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Improving treatment outcomes of Pradipta, Ivan

DOI: 10.33612/diss.113506043

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Download date: 05-10-2021 Improving treatment outcomes of tuberculosis Towards an antimicrobial stewardship program

Ivan Surya Pradipta

1

Improving treatment outcomes of tuberculosis Towards an antimicrobial stewardship program

ISBN : 978-94-034-2378-4 (printed book) ISBN : 978-94-034-2379-1 (electronic book)

Author : Ivan Surya Pradipta Cover : Ivan Surya Pradipta (concept) and Hartanto (graphic design) Lay-out : Lara Leijtens, persoonlijkproefschrift.nl Printing : Ridderprint BV | www.ridderprint.nl

This thesis was conducted within the Groningen University Institute for Drug Exploration (GUIDE). Financial support for the printing of this thesis was kindly provided by the Graduate School of Sciences, University of Groningen, the Netherlands.

Ivan Surya Pradipta received a Ph.D. scholarship from the Indonesia Endowment Fund for Education or LPDP to conduct all studies in this book.

Copyright ©Ivan Surya Pradipta, Groningen 2020. All rights reserved. No part of this thesis may be produced or transmitted in any from or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without written permission of author. The copyright of previously published chapters of this remains with the publisher or journal.

Improving treatment outcomes of tuberculosis

Towards an antimicrobial stewardship program

PhD thesis

to obtain the degree of PhD at the University of Groningen on the authority of the Rector Magnificus Prof. C. Wijmenga and in accordance with the decision by the College of Deans.

This thesis will be defended in public on

Monday 24 February 2020 at 11.00 hours

by

Ivan Surya Pradipta

born on 20 June 1983 in Yogyakarta, Indonesia

3

Supervisors Prof. E. Hak Prof. J.W.C. Alffenaar

Assessment Committee Prof. F.G.J. Cobelens Prof. K. Taxis Prof. T.S. van der Werf

4

To my respected teachers, my beloved parents, brothers, wife and children. Paranymphs: Sylvi Irawati Sofa Dewi Alfian

6

TABLE OF CONTENTS

Chapter 1 General Introduction 8

Chapter 2 Risk factors of multidrug-resistant tuberculosis: a global systematic 18 review and meta-analysis Journal of 2018 Dec;77(6):469-478.

Chapter 3 Predictors for treatment outcomes among patients with drug- 48 susceptible tuberculosis in the Netherlands: a retrospective cohort study Clinical Microbiology and Infection 2019 Jun;25(6):761.e1-761.e7.

Chapter 4 Treatment outcomes of drug-resistant tuberculosis in the 72 Netherlands, 2005-2015 Antimicrobial Resistance and Infection Control 2019 Jul 12;8:115.

Chapter 5 A systematic review of measures to estimate and 100 persistence to multiple Journal of Clinical Epidemiology 2019 Apr;108:44-53.

Chapter 6 Interventions to improve adherence in patients with 126 latent and active tuberculosis infection: a systematic review of randomized controlled studies Submitted

Chapter 7 Barriers and strategies to successful tuberculosis treatment in 150 a high-burden tuberculosis setting: a qualitative study from the patient’s perspective Submitted

Chapter 8 Discussion and future perspectives 178

Addendum Summary 194 Nederlandse Samenvatting 197 Ringkasan 201 Acknowledgements 205 About the Author 208 PhD Portfolio 209 Hamburg, 2018 CHAPTER GENERAL INTRODUCTION 1 Chapter 1

Tuberculosis (TB) remains a continuous global problem. TB, an infectious disease caused by Mycobacterium tuberculosis (M.tb), is an ancient disease that was described about 70,000 years ago.(1) The pathogen can easily transmit from a TB patient to healthy people by air transmission. Nowadays, TB has spread worldwide owing to its ability to establish a latent infection, the capability of long persistence in the host,(2) and variations in its sensitivity to .(3)

Global burden of tuberculosis disease Nowadays, TB is one of the top 10 causes of death.(4) The World Health Organization (WHO) estimated that 10 million people developed TB and 1.3 million patients died because of TB in 2017.(4) A global report estimated that a large proportion of the world population has a latent TB infection (an existing M.tb with no evidence of clinically manifest active tuberculosis).(4) In approximately 10% of the population infected with latent TB, active TB disease will develop during the lifetime.(4) As a consequence, an active TB status is associated with disease transmission and substantial morbidity, which is even more problematic in the case of drug-resistant tuberculosis (DR-TB).

Drug-resistant tuberculosis (DR-TB) is a resistance of M.tb to one or more anti-tuberculosis drugs. The resistance patterns can be classified into mono-, poly-, -, multidrug- and extensive drug-resistance.(5) Mono-resistant TB is defined as resistance to one first- line anti-TB drug only, while poly-resistant TB is resistance to more than one first-line anti-TB drugs other than and rifampicin.(5) The more extensive patterns of drug resistance are multi-drug resistant TB (MDR-TB) and extensive drug-resistant TB (XDR- TB). MDR-TB is defined as resistance to at least both of the most potent anti-TB drugs (isoniazid and rifampicin), while XDR-TB is resistance to any fluoroquinolone, and at least one of three second-line injectable drugs (, kanamycin and ) in addition to multidrug resistance.(5) As one of the most potent TB drugs, rifampicin resistance has been a great concern in TB treatment. Hence, the resistance of rifampicin has been included in the classification of DR-TB as rifampicin resistant-TB (RR-TB). Considering sensitivity to the anti-tuberculosis drugs, RR-TB is defined as resistance to rifampicin, with or without resistance to other anti-TB drugs, including any resistance to rifampicin, in the form of mono-resistance, poly-resistance, MDR or XDR.(5)

The WHO identified 558,000 people who developed RR-TB and among this group, 82% had MDR-TB.(4) MDR-TB was reported responsible for about a quarter of all deaths caused by antimicrobial-resistant (6) and contributed to an estimated 14% of TB deaths worldwide.(4) If it comes to the treatment of MDR-TB, such patients should take multiple drugs and the drug treatment can be up to 24 months.(7) Combination of prolonged , drug toxicities and cost burden, explain why MDR-TB patients have more frequent sub-

10 General Introduction optimal outcomes than DS-TB or mono-resistant TB,(4,8) with an overall low success rate for treatment of MDR-TB of 55%.(4)

The global target for TB elimination 2035: low versus high burden TB countries The WHO has divided all countries into two general categories of TB countries, i.e. low and high burden countries. TB low burden countries are the countries with TB incidence below 10 per 100,000 population,(9) while high burden countries are the countries with the highest absolute number of estimated incident cases and those with the most severe burden in terms of incidence rate per capita.(10) Although the quantitative burden level is 1 different among low and high burden countries, the global target defined by the WHO is to achieve a global incidence rate of TB below 10 per 100,000 population by the year 2035.(11)

To achieve the strict targets, the WHO initiated a global strategy for TB prevention, care and control. The program, called the END-TB strategy, includes three main pillars, i.e., 1) integrated, patient-centered care and prevention, 2) strong policies and supportive systems, and 3) intensified research and innovation.(11) To respond to the global target, the specific target set in the low burden TB countries is to reduce the TB incidence to below 1 case per 100,000 population in 2035.(12) This challenging goal was followed up by the low burden TB countries. As an example, the Netherlands, a country with a total population of 17 million inhabitants in 2017, committed to reducing the TB case burden from 5.9 cases per 100,000 population in 2016 (13) to below 1 case per 100,000 population by 2035.(14) As an example for the high burden countries, Indonesia with a total estimated population of 264 million people, set a target from 254 cases per 100,000 population in 2017 (15) to 10 cases per 100,000 population by 2035.(16) More intensified research and health programs and policies are hence needed to achieve such strict targets within the coming 15 years.

Therapeutic failure is an essential problem for controlling drug-resistant tuberculosis The mechanism of development of DR-TB can be classified into two main pathways: 1) primary drug-resistance which occurs when resistant strains are transmitted into a new host, and 2) secondary or acquired drug resistance which occurs through the acquisition of drug resistance mutations to one or more drugs.(17–19) Increasing the risk of primary drug resistance is higher in areas with a high prevalence of DR-TB. The transmission can be enhanced by environmental conditions such as crowding, poor ventilation and lack of infection control. On the other hand, secondary/acquired DR-TB is more affected by the low quality of drug treatment such as non-adherence to drugs and low drug exposure due to adverse drug reaction, drug interaction or inadequately dose.(5)

A history of previous TB treatment is one of the main risk factors for MDR-TB.(20) This evidence indicates that MDR-TB can be caused by secondary drug-resistance due to unsuccessful treatment from a prior treatment. A global report estimated that 3.5% of all

11 Chapter 1 new TB cases had MDR or RR-TB, while 18% of previously treated cases had MDR or RR-TB in 2017.(4) The report provides evidence that unsuccessful TB treatment in the first episode of disease should be a concern for controlling DR-TB.

Besides TB case-finding, improving the rational use of anti-TB drugs has been an essential part of DR-TB control. Several studies have reported acquired drug-resistance due to therapeutic failures of TB treatment.(21–25) A study also demonstrated that TB patients with acquired drug resistance were at significantly increased risk for poor treatment outcome.(26) The global TB problem will become even more complicated when the trend of DR-TB transmission is shifting from acquired drug resistance to primary drug resistance. A dynamic modeling study in showed that there is a predicted significant increasing transmission trend of primary drug-resistance from 15% in 2012 to 85% in 2032.(27) Therefore, emerging problems associated with TB drug treatment management should be primarily controlled to avoid the development of both primary and secondary DR-TB.

Antimicrobial stewardship program in TB disease Antimicrobial stewardship is defined as “a coherent set of activities which promotes using in ways that ensure sustainable access to effective therapy for all who need them”.(28) The primary goal is to optimize clinical outcomes and minimize unintended consequences of antimicrobial use, including toxicity, the selection of pathogenic organisms and the emergence of resistance.(29) An effective program should be designed based on setting priorities.(30) Engagement of multiple actors such as health care providers, government, patients, and society are also needed to enhance the benefits of an antimicrobial stewardship program.(28)

Although the terminology of antimicrobial stewardship is uncommonly used in the research and management of TB disease, the principles are still very relevant to achieve the global target of TB elimination in 2035. Activities related to treatment supervision can be part of antimicrobial stewardship programs for controlling DR-TB. Scientific knowledge of anti-TB drug use is needed to determine priority areas and plans for intervention.(31) The development of an effective program requires a more personalized approach that takes into account the heterogeneity in terms of patient characteristics across geographical areas and health care systems. In the development of intervention programs, it should be explored which high-risk groups of patients should be targeted for therapeutic failures. Furthermore, since treatment adherence is widely known as the main problem in TB disease, it should be explored who should be monitored and which intervention strategies should be developed to maintain good treatment adherence. Therefore, a comprehensive picture of unsuccessful treatment risks, treatment barriers and treatment non-adherence in TB disease should be obtained for developing an effective strategy to improve treatment outcome and control TB, notably DR-TB.

12 General Introduction

Unfortunately, comprehensive information on the current situation of high-risk groups for therapeutic failure and drug-related problems in TB disease is lacking. The controversial information is due to the differences in designs and settings of previous studies as well as the absence of more recent data, which is essential given the rapidly changing TB epidemiology. Updated studies and novel strategies are required for developing effective antimicrobial stewardship programs in TB disease. Therefore, this thesis attempted to provide a scientific knowledge base on the essential aspects to improve treatment outcomes of TB patients in the near future. 1 Thesis objectives and outlines In this thesis, we aim to research both low and high TB settings with the Netherlands as a low burden TB country and Indonesia as a high burden TB country. The empirical research will apply drug utilization and pharmacoepidemiological approaches to support potentially effective strategies to improve treatment outcomes as part of drug resistance control in TB disease. The specific objectives of this thesis can be listed as follows:

1. To analyze high-risk groups for therapeutic failure among TB patients. 2. To analyze potential interventions to improve treatment adherence in TB patients. 3. To analyze treatment barriers and potential strategies for successful TB treatment.

In Chapter 2, we aim to identify risk factors for MDR-TB as a fundamental part of this thesis. We will present a global systematic review and meta-analysis study that will identify patients’ and geographical characteristics that are associated with the development of MDR-TB.

Since acquired drug-resistant TB can develop after the therapeutic failure of drug- susceptible TB (DS-TB), predictors for unsuccessful TB treatment among DS-TB patients are studied in Chapter 3. Using a nationwide TB database from the Netherlands, a low TB incidence country, we aim to identify the incidence, high-risk groups and predictors for unsuccessful TB treatment outcomes in an adult DS-TB population.

In Chapter 4, the current situation of the high-risk DR-TB patients in the Netherlands is investigated. We will present the prevalence of different types of DR-TB cases and the characteristics of patients who were lost to follow-up for the treatment outcome. To gain more insights in the treatment outcomes, we will determine the incidence, high-risk groups and predictors for poor outcome of TB treatment among all types of DR-TB patients also including MDR-TB patients.

In Chapter 5, we will systematically review measures of adherence and persistence to multiple medications using a pharmacoepidemiological approach. Since there is a lack of

13 Chapter 1 studies in adherence to TB medications using real-world prescription databases, an example of cardio-metabolic medications that represent chronic diseases treated with multiple medications will be studied in this chapter. This review will implicitly provide scientific considerations for measuring adherence in TB disease using a prescription database.

Another TB drug adherence topic is continued in Chapter 6. This chapter will describe current studies on the impact of interventions to improve TB treatment adherence. In this chapter, we will also discuss several strategies to conduct valid intervention studies on TB treatment adherence.

In Chapter 7, we investigate treatment barriers among TB patients in a high burden TB setting. A qualitative study will be performed to determine treatment barriers from the patient perspective. In this chapter, we will discuss potential strategies to improve successful treatment among TB disease in Indonesia.

Finally, Chapter 8 will describe an overview, general discussion, implication and future perspectives related to the findings of the studies presented in this thesis.

14 General Introduction

REFERENCES

1. M Cristina G, Brisse S, Brosch R, Fabre M, Omaïs 13. ECDC. Tuberculosis surveillance and monitoring B, Marmiesse M, et al. Ancient origin and gene in Europe [Internet]. 2018th ed. WHO Regional mosaicism of the progenitor of Mycobacterium Office for Europe (WHO/Europe) and the tuberculosis. PLoS Pathog. 2005;1(1):55–61. European Centre for Disease Prevention and 2. Chaves AS, Rodrigues MF, Mattos AMM, Control (ECDC).; 2018. 206 p. Available from: Teixeira HC. Challenging Mycobacterium https://ecdc.europa.eu/sites/portal/files/ tuberculosis dormancy mechanisms and their documents/ecdc-tuberculosis-surveillance- immunodiagnostic potential. Brazilian J Infect monitoring-Europe-2018-19mar2018.pdf Dis. 2015;19(6):636–42. 14. de Vries G, Riesmeijer R. National Tuberculosis 3. Aldridge BB, Fernandez-Suarez M, Heller D, Control Plan 2016-2020 : Towards elimination. 1 Ambravaneswaran V, Irimia D, Toner M, et al. Bilthoven: National Institute for Public Health Asymmetry and aging of mycobacterial cells lead and the Environment; 2016. 1–8 p. to variable growth and susceptibility. 15. Ministry of health Republic of Indonesia. Science (80- ). 2012;335(6064):100–4. INFODATIN Tuberkulosis (Temukan Obati 4. World Health Organization (WHO). Global Sampai Sembuh) [Internet]. Infodatin. 2018 [cited Tuberculosis Report 2018. Geneva: World health 2019 Oct 7]. Available from: http://www.depkes. Organization; 2018. go.id/folder/view/01/structure-publikasi- pusdatin-info-datin.html 5. WHO. Companion Handbook to the WHO Guidelines for the Programmatic Management 16. Ministry of health Republic of Indonesia. of Drug-Resistant Tuberculosis. Companion Regulation of health minister RI no. 67 about Handbook to the WHO Guidelines for the management of tuberculosis disease. Ministry Programmatic Management of Drug-Resistant of Health, Republic of Indonesia. 2016. Tuberculosis. 2014. 1–20 p. 17. Dookie N, Rambaran S, Padayatchi N, Mahomed 6. O’Neill J. Tackling drug-resistant infections S, Naidoo K. Evolution of drug resistance in globally: final report and recomendations Mycobacterium tuberculosis: A review on [Internet]. Welcome Trust and HM Governemnet. the molecular determinants of resistance and London; 2016. Available from: https://amr- implications for personalized care. J Antimicrob review.org/sites/default/files/160518_Final Chemother. 2018;73(5):1138–51. paper_with cover.pdf 18. Palomino JC, Martin A. Drug resistance 7. Pontali E, Raviglione MC, Migliori GB. Regimens mechanisms in Mycobacterium tuberculosis. to treat multidrug-resistant tuberculosis: past, Antibiotics. 2014;3(3):317–40. present and future perspectives. Eur Respir Rev. 19. da Silva PEA, Palomino JC. Molecular basis and 2019;28(152):1–7. mechanisms of drug resistance in Mycobacterium 8. Pontali E, Visca D, Centis R, D’Ambrosio L, tuberculosis: Classical and new drugs. J Spanevello A, Migliori GB. Multi and extensively Antimicrob Chemother. 2011;66(7):1417–30. drug-resistant pulmonary tuberculosis: 20. Dean AS, Cox H, Zignol M. Epidemiology of Advances in diagnosis and management. Curr drug-resistant tuberculosis. In: Gagneux S, Opin Pulm Med. 2018;24(3):244–52. editor. Strain Variation in the Mycobacterium 9. Clancy L, Rieder HL, Enarson DA, Spinaci S. tuberculosis Complex: Its Role in Biology, Tuberculosis elimination in the countries of Epidemiology and Control. Springer, Cham; 2017. Europe and other industrialized countries. Eur p. 209–20. Respir J. 1991;4(10):1288–95. 21. Chiang CY, Schaaf HS. Management of drug- 10. WHO. Use of high burden country lists for TB resistant tuberculosis. Int J Tuberc Lung Dis. by WHO in the post-2015 era. WHO Press. 2010;14(6):672–82. 2015;(April):19. 22. Castillo-Chavez C, Feng Z. To treat or not to 11. World Health Organization. Implementing the treat: The case of tuberculosis. J Math Biol. END TB strategy: the essentials. WHO. Geneva: 1997;35(6):629–56. World health Organization; 2015. 23. Fodor T, Mitchison DA. How drug resistance 12. Lönnroth K, Migliori GB, Abubakar I, emerges as a result of poor compliance. Int J D’Ambrosio L, De Vries G, Diel R, et al. Towards Tuberc Lung Dis. 1999;3(2):174. tuberculosis elimination: An action framework 24. Gillespie SH. Evolution of drug resistance in for low-incidence countries. Eur Respir J. Mycobacterium tuberculosis: Clinical and 2015;45(4):928–52. molecular perspective. Antimicrob Agents Chemother. 2002;46(2):267–74.

15 Chapter 1

25. Ekaza E, N’Guessan RK, Kacou-N’Douba A, Aka 29. Dellit TH. Summary of the Infectious Diseases N, Kouakou J, Le Vacon F, et al. Emergence in Society of America and the Society for Western African countries of MDR-TB, focus on Healthcare Epidemiology of America guidelines côte d’ivoire. Biomed Res Int. 2013;2013:1–9. for developing an institutional program to 26. Kempker RR, Kipiani M, Mirtskhulava V, enhance antimicrobial stewardship. Infect Dis Tukvadze N, Magee MJ, Blumberg HM. Acquired Clin Pract. 2007;15(4):263–4. drug resistance in mycobacterium tuberculosis 30. Doron S, Davidson LE. Antimicrobial Stewardship. and poor outcomes among patients with Mayo Clin Proc [Internet]. 2011;86:1113– multidrug-resistant tuberculosis. Emerg Infect 23. Available from: http://10.0.15.225/ Dis. 2015;21(6):992–1001. mcp.2011.0358%5Cnhttps://ezp.lib.unimelb. 27. Law S, Piatek AS, Vincent C, Oxlade O, Menzies edu.au/login?url=https://search.ebscohost.com/ D. Emergence of drug resistance in patients with login.aspx?direct=true&db=edselp&AN=S0025 tuberculosis cared for by the Indian health-care 619611652026&site=eds-live&scope=site system: a dynamic modelling study. Lancet Public 31. Padayatchi N, Mahomed S, Loveday M, Heal. 2017;2(1):e47–55. Naidoo K. Antibiotic stewardship for drug 28. Dyar OJ, Huttner B, Schouten J, Pulcini C. What is resistant tuberculosis. Expert Opinion on antimicrobial stewardship? Clin Microbiol Infect. Pharmacotherapy. 2016;17(15):1981–3. 2017;23(11):793–8.

16 General Introduction

1

17 Berlin, 2018 CHAPTER RISK FACTORS OF MULTIDRUG- RESISTANT TUBERCULOSIS: A GLOBAL SYSTEMATIC REVIEW AND META-ANALYSIS 2 Ivan S. Pradipta Lina D. Forsman Judith Bruchfeld Eelko Hak Jan-Willem C. Alffenaar

This chapter is based on the published manuscript: Pradipta IS, Forsman LD, Bruchfeld J, Hak E, Alffenaar J-W. Risk factors of multidrug-resistant tuberculosis: A global systematic review and meta-analysis. J Infect. 2018;77:469–78. Chapter 2

ABSTRACT

Objectives: Since the risk of multidrug-resistant tuberculosis (MDR-TB) may depend on the setting, we aimed to determine the associations of risk factors of MDR-TB across different regions.

Methods: A systematic review and meta-analysis was performed with Pubmed and Embase databases. Information was retrieved on 37 pre-defined risk factors of MDR-TB. We estimated overall Mantel-Haenszel odds ratio as a measure of the association.

Results: Factors of previous TB disease and treatment are the most important risk factors associated with MDR-TB. There was also a trend towards increased risk of MDR-TB for patients 40 years and older, unemployed, lacking health insurance, smear positive, with non-completion and failure of TB treatment, showing adverse drug reaction, non-adherent, HIV positive, with COPD and with M. Tuberculosis Beijing infection. Effect modification by geographical area was identified for several risk factors such as male gender, married patients, urban domicile, homelessness and history of imprisonment.

Conclusions: Assessment of risk factors of MDR-TB should be conducted regionally to develop the most effective strategy for MDR-TB control. Across all regions, factors associated with previous TB disease and treatment are essential risk factors, indicating the appropriateness of diagnosis, treatment and monitoring are an important requirements.

20 Risk factors of MDR-TB

INTRODUCTION

According to the World Health Organization (WHO), tuberculosis (TB) remains a global problem with an increasing trend of new cases of TB from 6.1 million in 2015 to 6.3 million in 2016. (1) This global health problem has further worsened in recent years due to the increase in multidrug-resistant tuberculosis (MDR-TB, M. tuberculosis resistant to rifampicin and isoniazid), with an estimated 490 000 new patients in 2016.(1) From a health economics perspective, MDR-TB is a heavy burden on health care systems with treatment costs 20 times higher than the corresponding cost of drug-susceptible TB (DS-TB).(2)

The occurrence of drug-resistant tuberculosis (DR-TB) is not only determined by timely and 2 correct diagnosis, adequate use of anti-TB drugs, patient factors commonly associated with drug adherence (beliefs, barriers, behavior), but also determined by microbiological factors. (3) Since spontaneous resistance mutation occurs for isoniazid and rifampicin, a combination of several TB-drugs is mandatory to avoid development of drug resistance. Although the combination of antibiotics in TB treatment can prevent acquired drug resistance to some extent, problems of adverse drug reactions (ADRs), potentially leading to treatment failure, remain a challenge worldwide.(4)

In 2014 WHO formulated globally applicable programmatic management guidelines for drug-resistant tuberculosis.(5) However, several studies reported conflicting results for some risk factors of MDR-TB.(6–11) Thus, identification of the risk factors and possible effect modification by region are needed for developing optimal intervention strategies for MDR-TB control.

Four systematic reviews and meta-analyses on risk factors for MDR-TB were performed prior to our study.(12–15) The findings of these studies were limited for several reasons. Firstly, the focus of the studies was restricted to one region and the geographical effect of the risk factors from a global perspective could not be assessed. Secondly, the risk factors were analysed from a specific perspective, either host- or pathogen related. To support global strategies to target MDR-TB effectively, we therefore conducted a comprehensive systematic review and meta-analysis in predictive studies to determine risk factors for MDR-TB across different regions. These studies had five different perspectives, including host characteristics, previous TB disease and treatment, comorbidities, lifestyle and environmental characteristics, as well as microbiological aspects.

21 Chapter 2

MATERIAL AND METHODS

Search strategy and selection criteria A systematic review and meta-analysis study following PRISMA guidelines(16) was performed. The study was registered in PROSPERO, number CRD42016038014. We included experimental and observational predictive study designs, without language restrictions, in which one or multiple risk factors for MDR-TB were analysed during the study, from January 1, 2010, to March 26, 2016. We excluded cross-sectional studies, case reports, case series, review articles as well as conference abstract papers.

