INVESTIGATING THE ROLE OF ANTIBIOTIC EXPOSURE AND DEMOGRAPHIC/SOCIOECONOMIC FACTORS ON THE DEVELOPMENT OF BLOODSTREAM INFECTIONS IN PRIMARY CARE
Hannah Lishman
Imperial College London Department of Primary Care and Public Health
PhD in Clinical Medicine Research (Public Health) 2019
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Declaration of Originality
I, Hannah Lishman, confirm that the work presented in this thesis for examination for a PhD degree assessed and awarded by Imperial College London was carried out and written by myself, unless otherwise stated.
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The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution
Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work.
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Acknowledgements
“No (wo)man is an island entire of itself; every (wo)man is a piece of the continent, a part of the main…” - John Donne, 1624
There are many people who deserve my thanks in completing this piece of work. Firstly, my amazing supervisors. Professor Paul Aylin, I thank you for your keen insight and your encouragement from the beginning of my time at Imperial College to the end. Professor Alan Johnson, I thank you for your commitment to this work, your continuous guidance and your kindness. And Dr. Ceire Costelloe, my supervisor, mentor and friend, it has truly been a pleasure working with you; your endless curiosity and interest have inspired me throughout my PhD and will continue to do so as I take the next steps in my career.
I would like to acknowledge the support of the National Institute for Health Research (NIHR) who have funded this work. I would like to thank the countless amazing scientists I have had the privilege of working with within the Imperial College Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance (HPRU HCAI/AMR) and in the Primary Care and Public Health (PCPH) department. This work has benefitted from the support of a wide range of epidemiologists, clinicians, pharmacists and data managers and I feel very privileged to have collaborated with so many people. I would like to thank Myriam Gharbi, Mariam Molokhia, Mahsa Mazidi, Farzan Ramzan, Kate Honeyford, Anthony Thomas and Alison Holmes in particular. I have also had the privilege of making some incredible friends within the HPRU - these brilliant, kind and fiercely funny women have got me through this experience.
As part of this PhD I had the opportunity to work closely with Public Health England and I would like to thank the HCAI/AMR Surveillance Unit for their time and support, this was truly a wonderful collaborative experience for me. I would like to thank Russell Hope, Berit Muller-Pebody, Susan Hopkins, Mehdi Minaji, Miroslava Mihalkova and Sabine Bou-Antoun in particular. I would also like to acknowledge Cliodna McNulty and the PHE Primary Care Unit she leads in Gloucester for providing all the archived versions of PHE prescribing guidelines for a large portion of this work.
And lastly, and perhaps most importantly, I would like to thank my wonderful friends and family for their undying support. The person who deserves the most thanks is my amazing husband – you have lifted me up in the difficult times and celebrated with me in the exciting times. I would not be where I am without your love and support.
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Abstract
Background In recent years the UK has seen a rise in the incidence of Gram-negative bloodstream infections (BSIs), particularly those acquired in the community, half of which have been recorded as being preceded by a urinary tract infection (UTI) - one of the most common infections treated with antibiotics. Antibiotic resistance has been postulated as exacerbating or even partially causing the progression from a UTI to a BSI. For these reasons the UK Government has introduced a number of measures through the NHS Quality Premiums related to BSI incidence and the reduction of inappropriate antibiotic prescribing for UTIs in the community in the hope of reducing the incidence of BSIs by improving antibiotic stewardship for preceding infections.
Aim To investigate whether the risk of developing a BSI following antibiotic treatment for a community-acquired UTI is affected by differing patterns of antibiotic prescribing for community-acquired UTIs.
Methods For all studies a number of routinely-collected healthcare data sources linked at the national level are used. An ecological design is used to investigate the volume of GP practice antibiotic prescribing for UTI and UTI-related E. coli bacteraemia (ECB) risk, taking antibiotic susceptibility into account. An algorithm is built to determine the level of antibiotic prescribing for UTI in England was does not adhere to prescribing guidelines as well as the reasons for non-adherence at the patient-level. This algorithm is then used in a patient-level retrospective cohort study to determine whether developing a BSI is associated with guideline adherence of antibiotic treatment for a preceding UTI. Lastly, a retrospective cohort study is used to examine whether longer durations of antibiotic treatment for UTI confer a greater risk of subsequent BSI compared with shorter durations.
Results The ecological study demonstrates that GP practices with higher levels of trimethoprim prescribing, after adjusting for case-mix and practice characteristics, have a higher incidence of UTI-related trimethoprim-resistant ECB than practices with lower levels. The algorithm demonstrates that the majority of antibiotic prescriptions for UTI in England do not adhere to national prescribing guidelines, mainly due to antibiotic choice or duration of treatment. The first retrospective cohort study does not find evidence of greater or lesser risk of BSI following UTI antibiotic treatment which was not in line with national prescribing guidelines. The second retrospective cohort study found weak evidence of higher risk of BSI following a longer course of trimethoprim treatment for a UTI compared with a shorter course in women with uncomplicated UTI.
Conclusions Improving antibiotic prescribing for UTI in the community (through using nitrofurantoin as the first-choice antibiotic and prescribing shorter durations of treatment where appropriate) may have an effect on subsequent BSI risk, although improvements in data acquisition and linkage to overcome the limitations outlined in this work will provide further clarity to this infection pathway.
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Contents List of Tables ...... 8 List of Figures ...... 10 Glossary...... 12 CHAPTER ONE………………………………………………………………………………………………………………………………………………….13 INTRODUCTION AND BACKGROUND - UNDERSTANDING THE INFECTION PATHWAY Introduction ...... 13 1.1. What are the current challenges? ...... 13 1.2. Hypothesis ...... 15 1.3. Aims and objectives of the thesis...... 15 1.4. Thesis overview...... 16 Background ...... 19 1.5. Urinary tract infections – treatment practices and surveillance ...... 19 1.6. Bloodstream infections – risk factors and surveillance ...... 23 1.7. Prescribing and resistance – mapping the infection pathway ...... 28 1.8. How do social determinants of health fit in? ...... 31 1.9. Routinely collected data available in England ...... 32 1.10. Ethics approvals and exemptions ...... 33 CHAPTER TWO…………………………………………………………………………………………………………………………………………………35 EXPLORING THE EFFECT OF ANTIBIOTIC PRESCRIBING ON THE INCIDENCE AND SUSCEPTIBILITY PATTERNS OF E. COLI BACTERAEMIA - AN ECOLOGICAL STUDY 2.1. Introduction ...... 36 2.2. Aims and objectives ...... 36 2.3. Data sources and linkage ...... 37 2.4. Methods ...... 40 2.4.1. Study population ...... 40 2.4.2. Inclusion and exclusion criteria ...... 41 2.4.3. Statistical methods ...... 43 2.5. Results ...... 46 2.5.1. Descriptive analysis ...... 46 2.5.2. Model 1 – Prescribing and total E. coli bacteraemia incidence ...... 49 2.5.3. Model 2 – Prescribing and trimethoprim-resistant E. coli bacteraemia incidence ...... 50 2.5.4. Model 3 – Prescribing and incidence of E. coli bacteraemia subsequent to a nitrofurantoin- resistant UTI ...... 52 2.5.5. Trimethoprim:nitrofurantoin ratio analyses for Models 1, 2 and 3 ...... 54 2.6. Discussion ...... 55 2.6.1. Main findings ...... 55 2.6.2. Limitations...... 56 2.6.3. How these findings fit in with existing literature ...... 57 2.6.4. Implications for practice and policy ...... 58 2.7. How these finding fit in with the wider thesis ...... 59 CHAPTER THREE………………………………………………………………………………………………………………………………………………61
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MEASURING THE LEVEL OF OFF-GUIDELINE ANTIBIOTIC PRESCRIBING FOR COMMUNITY-ACQUIRED URINARY TRACT INFECTIONS IN PRIMARY CARE 3.1. Introduction ...... 62 3.2. Aims and objectives ...... 63 3.3. Data sources and linkage ...... 64 3.4. Methods ...... 66 3.4.1. Study population ...... 66 3.4.2. Inclusion/exclusion criteria ...... 66 3.4.3. Code list development, linkage strategy and data cleaning ...... 68 3.4.4. Defining episodes of infection ...... 75 3.4.5. Defining patient sub-groups and UTI severities ...... 76 3.4.6. Description of the algorithm ...... 79 3.4.7. Changes in PHE guidelines over time ...... 82 3.4.8. Statistical methods ...... 85 3.4.9. Sensitivity analysis ...... 85 3.5. Results ...... 87 3.5.1. Trends in antibiotics prescribed for UTI ...... 87 3.5.2. Trends in antibiotic prescribing in relation to the guideline ...... 91 3.5.3. Reasons for off-guideline prescribing (drug, dose, duration, frequency) ...... 96 3.5.4. Variation in off-guideline prescribing by GP practice ...... 97 3.5.5. Sensitivity analysis (3 and 6-month time lag) ...... 99 3.6. Discussion ...... 99 3.6.1. Main findings ...... 99 3.6.2. Limitations...... 101 3.6.3. How these findings fit in with existing literature ...... 102 3.6.4. Implications for practice and policy ...... 104 3.7. How these findings fit in with the wider thesis ...... 105 CHAPTER FOUR..……………………………………………………………………………………………………………………………………………106 INVESTIGATING THE EFFECT OF OFF-GUIDELINE ANTIBIOTIC PRESCRIBING IN PRIMARY CARE FOR URINARY TRACT INFECTION ON THE RISK OF BLOODSTREAM INFECTION 4.1. Introduction ...... 107 4.2. Aims, objectives and rationale ...... 107 4.3. Data sources and linkage ...... 109 4.4. Methods ...... 111 4.4.1. Study population and outcomes ...... 111 4.4.2. Sample size calculation ...... 114 4.4.3. Statistical methods ...... 114 4.5. Results ...... 116 4.5.1. Distribution of patient characteristics and outcomes...... 116 4.5.2. Logistic regression models investigating the effect of off-guideline antibiotic prescribing on BSI ...... 120 4.5.3. Logistic regression models investigating the effect of recurrent UTIs on BSI ...... 124
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4.5.4. Time-to-event analysis investigating the hazard of a BSI following off-guideline prescribing for a UTI ...... 132 4.5.5. Time-to-event analysis investigating the hazard of a BSI following a recurrent UTI ...... 136 4.5.6. Sensitivity analyses ...... 141 4.6. Discussion ...... 141 4.6.1. Main findings ...... 141 4.6.2. Limitations...... 143 4.6.3. How these findings fit in with existing literature ...... 146 4.6.4. Implications for practice and policy ...... 148 4.7. How these findings fit in with the wider thesis ...... 149 CHAPTER FIVE…..…………………………………………………………………………………………………………………………………………..150 INVESTIGATING THE EFFECT OF ANTIBIOTIC DURATION OF TREATMENT IN PRIMARY CARE FOR URINARY TRACT INFECTIONS ON BLOODSTREAM INFECTIONS 5.1. Introduction ...... 151 5.2. Aims/rationale ...... 151 5.3. Methods ...... 153 5.3.1. Study population ...... 153 5.3.2. Statistical methods ...... 153 5.4. Results ...... 154 5.4.1. Distribution of patient characteristics and outcomes...... 154 5.4.2. Univariate analyses ...... 161 5.4.3. Multivariable logistic regression models investigating effect of longer vs shorter treatment with trimethoprim 200mg on development of BSIs ...... 163 5.5. Discussion ...... 166 5.5.1. Main findings ...... 166 5.5.2. Limitations...... 168 5.5.3. How these findings fit in with existing literature ...... 170 5.5.4. Implications for practice and policy ...... 173 5.6. How these findings fit in with the wider thesis ...... 173 CHAPTERS SIX AND SEVEN.……………………………………………………………………………………………………………………………175 OVERARCHING DISCUSSION AND RECOMMENDATIONS 6.1. Looking back to the hypothesis ...... 176 6.2. Strengths and limitations ...... 179 6.3. How this thesis supports government policy ...... 184 6.4. Novel contributions of this thesis ...... 185 6.5. Further research questions generated from this thesis ...... 185 6.6. Dissemination and implementation into practice (WSIC dashboard) ...... 187 7.1. Conclusions ...... 190 7.2. Recommendations ...... 190 Publications and conference presentations ...... 193 References ...... 194
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List of Tables
Table 1.1. Overview of studies comprising the thesis……………………………………………………………………………………...18
Table 1.2. Definitions of bloodstream infection and associated entities………………………………………………………..24
Table 2.1. Specifications of included datasets for ecological study before linkage…………………………………………..38
Table 2.1. Trimethoprim and nitrofurantoin prescribing ranges by quintile - DDD/1000 registered patients (STAR-PU-adjusted)…………………………………………………………………………………………………………………………………………43
Table 2.2. Baseline distribution of total E. coli bacteraemia infections in women per year by GP practice characteristics (2012-2014)……………………………………………………………………………………………………………………………..49
Table 2.3. Adjusted associations between practice characteristics and total E. coli bacteraemia infections in women per practice (2012-2014)…………………………………………………………………………………………………………………….50
Table 2.4. Baseline distribution of trimethoprim-resistant UTI-related E. coli bacteraemia infections in women per year by GP practice characteristics (2012-2014)………………………………………………………………………………………..51
Table 2.5. Adjusted associations between practice characteristics and total trimethoprim-resistant E. coli bacteraemia infections in women per practice (2012-2014)……………………………………………………………………………52
Table 2.6. Baseline distribution of nitrofurantoin-resistant UTIs leading to an E. coli bacteraemia in women per year by GP practice characteristics (2014)……………………………………………………………………………………………………….53
Table 2.7. Adjusted associations between practice characteristics and total nitrofurantoin-resistant UTIs leading to an E. coli bacteraemia in women per practice (2014)………………………………………………………………………53
Table 3.1. Specifications of included datasets for off-guideline prescribing study……………………………………………65
Table 3.2. Stepwise specifications for initial exclusion criteria (SQL)……………………………………………………………….71
Table 3.3. Day count used to enable UTI episode categorisation…………………………………………………………………….76
Table 3.4. Day count used to enable year categorisation………………………………………………………………………………..78
Table 3.5. Treatment frequencies for UTIs in entire patient cohort (95th percentile only)……………………………….88
Table 3.6. Treatment frequencies for female and male uncomplicated UTI patients (95th percentile only)……..88
Table 3.7. Treatment frequencies for pregnant patients, excluding recurrent and severe UTIs (95th percentile only)………………………………………………………………………………………………………………………………………………………………..89
Table 3.8. Treatment frequencies for recurrent UTI patients, excluding pregnant and severe UTIs (95th percentile only)……………………………………………………………………………………………………………………………………………….89
Table 3.9. Duration frequencies for female and male uncomplicated UTI patients………………………………………….90
Table 3.10. Study characteristics for adults with a UTI receiving an antibiotic prescription (excluding recurrent UTIs) in England (2008-2017)…………………………………………………………………………………………………………………………..93
Table 4.1. Characteristics of adults with a BSI within 30 and 60 days of receiving antibiotics for a UTI (excluding recurrent UTIs) in England (2008-2017)…………………………………………………………………………………………………………118
Table 4.2. Crude odds ratios for investigating the effect of off-guideline prescribing for a UTI on developing a BSI within 30 and 60 days in England, (excluding recurrent UTIs)………………………………………………………………….121
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Table 4.3. Adjusted odds ratios for investigating the effect of off-guideline prescribing for a UTI on developing a BSI within 30 and 60 days in England, (excluding recurrent UTIs)……………………………………………………………….123
Table 4.4. Characteristics of adults with a BSI event within 30 and 60 days of receiving antibiotics for a UTI (including recurrent UTIs) in England (2008-2017)…………………………………………………………………………………………126
Table 4.5. Crude odds ratios for investigating the effect of whether a UTI is recurrent on developing a BSI within 30 and 60 days in England…………………………………………………………………………………………………………………..129
Table 4.6. Adjusted odds ratios for investigating the effect of whether a UTI is recurrent on developing a BSI within 30 and 60 days in England…………………………………………………………………………………………………………………..131
Table 4.7. Log-rank test for equality of survivor functions (non-recurrent UTIs only)…………………………………….134
Table 4.8. Adjusted Cox proportional hazards model for hazard of BSI outcome following antibiotic treatment for a UTI, censored at 60 days (non-recurrent UTIs only)……………………………………………………………………………….135
Table 4.9. Test of proportional hazards assumption using Schoenfeld residuals by individual predictor (non- recurrent UTIs only)……………………………………………………………………………………………………………………………………….136
Table 4.10. Log-rank test for equality of survivor functions (including recurrent UTIs)………………………………….138
Table 4.11. Adjusted Cox proportional hazards model for hazard of BSI outcome following antibiotic treatment for a UTI, censored at 60 days (including recurrent UTIs)………………………………………………………………………………140
Table 4.12. Test of proportional hazards assumption using Schoenfeld residuals by individual predictor (including recurrent UTIs)……………………………………………………………………………………………………………………………..141
Table 5.1. Distribution of adult women who developed a BSI within 60 days of receiving trimethoprim 200mg for an uncomplicated UTI in England (2008-2017)…………………………………………………………………………………………155
Table 5.2. Distribution of adult women who developed a BSI within 60 days of receiving nitrofurantoin 100mg for an uncomplicated UTI in England (2008-2017)…………………………………………………………………………………………155
Table 5.3. Distribution of treatment in adult men who developed a BSI within 60 days of receiving trimethoprim 200mg for an uncomplicated UTI in England (2008-2017)……………………………………………………….158
Table 5.4. Distribution of adult men who developed a BSI within 60 days of receiving nitrofurantoin 100mg for an uncomplicated UTI in England (2008-2017)………………………………………………………………………………………………158
Table 5.5. Crude odds ratios for the effect of duration of Trimethoprim 200mg for uncomplicated UTIs on BSI within 60 days in England in female patients…………………………………………………………………………………………………162
Table 5.6. Crude odds ratios for the effect of duration of trimethoprim 200mg for uncomplicated UTIs on BSI within 60 days in England in male patients…………………………………………………………………………………………………….163
Table 5.7. Adjusted odds ratios for the effect of duration of Trimethoprim 200mg for uncomplicated UTIs on BSI within 60 days in England in female patients…………………………………………………………………………………………..165
Table 5.8. Adjusted odds ratios for the effect of duration of trimethoprim 200mg for uncomplicated UTIs on BSI within 60 days in England in male patients………………………………………………………………………………………………166
Table 6.1. Thesis findings mapped to national policy recommendations……………………………………………………….184
Table 6.2. Novel contributions of this thesis and recommendations for implementation………………………………185
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List of Figures
Figure 1.1. Conceptual overview of the thesis…………………………………………………………………………………………………17
Figure 1.2. NICE guidance on UTI management and treatment for UTI (2018)…………………………………………………21
Figure 1.3. Diagnostic hierarchy from positive blood cultures to community-onset bloodstream infection…….24
Figure 1.4. Inflammation responses that occur in response to bacterial bloodstream infection………………………25
Figure 1.5. Trends in the all case and hospital-onset rate of MRSA bacteraemia in England……………………………26
Figure 1.6. Trends in the rate of E. coli bacteraemia in England, 2012/13 to 2017/18……………………………………..27
Figure 2.1. Overall linkage strategy of ecological study with dataset sources………………………………………………….39
Figure 2.2. Study profile………………………………………………………………………………………………………………………………….47
Figure 2.3. Total primary care prescribing of trimethoprim and nitrofurantoin in the study population by month (2012-2014)…………………………………………………………………………………………………………………..48
Figure 2.4. Total E. coli bacteraemia infections with a UTI focus in the study population by month (2012-2014)…………………………………………………………………………………………………………………………………………………….48
Figure 3.1. UTI episode construction……………………………………………………………………………………………………………….75
Figure 3.2. PHE prescribing guideline for UTIs with key elements highlighted for algorithm development………80
Figure 3.3. Changes in the Public Health England “Management and Treatment of Common Infections” guideline over the study period (2007-2017)…………………………………………………………………………………………………..84
Figure 3.4. The guideline adherence algorithm with no time lag vs. a 3 or 6-month time lag…………………………..86
Figure 3.5. Trends in relative antibiotic class usage for UTIs over time in adults receiving an antibiotic prescription in England……………………………………………………………………………………………………………………………………91
Figure 3.6. Trends in on/off guideline status over time for non-recurrent UTIs in adults receiving an antibiotic prescription in England……………………………………………………………………………………………………………………………………92
Figure 3.7. Trends in on/off guideline status over time for non-recurrent UTIs in adults receiving an antibiotic prescription in England stratified by patient gender……………………………………………………………………………………….94
Figure 3.8. Trends in on/off guideline status by age group for non-recurrent UTIs in adults receiving an antibiotic prescription in England stratified by patient gender………………………………………………………………………..95
Figure 3.9. Trends in on/off guideline status by region for non-recurrent UTIs in adults receiving an antibiotic prescription in England……………………………………………………………………………………………………………………………………95
Figure 3.10. Trends in reason for off-guideline status over time for non-recurrent UTIs in adults receiving an antibiotic prescription in England……………………………………………………………………………………………………………………96
Figure 3.11. Trends in reason for off-guideline status by age group for non-recurrent UTIs in adults receiving an antibiotic prescription in England stratified by patient gender………………………………………………………………………..97
Figure 3.12. Distribution of guideline status for prescribing for UTI by GP practice…………………………………………98
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Figure 3.13. Variation in the proportion of prescribing for UTI which was off-guideline by practice………………. 98
Figure 4.1. Overall linkage strategy between CPRD, HES, ONS, IMD2010 and Pregnancy register………………….110
Figure 4.2. Linkage strategy between “HES diagnosis hospital”, “HES hospitalisation” and “HES diagnosis episode” tables to identify BSI diagnoses which were both community-acquired and community-onset………113
Figure 4.3. Kaplan-Meier survival estimate by day since UTI diagnosis/treatment in non-recurrent UTI patients who developed BSI within 60 days…………………………………………………………………………………………………………………132
Figure 4.4. Kaplan-Meier survival estimate by day since UTI diagnosis/treatment in non-recurrent UTI patients who developed BSI within 60 days, stratified by treatment group…………………………………………………………………133
Figure 4.5. Kaplan-Meier survival estimate by day since UTI diagnosis/treatment in all UTI patients who developed BSI within 60 days………………………………………………………………………………………………………………………..137
Figure 4.6. Kaplan-Meier survival estimate by day since UTI diagnosis/treatment in all UTI patients who developed BSI within 60 days, stratified by recurrent UTI status…………………………………………………………………..137
Figure 5.1. Trends in duration of (a) trimethoprim 200mg and (b) nitrofurantoin 100mg over the study period in adult female uncomplicated UTI patients (2008-2017)……………………………………………………………………………..156
Figure 5.2. Trends in duration of (a) trimethoprim 200mg and (b) nitrofurantoin 100mg by age category in adult female uncomplicated UTI patients (2008-2017)………………………………………………………………………………….157
Figure 5.3. Trends in duration of (a) trimethoprim 200mg and (b) nitrofurantoin 100mg over the study period in adult male uncomplicated UTI patients (2008-2017)………………………………………………………………………………...159
Figure 5.4. Trends in duration of (a) trimethoprim 200mg and (b) nitrofurantoin 100mg by age category in adult male uncomplicated UTI patients (2008-2017)………………………………………………………………………………….….160
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Glossary
ABR Antibiotic resistance AMR Antimicrobial resistance ASP Antimicrobial Stewardship Programme BD Bis die sumendum (2 times a day) BSI Bloodstream infection CA Community-acquired CAP Community-acquire pneumonia CCG Clinical Commissioning Group CPRD Clinical Practice Research Datalink ECB E. coli bacteraemia ESPAUR English Surveillance Programme for Antimicrobial Utilisation and Resistance GNBSI Gram-negative bloodstream infection HCAI Healthcare-Associated Infection HES Hospital Episode Statistics HPRU Health Protection Research Unit HSCIC Health and Social Care Information Centre ICHP Imperial College Health Partners IMD Index of Multiple Deprivation ISAC Independent Scientific Advisory Committee LSHTM London School of Hygiene and Tropical Medicine LSOA Lower Super Output Area MR Modified-release MRSA Methicillin-resistant Staphylococcus aureus NHS National Health Service NHS BSA NHS Business Services Authority NICE National Institute for Health and Care Excellence NIHR National Institute for Health Research ONS Office for National Statistics PHE Public Health England QDS Quater die sumendum (4 times a day) QOF Quality Outcomes Framework QP Quality Premium RCT Randomised control trial SAIL Secure Anonymised Information Linkage Databank SIRS Severe Inflammatory Response Syndrome STAT Statim (immediately) TARGET “Treat Antibiotics Responsibly, Guidance, Education Tools” toolkit TDS Ter die sumendum (3 times a day) UTI Urinary tract infection WSIC Whole Systems Integrated Care
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CHAPTER 1: INTRODUCTION AND BACKGROUND: UNDERSTANDING THE INFECTION PATHWAY
Introduction
1.1. What are the current challenges?
Optimising the use of antibiotics is becoming an increasingly high priority in clinical care as one of the main drivers of antibiotic resistance (ABR) is the misuse and overuse of antibiotics (1, 2). For this reason,
Antimicrobial Stewardship Programmes (ASPs) (groups of measures to promote the appropriate use of antimicrobials) such as the Start Smart – Then Focus programme in secondary care (3) and the TARGET (Treat
Antibiotics Responsibly, Guidance, Education Tools) toolkit in primary care (4) have been put in place in recent years and are under constant review within the National Health Service (NHS). An important component of
ASPs is to provide evidence-based standards for antimicrobial prescribing.
Urinary tract infections (UTIs) are one of the most commonly-presenting bacterial infections in primary care
(5). Failure to adequately treat UTIs increases the likelihood that the causative bacteria (most commonly
Escherichia coli) will go on to invade the bloodstream and cause bacteraemia, which is a major cause of infectious disease morbidity and mortality (6, 7). As outlined in a recent publication looking at 30 day all-cause mortality in patients with E. coli bacteraemia (ECB), it was found that although bacteraemia with an underlying urinary tract focus had a low case fatality rate, the high prevalence of ECB with this underlying focus at the population level meant that it was associated with the largest number of deaths compared with other underlying foci of infection (8). Therefore, understanding the factors influencing the progression from a UTI to a bloodstream infection (BSI) such as ECB is important with regards to devising potential interventions to reduce BSI-related morbidity and mortality.
Data from the programme of mandatory surveillance of ECB in England (9) has shown that the overall number of ECBs in England increased by 24.3% from 32,405 cases in 2012 to 41,287 cases in 2017, equating to a rate of
74.3 cases per 100,000 population. The proportion of E. coli isolated from blood that were resistant to key
13 | Page antibiotics has remained relatively stable over this period but as the incidence of ECB increased, the burden of antibiotic-resistant ECBs has nonetheless increased correspondingly. Between 2014 and 2017, antibiotic prescribing (measured as daily doses per 1000 population per day) declined by 6.1% across most major antibiotic classes with significant regional variation in prescribing rates being shown (10). The vast majority of antibiotic prescriptions are written in primary care, which is where the largest reduction in antibiotic prescribing over these years was seen (13.2% reduction between 2013 and 2017), however this reduction was largely seen in penicillins, which are typically not used to treat UTIs. A study lead by the Imperial College
Health Protection Research Unit (HPRU) in Healthcare Associated Infections (HCAI) and Antimicrobial
Resistance (AMR) found that after the introduction of the new NHS Quality Premium (QP) in 2015, which incentivised the targeted reduction in total and broad-spectrum antibiotic prescribing in primary care, antibiotic items prescribed decreased by 8.2% (11). This represented 5,933,563 fewer antibiotic items prescribed post-intervention compared with expected numbers extrapolated from pre-intervention trends.
The trend in increasing Gram-negative BSI (GNBSI) burden in England in recent years prompted the UK government to announce in 2016 an ambition to reduce HCAI GNBSIs in England by 50% (of the 2017/18 baseline value) by March 2021 (12). In a review looking at the burden of BSI in the population across Europe, it was estimated that 1.2–1.4 million episodes of BSI occur per year, based on extrapolation of rates from studies in Denmark, Finland and the UK. The case fatality rate (CFR) was calculated to be between 13% and 20% and while the estimated episodes and CFRs were not specific to E. coli bacteraemia, E. coli was the most commonly reported aetiologic agent of BSI (13). Therefore, understanding how BSI can be prevented is an important area of research for reducing this cause of potentially avoidable morbidity and mortality.
There are many parts of the pathway from UTI to BSI, however, which are currently incompletely understood.
While national surveillance data from Public Health England (PHE) has shown that in England ECBs are most frequently recorded as being preceded by an underlying UTI compared with other infections (14), what is not known is the proportion of patients with UTI who go on to develop a BSI (such as ECB) when looking within a general UTI patient cohort. The time frame for the risk of patients with UTIs developing UTI-related BSI and whether certain patients are more at risk of developing a BSI than others is also currently ill defined. Lastly, whilst there is much literature on the association between antibiotic prescribing and antibiotic resistance, and increasingly so around how these factors affect treatment failure rates in UTIs, further research is needed to
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England.
Within the context of a very common infection with the potential for severe complications (particularly BSI), recently-implemented government policy to address this patient safety issue in the form of published ambitions to reduce GNBSIs, and a wealth of routinely-collected healthcare data currently available, this thesis seeks to investigate this infection pathway particularly with respect to the potential impact of differences in antibiotic use. Conditions such as an aging population, an increasing number of complex patients living with multiple morbidities and an increase in antibiotic resistance in England further signal the urgency of addressing this problem. This thesis describes the linkage of a number of existing national databases to build enriched datasets to provide a fuller picture of the patient care pathway and the use of quantitative modelling techniques to interrogate the relationship between these two infections, with the aim of addressing some of the gaps in the literature outlined above.
1.2. Hypothesis
This thesis addresses the hypothesis that the risk of developing a BSI following antibiotic treatment for a community-acquired UTI is associated with differing patterns of antibiotic prescribing for community-acquired
UTIs.
1.3. Aims and objectives of the thesis
The overarching aim of this project is to determine, using routinely-collected national data, whether differences in antibiotic prescribing for UTIs in primary care are associated with the risk of developing a subsequent BSI, after accounting for relevant patient or practice-related factors. To achieve this aim, it was necessary for this thesis to:
- Assess whether an association exists between the volume of trimethoprim and nitrofurantoin
prescribing for UTIs and the incidence of UTI-related ECB in adult women, stratified by GP practice;
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- Investigate whether this relationship changes when taking data on the antibiotic susceptibility of
causative pathogens into account;
- Quantify the levels of “off-guideline” vs. “changed” vs. “on-guideline” antibiotic prescribing for UTI
episodes within an English national primary care adult cohort, coupled with the investigation of the
reasons for off-guideline prescribing and trends over time, with a focus on patient characteristics;
- Assess whether an association exists between off-guideline antibiotic prescribing in patients presenting
with a UTI and the risk of developing a subsequent BSI;
- Determine the risk factors associated with developing a BSI after being treated with antibiotics for a
UTI in primary care;
- Determine the median time to developing a BSI following antibiotic treatment for a UTI in primary care;
- Determine whether diagnosis and treatment for a recurrent UTI plays a role in the risk of developing a
BSI compared with treatment for a non-recurrent UTI;
- Examine the trends in duration of treatment for the two main UTI antibiotic options (trimethoprim
200mg and nitrofurantoin 100mg) in an uncomplicated UTI patient cohort over time, stratified by
patient characteristics;
- Determine the effect of different durations of trimethoprim 200mg and nitrofurantoin 100mg for UTI
on the risk of BSI in an uncomplicated UTI patient cohort.
1.4. Thesis overview
This thesis consists of seven chapters. This first chapter provides a brief background of the area of research with respect to antibiotic prescribing for UTIs in England, along with the pathway from UTI to development of a BSI.
In addition, this chapter discusses the various routinely collected data sources for healthcare and demographic information in England to be made use of in this thesis. Chapters 2-5 present the studies conducted as part of this thesis and are structured using the format commonly required when manuscripts are submitted to peer- reviewed journals, comprising the Aims, Methods (with reference to previous applicable methods described),
Results and Discussion for each study. Chapter 2 presents an ecological study investigating UTI-related antibiotic prescribing by practice and UTI-related ECB incidence. Chapter 3 describes the data cleaning and linkage methods for building the linked dataset used in the studies presented in Chapters 3-5. Chapter 3 also presents
16 | Page the algorithm built to assess prescribing guideline adherence for UTI and the resulting trends in guideline adherence nationally. Chapter 4 utilises the algorithm built in Chapter 3 and presents the results of a retrospective cohort study investigating whether receiving an antibiotic prescription for UTI that is not in line with prescribing guidance is associated with developing a subsequent BSI. Chapter 5 presents a sub-analysis investigating the impact of duration of antibiotic treatment of uncomplicated UTI in adult women and men on the odds of developing a subsequent BSI. For this analysis, two antibiotic/dosage combinations were used to investigate the risk of BSI directly attributable to differences in duration of antibiotic treatment. Chapter 6 provides an overarching discussion of the findings of all chapters in the context of current scientific evidence, government policy and clinical practice, discussing the validation of existing evidence, novel contributions and the limitations of this study. It also discusses work I am currently doing to translate the findings of this thesis for informing clinical practice. Chapter 7 concludes the thesis and provides a series of clinical, epidemiological and surveillance recommendations. Figure 1.1. shows the conceptual overview of the thesis and Table 1.1. provides a brief overview of each of the studies conducted (the data sources outlined will be expanded upon later in this
Chapter and cited in the Chapters within which they are used).
Chapter 1: Aims and Background
Chapter 2: Volume of prescribing for UTI and incidence of E. coli bacteraemia
Chapter 3: Chapter 4: Effect of BSI-related UTI diagnosis Quantifying levels of off-guideline admission, and treatment off-guideline prescribing for UTI death or re- prescribing for UTI on BSI risk consultation
Chapter 5: Effect of duration of treatment for UTI on BSI risk
Chapter 6, 7: Discussion, conclusions and recommendations
Figure 1.1. Conceptual overview of the thesis.
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Table 1.1. Overview of studies comprising the thesis Chapter Objective Study design Data and data sources Ethics
PHE mandatory E. coli bacteraemia Surveillance Not required as I
accessed the data PHE Second Generation Determine the association National ecological under an honorary Surveillance System (SGSS): between antibiotic prescribing study aggregated by contract antibiotic susceptibility of for UTIs and the incidence of GP practice. arrangement with 2 blood culture isolates UTI-related ECB in female PHE – data were
adult patients at the GP Hierarchical Poisson not taken out of PHE SGSS: antibiotic practice level. models used. the secure PHE susceptibility of urine culture research isolates environment.
NHS Digital: Characteristics of GP practices
Clinical Practice Research Datalink (CPRD): primary care records
Hospital Episode Statistics Determine the proportion of (HES): inpatient records antibiotic prescriptions for UTI CPRD Independent in adult patients in primary Office for National Statistics Scientific Advisory care which are not following 3 Descriptive study (ONS): mortality data Committee national antibiotic prescribing approval guidelines and the reasons for Index of Multiple Deprivation (14_225RA2) “off-guideline” prescribing. (IMD): patient-level
deprivation data
CPRD/London School of Hygiene & Tropical Medicine (LSHTM): Pregnancy register
Determine the effect of “off- Retrospective guideline” antibiotic CPRD Independent cohort study prescribing for UTI patients in Scientific Advisory (univariate and As for Chapter 3 4 primary care on the risk of Committee multivariable logistic developing BSI, as well as the approval regression and median time-to-event in (14_225RA2) survival analyses) adults.
Determine the effect of the duration of antibiotic Retrospective CPRD Independent treatment for UTI on the risk cohort study Scientific Advisory As for Chapter 3 5 of a subsequent BSI event, (univariate and Committee
focusing only on trimethoprim multivariable logistic approval 200mg and nitrofurantoin regression) (14_225RA2) 100mg.
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Background
1.5. Urinary tract infections – treatment practices and surveillance
UTIs are among the most common bacterial infections seen in both the community and hospital settings. They are typically classified as either “lower UTI”, meaning the infection is confined to the bladder and is relatively less severe and easier to treat, or “upper UTI” (or pyelonephritis), which affects the kidneys and typically presents as a more severe infection, requiring longer treatment durations (5). UTI in men can also result from an infection of the prostate, known as prostatitis, which, like pyelonephritis, is considered to be a severe form of UTI which requires a longer duration of treatment than lower UTI (15). From a US-based survey it was found that 10.8% of women aged 18 years or older reported having had at least one UTI in the past 12 months, with an estimated extrapolation that by age 24, one-third of women will have had at least one diagnosed UTI episode in their lifetime (16). These findings have since been verified in a survey-based study in the UK which found that
37% of women aged 16 or older, reported having had at least one UTI in their lifetime, with 29% having had more than one UTI episode (17). In elderly patients in England between 2004 and 2014, the incidence of UTIs was found to range from 9.03 to 19.80 per 100 person-years at risk in women over 65 and from 2.81 to 10.54 per 100 person-years at risk in men over 65 (18).
Diagnosis of UTI can be challenging, as can be the differentiation between an upper and a lower UTI. Typically, lower UTI, sometimes referred to as cystitis, is characterised by three or more of the following urinary symptoms: frequency, dysuria, urgency, strangury, haematuria, suprapubic pain and/or a change in urine smell
(19). Typically, upper UTI is characterised by urinary symptoms (as above) as well as any of the following: fever, rigors, vomiting, loin pain or tenderness, and the onset of symptoms are usually rapid. However, UTI in the elderly can often cause sudden confusion, agitation or withdrawals, particularly in patients with dementia, and taking an accurate history can be challenging (20). It is recommended that UTI be diagnosed only in the presence of at least three urinary symptoms to avoid the overuse of antibiotics and antibiotic treatment is not recommended in elderly patients with asymptomatic bacteriuria as it is not associated with increased morbidity in this patient population (15, 20). However, even when symptoms of UTI are present, this does not always correspond with a positive urine culture as acute cystitis or “inflammation of the bladder” (which may present
19 | Page with the same symptoms as a bacterial UTI) may not be caused by a bacterial infection, adding further complication and uncertainty. In a recent prospective observational cohort study conducted by Butler et al. across England, Wales, Spain and the Netherlands, of 726 women presenting with symptoms of UTI who had microbiological results from a midstream urine sample, only 259 (35.8%, 95% CI 32.3-39.2) were culture-positive for a UTI, with the proportion of culture-positive UTIs being lowest in English primary care practices (24.3%, 95%
CI 19.1-30.4) and highest in Dutch primary care practices (63.8%, 95% CI 55.1-71.6) (21). With respect to antibiotic prescribing, 95.1% of study participants in England received empirical antibiotics, as opposed to 59.4% of study participants in the Netherlands.
When diagnosing a UTI, microbiological examination is not used as a default option; empirical antibiotic prescribing is more common and seen to be relatively safe in patients who are considered to be low risk (i.e. young otherwise healthy women who do not have a history of recurrent UTI). Typically, urine culture is only recommended when there are clinical signs of pyelonephritis, the UTI has failed to respond to previous antibiotics (a treatment failure possibly indicative of antibiotic resistance), the patient is pregnant or if the patient is immunocompromised or diabetic (19). Guidance from the National Institute for Health and Care
Excellence (NICE) also recommends that a male patient with clinical signs of a UTI may require a urine culture
(22). The evidence around the utility of point-of-care dipstick tests for the presence of nitrite, leukocyte esterase and/or blood in the urine to rule-in or rule-out an infection is heterogeneous with regards to providing an accurate diagnosis (20, 23, 24). In the UK, however, guidance around dipstick use is based upon the findings of a randomised control trial (RCT) conducted by Little et al. which showed that in women with uncomplicated UTI, the negative predictive value when nitrite, leukocytes and blood were all dipstick-negative was 76% and the positive predictive value when nitrite and either blood or leukocyte tests were positive was 92% (25). This lead to PHE recommending the use of dipsticks to guide whether or not to prescribe antibiotics in patients under 65 years of age if the urine is found to be cloudy, which could suggest a possible bacterial infection which a positive dipstick test could be used to lend further weight to a diagnosis of UTI (15).
Due to the uncertainty and heterogeneity around dipstick use however, improving point-of-care diagnostics for detecting bacterial UTI in primary care to reduce the processing of urine cultures and reduce unnecessary
20 | Page antibiotic prescribing is a priority for the future of UTI diagnosis and management. Figure 1.2. shows the most recent version of guidance for management and antibiotic prescribing for UTI released by NICE (22).
Figure 1.2. NICE guidance on UTI management and treatment for UTI (2018).
National Institute for Health and Care Excellence. Urinary tract infection (lower): antimicrobial prescribing 2018 [Available from: https://www.nice.org.uk/guidance/ng109.
Delayed antibiotic use has been increasingly recommended as a treatment strategy in an effort to reduce unnecessary antibiotic usage while still addressing symptom control. In another RCT conducted by Little et al., investigating different approaches to UTI management, 309 non-pregnant women suspected of having a UTI were recruited and randomised to one of five management strategies: immediate empirical antibiotics, 48-hour delayed empirical antibiotics, targeted antibiotics based on a symptom score (urine cloudiness, urine smell, nocturia or dysuria), a positive dipstick result or a positive midstream urine analysis (urine culture) (26). It was found that all management strategies achieved similar symptom control, and that there was no advantage to routinely sending urine samples for culturing as patients who waited for the urinalysis results for more than 48 hours before taking antibiotics may have poor symptom control. Thus managing patients using either dipstick tests with a backup delayed antibiotic prescription or by empirically using a delayed prescription could help to
21 | Page reduce unnecessary antibiotic usage. It is not currently possible, however, to identify delayed prescriptions written on the same day as the UTI consultation in routinely collected data, making it difficult to evaluate these strategies retrospectively.
Antibiotic treatment options for uncomplicated UTI include trimethoprim 200mg and nitrofurantoin 100mg, with pivmecillinam and fosfomycin being included in the guidance in 2017. Due to increasing rates of resistance to trimethoprim and a low and stable rate of resistance to nitrofurantoin over recent years, PHE recommended a switch from trimethoprim to nitrofurantoin as the first-choice treatment option in 2014 (15). Trimethoprim is now only recommended in patients who cannot tolerate nitrofurantoin (those with poor kidney function), in areas where trimethoprim resistance is low or where a patient’s urine culture susceptibility to trimethoprim has been confirmed.
According to the PHE ESPAUR 2017 report (12), some form of antibiotic resistance was common in the more than 1 million laboratory-confirmed UTIs caused by E. coli in England in 2017. In total, data from 1,007,684 urine isolates were available, 61% of which were from a primary care setting and 39% were from secondary care.
Ninety-eight percent of all isolates from primary and secondary care were tested for susceptibility to nitrofurantoin and trimethoprim (the two recommended first-line antibiotics). Of the isolates which were tested for susceptibility, 2.7% of the community isolates and 3.2% of the acute care isolates were resistant to nitrofurantoin, which was in stark contrast to 34% of community isolates and 37% of acute care isolates resistant to trimethoprim. When investigating the second-line treatment options, susceptibility testing for pivmecillinam was reported for 35% of isolates, with 9% of these demonstrating pivmecillinam-resistance while testing for fosfomycin was reported for 29% of isolates, 4% of which were reported as resistant. Resistance to ciprofloxacin, which is recommended for treating complicated or upper UTIs, was reported for 12% of community isolates and
15% of isolates from acute care settings, with 82% of UTI isolates being tested for susceptibility.
However, as discussed above, guidelines recommend that urine samples only be sent for laboratory testing from individuals exhibiting treatment failure, recurrent UTI or in high-risk patients who have a higher likelihood of a resistant infection. Hence, when attempting to inform antibiotic prescribing guidelines for the treatment of UTIs based on laboratory surveillance data which has an inherent bias towards samples from individuals who are at increased risk of antibiotic-resistant UTIs, it is important to be cognisant that such data are likely to overestimate
22 | Page the resistance rates in the general UTI population. As outlined in a review by Chin et al., participation in surveillance is voluntary (although improving) and case definitions and laboratory analysis methods vary (27).
Couple this with the requesting bias caused by selective midstream urine analysis (not requesting cultures for cases of uncomplicated UTI in non-pregnant women at first presentation) and we have a surveillance system which is inadequate for quantifying the risk of antibiotic resistance in UTIs presenting in the community (27). In recognition of this, PHE stated in the 2017 ESPAUR report that in order to improve data on unselected urines, a sentinel surveillance programme with unbiased data collection is required for national surveillance of UTIs (12).
Recent studies have consistently demonstrated a wide variation in the choice of antibiotic prescriptions for UTI
(and respiratory tract infections) in England (28) with variation between practices remaining even after accounting for patient comorbidities, smoking and deprivation levels within each practice (29). The study by
Butler et al. previously discussed showed that this wide variation in not only choice of antibiotic but likelihood of antibiotic prescribing for a UTI diagnosis is not just seen within the UK but also between European countries
(21). The low proportion of microbiologically-confirmed UTIs seen in this study suggested that there may be risk of over-prescribing of antibiotics for syndromes that present similarly to UTI perhaps caused by inflammation of the urinary tract from a non-infectious source. This suggests scope for improvement in the way antibiotics are used for UTI and warrants an investigation into how adherence to prescribing guidance can be improved.
1.6. Bloodstream infections – risk factors and surveillance
BSIs are a major cause of infectious disease morbidity and mortality both in the UK and globally and are diagnosed when there is evidence of microbial growth in a blood culture (6). Incidence of ECB in England was found to be 60.4 cases/100,000 population in 2012-13, increasing to 63.5 cases/100,000 population in 2013-14 in a study by Bou-Antoun et al. using laboratory data reported on a voluntary basis to PHE (30), with the rise in
ECB incidence rate averaging 5% every year between 2012 and 2017 (31). Figure 1.3., taken from the Laupland et al. review, demonstrates the breakdown from positive blood cultures to bloodstream infection.
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Figure 1.3. Diagnostic hierarchy from positive blood cultures to community-onset bloodstream infection.
Population-Based Epidemiology and Microbiology of Community-Onset Bloodstream Infections. Kevin B. Laupland, Deirdre L. Church. Clinical Microbiology Reviews Oct 2014, 27 (4) 647-664; DOI: 10.1128/CMR.00002-14
The base of the triangle represents all positive blood cultures, some of which can be due to contamination with, for example skin microflora, due to inadequate sterilisation techniques when obtaining/processing the blood sample, as opposed to the actual presence of bacteria in the blood. The next level up is presence of viable microbes in the blood, but which are present only transiently and do not produce signs of clinical infection in the host. Lastly, positive blood cultures are most commonly due to the presence of viable microbes in the blood which have caused a clinical infection; these can be further classified based on whether the infection was community-onset (CO-BSI), hospital-onset (HO-BSI), community-associated (CA-BSI) or healthcare-associated
(HCA-BSI). The definitions for bloodstream infection and all associated entities from the Laupland et al. review can be found in Table 1.2.
Table 1.2. Definitions of bloodstream infection and associated entities Entity Definition Blood cultures are positive for growth due to organisms that were not Contamination of blood cultures present in the bloodstream
Presence of viable bacteria in the blood; blood cultures positive for Bacteremia bacterial growth where contamination has been ruled out
Presence of viable fungi in the blood; blood cultures positive for fungal Fungemia growth where contamination has been ruled out
Brief episode of bacteremia/fungemia that is not associated with infection Transient bacteremia/fungemia
Bacteremia/fungemia that is associated with infection Bloodstream infection
Bloodstream infection that is first identified (culture drawn) within 48 h Hospital-onset bloodstream infection after hospital admission and within 48 h following hospital discharge*
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Bloodstream infection occurring in an outpatient or first identified (culture Community-onset bloodstream infection drawn) less than 48 h following admission to hospital*
Community-onset bloodstream infection associated with significant prior health care exposure (as evidenced by recent hospitalization, specialized Health care-associated community-onset in-home medical services, care in a hospital-based clinic or hemodialysis bloodstream infection unit, or residence in a nursing home)
Community-onset bloodstream infection not fulfilling criteria for health Community-associated community onset care-associated infection bloodstream infection
Episode of bloodstream infection associated with two or more different Polymicrobial bloodstream infection organisms isolated within 48 h of each other
Population-Based Epidemiology and Microbiology of Community-Onset Bloodstream Infections. Kevin B. Laupland, Deirdre L. Church. Clinical Microbiology Reviews Oct 2014, 27 (4) 647-664; DOI: 10.1128/CMR.00002-14 * 48-hour criteria not used in this thesis, 72-hour criteria used to align with UK-based studies
If appropriate treatment is not administered quickly enough, BSIs can progress to the more severe condition of sepsis and, in the worst case, septic shock and death. Sepsis is the result of an over-active immune response to the presence of microbes in the blood, causing inflammatory responses which can lead to organ failure in the most severe cases (32). Patients are therefore at risk from the microbial pathogens themselves or the inflammatory immune response to the presence of those pathogens, and patient survival is dependent upon balancing the patient’s need for a sufficiently intense immune response to eliminate the infection with bringing the immune response back to a normal functioning state with no associated immunopathology. Eliminating delays in the administration of appropriate intravenous antibiotic therapy has been shown to increase survival by 7 to 10% per hour (33). As outlined in Figure 1.4., BSI is not the only cause of sepsis (characterised by Severe
Inflammatory Response Syndrome (SIRS) – high temperature, high heart rate, high respiratory rate and/or high white blood cell count), but it is the main preceding cause (32).
Figure 1.4. Inflammation responses that occur in response to bacterial bloodstream infection.
Wolk, D., & Fiorello, A. B. (2010). Code Sepsis: Rapid Methods To Diagnose Sepsis and Detect Hematopathogens. Part I: The Impact and Attributes of Sepsis. Clinical Microbiology Newsletter, 32(5), 33- 37. https://doi.org/10.1016/j.clinmicnews.2010.02.001
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Surveillance data has shown that E. coli is the commonest cause of bacteraemia in the UK (34). According to annual mandatory surveillance data from PHE, the number of E. coli bacteraemias in England increased by 14% between 2011 and 2015, with a further increase of 10% to 2017/18, when 41,060 cases were reported (31).
Since 2004 there has been a dramatic decline in rates of methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia in England, previously the primary Gram-positive etiological agent of BSI (35). While it is not entirely clear what interventions this decline was directly attributable to, contributing factors would likely have included improved hand-hygiene campaigns, contact precautions, improved intravenous line management, active surveillance cultures, improved antibiotic stewardship, decolonisation of MRSA carriers, deep cleaning of clinical areas or a combination of the above interventions, known as the “care bundle approach” (35, 36). Figure 1.5. shows the sustained decline in total and hospital-onset cases of MRSA bacteraemia in England in the last 10 years (2007/08 to 2017/18), provided by the PHE mandatory surveillance team.
Figure 1.5. Trends in the all case and hospital-onset rate of MRSA bacteraemia in England. Public Health Health England. England. Annual epidemiologicalAnnual epidemiological commentary: Gram commentary:-negative bacteraemia, Gram- negativeMRSA bacteraemia, MSSA bacteraemiabacteraemia, and C. MRSA difficile bacteraemia, infections, up to and MSSA including bacteraemia financial year and April C. 2017 difficile to March infections, 2018. 2018. up to and including financial year April 2017 to March 2018. 2018.
However, while there has been a decline in MRSA (previously the most prevalent Gram-positive cause of BSI), over the same period an increase in the incidence of Gram-negative BSIs caused predominantly by E. coli,
Klebsiella spp. and Pseudomonas aeruginosa bacteraemia has been seen. Figure 1.6. shows the increase in all case and hospital-onset cases of E. coli bacteraemia from 2012-2018.
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Figure 1.6. Trends in the rate of E. coli bacteraemia in England, 2012/13 to 2017/18.
Public Health England. Annual epidemiological commentary: Gram-negative bacteraemia, MRSA bacteraemia, MSSA bacteraemia and C. difficile infections, up to and including financial year April 2017 to March 2018. 2018.
From Figure 1.6., it can be seen that the incidence of all cases of E. coli bacteraemia have increased while the incidence of hospital-onset cases has remained relatively stable over this period, suggesting that the increased incidence has been mainly due to a rise in incidence of community-onset cases. This trend has also been seen in a study conducted recently by the Imperial College HPRU using retrospective inpatient records from a large NHS
Trust in West London. It showed that E. coli bacteraemia incidence increased by 76% between 2011 and 2015 and that this increase was mainly due to community-onset cases (37). Factors which placed patients at highest risk of mortality were: older age, a higher comorbidity score and having an antibiotic-resistant bacteraemia. The authors concluded that a major focus to reduce the burden of E. coli bacteraemia should shift to initiatives aimed at preventing community-onset cases.
In 2012/13 a sentinel surveillance study was conducted by Abernathy et al. at PHE which involved enhanced data collection for 1,731 cases of E. coli bacteraemia nationally over a 3-month period (38). Of these cases, the urogenital tract was the most frequently reported primary source of BSI (51.2%). Previous antibiotic treatment for a UTI was the most frequently reported source of prior healthcare exposure, followed by antibiotic treatment for respiratory tract infections. Previous healthcare exposure was associated with a higher proportion of antibiotic resistance in blood culture isolates. UTI being recorded as the primary underlying source of E. coli bacteraemia has been seen in the national data in all years since the Abernathy study was conducted. Because
27 | Page of this, the most recent release of the NHS Quality Premium 2017-19 included new incentive schemes for reducing GNBSIs and inappropriate antibiotic prescribing in at-risk groups by reducing GNBSI across the whole health economy and by reducing inappropriate antibiotic prescribing for UTIs in primary care (39).
1.7. Prescribing and resistance – mapping the infection pathway
One of the first studies in the UK to show a correlation between antibiotic use at the general practice level and antibiotic resistance in coliform organisms in urine samples taken from patients with suspected urinary tract infection was by Magee et al. on behalf of the Welsh Antibiotic Study Group (40). The mechanism by which resistance occurs in urinary isolates proposed in this study was either through the prior selection of antibiotic- resistant organisms in the faecal flora of patients presenting with a UTI or through the transmission of these antibiotic-resistant organisms from others in the wider community. Data from around 30,000 isolates from 190
GP surgeries serving over 1.2 million patients were analysed and correlations between prescribing of an antibiotic and resistance to that antibiotic were often found to be significant. Most notably for this thesis, the correlation between higher rates of trimethoprim prescribing and higher rates of trimethoprim resistance in urinary isolates was highly significant (rs=0.331, 95% CI 0.201-0.450; p<0.001). However, it was not just trimethoprim use per se which was correlated with trimethoprim resistance, as amoxicillin use also demonstrated a significant correlation with trimethoprim resistance in urinary isolates (rs=0.172, 95% CI 0.003-
0.304; p=0.015). This co-selection was explained by ampicillin and trimethoprim resistance being encoded by genes occurring on the same transmissible plasmids in E. coli, allowing for selection pressure for resistance to one of these antibiotics also selecting for resistance to the other. The potential effect of bias due to selective sample submission between practices was investigated and there was no significant correlation between antibiotic use and the number of urine samples submitted or the number of urine samples positive for coliforms per 1000 registered patients. What this study was not able to do, however, was draw patient-level inference as the data were analysed at the practice level and factors such as antibiotic indication, patient demographics, co- morbidities and previous antibiotic exposure could not be determined or accounted for. Practice level characteristics were also not accounted for, leaving open the possibility of residual confounding.
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Following this investigation, a study focused solely on bacteriuria caused by trimethoprim-resistant bacteria was undertaken by Donnan et al. using a multilevel modelling approach to explore associations between trimethoprim prescribing and trimethoprim resistance at the GP practice level and the individual patient level simultaneously (41). This cross-sectional study employed the use of linked laboratory, demographic, prescribing and practice data from 8,833 patients registered at 28 general practices in Scotland. There was wide variation found between practices with respect to trimethoprim prescribing (67 to 357 prescriptions per 100 practice patients) and trimethoprim resistance in bacteria isolated from urine specimens (26% to 50%), potentially suggesting scope for improved antibiotic stewardship in the community. After adjusting for practice-level factors, there was no significant association between higher trimethoprim prescribing within a practice and levels of trimethoprim resistance (OR 1.01, 95% CI 0.99-1.02; p=0.101). However, in the multilevel model accounting for both patient and practice level factors, individual prescribing of trimethoprim was significantly associated with trimethoprim resistance (OR 1.22, 95% CI 1.16-1.28; p<0.001), with the association being strongest for patients receiving trimethoprim within 8-15 days before the UTI (OR 9.19, 95% CI 6.35-13.3) and being non-significant in patients receiving trimethoprim over 6 months before the UTI (OR 1.00, 95% CI 0.82-
1.23). Additionally, trimethoprim resistance was also significantly associated with individual prescribing of antibiotics other than trimethoprim (OR 1.18, 95% CI 1.06-1.32; p<0.01). While the types of antibiotics prescribed to individuals were not reported, this finding could be due to the co-selection of trimethoprim and ampicillin resistance genes discussed previously. This study highlighted that while large ecological studies can show associations between antibiotic prescribing and antibiotic resistance within a population, care must be taken when drawing conclusions, as many potential confounding factors cannot be adjusted for and, as seen in this study, associations seen at the individual level may be obscured at the population level. Ecological studies looking at antibiotic prescribing and resistance should be followed up with patient-level analyses.
A number of systematic reviews conducted in the past 5-10 years have since further characterised the relationship between antibiotic use and antibiotic resistance within individual patients and it is now well established that greater exposure to antibiotics leads to a greater likelihood of being colonised with antibiotic- resistant bacteria and/or developing antibiotic-resistant infections (42-44). In one such meta-analysis investigating the effect of primary care antibiotic prescribing on rates of antibiotic resistant infections, it was found that bacteria isolated from individuals who were prescribed an antibiotic for urinary or respiratory tract
29 | Page infections developed resistance to that antibiotic, with the likelihood of resistance developing being highest within the first month immediately after treatment (42). Within this meta-analysis, the pooled odds ratio for developing bacterial resistance to the antibiotic used for treatment within the first two months was 2.5 (95% CI
2.1-2.9) for UTIs and 2.4 (95% CI 1.4-3.4) for respiratory tract infections. Similar trends were seen when looking exclusively at paediatric populations (43, 45).
When looking at how this relates to the progression from UTI treatment to severe outcomes, the likelihood of treatment failure has been a key outcome measure in studies investigating treatment options for UTI (46-48) as treatment failure is often due to antibiotic resistance. Treatment failure has been defined as either a re- consultation within a defined period or, perhaps more appropriately, receiving a second antibiotic prescription within a defined period, indicative of persisting symptoms of infection. Rates of consultation or additional prescription in these studies vary based upon the antibiotic(s) used, the treatment duration chosen and the patient population – these studies will be elaborated upon further in later chapters.
If we revisit the Abernathy et al. study, it was found that among the E. coli bacteraemia infections, 47% of patient urine cultures and 40.5% of blood cultures exhibited non-susceptibility to trimethoprim, respectively (38). The authors interpreted this finding as potentially reflecting previous trimethoprim exposure for UTI treatment, subsequent selection of resistant strains which may have led to treatment failure and progression to bacteraemia.
In a recent large observational study conducted by Vihta et al. at Oxford University, all available data on E. coli bloodstream infections and E. coli UTIs from Oxfordshire over a period of 20 years were used to investigate the factors contributing to regional changes in incidence of E. coli BSI and antibiotic susceptibility (49). Their findings showed that the overall incidence of E. coli BSI increased year-on-year over the study period (aIRR 1.06, 95% CI
1.07-1.13) and that this increase was largely attributable to community (last hospital admission >365 days) and quasi-community (last hospital admission 31-365 days) cases. While trimethoprim-resistance was not specifically focussed on in this study, the authors did find that increased co-amoxiclav prescribing at the practice level was associated with increased rates of E. coli UTIs resistant to co-amoxiclav and an increase in the incidence of E. coli BSI resistant to co-amoxiclav was also seen over the study period. This increase in resistant BSIs did not
30 | Page confer an increase in mortality rates, however. This comprehensive study provides evidence that focussing on interventions targeted at reducing community-associated BSI may have a much greater impact on reducing the overall incidence of BSI in the UK – a strategy which differs from the UK NHS focus on reducing healthcare- associated BSIs. Importantly, this work was not able to make use of patient-level antibiotic prescribing data to analyse the effect of individual prescriptions on outcomes of interest, a line of inquiry which the authors note would be of importance.
What is also not known through this work and similar studies in the literature is the rate of progression from UTI
(particularly those treated with antibiotics) to a BSI or the time frame within which this is most likely to occur.
As touched upon in a recent editorial in the Journal of Hospital Infection: “What we need to understand is whether potentially preventable urosepsis is occurring because NICE guidance is not being followed in primary care, whether the NICE guidance is not fit for purpose, or whether the root cause is simply failure to prescribe appropriate and timely antibiotic therapy (which could be for clinical or laboratory reasons)” (50).
1.8. How do social determinants of health fit in?
Recent studies have shown evidence of a link between deprivation and rates of antibiotic prescribing, with investigations by Antibiotic Research UK (51) and the University of Strathclyde (52) showing higher rates of antibiotic prescribing in deprived areas in England and Scotland, respectively. Similarly, a national study looking at antibiotic prescribing volume by practice found that the area-level deprivation score was a significant predictor of antibiotic usage, although this association was weakened when the data were adjusted to take account of other practice-level factors (53). As touched on by Maureen Baker, former Chair of the Royal College of General Practitioners (RCGP), it is not yet known whether this trend in higher rates of prescribing in more deprived areas is due to a legitimate need for more antibiotic prescriptions in these neighbourhoods or whether it is due to more inappropriate prescribing (54). Recent studies have shown that the transmission of infections such as MRSA (55), antibiotic-resistant E. coli UTIs (56) and community-acquired BSIs (57) disproportionately affect those living in more deprived areas. It is therefore important to further explore the roles that both area- level and patient-level deprivation play in the likelihood of a) receiving a prescription that is not in agreement with national guidance and b) developing a severe outcome following treatment for a UTI.
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1.9. Routinely collected data available in England
There is a wealth of existing healthcare data pertaining to clinical diagnoses, prescriptions, patient demographics, medical history, procedures and organisational characteristics collected in England as a part of routine care through the NHS as well as through infection-specific enhanced surveillance systems. While there are important limitations to consider when using each of these sources of data individually, if linked together they can provide a rich picture of a patient’s interaction with the healthcare system and can allow for important audits and evaluations to take place, particularly those which require the entire patient pathway to be analysed.
By employing the use of such linked data, retrospective studies of long duration can be conducted relatively quickly and at lower cost in comparison to prospective studies with primary data collection and can build large cohorts to measure the incidence of relatively rare events. The use of national data also allows for more generalizable conclusions to be made when evaluating national policy initiatives.
All studies in this thesis employ the use of national datasets. The sources of national data used in this thesis will be described in detail in the chapters in which they are used, however a brief overview, expanding on the information presented in Table 1.1, is as follows:
1) PHE mandatory E. coli bacteraemia surveillance scheme (DCS) – reporting of all ECB cases through a web- based data capture system (DCS) which allows for the capture of clinical data related to the patient and the infection, the primary source of infection and prior healthcare exposure. Surveillance of ECB was made mandatory in England in June 2011.
2) PHE voluntary antibiotic susceptibility data (SGSS Communicable Disease Reporting) – reporting system for
NHS laboratories to feed isolate culture data (e.g. blood and urine) to PHE Colindale, including antibiotic susceptibility data.
3) NHS Digital (NHSBSA) prescriptions database – data on the use of antibiotics (and other medications) prescribed in the community which were extracted monthly from the GP Payments system maintained by NHS
Digital for reporting of antibiotic usage in the ESPAUR reports.
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4) NHS Digital General and Personal Medical Services (formerly HSCIC) – detailed data collected on the General
Practice workforce including data on practices, practice staff and registered patient list sizes. The general practice census is collected yearly.
5) Clinical Practice Research Datalink (CPRD) – a sample of pseudonymised patient-level primary care health records which represent approximately 7% of GP practices in the UK, updated monthly and available for linkage with approved datasets such as secondary care and disease registers. Access is granted upon ethical approval by the CPRD Independent Scientific Advisory Committee (ISAC) and through an institutional licence. PHE data is currently not part of the approved linkage scheme.
6) English Index of Multiple Deprivation 2010 (IMD) – a continuous measure of relative deprivation at the Lower
Super Output Area (LSOA) level (~1,500 people). Score is based on a weighted composite measure derived from seven domains identified in the English Indices of Deprivation: Income, Employment, Health and Disability,
Education Skills and Training, Housing and Services, Living Environment and Crime. The scores are then ranked and binned into quintiles and deciles. For the purposes of linkage with CPRD, they can be based on the patient’s residential postcode (“patient-level”) or based on the practice’s postcode (“practice-level”). IMD deciles and quintiles are available for linkage with CPRD.
7) NHS Digital Hospital Episode Statistics (HES) inpatient records – data warehouse containing data on all hospital admissions at NHS hospitals in England. Records include clinical data about diagnoses, operations and procedures, patient demographic data, administrative data about dates and methods of admission and discharge and geographic data. HES data are available for linkage with CPRD.
8) ONS Mortality data – national register of deaths in England and Wales including causes of death. ONS
Mortality data are available for linkage with CPRD.
9) CPRD/LSHTM Pregnancy Register – a new dataset available for linkage with CPRD which records pregnancies in CPRD alongside the end date of the pregnancy, estimated start date of the pregnancy and patient demographics of the mother.
1.10. Ethics approvals and exemptions
Ethics approval was not required for Chapter 2 as it employed the use of mandatory surveillance data collected and managed by PHE linked with open-access data made available through NHS Digital. All data extraction was
33 | Page conducted by trained PHE employees and provided to me and all data cleaning and analyses conducted by myself was done on-site at PHE Colindale. Data were never removed from the secure PHE servers, except as the findings found in the Tables and Figures in this thesis and published online.
Ethics approval for Chapters 3, 4 and 5 was obtained by the CPRD ISAC, with major and minor amendments made and approved over the period of this PhD (14_225RA2). After the study methodologies and proposed linkages were approved by the ISAC, all data extraction was conducted by the trained and approved CPRD Fob holder within the Imperial College Primary Care and Public Health department. All data management, cleaning and analyses were conducted within the HPRU secure research server which is managed by a trained data manager. No record level data were ever removed from the secure research environment.
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CHAPTER 2: EXPLORING THE EFFECT OF ANTIBIOTIC PRESCRIBING ON THE INCIDENCE AND SUSCEPTIBILITY PATTERNS OF E. COLI BACTERAEMIA – AN ECOLOGICAL STUDY.
Chapter 1: Aims and Background
Chapter 2: Volume of prescribing for UTI and incidence of E. coli bacteraemia
Chapter 3: Quantifying Chapter 4: Effect of BSI-related levels of off-guideline off-guideline UTI diagnosis admission, prescribing for UTI prescribing for UTI and treatment death or re- on BSI risk consultation
Chapter 5: Effect of duration of treatment for UTI on BSI risk
Chapter 6, 7: Discussion, conclusions and recommendations
Summary:
This chapter comprises an ecological study using an extract of infection and antibiotic susceptibility data from Public Health England linked with NHS primary care antibiotic prescribing data and open access GP practice characteristic data. It explores the relationships between practice-level antibiotic prescribing for UTI and UTI- related E. coli bacteraemia incidence in England. Practices with higher rates of antibiotic prescribing for UTI are demonstrated to have higher incidence of UTI-related E. coli bacteraemia (ECB), particularly when looking at trimethoprim prescribing and trimethoprim-resistant ECB. This work has been published in the International Journal of Antimicrobial Agents.
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2.1. Introduction
According to the first English Surveillance Programme Antimicrobial Utilisation and Resistance (ESPAUR) report published in 2014, between 2010 and 2014 the overall incidence of ECB in England, based on mandatory reporting, increased by 15.6% from 45.0 to 52.0 cases per 100,000 population, equating to approximately 34,000
ECB per year in England (58). Of those with a reported primary source of infection (between 80% and 86%), 48% of ECB had “Urinary tract” recorded as the primary source of infection, which made a UTI the source of the BSI in the large majority of cases. Alongside the rise in ECB incidence, it was also seen in the ESPAUR report that between 2010 and 2014 total antibiotic prescribing increased, the majority of antibiotic prescriptions were administered in the community setting (GP practices) and frequently areas with higher antibiotic prescribing were seen to have higher rates of antibiotic resistance. For this reason, in 2015, PHE along with the Imperial
College HPRU decided it would be a priority to further explore these trends and whether antibiotic prescribing for UTI was associated with ECB incidence.
In 2018, a study conducted in the West Midlands by Ironmonger et al. was published investigating the association between antibiotic prescribing by GP practice and antibiotic susceptibility of E. coli positive urine cultures, adjusted for GP practice characteristics (59). Findings showed that across multiple antibiotics (including trimethoprim and nitrofurantoin), small increases in antibiotic prescribing at the practice-level were associated with increases in the number of non-susceptible bacteria in urine cultures in the GP practices patient populations. This chapter will go one step further to examine whether an association exists when looking at antibiotic prescribing for UTIs at the practice-level and incidence of UTI-related ECB, adjusting for practice characteristics.
2.2. Aims and objectives
The overall aim of this chapter was to examine the trends seen in the ESPAUR reports further by examining the association between treatment of UTIs and development of BSIs at the GP practice level. The analyses focussed on a patient cohort comprising women aged >18 years, as UTIs seen in primary care are particularly common in this patient group.
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The objectives were to:
1) Assess the magnitude of any association between trimethoprim and nitrofurantoin prescribing for UTIs and the incidence of ECB in adult women by GP practice (Model 1);
2) Quantify the effect of prescribing of these two antibiotics separately on the incidence of trimethoprim- resistant ECB (Model 2);
3) Quantify the effect of prescribing of these two antibiotics on nitrofurantoin-resistant UTIs leading to an ECB
(Model 3).
The primary hypothesis was that practices with higher levels of prescribing of these antibiotics (adjusted for case-mix of each practice’s patient population) would have higher rates of ECB resulting from an underlying UTI due to higher levels of bacterial resistance and treatment failure. The secondary hypothesis was that this effect would be greater when looking only at antibiotic-resistant bloodstream infections (respective to the antibiotics of interest).
2.3. Data sources and linkage
Healthcare databases are often created to collect focussed information for a particular purpose, for example at one point of care (i.e. hospital records), one type of disease or infection (i.e. a national surveillance scheme for
ECB or a cancer registry) or one outcome (i.e. a death registry), as a few examples. By identifying a common key between one or more of these databases (such as a unique patient or practice identifier) these separate but complimentary sources of data can be linked together to build a more complete picture of a patient’s care. More specifically in epidemiological research, it allows for more accurate capture of potential exposures, covariates and outcomes of interest to allow for more complete models of risk to be built.
Table 2.1 summarises, for this chapter, the data sources used, their scope, access and linkage specifications.
Each linkage was deterministic and was based on either NHS number and specimen number (for the infection- related data) or GP practice code (for the prescribing and practice characteristic data). Data extracts of mandatory ECB data, SGSS blood culture susceptibility data and SGSS urine culture susceptibility data were
37 | Page provided by the HCAI/AMR surveillance team at PHE. Practice-level prescribing data, which was collected and cleaned by the HCAI/AMR surveillance team for the ESPAUR reports, was provided by PHE and extracts of practice-level characteristics (GP characteristics, registered patient list sizes and practice-level deprivation scores) where provided by NHS Digital as open access. The linkage strategy was discussed with the data manager within the HCAI/AMR department, and conducted by myself, along with data cleaning and analysis, using Stata version 13.1 (StataCorp, Texas, USA).
Table 2.1. Specifications of included datasets for ecological study before linkage Data sources Description Scope Access Linkage PHE mandatory E. Enhanced surveillance has been National Permission to use and Linkage with coli bacteraemia mandatory for NHS acute trusts (England) analyze an extract is susceptibility data surveillance since June 2011. Patient data of required from PHE on NHS number scheme any E. coli bacteraemia are HCAI/AMR department (deterministic), reported monthly to Public Health and work must be done in linkage with England (PHE) at the NHS Acute collaboration with the prescribing and Trust and CCG level. department (honorary practice contract). Monthly, characteristic data quarterly and annual on GP practice code epidemiology reports are obtained through released. NHS Digital Spine (deterministic).
PHE Second Antibiotic susceptibility test results National Permission to use and Linkage with Generation for bloodstream isolates supplied (England) analyze an extract is bacteraemia data on Surveillance by approximately 98% of English required from PHE NHS number and System (SGSS) hospital microbiology laboratories. HCAI/AMR department Bacteraemia ID Communicable Test results suppressed from and work must be done in (deterministic). Disease Report clinical reports by the sending collaboration with the (CDR) module (an laboratories are not captured. CDR department (honorary upgraded system module used for blood culture- contract). Monthly, developed from related analyses as the SGSS AMR quarterly and annual the former module had lower laboratory epidemiology reports are CoSurv/LabBase2 coverage prior to 2014. released. database)
PHE SGSS AMR The AMR module contains more National Permission to use and Linkage with module (which comprehensive antibiogram (England) analyze an extract is bacteraemia data on incorporated the information for urine isolates as it required from PHE NHS number and former AmSurv includes results for all antibiotics HCAI/AMR department Bacteraemia ID database) tested (including results and work must be done in (deterministic). suppressed from clinical reports). collaboration with the The AMR module only achieved department (honorary national coverage at the beginning contract). Monthly, of 2014 – urine culture-related quarterly and annual analyses therefore only used data epidemiology reports are from 2014 (calendar year). released.
NHS Business GP practice antibiotic prescribing National Permission to use and Linkage with Services Authority data obtained from the NHSBSA (England) analyze an extract is bacteraemia data (NHSBSA) database by PHE for the purposes required from PHE (linked with of the ESPAUR reports. Data are HCAI/AMR department susceptibility data) extracted each month from the GP and work must be done in on GP Practice Code Payments system maintained by collaboration with the and Year
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NHS Digital. Data were cleaned and department (honorary DDD/1000population was contract). Monthly, calculated for each antibiotic, quarterly and annual practice and month by the epidemiology reports are HCAI/AMR department at PHE. released.
GP registered patient lists, STAR-PU National Open access and freely Linkage with NHS Digital GP weights to allow for case-mix (England) downloadable (links bacteraemia data practice adjustment, practice characteristics available in the text). (linked with characteristic data (# of GPs, # of FTE, # of susceptibility and male/female GPs, age range of GPs, prescribing data) on # of GPs qualified in the UK) – all GP Practice Code freely available from HSCIC and Year website.
Figure 2.1 shows the overall data linkage strategy and the sources of data for each extract. The antibiotic susceptibility data for bacteria isolated from blood and urine cultures were available at the patient-level and were linked with ECB records first. Antibiotic prescribing data and practice characteristic data were available at the GP practice-level, which was therefore the unit of analysis. The linked infection-related data was then aggregated at the GP practice level and linked with antibiotic prescribing (by practice and year), practice characteristics and registered patient list sizes to conduct the analyses. This process will be elaborated on further in Section 2.4 (Methods).
Figure 2.1. Overall linkage strategy of ecological study with dataset sources.
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2.4. Methods
2.4.1. Study population
The study population comprised adult (age >18 years) female patients with bacteraemia caused by E. coli notified to the national mandatory surveillance programme (9) between January 2012 and December 2014, who had the underlying source of the bacteraemia recorded as “urinary tract”. The antibiotic prescribing data for primary care providers compiled by the NHS Business Services Authority (NHS BSA) which had previously been collected and collated for inclusion in the annual ESPAUR reports was used to derive information on prescribing of trimethoprim and nitrofurantoin. This dataset gave the quantities and formulations of these antibiotics prescribed by all GP practices within England by month; defined daily doses (DDDs) (60) were then calculated by
PHE and were the measure of prescribing used in these analyses.
Antibiotic susceptibility data provided by hospital microbiology laboratories on a voluntary basis were extracted from the two modules of PHE’s Second Generation Surveillance System (SGSS) database (61), namely the
Communicable Disease Reporting module (CDR; formerly CoSurv/LabBase2) for blood culture susceptibility data and the Antimicrobial Resistance Module (AMR; formerly AmSurv) for urine culture susceptibility data (62). Data pertaining to GP practice characteristics (such as the number, sex, age and country of medical qualification of full-time equivalent GPs within each practice) were obtained from the Health and Social Care Information Centre
(now called NHS Digital) (63) for each year of the study period. The Index of Multiple Deprivation 2010 data (64) were obtained at the Lower Super Output Area (LSOA) level and then mapped to GP practices based on the
LSOAs served by each GP practice in the study. Practice denominators were obtained by applying STAR-PU weightings (Specific Therapeutic Group Age-sex weightings Related Prescribing Units) (65) to registered patient list sizes for each practice and each year (66). STAR-PU weightings take account of variations in patient need for different therapeutic agents based on age and sex profiles and therefore allow for standardised comparisons of antibiotic prescribing between GP practices in England. As the weights were issued in 2009 and updated in 2013, the 2009 weights were applied to the 2012 patient list sizes and the 2013 weights were applied to the 2013 and
2014 patient list sizes (calculated separately for sex and age bands). STAR-PU weights can be found in Appendix
A.
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For the prescribing outcomes, the whole patient list size for each practice and each year (after STAR-PU weights were applied) was used as the denominator to reflect that these antibiotics could have been prescribed to women and men, adults and children. For the infection outcomes, however, only registered adult female patients per practice per year (after the STAR-PU weights were applied) were used as the denominator to reflect that a specific patient population was being investigated for this infection.
2.4.2. Inclusion and exclusion criteria
Choice of antibiotic
Only trimethoprim and nitrofurantoin were considered in this analysis, as they are the two main antibiotics used for treatment of uncomplicated UTIs in non-pregnant adult women and men (who do not have prostatitis) with normal kidney function (GFR > 45 ml/min) (67). In addition, as this is an ecological study, only antibiotics which could be used as a reliable proxy for UTI treatment at the practice-level between 2012 and 2014 could be included. As the other first-line option, pivmecillinam, was not added to the guidelines until the end of 2014 and second-line options, such as amoxicillin, have formulations for many other types of infections (68), only trimethoprim and nitrofurantoin prescribing was analysed. As nitrofurantoin susceptibility is not routinely tested for in blood cultures, only trimethoprim-resistant bacteraemia was measured as an outcome using the linked blood culture data. Nitrofurantoin resistance outcomes were instead assessed by looking at ECB resulting from a nitrofurantoin-resistant UTI (Model 3), which made use of antibiotic susceptibility data from urine cultures linked with ECB records.
Data extraction and linkage
Patient-level ECB records from the mandatory surveillance database were deterministically linked (using NHS number) with blood culture susceptibility records and urine culture susceptibility records (where available) that were positive for isolation of E. coli. Bacteraemia records were still included if they did not have linked microbiology data. E. coli blood culture susceptibility data were only linked if the blood culture specimen date
(in the susceptibility dataset) was within two weeks either side of the bacteraemia specimen date (in the mandatory surveillance dataset) to allow for reporting delays. Urine culture susceptibility data were only linked if the urine culture specimen date was within 30 days before or 2 days after the bacteraemia specimen date.
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This time window was selected by expert opinion after discussion with collaborators in the HCAI/AMR department at PHE and was supported by the literature with regards to the length of time (two to four weeks) within which it is warranted to obtain cultures to identify resistant organisms in patients for whom UTI-related symptoms are not clearing after initial antibiotic treatment (69, 70).
Repeat specimens were removed from the linked datasets after which the data were aggregated by GP practice and year. If susceptibility results differed between repeat specimens, the record reporting resistance was chosen. These practice-level infection and susceptibility data were then linked with trimethoprim and nitrofurantoin prescribing data, STAR-PU-adjusted registered patient list sizes and practice-level characteristics deterministically based on GP practice code - a unique identifier for each practice that was conserved across all datasets used in this chapter. The GP practice code is not included in the standard outputs of the ECB data and was included in these extracts specifically for the purposes of this study, by mapping the data to the NHS Spine and obtaining the GP practice code; this work was carried out by the HCAI/AMR department at PHE. Antibiotic susceptibility test results for isolates were reported as “susceptible”, intermediate” or “resistant” to the tested antibiotic. Non-susceptibility to a given antibiotic was classified as either “intermediate” or “resistant” for this study.
As the SGSS AMR voluntary surveillance scheme only attained extensive national coverage after 2013, urine culture data were only linked to bacteraemia data between January 1st, 2014 and December 31st, 2014 as this was the time period in the study in which participation in this reporting scheme was highest (89% of Laboratories reported data to the AMR module of SGSS in 2014). To increase the number of potential linkages with urine culture data, all urine cultures positive for either E. coli or a Coliform were included, as approximately 13% of
English laboratories contributing data to SGSS AMR in 2014 rarely identified Gram-negative bacteria from urine to species level, simply reporting such isolates as “Coliform”. Repeat specimens were removed from the linked infection data and were aggregated by year and GP practice.
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2.4.3. Statistical methods
Defining exposures and outcomes
A multi-level modelling approach was used to determine the association between prescribing of trimethoprim and nitrofurantoin for UTIs (measured as DDD/1000 population for each practice and year) and the incidence of
ECB (both resistant and susceptible) at the practice-level. Prescribing was grouped in quintiles in order to allow reporting of a clinically meaningful increase in exposure (i.e. an increase from the first 20th percentile of practices to the next 20th percentile of practices as opposed to an increase of one DDD/1000 population), the DDD/1000 population ranges of the quintiles for each antibiotic can be found in Table 2.1. The prescribing quintiles were considered continuous as this variable showed a linear relationship with the outcome. The total number of ECB in adult females (with a urinary tract source) per practice (Model 1), the total number of trimethoprim-resistant
ECB in adult females (with a urinary source) per practice (Model 2) and the total number of ECB in adult females originating from a nitrofurantoin-resistant UTI per practice (Model 3) were analysed. The models investigating trimethoprim-resistant bacteraemia and bacteraemia following a resistant UTI as the outcomes used a subset of patients for which trimethoprim susceptibility testing data from blood culture or nitrofurantoin susceptibility data from urine culture were available, respectively.
Table 2.1. Trimethoprim and nitrofurantoin prescribing ranges by quintile - DDD/1000 registered patients (STAR-PU- adjusted)
Trimethoprim (DDD/1000 population) Quintile Min .25 Median .75 Max 1 6.97 305.31 393.97 459.06 517.09 2 517.15 565.01 606.99 648.20 684.19 3 684.20 720.68 754.27 789.04 824.41 4 824.43 862.03 902.69 946.79 999.69 5 999.72 1064.33 1146.74 1274.97 1993.90
Nitrofurantoin (DDD/1000 population)
Quintile Min .25 Median .75 Max 1 1.35 119.12 174.31 216.97 254.34 2 254.41 287.41 318.49 347.82 375.83 3 375.84 401.48 427.38 455.26 484.35 4 484.35 515.60 548.68 586.61 629.34 5 629.39 680.63 748.57 855.06 1199.43
A sub-analysis investigating the effect of the ratio of trimethoprim prescribing to nitrofurantoin prescribing on each of the three infection outcomes was also performed. This ratio was calculated as (trimethoprim
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DDD/1000population) / (nitrofurantoin DDD/1000population + trimethoprim DDD/1000population) per practice per year. This measure is a key indicator of appropriate prescribing set out by PHE and is defined as the
“twelve month rolling proportion of trimethoprim class prescribed antibiotic items as a ratio of trimethoprim to nitrofurantoin” (71). For these sub-analyses, the ratio was used firstly to assess the longitudinal trend of this variable. Secondly it was used to analyse the impact on total UTI-related E. coli bacteraemia incidence, trimethoprim-resistant UTI-related E. coli bacteraemia and incidence of E. coli bacteraemia following a nitrofurantoin-resistant UTI infections in place of separate trimethoprim and nitrofurantoin prescribing. After categorising the trimethoprim to nitrofurantoin ratio values into quintiles (Quintile 1 including practices with lower levels of trimethoprim to nitrofurantoin prescribing and Quintile 5 including practices with higher levels of trimethoprim to nitrofurantoin prescribing), Models 1, 2 and 3 were re-run using the ratio quintiles as the independent variable.
Univariate analysis methods
Differences in means of the outcomes were analysed using t-tests for binary covariates and ANOVA tests for categorical outcomes. The final adjusted models were adjusted for covariates which were previously reported in the literature as being risk factors for patient safety outcomes at the GP practice level (72) and also demonstrated a statistically significant association with the outcome of interest (p<0.05) in the unadjusted analyses.
Determining the distribution
For the infection-related outcomes, both Poisson and negative binomial distributions were considered, as the study involved count data. To decide between Poisson and negative binomial distributions, overdispersion was tested for by investigating the mean and the variance of the outcome (ECB) to determine whether the variance differed significantly from the mean under an assumed Poisson distribution. The mean was 2.34 and the variance was 2.47, this was taken to indicate there was no evidence of overdispersion and a Poisson distribution was then used where ECB counts were the outcome.
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Determining parameters for linear regression models
For the models looking at rates of prescribing by practice as the outcome where the data were not presented as
counts, a fixed effects or random effects linear regression model was used, rather than Poisson or negative
binomial distribution. To test whether fixed or random effects were appropriate, unadjusted fixed effects and
random effects models were run and the estimates were stored. The Hausman test was then employed to test
whether the null hypothesis of the random effects model being the most appropriate fit to the data could be
rejected. The null hypothesis could not be rejected (p>0.05) after running the Hausman test and random effects
linear regression models were therefore run where antibiotic prescribing was the outcome.
Multivariable analysis methods
The rates of testing of blood culture isolates for susceptibility to trimethoprim and urine isolates for
susceptibility to nitrofurantoin in each of the then 15 PHE Centres in England (73) were adjusted for in Model 2
and Model 3, respectively, to account for any potential biases which differing susceptibility testing rates may
introduce. To compare the fits of the models with and without the testing weights, the models with the lowest
Schwartz Bayesian Information Criterion value (BIC) were selected; in both cases the models which were
adjusted for testing rates were a better fit to the data, and trimethoprim and nitrofurantoin susceptibility testing
weights were therefore included in Models 2 and 3, respectively. Model 1
Rate of trimethoprim prescribing, rate of nitrofurantoin prescribing, year and mean age of patients by practice
were adjusted for a priori in all models – all other possible covariates were included if they demonstrated a
statistically significant relationship with the outcome in the univariate analyses. Model fit was tested using the
BIC value to determine whether categorical variables should be specified as categorical or continuous in each
model. The practice characteristics of GP full time equivalents, single-handed practices, GPs aged over 50,
female GPs and GPs qualified in the UK were classified as binary (less than 50% and more than 50%). Linear
random effects models were used to determine prescribing trends over the study period, adjusted for practice
characteristics using similar adjustment methods as for the infection models.
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2.5. Results
2.5.1. Descriptive analysis
Between 1 Jan 2012 and 31 Dec 2014, 114,761 episodes of E. coli bacteraemia in adults were reported, 50,487
(44%) of which were in women. An underlying source of infection was recorded for 83% of the bacteraemias in women. The main recorded primary sources were: Urinary tract (53%), Unknown source (21%), Hepatobiliary
(12%), Gastrointestinal (excluding hepatobiliary) (5%), Respiratory tract (3%) and Other (3%). After de- duplication and exclusion of records without the urinary tract as the primary source or those with incomplete data for GP practice code, practice list size, prescribing data or north/south region, 19,874 patients from 5,916
GP practices were included in the study, comprising 11,135 practice-year observations (per practice per year aggregated measures) (Figure 2.2).
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114,761 total adult E. coli bacteraemia in study period (2012-2014)
64,274 male and undefined E. coli bacteraemia (49%)
50,487 female E. coli bacteraemia
5,463 without a practice code, list size, prescribing or region (11%)
25,150 without a urinary tract source (55%)
19,874 female patients with E. coli bacteraemia and a urinary tract source in 2012-2014 (7,175 in 2014)
11,388 aggregated GP 79,221 unlinked E. coli blood 897,442 unlinked urine cultures practice/year data points cultures in 2012-2014 in 2014 (E. coli + Coliform)
41,207 female blood cultures 646,134 female urine cultures 11,135 data points with (duplicates removed) (52%) (with NHS number + duplicates trim/nitro data (98%) removed) (72%)
15,140 blood cultures linked with bacteraemia data (76% 1,565 urine cultures linked of possible linkages)* with bacteraemia data (22% of possible linkages)**
7,672 847 1,529 1,505 trimethoprim nitrofurantoin trimethoprim nitrofurantoin tested (51%) tested (6%) tested (98%) tested (96%)
9,298 data points (aggregated Aggregated and linked with with trim/nitro data) GP practice prescribing data
Model 1 Model 2 Model 3
Figure 2.2. Study profile * Only blood cultures with specimen dates within 2 weeks before or after bacteraemia sample specimen date were used for linkage. **Only urine cultures (from 2014) taken within 30 days before and 2 days after the bacteraemia specimen date were used.
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Prescribing of trimethoprim decreased by 13.7% and prescribing of nitrofurantoin increased by 13.8% between
January 2012 and December 2014 (Figure 2.3). The adjusted analysis showed a reduction in trimethoprim
prescribing of 84.2 DDD/1000 STAR-PU year-on-year (95% CI -87.6 to -80.4, p<0.001) and a relatively stable rate
of nitrofurantoin prescribing (increase of 4.7 DDD/1000 STAR-PU year-on-year, 95% CI 2.2-7.3, p<0.001).
Figure 2.3. Total primary care prescribing of trimethoprim and nitrofurantoin in the study population by month (2012-2014)
Unadjusted ECB counts per month are shown in Figure 2.4. Between January 2012 and December 2014, the total Model 1 count of UTI-related ECB in adult women decreased by 17.8% (unadjusted analysis).
Figure 2.4. Total E. coli bacteraemia infections with a UTI focus in the study population by month (2012-2014)
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2.5.2. Model 1 – Prescribing and total ECB incidence
UTI-related ECB counts were higher in practices in the North of England and in practices that had more than one
GP, younger GPs, fewer GPs that had qualified in the UK or fewer female GPs (Table 2.2). Practices with higher trimethoprim prescribing had higher rates of ECB than those with lower levels of prescribing (p<0.001); a similar trend was observed for nitrofurantoin (p<0.001). ECB rates per practice did not vary by deprivation of the practice (where lower quintiles indicate less deprivation and higher quintiles greater deprivation (p=0.44) or by percentage of full-time vs part-time equivalents (p=0.395).
Table 2.2. Baseline distribution of total E. coli bacteraemia infections in women per year by GP practice characteristics (2012-2014) Total (%) bacteraemia Mean (# of SD p-value infections bacteraemia per practice-year) Year 0.522 2012 6,822 (34.3%) 1.79 1.16 2013 6,446 (32.4%) 1.77 1.13 2014 6,606 (33.3%) 1.80 1.15 Trimethoprim prescr. (DDD/1000pop) <0.001 Quintile 1 3,403 (17.1%) 1.53 0.91 Quintile 2 3,946 (19.9%) 1.77 1.11 Quintile 3 4,071 (20.5%) 1.83 1.16 Quintile 4 4,239 (21.3%) 1.90 1.24 Quintile 5 4,215 (21.2%) 1.89 1.23 Nitrofurantoin prescr. (DDD/1000pop) <0.001 Quintile 1 3,532 (17.8%) 1.59 0.95 Quintile 2 3,943 (19.8%) 1.77 1.14 Quintile 3 4,086 (20.6%) 1.83 1.18 Quintile 4 4,209 (21.2%) 1.89 1.27 Quintile 5 4,104 (20.7%) 1.84 1.16 North vs. South <0.001 Northern England 12,481 (62.8%) 1.87 1.21 Southern England 7,393 (37.2%) 1.66 1.03 Deprivation (228 missing data) 0.435 Quintile 1 3,901 (19.9%) 1.78 1.10 Quintile 2 3,989 (20.3%) 1.82 1.14 Quintile 3 3,971 (20.2%) 1.81 1.16 Quintile 4 3,924 (20.2%) 1.79 1.16 Quintile 5 3,861 (19.7%) 1.76 1.18 GPs full-time equivalents 0.395 50% or less 2,330 (11.7%) 1.76 1.15 More than 50% 17,544 (88.3%) 1.79 1.15 Single-handed practices <0.001 More than one GP 19,091 (96.1%) 1.81 1.16 One GP 783 (3.9%) 1.29 0.60 GPs aged 50 years and over <0.001 50% or less 15,756 (79.3%) 1.83 1.18 More than 50% 4,118 (20.7%) 1.63 1.03 GPs qualified in the UK <0.001 50% or less 4,266 (21.5%) 1.59 1.03 More than 50% 15,608 (78.5%) 1.85 1.18 Female GPs <0.001 50% or less 11,756 (59.2%) 1.75 1.14 More than 50% 8,118 (40.8%) 1.84 1.16
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ECB rates remained relatively stable year-on-year (IRR=1.014; 95% CI 1.00-1.03) in the adjusted analysis.
Following adjustment for practice characteristics and mean age of adult female patients, an increase from one quintile to the next in trimethoprim prescribing was associated with a 3% (95% CI 2.0%-4.0%) increase in the incidence in ECB, while an increase in quintile of nitrofurantoin prescribing was associated with 1.5% (95% CI
0.6%-2.5%) increased incidence in ECB (Table 2.3).
Table 2.3. Adjusted associations between practice characteristics and total E. coli bacteraemia infections in women per practice (2012-2014) IRR (95% CI)b p-value Yeara 1.014 (1.000-1.028) 0.049 Trimethoprim prescribing (Quintile 1 = baseline)a 1.030 (1.020-1.040) <0.001 Nitrofurantoin prescribing (Quintile 1 = baseline)a 1.015 (1.006-1.025) 0.002 a Association between year/prescribing and total bacteraemia infections adjusted for mean age, north vs. south, number of GPs, GPs over 50, GPs qualified in the UK and number of female GPs. b IRRs correspond to the increase from one prescribing quintile to the next.
As an illustrative example using the increase in prescribing from Quintile 2 to 3 (difference in medians), an increase in trimethoprim prescribing of approximately 150 DDD/1000 population (STAR-PU adjusted) corresponded to a 3% increase in ECB incidence. Similarly, an increase in nitrofurantoin prescribing of approximately 100 DDD/1000 population (STAR-PU adjusted) corresponded to an increase in ECB incidence of
1.5%.
2.5.3. Model 2 – Prescribing and trimethoprim-resistant ECB incidence
Of the 19,874 adult female ECB patients in the study population, 15,140 (76%) had linked blood culture data and were therefore the subset of patients used for this analysis. Of the 15,140 blood culture isolates, 7,672 (51%) were tested for trimethoprim susceptibility, with 4,213 (53.4%) of those tested being susceptible. Only 847
(5.6%) blood cultures were tested against nitrofurantoin, of which 813 (96%) were susceptible.
Trimethoprim-resistant ECB counts per practice per year did not change over the study period, were higher in practices in the North of England and in practices that had more than one GP, younger GPs and less GPs qualified in the UK (Table 2.4). Practices with higher trimethoprim prescribing had higher a higher rates of trimethoprim-
50 | Page resistant ECB than those with lower prescribing (p<0.001); a similar trend was seen for nitrofurantoin (p<0.001).
Practices in more deprived areas had higher mean number of trimethoprim-resistant ECB (p<0.001).
Trimethoprim-resistant ECB rates did not vary by percentage of full-time vs part-time equivalents or by proportion of female GPs at the practice.
Table 2.4. Baseline distribution of trimethoprim-resistant UTI-related E. coli bacteraemia infections in women per year by GP practice characteristics (2012-2014) Total (trimR bacteraemia Mean (# of trimR SD p-value infections) bacteraemia per practice-year) Year 0.075 2012 1,298 (36.2%) 0.411 0.663 2013 1,136 (31.7%) 0.379 0.642 2014 1,149 (32.1%) 0.178 0.631 Trimethoprim prescribing <0.001 (DDD/1000pop) Quintile 1 418 (11.7%) 0.283 0.526 Quintile 2 709 (19.8%) 0.373 0.630 Quintile 3 871 (24.3%) 0.423 0.678 Quintile 4 837 (23.4%) 0.414 0.662 Quintile 5 748 (20.9%) 0.429 0.686 Nitrofurantoin prescribing <0.001 (DDD/1000pop) Quintile 1 439 (12.3%) 0.314 0.545 Quintile 2 683 (19.1%) 0.400 0.646 Quintile 3 850 (23.7%) 0.428 0.699 Quintile 4 855 (23.9%) 0.405 0.666 Quintile 5 756 (21.1%) 0.379 0.629 North vs. South <0.001 Northern England 2,336 (65.2%) 0.433 0.669 Southern England 1,247 (34.8%) 0.329 0.606 Deprivation (37 missing data) <0.001 Quintile 1 562 (15.8%) 0.308 0.585 Quintile 2 836 (23.6%) 0.462 0.701 Quintile 3 833 (23.5%) 0.458 0.699 Quintile 4 689 (19.4%) 0.382 0.633 Quintile 5 626 (17.7%) 0.345 0.597 GPs full-time equivalents 0.141 50% or less 444 (12.4%) 0.417 0.674 More than 50% 3,139 (87.6%) 0.386 0.642 Single-handed practices <0.001 More than one GP 3,464 (96.7%) 0.397 0.653 One GP 119 (3.3%) 0.257 0.461 GPs aged 50 years and over <0.001 50% or less 2,890 (80.7%) 0.405 0.661 More than 50% 693 (19.3%) 0.336 0.588 GPs qualified in the UK <0.001 50% or less 676 (18.9%) 0.314 0.574 More than 50% 2907 (81.1%) 0.413 0.665 Female GPs 0.640 50% or less 2,154 (60.1%) 0.387 0.648 More than 50% 1,429 (39.9%) 0.394 0.643
Following adjustment for practice characteristics and mean age of adult female patients, an increase from one quintile to the next in trimethoprim prescribing was associated with a 4.5% (95% CI 0.4%-8.8%) increase in the
51 | Page incidence of trimethoprim-resistant ECB. Nitrofurantoin prescribing was not associated with the incidence of trimethoprim-resistant ECB (IRR=0.988; 95% CI 0.95-1.03) (Table 2.5).
Table 2.5. Adjusted associations between practice characteristics and total trimethoprim-resistant E. coli bacteraemia infections in women per practice (2012-2014) IRR (95% CI)b p-value Yeara 0.985 (0.930-1.042) 0.594 Trimethoprim prescribing (Quintile 1 = baseline)a 1.045 (1.004-1.088) 0.032 Nitrofurantoin prescribing (Quintile 1 = baseline)a 0.988 (0.951-1.027) 0.546 a Association between year/prescribing and trimethoprim-resistant bacteraemia infections adjusted for mean age, north vs. south, number of GPs, GPs over 50 and GPs qualified in the UK. b IRRs correspond to the increase from one prescribing quintile to the next.
As an illustrative example, again moving from prescribing Quintile 2 to 3, an increase in approximately 150
DDD/1000 population (STAR-PU adjusted) in trimethoprim prescribing corresponded to a 4.5% increase in incidence of trimethoprim-resistant ECB.
2.5.4. Model 3 – Prescribing and incidence of ECB subsequent to a nitrofurantoin- resistant UTI
Of the 7,175 adult female ECB patients in the study population in 2014, 1,565 (22%) had linked urine culture data and were therefore the subset of patients used for this analysis. Of the 1,565 urine isolates, 1,529 (98%) were tested for trimethoprim susceptibility, with 768 (50.2%) being susceptible. Of the 1,565 urine isolates,
1,505 (96%) were tested for nitrofurantoin susceptibility, with 1,452 (96%) being susceptible. Rates of ECB resulting from a nitrofurantoin-resistant UTI were higher in single-handed practices and in practices that had fewer GPs that had qualified in the UK (Table 2.6).
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Table 2.6. Baseline distribution of nitrofurantoin-resistant UTIs leading to an E. coli bacteraemia in women per year by GP practice characteristics (2014) Total (bacteraemia Mean SD p-value after nitroR-UTI) Nitrofurantoin prescribing (DDD/1000pop) 0.957 Quintile 1 313 (20%) 0.045 0.222 Quintile 2 313 (20%) 0.038 0.192 Quintile 3 313 (20%) 0.048 0.214 Quintile 4 313 (20%) 0.048 0.214 Quintile 5 313 (20%) 0.051 0.221 Trimethoprim prescribing (DDD/1000pop) 0.526 Quintile 1 313 (20%) 0.029 0.186 Quintile 2 313 (20%) 0.048 0.214 Quintile 3 313 (20%) 0.051 0.221 Quintile 4 313 (20%) 0.045 0.207 Quintile 5 313 (20%) 0.058 0.233 North vs. South 0.925 Northern England 948 (61%) 0.046 0.215 Southern England 617 (39%) 0.045 0.208 Deprivation (21 missing) 0.896 Quintile 1 309 (20%) 0.039 0.194 Quintile 2 308 (20%) 0.042 0.201 Quintile 3 310 (20%) 0.042 0.216 Quintile 4 309 (20%) 0.055 0.228 Quintile 5 308 (20%) 0.045 0.209 GPs FTE (6 missing) 0.414 50% or less 10 (0.6%) 0.100 0.316 More than 50% 1,549 (99.4%) 0.045 0.211 Single-handed practices (6 missing) 0.034 More than one GP 1,487 (95%) 0.043 0.206 One GP 72 (5%) 0.097 0.298 GPs aged ≥ 50 years (6 missing) 0.053 50% or less 1,222 (78%) 0.040 0.200 More than 50% 337 (22%) 0.065 0.247 GPs qualified in the UK (6 missing) 0.002 50% or less 335 (21%) 0.078 0.268 More than 50% 1,224 (79%) 0.037 0.193 Female GPs 0.589 50% or less 873 (56%) 0.048 0.214 More than 50% 686 (44%) 0.042 0.208
After adjusting for practice characteristics and mean age of adult female patients, ECB resulting from a nitrofurantoin-resistant UTI was not associated with a significant increase in nitrofurantoin (IRR=1.03; 95% CI
0.87-1.2) or trimethoprim prescribing (IRR=1.14; 95% CI 0.96-1.4) (Table 2.7).
Table 2.7. Adjusted associations between practice characteristics and total nitrofurantoin-resistant UTIs leading to an E. coli bacteraemia in women per practice (2014) IRR (95% CI) p-value Nitrofurantoin prescribing 1 1.033 (0.866-1.231) 0.719 Trimethoprim prescribing 1 1.148 (0.962-1.370) 0.126 a Association between prescribing and trimethoprim-resistant bacteraemia infections adjusted for mean age, north vs. south, number of GPs, GPs over 50 and GPs qualified in the UK. b IRRs correspond to the increase from one prescribing quintile to the next.
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2.5.5. Trimethoprim:nitrofurantoin ratio analyses for Models 1, 2 and 3
For each practice and each year, the ratio of trimethoprim DDD/1000 STAR-PU to nitrofurantoin DDD/1000
STAR-PU was generated and was used to first assess the longitudinal trend of this variable and secondly to analyse the impact on each of the three outcomes discussed in Sections 2.5.3. and 2.5.4., in place of nitrofurantoin and trimethoprim prescribing separately. After adjusting for practice-level covariates which were significant at the p<0.05 level in the unadjusted analyses (candidate covariates were the same as those used in
Models 1, 2 and 3), the ratio of trimethoprim to nitrofurantoin prescribing decreased year-on-year across
England (coefficient= -0.228; 95% CI -0.250 to -0.210).
After categorising the trimethoprim to nitrofurantoin ratio values into quintiles (Quintile 1 included practices with lower levels of trimethoprim to nitrofurantoin prescribing and Quintile 5 included practices with higher levels of trimethoprim to nitrofurantoin prescribing), Model 1 was re-run using the ratio quintiles as the independent variable. After adjusting for practice-level covariates which were significant at the p<0.05 level in the unadjusted analyses, an increase from one quintile to the next in the ratio of trimethoprim to nitrofurantoin prescribing was not associated with a change in total ECB incidence (IRR=0.999; 95% CI 0.989 to 1.010).
Similarly, Model 2 was re-run using the ratio quintiles as the independent variable to examine the effect of the prescribing ratio on the incidence of trimethoprim-resistant ECB. After adjusting for practice-level covariates which were significant at the p<0.05 level in the unadjusted analyses, an increase from one quintile to the next in the ratio of trimethoprim to nitrofurantoin prescribing was associated with a 3.7% (p=0.046; 95% CI 1.00 to
1.074) increase in the incidence of trimethoprim-resistant ECB.
Similarly, Model 3 was re-run using the ratio quintiles as the independent variable to examine the effect of the prescribing ratio on the incidence of ECB resulting from a nitrofurantoin-resistant UTI. After adjusting for practice-level covariates which were significant at the p<0.05 level in the unadjusted analyses, trimethoprim to nitrofurantoin prescribing ratio was not associated with a change in ECB incidence resulting from a nitrofurantoin-resistant UTI (IRR=0.986; 95% CI 0.837 to 1.161).
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2.6. Discussion
2.6.1. Main findings
This study demonstrates an association, at the GP practice-level, between the prescribing of the two first-line recommended antibiotics for UTI treatment and incidence of UTI-related ECB in adult women. The increase in bacteraemia incidence associated with trimethoprim prescribing was higher than that seen with nitrofurantoin prescribing. It has also demonstrated that higher prescribing of trimethoprim is associated with a higher incidence of UTI-related trimethoprim-resistant ECB. No association was found between a practice’s level of nitrofurantoin prescribing and the incidence of trimethoprim-resistant bacteraemia nor was an association found between nitrofurantoin or trimethoprim prescribing and the incidence of nitrofurantoin-resistant UTIs leading to an ECB.
This difference between the two antibiotics with regards to their association with ECB may be at least in part due to the difference in their overall resistance rates in the community, as both observed in this study and reported in the wider literature. In this study it was found that 46.6% of blood cultures and 49.6% of urine cultures tested against trimethoprim were non-susceptible to this antibiotic, whereas only 3.7% of urine cultures tested against nitrofurantoin were non-susceptible to this antibiotic. In a study conducted in the Yorkshire and
Humber region of England, looking at resistance rates in ECB blood isolates between 2010 and 2012, resistance to trimethoprim was seen in 40% of 770 isolates (74). In the 2015 ESPAUR report, resistance to trimethoprim was found in 35-37% of the 711,960 isolates of E. coli from urine samples submitted for susceptibility testing by reporting laboratories in 2014, whereas resistance to nitrofurantoin was found in only 3% (75).
The higher rates of resistance seen in the study population could be due to several possible factors: 1) as the women in the study were only those with a reported bloodstream infection originating from a UTI, one would expect the urine isolates from these women to have a higher likelihood of antibiotic resistance compared to those for whom the UTI did not progress to a severe infection, 2) this urine culture sample was smaller than that which was used by PHE to report their resistance rates, which were all of the submitted urine samples in England or 3) this sample of only adult women may have differing levels of risk with regards to being infected with a resistant strain of E. coli than the entire population of UTI patients.
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2.6.2. Limitations
The analyses reported here have several limitations. Firstly, as with any ecological study design, patient-level conclusions cannot be drawn, as patient-level factors which may contribute to the overall association cannot be accounted for (ecological fallacy). However, at the time this study was conducted, the use of GP practice-level data which allows the lowest level of granularity that a national analysis could be performed at based upon the data available, had not been described previously in the subject area and all GP practices were included.
Additionally, reporting results at the GP practice-level allows for recommendations to be made with regards to changing practice-level policy.
Secondly, completeness of data reporting is often a limitation in studies using routinely collected data. Only bacteraemia records with a recorded urinary source of infection (17% missing data; of those with data, 21% were reported as “unknown”), denominator data (14% of practices had missing data) and practice prescribing data were included, as these variables were essential to the analyses. Practice-year observations that fell outside of the normal distributions for prescribing were considered as outliers and were not included (2.2%) as they were deemed to represent reporting error and if their characteristics differed from the study population this could introduce bias. However, as 52% of these practices were also missing practice characteristic data, it is not possible to determine the direction of this potential source of bias.
Thirdly, the antibiotic susceptibility data from blood cultures were collected through a voluntary surveillance scheme, participation in which improved over time. The proportion of laboratories submitting data to the CDR modules of SGSS was approximately 90% across the country and remained stable at this level between 2012 and
2014. The AMR module of SGSS in comparison, is a more recent system and participation was not at an acceptable level until the end of 2013 (when it reached 89%), which is why the third model was restricted to using urine culture data only for isolates obtained in 2014. This comparatively small amount of data used for the third model may also have had an effect on whether an association between prescribing and incidence of bacteraemia resulting from a nitrofurantoin-resistant UTI could be seen.
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Additionally, because the prescription data was at the practice-level, it was not possible to determine which antibiotics individual patients were prescribed. To account for this, the whole patient list size for each practice and each year was used as the denominator in calculating antibiotic prescribing as opposed to only using registered adult female patients as the denominator, to reflect that these antibiotics could have been prescribed to both women and men and both adults and children. By using antibiotic-specific STAR-PUs it was possible to account for the fact that GP practices serving different patient populations may have had different rates of antibiotic prescribing (i.e. a GP practice with a higher proportion of elderly and/or female patients may prescribe a higher level of UTI-related antibiotics than practices serving younger patients).
Finally, due to the ecological nature of the study design, a direct causal relationship between prescribing for UTIs and total ECB incidence cannot be implied as it was not possible to determine the severity of illness at the point of prescribing, possible prophylactic antibiotic use, prevalence of co-morbidities or the relation between community prescribing and recent hospital admission, all of which are potential confounders which could have introduced residual bias. As national-level data were not available it was not possible to adjust for the number of diagnosed UTIs per year, however the STAR-PU weighting applied to the prescribing and infection denominators were used as a way of addressing this limitation.
2.6.3. How these findings fit in with existing literature
Higher levels of trimethoprim resistance observed in the community led to PHE changing the national primary care prescribing guidelines in November 2014 from trimethoprim to nitrofurantoin as the first choice of treatment for uncomplicated UTIs in adults (23). The trend of an increase in nitrofurantoin prescribing compared to trimethoprim prescribing by practice between 2012 and 2014 was demonstrated in this study and the impact of a higher trimethoprim to nitrofurantoin ratio being associated with a higher rate of trimethoprim-resistant bacteraemia indicates a possible negative effect of not following this national recommendation to switch first- line treatment. With higher levels of trimethoprim resistance in the community, a higher level of treatment failure for UTIs treated with trimethoprim is expected, leading to a greater association between trimethoprim use and subsequent ECB as compared with nitrofurantoin, which has been demonstrated.
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Similar results were seen in an enhanced antibiotic stewardship initiative introduced in Nottinghamshire in 2015, where new local guidelines and education campaigns to encourage guideline implementation led to dramatic decreases in the use trimethoprim compared to nitrofurantoin, while maintaining low levels of broad-spectrum antibiotic use (e.g. co-amoxiclav, ciprofloxacin and cephalosporin). This was associated with a reduction in trimethoprim-resistant E. coli urine cultures from 35% pre-intervention to 23% post-intervention. A reduction in trimethoprim resistance in community-onset E. coli BSIs was also seen during this period, with resistance rates falling from 43% to 31% (76). It was argued that by reducing the selective pressure driven by over-use of trimethoprim in the community, resistance rates could be brought down, and the future effectiveness of this antibiotic could be restored.
However, as shown in a study by Pouwels et al., usage of antibiotics other than trimethoprim was found to be associated with trimethoprim resistance among Enterobacteriaceae urine samples, in particular usage of amoxicillin/ampicillin (relative risk 1.19, 95% CI 1.11–1.30) (77). These results complimented the results found in the study by Magee et al. discussed in the previous chapter, where significant associations between amoxicillin/ampicillin use and trimethoprim resistance (and vice versa) were found at the practice-level (40).
Nitrofurantoin usage was found to be associated with lower trimethoprim resistance levels (relative risk 0.83,
95% CI" 0.57–0.96), which is broadly in line with this chapter where it was demonstrated that higher nitrofurantoin usage was not associated with trimethoprim-resistant ECB incidence. The Pouwels et al. study indicates that reductions in the use of broad spectrum antibiotics such as amoxicillin, alongside reductions in the use of trimethoprim, could also have an effect on lowering the incidence of trimethoprim-resistant infections.
2.6.4. Implications for practice and policy
Findings from this study support two UK government policies in relation to antibiotic prescribing. Namely, the updated version of the NHS Quality Premium 2015/16 guidance for Clinical Commissioning Groups (CCGs), released in April of 2015, which called for a) a reduction of total antibiotics prescribed in primary care by 1% from each CCG’s 2013/14 value and b) a reduction in the proportion of broad-spectrum antibiotics (co- amoxiclav, cephalosporins and quinolones) prescribed in primary care by 10% from each CCGs 2013/14 value
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(28). In support of this policy, I have demonstrated in this study that total prescribing for UTIs in primary care should be reduced where symptoms are mild (with pain relief or delayed antibiotic prescribing as possible alternatives) as advised in the PHE TARGET UTI leaflet (4), as practices with higher prescribing of both trimethoprim and nitrofurantoin were associated with a higher incidence of ECB following a UTI, adjusting for patient case-mix.
Additionally, in November 2014 PHE revised their primary care prescribing guidelines to advise GPs to prescribe nitrofurantoin as opposed to trimethoprim as the first choice for treatment of symptomatic, uncomplicated UTIs for adult women (who are not pregnant) if the patients GFR is above 45ml/min (23). This change was due to the high levels of resistance to trimethoprim in the community in patients with a UTI. As a result, one of the key indicators to measure guideline compliance for the PHE Public Health Profiles (AMR Local Indicators) is a twelve month rolling proportion of trimethoprim antibiotics prescribed by CCG and GP as a ratio of the proportion of trimethoprim to nitrofurantoin prescriptions (29). I therefore ran the models using the ratio of trimethoprim to nitrofurantoin prescribing to investigate the risk of severe outcomes associated with practices prescribing high levels of trimethoprim in relation to nitrofurantoin. This study supports the above policy in the conclusion that where antibiotics must be prescribed for a UTI, nitrofurantoin should be used as the first-line agent of choice
(dependent upon local resistance rates and patient GFR) as a higher ratio of trimethoprim to nitrofurantoin prescribing by practice was significantly associated with an increased incidence of trimethoprim-resistant ECB.
This study therefore has shown that there potentially could be effects on patient safety if these revised PHE recommendations are not implemented in GP practices.
Finally, as stronger associations were seen when considering only trimethoprim-resistant ECB outcomes (as opposed to total ECB), weight is lent to the hypothesis that the risk of ECB in relation to UTI treatment is most likely characterized by antibiotic resistance.
2.7. How these finding fit in with the wider thesis
The majority of the work conducted in this chapter was published in the International Journal of Antimicrobial
Agents in 2018 (78) and the publication can be found in Appendix B. Having shown an association at the
59 | Page ecological level, this study would benefit from validation through an investigation into the relationship between prescribing for UTIs and subsequent BSI risk at the patient-level to allow for adjustment for comorbidities, UTI severity, previous antibiotic use and other patient-level characteristics. This study shows that such an investigation would be justified in order to measure patient-level associations between treatment for UTIs in the community and subsequent severe outcomes with the aim of preventing future UTI-related ECB infections.
It is for this reason that I feel this work paves the way for further study on the pathway from UTI diagnosis and treatment to the development of a BSI to be conducted using individual patient data so that these patient-level factors can be adjusted for, which is what I will endeavour to do in the following chapters.
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CHAPTER 3: MEASURING THE LEVEL OF OFF- GUIDELINE ANTIBIOTIC PRESCRIBING FOR COMMUNITY-ACQUIRED URINARY TRACT INFECTIONS IN PRIMARY CARE.
Chapter 1: Aims and Background
Chapter 2: Volume of prescribing for UTI and incidence of E. coli bacteraemia
Chapter 3: Chapter 4: Effect of BSI-related UTI diagnosis Quantifying levels of off-guideline admission, off-guideline prescribing for UTI and treatment death or re- prescribing for UTI on BSI risk consultation
Chapter 5: Effect of duration of treatment for UTI on BSI risk
Chapter 6, 7: Discussion, conclusions and recommendations
Summary:
This chapter uses an extract of nationally-representative English primary care electronic health records linked with hospital admissions and a death registry at the patient level. It describes the development of an algorithm designed to assess antibiotic prescribing adherence to national prescribing guidance for individual prescriptions written for UTI. It uses this algorithm to describe national trends in prescribing adherence for UTI in adults in England over 9 years. The majority of antibiotic prescriptions for adult UTI in England were not found to adhere to national guidance, particularly for female and elderly patients. The reason is most often due to the choice of duration of treatment. This work, in conjunction with Chapter 4, is being written as a manuscript for submission to a medical journal.
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3.1. Introduction
As touched upon in Chapter 1, optimising the use of antibiotics is a high priority in antibiotic stewardship as we see rising rates of antibiotic resistance both within the UK and worldwide due to the misuse and overuse of antibiotics. Studies have shown that in Europe up to one third of antibiotic prescriptions are not compliant with evidence-based guidelines (79) and there have been several studies demonstrating wide variation in antibiotic choice for infections most commonly presenting in primary care, such as urinary and respiratory tract infection
(80, 81). For this reason, Antimicrobial Stewardship Programmes (ASPs) have been put in place within the NHS to improve antibiotic prescribing and control antibiotic resistance (3). One component of Antimicrobial
Stewardship is to provide evidence-based standards for routine antibiotic prescribing, with particular reference to the choice of appropriate agent, dose, route of administration and duration of therapy (15, 22), which are routinely reviewed and updated when new and relevant evidence becomes available.
Antibiotic-resistant infections respond poorly or not at all if treated with the antibiotics to which they are resistant, making them more persistent and harder to treat. Moreover, failure to adequately treat UTI means there is an increased likelihood that the bacteria will go on to invade the bloodstream (6). Some of the most common etiological agents of bloodstream infections such as E. coli and Klebsiella species cause genitourinary infections that may lead to bloodstream infections if not treated properly (7). It is for this reason that the role that antibiotic prescribing for infections upstream of a BSI may or may not play in the risk of developing this outcome needs to be understood, in particular the role of stewardship measures such as the choice of treatment. But firstly, the types of prescriptions being written for UTI in English primary care practices must be explored and evaluated. Unlike a recent study by Pouwels et al. which assessed the “ideal” prescribing proportions for common infections (i.e. the proportion of consultations that should result in an antibiotic prescription), this chapter focusses on the quality of antibiotic prescriptions, once they have been written, in relation to antibiotic prescribing guidance.
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3.2. Aims and objectives
This chapter aims to describe the characteristics of a cohort of adult patients receiving antibiotic treatment for
UTIs in primary care as well as reporting the distribution of antibiotic type, dose, frequency and duration of treatment. The trends in “off-guideline”, “on-guideline” and “changed” antibiotic prescribing with respect to a national antibiotic prescribing guideline will be examined both regionally and temporally.
Primary objective:
1) To quantify the levels of “off-guideline” vs. “changed” vs. “on-guideline” antibiotic prescribing for UTI episodes within an English national primary care adult cohort.
Secondary objectives:
2) To report on the reasons for “off-guideline” and “changed” antibiotic treatment with respect to antibiotic, dose, frequency and duration of treatment for UTI infection episodes.
3) To report on the distribution of patient characteristics (age, sex, region, deprivation, co-morbidities, previous antibiotic usage and UTI severity) by the outcome (off-guideline, changed or on-guideline treatment).
Rationale:
These specific aims will allow, for the first time in England, for prescriber adherence to antibiotic guidelines for
UTIs to be evaluated on a national scale using routinely-collected data. It will also shed light on the reasons why prescribing guidelines are not being followed and what patient groups may be most likely to receive off-guideline or changed treatment, which will be useful to policy-makers when designing interventions to improve guideline adherence, if such a thing is desired. Additionally, the algorithm developed in this chapter to determine prescribing adherence to guidelines is currently being used to develop an interactive dashboard for use in North
West London GP practices as part of ongoing collaborative work with Imperial College Healthcare Partners (ICHP) to measure prescribing behaviours for monthly in-practice monitoring and feedback. The methodology used to develop the algorithm could easily be adapted to suit prescribing guidelines for other common infection types in primary care such as community-acquired pneumonia. Finally, the findings of this chapter will be used in
Chapter 4 to determine whether there are severe clinical outcomes associated with off-guideline vs. on- guideline prescribing for UTI.
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3.3. Data sources and linkage
This study makes use of primary care anonymised electronic health records available from the Clinical Practice
Research Datalink (CPRD) which is a database covering 11.3 million patients from 674 GP practices in the UK, equating to roughly 7% of the UK population (82). Patients have been shown to be representative of the general
UK population with respect to age, gender and ethnicity. The CPRD provides information on the patient, practice, staff, clinical diagnoses, therapies, referrals, tests and immunisations for any consultation occurring at the practices which are represented. Additionally, a linkage scheme is available for consenting practices (75% currently which represents 58% of UK CPRD practices) which provides patient-level linkage with hospital admissions through Hospital Episodes Statistics (HES), mortality data through the Office for National Statistics
(ONS), deprivation data through the Index of Multiple Deprivation (IMD) and disease-specific registries which were not used for this study. Additionally, in 2018 the CPRD/LSHTM Pregnancy Register, documenting data related to pregnancy (irrespective of the outcome), became available and was used for this study. All linkages are carried out by a trusted third party (the Health and Social Care Information Centre) to datasets which the study has been approved to use. The ethics application (including amendments) approved by the CPRD
Independent Scientific Advisory Committee (ISAC) for the work conducted in Chapters 3-5 can be found in
Appendix C. Table 3.1. provides a brief overview of the data sources used in Chapters 3-5.
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Table 3.1. Specifications of included datasets for off-guideline prescribing study Data source Description Scope Access Linkage CPRD: Patient Contains patient demographics 7% national Imperial license Connects with all tables and patient registration details. sample and ethics using patid (unique patient (England) approval granted ID) apart from Practice and by ISAC * Staff. Practice Contains details of each 7% national Imperial license Last three digits of the patid practice, including region and a sample and ethics and staffid is the pracid data collection quality marker. (England) approval granted (unique practice ID) which by ISAC can be used to link to the Patient and Staff tables. Staff Contains practice staff details, 7% national Imperial license Connects with the with one record per member of sample and ethics Consultation table using staff. (England) approval granted staffid (unique staff ID). by ISAC Consultation Contains information relating 7% national Imperial license Connects to the events that to the type of consultation as sample and ethics occur as part of the entered by the GP. (England) approval granted consultation via consid by ISAC (unique consultation ID). Clinical Contains all the medical history 7% national Imperial license Contains patid, staffid, data entered on the GP system, sample and ethics consid to link to these including symptoms, signs and (England) approval granted tables as well as adid diagnoses. Data is coded using by ISAC (unique additional clinical Read codes. ID) which links with Additional Clinical Details table. Referral Contains information involving 7% national Imperial license Contains patid, staffid, patient referrals to external sample and ethics consid to link to these care centres (secondary care (England) approval granted tables. locations such as hospitals for by ISAC inpatient or outpatient care) and includes specialty and referral type. Test Contains records of test data 7% national Imperial license Contains patid, staffid, on the GP system. The data is sample and ethics consid to link to these coded using a Read code which (England) approval granted tables. will generally identify the type by ISAC of test used. Test results can be looked up in the Additional Clinical data using the entity type and “adid” (identifier used to link with the Additional Clinical dataset). Therapy Contains details of all 7% national Imperial license Contains patid, staffid, prescriptions on the GP system sample and ethics consid to link to these issued by the GP. Drug (England) approval granted tables as well as the products are recorded by the by ISAC dosageid (unique dosage GP using the Gemscript product ID) to link with the code system. Common Dosages Lookup file which provides dosage and frequency data about the prescription. CPRD/LSHTM Lists all pregnancies identified 7% national Imperial license Contains patid of mother pregnancy in CPRD and includes details of sample and ethics which links with CPRD register each one. A single record (England) approval granted tables. Pregnancy start date represents a unique pregnancy by ISAC (minor and pregnancy end date episode and there may be amendment to used to determine which more than one per woman. For original protocol UTI episodes were pregnancies resulting in live to include newly- diagnosed during births, patient identifiers of created pregnancy. linked babies identified through pregnancy register)
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the CPRD Mother Baby Link are also provided. IMD 2010 Index of Multiple Deprivation 7% national Imperial license Contains patid and pracid patient-level score given in 2010 which is a sample and ethics to allow for linkage with deprivation data composite measure based on a (England) approval granted Patient, Practice or number of indicators of by ISAC (minor Consultation tables. deprivation assigned to the amendment to Lower Super Output Area original protocol (LSOA) that the patient resides to include in. Scores are binned into patient-level quintiles. deprivation data) HES for The HES data contains details Practices within Imperial license Contains patid and pracid comorbidities of all admissions to, or CPRD’s 7% and ethics to allow for linkage with attendances at, English NHS national sample approval granted Patient, Practice or health care providers. which have by ISAC Consultation tables. Diagnosis codes are in the form consented to be of ICD-10 codes. part of linkage scheme (~55%) ONS Mortality National death register Practices within Imperial license Contains patid and pracid data managed by the Office for CPRD’s 7% and ethics to allow for linkage with National Statistics which national sample approval granted Patient, Practice or captures all deaths in England which have by ISAC Consultation tables. (both inside and outside of consented to be hospital) as well as underlying part of linkage cause of death. scheme (~55%)
* ISAC-approved protocol (with amendments) for studies conducted in Chapters 3-5 can be found in Appendix C.
3.4. Methods
3.4.1. Study population
The study population for this chapter was all adult patients (aged 18 and above) who were registered with a GP
practice in England, had been diagnosed with a UTI, suspected UTI or cystitis and were concurrently prescribed
a course of antibiotics between October 1st, 2007 and December 1st, 2017. Unlike in Chapter 2, both female and
male patients were examined in this study as this was a patient-level analysis as opposed to an ecological analysis
which allowed for consideration of individual patient characteristics. Due to this ability for more refined
adjustment, a more heterogeneous adult patient population could be examined than in the previous study.
3.4.2. Inclusion/exclusion criteria
Inclusion criteria:
The scope was all GP practices in England that used the Vision data input system (the data management software
which CPRD extracts patient data from) which were classified as “up-to-standard” before the start of the study
66 | Page period, therefore having data deemed to be of research quality. Similarly, patient data was only included if the patient had an acceptability flag of “acceptable” at the time of their recruitment. The quality of CPRD can be variable as data are entered on a routine basis for the purposes of clinical care, not for the purposes of research.
To account for potential variations in the quality of data entry between practices and between patients, CPRD have devised two quality checks: an “acceptability flag” (pertaining to an individual patient’s data) and an “up- to-standard date” (pertaining to the data entered by a GP practice as a whole).
In order for a patient’s record to be “acceptable”, the patient must have recorded: a valid gender (male, female, indeterminate), a birth year, no events prior to their birth year in any of the separate tables, an age of less than or equal to 115 at the last collection date or transfer out date, a first registration date on or after the birth date, permanent registration at a practice, no missing or invalid event dates (missing dates are blank, invalid dates are any date before 01/01/1800 or after the last date of the current data release). Patient data can move from
“not acceptable” to “acceptable” and vice versa with each data release. In order for a practice’s data to be deemed “up-to-standard” (UTS): practice mortality rates must be within an expected range (which ensures that data is provided for patients who have died, that deaths are being recorded and that irregularities in practice are noted) and there must be a longitudinal continuity in data recording within a practice which is ascertained by carrying out “gap analysis” (83). The UTS date is the earliest date on which the practice’s data met the quality criteria. The extract therefore was limited to only include data deemed to be of research quality by including only patients who were “acceptable” during their follow-up and only practices with UTS dates before the start of the study period.
Exclusion criteria:
Patients with clinical, test or referral Read codes for an immunocompromising or auto-immune condition, were an oncology or haematology patient or were HIV-positive during the study period were excluded from the study as they are often correctly written different antibiotic prescriptions than patients who do not fall into these groups and their treatment would therefore appropriately differ from the national treatment guidelines. This approach was informed by similar studies in the literature (84-86) and by clinical input from an academic GP who I collaborated with on this project.
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3.4.3. Code list development, linkage strategy and data cleaning
Code list development:
The lists of medcodes for each condition examined in this study (UTI, severe UTIs and comorbidities) were initially built by either searching for previously published code lists for each condition using the University of
Manchester ClinicalCodes Repository (https://clinicalcodes.rss.mhs.man.ac.uk/) where available, or by using code lists previously built by researchers within the Department of Primary Care and Public Health (code lists were inclusive of Quality Outcomes Framework [QOF] codes where applicable). If code lists were not available from these two options, I constructed them by building a comprehensive list based on keyword searches within the CPRD medical code browser. These deliberately broad lists were then checked by my GP collaborator to see whether any codes were not relevant and should be excluded or whether known codes were not present in the lists and should be added. The lists of “prodcodes” (medications) included a list of “all antibiotics”, a list for each antibiotic/dose combination included in each UTI prescribing guideline and a list of immunocompromising medications. The list of “all antibiotics” was developed by including all “prodcodes” which fell under each BNF antibiotic chapter by class in the CPRD product code browser (penicillins, macrolides, cephalosporins, fluoroquinolones, sulphonamides, tetracyclines, aminoglycosides, carbapenems, glycopeptides and nitrofurans). The individual antibiotic/dosage combination lists were developed by using keyword searches within the product code browser and including only those codes which specified the correct dosage (e.g. all codes for Trimethoprim 100mg from all providers within the Read code system). The list of medications which contribute to a patient being immunocompromised was provided by my GP collaborator which was developed for a previous project.
Linkage strategy and data cleaning:
The extract was defined as all patients with one of the UTI-related Read codes (specified in Appendix D) diagnosed between October 1st, 2007 and December 1st, 2017 with gender being specified as “male” or “female”, being at least 18 years old at the start of the study with an acceptability flag equal to 1 (patient data meeting research quality standard). The number of patients meeting these criteria, constituting the initial extract, was
1,406,729. These patients were then checked for linkage eligibility with HES and ONS (the outcome datasets, elaborated upon further in Chapters 4 and 5) and those registered in practices which had consented to be part
68 | Page of the CPRD linkage scheme were 809,941 (58%). CPRD policy constitutes that a complete data extract (including all available variables) could only be released for a maximum of 600,000 patients; to comply with this I requested a random sample of 600,000 patients of the available 809,941 be extracted as the final data extract. The random sample was acquired by assigning a random number to each patient ID, then sorting the patients by these random numbers in ascending order and selecting the top 600,000 patient IDs. All initial extraction of the patient identifiers and selection of the random sample was carried out by the designated Department of Primary Care and Public Health CPRD fob holder and database manager.
To ensure that the sample was random, I compared the age and sex distributions of the 600,000-person sample extract and the initial 809,941-person extract and found them to be highly comparable across these characteristics, I therefore determined the sample to be an accurate representation of all patients meeting initial inclusion criteria for this study.
After patient ID extraction, all records held within CPRD for each of the included patients were extracted. The data for each consultation are held across multiple tables, namely the Patient, Practice, Staff, Consultation,
Clinical, Additional Clinical, Referral, Immunisation, Test and Therapy tables (as outlined above in Table 3.1).
Each table contains a unique identifier which enabled deterministic linkage between tables. A unique Patient ID
(patid) was provided across the Patient, Consultation, Clinical, Additional Clinical, Referral, Immunisation, Test and Therapy tables. A unique Staff ID (staffid) was provided in the Staff and Consultation tables. A unique
Practice ID (pracid) was provided in the Practice and Patient tables (present as the last three digits of the Patient
ID). And finally, a unique Additional Information ID (adid) was provided in the Clinical and Additional Clinical tables. Using these identifiers, the tables were linked directly in the structure designed by CPRD and specified in
Table 3.2 (Step 1). I specified the linkages between the Patient, Consultation, Clinical and Therapy tables to be inner joins (data in each table available for linkage must be present for the record to remain in the study) as I was only interested in consultations with both a UTI diagnosis and an antibiotic prescription. Linkages with all other tables I specified to be outer joins (data in the linking table does not have to be present for the record to remain in the study). These initial basic linkages and the following application of the exclusion criteria were designed and specified by myself and performed by the HPRU database manager and myself using SQL Studio as the datasets were too large to manage initially in Stata, the software package which I have expertise in using.
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After linkage of the separate tables, records were filtered out which met any one of the following exclusion criteria, as outlined in Table 3.2.:
1) Practice up-to-standard date is after the start of the study period;
2) Patient does not have “acceptable” data;
3) Patient data is not eligible for the linkage scheme;
4) Patient has a Read code specifying that they are HIV-positive, have a cancer diagnosis or are
immunocompromised due to a haematology or other immunocompromising condition (Read
codes searched for across Clinical, Test and Referral tables) within the study period;
5) Patient has been written a prescription which would cause them to be immunocompromised
(“prodcodes” searched for in the Therapy table) during the study period;
6) Patient’s UTI diagnosis and antibiotic prescription are not on the same day.
All Read code lists for HIV, cancer, haemotology or other immunocompromising condition and all “prodcodes” for immunocompromising prescriptions can be found in Appendix D.