The study domain was restricted to adult TB-patients, 18 years and older. For cohort studies we included adult DS-TB patients as the population at risk, with MDR-TB as the outcome. We compared the risk factors of adult DS-TB and MDR-TB patients in included case-control studies. DS-TB was defined as fully sensitive of all anti-tuberculosis drugs to the Mycobacterium tuberculosis (M.tb) in a TB patient, while MDR-TB was defined as resistance to the first-line TB drugs rifampicin and isoniazid, with or without resistance to other first- line TB drugs. Microbiological verification was needed to confirm resistance type of the patients in this study.

We excluded studies restricted to specific high-risk MDR-TB patient groups, such as TB patients with HIV, prior TB treatment, neoplastic disease or mellitus. We also excluded studies that only used clinical or histopathological information for defining the type of TB without microbiological confirmation. Six perspectives of risk factors, comprising a list of 37 pre-defined variables in total, were analysed. The perspectives and risk factors were developed from a conceptual framework of pathogen-host-environment interplay in the emerging infectious disease (17) as well as previously published studies(12–15) providing potential targets for controlling MDR-TB. The definition criteria for the risk factors can be found in the Appendix Table E1.

The outcome measure was MDR-TB defined as a resistance to the first-line TB drugs rifampicin and isoniazid, with or without resistance to other first-line TB drugs. MDR-TB status was verified by microbiological test using either phenotyping drug susceptibility test or polymerase chain reaction (PCR) based on the identification of mutations linked to resistance of M.tb.

Both Pubmed and Embase databases were used to find potentially eligible articles. We developed the search term and strategy together with a medical information specialist at the Central Medical Library, University of Groningen, resulting in selecting the following root terms : “tuberculosis”,“multiple drug resistant tuberculosis”, “risk factor”, “epidemiologic factor”, “risk assessment”, “determinant”, “social determinant of health”, “predictor”. We used

22 Risk factors of MDR-TB

MeSH terms for the PubMed database and Emtree for the Embase database. Duplicate studies from the two databases were removed using the RefWorks® program. The comprehensive search terms are provided in the Appendix Table E2.

Data abstraction and assessment of quality Two reviewers (ISP, LDF) independently screened abstracts, full-text articles, and performed bias assessments. Disagreements between the two independent reviewers (ISP, LDF) were discussed and resolved by a third reviewer (EH). The level of disagreement was calculated using a percentage of agreement and reliability, Cohen’s Kappa.(18) Data were extracted by the first reviewer (ISP) from the included articles, evaluated by the second reviewer (LDF) and final evaluation was conducted by the third reviewer (EH). We attempted to contact 2 study authors when more data were needed; however, if the information was not received, we assumed that data were missing. We conducted a risk of bias assessment using the Risk of Bias Assessment Tool for Non-randomized studies which is compatible with the Cochrane risk of bias tool and has an acceptable validity and reliability value.(19)

Statistical analysis A dichotomous variable was applied for each factor that was analysed. We pooled all risk factors that had a similar definition using Mantel-Haenszel Odds Ratio (mhOR) with a 95% confidence interval (95% CI). The significance threshold was set at p-value < 0·05. If data about a risk factor were only available in one study, Odds Ratio (OR) instead of mhOR was calculated. The level of heterogeneity (I2 and p-value) was calculated to identify variation in association measures across the studies. We defined considerable heterogeneity as 2I ≥ 75%(20) and/or a p-value of heterogeneity < 0·05.(21) If the data were heterogeneous, we applied a random effects model to estimate the overall effect size. Furthermore, we performed a subgroup analysis to identify sources of heterogeneity. The geographic area of the study was used for stratification in subgroup analysis. Additionally, we performed sensitivity analysis for risk factors with heterogeneous data that excluded the high potential risk of bias studies, to identify the effect size of each risk factor. Heterogeneity level and direction of the effect size among the group were considered in defining the effect estimated in the sensitivity analysis. We used Review Manager version 5.3 to analyse the effect size of the study.

RESULTS

The search process found 644 original publications from Pubmed and 764 publications from Embase. A total of 1,056 abstracts were screened after duplications were removed, and 1,036 articles were excluded for several reasons (Figure 1). There were 47 discrepancies between the two independent reviewers in the title-abstract screening. The level of agreement was 96% (good), and the reliability according to Cohen’s Kappa was 0·78

23 Chapter 2

(good). Furthermore, the disagreement arose in seven out of the 117 articles in the full-text screening, with a level of agreement of 94% (good), and reliability according to Cohen’s kappa was 0·84 (good). We found 20 studies fulfilling the inclusion criteria from the following continents; Asia (14), Africa (2), North America (1), South America (2) and Europe (1). The total number of patients included was 20 017, among which 1814 were MDR-TB patients and 18 203 DS-TB patients. Study characteristics are shown in Table 1.

Figure 1. Flow diagram, literature search and screening process.

24 Risk factors of MDR-TB Beijing genotype strain M. tb 2 Beijing genotype strain Risk factors identified Marital status, non-BCG gender, vaccination, previous treatment, smoking, known contact with TB patient Gender, non-completion and failure of TB treatment, HIV Gender, previous treatment, smear positivity, smoking, known contact with TB patient Gender, lung cavity, HIV Age 40 years non-completion gender, and older, and failure of TB treatment, CVD, DM, low BMI HIV, Employment status, previous gender, treatment, smear positivity, DM, known HIV, contact with TB patient, daily consumption alcohol Age 40 years employment and older, status, gender, previous treatment, known HIV, contact with TB patient, alcoholdaily consumption Marital status, previous gender, TB disease, COPD, HIV, history of imprisonment, history homeless, daily alcohol consumption Gender, previous treatment, COPD, positivity, history lung cavity, HIV, of imprisonment, smoking, history of homeless, known contact with TB patient, daily alcohol consumption M. tb non-completionGender, DOT, and failure of TB treatment, previous TB disease, , lung cavity, HIV Age 40 years ADRs, gender, and older, previous treatment, low BMI Gender, HIV smoking, 75 36 84 117 116 145 298 209 234 508 304 1977 1609 (DS-TB) Control 76 17 56 15 12 50 22 48 30 44 145 105 123 Case (MDR-TB) 2007-2013 2010-2014 1995-2001 2007-2009 2007-2008 2002-2007 2008-2010 2002-2009 2002-2005 2005-2007 2005-2006 2000-2002 2000-2004 Study period Study Cohort Case-control Case-control Case-control Case-control Case-control Case-control Case-control Case-control Case-control Case-control Case-control Case-control Study design Study Iran Faso India Israel Brazil China States Burkina Burkina Vietnam Country Pakistan Thailand Malaysia Colombia The United United The South Characteristics of the studies included in the systematic review and meta-analysis. Author (year publication) Ahmad et al. (2012)(22) Andrew et al. (2010)(23) Baghaei et al. (2009)(24) Balaji et al (2010)(25) Chuchottaworn et al. (2015) (8) De Souza et al. (2006)(26) Diande et al. (2009)(10) SahlyEl et al. (2006)(27) etElmi al. (2015)(9) Ferro et al (2011)(28) et al.Fox (2011)(7) Gao et al (2016)(29) Hang et al. (2013)(6) Table 1. Table

25 Chapter 2 M. Beijing genotype strain Gender, previous TB disease, lung cavity, HIV Age 40 years previous gender, and older, TB treatment Risk factors identified Non-coverage health insurance, previous gender, treatment, lung cavity, known contact with TB patient, tb Marital status, non-adherence, gender, higher education, previous treatment, smear positive, DM, history HIV, of imprisonment, smoking, urban area, known contact with TB patient, daily alcohol consumption Gender, previous treatment, smear history positivity, HIV, imprisonment of Gender, previous TB disease, smear positivity, known contact with TB patient Gender, previous treatment, smear positivity, lung cavity, urban area 97 56 84 120 3107 7018 2009 (DS-TB) Control 42 30 175 207 184 333 100 Case (MDR-TB) 2009 2013-2014 1982-2004 2007-2009 1999-2000 2004-2005 2000-2006 Study period Study Case-control Case-control Case-control Case-control Case-control Case-control Case-control Study design Study India Israel China China China England Country Malaysia Characteristics of the studies included in the systematic review and meta-analysis. Zhao et al (2012)(35) Author (year publication) He et al. (2011)(30) Mohd Sharrif et al. (2016) (31) Mor et al. (2014)(32) O’Riordan et al. (2008)(33) Shen et al. (2009)(34) Vadwai et al. (2011)(11) Information: multidrug-resistantMDR-TB: tuberculosis; DS-TB: drug-susceptible tuberculosis; BCG: Bacille Calmette-Guérin; HIV: human immunodeficiency virus; CVD: cardiovascular; DM: diabetes mellitus; BMI: ; COPD: chronic obstructive pulmonary disease; M.tb: Mycobacterium tuberculosis; directly observedDOT: treatment; ADRs: adverse drug reactions. Table 1 (Continued).Table

26 Risk factors of MDR-TB

Potential bias was analysed for the 20 included studies. Thirty percent of all included studies displayed a high potential for bias in the measurement of exposure. Interview bias, recall bias, self-reported data, and unclear definition of the exposure were identified as common sources of bias. However, the overall risk of bias was low (see Appendix Fig. E1, E2). Not all pre-defined perspectives could be analysed due to lack of availability of the data in the included articles. We were not able to analyse risk factors from a health services perspective. Therefore five of the six different perspectives of risk factors, comprising 29 specific factors from the included studies, were analysed in this study. Additional data were received upon request for one study.(6) We identified significant risk factors of MDR-TB (p<0.05) from four perspectives, namely patient characteristics (i.e. unemployed, lacking health insurance coverage, smear positive, mantoux test positive and lung cavity), TB history 2 and treatment (i.e. previous TB disease, previous TB treatment, non-completion and failure of TB treatment, adverse drug reaction, non-BCG vaccination, non-adherence), comorbidity (i.e. Chronic Obstructive Pulmonary Disease, COPD) and strain (i.e. M.tb Beijing strain). However, several risk factors of MDR-TB showed heterogeneous results (I2 ≥ 75% or p-value heterogeneity < 0·05), i.e. age 40 years and older, male gender, married patients, lung cavity, previous TB disease, previous TB treatment, HIV, known contact with TB patients, low BMI, urban domicile, homelessness, and history of imprisonment. The pooled effect estimated for all risk factors can be found in Table 2.

Subgroup analysis was performed for factors with heterogeneous results to identify the effect of geographical area. When stratifying patients by setting, homogenous results appeared within subgroups for variables of gender, marital status, previous TB disease, domicile area, nature of abode, and history of imprisonment status (Fig. 2, 3), while heterogeneous results appeared within subgroups for variables of age, BMI, status of lung cavity, previous TB treatment, HIV and known contact with TB patients (see Appendix Figure E3, E4). Subgroup analysis indicated variations dependent on setting for several risk factors of MDR-TB, such as male gender, married patients, urban domicile, homelessness, having a previous TB disease and a history of imprisonment. For example, pooled effect estimates of studies in America (Brazil and USA)(26,27) showed female patients and unmarried patients were more likely to be diagnosed with MDR-TB than DS-TB. On the contrary, effect estimates from studies in Western Asia (Iran and Israel)(7,24,32) revealed that males were more prone to MDR-TB and marital status was not a risk factor for MDR-TB in Asia (Fig. 2A, 2B).(22,31) Likewise, studies from North America(27) described a protective effect of MDR-TB for subjects who had a history of imprisonment, whereas several Asian studies failed to prove any association with history of imprisonment and MDR-TB (Fig. 3C). (9,31,32)

27 Chapter 2 † † † † † † † n/a n/a n/a n/a n/a n/a n/a n/a n/a (p-value) 2 0%; (0·37) 41% (0·13) 41% I 44% (0·15) 40% (0·20) 69% (0·07)69% 76% (0·006)76% 67% (<0·001)67% 81% (< 0·001) (< 81% 86% (< 0·001)86% (< 87% (< 0·001)87% (< 89% (< 0·001)89% (< 88% (< 0·001)88% (< Heterogeneity † † † * † † *† † *† † † *† *† 1·34 (0.75-2.39)1·34 0·75 (0·36-1·58) 0·75 1·36 (0·47-3·95) 1·36 1·07 (0·85-1.36)1·07 0·64 (0·13-3·11) 1·69 (0·73-3·87)1·69 0·42 (0·13-1·40) 0·42 1·49 (0·73-3·06)1·49 1·30 (0·91-1·86) 1·99 (1·12-3·54) 2·79 (1·13-6·85) 2·79 2·31 (1·14-4·69) 2·31 3·38 (1·45-7·89) 1·92 (1·02-3·62) 1·92 Effect Estimated 1·72 (1·40-2·12) 1·72 2·53 (1·05-6·14) 2·53 4·50 (1·71-11·82) 3·00 (1·69-5·30) 5·60 (3·36-9·32) 7·24 (4·06-12·89) 7·24 4·42 (1·46-13·37) Odds CI) Ratio (95% 552 552 552 197 103 290 150 150 125 802 408 2267 2907 1354 8825 2306 4460 11 161 19 856 10 736 15 657 Participants Number of Studies 4(8,10,29,35) 19(6–10,22–27,29–36) 1(31) 2(10,26) 1(30) 3(22,27,31) 6(24,26,31–34) 1(9) 7(7–9,25,30,34,36) 4(7,27,33,36) 11(9,10,22,24,26,29–32,34,35) 3(7,8,23) 1(7) 1(29) 1(22) 1(31) 11(6–10,23,25,27,31,32,36) 4(8,9,26,31) 1(8) 2(9,27) 1(7) ‡ Effect estimates for risk factors of Multidrug-Resistant (MDR-TB) Tuberculosis Risk factors Risk Patients characteristics Age 40 years and older gender Male Higher education Unemployment Lack of health insurance coverage Married patient Smear positive Mantoux test positive Lung cavity TB history & treatment Presence of previous TB disease Presence of previous TB treatment Non-completion and failure of TB treatment DOT program Presence of adverse Drug Reaction Non-BCG vaccination Non-adherence comorbidity or Disease HIV positive Diabetes mellitus disease Cardiovascular COPD Hepatitis Table 2. Table

28 Risk factors of MDR-TB † † † † † (p-value) 2 49% (0·10) I 21% (0·28) 21% 66% (0·05) 82% (0·02) 63% (0·04) 87% (0·006) 67% (0·004)67% 91% (< 0·001) (< 91% Heterogeneity * * *† 1·30 (0·74-2·29) 1·30 0·86 (0·27-2·78) 0·86 (0·17-4·27) 0·88 (0·20-3·89) 0·90 (0·66-1·22) 0·80 (0·49-1·30) Effect Estimated 2·73 (0·18-40·95) 2·73 5·58 (1·66-18·76) Odds CI) Ratio (95% 2 665 1189 1453 7501 2720 1865 5770 2306 Participants including non-cure, non-completion, default and failure treatment; ** Body Mass Index (BMI) < ‡ Number of Studies 8(9,10,22,24,26,30,31,33) 5(6,9,22,24,31) 2(8,29) 2(31,34) 5(9,10,26,27,31) 2(9,27) 4(9,27,31,32) 3(6,28,30) Significant value (p< 0.05); † Effect estimates for risk factors of Multidrug-Resistant (MDR-TB) Tuberculosis ; n/a : not; n/a applicable; COPD: Chronic obstructive pulmonary disease; Human HIV: Immunodeficiency Direct Virus; ObservedDOT: Treatment. 2 Risk factors Risk style&Life Environmental Known contact with TB patient Smoker Low BMI** domicile Urban alcoholDaily consumption Homelessness imprisonment of History Strain Beijing strain Table 2 (Continued).Table Information: * Fixed effect model; 18 kg/m

29 Chapter 2

A. Gender (female vs. male)

Figure 2. Homogeneous effect estimated within the subgroup of gender, marital status and previ- ous tuberculosis disease, stratified by area of study. Notes: reference group in each of factors: (A) female, (B) unmarried (C) non-previous TB disease

30 Risk factors of MDR-TB

B. Marital status (unmarried vs. married)

2

C. Previous TB disease (non-previous TB disease vs. previous TB disease)

Figure 2 (Continued). Homogeneous effect estimated within the subgroup of gender, marital status and previous tuberculosis disease, stratified by area of study. Notes: reference group in each of factors: (A) female, (B) unmarried (C) non-previous TB disease

31 Chapter 2

A. Domicile area (rural vs. urban)

B. Nature of abode (non-homelessness vs. homelessness)

C. History of imprisonment (non-history of imprisonment vs. history of imprisonment)

Figure 3. Homogeneous effect estimated within the subgroup of domicile area, nature of abode and history of imprisonment, stratified by area of study. Notes: reference group in each of factors: (A) rural domicile, (B) non-homelessness, (C) non-history of imprisonment

32 Risk factors of MDR-TB

Regarding variables of previous TB disease status and domicile area, we analysed that having a previous TB disease remained a significant risk factor of MDR-TB in the pooled estimate (p< 0·001; OR 4·42; 95%CI 1·46-13·37). Although risk factors of previous TB disease showed heterogeneous result (I2: 86%), the forest plot described the same directions for a risk factor of MDR-TB in the all subgroups of variable previous TB disease (Fig. 2C). On the contrary, the risk factor of urban domicile differed significantly depending on the setting, where a Malaysian study indicated a protective effect of urban dwelling (p=0·03; OR 0·39; 95%CI 0·16-0·93) whereas a study in China showed an increased risk (p= 0·001; OR 1·77; 95%CI 1·42-2·21) (Fig. 3A).

Since heterogeneity in several variables, such as age, BMI and status of lung cavity, previous 2 TB treatment, HIV, known contact with TB remained high (see Supplementary Fig. E3, E4), we therefore conducted a sensitivity analysis of these variables by excluded studies with high risk of bias. The studies that were exluded in the sensitivity analysis, i.e. studies assessing age (three studies(8,10,35)), lung cavity (five studies(8,9,25,34,36)), previous TB treatment(eight studies(9,10,22,24,26,31,34,35)), HIV (six studies(8–10,25,27,31)), known contact with TB (six studies(9,10,22,24,26,31)) and BMI (one study(8)). The sensitivity analysis showed being HIV positive, previous TB treatment and age 40 years and older to be risk factors of MDR-TB (Table 3). However, the variables ‘previous TB treatment’ and ‘lung cavity status’ displayed a heterogenous association with the risk of MDR-TB and should therefore be interpreted carefully. Regarding previous TB treatment, despite heterogeneity all effect estimates of the studies were of the same nature as risk factors of MDR-TB, while the presence of lung cavity cannot be interpreted as a risk factor for MDR-TB since effect estimates across studies showed conflicting results (Appendix Fig. E4B).

DISCUSSION

We identified an effect modification by geographic area for several risk factors of MDR-TB, such as male gender, married patient, urban domicile, homelessness and having a history of imprisonment. Our results confirm prior reviews that having a previous TB disease and treatment are the most influential risk factors for developing MDR-TB, independent of the setting. Furthermore, patients 40 years and older, lacking health insurance, unemployed, non-adherent, ADRs, with a history of non-completion or failure of TB treatment, without BCG vaccination, HIV positive, with COPD, with infection with M. tb Beijing strain, smear and mantoux test positivite, show significant risk factors for developing MDR-TB. On the contrary, other risk factors identified in prior studies, such as, low education status, non- Directly Observed Treatment (DOT), diabetes mellitus, cardiovascular diseases, hepatitis, known contact with TB patients, smoking, low BMI and daily alcohol intake, did not show a clear association with MDR-TB in our study.

33 Chapter 2

Table 3. Sensitivity analysis of heterogeneous’ factors

No Risk factors Pre-sensitivity analysis Post-sensitivity analysis Number Odd Ratio I2 Number Odd Ratio I2 of Studies (95% CI) (p-value) of Studies (95% CI) (p-value) 1 Age 40 years 4 1·34 76% 1 14·18 n/a and older (0·75-2·39) ( 0·006)† (1·88-107·18) † 2 Lung cavity 7 1·92 89% 2 1·10 82% (1·02-3·62)† (< 0·001) † (0·40-3·02) (0·02) † 3 Presence of 11 7·24 88% 3 5·38 80% previous TB (4·06-12·89) † (< 0·001) † (1·67-13·37)† (0·007)† treatment 4 HIV positive 11 1·49 81% 5 3·04 55% (0·73-3·06) (< 0·001) † (1·60-5·77) † (0·08) 5 Known 8 1·30 67% 2 0·80 58% contact with (0·74-2·29) (0·004) † (0·22-2·85) (0·12) TB patient 6 Low BMI 2 0·86 82% 1 0·34 n/a (0·17-4·27) (0·02) † (0·10-1·19)

Information : I2: heterogeneity; †Significant value; Low body mass index (BMI) : BMI < 18 kg/m2; 95%CI : 95% confidence interval; HIV: Human Immunodeficiency Virus; TB: tuberculosis

In terms of microbiological aspect, our study was supported by other studies. Beijing M. tb strains are more likely to be MDR-TB than non-Beijing M. tb strains, according to studies from Indonesia,(37) Vietnam,(38) and Russia,(39) linking the M. tb Beijing genotype strain with a history of previous TB treatment and treatment failure. Animal studies have shown Beijing M. tb strains to be more virulent with more extensive tissue destruction, rapid outgrowth, and increased mortality.(40) Suggested hypotheses for this association regard differences in cell wall structure, leading to suboptimal intracellular drug concentrations, as well as a higher virulence per se, resulting in longer persistent infection.(41)

Regarding comorbidities, it is a matter of debate whether HIV is a risk factor for MDR- TB. A previous systematic review showed no association between HIV and primary or secondary MDR-TB.(42) However, our study indicated that HIV is a risk factor for MDR-TB after sensitivity analysis was performed. This can be explained by both immune status and drug-related factors. Immunosuppression can lead to reactivation of latent TB, increased risk of re-infection recurrence due to new M.tb infection and rapid progression to active TB.(43) Furthermore, problems relating to drug interactions, overlapping drug toxicities, high pill burden, drug and immune reconstitution inflammatory syndrome (IRIS) can potentially lead to the development of drug resistance and therapeutic failure in co-infected TB-HIV patients.(44) Hence, there is biological plausibility for HIV being a risk factor of MDR-TB and this finding has been supported by Faustini and co-authors, showing that HIV is associated with MDR-TB (OR 3·5; 95% CI 2·48-5·01).(14)

34 Risk factors of MDR-TB

Another comorbidity, COPD, has also been discussed as a risk factor of MDR-TB. A prospective study of pulmonary tuberculosis (PTB) patients aged ≥ 40 years with concomitant COPD had an increased risk of developing MDR-TB. (45) There is also evidence of an inverse relationship; TB patients can develop COPD as a result of long-term damage of structural and functional of the lung.(46,47) In our study, we analysed two case-control studies from Malaysia and USA, with 120 MDR-TB patients as cases and 2,186 DS-TB patients as controls. Our study indicated that COPD patients were more likely to have MDR-TB than patients without COPD, with a pooled estimate 2.5 times higher for COPD patients than non-COPD patients.

Our study demonstrated that failed TB treatment is a considerable risk factor for MDR- 2 TB. Although non-adherence to treatment is believed to be a cause of treatment failure in TB patients, a pre-clinical study showed that non-adherence alone was not sufficient for the development of MDR-TB, but in-between patient pharmacokinetic variability was necessary.(48) Similarly, a meta-analysis identified pharmacokinetic variability of isoniazid to be associated with therapeutic failure and acquired drug resistance.(49) Another meta- analysis analysed genetic factors such as rate of acetylation, where patients who have rapid acetylation of isoniazid were more likely to have microbial failure, acquire drug resistance and relapse than patients with slow acetylation.(49) On the other hand, patients with a slow isoniazid acetylation profile were more prone to hepatotoxicity than patients with a rapid acetylation profile.(50) It is apparent that pharmacogenetics variation plays an important role in therapeutic response and ADRs, besides inter-individual variability of pharmacokinetics profile.(50)

Our study corroborated the results of prior meta-analyses showing that previous TB disease and treatment were essential risk factors of MDR-TB, while alcohol abuse and low education were not.(12–14)Moreover, meta-analyses in China pointed out pulmonary cavity and living in rural area as risk factors of MDR-TB,(12,13) while studies in Europe showed that male gender, homelessness and urban domicile to be risk factors of MDR-TB.(14) As described in the aforementioned studies, the impact of risk factors can differ according to geographical area.

Our study suggests that identifying risk factors of MDR-TB regionally is important in developing strategies for MDR-TB control as a result of regional differences in the risk factors due to variation of healthcare quality, socio-behavioral and poor living conditions. Since unemployment and lack of health insurance coverage are risk factors of MDR-TB in our study, government support is crucial to organise universal health coverage that will cover not only drug cost but also diagnosis, treatment and monitoring for TB patients. In addition, enhancing access to health facilities and laboratories, including qualified drug

35 Chapter 2 susceptible tests, are required for appropriate diagnosis and treatment as well as for correct surveillance of the MDR-TB epidemic.