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Table 3.2. Stepwise specifications for initial exclusion criteria (SQL). Description Datasets involved Specification 1. Linkage of CPRD tables according to CPRD linkage CPRD GOLD (tables) * strategy (Patient, Practice, Staff, Consultation, Clinical, Additional Clinical, Referral, Immunisation, Test, Therapy)
Save resulting table as “CPRD_raw.db”
CPRD_raw.db CPRD_raw.db 2. Filter out practices with up-to-standard date after start CPRD GOLD of study period
CPRD_clean.db
Exclude if uts > Nov 1st, 2007
3. Filter out patients who do not have high quality data CPRD GOLD (acceptability flag = 0) Exclude if accept = 0 4. Filter out patients who are not eligible for linkage CPRD GOLD Exclude if hes_e = 0 Or if death_e = 0
5. Identify UTIs between Nov 1st, 2008 – Sept 30th, 2017 CPRD GOLD + UTI medcode lookup file
UTI_codes (cystitis).txt CPRD_clean.db
medcode
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6. Identify patients with cancer, HIV, haematology or CPRD GOLD + medcode other immunocompromised medcodes lookup files Cancer_codes.txt CPRD_clean.db HIV_codes.txt
Clinical, test or Immuno_codes.txt referral medcode
Haema_codes.txt
7. Identify patients prescribed an immunocompromising CPRD GOLD + prodcode drug lookup file Immuno_drug_codes.txt CPRD_clean.db
Therapy prodcode
8. Filter out patients with one of the above medcodes or CPRD GOLD prodcodes within study period Drop patients where cancer=1 or HIV=1 or haematology=1 or immunocomp=1 or immunodrug=1 within study period
9. Identify where prescriptions are antibiotics using “All CPRD GOLD + all Antibiotic” lookup antibiotic lookup file All_antibiotics.txt CPRD_clean.db
Therapy prodcode
10. Filter out records where the diagnosis and the CPRD GOLD prescription are not on the same day Drop records where uti==1 & Consultationeventdate != Therapyeventdate
* Linkage structure outlined Step 1 was provided in CPRD training materials available through the Imperial Primary Care and Public Health Department’s access licence.
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Due to restrictions in the size of files which could be opened in Stata, it was not possible to export the fully linked dataset with the exclusion criteria applied from SQL Studio to Stata. To circumvent this restriction, the patient records which were excluded from the large linked dataset in SQL were then dropped from the raw individual tables (based on the combination of Patient ID and Consultation ID) within SQL Studio. The filtered tables were then exported to Stata as five tables: a linked table comprised of Consultation, Staff, Patient and Practice data
(ConsStfPtPrac), a linked table comprised of Clinical and Additional Clinical data (Clin_Add), the Therapy table
(Therapy), the Test table (Test) and the Referral table (Referral). The Immunisation table was not needed for further analyses as the patients’ immunisations history was not of interest for exclusion or as an exposure, covariate or outcome for this study. I performed all data cleaning, linkage and analytical work once the data was moved out of SQL and into Stata.
The Referral table was used as a look-up table and therefore did not undergo any further cleaning. The Therapy,
Test, ConsStfPtPrac and Clin_Add tables were cleaned using bespoke criteria (cleaning flow charts for each table can be found in Appendix E) to filter out duplicate data or non-duplicate data which was not relevant to the study. In this way all relevant data were captured on one row to allow for unique identification of each patient’s
UTI consultation which would then facilitate linkage between tables. Before linkage between tables, however, additional datasets were linked to the appropriate individual tables to capture further necessary information.
To capture the antibiotic frequency information (how often an antibiotic was taken per day), the “Common
Dosages Lookup” file was used to merge frequency data with the Therapy table using a 64-character unique
Dosage ID. This lookup file contains extracted numerical information from the dosage text and over 95% of dosage text which occur in the data have been coded and are available for linkage. The antibiotic frequency data were essential for calculating duration of treatment (total quantity / frequency = duration of prescription) as well as for categorising whether the prescription followed the guideline used in this study.
To capture patient-level deprivation data (as outlined in Table 3.1.), IMD2010 quintile data were merged with the ConsStfPtPrac table using the unique Patient ID. The IMD score, which is a weighted score based upon seven domains of deprivation, namely Income, Employment, Housing, Health, Education, Crime and Living
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Environment, is assigned to each Lower Super Output Area (neighbourhood level area of ~1,500 people, as discussed previously in Chapter 2) (87). The scores are then ranked and categorised into deciles and quintiles.
CPRD provides the opportunity for linkage with either patient-level IMD decile and quintile data (based upon the patient’s residential postcode) or practice-level IMD decile and quintile data (based upon the GP practice’s postcode). Unlike in Chapter 2 where the analyses were at the practice-level and therefore practice-level deprivation data was used, as this study was at the patient-level I chose to link with IMD2010 data based on the patient’s residential postcode.
Finally, to capture whether a patient was pregnant at the time of the UTI diagnosis and treatment I used the
CPRD Pregnancy Register recently developed in collaboration with the London School of Hygiene and Tropical
Medicine (LSHTM) to identify all pregnancies in CPRD and capture corresponding information such as the estimated pregnancy start date, the pregnancy end date and the outcome of the pregnancy. I decided not to use the Mother Baby Link provided by CPRD for this study as I determined it would be an underestimation of pregnancies in my dataset as the Mother Baby Link only provides data for live births. As I was interested in evaluating prescriber decision-making at the time of UTI diagnosis (i.e. whether the patient was pregnant on the day), I did not want to exclude patients who had a pregnancy end in a stillbirth or through miscarriage or termination of the pregnancy. I generated a “pregnancy flag” by using the “rangejoin” function within the
“rangestat” package in Stata to identify records where the UTI consultation date was found to be within the range of the estimated start date of the pregnancy (“pregstart”) and the end date of the pregnancy (“pregend”) for each unique Patient ID. Where the UTI consultation date fell within the range of these two dates, I flagged the patient as pregnant, which allowed me to analyse their UTI prescriptions under the “UTI in pregnancy” section of the chosen guideline and account for the fact that these patients differed from the general UTI patient population.
UTI consultations with more than one recorded antibiotic prescription or more than one recorded dosage or duration of treatment were excluded from the cohort as it was not possible to determine whether this was due to recording error or another indication other than UTI and their adherence to the guidance would therefore not be able to be determined. This approach was taken after discussion with an academic GP and was guided by
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the literature (88). Before de-duplication of therapy records there were 121,001 records (about 66,638
consultations) of 2,820,436 records where this is the case (4.3%).
3.4.4. Defining episodes of infection
To construct episodes of UTI, I identified the first UTI consultation for each patient in the dataset and built
episodes of UTI from that starting point. An episode of UTI was defined as the first UTI consultation (index
diagnosis) and any UTI-related re-consultations within 60 days of the index diagnosis. There was then a 30-day
“gap” period between each UTI episode to allow for identification of any antibiotic exposure (recorded within
that practice) in the previous month of the next UTI episode. This logic is illustrated in Figure 3.1. As mentioned
previously, the full data extract ran from November 1st, 2007 to December 1st, 2017, however the UTI episodes
were not counted until a year into the data extract to allow for patients at the start of the study to have a 1-year
look-back for exclusion codes (HIV-positive, cancer, immunocompromised etc.) as well as history of
comorbidities (diabetes, cardiovascular disease, renal abnormalities, STI, urinary catheter usage, penicillin
allergy). The start of the active study period was therefore November 1st, 2008. The last date that an index UTI
consultation could be considered was September 30th, 2017 to allow the last index UTI consultation to have the
full 60-day follow-up period (for the purposes of both this chapter and Chapter 4). The end of the active study
period was therefore September 30th, 2017.
Episode of UTI Episode of UTI
UTI (index diagnosis) UTI (index diagnosis) 90 days
30 days 60 day follow-up 30 days 60 day follow-up Previous antibiotic Previous antibiotic treatment treatment
Start of study with first End of study with last inclusion Nov 1st, 2008 inclusion Sep 30th, 2017
Figure 3.1. UTI episode construction. Full extract runs from Nov 1st, 2007 to Dec 1st, 2017, active study period runs from Nov 1st, 2008 to Sep 30th, 2017 - allows 1-year look-back for co-morbidities from first UTI inclusion and 60-day follow-up for last UTI inclusion.
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To capture which episodes or “gap periods” each UTI consultation fell into, I created a day count from the start of the active study period to the end by counting 60 days between start and end of the “episode” with a 30-day gap in between each “episode”. This is demonstrated in Table 3.3.
Table 3.3. Day count used to enable UTI episode categorisation Group Initial Day Last Day Episode 1 0 60 Episode 2 90 150 Episode 3 180 240 Episode 4 270 330 Episode 5 360 420 Episode 6 450 510 Episode 7 540 600 Episode 8 630 690 Episode 9 720 780 Episode 10 810 870 . . .
For each patient, I then calculated the date difference between the date of each UTI consultation and the date of the first UTI they entered into the study with. I then mapped these UTIs to the date ranges in the above table using the “rangejoin” function in the “rangestat” package in Stata to determine which UTI consultations fell within which episodes (or “gaps”, which were excluded) after the first UTI episode for each patient. Each episode was then assigned a unique UTI identifier for identification of the number of unique UTI episodes per patient.
Lastly, I determined which UTIs were community-acquired by excluding those which had a record of a hospital discharge within the previous 30 days before the index UTI consultation date of each UTI episode. This was determined through the linked HES data. This will be elaborated upon further in Chapter 4.
3.4.5. Defining patient sub-groups and UTI severities
Patients who were pregnant, had clinical Read codes for acute prostatitis or acute pyelonephritis at the time of their UTI diagnosis, or had recurrent UTIs were classified as “complicated UTIs” (all other patients fell into the
“uncomplicated UTI” category). Patients who were pregnant at the time of their UTI diagnosis were identified either through a pregnancy-related UTI Read code (Appendix D) or through linkage with the CPRD/LSHTM
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Pregnancy Register as described previously in Section 3.4.3. Patients with prostatitis or pyelonephritis were identified using clinical Read codes for these two conditions (Appendix D).
To identify patients with recurrent UTIs, I developed a composite definition based on expert opinion (a GP with experience using CPRD data) and definitions found in the literature (89, 90) to capture the presence of recurrent
UTI either through clinical diagnosis, repeat/extended prescription of antibiotics for UTI or through frequency of UTI-related visits. The definition for a recurrent UTI was met if one or more of the follow were true: a) there was a clinical Read code specifying “recurrent UTI”, b) the patient received antibiotic treatment for a UTI of 28 days or more (and did not have a code for prostatitis which required a 28-day course), c) the patient was written an antibiotic prescription for the UTI which was recorded as being part of a repeat schedule (89) or d) the patient had three or more UTI episodes within a year (90). Recurrent UTIs have been reported on in this chapter and BSI outcomes for these patients have been analysed but they were not included in the on/off guideline analyses as treatment duration is not specified in the guidelines for these patients and they therefore could not be categorised in the same way.
The recurrent UTI composite definition was comprised of one of the following:
1) Clinical Read code for a recurrent UTI (1AG..00, K190311, K190.11 or K190300);
2) Prescription of an antibiotic course for UTI (not prostatitis) with a duration of 28 days or more;
3) Prescription of an antibiotic course for UTI that is part of a repeat schedule (issue sequence > 0);
4) Three or more UTI episodes within one year.
To capture “three or more UTIs within a year” a similar method was used to that employed for defining episodes of infection in Section 3.4.4. I created a day count from the start of the study period to the end of the study period, binning every 365 days into “Yr1”, “Yr2”, “Yr3” etc as seen in Table 3.4.
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Table 3.4. Day count used to enable year categorisation Group Initial Days Last Days Yr1 0 365 Yr2 366 731 Yr3 732 1097 Yr4 1098 1463 Yr5 1464 1829 Yr6 1830 2195 Yr7 2196 2561 Yr8 2562 2927 Yr9 2928 3293 Yr10 3294 3659
For each patient, I then calculated the date difference between the index date of each episode of UTI and the date of the first UTI. I again mapped these UTI episodes to the date ranges in the above table using the
“rangejoin” function to determine which UTI episodes fell within which years after the first UTI episode for each patient. I then counted how many UTI episodes fell within each year to determine if they met the definition of
“recurrent UTI”. In an attempt to accurately reflect prescriber decision-making, after discussing with a GP, where there were three or more recurrent UTIs in a year I coded the first UTI that the patient entered into the study with as being “non-recurrent” and all other UTIs which followed and met the definition of “3 or more UTIs in a year” being coded as “recurrent”. This was done to reflect that a GP would not know upon presentation of the first UTI how many UTI diagnoses would follow and would therefore treat the UTI as non-recurrent and prescribe as such. I did not want my ability to see all records retrospectively to hinder an accurate reflection of real-time infection presentation, clinical decision-making and subsequent prescribing behaviour by introducing bias to the infection categorisation.
It is worth noting that men presenting with a UTI are often automatically considered to be a “complicated UTI”
(91) however the patient subgroup definitions used in Chapters 3 and 4 were designed to directly correlate with the patient subgroups used in the chosen prescribing guideline, namely: ”UTI in adults (no fever or flank pain) – women and men”, “Acute prostatitis”, “UTI in pregnancy”, “Acute pyelonephritis” and “Recurrent UTI in non- pregnant women ≥3 UTIs/year” (15).
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3.4.6. Description of the algorithm
As this study was national in scope and the pseudonymised nature of the data meant that region was the only knowledge that I had of where these patients were treated in England, a national guideline had to be chosen to compare prescriptions against. The Public Health England “Management and Treatment of Common Infections: antibiotic guidance for primary care - for consultation and local adaptation” (15), which is managed by the PHE
Primary Care Unit, is produced in consultation with the Association of Medical Microbiologists, general practitioners, nurses, specialists and patient representatives, was chosen for this study. The PHE Primary Care
Unit in Gloucester kindly provided all archived versions of this guideline to cover the entirety of the study period.
The guideline is reviewed every three years, with updates occurring more frequently if there are significant findings in the field which are relevant to treatment of the infections covered in the guideline. This guideline agrees with other national guidelines such as those produced by the National Institute for Clinical Excellence
(NICE) Clinical Knowledge Summaries (CKS) and the Scottish Intercollegiate Guidelines Network (SIGN) and all evidence is fully referenced with the quality of the included studies graded. This guideline is intended as a quick guide which allows flexibility for local adaptation by including a number of options for treatment within each patient group, depending on local service delivery, sampling protocols and antibiotic resistance rates.
While this guideline is relatively robust and flexible, it must be said that a certain amount of nuance and complexity with regards to clinical treatment may not be covered by this reference document and there may be a certain amount of “appropriate deviation” from this guideline based on the individual circumstances of patient presentation. It is for this reason that a certain degree of caution will be exercised when interpreting the results of this study.
To construct the algorithm for determining whether an antibiotic prescription was in line with guidance, several factors were taken into consideration. The algorithm was firstly responsive to the patient’s gender which was used to classify whether the duration of treatment was in line with guidance (typically a recommendation of 3 days of treatment in women and 7 days of treatment in men). Secondly, the guideline was responsive to the severity or complexity of the UTI, namely whether the UTI was diagnosed in a pregnant patient, whether it was a recurrent UTI or whether it was diagnosed as acute prostatitis or pyelonephritis. This allowed the patient’s
79 | Page prescription to be assessed in the specific category in which the patient fell in the guideline. Lastly, the algorithm accounted for updates in guidance over time and the definition of “on-guideline” took into consideration when the UTI diagnosis occurred and what the circulating version of the guideline was at the time of diagnosis. This will be elaborated on more in Section 3.4.7. These three considerations are outlined in Figure 3.2.
A
B
C
Figure 3.2. PHE prescribing guideline for UTIs with key elements highlighted for algorithm development. (A) Gender considered for correct categorisation of treatment duration, (B) severity/complexity for correct categorisation of overall prescription, (C) time-period of UTI diagnosis considered for correct categorisation of guideline version
Once the patient was mapped to a category based on gender, complexity and time of diagnosis, the prescription was compared to the guideline based on antibiotic choice, dosage, frequency and duration of treatment. Some flexibility was built into the algorithm to avoid spurious miscategorization. Firstly, drug code lists were developed for each antibiotic/dosage combination to account for the fact that there are multiple “prodcodes” for each drug
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(i.e. Trimethoprim 200mg can be purchased from many different providers and there are therefore multiple product codes which indicate the same antibiotic and dosage). Secondly, where a range of dosages were provided, any dosage for that antibiotic within the specified range was considered “on-guideline” (i.e. any dosage in the range of Nitrofurantoin 50-100mg would be considered to follow the guideline). Thirdly, “buffer zones” were created around the durations (i.e. 3 days of treatment was coded as 2.5 days-3.5 days etc.) to account for the fact that the pack size available may be slightly higher than the exact number of tablets needed and may therefore not divide by the frequency perfectly to give the exact recommended duration (i.e. a pack size of 22 tablets may be given to a patient for a three times daily 7-day prescription, 22/3=7.33 which in this case would still be considered to be “on-guideline” as it was clear that the prescriber was following guidance but the packs were only available as 22 tablets). And lastly, the guideline provides a number of treatment options for each patient group, usually specified as first-line, second-line etc. As the decision for using first or second line treatment usually relies on clinical judgement, consideration of local resistance rates and/or microbiological examination, any of the possible prescriptions within each patient group were classified as “on-guideline” to allow for clinical judgement and adaptation to the local context.
In this way, prescriptions were assigned as being either “on-guideline” or “off-guideline”. To capture patients which fell into the “changed treatment” group, I investigated only patients who had multiple GP visits for a UTI within the period of one UTI episode and determined whether one of the prescriptions was “on-guideline” and another was “off-guideline”. If this was the case, they were categorised as “changed treatment”, however this definition did not discriminate between whether the “off-guideline” prescription was given before or after the
“on-guideline” prescription. It also did not discriminate whether two or more “on-guideline” or “off-guideline” prescriptions which differed from each other were given over the period of the UTI episode.
To determine the reason for off-guideline prescribing, I developed an algorithm that determined for each guideline update, UTI severity and patient gender group whether the prescription differed from the guideline with respect to firstly a) antibiotic, then b) dosage, then c) frequency and lastly d) duration of treatment. As an illustrative example of this, let us take Trimethoprim 200mg BD for 3 days, one of the options for uncomplicated
UTIs in adult women. If the prescription differed from the guideline with respect to (a) antibiotic I would expect to see the patient receive Ciprofloxacin, for example. If the prescription differed with respect to (b) dosage, I
81 | Page would expect to see the patient receive Trimethoprim 100mg, for example. If the prescription differed with respect to (c) frequency, I would expect to see the patient receive Trimethoprim 200mg TDS, for example. And lastly, if the prescription differed with respect to (d) duration, I would expect to see the patient receive
Trimethoprim 200mg BD for 7 days, for example.
3.4.7. Changes in PHE guidelines over time
As can be seen in Figure 3.3., there were a total of nine versions of the guideline over the study period when looking across all patient groups. Rows highlighted in green are antibiotic prescription options which were new additions to the guideline (compared to the previous version). Rows highlighted in red are antibiotic prescription options from the previous version of the guideline which have been omitted in the current version. Rows highlighted in yellow are antibiotic prescriptions for which the dosage, frequency or duration of treatent has changed from the previous version. Most of the guideline changes relate to dosing, frequency or duration of already recommended antibiotics (yellow) as opposed to additions of new antibiotics or omissions of previously- used antibiotics. Notable exceptions to this are the additions of pivmecillinam and amoxicillin to the guideline in November 2014 and the addition of fosfomycin in February 2017, all for uncomplicated UTIs in adult women and men. Other notable additions are specific antibiotic guidance (ciprofloxacin, ofloxacin and trimethoprim) for acute prostatitis as a separate category in December 2012 as well as the addition of trimethoprim to the guideline for acute pyelonephritis in November 2014. Omissions include amoxicillin for pregnant patients in
October 2017 as well as trimethoprim for patients with acute pyelonephritis in September 2010 (although this antibiotic was added back into the guideline in November 2014 for this patient group).
It can been seen in the guidance after February 2017 that the recommended Pivmecillinam prescription for uncomplicated UTI was 400mg STAT followed by 200mg TDS for three days in women and seven days in men.
After consulting a pharmacist on how this type of prescription may appear in the data, I was advised that the prescription would most likely take the form of the first (STAT) dosage being 2x200mg tablets, followed by the normal 200mg TDS schedule. This would result in 10 tablets for women (10/3=3.33 days) and 22 tablets for men
(22/3=7.33 days). This type of prescription was therefore correctly captured as “on-guideline” through the usage of the “buffer zone” around the duration (2.5-3.5 is counted as 3 days etc.). The “Fosfomycin 3g STAT”
82 | Page recommendation after February 2017 for uncomplicated female UTIs was simply coded as Fosfomycin 3g with a frequency of one (once per day) and a duration of one day. There was a higher level of uncertainty as to how the “Fosfomycin 3g STAT and 3g three days later” recommendation for men would be coded, I therefore coded the quantity as two tablets with a frequency of once per day and a duration of anywhere between 2 and 4 days to account for this uncertainty.
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Date range of guidance UTI in adults (no fever/flank pain) FEMALE UTI in adults (no fever/flank pain) MALE Acute prostatitis UTI in pregnancy Acute pyelonephritis Antibiotic Dose Freq. Duration Antibiotic Dose Freq. Duration Antibiotic Dose Freq. Duration Antibiotic Dose Freq. Duration Antibiotic Dose Freq. Duration Start of study - May 08 Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Nitrofurantoin 50-100mg QDS 7 days Ciprofloxacin 500mg BD 7 days Nitrofurantoin 50-100mg QDS 3 days Nitrofurantoin 50-100mg QDS 7 days Trimethoprim 200mg BD 7 days Co-amoxiclav 500/125mg TDS 14 days Cefalexin 500mg BD 7 days Trimethoprim 200mg BD 14 days Amoxicillin 250mg TDS 7 days June 08 - August 10 Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 7 days Nitrofurantoin 100mg m/r BD 3 days Nitrofurantoin 100mg m/r BD 7 days Trimethoprim 200mg BD 7 days Co-amoxiclav 500/125mg TDS 14 days Cefalexin 500mg BD 7 days Trimethoprim 200mg BD 14 days Amoxicillin 250mg TDS 7 days Sep 10 - Nov 12 Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 7 days Nitrofurantoin 100mg m/r BD 3 days Nitrofurantoin 100mg m/r BD 7 days Amoxicillin 500mg TDS 7 days Co-amoxiclav 500/125mg TDS 14 days Trimethoprim 200mg BD 7 days Cefalexin 500mg BD 7 days Dec 12 - Oct 14 Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Ciprofloxacin 500mg BD 28 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 7 days Nitrofurantoin 100mg m/r BD 3 days Nitrofurantoin 100mg m/r BD 7 days Ofloxacin 200mg BD 28 days Amoxicillin 500mg TDS 7 days Co-amoxiclav 500/125mg TDS 14 days Trimethoprim 200mg BD 28 days Trimethoprim 200mg BD 7 days Cefalexin 500mg BD 7 days Nov 14 - April 15 Nitrofurantoin 100mg m/r BD 3 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 28 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 7 days Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Ofloxacin 200mg BD 28 days Amoxicillin 500mg TDS 7 days Co-amoxiclav 500/125mg TDS 7 days Pivmecillinam 400mg STAT TDS 3 days Pivmecillinam 400mg STAT TDS 7 days Trimethoprim 200mg BD 28 days Trimethoprim 200mg BD 7 days Trimethoprim 200mg BD 14 days then 200mg then 200mg Cefalexin 500mg BD 7 days Amoxicillin 500mg TDS 3 days Amoxicillin 500mg TDS 7 days May 15 - May 16 Nitrofurantoin 100mg m/r BD 3 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 28 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 7 days Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Ofloxacin 200mg BD 28 days Amoxicillin 500mg TDS 7 days Co-amoxiclav 500/125mg TDS 7 days Pivmecillinam 400mg TDS 3 days Pivmecillinam 400mg TDS 7 days Trimethoprim 200mg BD 28 days Trimethoprim 200mg BD 7 days Trimethoprim 200mg BD 14 days Amoxicillin 500mg TDS 3 days Amoxicillin 500mg TDS 7 days Cefalexin 500mg BD 7 days June 16 - Jan 17 Nitrofurantoin 100mg m/r BD 3 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 28 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 7 days Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Ofloxacin 200mg BD 28 days Amoxicillin 500mg TDS 7 days Co-amoxiclav 500/125mg TDS 7 days Pivmecillinam 200mg TDS 3 days Pivmecillinam 200mg TDS 7 days Trimethoprim 200mg BD 28 days Trimethoprim 200mg BD 7 days Trimethoprim 200mg BD 14 days if resistance risk (?) 400mg TDS 3 days if resistance risk 400mg TDS 7 days Cefalexin 500mg BD 7 days Amoxicillin 500mg TDS 3 days Amoxicillin 500mg TDS 7 days Feb 17 - Sep 17 Nitrofurantoin 100mg m/r BD 3 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 28 days Nitrofurantoin 100mg m/r BD 7 days Co-amoxiclav 500/125mg TDS 7 days Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Ofloxacin 200mg BD 28 days Amoxicillin 500mg TDS 7 days Ciprofloxacin 500mg BD 7 days Pivmecillinam 400mg STAT Pivmecillinam 400mg STAT Trimethoprim 200mg BD 28 days Trimethoprim 200mg BD 7 days Trimethoprim 200mg BD 14 days then 200mg TDS 3 days then 200mg TDS 7 days Cefalexin 500mg BD 7 days Fosfomycin 3g STAT Fosfomycin 3g STAT, 3g 3 days later Amoxicillin 500mg TDS 3 days Amoxicillin 500mg TDS 7 days Oct 17 - end of study Nitrofurantoin 100mg m/r BD 3 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 28 days Nitrofurantoin 100mg m/r BD 7 days Ciprofloxacin 500mg BD 7 days or 50mg i/r QDS 3 days or 50mg i/r QDS 7 days Ofloxacin 200mg BD 28 days or 50mg i/r QDS 7 days Co-amoxiclav 500/125mg TDS 7 days Trimethoprim 200mg BD 3 days Trimethoprim 200mg BD 7 days Trimethoprim 200mg BD 28 days Trimethoprim 200mg BD 14 days Pivmecillinam 400mg STAT Pivmecillinam 400mg STAT Trimethoprim 200mg BD 7 days then 200mg TDS 3 days then 200mg TDS 7 days Cefalexin 500mg BD 7 days Amoxicillin 500mg TDS 3 days Amoxicillin 500mg TDS 7 days Fosfomycin 3g STAT Fosfomycin 3g STAT, 3g 3 days later
Changed dosage, duration or frequency of same antibiotic Removal of previously-recommended antibiotic Addition of newly-recommended antibiotic
Figure 3.3. Changes in the Public Health England “Management and Treatment of Common Infections” guideline over the study period (2007-2017).
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3.4.8. Statistical methods
Based on the literature (42, 92, 93) and discussions with my GP collaborator, the patient-level factors which I examined in relation to prescribing for UTI were: age, gender, region, patient-level deprivation, pregnancy, antibiotic prescription within 30 days before the UTI episode, record of a urine test (dipstick or culture), severity of UTI (prostatitis or pyelonephritis), number of UTI-related GP visits per episode or a record for diabetes, cardiovascular disease, renal abnormality, sexually transmitted infection, urinary catheterisation or penicillin allergy (in one of the Clinical, Referral or Test files) in the year prior to the UTI episode.
A descriptive analysis was performed to describe the frequencies and distribution of the outcome (guideline status) in the study participants according to the risk factors of interest. As the outcome was categorical and all candidate risk factors were categorical (aside from mean age), testing for significant differences in the distributions across the outcome was performed using either the Chi-square test or Fisher’s exact test if the observed values were less than five. Evidence of a significant difference in mean age across the outcome groups was tested using one-way ANOVA. All reported p-values were considered as two-tailed, and a p-value <0.05 was considered to be significant.
3.4.9. Sensitivity analysis
To investigate whether GP practice adherence to the prescribing guideline improved 3 to 6 months after the introduction of a guideline update, I conducted a sensitivity analysis building in time lags and recategorizing the
“on, off or changed guideline” variable. To do this for the 3-month time lag, I altered the adherence algorithm to categorise a prescription as “on-guideline” if it either a) followed the current guideline between its introduction to the day it was updated (as before) or b) followed the previous version of the guideline up to 3 months after it was updated. If the prescription did not meet either of these criteria (still responsive to patient characteristics and UTI complexity as before), it was classified as “off-guideline”. If two or more prescriptions were given for UTI within one UTI episode and they differed with respect to being “on” or “off-guideline” they were classified as “changed treatment” as before. This algorithm alteration was repeated to build in a 6-month delay in adherence. This logic is demonstrated in Figure 3.4.
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Figure 3.4. The guideline adherence algorithm with no time lag vs. a 3 or 6-month time lag. Each colour represents a different version of the PHE prescribing guideline with the start and end dates when they came into effect. The width of each coloured box is not representative of time. The brackets represent the overlap period (of 3 or 6 months) when following either the current or the previous version of the guideline is considered to be “on-guideline”.
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3.5. Results
Over the study period, a total of 210,818 patients from 355 GP practices met all inclusion criteria, contributing a total of 316,226 UTI episodes to the study (1.5 UTI episodes per patient and 891 UTI episodes per practice, on average). The age range in this cohort was 19-112 years and the mean age of the patient population was 56 years (SD 20.3 years). The cohort, as to be expected, was largely female with 181,337 female patients (86%) and
29,481 male patients (14%). A total of 10,282 of the UTI episodes were in pregnant women which equated to
8.1% of the UTIs in women of child-bearing age (≤50 years old). A total of 43,558 of the UTI episodes (13.8%) were categorised as being recurrent and were excluded from the analyses in this chapter related to on/off/changed prescriptions as the treatment of recurrent UTIs does not have a specified duration in the guideline. The antibiotic treatment frequencies for these patients have, however, been reported on. In terms of geographical distribution of UTI patients, the frequencies in descending order were: North West: 33,146 (15.7%),
South East Coast: 30,518 (14.5%), West Midlands: 30,177 (14.3%), South Central: 29,986 (14.2%), South West:
25,486 (12.1%), London: 23,688 (11.2%), East of England: 22,853 (10.8%), Yorkshire & Humber: 5,673 (2.7%),
North East: 5,020 (2.4%), East Midlands: 4,271 (2.0%).
3.5.1. Trends in antibiotics prescribed for UTI
Overall, 57.3% of prescriptions were trimethoprim, 21.7% were nitrofurantoin, 9.0% were a penicillin, 8.6% were a cephalosporin, 2.8% were a quinolone and 0.6% were other. Table 3.5. displays the frequency distribution of antibiotic/dosage combinations across the entire UTI patient cohort, only antibiotics in the 95th percentile have been shown. It can be seen that the most commonly prescribed antibiotic was Trimethoprim 200mg (56.4%), followed by Nitrofurantoin 50mg (16.1%) and 100mg (4.5%), followed by Cefalexin 500mg (3.7%).
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Table 3.5. Treatment frequencies for UTIs in entire patient cohort (95th percentile only)
Antibiotic Dosage Frequency Percent Trimethoprim 200mg 178,182 56.4% Nitrofurantoin 50mg 50,925 16.1% Nitrofurantoin 100mg 14,308 4.5% Cefalexin 500mg 11,597 3.7% Cefalexin 250mg 9,991 3.2% Amoxicillin 250mg 7,520 2.4% Co-amoxiclav 250mg/125mg 7,243 2.3% Amoxicillin 500mg 7,083 2.2% Co-amoxiclav 500mg/125mg 5,343 1.7% Ciprofloxacin 500mg 4,242 1.3% Ciprofloxacin 250mg 3,494 1.1%
Table 3.6. shows the frequency distribution of antibiotic/dosage combinations broken down by female and male uncomplicated patients (excluding pregnant patients, recurrent UTIs, prostatitis and pyelonephritis), only the
95th percentile is shown. It can be seen that for female and male patients, the most commonly prescribed antibiotic treatment is Trimethoprim 200mg (61.9% in women, 56.7% in men), followed by Nitrofurantoin 50mg
(15.9% in women, 12.8% in men). Following these top two prescribed antibiotics, in women these were followed by Nitrofurantoin 100mg (4.1%), Cefalexin 500mg (3.3%) and Cefalexin 250mg (2.7%). In men, these top two prescribed antibiotics were followed by Ciprofloxacin 500mg (4.1%), Cefalexin 500mg (3.9%) and Nitrofurantoin
100mg (3.7%).
Table 3.6. Treatment frequencies for female and male uncomplicated UTI patients (95th percentile only) Female Male Antibiotic Dosage Freq. % Antibiotic Dosage Freq. % Trim. 200mg 142,848 61.9% Trim. 200mg 18,308 56.7% Nitro. 50mg 36,611 15.9% Nitro. 50mg 4,116 12.8% Nitro. 100mg 9,420 4.1% Cipro. 500mg 1,328 4.1% Cefalexin 500mg 7,498 3.3% Cefalexin 500mg 1,251 3.9% Cefalexin 250mg 6,317 2.7% Nitro. 100mg 1,191 3.7% Co-amox. 250/125mg 4,910 2.1% Co-amox. 500/125mg 1,089 3.4% Amox. 250mg 4,242 1.8% Co-amox. 250/125mg 904 2.8% Amox. 500mg 3,939 1.7% Amox. 500mg 868 2.7% Co-amox. 500/125mg 3,224 1.4% Cefalexin 250mg 763 2.4% Amox. 250mg 626 1.9%
Table 3.7. shows the frequency distribution of antibiotic/dosage combinations for pregnant patients, excluding patients with a recurrent UTI and patients with pyelonephritis or prostatitis, only the 95th percentile is shown.
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For pregnant patients, the top three most commonly prescribed antibiotics were Amoxicillin 250mg (20.6%),
Cefalexin 250mg (14.8%) and Amoxicillin 500mg (14.3%).
Table 3.7. Treatment frequencies for pregnant patients, excluding recurrent and severe UTIs (95th percentile only) Antibiotic Dosage Frequency % Amoxicillin 250mg 1,936 20.6% Cefalexin 250mg 1,388 14.8% Amoxicillin 500mg 1,341 14.3% Cefalexin 500mg 1,295 13.8% Trimethoprim 200mg 1,275 13.6% Nitrofurantoin 50mg 1,086 11.6% Co-amoxiclav 250/125mg 274 2.9% Nitrofurantoin 100mg 157 1.7% Cefradine 250mg 113 1.2%
Table 3.8. shows the frequency distribution of antibiotic/dosage combinations for patients with a recurrent UTI, excluding pregnant patients and those with pyelonephritis or prostatitis, only the 95th percentile is shown. For these patients, the top three most commonly prescribed antibiotics were Trimethoprim 200mg (36.7%),
Nitrofurantoin 50mg (21%) and Nitrofurantoin 100mg (9.4%).
Table 3.8. Treatment frequencies for recurrent UTI patients, excluding pregnant and severe UTIs (95th percentile only) Antibiotic Dosage Frequency % Trimethoprim 200mg 15,653 36.7% Nitrofurantoin 50mg 8,951 21.0% Nitrofurantoin 100mg 4,012 9.4% Cefalexin 250mg 1,848 4.3% Trimethoprim 50mg/5ml 1,654 3.9% Cefalexin 500mg 1,561 3.7% Co-amoxiclav 250/125mg 1,479 3.5% Co-amoxiclav 500/125mg 916 2.2% Amoxicillin 250mg 876 2.1% Amoxicillin 500mg 837 2.0% Ciprofloxacin 250mg 780 1.8% Ciprofloxacin 500mg 772 1.8% Trimethoprim 100mg 756 1.8%
For the 36 pyelonephritis patients identified in this cohort, the top three most commonly prescribed antibiotics were Co-amoxiclav 500/125mg (27.8%), Nitrofurantoin 50mg (22.2%) and Ciprofloxacin 500mg (19.4%). For the
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9 prostatitis patients identified in this cohort, the top three most commonly prescribed antibiotics were
Ciprofloxacin 500mg (66.7%), Trimethoprim 200mg (11.1%) and Cefalexin 250mg (11.1%).
Table 3.9. shows the frequency distribution of antibiotic durations of all prescriptions for female and male uncomplicated UTI patients. “Missing” durations were any duration falling outside of buffer zones around each duration, as outlined in Section 3.4.6. For female patients, the most common duration was three days (40.8%) which is the recommendation for all antibiotics, closely followed by 7 days (34.7%) and then 5 days (21.8%). In male patients, the majority received 7 days of treatment (67.8%) which is the recommendation for all antibiotics, with far fewer receiving 5 days (18.6%) and 3 days (7.8%) of treatment.
Table 3.9. Duration frequencies for female and male uncomplicated UTI patients Female Male Duration Freq. % Duration Freq. % 3 days 94,183 40.8% 3 days 2,503 7.8% 5 days 50,287 21.8% 5 days 5,993 18.6% 7 days 80,056 34.7% 7 days 21,872 67.8% 10+ days 3,702 1.6% 10+ days 1,588 4.9% Missing 2,747 1.2% Missing 308 1.0% Total 230,975 Total 32,264
Figure 3.5. shows the trends in relative antibiotic prescribing by class over the study period for all UTI episodes in the cohort. It can be seen that trimethoprim remained the most commonly-prescribed antibiotic for every year of the study, however trimethoprim prescribing declined between 2015 and 2017. The prescribing of nitrofurantoin increased markedly over time. The prescribing of cephalosporins decreased as did the prescribing of broad-spectrum penicillins and quinolones, slightly. The prescribing of other antibiotics increased slightly between 2014 and 2017, this was largely due to the prescribing of pivmecillinam which has been recently added to the guideline as a recommended option in uncomplicated UTI patients.
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100%
90%
80% 70%
60%
50% 40%
30%
20%
10% 0% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Trimethoprim Nitrofurantoin Cephalosporin Broad spectrum penicillin Quinolone Other
Fig 3.5. Trends in relative antibiotic class usage for UTIs over time in adults receiving an antibiotic prescription in England
3.5.2. Trends in antibiotic prescribing in relation to the guideline
In the non-recurrent UTI patient cohort over the entire study period, 175,120 UTI episodes (64.2%) were classified as having been prescribed antibiotics “off-guideline”, 88,777 UTI episodes (32.6%) were classified as having been prescribed antibiotics “on-guideline” and 8,770 UTI episodes (3.2%) were classified as having
“changed” treatment. Figure 3.6. shows the trends in antibiotic prescribing for non-recurrent UTIs in relation to the PHE national prescribing guideline over time (2008-2017). It can be seen that the proportion of “off- guideline” prescriptions is higher than “on-guideline” prescriptions consistently over time, however the proportion of “off-guideline” compared to “on-guideline” prescriptions decreased between 2012 and 2015 and stabilized between 2015 and 2017.
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100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
On-guideline Off-guideline Changed Rx
Fig 3.6. Trends in on/off guideline status over time for non-recurrent UTIs in adults receiving an antibiotic prescription in England
Table 3.10. shows the distribution of guideline status for non-recurrent UTIs across all patient-level factors of interest. From the table it can be seen that patients who received an off-guideline prescription were more likely to be older, female, treated in London and receiving antibiotics in the month before the UTI episode. Patients who were pregnant at the time of their UTI diagnosis were also more likely to receive an off-guideline prescription. There was strong evidence of significant differences in guideline status across the groups for each patient factor tested (number of visits per episode which was not tested), aside from UTI severity which demonstrated little to no significant difference in guideline status depending on whether the UTI was severe or not, although this could be due to there being so few patients diagnosed with pyelonephritis or prostatitis in this cohort.
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Table 3.10. Study characteristics for adults with a UTI receiving an antibiotic prescription (excluding recurrent UTIs) in England (2008-2017) Variable Off-guideline On-guideline Changed status Total P value n=175,120 n=88,777 N=8,770 n=272,668
Mean age (SD) 56.3 (20.3) 53.1 (19.6) 55.6 (20.0) 56.0 (20.3) <0.001
Age category <0.001 19-44 57,737 (33.0%) 34,202 (38.5%) 2,951 (33.7%) 94,890 (34.8%) 45-64 50,531 (28.9%) 27,121 (30.6%) 2,638 (30.1%) 80,290 (29.5%) 65-74 26,149 (14.9%) 11,791 (13.3%) 1,317 (15.0%) 39,257 (14.4%) 75+ 40,703 (23.2%) 15,663 (17.6%) 1,864 (21.3%) 58,230 (21.4%) Gender <0.001 Female 156,060 (89.1%) 76,748 (86.5%) 7,580 (86.4%) 278,037 (87.9%) Male 19,060 (10.9%) 12,029 (13.6%) 1,190 (13.6%) 38,189 (12.1%) Region <0.001 North of England 34,237 (19.6%) 20,770 (23.4%) 2,021 (23.0%) 65,877 (20.8%) Midlands/East of England 48,368 (27.6%) 24,558 (27.7%) 2,741 (31.3%) 87,289 (27.6%) South of England 71,911 (41.1%) 35,222 (39.7%) 3,319 (37.8%) 129,706 (41.0%) London 20,604 (11.8%) 8,227 (9.3%) 689 (7.9%) 33,354 (10.6%) Deprivation (n=163 miss) <0.001 Quintile 1 41,988 (24%) 19,764 (22.3%) 2,025 (23.1%) 74,313 (23.5%) Quintile 2 41,864 (23.9%) 20,036 (22.6%) 2,115 (24.1%) 74,449 (23.6%) Quintile 3 34,546 (19.7%) 18,923 (21.3%) 1,819 (20.8%) 64,118 (20.3%) Quintile 4 31,571 (18.0%) 16,442 (18.5%) 1,581 (18.0%) 57,377 (18.2%) Quintile 5 25,068 (14.3%) 13,554 (15.3%) 1,224 (14.0%) 45,806 (14.5%) Pregnant (women ≤50) <0.001 Yes 7,634 (11.1%) 1,605 (4.0%) 142 (4.0%) 9,381 (8.3%) No 61,406 (88.9%) 38,714 (96.0%) 3,424 (96.0%) 103,544 (91.7%) Abx in past 30days <0.001 Yes 28,725 (16.4%) 7,162 (8.1%) 1,022 (11.7%) 48,541 (15.4%) No 146,395 (83.6%) 81,615 (91.9%) 7,748 (88.4%) 267,685 (84.6%) Urine test <0.001 Yes 20,942 (12.0%) 12,588 (14.2%) 2,009 (22.9%) 40,390 (12.8%) No 154,178 (88.0%) 76,189 (85.8%) 6,761 (77.1%) 275,836 (87.2%) Severe UTI Yes 38 (0.02%) 8 (0.01%) 1 (0.01%) 51 (0.02%) 0.053 No 175,082 (99.9%) 88,769 (99.9%) 8,769 (99.9%) 316,175 (99.9%) # of visits per episode / 1 161,132 (92.0%) 87,109 (98.1%) / 282,795 (89.4%) 2 12,674 (7.2%) 1,639 (1.9%) 7,712 (87.9%) 29,413 (9.3%) 3+ 1,314 (0.8%) 29 (0.03%) 1,058 (12.1%) 4,018 (1.3%) Diabetes <0.001 Yes 4,575 (2.6%) 1,663 (1.9%) 722 (8.2%) 8,688 (2.8%) No 170,545 (97.4%) 87,114 (98.1%) 8,048 (91.8%) 307,538 (97.2%) CVD <0.001 Yes 8,482 (4.8%) 3,246 (3.7%) 1,090 (12.4%) 16,190 (5.1%) No 166,638 (95.2%) 85,531 (96.3%) 7,680 (87.6%) 300,036 (94.9%) Renal abnormality <0.001 Yes 5,641 (3.2%) 1,664 (1.9%) 872 (9.9%) 10,600 (3.4%) No 169,479 (96.8%) 87,113 (98.1%) 7,898 (90.1%) 305,626 (96.7%) STI <0.001 Yes 216 (0.1%) 125 (0.1%) 45 (0.5%) 454 (0.1%) No 174,904 (99.9%) 88,652 (99.9%) 8,725 (99.5%) 315,772 (99.9%) Urinary catheter <0.001 Yes 974 (0.6%) 302 (0.3%) 130 (1.5%) 2,096 (0.7%) No 174,146 (99.4%) 88,475 (99.7%) 8,640 (98.5%) 314,130 (99.3%) Penicillin allergy <0.001 Yes 2,780 (1.6%) 951 (1.1%) 514 (5.9%) 5,473 (1.7%) No 172,340 (98.4%) 87,826 (98.9%) 8,256 (94.1%) 310,753 (98.3%)
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Figure 3.7. shows the trends in guideline status of prescriptions for non-recurrent UTIs over time (2008-2017), stratified by patient gender. It can be seen that the proportion of off-guideline prescriptions in women decreased slightly between 2011 and 2015 but then rose again between 2015 and 2017. This trend remained largely stable, however, in comparison with the trend seen in men. It can be seen that the proportion of off- guideline prescriptions in men has dropped dramatically over the study period, from 70% in 2008 to 38% in
2017. Between 2014 and 2015, the proportion of on-guideline prescriptions for UTI in men surpassed those that were off-guideline. The trends in changed treatment remained stable for women and men across the study period.
100%
90% 80% 70%
60% 50%
40% 30%
20% 10% 0% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
On-guideline (female) Off-guideline (female) Changed Rx (female) On-guideline (male) Off-guideline (male) Changed Rx (male)
Fig 3.7. Trends in on/off guideline status over time for non-recurrent UTIs in adults receiving an antibiotic prescription in England stratified by patient gender
Figure 3.8. shows the trend in guideline status of prescriptions for non-recurrent UTIs across the four age groups used in this study, stratified by patient gender. It can be seen that the proportion of off-guideline prescriptions increased with increasing age for both women and men. The proportion of off-guideline prescriptions remained consistently higher in women than in men across all age groups, increasingly so in patients over 65 years. The trend in changed treatment appeared to remain relatively stable for women and men across all age groups.
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100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 19-44 45-64 65-74 75+ On-guideline (female) Off-guideline (female) Changed Rx (female) On-guideline (male) Off-guideline (male) Changed Rx (male)
Fig 3.8. Trends in on/off guideline status by age group for non-recurrent UTIs in adults receiving an antibiotic prescription in England stratified by patient gender
Figure 3.9. shows the distribution of guideline status stratified by the region of England within which the practice was located. The regions with the highest proportions of off-guideline prescribing for non-recurrent UTI are
London and East of England and the regions with the lowest proportions of off-guideline prescribing for non- recurrent UTI are North East and Yorkshire and the Humber.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
North East North West Yorkshire&Humber East Midlands West Midlands East of England South West South Central London South East Coast
On-guideline Off-guideline Changed Rx
Fig 3.9. Trends in on/off guideline status by region for non-recurrent UTIs in adults receiving an antibiotic prescription in England
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3.5.3. Reasons for off-guideline prescribing (drug, dose, duration, frequency)
The following results describe in more detail the components of the guideline (drug, dosage, frequency or duration) that the prescriptions being “off-guideline” are due to. When looking at the off-guideline prescriptions
(again, non-recurrent UTIs only), the number of UTI episodes being off-guideline due to antibiotic choice were
46,424 (26.5%), being due to dosage choice were 45,200 (25.8%), being due to frequency choice were 6,924
(4.0%) and being due to duration choice were 76,572 (43.7%). Figure 3.10. shows the trends in these reasons for being categorised as off-guideline over the study period. It can be seen that in all years, duration was the most common reason for a prescription being off-guideline (ie. the antibiotic, dosage and frequency were all on- guideline) and was on an upward trajectory between 2013 and 2017. Between 2008 and 2011, the choice of antibiotic was the second most common reason for a prescription to be off-guideline, however the trend was on a continuous downward slope and was surpassed by the dosage choice after 2011. The choice of frequency was the least frequent reason for a prescription being off-guideline across all years of the study. In other words, the choice of antibiotic became increasingly on-guideline over time but the choice of dosage for these recommended antibiotics was not in agreement with guidance.
100% 90% 80% 70% 60%
50% 40% 30% 20% 10%
0% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Drug Dose Indication Duration
Fig 3.10. Trends in reason for off-guideline status over time for non-recurrent UTIs in adults receiving an antibiotic prescription in England
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One possible explanation for this could be a trend in increased prescribing of Nitrofurantoin over time, as can be seen in Figure 3.5., but the persistently higher prescribing of Nitrofurantoin 50mg over Nitrofurantoin 100mg
(the recommended dosage during the study period), seen in Tables 3.5. to 3.8.
Figure 3.11. shows the breakdown of reasons for off-guideline prescribing for women and men across the four different age groups used in this study. There does not appear to be a clear trend in reason for off-guideline prescribing by age, however choice of duration is the predominant reason for a prescription being off-guideline in women whereas choice of antibiotic is the predominant reason for a prescription being off-guideline in men, followed by duration.
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
Drug
Dose
Female Frequency
Duration
Drug
Dose
Male Frequency
Duration
19-44 45-64 65-74 75+
Fig 3.11. Trends in reason for off-guideline status by age group for non-recurrent UTIs in adults receiving an antibiotic prescription in England stratified by patient gender
3.5.4. Variation in off-guideline prescribing by GP practice
To investigate the variation in proportions of off-guideline prescribing by GP practice, I calculated the percentage of UTI episodes receiving on-guideline, off-guideline and changed treatment for each of the 355 GP practices.
As shown in Figure 3.12., by practice, the median percentage of on-guideline prescribing was 27% with an interquartile range of 18.3% to 37.0%, with the lowest percentage being 0.10% and the highest percentage being
58.8%. By practice, the median percentage of off-guideline prescribing was 57.4% with an interquartile range of
46.3% to 69.2%, with the lowest percentage being 19.7% and the highest percentage being 93.1%. Lastly, by
97 | Page practice, the median percentage of changed prescribing was 2.3% with an interquartile range of 1.4% to 3.7%, with the lowest percentage being 0.15% and the highest percentage being 6.9%.
Fig 3.12. Distribution of guideline status for prescribing for UTI by GP practice
Figure 3.13. shows the distribution of proportions of off-guideline prescribing for UTI by GP practice. It can be seen that there is a large amount of variation in the proportion of prescriptions for UTI which were off-guideline across all 355 GP practices in the study.
Fig 3.13. Variation in the proportion of prescribing for UTI which was off-guidelineby practice. The solid red line is the weighted mean and the blue dashed lines are the 95% and 99% CIs.
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3.5.5. Sensitivity analysis (3 and 6-month time lag)
When a 3-month time lag was introduced to the guideline adherence algorithm, 93,027 (32.1%) UTI episodes were written prescriptions which were “on-guideline”, 187,065 (64.6%) UTI episodes were written prescriptions which were “off-guideline” and 9,395 (3.3%) UTI episodes had changed treatment. When a 6-month time lag was introduced to the guideline adherence algorithm, 93,065 (32.2%) UTI episodes were written prescriptions which were “on-guideline”, 187,021 (64.6%) UTI episodes were written prescriptions which were “off-guideline” and 9,401 (3.2%) UTI episodes had changed treatment. Introducing time lags into the algorithm therefore did not appear to change the proportions of on or off-guideline prescribing within practices.
3.6. Discussion
3.6.1. Main findings
In this chapter, I described the variation in antibiotics prescribed for community-acquired UTI in an adult patient cohort as part of a nationally-representative sample of English primary care practices. I specifically focussed on how these prescriptions related to those recommended by the PHE national primary care prescribing guideline for UTI. This chapter has shown that the majority of prescriptions for patients with a community-acquired UTI who received an antibiotic in England between 2008 and 2017 were not in agreement with the PHE guideline.
Between 2010 and 2017, women consistently received a higher proportion of off-guideline prescriptions than men and this difference was more pronounced in older age groups than in younger. The trend in off-guideline prescribing for male patients dropped dramatically between 2010 and 2017 and in 2014 the proportion of prescriptions for men which were on-guideline was higher than those which were off-guideline. Off-guideline prescribing appeared to be highest in practices in London and the East of England and lowest in the North East and in Yorkshire and the Humber, although some caution should be used in interpreting this finding as these are large geographical areas over which it is difficult to draw generalisable conclusions about practice-level decision making. Over the study period, the use of trimethoprim, cephalosporins, broad-spectrum penicillins and quinolones decreased and there was a large increase in relative nitrofurantoin usage. Interestingly,
Nitrofurantoin 50mg was consistently used more frequently than Nitrofurantoin 100mg in all patient groups in
99 | Page the study, despite the fact that the 100mg prescription has been recommended in the guideline for female and male patients with uncomplicated UTI for the entirety of the study period. Women were often receiving longer courses of antibiotics than what was recommended. Lastly, I demonstrated that there is a large amount of variation in the proportion of off-guideline prescribing for UTIs between practices, with some practices having as much as 93% of their prescriptions for UTI be off-guideline and others having as little as 20% of their prescriptions for UTI be off-guideline. This amount of variation between practices demonstrates that, if there is in fact a “gold standard” prescription for each patient group (whether it be in line with this guideline or not), then there is much scope for change. Introducing a time lag to allow for new guidance materials to be implemented in practice did not alter guideline adherence in this study.
The purpose of this chapter, however, is not to pass judgement on those working in the primary care setting ascribed to whether they followed a specific guideline or not. Firstly, a guideline is just that. This document produced by PHE was intended as a source of guidance to clinicians working in a complex environment with high levels of diagnostic uncertainty to assist them in deciding what to prescribe for the patient presenting before them. It was also produced, like any other paper-based decision support tool, with a degree of convenience and generalisability in mind and as such a certain amount of complexity is lost, as one would expect. Ultimately, the decision must lie with the GP as to what is most appropriate for their patient and there may therefore be a certain degree of “appropriate deviation” from the guideline based on the patient’s (and perhaps the practice’s) unique circumstances. In a recent study in BMJ Open investigating Read code list development in CPRD, it was found that Read codes with the highest degree of uncertainty determined by a panel of GPs (uncertainty being defined as it being unclear whether the code accurately reflects the clinical feature of interest) tended to be the codes which were used most rarely in practice (94). However, while there may be a reasonable amount of agreement over the codes which reflect a clinical diagnosis, the nature of administrative data is such that not all complexity and nuance related to clinical presentation and what led a GP to make their diagnosis will be recorded uniformly, if at all. This is important as it means that information such as severity of UTI (in terms of pain, fever, history, general distress to the patient), which may not change the Read code allocation (it may still be classified as a UTI and not pyelonephritis or prostatitis) but may change the decision-making in terms of type of prescription to give, may not be captured.
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3.6.2. Limitations
Firstly, analyses have not been restricted to microbiologically-confirmed UTI and have instead used all clinically- diagnosed and empirically-treated UTIs (ie. those with a UTI-related diagnosis receiving antibiotics during the same consultation) so these findings may have overestimated UTIs in each practice. This approach, however, has been widely used in the literature (18, 46, 47) as it is more likely to reflect the true burden of UTI in primary care as many UTIs are treated empirically without laboratory confirmation. It has been outlined by Schmeimann et al. that absolute diagnostic certainty and specific therapy choice could only be achieved if the gold standard testing method of urine culture were used for each patient, this is however unrealistic as this would require considerable diagnostic resources and would delay antibiotic therapy to the patient (95). Urine culture is also unnecessary for most patients with consistent and typical symptoms of UTI (and with a positive dipstick test if it is carried out), as outlined by Car et al., unless there is a history of predisposing factors which the GP believes could put the patient at risk of upper UTI (19).
Secondly, as this study is reliant on clinical coding, it is subject to a degree of uncertainty around the consistency of coding practice (between prescribers, between practices and over time). However, the approach that I used to construct all Read codes lists for UTI and relevant patient characteristics, as outlined in Section 3.4.3., was designed to be as accurate and reflective of practice as possible. This was achieved by using a combination of code lists sourced from the literature, broad searches compiled from the Read code browser refined by clinical input from a practicing GP and inclusion of QOF codes where relevant. It should be highlighted that the codes for “prostatitis” and “pyelonephritis” were not included in the initial list of Read codes used for data extraction.
These conditions would only be identified if the patient had visited the GP for a UTI at some point during the study period and the proportion of patients with these severe conditions in this cohort may therefore be an underestimate.
Thirdly, the way in which the episodes of infection were constructed, as outlined in Section 3.4.4., means that not all UTI consultations and/or prescriptions were analysed, such as the UTI revisits following the initial visit
(index UTI) of an episode (prescription was not captured unless it’s guideline status differed from that of the index UTI, whereby it was classified as “changed treatment”) or the UTI-related visits which occurred in the
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“gap” periods between UTI episodes. Time periods for constructing the UTI episodes were slightly broader than some similar studies in the literature (60 days vs. 30 days) (18), however this was done deliberately to align with the cohort study follow-up time for bloodstream infections, covered in Chapters 4 and 5. While episode construction may be slightly arbitrary, I believe it is preferable to treating all UTI-related visits as independent from each other as this would not accurately reflect real practice wherein a GP would have the patient’s previous case notes at hand and would take the patient’s history into account to make an informed decision about diagnosis and treatment.
3.6.3. How these findings fit in with existing literature
Whilst the methodology may not be as accurate as individual case note review and while there may be gaps in what the algorithm can pick up with respect to consultation complexity, it is the first time that prescriptions for
UTI in England have been analysed nationally in this level of detail using administrative data and in the context of patient demographics, comorbidities and previous antibiotic usage history. It is also the first time that the
PHE primary care prescribing guideline for common infections has been evaluated in relation to day-to-day clinical uptake and usage.
The broad trends described in this chapter with respect to choice of antibiotic were validated in a large UK population-based cohort study estimating incidence of UTI in the elderly conducted by researchers at Cardiff
University and the University of Oxford, whereby the prescribing of nitrofurantoin has been shown to be increasing alongside the decrease in cephalosporin and quinolone prescribing (18), with trimethoprim prescribing remaining largely stable (until 2014 when the study ended). I have since seen a decline in trimethoprim usage from 2014 to 2017 in my study with a continued increase in nitrofurantoin usage, which is most likely due to the recommended switch from trimethoprim to nitrofurantoin as the first-line choice for uncomplicated UTI discussed in Chapter 2. Therefore, this study is complementary to and expands upon the work carried out by the Cardiff and Oxford teams. This trend is also validated across the ESPAUR reports monitoring antibiotic consumption in primary care from 2014-2018 (10, 12, 96).
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With respect to evaluating whether UTI prescribing was in line with treatment guidelines, a research group in
Israel investigating antibiotic prescriptions for 7,738 non-pregnant women over 18 who did not have signs of a severe UTI found that 40.5% of prescriptions were in line with guidelines – however their definition of following the guideline was simply whether the patient received a trimethoprim/sulfamethoxazole or nitrofurantoin prescription (on-guideline) or whether they received any other antibiotic (off-guideline) (85). This proportion is slightly higher than the proportion of on-guideline prescribing found in this study (33%). This could be due to the fact that the patient population in this chapter was much more heterogeneous (including pregnant patients, male patients and some severe UTI patients) and my definition of on/off guideline prescribing was much more detailed as I evaluated every component of the prescription as opposed to just looking at the antibiotic type.
The overall trend of the majority of prescriptions for UTI not being in line with recommended practice is, however, broadly comparable with my findings.
A similar audit conducted in Spain investigating diagnosis and treatment of UTI in 658 lower UTI episodes in adult women found similarly poor agreement with practice guidelines (84). Only 17.5% of patients were prescribed first-choice antibiotic treatment and 39.3% received a long course of treatment (7 days or more) as opposed to the recommended shorter courses of treatment. A US-based study which looked at the treatment of 68 adult female UTI patients found similarly low rates of prescribing in line with recommendations, with only
25% of patients receiving the recommended empirical treatment (97).
In terms in duration of treatment being a particularly large contributor to prescriptions being off-guideline in this study, this finding is reflected in a recent study by Curtis et al. at the University of Oxford where they analysed the temporal and spatial variation of antibiotic prescribing by practice in England (81). As part of their analysis, they found that the average duration of treatment for antibiotics used to treat UTI was between 5 and
7 days, indicating a general tendency for longer courses of treatment for UTI than the recommended 3 days.
This analysis was at the practice-level however and therefore would have included patients receiving treatment for recurrent UTI and more complicated cases of UTI which would correctly require longer courses of treatment and is therefore most likely contributing to the higher average duration.
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A recent study by Pouwels et al., however, demonstrated that a large amount of variation in antibiotic prescribing between practices remains even upon adjustment for differences in patient comorbidities, smoking and deprivation, indicating that differences in antibiotic prescribing most likely go beyond what is appropriate based on the needs of the underlying patient population that a practice serves (80).
3.6.4. Implications for practice and policy
I believe that both the findings and the developed methodology outlined in this chapter are useful for demonstrating clinical practice in England in the treatment of one of the most commonly-presenting adult infections in primary care and for influencing policy in the future with regards to guideline evaluation. The methodology developed to evaluate adherence of individual prescriptions with a national standard at the level of antibiotic, dosage, frequency and duration of treatment has allowed me to report on the extent to which this guideline is followed, which types of patients it is most likely to not be followed in, the reasons for it not being followed and the amount of variation in guideline adherence across practices. This type of algorithm could be adapted to a local guideline or used and updated continuously over time at the national level to monitor uptake if the Department of Health should wish to improve adherence to antibiotic prescribing guidance for this infection. This type of analysis could also be used to inform whether it is cost and time-efficient to create, update and disseminate a national prescribing guideline if clinicians are using either other guidance or are more reliant on their own clinical judgement. This algorithm could also be easily adapted to monitor prescribing for other common bacterial infections such as community-acquired pneumonia (CAP) in primary care; because of this I have also received ethical approval from the CPRD ISAC for our team to replicate this work in an adult CAP cohort and this work will be carried out after the completion of this PhD.
The findings from this chapter have given a more detailed view of the broader trends in antibiotic prescribing for UTI seen in the literature by breaking each individual prescription given specifically for UTI down to the component level which builds upon the existing work monitoring general trends in antibiotic usage (either not linked to indication or not linked to patient characteristics). This type of analysis is also useful for hypothesis generation in terms of which avenues of research to pursue which could be useful for investigating the
104 | Page progression from common to severe infections (in this case UTI to BSI) and in which patient groups we may wish to focus our efforts.
3.7. How these findings fit in with the wider thesis
Following on from Chapter 2 where prescribing of trimethoprim and nitrofurantoin by practice were used as a proxy for UTI treatment, this chapter has examined in more depth the specific antibiotic prescriptions being given for UTI at the patient level, specifically in how they relate to a national antibiotic prescribing guideline. It builds upon Chapter 2 by exploring what patient factors should be treated as covariates in future adjusted analyses, insight which I could not explore in the ecological study due to the level of granularity of the prescribing data. In Chapter 2 I explored the incidence of UTI-related ECB and its relationship with volume of prescribing for
UTI at the practice level and found there to be an association between higher prescribing of trimethoprim and higher incidence of trimethoprim-resistant UTI-related ECB. This indicated that 1) there appears to be a relationship between UTI and BSI, 2) it may be influenced by antibiotic prescribing and 3) antibiotic resistance may be one of the mechanisms of this influence. This chapter has shown that the majority of antibiotic prescriptions for UTI in England are not following the evidence-based guideline released by PHE, which is commonly due to the choice of duration of treatment in women and the choice of antibiotic in men and that the proportion of off-guideline prescribing varies widely across practices. This chapter alone does not properly evaluate the use of the guideline as it does not provide analysis of whether there are detrimental consequences to patient safety if the guideline is not followed. The next critical step in both evaluating the use of this guideline and in attempting to provide insight into what puts UTI patients at risk of developing BSIs is to investigate whether patients for whom the guideline was not followed were at greater, lesser or equal risk of developing a
BSI than those receiving an on-guideline antibiotic prescription for UTI in primary care. This is what I will endeavour to do in Chapter 4.
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CHAPTER 4: INVESTIGATING THE EFFECT OF OFF- GUIDELINE ANTIBIOTIC PRESCRIBING IN PRIMARY CARE FOR URINARY TRACT INFECTION ON THE RISK OF BLOODSTREAM INFECTION.
Chapter 1: Aims and Background
Chapter 2: Volume of prescribing for UTI and incidence of E. coli bacteraemia
Chapter 3: Quantifying Chapter 4: Effect of BSI-related levels of off-guideline UTI diagnosis off-guideline admission, prescribing for UTI and treatment prescribing for UTI death or re- on BSI risk consultation
Chapter 5: Effect of duration of treatment for UTI on BSI risk
Chapter 6, 7: Discussion, conclusions and recommendations
Summary:
This chapter makes use of the national linked dataset and algorithm previously described in Chapter 3 to assess the risk of bloodstream infection following antibiotic treatment for a UTI in primary care. Particular attention is paid to differences in risk in relation to prescribing guideline adherence for UTI. The study takes the form of a retrospective cohort study and the analytical methods include univariate and multivariable logistic regression and survival analysis. No relationship between whether a UTI prescription adhered to guidance and risk of subsequent BSI was demonstrated. This chapter quantifies the rate of progression from a UTI treated with antibiotics to a subsequent BSI as well as the median time to event in various patient sub populations. This work, in conjunction with Chapter 3, is being written as a manuscript for submission to a medical journal.
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4.1. Introduction
As discussed in Chapter 1, bloodstream infections (BSIs) are a major cause of infectious disease morbidity and mortality worldwide (6). In England, the overall incidence of ECB increased from approximately 34,000 per year in 2012 to approximately 41,000 per year in 2017 (12). Of those with a reported underlying source of infection, about half reported a UTI as the source of the BSI (38, 98). Alongside the rise in BSI incidence, it has also been seen that since 2010 total antibiotic prescribing in England has been increasing. The majority of antibiotic prescriptions are administered in primary care and frequently areas with higher antibiotic prescribing are seen to have higher rates of antibiotic resistance (99).
While research around BSI has mainly focused on rapid diagnosis and treatment (100), from a public health perspective prevention of BSI should now be a focus of increasing importance. This shift in focus has been reflected in the NHS England Quality Premium (QP) guidance, updated in April 2018 after this study was designed and partially conducted, to include incentivised targets to: a) reduce Gram-negative bacterial BSIs across the whole health economy and: b) reduce inappropriate antibiotic prescribing for UTIs in primary care (39). Building on the previous chapter, this study aims to explore how some BSIs might be prevented in the community by investigating how UTIs, as a commonly-reported underlying infection that may progress to infection of the bloodstream, are treated with antibiotics. Concurrently, this chapter will investigate patient demographic and socioeconomic factors to understand their role in this infection pathway. From the previous chapter we know there is a large amount of prescribing of antibiotics for UTIs which are not in line with evidence-based recommendations with wide variation in prescribing practices across the country. The natural progression of that work is to investigate whether there is a consequence (in terms of the risk of developing a BSI) to not following prescribing guidelines for UTIs with respect to one of the patient safety outcomes of high priority to the UK government.
4.2. Aims, objectives and rationale
In this chapter I aim to investigate the risk of BSI in patients with a UTI in primary care who were given an antibiotic prescription that either adhered to national guidelines (“on-guideline”), didn’t adhere to national
107 | Page guidelines (off-guideline) or was changed. I also aim to describe other factors which may put patients with UTI at greater risk of developing a BSI as well as quantifying the median time to a BSI following treatment for a UTI in the patient cohort described previously in Chapter 3.
Primary objective:
1) To determine the effect of off-guideline antibiotic prescribing in patients presenting with a UTI at the
GP practice on the incidence of BSI within 30 and 60 days.
Secondary objectives:
2) To determine the risk factors associated with developing a BSI after being treated with antibiotics
for a UTI;
3) To determine the median time to developing a BSI following antibiotic treatment for a UTI in primary
care (stratified by whether the treatment followed the prescribing guideline or not);
4) To determine whether diagnosis and treatment for a recurrent UTI places patients at greater risk of
developing a BSI within 30 and 60 days and determine the median time-to-event.
Rationale:
These specific aims will allow for the national antibiotic prescribing guideline for UTI treatment to be evaluated with respect to whether not following the guideline confers a greater risk of severe outcomes (in this case, BSI).
This chapter will also shed light on the pathway between being treated for a UTI and developing a BSI with regards to what patient factors contribute to the risk of this outcome as well as the time windows within which patients are most at risk of developing a BSI. These measures will assist government bodies such as PHE and the
NHS in making informed policy decisions with regards to understanding a) whether there is a consequence to
GPs not following the treatment guidelines for UTI, b) what certain patient groups are most at risk of this outcome and c) the time window within which the risk is highest. As the Department of Health has identified a reduction in healthcare-associated Gram-negative BSIs as a national health priority, investigating the upstream
108 | Page factors related to antibiotic usage potentially contributing to BSI incidence will be explored in this chapter. The methodology developed and used in this chapter will also contribute to the analyses conducted in Chapter 5.
4.3. Data sources and linkage
This chapter uses the CPRD dataset linked with HES, ONS Mortality, IMD2010 deprivation data and the
CPRD/LSHTM Pregnancy Register, as described in detail in the previous chapter. Figure 4.1. illustrates the overall linkage strategy employed to link all datasets together. Orange boxes relate to the datasets used to define exposure groups and blue boxes relate to datasets used to define outcome groups. The green box is the final linked dataset. The dashed lines indicate where a select number of variables were taken from the primary care data over to the linking datasets to generate dummy variables to be brought back into the primary care dataset.
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Patient Practice Staff Consultation Clinical Add Clin Referral Test Therapy
1. Inclusion/exclusion criteria Merge Common Dosages 2. Cleaning Lookup file with Therapy file to 3. De-duplication Merge total antibiotics list to determine dosage/frequency CPRD records to identify antibiotic prescriptions Patient IMD 2010 data Pregnancy register data
Cleaned CPRD dataset Generate dummy Take patid, uti pregnancy Direct link on patid consultation date variable (uti over to register between pregstart and pregend), Cleaned CPRD dataset with IMD take to CPRD data and pregnancy data
ONS Mortality data
Cleaned CPRD dataset with IMD Take over patid + uti Patient Hospitalisation Episode Diagnosis Procedure and pregnancy data linked with consultation date ONS dummy variable Generate dummy variable if dod within 30d and 60d of uti consultation date HES data (basic) 1. Inclusion/exclusion criteria 2. Cleaning Cleaned CPRD dataset with IMD Take over patid + UTI 3. De-duplication and pregnancy data linked with consultation date + ONS dummy variable generated 30d and Cleaned HES data (basic) 60d variable
Generate dummy Final CPRD dataset with all variable if CA-BSI = 1 covariate and outcome linked within 30d and 60d of data UTI consultation date
Figure 4.1. Overall linkage strategy between CPRD, HES, ONS, IMD2010 and CPRD/LSHTM Pregnancy register. Add Clin = Additional Clinical; patid = unique patient identifier; pregstart = estimated pregnancy start date; pregend = pregnancy end date (irrespective of pregnancy outcome); dod = date of death; 30d = 30 days; 60d = 60 days 110 | Page
4.4. Methods
4.4.1. Study population and outcomes
The study population and inclusion/exclusion criteria outlined in Chapter 3 are the same for this chapter.
Analyses investigating the association between off-guideline UTI prescribing and BSI risk excluded recurrent UTI episodes as they could not be categorised in relation to the guideline (as they do not have a specified duration of treatment). All analyses have therefore been run separately looking at 1) all non-recurrent UTI patients and whether their risk of BSI differed based on whether their UTI prescription was on-guideline/off- guideline/changed and 2) all patients in the cohort and investigating whether being diagnosed with a recurrent
UTI is associated with developing a BSI subsequent to antibiotic treatment for the recurrent UTI. As outlined in the previous chapter, only community-acquired UTIs (those who did not have evidence of a hospital discharge in the previous 30 days) were included in the study.
Defining the outcome:
BSI as an outcome was captured in three ways: 1) a diagnosis at the hospital captured in HES data, 2) a revisit to the GP practice for a BSI-related cause captured in CPRD data or 3) a record of death due to a BSI captured in
ONS Mortality data. For the HES data, diagnoses were identified using ICD-10 codes and for the CPRD data, diagnoses were identified using Read codes; the two code lists for BSI can be found in Appendix D. The Read code list was again developed in collaboration with my academic GP collaborator, similarly to the Read code lists for UTI and comorbidities outlined in Chapter 3. The ICD-10 code list for BSI was based on the ICD-10 Diagnostic
Related Groups list for “Septicemia or severe sepsis with/without mv >96 hours with mcc”
(https://www.icd10data.com/ICD10CM/DRG/871) and altered to exclude any code related specifically to a viral or fungal BSI. This list was cross-referenced with several other researchers within the Imperial HPRU working on
ICD-10 coding for BSI to ensure agreement. If a patient met any one of these criteria (a BSI code in HES, CPRD or
ONS) within either 30 or 60 days, they were assigned to the “case” group. Patients who failed to meet any of these criteria in the respective time frames comprised the “control” group. A flag was generated to indicate whether each patient was a case at 30 days (Y/N), a case at 60 days (Y/N) or neither for the purposes of the
111 | Page logistic regression modelling. In addition to the BSI flags, the exact date of the BSI code (in HES, CPRD or ONS) was retained for the purposes of the survival analyses to determine time-to-event. All linkages between datasets were deterministic (based on a combination of Patient ID, UTI ID and/or Hospitalisation ID) and employed the use of the “rangestat” package in Stata described in the previous chapter.
All analyses were restricted to infections that were considered to be both community-onset and community- acquired. Firstly, in order to filter out the infections which were not community-acquired, patients with a BSI outcome could not have had a hospital discharge date in HES within 30 days before the BSI event date (38).
Additionally, in order to further filter out the infections which were not community-onset, BSI diagnoses had to occur on or before the third day after admission, with day 1 being the day of admission. This is in line with the definition used by PHE in defining community vs. hospital-onset infections (101). This part of the definition was slightly more challenging to achieve due to the structure of HES data. Within HES, each patient can have one or more “spells” which is a single hospitalisation and contains an admission date and a discharge date. Further, for each “spell” each patient can have one or more “episodes” which contain episode start dates and episode end dates – which when pieced together create a spell. Each episode represents a “finished consultant episode of care” containing patient information and diagnoses, meaning that every time a patient is placed under a new consultant within their hospitalisation, a new episode begins. The implication of this is that if an episode lasts longer than a day (which is often the case), it is impossible to know the exact day on which a diagnosis of interest occurred as a “diagnosis date” is not given, just episode start and end dates. For this reason, determining if a diagnosis was made “within 2 days after the admission date” is therefore a challenge. To combat this, I selected patients where the BSI-related ICD-10 code either occurred in the first episode of the spell (their primary reason for being admitted) or occurred in a subsequent episode that ended within 3 days of admission (episode end date – admission date ≤ 3).
As there was some uncertainty around whether this definition was appropriate and after discovering many differing methods in the literature, I examined the proportion of BSI cases which were recorded in the first episode, similar to the method employed by researchers at LSHTM for defining community-onset pneumonia in
HES (102) and the proportion of BSI cases recorded in a subsequent episode which ended within 72 hours of
112 | Page admission. These results can be found in Section 4.5.6. (“Sensitivity analyses”). The strategy for linking the different HES datasets to identify episodes containing BSI diagnostic codes is shown in Figure 4.2.
HES_diag_hosp.dta HES_hospitalisation.dta
Identify BSI codes, save patid Deduplicate on spno and spno where bsi==1
Merge on N = patid + Identify BSI records based on 33,29031,315 BSI_lookup.dta spno match
N = Identify CA-BSI by counting back 30,290 Deduplicate on spno 31,315 30 days from admidate
N = CA_BSI_lookup.dta 18,677 18,677
HES_diag_episode.dta
Calculate date difference between each epistart and admidate Merge on patid + spno
Identify BSI codes within 72 hours of admission
Identify comm acquired BSIs which are comm associated based on match
N = 16,01816,018 Comm_BSI_final.dta
Fig 4.2. Linkage strategy between “HES diagnosis hospital”, “HES hospitalisation” and “HES diagnosis episode” tables to identify BSI diagnoses which were both community-acquired and community-onset. Patid = patient ID, spno = spell number (the unique identifier for a hospitalisation).
To capture patients which were “lost-to-follow up”, I firstly identified whether patients transferred out of the
GP practice (using “transfer out date” and “transfer out reason” in CPRD) within 30 days or 60 days and, if so, whether that transfer was before the BSI outcome if one existed. Secondly, I identified whether patients died
113 | Page from a non-BSI related cause within 30 and 60 days using ONS Mortality data. Patients who were lost-to-follow up within 30 days and 60 days were excluded from the models looking at the 30-day and 60-day BSI outcomes, respectively.
4.4.2. Sample size calculation
As it was not known what the rate of progression from a UTI treated with antibiotics in the general adult population to a BSI was from the literature, a best estimate was made. Based on pilot data from another Imperial
HPRU project (the manuscript of which has recently been accepted in the BMJ), it was estimated that 1% of patients in the reference group (on-guideline prescription) would develop a community-acquired BSI within 60 days after being diagnosed with a UTI. In order to detect twice the rate of bacteraemia per unit time in the exposed groups (off-guideline and changed prescription) compared with the reference group (on-guideline)
(Odds Ratio=2), with a 2-sided α=0.05, a β=0.20, and a 5% loss to follow-up rate, a total of 10,350 patients (3,450 per group) was the minimum required.
4.4.3. Statistical methods
For determining the factors predictive of developing a BSI following on or off-guideline antibiotic treatment for a non-recurrent UTI, univariate and multivariable logistic regression models were run for two separate outcomes: 1) BSI event within 30 days and 2) BSI event within 60 days. To determine the factors predictive of developing a BSI following a recurrent UTI, univariate and multivariable logistic regression models were run, again looking at BSI outcomes separately at 30 days and 60 days. To build the multivariable logistic regression models, all potential risk factors which were determined a priori to potentially have an effect on the outcome
(informed by clinical input and the literature and findings outlined in Chapter 3) were included and inference was drawn on a global model (all predictors present) as opposed to using a form of stepwise regression.
The predetermined factors included in analyses were (as in the previous chapter): age category, gender, region, patient-level deprivation, pregnancy, antibiotic prescription within 30 days before the first date of the UTI episode, record of a urine test (dipstick or urine culture), severity of UTI (prostatitis or pyelonephritis), number
114 | Page of UTI-related GP visits per episode or record of diabetes, cardiovascular disease, renal abnormality, sexually transmitted infection, urinary catheter usage or penicillin allergy (in one of the Clinical, Referral or Test files) in the year prior to the UTI episode. The final multivariable logistic regression model included a cluster function, clustering on GP Practice ID to account for the fact that antibiotic prescribing within a GP practice may be more similar than between practices based on local guideline adaptation, serving of similar patient populations and the potential influence of GPs within a practice on each other’s clinical decision-making and prescribing.
Interaction was tested for in the guideline status models between exposure group and age category and between exposure group and deprivation quintile to determine whether different groups within age category or deprivation quintile influenced the effect of the exposure group on the outcome. For the purposes of testing interaction, age was re-categorised into “under 75 years old” vs. “75 years or older” and deprivation was re- categorised into “Quintile 1,2,3,4” vs. “Quintile 5 (most deprived)”. These variables were re-categorised in this way as being 75 or over and living in the most deprived neighbourhoods were the strata of these categorical variables which showed the strongest and most significant associations with the outcome in the adjusted models. For the recurrent UTI models, interaction between having a recurrent UTI and having antibiotics in the previous month was tested. To test for interaction, models were run with the dependant variable (BSI at 30 or
60 days) and the two independent variables of interest separately (i.e. exposure group and binary age variable) and with the independent variables including an interaction term. The Hosmer-Lemeshow goodness-of-fit test was used to test the null hypothesis that the variables separately were nested within the model including the interaction term (103).
Secondary analysis
To account for the fact that the risk of the outcome may change over time, I chose to supplement the logistic regression analyses (whether the event occurred Y/N) with a series of survival analyses to determine the time- to-event as well as the instantaneous risk of the outcome occurring at time t, using methodology outlined by
Kirkwood and Sterne (104) and by Cleves, Gould and Marchenko (105). For each unique UTI episode (ID = utiID),
I set the survival time to begin on the day of UTI diagnosis (entry = “consultation_event_date”) and the outcome event to be the date of BSI (failure = BSI_admission_date, BSI_consultation_date or BSI_death_date). All patients
115 | Page were censored at 60 days following the UTI diagnosis date. Curves were plotted to show the Kaplan-Meier estimates of the survivor functions for a) all non-recurrent UTIs, b) all non-recurrent UTIs, stratified by treatment group, c) all UTIs (recurrent and non-recurrent) and d) all UTIs, stratified by recurrent status. This was to graphically illustrate the change in the probability that an individual will not experience (or “survive”) the event of interest up to and including time t. Median survival times and their respective confidence intervals were calculated using the “stci” command in Stata. The log-rank test of equality across strata was used to conduct the univariate analyses for each categorical or binary variable of interest separately and its relation to each of the outcomes. To determine the instantaneous rate of BSI at time t, adjusted for potential confounding factors, I ran Cox proportional hazards models investigating separately whether treatment group affected the hazard of the outcome or whether having a recurrent UTI affected the hazard of the outcome. The proportional hazards assumption for the Cox hazards model was examined using the scaled Schoenfeld residuals for each of the individual covariates as well as the exposure variable (treatment group or recurrent UTI) in both models as well as examining the global tests for the models overall. I also plotted the Schoenfeld residuals for each variable in relation to the failure event to interrogate the data visually, these plots can be found in Appendix F. If the hazard ratio was proportional for each variable, then the null hypothesis of a “zero slope” relationship between the residuals of the variables would have to be accepted and the hazards would be assumed to be proportional, thereby meeting the proportional hazards assumption.
4.5. Results
4.5.1. Distribution of patient characteristics and outcomes
As shown in Figure 4.2., 31,315 BSI records were initially identified in HES. After de-duplication based on unique hospitalisation number per patient (spno) the number of records in the UTI patient cohort was reduced to 30,290
BSI events. After applying the “community-acquired” criteria (no hospital discharge in the previous 30 days), this was reduced to 18,677 BSIs and after further applying the “community-onset” criteria (diagnosis within 3 days of admission) the final number of BSIs eligible for linkage from HES was 16,018. I further identified 1,682 BSI- related re-visits within CPRD and 215 BSI-related deaths within ONS, bringing the total number of BSI outcome events eligible for linkage with UTI records to be 17,915. After linking the BSI events with the primary care
116 | Page records, 361 BSI events occurred within 30 days and 539 BSI events occurred within 60 days in the whole patient cohort (recurrent and non-recurrent UTIs). When looking just at the non-recurrent UTI patient sample, 292 BSI events occurred within 30 days and 428 BSI events occurred within 60 days. One thousand five hundred and sixty-three patients were lost-to-follow up within the 30-day mark, one of whom had a BSI outcome, while 3,109 patients were lost-to-follow up within the 60-day mark, four of whom had a BSI outcome. BSI patients lost-to- follow up were not included in the respective 30 and 60-day models.
Table 4.1. shows the distribution of BSI events for non-recurrent UTIs across all patient-level factors of interest where the event occurred either within 30 days or 60 days of antibiotic treatment for a UTI in primary care.
From the Table it can be seen that patients who developed a BSI within 30 or 60 days were more likely to be older, male, treated for UTI in the Midlands/East of England and had received antibiotics in the month prior to
UTI diagnosis. Patients with a history in the previous year of diabetes, cardiovascular disease, renal abnormality and urinary catheter usage were also more likely to develop a BSI within 30 or 60 days of UTI treatment. There was no evidence of significant differences in development of BSI with regards to whether the UTI prescription followed the guidelines or not at either outcome time point. There were too few BSI patients (less than 5) who had prostatitis/pyelonephritis, were pregnant at the time of UTI diagnosis, had three or more visits to the GP within one infection episode or had a history of STI in the previous year to warrant including these variables in the univariate or multivariable analyses.
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Table 4.1. Characteristics of adults with a BSI within 30 and 60 days of receiving antibiotics for a UTI (excluding recurrent UTIs) in England (2008-2017) Variable No BSI (30 d) BSI (30 d) Total P value No BSI (60 d) BSI (60 d) Total P value n=272,376 n=292 n=272,668 n=272,240 n=428 n=272,668 Treatment group 0.163 0.100 On-guideline 88,693 (32.6%) 84 (28.8%) 88,777 (32.6%) 88,656 (32.6%) 121 (28.3%) 88,777 (32.6%) Off-guideline 174,918 (64.2%) 202 (69.2%) 175,120 (64.2%) 174,824 (64.2%) 296 (69.2%) 175,120 (64.2%) Changed treatment 8,764 (3.2%) 6 (2.1%) 8,770 (3.2%) 8,759 (3.2%) 11 (2.6%) 8,770 (3.2%)
Mean age (SD) 55.2 (SD 20.1) 77.2 (SD 16.5) 55.2 (SD 20.1) <0.001 55.2 (SD 20.1) 77.5 (SD 16.3) 55.2 (SD 20.1) <0.001
Age category <0.001 <0.001 19-44 94,867 (34.8%) 23 (7.9%) 94,890 (34.8%) 94,857 (34.8%) 33 (7.7%) 94,890 (34.8%) 45-64 80,267 (29.5%) 24 (8.2%) 80,291 (29.5%) 80,256 (29.5%) 35 (8.2%) 80,291 (29.5%) 65-74 39,216 (14.4%) 41 (14.0%) 39,257 (14.4%) 39,202 (14.4%) 55 (12.9%) 39,257 (14.4%) 75+ 58,026 (21.3%) 204 (69.9%) 58,230 (21.4%) 57,925 (21.3%) 305 (71.3%) 58,230 (21.4%) Gender <0.001 <0.001 Male 32,166 (11.8%) 114 (39.0%) 32,280 (11.8%) 32,120 (11.8%) 160 (37.4%) 32,280 (11.8%) Female 240,210 (88.2%) 178 (61.0%) 240,388 (88.2%) 240,120 (88.2%) 268 (62.6%) 240,388 (88.2%) Region 0.037 0.043 North of England 56,970 (20.9%) 58 (19.9%) 57,028 (20.9%) 56,940 (20.9%) 88 (20.6%) 57,028 (20.9%) Midlands/East of England 75,566 (27.7%) 101 (34.6%) 75,667 (27.8%) 75,524 (27.7%) 143 (33.4%) 75,667 (27.8%) South of England 110,342 (40.5%) 111 (38.0%) 110,453 (40.5%) 110,292 (40.5%) 161 (37.6%) 110,453 (40.5%) London 29,498 (10.8%) 22 (7.5%) 29,520 (10.8%) 29,484 (10.8%) 36 (8.4%) 29,520 (10.8%) Deprivation (n=163 missing, 0 from BSI) 0.137 0.044 Quintile 1 63,726 (23.4%) 51 (17.5%) 63,777 (23.4%) 63,703 (23.4%) 74 (17.3%) 63,777 (23.4%) Quintile 2 63,935 (23.5%) 80 (27.4%) 64,015 (23.5%) 63,899 (23.5%) 116 (27.1%) 64,015 (23.5%) Quintile 3 55,229 (20.3%) 60 (20.6%) 55,289 (20.3%) 55,198 (20.3%) 91 (21.3%) 55,289 (20.3%) Quintile 4 49,541 (18.2%) 53 (18.2%) 49,594 (18.2%) 49,514 (18.2%) 80 (18.7%) 49,594 (18.2%) Quintile 5 39,798 (14.6%) 48 (16.4%) 39,846 (14.6%) 39,779 (14.6%) 67 (15.7%) 39,846 (14.6%) Pregnant (women <=50) 0.423 0.336 No 103,525 (91.7%) 19 (86.4%) 103,544 (91.7%) 103,516 (91.7%) 28 (87.5%) 103,544 (91.7%) Yes 9,378 (8.3%) 3 (13.6%) 9,381 (8.3%) 9,377 (8.3%) 4 (12.5%) 9,381 (8.3%) Antibiotics in past 30days <0.001 <0.001 No 235,527 (86.5%) 232 (79.5%) 235,759 (86.5%) 235,436 (86.5%) 323 (75.5%) 235,759 (86.5%) Yes 36,849 (13.5%) 60 (20.6%) 36,909 (13.5%) 36,804 (13.5%) 105 (24.5%) 36,909 (13.5%) Urine test 0.480 0.160 No 236,871 (87.0%) 258 (88.4%) 237,129 (87.0%) 236,747 (87.0%) 382 (89.3%) 237,129 (87.0%) Yes 35,505 (13.0%) 34 (11.6%) 35,539 (13.0%) 35,493 (13.0%) 46 (10.8%) 35,539 (13.0%) Severe UTI / / No 272,328 (99.9%) 292 (100.0%) 272,620 (99.9%) 272,192 (99.9%) 428 (100.0%) 272,620 (99.9%)
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Yes 48 (0.02%) 0 (0.0%) 48 (0.02%) 48 (0.02%) 0 (0.0%) 48 (0.02%) # of visits per episode 0.738 0.246 1 247,973 (91.0%) 269 (92.1%) 248,242 (91.0%) 247,855 (91.0%) 387 (90.4%) 248,242 (91.0%) 2 22,003 (8.1%) 22 (7.5%) 22,025 (8.1%) 21,991 (8.1%) 34 (7.9%) 22,025 (8.1%) 3+ 2,400 (0.9%) 1 (0.3%) 2,401 (0.9%) 2,394 (0.9%) 7 (1.6%) 2,401 (0.9%) Diabetes <0.001 <0.001 No 265,433 (97.5%) 275 (94.2%) 265,708 (97.5%) 265,308 (97.5%) 400 (93.5%) 265,708 (97.5%) Yes 6,943 (2.6%) 17 (5.8%) 6,960 (2.6%) 6,932 (2.6%) 28 (6.5%) 6,960 (2.6%) CVD <0.001 <0.001 No 259,602 (95.3%) 248 (84.9%) 259,850 (95.3%) 259,488 (95.3%) 362 (84.6%) 259,850 (95.3%) Yes 12,774 (4.7%) 44 (15.1%) 12,668 (4.7%) 12,752 (4.7%) 66 (15.4%) 12,668 (4.7%) Renal abnormality <0.001 <0.001 No 264,220 (97.0%) 271 (92.8%) 264,491 (97.0%) 264,097 (97.0%) 394 (92.1%) 264,491 (97.0%) Yes 8,156 (3.0%) 21 (7.2%) 8,177 (3.0%) 8,143 (3.0%) 34 (7.9%) 8,177 (3.0%) STI / / No 271,990 (99.9%) 292 (100.0%) 272,282 (99.9%) 271,854 (99.9%) 428 (100.0%) 272,282 (99.9%) Yes 386 (0.1%) 0 (0.0%) 386 (0.1%) 386 (0.1%) 0 (0.0%) 386 (0.1%) Urinary catheter <0.001 <0.001 No 270,982 (99.5%) 280 (95.9%) 271,262 (99.5%) 270,852 (99.5%) 410 (95.8%) 271,262 (99.5%) Yes 1,394 (0.5%) 12 (4.1%) 1,406 (0.5%) 1,388 (0.5%) 18 (4.2%) 1,406 (0.5%) Penicillin allergy 0.246 0.090 No 268,138 (98.4%) 285 (97.6%) 268,423 (98.4%) 268,006 (98.4%) 417 (97.4%) 268,423 (98.4%) Yes 4,238 (1.6%) 7 (2.4%) 4,245 (1.6%) 4,234 (1.6%) 11 (2.6%) 4,245 (1.6%)
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4.5.2. Logistic regression models investigating the effect of off-guideline antibiotic prescribing on BSI
Table 4.2. shows the results of the univariate logistic regression models investigating the effect of each individual potential predictor of developing a BSI within 30 days. Crude odds ratios, 95% confidence intervals and p-values of each association are displayed for each of the factors (all of which are either binary or categorical), the main factor of interest being the treatment group. The factors significantly associated with the outcome of developing a BSI within 30 days were age category, gender, deprivation, having received previous antibiotics, a history of diabetes, cardiovascular disease, renal abnormality or urinary catheter usage. Taking account of these factors, patients who were in older age categories, male, had a history of receiving previous antibiotics before the UTI and had evidence of any of the comorbidities mentioned above were shown to have a higher risk of developing the outcome. The association between deprivation and the outcome was less clear as the risk of developing the outcome was higher in all groups which were more deprived than the most affluent group (Quintile 1) however the association was only significant between living in Quintile 2 and living in Quintile 5 (most deprived) compared to living in Quintile 1. The risk of developing the outcome was higher in patients who received an off-guideline prescription compared with those who received an on-guideline prescription (OR 1.22, 95% CI 0.95-1.58) and lower in those who received changed treatment during their infection episode compared with those who received one prescription which was on-guideline (OR 0.72, 95% CI 0.32-1.65), however these differences were not statistically significant. Equally, there did not appear to be a significant association between developing the outcome and region where the UTI was treated, having received a UTI test, or having evidence of a penicillin allergy.
Table 4.2. also shows the results of the univariate logistic regression models investigating the individual potential predictors of developing a BSI within 60 days. The factors identified as being significantly associated with developing a BSI within 60 days of being treated for a UTI were age category, gender, deprivation, having received previous antibiotics, and a history of diabetes, cardiovascular disease, renal abnormality or urinary catheter usage within the previous year. Patients who were older, male, not living in affluent communities, had received antibiotics within 30 days before the UTI or had a history of the comorbidities mentioned above were more at risk of developing the outcome.
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Table 4.2. Crude odds ratios for investigating the effect of off-guideline prescribing for a UTI on developing a BSI within 30 and 60 days in England, (excluding recurrent UTIs) BSI event with 30 days BSI event within 60 days Variable Crude OR 95% CI P-value Crude OR 95% CI P-value Treatment group On-guideline REF REF REF REF Off-guideline 1.22 (0.95-1.58) 0.124 1.24 (1.01-1.54) 0.043 Changed treatment 0.72 (0.32-1.65) 0.440 0.92 (0.50-1.70) 0.787 Age category 19-44 REF REF REF REF 45-64 1.23 (0.70-2.18) 0.475 1.25 (0.78-2.01) 0.358 65-74 4.31 (2.59-7.18) <0.001 4.03 (2.62-6.20) <0.001 75+ 14.67 (9.53-22.58) <0.001 15.46 (10.79-22.14) <0.001 Gender Male REF REF REF REF Female 0.21 (0.16-0.26) <0.001 0.22 (0.18-0.27) <0.001 Region North of England REF REF REF REF Midlands/East of England 1.31 (0.95-1.82) 0.097 1.23 (0.94-1.60) 0.132 South of England 0.99 (0.72-1.36) 0.949 0.95 (0.73-1.23) 0.679 London 0.73 (0.45-1.20) 0.216 0.79 (0.54-1.17) 0.237 IMD2010 (n=163 missing) Quintile 1 REF REF REF REF Quintile 2 1.56 (1.10-2.22) 0.013 1.56 (1.17-2.10) 0.003 Quintile 3 1.36 (0.94-1.97) 0.108 1.42 (1.05-1.93) 0.025 Quintile 4 1.34 (0.91-1.96) 0.139 1.39 (1.01-1.91) 0.041 Quintile 5 1.51 (1.02-2.24) 0.042 1.45 (1.04-2.02) 0.028 Antibiotics in past 30days No REF REF REF REF Yes 1.66 (1.25-2.20) <0.001 2.09 (1.68-2.61) <0.001 Urine test No REF REF REF REF Yes 0.88 (0.61-1.25) 0.473 0.80 (0.59-1.09) 0.153 Diabetes No REF REF REF REF Yes 2.37 (1.44-3.87) 0.001 2.69 (1.83-3.95) <0.001 CVD No REF REF REF REF Yes 3.63 (2.63-5.00) <0.001 3.76 (2.89-4.89) <0.001 Renal abnormality No REF REF REF REF Yes 2.53 (1.62-3.94) <0.001 2.83 (1.99-4.01) <0.001 Urinary catheter No REF REF REF REF Yes 8.43 (4.72-15.06) <0.001 8.73 (5.43-14.04) <0.001 Penicillin allergy No REF REF REF REF Yes 1.55 (0.73-3.29) 0.250 1.67 (0.92-3.04) 0.093
Factors which appeared to have either very weak evidence or no evidence of a statistically significant association with the outcome were region, whether a urine test was conducted or whether the patient had a record of penicillin allergy. Interestingly, when looking at the 60-day outcome, receiving a prescription for the UTI which was off-guideline was associated with 24% higher odds of developing a BSI within 60 days (OR 1.24, 95% CI 1.01-
1.54) when compared with receiving a prescription for the UTI which was on-guideline. As described in the
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Methods, all factors which were postulated to affect the association between prescribing for a UTI and development of a BSI were included in the multivariable analysis, rather than selecting factors based on the significance of their association with the outcome in the univariate analyses. All factors from the univariate analyses were therefore included in the adjusted models. Both multivariable models included a cluster function clustering on practice identifier, as outlined in the Methods.
Table 4.3. shows the results of the multivariable logistic regression model investigating the effect of treatment group on developing a BSI within either 30 or 60 days, adjusted for covariates of interest. After adjusting for age category, gender, region, deprivation, previous antibiotic use, urine testing and comorbidities, there was no statistically significant relationship between receiving an off-guideline or changed prescription for a UTI and developing a BSI within 30 days compared to receiving an on-guideline prescription (OR 1.10, 95% CI 0.85-1.43;
OR 0.54, 95% CI 0.24-1.23, respectively). After adjustment for all other factors, being over 75 years old placed patients at being almost 11 times the odds of developing a BSI within 30 days compared with patients under 45 years old (OR 10.95, 95% CI 6.86-17.48). Being female was protective against developing a BSI within 30 days compared to being male (OR 0.32, 95% CI 0.25-0.42). Living in the most deprived areas (IMD2010 Quintile 5) placed patients at 85% higher odds of the outcome within 30 days after treatment for a UTI (OR 1.85, 95% CI
1.15-2.99) compared with living in the most affluent areas (IMD2010 Quintile 1). After adjustment for all other factors of interest, having a history of urinary catheterization within the previous year remained the only comorbidity with a statistically significant association with the outcome, increasing the odds by almost 2 times compared to patients with no history of catheter usage (OR 1.98, 95% CI 1.03-3.78).
When investigating the BSI outcome within 60 days of UTI diagnosis and treatment, after adjusting for age category, gender, region, deprivation, previous antibiotic use, urine testing and comorbidities, there was no statistically significant relationship between receiving an off-guideline or changed prescription for a UTI and developing a BSI within 60 days compared with receiving an on-guideline prescription (OR 1.08, 95% CI 0.86-
1.36; OR 0.67, 95% CI 0.36-1.24, respectively). All other associations between the different patient factors and the outcome were similar to those seen in the previous model looking at BSI within 30 days as the outcome.
When testing for interaction between a) age and treatment group and b) deprivation and treatment group as
122 | Page outlined in the Methods, there was no evidence of interaction in either comparison and interaction terms were therefore not included in the adjusted models.
Table 4.3. Adjusted odds ratios for investigating the effect of off-guideline prescribing for a UTI on developing a BSI within 30 and 60 days in England, (excluding recurrent UTIs) BSI event with 30 days BSI event within 60 days Variable Adj. OR 95% CI P-value Adj. OR 95% CI P-value Treatment group On-guideline REF REF REF REF Off-guideline 1.10 (0.85-1.43) 0.463 1.08 (0.86-1.36) 0.151 Changed treatment 0.54 (0.24-1.23) 0.143 0.67 (0.36-1.24) 0.202 Age category 19-44 REF REF REF REF 45-64 1.10 (0.61-1.96) 0.754 1.11 (0.68-1.80) 0.677 65-74 3.46 (2.00-5.97) <0.001 3.19 (2.00-5.08) <0.001 75+ 10.95 (6.86-17.48) <0.001 11.3 (7.56-16.92) <0.001 Gender Male REF REF REF REF Female 0.32 (0.25-0.42) <0.001 0.35 (0.28-0.44) <0.001 Region North of England REF REF REF REF Midlands/East of England 1.37 (0.90-2.10) 0.146 1.28 (0.90-1.80) 0.168 South of England 1.04 (0.73-1.48) 0.824 0.99 (0.74-1.31) 0.922 London 0.87 (0.52-1.44) 0.587 0.94 (0.59-1.49) 0.784 IMD2010 (n=163 missing) Quintile 1 REF REF REF REF Quintile 2 1.48 (1.06-2.08) 0.021 1.48 (1.11-1.98) 0.008 Quintile 3 1.37 (0.93-2.03) 0.110 1.43 (1.04-1.96) 0.026 Quintile 4 1.54 (1.02-2.33) 0.042 1.57 (1.10-2.24) 0.013 Quintile 5 1.85 (1.15-2.99) 0.011 1.73 (1.16-2.58) 0.007 Antibiotics in past 30days No REF REF REF REF Yes 1.13 (0.84-1.51) 0.415 1.43 (1.16-1.76) 0.001 Urine test No REF REF REF REF Yes 0.97 (0.68-1.38) 0.852 0.88 (0.61-1.27) 0.490 Diabetes No REF REF REF REF Yes 1.31 (0.77-2.21) 0.319 1.46 (0.95-2.24) 0.086 CVD No REF REF REF REF Yes 1.29 (0.94-1.76) 0.121 1.29 (0.97-1.73) 0.085 Renal abnormality No REF REF REF REF Yes 0.99 (0.62-1.56) 0.954 1.05 (0.73-1.53) 0.778 Urinary catheter No REF REF REF REF Yes 1.98 (1.03-3.78) 0.039 2.01 (1.16-3.49) 0.013 Penicillin allergy No REF REF REF REF Yes 1.32 (0.64-2.73) 0.448 1.34 (0.71-2.53) 0.359
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4.5.3. Logistic regression models investigating the effect of recurrent UTIs on BSI
Prescriptions for recurrent UTIs could not be assessed against prescribing guidelines in the same way as prescriptions for non-recurrent UTIs as treatment duration is not specified. I nonetheless wanted to investigate the risk of developing a BSI following a recurrent UTI (again only those treated with antibiotics), compared with the risk of BSI following a non-recurrent UTI. Patients with recurrent UTIs are a group of particular interest due to their relatively increased frequency of antibiotic exposure. Patients who visit their GP for UTI frequently are not uncommonly prescribed a first line antibiotic to take prophylactically as a single dose when exposed to an identifiable trigger (such as sexual intercourse) or once a day, with the treatment being reviewed at 6 months
(22). The risk that this antibiotic usage puts them at with respect to antibiotic resistance and severe infection- related outcomes is not very well understood (106) and provided the rationale for building risk models specifically to investigate BSI-related outcomes in this patient population.
Table 4.4. shows the distribution of BSI events for all UTIs in the cohort (including recurrent UTIs) across all patient-level factors of interest within 30 days or 60 days of UTI diagnosis and antibiotic treatment. Trends were similar with respect to patient characteristics for those patients developing a BSI up to 30 and 60 days in the whole patient cohort as they were in the more restricted cohort of only non-recurrent UTI patients described previously. More specifically, patients who developed a BSI within 30 or 60 days were more likely to be older, male, treated for UTI in the Midlands/East of England and had received antibiotics in the month prior to the UTI diagnosis. Patients with a history in the previous year of diabetes, cardiovascular disease, renal abnormality, urinary catheter usage and, to a lesser extent, a recorded penicillin allergy were also more likely to develop a
BSI within 30 or 60 days of a UTI. There were too few BSI patients who had prostatitis/pyelonephritis, were pregnant at the time of UTI diagnosis, had three or more visits to the GP within one infection episode or had a history of STI in the previous year to warrant including these variables in the univariate or multivariable analyses.
For the 60-day outcome, the number of BSI patients falling into each of the five quintiles of deprivation did appear to be significantly different, although a clear trend was not yet apparent.
The main patient factor of interest in Table 4.4., however, is whether the UTI was determined to be recurrent or not and whether there were significant differences in the outcome between having a recurrent UTI or not.
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Within both 30 days and 60 days, there did appear to be evidence of a significantly higher proportion of patients with recurrent UTIs than would be expected based on the underlying distribution of this factor in the patient cohort.
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Table 4.4. Characteristics of adults with a BSI event within 30 and 60 days of receiving antibiotics for a UTI (including recurrent UTIs) in England (2008-2017) Variable No BSI (30 d) BSI (30 d) Total P value No BSI (60 d) BSI (60 d) Total P value n=315,865 n=361 n=316,226 n=315,687 n=539 n=316,226 Recurrent UTI 0.003 <0.001 No 272,376 (86.2%) 292 (80.9%) 272,668 (86.2%) 272,240 (86.2%) 428 (79.4%) 272,668 (86.2%) Yes 43,489 (13.8%) 69 (19.1%) 43,558 (13.8%) 43,447 (13.8%) 111 (20.6%) 43,558 (13.8%)
Mean age (SD) 56.0 (SD 20.3) 77.0 (SD 16.2) 56.0 (SD 20.3) <0.001 56.0 (20.3) 77.6 (SD 15.8) 56.0 (SD 20.3) <0.001
Age category <0.001 <0.001 19-44 105,908 (33.5%) 26 (7.2%) 105,934 (33.5%) 105,898 (33.6%) 36 (6.7%) 105,934 (33.5%) 45-64 91,846 (29.1%) 32 (8.9%) 91,878 (29.1%) 91,832 (29.1%) 46 (8.5%) 91,878 (29.1%) 65-74 46,391 (14.7%) 56 (15.5%) 46,447 (14.7%) 46,368 (14.7%) 79 (14.7%) 46,447 (14.7%) 75+ 71,720 (22.7%) 247 (68.4%) 71,967 (22.8%) 71,589 (22.7%) 378 (70.1%) 71,967 (22.8%) Gender <0.001 <0.001 Male 38,056 (12.1%) 133 (36.8%) 38,189 (12.1%) 37,992 (12.0%) 197 (36.6%) 38,189 (12.1%) Female 277,809 (87.9%) 228 (63.2%) 278,037 (87.9%) 277,695 (88.0%) 342 (63.5%) 278,037 (87.9%) Region 0.021 0.039 North of England 65,798 (20.8%) 79 (21.9%) 65,877 (20.8%) 65,763 (20.8%) 114 (21.2%) 65,877 (20.8%) Midlands/East of 87,168 (27.6%) 121 (33.5%) 87,289 (27.6%) 87,113 (27.6%) 176 (32.7%) 87,289 (27.6%) England South of England 129,571 (41.0%) 135 (37.4%) 129,706 (41.0%) 129,504 (41.0%) 202 (37.5%) 129,706 (41.0%) London 33,328 (10.6%) 26 (7.2%) 33,354 (10.6%) 33,307 (10.6%) 47 (8.7%) 33,354 (10.6%) Deprivation (n=163 0.120 0.025 missing, 0 from BSI) Quintile 1 74,246 (23.5%) 67 (18.6%) 74,313 (23.5%) 74,217 (23.5%) 96 (17.8%) 74,313 (23.5%) Quintile 2 74,355 (23.6%) 94 (26.0%) 74,449 (23.6%) 74,309 (23.6%) 140 (26.0%) 74,449 (23.6%) Quintile 3 64,042 (20.3%) 76 (21.1%) 64,118 (20.3%) 63,998 (20.3%) 120 (22.3%) 64,118 (20.3%) Quintile 4 57,316 (18.2%) 61 (16.9%) 57,377 (18.2%) 57,282 (18.2%) 95 (17.6%) 57,377 (18.2%) Quintile 5 45,743 (14.5%) 63 (17.5%) 45,806 (14.5%) 45,718 (14.5%) 88 (16.3%) 45,806 (14.5%) Pregnant (women 0.466 0.540 <=50) No 115,957 (91.9%) 23 (88.5%) 115,980 (91.9%) 115,947 (91.9%) 33 (89.2%) 115,980 (91.9%) Yes 10,278 (8.1%) 3 (11.5%) 10,281 (8.1%) 10,277 (8.1%) 4 (10.8%) 10,281 (8.1%) Antibiotics in past <0.001 <0.001 30days No 267,409 (84.7%) 276 (76.5%) 267,685 (84.7%) 267,289 (84.7%) 396 (73.5%) 267,685 (84.7%) Yes 48,456 (15.3%) 85 (23.6%) 48,541 (15.4%) 48,398 (15.3%) 143 (26.5%) 48,541 (15.4%) Urine test 0.420 0.055 No 275,516 (87.2%) 320 (88.6%) 275,836 (87.2%) 275,351 (87.2%) 485 (90.0%) 275,836 (87.2%)
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Yes 40,349 (12.8%) 41 (11.4%) 40,390 (12.8%) 40,336 (12.8%) 54 (10.0%) 40,390 (12.8%) Severe UTI / / No 315,814 (99.9%) 361 (100.0%) 316,175 (99.9%) 315,636 (99.9%) 539 (100.0%) 316,175 (99.9%) Yes 51 (0.1%) 0 (0.0%) 51 (0.1%) 51 (0.1%) 0 (0.0%) 51 (0.1%) # of visits per episode 0.775 0.016 1 282,472 (89.4%) 323 (89.5%) 282,795 (89.4%) 282,325 (89.4%) 470 (87.2%) 282,795 (89.4%) 2 29,381 (9.3%) 32 (8.9%) 29,413 (9.3%) 29,358 (9.3%) 55 (10.2%) 29,413 (9.3%) 3+ 4,012 (1.3%) 6 (1.7%) 4,018 (1.3%) 4,004 (1.3%) 14 (2.6%) 4,018 (1.3%) Diabetes <0.001 <0.001 No 307,199 (97.3%) 339 (93.9%) 307,538 (97.3%) 307,038 (97.3%) 500 (92.8%) 307,538 (97.3%) Yes 8,666 (2.7%) 22 (6.09%) 8,688 (2.8%) 8,649 (2.7%) 39 (7.2%) 8,688 (2.8%) CVD <0.001 <0.001 No 299,729 (94.9%) 307 (85.0%) 300,036 (94.9%) 299,579 (94.9%) 457 (84.8%) 300,036 (94.9%) Yes 16,136 (5.1%) 54 (15.0%) 16,190 (5.1%) 16,108 (5.1%) 82 (15.2%) 16,190 (5.1%) Renal abnormality <0.001 <0.001 No 305,294 (96.7%) 332 (92.0%) 305,626 (96.7%) 305,137 (96.7%) 489 (90.7%) 305,626 (96.7%) Yes 10,571 (3.3%) 29 (8.0%) 10,600 (3.4%) 10,550 (3.3%) 50 (9.3%) 10,600 (3.4%) STI / / No 315,411 (99.9%) 361 (100.0%) 315,772 (99.9%) 315,233 (99.9%) 539 (100.0%) 315,772 (99.9%) Yes 454 (0.1%) 0 (0.0%) 454 (0.1%) 454 (0.1%) 0 (0.0%) 454 (0.1%) Urinary catheter <0.001 <0.001 No 313,789 (99.3%) 341 (94.5%) 314,130 (99.3%) 313,618 (99.3%) 512 (95.0%) 314,130 (99.3%) Yes 2,076 (0.7%) 20 (5.5%) 2,096 (0.7%) 2,069 (0.7%) 27 (5.0%) 2,096 (0.7%) Penicillin allergy 0.055 0.004 No 310,403 (98.3%) 350 (97.0%) 310,753 (98.3%) 310,232 (98.3%) 521 (96.7%) 310,753 (98.3%) Yes 5,462 (1.7%) 11 (3.0%) 5,473 (1.7%) 5,455 (1.7%) 18 (3.3%) 5,473 (1.7%)
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Table 4.5. shows the results of the univariate logistic regression models investigating the effect of each individual potential predictor of developing a BSI within 30 and 60 days, the main factor of interest being whether the UTI was recurrent or non-recurrent. The factors identified as being significantly associated with the outcome of developing a BSI within 30 days were age category, gender, deprivation (most deprived compared with least deprived), having received previous antibiotics, having a history of diabetes, cardiovascular disease, renal abnormality or urinary catheter usage. Similar to the previous univariate analyses in Section 4.5.2., patients who were in older age categories, male, had received previous antibiotics before the UTI and with evidence of any of the comorbidities mentioned above were shown to have a higher risk of developing the outcome. The association between deprivation and the outcome was less clear as the risk of developing the outcome was higher in all groups which were more deprived than the most affluent group (Quintile 1) although the association was only significant between living in either Quintile 2 or Quintile 5 (most deprived) compared to living in
Quintile 1. When looking at the 60-day outcome, however, the odds of BSI were higher in patients living in all neighbourhoods which were more deprived than those in the most affluent quintile (Quintiles 2, 3, 4 and 5); the higher odds were significant for all quintiles aside from Quintile 4 (OR 1.28, 95% CI 0.97-1.70).
The risk of developing the outcome within 30 days was almost 50% higher in patients who had a recurrent UTI compared with those who had a non-recurrent UTI (OR 1.49, 95% CI 1.14-1.93) and the risk of developing the outcome within 60 days was 64% higher in recurrent UTI patients (OR 1.64, 95% CI 1.33-2.02). Equally, there did not appear to be a significant association between developing the outcome and region where the UTI was treated, having received a UTI test, or having evidence of a penicillin allergy.
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Table 4.5. Crude odds ratios for investigating the effect of whether a UTI is recurrent on developing a BSI within 30 and 60 days in England BSI event with 30 days BSI event within 60 days Variable Crude OR 95% CI P value Crude OR 95% CI P value Recurrent UTI Non-recurrent UTI REF REF Recurrent UTI 1.49 (1.14-1.93) 0.003 1.64 (1.33-2.02) <0.001 Age category 19-44 REF REF 45-64 1.42 (0.85-2.38) 0.186 1.47 (0.95-2.27) 0.084 65-74 4.92 (3.09-7.83) <0.001 5.01 (3.38-7.43) <0.001 75+ 14.22 (9.49-21.30) <0.001 15.91 (11.30-22.40) <0.001 Gender Male REF REF Female 0.23 (0.19-0.29) <0.001 0.23 (0.20-0.28) <0.001 Region North of England REF REF Midlands/East of England 1.16 (0.87-1.54) 0.313 1.17 (0.92-1.48) 0.200 South of England 0.87 (0.66-1.15) 0.323 0.90 (0.72-1.13) 0.379 London 0.65 (0.42-1.01) 0.057 0.82 (0.58-1.15) 0.239 IMD2010 (n=163 missing) Quintile 1 REF REF Quintile 2 1.40 (1.02-1.92) 0.035 1.46 (1.12-1.89) 0.004 Quintile 3 1.32 (0.95-1.83) 0.101 1.45 (1.11-1.90) 0.006 Quintile 4 1.18 (0.83-1.67) 0.352 1.28 (0.97-1.70) 0.086 Quintile 5 1.53 (1.08-2.15) 0.016 1.49 (1.11-1.99) 0.007 Antibiotics in past 30days No REF REF Yes 1.71 (1.34-2.18) <0.001 2.01 (1.66-2.43) <0.001 Urine test No REF REF Yes 0.87 (0.63-1.21) 0.411 0.76 (0.57-1.00) 0.052 Diabetes No REF REF Yes 2.30 (1.50-3.55) <0.001 2.78 (2.01-3.85) <0.001 CVD No REF REF Yes 3.29 (2.46-4.39) <0.001 3.38 (2.67-4.28) <0.001 Renal abnormality No REF REF Yes 2.54 (1.74-3.71) <0.001 2.99 (2.23-4.00) <0.001 Urinary catheter No REF REF Yes 8.98 (5.71-14.13) <0.001 8.16 (5.53-12.04) <0.001 Penicillin allergy No REF REF Yes 1.79 (0.98-3.26) 0.058 1.97 (1.23-3.15) 0.005
Table 4.6. shows the two multivariable logistic regression models used to examine the odds of developing a BSI within either 30 or 60 days following a recurrent UTI, adjusting for covariates of interest and clustering on GP
Practice ID.
After adjusting for age category, gender, region, deprivation, previous antibiotic use, urine testing and comorbidities, there was no statistically significant relationship between having a recurrent UTI and developing
129 | Page a BSI within 30 days compared to not having a recurrent UTI (OR 1.08, 95% CI 0.85-1.37). The effect seen between recurrent UTI status and BSI seen in the univariate analyses was therefore confounded by the effects of the covariates on the outcome. Similarly to the off-guideline adjusted logistic regression model, after adjusting for all other factors, being over 75 years old placed patients at being almost 11 times the odds of developing a BSI within 30 days compared with patients under 45 years old (OR 10.96, 95% CI 7.11-16.90). Being female was protective against developing a BSI within 30 days compared to being male (OR 0.36, 95% CI 0.29-
0.46). Living in the most deprived areas (IMD2010 Quintile 5) placed patients at 83% higher odds of the outcome within 30 days after treatment for a UTI (OR 1.83, 95% CI 1.14-2.94) compared with living in the most affluent areas (IMD2010 Quintile 1). After adjustment for all other factors of interest, having a history of urinary catheterization within the previous year remained the only comorbidity with a statistically significant association with the outcome, increasing the odds by over 2 times compared to patients with no history of catheter usage
(OR 2.38, 95% CI 1.51-3.76).
When investigating the occurrence of BSI within 60 days of UTI, after adjusting for age category, gender, region, deprivation, previous antibiotic use, urine testing and comorbidities, there was no statistically significant relationship between having a recurrent UTI and developing a BSI (OR 1.15, 95% CI 0.94-1.40). In the adjusted model, living in any area that was more deprived than the most affluent quintile (Quintile 1) was associated with higher odds of developing a BSI within 60 days and all associations were statistically significant. As an example, living in the most deprived areas (Quintile 5) was associated with 77% higher odds of developing a BSI within 60 days compared with living in the most affluent areas (OR 1.77, 95% CI 1.19-2.63). There was also weak evidence of a diabetes record within the previous year being associated with higher odds of the outcome, after adjusting for other patient factors (OR 1.45, 95% CI 0.99-2.11). All other associations between the different patient factors and the outcome were similar to those seen in the previous model looking at BSI within 30 days as the outcome.
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Table 4.6. Adjusted odds ratios for investigating the effect of whether a UTI is recurrent on developing a BSI within 30 and 60 days in England BSI event with 30 days BSI event within 60 days Variable Adj. OR 95% CI P value Adj. OR 95% CI P value Recurrent UTI Non-recurrent UTI REF REF Recurrent UTI 1.08 (0.85-1.37) 0.550 1.15 (0.94-1.40) 0.166 Age category 19-44 REF REF 45-64 1.28 (0.76-2.17) 0.354 1.31 (0.84-2.04) 0.232 65-74 4.07 (2.43-6.81) <0.001 4.03 (2.60-6.34) <0.001 75+ 10.96 (7.11-16.90) <0.001 11.82 (8.07-17.32) <0.001 Gender Male REF REF Female 0.36 (0.29-0.46) <0.001 0.37 (0.30-0.44) <0.001 Region North of England REF REF Midlands/East of England 1.20 (0.78-1.83) 0.404 1.21 (0.86-1.71) 0.281 South of England 0.92 (0.66-1.27) 0.608 0.95 (0.73-1.23)) 0.676 London 0.79 (0.49-1.26) 0.319 0.99 (0.69-1.41) 0.941 IMD2010 (n=163 missing) Quintile 1 REF REF Quintile 2 1.34 (0.96-1.85) 0.083 1.38 (1.05-1.81) 0.020 Quintile 3 1.33 (0.93-1.91) 0.118 1.47 (1.11-1.93) 0.007 Quintile 4 1.34 (0.89-2.00) 0.159 1.43 (1.03-2.00) 0.033 Quintile 5 1.83 (1.14-2.94) 0.013 1.77 (1.19-2.63) 0.005 Antibiotics in past 30days No REF REF Yes 1.16 (0.90-1.49) 0.248 1.34 (1.11-1.62) 0.002 Urine test No REF REF Yes 0.94 (0.68-1.30) 0.708 0.81 (0.59-1.12) 0.202 Diabetes No REF REF Yes 1.22 (0.76-1.97) 0.407 1.45 (0.99-2.11) 0.054 CVD No REF REF Yes 1.16 (0.86-1.57) 0.324 1.15 (0.88-1.52) 0.300 Renal abnormality No REF REF Yes 1.00 (0.68-1.48) 0.992 1.14 (0.83-1.54) 0.419 Urinary catheter No REF REF Yes 2.38 (1.51-3.76) <0.001 2.04 (1.36-3.06) 0.001 Penicillin allergy No REF REF Yes 1.41 (0.71-2.80) 0.327 1.50 (0.90-2.52) 0.123
When testing for interaction between previous antibiotic usage and recurrent UTI status as outlined in the
Methods, there was no evidence of interaction for this comparison and an interaction term was therefore not included in the adjusted models.
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4.5.4. Time-to-event analysis investigating the hazard of a BSI following off-guideline prescribing for a UTI
Kaplan Meier survival estimates
As developing a BSI was a relatively infrequent event in this UTI patient cohort, the survival function in the overall study patient population did not appear to change over the 60 days (details shown in Appendix G). To determine the median time-to-event and for visualisation purposes the Kaplan-Meier survival estimate by day following a non-recurrent UTI for patients who developed a BSI within 60 days is shown in Figure 4.3. The plot shows that the median time to developing a BSI (censored at 60 days) in the non-recurrent UTI population was 18 days (SE
1.51, 95% CI 15-21).
Fig 4.3. Kaplan-Meier survival estimate by day since UTI diagnosis/treatment in non-recurrent UTI patients who developed BSI within 60 days. Red dashed line indicates the median time-to-event.
Figure 4.4. shows the Kaplan-Meier survival estimate by day following a non-recurrent UTI for patients who developed a BSI within 60 days stratified by UTI treatment group. The plot shows that the median time to developing a BSI (censored at 60 days) in the non-recurrent UTI population was 18 days (SE 1.95, 95% CI 14-22) for patients receiving an on-guideline prescription, 18 days (SE 2.22, 95% CI 14-22) for patients receiving an off- guideline prescription and 24 days (SE 17.06, 95% CI 2-42) for patients receiving changed treatment.
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Fig 4.4. Kaplan-Meier survival estimate by day since UTI diagnosis/treatment in non-recurrent UTI patients who developed BSI within 60 days, stratified by treatment group. Red, blue and green dashed lines indicate the median time-to-event for each treatment group.
Cox proportional hazards model
To conduct the univariate analyses investigating survival (from BSI) for each potential categorical variable, the log-rank test for equality of survivor functions across strata was used. An arbitrary p-value cut-off point under which a covariate would not be included in the multivariable Cox proportional hazards model was not set in order to decide a priori which predictors would be of interest to include in the model. Table 4.7. shows the results of the individual log-rank tests for equality of survivor functions for the non-recurrent UTI patients – survival being defined as not developing a BSI within 60 days of UTI diagnosis/treatment.
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Table 4.7. Log-rank test for equality of survivor functions (non-recurrent UTIs only)
Variable Events observed Events Expected P-value Treatment group 0.1001 On-guideline 121 139.4 Off-guideline 296 274.9 Changed treatment 11 13.8 Age category <0.001 19-44 33 149.0 45-64 35 126.1 65-74 55 61.6 75+ 305 91.3 Gender <0.001 Male 160 50.6 Female 268 377.4 Region 0.0426 North of England 88 89.5 Midlands/East of England 143 118.8 South of England 161 173.4 London 36 46.4 IMD2010 (n=163 missing) 0.0440 Quintile 1 74 100.2 Quintile 2 116 100.5 Quintile 3 91 86.8 Quintile 4 80 77.9 Quintile 5 67 62.6 Antibiotics in past 30days <0.001 No 323 370.1 Yes 105 57.9 Urine test 0.1599 No 382 372.2 Yes 46 55.8 Diabetes <0.001 No 400 417.1 Yes 28 10.9 CVD <0.001 No 362 407.9 Yes 66 20.1 Renal abnormality <0.001 No 394 415.2 Yes 34 12.8 Urinary catheter <0.001 No 410 425.8 Yes 18 2.2 Penicillin allergy 0.0901 No 417 421.3 Yes 11 6.7
As can be seen in Table 4.7., statistically significant differences in survival functions across different strata of predictors were seen for age category, gender, region, deprivation, previous antibiotic use, diabetes, cardiovascular disease, renal abnormality and catheter usage in the previous year. There were no significant differences across strata for urine test, penicillin allergy or, most importantly for this chapter, treatment group.
Simply put, there was no observable difference in survival function based upon the treatment group that patients fell into. These variables were then added to the Cox proportional hazards model to obtain adjusted
134 | Page estimates of the hazard of developing a BSI at any point within 60 days of UTI diagnosis and treatment. As can be seen in Table 4.8., after adjusting for all other covariates of interest, there was no statistically significant relationship between treatment group and the hazard of developing a BSI at any point in time within 60 days of
UTI diagnosis and treatment.
Table 4.8. Adjusted Cox proportional hazards model for hazard of BSI outcome following antibiotic treatment for a UTI, censored at 60 days (non-recurrent UTIs only) Variable Hazard ratio SE 95% CI P-value Treatment group On-guideline REF Off-guideline 1.08 0.12 (0.87-1.33) 0.504 Changed treatment 0.67 0.22 (0.36-1.26) 0.219 Age category 19-44 REF 45-64 1.12 0.27 (0.69-1.80) 0.653 65-74 3.20 0.71 (2.07-4.96) <0.001 75+ 11.06 2.09 (7.64-16.02) <0.001 Gender Male REF Female 0.35 0.04 (0.29-0.43) <0.001 Region North of England REF Midlands/East of England 1.27 0.18 (0.97-1.67) 0.084 South of England 0.98 0.14 (0.75-1.29) 0.893 London 0.94 0.19 (0.63-1.39) 0.748 IMD2010 (n=163 missing) Quintile 1 REF Quintile 2 1.47 0.22 (1.10-1.97) 0.010 Quintile 3 1.42 0.22 (1.04-1.93) 0.026 Quintile 4 1.56 0.25 (1.14-2.15) 0.006 Quintile 5 1.72 0.30 (1.22-2.42) 0.002 Abx in past 30days No REF Yes 1.41 0.16 (1.13-1.76) 0.003 Urine test No REF Yes 0.89 0.14 (0.65-1.21) 0.448 Diabetes No REF Yes 1.46 0.29 (0.99-2.15) 0.059 CVD No REF Yes 1.28 0.18 (0.98-1.69) 0.073 Renal abnormality No REF Yes 1.06 0.19 (0.74-1.51) 0.767 Urinary catheter No REF Yes 1.99 0.50 (1.22-3.25) 0.006 Penicillin allergy No REF Yes 1.35 0.41 (0.74-2.46) 0.335
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As outlined previously in Methods section 4.4.3, the proportional hazards assumption for the Cox proportional hazards model was investigated by examining the scaled Schoenfeld residuals for each component variable in the non-recurrent UTI model, as well as examining the global test for the model as a whole. Table 4.9. shows the results of the test of the proportional hazards assumption using scaled
Schoenfeld residuals, displaying the individual component p-values and the global p-value. The results show that for each variable and globally there was no reason to reject the null hypothesis of a zero slope, the proportional hazards assumptions were therefore not violated, and a Cox proportional hazards model was therefore the correct choice for time-to-event analysis. The plots of the scaled Schoenfeld residuals for each variable can be found in Appendix F for reference.
Table 4.9. Test of proportional hazards assumption using Schoenfeld residuals by individual predictor (non- recurrent UTIs only) Variable rho chi2 df p value Treatment group -0.0193 0.15 1 0.6969 Age category 0.0482 1.19 1 0.2762 Gender 0.0517 1.21 1 0.2716 Region 0.0068 0.02 1 0.8934 IMD2010 0.0082 0.03 1 0.8694 Antibiotics in past 30 days 0.0852 3.22 1 0.0729 Urine test -0.0196 0.16 1 0.6852 Diabetes 0.0124 0.07 1 0.7922 CVD 0.0092 0.04 1 0.8466 Renal abnormality 0.0389 0.68 1 0.4113 Urinary catheter -0.0004 0.00 1 0.9941 Penicillin allergy -0.0019 0.00 1 0.9677 Global test 7.13 12 0.8491
4.5.5. Time-to-event analysis investigating the hazard of a BSI following a recurrent UTI
Kaplan Meier survival estimates
As in the previous analyses looking at BSI risk in the non-recurrent UTI patient population, the same problem was experienced when looking at the entire patient cohort (recurrent and non-recurrent UTIs).
The survival function therefore did not appear to change over the 60 days (details shown for reference in
Appendix G). To determine the median time-to-event the Kaplan-Meier survival estimate by day following
UTI (both recurrent and non-recurrent) diagnosis and treatment only for patients who developed a BSI within 60 days is shown in Figure 4.5. The plot shows that the median time to developing a BSI (censored at 60 days) in the whole UTI population was 19 days (SE 1.38, 95% CI 16-21).
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Fig 4.5. Kaplan-Meier survival estimate by day since UTI diagnosis/treatment in all UTI patients who developed BSI within 60 days. Red dashed line indicates the median time-to- event.
Figure 4.6. shows the Kaplan-Meier survival estimate by day since UTI diagnosis and treatment for only patients who developed a BSI within 60 days stratified by whether the UTI was recurrent or non-recurrent.
The plot shows that the median time to developing a BSI (censored at 60 days) in the overall UTI patient population was 18 days (SE 1.51, 95% CI 15-21) for patients with a non-recurrent UTI and 20 days (SE 2.83,
95% CI 14-26) for patients with a recurrent UTI.
Fig 4.6. Kaplan-Meier survival estimate by day since UTI diagnosis/treatment in all UTI patients who developed BSI within 60 days, stratified by recurrent UTI status. Red and blue dashed lines indicate the median time-to-event for each treatment group.
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Cox proportional hazards model
Similar to Section 4.5.4, univariate analyses (in the form of log-rank tests) and an adjusted Cox proportional hazards model were run to investigate the hazard of developing a BSI at any point within 60 days of a recurrent vs. non-recurrent UTI. Table 4.10. shows the results of the individual log-rank tests for equality of survivor functions for all UTI patients.
Table 4.10. Log-rank test for equality of survivor functions (including recurrent UTIs)
Variable Events observed Events Expected P-value Recurrent UTI <0.001 Non-recurrent UTI 428 464.8 Recurrent UTI 111 74.2 Age category <0.001 19-44 36 180.7 45-64 46 156.7 65-74 79 79.2 75+ 378 122.5 Gender <0.001 Male 197 65.0 Female 342 474.0 Region 0.0388 North of England 114 112.3 Midlands/East of England 176 148.8 South of England 202 221.1 London 47 56.9 IMD2010 (n=163 missing) 0.0246 Quintile 1 96 126.8 Quintile 2 140 126.9 Quintile 3 120 109.3 Quintile 4 95 97.9 Quintile 5 88 78.1 Antibiotics in past 30days <0.001 No 396 456.3 Yes 143 82.7 Urine test 0.0553 No 485 470.2 Yes 54 68.8 Diabetes <0.001 No 500 524.2 Yes 39 14.8 CVD <0.001 No 457 511.4 Yes 82 27.6 Renal abnormality <0.001 No 489 520.9 Yes 50 18.1 Urinary catheter <0.001 No 512 535.5 Yes 27 3.5 Penicillin allergy 0.0041 No 521 529.7 Yes 18 9.3
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As can be seen in Table 4.10., statistically significant differences in survival functions across different strata of variables were seen for age category, gender, region, deprivation, previous antibiotic use, diabetes, cardiovascular disease, renal abnormality, catheter usage and penicillin allergy in the previous year. There was weaker evidence of a difference in survival function across the strata of urine test. Most importantly for this study, there was also evidence of a difference in the survival function between the patients with a recurrent vs. a non-recurrent UTI (p<0.001). These variables were then added to the Cox proportional hazards model to obtain adjusted estimates of the hazard of developing a BSI at any point within 60 days of UTI diagnosis and treatment. As can be seen in Table 4.11., after adjusting for all other covariates of interest, there was no statistically significant relationship between having a recurrent UTI and the hazard of developing a BSI at any point in time within 60 days of diagnosis and treatment. The effect seen in the univariate analysis was confounded by other patient-related factors in the model upon adjustment.
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Table 4.11. Adjusted Cox proportional hazards model for hazard of BSI outcome following antibiotic treatment for a UTI, censored at 60 days (including recurrent UTIs) Variable Hazard ratio SE 95% CI P-value Recurrent UTI Non-recurrent UTI REF Recurrent UTI 1.14 0.12 (0.92-1.41) 0.235 Age category 19-44 REF 45-64 1.32 0.29 (0.85-2.04) 0.216 65-74 4.04 0.82 (2.71-6.02) <0.001 75+ 11.52 2.06 (8.11-16.37) <0.001 Gender Male REF Female 0.37 0.03 (0.31-0.45) <0.001 Region North of England REF Midlands/East of England 1.21 0.15 (0.95-1.54) 0.128 South of England 0.94 0.11 (0.74-1.19) 0.614 London 0.99 0.17 (0.70-1.39) 0.936 IMD2010 (n=163 missing) Quintile 1 REF Quintile 2 1.38 0.18 (1.06-1.78) 0.016 Quintile 3 1.45 0.20 (1.11-1.90) 0.007 Quintile 4 1.43 0.21 (1.07-1.90) 0.015 Quintile 5 1.76 0.27 (1.30-2.37) <0.001 Antibiotics in past 30days No REF Yes 1.33 0.13 (1.09-1.62) 0.004 Urine test No REF Yes 0.82 0.12 (0.62-1.09) 0.174 Diabetes No REF Yes 1.45 0.25 (1.04-2.02) 0.029 CVD No REF Yes 1.15 0.14 (0.90-1.47) 0.256 Renal abnormality No REF Yes 1.14 0.17 (0.85-1.54) 0.393 Urinary catheter No REF Yes 2.02 0.42 (1.35-3.03) 0.001 Penicillin allergy No REF Yes 1.51 0.36 (0.94-2.42) 0.090
Table 4.12. shows the results of the test of the proportional hazards assumption for the whole UTI model
(recurrent and non-recurrent UTI) using scaled Schoenfeld residuals, displaying the individual component p-values and the global p-value. The results show that for each variable and globally there was no reason to reject the null hypothesis of a zero slope, the proportional hazards assumptions were therefore not violated, and a Cox proportional hazards model was therefore the correct choice for time-to-event analysis. The plots of the scaled Schoenfeld residuals for each variable can be found in Appendix F for reference.