The fact that we identified non-adherence, previous and failed TB treatment as considerable risk factors of MDR-TB in our study, indicates that variation of adherence, pharmacokinetics and pharmacogenetics profile among TB patients is a factor that should be considered to avoid development of MDR-TB. Antibiotic stewardship program for drug- resistant tuberculosis is required to be established at an institution level, specifically in high-burden areas of TB. The collaborative team should include physicians, pharmacists, microbiologists, nurses and administrators, all with a common goal to improve diagnosis, treatment and monitoring of TB patients. Personalised treatment could be a promising approach for controlling MDR-TB, especially in patients at high risk of MDR-TB. Therapeutic drug monitoring and intervention with individual non-adherence can be implemented as a program to achieve treatment success.(51) However, since personalised treatment needs advanced resources, free consultation of TB experts should be widely available for health care providers to make rapid decisions on the management of complex TB cases, particularly in an area with limited resources.

There are several limitations in our study. Firstly, most of our included studies were case- control studies where recall bias may have occurred. Secondly, not all countries and included risk factors could be assessed due to unavailability of data. Thirdly, since the majority of studies were predictive studies, the causality of risk factors and outcome should be explored further using an appropriate study design. Finally, the power of the study was low for some risk factors of MDR-TB, such as non-BCG vaccination and positive Mantoux test. We noticed a potential information bias due to missing data in the only included study which analysed positive Mantoux test as a risk factor for MDR-TB. The study showed a high proportion of participants who had unknown information of Mantoux test results in the MDR-TB group (58.8%). The multivariate analysis indicated that Mantoux test positivity and non-BCG status were not significant risk factors for MDR-TB. (p≥0·05).(9,22) Hence, there is no clear support of an association of Mantoux test and BCG vaccination with MDR-TB.

On the other hand, we performed a thorough full-text screening, excluding studies with a high level of bias in the sensitivity analysis. We also assessed statistical heterogeneity and biological plausibility from the current evidence. Furthermore, we attempted to contact study authors to obtain more comprehensive data in our study.

In conclusion, factors of previous TB disease and treatment are the major risk factors for MDR-TB across all settings. Subsequently, we identified patients age 40 years and older, unemployed, lacking health insurance, smear positive, with a history of non-completion and failure of TB treatment, with adverse drug reaction, non-adherent, HIV positive, with

36 Risk factors of MDR-TB

COPD and with M. Tuberculosis Beijing infection who should be carefully monitored during their TB treatment to avoid development of MDR-TB. Equally important, risk factors of MDR-TB related to male gender, married patient, urban domicile, homelessness and having a history of imprisonment can vary depending on the setting. Therefore, assessment of risk factors of MDR-TB should be conducted regionally to develop the most effective strategy for MDR-TB control.

Funding This work was supported by Indonesia Endowment Fund for Education or LPDP in the form of a Ph.D. scholarship to ISP. 2 Acknowledgment We thank Prof. Naoto Keicho, The Research Institute of Tuberculosis and Japan Anti- tuberculosis Association, and Nguyen Thi Le Hang, MD, PhD for providing additional information. We also thank Brian Davies for language correction.

Conflict of interests ISP, LDF, JB, EH and JWA have no competing financial or non-financial interests in this work

37 Chapter 2

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38 Risk factors of MDR-TB

23. Andrews JR, Shah NS, Weissman D, Moll AP, 33. O’Riordan P, Schwab U, Logan S, Cooke G, Friedland G, Gandhi NR. Predictors of multidrug- Wilkinson RJ, Davidson RN, et al. Rapid and extensively drug-resistant tuberculosis in a molecular detection of rifampicin resistance high HIV prevalence community. PLoS One. facilitates early diagnosis and treatment of multi- 2010;5(12):1–6. drug resistant tuberculosis: Case control study. 24. Baghaei P, Tabarsi P, Chitsaz E, Novin A, Alipanah PLoS One. 2008;3(9):1–7. N, Kazempour M, et al. Risk factors associated 34. Shen X, DeRiemer K, Yuan ZA, Shen M, Xia Z, Gui with multidrug-resistant tuberculosis. Tanaffos. X, et al. Drug-resistant tuberculosis in Shanghai, 2009;8(3):17–21. China, 2000-2006: Prevalence, trends and risk 25. Balaji V, Daley P, Anand AA, Sudarsanam T, factors. Int J Tuberc Lung Dis. 2009;13(2):253–9. Michael JS, Sahni RD, et al. Risk factors for MDR 35. Zhao Y, Xu S, Wang L, Chin DP, Wang S, and XDR-TB in a tertiary referral hospital in Jiang G, et al. National Survey of Drug- India. PLoS One. 2010;5(3):1–6. Resistant Tuberculosis in China. N Engl J Med. 26. de Souza M, Antunes C, Garcia G. Multidrug- 2012;366(23):2161–70. resistant Mycobacterium tuberculosis at a 36. Vadwai V, Shetty A, Soman R, Rodrigues C. referral center for infectious diseases in the Determination of risk factors for isoniazid 2 state of Minas Gerais, Brazil: sensitivity profile monoresistance and multidrug-resistant and related risk factors. J Bras Pneumol. tuberculosis in treatment failure patients. 2006;32(5):430–7. Scand J Infect Dis [Internet]. 2012;44(1):48–50. 27. El Sahly HM, Teeter LD, Pawlak RR, Musser Available from: http://www.ncbi.nlm.nih.gov/ JM, Graviss EA. Drug-resistant tuberculosis: A pubmed/21923626 disease of target populations in Houston, Texas. 37. Parwati I, Alisjahbana B, Apriani L, Soetikno J Infect. 2006;53(1):5–11. RD, Ottenhoff TH, van der Zanden AGM, et al. 28. Ferro BE, Nieto LM, Rozo JC, Forero L, van Mycobacterium tuberculosis Beijing genotype Soolingen D. Multidrug-resistant Mycobacterium is an independent risk factor for tuberculosis tuberculosis, southwestern Colombia. Emerg treatment failure in Indonesia. J Infect Dis. Infect Dis. 2011;17(7):1259–62. 2010;201(4):553–7. 29. Gao J, Ma Y, Du J, Zhu G, Tan S, Fu Y, et al. Later 38. Lan NTN, Lien HTK, Tung LB, Borgdorff MW, emergence of acquired drug resistance and its Kremer K, Van Soolingen D. Mycobacterium effect on treatment outcome in patients treated tuberculosis Beijing Genotype and Risk for with Standard Short-Course Treatment Failure and Relapse, Vietnam. Vol. 9, for tuberculosis. BMC Pulm Med [Internet]. Emerging Infectious Diseases. 2003. p. 1633–5. 2016;16:26. Available from: http://www. 39. Drobniewski F, Balabanova Y, Nikolayevsky pubmedcentral.nih.gov/articlerender.fcgi?artid= V, Ruddy M, Kuznetzov S, Zakharova S, et al. 4743330&tool=pmcentrez&rendertype= Drug-resistant tuberculosis, clinical virulence, abstract and the dominance of the Beijing strain family in 30. He GX, Wang HY, Borgdorff MW, van Soolingen Russia. Jama [Internet]. 2005;293(22):2726–31. D, van der Werf MJ, Liu ZM, et al. Multidrug- Available from: http://www.ncbi.nlm.nih.gov/ resistant Tuberculosis, People’s Republic pubmed/15941801 of China, 2007-2009. Emerg Infect Dis. 40. Dormans J, Burger M, Aguilar D, Hernandez- 2011;17(10):1831–8. Pando R, Kremer K, Roholl P, et al. Correlation 31. Mohd Shariff N, Shah SA, Kamaludin F. Previous of virulence, lung pathology, bacterial load treatment, sputum-smear nonconversion, and and delayed type hypersensitivity responses suburban living: The risk factors of multidrug- after infection with different Mycobacterium resistant tuberculosis among Malaysians. Int J tuberculosis genotypes in a BALB/c mouse Mycobacteriology [Internet]. 2016;5(1):51–8. model. Clin Exp Immunol. 2004;137(3):460–8. Available from: http://dx.doi.org/10.1016/j. 41. Parwati I, van Crevel R, van Soolingen D. ijmyco.2015.11.001 Possible underlying mechanisms for successful 32. Mor Z, Goldblatt D, Kaidar-Shwartz H, Cedar emergence of the Mycobacterium tuberculosis N, Rorman E, Chemtob D. Drug-resistant Beijing genotype strains. Vol. 10, The Lancet tuberculosis in Israel: Risk factors and Infectious Diseases. 2010. p. 103–11. treatment outcomes. Int J Tuberc Lung Dis. 42. Suchindran S, Brouwer ES, Van Rie A. Is HIV 2014;18(10):1195–201. infection a risk factor for multi-drug resistant tuberculosis? A systematic review. Vol. 4, PLoS ONE. 2009.

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43. Houben RMGJ, Crampin AC, Ndhlovu R, 48. Srivastava S, Pasipanodya JG, Meek C, Leff Sonnenberg P, Godfrey-Faussett P, Haas WH, et R, Gumbo T. Multidrug-resistant tuberculosis al. Human immunodeficiency virus associated not due to noncompliance but to between- tuberculosis more often due to recent infection patient pharmacokinetic variability. J Infect Dis. than reactivation of latent infection. Int J Tuberc 2011;204(12):1951–9. Lung Dis. 2011;15(1):24–31. 49. Pasipanodya JG, Srivastava S, Gumbo T. 44. Aaron L, Saadoun D, Calatroni I, Launay O, Meta-analysis of clinical studies supports Mémain N, Vincent V, et al. Tuberculosis in HIV- the pharmacokinetic variability hypothesis infected patients: a comprehensive review. Clin for acquired drug resistance and failure of Microbiol Infect [Internet]. 2004;10(5):388–98. antituberculosis therapy. Vol. 55, Clinical Available from: http://www.ncbi.nlm.nih.gov/ Infectious Diseases. 2012. p. 169–77. pubmed/15113314 50. Ramachandran G, Swaminathan S. Role 45. Zhao J-N, Zhang X-X, He X-C, Yang G-R, Zhang of pharmacogenomics in the treatment of X-Q, Xin W-G, et al. Multidrug-Resistant tuberculosis: A review. Vol. 5, Pharmacogenomics Tuberculosis in Patients with Chronic and Personalized Medicine. 2012. p. 89–98. Obstructive Pulmonary Disease in China. 51. Nahid P, Dorman SE, Alipanah N, Barry PM, PLoS One [Internet]. 2015;10(8):e0135205. Brozek JL, Cattamanchi A, et al. Official Available from: http://www.pubmedcentral.nih. American Thoracic Society / Centers for Disease gov/articlerender.fcgi?artid=4540442&tool= Control and Prevention / Infectious Diseases pmcentrez&rendertype=abstract Society of America Clinical Practice Guidelines : 46. Inghammar M, Ekbom A, Engstrom G, Ljungberg Treatment of Drug-Susceptible Tuberculosis. Clin B, Romanus V, Lofdahl CG, et al. COPD and the Infect Dis Guidel. 2016;63:147–95. Risk of Tuberculosis - A Population-Based Cohort 52. Nayak S, Acharjya B. Mantoux test and its Study. PLoS One. 2010;5:7 ST-COPD and the interpretation. Indian Dermatol Online J. Risk of Tuberculosis-A Popu. 2012;3(1):2. 47. Kim J-H, Park J-S, Kim K-H, Jeong H-C, Kim E-K, Lee J-H. Inhaled is associated with an increased risk of TB in patients with COPD. Chest [Internet]. 2013;143(4):1018– 24. Available from: http://www.ncbi.nlm.nih. gov/pubmed/23079688%5Cnhttp://dx.doi. org/10.1378/chest.12-1225

40 Risk factors of MDR-TB

2 non-completion: participants who discontinued/ stopped treatment before defined period of treatment, OR non-cure or failure treatment: a patient who sputum is smear or sputum culture positive at five months lateror after the initiation of anti TB treatment, OR default: participants who interrupted have TB treatment for two or more consecutive months Operational Definition Age of the participants, divided between < 40 and ≥ 40 years old female or Male Categorized as lower education (below diploma level or non-education) and higher education (diploma, bachelor, master or doctoral degree) Participants who can explain the basic understanding of tuberculosis correctly, and MDR-TB i.e., signs, symptoms, process of TB spreading, the definitionand awareness of MDR-TB of the long treatment duration identified is as good knowledge. Occupation of the participants unmarried or Married Participants who no have health insurance coverage Participants who received have at least one drugs month in the of anti-Tb past, regardless of their treatment outcome. Participants with a history of previous TB, regardless of treatment status Participants who one have of the criteria below : • • • Participants treated with FDC rather than multiple single anti-tuberculosis drug use Intravenous versus oral treatment Participants who experienced ADRs during TB treatment Participants who had a previous BCG-vaccination. Participants who completed the duration of treatment, who however took less than 90% of the prescribed anti-tuberculosis drug without clinical reasons, such as adverse drug reaction or drug interaction. Exposure/Risk factorExposure/Risk Age Gender Level of education MDR-TB of Knowledge Occupation Marital status Lack of health insurance Previous treatment Previous TB disease Non-completion and failure of TB treatment Fixed Dose Combination (FDC) Route of administration Adverse drug reactions (ADRs) Previous BCG vaccination Non-adherence Exposure criteria of systematic review and meta-analysis study on risk factors of multidrug-resistant tuberculosis Perspectives Patients characteristics Tuberculosis (TB) history and treatment No 1 2 APPENDIX. E1. Table

41 Chapter 2 2 Operational Definition Clinically or lab-confirmeddiagnosis of HIV Diagnostic criteria of fasting plasma mmol/l glucose mg/dl) plasma or ≥ 7.0 2-h glucose (126 ≥ mmol/l11.1 (200 mg/dl) or diagnosed DM by the clinician. Clinically confirmeddiagnosis COPD, of including emphysema, chronic bronchitis, refractory (non-reversible, asthma, and bronchiectasis). Includes diagnoses such as previous heart attack, ischemic stroke, heart failure, arrhythmia, and health valve disease Any viral hepatitis with laboratory confirmation or stated in medical records by a medical doctor. Any diagnosis of liver disease and/or three times elevated the normal value of Transaminase (ALT) unit/L) in the 7-56 (ref Presence of lung cavity on chest x-ray Plasma albumin < 35 g/dL Visible M. tb in the sputum during microscopy (Ziehl-Neehlsen or auramine stain) BMI Kg/m < 18 A positive tuberculin skin test, defined accordingto a medical doctor’s interpretation after dermal injection of protein derivative of tubercle bacillus, with mm a raised red area of 5-10 appearing hours(52). 48-72 Including former and current smoking habit, regardless of duration Known previous contact with a patient diagnosed with contagious TB Participants who consume alcohol on a daily basis divided is It homelessness into and non-homelessness. Homelessness defined is as participants who a previous have or current history of homelessness Participants who are currently imprisoned or a previous have history of imprisonment. Domicile area of the participant. divided is It two into categories, i.e. urban domicile and rural domicile. numberTotal of rooms in the household M. tb type Beijing strain Distance and travel time the to health facility Availability of anti-tuberculosis drugs in the health facility Low quality of medicine refers counterfeit to and/or poor drugs quality based anti-TB on physical and chemical criteria of the drug. Exposure/Risk factorExposure/Risk Human Immunodeficiency Virus (HIV) (DM) Diabetes mellitus Chronic Obstructive Pulmonary (COPD) Diseases Cardiovascular diseases (CVD) Hepatitis Liver diseases Lung cavity Hypoalbuminea Sputum smear positivity Low Body Mass Index (BMI) Positive Mantoux test Smoking Known contact with TB patient alcoholDaily consumption Nature of abode imprisonment of History Domicile area Room spaces Beijing strain health facility of Accessibility Drug supply medicines Low-quality Exposure criteria of systematic review and meta-analysis study on risk factors of multidrug-resistant tuberculosis Perspectives Comorbidities Lifestyle and Environmental Microbiology Health services No 3 4 5 6 Table E1 (Continued). E1 Table

42 Risk factors of MDR-TB

Table E2. Search terms of the study.

No Bibliographic Key terms database 1 Pubmed ((((“Tuberculosis, Multidrug-Resistant”[Mesh] OR multidrug resistant tuberculosis[tiab] OR mdr tb[tiab] OR mdr tuberculosis[tiab] OR mdr- tb[tiab] OR multi drug resistant tuberculosis[tiab] OR multi-drug resistant tuberculosis[tiab] OR multiple drug resistant tuberculosis[tiab])) AND (“Risk Factors”[Mesh] OR “Epidemiologic Factors”[Mesh] OR predictor*[tiab] OR determinant*[tiab] OR risk factor*[tiab] OR epidemiologic factor*[tiab]))) NOT (“Cross-Sectional Studies”[Mesh] OR cross-sectional [tiab] OR cross sectional [tiab] OR crosssectional [tiab]) Filters:Publication date from 2006/01/01 to 2016/03/26 2 Embase # 1 2 ‘risk assessment’/exp OR ‘risk assessment’ OR ‘risk factor’/exp OR ‘risk factor’ OR ‘predictor variable’/exp OR ‘predictor variable’ OR ‘social determinants of health’/exp OR ‘social determinants of health’ OR ‘risk factor’:ab,ti OR ‘risk factors’:ab,ti OR ‘epidemiologic factor’:ab,ti OR ‘epidemiologic factors’:ab,ti OR ‘predictor*’:ab,ti OR ‘determinant*’:ab,ti #2 ‘multidrug resistant tuberculosis’/exp OR ‘multidrug resistant tuberculosis’:ab,ti OR ‘mdr tb’:ab,ti OR ‘multi-drug resistant tuberculosis’:ab,ti OR ‘mdr-tb’:ab,ti OR ‘mdr tuberculosis’:ab,ti OR ‘multi drug resistant tuberculosis’:ab,ti OR ‘multiple drug resistant tuberculosis’:ab,ti #3 ‘cross-sectional study’/exp OR ‘cross-sectional study’ OR ‘cross sectional’:ab,ti OR ‘çross-sectional’:ab,ti OR ‘crosssectional’:ab,ti ((#1 AND #2) NOT #3) AND [2006-2016]/py

Figure E1. Risk of bias graph: review authors’ judgments about the risk of bias per variable, present- ed as percentages across all included studies.

43 Chapter 2

Figure E2. Risk of bias summary: review authors’ judgment about each risk of bias item for each included study. Information: Green (+) shows low risk of bias, yellow (?) shows unclear risk of bias, and red (-) shows high potential risk of bias. Notes: Citation of the studies refers to Table 1.

44 Risk factors of MDR-TB

A. Age (< 40 years vs. ≥ 40 years)

2

B. HIV (non-HIV vs. HIV)

Figure E3. Heterogeneous effect estimated in several risk factors of MDR-TB stratified by area of study. Notes: TB: tuberculosis; reference group in each factor: (A) age less than 40 years, (B) HIV negative, (C) non-previous TB treatment

45 Chapter 2

C. Previous TB treatment (non-previous TB treatment vs. previous TB treatment)

Figure E3 (Continued). Heterogeneous effect estimated in several risk factors of MDR-TB stratified by area of study. Notes: TB: tuberculosis; reference group in each factor: (A) age less than 40 years, (B) HIV negative, (C) non-previous TB treatment

A. BMI (BMI≥ 18 Kg/m2 vs. BMI < 18 Kg/m2)

Figure E4. Heterogeneous effect estimated in several risk factors of MDR-TB. Notes: TB: tuberculo- sis; reference groups in each factor: (A) BMI ≥ 18 Kg/m2, (B) non-lung cavity, (C) No contact with TB patient; BMI: body mass index.

46 Risk factors of MDR-TB

B. Lung Cavity (non-lung cavity vs. lung cavity)

C. Known contact with TB patient (no contact vs. contact) 2

Figure E4 (Continued). Heterogeneous effect estimated in several risk factors of MDR-TB. Notes: TB: tuberculosis; reference groups in each factor: (A) BMI ≥ 18 Kg/m2, (B) non-lung cavity, (C) No contact with TB patient; BMI: body mass index.

47 Groningen, 2018 CHAPTER PREDICTORS FOR TREATMENT OUTCOMES AMONG PATIENTS WITH DRUG-SUSCEPTIBLE TUBERCULOSIS IN THE NETHERLANDS: A RETROSPECTIVE 3 COHORT STUDY

Ivan S. Pradipta Natasha van’t Boveneind-Vrubleuskaya Onno W. Akkerman Jan-Willem C. Alffenaar Eelko Hak

This chapter is based on the published manuscript: Pradipta IS, Boveneind-Vrubleuskaya N van’t, Akkerman OW, Alffenaar J-WC, Hak E. Predictors for treatment outcomes among patients with drug-susceptible tuberculosis in the Netherlands: a retrospective cohort study. Clin Microbiol infect. 2019;25(6):761.e1-761.e7. Chapter 3

ABSTRACT

Objectives: We evaluated treatment outcomes and predictors for poor treatment outcomes for tuberculosis (TB) among native- and foreign-born patients with drug-susceptible TB (DSTB) in the Netherlands.

Methods: This retrospective cohort study included adult patients with DSTB treated from 2005 to 2015 from a nationwide exhaustive registry. Predictors for unsuccessful treatment outcomes (default and failure) and TB-associated mortality were analysed using multivariate logistic regression.

Results: Among 5,674 identified cases, the cumulative incidence of unsuccessful treatment and mortality were 2.6% (n/N = 146/5,674) and 2.0% (112/5,674), respectively. Although most patients were foreign-born (71%; 4,042/5,674), no significant differences in these outcomes were observed between native- and foreign-born patients (p > 0.05). Significant predictors for unsuccessful treatment were age of 18–24 years [odds ratio (OR), 2.04; 95% confidence interval (CI): 1.34–3.10], homelessness (OR, 2.56; 95% CI: 1.16–5.63), prisoner status (OR, 5.39; 95% CI: 2.90–10.05) and diabetes (OR, 2.02; 95% CI: 1.03-3.97). Furthermore, predictors for mortality were age of 74–84 (OR, 5.58; 95% CI: 3.10–10.03) or ≥85 years (OR, 9.35, 95% CI: 4.31–20.30), combined pulmonary and extra-pulmonary TB (OR, 4.97; 95% CI: 1.42–17.41), central (OR, 120, 95% CI: 34.43–418.54) or miliary TB (OR, 10.73, 95% CI: 2.50–46.02), drug addiction (OR, 3.56; 95% CI: 1.34–9.47) and renal insufficiency/dialysis (OR, 3.23; 95% CI: 1.17–8.96).

Conclusions: Native- and foreign-born patients exhibited similar TB treatment outcomes. To further reduce disease transmission and inhibit drug resistance, special attention should be given to high-risk patients.

50 Predictors for treatment outcomes of DS-TB

INTRODUCTION

Although tuberculosis (TB) is a global health problem (1), the associated burden in Europe has been mainly attributed to the travel and migration of people from high- to low-TB burden countries (2–4). Several groups, including immigrants, asylum seekers, prisoners and homeless individuals, have been identified as high-risk groups (4,5). Hence, adequate treatment management is required, especially for high-risk groups.

The Netherlands has a low TB incidence, with an estimated incidence of 5.9/100,000 population in 2016 (5). According to the Netherlands Tuberculosis Registry (NTR), drug- susceptible TB (DSTB) is the most common form of TB in the Netherlands. From 2005 to 2015, 72% of cases (n/N= 7,416/10,303) were identified as using standard treatment for DSTB. A previous study from the Netherlands (1993–1997) identified a higher probability 3 of treatment default among asylum seekers, immigrants and illegal immigrants (6). However, updated data are needed to determine whether being in a risk group or other factors contribute to poor outcomes of TB treatment and to evaluate the success of current treatment programmes in the Netherlands. We therefore aimed to evaluate treatment outcomes and predictors for poor treatment outcomes for tuberculosis (TB) among native- and foreign-born patients with drug-susceptible TB (DSTB) in the Netherlands.

METHODS

Study design and setting This retrospective cohort study included patients treated for DSTB between 1 January 2005 and 31 December 2015. Anonymised data were obtained from the NTR database on 23 January 2017 following approval from the NTR committee. The NTR is an exhaustive national database managed by the Dutch National Institute for Public Health and the Environment (RIVM). Real-time surveillance data are routinely collected by RIVM in close collaboration with the TB control department of the Municipal Public Health Services (MPHS) and Royal Netherlands Tuberculosis Association/ KNCV TB. MPHS are legally required to record and register all patients with TB in the Netherlands, including those treated in hospitals. NTR data collection occurs throughout the TB diagnostic and treatment period, and the information is entered by the physician or nurse into an electronic report via the Online Registration System for Infectious Diseases in Infectious Diseases Surveillance Information System (OSIRIS) after the diagnosis is made. KNCV TB and MPHS check the registrations for completeness and consistency through an interactive process. MPHS receives reminders when records remain incomplete. The online system enables MPHS to correct and add information to patient records.