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Table 4.12. Test of proportional hazards assumption using Schoenfeld residuals by individual predictor (including recurrent UTIs) Variable rho chi2 df p value Recurrent UTI 0.0142 0.11 1 0.7397 Age category 0.0661 2.66 1 0.1031 Gender 0.0130 0.10 1 0.7548 Region 0.0607 1.87 1 0.1715 IMD2010 0.0167 0.14 1 0.7058 Antibiotics in past 30days 0.0492 1.34 1 0.2466 Urine test -0.0338 0.61 1 0.4336 Diabetes 0.0235 0.31 1 0.5749 CVD 0.0121 0.08 1 0.7742 Renal abnormality 0.0466 1.20 1 0.2739 Urinary catheter -0.0238 0.32 1 0.5717 Penicillin allergy 0.0055 0.02 1 0.8986 Global test 10.18 12 0.6001
4.5.6. Sensitivity analyses
As there was no demonstrable change in guideline adherence once 3 and 6-month time lags were introduced to the guideline adherence algorithm in Chapter 3, I did not re-run the models in this chapter with the 3 and 6-month time lags included.
To investigate the definition I used to identify community-onset BSI cases (including BSI cases recorded in the first episode, regardless of episode end date and including a BSI recorded in an episode after the first which ended within 72 hours of admission), of the 16,018 community-onset BSI cases, 13,942 were recorded upon admission (within the first episode) and 2,076 were recorded after the first episode but within 72 hours of admission. This shows that 13% BSI cases which fit the definition of being recorded
“within 72 hours of admission” would have been missed if the more restrictive definition found in the literature (“recorded in the first episode”) (102) were used without this addition.
4.6. Discussion
4.6.1. Main findings
In this study, I described the distribution of characteristics for patients who developed a community- acquired BSI following diagnosis and treatment for a community-acquired UTI in primary care – a patient group which is of particular interest to PHE and the UK Department of Health. I specifically focused on
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whether a) being prescribed an antibiotic prescription which was not in line with PHE prescribing guidelines for UTI (available during the study period) or b) whether having a UTI which was classified as
“recurrent”, placed patients at greater risk of developing a BSI within 30 or 60 days of the UTI. I also examined the time window within which patients were most at risk of developing this severe subsequent infection – with a view to generating data which could be useful to both clinicians and policy-makers.
With regards to the modelling methodology, I specifically chose to avoid using a stepwise technique when choosing variable components for the models as stepwise regression techniques have generally been deemed by the statistics community to be less appropriate for models exploring a possible relationship between an exposure and an outcome as opposed to prediction (under the assumption that each factor is independent) or hypothesis generation (using the regression modelling to generate future research questions) (107).
This chapter has not demonstrated an association between whether a UTI prescription followed the PHE guideline or not and the risk of developing a community-acquired BSI within 30 or 60 days, although this result should be interpreted with caution as the models in this chapter may have been underpowered to detect a difference in BSI risk. This chapter has shown that when considered on its own, having a recurrent
UTI put patients at greater risk of developing a subsequent BSI, yet when other patient factors were taken into account, this effect was no longer significant. The adjusted models examining BSI risk in non-recurrent
UTIs only showed that patients who were older (particularly over 75 years), male, living in more deprived neighbourhoods, receiving antibiotics in the month prior to the UTI diagnosis and/or having had a history of urinary catheter usage in the previous year were at a significantly higher risk of community-acquired
BSI within either 30 or 60 days, after taking all other potential factors into consideration. This chapter also demonstrated that the median time to a BSI following a UTI was approximately 19 days (varying between
18 and 24 days depending on the patient subgroup being investigated).
Using a series of nationally representative datasets linked together at the patient-level, I have been able to explore whether the risk of developing a BSI following a UTI, which is the most commonly reported underlying source of ECB, is strongly associated with whether or not antibiotic prescribing guidelines were
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followed. Following an evidence-based guideline when making decisions related to prescribing antibiotics is one of the bedrocks of antimicrobial stewardship and this chapter’s aim was to evaluate whether not following this guidance conferred a risk of one of the most severe outcomes, namely a systemic infection following treatment failure for a UTI. While the analyses undertaken did not demonstrate a relationship between prescriber adherence to guidelines and risk of BSI, this result should be interpreted with caution as it may have suffered from being under-powered to detect a difference in effect as the rate of community-acquired BSI in this patient cohort was much lower than originally estimated.
4.6.2. Limitations
Due to the fact that a rate of progression from a UTI treated with antibiotics to a BSI was not published in the literature at the time of defining the sample size calculation for this study (2014/15), the rate of progression used for this work and a similar study conducted in the Imperial HPRU was estimated through discussion with my expert colleagues to be 1%. The rate of progression that I actually found was much lower, between 0.11% and 0.2%, meaning that the models in this study could have been underpowered to detect a measurable effect in BSI risk between the guideline status groups. The findings should therefore be interpreted with caution and should be viewed as results to be validated or refuted in future studies.
This study is a good example of the need for observational studies employing the use of large national administrative linked datasets to investigate infrequent outcomes. Taking into consideration the large number of patient records over approximately 10 years which was needed to pick up 539 BSI events after applying inclusion and exclusion criteria, it is evident that a study looking at this infection pathway could never be conducted using an interventional design such as a randomised control trial or even a single centre observational study. The initial cohort size required to measure significant differences in the outcome between groups would simply be unachievable. When reflecting on whether the study design could be improved to maximize the number of outcome events detected, I considered whether recruiting patients based on the outcome and conducting a case-control study to look back at their UTI treatment would be feasible. To do this type of study, the PHE mandatory E. coli bacteraemia dataset used in Chapter
2 would have to be used for recruitment which would allow a starting point of between 34,000 and 41,000 143
BSI-related outcomes per year. The reason this method would not work for this particular question, however, is that while it is possible to link the bacteraemia records with blood cultures, urine cultures, certain patient demographic data, deprivation data and GP practice prescribing, it is not currently possible to link with patient-level antibiotic prescribing data or comorbidities. Such a detailed analysis of antibiotic prescriptions, antibiotic stewardship within practice or an evaluation of the prescribing guideline would therefore not be possible using PHE data in this way. One way in which this study could possibly be improved upon in the future is by requesting a restricted dataset from CPRD and acquiring restricted records for all patients eligible for the study to increase the sample size. As outlined in Chapter 3, CPRD policy states that records for a maximum of 600,000 patients can be released if a full extract (all available variables) is required. Now that it is known what variables are necessary to conduct this study and build these models, the study could be re-conducted including all 809,941 eligible patients. However, after application of exclusion criteria, it is difficult to know whether this would result in a sample large enough to detect a measurable difference in the outcome or whether it would still suffer from being potentially underpowered.
There are a few limitations to discuss with respect to the use of antibiotic prescription as an exposure variable. Firstly, it is important to note that these analyses were restricted to patients who were treated for their UTI with antibiotics, which is a subset of UTI patients. The ideal proportion of UTIs (in non- pregnant women without comorbidities presenting with a non-recurrent bacterial UTI) which should receive antibiotics in primary care has been agreed upon as 75% by a panel of subject experts in a study conducted by the Modelling and Economics Unit at PHE (108), with actual proportions in routinely- collected data being found to be about 92% in UTI patients over the age of 14 (109). While the majority of patients diagnosed with a UTI in primary care are likely to receive antibiotics, the implication of this is that the risk of BSI in the general UTI patient population cannot be commented on and the results are therefore not fully generalizable. The reason I focused on this patient sub-population was to be able to measure the risk of BSI which was directly attributable to differences in antibiotic prescribing (adjusted for other patient factors), as in essence I wanted to keep the analyses as clean as possible. The implication of this, however, is that patients who do not receive antibiotics for their confirmed or suspected UTI may be a less at-risk group as GPs were willing to use alternative management strategies such as
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recommending a “wait and see” approach, prioritizing pain management over antibiotics by prescribing paracetamol, or advising on drinking enough fluid to avoid dehydration (22). By only including patients for whom GPs thought it necessary to prescribe antibiotics for their UTI - patients who may have been perceived to be at a higher risk of subsequent complications - the probability of BSI following a UTI may be an overestimate if I were to discuss these results in relation to the general UTI patient population. It is therefore imperative that the sub-population of UTI patients which this study represents is emphasised and does not include “suspected UTI” with alternative treatment and management strategies.
Secondly, as the data are currently recorded, it cannot be ascertained whether the antibiotic prescription written on the day of the UTI diagnosis was written as part of a delayed prescribing strategy, whereby the prescription is written and kept at reception for the patient to collect 48 hours later if their symptoms have not resolved, or whether it was in fact given to the patient on the day of their consultation. In a randomised control trial conducted by Little et al. investigating the differences in symptom duration between female UTI patients given immediate vs. delayed antibiotics, they found that 98% of women prescribed immediate antibiotics took them as opposed to 77% of patients written delayed prescriptions
(26). As the antibiotic prescribing data I had only provided information on whether a prescription had been written and what it comprised of, not whether advice was given to only fill the prescription if symptoms did not resolve within 2 days, the implication of this could be that my study population of UTI patients receiving antibiotics could be an overestimate of the number of patients actually receiving a course of antibiotics. However, as the analyses in this Chapter were concerned with detecting a difference in the outcome between patients receiving different kinds of antibiotic courses and not whether antibiotics were prescribed or not, this potential source of bias would only affect the results if certain types of antibiotic prescribing (i.e. drug choice, on/off guideline etc.) were systematically more likely to be recommended as part of a delayed prescribing strategy over others. This does not appear to be likely given the advice around delayed prescribing from PHE or NICE.
Lastly, a common limitation in observational studies using electronic health records to determine antibiotic use is that there is no data on whether the prescription given to the patient was filled at their pharmacy and whether the patient took the entire course. This study therefore has had to operate under
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the assumption that the prescription which was written for the patient was filled and complied with by the patient. Together with the previous point, this demonstrates that this study may be an overestimation of patients receiving antibiotics.
4.6.3. How these findings fit in with existing literature
As this is the first time a patient-level cohort study investigating the link between guideline adherence for
UTI prescribing and BSI development has been reported on, it is challenging to make direct comparisons with the existing literature. What is often reported on, however, is the rate of treatment failure (revisit to the GP requiring further antibiotic treatment) and other adverse outcomes following antibiotic treatment for a UTI. As this is the proposed mechanism of action between UTI treatment and BSI development, it seems a suitable outcome to discuss here.
In a study by Ahmed et al. examining treatment failure, admission for acute kidney injury, admission for sepsis or death following treatment of UTIs in an elderly patient population, it was found that patients with poor kidney function were more likely to die or be admitted to hospital for a UTI-related cause within
14 days of antibiotic treatment for a UTI than patients with good kidney function (110). The percentage of UTI patients admitted with sepsis within 14 days was 0.2%, which is broadly comparable with the rate seen at 30 days in this study (0.11%) although it is very difficult to compare directly as the patient population in the Ahmed et al. study was elderly (>65), were more likely to have renal impairment and were being followed up for a shorter period of time. When investigating the effect of the UTI treatment on the outcomes, when compared with trimethoprim, the use of nitrofurantoin was associated with a lower risk of acute kidney injury and was not associated with increased risk of any of the adverse outcomes.
In a study by Lawrenson et al. exploring factors which were predictive of treatment failure within 28 days of UTI antibiotic treatment for women under the age of 44yo using CPRD data, 14% of UTI episodes resulted in requiring further antibiotic treatment for their UTI (46). Women who were older, pregnant and those with diabetes were more likely to experience treatment failure within 28 days. These trends were 146
broadly mirrored in this study when looking at factors which placed patients at risk of poor infection- related outcomes, however I did not have large enough numbers of pregnant patients developing BSI to include them in the regression analyses. In the Lawrenson study, when compared to trimethoprim, receiving amoxicillin as the initial treatment placed patients at a significantly higher risk of treatment failure. Nitrofurantoin was not as commonly used as it was in my patient cohort, although the study was conducted between 1992 and 1999 – nitrofurantoin was therefore less likely to have been a first-line antibiotic at the time and choices such as penicillins and fluoroquinolones which have been discouraged in recent years were seemingly used more frequently.
Interestingly, when we look at studies conducted more recently exploring antibiotic use for UTI and risk of subsequent adverse events, trends related to trimethoprim usage change. In a study conducted by
Crellin et al., patients over the age of 65 years treated for a UTI between 1997-2015 were followed up for record of acute kidney injury, hyperkalaemia or death within 14 days of UTI treatment (88). In this setting, patients who received trimethoprim were at significantly higher risk of acute kidney injury and hyperkalaemia than patients receiving other antibiotics for UTI, they were not at higher risk of death however. This higher risk in comparison to the previous study may be in part due to the increase in trimethoprim-resistant bacterial infections in recent years previously described in Chapter 2 – we may therefore be seeing an increase in adverse events related to high trimethoprim usage over time as resistant strains become more prevalent in the community.
Lastly, In a paper by Lee et al., bacteraemia following UTI was explored, albeit in a subpopulation which are at much higher risk of the outcome than the general population. This paper explored factors predictive of bacteraemia in patients admitted to the emergency department with febrile UTI at a tertiary care hospital in Korea in 2012. As this was an already at-risk group (severe UTI), 33% of the admitted patients were found to have confirmed bacteraemia. Similar to my study, older age was a significant predictor of bacteraemia in both the univariate and adjusted analyses and diabetes was predictive of the outcome in the univariate analyses but not upon adjustment of other potential confounders. The Lee paper placed a much heavier focus on clinical predictors upon admission such as flank pain, platelet count, C-reactive
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protein, procalcitonin etc. as opposed to patient demographics and comorbidities that my study did so direct comparison with other confounders was not possible (111).
4.6.4. Implications for practice and policy
By using the richest and most representative data currently available in England, further enriched through linkage with multiple other datasets, this chapter has not been able to demonstrate an association between national prescribing guideline adherence for UTI antibiotic treatment and risk of a community- acquired bloodstream infection within 1-2 months. I therefore cannot reject the null hypothesis that there is no difference in risk of the outcome based on whether the PHE prescribing guideline was followed for
UTI treatment, although this result should be interpreted cautiously in light of the smaller than expected number of BSI events in this study. If there is truly no effect of prescribing guideline adherence as a potential source of risk difference, it would focus research efforts on other areas with respect to the contributors to risk of a BSI following a UTI. However, given that these analyses may have been underpowered to detect an association, the novel findings in this chapter stand to be confirmed or refuted by future studies should larger patient cohorts become available for use. Epidemiological and data linkage recommendations in regard to this point will be discussed in Chapter 7.
This study has contributed evidence related to recurrent UTI patients and whether they are at higher risk of developing severe outcomes. While there was a significantly higher risk of developing a BSI following a recurrent UTI compared to a non-recurrent UTI, once other patient factors were accounted for this effect disappeared – suggesting the effect was completely confounded by these other patient factors.
What this study did highlight was the patient characteristics which put patients most at risk of this outcome. These results compliment and validate previous similar findings and could be used by GPs in practice to identify patients who should perhaps have cultures sent off for more routinely when the diagnosis is being made and/or be monitored more carefully following treatment for a UTI for signs of treatment failure and deterioration. Namely, elderly patients, male UTI patients, those with recent previous antibiotic use, a history of urinary catheterisation and those living in more deprived areas. This study has also shown the time window within which patients are most at risk of the outcome, with the 148
median time to event being about 19 days for patients treated with antibiotics. This could be very useful for clinicians treating patients with the above characteristics for UTI as it demonstrates that revisits to the
GP related to a failure of symptom resolution within about 3 weeks of UTI treatment should be flagged to the GP and monitored carefully to potentially avoid admission to hospital for a bloodstream infection.
4.7. How these findings fit in with the wider thesis
Following on from Chapter 3 where it was shown that the majority of antibiotic prescriptions for UTI over the past 9 years in England were not in line with the PHE prescribing guideline, this chapter found that this did not appear to confer a greater risk of BSI in adult UTI patients. What Chapter 2 demonstrated, however, was that GP practices prescribing higher volumes of trimethoprim for UTI had significantly higher rates of trimethoprim-resistant UTI-related E. coli bacteraemia. Chapter 3 then demonstrated that the most common reason for a prescription not being in line with treatment guidelines was the duration of treatment, particularly for trimethoprim. While it now appears as though following the guideline in general does not confer a greater risk of community-acquired BSI, perhaps we need to look more specifically at whether differences in duration of treatment confer differing levels of risk of developing the outcome. This is what I will endeavor to do in Chapter 5.
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CHAPTER 5: INVESTIGATING THE EFFECT OF ANTIBIOTIC DURATION OF TREATMENT IN PRIMARY CARE FOR URINARY TRACT INFECTIONS ON THE RISK OF BLOODSTREAM INFECTION.
Chapter 1: Aims and Background
Chapter 2: Volume of prescribing for UTI and incidence of E. coli bacteraemia
Chapter 3: Chapter 4: Effect BSI-related UTI diagnosis Quantifying levels of of off-guideline admission, and treatment off-guideline prescribing for UTI death or re- prescribing for UTI on BSI risk consultation
Chapter 5: Effect of duration of treatment for UTI on BSI risk
Chapter 6, 7: Discussion, conclusions and recommendations
Summary:
This chapter makes use of the national linked dataset described in Chapters 3 and 4 to conduct a sub analysis of adult patients with an uncomplicated UTI which is not recurrent treated with one of the two first line options for antibiotic treatment of this patient sub population. It explores the relationship between differences in duration of treatment with these two antibiotics for UTI in primary care and risk of subsequent BSI. Longer durations of trimethoprim 200mg for uncomplicated UTI in female patients conferred a greater risk of BSI than shorter durations, although there may be residual confounding. This work is being written as a manuscript for joint submission with the manuscript covering Chapters 3 and 4 in a medical journal.
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5.1. Introduction
In 2017, an article was published in the BMJ provocatively entitled “The Antibiotic Course Has Had Its Day”
(112) which called into question the concept which has been central to guiding antibiotic treatment for decades, namely that longer courses are better at achieving bacterial clearance and that patients should finish the antibiotic course they have been given, even after symptoms have improved. The authors, a collection of UK-based researchers and clinicians, argued that: “Historically, antibiotic courses were set by precedent, driven by fear of undertreatment, with less concern about overuse. For many indications, recommended durations have decreased as evidence of similar clinical outcomes with shorter courses has been generated”. In particular, systematic reviews and meta-analyses have confirmed this with respect to UTI treatment (113, 114), the focus of this PhD, with one meta-analysis going one step further to show that longer duration prescriptions and multiple courses of antibiotics are associated with higher rates of antibiotic resistance (42) highlighting that not only do shorter courses appear to hold equivalence to longer courses in curing infection (in uncomplicated UTI), but that by reducing unnecessarily prolonged antibiotic exposure, they may reduce the risk of patients becoming colonised with antibiotic-resistant bacteria. Therefore, should the new antibiotic mantra be “shorter is better” as proposed by Brad
Spellberg’s 2016 JAMA editorial (115)?
This chapter builds on the previous chapters where it was found that differences in duration of UTI treatment in England are a primary reason for guideline non-adherence and that GP practices with higher volumes of trimethoprim prescribing (which could be due to either more prescriptions or longer duration prescriptions) appear to have higher incidences of trimethoprim-resistant UTI-related E. coli bacteraemia.
This chapter investigates the trends in differences in duration of treatment with two antibiotics, trimethoprim 200mg and nitrofurantoin 100mg, in patients with uncomplicated UTI and whether these differences confer a difference in risk of developing a subsequent BSI.
5.2. Aims/rationale
This chapter aims to investigate the risk of a community-acquired BSI (CA-BSI) occurring following treatment of a UTI with either trimethoprim 200mg or nitrofurantoin 100mg, with particular attention 151
paid to the duration of treatment and whether longer vs. shorter courses of these antibiotics place patients at higher risk of developing a BSI. I aim to model these relationships separately for women and men as they are recommended different durations of treatment in national prescribing guidelines.
Primary objectives:
1) To determine the effect of different durations of trimethoprim 200mg for UTI on the risk of
developing a BSI within 60 days of diagnosis and treatment of uncomplicated, non-recurrent UTI.
2) To determine the effect of different durations of nitrofurantoin 100mg for UTI on the risk of
developing a BSI within 60 days of diagnosis and treatment of uncomplicated, non-recurrent UTI.
Secondary objectives:
3) To determine the proportion of patients with uncomplicated, non-recurrent UTI being treated
with trimethoprim 200mg and nitrofurantoin 100mg in the adult UTI cohort.
4) To explore the factors putting patients most at risk of developing a BSI following treatment
with these antibiotics.
Rationale:
These specific aims will allow for the risk of developing a BSI to be evaluated with respect to differing durations of treatment for the two main treatment options for uncomplicated UTI in women and men. By holding the antibiotics and dosages constant (only looking at trimethoprim 200mg and nitrofurantoin
100mg, separately), the individual effect of a difference in duration on the outcome will be ascertained, following adjustment for potential confounding factors. Following on from the findings of Chapter 4 (no demonstrated evidence of an effect of guideline adherence on the risk of developing of a BSI), and given that differing durations of treatment were the main reason for UTI prescriptions being classified as “off- guideline” in Chapter 3, the important next step is to evaluate the specific effect of duration of treatment on the risk of developing a BSI. This chapter will contribute to the growing body of evidence around duration of UTI treatment on patient outcomes using a large, nationally-representative patient cohort as
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opposed to a single-site study. It will also provide further insight into this infection pathway by focussing on one specific reason for “guideline non-adherence”.
5.3. Methods
5.3.1. Study population
The study population and inclusion/exclusion criteria for the underlying patient cohort outlined in Chapter
3 are the same for this chapter. Analyses were restricted to patients receiving either trimethoprim 200mg or nitrofurantoin 100mg for their index UTI (first UTI consultation of the episode). Analyses were further restricted to uncomplicated UTI patients, with “uncomplicated” being defined as not meeting the criteria for any of the following: a recurrent UTI, a UTI during pregnancy, pyelonephritis or prostatitis. This definition was the same definition as that used in Chapters 3 and 4 and was in line with PHE prescribing guidelines (15). Female and male patients were recommended different durations of treatment for trimethoprim and nitrofurantoin (3 days for women, 7 days for men) across all versions of the prescribing guideline analysed in this thesis. For this reason, female and male patient outcomes were modelled separately. To generate the “treatment duration” variable, similar methods to those employed in Chapter
3 were used. Namely, each duration of interest was coded using a “buffer window” around the number of days to allow for minor variation in treatment duration due to standard pack sizes. Therefore, “3-day treatment” included any treatment duration between 2.5 and 3.5 days, “5-day treatment” included any treatment duration between 4.5 and 5.5 days, “7-day treatment” included any treatment duration between 6.5 and 7.5 days and “10+ day treatment” included any treatment duration above 9.5 days. The
“Other” treatment duration category therefore included any duration which did not fall into any of the above duration ranges.
5.3.2. Statistical methods
For determining the effect of UTI antibiotic treatment duration on the odds of developing a BSI within 60 days of treatment and to determine the factors predictive of this outcome in the sub-groups of interest,
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univariate and multivariable logistic regression models similar to those built in Chapter 4 were constructed for the following sub-groups:
1) Female uncomplicated UTI patients receiving trimethoprim 200mg for their UTI
2) Female uncomplicated UTI patients receiving nitrofurantoin 100mg for their UTI
3) Male uncomplicated UTI patients receiving trimethoprim 20mg for their UTI
4) Male uncomplicated UTI patients receiving nitrofurantoin 100mg for their UTI
Odds ratios and 95% confidence intervals were calculated for all predictors. Similar to the models constructed in Chapter 4, the potential confounders that were hypothesised to have an effect on the outcome a priori were included in the final adjusted models, rather than adding predictors to the final model in a stepwise manner. All potential confounders explored in Chapter 4 were included in the univariate and multivariable models investigating duration apart from patient sex (as the models were run separately for females and males) and penicillin allergy, as the decision to prescribe trimethoprim or nitrofurantoin would not have been affected by a history of penicillin allergy. All models included a cluster function clustering on GP practice to reflect the fact that antibiotic prescribing practices with respect to duration of treatment for uncomplicated UTI cases may be more similar within GP practices than between them.
5.4. Results
5.4.1. Distribution of patient characteristics and outcomes
Of the 230,975 female patients with uncomplicated UTI over the study period, 142,863 (62%) were prescribed trimethoprim 200mg, of which 141 (0.1%) developed a BSI within 60 days. Table 5.1. shows the distribution of duration of treatment for adult female patients prescribed trimethoprim 200mg for an uncomplicated UTI by outcome group. From the Table it can be seen that just over half (55.6%) of female patients prescribed this antibiotic were given a 3-day course (which was in agreement with the guideline throughout this study). For those patients prescribed longer treatment durations, 20% were prescribed a
5-day course and 22.5% were prescribed a 7-day course. Very few were prescribed a course of 10 days or longer (which was to be expected as this analyses was restricted to uncomplicated, non-recurrent UTIs)
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and 1.3% were given a course which did not fit into any of the above categories. When looking only at the patients who developed a BSI within 60 days, a smaller proportion of patients (40.4%) prescribed trimethoprim 200mg were given 3-day courses than patients who did not develop a BSI and a greater proportion of patients who developed BSI were prescribed 7-day courses (34.0%). The differences between patients who did and did not develop BSI with regards to duration of Trimethoprim 200mg treatment were significant (p=0.003).
Table 5.1. Distribution of adult women who developed a BSI within 60 days of receiving trimethoprim 200mg for an uncomplicated UTI in England (2008-2017) Treatment duration No BSI (%) BSI (%) Total P value n=142,722 n=141 n=142,863 0.003 3 days 79,331 (55.6%) 57 (40.4%) 79,388 (55.6%) 5 days 28,554 (20.0%) 33 (23.4%) 28,587 (20.0%) 7 days 32,072 (22.5%) 48 (34.0%) 32,120 (22.5%) 10+ days 935 (0.7%) 1 (0.7%) 936 (0.7%) Other 1,830 (1.3%) 2 (1.4%) 1,832 (1.3%)
Of the 230,975 female patients with uncomplicated UTI over the study period, 11,510 (5.0%) were prescribed nitrofurantoin 100mg. Table 5.2. shows the distribution of duration of nitrofurantoin 100mg treatment in these patients. As can been seen from the Table, only 8 patients who were prescribed nitrofurantoin 100mg for their initial UTI developed a BSI within the follow up period.
Table 5.2. Distribution of adult women who developed a BSI within 60 days of receiving nitrofurantoin 100mg for an uncomplicated UTI in England (2008-2017) Treatment duration No BSI (%) BSI (%) Total P value n=11,502 n=8 n=11,510 0.469 3 days 3,091 (26.9%) 4 (50.0%) 3,095 (26.9%) 5 days 1,734 (15.1%) 0 (0.0%) 1,734 (15.1%) 7 days 6,321 (55.0%) 4 (50.0%) 6,325 (55.0%) 10+ days 230 (2.0%) 0 (0.0%) 230 (2.0%) Other 126 (1.1%) 0 (0.0%) 126 (1.1%)
Figure 5.1. shows the trends in duration of treatment in female patients with uncomplicated UTIs prescribed either (a) trimethoprim 200mg or (b) nitrofurantoin 100mg over the study period. Three days of treatment was recommended for both antibiotics within this patient group between 2008 and 2017.
From Figure 5.1. (a) it can be seen that 3 days of treatment was the most common option across all years, increasing from 52% in 2008 to 60% in 2015, then decreasing to 56% in 2017. Five days and 7 days of
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treatment were prescribed at a similar frequency until 2012, after which 7 days of treatment became more common, increasing from 22% in 2012 to 29% in 2017.
From Figure 5.1 (b) it can be seen that 7 days of treatment was more commonly prescribed for nitrofurantoin 100mg over the study period, although the proportion declined from 70% in 2008 to 42% in 2017. Over the same time period the frequency of 3-day duration of treatment for nitrofurantoin
100mg increased from 11% to 41% in 2017.
Figure 5.1. Trends in duration of (a) trimethoprim 200mg and (b) nitrofurantoin 100mg
over the study period in adult female uncomplicated UTI patients (2008-2017).
Figure 5.2. shows the trends in treatment duration in female patients with uncomplicated UTIs prescribed either (a) trimethoprim 200mg or (b) nitrofurantoin 100mg stratified by age group. From Figure 5.2. (a) it can be seen that 3 days of treatment with trimethoprim 200mg was the most common option across all 156
age groups, although this trend decreased with increasing age. The proportion of patients under 44 years of age receiving a 3-day course was 62%, decreasing to 46% in patients over 75 years of age. From Figure
5.2. (b) it can be seen that 7 days of treatment was most common across all age groups in uncomplicated female UTI patients receiving nitrofurantoin 100mg, with the proportion receiving 7 days increasing from
50% in women under 44 years of age to 59% in women over 75 years of age.
Figure 5.2. Trends in duration of (a) trimethoprim 200mg and (b) nitrofurantoin 100mg by age category in adult female uncomplicated UTI patients (2008-2017).
Of the 32,266 male patients with uncomplicated UTIs over the study period, 18,310 (57%) were prescribed trimethoprim 200mg, 85 (0.5%) of whom developed a BSI within 60 days. Table 5.3. shows the distribution of duration of treatment for adult male patients prescribed trimethoprim 200mg for an uncomplicated
UTI. From the Table it can be seen that the majority of patients (65.2%) were prescribed a 7-day course
(which was in agreement with the guideline throughout the study). The remaining patients were given
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either a 5-day course (18.2%), a 3-day course (12.1%), a 10-day or longer course (3.4%) or another duration which did not fall into any of these categories (1.2%). When looking only at the patients who developed a BSI within 60 days, a slightly smaller proportion of patients prescribed trimethoprim 200mg were either given 3-day courses (10.6%) or 5-day courses (10.6%) compared to patients who did not develop a BSI. A greater proportion of patients developing BSI within 60 days received 7-day courses
(75.3%) than patients who did not develop a BSI. The differences between patients who did and did not develop BSI with regards to duration of trimethoprim 200mg treatment were not significant (p=0.309).
Table 5.3. Distribution of treatment in adult men who developed a BSI within 60 days of receiving trimethoprim 200mg for an uncomplicated UTI in England (2008-2017) Treatment duration No BSI (%) BSI (%) Total P value n=18,225 n=85 n=18,310 0.309 3 days 2,208 (12.1%) 9 (10.6%) 2,217 (12.1%) 5 days 3,325 (18.2%) 9 (10.6%) 3,334 (18.2%) 7 days 11,865 (65.1%) 64 (75.3%) 11,929 (65.2%) 10+ days 615 (3.4%) 3 (3.5%) 618 (3.4%) Other 212 (1.2%) 0 (0.0%) 212 (1.2%)
Of the 32,266 male patients with uncomplicated UTI over the study period, 1,454 (4.5%) were prescribed nitrofurantoin 100mg. Table 5.4. shows the distribution of treatment duration in male patients with uncomplicated UTIs prescribed nitrofurantoin 100mg. As can been seen from the Table, only 9 patients who were prescribed nitrofurantoin 100mg for their initial UTI developed a BSI within the follow up period.
Table 5.4. Distribution of adult men who developed a BSI within 60 days of receiving nitrofurantoin 100mg for an uncomplicated UTI in England (2008-2017) Treatment duration No BSI (%) BSI (%) Total P value n=1,445 n=9 n=1,454 0.377 3 days 50 (3.5%) 1 (11.1%) 51 (3.5%) 5 days 132 (9.1%) 0 (0.0%) 132 (9.1%) 7 days 1,203 (83.3%) 8 (88.9%) 1,211 (83.3%) 10+ days 59 (4.1%) 0 (0.0%) 59 (4.1%) Other 1 (0.1%) 0 (0.0%) 1 (0.1%)
Figure 5.3. shows the trends in treatment duration in male patients with uncomplicated UTIs prescribed either (a) trimethoprim 200mg or (b) nitrofurantoin 100mg over the study period. Seven days of treatment was recommended for both antibiotics within this patient group between 2008 and 2017. From Figure
5.3. (a) it can be seen that 7 days of treatment was the most common option across all years, increasing 158
from 54% in 2008 to 82% in 2017. Three days and 5 days of treatment were prescribed at decreasing frequencies over the study period.
From Figure 5.3. (b) it can be seen that 7 days of treatment was more commonly prescribed for nitrofurantoin 100mg over the study period, with a relatively stable rate over time (fluctuating between
76% and 91%).
Figure 5.3. Trends in duration of (a) trimethoprim 200mg and (b) nitrofurantoin 100mg over the study period in adult male uncomplicated UTI patients (2008-2017).
Figure 5.4. shows the trends in treatment duration in male patients with uncomplicated UTIs prescribed either (a) trimethoprim 200mg or (b) nitrofurantoin 100mg, stratified by age group. From Figure 5.4. (a) it can be seen that 7 days of treatment with trimethoprim 200mg was the most common option across all 159
age groups, with a similar distribution of durations seen across all ages. From Figure 5.2. (b) it can be seen that 7 days of treatment was also most common across all age groups in uncomplicated male UTI patients receiving nitrofurantoin 100mg, with a similar distribution of durations seen across all ages.
Figure 5.4. Trends in duration of (a) trimethoprim 200mg and (b) nitrofurantoin 100mg by age category in adult male uncomplicated UTI patients (2008-2017).
As so few female and male patients treated for an uncomplicated UTI with nitrofurantoin 100mg went on to develop a BSI within the follow up period (Tables 5.2. and 5.4.), there were too few observations to run univariate or multivariable logistic regression models investigating the effect of difference in duration of treatment on the subsequent development of a BSI. As a result, the rest of this chapter will only investigate trimethoprim 200mg prescribing.
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5.4.2. Univariate analyses
Table 5.5. shows the results of the univariate logistic regression models investigating the effect of each of the individual potential predictors explored in Chapter 4 on the risk of female patients with uncomplicated, non-recurrent UTIs developing a BSI within 60 days of their UTI being treated with trimethoprim 200mg. Crude odds ratios, 95% confidence intervals and p-values of each association are displayed for each of the factors, the main factor of interest being the duration of treatment. Similar to the univariate models described in Chapter 4, the factors which were identified as being significantly associated with the outcome of developing a BSI within 60 days were age category, deprivation, a history of previous antibiotic use, having received a urine test and a history of diabetes, cardiovascular disease, renal abnormality or urinary catheterisation. Within these factors, female patients who were in older age categories, those receiving antibiotics before the UTI and patients with evidence of any of the comorbidities mentioned above were shown to have a higher risk of developing a BSI. Patients who had a urine test (either dipstick or culture) had a significantly lower risk of developing a BSI than patients who did not have a urine test performed (OR 0.47, 95% CI 0.25-0.90). The association between deprivation and the outcome was less clear as the risk of developing a BSI was higher in all groups which were more deprived than the most affluent group (Quintile 1) although the association was only significant between living in Quintile 4 compared to living in Quintile 1. Of particular interest in this chapter, the risk of developing a BSI in female patients with uncomplicated UTI prescribed trimethoprim 200mg was significantly associated with duration of treatment. Patients receiving 5 days of treatment had over one and a half times the odds of developing a BSI compared to patients receiving 3 days of treatment (OR
1.61, 95% CI 1.05-2.48) and patients receiving 7 days of treatment had over twice the odds of developing a BSI compared to patients receiving 3 days of treatment (OR 2.09, 95% CI 1.42-3.07). Patients receiving
10 days or longer treatment were not at significantly higher risk of developing BSI compared with patients receiving 3 days of treatment, but this may be due to low numbers of non-recurrent UTI patients receiving long courses of this antibiotic who developed the outcome. There did not appear to be a significant association between developing a BSI and the region where the UTI was treated.
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Table 5.5. Crude odds ratios for the effect of duration of Trimethoprim 200mg for uncomplicated UTIs on BSI within 60 days in England in female patients Crude OR 95% CI P value Treatment duration 3 days REF REF 5 days 1.61 (1.05-2.48) 0.029 7 days 2.09 (1.42-3.07) <0.001 10+ days 1.49 (0.21-10.76) 0.694 Age category 19-44 REF REF 45-64 1.08 (0.48-2.44) 0.857 65-74 2.67 (1.20-5.94) 0.016 75+ 17.21 (9.47-31.28) <0.001 Region North of England REF REF Midlands/East of England 1.20 (0.75-1.94) 0.441 South of England 0.93 (0.59-1.48) 0.763 London 1.20 (0.66-2.20) 0.546 IMD2010 (n=163 missing) Quintile 1 REF REF Quintile 2 1.45 (0.86-2.44) 0.161 Quintile 3 1.27 (0.73-2.20) 0.393 Quintile 4 1.99 (1.19-3.32) 0.009 Quintile 5 1.26 (0.68-2.32) 0.462 Antibiotics in past 30 days No REF REF Yes 2.69 (1.78-4.04) <0.001 Urine test No REF REF Yes 0.47 (0.25-0.90) 0.022 Diabetes No REF REF Yes 2.78 (1.36-5.67) 0.005 CVD No REF REF Yes 3.58 (2.15-5.94) <0.001 Renal abnormality No REF REF Yes 3.11 (1.68-5.76) <0.001 Urinary catheter No REF REF Yes 14.93 (4.72-47.25) <0.001
Table 5.6. shows the results of the univariate logistic regression models investigating the effect of each of the individual potential predictors on the development of BSIs in male patients with uncomplicated, non- recurrent UTIs prescribed trimethoprim 200mg. The factors identified as being significantly associated with the outcome of developing a BSI within 60 days were age category, region, having received previous antibiotics and having a history of urinary catheter usage. Within these factors, male patients who were in older age categories, those being treated in the Midlands/East of England or the South of England compared with the North of England, those receiving previous antibiotics before the UTI and patients with a history of urinary catheter usage were shown to have a higher risk of developing a BSI. When looking at the primary factor of interest, the duration of trimethoprim 200mg in male patients with uncomplicated, non-recurrent UTIs did not appear to have a significant association with the risk of developing a BSI (5 162
days vs. 3 days OR 0.66, 95% CI 0.26-1.68; 7 days vs. 3 days OR 1.30, 95% CI 0.64-2.61). There did not appear to be a significant association between developing a BSI and any of the other factors of interest.
Table 5.6. Crude odds ratios for the effect of duration of trimethoprim 200mg for uncomplicated UTIs on BSI within 60 days in England in male patients Crude OR 95% CI P value Treatment duration 3 days REF 5 days 0.66 (0.26-1.68) 0.386 7 days 1.30 (0.64-2.61) 0.467 10+ days 1.18 (0.32-4.39) 0.800 Age category 19-44 REF 45-64 0.86 (0.24-3.06) 0.819 65-74 3.28 (1.09-9.88) 0.035 75+ 8.30 (3.01-22.86) <0.001 Region North of England REF Midlands/East of England 2.18 (1.06-4.50) 0.034 South of England 2.07 (1.03-4.14) 0.040 London 1.38 (0.50-3.82) 0.530 IMD2010 (n=163 missing) Quintile 1 REF Quintile 2 1.25 (0.65-2.39) 0.508 Quintile 3 1.49 (0.78-2.83) 0.229 Quintile 4 1.02 (0.49-2.13) 0.953 Quintile 5 1.21 (0.57-2.57) 0.615 Antibiotics in past 30 days No REF Yes 1.97 (1.16-3.36) 0.013 Urine test No REF Yes 0.62 (0.28-1.34) 0.221 Diabetes No REF Yes 2.00 (0.96-4.16) 0.063 CVD No REF Yes 1.72 (0.95-3.12) 0.072 Renal abnormality No REF Yes 1.53 (0.62-3.78) 0.360 Urinary catheter No REF Yes 3.91 (1.79-8.54) 0.001
5.4.3. Multivariable logistic regression models investigating effect of longer vs shorter treatment with trimethoprim 200mg on development of BSIs
Table 5.7. shows the results of the multivariable logistic regression model investigating the effect of duration of trimethoprim 200mg treatment in female patients with uncomplicated, non-recurrent UTIs on the risk of developing a BSI within 60 days of UTI diagnosis/treatment, adjusted for covariates of
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interest. After adjusting for age category, region, deprivation, previous antibiotic use, urine testing and comorbidities, 7 days of trimethoprim 200mg treatment placed patients at an almost 50% higher odds of developing a BSI compared with patients receiving 3 days of trimethoprim 200mg treatment, although this association was weakly significant (OR 1.46, 95% CI 0.99-2.15, p=0.054). After adjustment for all other factors, the odds of patients greater than 75 years of age developing a BSI within 60 days was over 15 times higher compared with patients under 45 years of age (OR 15.12, 95% CI 7.99-28.61). Living in one of the more deprived areas (IMD2010 Quintile 4) placed patients at an over 2 times higher odds of developing a BSI within 60 days after treatment for a UTI compared to living in the most affluent areas
(OR 2.17, 95% CI 1.28-3.69). Receiving a urine test remained a statistically significant protective factor against developing a BSI in this patient cohort, reducing the odds by half (OR 0.51, 95% CI 0.28-0.95) and previous antibiotic usage remained a statistically significant predictor of developing a BSI (OR 1.74, 95%
CI 1.13-2.69). After adjustment for all other factors, having a history of urinary catheter usage within the previous year remained the only comorbidity with a statistically significant association with the outcome, increasing the odds of developing a BSI by more than 4 times compared to patients with no history of catheter usage (OR 4.39, 95% CI 1.03-18.73).
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Table 5.7. Adjusted odds ratios for the effect of duration of Trimethoprim 200mg for uncomplicated UTIs on BSI within 60 days in England in female patients Adj. OR 95% CI P value Treatment duration 3 days REF 5 days 1.28 (0.83-1.97) 0.259 7 days 1.46 (0.99-2.15) 0.054 10+ days 1.24 (0.17-8.90) 0.829 Age category 19-44 REF 45-64 1.06 (0.45-2.50) 0.885 65-74 2.55 (1.16-5.59) 0.020 75+ 15.12 (7.99-28.61) <0.001 Region North of England REF Midlands/East of England 1.26 (0.74-2.13) 0.390 South of England 0.91 (0.57-1.46) 0.710 London 1.31 (0.72-2.37) 0.382 IMD2010 (n=163 missing) Quintile 1 REF Quintile 2 1.36 (0.82-2.29) 0.255 Quintile 3 1.29 (0.74-2.24) 0.372 Quintile 4 2.17 (1.28-3.69) 0.004 Quintile 5 2.52 (0.77-3.02) 0.230 Urine test No REF Yes 0.51 (0.28-0.95) 0.035 Antibiotics in past 30 days No REF Yes 1.74 (1.13-2.69) 0.012 Diabetes No REF Yes 1.39 (0.60-3.19) 0.441 CVD No REF Yes 1.23 (0.74-2.04) 0.417 Renal abnormality No REF Yes 1.05 (0.55-1.99) 0.891 Urinary catheter No REF Yes 4.39 (1.03-18.73) 0.045
Table 5.8. shows the results of the multivariable logistic regression model investigating the effect of duration of trimethoprim 200mg treatment in male patients with uncomplicated, non-recurrent UTIs on the risk of developing a BSI within 60 days of UTI diagnosis/treatment, adjusted for covariates of interest.
After adjusting for age category, region, deprivation, previous antibiotic use, urine testing and comorbidities, duration of trimethoprim 200mg treatment did not have a statistically significant association with developing a BSI (5 days vs. 3 days OR 0.60, 95% CI 0.24-1.49; 7 days vs. 3 days OR 1.29,
95% CI 0.65-2.55). No other factors appeared to have a significant association with the outcome in this patient cohort, aside from region where being treated in the South of England appeared to place patients
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at over twice the odds of developing a BSI than being treated in the North of England (OR 2.08, 95% CI
1.05-4.11), after adjusting for all other factors.
Table 5.8. Adjusted odds ratios for the effect of duration of trimethoprim 200mg for uncomplicated UTIs on BSI within 60 days in England in male patients Adj. OR 95% CI P value Treatment duration 3 days REF 5 days 0.60 (0.24-1.49) 0.272 7 days 1.29 (0.65-2.55) 0.465 10+ days 1.28 (0.33-5.02) 0.721 Age category 19-44 REF 45-64 0.82 (0.23-2.94) 0.758 65-74 3.01 (0.98-9.22) 0.054 75+ 7.48 (2.58-21.64) <0.001 Region North of England REF Midlands/East of England 2.24 (1.03-4.90) 0.042 South of England 2.08 (1.05-4.11) 0.035 London 1.54 (0.52-4.51) 0.433 IMD2010 (n=163 missing) Quintile 1 REF Quintile 2 1.22 (0.59-2.49) 0.594 Quintile 3 1.58 (0.76-3.29) 0.224 Quintile 4 1.20 (0.51-2.81) 0.682 Quintile 5 1.77 (0.73-4.31) 0.208 Urine test No REF Yes 0.65 (0.29-1.45) 0.287 Antibiotics in past 30 days No REF Yes 1.59 (0.95-2.68) 0.080 Diabetes No REF Yes 1.70 (0.80-3.61) 0.166 CVD No REF Yes 0.95 (0.52-1.72) 0.866 Renal abnormality No REF Yes 0.83 (0.33-2.12) 0.701 Urinary catheter No REF Yes 2.18 (0.94-5.08) 0.070
5.5. Discussion
5.5.1. Main findings
In this study, I investigated the treatment of adult female and male patients with uncomplicated UTIs who presented in primary care between 2008 and 2017 in England. In particular I focussed on the proportion of patients prescribed either of the two main antibiotics recommended for this patient group, the trends in duration of treatment over time stratified by age group, and the distribution of patients receiving these 166
antibiotics who did or did not develop a BSI within 60 days following their UTI. Additionally, this chapter explored the effect of treatment duration for the two antibiotic options separately by patient sex on the odds of developing a BSI within 60 days of UTI antibiotic treatment, adjusted for relevant potential confounding factors.
This study found that 62% of female and 57% of male patients with an uncomplicated UTI were prescribed trimethoprim 200mg while 5% of female and 4.5% of male patients were prescribed nitrofurantoin
100mg. Thus, more patients received a prescription for trimethoprim 200mg than nitrofurantoin 100mg in this cohort, despite guidance recommending nitrofurantoin 100mg as the first choice of treatment for this infection in women and men (15). Women prescribed trimethoprim 200mg were more commonly prescribed a 3-day rather than a 7-day course, with this trend increasing slightly over time and decreasing with older age. Women prescribed nitrofurantoin 100mg were more commonly prescribed a 7-day rather than a 3-day course, with this trend markedly decreasing over time and remaining stable with age. Men prescribed trimethoprim 200mg were more commonly given a 7-day rather than a 3-day course, with this trend increasing over time and remaining stable with age. Lastly, men prescribed nitrofurantoin 100mg were more commonly given a 7-day course than 3-day, with this trend also remaining relatively stable over time and with age.
Among those patients who developed a BSI, the number initially prescribed nitrofurantoin 100mg was too low to allow the effect measures of interest in these patients to be robustly modelled. From the univariate analyses of female patients prescribed trimethoprim 200mg, all postulated predictive factors demonstrated a statistically significant association with the outcome apart from geographical region.
From the univariate analyses in male patients, only age, region, previous antibiotic usage and a history of urinary catheter usage showed statistically significant associations with the outcome, although caution must be exercised when interpreting the findings in the male-related models as far fewer male patients developed a BSI than female patients and these models were therefore based on a much smaller sample.
Upon adjustment for all factors of interest, a weak association was found between duration of trimethoprim treatment in female patients with uncomplicated UTIs and the odds of developing a BSI.
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Being prescribed a seven-day course of this antibiotic placed patients at an almost 50% higher odds of developing a BSI than being prescribed this antibiotic for three days. The adjusted models showed that patients who were older, had not had a urine test, had previous antibiotic usage before their UTI or had a history of catheter usage were more likely to develop a BSI after trimethoprim 200mg treatment for an uncomplicated UTI. There was also weak evidence of a higher odds of the outcome in patients living in more deprived neighbourhoods, although this was only true of those with a deprivation score falling in
Quintile 4 compared with Quintile 1. When looking at the odds of male patients with uncomplicated UTIs developing a BSI, no association with duration of trimethoprim 200mg treatment was found. However, this observation should be interpreted with caution as the model may have been underpowered to detect an association due to the low number of male patients with uncomplicated UTI treated with this antibiotic who developed BSIs.
By making use of a large, nationally representative linked dataset, I have been able to explore a specific reason for guideline non-adherence and its effect on the development of a subsequent severe infection.
In this study I was able to demonstrate a weak association between the use of longer courses of trimethoprim 200mg compared with shorter courses (which are recommended by the guideline) in female patients with uncomplicated UTIs, however for the reasons outlined in the following section, I believe this result should be interpreted with caution in light of the limitations that this particular study is potentially subject to.
5.5.2. Limitations
Upon discussion of these findings with practicing GPs, a key potential limitation was highlighted, namely, that data on severity of UTI, a factor which could influence the decision-making of the prescriber, is typically not recorded in the primary care dataset used in this study. While the models built in this chapter adjusted for factors such as age and patient comorbidities, which one would expect to be potentially associated with likelihood of increased symptom severity, the presentation of UTI symptoms in the form of symptom-related Read codes (as opposed to diagnosis Read codes) are the least reliable method for case acquisition (116) and there is currently no option for recording “UTI severity”, beyond a diagnosis of
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upper UTI which is a separate clinical condition. If a patient presents with severe symptoms that are indicative of a UTI but not sufficiently severe to warrant a diagnosis of pyelonephritis or prostatitis (i.e. fever and flank pain), this could lead to the GP deciding to prescribe a longer course of trimethoprim or nitrofurantoin with a view to increasing the chances of bacterial clearance. Based on the pragmatic assumption that patients who present with more severe symptoms could also be at higher risk of a severe outcome such as BSI, this then begs the question, how often does severity of UTI affect decision-making relating to duration of treatment without affecting the coding of UTI? This is a difficult question to answer as severity is not routinely collected in administrative data, although a qualitative investigation conducted by Butler et al. has provided some insight (17). In a household survey run by Ipsos MORI in 2014, 2,424 women over the age of 16 were interviewed about their history of developing UTIs, their symptom severity, help-seeking behaviours and management. In the sample, 37% of women reported having had a
UTI in their lifetime, with 48% of these women reporting the severity of their last UTI to be “fairly severe” or “very severe”. Almost all women (95%) had contacted a healthcare professional about their UTI, of whom 76% had a urine test and 74% were prescribed an antibiotic with only 63% of those prescribed an antibiotic actually taking it.
If we assume from the Butler study that a patient’s perception of the severity of their symptoms directly translates to a similar perception of the symptoms by the prescriber, and that perception of severity influences prescribing decisions around shorter vs. longer durations of treatment, 48% of otherwise uncomplicated UTIs being classified as having “moderately to fairly” severe symptoms could account for some of the 7-day prescriptions of trimethoprim 200mg seen for uncomplicated female patients. In addition to the above considerations, there could however be other non-clinical factors that may potentially influence GPs with regard to their choice of duration of therapy for UTIs. In a study investigating factors influencing prescriber decision-making when managing patients in long-term care facilities in Ontario, Canada, it was found that historical tendencies (within the previous year) of choosing longer-term duration of antibiotic treatment was a strong predictor of choosing longer-term duration of antibiotic treatment currently (117). This association remained significant even after adjusting for patient characteristics such as comorbidities, frailty levels and functional status.
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The variation in duration between 3 and 7 days for trimethoprim treatment of female patients with uncomplicated UTIs may therefore be influenced either by the presentation of symptom severity, the tendency of clinicians to continue “prescribing the way they have always prescribed”, or it may be a combination of the two. A study of the kind conducted in this chapter cannot answer this question as severity of the UTI is not known and therefore cannot be accounted for in the final models. Other methods such as qualitative methods (for example, questionnaires, interviews, focus groups, ethnographic approaches) must therefore be employed to further explore this more refined question.
Another limitation, touched upon early in the chapter, is the low number of BSIs seen among a) all patients receiving nitrofurantoin 100mg and among b) male patients with uncomplicated UTIs prescribed trimethoprim 200mg. This limitation meant I was not able to examine the association between duration of nitrofurantoin treatment and subsequent development of BSI in either women or men and also raised the question as to whether the model investigating duration of trimethoprim treatment and subsequent development of BSI in male patients was powered to be able to detect this association; this limitation was discussed at length in the previous chapter and could apply in this chapter as well. The results from the male UTI model should therefore be interpreted with caution.
5.5.3. How these findings fit in with existing literature
In the most recent ESPAUR report, it was noted that the number of prescribed antibiotic items decreased overall between 2012 and 2016 (from 2.17 to 1.88 items per 1000 inhabitants per day) and that this decrease was greater than the decrease in antibiotic use measured using defined daily doses (DDDs). One of the suggested reasons that might have accounted for this disparity was the greater use of longer duration prescriptions (10). In the analysis of trimethoprim 200mg usage in female patients, there was a trend in increased usage of 7-day courses between 2012 and 2018, although this comparison may be too tenuous, as the ESPAUR data were not stratified by antibiotic type.
In an English cohort study conducted by Lawrenson et al. investigating antibiotic treatment failure in female patients with uncomplicated UTIs under 44 years of age, it was found that between 1992 and 1999,
8% of trimethoprim prescriptions were for three days, 60% were for five days and 32% were for seven 170
days, with the percentage of 3-day prescriptions increasing from 6% in 1992 to 27% in 1999 and the percentage of 7-day prescriptions falling from 36% to 15% over the study period (46). When looking at whether duration of trimethoprim treatment had an effect on the risk of being prescribed another antibiotic within 28 days (a likely marker of treatment failure), there was no difference in the outcome between female patients receiving three, five or seven days of treatment.
As highlighted in the BMJ article by Llewelyn et al. touched upon at the beginning of this chapter (112), there is a significant and growing body of evidence to support the use of shorter courses of antibiotics for many bacterial infections, particularly those treated in primary care. There are a number of systematic reviews of clinical trials demonstrating equivalency in symptomatic and/or bacteriological cure between shorter vs. longer courses of antibiotics across many different bacterial infections. In a review of randomized control trials conducted by Hanretty et al., they concluded that duration selection should be guided by antibiotic choice but that there is sufficient evidence to support the use of shorter courses of all antibiotics used to treat uncomplicated UTI, as recommended by the Infectious Diseases Society of
America and the European Society for Microbiology and Infectious Diseases (114). They argued that any antibiotic exposure is an opportunity for the development of antibiotic resistance and that antimicrobial stewardship programs which systematically address treatment duration can affect the volume of antibiotic use within an institution without compromising on patient care.
A Cochrane systematic review conducted by Milo et al. demonstrated that three days of antibiotic therapy was similar to 5-10 days in achieving symptomatic cure for an uncomplicated UTI, while longer durations were more effective at achieving bacteriological cure, although were more likely to cause adverse effects
(118). They concluded that for the majority of female patients with uncomplicated UTIs, three days of treatment is most likely to be sufficient. Antibiotic treatment for 5-10 days, however, should be considered for women in whom bacteriological eradication might be of importance (such as women with recurrent or severe lower UTIs, women who are planning on becoming pregnant or women with underlying disorders). Similar trends with respect to equivalency in cure rates between short course and longer course antibiotic therapy were demonstrated in a paediatric UTI patient population in a systematic
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review of randomized control trials by Michael et al. (113), concluding that a 2-4 day course of oral antibiotics was as effective as 7-14 days in clearing lower UTI in children.
When looking specifically at antibiotic use in secondary care, an overview of systematic reviews conducted by Onakpoya et al. investigating the evidence for shorter vs longer duration antibiotic treatments for bacterial infections found that in adult patients there was no difference between shorter and longer durations in clinical resolution rates for a range of infections, including acute pyelonephritis and septic
UTI (119). Similarly, there was no evidence of a difference in clinical resolution rates by duration of treatment for children treated for pyelonephritis. It was highlighted, however, that the quality of evidence for both adults and children treated in secondary care with respect to antibiotic duration was low to moderate and that high-quality trials assessing this relationship in a hospital setting should now be a priority.
These large pieces of work have demonstrated that shorter courses, in patients for whom there is a low risk of complication, are largely equivalent to longer courses of antibiotics for treating and curing lower
UTI. Yet a number of the above articles mentioned not only the non-inferiority of shorter treatment, but the potential danger of longer treatment due to unnecessary increased exposure to antibiotics and, as a result, an increased risk of antibiotic resistance both for the patient and for the wider patient population.
This concept was explored in a systematic review conducted by Costelloe et al., touched upon at the beginning of this chapter, investigating the effect of antibiotic prescribing in primary care on the development of antibiotic resistance in individual patients (42). In this review, a case-control study was identified, conducted by Hillier et al., which demonstrated that for primary care patients with a trimethoprim-resistant community-acquired E. coli UTI, there was a much higher odds of this infection if the patient had received a trimethoprim prescription in the previous month, the duration of which was for ≥ 7 days compared with patients who received a shorter course of trimethoprim in the previous month.
Similar trends were seen for amoxicillin prescribing and amoxicillin-resistant community-acquired E. coli
UTI. The authors recommended that high-dose, shorter duration antibiotic regimens could reduce the selective pressure likely contributing to the emergence of antibiotic resistance in this patient population.
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5.5.4. Implications for practice and policy
In this chapter I have discussed the large amount of evidence showing that shorter compared with longer courses of many antibiotics are equivalent in effectively treating uncomplicated UTIs in female patients where symptomatic cure is sufficient (as opposed to bacteriological cure as well). I have also taken account of the work of Hillier et al. which demonstrated that longer versus shorter durations of previous trimethoprim use was associated with greater odds of trimethoprim-resistant UTI in primary care.
Together, this evidence suggests that not only are shorter antibiotic courses sufficient for treatment of most uncomplicated female UTIs, but they may place patients at lower risk of antibiotic-resistant UTIs in the future. If we postulate that the mechanism of the pathway between treatment for a UTI and development of a BSI is due to antibiotic resistance, then the findings of this chapter (that longer durations of trimethoprim were weakly associated with a greater risk of BSI) could be used as evidence to further support this argument. However, there are important limitations to consider in this chapter, namely that
UTI severity was not captured in the routinely-collected data used in this study. For this reason, rather than concluding that 3-day courses of trimethoprim 200mg should be prescribed for women with uncomplicated UTIs to avoid the risk of a subsequent BSI, I believe the main recommendation from this chapter needs to be a call for further evidence. More specifically, there is a need for more of a mixed- methods approach to provide data on the question of UTI symptom severity and its influence on selection of antibiotic duration, similar to the patient interviews conducted in the study by Butler et al. discussed above. By collecting these data, we could more easily ascertain whether longer duration selection is solely due to a more severe presentation of UTI or whether it is due prescriber preference (or a mixture of the two). This would shed more light on whether the effect demonstrated in this chapter is real, or whether it is likely to be confounded by UTI symptom severity. This type of investigation could also provide insight into whether the national prescribing guidelines are specific enough with respect to treatment duration and illness severity.
5.6. How these findings fit in with the wider thesis
This chapter concludes the studies conducted in this thesis. It followed on from Chapter 4 by focusing on a sub-group of the UTI cohort - uncomplicated UTIs prescribed one of the two first line treatment options
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- to investigate one specific reason for a prescription being “off-guideline” and its effect on the development of BSI. However, there were limitations in this chapter which introduced uncertainty into the findings and means that making recommendations with respect to clinical practice is premature at this stage. What it does serve to emphasize, however, are the limitations of using routinely-collected administrative data for studying antibiotic usage in treating infections in primary care. Certain intricacies in a GP consultation are not recorded in the routine data – intricacies which may have a large impact on antibiotic decision-making. This demonstrates that observational studies using linked, routinely-collected data can be extremely informative but can currently only answer part of the question and must be supplemented with other kinds of methodologies, such as interview-based studies of both patient and prescriber or even ethnographic approaches (while recognizing the limitations of these approaches as well). Large-scale quantitative studies using national data can be incredibly powerful in using large enough sample sizes (which often cannot be achieved using trial or single-site approaches) for the purposes of identifying trends in the population, focusing the research agenda to specific questions which need further investigation and hypothesis-generation. However, they will always be limited by the fact that data is collected for the purposes of clinical practice, not for the purposes of research. Where there are gaps, such as in this study where we cannot know to what extent a certain factor may be confounding the relationship, other methods must be employed to explore and supplement these areas with a more nuanced and tailored approach. Hopefully, the findings and recommendations presented here will serve as a stimulus for future work on this clinically important topic.
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CHAPTER 6: OVERARCHING DISCUSSION
Chapter 1: Aims and Background
Chapter 2: Volume of prescribing for UTI and incidence of E. coli bacteraemia
Chapter 3: Quantifying Chapter 4: Effect of BSI-related levels of off-guideline off-guideline UTI diagnosis admission, prescribing for UTI prescribing for UTI and treatment death or re- on BSI risk consultation
Chapter 5: Effect of duration of treatment for UTI on BSI risk
Chapter 6, 7: Discussion, conclusions and recommendations
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6.1. Looking back to the hypothesis
This thesis aimed to explore the pathway between treatment with antibiotics for a urinary tract infection in primary care and the development of a subsequent bloodstream infection, with the aim of better understanding whether the rates of BSI could be reduced by improving antibiotic stewardship for the underlying infection. The overall hypothesis for the PhD was as follows:
“This thesis addresses the hypothesis that the risk of developing a BSI following antibiotic treatment for a community-acquired UTI is associate with differing patterns of antibiotic prescribing for community- acquired UTIs.”
In Chapter 2 I explored, at the ecological level, the pathway by which a UTI may lead to an E. coli bacteraemia and found that GP practices with higher rates of both trimethoprim and nitrofurantoin prescribing, after adjusting for patient case-mix and GP practice characteristics, had significantly higher incidences of UTI-related E. coli bacteraemia in adult women than practices with lower rates of prescribing of these two antibiotics. The effect size of this relationship became stronger when looking only at UTI- related E. coli bacteraemia which were resistant to trimethoprim and prescribing of trimethoprim; not unexpectedly, higher prescribing of nitrofurantoin no longer had an effect on the incidence of these trimethoprim-resistant infections. This relationship was further explored when I investigated the effect of the prescribing of trimethoprim and nitrofurantoin in relation to each other (as opposed to being adjusted for each other), expressed as the ratio of trimethoprim to nitrofurantoin prescribing by practice. This is a measure reported by PHE as a proxy for appropriate prescribing for UTI (the rationale being that the ratio of trimethoprim to nitrofurantoin should decrease if practices increasingly adopt nitrofurantoin as the first choice for UTI treatment to comply with national guidance).
Chapter 2 also demonstrated that a relationship between volume of antibiotic prescribing for UTI and incidence of UTI-related E. coli bacteraemia existed in the study period, and that the relationship became stronger when looking at resistant bacteraemia, suggesting that antibiotic resistance may be a contributing mechanism to this progression. These associations, however, did not take account of patient characteristics and comorbidities. These findings indicated the need for further investigation into different 176
patterns of antibiotic prescribing for UTI and the risk of BSI at the patient-level as well as determining which patient groups were most at risk of this severe outcome. What is interesting to note is that the investigation of “volume” of prescribing in a practice does not distinguish between higher numbers of antibiotic prescriptions per patient and longer durations of antibiotic prescriptions across all patients. This is useful to bear in mind when taking all chapters into consideration.
Moving from “volume” of prescribing, I then investigated “quality” of antibiotic prescribing for UTI at the patient-level in Chapter 3 by developing an algorithm to assess adherence of antibiotic prescriptions to a national prescribing guideline over a 9-year study period. I firstly determined that the majority of antibiotic prescriptions for patients with a CA-UTI over the study period were not in line with national guidance (due to a discrepancy in choice of either antibiotic, dose, frequency or duration). This was particularly prevalent for female patients and older patients. Adherence to prescribing guidelines increased markedly over the study period for male patients but remained relatively low over the study period for female patients. Interestingly, when introducing a 3 or 6-month time lag in the definition of
“on-guideline” (i.e. allowing 3 or 6 months for a new guideline to be implemented in practice by classifying a prescription as “on-guideline” if it adhered to the updated or the previous guideline for the time lag period), the proportion of “on-guideline” prescriptions did not change from when no time lag was used.
This appears to suggest that the low levels of guideline adherence are not due to delays in updated guideline implementation in practice. The reason for prescriptions being “off-guideline” was most commonly related to the duration of treatment or the antibiotic prescribed – female patients in particular were commonly seen to be prescribed longer courses than recommended for UTI. I also demonstrated that there was a large amount of variation in guideline adherence between practices, suggesting that there is not another source of guidance that practices are uniformly following.
In Chapter 4 I then sought to determine whether this variation in adherence to national prescribing guidance conferred different levels of risk with respect to patients developing a BSI following antibiotic treatment for a UTI. After linking the primary care data with secondary care and death registry data, and adjusting for postulated patient-level confounding factors, I could not demonstrate an effect between guideline adherence for UTI treatment and the risk of subsequent BSI within 30 or 60 days. As the rate of
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progression from UTI to BSI was not previously known, these results must therefore be interpreted with caution and validated in future work before they can be definitively accepted. There was a risk of these models being underpowered to detect a significant difference between the treatment groups. The rate of progression and the median time-to-event found in Chapter 4, however, will be useful measures for future work in this area. While I could not demonstrate that guideline adherence had an effect on BSI risk, I subsequently sought to determine whether the type of non-adherence had an effect on development of a BSI. In other words, were any specific types of deviation from the guideline associated with an increased risk of patients developing a BSI?
Following on from the results of Chapter 2 (where a higher volume of UTI prescribing was associated with a higher incidence of UTI-related ECB) and Chapter 3 (where duration of treatment was the main reason for prescriptions not adhering to guidelines), I decided to investigate whether differences in duration of antibiotic treatment for the two first-line UTI treatment options was associated with the risk of developing a BSI. I explored this relationship separately for trimethoprim 200mg and nitrofurantoin 100mg, stratified by female and male patients. There was an insufficient number of patients treated with nitrofurantoin
100mg who developed BSI within 60 days to model these effects. In the trimethoprim 200mg model looking at the occurrence of BSIs in women, there was weak evidence of a higher odds of BSI following treatment with a 7-day course of trimethoprim 200mg than following a 3-day treatment course. This result could be affected by residual confounding – I was unable to adjust for symptom severity, which may have impacted the results by a) affecting the duration of treatment chosen and b) affecting the risk of BSI.
As a result, I postulated that duration of treatment may play a role in the risk of developing a BSI due to higher unnecessary antibiotic exposure and potential development of antibiotic resistance in the causative organism. However more mixed methods research needs to be conducted to determine what role the severity of UTI symptoms upon presentation (which may be poorly recorded, if at all) may play in prescriber decision-making.