51 Chapter 3

Study subjects We included patients with TB aged ≥18 years who were registered in the NTR database and classified as being infected withMycobacterium tuberculosis strain that was considered fully sensitive to first-line anti-TB drugs and treated during the study period. From this cohort of eligible patients, those with an unknown treatment outcome, i.e. no treatment initiated, treatment ongoing and treatment continued elsewhere with unknown results during a 1-year period, were excluded.

Potential predictors and definitions Potential predictors for a poor outcome of TB treatment were identified at baseline (before or during diagnosis) to predict the incidence of the study outcome. We selected a set of potential predictors based on previously published articles (see Appendix 1), input from TB practitioners and information from the NTR database. These potential predictors were classified into five categories: (1) socio-demographic characteristics (age, sex, birth country, domicile area, insurance coverage for TB), (2) current TB diagnosis (pulmonary TB type, TB location, place of diagnosis, treatment delay), (3) history of TB disease and treatment [previously diagnosed TB, treated latent TB infection (LTBI), Bacillus Calmette–Guérin (BCG) vaccination status] (4) risk groups (those in contact with patients with TB, immigrants, asylum seekers, illegal immigrants, homeless individuals, healthcare workers, travellers from/in endemic area, prisoners, alcohol and drug addicts) and (5) high-risk comorbidities [diabetes, human immunodeficiency virus (HIV), , renal insufficiency/dialysis, organ transplantation].

Primary outcomes We retrospectively followed patients from the beginning to the end of DSTB treatment for one episode of TB during a 1-year period. According to the WHO criteria (7), we categorised the study outcomes into unsuccessful treatment and TB-associated mortality. Unsuccessful treatment was defined as a combination of defaulted and failed treatment. Treatment default cases met one of the following four conditions: interruption of TB treatment for ≥2 consecutive months, incomplete treatment for 6 months within a 9-month treatment period, incomplete treatment for 9 months within a 12-month treatment period and completion of <80% of the treatment. Failed treatment was defined as a positive sputum smear or culture at 5 months or more after treatment initiation. For extra-pulmonary TB, treatment failure was defined by a physician according to a national guideline (8). All treatment outcomes were determined by a physician in daily clinical practice. The operational definitions of these variables followed those in the manual OSIRIS guideline published by RIVM (9) (Appendix Table S1).

52 Predictors for treatment outcomes of DS-TB

Statistical analysis Distributions of subjects’ characteristics and the cumulative incidences were examined using descriptive statistics. The cumulative incidence of the study outcomes were calculated by dividing incidence of the outcome with the number of DSTB cases during the observation period. We eliminated potential predictors if >10% of the data were missing. We used the chi-square test or Fisher’s exact test (when expected cell size was <5) for univariate analyses of categorical covariates. Variables with a p-value of <0.25 in the univariate analysis were considered for inclusion in the multivariate analysis. If the number of variables exceeded the assumption of 10 events per variable examined, we considered a stricter univariate p-value (<0.15) for inclusion in the multivariate analysis (10). To increase the statistical power and validity, we minimised the degree of freedom in the predictor model by combining predictors that measured a similar concept and had similar estimated risks in the univariate analysis (10). Variables for which there were no incidences of the study outcome in the 3 indicator group were not included in the multivariate analysis. A backward step elimination based on a p-value of >0.05 was used for the multivariate analysis. We used complete case analysis that excluded patients with missing values (10). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to quantify the level of association between variables and outcomes. The calibration of the multivariate analysis model was assessed using the Hosmer–Lemeshow test, while discrimination was estimated using a receiver operating characteristic curve with a 95% CI. We used Statistical Package for the Social Science, version 23 (SPSS; IBM Corp., NY, USA) for Windows™ in all statistical analyses; a final p-value of <0.05 was considered significant in the multivariate analysis. We followed the STROBE guidelines for reporting this study (11).

RESULTS

Baseline characteristics of study subjects Of the 10,303 adult cases with TB registered during the study period, we identified 5,674 cases with DSTB who fulfilled the study criteria (Figure 1). Most patients with DSTB were foreign-born (71%, n/N = 4,042/5,674; Table 1). As described in Figure 1, 192 patients with DSTB were lost to observation and had missing information about treatment outcomes. Missing information about TB treatment outcomes was significantly more frequent (p < 0.05) among males, foreign-born patients, prisoners, those with pulmonary TB, those with TB diagnosis from outside the Netherlands, immigrants, illegal immigrants and those with a history of travel from/to an endemic area >3 months earlier (Appendix Table S2).

Incidence of DSTB We observed a significant declining trend in the number of DSTB cases within the study period (p <0.05), with cumulative incidences of unsuccessful TB treatment and TB-

53 Chapter 3 associated mortality as 2.6% (146/5,674) and 2.0% (112/5,674), respectively. The highest annual cumulative incidence for both these outcomes was identified in 2011 (Fig. 2).

Predictors for outcomes We combined asylum seekers and immigrants as one covariate in the analysis because similar residential status outside the Netherlands was thought to yield relatively similar statistical associations in the univariate analysis. In the univariate analysis, immigrants and asylum seekers had ORs (95% CI) of 0.90 (0.48–1.67) and 1.57 (0.97–2.54) for unsuccessful treatment outcome, while for mortality outcome had ORs (95% CI) of 0.19 (0.05–0.80) and 0.09 (0.12–0.62), respectively.

Figure 1. Flow diagram of the included subjects. M. tb, Mycobacterium tuberculosis; H, isoniazid; R, rifampicin; E, ; Z, ; MDR, multi-drug-resistant; XDR, extensively drug-re- sistant; DSTB, drug-susceptible tuberculosis; DRTB, drug-resistant tuberculosis.

54 Predictors for treatment outcomes of DS-TB

Table 1. Characteristics of subjects (N = 5,674)

No Characteristics Frequency (%) 1 Socio-demographic Male 3,426 (60.4) Age (years): 18–24 867 (15.3) 25–74 4,246 (74.8) 75–84 422 (7.2) ≥85 139 (2.4) Country of birth*: The Netherlands 1,617 (28.5) Somalia 741 (13.1) Maroco 539 (9.5) Indonesia 275 (4.8) 3 Suriname 274 (4.8) Turkey 187 (3.3) Others 2,041 (36) Urban domicile† 1,997 (35.2) Insurance coverage for TB*§ 57 (10.3) 2 Current TB diagnosis Pulmonary diagnosis ETB 1,890 (33.3) PTB 3,012 (53.1) ETB + PTB 772 (13.6) Initial TB location Lungs 3,505 (61.8) 70 (1.2) Miliary 125 (2.2) Others 1,974 (34.8) TB diagnosis outside of the Netherlands 50 (0.9) Treatment delay >4 weeks* 1,053 (18.5) 3 History of TB disease & treatment Previously diagnosed TB* 358 (6.3) Previously treated LTBI* 184 (3.2) BCG vaccination* 1,555 (27.4) 4 TB risk group TB contact 375 (6.6) Immigrant 471 (8.3) Asylum seeker 527 (9.3) Illegal immigrant 201 (3.5) Homeless individuals 132 (2.3) Health care workers 46 (0.8) Travelers from/in endemic area >3 month 130 (2.3) Prisoners 143 (2.5)

55 Chapter 3

Table 1 (Continued). Characteristics of subjects (N = 5,674)

No Characteristics Frequency (%) Alcohol addicts 111 (2.0) Drug addicts 152 (2.7) 5 Comorbidities Diabetes 268 (4.7) HIV positive 229 (4.0) Malignancy 135 (2.4) Renal insufficiency/ dialysis 91 (1.6) Organ transplantation 22 (0.4) 6 Outcomes Cure or completed treatment 5,190 (91.5) Defaulted treatment 144 (2.5) Failed treatment 2 (0.0) Death due to TB 112 (2.0) Death due to non-TB 226 (4.0)

Information: *missing data : Country of birth 15 (0.3%), Previously diagnosed TB 437 (7.7%), Previously treated LTBI 466 (8.2%), BCG vaccination 2,812 (49.6%), HIV positive 3,329 (58.7%), treatment delay 4,056 (71.5), insurance coverage for TB 5,062 (89.2%); §the information was documented from 2014; †Urban domicile : Amsterdam, Rotterdam, the Hague and Utrecht; TB, tuberculosis; ETB, extra- pulmonary tuberculosis; PTB, pulmonary tuberculosis; LTBI, infection; BCG, Bacillus Calmette–Guérin; HIV, human immunodeficiency virus.

Figure 2. Annual cumulative incidence for TB treatment outcomes during 2005–2015. DSTB, drug-susceptible tuberculosis; TB, tuberculosis

In the univariate analysis, sex, age, homelessness and prisoner status were significantly associated (p < 0.05) with unsuccessful treatment. Furthermore, multivariate analyses revealed a final prediction model comprising age of 18–24 years (OR, 2.04; 95% CI: 1.34–3.10), homelessness

56 Predictors for treatment outcomes of DS-TB

(OR, 2.56; 95% CI: 1.16–5.63), prisoner status (OR, 5.39; 95% CI: 2.90–10.05) and diabetes (OR, 2.02; 95% CI: 1.03–3.97) as significant predictors for unsuccessful treatment (Table 2).

Regarding mortality, age; pulmonary diagnostic type; initial TB location, such as lung, CNS and miliary TB; previous TB diagnosis; non-immigrant status; non-asylum seeker; native- born status and comorbidities, such as diabetes, malignancy, renal insufficiency/dialysis and organ transplantation, were significantly associated with death in the univariate analysis (p < 0.05). Finally, we identified age of 75–84 (OR, 5.58; 95% CI: 3.10–10.03) or ≥85 years (OR, 9.35; 95% CI: 4.31–20.30), combined pulmonary and extra-pulmonary TB (OR, 4.97; 95% CI: 1.42–17.41), central nervous system (OR, 120; 95% CI: 34.43–418.54) or miliary TB (OR, 10.73; 95% CI: 2.50–46.02), drug addiction (OR, 3.56; 95% CI: 1.34–9.47), renal insufficiency/dialysis (OR, 3.23; 95% CI: 1.17–8.96) and immigrant or asylum seeker status (OR, 0.11; 95% CI :0.01–0.84) as significant predictors for mortality (Table 3). 3

DISCUSSION

Although most cases in our study involved foreign-born patients, no significant differences in treatment outcomes were observed between native- and foreign-born patients. Immigrants and asylum seekers had a lower risk of death than other patients and no significant difference in the risk for unsuccessful TB treatment. Overall, approximately 5 in 100 treated DSTB cases had a poor TB treatment outcome, of which 2.6% (146/5,674) were attributed to unsuccessful treatment and 2.0% (112/5,674) to TB-associated mortality. Predictors for unsuccessful treatment included age of 18–24 years, homelessness, prisoner status and diabetes. Furthermore, age of ≥75 years, drug addiction, combined pulmonary and extra-pulmonary TB and several comorbidities [renal insufficiency, central nervous system (CNS) and miliary TB] were predictors for TB-associated mortality. Moreover, male sex, foreign-born patients, immigrants, illegal immigrants, travellers from/in endemic areas for >3 months, those diagnosed with TB from outside of the Netherlands, those with pulmonary TB and prisoners were more likely to be lost to treatment follow-up which indicates potential high risk of poor outcomes.

Diabetes was identified as a risk factor for unsuccessful TB treatment in this study. Previous studies have demonstrated that the correlation of diabetes with TB treatment failure (12) could be attributed to altered drug absorption (13) and as well as drug interaction (14). We further identified renal insufficiency/dialysis as a risk factor for TB- associated mortality. In patients undergoing dialysis, altered immune response associated with uraemia and dialysis exacerbation have been identified as predisposing factors for active TB development (15). Patients with end-stage renal disease are more susceptible to TB (16). Furthermore, drug-induced hepatitis has been identified more frequently in patients with TB and chronic renal failure than in those with TB but without chronic renal failure that increase the risk of TB-associated mortality (17).

57 Chapter 3 - - - - - 0.14 0.13 0.52 0.26 0.004 p-value n/a n/a Ref. Ref. Multivariate analysis* Not included Not included Not included Not included Not included aOR (95%CI) 0.75 (0.52-1.10) 0.75 1.35 (0.91-2.01) 1.35 1.46 (0.75-2.81) 0.83 (0.36-1.93) 0.83 2.24 (0.89-5.67) 2.04 (1.34-3.10) 2.04 1.82 (0.83-4.00) 1.82 0.76 0.11 0.17 0.95 0.52 0.37 0.23 0.83 0.025 0.000 p-value n/a Ref. Ref. Ref. Univariate analysis Univariate OR (95%CI) 0.81 (0.47-1.42) 0.96 (0.67-1.37) 0.93 (0.47-1.83) 1.66 (1.11-2.48) 1.66 0.89 (0.61-1.29) 0.89 1.35 (1.04-1.76) 1.35 0.71 (0.33-1.53) 0.71 0.99 (0.70-1.40) 0.99 1.56 (0.72-3.39) 2.22 (1.01-4.87) 2.22 0.89 (0.64-1.25) 0.28 (0.04-2.01) 0.28 1.50 (0.84-2.68) 1.59 (0.38-6.58) 1.59 0 (0) 2 (1.4) 7 (5.3) 7 (4.8) 7 (4.8) 9 (6.2) 1 (0.7) 89 (61) 13 (9.8) 99 (67.8) 51 (34.9) 51 (34.9) 17 (11.6) 78 (53.4) 56 (38.4) 33 (22.6) 38 (26.2) 101 (69.2) 101 Yes (n= 146; %) (n= 146; Yes Unsuccessful treatment 70 (1.3) 48 (0.9) 415 (7.5) 415 132 (2.4) 177 (3.5)177 366 (6.6) 124 (2.2)124 345 (6.8) 345 4147 (75) 4147 755 (13.7) 834 (15.1) 3416 (61.8) 3416 1918 (34.7) 2934 (53.1) 1946 (35.2)1946 3325 (60.1) 1579 (28.6) 1839 (33.3) No (n = 5,528; %) Predictors for unsuccessful tuberculosis treatment outcome (N = 5,674) Predictors characteristics Socio-demographic Male Age (years) 18–24 25–74 75–84 ≥85 Born in the Netherlands** domicile Urban diagnosis TB Current Pulmonary diagnosis ETB PTB ETB + PTB Initial TB location Lungs nervous system Central Miliary Others TB diagnosis outside of the Netherlands History of TB disease & treatment Previously diagnosed TB** Previously treated LTBI** TB risk group TB contacts No 1 2 3 4 Table 2. Table

58 Predictors for treatment outcomes of DS-TB - - - - - 0.13 0.02 0.22 0.04 0.000 p-value Multivariate analysis* Not included Not included Not included Not included Not included Not included Not included aOR (95%CI) 2.56 (1.16-5.63) 2.09 (0.81-5.35) 2.02 (1.03-3.97) 2.02 1.34 (0.84-2.14) 1.34 5.39 (2.90-10.05) 0.11 0.16 0.78 0.52 0.24 0.32 0.26 0.28 0.44 0.54 0.007 0.000 p-value 3 Univariate analysis Univariate OR (95%CI) 0.57 (0.18-1.79) 0.57 0.59 (0.14-2.39) 0.59 1.79 (0.78-4.14) 5.23 (3.03-9.06) 5.23 1.67 (0.89-3.13) 1.43 (0.52-3.93) 1.27 (0.85-1.90) 1.27 0.20 (0.01-3.28) 0.20 1.58 (0.69-3.64) 1.58 0.40 (0.02-6.56) 0.40 2.89 (1.44-5.80) 2.89 1.81 (0.24-13.54) 0(0) 0 (0) 6 (4.1) 6 (4.1) 2 (1.4) 3 (2.1) 9 (6.2) 4 (2.7) 1 (0.7) 16 (11) 11 (7.5) 11 31 (21.2)31 Yes (n= 146; %) (n= 146; Yes Unsuccessful treatment 91 (1.6) 21 (0.4) 46 (0.8) 107 (1.9) 198 (3.6) 257 (4.6) 146 (2.6) 127 (2.3) 129 (2.3) 128 (2.3) 123 (2.2) 966 (17.5) No (n = 5,528; %) Predictors for unsuccessful tuberculosis treatment outcome (N = 5,674) *Number of analysed cases, Hosmer 5,674; & Lemeshow test, 0.99; area under the curve, 0.64 (0.59–0.69); n/a, not applicable due a small to number : Predictors Immigrants & asylum seekers immigrants Illegal Homeless individuals Health care workers from/inTravelers endemic area >3 month Prisoners Alcohol addicts Drug addicts Comorbidities Diabetes Malignancy insufficiency/dialysis Renal transplantation Organ No 5 Table 2 (Continued).Table Information of events; Ref., reference; OR, odds ratio; aOR, adjusted odds ratio; **missing values: country of birth, 15 (0.3%); previous TB diagnosis, previous 437 LTBI (7.7%); treatment, 466 (8.21%); ETB, extra-pulmonary tuberculosis; PTB, pulmonary tuberculosis; TB, tuberculosis; latent tuberculosis LTBI, infection.

59 Chapter 3 - - - - - 0.57 0.38 0.038 0.000 0.000 p-value Ref. Ref. Ref. Multivariate analysis* Not included Not included Not included Not included Not included aOR CI) (95% 0.45 (0.13-1.52) 0.45 1.23 (0.61-2.48) 1.23 1.26 (0.75-2.12) 2.03 (0.45-9.04) 2.03 4.97 (1.42-17.41) 4.97 4.04 (0.92-17.75) 9.35 (4.31-20.30) 9.35 5.58 (3.10-10.03) 10.73 (2.50-46.02)10.73 120 (34.43-418.54) 120 0.19 0.47 0.98 0.69 0.39 0.008 0.000 0.000 0.000 0.000 p-value Ref Ref. Ref. Univariate analysis Univariate OR (95%CI) 0.52 (0.19-1.4) 0.52 1.01 (0.14-7.41) 1.01 0.76 (0.18-3.10) 1.15 (0.78-1.69) 1.19 (0.80-1.75)1.19 5.73 (3.71-8.84) 0.31 (0.11-0.86) 0.31 2.77 (1.55-4.97) 2.77 2.35 (1.23-4.49) 2.75 (1.88-4.02) 6.96 (3.73-12.99) 5.98 (2.75-13.01) 5.98 6.75 (3.63-12.55) 50.37 (20.72-122.45) 64.09 (24.64-166.68) 64.09 1 (0.9) 4 (3.6) 4 (3.6) 7 (6.3) 2 (2.7) 19 (17) 37 (33) 11 (14.5) 11 13 (11.6) 13 (11.6) 62 (55.4) 57 (51.8) 33 (29.5) 14 (12.5)14 61 (54.5)61 73 (65.2)73 72 (64.3) 43 (38.4) Yes (n=112; %) (n=112; Yes Mortality due TB to 57 (1) 389 (7) 49 (0.9) 106 (1.9) 371 (6.7) 347 (6.7) 182 (3.5)182 126 (2.3) 735 (13.2) 863 (15.5) 2951 (53.1) 1967 (35.4) 1954 (35.1) 1876 (33.7) 4184 (75.2)4184 3432 (61.7) 3432 1560 (28.1) 3354 (60.3) No (n=5,562; %) Predictors for mortality outcome due tuberculosis to (N = 5,674) Lungs nervous system Central Miliary Others Previously diagnosed TB** Previously treated LTBI** Risk group of TB TB contact Pulmonary diagnosis ETB PTB ETB + PTB Initial TB location TB diagnosis outside of the Netherlands History of TB disease & treatment 18–24 25–74 75–84 ≥85 Born in the Netherlands** domicile Urban diagnosis TB Current Predictors characteristics Socio-demographic Male Age (years) 3 4 2 No 1 Table 3. Table

60 Predictors for treatment outcomes of DS-TB - - - - - 0.01 0.89 0.03 0.60 0.84 0.024 p-value Multivariate analysis* Not included Not included Not included Not included Not included Not included aOR CI) (95% 3.23 (1.17-8.96) 3.23 2.13 (0.89-5.11) 3.56 (1.34-9.47) 3.56 1.10 (0.46-2.65)1.10 0.11 (0.01-0.84) 0.11 1.88 (0.18-19.54) 0.19 0.19 0.12 0.72 0.21 0.89 0.60 0.017 0.009 0.003 0.000 0.000 p-value 3 Univariate analysis Univariate OR (95%CI) 0.17 (0.01-2.71) 0.17 2.10 (0.91-4.86) 0.77 (0.18-3.16) 0.91 (0.22-3.73) 2.49 (1.35-4.59) 1.10 (0.15-8.08) 0.13 (0.04-0.40) 0.13 2.00 (0.80-4.99) 2.83 (1.29 -6.20)2.83 (1.29 0.24 (0.034-1.74) 0.24 5.84 (2.86-11.94) 5.84 8.03 (2.34-27.53) 8.03 9 (8) 0 (0) 1 (0.9) 1 (0.9) 6 (5.4) 2 (1.8) 2 (1.8) 7 (6.3) 5 (4.5) 3 (2.7) 3 (2.7) 12 (10.7) Yes (n=112; %) (n=112; Yes Mortality due TB to 109 (2) 109 82 (1.5)82 19 (0.3) 45 (0.8) 256 (4.6) 143 (2.6) 146 (2.6) 127 (2.3) 128 (2.3) 128 (2.3) 200 (3.6) 994 (17.9) No (n=5,562; %) Predictors for mortality outcome due tuberculosis to (N = 5,674) * Number of analysed cases Hosmer 5,674, & Lemeshow area test under 0.59, curve 0.85 (0.82-0.88); n/a, not applicable due a small to number : Diabetes Malignancy insufficiency/dialysis Renal Health care workers from/inTravelers endemic month area >3 Prisoners Alcohol addicts Drug addicts Comorbidities transplantation Organ Predictors Immigrants and asylum seekers immigrants Illegal Homeless individuals No 5 Information of event; Ref., reference; OR, odds ratio; aOR, adjusted odds ratio; **missing value: Country of birth 15 (0.3%), previously diagnosed previously TB 437 (7.7%), 466treated (8.21%); LTBI ETB, extra-pulmonary tuberculosis; PTB, pulmonary tuberculosis; TB, tuberculosis; latent tuberculosis LTBI, infection. Table 3 (Continued).Table

61 Chapter 3

Diabetes was identified as a risk factor for unsuccessful TB treatment in this study. Previous studies have demonstrated that the correlation of diabetes with TB treatment failure (12) could be attributed to altered drug absorption (13) and immune system as well as drug interaction (14). We further identified renal insufficiency/dialysis as a risk factor for TB- associated mortality. In patients undergoing dialysis, altered immune response associated with uraemia and dialysis exacerbation have been identified as predisposing factors for active TB development (15). Patients with end-stage renal disease are more susceptible to TB (16). Furthermore, drug-induced hepatitis has been identified more frequently in patients with TB and chronic renal failure than in those with TB but without chronic renal failure that increase the risk of TB-associated mortality (17).

Our finding of age being a relevant predictor was supported by a retrospective population- based pulmonary TB study in a South African province, in which younger patients (age <25 years) more likely defaulted treatment (18). Moreover, a multi-centre prospective cohort study in Spain reported that elderly people were more likely to die from TB (19).

A previous Dutch study (1993–1997) showed an association between the risk of treatment default and being in the high-risk group (asylum seekers, immigrants, illegal immigrants, homeless individuals, prisoners and eastern European nationals) (6). However, the present study did not show that immigrants and asylum seekers as a high-risk group in terms of outcomes (unsuccessful treatment and TB-associated mortality). It seems that asylum seekers and immigrants received a successful treatment during the study period.

According to the national guideline, immigrants and asylum seekers comprise a high-risk priority group for TB screening and monitoring (20). People from TB-endemic countries who plan to reside in the Netherlands for >3 months are required to undergo regular chest X-ray for 2 years. TB diagnosis leads to the administration of regular treatment and monitoring, together with treatment support from a nurse at Municipal Public Health Services. To ensure TB treatment compliance, municipal health centres work closely with medical service providers to asylum seekers and prisoners as well as with social workers from institutions for homeless care. Total TB control expenditures are covered by health insurance and funding from municipal authorities and the government (21). For uninsured patients, the treatment cost is covered by municipalities via the public health act or budgeted financial support for illegal immigrants (22). Two modern TB hospitals have been established for the long-term admission and specialised treatment of clinically complex or socially problematic TB cases to support successful treatment (23). TB management is standardised according to a national TB guideline (8) and framework of the National Tuberculosis Control and Plan (21).

62 Predictors for treatment outcomes of DS-TB

We identified homeless individuals and prisoners as being at a risk of unsuccessful TB treatment and drug addicts as a dominant risk group for TB-associated mortality. These vulnerable and hard-to-reach patients have both individual problems and challenges related to healthcare facility access. Specifically, individuals in these groups lack awareness and knowledge of TB and experience stigma, unstable accommodation and challenges in terms of transportation, costs and treatment duration (24). Furthermore, drug users are frequently homeless individuals, prisoners or HIV-positive (25), all of which further increase the risk of poor TB treatment outcome. Therefore, hard-to-reach patients should be admitted into a modern TB hospital to intensify treatment and monitoring and enable successful outcomes.

Our results were inconsistent with those of several other local studies regarding the determinants for poor TB treatment outcomes in Pakistan (26), China (27), South Korea (28), and Germany (29). For instance, a study in Hamburg identified alcohol dependence 3 as a determinant for disease persistence and treatment interruption. These inter-study differences can be explained by differences in risk factors across settings due to differences in healthcare systems, government support and patients’ social, clinical and behavioural characteristics. Previous analyses also included subjects with drug-resistant TB, a specific high-risk group that requires longer and other treatment, and more study on their prognosis is needed.

Several potential limitations need to be acknowledged. First, because we used data from an administrative database, our dataset relied on reports from clinicians without any direct observations by current investigators, which may have led to inaccuracies. Second, several prominent predictors which may have further increased the discriminative value of multivariate models, such as HIV, treatment delay duration, BCG vaccination history, insurance coverage, education level, income and patient beliefs, could not be analysed due to unavailability of data for a large number of patients. Third, a low mortality rate in this study led to low precision of the associations between mortality outcome and some predictors, such as age and initial TB location (CNS and miliary TB). However, we believe that the systematic approach for data collection supported by information technology, national guideline, control system for data collection and an integrated referral system for patients with TB in the Netherlands led to a minimal bias in this study. Importantly, expanding the national database coverage to include patients throughout the Netherlands will improve the applicability of our results to the Dutch DSTB population.

In conclusion, although most DSTB cases included foreign-born patients, these patients achieved similar TB treatment success compared with native-born patients. We observed a relatively low incidence of unsuccessful TB treatment and TB-associated mortality among DSTB cases in the Netherlands. However, to reduce further disease transmission and inhibit drug resistance, the potential for unsuccessful treatment should be considered

63 Chapter 3 among patients with DSTB aged 18–24 years and those who are homeless, prisoners or diabetic. Furthermore, patients aged ≥75 years, drug addicts, those diagnosed with CNS TB, miliary TB, renal insufficiency comorbidity, combined pulmonary and extra-pulmonary TB should be carefully monitored to prevent premature mortality. Further study is needed to investigate the quality of TB management, barriers and effective interventions for improved treatment in high-risk groups.

Funding This work was supported by the Indonesia Endowment Fund for Education or LPDP in the form of a Ph.D. scholarship to ISP; this funding source had no role in the concept development, study design, data analysis or article preparation.

Acknowledgements We thank Ms. Henrieke Schimmel, RIVM, Bilthoven, The Netherlands, for providing additional information and Ms. Jasmin for language correction.

Conflict of Interest All authors report no conflicts of interest relevant to this article.

64 Predictors for treatment outcomes of DS-TB

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66 Predictors for treatment outcomes of DS-TB

APPENDIX.

Appendix 1. Published articles for defining a set of potential predictors. 1 Faustini A, Hall AJ, Perucci CA. Tuberculosis treatment outcomes in Europe: A systematic review. Eur Respir J 2005. Doi: 10.1183/09031936.05.00103504. 2 Kigozi G, Heunis C, Chikobvu P, Botha S, van Rensburg D. Factors influencing treatment default among tuberculosis patients in a high burden province of South Africa. Int J Infect Dis 2017;54:95–102. Doi: 10.1016/j.ijid.2016.11.407. 3 Rutherford Merrin E, Hill PC, Maharani W, Sampurno H, Ruslami R. Risk factors for treatment default among adult tuberculosis patients in Indonesia. Int J Tuberc Lung Dis 2013;17(10):1304–9. Doi: 10.5588/ijtld.13.0084. 4 Alobu Isaac, Oshi Sarah N, Oshi Daniel C, Ukwaja Kingsley N. Risk factors of treatment default and death among tuberculosis patients in a resource- 3 limited setting. Asian Pac J Trop Med 2014;7(12):977–84. Doi: 10.1016/S1995- 7645(14)60172-3. 5 Brasil Pedro Emmanuel Alvarenga Americano do, Braga José Ueleres. Meta- analysis of factors related to health services that predict treatment default by tuberculosis patients. Cad Saude Publica 2008;24(suppl 4):s485–502. Doi: 10.1590/S0102-311X2008001600003. 6 Baussano Iacopo, Pivetta E, Vizzini L, Abbona F, Bugiani M. Predicting tuberculosis treatment outcome in a low-incidence area. Int J Tuberc Lung Dis 2008. 7 Waitt CJ, Squire SB. A systematic review of risk factors for death in adults during and after tuberculosis treatment. Int J Tuberc Lung Dis 2011. Doi: 10.5588/ ijtld.10.0352.

Table S1. The operational definition of study

No Variables Operational definition Predictors 1 Age Age when the current TB diagnosis was made. 2 Pulmonary diagnosis Pulmonary TB is defined by a medical doctor based on the ICD code. TB is divided into three categories: pulmonary TB (PTB), extra-pulmonary TB (ETB), and combined PTB and ETB. 3 Type of the initial TB location The initial TB location is based on ICD-9 codes and is defined by a medical doctor. It is divided into lung, central nervous system (CNS) and miliary TB and other TB (respiratory tract, intestinal, urogenital, , and others). 4 TB diagnosis outside of the Subjects diagnosed with TB outside the Netherlands and Netherlands who continue to receive treatment in the Netherlands. 5 Previously diagnosed with TB Subjects previously diagnosed with a different episode of TB, regardless of relapse or non-relapse status.

67 Chapter 3

Table S1 (Continued). The operational definition of study No Variables Operational definition 6 Previous latent TB infection Subjects previously treated for LTBI based on clinical (LTBI) treatment findings and documentation. 7 TB contact Subjects who have been identified by the Municipal Public Health Services (MPHS) as having contact with a patient with TB. 8 Immigrants Subjects who have been identified as having a legal residence status other than a tourist, refugee or asylum seeker. 9 Asylum seekers Subjects who left their home country as a political refugee and are seeking asylum elsewhere. 10 Illegal immigrants Subjects without a legal residence status in the Netherlands at the time of diagnosis, regardless of the length of stay in the Netherlands. 11 Homeless Subjects with no fixed residence and those who regularly sleep on the street or use marginal temporary accommodation. 12 Healthcare workers Subjects who work as healthcare providers 13 Travellers from/to endemic Subjects who travelled from or to a TB-endemic area for areas for >3 months >3 months 14 Prisoners Subjects who were imprisoned at the time of diagnosis, including those who had been screened while in prison but were not diagnosed until after discharge from the prison 15 Alcohol addicts Subjects exhibiting problematic alcohol consumption at the time of diagnosis. Problematic alcohol consumption is related to a drinking pattern that leads to physical complaints and/or psychological or social problems. The amount of alcohol consumed was not considered when defining alcoholic status. 16 Drugs addicts Subjects who regularly use drugs, including methadone and cocaine 17 Comorbidities Subjects with a disease or compelling indication concomitant with TB as defined by a medical doctor. This was divided into diabetes mellitus, malignancy, insufficiency renal/dialysis and organ transplantation. Outcomes 18 Unsuccessful treatment Combination of defaulted and failed treatment. Defaulted treatment was defined as an interruption of TB treatment for ≥2 consecutive months as estimated by a physician or nurse, 6 months of uncompleted treatment within 9 months, 9 months of uncompleted treatment within 12 months or completion of <80% of the treatment. Failed treatment was defined as a positive sputum smear or culture at fifth months after treatment initiation. 19 Mortality associated with TB Subjects who died because of TB as defined by a physician. 20 Poor outcome of TB treatment Subjects with unsuccessful TB treatment and TB- associated death.

68 Predictors for treatment outcomes of DS-TB

Table S2. Characteristics of lost to observation drug susceptible tuberculosis patients compared with the completed outcome treatment patients

No Characteristics Completed Lost to OR (95% CI) p-value outcome observation treatment (n= 192) (n=5,674) 1 Socio-demographic Male 3,426 (60.4) 133 (69.3) 1.48 (1.08-2.02) 0.014† Age (years) 0.28 18–24 867 (15.3) 43 (22.4) Ref. 25–74 4,246 (74.8) 149 (77.6) 0.71 (0.50-1.00) 75–84 422 (7.2) 0 n/a ≥85 139 (2.4) 0 n/a Country of birth*: 0.000† The Netherlands 1,617 (28.5) 4 (2.1) Ref. 3 Somalia 741 (13.1) 8 (4.2) 4.36 (1.31-14.53) Maroco 539 (9.5) 12 (6.3) 9.0 (2.89-28.02) Indonesia 275 (4.8) 21 (10.9) 30.87 (10.52-90-62) Suriname 274 (4.8) 4 (2.1) 5.90 (1.47-23.74) Turkey 187 (3.3) 3 (1.6) 6.48 (1.44-29.19) Others 2,041 (36) 138 (71.9) 27.54 (10.16-74.57) Urban domicile** 1,997 (35.2) 67 (34.9) 0.99 (0.73-1.33) 0.93 2 Current TB diagnosis Pulmonary diagnosis 0.003† ETB 1,890 (33.3) 45 (23.4) Ref. PTB 3,012 (53.1) 126 (65.6) 1.76 (1.24-2.48) ETB + PTB 772 (13.6) 21 (10.9) 1.14 (0.68-1.93) Initial TB location 0.013† Lungs 3,505 (61.8) 139 (72.4) 1.74 (1.24-2.45) Central nervous system 70 (1.2) 2 (1) 1.25 (0.29-5.27) Miliary 125 (2.2) 6 (3.1) 2.11 (0.88-5.03) Others 1,974 (34.8) 45 (23.4) Ref. TB diagnosis outside of the 50 (0.9) 6 (3.1) 3.62 (1.53-8.57) 0.003† Netherlands 3 History of TB disease & treatment Previously diagnosed TB* 358 (6.3) 8 (4.2) 0.74 (0.36-1.52) 0.42 Previously treated LTBI* 184 (3.2) 1 (0.5) 0.18 (0.02-1.30) 0.08 4 TB risk group TB contact 375 (6.6) 4 (2.1) 0.30 (0.11-0.81) 0.01† Immigrants 471 (8.3) 49 (25.5) 3.78 (2.70-5.31) 0.000† Asylum seekers 527 (9.3) 16 (8.3) 0.89 (0.53-1.49) 0.65 Illegal immigrants 201 (3.5) 20 (10.4) 3.17 (1.95-5.14) 0.000† Homeless individuals 132 (2.3) 8 (4.2) 1.83 (0.88-3.78) 0.11 Health care workers 46 (0.8) 1 (0.5) 0.64 (0.08-4.69) 0.66

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Table S2 (Continued). Characteristics of lost to observation drug susceptible tuberculosis patients compared with the completed outcome treatment patients No Characteristics Completed Lost to OR (95% CI) p-value outcome observation treatment (n= 192) (n=5,674) Travelers from/in endemic area 130 (2.3) 26 (13.5) 6.68 (4.26-10.46) 0.000† more than 3 month Prisoners 143 (2.5) 18 (9.4) 4.00 (2.39-6.68) 0.000† Alcohol addicts 111 (2.0) 2 (1.0) 0.52 (0.13-2.15) 0.37 Drug addicts 152 (2.7) 4 (2.1) 0.77 (0.28-2.10) 0.61 5 Comorbidities Diabetes 268 (4.7) 3 (1.6) 0.32 (0.10-1.00) 0.05 Malignancy 135 (2.4) 0 0.10 (0.00-1.71) 0.11 Renal insufficiency/ dialysis 91 (1.6) 0 0.15 (0.00-2.56) 0.19 Organ transplantation 22 (0.4) 0 0.65 (0.03-10.79) 0.76

Information Table S2: †Significant value (p<0.05); OR, odds ratio; CI, confidence interval; *missing data : 1) completed treatment group, i.e. country of birth 15 (0.3%), Previously diagnosed TB 437 (7.7%), Previously treated LTBI 466 (8.2%), 2) lost to observation group, i.e. country of birth 2 (1%), Previously diagnosed TB 37 (19.3%), Previously treated LTBI 40 (20.8%); **Urban domicile: Amsterdam, Rotterdam, the Hague and Utrecht; TB, tuberculosis; ETB, extra-pulmonary tuberculosis; PTB, pulmonary tuberculosis; LTBI, latent tuberculosis infection; BCG, Bacillus Calmette–Guérin; HIV, human immunodeficiency virus.

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71 Hamburg, 2018 CHAPTER TREATMENT OUTCOMES OF DRUG-RESISTANT TUBERCULOSIS IN THE NETHERLANDS, 2005-2015 4 Ivan S. Pradipta Natasha van’t Boveneind-Vrubleuskaya Onno W. Akkerman Jan-Willem C. Alffenaar Eelko Hak

This chapter is based on the published manuscript: Pradipta IS, Van’T Boveneind-Vrubleuskaya N, Akkerman OW, Alffenaar JWC, Hak E. Treatment outcomes of drug-resistant tuberculosis in the Netherlands, 2005-2015. Antimicrob Resist Infect Control 2019; 8: 115. Chapter 4

ABSTRACT

Background: Since in low incidence TB countries population migration and complex treatment of drug-resistant tuberculosis (DR-TB) patients are major issues, we aimed to analyse patient risk factors associated with the incidence of poor outcome of TB treatment among DR-TB patients in the Netherlands.

Methods: This retrospective cohort study included adult patients with confirmed DR-TB treated from 2005 to 2015. We obtained data from a nationwide exhaustive registry of tuberculosis patients in the Netherlands. Predictors for unsuccessful TB treatment (defaulted and failed treatment) and TB-associated mortality were analysed using multivariate logistic regression.

Results: Among 10,303 registered TB patients, 545 patients with DR-TB were analysed. Six types of DR-TB were identified from the included patients, i.e. isoniazid mono- or poly-resistance (68%); rifampicin mono- or poly-resistance (3.1%); pyrazinamide mono- resistance (8.3%); ethambutol mono-resistance (0.1%); multidrug-resistance (18.9%); and extensively drug-resistance (0.7%). The majority of patients were foreign-born (86%) and newly diagnosed TB (89%) patients. The cumulative incidence of unsuccessful treatment and mortality were 5% and 1%, respectively. Among all DR-TB cases, patients with Multi Drug-Resistant Tuberculosis (MDR-TB) (OR 4.43; 95%CI 1.70-11.60) were more likely to have unsuccessful treatment, while miliary and central nervous system TB (OR 15.60; 95%CI 2.18-111.52) may also be predictors for TB mortality. Additionally, patients with substance abuse and homelessness tend to have unsuccessful treatment.

Conclusions: In recent years, we identified a low incidence of DR-TB as well as the poor outcome of DR-TB treatment. The majority of cases were primary drug-resistant and foreign-born. To further improve treatment outcome, special attention should be given to the high-risk DR-TB patients.

74 Treatment outcomes of DR-TB

INTRODUCTION

Drug-resistant tuberculosis (DR-TB), infection with a strain of M. tuberculosis (M. tb) that is resistant to one or more of the first-line anti-tuberculosis drug, is an ongoing global threat. DR-TB can be classified into mono-, rifampicin-, poly-, multidrug- and extensive drug- resistance. The World Health Organization (WHO) globally recorded 160,684 cases of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in 2017.(1) However, treatment success remains low at 55% globally(1) and the cost of treating Multi- or Extensively Drug-resistant Tuberculosis (M/XDR-TB) is up to 25 times higher than the cost of drug-susceptible tuberculosis.(2)

Although in the WHO European region the fastest decline in incidence and mortality rate of TB has been reported since 2010,(3) DR-TB remains out of control. One-third of notified MDR-TB cases identified globally are people who live in the WHO European Region, and additional resistance commonly exists with MDR-TB in this region.(4) Furthermore, XDR-TB 4 shows an increasing trend. Among 91.3% second-line Drug Susceptibility Test (DST), 18.6% of pulmonary MDR-TB cases had XDR-TB in 2017.(5) A recent study showed different rates of treatment success, treatment failure and death of MDR-TB patients in 16 European countries.(6) The problem is more complex as travel and migration of people have been identified as a risk factor of TB burden in the European countries.(7,8) This can lead to the transmission of DR-TB from high to low incidence TB countries which in increasingly being reported from some European countries.(9,10)

The Netherlands is one of the low incidence TB countries(8). The government has formulated a national tuberculosis control plan that set 1 case/100.000 people as a target for TB elimination by 2035.(11) Previously published studies reported highly successful treatment of MDR-TB in the Netherlands from 1985-2009.(12,13) However, these studies neither analysed all types of drug-resistant TB nor identified predictors for poor outcome of TB treatment. Hence, updated data are required to describe the current situation, evaluate current programmes and identify potential interventions to improve treatment outcomes of the overall DR-TB types in the Netherlands as well as to achieve the national target. Since mono- or poly-resistant TB can potentially develop into the poor outcome of TB treatment and a further level of resistance, we therefore aimed to determine the prevalence of different types of DR-TB cases and its characteristics of the not-evaluated patient for the treatment outcome over the recent years from 2005 to 2015 in the Netherlands. Additionally, we also examined the incidence and predictors for poor outcome of TB treatment among the subgroup of MDR-TB patients.

75 Chapter 4

METHODS

Study design and setting We conducted a retrospective cohort study using a database from the Netherlands Tuberculosis Registry (NTR) covering the period from January 1, 2005 to December 31, 2015. De-identified data were obtained from the NTR on January 23, 2018. The NTR is an exhaustive nationwide database for tuberculosis disease in the Netherlands managed by the Dutch National Institute for Public Health and the Environment (RIVM). Real-time surveillance data are routinely collected by RIVM in close collaboration with the TB control department of the Municipal Public Health Services (MPHS) and the Royal Netherlands Tuberculosis Association (KNCV). NTR data collection occurs throughout the TB diagnostic and treatment period, and the information is entered by the physician or nurse into an electronic report via the Online Registration System for Infectious Diseases in the Infectious Diseases Surveillance Information System (OSIRIS) after the diagnosis is made. The data were validated by KNCV and MPHS for the completeness and consistency through an interactive process.(14) MPHS received reminders when the data entered in OSIRIS were incomplete and online system enables MPHS to correct and add the information.

Study patients In the present study, adult DR-TB patients in the Netherlands were our population of interest. We included adult patients 18 years and older who were diagnosed with tuberculosis disease during the period 2005-2015, caused by M. tb pathogen proven to be resistant to at least one of the first-line antituberculosis drugs. A phenotypical confirmation test has been used as a standard test in the Netherlands between 2005-2007. However, a combination test, i.e., phenotypic test (Bactec MGIT 960 system) and genotypic test (Genotype MTBDR plus assay or Line Probe Assay (LPA)), have been applied since 2007. Drug susceptibility testing (DST) was conducted to determine resistance to first-line anti- tuberculosis drugs. If the resistance had been confirmed, the DST was extended to the second-line drugs, except for isoniazid, pyrazinamide and ethambutol mono-resistance. We retrospectively followed-up up to 24 months for patients identified as DR-TB. The observation started from the time the diagnosis of DR-TB was made until the outcome of TB treatment was reported. Patients who had not started treatment and had an unknown treatment outcome were excluded from the analysis for the incidence and patient risk factors for poor outcome of TB treatment. Moreover, patients who had an unknown treatment outcome were included for further analysis of a not-evaluated patient outcome.

Potential predictors and definitions Potential predictors were identified at baseline of TB diagnosis, and were selected from a previous meta-analytical study,(15) input from TB practitioners and information from the NTR database. Five categories of potential predictors were analysed in this study, including

76 Treatment outcomes of DR-TB socio-demographic information (age, gender, country of birth, and domicile area), current TB diagnosis (type of pulmonary TB diagnosis, initial TB location, country of the TB diagnosis, and type of drug resistance), history of TB disease and treatment (BCG vaccination, previously diagnosed TB and treated latent TB infection / LTBI), risk groups (TB contacts, immigrants, asylum seekers, homeless individuals, health care workers, travellers from high endemic area, prisoners, alcohol dependence, and drugs dependence), and comorbidities (diabetes, , HIV, renal insufficiency/on dialysis, and organ transplantation). Operational definitions of the variables and terminology followed the definitions stated in the OSIRIS and WHO guideline (14,16) (See Appendix Table A1).

Study outcomes We defined unsuccessful TB treatment and TB associated mortality as the primary outcome for the predictors of poor outcome treatment. Unsuccessful treatment was a combination of defaulted and failed treatments, while TB associated mortality was mortality due to tuberculosis disease that was defined by a doctor who treated the 4 patient. Defaulted treatment was defined as such if one of the following four conditions was met: 1) an interruption of TB treatment, that was not decided by the clinician, for two or more consecutive months, 2) an uncompleted 6-month treatment in a 9-month period, 3) an uncompleted 9-month treatment in a 12-month period, and 4) treatments where patients took less than 80% of their medication.(14,16) Failed treatments were defined as having a TB-positive sputum smear or culture on the fifth month or later after treatment initiation(14,16). In case of RR/M/XDR-TB, treatment failure was defined if one of the following five conditions was met: 1) lack of conversion by the end of the intensive phase, 2) bacteriological reversion in the continuation phase after conversion to negative, 3) evidence of additional acquired resistance to fluoroquinolone or second-line injectable drugs 4) adverse drug reactions, or 5) a TB-positive sputum smear or culture were defined after 12 month or later from the initial TB treatment.(14,16) As a secondary outcome, we studied the characteristic of patients who were not evaluated for the treatment outcome. The patient who started the treatment but were unknown for the treatment outcome, e.g., in transferred out cases, were defined as not-evaluated patients.

Data analysis We used descriptive analysis to describe characteristics of the study patients, trends of DR-TB cases and incidence of poor treatment outcomes during the study period. The cumulative incidence was used to express incidence for the poor outcome by dividing the number of cases of poor outcomes of TB treatment (unsuccessful treatment or TB- associated mortality) by the number of patients diagnosed with DR-TB. A univariate analysis was conducted for each of the potential predictors and outcomes. We used the chi-square test or the Fisher’s exact test (when expected cell size was < 5) for the categorical data in the univariate analysis. Potential predictors that had a p-value ≤ 0.15 were included in

77 Chapter 4 the multivariate analysis. The logistic regression analysis with a backward step elimination based on a p-value > 0.05 and entry method were used for the multivariate analysis. We used a complete case analysis in the multivariate analysis considering the low percentage of the missing data from the variables analysed. We identified 1 (0.2%) missing values for gender, 2 (0.4%) missing values for country of birth, 53 (9.7%) missing values for newly diagnosed TB, and 58 (10.6%) missing values for previous LTBI treatment. Furthermore, some potential predictors were not included in the analysis due to a high level of missing values, i.e. presence of HIV (51%) and BCG vaccination (51%). To quantify the level of the association between predictors and the outcome, an odds ratio (OR) with 95% confidence interval (95%CI) was calculated. Calibration values of the final model were assessed by the Hosmer-Lemeshow test. Statistical Package for the Social Science version 23 was used for the statistical analysis in this study, and we followed the STROBE statement for reporting the study results.(17)

Results Out of 10,303 adult TB cases identified during the study period, we included 545 (5.3%) DR-TB cases that fulfilled criteria of the study (See flowchart inFigure 1). During the same period, the prevalence of MDR-TB was 1% (n/N= 103/10,303).

We identified that the highest proportion of DR-TB during the study period existed of isoniazid mono- or poly-resistant TB cases (375 cases), while the highest number of patients diagnosed with all type of DR-TB was in 2010 (68 cases). As the second highest proportion of DR-TBs, MDR-TB was identified in each of the years, with 103 diagnosed cases during the study period. However, some types of DR-TB, such as rifampicin mono- or poly-resistant strains, ethambutol mono-resistant and XDR-TB, were not consistently found every year during the study period. Overall, there was a declining trend of DR-TB cases during the study period, from 54 cases (2005) to 33 cases (2015) (Fig. 2).

With regard to the patient risk factors, DR-TB patients were slightly more often in the male (54%), rural domicile (66%), and pulmonary TB diagnosis (52%) group, while most cases were newly diagnosed with TB (88%), foreign-born (86%), isoniazid or rifampicin mono-/ poly-resistant TB (72%), with TB diagnosis in the Netherlands (98%) and aged between 25-64 years old (74%) (Table 1).

As described in Figure 1, we identified 28 patients with unknown treatment outcome. We observed that previously diagnosed TB patients, illegal immigrants, travelers from/ in endemic areas and prisoners were more likely to be not evaluated for their treatment outcome (p < 0.05) (Appendix Table A2).

78 Treatment outcomes of DR-TB

4

Figure 1. Flow diagram of the included patients. *The proportion of drug-resistant M. tb complex was 8.2% (49/582); The proportion of M. tb complex with the known type of strain was 1.2% (7/582).