While these findings lean towards supporting the hypothesis that different patterns of antibiotic prescribing affect subsequent BSI risk (potentially with particular focus on duration of trimethoprim treatment), there is insufficient evidence to definitively support or refute the hypothesis without further
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investigation. I believe that the methodology developed in this thesis and the “lessons learned” which I discuss in detail throughout, are as interesting as the findings themselves, if not more so. In the following sections of Chapter 6 and 7 I lay out my recommendations for how English primary care and national surveillance data collection and linkage can be improved to build upon this work and ultimately address the residual questions generated from this thesis.
6.2. Strengths and limitations
Strengths:
This work is of relevance to a key national infection-related priority (namely, the UK government’s ambition to halve Gram-negative BSIs by 2021 (39)) and was undertaken using several disparate but complementary national healthcare datasets. This approach allowed for analysis of data that was representative of the general population as well as allowing analysis of trends over reasonably long periods of time (3 years in Chapter 2 and 9 years in Chapters 3-5). Importantly, this novel work plan was developed in collaboration with a multi-disciplinary range of colleagues comprising clinicians, pharmacists, surveillance experts and those who advise on national antibiotic prescribing policy, ensuring this work was of relevance to both clinical practice and the formulation of policy interventions for tackling the upsurge in BSIs.
As this is the first time this question has been addressed using these data, I deliberately took a broad approach to the analyses, creating pragmatic code lists which focussed on inclusivity (i.e. defining the outcome as “bloodstream infections” in the CPRD work rather than just “ECB” or “sepsis” and using UTI codes such as “suspected UTI” to reflect clinical prescribing behaviour rather than limiting the data to confirmed UTIs), conducting sub-analyses for patient subgroups, performing sensitivity analyses to test my definitions, investigating two follow-up times and using multiple analytical techniques to fully explore this infection pathway from myriad angles. In this way, beyond contributing novel findings and a novel methodology to the evidence base, I believe that I have generated many “lessons learned” with respect to working with these datasets that should aid future attempts to answer infection-related questions by providing a strong foundation on which others seeking to replicate, validate, build upon or improve this
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work can base their studies. Through a broad discussion of the limitations of what one can learn from these data about this infection pathway, this thesis can be used as an exemplar case study for the inclusion of PHE mandatory infection surveillance data and antibiotic susceptibility data into the CPRD linkage scheme. In this way a case-control study, of the kind outlined in Chapter 4, could be conducted to validate these findings with susceptibility data from urine and blood cultures being used to determine whether antibiotic resistance is the major mechanism for defining this pathway.
Through my honorary contract with PHE and through use of the Imperial College CPRD licence, I have been in a privileged position of being able to make use of the best national infection surveillance and primary/secondary care datasets of their kind in England, and arguably in the world (82), to complete this work. Wherever possible, I have sought to supplement core datasets with linked data to accurately capture further covariate and outcome information. I have also re-evaluated my approach when I thought it could be improved upon. In particular, in 2017 I presented some preliminary work to an audience of
CPRD users and developers; at this event I asked the audience if there was a better way to capture pregnancy, beyond the use of Read codes, as I was noticing particularly low rates of pregnancy among women of child-bearing age in my cohort. I was informed by a CPRD developer that a new dataset, the
Pregnancy Register, was about to be made available and that my study could be one of the first to make use of it. I took this opportunity to amend my protocol to request the use of this new dataset and at the same time requested to update the timeline for my study, from the end of 2014 to December 2017. While incorporating an additional three years of data along with the linking pregnancy data took more time to work with and resulted in a re-analysis of some of my previous work, I made this decision because I believed it would greatly improve the validity and relevance of the study.
During this PhD, aside from conducting epidemiological studies, I developed and tested a new tool for identifying whether an antibiotic prescription is in line with guidance. This tool allowed assessment of adherence to guidelines in more detail (antibiotic type, dose, frequency and duration of treatment for the patient and time period) and on a larger scale (by utilizing national, routinely-collected data) than anything previously reported in the literature. This tool could be used to evaluate national guideline adherence continuously over time for UTIs or it could be adapted for use on other infections commonly treated in
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primary care. For example, the Imperial College HPRU currently holds ethics approval to replicate this study in a community-acquired pneumonia patient cohort. This tool could also be adapted for use at the
CCG or GP practice level, should differences between local and national guidance be noted. To achieve this, together with others within the HPRU I am are currently working with a North West London partner organisation to adapt this tool to local guidance and data and design a dashboard which utilises this algorithm for use in clinical practice. This will be elaborated upon further in Section 6.6.
Limitations:
While I consider the use of national, routinely-collected data to be a strength of this PhD, it does not come without limitations. As discussed in previous chapters, using data which were not collected for the purposes of research can be complicated to work with without introducing systematic bias. Intricacies around how one defines clinical diagnosis code lists, time periods for investigating previous healthcare exposures and comorbidities, episodes of infection, outcomes and follow-up times can all potentially lead to the introduction of bias. By solely employing the use of administrative data and therefore being confined to the variables collected (and further restricted to using only variables with a high degree of completeness), I cannot rule out the risk that residual confounding may exist in any or all studies in this thesis and may therefore have influenced the results. In this project, however, all definitions and rationale used were either based on the literature and/or expert opinion and were tested with sensitivity analyses where there was uncertainty around the approach chosen. I have been transparent in outlining my methodology and reasoning, as findings must always be weighed against the potential limitations of a study’s conduct.
There are also limitations with attempting to define a “UTI patient population” using CPRD data. As discussed in Chapter 1, clinical diagnostic codes may not accurately reflect the true underlying cause of the urinary symptoms exhibited by a patient. For example, a diagnosis of “cystitis” might be recorded where a GP believes a patient has a bladder infection, for which a patient may be prescribed an antibiotic.
However, cystitis may reflect inflammation of the bladder or urethra due to causes other than bacterial infection (associated with culture-negative urine samples). While such cases may have similar or identical symptoms to a bacterial UTI, their differing aetiology means they would not be expected to respond to
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antibiotics. Thus, by using only diagnostic read codes, the patient population defined in Chapters 3-5 could be an overestimate of the true bacterial UTI population for whom prescribing a course of antibiotic treatment would often be appropriate. Other observational studies of UTI patients have avoided this limitation by restricting the patient population to (or reported on the proportion of) microbiologically- confirmed UTIs (as discussed in Chapter 1), although this approach is victim to culturing bias whereby urine cultures are only taken from select patient groups. The patient populations represented in these studies may therefore be an underestimate of the true underlying UTI patient population. This is a challenge in all studies related to UTI.
As discussed in Chapter 4, due to the fact that the rate of progression from a CA-UTI treated with antibiotics to a subsequent associated BSI was previously unknown, I captured fewer BSI cases than I had expected to when calculating sample size. This could have resulted in the models built in Chapters 4 and
5 being underpowered to detect significant associations between the exposure (on/off guideline status or duration of treatment) and the outcome (BSI at 30 or 60 days). This has led to relatively weak conclusions from these models and an inability to make definitive clinical recommendations. However, with the data currently available, these models are difficult to improve upon as I extracted the maximum amount of data possible (data for 600,000 patients) while still maintaining a full dataset from one of the largest primary care data repositories in the world.
This is where, as I have highlighted before, the case needs to be made to link PHE infection surveillance data with CPRD (or the SAIL Databank, the Welsh equivalent to CPRD, although this is a much smaller database) to allow for the recruitment of patients based on the outcome and looking back for the exposure, in other words, a case-control design. Using the methodology developed in this thesis, this proposed use of data would allow for my findings to be validated (or refuted) and would allow for adjustment for antibiotic susceptibility – both of which would greatly improve the evaluation of the prescribing guideline and provide insight into the best strategies for reducing community-acquired GNBSI, as outlined in UK Government policy.
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Similarly, as discussed in Chapters 3-5, there are some ways in which data collection for antibiotic prescribing are not yet optimised for conducting national evaluations such as these. In particular, information about when a prescription has been written as part of a delayed prescribing strategy should be recorded as the data most likely currently over-estimates the amount of antibiotics being consumed in the population if many antibiotic prescriptions are written with the advice to “take them in a few days if symptoms do not resolve on their own”. Without the inclusion of a “delayed antibiotic flag” in primary care data, it is currently very difficult to evaluate delayed prescribing strategies using electronic health records. Like all studies investigating antibiotic usage in primary care nationally, this lack of information may have affected the studies in this thesis by overestimating antibiotic exposure. Similarly, the inability to account for potential residual confounding due to UTI severity upon patient presentation made it difficult to draw strong conclusions when investigating the role of duration of treatment on the risk of BSI outcomes in Chapter 5. Having some “marker of severity” flag, “justification for longer treatment duration” flag or incentivising the recording of symptoms which might affect a prescriber’s decision- making could all be useful tools in better understanding what drives the demonstrated variation in antibiotic prescribing for uncomplicated UTI in adults in England.
I would like to make the case for a more multi-faceted approach to epidemiological research by highlighting that it is unrealistic to expect any single study design to fully answer a research question. This thesis has shown what can be done with ecological and patient-level administrative data by employing the use of linkage techniques and statistical modelling, however there are limits to what this routine data can provide with regards to understanding this infection pathway. I believe that these studies (besides being built upon, validated and ultimately improved) must be supplemented by qualitative study designs to better understand why prescribers choose one prescription over another for what is recorded in the routine data as the same infection (uncomplicated UTI). By interviewing GPs, patients or even employing an ethnographic approach, we will begin to be able to fill in the missing pieces of the puzzle and better understand whether other sources of residual confounding exist. A mixed-methods approach going forward will give us a much richer understanding of prescribing behaviour in primary care, why such large amounts of variation occur in antibiotic choice in this country, whether this is indeed appropriate and what consequences this might have on patient safety outcomes.
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6.3. How this thesis supports government policy
There are a number of ways in which findings from this PhD validate and support various government policies and interventions from 2014 to 2018; these are mapped in Table 6.1. below.
Table 6.1. Thesis findings mapped to national policy recommendations Source National policy recommendation Finding from this PhD Chapter
NHS Quality Decreased overall antibiotic use in Higher prescribing of both trimethoprim and 2 Premium uncomplicated female UTI cases nitrofurantoin were associated with higher 2015/16 (120) (within the lowest risk patient groups) UTI-related ECB rates - reducing overall prescribing for UTI in low risk patients at the practice level could impact UTI-related ECB incidence
PHE 2014- Switch from trimethoprim to Low rates of nitrofurantoin resistance found in 2 current nitrofurantoin as the first line blood and urine cultures, lower association guidance (15) treatment option in uncomplicated UTI between nitrofurantoin prescribing and UTI- cases related ECB incidence, no association between nitrofurantoin prescribing and trimethoprim- resistant ECB incidencev– where antibiotics are needed, and it is safe to do so, opt for nitrofurantoin as first choice
PHE 2014- Decrease the ratio of trimethoprim to Higher ratio of trimethoprim to nitrofurantoin 2 current nitrofurantoin prescribing per practice prescribing was associated with higher guidance (71) (this ratio is used as a measure of trimethoprim-resistant ECB, supporting switch inappropriate prescribing) from trimethoprim to nitrofurantoin where appropriate
PHE/NICE Elderly patients, male patients, These factors, along with deprivation, 4 guidance (15, previous antibiotic use and those with remained significant predictors of BSI 22, 38, 42) a history of urinary catheter usage are following UTI after adjustment (for all models) at higher risk of severe outcomes
PHE/NICE Shorter (3 days) instead of longer (5-10 3 days compared with 7 days of trimethoprim 5 guidance (15, days) antibiotic courses recommended 200mg had lower risk of developing CA-BSI in 22) for female uncomplicated UTIs uncomplicated female UTIs, although requires further study to eliminate the potential for confounding due to symptom severity
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6.4. Novel contributions of this thesis
Aside from supporting several existing policies, as outlined in Section 6.3., there are a number of novel contributions arising from this thesis. These, along with how best they can be implemented into practice are outlined below in Table 6.2.
Table 6.2. Novel contributions of this thesis and recommendations for implementation Novel contributions from PhD Proposed implementation Chapter
Development of an algorithm for determining This methodology is being developed into a dashboard 3 whether an individual patient prescription in collaboration with Imperial College Health Partners agrees with the national guideline. to feedback monthly UTI prescribing patterns and guideline adherence rates to GP practices in NW London.
Analysis of national levels of adherence to UTI These findings can provide insight into how and where 3 treatment guidelines (stratified by patient guideline adherence can be improved and provide a groups and reasons for non-adherence; analysed national benchmark for practices to compare temporally and regionally). themselves against with respect to UTI prescribing guideline adherence.
Quantification of the rate of developing a BSI This measure can be used in sample size calculations 4 following antibiotic treatment for a CA-UTI. for future studies investigating this infection pathway.
Evaluation of the median time-to-event for a BSI This measure can be used by clinicians to determine 4 following antibiotic treatment for a CA-UTI. an “at-risk” window within which to more closely monitor UTI patients for revisits and hospital admission.
Outlined the consistent importance of the role of This finding opens up area for further research into 3,4,5 patient-level deprivation on the development of social determinants of health with respect to this BSI following antibiotic treatment for a CA-UTI. infection pathway.
6.5. Further research questions generated from this thesis
The immediate work following on from this PhD is the implementation of the algorithm developed in
Chapter 3 into a dashboard for use in North West London GP practices, as outlined in further detail in the section below. Additionally, I am currently working with health economists based at PHE and the London
School of Hygiene and Tropical Medicine on exploring the possibility of assigning costs to the measures of association found in Chapters 4 and 5. This work would involve modelling the potential costs to the healthcare system of different antibiotic prescribing practices in primary care when severe outcomes are accounted for, under the assumption that these effects are real and taking account of the limitations
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which exist in the studies. Beyond this current work, I believe there are several avenues for further study which have come out of this thesis, which are as follows:
1) Qualitative studies to better understand the role of 1) UTI severity at the time of presentation,
2) prescriber preference (or “prescribing as one always has”) and 3) other potential clinical or
behavioural drivers in the selection of antibiotic type (trimethoprim vs. nitrofurantoin) and
duration (shorter vs. longer treatment courses) for uncomplicated UTI in adults.
2) Making a case for PHE mandatory infection surveillance data and antibiotic susceptibility data to
be included in the CPRD linkage scheme. This could then allow for patients to be recruited into
the study based on whether they were diagnosed with a laboratory-confirmed ECB, with a look-
back to previous antibiotic treatment for UTI in primary care. The methodology developed in this
thesis and in the study by Abernathy et al. could then be replicated and improved upon with a
much larger sample size (of ECB patients) and supplemented with the inclusion of urine and
blood culture susceptibility data.
3) One key question which has arisen out of this PhD is whether practices prescribing higher
volumes of antibiotics such as trimethoprim (shown in Chapter 2 to have an effect on the
incidence of trimethoprim-resistant UTI-related ECB) are actually writing more individual
prescriptions of trimethoprim or are in fact writing trimethoprim prescriptions of longer duration
(suggested in Chapter 5 to be weakly associated with BSI risk, acknowledging the limitations).
Accounting for this distinction in how we monitor antibiotic usage nationally, I believe, will need
to be a key consideration in future for antimicrobial surveillance programmes.
4) This work makes the case for a much more in-depth analysis of the role that both patient-level
and area-level deprivation play on differences in both antibiotic prescribing for UTI in primary
care and the risk of developing subsequent severe outcomes, through multi-level modelling or
mediation analysis techniques, for example. Other social factors that may confound or mediate
the relationship such as patient ethnicity and travel should also be explored within this realm. I
am beginning to explore this avenue through work I am leading on outside of this PhD (121) but
I believe there is scope for much more work in this area.
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5) In the discussion of the limitations of this work, along with all antibiotic-related work using
routinely-collected data, exploring the case for including a new “marker of infection severity”
and a “marker for a delayed antibiotic prescription” could make these data available to practice
managers, researchers and policy-makers to greatly improve the understanding of the role of
these influencers in the decision-making of antibiotic prescribers in primary care.
6.6. Dissemination and implementation into practice (Whole Systems Integrated Care dashboard)
To work towards implementation of the methodology developed in Chapter 3 into clinical practice, the
Imperial College HPRU in HCAI/AMR have begun a collaboration with Imperial College Health Partners
(ICHP), a partnership organisation bringing together NHS healthcare providers, clinical commissioning groups (CCGs) and academic partners within North West London, to build a monthly monitoring dashboard for use in primary care. By using the algorithm developed to identify whether an antibiotic prescription for a UTI adhered to guidelines (in terms of antibiotic, dose, duration and frequency) based upon patient characteristics, illness severity and time period, I am working with analysts at ICHP and with the HPRU data manager to translate this methodology into a tool to be used in practice for monthly feedback. I am currently adapting the algorithm to the data available in the North West London Whole
Systems Integrated Care (WSIC) dataset managed by ICHP and am also currently exploring whether there are changes between the national guideline (which the algorithm is based upon) and the local guideline(s) used by North West London GP practices. If there are, the algorithm will be adapted to compare prescriptions to local guidance. The WSIC dataset includes data for 88% of the patient population in North
West London, covering the CCGs of Central London, West London, Hammersmith and Fulham, Brent,
Ealing, Hounslow, Hillingdon and Harrow.
By doing this we will be able to build a dashboard using Tableau software which displays to GPs the number of UTI patients treated at their practice in the previous month (up to the past 5 years, depending on data availability), the proportion of patients who received a prescription which was considered “off- guideline” as well as an overall breakdown of antibiotics used and patient demographics. Alongside the initial work of adapting the algorithm to the available data and guideline, the HPRU is also exploring other
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functionality which could include local system-wide benchmarking, analysis of trends in adherence over time and reporting of reasons for non-adherence (i.e. certain patient groups, whether it is a different antibiotic, dose or duration being prescribed consistently in a practice etc.).
There are other dashboards which have been recently developed for the purpose of feeding back antibiotic prescribing data to GPs – a few examples include PHE Fingertips (AMR local indicators) (122), the NHS QP Antibiotic Prescribing dashboard (123) and OpenPrescribing.net (run by the EBM DataLab,
University of Oxford). Our proposed dashboard differs from these, however, in that it monitors antibiotic prescribing for a specific indication within the context of individual patient characteristics as well as monitoring adherence to prescribing guidance and the reasons for non-adherence, as opposed to monitoring aggregated usage by practice and antibiotic class. All of the above dashboards serve an important role in monitoring trends in usage of certain classes of antibiotics nationally to evaluate adherence with national prescribing policy, however our dashboard is a tool which I believe is more clinically-focussed and relevant, which can potentially change prescribing behaviour and improve guideline adherence for antibiotic prescribing for this common infection.
Developing this tool will form part of a wider body of work including securing an appropriate amount of baseline prescribing and infection data, working with clinicians to design the implementation of the tool in practice, conducting the evaluation of its use and the analysis of whether it has an impact on antibiotic prescribing decision making and clinical outcomes. The design of this work will be guided by the Medical
Research Council’s (MRC) Guidelines on Developing and Evaluating Complex Interventions (124).
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CHAPTER 7: THESIS CONCLUSIONS AND RECOMMENDATIONS
Chapter 1: Aims and Background
Chapter 2: Volume of prescribing for UTI and incidence of E. coli bacteraemia
Chapter 3: Quantifying Chapter 4: Effect of BSI-related levels of off-guideline off-guideline UTI diagnosis admission, prescribing for UTI prescribing for UTI and treatment death or re- on BSI risk consultation
Chapter 5: Effect of duration of treatment for UTI on BSI risk
Chapter 6, 7: Discussion, conclusions and recommendations
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7.1. Conclusions
This thesis has demonstrated that differences in antibiotic treatment for UTI in primary care most likely confer differences in the risk of developing a BSI subsequent to a UTI, most definitively at the practice level, and that antibiotic resistance most likely plays a role in this pathway. It demonstrates that the majority of antibiotic prescriptions for UTI in England do not agree with national evidence-based guidance released by PHE and that there is a large amount of variation in guideline adherence between practices.
The reasons most commonly are due to antibiotic duration in women and antibiotic choice in men. There does not appear to be evidence of a greater risk of BSI occurring in patients receiving “off-guideline” antibiotic prescriptions for prior UTI, although further study is required to validate this finding. Longer antibiotic courses for female patients with uncomplicated UTIs treated with trimethoprim 200mg may confer a greater risk of subsequent BSI, although this relationship may be confounded by UTI severity and requires supplementation with qualitative research. This thesis has also provided new methodology in analysing guideline adherence, along with determining the rate of BSI following antibiotic treatment for a CA-UTI along with the time window within which this event is most likely to occur. The work conducted as part of this PhD has strengthened the case for future data linkages and has laid the groundwork for further study to validate and improve upon these designs.
7.2. Recommendations
Clinical:
- GP practices should aim to reduce the overall use of antibiotics for low-risk patients (typically
young, otherwise healthy women without recurrent UTI) as it could help to lower the incidence
of UTI-related E. coli bacteraemia.
- Where antibiotics are needed, GPs should follow national guidance and opt for nitrofurantoin
100mg as their first-choice antibiotic in uncomplicated UTI patients with normal kidney function
as opposed to trimethoprim 200mg to reduce the incidence of UTI-related trimethoprim-
resistant E. coli bacteraemia within their practice.
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- GP practices should pay close attention to their ratio of trimethoprim to nitrofurantoin
prescribing (provided through PHE Fingertips or OpenPrescribing.net) as a lower ratio is
associated with lower rates of E. coli bacteraemia by practice.
- GPs should be aware that patients returning within 18 to 24 days of antibiotic treatment for UTI
with non-resolving or worsening symptoms should be monitored carefully for signs of a BSI,
particularly patients who are elderly, male, living in more deprived areas, and/or with a history
of urinary catheter usage.
- When deciding on duration of treatment (in this case, trimethoprim 200mg), GPs should follow
guidance and select a 3-day course in uncomplicated female UTI patients to reduce unnecessary
antibiotic exposure and the risk of BSI, although as these associations are relatively weak and
may be subject to residual confounding, further research is therefore needed to make this
recommendation definitive.
Epidemiological:
- For future epidemiological studies investigating the progression from CA-UTI to BSI, 0.11% of
adult CA-UTI patients treated with antibiotics can be expected to develop a CA-BSI within 30 days
and 0.17% within 60 days – these measures can be used in sample size calculations going
forward.
- Feedback of guideline adherence for UTI treatment in adults based on individual patient data
should be improved to inform GP practices of their prescribing patterns and provide an
opportunity for investigation, reflection and potentially behaviour change to improve antibiotic
stewardship in primary care.
- More research needs to be conducted, with a larger sample size of outcome events, to validate
the findings of no association between receiving an “off-guideline” antibiotic prescription for CA-
UTI and risk of a subsequent BSI.
- Qualitative studies need to be conducted to supplement the duration-related work to better
understand what influences prescriber decision-making with respect to treatment duration for
uncomplicated UTIs in adults.
191
- In line with the literature, risks of developing a BSI following treatment for a CA-UTI are higher in
patients who are older, male, living in more deprived areas, those having had antibiotics in the
previous month before the UTI, those with a history of urinary catheter usage and, less
consistently (only before adjustment), those with a history of diabetes, cardiovascular disease or
renal abnormality. More research into the role that area-level and patient-level deprivation play
in the risk of this outcome should be conducted to understand the reasons behind poorer
infection-related outcomes and prescribing in poorer neighbourhoods.
Data linkage and collection:
- This thesis further strengthens the case for PHE mandatory infection data and antibiotic
susceptibility data to be included in the CPRD linkage scheme to allow for case-control studies to
be conducted to validate these findings using a larger sample size of BSI outcomes.
- Given that delayed antibiotic prescribing is now a more commonly-recommended strategy for
reducing antibiotic usage for common infections in primary care, there is a case to be made for
building a “delayed prescription” variable into routine data collection in practice to allow for this
strategy to be monitored, evaluated and accounted for in research.
- There may be a similar case to be made for introducing a “symptom severity marker” variable to
indicate when a patient’s symptoms caused the prescriber to recommend a longer course
without changing the diagnostic code beyond an uncomplicated UTI.
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Publications and conference presentations
Title Type “Exploring the relationship between primary care antibiotic prescribing for Peer-reviewed paper urinary tract infections, Escherichia coli bacteraemia incidence and antimicrobial resistance: an ecological study.” Lishman H, Costelloe C, Hopkins S, Johnson AP, Hope R, Guy R, Muller-Pebody B, Aylin P. International Journal of Antimicrobial Agents. 2018;52(6):790-8.
“Measuring levels of antibiotic prescribing for urinary tract infections in Oral conference primary care in England which does not adhere with national prescribing presentation guidelines”, Lishman H, Costelloe C, Gharbi M, Johnson AP, Molokhia M, Aylin P. Presented at the General Practice Research in Infections Network meeting (GRIN) 2017, Oslo, Norway
“Investigating the effect of suboptimal antibiotic prescribing for UTIs in Oral conference primary care on hospital admissions due to bloodstream infections: study presentation protocol.” Lishman H, Costelloe C, Gharbi M, Johnson AP, Molokhia M, Aylin P. Presented at the General Practice Research in Infections Network meeting (GRIN) 2016, Oxford University, UK
“Exploring the relationship between primary care antibiotic prescribing for Oral ePoster conference urinary tract infections, Escherichia coli bacteraemia incidence and presentation antimicrobial resistance: an ecological study.” Lishman H, Costelloe C, Hopkins S, Johnson AP, Hope R, Guy R, Muller-Pebody B, Aylin P. European Congress on Clinical Microbiology and Infectious Diseases (ECCMID) 2016, Amsterdam, NL
“Exploring the relationship between primary care antibiotic prescribing for Poster urinary tract infections, Escherichia coli bacteraemia incidence and antimicrobial resistance: an ecological study.” Lishman H, Costelloe C, Hopkins S, Johnson AP, Hope R, Guy R, Muller-Pebody B, Aylin P. Annual Public Health England Applied Epidemiology Conference 2016, Warwick, UK
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83. Williams T, van Staa T, Puri S, Eaton S. Recent advances in the utility and use of the General Practice Research Database as an example of a UK Primary Care Data resource. Therapeutic Advances in Drug Safety. 2012;3(2):89-99. 84. Llor C, Rabanaque G, López A, Cots JM. The adherence of GPs to guidelines for the diagnosis and treatment of lower urinary tract infections in women is poor. Family Practice. 2011;28(3):294-9. 85. Kahan E, Kahan NR, Chinitz DP. Urinary tract infection in women—physician's preferences for treatment and adherence to guidelines: a national drug utilization study in a managed care setting. European Journal of Clinical Pharmacology. 2003;59(8):663-8. 86. Little P, Stuart B, Smith S, Thompson MJ, Knox K, van den Bruel A, et al. Antibiotic prescription strategies and adverse outcome for uncomplicated lower respiratory tract infections: prospective cough complication cohort (3C) study. BMJ. 2017;357:j2148. 87. Department for Communities and Local Government. English indices of deprivation 2010. 2011 [Available from: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2010. 88. Crellin E, Mansfield KE, Leyrat C, Nitsch D, Douglas IJ, Root A, et al. Trimethoprim use for urinary tract infection and risk of adverse outcomes in older patients: cohort study. BMJ. 2018;360. 89. Dolk FCK, Pouwels KB, Smith DRM, Robotham JV, Smieszek T. Antibiotics in primary care in England: which antibiotics are prescribed and for which conditions? Journal of Antimicrobial Chemotherapy. 2018;73(suppl_2):ii2-ii10. 90. Hooton TM. Recurrent urinary tract infection in women. International Journal of Antimicrobial Agents. 2001;17(4):259-68. 91. Bardsley A. Assessment, management and prevention of urinary tract infections in men. Nursing Standard. 2018. 92. McDonald HI, Nitsch D, Millett ER, Sinclair A, Thomas SL. New estimates of the burden of acute community-acquired infections among older people with diabetes mellitus: a retrospective cohort study using linked electronic health records. Diabetic medicine : a journal of the British Diabetic Association. 2014;31(5):606-14. 93. Foxman B. Epidemiology of urinary tract infections: incidence, morbidity, and economic costs. Am J Med. 2002;113 Suppl 1A:5s-13s. 94. Watson J, Nicholson BD, Hamilton W, Price S. Identifying clinical features in primary care electronic health record studies: methods for codelist development. BMJ Open. 2017;7(11). 95. Schmiemann G, Kniehl E, Gebhardt K, Matejczyk MM, Hummers-Pradier E. The Diagnosis of Urinary Tract Infection: A Systematic Review. Deutsches Ärzteblatt International. 2010;107(21):361-7. 96. Public Health England. English surveillance programme for antimicrobial utilisation and resistance (ESPAUR): Report 2016. 2016. 97. Grover ML, Bracamonte JD, Kanodia AK, Bryan MJ, Donahue SP, Warner A-M, et al. Assessing Adherence to Evidence-Based Guidelines for the Diagnosis and Management of Uncomplicated Urinary Tract Infection. Mayo Clinic Proceedings. 2007;82(2):181-5. 98. England PH. Annual epidemiological commentary: MRSA, MSSA and E. coli bacteraemia and C. difficile infection data, up to and including financial year April 2014 to March 2015. 2015. 99. Public Health England. English surveillance programme for antimicrobial utilisation and resistance (ESPAUR) 2010 to 2014: report 2015. 2015 [Available from: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/477962/ESPAUR_Rep ort_2015.pdf. 100. Cohen J, Vincent J-L, Adhikari NKJ, Machado FR, Angus DC, Calandra T, et al. Sepsis: a roadmap for future research. The Lancet Infectious Diseases. 2015;15(5):581-614. 101. Deeny SR, van Kleef E, Bou-Antoun S, Hope RJ, Robotham JV. Seasonal changes in the incidence of Escherichia coli bloodstream infection: variation with region and place of onset. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases. 2015;21(10):924-9. 102. Millett ERC, De Stavola BL, Quint JK, Smeeth L, Thomas SL. Risk factors for hospital admission in the 28 days following a community-acquired pneumonia diagnosis in older adults, and their contribution to increasing hospitalisation rates over time: a cohort study. BMJ Open. 2015;5(12). 103. Hosmer D LS. Applied Logisitic Regression. 1st ed ed. New York: John Wiley & Sons; 1989. 104. Betty R. Kirkwood JACS. Essential Medical Statistics 2nd ed. Hoboken, NJ: Wiley-Blackwell; 2003. 105. Mario Cleves WG, Yulia Marchenko. An Introduction to Survival Analysis Using Stata. Revised 3rd ed. Texas: Stata Press; 2016.
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106. Ahmed H, Farewell D, Jones HM, Francis NA, Paranjothy S, Butler CC. Antibiotic prophylaxis and clinical outcomes among older adults with recurrent urinary tract infection: cohort study. Age and Ageing. 2018:afy146-afy. 107. Sun G-W, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. Journal of Clinical Epidemiology. 1996;49(8):907-16. 108. Smith DRM, Dolk FCK, Pouwels KB, Christie M, Robotham JV, Smieszek T. Defining the appropriateness and inappropriateness of antibiotic prescribing in primary care. Journal of Antimicrobial Chemotherapy. 2018;73(suppl_2):ii11-ii8. 109. Pouwels KB, Dolk FCK, Smith DRM, Robotham JV, Smieszek T. Actual versus ‘ideal’ antibiotic prescribing for common conditions in English primary care. Journal of Antimicrobial Chemotherapy. 2018;73(suppl_2):19-26. 110. Ahmed H, Farewell D, Francis NA, Paranjothy S, Butler CC. Risk of adverse outcomes following urinary tract infection in older people with renal impairment: Retrospective cohort study using linked health record data. PLOS Medicine. 2018;15(9):e1002652. 111. Lee H, Lee YS, Jeong R, Kim YJ, Ahn S. Predictive factors of bacteremia in patients with febrile urinary tract infection: an experience at a tertiary care center. Infection. 2014;42(4):669-74. 112. Llewelyn MJ, Fitzpatrick JM, Darwin E, SarahTonkin-Crine, Gorton C, Paul J, et al. The antibiotic course has had its day. BMJ. 2017;358. 113. Michael M, Hodson EM, Craig JC, Martin S, Moyer VA. Short compared with standard duration of antibiotic treatment for urinary tract infection: a systematic review of randomised controlled trials. Archives of Disease in Childhood. 2002;87(2):118-23. 114. Hanretty AM, Gallagher JC. Shortened Courses of Antibiotics for Bacterial Infections: A Systematic Review of Randomized Controlled Trials. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2018;38(6):674-87. 115. Spellberg B. The New Antibiotic Mantra-"Shorter Is Better". JAMA Intern Med. 2016;176(9):1254-5. 116. Quint JK, Mullerova H, DiSantostefano RL, Forbes H, Eaton S, Hurst JR, et al. Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD). BMJ Open. 2014;4(7):e005540. 117. Daneman N, Campitelli MA, Giannakeas V, Morris AM, Bell CM, Maxwell CJ, et al. Influences on the start, selection and duration of treatment with antibiotics in long-term care facilities. Canadian Medical Association Journal. 2017;189(25):E851-E60. 118. Milo G, Katchman E, Paul M, Christiaens T, Baerheim A, Leibovici L. Duration of antibacterial treatment for uncomplicated urinary tract infection in women. Cochrane Database of Systematic Reviews. 2005(2). 119. Onakpoya IJ, Walker AS, Tan PS, Spencer EA, Gbinigie OA, Cook J, et al. Overview of systematic reviews assessing the evidence for shorter versus longer duration antibiotic treatment for bacterial infections in secondary care. PLOS ONE. 2018;13(3):e0194858. 120. NHS England. Quality Premium: 2015/16 guidance for CCGs 2015 [Available from: https://www.england.nhs.uk/resources/resources-for-ccgs/ccg-out-tool/ccg-ois/qual-prem/. 121. Lishman H, Aylin P, Alividza V, Castro-Sanchez E, Chatterjee A, Mariano V, et al. Investigating the burden of antibiotic resistance in ethnic minority groups in high-income countries: protocol for a systematic review and meta-analysis. Systematic Reviews. 2017;6:251. 122. Johnson AP, Muller-Pebody B, Budd E, Ashiru-Oredope D, Ladenheim D, Hain D, et al. Improving feedback of surveillance data on antimicrobial consumption, resistance and stewardship in England: putting the data at your Fingertips. Journal of Antimicrobial Chemotherapy. 2017;72(4):953-6. 123. NHS England. Antibiotic quality premium monitoring dashboard 2016 [Available from: https://www.england.nhs.uk/ccg-out-tool/anti-dash/. 124. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ. 2008;337. 125. Costelloe C, Metcalfe C, Lovering A, Mant D, Hay AD. Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis2010 2010- 05-18 23:05:48.
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APPENDICES
Table of contents
Appendix A – Specific Therapeutic Group Age-Sex weightings Related Prescribing Units (STAR-PU)…...... 201
Appendix B – IJAA publication of work conducted in Chapter 2…………………………………………………………..202
Appendix C – ISAC approved ethics application for work conducted in Chapters 3-5 using CPRD data……………………………………………………………………………………………………………………………………………211
Appendix D – Read code lists for diagnoses in primary care, product code lists for immunocompromising medications and ICD-10 code list for BSI in secondary care…………………………….227
Appendix E – Data cleaning processes for Therapy, Test and Consultation/Staff/Patient/Practice Files……………………………………………………………………………………………………………………………………………………..276
Appendix F – Scaled Schoenfeld residuals plots for testing the proportionality assumption for the survival analyses in Chapter 4………………………………………………………………………………………………….280
Appendix G – Kaplan-Meier survival estimates by day since UTI diagnosis in non-recurrent UTI patients…………………………………………………………………………………………………………………………………………282
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Appendix A - Specific Therapeutic Group Age-Sex weightings Related Prescribing Units (STAR-PU)
ITEM Based ITEM Based
BNF 5.1 (sub-set) BNF 5.1 (sub-set)
Therapeutic area: Oral Antibacterials Therapeutic area: Oral Antibacterials Age Band Male Female Age Band Male Female
0-4 0.8 0.8 0-4 0.8 0.7
5-14 0.3 0.4 5-14 0.3 0.4
15-24 0.3 0.6 15-24 0.4 0.6
25-34 0.2 0.6 25-34 0.3 0.6
35-44 0.3 0.6 35-44 0.3 0.6
45-54 0.3 0.6 45-54 0.3 0.6
55-64 0.4 0.7 55-64 0.4 0.7
65-74 0.7 1.0 65-74 0.6 0.9
75+ 1.0 1.3 75+ 0.9 1.1
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Appendix B – IJAA publication of work conducted in Chapter 2
202
203
204
205
206
207
208
209
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Appendix C - ISAC approved ethics application for work conducted in Chapters 3-5 using CPRD data
ISAC use only: IMPORTANT Protocol Number ...... If you have any queries, please contact ISAC Secretariat: Date submitted ...... [email protected]
1. Study Title
Investigating the effect of suboptimal antimicrobial prescribing for patients with a urinary tract infection or community- acquired pneumonia at the GP practice on hospital admission or GP presentation due to a bloodstream infection.
2. Principal Investigator (full name, job title, organisation & e-mail address for correspondence regarding this protocol)
Professor Paul Aylin (Professor of Epidemiology and Public Health, Imperial College London, [email protected]) Professor Alan Johnson (Head of the Department of Healthcare-associated Infections and Antimicrobial Resistance, Centre for Infectious Disease Surveillance and Control, Public Health England, [email protected])
3. Affiliation (full address)
AMR/HCAI Health Protection Research Unit, Faculty of Medicine, Infectious Diseases Department, 8th Floor, Commonwealth Building, Hammersmith Hospital, Imperial College London, W12 0HS
4. Protocol’s Author (if different from the principal investigator)
5. List of all investigators/collaborators (please list the names, affiliations and e-mail addresses* of all collaborators, other than the principal investigator)
Professor Alison Holmes (Professor of Infectious Diseases and Director of Infection, Prevention and Control for Imperial College Healthcare NHS Trust, [email protected]) Dr. Mariam Molokhia (Reader in Clinical Epidemiology & Primary Care, King’s College London, [email protected]) Dr. Myriam Gharbi (Research Associate, Imperial College London, [email protected]) Miss Hannah Lishman (Research Assistant, Imperial College London, [email protected]) Mr. Oliver Blandy (Research Assistant, Imperial College London, [email protected])
*Please note that your ISAC application form and protocol must be copied to all e-mail addresses listed above at the time of submission of your application to the ISAC mailbox. Failure to do so will result in delays in the processing of your application.
6. Type of Institution (please tick one box below)
Academia Research Service Provider Pharmaceutical Industry NHS Government Departments Others
7. Financial Sponsor of study
Pharmaceutical Industry (please specify) Academia(please specify) Government / NHS (please specify) NIHR None Other (please specify)
8. Data source (please tick one box below)
Sponsor has on-line access Purchase of ad hoc dataset Commissioned study Other (please specify)
9. Has this protocol been peer reviewed by another Committee?
Yes* No
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* Please state in your protocol the name of the reviewing Committee(s) and provide an outline of the review process and outcome.
10. Type of Study (please tick all the relevant boxes which apply)
Adverse Drug Reaction/Drug Safety Drug Use Disease Epidemiology Drug Effectiveness Pharmacoeconomic Other
11. This study is intended for:
Publication in peer reviewed journals Presentation at scientific conference Presentation at company/institutional meetings Other
12. Does this protocol also seek access to data held under the CPRD Data Linkage Scheme?
Yes No
13. If you are seeking access to data held under the CPRD Data Linkage Scheme*, please select the source(s) of linked data being requested.
Hospital Episode Statistics Cancer Registry Data** MINAP ONS Mortality Data Index of Multiple Deprivation/ Townsend Score Mother Baby Link Other: (please specify)
* As part of the ISAC review of linkages, the protocol may be shared - in confidence - with a representative of the requested linked data set(s) and summary details may be shared - in confidence - with the Confidentiality Advisory Group of the Health Research Authority.
**Please note that applicants seeking access to cancer registry data must provide consent for publication of their study title and study institution on the UK Cancer Registry website. Please contact the CPRD Research Team on +44 (20) 3080 6383 or email [email protected] to discuss this requirement further.
14. If you are seeking access to data held under the CPRD Data Linkage Scheme, have you already discussed your request with a member of the Research team?
Yes No*
*Please contact the CPRD Research Team on +44 (20) 3080 6383 or email [email protected] to discuss your requirements before submitting your application.
Please list below the name of the person/s at the CPRD with whom you have discussed your request.
Helen Strongman, Research Scientist, MHRA/Clinical Practice Research Datalink
15. If you are seeking access to data held under the CPRD Data Linkage Scheme, please provide the following information:
The number of linked datasets requested: One dataset with CPRD, HES and ONS linked data.
A synopsis of the purpose(s) for which the linkages are required:
We are requesting for HES and ONS to be linked to primary care data to allow us to track patients with UTIs and community-acquired pneumonia in the community and follow them up for their subsequent admissions to hospital or potential death (should these outcomes occur).
Is linkage to a local dataset with <1 million patients being requested?
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Yes* No
* If yes, please provide further details:
16. If you have requested linked data sets, please indicate whether the Principal Investigator or any of the collaborators listed in response to question 5 above, have access to any of the linked datasets in a patient identifiable form, or associated with a patient index.
Yes* No
* If yes, please provide further details: Paul Aylin has access to identifiable HES data, but the extract requested above will be kept on a completely separate system, and there will be no intention to link these datasets.
17. Does this protocol involve requesting any additional information from GPs?
Yes* No
* Please indicate what will be required: Completion of questionnaires by the GPy Yes No Provision of anonymised records (e.g. hospital discharge summaries) Yes No Other (please describe) y Any questionnaire for completion by GPs or other health care professional must be approved by ISAC before circulation for completion. 18. Does this protocol describe a purely observational study using CPRD data (this may include the review of anonymised free text)?
Yes* No**
* Yes: If you will be using data obtained from the CPRD Group, this study does not require separate ethics approval from an NHS Research Ethics Committee. ** No: You may need to seek separate ethics approval from an NHS Research Ethics Committee for this study. The ISAC will provide advice on whether this may be needed.
19. Does this study involve linking to patient identifiable data from other sources?
Yes No
20. Does this study require contact with patients in order for them to complete a questionnaire?
Yes No
N.B. Any questionnaire for completion by patients must be approved by ISAC before circulation for completion. 21. Does this study require contact with patients in order to collect a sample?
Yes* No
* Please state what will be collected
22. Experience/expertise available
Please complete the following questions to indicate the experience/expertise available within the team of researchers actively involved in the proposed research, including analysis of data and interpretation of results Previous GPRD/CPRD Studies Publications using GPRD/CPRD data
None 1-3 > 3
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Yes No Is statistical expertise available within the research team? If yes, please outline level of experience
Professor Aylin and members of his team have extensive experience (10+ years) in producing quantitative epidemiological publications related to primary and secondary care outcomes.
Is experience of handling large data sets (>1 million records) available within the research team? If yes, please outline level of experience
Yes, Professor Aylin’s team is used to handling GPRD data. Tsang C, Bottle A, Majeed A, Aylin P. Cancer diagnosed by emergency admission in England: an observational study using the general practice research database, BMC Health Services Research, 2013;13:308-313
Mariam Molokhia has extensive GPRD /large EHR database experience and several publications in pharmaco- epidemiological studies.
Is UK primary care experience available within the research team?
If yes, please outline level of experience
Mariam Molokhia is a senior primary care researcher and practicing GP with extensive experience in pharmaco- epidemiological studies. Professor Aylin is a consultant in public health. Professor Johnson is a member of the Steering Group of the PHE Primary Care Unit.
23. References relating to your study
Please list up to 3 references (most relevant) relating to your proposed study.
1. Public Health England. Managing common infections: guidance for primary care 2014. Available from: https://www.gov.uk/government/publications/managing-common-infections-guidance-for- primary-care. 2. Costelloe C, Metcalfe C, Lovering A, Mant D, Hay AD. Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis. 2010;23:05:48.
3. Wilson J, Elgohari S, Livermore DM, Cookson B, Johnson A, Lamagni T, et al. Trends among pathogens reported as causing bacteraemia in England, 2004–2008. Clinical Microbiology and Infection. 2011;17(3):451-8.
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PROTOCOL CONTENT CHECKLIST In order to help ensure that protocols submitted for review contain adequate information for protocol evaluation, ISAC have produced instructions on the content of protocols for research using CPRD data. These instructions are available on the CPRD website (www.cprd.com/ISAC). All protocols using CPRD data which are submitted for review by ISAC must contain information on the areas detailed in the instructions. IF you do not feel that a specific area required by ISAC is relevant for your protocol, you will need to justify this decision to ISAC.
Applicants must complete the checklist below to confirm that the protocol being submitted includes all the areas required by ISAC, or to provide justification where a required area is not considered to be relevant for a specific protocol. Protocols will not be circulated to ISAC for review until the checklist has been completed by the applicant.
Please note, your protocol will be returned to you if you do not complete this checklist, or if you answer ‘no’ and fail to include justification for the omission of any required area.
Included in protocol? Required area Yes No If no, reason for omission Lay Summary (max.200 words) Background Objective, specific aims and rationale Study Type Descriptive Not the study type of interest. Hypothesis Generating Hypothesis Testing Study Design Sample size/power calculation (Please provide justification of sample size in the protocol) Study population (including estimate of expected number of relevant patients in the CPRD) Selection of comparison group(s) or controls Exposures, outcomes and covariates Exposures are clearly described Outcomes are clearly described Use of linked data
(if applicable) Data/ Statistical Analysis Plan There is plan for addressing confounding There is a plan for addressing missing data Patient/ user group involvement † Limitations of the study design, data sources and analytic methods Plans for disseminating and communicating study results
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† It is expected that many studies will benefit from the involvement of patient or user groups in their planning and refinement, and/or in the interpretation of the results and plans for further work. This is particularly, but not exclusively true of studies with interests in the impact on quality of life. Please indicate whether or not you intend to engage patients in any of the ways mentioned above.
Voluntary registration of ISAC approved studies: Epidemiological studies are increasingly being included in registries of research around the world, including those primarily set up for clinical trials. To increase awareness amongst researchers of ongoing research, ISAC encourages voluntary registration of epidemiological research conducted using MHRA databases. This will not replace information on ISAC approved protocols that may be published in its summary minutes or annual report. It is for the applicant to determine the most appropriate registry for their study. Please inform the ISAC secretariat that you have registered a protocol and provide the location.
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Investigating the effect of suboptimal antimicrobial prescribing for patients with a UTI / community- acquired pneumonia at the GP practice on hospital admission or GP presentation due to a bloodstream infection
1. Lay summary
If antibiotic prescribing guidelines are not followed in GP practices, patients presenting with an infection can receive an incorrect type, dosage or duration of antibiotic which could lead to antibiotic resistance. Antibiotic resistant bacterial infections are more difficult to treat and, if treatment fails, can lead to infections of the blood which put the patient at a high risk of severe illness and death and result in a greater economic burden for the healthcare system. Two types of infection that can lead to infections of the blood are urinary tract infections (UTI) and respiratory tract infections (RTI) such as pneumonia acquired in the community. The purpose of this study is to use routinely collected data from a sample of NHS GP practices and hospitals across England to investigate the effect of incorrect or inappropriate antibiotic prescribing for patients diagnosed with a urinary tract infection or community-acquired pneumonia on subsequent complications such as hospital admission or GP presentation due to an infection of the blood. Findings from this study could provide evidence of incorrect antibiotic prescribing leading to increased numbers of cases of bloodstream infections which could help to shed light on the importance of stringent antibiotic prescribing guidelines at GP practices within the community.
2. Background
Optimising the use of antibiotics is becoming an increasingly high priority as we see rising rates of antimicrobial resistance both within the UK and worldwide due to the misuse of antibiotics. Studies have shown that in Europe up to one third of antibiotic prescriptions are not compliant with evidence-based guidelines (79). For this reason, Antimicrobial Stewardship Programmes (ASPs) (which are a group of measures that can be adopted to promote the correct use of antimicrobials) such as the Start Smart – Then Focus programme, have been put in place and are under constant review within the NHS to improve antibiotic prescribing and control antimicrobial resistance (3). One component of Antimicrobial Stewardship is to provide evidence-based standards for routine antimicrobial prescribing, with particular reference to the choice of appropriate agent, dose, route of administration and duration of therapy. In a recent meta-analysis investigating the effect of primary care antibiotic prescribing on rates of antimicrobial resistance, it was found that individuals who were prescribed an antibiotic for urinary or respiratory tract infections developed resistance to that antibiotic, with the likelihood of developing antibiotic resistance being highest within the first month immediately after treatment (125). Within this meta-analysis, the pooled odds ratio for developing resistance to the antibiotic used for treatment within the first two months was 2.5 (95% CI 2.1-2.9) for UTI and 2.4 (95% CI 1.4-3.4) for respiratory tract infections (125). Antimicrobial-resistant infections respond poorly or not at all if treated with the antibiotics to which they are resistant, making them more persistent and harder to treat. Moreover, failure to adequately treat UTI or pneumonia means there is an increased likelihood that the bacteria will go on to invade the bloodstream (6). Some of the most common etiological agents of bloodstream infections such as E. coli and Klebsiella species cause genitourinary and respiratory tract infections that may lead to bloodstream infections if not treated properly (7). To investigate this trend further, our study will investigate the effect of suboptimal antimicrobial prescribing in primary care for patients with a UTI or respiratory tract infection on complications such as hospital admission or GP presentation due to a bloodstream infection. Prescribing will be classified as “suboptimal” if it differs from predetermined treatment guidelines in regards to the type, dose and duration of the antibiotic course being given to the patient for their particular infection.
Primary hypothesis:
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1) Suboptimal antibiotic prescribing in patients presenting with a urinary tract infection or a respiratory tract infection at the GP practice may increase the incidence of bloodstream infections in England between 2004 and 2014.
Secondary hypotheses:
2) There are risk factors associated with developing bloodstream infections after a suboptimal antibiotic dose has been taken for urinary tract infections and respiratory tract infections. 3) Trends in suboptimal prescribing and incidence of bloodstream infections can be identified over time. Then, the cross-correlation between prescribing and time can be used to forecast incidence of bloodstream infections in subsequent months. 4) The proportions of patients receiving suboptimal antibiotic prescriptions, developing a bloodstream infection and being picked up at either the GP practice or the hospital with such an infection will reflect what has been found in the literature.
3. Objectives, specific aims and rationale
The primary objective of this study is to:
1) Determine the effect of suboptimal antibiotic prescribing in patients presenting with a UTI or respiratory tract infection at the GP practice on the incidence of bloodstream infections.
Secondary objectives of this study are:
2) To determine the risk factors associated with developing bloodstream infections after a suboptimal antibiotic dose has been taken; 3) To analyse time trends of both suboptimal antibiotic prescribing and the incidence of bloodstream infections over the study period and to forecast the incidence of bloodstream infections in the subsequent months using the cross-correlation between prescribing and time.; 4) To perform a descriptive analysis reporting on the proportions of patients receiving a suboptimal antibiotic prescription, developing a bloodstream infection and being picked up at the GP practice or the hospital with such an infection.
Rationale:
These specific aims will allow the ascertainment of the patients exposed to a suboptimal antibiotic prescription and the patients not exposed to a suboptimal antibiotic prescription in the study population as well as the patients who develop a bloodstream infection and present to the hospital or GP (cases) and the patients who do not (non-cases). These aims will also give an insight into what prescribing practises are most greatly contributing to suboptimal prescribing as well as where the most patients presenting with a bloodstream infection can be picked up in the NHS (hospital or GP practice).
4. Study type
A retrospective cohort study. This will be a hypothesis testing study as we will be testing the hypothesis that suboptimal antibiotic prescribing at the GP practice may lead to subsequent complications such as hospitalization or presentation to the GP due to a bloodstream infection.
5. Study design
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A population-based retrospective cohort study will be conducted. The study population will be all patients at a sentinel GP practice (those included in the CPRD) diagnosed with a UTI or respiratory tract infection (community-acquired pneumonia) within the study period of October 1st, 2004 – December 31st, 2014. A 10 year study period will not only give us a large sample size which will lead to greater statistical power for the study, it will also allow us to examine trends in antibiotic prescribing over time. The patients will be classified into one of three groups: 1) those that received a suboptimal antibiotic prescription (i.e. not included in national treatment guidelines), 2) those that had multiple GP visits for the same episode within the follow up period and received both suboptimal and optimal antibiotic prescriptions (i.e. changed treatment during the follow up) and 3) those that received an optimal antibiotic prescription for the particular infection that is included in national treatment guidelines. Group 1 (suboptimal) and Group 2 (changed treatment) will both be considered “exposed” and Group 3 (optimal) will be considered “unexposed”. These three groups will then be followed up for 60 days to monitor which patients develop a bloodstream infection, determined either by hospital admission or a second visit to the GP practice (cases) and which patients do not develop a bloodstream infection (comparison group). This study design has been chosen in order to be able to attribute all captured cases of bloodstream infection to the original patient population of individuals diagnosed with a UTI or respiratory tract infection and receiving an antibiotic. The study is retrospective because both the exposure data (sourced from the GP practice) and the outcome data (sourced from HES and the GP practice) are already available either within or linked to the CPRD database.
6. Sample size/power calculation
Based on data from the literature and pilot data from a previous study, we estimated that 1% of patients in the reference group (optimal treatment) would develop a community-acquired E. coli bacteraemia by 60 days after being diagnosed with a UTI. In order to detect in the exposed groups (suboptimal and changed treatment) twice the rate of bacteraemia per unit time as the reference group (optimal treatment) (Hazard Ratio=2), with a 2-sided α=0.05, a β=0.20, and a 5% loss to follow-up rate, a total of 10,350 patients (3450 per group) is the minimum required.
Based on data from the literature and pilot data from a previous study, we estimated that 0.5% of patients in the reference group (optimal treatment) would develop a community-acquired E. coli bacteraemia by 60 days after being diagnosed with a community-acquired pneumonia. In order to detect in the exposed group (suboptimal and changed treatment) twice the rate of bacteraemia per unit time as the reference group (Hazard Ratio=2), with a 2-sided α=0.05, a β=0.20, and a 5% loss to follow-up rate, a total of 20,568 patients (6862 per group) is the minimum required.
7. Data linkage This study will require the CPRD (Clinical Practice Research Database) data to be linked with HES (Hospital Episode Statistic) data and ONS (Office for National Statistics) data in order to capture all possible patient outcomes during the follow up period. While the CPRD data will provide the necessary demographic, diagnostic and prescribing data for the initial and secondary GP visits (if they occur), linkages with HES will allow the clinical outcome of hospital admission to be measured and any losses to follow up due to death of the patient will be ascertained from the ONS data. Each patient that enters the study from the CPRD dataset will be followed up and the linked datasets will be checked for these patients’ records. We will restrict the study population to patients with records in the CPRD-HES linked dataset. Patients will be included in the study from October 1st, 2004 up to the most recent available CPRD-HES linkage up to a cut-off point of December 31st, 2014. The most recent may either be the March 2014 update or the next update (which should be six months later, September 2014) if it becomes available before our analysis begins. One of the researchers on our team works at an organization with access to patient-identifiable HES data, however this data is held on a completely independent Local Area Network and staff members working on this network have to be physically present at their desks in the organization’s Research Office. The private network can only be accessed by direct connection. VPN's (Virtual Private Networks), Extranets, wireless networks and Remote Access are NOT 219
allowed. The researcher on our team has no access to these data, and only named researchers working under him have access to the identifiable data. By contrast, the CPRD data for this application will be held at a completely different site, on a different server and access will only be permitted to different named researchers, who have no access to the physical site where identifiable data are held, and where remote access to these data is impossible.
8. Study population
Inclusion criteria:
The study population will be all patients aged 18 and above who are registered with a GP in England, have been diagnosed with a UTI or community-acquired pneumonia and have subsequently been prescribed a course of antibiotics between October 1st, 2004 and December 31st, 2014. As the CPRD is a 6% national sample, the scope will be all GP practices in England that use the Vision data input system and have been classified as “up to standard” before the start of the study period, therefore having data of research quality included in the CPRD. Similarly, patient data will only be included if the patient has an acceptability flag of “acceptable” at the time of their recruitment and during their follow up period. For patients being admitted to hospital for a bloodstream infection, the ICD-10 diagnostic code upon admission must be one of the codes for an adult bloodstream infection, ascertained by the linked HES data. For patients presenting to the GP for a bloodstream infection, they cannot have been an inpatient in a hospital 30 days prior to the initial GP visit of an episode to ensure that the bloodstream infection is community-acquired as opposed to hospital-associated community- onset. If one patient has multiple diagnoses of UTI or community-acquired pneumonia (and antibiotic prescriptions) at their GP practice within the study period, only one UTI and/or community-acquired pneumonia diagnosis and antibiotic prescription per year will be included in the study. If a patient has more than one UTI diagnosis during the study period, the diagnoses will only be included and analysed if they are at least 365 days apart from each other (counted outside of the 60 day follow up period). The same logic would apply to multiple CAP diagnoses in the study period. This rule does not apply, however, if one patient has a mixture of UTI and CAP diagnoses - a patient can have a UTI diagnosis and a CAP diagnosis that are less than 365 days apart as they are different infections. This system will eliminate recurrent UTI or CAP infections from the analysis, thereby removing this potential source of confounding while taking into consideration potential repeat GP visits within the same episode of infection. Recruitment of patients will occur over the entire study period and each patient will be followed up for a complication due to a bloodstream infection for 60 days after diagnosis at a GP practice.
Exclusion criteria:
Patients who are immunocompromised or have an auto-immune disease, are an oncology or haematology patient or are HIV positive will be excluded from the study as they will be prescribed different antibiotic courses than patients who do not fall into these groups and their treatment would therefore differ from the optimal treatment guidelines which could subsequently introduce a possible source of confounding.
9. Selection of comparison group or controls
The comparison group selected will be all patients who are part of the described study population and have received an antibiotic prescription recommended for treatment in the PHE national treatment guidelines for their diagnosed UTI or community-acquired pneumonia. Whether or not the antibiotic prescription is optimal will be determined and guided by the Public Health England primary care treatment guidelines (67). Because both exposed (receiving a suboptimal antibiotic prescription) and unexposed (receiving an optimal antibiotic prescription) groups have been diagnosed with a UTI or community-acquired pneumonia at the GP practice and have received an antibiotic prescription, any differences in the incidence of bloodstream infections seen 220
between these two groups will most likely be due to differences in the optimal/suboptimal nature of the antibiotics that they have received after adjusting for the potential confounding factors.
10. Exposures, outcomes and covariates
The two possible exposures are suboptimal antibiotic prescribing for a urinary tract infection or suboptimal antibiotic prescribing for community-acquired pneumonia and will be defined as follows:
- Patients will receive either a suboptimal prescription (wrong drug or wrong dose or wrong duration), an optimal prescription (follows PHE guidelines) or will have received both optimal and suboptimal prescriptions (due to a change in treatment after repeat visits during the same episode). These three categories must be considered separately as we hypothesize that these patients will be at differing risks of developing a bloodstream infection.
1) Suboptimal antibiotic prescribing for a UTI will be defined as follows and has been written in accordance with the Public Health England guidelines (67):
- Incorrect antibiotic prescribed (not one of trimethoprim, nitrofurantoin, amoxicillin or pivmecillinam), taking into consideration drug allergies. - Incorrect daily dose for patient depending on age, sex, advanced vs. not advanced symptoms (advanced being defined as having a prostatitis or pyelonephritis diagnosis) and the underlying condition of pregnancy. - Incorrect duration of antibiotic course depending on the above patient characteristics.
2) Suboptimal antibiotic prescribing for community-acquired pneumonia will be defined as follows and has been written in accordance with the Public Health England guidelines (67):
- Incorrect antibiotic prescribed for community-acquired pneumonia (not one of amoxicillin and co-amoxiclav, doxycycline or clarithromycin), taking into consideration drug allergies. - Incorrect daily dose for patient depending on age and the underlying condition of COPD. - Incorrect duration of antibiotic course depending on the above patient characteristics.
Outcomes (with the main outcome of interest being the first):
1) Bloodstream infections diagnosed either at the hospital or at the GP practice within 60 days of follow-up from prescription (case): - Hospital admission with ICD-10 codes involving adult bloodstream infections (obtained from HES). - Diagnosis of a bloodstream infection at GP practice (either same practice as initial visit or different practice) (obtained from CPRD).
2) Death of the patient within 60 days of follow-up from prescription (case or loss to follow up): - Record of death of the patient within the 60 day follow-up period will determine either a case (if recorded cause of death is a bloodstream infection) or loss to follow-up due to death (if recorded cause of death is not a bloodstream infection) (obtained from ONS).
3) No bloodstream infection diagnosed at the hospital or GP practice within 60 days of follow-up from prescription (non-case).
Covariates will be as follows (after reviewing covariates from the literature that should be adjusted for and what variables are available in CPRD):
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- Age (yob), gender (gender), region (region), underlying diseases/comorbidities (such as diabetes, cardiovascular (i.e. stroke)/respiratory diseases (i.e. COPD, asthma), STIs), socio-economic factors (ses), number of routine medications (other than antibiotics), taking drugs that interact with antibiotic availability (oral contraceptives, coils etc.), antibiotic allergies (especially to penicillin), pregnancy status and type of immunisation/vaccination (immstype, stage and status), length between GP diagnosis and hospital admission, having had a test (mid-stream urine or dipstick for UTI, chest x-ray or sputum culture for CAP), smoking, alcohol, BMI. - All covariates without a given variable field name (written in bold) will be available from the “Additional Clinical Details” entity types or the “Test” entity types.
11. Data/Statistical analysis plan
The statistical analysis plan will be as follows:
1) A descriptive analysis to describe the frequencies and distribution of the outcome in the study participants according to the risk factors and covariates of interest. Analyses will include cross tabulations, tests of central tendency and logistic regression. All reported p-values will be considered as two-tailed, and a p-value <0.05 will be considered to be significant.
2) A Cox proportional hazards model to do time-to-event analyses to derive a hazard ratio and 95% Confidence Interval for the study outcomes in patients with an optimal antibiotic prescription, without an optimal antibiotic prescription and with both an optimal and a suboptimal antibiotic prescription.
- We will censor participants—ie. recording them as not having had a study outcome event—at the end of the 60 day follow-up period or at time of death.
- We will adjust the hazards ratios for the potential confounders listed above.
- We will be using study period proportions for this analysis.
3) Time series analysis methods will be applied over a 10 year-period to develop a forecasting model of the outcome i.e. the incidence rate of bloodstream infections using current or past values (lags) of the exposure i.e. suboptimal antibiotic prescribing or changed treatment (explanatory variables). We will identify the point in time where both series, outcome and exposure, are best aligned using cross-correlation analysis (Pearson test) at different time lags. We will then develop our forecasting model using Autoregressive Integrated Moving Average (ARIMA) models.
- We will be using monthly proportions if the time series analysis is performed on a month-by-month time scale, if that is not possible we will use yearly proportions if the time series analysis is performed on a year-by-year time scale. - Statistical analysis will be performed using STATA version 12 (STATA Corp, College Station, Texas). - The missing data will be treated using the multiple imputation method.
12. Patient/user group involvement
This project would be developed within a context of strong patient and public involvement, already established within the University, the Trust and the AMR/HCAI Health Protection Research Unit. In particular the strength of patient involvement within the Trust via its Patient Panels and “shadow foundation trust
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members” provides a pool of individuals willing to serve the patient interest in future initiatives, including becoming involved in health services research.
Two patients, Fran Husson and Tim Sims, who we are involved with the Centre for Infection Prevention and Management, have been consulted for being part of the project. These patients have been initially contacted via email at the first stage of the project. Since then, a regular discussion via phone call or email has been established between us. These individuals have experience contributing to research projects at steering/strategy group level. Ms. Husson is involved in a Collaboration for Leadership in Applied Health Research and Care (CLAHRC) project, which aims to help patients with their medication and associated risks, such as antibiotic resistance and Mr. Sims is a contributor to his local Healthwatch where he is much involved in Patient Lead Assessments of the Care Environment inspections.
The patient representatives, as active members of the research, have been involved in both the design and the definition of the research questions. They enabled us to identify and ask the right questions in the right way, ensuring research remains relevant to patients using the NHS services and the public. Their input has resulted in greater consideration of the patients’ perspective in the process of managing the project, has helped to clearly define the objectives of the research and has opened new directions of the work. The project summary in plain English was clear and comprehensive for the patient representatives.
13. Limitations of the study design, data sources and analytical methods
Limitations due to the retrospective study design include:
- Patients without a bloodstream infection may not have the same accurately recorded information or the same laboratory tests as blood tests may not be ordered for patients without clinical complications.
Limitations due to the data sources include:
- The CPRD is only a 6% sample of the national data which could make the study population less representative of the general population than a larger sample may have been. - The data sources are only routinely collected databases therefore the information is not recorded for a research purpose and there may be problems with data quality, missing data etc. - Some GPs may diagnose a respiratory tract infection as bacterial when in fact it is viral. We will receive test results from the CPRD but not all GPs will request for a lab test to be done which means that some infections for which an antibiotic was prescribed could have been viral. If a significant number of the patients prescribed an antibiotic did not have a lab test and had a viral respiratory tract infection, depending on the proportion of which received a suboptimal or an optimal antibiotic prescription (which group they fall into), this could skew the results as the patients who had a viral infection would be less likely to develop a bloodstream infection than the patients who had a bacterial respiratory tract infection. If the proportion of viral cases is distributed evenly across the exposed and unexposed groups then the misclassification bias will be non-differential and should therefore not skew the results.
14. Plans for disseminating and communicating study results
We will seek to communicate about our work and implications to a wide audience through a number of channels and in liaison with each partner organisation. The findings of our research will be disseminated to the research committee, the healthcare professionals, the patients and the public through written communication, events and conferences, networks and social media.
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Peer-reviewed publications: We aim to publish three main research papers in international peer reviewed journals with a high-impact factor. A few other publications - methodology, literature reviews, opinions, and collaborators’ papers will derivate from this research.
Conferences and events: We will communicate our findings through attendance at national, European and international conferences (International Conference on Antimicrobial Agents and Chemotherapy (ICAAC), European Conference of Clinical Microbiology and Infectious Diseases (ECCMID), Federation of Infection Societies conferences (FIS), International Society of Infectious Diseases (ISID) and events.
We will also hold events organised by Imperial College London and other partners, especially NHS hospitals for presenting our findings to the healthcare professionals, academics, patients and public (seminars, workshops, and awareness days). More specifically, we will present our findings at the annual Healthcare Associated Infections/Antimicrobial Resistance (HCAI/AMR) HPRU conference open to the public, patients, researchers, healthcare professionals, and Public Health England (PHE) staff.
Website: We will plan to regularly update the dedicated HPRU (HCAI/AMR Health Protection Research Unit) website, as well as the CIPM (Centre for Infection Prevention and Management) website www.imperial.ac.uk/cipm, with news or outputs.