Treatment outcomes and its predictors among the overall drug-resistant tuberculosis patients We observed that there was no failed treatment outcome in DR-TB patients. The treatment outcomes among the overall DR-TB patients (N = 545) were cured treatment (n = 44), completed treatment (n = 463), defaulted treatment (n = 25), TB-associated mortality (n = 6), and non-TB-associated mortality (n = 7). Therefore, the cumulative incidence of unsuccessful treatment and death were 5% and 1%, respectively. In the univariate analysis, several variables such as being male, having MDR-TB, homelessness, alcohol dependence, and substance abuse were significantly associated with unsuccessful treatment (p < 0.05). Furthermore, we included those predictors together with potential predictors that have a p≤ 0.15 in the multivariate analysis. Finally, we identified three significant predictors (p < 0.05) for the unsuccessful treatment of TB in the multivariate analysis, i.e., having MDR-TB (OR 4.43; 95%CI 1.70-11.60), homelessness (OR 9.10; 95%CI 2.32-35.74), and substance abuse (OR 6.66; 95%CI 1.72-25.82). Performance of the final model was acceptable, with a Hosmer-Lemeshow test p-value of 0.88 (Table 2).

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In a univariate analysis, we found miliary and central nervous system (CNS) TB (OR 14.96; 95%CI 2.47-90.52) as a potential predictor for the mortality outcome. Our final model in the multivariate analysis showed that miliary and CNS TB (OR 15.60; 95%CI 2.18-111.52) were more prone to having death as an outcome than any other TB site adjusted by variables of age and illegal immigrant. Our final model demonstrated an acceptable calibration with a Hosmer-Lemeshow test p-value of 0.85 (Table 3).

Figure 2. The number of drug-resistant cases in the Netherlands from 2005-2015. Notes: H, isoniazid; R, rifampicin; E, ethambutol; Z, pyrazinamide; MDR, multidrug-resistant; XDR, extensively drug-resistant.

Table 1. Characteristics of patients (n=545)

No Characteristics Frequency (%) 1 Socio-demographic Male* 295 (54.1) Age (years): 18-24 106 (19.4) 25-64 404 (74.1) 65+ 35 (6.4) Country of birth*: Somalia 109 (20) The Netherlands 74 (13.6) Morocco 41 (7.5) Indonesia 28 (5.1) Others 293 (53.8) Rural domicile** 359 (65.9) 2 Current TB diagnosis Pulmonary diagnosis: ETB 191 (35) PTB 283 (51.9)

80 Treatment outcomes of DR-TB

Table 1 (Continued). Characteristics of patients (n=545) No Characteristics Frequency (%) ETB + PTB 71 (13) Type of TB location: Lungs 333 (61.1) Miliary and central nervous system TB 13 (2.4) Respiratory tract 38 (7) Intestinal tract 15 (2.8) Bone and joint 28 (5.1) Urogenital tract 9 (1.7) Other organ 109 (20) Diagnosed by doctors abroad 11 (2) 3 History of TB disease & treatment Previously diagnosed TB* 59 (10.8) Previously treated LTBI* 24 (4.4) 4 The risk group of TB 4 TB contacts 29 (5.3) Immigrants 68 (12.5) Asylum seekers 87 (16) Illegal immigrants 14 (2.6) Homeless individual 15 (2.8) Alcohol dependence 8 (1.5) Substance abuse 18 (3.3) Health care workers 4 (0.7) Travelers from/in endemic areas for more than three month 19 (3.5) Prisoners 12 (2.2) 5 Comorbidities Diabetes 18 (3.3) Malignancy 11 (2) Insufficient renal function or on dialysis 5 (0.9) Organ transplantation 2 (0.4)

Information: *missing value: Gender 1 (0.2%), Country of birth 2 (0.4%), Newly diagnosed TB 53 (9.7%), previous LTBI treatment 58 (10.6%); **Urban domicile : Amsterdam, Rotterdam, the Hague and Utrecht; TB, tuberculosis; ETB, extra-pulmonary tuberculosis; PTB, pulmonary tuberculosis; LTBI, latent tuberculosis infection.

Treatment outcomes and its predictors among the multidrug-resistant tuberculosis patients Since MDR-TB was a risk factor for poor outcome treatment among all DR-TB patients, we attempted to gain more insight about the predictors of treatment outcome in the subgroup of MDR-TB patients. We observed that among the 103 MDR-TB cases, most cases were from the group of foreign-born patients, followed by those living in rural domiciles, having lung- TB, newly diagnosed with TB without any previous TB treatment and identified as DR-TB-

81 Chapter 4 positive in the Netherlands. Figure 2 reveals that there has been a fluctuating trend in the number of MDR-TB cases from 2005-2015. Treatment outcomes of the MDR-TB patients (N=103) were cured treatment (n = 4), completed treatment (n = 85), defaulted treatment (n = 11), TB associated mortality (n = 1), and non-TB associated mortality (n = 2). Overall, the cumulative poor TB treatment outcome incidence (a combination of unsuccessful treatment and death due to tuberculosis) was 12%. The significant differences (p<0.05) for the poor TB treatment outcome were found in patients with male gender, homelessness, and substance abuse in the univariate analysis. In the final model, male gender (OR 9.80; 95%CI 1.18- 81.68) and substance abuse (OR 7.50; 95%CI 1.07-52.37) were identified as independent predictors for poor TB treatment outcomes in MDR-TB cases. A Hosmer-Lemeshow test was shown on p-value of 1 (Table 4).

DISCUSSION

Our study demonstrated that the overall prevalence and poor outcomes of DR-TB cases among adults in the Netherlands were relatively low. Most DR-TB cases were foreign-born, newly diagnosed TB and isoniazid mono-/poly-resistant TB patients. Though the numbers were low, we identified that MDR-TB, homelessness, and substance abuse were statistically significant predictors for unsuccessful treatment, while miliary and CNS-TB were analysed as predictors for TB-associated mortality among overall DR-TB cases. Additionally, we noted that patients with male gender and substance abuse were more likely to have a poor outcome after MDR-TB treatment. Among all DR-TB cases, we found that previously diagnosed TB patients, illegal immigrants, travelers from/in endemic areas and prisoners were more likely not to be evaluated for their treatment outcome, which indicates potential risk of poor outcome treatment.

Our study showed that the Netherlands has a low prevalence of DR-TB and poor DR-TB treatment outcomes. Several studies described that the prevalence of DR-TB and MDR-TB across the 27 European Union (EU) and European Economic Area (EEA) countries were 10% and 2%, respectively(18), while our data demonstrated that the Netherlands has a 5.3% prevalence of DR-TB and a 1% prevalence of MDR-TB. In case of MDR-TB, the treatment success rate in the Netherlands was 88%, which is higher than the globally reported rates (46-58%)(3) and the 27 EU/EEA countries (48%).(18)

82 Treatment outcomes of DR-TB ------0.19 0.13 0.39 0.05 p-value ------n/a Ref. Ref. Multivariate analysis* Not included Not included Not included 2.44 (0.66-9.05) 2.30 (0.79-6.69) 2.30 1.85 (0.74-4.63) 1.85 2.96 (0.73-12.07) 2.96 1.68 (0.19-15.22) 4.43 (1.70-11.60) 4.43 2.50 (0.49-12.66) 2.50 Odds ratio (95%CI) ------0.13 0.07 0.81 0.03 0.20 0.88 0.008 p-value n/a n/a n/a Ref. Ref. Ref. Ref. 4 Univariate analysis Univariate 3.55 (1.31-9.60) 3.55 0.99 (0.36-2.74) 0.99 2.28 (0.28-18.9) 2.54 (0.68-9.61) 2.48 (0.92-6.71) 2.48 0.55 (0.24-1.22) 1.12 (0.34-3.99) 0.59 (0.06-5.27) 0.59 3.74 (0.82-17.15) 3.74 4.26 (1.24-14.66) 4.26 4.36 (1.80-10.59) Odds ratio (95%CI) Yes 1 (4) 1 (4) 0 (0) 0 (0) 0 (0) 4 (16) 3 (12) 3 (12) 5 (20) 5 (20) 5 (n=25) 19 (76) 11 (44) 18 (72)18 10 (40) 12 (48) 22 (88) 20 (80) 20 (80) 20 No 4 (0.8) 11 (2.1) 16 (3.1) 13 (2.5)13 34 (6.5) 43 (8.3) (n=520) 265 (51) 275 (53) 385 (74) 92 (17.7) 67 (12.9) 101 (19.4) 194 (37.3) Unsuccessful treatment 174 (33.5) 174 313 (60.2)313 365 (70.2) 188 (36.2) 447 (86.3) ‡ 18-24 25-64 65+ ETB PTB ETB + PTB Lungs Miliary and central nervous system Others mono-/poly-resistant Isoniazid mono-/poly-resistant Rifampicin Pyrazinamide/ mono-resistant ethambutol MDR-TB XDR-TB . Predictors for the unsuccessful treatment of tuberculosis among drug-resistant tuberculosis patients (n= 545) Predictors Socio-demographic Male** Age (years): Foreign-born patients** domicile Urban diagnosis TB Current Pulmonary diagnosis: of TB location:Type Diagnosed by doctors abroad of resistance:Type No 1 2 Table 2

83 Chapter 4 ------0.14 0.002 0.006 p-value Multivariate analysis* Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included 0.43 (0.05-3.94) 0.43 9.10 (2.32-35.74) 6.66 (1.72-25.82) 4.35 (0.60-31.31) 4.35 Odds ratio (95%CI) 0.76 0.10 0.94 0.51 0.49 0.62 0.62 0.26 0.46 0.33 0.66 0.58 0.54 0.004 0.000 0.000 p-value Urban domicile : Amsterdam, Rotterdam, The ‡ n/a n/a n/a n/a n/a n/a Univariate analysis Univariate 1.59 (0.36-7.09) 1.59 1.23 (0.16-9.65) 1.59 (0.52-4.83) 1.59 1.71 (0.66-4.42)1.71 0.96 (0.28-3.28)0.96 4.44 (0.92-21.42) 1.63 (0.20-12.94) 17.93 (5.79-55.50) 17.93 13.37 (4.53-39.43) 14.05 (3.15-62.54) 14.05 Odds ratio (95%CI) Yes 0(0) 1 (4) 1 (4) 2 (8) 2 (8) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 6 (24) 6 (24) 6 (24) 3 (12) 3 (12) (n=25) 4 (17.4) No 5 (1) 5 (1) 9 (1.7) 9 2 (0.4) 4 (0.8) 10 (1.9) 11 (2.1) 19 (3.7) 24 (5.2)24 17 (3.3) 27 (5.2)27 13 (2.5)13 12 (2.3) (n=520) 55 (11.7) 55 81 (15.6) 81 65 (12.5)65 Unsuccessful treatment . Predictors for the unsuccessful treatment of tuberculosis among drug-resistant tuberculosis patients (n= 545) Predictors History of TB disease & treatment Previously diagnosed with TB** Previously treated LTBI** Risk group of TB TB contacts Immigrants Asylum seekers Illegal residence persons Homeless individuals Alcohol dependence abuse Substance Health care workers from/inTravellers endemic areas for more than 3 month Prisoners Comorbidities Diabetes Malignancy Insufficient renal function or dialysison transplantation Organ No 3 4 5 Table 2 (Continued)Table Information: *Number of cases analysed 544 with backward elimination method the in multivariate analysis; Hosmer & Lemeshow test 0.88; not n/a: applicable due small to number of event; Reference; Ref.: Not included: the predictor was not included due p-value to > 0.15 in the univariate analysis; **missing value: Gender 1 (0.2%), Country of birth 2 (0.4%), Newly diagnosed previous treatment LTBI 58 (10.6%); TB 53 (9.7%), Hague and Utrecht; TB, tuberculosis; ETB, extra-pulmonary tuberculosis; PTB, pulmonary tuberculosis; multidrug-resistant MDR-TB, tuberculosis; XDR-TB, extensively drug-resistant tuberculosis; latent tuberculosis LTBI, infection; CI, confidence interval.

84 Treatment outcomes of DR-TB ------0.05 0.024 p-value ------n/a Ref. Ref. Multivariate analysis* Not included Not included Not included Not included Not included Not included 0.65 (0.06-6.91) 0.65 8.24 (0.63-107. 05) 8.24 (0.63-107. Odds ratio (95%CI) 15.60 (2.18-111.52) ------0.97 0.72 0.42 0.07 0.23 0.58 0.83 0.013 p-value 0 n/a n/a n/a n/a Ref. Ref. Ref. Ref.

Univariate analysis Univariate 4 1.95 (0.39-9.73) 0.78 (0.08-7.63) 0.78 0.84 (0.17-4.21) 0.84 0.24 (0.05-1.23) 0.24 0.91 (0.10-8.23) 0.91 0.78 (0.09-6.83) 0.78 2.06 (0.23-18.85) 2.06 6.36 (0.56-72.44) 14.96 (2.47-90.52) Odds ratio (95%CI) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 3 (50) 3 (50) 3 (50) 3 (50) 3 (50) 1 (16.7) 1 (16.7) 1 (16.7) 4 (66.7) 4 (66.7) 2 (33.3) 2 (33.3) 5 (83.3) Yes (n=6; %) Yes 11 (2) 11 (2) 11 4 (0.7) 33 (6.1) 17 (3.2)17 45 (8.3) 329 (61) 183 (34) 68 (12.6) 105 (19.5) 105 280 (51.9) 102 (18.9) 191 (35.4) 199 (36.9) 401 (74.4) 371 (68.8) TB-associated mortality 292 (54.3) 292 464 (86.4) No (n=539; %) No (n=539; ‡ 18-24 25-64 65+ ETB PTB ETB + PTB Lungs Miliary and central nervous system Others mono-/poly-resistant Isoniazid mono-/poly-resistant Rifampicin Pyrazinamide/ mono-resistant ethambutol MDR-TB XDR-TB . Predictors for mortality outcomes due tuberculosis to among drug-resistant tuberculosis patients (n= 545) Predictors Socio-demographic Male** Age (years): Foreign-born patients** domicile Urban diagnosis TB Current Pulmonary diagnosis: of TB location:Type Diagnosed by doctors abroad of resistance:Type No 1 2 Table 3

85 Chapter 4 ------0.09 p-value Multivariate analysis* Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included 8.87 (0.71-111.40) Odds ratio (95%CI) 0.76 0.41 0.96 0.71 0.61 0.18 0.15 0.72 0.81 0.55 0.65 0.56 0.68 0.83 0.88 0.64 p-value 0 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Univariate analysis Univariate 1.05 (0.12-9.13) 1.41 (0.16-12.24) 8.09 (0.88-74.24) 6.14 (0.68-55.46) Odds ratio (95%CI) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (16.7) 1 (16.7) 1 (16.7) 1 (16.7) Yes (n=6; %) Yes 24 (5) 24 11 (2) 11 5 (0.9) 8 (1.5) 2 (0.4) 4 (0.7) 86 (16) 29 (5.4) 19 (3.5)19 13 (2.4) 17 (3.2)17 18 (3.3) 15 (2.8) 12 (2.2) 59 (12.1) 67 (12.4) TB-associated mortality No (n=539; %) No (n=539; . Predictors for mortality outcomes due tuberculosis to among drug-resistant tuberculosis patients (n= 545) Predictors History of TB disease & treatment Previously diagnosed with TB** Previously treated LTBI** The risk group of TB TB contacts Immigrants Asylum seekers Illegal residence persons Homeless individual Alcohol dependence abuse Substance Health care workers from/inTravellers endemic areas for more than 3 month Prisoners Comorbidities Diabetes Malignancy Insufficient renal function or dialysison transplantation Organ No 3 4 5 Table 3 (Continued)Table Information: * Number of cases analysed 545 with entry method in the multivariate analysis; Hosmer & Lemeshow test 0.85; not n/a: applicable due small to number of event; Reference; Ref.: Not included: the predictor was not included due p-value to > 0.15 in the univariate analysis; **missing value: Gender 1 (0.2%), Countryof birth (0.4%),2 Newly diagnosed previous treatment ‡Urban 58 LTBI (10.6%); domicile TB 53 (9.7%), Amsterdam,: Rotterdam, The Hague and Utrecht; TB, tuberculosis; ETB, extra-pulmonary tuberculosis; PTB, pulmonary tuberculosis; multidrug-resistant MDR-TB, tuberculosis; extensively XDR-TB, drug-resistant tuberculosis; latent tuberculosis LTBI, infection; CI, confidence interval.

86 Treatment outcomes of DR-TB ------0.035 p-value ------Multivariate analysis* Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included Not included 9.80 (1.18-81.68) 9.80 Odds ratio (95%CI) ------0.17 0.41 0.71 0.75 0.47 0.47 0.99 0.42 0.45 0.38 0.88 0.003 p-value n/a n/a n/a Ref. Ref. Ref.

Univariate analysis Univariate 4 0.42 (0.12-1.46) 1.53 (0.30-7.79) 1.53 1.86 (0.38-9.19) 1.72 (0.47-6.38) 1.72 0.95 (0.23-3.86) 0.95 0.32 (0.04-2.65) 0.32 1.18 (0.33-4.27) 1.18 1.40 (0.17-11.08) 1.40 1.98 (0.20-19.32) 2.56 (0.19-33.16) 6.50 (0.39-106.71) 6.50 Odds ratio (95%CI) 12.83 (1.59-103.57)

‡ 0 (0) 0 (0) 9 (75) 3 (25) 1 (8.3) 1 (8.3) 1 (8.3) 1 (8.2) 5 (41.7) 5 2 (16.7) 2 (16.7) 2 (16.7) 8 (66.7) 8 (66.7) 4 (33.3) 4 (33.3) 12 (100) 12 11 (91.7) 11 (n=12; %) (n=12; Poor outcome 4 (4.4) 5 (6.4) 5 (5.5) 3 (3.3) 2 (2.2) 20 (22) 89 (97.8) 63 (69.2) 27 (29.7) 21 (23.1) 21 (23.1) 55 (60.4) 65 (71.4) 15 (16.5) 26 (28.6) 23 (25.3) 42 (46.2)42 18 (22.5) Non-Poor outcome %) (n=91; § †† 18-24 25-64 65+ ETB PTB ETB + PTB Lungs Miliary and central nervous system Others . Predictors of poor outcomes of TB treatment among multidrug-resistant tuberculosis patients (n=103) Predictors Socio-demographic Male Age (years): Foreign-born patients domicile Urban diagnosis TB Current Pulmonary diagnosis: of TB location:Type Diagnosed by a doctor abroad History of TB disease & treatment Previously diagnosed with TB** Previously treatment** LTBI The risk group of TB TB contacts Immigrants Asylum seekers No 1 2 3 4 Table 4

87 Chapter 4 ------0.54 0.04 p-value Foreign-born Foreign-born § n/a n/a n/a n/a Multivariate analysis* Not included Not included Not included Not included Not included 7.50 (1.07-52.37) 7.50 2.15 (0.19-24.28) Odds ratio (95%CI) n/a n/a 0.41 0.41 0.12 0.72 0.01 0.39 0.02 0.46 0.60 p-value n/a n/a n/a n/a n/a n/a n/a n/a Univariate analysis Univariate 8.9 (1.13-70.26) 8.9 2.67 (0.26-27.92) Odds ratio (95%CI) 14.83 (2.18-100.78)

‡ 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 3 (25) 1 (8.3) 1 (8.3) 2 (16.7) (n=12; %) (n=12; Poor outcome Poor outcome of treatment is a combination of unsuccessful treatment and death ‡ 0 (0) 0 (0) 0 (0) 1 (1.1) 4 (4.4) 5 (5.5) 5 (5.5) 3 (3.3) 2 (2.2) 2 (2.2) 2 (2.2) Non-Poor outcome %) (n=91; Urban domicile : Amsterdam, Rotterdam, The Hague and Utrecht; not n/a: applicable due small to number of event; †† . Predictors of poor outcomes of TB treatment among multidrug-resistant tuberculosis patients (n=103) Predictors Illegal residence persons Homeless individual Alcohol dependence abuse Substance Health care workers from/inTravellers endemic areas for more than 3 month Prisoners Comorbidities Diabetes Malignancy Insufficient renal function or undergoingdialysis transplantation Organ No 5 Table 4 (Continued)Table Information: * Number of cases analysed with 103 backward step elimination method in the multivariate analysis; The Hosmer and Lemeshow test: 1.00; Ref.: Reference; Not included: the predictor was not included due p-value to > 0.15 in the univariate analysis; ** missing data: previously diagnosed (10.7%), TB: 11 Latent Infection. Tuberculosis LTBI: Previously (13.6%). 14 treated LTBI: outcome due tuberculosis; to countries: Somalia 25 (24.3%), Georgia 6 (5.8%), Russia Others India 8 (7.8%), 5 (49%), (55.33%). 57

88 Treatment outcomes of DR-TB

Our study determined that homelessness and substance abuse are risk factors for having an unsuccessful TB treatment outcome in overall DR-TB patients. Homeless patients are faced with several problems, such as unstable accommodation, lack of infection awareness, difficulties of accessing healthcare services, stigmatization, problems with access to proper nutrition and suffering from comorbidities(19). Those problems can lead to increasing discontinuation rate and non-adherence to the medication. A published review stated that drug users are associated with vulnerable TB condition, such as homelessness and HIV status.(20) It can be argued that homeless patients are a susceptible group to have poor TB treatment outcomes. Although due to low numbers of outcomes, we observed statistically significant associations. However, the precision of the estimates was low, especially for the factors homelessness and substance abuse.

CNS and miliary TB should be a concern in the management of TB as their mortality risk was the highest of all TB forms. This finding was supported by a study in Denmark (21) that showed that CNS-TB was a factor strongly associated with mortality in TB patients. 4 Another study reported that CNS-TB was frequently accompanied with miliary TB(22). The multifaceted problems in the management of CNS-TB relate to delays in clinical recognition, diagnosis, treatment and drug penetration in , have been determined as the main issues to improve successful treatment.(23)

As expected, although isoniazid mono-/poly-resistant TB was presented in the majority of cases in this study, MDR-TB cases tend to have more frequently the unsuccessful treatment outcome. Additional use of in first-line TB regimen may give a positive effect for the outcome of isoniazid mono-/poly-resistant TB patients. A meta-analytical study that included data from the Netherlands supported that addition of a fluoroquinolone to 6 months or more of first-line regimen was associated with significantly greater treatment success.(24) On the other hand, the complexity of the MDR-TB treatment regimen that uses a combination of first- and second-line drugs based on susceptibility testing can result in unsuccessful treatment. The treatment is longer, less effective and less tolerable than standard treatments, and involves injectable drugs as well. Hence, adverse events can occur among MDR-TB patient and are a factor in the decision to discontinued treatment.

We also found that males and substance abuse are associated with poor MDR-TB treatment outcomes. The finding of an association between gender and tuberculosis treatment outcomes remains a contested debate. Several studies have reported that there is no association between gender and treatment outcomes among DR-TB patients.(25–27) In contrast, studies in Nigeria(28) and Taiwan(29) found that male gender is associated with poorer tuberculosis treatment outcomes, while a review explained an opposite statement(30). The disparity of the result can be explained by differences in social, cultural, economic and clinical factors between patients and geographical area. Financial

89 Chapter 4 dependence, cultural inequality and greater fear of the stigmatization make it more difficult for women to access qualified medical care in some areas.(31,32) On the other hand, gender-specific social role makes men to have more social contact in other areas, thereby increasing the risk of TB exposure.(33) Furthermore, clinical aspects can also play a role in the treatment outcome. A study in Nigeria(28) showed that male patients were older, while a study in Taiwan(29) described that males were more likely to smoke, have COPD, malignancy, cirrhosis, low body weight, pleural effusion or hemoptysis. In our data, the prevalence of the poor outcome in MDR-TB was higher in males (10.68 %) than females (0.97 %). We found that substance abuse was the only one characteristic that associated with poor outcome in the male group, while there was no characteristic associated with poor outcome in the female group (see. Table A3). Although substance abuse was indicated as a factor that affected the poor outcome in the different gender, a further study that considers social, cultural, economic and clinical aspects is required to obtain a comprehensive picture across geographical areas.

The present study indicated that most DR-TB patients were foreign-born, with primary drug-resistant M. tb. This finding can be explained by the fact that the most DR-TB patients had a newly diagnosed TB Since the Netherlands has a low TB prevalence, it seems that immigration and activation of latent TB were essential factors of DR-TB cases in the Netherlands.