Additionally, our work will be disseminated through newsletters to patients, researchers, healthcare professionals and key stakeholders (CIPM’s newsletter is produced 4 times a year); public engagement events (e.g. the Imperial Festival) and networks involving patients and the public including organisations such as Healthwatch and the NWL CLAHRC.
AMENDMENTS
Major amendments: 1) Request for linked patient-level deprivation data from IMD 2010. This will be used as a key covariate to adjust for in all multivariable models in the study as the socioeconomic data is not well recorded in CPRD is therefore not reliable enough to use.
2) Request for permission to link patient IDs to the new CPRD Pregnancy Register using the December 2017 release. We have discussed the use of this dataset with scientists at CPRD and have determined that it will greatly improve our ability to identify pregnant patients receiving antibiotic treatment for a UTI which is necessary for determining the adherence of their prescription to guidelines. The strategy we were previously using for identifying pregnant patients was most likely resulting in a large underestimate.
Minor amendments:
1) With the next linkage request (see above), we will be extending the study time period from ending in December 31st, 2014 to ending in September 30th, 2017 (or the most recent available upload). This will apply to the CPRD data and all linked datasets approved by ISAC.
2) In addition to the survival analysis using a Cox proportional hazards model outlined in Section 11, we will be running a clustered logistic regression model to determine the odds of developing a bloodstream infection (Y/N) after an adherent vs. non-adherent UTI prescription. In this way we can determine a) the risk of a BSI following a non-adherent UTI prescription and b) the median time to a
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BSI following treatment failure for a UTI. We no longer plan on conducting a time series analysis as we feel our question will be sufficiently answered using the above two models.
3) We plan on conducting a sub-analysis looking at only those who received a non-adherent prescription and examining the effect of differing durations of treatment on the risk of developing a BSI. This has been added as there has been much discussion recently about the importance of determining if longer or shorter courses of antibiotics put patients at greater risk of treatment failure.
4) We are revising the listed personnel on this study (not the PI) – Oliver Blandy will no longer be a co- author on these publications. Dr. Ceire Costelloe will now be a co-author based on significant contributions.
5) We have changed the wording of “suboptimal/optimal” to “non-adherent/adherent” as this is more representative of our question and less subjective than the previously used wording.
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ISAC EVALUATION OF PROTOCOLS FOR RESEARCH INVOLVING CPRD DATA
FEEDBACK TO APPLICANTS
CONFIDENTIAL by e-mail
PROTOCOL NO: 14_225RA2
PROTOCOL TITLE: Investigating the effect of suboptimal antimicrobial prescribing for patients with a urinary tract infection or community-acquired pneumonia at the GP practice on hospital admission or GP presentation due to a bloodstream infection. APPLICANT: Professor Paul Aylin (Professor of Epidemiology and Public Health, Imperial College London, [email protected]) Professor Alan Johnson (Head of the Department of Healthcare-associated Infections and Antimicrobial Resistance, Centre for Infectious Disease Surveillance and Control, Public Health England, [email protected]) APPROVED APPROVED WITH COMMENTS REVISION/ REJECTED (resubmission not required) RESUBMISSION
REQUESTED
INSTRUCTIONS: Protocols with an outcome of ‘Approved’ or ‘Approved with comments’ do not require resubmission to the ISAC.
REVIEWER COMMENTS:
APPLICANT FEEDBACK:
DATE OF ISAC FEEDBACK: 18/01/18
DATE OF APPLICANT FEEDBACK:
For protocols approved from 01 April 2014 onwards, applicants are required to include the ISAC protocol in their journal submission with a statement in the manuscript indicating that it had been approved by the ISAC (with the reference number) and made available to the journal reviewers. If the protocol was subject to any amendments, the last amended version should be the one submitted.
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Appendix D – Read code lists for diagnoses in primary care, product code lists for immunocompromising medications and ICD-10 code list for BSI in secondary care
Appendix D.1. UTI code list (Read codes) Readcode Read term K15..00 Cystitis K190z00 Urinary tract infection, site not specified NOS K190.00 Urinary tract infection, site not specified 1J4..00 Suspected UTI 1AG..00 Recurrent urinary tract infections K190311 Recurrent UTI K150.00 Acute cystitis K190.11 Recurrent urinary tract infection K190300 Recurrent urinary tract infection K190500 Urinary tract infection K15z.00 Cystitis NOS K190100 Pyuria, site not specified L166800 Urinary tract infection complicating pregnancy K190200 Post operative urinary tract infection L166.00 Genitourinary tract infections in pregnancy L166z11 UTI - urinary tract infection in pregnancy K190400 Chronic urinary tract infection L166600 Urinary tract infection following delivery L166.11 Cystitis of pregnancy L166z00 Genitourinary tract infection in pregnancy NOS L166300 Genitourinary tract infection in pregnancy - not delivered L166000 Genitourinary tract infection in pregnancy unspecified L166.11 Cystitis of pregnancy 8CMWE00 On urinary tract infection care pathway L166100 Genitourinary tract infection in pregnancy - delivered SP07Q00 Catheter-associated urinary tract infection SP07700 Infect+inflam react due pros dev,implt+graft in urinary syst Lyu6100 [X]Other genitourinary tract infections following delivery L166400 Genitourinary tract infection in pregnancy with p/n comp SP07Q11 CAUTI - catheter-associated urinary tract infection Lyu2400 [X]Other+unspcf genitourinary tract infection in pregnancy L09y400 Urinary tract infection following abortive pregnancy
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Appendix D.2. Pyelonephritis code list (Read codes) Readcode Read term K10y000 Pyelonephritis unspecified K101.00 Acute pyelonephritis K100600 Calculous pyelonephritis K104.00 Xanthogranulomatous pyelonephritis K101z00 Acute pyelonephritis NOS A160200 Tuberculous pyelonephritis K10y.00 Pyelonephritis and pyonephrosis unspecified K10yz00 Unspecified pyelonephritis NOS K101000 Acute pyelonephritis without medullary necrosis K10y300 Pyelonephritis in diseases EC K106.00 Candida pyelonephritis K101400 Emphysematous pyelonephritis K10..00 Infections of kidney K10..11 Renal infections K10z.00 Infection of kidney NOS L166500 Infections of kidney in pregnancy
Appendix D.3. Prostatitis code list (Read codes) Readcode Read term K21z.00 Prostatitis NOS K210.00 Acute prostatitis K21..11 Prostatitis and other inflammatory diseases of prostate A981200 Acute gonococcal prostatitis K214.00 Prostatitis in diseases EC K214z00 Prostatitis in diseases EC NOS
Appendix D.4. HIV-positive code list (Read codes) Readcode Read term 43C3.11 HIV positive 65QA.00 AIDS carrier 65VE.00 Notification of AIDS A788.00 Acquired immune deficiency syndrome A788.11 Human immunodeficiency virus infection A788200 HIV infection with persistent generalised lymphadenopathy A788300 Human immunodeficiency virus with constitutional disease A788400 Human immunodeficiency virus with neurological disease A788500 Human immunodeficiency virus with secondary infection A788600 Human immunodeficiency virus with secondary cancers A788U00 HIV disease result/haematological+immunologic abnorms,NEC A788V00 HIV disease resulting in multiple diseases CE
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A788W00 HIV disease resulting in unspecified malignant neoplasm A788X00 HIV disease resulting/unspcf infectious+parasitic disease A788y00 Human immunodeficiency virus with other clinical findings A789.00 Human immunodef virus resulting in other disease A789000 HIV disease resulting in mycobacterial infection A789100 HIV disease resulting in cytomegaloviral disease A789200 HIV disease resulting in candidiasis A789300 HIV disease resulting in Pneumocystis carinii pneumonia A789400 HIV disease resulting in multiple infections A789500 HIV disease resulting in Kaposi's sarcoma A789600 HIV disease resulting in Burkitt's lymphoma A789700 HIV dis resulting oth types of non-Hodgkin's lymphoma A789800 HIV disease resulting in multiple malignant neoplasms A789900 HIV disease resulting in lymphoid interstitial pneumonitis A789A00 HIV disease resulting in wasting syndrome A789X00 HIV dis reslt/oth mal neopl/lymph,h'matopoetc+reltd tissu AyuC000 [X]HIV disease resulting in other bacterial infections AyuC100 [X]HIV disease resulting in other viral infections AyuC200 [X]HIV disease resulting in other mycoses AyuC300 [X]HIV disease resulting in multiple infections AyuC400 [X]HIV disease resulting/other infectious+parasitic diseases AyuC500 [X]HIV disease resulting/unspcf infectious+parasitic disease AyuC600 [X]HIV disease resulting in other non-Hodgkin's lymphoma AyuC700 [X]HIV dis reslt/oth mal neopl/lymph,h'matopoetc+reltd tissu AyuC800 [X]HIV disease resulting in other malignant neoplasms AyuC900 [X]HIV disease resulting in unspecified malignant neoplasm AyuCA00 [X]HIV disease resulting in multiple diseases CE AyuCB00 [X]HIV disease result/haematological+immunologic abnorms,NEC AyuCC00 [X]HIV disease resulting in other specified conditions AyuCD00 [X]Unspecified human immunodeficiency virus [HIV] disease Eu02400 [X]Dementia in human immunodef virus [HIV] disease R109.00 [D]Laboratory evidence of human immunodefiency virus [HIV]
Appendix D.5. Cancer code list (Read codes) Readcode Read term 142..00 H/O: malignant neoplasm (*) 142..11 H/O: cancer 142..13 H/O: malignancy 142..15 H/O: neoplasm 1425000 H/O Malignant melanoma 14CB.00 H/O Upper GIT Neoplasm 14CC.00 H/O Lower GIT Neoplasm 1D18.00 Pain from metastases
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1O0..00 Cancer confirmed 4C54.00 Bone marrow: tumour cells 4D56.00 Pleural fluid: malignant cells 4E33.00 Sputum: malignant cells 4F32.00 Ascitic fluid: malignant cells 4M...00 Tumour staging 4M0..00 Gleason grading of prostate cancer 4M4..00 FIGO staging of gynaecological malignancy 4M5..00 TNM tumour staging 5135.00 Radiological tumour control 5136.00 X-ray metastasis control 5149.00 Radiotherapy-tumour palliation 5A12.00 Thyroid tumour/metast irradiat 5A15.00 Bone tumour/metast.irradiat. 7008500 Insertion of carmustine wafers in cerebral neoplasm 7B2C700 Intravesical install chemotherapeutic agent for malignancy 7G03K00 Excision malignant skin tumour 7L1b.00 Procurement drugs for chemotherapy for neoplasm in bands 1-5 7L1b000 Procurement drugs chemotherapy neoplasm regimens in band 1 7L1b100 Procurement drugs chemotherapy neoplasm regimens in band 2 7L1b200 Procurement drugs chemotherapy neoplasm regimens in band 3 7L1b300 Procurement drugs chemotherapy neoplasm regimens in band 4 7L1b400 Procurement drugs chemotherapy neoplasm regimens in band 5 7L1by00 OS procurement drugs chemotherapy for neoplasm in bands 1-5 7L1bz00 Procurement drugs chemotherapy for neoplasm in bands 1-5 NOS 7L1c.00 Procurement of drugs chemotherapy for neoplasm in bands 6-10 7L1c000 Procurement drugs chemotherapy neoplasm regimens in band 6 7L1c100 Procurement drugs chemotherapy neoplasm regimens in band 7 7L1c200 Procurement drugs chemotherapy neoplasm regimens in band 8 7L1c300 Procurement drugs chemotherapy neoplasm regimens in band 9 7L1c400 Procurement drugs chemotherapy neoplasm regimens in band 10 7L1cy00 OS procurement drugs chemotherapy for neoplasm in bands 6-10 7L1cz00 Procurement drugs chemotherapy for neoplasm bands 6-10 NOS 7L1d.00 Delivery of chemotherapy for neoplasm 7L1d300 Delivery subsequent element cycle chemotherapy for neoplasm 7L1dy00 Other specified delivery of chemotherapy for neoplasm 7L1dz00 Delivery of chemotherapy for neoplasm NOS 7L1e.00 Delivery of oral chemotherapy for neoplasm 7L1e000 Delivery of exclusively oral chemotherapy for neoplasm 7L1ey00 Other specified delivery of oral chemotherapy for neoplasm 7L1ez00 Delivery of oral chemotherapy for neoplasm NOS 7L1K300 Debulking of tumour of unspecified organ 8B3p.00 Administration of cancer treatment 8BAD000 Cancer chemotherapy
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8BAV.00 Cancer care review 8BC3.00 Cancer care plan given 8BC6.00 Cancer treatment started 8BCF.00 Cancer hospital treatment completed 8CL0.00 Cancer diagnosis discussed 8CL1.00 Cancer diagnosis discussed with significant other 8CL2.00 Cancer diagnosis discussed with patient 8CM0.00 Cancer care plan 8CP0.00 Cancer care plan discussed with patient 8CP1.00 Cancer care plan discussed with significant other 8CR8.00 Cancer shared care medication card 8HH8.00 Referred to cancer primary healthcare multidisciplinary team 9e00.00 GP out of hours service notified of cancer care plan 9h8..00 Exception reporting: cancer quality indicators 9h81.00 Excepted from cancer quality indicators: Patient unsuitable 9h82.00 Excepted from cancer quality indicators: Informed dissent 9N4S.00 DNA - Did not attend cancer clinic 9Nh1.00 Under care cancer primary healthcare multidisciplinary team 9NX0.00 Cancer primary healthcare multidisciplinary team 9NX1.00 Cancer supportive care worker 9Ok..00 Cancer monitoring administration 9Ok0.00 Cancer monitoring first letter 9Ok1.00 Cancer monitoring second letter 9Ok2.00 Cancer monitoring third letter 9Ok3.00 Date cancer diagnosis received in primary care 9Ok5.00 Cancer pain and symptom management 9Ok6.00 Cancer short term health assessment 9Ok7.00 Cancer rehabilitation and readaption 9Ok9.00 Cancer screening follow up 9OkA.00 Cancer monitoring verbal invitation 9OkB.00 Cancer monitoring telephone invitation 9OkC.00 Patient on regional cancer register A788600 Human immunodeficiency virus with secondary cancers A788W00 HIV disease resulting in unspecified malignant neoplasm A789800 HIV disease resulting in multiple malignant neoplasms AyuC800 [X]HIV disease resulting in other malignant neoplasms AyuC900 [X]HIV disease resulting in unspecified malignant neoplasm B....11 Cancers B0...00 Malignant neoplasm of lip, oral cavity and pharynx B00..00 Malignant neoplasm of lip B000.00 Malignant neoplasm of upper lip, vermilion border B000000 Malignant neoplasm of upper lip, external B000100 Malignant neoplasm of upper lip, lipstick area B000z00 Malignant neoplasm of upper lip, vermilion border NOS
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B001.00 Malignant neoplasm of lower lip, vermilion border B001000 Malignant neoplasm of lower lip, external B001100 Malignant neoplasm of lower lip, lipstick area B001z00 Malignant neoplasm of lower lip, vermilion border NOS B002.00 Malignant neoplasm of upper lip, inner aspect B002000 Malignant neoplasm of upper lip, buccal aspect B002100 Malignant neoplasm of upper lip, frenulum B002200 Malignant neoplasm of upper lip, mucosa B002300 Malignant neoplasm of upper lip, oral aspect B002z00 Malignant neoplasm of upper lip, inner aspect NOS B003.00 Malignant neoplasm of lower lip, inner aspect B003000 Malignant neoplasm of lower lip, buccal aspect B003100 Malignant neoplasm of lower lip, frenulum B003200 Malignant neoplasm of lower lip, mucosa B003300 Malignant neoplasm of lower lip, oral aspect B003z00 Malignant neoplasm of lower lip, inner aspect NOS B004.00 Malignant neoplasm of lip unspecified, inner aspect B004000 Malignant neoplasm of lip unspecified, buccal aspect B004100 Malignant neoplasm of lip unspecified, frenulum B004200 Malignant neoplasm of lip unspecified, mucosa B004300 Malignant neoplasm of lip, oral aspect B004z00 Malignant neoplasm of lip, inner aspect NOS B005.00 Malignant neoplasm of commissure of lip B006.00 Malignant neoplasm of overlapping lesion of lip B007.00 Malignant neoplasm of lip, unspecified B00y.00 Malignant neoplasm of other sites of lip B00z.00 Malignant neoplasm of vermilion border of lip unspecified B00z000 Malignant neoplasm of lip, unspecified, external B00z100 Malignant neoplasm of lip, unspecified, lipstick area B00zz00 Malignant neoplasm of lip, vermilion border NOS B01..00 Malignant neoplasm of tongue B010.00 Malignant neoplasm of base of tongue B010.11 Malignant neoplasm of posterior third of tongue B010000 Malignant neoplasm of base of tongue dorsal surface B010z00 Malignant neoplasm of fixed part of tongue NOS B011.00 Malignant neoplasm of dorsal surface of tongue B011000 Malignant neoplasm of anterior 2/3 of tongue dorsal surface B011100 Malignant neoplasm of midline of tongue B011z00 Malignant neoplasm of dorsum of tongue NOS B012.00 Malignant neoplasm of tongue, tip and lateral border B013.00 Malignant neoplasm of ventral surface of tongue B013000 Malignant neoplasm of anterior 2/3 of tongue ventral surface B013100 Malignant neoplasm of frenulum linguae B013z00 Malignant neoplasm of ventral tongue surface NOS
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B014.00 Malignant neoplasm of anterior 2/3 of tongue unspecified B015.00 Malignant neoplasm of tongue, junctional zone B016.00 Malignant neoplasm of lingual tonsil B017.00 Malignant overlapping lesion of tongue B01y.00 Malignant neoplasm of other sites of tongue B01z.00 Malignant neoplasm of tongue NOS B02..00 Malignant neoplasm of major salivary glands B020.00 Malignant neoplasm of parotid gland B021.00 Malignant neoplasm of submandibular gland B022.00 Malignant neoplasm of sublingual gland B023.00 Malignant neoplasm, overlapping lesion of major saliv gland B02y.00 Malignant neoplasm of other major salivary glands B02z.00 Malignant neoplasm of major salivary gland NOS B03..00 Malignant neoplasm of gum B030.00 Malignant neoplasm of upper gum B031.00 Malignant neoplasm of lower gum B03y.00 Malignant neoplasm of other sites of gum B03z.00 Malignant neoplasm of gum NOS B04..00 Malignant neoplasm of floor of mouth B040.00 Malignant neoplasm of anterior portion of floor of mouth B041.00 Malignant neoplasm of lateral portion of floor of mouth B042.00 Malignant neoplasm, overlapping lesion of floor of mouth B04y.00 Malignant neoplasm of other sites of floor of mouth B04z.00 Malignant neoplasm of floor of mouth NOS B05..00 Malignant neoplasm of other and unspecified parts of mouth B050.00 Malignant neoplasm of cheek mucosa B050.11 Malignant neoplasm of buccal mucosa B051.00 Malignant neoplasm of vestibule of mouth B051000 Malignant neoplasm of upper buccal sulcus B051100 Malignant neoplasm of lower buccal sulcus B051200 Malignant neoplasm of upper labial sulcus B051300 Malignant neoplasm of lower labial sulcus B051z00 Malignant neoplasm of vestibule of mouth NOS B052.00 Malignant neoplasm of hard palate B053.00 Malignant neoplasm of soft palate B054.00 Malignant neoplasm of uvula B055.00 Malignant neoplasm of palate unspecified B055000 Malignant neoplasm of junction of hard and soft palate B055100 Malignant neoplasm of roof of mouth B055z00 Malignant neoplasm of palate NOS B056.00 Malignant neoplasm of retromolar area B05y.00 Malignant neoplasm of other specified mouth parts B05z.00 Malignant neoplasm of mouth NOS B06..00 Malignant neoplasm of oropharynx
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B060.00 Malignant neoplasm of tonsil B060000 Malignant neoplasm of faucial tonsil B060100 Malignant neoplasm of palatine tonsil B060200 Malignant neoplasm of overlapping lesion of tonsil B060z00 Malignant neoplasm tonsil NOS B061.00 Malignant neoplasm of tonsillar fossa B062.00 Malignant neoplasm of tonsillar pillar B062000 Malignant neoplasm of faucial pillar B062100 Malignant neoplasm of glossopalatine fold B062200 Malignant neoplasm of palatoglossal arch B062300 Malignant neoplasm of palatopharyngeal arch B062z00 Malignant neoplasm of tonsillar fossa NOS B063.00 Malignant neoplasm of vallecula B064.00 Malignant neoplasm of anterior epiglottis B064000 Malignant neoplasm of epiglottis, free border B064100 Malignant neoplasm of glossoepiglottic fold B064z00 Malignant neoplasm of anterior epiglottis NOS B065.00 Malignant neoplasm of junctional region of epiglottis B066.00 Malignant neoplasm of lateral wall of oropharynx B067.00 Malignant neoplasm of posterior wall of oropharynx B06y.00 Malignant neoplasm of oropharynx, other specified sites B06y000 Malignant neoplasm of branchial cleft B06yz00 Malignant neoplasm of other specified site of oropharynx NOS B06z.00 Malignant neoplasm of oropharynx NOS B07..00 Malignant neoplasm of nasopharynx B070.00 Malignant neoplasm of roof of nasopharynx B071.00 Malignant neoplasm of posterior wall of nasopharynx B071000 Malignant neoplasm of adenoid B071100 Malignant neoplasm of pharyngeal tonsil B071z00 Malignant neoplasm of posterior wall of nasopharynx NOS B072.00 Malignant neoplasm of lateral wall of nasopharynx B072000 Malignant neoplasm of pharyngeal recess B072100 Malignant neoplasm of opening of auditory tube B072z00 Malignant neoplasm of lateral wall of nasopharynx NOS B073.00 Malignant neoplasm of anterior wall of nasopharynx B073000 Malignant neoplasm of floor of nasopharynx B073100 Malignant neoplasm of nasopharyngeal soft palate surface B073200 Malignant neoplasm posterior margin nasal septum and choanae B073z00 Malignant neoplasm of anterior wall of nasopharynx NOS B074.00 Malignant neoplasm, overlapping lesion of nasopharynx B07y.00 Malignant neoplasm of other specified site of nasopharynx B07z.00 Malignant neoplasm of nasopharynx NOS B08..00 Malignant neoplasm of hypopharynx B080.00 Malignant neoplasm of postcricoid region
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B081.00 Malignant neoplasm of pyriform sinus B082.00 Malignant neoplasm aryepiglottic fold, hypopharyngeal aspect B083.00 Malignant neoplasm of posterior pharynx B084.00 Malignant neoplasm, overlapping lesion of hypopharynx B08y.00 Malignant neoplasm of other specified hypopharyngeal site B08z.00 Malignant neoplasm of hypopharynx NOS B0z0.00 Malignant neoplasm of pharynx unspecified B0z1.00 Malignant neoplasm of Waldeyer's ring B0z2.00 Malignant neoplasm of laryngopharynx B0zy.00 Malignant neoplasm of other sites lip, oral cavity, pharynx B0zz.00 Malignant neoplasm of lip, oral cavity and pharynx NOS B1...00 Malignant neoplasm of digestive organs and peritoneum B10..00 Malignant neoplasm of oesophagus B100.00 Malignant neoplasm of cervical oesophagus B101.00 Malignant neoplasm of thoracic oesophagus B102.00 Malignant neoplasm of abdominal oesophagus B103.00 Malignant neoplasm of upper third of oesophagus B104.00 Malignant neoplasm of middle third of oesophagus B105.00 Malignant neoplasm of lower third of oesophagus B106.00 Malignant neoplasm, overlapping lesion of oesophagus B10y.00 Malignant neoplasm of other specified part of oesophagus B10z.00 Malignant neoplasm of oesophagus NOS B10z.11 Oesophageal cancer B11..00 Malignant neoplasm of stomach B11..11 Gastric neoplasm B110.00 Malignant neoplasm of cardia of stomach B110000 Malignant neoplasm of cardiac orifice of stomach B110100 Malignant neoplasm of cardio-oesophageal junction of stomach B110111 Malignant neoplasm of gastro-oesophageal junction B110z00 Malignant neoplasm of cardia of stomach NOS B111.00 Malignant neoplasm of pylorus of stomach B111000 Malignant neoplasm of prepylorus of stomach B111100 Malignant neoplasm of pyloric canal of stomach B111z00 Malignant neoplasm of pylorus of stomach NOS B112.00 Malignant neoplasm of pyloric antrum of stomach B113.00 Malignant neoplasm of fundus of stomach B114.00 Malignant neoplasm of body of stomach B115.00 Malignant neoplasm of lesser curve of stomach unspecified B116.00 Malignant neoplasm of greater curve of stomach unspecified B117.00 Malignant neoplasm, overlapping lesion of stomach B11y.00 Malignant neoplasm of other specified site of stomach B11y000 Malignant neoplasm of anterior wall of stomach NEC B11y100 Malignant neoplasm of posterior wall of stomach NEC B11yz00 Malignant neoplasm of other specified site of stomach NOS
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B11z.00 Malignant neoplasm of stomach NOS B12..00 Malignant neoplasm of small intestine and duodenum B120.00 Malignant neoplasm of duodenum B121.00 Malignant neoplasm of jejunum B122.00 Malignant neoplasm of ileum B123.00 Malignant neoplasm of Meckel's diverticulum B124.00 Malignant neoplasm, overlapping lesion of small intestine B12y.00 Malignant neoplasm of other specified site small intestine B12z.00 Malignant neoplasm of small intestine NOS B13..00 Malignant neoplasm of colon B130.00 Malignant neoplasm of hepatic flexure of colon B131.00 Malignant neoplasm of transverse colon B132.00 Malignant neoplasm of descending colon B133.00 Malignant neoplasm of sigmoid colon B134.00 Malignant neoplasm of caecum B135.00 Malignant neoplasm of appendix B136.00 Malignant neoplasm of ascending colon B137.00 Malignant neoplasm of splenic flexure of colon B138.00 Malignant neoplasm, overlapping lesion of colon B13y.00 Malignant neoplasm of other specified sites of colon B13z.00 Malignant neoplasm of colon NOS B13z.11 Colonic cancer B14..00 Malignant neoplasm of rectum, rectosigmoid junction and anus B140.00 Malignant neoplasm of rectosigmoid junction B141.00 Malignant neoplasm of rectum B142.00 Malignant neoplasm of anal canal B142000 Malignant neoplasm of cloacogenic zone B143.00 Malignant neoplasm of anus unspecified B14z.00 Malignant neoplasm rectum,rectosigmoid junction and anus NOS B15..00 Malignant neoplasm of liver and intrahepatic bile ducts B150.00 Primary malignant neoplasm of liver B150z00 Primary malignant neoplasm of liver NOS B151.00 Malignant neoplasm of intrahepatic bile ducts B151000 Malignant neoplasm of interlobular bile ducts B151100 Malignant neoplasm of interlobular biliary canals B151200 Malignant neoplasm of intrahepatic biliary passages B151300 Malignant neoplasm of intrahepatic canaliculi B151400 Malignant neoplasm of intrahepatic gall duct B151z00 Malignant neoplasm of intrahepatic bile ducts NOS B152.00 Malignant neoplasm of liver unspecified B153.00 Secondary malignant neoplasm of liver B15z.00 Malignant neoplasm of liver and intrahepatic bile ducts NOS B16..00 Malignant neoplasm gallbladder and extrahepatic bile ducts B160.00 Malignant neoplasm of gallbladder
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B161.00 Malignant neoplasm of extrahepatic bile ducts B161000 Malignant neoplasm of cystic duct B161100 Malignant neoplasm of hepatic duct B161200 Malignant neoplasm of common bile duct B161300 Malignant neoplasm of sphincter of Oddi B161z00 Malignant neoplasm of extrahepatic bile ducts NOS B162.00 Malignant neoplasm of ampulla of Vater B163.00 Malignant neoplasm, overlapping lesion of biliary tract B16y.00 Malignant neoplasm other gallbladder/extrahepatic bile duct B16z.00 Malignant neoplasm gallbladder/extrahepatic bile ducts NOS B17..00 Malignant neoplasm of pancreas B170.00 Malignant neoplasm of head of pancreas B171.00 Malignant neoplasm of body of pancreas B172.00 Malignant neoplasm of tail of pancreas B173.00 Malignant neoplasm of pancreatic duct B174.00 Malignant neoplasm of Islets of Langerhans B175.00 Malignant neoplasm, overlapping lesion of pancreas B17y.00 Malignant neoplasm of other specified sites of pancreas B17y000 Malignant neoplasm of ectopic pancreatic tissue B17yz00 Malignant neoplasm of specified site of pancreas NOS B17z.00 Malignant neoplasm of pancreas NOS B18..00 Malignant neoplasm of retroperitoneum and peritoneum B180.00 Malignant neoplasm of retroperitoneum B180000 Malignant neoplasm of periadrenal tissue B180100 Malignant neoplasm of perinephric tissue B180200 Malignant neoplasm of retrocaecal tissue B180z00 Malignant neoplasm of retroperitoneum NOS B18y.00 Malignant neoplasm of specified parts of peritoneum B18y000 Malignant neoplasm of mesocolon B18y100 Malignant neoplasm of mesocaecum B18y200 Malignant neoplasm of mesorectum B18y300 Malignant neoplasm of omentum B18y400 Malignant neoplasm of parietal peritoneum B18y500 Malignant neoplasm of pelvic peritoneum B18y600 Malignant neoplasm of the pouch of Douglas B18y700 Malignant neoplasm of mesentery B18yz00 Malignant neoplasm of specified parts of peritoneum NOS B18z.00 Malignant neoplasm of retroperitoneum and peritoneum NOS B1z0.00 Malignant neoplasm of intestinal tract, part unspecified B1z0.11 Cancer of bowel B1z1.00 Malignant neoplasm of spleen NEC B1z1z00 Malignant neoplasm of spleen NOS B1z2.00 Malignant neoplasm, overlapping lesion of digestive system B1zy.00 Malignant neoplasm other spec digestive tract and peritoneum
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B1zz.00 Malignant neoplasm of digestive tract and peritoneum NOS B200.00 Malignant neoplasm of nasal cavities B200000 Malignant neoplasm of cartilage of nose B200100 Malignant neoplasm of nasal conchae B200200 Malignant neoplasm of septum of nose B200300 Malignant neoplasm of vestibule of nose B200z00 Malignant neoplasm of nasal cavities NOS B201000 Malignant neoplasm of auditory (Eustachian) tube B201100 Malignant neoplasm of tympanic cavity B201200 Malignant neoplasm of tympanic antrum B201300 Malignant neoplasm of mastoid air cells B202.00 Malignant neoplasm of maxillary sinus B203.00 Malignant neoplasm of ethmoid sinus B204.00 Malignant neoplasm of frontal sinus B205.00 Malignant neoplasm of sphenoidal sinus B206.00 Malignant neoplasm, overlapping lesion of accessory sinuses B20z.00 Malignant neoplasm of accessory sinus NOS B21..00 Malignant neoplasm of larynx B210.00 Malignant neoplasm of glottis B211.00 Malignant neoplasm of supraglottis B212.00 Malignant neoplasm of subglottis B213.00 Malignant neoplasm of laryngeal cartilage B213000 Malignant neoplasm of arytenoid cartilage B213100 Malignant neoplasm of cricoid cartilage B213200 Malignant neoplasm of cuneiform cartilage B213300 Malignant neoplasm of thyroid cartilage B213z00 Malignant neoplasm of laryngeal cartilage NOS B214.00 Malignant neoplasm, overlapping lesion of larynx B215.00 Malignant neoplasm of epiglottis NOS B21y.00 Malignant neoplasm of larynx, other specified site B21z.00 Malignant neoplasm of larynx NOS B22..00 Malignant neoplasm of trachea, bronchus and lung B220.00 Malignant neoplasm of trachea B220000 Malignant neoplasm of cartilage of trachea B220100 Malignant neoplasm of mucosa of trachea B220z00 Malignant neoplasm of trachea NOS B221.00 Malignant neoplasm of main bronchus B221000 Malignant neoplasm of carina of bronchus B221100 Malignant neoplasm of hilus of lung B221z00 Malignant neoplasm of main bronchus NOS B222.00 Malignant neoplasm of upper lobe, bronchus or lung B222000 Malignant neoplasm of upper lobe bronchus B222100 Malignant neoplasm of upper lobe of lung B222z00 Malignant neoplasm of upper lobe, bronchus or lung NOS
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B223.00 Malignant neoplasm of middle lobe, bronchus or lung B223000 Malignant neoplasm of middle lobe bronchus B223100 Malignant neoplasm of middle lobe of lung B223z00 Malignant neoplasm of middle lobe, bronchus or lung NOS B224.00 Malignant neoplasm of lower lobe, bronchus or lung B224000 Malignant neoplasm of lower lobe bronchus B224100 Malignant neoplasm of lower lobe of lung B224z00 Malignant neoplasm of lower lobe, bronchus or lung NOS B225.00 Malignant neoplasm of overlapping lesion of bronchus & lung B22y.00 Malignant neoplasm of other sites of bronchus or lung B22z.00 Malignant neoplasm of bronchus or lung NOS B22z.11 Lung cancer B23..00 Malignant neoplasm of pleura B230.00 Malignant neoplasm of parietal pleura B231.00 Malignant neoplasm of visceral pleura B23y.00 Malignant neoplasm of other specified pleura B23z.00 Malignant neoplasm of pleura NOS B24..00 Malignant neoplasm of thymus, heart and mediastinum B240.00 Malignant neoplasm of thymus B241.00 Malignant neoplasm of heart B241000 Malignant neoplasm of endocardium B241100 Malignant neoplasm of epicardium B241200 Malignant neoplasm of myocardium B241300 Malignant neoplasm of pericardium B241z00 Malignant neoplasm of heart NOS B242.00 Malignant neoplasm of anterior mediastinum B243.00 Malignant neoplasm of posterior mediastinum B24X.00 Malignant neoplasm of mediastinum, part unspecified B24z.00 Malignant neoplasm of heart, thymus and mediastinum NOS B26..00 Malignant neoplasm, overlap lesion of resp & intrathor orgs B2zy.00 Malignant neoplasm of other site of respiratory tract B2zz.00 Malignant neoplasm of respiratory tract NOS B30..00 Malignant neoplasm of bone and articular cartilage B300.00 Malignant neoplasm of bones of skull and face B300000 Malignant neoplasm of ethmoid bone B300100 Malignant neoplasm of frontal bone B300200 Malignant neoplasm of malar bone B300300 Malignant neoplasm of nasal bone B300400 Malignant neoplasm of occipital bone B300500 Malignant neoplasm of orbital bone B300600 Malignant neoplasm of parietal bone B300700 Malignant neoplasm of sphenoid bone B300800 Malignant neoplasm of temporal bone B300900 Malignant neoplasm of zygomatic bone
239
B300A00 Malignant neoplasm of maxilla B300B00 Malignant neoplasm of turbinate B300C00 Malignant neoplasm of vomer B300z00 Malignant neoplasm of bones of skull and face NOS B301.00 Malignant neoplasm of mandible B302.00 Malignant neoplasm of vertebral column B302000 Malignant neoplasm of cervical vertebra B302100 Malignant neoplasm of thoracic vertebra B302200 Malignant neoplasm of lumbar vertebra B302z00 Malignant neoplasm of vertebral column NOS B303.00 Malignant neoplasm of ribs, sternum and clavicle B303000 Malignant neoplasm of rib B303100 Malignant neoplasm of sternum B303200 Malignant neoplasm of clavicle B303300 Malignant neoplasm of costal cartilage B303400 Malignant neoplasm of costo-vertebral joint B303500 Malignant neoplasm of xiphoid process B303z00 Malignant neoplasm of rib, sternum and clavicle NOS B304.00 Malignant neoplasm of scapula and long bones of upper arm B304000 Malignant neoplasm of scapula B304100 Malignant neoplasm of acromion B304200 Malignant neoplasm of humerus B304300 Malignant neoplasm of radius B304400 Malignant neoplasm of ulna B305.00 Malignant neoplasm of hand bones B305.11 Malignant neoplasm of carpal bones B305.12 Malignant neoplasm of metacarpal bones B305000 Malignant neoplasm of carpal bone - scaphoid B305100 Malignant neoplasm of carpal bone - lunate B305200 Malignant neoplasm of carpal bone - triquetrum B305300 Malignant neoplasm of carpal bone - pisiform B305400 Malignant neoplasm of carpal bone - trapezium B305500 Malignant neoplasm of carpal bone - trapezoid B305600 Malignant neoplasm of carpal bone - capitate B305700 Malignant neoplasm of carpal bone - hamate B305800 Malignant neoplasm of first metacarpal bone B305900 Malignant neoplasm of second metacarpal bone B305A00 Malignant neoplasm of third metacarpal bone B305B00 Malignant neoplasm of fourth metacarpal bone B305C00 Malignant neoplasm of fifth metacarpal bone B305D00 Malignant neoplasm of phalanges of hand B305z00 Malignant neoplasm of hand bones NOS B306.00 Malignant neoplasm of pelvic bones, sacrum and coccyx B306000 Malignant neoplasm of ilium
240
B306100 Malignant neoplasm of ischium B306200 Malignant neoplasm of pubis B306300 Malignant neoplasm of sacral vertebra B306400 Malignant neoplasm of coccygeal vertebra B306500 Malignant sacral teratoma B306z00 Malignant neoplasm of pelvis, sacrum or coccyx NOS B307.00 Malignant neoplasm of long bones of leg B307000 Malignant neoplasm of femur B307100 Malignant neoplasm of fibula B307200 Malignant neoplasm of tibia B307z00 Malignant neoplasm of long bones of leg NOS B308.00 Malignant neoplasm of short bones of leg B308.11 Malignant neoplasm of metatarsal bones of foot B308000 Malignant neoplasm of patella B308100 Malignant neoplasm of talus B308200 Malignant neoplasm of calcaneum B308300 Malignant neoplasm of medial cuneiform B308400 Malignant neoplasm of intermediate cuneiform B308500 Malignant neoplasm of lateral cuneiform B308600 Malignant neoplasm of cuboid B308700 Malignant neoplasm of navicular B308800 Malignant neoplasm of first metatarsal bone B308900 Malignant neoplasm of second metatarsal bone B308A00 Malignant neoplasm of third metatarsal bone B308B00 Malignant neoplasm of fourth metatarsal bone B308C00 Malignant neoplasm of fifth metatarsal bone B308D00 Malignant neoplasm of phalanges of foot B308z00 Malignant neoplasm of short bones of leg NOS B309.00 Malignant neoplasm, overlap les bone and artic cart of limbs B30W.00 Malignant neoplasm/overlap lesion/bone+articulr cartilage B30X.00 Malignant neoplasm/bones+articular cartilage/limb,unspfd B30z.00 Malignant neoplasm of bone and articular cartilage NOS B31..00 Malignant neoplasm of connective and other soft tissue B310000 Malignant neoplasm of soft tissue of head B310100 Malignant neoplasm of soft tissue of face B310200 Malignant neoplasm of soft tissue of neck B310300 Malignant neoplasm of cartilage of ear B310400 Malignant neoplasm of tarsus of eyelid B310500 Malignant neoplasm soft tissues of cervical spine B311000 Malignant neoplasm of connective and soft tissue of shoulder B311100 Malignant neoplasm of connective and soft tissue, upper arm B311200 Malignant neoplasm of connective and soft tissue of fore-arm B311300 Malignant neoplasm of connective and soft tissue of hand B311400 Malignant neoplasm of connective and soft tissue of finger
241
B311500 Malignant neoplasm of connective and soft tissue of thumb B312000 Malignant neoplasm of connective and soft tissue of hip B312400 Malignant neoplasm of connective and soft tissue of foot B312500 Malignant neoplasm of connective and soft tissue of toe B313.00 Malignant neoplasm of connective and soft tissue of thorax B313000 Malignant neoplasm of connective and soft tissue of axilla B313100 Malignant neoplasm of diaphragm B313200 Malignant neoplasm of great vessels B313300 Malig neoplasm of connective and soft tissues of thor spine B314.00 Malignant neoplasm of connective and soft tissue of abdomen B314100 Malig neoplasm of connective and soft tissues of lumb spine B315.00 Malignant neoplasm of connective and soft tissue of pelvis B315000 Malignant neoplasm of connective and soft tissue of buttock B315200 Malignant neoplasm of connective and soft tissue of perineum B317.00 Malignant neoplasm, overlap lesion connective & soft tissue B31z.00 Malignant neoplasm of connective and soft tissue, site NOS B32..00 Malignant melanoma of skin B320.00 Malignant melanoma of lip B321.00 Malignant melanoma of eyelid including canthus B322.00 Malignant melanoma of ear and external auricular canal B322000 Malignant melanoma of auricle (ear) B322100 Malignant melanoma of external auditory meatus B322z00 Malignant melanoma of ear and external auricular canal NOS B323.00 Malignant melanoma of other and unspecified parts of face B323000 Malignant melanoma of external surface of cheek B323100 Malignant melanoma of chin B323200 Malignant melanoma of eyebrow B323300 Malignant melanoma of forehead B323400 Malignant melanoma of external surface of nose B323500 Malignant melanoma of temple B323z00 Malignant melanoma of face NOS B324.00 Malignant melanoma of scalp and neck B324000 Malignant melanoma of scalp B324100 Malignant melanoma of neck B324z00 Malignant melanoma of scalp and neck NOS B325.00 Malignant melanoma of trunk (excluding scrotum) B325000 Malignant melanoma of axilla B325100 Malignant melanoma of breast B325200 Malignant melanoma of buttock B325300 Malignant melanoma of groin B325400 Malignant melanoma of perianal skin B325500 Malignant melanoma of perineum B325600 Malignant melanoma of umbilicus B325700 Malignant melanoma of back
242
B325800 Malignant melanoma of chest wall B325z00 Malignant melanoma of trunk, excluding scrotum, NOS B326.00 Malignant melanoma of upper limb and shoulder B326000 Malignant melanoma of shoulder B326100 Malignant melanoma of upper arm B326200 Malignant melanoma of fore-arm B326300 Malignant melanoma of hand B326400 Malignant melanoma of finger B326500 Malignant melanoma of thumb B326z00 Malignant melanoma of upper limb or shoulder NOS B327.00 Malignant melanoma of lower limb and hip B327000 Malignant melanoma of hip B327100 Malignant melanoma of thigh B327200 Malignant melanoma of knee B327300 Malignant melanoma of popliteal fossa area B327400 Malignant melanoma of lower leg B327500 Malignant melanoma of ankle B327600 Malignant melanoma of heel B327700 Malignant melanoma of foot B327800 Malignant melanoma of toe B327900 Malignant melanoma of great toe B327z00 Malignant melanoma of lower limb or hip NOS B32y.00 Malignant melanoma of other specified skin site B32y000 Overlapping malignant melanoma of skin B32z.00 Malignant melanoma of skin NOS B33..00 Other malignant neoplasm of skin B33..14 Malignant neoplasm of sebaceous gland B33..15 Malignant neoplasm of sweat gland B330.00 Malignant neoplasm of skin of lip B331.00 Malignant neoplasm of eyelid including canthus B331000 Malignant neoplasm of canthus B331100 Malignant neoplasm of upper eyelid B331200 Malignant neoplasm of lower eyelid B332.00 Malignant neoplasm skin of ear and external auricular canal B332000 Malignant neoplasm of skin of auricle (ear) B332100 Malignant neoplasm of skin of external auditory meatus B332200 Malignant neoplasm of pinna NEC B333.00 Malignant neoplasm skin of other and unspecified parts face B333000 Malignant neoplasm of skin of cheek, external B333100 Malignant neoplasm of skin of chin B333200 Malignant neoplasm of skin of eyebrow B333300 Malignant neoplasm of skin of forehead B333400 Malignant neoplasm of skin of nose (external) B333500 Malignant neoplasm of skin of temple
243
B333z00 Malignant neoplasm skin other and unspec part of face NOS B334.00 Malignant neoplasm of scalp and skin of neck B334000 Malignant neoplasm of scalp B334100 Malignant neoplasm of skin of neck B334z00 Malignant neoplasm of scalp or skin of neck NOS B335.00 Malignant neoplasm of skin of trunk, excluding scrotum B335000 Malignant neoplasm of skin of axillary fold B335100 Malignant neoplasm of skin of chest, excluding breast B335200 Malignant neoplasm of skin of breast B335300 Malignant neoplasm of skin of abdominal wall B335400 Malignant neoplasm of skin of umbilicus B335500 Malignant neoplasm of skin of groin B335600 Malignant neoplasm of skin of perineum B335700 Malignant neoplasm of skin of back B335800 Malignant neoplasm of skin of buttock B335900 Malignant neoplasm of perianal skin B335A00 Malignant neoplasm of skin of scapular region B335z00 Malignant neoplasm of skin of trunk, excluding scrotum, NOS B336.00 Malignant neoplasm of skin of upper limb and shoulder B336000 Malignant neoplasm of skin of shoulder B336100 Malignant neoplasm of skin of upper arm B336200 Malignant neoplasm of skin of fore-arm B336300 Malignant neoplasm of skin of hand B336400 Malignant neoplasm of skin of finger B336500 Malignant neoplasm of skin of thumb B336z00 Malignant neoplasm of skin of upper limb or shoulder NOS B337.00 Malignant neoplasm of skin of lower limb and hip B337000 Malignant neoplasm of skin of hip B337100 Malignant neoplasm of skin of thigh B337200 Malignant neoplasm of skin of knee B337300 Malignant neoplasm of skin of popliteal fossa area B337400 Malignant neoplasm of skin of lower leg B337500 Malignant neoplasm of skin of ankle B337600 Malignant neoplasm of skin of heel B337700 Malignant neoplasm of skin of foot B337800 Malignant neoplasm of skin of toe B337900 Malignant neoplasm of skin of great toe B337z00 Malignant neoplasm of skin of lower limb or hip NOS B33X.00 Malignant neoplasm overlapping lesion of skin B33y.00 Malignant neoplasm of other specified skin sites B33z.00 Malignant neoplasm of skin NOS B34..00 Malignant neoplasm of female breast B340.00 Malignant neoplasm of nipple and areola of female breast B340000 Malignant neoplasm of nipple of female breast
244
B340100 Malignant neoplasm of areola of female breast B340z00 Malignant neoplasm of nipple or areola of female breast NOS B341.00 Malignant neoplasm of central part of female breast B342.00 Malignant neoplasm of upper-inner quadrant of female breast B343.00 Malignant neoplasm of lower-inner quadrant of female breast B344.00 Malignant neoplasm of upper-outer quadrant of female breast B345.00 Malignant neoplasm of lower-outer quadrant of female breast B346.00 Malignant neoplasm of axillary tail of female breast B347.00 Malignant neoplasm, overlapping lesion of breast B34y.00 Malignant neoplasm of other site of female breast B34y000 Malignant neoplasm of ectopic site of female breast B34yz00 Malignant neoplasm of other site of female breast NOS B34z.00 Malignant neoplasm of female breast NOS B35..00 Malignant neoplasm of male breast B350.00 Malignant neoplasm of nipple and areola of male breast B350000 Malignant neoplasm of nipple of male breast B350100 Malignant neoplasm of areola of male breast B350z00 Malignant neoplasm of nipple or areola of male breast NOS B35z.00 Malignant neoplasm of other site of male breast B35z000 Malignant neoplasm of ectopic site of male breast B35zz00 Malignant neoplasm of male breast NOS B4...00 Malignant neoplasm of genitourinary organ B40..00 Malignant neoplasm of uterus, part unspecified B41..00 Malignant neoplasm of cervix uteri B410.00 Malignant neoplasm of endocervix B410000 Malignant neoplasm of endocervical canal B410100 Malignant neoplasm of endocervical gland B410z00 Malignant neoplasm of endocervix NOS B411.00 Malignant neoplasm of exocervix B412.00 Malignant neoplasm, overlapping lesion of cervix uteri B41y.00 Malignant neoplasm of other site of cervix B41y000 Malignant neoplasm of cervical stump B41y100 Malignant neoplasm of squamocolumnar junction of cervix B41yz00 Malignant neoplasm of other site of cervix NOS B41z.00 Malignant neoplasm of cervix uteri NOS B42..00 Malignant neoplasm of placenta B43..00 Malignant neoplasm of body of uterus B430.00 Malignant neoplasm of corpus uteri, excluding isthmus B430000 Malignant neoplasm of cornu of corpus uteri B430100 Malignant neoplasm of fundus of corpus uteri B430200 Malignant neoplasm of endometrium of corpus uteri B430211 Malignant neoplasm of endometrium B430300 Malignant neoplasm of myometrium of corpus uteri B430z00 Malignant neoplasm of corpus uteri NOS
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B431.00 Malignant neoplasm of isthmus of uterine body B431000 Malignant neoplasm of lower uterine segment B431z00 Malignant neoplasm of isthmus of uterine body NOS B432.00 Malignant neoplasm of overlapping lesion of corpus uteri B43y.00 Malignant neoplasm of other site of uterine body B43z.00 Malignant neoplasm of body of uterus NOS B44..00 Malignant neoplasm of ovary and other uterine adnexa B440.00 Malignant neoplasm of ovary B440.11 Cancer of ovary B441.00 Malignant neoplasm of fallopian tube B442.00 Malignant neoplasm of broad ligament B443.00 Malignant neoplasm of parametrium B444.00 Malignant neoplasm of round ligament B44y.00 Malignant neoplasm of other site of uterine adnexa B44z.00 Malignant neoplasm of uterine adnexa NOS B450.00 Malignant neoplasm of vagina B450000 Malignant neoplasm of Gartner's duct B450100 Malignant neoplasm of vaginal vault B450z00 Malignant neoplasm of vagina NOS B451.00 Malignant neoplasm of labia majora B451000 Malignant neoplasm of greater vestibular (Bartholin's) gland B451z00 Malignant neoplasm of labia majora NOS B452.00 Malignant neoplasm of labia minora B453.00 Malignant neoplasm of clitoris B454.00 Malignant neoplasm of vulva unspecified B454.11 Primary vulval cancer B45X.00 Malignant neoplasm/overlapping lesion/feml genital organs B45y.00 Malignant neoplasm of other specified female genital organ B45y000 Malignant neoplasm of overlapping lesion of vulva B45z.00 Malignant neoplasm of female genital organ NOS B46..00 Malignant neoplasm of prostate B47..00 Malignant neoplasm of testis B470.00 Malignant neoplasm of undescended testis B470000 Malignant neoplasm of ectopic testis B470100 Malignant neoplasm of retained testis B470z00 Malignant neoplasm of undescended testis NOS B471.00 Malignant neoplasm of descended testis B471z00 Malignant neoplasm of descended testis NOS B47z.00 Malignant neoplasm of testis NOS B48..00 Malignant neoplasm of penis and other male genital organs B480.00 Malignant neoplasm of prepuce (foreskin) B481.00 Malignant neoplasm of glans penis B482.00 Malignant neoplasm of body of penis B483.00 Malignant neoplasm of penis, part unspecified
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B484.00 Malignant neoplasm of epididymis B485.00 Malignant neoplasm of spermatic cord B486.00 Malignant neoplasm of scrotum B487.00 Malignant neoplasm, overlapping lesion of penis B48y.00 Malignant neoplasm of other male genital organ B48y000 Malignant neoplasm of seminal vesicle B48y100 Malignant neoplasm of tunica vaginalis B48y200 Malignant neoplasm, overlapping lesion male genital orgs B48yz00 Malignant neoplasm of other male genital organ NOS B48z.00 Malignant neoplasm of penis and other male genital organ NOS B49..00 Malignant neoplasm of urinary bladder B490.00 Malignant neoplasm of trigone of urinary bladder B491.00 Malignant neoplasm of dome of urinary bladder B492.00 Malignant neoplasm of lateral wall of urinary bladder B493.00 Malignant neoplasm of anterior wall of urinary bladder B494.00 Malignant neoplasm of posterior wall of urinary bladder B495.00 Malignant neoplasm of bladder neck B496.00 Malignant neoplasm of ureteric orifice B497.00 Malignant neoplasm of urachus B49y.00 Malignant neoplasm of other site of urinary bladder B49y000 Malignant neoplasm, overlapping lesion of bladder B49z.00 Malignant neoplasm of urinary bladder NOS B4A..11 Renal malignant neoplasm B4A0.00 Malignant neoplasm of kidney parenchyma B4A1.00 Malignant neoplasm of renal pelvis B4A1000 Malignant neoplasm of renal calyces B4A1100 Malignant neoplasm of ureteropelvic junction B4A1z00 Malignant neoplasm of renal pelvis NOS B4A2.00 Malignant neoplasm of ureter B4A3.00 Malignant neoplasm of urethra B4A4.00 Malignant neoplasm of paraurethral glands B4Ay.00 Malignant neoplasm of other urinary organs B4Ay000 Malignant neoplasm of overlapping lesion of urinary organs B4Az.00 Malignant neoplasm of kidney or urinary organs NOS B4y..00 Malignant neoplasm of genitourinary organ OS B4z..00 Malignant neoplasm of genitourinary organ NOS B5...00 Malignant neoplasm of other and unspecified sites B50..00 Malignant neoplasm of eye B500000 Malignant neoplasm of ciliary body B500100 Malignant neoplasm of iris B500200 Malignant neoplasm of crystalline lens B500300 Malignant neoplasm of sclera B500z00 Malignant neoplasm of eyeball NOS B501.00 Malignant neoplasm of orbit
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B501000 Malignant neoplasm of connective tissue of orbit B501100 Malignant neoplasm of extraocular muscle of orbit B501z00 Malignant neoplasm of orbit NOS B502.00 Malignant neoplasm of lacrimal gland B503.00 Malignant neoplasm of conjunctiva B504.00 Malignant neoplasm of cornea B505.00 Malignant neoplasm of retina B506.00 Malignant neoplasm of choroid B507.00 Malignant neoplasm of lacrimal duct B507000 Malignant neoplasm of lacrimal sac B507100 Malignant neoplasm of nasolacrimal duct B507z00 Malignant neoplasm of lacrimal duct NOS B508.00 Malignant neoplasm, overlapping lesion of eye and adnexa B50y.00 Malignant neoplasm of other specified site of eye B50z.00 Malignant neoplasm of eye NOS B51..00 Malignant neoplasm of brain B51..11 Cerebral tumour - malignant B510.00 Malignant neoplasm cerebrum (excluding lobes and ventricles) B510000 Malignant neoplasm of basal ganglia B510100 Malignant neoplasm of cerebral cortex B510200 Malignant neoplasm of corpus striatum B510300 Malignant neoplasm of globus pallidus B510400 Malignant neoplasm of hypothalamus B510500 Malignant neoplasm of thalamus B510z00 Malignant neoplasm of cerebrum NOS B511.00 Malignant neoplasm of frontal lobe B512.00 Malignant neoplasm of temporal lobe B512000 Malignant neoplasm of hippocampus B512100 Malignant neoplasm of uncus B512z00 Malignant neoplasm of temporal lobe NOS B513.00 Malignant neoplasm of parietal lobe B514.00 Malignant neoplasm of occipital lobe B515.00 Malignant neoplasm of cerebral ventricles B515000 Malignant neoplasm of choroid plexus B515100 Malignant neoplasm of floor of cerebral ventricle B515z00 Malignant neoplasm of cerebral ventricle NOS B516.00 Malignant neoplasm of cerebellum B517.00 Malignant neoplasm of brain stem B517000 Malignant neoplasm of cerebral peduncle B517100 Malignant neoplasm of medulla oblongata B517200 Malignant neoplasm of midbrain B517300 Malignant neoplasm of pons B517z00 Malignant neoplasm of brain stem NOS B51y.00 Malignant neoplasm of other parts of brain
248
B51y000 Malignant neoplasm of corpus callosum B51y100 Malignant neoplasm of tapetum B51y200 Malignant neoplasm, overlapping lesion of brain B51yz00 Malignant neoplasm of other part of brain NOS B51z.00 Malignant neoplasm of brain NOS B520.00 Malignant neoplasm of cranial nerves B520000 Malignant neoplasm of olfactory bulb B520100 Malignant neoplasm of optic nerve B520200 Malignant neoplasm of acoustic nerve B520z00 Malignant neoplasm of cranial nerves NOS B521.00 Malignant neoplasm of cerebral meninges B521000 Malignant neoplasm of cerebral dura mater B521100 Malignant neoplasm of cerebral arachnoid mater B521200 Malignant neoplasm of cerebral pia mater B521z00 Malignant neoplasm of cerebral meninges NOS B522.00 Malignant neoplasm of spinal cord B523.00 Malignant neoplasm of spinal meninges B523000 Malignant neoplasm of spinal dura mater B523100 Malignant neoplasm of spinal arachnoid mater B523200 Malignant neoplasm of spinal pia mater B523z00 Malignant neoplasm of spinal meninges NOS B524000 Malignant neoplasm of peripheral nerves of head, face & neck B524100 Malignant neoplasm of peripheral nerve,upp limb,incl should B524200 Malignant neoplasm of peripheral nerve of low limb, incl hip B524300 Malignant neoplasm of peripheral nerve of thorax B524400 Malignant neoplasm of peripheral nerve of abdomen B524500 Malignant neoplasm of peripheral nerve of pelvis B524600 Malignant neoplasm,overlap lesion periph nerve & auton ns B524W00 Mal neoplasm/periph nerves+autonomic nervous system,unspc B524X00 Malignant neoplasm/peripheral nerves of trunk,unspecified B525.00 Malignant neoplasm of cauda equina B52X.00 Malignant neoplasm of meninges, unspecified B52y.00 Malignant neoplasm of other specified part of nervous system B52z.00 Malignant neoplasm of nervous system NOS B53..00 Malignant neoplasm of thyroid gland B540.00 Malignant neoplasm of adrenal gland B540000 Malignant neoplasm of adrenal cortex B540100 Malignant neoplasm of adrenal medulla B540z00 Malignant neoplasm of adrenal gland NOS B541.00 Malignant neoplasm of parathyroid gland B542.00 Malignant neoplasm pituitary gland and craniopharyngeal duct B542000 Malignant neoplasm of pituitary gland B542100 Malignant neoplasm of craniopharyngeal duct B543.00 Malignant neoplasm of pineal gland
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B544.00 Malignant neoplasm of carotid body B545.00 Malignant neoplasm of aortic body and other paraganglia B545000 Malignant neoplasm of glomus jugulare B545100 Malignant neoplasm of aortic body B545200 Malignant neoplasm of coccygeal body B545z00 Malignant neoplasm of aortic body or paraganglia NOS B54X.00 Malignant neoplasm-pluriglandular involvement,unspecified B54y.00 Malignant neoplasm of other specified endocrine gland B55..00 Malignant neoplasm of other and ill-defined sites B550.00 Malignant neoplasm of head, neck and face B550000 Malignant neoplasm of head NOS B550100 Malignant neoplasm of cheek NOS B550200 Malignant neoplasm of nose NOS B550300 Malignant neoplasm of jaw NOS B550400 Malignant neoplasm of neck NOS B550500 Malignant neoplasm of supraclavicular fossa NOS B550z00 Malignant neoplasm of head, neck and face NOS B551.00 Malignant neoplasm of thorax B551000 Malignant neoplasm of axilla NOS B551100 Malignant neoplasm of chest wall NOS B551200 Malignant neoplasm of intrathoracic site NOS B551z00 Malignant neoplasm of thorax NOS B552.00 Malignant neoplasm of abdomen B553.00 Malignant neoplasm of pelvis B553000 Malignant neoplasm of inguinal region NOS B553100 Malignant neoplasm of presacral region B553200 Malignant neoplasm of sacrococcygeal region B553z00 Malignant neoplasm of pelvis NOS B554.00 Malignant neoplasm of upper limb NOS B555.00 Malignant neoplasm of lower limb NOS B55y.00 Malignant neoplasm of other specified sites B55y000 Malignant neoplasm of back NOS B55y100 Malignant neoplasm of trunk NOS B55y200 Malignant neoplasm of flank NOS B55yz00 Malignant neoplasm of specified site NOS B55z.00 Malignant neoplasm of other and ill defined site NOS B56..00 Secondary and unspecified malignant neoplasm of lymph nodes B56..11 Lymph node metastases B560100 Secondary and unspec malignant neoplasm mastoid lymph nodes B560300 Secondary and unspec malignant neoplasm occipital lymph node B57..11 Metastases of respiratory and/or digestive systems B570.00 Secondary malignant neoplasm of lung B571.00 Secondary malignant neoplasm of mediastinum B572.00 Secondary malignant neoplasm of pleura
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B573.00 Secondary malignant neoplasm of other respiratory organs B574.00 Secondary malignant neoplasm of small intestine and duodenum B574000 Secondary malignant neoplasm of duodenum B574100 Secondary malignant neoplasm of jejunum B574200 Secondary malignant neoplasm of ileum B575.00 Secondary malignant neoplasm of large intestine and rectum B575000 Secondary malignant neoplasm of colon B575100 Secondary malignant neoplasm of rectum B576000 Secondary malignant neoplasm of retroperitoneum B576100 Secondary malignant neoplasm of peritoneum B576200 Malignant ascites B577.00 Secondary malignant neoplasm of liver B577.11 Liver metastases B57y.00 Secondary malignant neoplasm of other digestive organ B58..00 Secondary malignant neoplasm of other specified sites B580.00 Secondary malignant neoplasm of kidney B581.00 Secondary malignant neoplasm of other urinary organs B581000 Secondary malignant neoplasm of ureter B581100 Secondary malignant neoplasm of bladder B581200 Secondary malignant neoplasm of urethra B581z00 Secondary malignant neoplasm of other urinary organ NOS B582.00 Secondary malignant neoplasm of skin B582000 Secondary malignant neoplasm of skin of head B582100 Secondary malignant neoplasm of skin of face B582200 Secondary malignant neoplasm of skin of neck B582300 Secondary malignant neoplasm of skin of trunk B582400 Secondary malignant neoplasm of skin of shoulder and arm B582500 Secondary malignant neoplasm of skin of hip and leg B582600 Secondary malignant neoplasm of skin of breast B582z00 Secondary malignant neoplasm of skin NOS B583.00 Secondary malignant neoplasm of brain and spinal cord B583000 Secondary malignant neoplasm of brain B583100 Secondary malignant neoplasm of spinal cord B583200 Cerebral metastasis B583z00 Secondary malignant neoplasm of brain or spinal cord NOS B584.00 Secondary malignant neoplasm of other part of nervous system B585.00 Secondary malignant neoplasm of bone and bone marrow B585000 Pathological fracture due to metastatic bone disease B586.00 Secondary malignant neoplasm of ovary B587.00 Secondary malignant neoplasm of adrenal gland B58y.00 Secondary malignant neoplasm of other specified sites B58y000 Secondary malignant neoplasm of breast B58y100 Secondary malignant neoplasm of uterus B58y200 Secondary malignant neoplasm of cervix uteri
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B58y211 Secondary cancer of the cervix B58y300 Secondary malignant neoplasm of vagina B58y400 Secondary malignant neoplasm of vulva B58y411 Secondary cancer of the vulva B58y500 Secondary malignant neoplasm of prostate B58y600 Secondary malignant neoplasm of testis B58y700 Secondary malignant neoplasm of penis B58y800 Secondary malignant neoplasm of epididymis and vas deferens B58y900 Secondary malignant neoplasm of tongue B58yz00 Secondary malignant neoplasm of other specified site NOS B58z.00 Secondary malignant neoplasm of other specified site NOS B59..00 Malignant neoplasm of unspecified site B590.00 Disseminated malignancy NOS B591.00 Other malignant neoplasm NOS B592.00 Malignant neoplasms of independent (primary) multiple sites B593.00 Primary malignant neoplasm of unknown site B594.00 Secondary malignant neoplasm of unknown site B59z.00 Malignant neoplasm of unspecified site NOS B5y..00 Malignant neoplasm of other and unspecified site OS B5z..00 Malignant neoplasm of other and unspecified site NOS B6...00 Malignant neoplasm of lymphatic and haemopoietic tissue B6...11 Malignant neoplasm of histiocytic tissue B62..00 Other malignant neoplasm of lymphoid and histiocytic tissue B623.00 Malignant histiocytosis B623000 Malignant histiocytosis of unspecified site B623100 Malignant histiocytosis of lymph nodes head, face and neck B623200 Malignant histiocytosis of intrathoracic lymph nodes B623300 Malignant histiocytosis of intra-abdominal lymph nodes B623400 Malignant histiocytosis of lymph nodes of axilla and arm B623500 Malignant histiocytosis of lymph nodes inguinal and leg B623600 Malignant histiocytosis of intrapelvic lymph nodes B623700 Malignant histiocytosis of spleen B623800 Malignant histiocytosis of lymph nodes of multiple sites B623z00 Malignant histiocytosis NOS B626.00 Malignant mast cell tumours B626000 Mast cell malignancy of unspecified site B626100 Mast cell malignancy of lymph nodes of head, face and neck B626200 Mast cell malignancy of intrathoracic lymph nodes B626300 Mast cell malignancy of intra-abdominal lymph nodes B626400 Mast cell malignancy of lymph nodes of axilla and upper limb B626500 Mast cell malignancy of lymph nodes inguinal region and leg B626600 Mast cell malignancy of intrapelvic lymph nodes B626700 Mast cell malignancy of spleen B626800 Mast cell malignancy of lymph nodes of multiple sites
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B626z00 Malignant mast cell tumour NOS B62x.00 Malignant lymphoma otherwise specified B62x300 Malignant reticuloendotheliosis B62x400 Malignant reticulosis B62x500 Malignant immunoproliferative small intestinal disease B62y.00 Malignant lymphoma NOS B62y000 Malignant lymphoma NOS of unspecified site B62y100 Malignant lymphoma NOS of lymph nodes of head, face and neck B62y200 Malignant lymphoma NOS of intrathoracic lymph nodes B62y300 Malignant lymphoma NOS of intra-abdominal lymph nodes B62y400 Malignant lymphoma NOS of lymph nodes of axilla and arm B62y500 Malignant lymphoma NOS of lymph node inguinal region and leg B62y600 Malignant lymphoma NOS of intrapelvic lymph nodes B62y700 Malignant lymphoma NOS of spleen B62y800 Malignant lymphoma NOS of lymph nodes of multiple sites B62yz00 Malignant lymphoma NOS B62z.00 Malignant neoplasms of lymphoid and histiocytic tissue NOS B62zz00 Lymphoid and histiocytic malignancy NOS B62zz11 Immunoproliferative neoplasm B63..00 Multiple myeloma and immunoproliferative neoplasms B630000 Malignant plasma cell neoplasm, extramedullary plasmacytoma B63y.00 Other immunoproliferative neoplasms B63z.00 Immunoproliferative neoplasm or myeloma NOS B6y..00 Malignant neoplasm lymphatic or haematopoietic tissue OS B6z..00 Malignant neoplasm lymphatic or haematopoietic tissue NOS B911000 Malignant hydatidiform mole BB02.00 [M]Neoplasm, malignant BB03.00 [M]Neoplasm, metastatic BB03.11 [M]Secondary neoplasm BB03.12 [M]Tumour embolus BB03.13 [M]Tumour embolism BB04.00 [M]Neoplasm, malig, uncertain whether primary or metastatic BB07.00 [M]Tumour cells, malignant BB08.00 [M]Malignant tumour, small cell type BB09.00 [M]Malignant tumour, giant cell type BB0A.00 [M]Malignant tumour, fusiform cell type BB13.00 [M]Carcinoma, metastatic, NOS BB16.00 [M]Epithelioma, malignant BB2..00 [M]Papillary and squamous cell neoplasms BB2..12 [M]Squamous cell neoplasms BB2B.00 [M]Squamous cell carcinoma, metastatic NOS BB2z.00 [M]Papillary or squamous cell neoplasm NOS BB53.00 [M]Adenocarcinoma, metastatic, NOS BB5a011 [M]Grawitz tumour
253
BB5B300 [M]Insulinoma, malignant BB5B311 [M]Beta-cell tumour, malignant BB5B500 [M]Glucagonoma, malignant BB5B511 [M]Alpha-cell tumour,malignant BB5C011 [M]G cell tumour NOS BB5C100 [M]Gastrinoma, malignant BB5C111 [M]G cell tumour, malignant BB5D512 [M]Hepatoma, malignant BB5h.00 [M]Adrenal cortical tumours BB5hz00 [M]Adrenal cortical tumours NOS BB5j100 [M]Endometrioid adenoma, borderline malignancy BB5j400 [M]Endometrioid adenofibroma, borderline malignancy BB5j500 [M]Endometrioid adenofibroma, malignant BB5R100 [M]Carcinoid tumour, malignant BB5R300 [M]Carcinoid tumour, argentaffin, malignant BB5R500 [M]Carcinoid tumour, nonargentaffin, malignant BB5R600 [M]Mucocarcinoid tumour, malignant BB5R611 [M]Goblet cell tumour BB5R800 [M]Adenocarcinoid tumour BB5Y.00 [M]Hypernephroid tumour BB5y200 [M]Klatskin's tumour BB6..00 [M]Adnexal and skin appendage neoplasms BB6z.00 [M]Adnexal and skin appendage neoplasm NOS BB7..00 [M]Mucoepidermoid neoplasms BB70.00 [M]Mucoepidermoid tumour BB7z.00 [M]Mucoepidermoid neoplasm NOS BB8..00 [M]Cystic, mucinous and serous neoplasms BB81.00 [M]Ovarian cystic, mucinous and serous neoplasms BB81.12 [M]Ovarian mucinous tumour BB81.13 [M]Ovarian papillary tumour BB81.14 [M]Ovarian serous tumour BB81100 [M]Serous cystadenoma, borderline malignancy BB81400 [M]Papillary cystadenoma, borderline malignancy BB81700 [M]Papillary serous cystadenoma, borderline malignancy BB81A00 [M]Serous surface papilloma, borderline malignancy BB81D00 [M]Mucinous cystadenoma, borderline malignancy BB81G00 [M]Papillary mucinous cystadenoma, borderline malignancy BB81J00 [M]Serous cystadenoma, borderline malignancy BB81K00 [M]Papillary cystadenoma, borderline malignancy BB81L00 [M]Papillary cystic tumour BB81M00 [M]Papillary serous cystadenoma, borderline malignancy BB81z00 [M]Ovarian cystic, mucinous or serous neoplasm NOS BB85100 [M]Metastatic signet ring cell carcinoma BB85111 [M]Krukenberg tumour
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BB8z.00 [M]Cystic, mucinous or serous neoplasm NOS BB9..00 [M]Ductal, lobular and medullary neoplasms BB9z.00 [M]Ductal, lobular or medullary neoplasm NOS BBA..00 [M]Acinar cell neoplasms BBa..00 [M]Miscellaneous tumours BBa0.11 [M]Rathke's pouch tumour BBA1.00 [M]Acinar cell tumour BBa4.00 [M]Melanotic neuroectodermal tumour BBa4.13 [M]Retinal angle tumour BBAz.00 [M]Acinar cell neoplasm NOS BBaz.00 [M]Miscellaneous tumour NOS BBB..00 [M]Complex epithelial neoplasms BBb0.00 [M]Glioma, malignant BBb6.00 [M]Choroid plexus papilloma, malignant BBB6100 [M]Thymoma, malignant BBba.00 [M]Primitive neuroectodermal tumour BBBz.00 [M]Complex epithelial neoplasm NOS BBc..00 [M]Neuroepitheliomatous neoplasms BBC..00 [M]Specialised gonadal neoplasms BBC0.00 [M]Sex cord-stromal tumour BBC0.11 [M]Gonadal stromal tumour BBC0.12 [M]Ovarian stromal tumour BBC0.13 [M]Testicular stromal tumour BBC0000 [M]Sex cord tumour with annular tubules BBc0z00 [M]Ganglioneuromatous neoplasm NOS BBC1.00 [M]Thecal cell neoplasms BBC1z00 [M]Thecal cell neoplasm NOS BBC3.00 [M]Granulosa cell tumour NOS BBC3000 [M]Juvenile granulosa cell tumour BBC4.00 [M]Granulosa cell tumour, malignant BBC5.00 [M]Granulosa cell-theca cell tumour BBC6100 [M]Androblastoma, malignant BBC6111 [M]Arrhenoblastoma, malignant BBC7.00 [M]Sertoli-Leydig cell tumour BBc8.00 [M]Pacinian tumour BBC9.13 [M]Sertoli cell tumour BBcA.00 [M]Olfactory neurogenic tumour BBCB.11 [M]Sertoli cell tumour with lipid storage BBCC.00 [M]Leydig cell tumour BBCC100 [M]Leydig cell tumour, malignant BBCC111 [M]Interstitial cell tumour, malignant BBCD.00 [M]Hilar cell tumour BBCE.00 [M]Lipid cell tumour of ovary BBCF.00 [M]Adrenal rest tumour
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BBCG.00 [M]Sclerosing stromal tumour BBcz.00 [M]Neuroepitheliomatous neoplasm NOS BBCz.00 [M]Specialised gonadal neoplasm NOS BBD..00 [M]Paragangliomas and glomus tumours BBD1.00 [M]Paraganglioma, malignant BBd2.00 [M]Meningioma, malignant BBD8.00 [M]Extra-adrenal paraganglioma, malignant BBD9.12 [M]Chromaffin tumour BBDA.00 [M]Phaeochromocytoma, malignant BBDz.00 [M]Paraganglioma or glomus tumour NOS BBe..00 [M]Nerve sheath tumour BBE1.00 [M]Malignant melanoma NOS BBE1000 [M]Malignant melanoma, regressing BBE1100 [M]Desmoplastic melanoma, malignant BBe7.00 [M]Neurilemmoma, malignant BBe7.11 [M]Schwannoma, malignant BBe9.00 [M]Triton tumour, malignant BBEC.00 [M]Malignant melanoma in junctional naevus BBEE.00 [M]Malignant melanoma in precancerous melanosis BBEG.00 [M]Malignant melanoma in Hutchinson's melanotic freckle BBEG000 [M]Acral lentiginous melanoma, malignant BBEM.00 [M]Malignant melanoma in giant pigmented naevus BBEV.00 [M]Blue naevus, malignant BBez.00 [M]Nerve sheath tumour NOS BBf..00 [M]Granular cell tumours and alveolar soft part sarcoma BBF..00 [M]Soft tissue tumours and sarcomas NOS BBf0.00 [M]Granular cell tumour NOS BBf1.00 [M]Granular cell tumour, malignant BBfz.00 [M]Granular cell tumour or alveolar soft part sarcoma NOS BBFz.00 [M]Soft tissue tumour or sarcoma NOS BBG..00 [M]Fibromatous neoplasms BBg1.00 [M]Malignant lymphoma NOS BBg1000 [M]Malignant lymphoma, diffuse NOS BBg2.00 [M]Malignant lymphoma, non Hodgkin's type BBg3.00 [M]Malignant lymphoma, undifferentiated cell type NOS BBg4.00 [M]Malignant lymphoma, stem cell type BBg5.00 [M]Malignant lymphoma, convoluted cell type NOS BBg7.00 [M]Malignant lymphoma, lymphoplasmacytoid type BBg8.00 [M]Malignant lymphoma, immunoblastic type BBg9.00 [M]Malignant lymphoma, mixed lymphocytic-histiocytic NOS BBgA.00 [M]Malignant lymphoma, centroblastic-centrocytic, diffuse BBgB.00 [M]Malignant lymphoma, follicular centre cell NOS BBgC.00 [M]Malignant lymphoma, lymphocytic, well differentiated NOS BBgE.00 [M]Malignant lymphoma, centrocytic
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BBGF.00 [M]Fibrous histiocytoma, malignant BBgF.00 [M]Malignant lymphoma, follicular centre cell, cleaved NOS BBgG.00 [M]Malignant lymphoma, lymphocytic, poorly different NOS BBGJ.00 [M]Fibroxanthoma, malignant BBgJ.00 [M]Malignant lymphoma, centroblastic type NOS BBgL.00 [M]Malignant lymphoma, small lymphocytic NOS BBgM.00 [M]Malignant lymphoma, small cleaved cell, diffuse BBgP.00 [M]Malignant lymphoma, mixed small and large cell, diffuse BBgQ.00 [M]Malignant lymphomatous polyposis BBgR.00 [M]Malignant lymphoma, large cell, diffuse NOS BBgS.00 [M]Malignant lymphoma, large cell, cleaved, diffuse BBgT.00 [M]Malignant lymphoma, large cell, noncleaved, diffuse BBgV.00 [M]Malignant lymphoma, small cell, noncleaved, diffuse BBGz.00 [M]Fibromatous neoplasm NOS BBj0.11 [M]Lymphogranuloma, malignant BBk0.00 [M]Malignant lymphoma, nodular NOS BBK1z00 [M]Angiomyomatous neoplasm NOS BBk2.00 [M]Malignant lymphoma, centroblastic-centrocytic, follicular BBK3.00 [M]Rhabdomyomatous neoplasms BBK3z00 [M]Rhabdomyomatous neoplasm NOS BBk7.00 [M]Malignant lymphoma, centroblastic type, follicular BBL..00 [M]Complex mixed and stromal neoplasms BBL3.12 [M]Mixed tumour NOS BBL4.00 [M]Mixed tumour, malignant, NOS BBL5.00 [M]Mullerian mixed tumour BBL6.00 [M]Mesodermal mixed tumour BBL7.00 [M]Mixed and stromal renal neoplasms BBL7112 [M]Wilms' tumour BBL7z00 [M]Mixed or stromal renal neoplasm NOS BBLC100 [M]Mesenchymoma, malignant BBLz.00 [M]Complex mixed or stromal neoplasm NOS BBM..00 [M]Fibroepithelial neoplasms BBm..00 [M]Miscellaneous reticuloendothelial neoplasms BBM0.00 [M]Brenner tumours BBM0000 [M]Brenner tumour, borderline malignancy BBM0100 [M]Brenner tumour, malignant BBM0z00 [M]Brenner tumour NOS BBm1.00 [M]Malignant histiocytosis BBm1.11 [M]Malignant reticulosis BBM9.00 [M]Cystosarcoma phyllodes, malignant BBMz.00 [M]Fibroepithelial neoplasm NOS BBmz.00 [M]Miscellaneous reticuloendothelial neoplasm NOS BBn..00 [M]Plasma cell tumours BBN..00 [M]Synovial neoplasms
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BBn3.00 [M]Plasma cell tumour, malignant BBnz.00 [M]Plasma cell tumour NOS BBNz.00 [M]Synovial neoplasm NOS BBp..00 [M]Mast cell tumours BBP..00 [M]Mesothelial neoplasms BBP1.00 [M]Mesothelioma, malignant BBp2.00 [M]Malignant mastocytosis BBP3.00 [M]Fibrous mesothelioma, malignant BBP5.00 [M]Epithelioid mesothelioma, malignant BBP7.00 [M]Mesothelioma, biphasic type, malignant BBpz.00 [M]Mast cell tumour NOS BBPz.00 [M]Mesothelial neoplasm NOS BBq..00 [M]Burkitt's tumours BBQ..00 [M]Germ cell neoplasms BBq0.00 [M]Burkitt's tumour BBQ4.00 [M]Endodermal sinus tumour BBQ4.13 [M]Polyvesicular vitelline tumour BBQ4.14 [M]Yolk sac tumour BBQ7200 [M]Teratoma, malignant, NOS BBQ7213 [M]Teratoblastoma, malignant BBQ7400 [M]Malignant teratoma, undifferentiated type BBQ7500 [M]Malignant teratoma, intermediate type BBQ9.00 [M]Dermoid cyst with malignant transformation BBQA.00 [M]Strumal neoplasms BBQA100 [M]Struma ovarii, malignant BBQAz00 [M]Strumal neoplasm NOS BBQB.00 [M]Mixed germ cell tumour BBqz.00 [M]Burkitt's tumour NOS BBQz.00 [M]Germ cell neoplasm NOS BBR..00 [M]Trophoblastic neoplasms BBR4.00 [M]Malignant teratoma, trophoblastic BBR6.00 [M]Placental site trophoblastic tumour BBRz.00 [M]Trophoblastic neoplasm NOS BBS2.00 [M]Mesonephroma, malignant BBT7100 [M]Haemangioendothelioma, malignant BBTD.00 [M]Haemangiopericytic neoplasms BBTD200 [M]Haemangiopericytoma, malignant BBTDz00 [M]Haemangiopericytic neoplasm NOS BBTK.00 [M]Epithelioid haemangioendothelioma, malignant BBTL.00 [M]Intravascular bronchial alveolar tumour BBW..00 [M]Chondromatous neoplasms BBW7.11 [M]Chondromatous giant cell tumour BBW8.00 [M]Chondroblastoma, malignant BBWz.00 [M]Chondromatous neoplasm NOS
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BBX..00 [M]Giant cell tumours BBX0.00 [M]Giant cell tumour of bone NOS BBX1.00 [M]Giant cell tumour of bone, malignant BBX1.12 [M]Osteoclastoma, malignant BBX2.00 [M]Giant cell tumour of soft parts NOS BBX3.00 [M]Malignant giant cell tumour of soft parts BBXz.00 [M]Giant cell tumour NOS BBy1.00 [M]No microscopic confirmation of tumour, clinically malig BBy2.00 [M]No microscopic confirmation tumour, clinically metastatic BBZ2.00 [M]Odontogenic tumour, malignant BBZG.00 [M]Ameloblastoma, malignant BBZG.11 [M]Adamantinoma, malignant BBZJ.00 [M]Squamous odontogenic tumour BBZP.00 [M]Calcifying epithelial odontogenic tumour By...00 Neoplasms otherwise specified Byu..00 [X]Additional neoplasm classification terms Byu0.00 [X]Malignant neoplasm of lip, oral cavity and pharynx Byu1.00 [X]Malignant neoplasm of digestive organs Byu1200 [X]Malignant neoplasm of intestinal tract, part unspecified Byu1300 [X]Malignant neoplsm/ill-defin sites within digestive system Byu2.00 [X]Malignant neoplasm of respiratory and intrathoracic orga Byu2000 [X]Malignant neoplasm of bronchus or lung, unspecified Byu2100 [X]Malignant neoplasm/overlap lesion/heart,mediastinm+pleura Byu2200 [X]Malignant neoplasm/upper resp tract, part unspecified Byu2300 [X]Malignant neopl/overlapping les/resp+intrathoracic organs Byu2400 [X]Malignant neoplasm/ill-defined sites within resp system Byu2500 [X]Malignant neoplasm of mediastinum, part unspecified Byu3.00 [X]Malignant neoplasm of bone and articular cartilage Byu3000 [X]Mal neoplasm/overlap lesion/bone+articular cartilage/limb Byu3100 [X]Malignant neoplasm/bones+articular cartilage/limb,unspfd Byu3200 [X]Malignant neoplasm/overlap lesion/bone+articulr cartilage Byu3300 [X]Malignant neoplasm/bone+articular cartilage, unspecified Byu4.00 [X]Melanoma and other malignant neoplasms of skin Byu4000 [X]Malignant melanoma of other+unspecified parts of face Byu4100 [X]Malignant melanoma of skin, unspecified Byu4200 [X]Oth malignant neoplasm/skin of oth+unspecfd parts of face Byu4300 [X]Malignant neoplasm of skin, unspecified Byu5.00 [X]Malignant neoplasm of mesothelial and soft tissue Byu5400 [X]Malignant neoplasm/peripheral nerves of trunk,unspecified Byu5500 [X]Mal neoplasm/overlap les/periph nerv+autonomic nerv systm Byu5600 [X]Mal neoplasm/periph nerves+autonomic nervous system,unspc Byu5700 [X]Malignant neoplasm of peritoneum, unspecified Byu5800 [X]Mal neoplasm/connective+soft tissue of trunk,unspecified Byu5900 [X]Malignant neoplasm/connective + soft tissue,unspecified
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Byu5A00 [X]Malignant neoplasm overlapping lesion of skin Byu6.00 [X]Malignant neoplasm of breast Byu7.00 [X]Malignant neoplasm of female genital organs Byu7000 [X]Malignant neoplasm of uterine adnexa, unspecified Byu7100 [X]Malignant neoplasm/other specified female genital organs Byu7200 [X]Malignant neoplasm/overlapping lesion/feml genital organs Byu7300 [X]Malignant neoplasm of female genital organ, unspecified Byu8.00 [X]Malignant neoplasm of male genital organs Byu8000 [X]Malignant neoplasm/other specified male genital organs Byu8100 [X]Malignant neoplasm/overlapping lesion/male genital organs Byu8200 [X]Malignant neoplasm of male genital organ, unspecified Byu9.00 [X]Malignant neoplasm of urinary tract Byu9000 [X]Malignant neoplasm of urinary organ, unspecified ByuA.00 [X]Malignant neoplasm of eye, brain and other parts of cent ByuA000 [X]Malignant neoplasm/other and unspecified cranial nerves ByuA100 [X]Malignant neoplasm/central nervous system, unspecified ByuA200 [X]Malignant neoplasm of meninges, unspecified ByuB.00 [X]Malignant neoplasm of thyroid and other endocrine glands ByuB000 [X]Malignant neoplasm-pluriglandular involvement,unspecified ByuB100 [X]Malignant neoplasm of endocrine gland, unspecified ByuC.00 [X]Malignant neoplasm of ill-defined, secondary and unspeci ByuC000 [X]Malignant neoplasm of other specified sites ByuC100 [X]Malignant neoplasm/overlap lesion/other+ill-defined sites ByuC200 [X]2ndry+unspcf malignant neoplasm lymph nodes/multi regions ByuC300 [X]Secondary malignant neoplasm/oth+unspc respiratory organs ByuC400 [X]Secondary malignant neoplasm/oth+unspcfd digestive organs ByuC500 [X]2ndry malignant neoplasm/bladder+oth+unsp urinary organs ByuC600 [X]2ndry malignant neoplasm/oth+unspec parts/nervous system ByuC700 [X]Secondary malignant neoplasm of other specified sites ByuC800 [X]Malignant neoplasm without specification of site ByuD.00 [X]Malignant neoplasms of lymphoid, haematopoietic and rela ByuD400 [X]Other malignant immunoproliferative diseases ByuDB00 [X]Mal neoplasm/lymphoid,haematopoietic+related tissu,unspcf ByuE.00 [X]Malignant neoplasms/independent (primary) multiple sites ByuE000 [X]Malignant neoplasms/independent(primary)multiple sites C13y300 Diencephalic syndrome secondary to tumour C354A00 Metastatic calcification D212.00 Anaemia in neoplastic disease F11x400 Cerebral degeneration due to neoplastic disease F163100 Myelopathy due to neoplastic disease F326400 Multiple cranial nerve palsies in neoplastic disease F337000 Nerve root and plexus compressions in neoplastic disease F373.00 Polyneuropathy in malignant disease F381100 Myasthenic syndrome due to other malignancy
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F396200 Myopathy due to malignant disease F456400 Glaucoma due to ocular tumour or cyst F4H5100 Disorder of optic chiasm due to non-pituitary neoplasm F4H6000 Visual pathway disorder due to neoplasm F4H7000 Visual cortex disorder due to neoplasm Fyu1300 [X]Paraneoplastic neuromyopathy and neuropathy Fyu1400 [X]Oth systemic atrophy affecting CNS in neoplastc diseas CE Fyu6300 [X]Multiple cranial nerve palsies/neoplastic disease CE Fyu6600 [X]Nerve root+plexus compressions in neoplastic disease CE Fyu7400 [X]Polyneuropathy in neoplastic disease CE Fyu8400 [X]Other myasthenic syndromes in neoplastic disease CE FyuA400 [X]Hydrocephalus in neoplastic disease classified elsewhere H51y700 Malignant pleural effusion K0A6.00 Glomerular disorders in neoplastic diseases K0B1.00 Renal tubulo-interstitial disorder/ neoplastic diseases Kyu0100 [X]Glomerular disorders in neoplastic diseases CE Kyu1800 [X]Renal tubulo-interstitial disordrs/neoplastic diseases CE M144400 Malignant pemphigus N003100 Dermatopolymyositis in neoplastic disease N069.00 Arthropathy in neoplastic disease N311000 Osteitis deformans in neoplastic disease N331700 Fracture of bone in neoplastic disease Nyu4800 [X]Dermato(poly)myositis in neoplastic disease CE Nyu4900 [X]Arthropathy in neoplastic disease classified elsewhere NyuCD00 [X]Osteitis deformans in neoplastic diseases CE NyuCE00 [X]Fracture of bone in neoplastic diseases CE SL07.00 Antineoplastic antibiotic poisoning SL07z00 Antineoplastic antibiotic poisoning NOS SL31.00 Antineoplastic and immunosuppressive poisoning SL31.11 Antineoplastic poisoning SL31z00 Antineoplastic or immunosuppressive poisoning NOS TJ07.00 Adverse reaction to antineoplastic antibiotics TJ07z00 Adverse reaction to antineoplastic antibiotics NOS TJ31.00 Adverse reaction to antineoplastic & immunosuppressive drugs TJ31z00 Adverse reaction to antineoplastic/immunosuppress drugs NOS U603100 [X]Antineoplast antimetabs caus adverse eff in therap use U603200 [X]Antineoplast natural prod caus adverse eff in therap use U603300 [X]Other antineoplast drugs caus adverse eff in therap use U603311 [X] Adverse reaction to antineoplastic antibiotics U60331D [X] Adverse reaction to antineoplastic antibiotics NOS U60331J [X] Adverse react to antineoplastic/immunosuppress drugs NOS ZVu6H00 [X]Personal history/malignant neoplasms/other organs+systems ZVu6J00 [X]Personal history of other neoplasms 4KJ..00 Tumour hormone receptor status
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4KJ0.00 Oestrogen receptor positive tumour 4KJ1.00 Progesterone receptor positive tumour 4KJ2.00 Oestrogen receptor negative tumour 4KJ3.00 Progesterone receptor negative tumour 4M6..00 Recurrence of tumour 9Ow1.00 Bowel cancer detected by national screening programme B937W00 Myelodysplastic syndrome, unspecified ByuHD00 [X]Myelodysplastic syndrome, unspecified
Appendix D.6. Haematology code list (Read codes) Readcode Read term D400400 Agranulocytosis due to infection D400z00 Agranulocytosis NOS D400y00 Other specified agranulocytosis 42H4.00 Agranulocytosis D400.00 Agranulocytosis D400200 Agranulocytosis - drug induced D400811 Acquired agranulocytosis NEC D400000 Idiopathic agranulocytosis D400800 Acquired neutropenia NEC D400900 Cyclical neutropenia D400011 Idiopathic neutropenia D400600 Drug-induced neutropenia D400211 Neutropenia - drug induced D400100 Primary splenic neutropenia D400.12 Neutropenia 42J2.00 Neutropenia D400411 Neutropenia due to infection D400312 Neutropenia due to irradiation 7840300 Splenectomy NEC 7841000 Partial splenectomy 7840100 Total splenectomy 14N7.00 H/O: splenectomy 7840400 Laparoscopic total splenectomy 7840.11 Total splenectomy
Appendix D.7. Other immunocompromising conditions code list (Read codes) Readcode Read term 2J30.00 Patient immunocompromised 2J31.00 Patient immunosuppressed 43C3.00 HTLV-3 antibody positive A788.00 Acquired immune deficiency syndrome AyuC.00 [X]Human immunodeficiency virus disease
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AyuCD00 [X]Unspecified human immunodeficiency virus [HIV] disease A788400 Human immunodeficiency virus with neurological disease A788300 Human immunodeficiency virus with constitutional disease C332.00 Other paraproteinaemias C332z00 Paraproteinaemia NOS C333.00 Macroglobulinaemia D41y100 Myelofibrosis B6..00 Malignant neoplasm of lymphatic and maemopoietic tissue B937W00 Myelodysplatic syndrome, unspecified ByuHD00 [X]Myelodysplastic syndrom, unspecified ByuD.00 [X]Malignant neoplasms of lymphoid, haematopoietic and rela
Appendix D.8. Immunosuppressing medications code list (Product codes) Prodcode Product name Drug substance name Substance BNF code strength 31193 Endoxana 10mg Tablet (Baxter Healthcare Cyclophosphamide 10mg 8010100 Ltd) 26301 Myleran 2mg Tablet (Wellcome Medical Busulfan 2mg 8010100 Division) 37396 Myelobromol 125mg Tablet (Durbin Plc) Mitobronitol 125mg 8010100 34728 Cyclophosphamide 50mg Tablet (Pharmacia Cyclophosphamide 50mg 8010100 Ltd) monohydrate 24448 Treosulfan 250mg capsules Treosulfan 250mg 8010100 29840 Cyclophosphamide 1g powder for solution for Cyclophosphamide 1gram 8010100 injection vials monohydrate 16105 Cyclophosphamide 500mg powder for Cyclophosphamide 500mg 8010100 solution for injection vials monohydrate 26343 Alkeran 2mg Tablet (Wellcome Medical Melphalan 2mg 8010100 Division) 41281 Carmustine 7.7mg implant Carmustine 7.7mg 8010100 12067 Lomustine 40mg capsules Lomustine 40mg 8010100 16838 Leukeran 2mg Tablet (Wellcome Medical Chlorambucil 2mg 8010100 Division) 3985 Cyclophosphamide 50mg tablets Cyclophosphamide 50mg 8010100 monohydrate 22204 Busulfan 500micrograms tablets Busulfan 500mg 8010100 3874 Busulfan 2mg tablets Busulfan 2mg 8010100 8404 Ccnu 40mg Capsule (Lundbeck Ltd) Lomustine 40mg 8010100 26119 Chlormethine 10mg/ml Injection (Sovereign Chlormethine 10mg/ml 8010100 Medical Ltd) Hydrochloride 47767 Bendamustine 25mg powder for solution for Bendamustine 25mg 8010100 infusion vials Hydrochloride 5600 Chlorambucil 2mg tablets Chlorambucil 2mg 8010100 33385 Thiotepa 15mg powder for solution for Thiotepa 15mg 07040400/ injection vials 08010100 10729 Endoxana 50mg tablets (Baxter Healthcare Cyclophosphamide 50mg 8010100 Ltd) monohydrate 23871 Treosulfan 250mg Capsule (Farillon Ltd) Treosulfan 250mg 8010100 26580 Melphalan 100mg/vial Injection Melphalan Hydrochloride 100mg/vial 8010100 13735 Estramustine 140mg capsules Estramustine sodium 140mg 8010100 phosphate 25848 Ccnu 10mg Capsule (Lundbeck Ltd) Lomustine 10mg 8010100 263
13604 Estracyt 140mg capsules (Pfizer Ltd) Estramustine sodium 140mg 8010100 phosphate 47752 Cyclophosphamide 25mg tablets Cyclophosphamide 25mg 8010100 57939 Bendamustine 100mg powder for solution for Bendamustine 100mg 8010100 infusion vials hydrochloride 16929 Melphalan 2mg tablets Melphalan 2mg 8010100 26322 Cyclophosphamide 100mg injection Cyclophosphamide 100mg 8010100 43168 Ifosfamide 2g powder for solution for Ifosfamide 2gram 8010100 injection vials 32412 Myleran 500microgram Tablet (Wellcome Busulfan 500microgra 8010100 Medical Division) m 44309 Endoxana 1g powder for solution for injection Cyclophosphamide 1gram 8010100 vials (Baxter Healthcare Ltd) monohydrate 3984 Cyclophosphamide 10mg tablets Cyclophosphamide 10mg 8010100 37099 Melphalan 50mg powder and solvent for Melphalan hydrochloride 50mg 8010100 solution for injection vials 30051 Mitomycin-C Kyowa 2mg powder for solution Mitomycin 2mg 8010200 for injection vials (ProStrakan Ltd) 39387 Epirubicin 100mg/50ml solution for infusion Epirubicin Hydrochloride 100mg/50ml 8010200 vials 37784 Caelyx 20mg/10ml concentrate for solution Doxorubicin 2mg/1ml 8010200 for infusion vials (Janssen-Cilag Ltd) hydrochloride 40251 Epirubicin 200mg/100ml solution for infusion Epirubicin hydrochloride 2mg/1ml 8010200 vials 30836 Doxorubicin 10mg powder for solution for Doxorubicin 10mg 07040000/ injection vials hydrochloride 08010200 48007 Mitomycin-C Kyowa 40mg powder for Mitomycin 40mg 8010200 solution for injection vials (ProStrakan Ltd) 44594 Mitomycin-C Kyowa 10mg powder for Mitomycin 10mg 8010200 solution for injection vials (ProStrakan Ltd) 26947 Doxorubicin 2mg/ml injection Doxorubicin 2mg/ml 07040400/ Hydrochloride 08010200 25851 Bleo-Kyowa 15,000unit powder for solution Bleomycin sulfate 15000unit 8010200 for injection vials (ProStrakan Ltd) 15405 Novantrone 2mg/ml Concentrate for solution Mitoxantrone 2mg/ml 8010200 for infusion (Wyeth Pharmaceuticals) Hydrochloride 47695 Actinomycin D 500micrograms/vial sterile Dactinomycin 500mg/vial 8010200 powder 28889 Pharmorubicin 2mg/ml Solution for injection Epirubicin Hydrochloride 2mg/ml 07040400/ (Pharmacia Ltd) 08010200 37942 Doxorubicin (liposomal) 50mg powder and Doxorubicin 50mg 8010200 solvent for suspension for infusion vials hydrochloride 11003 Cerubidin 20mg/vial Powder for solution for Daunorubicin 20mg 8010200 injection (Rhone-Poulenc Rorer Ltd) hydrochloride 28325 Epirubicin 50mg powder for solution for Epirubicin hydrochloride 50mg 8010200 injection vials 40816 Epirubicin 50mg/25ml solution for injection Epirubicin hydrochloride 2mg/1ml 8010200 vials 29652 Epirubicin HCL 2mg/ml injection Epirubicin Hydrochloride 2mg/ml 07040400/ 08010200 33878 Myocet 50mg powder and solvent for Doxorubicin 50mg 8010200 suspension for infusion vials (Teva UK Ltd) hydrochloride 31494 Mithramycin 2.5mg/vial Injection Plicamycin 2.5mg/vial 8010200 43805 Daunorubicin 20mg powder for solution for Daunorubicin 20mg 8010200 infusion vials hydrochloride 27469 Adriamycin 50mg/vial Injection (Pharmacia Doxorubicin 50mg 07040400/ Ltd) hydrochloride 08010200 45026 Doxorubicin 200mg/100ml solution for Doxorubicin 2mg/1ml 07040000/ infusion vials hydrochloride 08010200
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32593 Mitomycin 40mg powder for solution for Mitomycin 40mg 8010200 injection vials 43639 Pharmorubicin Rapid Dissolution 50mg Epirubicin hydrochloride 50mg 8010200 powder for solution for injection vials (Pfizer Ltd) 113 Mitomycin 10mg powder for solution for Mitomycin 10mg 8010200 injection vials 24997 Caelyx 2mg/ml Concentrate for solution for Doxorubicin 2mg/ml 8010200 infusion (Schering-Plough Ltd) Hydrochloride 32261 Bleomycin 15,000unit powder for solution for Bleomycin sulfate 15000unit 8010200 injection vials 31539 Epirubicin 20mg powder for solution for Epirubicin hydrochloride 20mg 8010200 injection vials 60004 Mitomycin 0.04% eye drops preservative free 8010200 40250 Caelyx 50mg/25ml concentrate for solution Doxorubicin 2mg/1ml 8010200 for infusion vials (Janssen-Cilag Ltd) hydrochloride 31339 Idarubicin 10mg capsules Idarubicin hydrochloride 10mg 8010200 61458 Mitomycin 20mg powder for solution for Mitomycin 20mg 8010200 injection vials 26680 Adriamycin 10mg/vial Injection (Pharmacia Doxorubicin 10mg 07040400/ Ltd) hydrochloride 08010200 31984 Idarubicin 5mg capsules Idarubicin hydrochloride 5mg 8010200 39307 Doxorubicin 10mg/5ml solution for injection Doxorubicin 2mg/1ml 07040000/ vials hydrochloride 08010200 33174 Mitoxantrone 2mg/ml Concentrate for Mitoxantrone 2mg/ml 8010200 solution for infusion Hydrochloride 56410 Caelyx 50mg/25ml concentrate for solution Doxorubicin 2mg/1ml 8010200 for infusion vials (Janssen-Cilag Ltd) hydrochloride liposomal pegylated 41267 Mitoxantrone 20mg/10ml solution for Mitoxantrone 2mg/1ml 8010200 infusion vials hydrochloride 19287 Mitomycin 2mg powder for solution for Mitomycin 2mg 8010200 injection vials 55271 Doxorubicin encapsulated in liposomes Doxorubicin 8010200 2mg/ml concentrate solution for infusion Hydrochloride 30014 Mithracin 2.5mg/vial Injection (Pfizer Ltd) Plicamycin 2.5mg/vial 8010200 18476 Fludarabine phosphate 10mg tablets Fludarabine phosphate 10mg 8010300 60979 Methotrexate 2.5mg tablets (Morningside Methotrexate 2.5mg 01050300/ Healthcare Ltd) 08010300/ 10010302/ 13050300 14348 Metoject 20mg/2ml solution for injection pre- Methotrexate sodium 10mg/1ml 01050300/ filled syringes (medac UK) 08010300/ 10010302/ 13050300 29743 Fludara 10mg tablets (Sanofi) Fludarabine phosphate 10mg 8010300 19556 Fluoro-uracil 250mg Capsule (Cambridge Fluorouracil 250mg 8010300 Laboratories Ltd) 52333 Mercaptopurine 75mg/5ml oral suspension Mercaptopurine 15mg/1ml 01050300/ 08010300 41086 Methotrexate 5g/50ml solution for infusion Methotrexate sodium 100mg/1ml 01050300/ vials 08010300/ 13050300 32111 Methotrexate 2.5mg tablets (Hospira UK Ltd) Methotrexate 2.5mg 01050300/ 08010300/ 10010302/ 13050300 47369 Mercaptopurine Oral solution Mercaptopurine 01050300/ 08010300
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55091 Fluorouracil 500mg/10ml solution for Fluorouracil sodium 50mg/1ml 8010300 injection vials 8583 Methotrexate 25mg/ml Injection Methotrexate Sodium 25mg/ml 01050300/ 08010300 7340 Capecitabine 150mg tablets Capecitabine 150mg 8010300 33127 Xeloda 150mg tablets (Roche Products Ltd) Capecitabine 150mg 8010300 56753 Mercaptopurine 25mg tablets Mercaptopurine 25mg 01050300/ 08010300 33601 Metoject 25mg/2.5ml solution for injection Methotrexate sodium 10mg/1ml 01050300/ pre-filled syringes (medac UK) 08010300/ 10010302/ 13050300 46039 Methotrexate 30mg/1.5ml solution for Methotrexate 30mg/1.5ml 01050300/ injection pre-filled syringes 08010300/ 10010302/ 13050300 18063 Xeloda 500mg tablets (Roche Products Ltd) Capecitabine 500mg 8010300 17035 Methotrexate 2.5mg/5ml oral suspension Methotrexate 500mg/1ml 08010300/ 10010302/ 13050300 61545 Mercaptopurine 50mg tablets (Aspen Pharma Mercaptopurine 50mg 01050300/ Trading Ltd) 08010300 7337 Methotrexate 10mg/0.4ml solution for Methotrexate sodium 25mg/1ml 08010300/ injection pre-filled syringes 10010302/ 13050300 21753 Maxtrex 10mg tablets (Pfizer Ltd) Methotrexate 10mg 01050300/ 08010300/ 10010302/ 13050300 36575 Fluorouracil 50mg/ml Injection Fluorouracil 50mg/ml 8010300 36167 Methotrexate 1g/10ml solution for injection Methotrexate sodium 100mg/1ml 01050300/ vials 08010300/ 13050300 32229 Methotrexate 500mg/20ml solution for Methotrexate sodium 25mg/1ml 01050300/ injection vials 08010300/ 13050300 35402 Methotrexate 7.5mg/0.75ml solution for Methotrexate 7.5mg/0.75ml 01050300/ injection pre-filled syringes 08010300/ 10010302/ 13050300 57239 Mercaptopurine 20mg/ml oral suspension Mercaptopurine 20mg/1ml 8010300 57441 Methotrexate 10mg tablets (A A H Methotrexate 10mg 01050300/ Pharmaceuticals Ltd) 08010300/ 10010302/ 13050300 27404 Methotrexate 15mg/1.5ml solution for Methotrexate 15mg/1.5ml 01050300/ injection pre-filled syringes 08010300/ 10010302/ 13050300 20094 Tioguanine 40mg tablets Tioguanine 40mg 8010300 44222 Raltitrexed 2mg powder for solution for Raltitrexed 2mg 8010300 infusion vials 30780 Methotrexate 2.5mg Tablet (Pharmacia Ltd) Methotrexate 2.5mg 01050300/ 08010300 27642 Methotrexate 27.5mg/1.1ml solution for Methotrexate sodium 25mg/1ml 08010300/ injection pre-filled syringes 10010302/ 13050300 56037 Methotrexate 2.5mg tablets (A A H Methotrexate 2.5mg 01050300/ Pharmaceuticals Ltd) 08010300/ 10010302/ 266
13050300 32972 Mercaptopurine 10mg tablets Mercaptopurine 10mg 01050300/ 08010300 7336 Methotrexate 12.5mg/0.5ml solution for Methotrexate sodium 25mg/1ml 08010300/ injection pre-filled syringes 10010302/ 13050300 59538 Methotrexate 10mg tablets (Teva UK Ltd) Methotrexate 10mg 01050300/ 08010300/ 10010302/ 13050300 55772 Mercaptopurine 25mg/5ml oral suspension Mercaptopurine 5mg/1ml 01050300/ 08010300 35752 Methotrexate 7.5mg/5ml oral suspension Methotrexate 1.5mg/1ml 08010300/ 10010302/ 13050300 24634 Methotrexate 25mg/2.5ml solution for Methotrexate 25mg/2.5ml 01050300/ injection pre-filled syringes 08010300/ 10010302/ 13050300 27400 Metoject 15mg/1.5ml solution for injection Methotrexate sodium 10mg/1ml 01050300/ pre-filled syringes (medac UK) 08010300/ 10010302/ 13050300 58885 Methotrexate 10mg tablets (Sigma Methotrexate 10mg 01050300/ Pharmaceuticals Plc) 08010300/ 10010302/ 13050300 40780 Gemcitabine 1g powder for solution for Gemcitabine 1gram 8010300 infusion vials hydrochloride 59842 Azacitidine 100mg powder for suspension for 8010300 injection vials 37117 Metoject 10mg/1ml solution for injection pre- Methotrexate 10mg/1ml 01050300/ filled syringes (medac UK) 08010300/ 10010302/ 13050300 58303 Methotrexate 2.5mg tablets (Orion Pharma Methotrexate 2.5mg 01050300/ (UK) Ltd) 08010300/ 10010302/ 13050300 16570 Methotrexate 7.5mg/0.3ml solution for Methotrexate sodium 25mg/1ml 08010300/ injection pre-filled syringes 10010302/ 13050300 33418 Gemcitabine 200mg powder for solution for Gemcitabine 200mg 8010300 infusion vials hydrochloride 49951 Methotrexate 2.5mg tablets (Sandoz Ltd) Methotrexate 2.5mg 01050300/ 08010300/ 10010302/ 13050300 26502 Lanvis 40mg tablets (Aspen Pharma Trading Tioguanine 40mg 8010300 Ltd) 29069 Methotrexate 500mg/vial sterile powder Methotrexate Sodium 500mg/vial 01050300/ 08010300 36849 Methotrexate 10mg/5ml oral suspension Methotrexate 2mg/1ml 08010300/ 10010302/ 13050300 18890 Methotrexate 17.5mg/0.7ml solution for Methotrexate sodium 25mg/1ml 08010300/ injection pre-filled syringes 10010302/ 13050300 37272 Pemetrexed 500mg powder for solution for Pemetrexed disodium 500mg 8010300 infusion vials 47522 Cytarabine 100mg/5ml solution for injection Cytarabine 20mg/1ml 8010300
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vials 45558 Methotrexate 25mg/1.25ml solution for Methotrexate 25mg/1.25ml 01050300/ injection pre-filled syringes 08010300/ 10010302/ 13050300 51120 Methotrexate 2.5mg tablets (Alliance Methotrexate 2.5mg 01050300/ Healthcare (Distribution) Ltd) 08010300/ 10010302/ 13050300 19982 Mercaptopurine 10mg capsules Mercaptopurine 10mg 01050300/ 08010300 57174 Methotrexate 10mg tablets (Waymade Methotrexate 10mg 01050300/ Healthcare Plc) 08010300/ 10010302/ 13050300 34258 Methotrexate 20mg/0.8ml Injection (Central Methotrexate Sodium 20mg/0.8ml 01050300/ Homecare) 08010300 36800 Methotrexate 10mg/5ml oral solution Methotrexate 2mg/1ml 08010300/ 10010302/ 13050300 14748 Methotrexate sodium 25mg/ml Injection Methotrexate Sodium 25mg/ml 01050300/ 08010300 33519 Uftoral capsules (Merck Serono Ltd) Tegafur/Uracil 100mg + 8010300 224mg 823 Methotrexate 2.5mg tablets Methotrexate 2.5mg 01050300/ 08010300/ 10010302/ 13050300 47450 Cytosar 100mg Injection (Pharmacia Ltd) Cytarabine 100mg 8010300 53696 Methotrexate 50mg/2ml solution for Methotrexate sodium 25mg/1ml 01050300/ injection vials (A A H Pharmaceuticals Ltd) 08010300/ 13050300 46407 Methotrexate 1g/40ml solution for injection Methotrexate sodium 25mg/1ml 01050300/ vials 08010300/ 13050300 30932 Methotrexate 5mg/0.2ml solution for Methotrexate sodium 25mg/1ml 08010300/ injection pre-filled syringes 10010302/ 13050300 35865 Metoject 7.5mg/0.75ml solution for injection Methotrexate 7.5mg/0.75ml 01050300/ pre-filled syringes (medac UK) 08010300/ 10010302/ 13050300 8327 Methotrexate 50mg/3ml Injection Methotrexate Sodium 50mg/3ml 01050300/ 08010300 20951 Methotrexate 2.5mg tablets (Mercury Methotrexate 2.5mg 01050300/ Pharma Group Ltd) 08010300/ 10010302/ 13050300 41266 Cytarabine 1g/10ml solution for injection vials Cytarabine 100mg/1ml 8010300 29675 Puri-Nethol 50mg tablets (Aspen Pharma Mercaptopurine 50mg 01050300/ Trading Ltd) 08010300 24681 Fludarabine phosphate 50mg powder for Fludarabine phosphate 50mg 8010300 solution for injection vials 58240 Fluorouracil 2.5g/100ml solution for infusion Fluorouracil sodium 25mg/1ml 8010300 vials 39388 Fluorouracil 1g/20ml solution for injection Fluorouracil sodium 50mg/1ml 8010300 vials 3450 Mercaptopurine 50mg tablets Mercaptopurine 50mg 01050300/ 08010300 51667 Methotrexate 200mg/8ml solution for Methotrexate sodium 25mg/1ml 01050300/ injection vials 08010300/ 268
13050300 7341 Capecitabine 500mg tablets Capecitabine 500mg 8010300 26064 Methotrexate 20mg/2ml solution for Methotrexate sodium 10mg/1ml 01050300/ injection pre-filled syringes 08010300/ 10010302/ 13050300 49547 Methotrexate 5g/200ml solution for infusion Methotrexate sodium 25mg/1ml 01050300/ vials 08010300/ 13050300 59723 Methotrexate 7.5mg/5ml oral solution Methotrexate 1.5mg/1ml 08010300/ 10010302/ 13050300 41585 Methotrexate sodium 2.5mg Tablet (Wyeth Methotrexate 2.5mg 01050300/ Pharmaceuticals) 08010300 34929 Methotrexate 10mg tablets (Hospira UK Ltd) Methotrexate 10mg 01050300/ 08010300/ 10010302/ 13050300 41104 Methotrexate 2.5mg tablets (Wockhardt UK Methotrexate 2.5mg 01050300/ Ltd) 08010300/ 10010302/ 13050300 9528 Methotrexate 5mg/2ml solution for injection Methotrexate sodium 2.5mg/1ml 01050300/ vials 08010300/ 13050300 14347 Methotrexate 20mg/0.8ml solution for Methotrexate Sodium 20mg/0.8ml 08010300/ injection pre-filled syringes 10010302/ 13050300 28041 Methotrexate 12.5mg/5ml oral suspension Methotrexate 2.5mg/1ml 08010300/ 10010302/ 13050300 12816 Methotrexate 100mg/ml Injection Methotrexate Sodium 100mg/ml 01050300/ 08010300 27342 Maxtrex 2.5mg/ml Injection (Pharmacia Ltd) Methotrexate sodium 2.5mg/1ml 01050300/ 08010300 18424 Methotrexate sodium 2.5mg Tablet Methotrexate 2.5mg 01050300/ 08010300 40454 Cladribine 10mg/5ml solution for injection Cladribine 2mg/1ml 8010300 vials 53385 Methotrexate 2.5mg tablets (Waymade Methotrexate 2.5mg 01050300/ Healthcare Plc) 08010300/ 10010302/ 13050300 51321 Methotrexate 50mg/2ml solution for Methotrexate sodium 25mg/1ml 01050300/ injection vials 08010300/ 13050300 32865 Methotrexate 10mg/1ml solution for Methotrexate 10mg/1ml 01050300/ injection pre-filled syringes 08010300/ 10010302/ 13050300 52606 Methotrexate 2.5mg tablets (Sigma Methotrexate 2.5mg 01050300/ Pharmaceuticals Plc) 08010300/ 10010302/ 13050300 877 Methotrexate 10mg tablets Methotrexate 10mg 01050300/ 08010300/ 10010302/ 13050300 30703 Methotrexate 30mg/1.2ml solution for Methotrexate sodium 25mg/1ml 08010300/ injection pre-filled syringes 10010302/ 13050300 269
17672 Methotrexate 22.5mg/0.9ml solution for Methotrexate Sodium 22.5mg/0.9ml 08010300/ injection pre-filled syringes 10010302/ 13050300 13428 Maxtrex 2.5mg tablets (Pfizer Ltd) Methotrexate 2.5mg 01050300/ 08010300/ 10010302/ 13050300 16540 Methotrexate 15mg/0.6ml solution for Methotrexate Sodium 15mg/0.6ml 08010300/ injection pre-filled syringes 10010302/ 13050300 24783 Methotrexate 50mg/2ml Injection Methotrexate Sodium 50mg/2ml 01050300/ 08010300 33520 Tegafur 100mg / Uracil 224mg capsules Tegafur/Uracil 100mg + 8010300 224mg 61085 Methotrexate 2.5mg tablets (Waymade Methotrexate 2.5mg 01050300/ Healthcare Plc) 08010300/ 10010302/ 13050300 16519 Methotrexate 25mg/1ml solution for Methotrexate Sodium 25mg/1ml 08010300/ injection pre-filled syringes 10010302/ 13050300 59685 Methotrexate 2.5mg tablets (Teva UK Ltd) Methotrexate 2.5mg 01050300/ 08010300/ 10010302/ 13050300 45165 Methotrexate 20mg/1ml solution for Methotrexate 20mg/1ml 01050300/ injection pre-filled syringes 08010300/ 10010302/ 13050300 20229 Fluoro-uracil 25mg/ml Injection (Cambridge Fluorouracil 25mg/ml 8010300 Laboratories Ltd) 19335 Cytosar 500mg Injection (Pharmacia Ltd) Cytarabine 500mg 8010300 27224 Oncovin 2mg Injection (Eli Lilly and Company Vincristine Sulphate 2mg 8010400 Ltd) 18751 Etoposide 100mg capsules Etoposide 100mg 8010400 26316 Vincristine sulphate 2mg injection Vincristine Sulphate 2mg 8010400 25589 Vincristine sulphate 1mg/ml injection Vincristine Sulphate 1mg/ml 8010400 18914 Vincristine sulphate 1mg injection Vincristine Sulphate 1mg 8010400 40659 Vincristine sulphate 5mg injection Vincristine Sulphate 5mg 8010400 32604 Vinblastine 10mg/10ml solution for injection Vinblastine sulfate 1mg/1ml 8010400 vials 48177 Etoposide 100mg/5ml solution for infusion Etoposide 20mg/1ml 8010400 vials 46838 Vinorelbine 80mg capsules Vinorelbine Tartrate 80mg 8010400 37375 Vepesid 100mg capsules (Bristol-Myers Etoposide 100mg 8010400 Squibb Pharmaceuticals Ltd) 36263 Eposin 20mg/ml Concentrate for solution for Etoposide 20mg/ml 8010400 infusion (medac UK) 44387 Etoposide 100mg powder for solution for Etoposide phosphate 100mg 8010400 injection vials 8756 Etoposide 50mg capsules Etoposide 50mg 8010400 42684 Vinorelbine 10mg/1ml solution for infusion Vinorelbine tartrate 10mg/1ml 8010400 vials 55823 Vincristine 5mg/5ml solution for injection Vincristine sulfate 1mg/1ml 8010400 vials 29761 Vepesid 50mg capsules (Bristol-Myers Squibb Etoposide 50mg 8010400 Pharmaceuticals Ltd) 33171 Vinorelbine 10mg/ml injection solution Vinorelbine 10mg/ml 8010400
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31115 Etoposide 20mg/ml Solution for infusion Etoposide 20mg/ml 8010400 32774 Vinorelbine 20mg capsules Vinorelbine Tartrate 20mg 8010400 40649 Vincristine 1mg/1ml solution for injection Vincristine sulfate 1mg/1ml 8010400 vials 42372 Temozolomide 140mg capsules Temozolomide 140mg 8010500 21250 Temozolomide 250mg capsules Temozolomide 250mg 8010500 25904 Arsenic 10mg/10ml solution for infusion Arsenic trioxide 1mg/1ml 8010500 ampoules 44740 Bortezomib 3.5mg powder for solution for Bortezomib 3.5mg 8010500 injection vials 40094 Erlotinib 100mg tablets Erlotinib hydrochloride 100mg 08010500/ 08010551 29700 Temodal 250mg capsules (Merck Sharp & Temozolomide 250mg 8010500 Dohme Ltd) 3873 Hydroxyurea Capsule Hydroxycarbamide 500mg 8010500 35826 Mitotane 500mg tablets Mitotane 500mg 8010500 27071 Dactinomycin 500microgram powder for Dactinomycin 500mg 8010500 solution for injection vials 25740 Avastin 100mg/4ml solution for infusion vials Bevacizumab 25mg/1ml 8010500 (Roche Products Ltd) 6333 Hydroxycarbamide 500mg capsules Hydroxycarbamide 500mg 08010500/ 09010300 38081 Erwinase 10,000unit powder for solution for Crisantaspase 10000unit 8010500 injection vials (EUSA Pharma Ltd) 43120 Trisenox 10mg/10ml concentrate for solution Arsenic trioxide 1mg/1ml 8010500 for infusion ampoules (Teva UK Ltd) 27292 Herceptin 150mg powder for solution for Trastuzumab 150mg 8010500 infusion vials (Roche Products Ltd) 59362 Trabectedin 1mg powder for solution for Trabectedin 1mg 8010500 infusion vials 12066 Razoxin 125mg Tablet (Cambridge Razoxane 125mg 8010500 Laboratories Ltd) 47194 Asparaginase 5000 unit/vial Powder for Colaspase 5000 Unit/vial 8010500 solution for injection (Imported) 33330 Hydroxycarbamide 500mg capsules (medac Hydroxycarbamide 500mg 08010500/ UK) 09010300 43888 Pentostatin 10mg powder for solution for Pentostatin 10mg 8010500 injection vials 58142 Brentuximab vedotin 50mg powder for solution for infusion vials 8010500 28682 Dacarbazine 200mg powder for solution for Dacarbazine citrate 200mg 8010500 injection vials 38145 Bevacizumab 100mg/4ml solution for infusion Bevacizumab 25mg/1ml 8010500 vials 58496 Cosmegen Lyovac 500microgram powder for Dactinomycin 500mg 8010500 solution for injection vials (Lundbeck Pharmaceuticals Ireland Ltd) 17186 Procarbazine 50mg capsules Procarbazine 50mg 8010500 hydrochloride 6884 Hydrea 500mg capsules (Bristol-Myers Squibb Hydroxycarbamide 500mg 08010500/ Pharmaceuticals Ltd) 09010300 31076 Trastuzumab 150mg powder for solution for Trastuzumab 150mg 8010500 infusion vials 35226 Temodal 20mg capsules (Merck Sharp & Temozolomide 20mg 8010500 Dohme Ltd) 37441 Tarceva 25mg tablets (Roche Products Ltd) Erlotinib hydrochloride 25mg 08010500/ 08010551 38158 Cetuximab 100mg/20ml solution for infusion Cetuximab 5mg/1ml 8010500 vials
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56041 Ipilimumab 50mg/10ml solution for infusion vials 8010500 32204 Dtic-dome 100mg/vial Injection (Bayer Plc) Dacarbazine citrate 100mg 8010500 44478 Lysodren 500mg tablets (HRA Pharma UK Ltd) Mitotane 500mg 8010500 33827 Erlotinib 150mg tablets Erlotinib hydrochloride 150mg 08010500/ 08010551 39791 Metvix 16% cream (Galderma (UK) Ltd) Methyl aminolevulinate 160mg/1g 08010500/ hydrochloride 13080100 45992 Erlotinib 25mg tablets Erlotinib hydrochloride 25mg 08010500/ 08010551 21249 Temozolomide 100mg capsules Temozolomide 100mg 8010500 58862 Ipilimumab 200mg/40ml solution for infusion Ipilimumab 5mg/1ml 8010500 vials 32490 Temozolomide 20mg capsules Temozolomide 20mg 8010500 47556 Tarceva 100mg tablets (Roche Products Ltd) Erlotinib hydrochloride 100mg 08010500/ 08010551 831 Methyl aminolevulinate 16% cream Methyl aminolevulinate 160mg/1g 08010500/ hydrochloride 13080100 50565 Hydroxycarbamide 300mg capsules Hydroxycarbamide 300mg 08010500/ 09010300 38319 Hydroxycarbamide 500mg/5ml oral solution Hydroxycarbamide 100mg/1ml 08010500/ 09010300 28605 Natulan 50mg Capsule (Cambridge Procarbazine 50mg 8010500 Laboratories Ltd) hydrochloride 36769 Bexarotene 75mg capsules Bexarotene 75mg 8010500 25262 Tarceva 150mg tablets (Roche Products Ltd) Erlotinib hydrochloride 150mg 08010500/ 08010551 18238 Dacarbazine 100mg powder for solution for Dacarbazine citrate 100mg 8010500 injection vials 45122 Panitumumab 100mg/5ml solution for Panitumumab 20mg/1ml 8010500 infusion vials 33803 Temodal 5mg capsules (Merck Sharp & Temozolomide 5mg 8010500 Dohme Ltd) 59276 Erbitux 100mg/20ml solution for infusion vials Cetuximab 5mg/1ml 8010500 (Merck Serono Ltd) 58819 Herceptin 600mg/5ml solution for injection vials (Roche Products Ltd) 8010500 55077 Temodal 100mg capsules (Merck Sharp & Temozolomide 100mg 8010500 Dohme Ltd) 40732 Velcade 3.5mg powder for solution for Bortezomib 3.5mg 8010500 injection vials (Janssen-Cilag Ltd) 59007 Hydroxycarbamide 500mg capsules (A A H Hydroxycarbamide 500mg 08010500/ Pharmaceuticals Ltd) 09010300 27922 Temozolomide 5mg capsules Temozolomide 5mg 8010500 61366 Abraxane 100mg powder for suspension for Paclitaxel albumin 100mg 8010500 infusion vials (Celgene Ltd) 40379 Targretin 75mg capsules (Eisai Ltd) Bexarotene 75mg 8010500 60112 Erivedge 150mg capsules (Roche Products Vismodegib 150mg 8010500 Ltd) 53166 Sodium aurothiomalate 50mg/0.5ml injection Sodium Aurothiomalate 10010303 16606 Sodium aurothiomalate 20mg/0.5ml solution Sodium aurothiomalate 40mg/1ml 0 for injection ampoules 10842 Sodium aurothiomalate 10mg/0.5ml solution Sodium aurothiomalate 20mg/1ml 10010303 for injection ampoules 4470 Sodium aurothiomalate 50mg/0.5ml solution Sodium aurothiomalate 100mg/1ml 10010303 for injection ampoules 52868 Penicillamine 250mg tablets (Phoenix Penicillamine 250mg 10010300 Healthcare Distribution Ltd) 48061 Penicillamine oral liquid Penicillamine 09080150/ 54070000 272
58953 Penicillamine 250mg tablets (Waymade Penicillamine 250mg 10010300 Healthcare Plc) 54257 Penicillamine 250mg tablets (Alliance Penicillamine 250mg 10010300 Healthcare (Distribution) Ltd) 57183 Penicillamine 125mg tablets (A A H Penicillamine 125mg 10010300 Pharmaceuticals Ltd) 60689 Penicillamine 125mg/5ml oral solution Penicillamine 25mg/1ml 10010300 643 Penicillamine 125mg tablets Penicillamine 125mg 10010300 267 Penicillamine 50mg tablets Penicillamine 50mg 09080150/ 10010300 40170 Penicillamine 125mg tablets (Generics (UK) Penicillamine 125mg 10010300 Ltd) 30925 Penicillamine 250mg tablets (Actavis UK Ltd) Penicillamine 250mg 10010300 604 Penicillamine 250mg tablets Penicillamine 250mg 10010300 59312 Penicillamine 125mg tablets (Kent Penicillamine 125mg 10010300 Pharmaceuticals Ltd) 56872 Penicillamine 125mg tablets (Phoenix Penicillamine 125mg 10010300 Healthcare Distribution Ltd) 31217 Penicillamine 250mg tablets (A A H Penicillamine 250mg 10010300 Pharmaceuticals Ltd) 31120 Penicillamine 125mg Tablet (IVAX Penicillamine 125mg 09080150/ Pharmaceuticals UK Ltd) 10010300 34684 Penicillamine 250mg tablets (Generics (UK) Penicillamine 250mg 10010300 Ltd) 58468 Penicillamine 125mg tablets (Teva UK Ltd) Penicillamine 125mg 10010300 55433 Penicillamine 250mg tablets (Kent Penicillamine 250mg 10010300 Pharmaceuticals Ltd) 57789 Penicillamine 125mg tablets (Alliance Penicillamine 125mg 10010300 Healthcare (Distribution) Ltd) 31216 Penicillamine 125mg tablets (Actavis UK Ltd) Penicillamine 125mg 10010300 4971 Leflunomide 10mg tablets Leflunomide 10mg 10010302 48217 Leflunomide 10mg tablets (medac UK) Leflunomide 10mg 10010302 4970 Leflunomide 100mg tablets Leflunomide 100mg 10010302 6934 Leflunomide 20mg tablets Leflunomide 20mg 10010302
Appendix D.9. Bloodstream infection code list (ICD 10 codes in HES data) CODE ALT_CODE DESCRIPTION A02.1 A021 Salmonella sepsis A22.7 A227 Anthrax sepsis A26.7 A267 Erysipelothrix sepsis A32.7 A327 Listerial sepsis A40 A40 Streptococcal sepsis A40.0 A400 Sepsis due to streptococcus, group A A40.1 A401 Sepsis due to streptococcus, group B A40.2 A402 Sepsis due to streptococcus, group D A40.3 A403 Sepsis due to Streptococcus pneumoniae A40.8 A408 Other streptococcal sepsis A40.9 A409 Streptococcal sepsis, unspecified A41 A41 Other sepsis A41.0 A410 Sepsis due to Staphylococcus aureus
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A41.1 A411 Sepsis due to other specified staphylococcus A41.2 A412 Sepsis due to unspecified staphylococcus A41.3 A413 Sepsis due to Haemophilus influenzae A41.4 A414 Sepsis due to anaerobes A41.5 A415 Sepsis due to other Gram-negative organisms A41.8 A418 Other specified sepsis A41.9 A419 Sepsis, unspecified A42.7 A427 Actinomycotic sepsis R57.2 R572 Septic shock A49.9 A499 Bacteremia NOS A20.7 A207 Septicaemic plague A39.2 A392 Acute meningococcaemia A39.3 A393 Chronic meningococcaemia A39.4 A394 Meningococcaemia, unspecified R65.0 R650 Systemic Inflammatory Response Syndrome of infectious origin without organ failure R65.1 R651 Systemic Inflammatory Response Syndrome of infectious origin with organ failure
Appendix D.10. Bloodstream infection code list (Read codes in CPRD data) Readcode Read term A38z.11 Sepsis A38..00 Septicaemia A3C..00 Sepsis A362.00 Meningococcal septicaemia K190600 Urosepsis A38z.00 Septicaemia NOS J666.00 Biliary sepsis A381.00 Staphylococcal septicaemia R106.00 [D]Unspecified bacteraemia A380.00 Streptococcal septicaemia A382.00 Pneumococcal septicaemia A384200 Escherichia coli septicaemia A384211 E.coli septicaemia 1JN0.00 Suspected sepsis L40..11 Sepsis - puerperal A384.00 Septicaemia due to other gram negative organisms A021.00 Salmonella septicaemia A3Cz.00 Sepsis NOS A3Cy.00 Other specified sepsis A381000 Septicaemia due to Staphylococcus aureus A38y.00 Other specified septicaemias A380100 Septicaemia due to streptococcus, group B A384300 Pseudomonas septicaemia A384000 Gram negative septicaemia NOS
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A380300 Septicaemia due to streptococcus pneumoniae A384100 Haemophilus influenzae septicaemia A380400 Septicaemia due to enterococcus A380000 Septicaemia due to streptococcus, group A Ayu3J00 [X]Septicaemia, unspecified A270100 Listeria septicaemia A383.00 Septicaemia due to anaerobes A3C3.00 Sepsis due to Gram negative bacteria A381100 Septicaemia due to coagulase-negative staphylococcus A98yz12 Gonococcal septicaemia L403.00 Puerperal septicaemia A3C0100 Sepsis due to Streptococcus group B A380500 Vancomycin resistant enterococcal septicaemia A3C1.00 Sepsis due to Staphylococcus A384400 Serratia septicaemia H5y0100 Tracheostomy sepsis A3C2.11 Sepsis due to anaerobes A3C1000 Sepsis due to Staphylococcus aureus A3C0300 Sepsis due to Streptococcus pneumoniae A3C0000 Sepsis due to Streptococcus group A A3C0.00 Sepsis due to Streptococcus A270611 Listerial sepsis A384z00 Other gram negative septicaemia NOS A3C0z00 Streptococcal sepsis, unspecified A3C0y00 Other streptococcal sepsis A023.00 Salmonella sepsis Ayu3F00 [X]Streptococcal septicaemia, unspecified A396.00 Sepsis due to Actinomyces A3C2.00 Sepsis due to anaerobic bacteria Ayu3E00 [X]Other streptococcal septicaemia A271100 Erysipelothrix septicaemia
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Appendix E – Data cleaning processes for Therapy, Test and Consultation/Staff/Patient/Practice files
Appendix E.1. Data cleaning process for Therapy file, including direct linkage with the Common Dosages Lookup file.
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Appendix E.2. Data cleaning process for Test file.
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Appendix E.3. Data cleaning process for Consultation/Staff/Patient/Practice file, including direct linkage with IMD 2010 file.
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Raw Clinical File N=65,696,729 Unique patids=420,922
Drop records that are not UTI-related N=65,028,322
UTI medcodes only N=668,407
Drop records that are in Pre-Study period N=87,193
UTI medcodes in Study or Post-Study period only N=581,214
True and pseudo duplicate records removed (across “not applicable” variables) N=3,799
Final de-duplicated Clinical dataset N=577,415 Unique patids=260,148
Cleaned Clinical file
Clin_eventdate Pregnancy register (all pregnancies in CPRD) – merge on UTI in gestational date range
Clinical file with UTIs in pregnancy flagged N=19,663 UTIs in pregnancy Unique patid=14,367 UTI_preg_flag
Appendix E.4. Data cleaning process for Clin_Add file, including data range linkage lookup with Pregnancy register.
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Appendix F - Scaled Schoenfeld residuals plots for testing the proportionality assumption for the survival analyses in Chapter 4
Appendix F.1. Scaled Schoenfeld residual plots to test the proportionality assumption. (Top left) exposure group; (top right) age category; (middle left) patient gender; (middle right) region category; (bottom left) deprivation quintile; (bottom right) antibiotics in the month previous to UTI diagnosis. 280
Appendix F.2. Scaled Schoenfeld residual plots to test the proportionality assumption. (Top left) UTI test; (top right) history of diabetes in previous year; (middle left) history of cardiovascular disease in previous year; (middle right) history of renal abnormality in previous year; (bottom left) history of urinary catheterization in previous year; (bottom right) history of penicillin allergy in previous year.
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Appendix G – Kaplan-Meier survival estimates by day since UTI diagnosis in non-recurrent UTI patients
Appendix G.1. Kaplan-Meier survival estimate by day since UTI diagnosis in non- recurrent UTI patients (regardless of outcome).
Appendix G.2. Kaplan-Meier survival estimate by day since UTI diagnosis in non-recurrent UTI patients (regardless of outcome), stratified by treatment group. 282