Several potential limitations in our study need to be mentioned. First, some potential predictors such as HIV status, treatment delay, history of BCG vaccine, level of education, the income of patients, and patients’ beliefs, could not be analysed due to the unavailability of the data for a large number of patients. Second, since the data were collected from a national database, we relied on administrative input without any direct investigation. Third, the low incidence of the study outcomes (unsuccessful treatment and death) led to potential overestimations and a wide confidence interval around the odd ratios in some associations between predictors and the outcomes. The inaccuracy of point estimate may exist in the association between gender and poor treatment outcome among MDR-TB patients. It is due to the uncommon incidence of poor treatment outcome in the female group. However, we identified that the probability of poor treatment outcome was significantly higher in the male (91.7%) than female group (8.3%). Additionally, a factor that was associated with poor treatment outcome in the MDR-TB group, i.e., substance abuse, was significantly dominated by male patients (Table. A3). These reasons seem to suggest that males are more likely to have poor treatment outcome among MDR-TB patients. Fourth, analysis of the appropriateness of medication cannot be performed due to lack of detailed treatment history and regimen in the database. However, we believe that integrating documentation and data collection of TB information, supported with integrated information technology and a referral system of healthcare services in the Netherlands, will minimize potential bias

90 Treatment outcomes of DR-TB and results can be generalized to the Dutch population. Importantly the information may also be useful for low-incidence TB countries in general.

A high success rate for MDR-TB treatment in the Netherlands was constantly reported from the previous studies(12,13) to the present study. Integrated systems and collaboration between all stakeholders may be the key to this success. Municipal Public Health Services (MPHSs) have an important role in controlling TB in the Netherlands. Twenty-five MPHSs, staffed by public health TB control officers, physicians, nurses, and administrative staff, are spread widely across the Netherlands.(34) They have the responsibility to diagnose, treat and monitor TB and LTBI patients for TB control. Suspected TB patients from the general practice or at-risk groups, such as immigrants, asylum seekers, and prisoners will have a TB examination in MPHSs to identify TB cases. A dedicated hospital TB coordinator in the Netherlands manages TB cases in the hospital setting. To optimize treatment adherence, TB nurses in MPHSs have been trained as treatment supporters in order to monitor drug adherence during the treatment period. Two special hospitals for TB, called 4 modern TB centres, are available for long-term admissions, socially problematic cases and clinically complex patients, such as TB or M- and XDR-TB patients.(35) If a contagious TB patient refuses treatment and poses a risk to the general population, the patient can be compulsorily isolated according to the Dutch Public Health Act. TB centre Beatrixoord is designated by the Dutch government for compulsory isolation. Moreover, pharmacokinetics/pharmacodynamics modeling has been used for therapeutic drug monitoring (TDM) in the MDR-TB treatment for years.(36) Since the treatment can be up to 24-month treatment, the TDM supported for shortening the regimen due to low drug exposure as well as improve safety and efficacy of the drugs.(37)

However, to optimize treatment outcome among DR-TB patients, special attention should be given to patients with MDR-TB, homelessness, substance abuse, as well as miliary and CNS-TB. Admission of these patients to a modern TB centre may be an option to intensify the treatment and monitoring of these high-risk patients. It can also prevent further development of drug resistance and transmission of tuberculosis in the community.(35) The treatment management for these patients should not only focus on medical support but also on social support. Treatment should not only be seen from the perspective of delivery to the patients but should also be seen from a comprehensive care perspective that should consider the patient’s ability to take medicine, to make a right life choice, and the treatment should support their circumstances to ensure an adherence to the treatment and an improvement in the quality of life.(20,35)

91 Chapter 4

CONCLUSIONS

We observed a low incidence of poor tuberculosis treatment outcomes among DR-TB patients. The majority of DR-TB cases were foreign-born patients with a newly diagnosed TB. To avoid unsuccessful treatment amongst DR-TB patients in the Netherlands; special attention should be given to patients with MDR-TB, homelessness, and substance abuse. Furthermore, miliary and CNS TB treated in general hospitals should be monitored carefully and/or treated together with TB specialists, or admitted to a modern TB center, to prevent premature mortality due to TB. We also identified that patients with male gender and substance abuse were more likely to have poor MDR-TB treatment outcomes. Close monitoring should be given to DR-TB patients with previous TB diagnosis, illegal status, traveling status from/in endemic areas and prisoner status to decrease the number of not- evaluated patient outcome. Further studies are required to identify critical factors for poor TB treatment outcomes, particularly in identified high-risk groups.

Funding This work was supported by the Indonesia Endowment Fund for Education or LPDP in the form of a Ph.D. scholarship to ISP; this funding source had no role in the concept development, study design, data analysis or article preparation.

Acknowledgments We thank Ms. Henrieke Schimmel, RIVM, Bilthoven, the Netherlands, for providing additional information. We also thank Dr. Hans Wouters, for giving us constructive suggestions in this study.

Conflict of interest All authors report no conflicts of interest relevant to this article

92 Treatment outcomes of DR-TB

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24. Fregonese F, Ahuja SD, Akkerman OW, 31. Yamasaki-Nakagawa M, Ozasa K, Yamada N, Arakaki-Sanchez D, Ayakaka I, Baghaei P, et Osuga K, Shimouchi A, Ishikawa N, et al. Gender al. Comparison of different treatments for difference in delays to diagnosis and health care isoniazid-resistant tuberculosis: an individual seeking behavior in a rural area of Nepal. Int J patient data meta-analysis. Lancet Respir Med. Tuberc Lung Dis. 2001;5(1):24–31. 2018;6(4):265–75. 32. Karim F, Ahmed F, Begum I, Johansson E, 25. Kurbatova E V., Taylor A, Gammino VM, Bayona J, Diwan VK. Female-male differences at various Becerra M, Danilovitz M, et al. Predictors of poor clinical steps of outcomes among patients treated for multidrug- in rural Bangladesh. Int J Tuberc Lung Dis. resistant tuberculosis at DOTS-plus projects. 2008;12(11):1336–9. Tuberculosis. 2012;92(5):397–403. 33. Liefooghe R, Michiels N, Habib S, Moran MB, De 26. Oliveira O, Gaio R, Villar M, Duarte R. Muynck A. Perception and social consequences Predictors of treatment outcome in multidrug- of tuberculosis: A focus group study of resistant tuberculosis in Portugal. Eur Respir J. tuberculosis patients in Sialkot, Pakistan. Soc 2013;42(6):1747–9. Sci Med. 1995;41(12):1685–92. 27. Kliiman K, Altraja A. Predictors of poor treatment 34. de Vries G, van Hest R, Bakker M, Erkens outcome in multi- and extensively drug-resistant C, van den Hof S, Meijer W, et al. Policy and pulmonary TB. Eur Respir J. 2009;33(5):1085– practice of programmatic management of latent 94. tuberculosis infection in The Netherlands. J Clin 28. Oshi SN, Alobu I, Ukwaja KN, Oshi DC. Tuberc Other Mycobact Dis. 2017;7:40–8. Investigating gender disparities in the profile 35. Akkerman OW, Grasmeijer F, De Lange WCM, and treatment outcomes of tuberculosis Kerstjens HAM, de Vries G, Bolhuis MS, et al. in Ebonyi state, Nigeria. Epidemiol Infect. Cross border, highly individualised treatment 2015;143(5):932–42. of a patient with challenging extensively 29. Feng JY, Huang SF, Ting WY, Chen YC, Lin YY, drug-resistant tuberculosis. Eur Respir J. Huang RM, et al. Gender differences in treatment 2018;51(3):1–4. outcomes of tuberculosis patients in Taiwan: A 36. Bolhuis MS, Akkerman OW, Sturkenboom MGG, prospective observational study. Clin Microbiol de Lange WCM, van der Werf TS, Alffenaar JWC. Infect. 2012;18(9):E331-337. Individualized treatment of multidrug-resistant 30. Connolly M, Nunn P. Women and tuberculosis. tuberculosis using therapeutic drug monitoring. World Heal Stat Q. 1996; Int J Mycobacteriology. 2016;5:S44–5. 37. van Altena R, Akkerman OW, Alffenaar J-WC, Kerstjens HAM, Magis-Escurra C, Boeree MJ, et al. Shorter treatment for multidrug-resistant tuberculosis: the good, the bad and the ugly. Eur Respir J. 2016;48(6):1800–2.

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APPENDIX.

Table A1. The operational definition of study

No Variables Operational definition Predictors 1 Age Age when the current diagnosis of TB was made 2 Pulmonary diagnosis Type of pulmonary TB defined by a medical doctor based on an ICD code. It was divided into three categories: pulmonary TB (PTB), extra-pulmonary TB (ETB) and the combination of PTB and ETB. 3 Type of TB location Type of localization of TB based on an ICD-9 defined by a medical doctor. It was divided into lung tuberculosis, central nervous system (CNS) and miliary TB, and other tuberculosis (respiratory tract, intestinal, urogenital, bone, joint and others) 4 Diagnosed by a doctor Patients who were diagnosed with TB outside the Netherlands and abroad continue their treatment in the Netherlands 5 Previously diagnosed Patients who were previously diagnosed with TB based on clinical with TB findings and or documentation. 4 6 Previously undergoing Patients who were previously treated for Latent Tuberculosis LTBI treatment Infection (LTBI) based on clinical findings and or documentation 7 TB contacts Patients who have been identified as having had contact with a TB patient. 8 Immigrants Patients with a legal residence status other than a tourist or refugee or asylum seeker. 9 Asylum seekers Patients who left their home country as political refugees and are seeking asylum in another country. 10 Illegal Patients without legal residence status in the Netherlands at the time of diagnosis, regardless of the length of stay in the Netherlands. 11 Homelessness Patients who have not had a fixed residence or regularly sleep on the street and/or use marginal temporary accommodation. 12 Health care workers Patients who work as health care providers 13 Travelers from the high Patients who have traveled from or to the high endemic area for endemic area more more than three months than three month 14 Prisoners Patients who were staying in prison at the time of the diagnosis, including patients who were screened in prison, but the diagnosis was made after discharge from the prison. 15 Alcohol dependence Patients who have problematic alcohol consumption at the time of diagnosis. Problematic alcohol is related to a drinking pattern that leads to physical complaints and/or psychological or social problems. The amount of alcohol consumed was not considered for defining the status of an alcoholic. 16 Substance abuse Patients who have regularly used drugs, including narcotics, methadone and cocaine, which has led to some degree of social disorder. 17 Comorbidities Patients who have a disease or compelling indication with TB defined by medical doctors. This was divided into diabetes mellitus, malignancy, insufficient renal function/on dialysis and organ transplantation.

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Table A1 (Continued). The operational definition of study No Variables Operational definition 18 Drug-resistant TB Patients who have resistance to at least one of the first-line anti-TB (DR-TB) drugs. They were divided into sixth categories: 1) isoniazid mono-/ poly-resistant TB; 2) rifampicin mono-/ poly-resistant TB; 3) ethambutol mono-resistant TB; 4) pyrazinamide mono-resistant TB; 5) multidrug-resistant TB (MDR-TB; 6) extensively drug-resistant TB (XDR-TB). 19 Mono-resistant Resistance to one of the first-line anti-TB drug only tuberculosis 20 Poly-resistant Resistance to more than one first-line anti-TB drug (other than both tuberculosis isoniazid and rifampicin) 21 Isoniazid mono-/ poly- a resistant M.tb strain to isoniazid alone; OR poly resistant M.tb resistant tuberculosis strain to isoniazid with one or more first-line anti-tuberculosis drugs other than rifampicin 22 Rifampicin mono-/ a resistant M. Tb strain to rifampicin alone; OR poly-resistant M. poly-resistant Tb strain to rifampicin with one or more first-line anti-tuberculosis tuberculosis drugs other than isoniazid 23 Multidrug-resistant a M.tb strain that is resistant to at least both isoniazid and tuberculosis rifampicin. 24 Extensively drug- a MDR-TB in addition to resistance to any of the fluoroquinolones resistant tuberculosis ( or moxifloxacin) and at least one of the three injectable second-line drugs (amikacin, capreomycin or kanamycin) Outcomes 25 Unsuccessful A combination of defaulted and failed treatments. treatment 26 Death Patients who die due to tuberculosis disease, as defined by a doctor. 27 Not-evaluated Patients who started the treatment but were unknown for the treatment outcome, including transferred out cases. 28 Poor TB treatment A combination of the unsuccessful TB treatment and death due to outcome tuberculosis disease.

Table A2. Characteristics of not-evaluated patients (n= 573)

No Characteristics Not-evaluated patient No (n=545) Yes (n=28) Odds ratio (95%CI) p-value 1 Socio-demographic Male** 295 (54.1) 20 (71.4) 2.04 (0.91-4.55) 0.07 Age (years): 0.11 18-24 106 (19.4) 4 (14.3) Ref. 25-64 404 (74.1) 24 (85.7) 1.57 (0.54-4.64) 65+ 35 (6.4) 0 (0) n/a Country of birth*: 0.02* The Netherlands 74 (13.6) 0 (0) n/a Somalia 109 (20) 2 (7.1) 0.22 (0.52-0.96) Morocco 41 (7.5) 1 (3.6) 0.29 (0.04-2.26) Indonesia 28 (5.1) 1 (3.6) 0.44 (0.06-3.35)

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Table A2 (Continued). Characteristics of not-evaluated patients (n= 573) No Characteristics Not-evaluated patient No (n=545) Yes (n=28) Odds ratio (95%CI) p-value Others 293 (53.8) 24 (85.7) Ref. Urban domicile‡ 186 (34.1) 11 (39.3) 1.24 (0.59-2.59) 0.58 2 Current TB diagnosis Pulmonary diagnosis: 0.16 PTB 283 (51.9) 19 (67.9) Ref. ETB 191 (35) 5 (17.9) 0.39 (0.14-1.06) ETB + PTB 71 (13) 4 (14.3) 0.84 (0.28-2.54) Type of TB location: 0.43 Lungs 333 (61.1) 22 (78.6) Ref. Respiratory tract 38 (7) 1 (3.6) 0.39 (0.05-3.04) Miliary and central nervous 13 (2.4) 1 (3.6) 1.16 (0.15-9.31) system Intestinal tract 15 (2.8) 1 (3.6) 1.01 (0.13-7.80) 4 Bone and joint 28 (5.1) 1 (3.6) 0.54 (0.07-4.16) Urogenital tract 9 (1.7) 0 (0) n/a Others 109 (20) 2 (7.1) 0.28 (0.06-1.20) Diagnosed by doctors abroad 11 (2) 0 (0) n/a 0.45 Type of resistance: 0.97 Isoniazid mono-/poly-resistant 375 (68.8) 20 (71.4) Ref. Rifampicin mono-/poly-resistant 17 (3.1) 0 (0) n/a Pyrazinamide/ ethambutol 46 (8.5) 0 (0) n/a mono-resistant MDR-TB 103 (18.9) 8 (28.6) 1.46 (0.62-3.40) XDR-TB 4 (0.7) 0 (0) n/a 3 History of TB disease & treatment Previously diagnosed with TB** 59 (12) 7 (31.8) 3.43 (1.34-8.75) 0.015* Previously treated LTBI** 24 (4.9) 0 (0) n/a 0.99 4 The risk group of TB TB contacts 29 (5.3) 0 (0) n/a 0.39 Immigrants 68 (12.5) 4 (14.3) 1.17 (0.39-3.47) 0.77 Asylum seekers 87 (16) 3 (10.7) 0.63 (0.19-2.14) 0.60 Illegal residence persons 14 (2.6) 4 (14.3) 6.32 (1.94-20.66) 0.009* Homeless individuals 15 (2.8) 2 (7.1) 2.72 (0.59-12.52) 0.19 Alcohol dependence 8 (1.5) 0 (0) n/a 0.99 Substance abuse 18 (3.3) 2 (7.1) 2.25 (0.50-10.23) 0.26 Health care workers 4 (0.7) 0 (0) n/a 0.99 Travellers from/in endemic areas 19 (3.5) 5 (17.9) 6.02 (2.064-17.55) 0.004* for more than 3 month Prisoners 12 (2.2) 5 (17.9) 9.66 (3.14-29.70) 0.001* 5 Comorbidities Diabetes 18 (3.3) 0 (0) n/a 0.99 Malignancy 11 (2) 1 (3.6) 1.73 (0.266-11.72) 0.46

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Table A2 (Continued). Characteristics of not-evaluated patients (n= 573) No Characteristics Not-evaluated patient No (n=545) Yes (n=28) Odds ratio (95%CI) p-value Insufficient renal function or on 5 (0.9) 0 (0) n/a 0.99 dialysis Organ transplantation 2 (0.4) 0 (0) n/a 0.99

Information: *significant (p-value < 0.05); n/a: not applicable due to small number of event; **missing value: Country of birth 2 (0.03%), Previously diagnosed TB 59 (10.3%), previous LTBI treatment 67 (11.7%); ‡Urban domicile : Amsterdam, Rotterdam, The Hague and Utrecht; TB, tuberculosis; ETB, extra-pulmonary tuberculosis; PTB, pulmonary tuberculosis; MDR-TB, multidrug-resistant tuberculosis; XDR-TB, extensively drug-resistant tuberculosis; LTBI, latent tuberculosis infection; CI, confidence interval.

Table A3 . Poor outcome of TB treatment between males and females among MDR-TB patients (N= 103)

No Characteristics Male (n=53) Female (n=50) Poor outcome p-value Poor outcome p-value No (n=42) Yes (n=11) No (n=49) Yes (n=1) 1 Socio-demographic Age (years): 0.82 1.00 18-24 10 (23.8) 2 (18.2) 16 (32.7) 0 (0) 25-64 30 (71.4) 8 (72.7) 33 (67.3) 1 (100) 65+ 2 (4.8) 1 (9.1) 0 (0) 0 (0) Foreign-born patients 40 (95.2) 11 (100) 1.00 49 (100) 1 (100) n/a Urban domicile 9 (21.4) 5 (45.5) 0.13 12 (24.5) 0 (0) 1.00 2 Current TB diagnosis Pulmonary diagnosis: 0.68 0.66 ETB 13 (31) 2 (18.2) 8 (16.3) 0 (0) PTB 23 (54.8) 7 (63.6) 32 (65.3) 1 (100) ETB + PTB 6 (14.3) 2 (18.2) 9 (18.4) 0 (0) Type of TB location: 0.96 1.00 Lungs 26 (61.9) 7 (63.6) 39 (79.6) 1 (100) Miliary and central 3 (7.1) 1 (9.1) 0 (0) 0 (0) nervous system Others 13 (31) 3 (27.3) 10 (20.4) 0 (0) Diagnosed by a doctor 2 (4.8) 0 (0) 1.00 3 (6.1) 0 (0) 1.00 abroad 3 History of TB disease & treatment Previously diagnosed 9 (23.7) 3 (27.3) 1.00 9 (21.4) 1 (100) 0.23 with TB* Previously LTBI 3 (8.1) 0 (0) 1.00 2 (4.9) 0 (0) n/a treatment**

98 Treatment outcomes of DR-TB

Table A3 (Continued). Poor outcome of TB treatment between males and females among MDR-TB patients (N= 103) No Characteristics Male (n=53) Female (n=50) Poor outcome p-value Poor outcome p-value No (n=42) Yes (n=11) No (n=49) Yes (n=1) 4 The risk group of TB TB contacts 1 (2.4) 1 (9.1) 0.38 3 (6.1) 0 (0) 1.00 Immigrants 7 (16.7) 1 (9.1) 1.00 13 (26.5) 0 (0) 1.00 Asylum seekers 12 (28.6) 4 (36.4) 0.72 15 (30.6) 0 (0) 1.00 Illegal residence persons 5 (11.9) 0 (00 0.57 0 (0) 0 (0) n/a Homeless individual 2 (4.8%) 2 (18.2) 0.19 0 (0) 0 (0) n/a Alcohol dependence 0 (0) 1 (9.1) 0.21 0 (0) 0 (0) n/a Substance abuse 2 (4.8) 3 (27.3) 0.05 0 (0) 0 (0) n/a Health care workers 1 (2.4) 0 (0) 1.00 0 (0) 0 (0) n/a Travellers from/in 2 (4.8) 0 (0) 1.00 3 (6.1) 0 (0) 1.00 endemic areas for more 4 than 3 month Prisoners 4 (9.5) 0 (0) 0.57 0 (0) 0 (0) n/a 5 Comorbidities Diabetes 2 (4.8) 1 (9.1) 0.51 1 (2) 0 (0) 1.00 Malignancy 2 (4.8) 0 (0) 1.00 0 (0) 0 (0) n/a Insufficient renal 0 (0) 0 (0) n/a 0 (0) 0 (0) n/a function or undergoing dialysis Organ transplantation 0 (0) 0 (0) n/a 0 (0) 0 (0) n/a

Information. n/a : not applicable due to no case in the comparator group; * missing data: male (4) & female (7); ** missing data : male (5) & female (9);‡ Substance abuse in male: OR 7.5 (95%CI 1.074- 52.28).

99 Budapest, 2019 CHAPTER A SYSTEMATIC REVIEW OF MEASURES TO ESTIMATE ADHERENCE AND PERSISTENCE TO MULTIPLE MEDICATIONS 5 Sofa D. Alfian Ivan S. Pradipta Eelko Hak Petra Denig

This chapter is based on the published manuscript: Alfian SD, Pradipta IS, Hak E, Denig P., A systematic review finds inconsistency in the measures used to estimate adherence and persistence to multiple cardiometabolic medications. J Clin Epidemiol. 2019;108:44-53. Chapter 5

ABSTRACT

Objectives: We reviewed measures used to estimate adherence and persistence to multiple cardiometabolic medications from prescription data, particularly for blood pressure- lowering, lipid-lowering, and/or glucose-lowering medication, and give guidance on which measures to choose.

Methods: A literature search of Medline, Embase, and PsycINFO databases was conducted to identify studies assessing medication adherence and/or persistence for patients using multiple cardiometabolic medications. Two reviewers performed the study selection process independently.

Results: From the 54 studies assessing adherence, only 36 (67%) clearly described the measures used. Five measures for adherence were identified, including adherence to ‘all’, to ‘any’, to ‘both’ medication, ‘average adherence’ and ‘highest/lowest adherence’. From the 22 studies assessing persistence, only 6 (27%) clearly described the measures used. Three measures for persistence were identified, including persistence with ‘all’, with ‘both’, and with ‘any’ medication. Less than half of the studies explicitly considered medication switches when relevant.

Conclusion: From the identified measures, the “any medication” measure is most suitable for identifying patients in need of an intervention, whereas the “all medication” measure is useful for assessing the effect of interventions. More attention is needed for adequate measurement definitions when reporting on and interpreting adherence or persistence estimates to multiple medications.

102 Adherence and persistence measurements

Key findings · We identified five distinct measures to estimate adherence and three distinct measures to estimate persistence in patients using multiple medications from prescription data, which can be used by future adherence researchers. · Many studies were flawed due to inadequate description of the methods or how switching or additions were dealt with.

What this adds to what was known? · To our knowledge, this is a first study systematically review the measures used to estimate medication adherence and medication persistence to multiple medications. · This review extends previous literature on adherence measures to multiple medications by identifying distinct measures to estimate multiple medications adherence and multiple medications persistence that may lead to different estimates.

What is the implication and what should change now? 5 · Researchers and practitioners need to be aware of unclear or inadequate definitions of the adherence and persistence measures when interpreting results for patients using multiple medications and targeting interventions to improve medication use. · More attention is needed for providing adequate measurement definitions in studies reporting on adherence or persistence to multiple medications.

103 Chapter 5

INTRODUCTION

Adherence and persistence to preventive medication are known to be suboptimal in daily practice.(1) This is recognized as a significant public health issue, since medication non- adherence leads to poor health outcomes and increased healthcare costs.(2) Medication adherence refers to whether patients take their medications as prescribed, whereas persistence refers to whether they continue to take the medication.(3) As patient behavior is a modifiable, it is important to assess adherence and persistence, and subsequently develop interventions to improve their medication-taking behaviors. However, most adherence measurements in interventions trials were found of low quality, which may influence the precision of adherence rates and subsequently leads to inefficient or even ineffective interventions.(4)

Because of the increase rate of polypharmacy,(5) it becomes very relevant to monitor adherence and persistence to multiple medications for the same indication. Adherence assessment is more complex for these patients, particularly when drugs can be switched or added over time. In addition, it is important to make a distinction between adherence and persistence. Although these are related concepts, they occur at different times of drug taking behavior, that is, in the implementation phase or the discontinuation phase.(3) Only a patient who is still persistent (i.e., continuing therapy) can be non-adherent to a medication (i.e., taking less medication).(3) This distinction seems not always sufficiently addressed when assessing adherence to multiple medications.(6,7)

The primary objective of this study is to systematically review the measures that are used to calculate adherence and persistence to multiple preventive medications from prescription data, and give guidance on when and why one should choose one measure over another. We focus on cardiometabolic medication, including blood pressure-lowering, lipid-lowering, and glucose-lowering medication. The secondary objective is to assess whether studies sufficiently describe the measures used, particularly in relation to addressing issues of switching and adding medication at drug class or therapeutic level.

METHODS

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)(8) guideline to report this systematic review. This systematic review was registered in International Prospective Register of Systematic Review (PROSPERO; www. crd.york.ac.uk) with registration number CRD42017069299.

104 Adherence and persistence measurements

Search strategy and selection criteria A literature search of Medline, Embase, and PsycINFO databases up to June 16th of 2017 was conducted to identify studies assessing medication adherence and/or persistence to multiple cardiometabolic medications. The full search strategy using a combination of medical subject heading (MeSH) terms and text words can be found in the Supplementary data. In short, we included experimental, cohort and case control studies among adults (age 18 years or older) that calculated medication adherence and/or persistence to multiple cardiometabolic oral medications (i.e., blood pressure-lowering, lipid-lowering, and/or glucose-lowering medication) from prescription data (i.e., prescribing, dispensing, or claims databases), and were published in English. Studies assessing adherence and/or persistence to treatment guideline or to diet, predicting adherence from model analysis, only focusing on primary non-adherence (that is, patients not obtaining the initial prescription), assessing adherence and/or persistence from pill counts, self-report, provider or care-giver assessment, or from electronic monitoring devices were excluded. Also, studies in which the adherence and/or persistence measures were not described (that is, measure was either not defined or only referred to another paper), adherence and/or persistence measures produced non-numeric values, and case reports or abstract from conference proceeding 5 were excluded.

Review process Eligibility assessment based on title and abstract was conducted independently by two reviewers (SDA, ISP). The full texts of potentially eligible articles were retrieved and reviewed in the second stage of the screening process by SDA and ISP. Disagreements between two reviewers were resolved by consensus with a third reviewer (PD). Inter- rater agreement in the title-abstract and full-text screening was calculated using percent agreement and Cohen’s kappa (ĸ) statistic. Data from the selected articles were extracted by SDA and any doubts from the data extraction process were resolved by consensus with ISP. We extracted the following information: country of study, study design, period of study, research question, type of data and/or database, characteristics of participants of the study (inclusion/exclusion criteria), type of medication studied, type of medication user studied (incident and/or prevalent), sample size of source population, definition of adherence and/or persistence (including the numerator and denominator, type of methods used to assess adherence (e.g., proportion of days covered (PDC) or medication possession ratio (MPR), any defined cut-off points and information on incorporating medication switches and/or additions at class or therapeutic level, when relevant), association of adherence or persistence measures with clinical outcome (when presented), and funding sources.

We defined medication class level as including medication with a similar mechanism of action (e.g., sulfonylureas), whereas therapeutic level was defined as including medication with similar pharmacological effects (e.g., glucose-lowering medication). We classified

105 Chapter 5 the defined period for the denominator in the adherence measures as prescription-based or interval-based approach. The assessment period in a prescription-based approach is defined as the number of days between two prescriptions (variable period ending with a prescription), whereas the period in an interval-based approach is defined as the total number of days in the given interval (fixed time interval). This distinction is relevant, since the interval-based approach may lead to underestimating adherence when medication switches are not taken into account. Incident users were defined as patients who initiate medication of interest without prior use in a specified period before the measurement period, whereas prevalent users were defined as patients already taking a medication of interest before the measurement period.

Data Analysis Descriptive statistics were used to present proportions of studies with particular characteristics. We determined at study level whether measures of adherence and persistence to multiple medications were clearly defined with regard to the numerator and denominator. We also assessed whether medication switches and additions were taken into account. Clearly defined measures were grouped to represent distinct methods of calculation.

RESULTS

The literature search resulted in 1,803 records across three databases. After removing duplicates, 1,660 abstracts were screened and 179 were selected for full-text review. A total of 63 articles met the eligibility criteria (Figure 1). The inter-rater agreement and reliability Cohen’s kappa after both title-abstract and full-text screening were high (97.5% with kappa 0.88, and 98.3% with kappa 0.93, respectively). The most common medication evaluated was glucose-lowering medication (n=26), followed by blood pressure-lowering medication (n=23). The majority of the studies were conducted using prescription data from USA (n=42). The mean sample size of source population was 68,621 participants, ranging from 568(9) to 706,032(10) participants. Table 1 summarizes characteristics of the studies. Study details from studies that clearly and not clearly described adherence and persistence are presented in Table S1 and S2 in Supplementary data, respectively.

Multiple medications adherence measures Of the 54 identified studies on adherence to multiple medication, 36 studies (67%) clearly described the adherence measures with MPR or PDC as the common methods. In 31 of these 36 studies switches or additions at class or therapeutic level were possible. Only 16 of those studies explicitly considered medication switches and/or additions.(6,7,18–23,10–17) The majority of 36 studies (n=23) looked at patients who initiated with one or more of the medications of interest.(7,10,22–31,11,32–34,12,13,15–19) Half of the studies (n=18) used

106 Adherence and persistence measurements the interval-based approach(6,10,28,30–36,11–13,15,17,21,24,27), while 16 studies used the prescription-based(14,16,38–43,18–20,23,25,26,29,37) and two studies used both the interval and prescription-based approach.(7,22) Of the 18 studies using the interval-based approach, only 6 studies took medication switching into account.(6,10,12,21,33,36)

Table 1. Characteristics of multiple medications adherence and/or persistence studies

Characteristics Number of studies (%) Country of study USA 42 (66.7) Australia 2 (3.2) Germany 3 (4.8) Hungary 2 (3.2) Italy 4 (6.3) Sweden 1 (1.6) The Netherlands 3 (4.8) Taiwan 2 (3.2) Hawaii 1 (1.6) Canada 2 (3.2) 5 China 1 (1.6) Sample size of source population 500 – 4,999 17 (27.0) 5,000 – 9,999 11 (17.5) 10,000 – 99,999 23 (36.5) >100,000 12 (19.0) Type of medication studied Blood pressure-lowering 23 (36.5) Lipid-lowering 3 (4.8) Glucose-lowering 26 (41.3) Combination of blood pressure-, lipid-, and/or glucose-lowering medications 11 (17.5) Type of medication users Incident users 32 (50.8) Prevalent users 21 (33.3) Incident and prevalent users 10 (15.9)

There were five distinct measures to estimate multiple medication adherence (see Figure 2): First, measuring adherence to “all medications”: Four studies assessed adherence to each medication separately, and defined patients as being adherent when they had collected at least 80% of each, that is, “all medications”.(6,7,16,21) All four studies assessed adherence to medication at class level, considering individual drugs within the same medication class as interchangeable, and then calculated adherence to multiple classes at therapeutic level, either for oral glucose-lowering(6,7,21) or blood pressure-lowering medication.(16)

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Figure 1. Flow diagram of the systematic review process. *Several studies assessed adherence and persistence simultaneously

Second, measuring adherence to “both medications”: Twelve studies assessed adherence to two medications, by calculating the number of days when both medications were available, which was indicated by concurrent prescriptions.(6,11,34,38,13,15,24,27,29– 32) The majority of studies (n=11) used a value of 80% or higher to define patients as

108 Adherence and persistence measurements adherent, whereas one study measured adherence as a continuous variable. Eight studies assessed adherence between two drugs or two classes from the same therapeutic level (e.g., glyburide and metformin or angiotensin II receptor blockers (ARBs) and (CCB)).(6,15,27,29,30,32,34,38) Four studies assessed adherence to two medications from different therapeutic levels (e.g., CCB and ).(11,13,24,31) To define whether drugs were considered as used concurrently, time periods need to be defined to distinguish between concurrent use and a medication switch or a medication addition. Only two studies stated this explicitly.(11,15) For example, Ferrario et al.,(15) used a period of at least 60 days prior to discontinuation of index therapy to define a medication addition for a blood pressure-lowering medication in a class other than the index drug, whereas An and Nichol(11) defined addition as medications prescribed to treat the comorbid conditions other than the index condition during the 6-month period (index diabetes with comorbid hypertension or vice versa).

Third, measuring adherence to “any medication”: Twelve studies assessed adherence from number of days with at least one medication available, and defined patients as being adherent when they had collected at least 80% of one, that is, “any medication”.(6,7,35,39,12,16,17,21 5 ,22,24,28,33) Also the studies using this measure first assessed adherence for medication at class level and then calculated adherence to multiple medication classes at therapeutic level for glucose-lowering,(6,7,12,21,33,39) blood pressure-lowering,(12,16,17,22,24,28,35) or lipid-lowering medication.(12,28) Two of these studies validated the proposed measure by assessing its association with clinical outcomes. The study by Tang et al.,(22) showed that adherence to any blood pressure-lowering medication was inversely associated with death (OR=0.70;95%CI:0.51-0.97). Fung et al.,(35) showed that adherence to any blood pressure-lowering medication was associated with lower odds of having elevated systolic blood pressure (OR=0.89;95%CI:0.85-0.93).

Fourth, measuring adherence by calculating the “average” adherence: Nineteen studies assessed adherence by first calculating adherence for the medication at individual drug level(14,20,25,26,37,41,43) or class level(6,7,40,42,10,18,19,21–23,35,36) and then calculate the overall average. The most common medication evaluated was glucose- lowering (n=13), followed by blood pressure-lowering (n=3), and lipid-lowering (n=1) medication. The majority of studies defined adherence as an average level as 80% or more,(6,7,25,26,35,37,40,10,14,18–23) whereas four studies reported adherence as a continuous variable.(36,41–43) Two of these studies validated the proposed measure by assessing its association with clinical outcomes. The study by Tang et al.,(22) showed that the average of the class-specific adherence with an 80% cut-off level to blood pressure- lowering medication was inversely associated with death (OR=0.71;95%CI:0.53-0.95). Fung et al.,(35) showed that the average also with an 80% cut-off level to blood pressure-lowering

109 Chapter 5 medication was associated with lower odds of having elevated systolic blood pressure (OR=0.87;95%CI:0.84-0.89).

Fifth, measuring the “highest” or “lowest” adherence: One study assessed adherence to blood pressure-lowering medication by calculating adherence for each medication class, and then presented both the “highest” and the “lowest” as measure of adherence.(22) The study by Tang et al.,(22) however, showed that no significant association was found between either the highest or the lowest class-specific adherence and death.

Multiple medications persistence measures Of the 22 identified studies on persistence to multiple medications, 6 (27%) studies clearly described the persistence measures. Only one of these studies clearly described how they dealt with medication switches,(44) where switches at class or therapeutic level were possible for all studies. Three distinct measures to estimate multiple medication persistence were identified (Figure 3).

First, measuring persistence to “all medications”: One study calculated persistence to all medications, and defined patients as persistent when all medications were without a medication gap of 30 days or more.(45) Persistence was first assessed for individual drugs, and then overall persistence was defined as being persistent on all medications from the same therapeutic level (e.g., metoprolol, hydrochlorothiazide, and amlodipine were without a medication gap).(45)

Second, measuring persistence to “both medications”: Two studies assessed persistence for two medication classes as follow: which days are covered by both classes (e.g., ARB and CCBs) and identify whether there is a gap without coverage of both classes. Patients are considered persistent if they have no such gaps in both drug classes concurrently.(30,34) Zeng et al.,(34) used a 30 days permissible gap, whereas Hsu et al.,(30) used a 56 days gap to define persistence.

Third, measuring persistence to “any medication”: Two studies defined patients as being persistent when either drug class A OR drug class B from the same therapeutic level were without a medication gap (e.g., ≤ 180 days gap).(44,46) In other words, patients were considered non-persistent to blood pressure-lowering medication if they were not receiving any blood pressure-lowering medication in a period of more than 180 days since the last prescription.(44) One study defined persistence to any medication by using the treatment anniversary method, that is, assessing whether or not patients are still receiving the medication in one year after treatment initiation. Patients were considered to be persistent if “any” (at least one) blood pressure-lowering medication was still available on the 365th day after initiation.(17)

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5 Methods estimate to multiple medications adherence. Figure 2.

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Figure 3. Methods to estimate multiple medications persistence.

DISCUSSION

We reviewed the measures that have been proposed or used to estimate medication adherence and medication persistence to multiple cardiometabolic medications. Such medication is usually intended for chronic use. From the 54 studies assessing adherence, only 36 (67%) clearly described how they calculated adherence to multiple medications. Five distinct adherence measures were identified from these studies. Of the 31 studies in which switches or additions at class or therapeutic level were possible, only 16 explicitly considered medication switches and/or additions. From the 22 studies assessing persistence, only 6 (27%) clearly described how they calculated persistence to multiple medications. Three distinct persistence measures were identified from these studies. Only one of the studies explicitly considered medication switches, where switches at class or therapeutic level were possible in all studies.

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Most of the included studies in this review were conducted in USA, which can in part be explained by the wide availability of longitudinal databases with prescriptions across a range of health care settings.(47) Most of studies used 80% as a cut-off point to determine adherence status, which is widely used and has shown to be a reasonable cut-off point for single drug adherence based on its ability of predicting subsequent hospitalization in diabetes, hypertension, and hyperlipidemia patients.(48)

This is a first systematic literature review summarizing the measures used to calculate medication adherence and medication persistence to multiple cardiometabolic medications. This review extends previous literature on adherence measures to multiple medications,(6,7) by identifying distinct measures to estimate multiple medication adherence and multiple medication persistence. Using disparate definitions, these measures will result in different estimates.(6,7) The “all medications” and “both medications” measures are very restrictive, in such a way that they will classify relatively few patients as adherent or persistent. The “all medications” measures were used in few studies, while the “both medications” measures were used more often, in particular to assess adherence and persistence to concurrent medication from different class or therapeutic levels. The “any medication” measure is 5 likely to lead to relatively high adherence or persistence rates, since patients are classified as adherent or persistent when they use only one of their drugs regularly. Use of the “any medication” adherence measure with an 80% cut-off level was relatively common and showed to be associated with clinical outcomes, indicating that it may be adequate for identifying clinically relevant non-adherence.(22,35) Also, the “average” adherence measure with an 80% cut-off level, which will result in intermediate scores, was common and showed to be associated with clinical outcomes.(22,35) Both the “any medication” for adherence and persistence measures and the “average” adherence measure should only be used to medication from the same therapeutic level, assuming that these drugs are partly interchangeable regarding their therapeutic effects. The “highest” or “lowest” adherence measures showed not to be associated with clinical outcomes.(22) These measures only reflect the adherence level to one drug and do not set a benchmark by using cut-off level. As such, they seem more difficult to interpret from a clinical perspective.

Adherence to individual drug classes or adherence to any medication can be calculated with MPR or PDC for patients on multiple medications.(22) However, there is a discrepancy between MPR and PDC methods when using the “adherence to any” measure. Adding the days supply for all medications in the numerator for the MPR may lead to overestimating adherence, when a patient uses multiple medications simultaneously or switches between medication with an overlap of the new drug with the prior drug.(49) Since the PDC focusses on days with or without medication, the presence of multiple medications on the same day does not lead to such overestimations.(49) Thus, PDC is preferred for calculating adherence to multiple medications due to its lower risk of overestimation.(50,51) Alternatively, Basak

113 Chapter 5 et al. proposed that switches between equivalent agents should be carried forward, under the assumption that the patient was supposed to consume all medication, whereas switches between different therapeutic agents should not be carried forward, assuming that the first treatment was to be discontinued at the time of the switch.(6) We found that many studies that used the interval-based approach to calculate adherence, however, did not consider medication switches. This is a matter of concern, since the interval-based approach is likely to underestimate adherence by classifying patients who switch from one drug to another during the interval as being non-adherent. This is supported by previous studies showing that the interval-based approach provides lower adherence estimates than the prescription-based approach.(7,22)

This review can help researchers and practitioners in choosing the measures to estimate medication adherence and persistence to multiple medications from prescription data. To identify patients for interventions to improve their adherence, the “any medication” measure may be applied, which is more sensitive to identify non-adherence. The “average adherence” and the “highest” or “lowest” adherence measures are less suitable to identify patients for interventions. In the “average adherence” measure, the high adherence to one medication may compensate poor adherence to another medication and lead to an acceptable average for the entire regimen. This measure has shown to not only overestimate but also underestimate adherence to multiple medications.(52) The “highest” or “lowest” adherence measures only reflect the adherence level to one drug, thereby disregarding poor adherence to other drugs. On the other hand, to measure the effect of interventions to improve adherence, one may select a measure with a high specificity, such as the “all medications” measure. Furthermore, the optimal adherence threshold may differ based on the measures used.(22,48) For single medication adherence, a threshold of 80% is commonly used. This may also be appropriate when using the “any medication” measure. (22) In contrast, when using the more stringent “all medication” or “both medications” adherence measures, a lower threshold, such as 70%, might be preferred, assuming that this is sufficient to achieve the desired clinical effect. In addition, the association of adherence level with clinical outcomes may also differ based on the dose and type of medication used. (53) Higher adherence threshold for low dose medications might be preferred than for high dose medications to obtain a similar clinical effect. In persistence studies, the focus can be either on persistence of the initial medication/medication class or on any medication to treat a condition. To monitor whether patients are still being treated for their condition, the “any medication” and “treatment anniversary” measures may suffice, since they are not restricted to a particular medication. The “treatment anniversary” measure, however, is not sensitive to early discontinuation followed by a restart before the treatment anniversary. To measure the effect of interventions on persistence, one may select the more specific “all medication” measure.

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Furthermore, we found that a substantial number of studies were flawed due to inadequate description of the methods or how switching or additions were dealt with. More than 10 years ago a checklist was developed for medication adherence and persistence studies using retrospective databases, recommending the researchers to provide a rationale and/or a formula for studies using multiple medications and explain how the analysis handled patients who switched to another medication.(54) Our study illustrates that the implementation of those recommendations is still insufficient. Therefore, both authors and reviewers of manuscripts on adherence or persistence should pay more attention that adequate measurement definitions are provided. In addition, researchers and practitioners need to be aware of these shortcomings when interpreting results for patients using multiple medications. Both the quality of the studies and the quality of the reporting will determine whether appropriate interpretations can be made and relevant interventions can be developed.

Some strengths and limitations of our review should be mentioned. We conducted a systematic search using three databases but only considered articles published in English and studies using prescription data from prescribing, dispensing, or claim databases (health 5 insurance). We did not include studies using electronic devices. The use of multiple electronic devices is impractical for patients using multiple medications. Therefore, it is usually decided to monitor just one medication with electronic devices in interventional studies, and hence there are too few studies using such data for multiple drug use. Two reviewers assessed the study eligibility and the inter-rater agreement for this was high. We found only two studies that analyzed the association of adherence or persistence measures with clinical outcomes. Therefore, future studies are needed to validate the various multiple medications adherence and persistence measures with clinical outcomes. In addition, more studies are needed comparing these prescription-based measures with other methods to get better insight in potential underestimations of adherence and persistence. For example, linking prescription data with medical records could reduce some of the risk of overestimating non-persistence when medication is stopped by the prescriber and reasons for stopping are documented.

CONCLUSION

A variety of measures has been proposed or used to estimate adherence and persistence to multiple medications. The “any medication” measure is helpful to monitor adherence and persistence, and to identify patients in need of an intervention. The “all medication” measure is more useful for assessing the effect of interventions. Many studies were flawed due to inadequate description of methods or how switching or additions were dealt with. Researchers and practitioners need to be aware of these shortcomings when interpreting results for patients using multiple medications. More attention is needed for providing

115 Chapter 5 adequate measurement definitions in reporting on adherence or persistence to multiple medications.

Funding S.D.A and I.S.P are supported by a scholarship from Indonesia Endowment Fund for Education (LPDP). This funding body did not have any role in designing the study, in analyzing and interpreting the data, in writing this manuscript, and in deciding to submit it for publication.

Conflict of interest The authors of this manuscript declare that they do not have any conflict of interest related to the content of this manuscript.

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66. Kreyenbuhl J, Dixon LB, Mccarthy F, Soliman S, 73. Van Wijk BLG, Klungel OH, Heerdink ER, De Boer Ignacio R V, Valenstein M. Does adherence to A. Rate and determinants of 10-year persistence medications for type 2 diabetes differ between with antihypertensive drugs. J Hypertens. individuals with vs without schizophrenia ? 2005;23:2101–7. Schizophr Bull. 2010;36(2):428–35. 74. Vink NM, Pharmd OHK, Stolk RP, Denig 67. Levi M, Pasqua A, Cricelli I, Cricelli C, Piccinni P. Comparison of various measures for C, Parretti D, et al. Patient adherence to assessing medication refill adherence using olmesartan/amlodipine combinations: fixed prescription data. Pharmacoepidemiol Drug Saf. versus extemporaneous combinations. J Manag 2009;18:159–65. care Spec Pharm. 2016;22(3):255–62. 75. Wang T, Chen Y, Huang C, Chen W, Chen 68. Melikian C, White J, Vanderplas A, Dezii CM, M. Combinations of antihypertensive Chang E. Adherence to oral antidiabetic therapy drugs bidirectional adherence changes and in a managed care organization: a comparison associated factors in patients switched from of monotherapy , combination therapy, and free combinations to equivalent single-pill fixed-dose combination therapy. Clin Ther. combinations of antihypertensive drugs. 2002;24(3):460–7. Hypertension. 2014;63(7):958–67. 69. Nelson LA, Pharm D, Graham MR, Pharm 76. White TJ, Vanderplas A, Chang E, Dezii CM, D, Lindsey CC, Pharm D, et al. Adherence Abrams GD. The costs of non-adherence to oral to antihyperlipidemic medication and lipid antihyperglycemic medication in individuals disorders. Psychosomatics. 2011;52(4):310–8. with diabetes mellitus and concomitant diabetes 70. Pan F, Chernew ME, Fendrick AM. Impact of mellitus and cardiovascular disease in a managed fixed-dose combination drugs on adherence care environment. Dis Manag Heal Outcomes. to prescription medications. J Gen Intern Med. 2004;12(3):181–8. 2008;23(5):611–4. 77. Ziyadeh N, Mcafee AT, Koro C, Landon J, Chan 71. Poluzzi E, Strahinja P, Vaccheri A, Vargiu A, KA. The thiazolidinediones rosiglitazone and Silvani MC, Motola D, et al. Adherence to chronic pioglitazone and the risk of coronary heart cardiovascular therapies: persistence over the disease: a retrospective cohort study using years and dose coverage. Br J Clin Pharmacol. a US health insurance database. Clin Ther. 2006;63(3):346–55. 2009;31(11):2665–77. 72. Rozenfeld Y, Hunt JS, Plauschinat C, Wong KS. Oral antidiabetic medication adherence and glycemic control in managed care. Am J Manag Care. 2008;14(2):71–5.

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APPENDIX.

Search strings in Medline (“Medication Adherence”[Mesh] OR complian*[tiab] OR noncomplian*[tiab] OR adher*[tiab] OR nonadher*[tiab] OR comply[tiab] OR complies[tiab] OR complying[tiab] OR concordance[tiab] OR “proportion of days covered”[tiab] OR “medication possession ratio”[tiab] OR possession rate*[tiab] OR refill[tiab] OR discontinu*[tiab] OR continu*[tiab] OR persist*[tiab] OR “treatment refusal”[tiab] OR switch*[tiab] OR addition[tiab] OR medication gap*[tiab] OR treatment gap*[tiab]OR day gap*[tiab] OR gaps refill*[tiab] OR daily polypharmacy possession ratio*[tiab] OR MPR[tiab] OR PDC[tiab] OR “medication acquisition”[tiab]) AND (“Polypharmacy”[Mesh] OR “Drug Therapy, Combination” [Mesh] OR “Drug Substitution”[Mesh] OR “Comorbidity”[Mesh] OR multiple therap*[tiab] OR multiple drug*[tiab] OR multiple medication*[tiab] OR polypharmac*[tiab] OR “overlapping prescription”[tiab] OR “multi drug”[tiab] OR combination therap*[tiab] OR pill combination*[tiab] OR “concurrent medication”[tiab] OR “concurrent use”[tiab] OR 5 “concurrent adherence”[tiab]) AND (“Drug prescriptions”[Mesh] OR “Dyslipidemias/drug therapy”[Mesh] OR “Hypolipidemic Agents”[Mesh] OR “Hypolipidemic Agents” [Pharmacological Action] OR “Antihypertensive Agents”[Mesh] OR “Antihypertensive Agents” [Pharmacological Action] OR “Angiotensin Receptor Antagonists”[Mesh] OR “Angiotensin-Converting Enzyme Inhibitors” [Mesh] OR “Calcium Channel Blockers”[Mesh] OR “Diuretics”[Mesh] OR “Blood Pressure” [Mesh] OR “Hypertension” [Mesh] OR “Cardiovascular Diseases”[Mesh] OR “Diabetes Mellitus, Type 2”[Mesh] OR “Hypoglycemic Agents”[Mesh] OR “Hypoglycemic Agents” [Pharmacological Action] OR “metformin”[Mesh] OR “Sulfonylurea Compounds”[Mesh] OR “hydroxymethylglutaryl-coa”[tiab] OR statin*[tiab] OR cardiovascular[tiab] OR hypertens*[tiab] OR antihypertens*[tiab] OR hyperlipid*[tiab] OR dyslipid*[tiab] OR “lipid- lowering”[tiab] OR antihyperlipid*[tiab] OR “blood pressure-lowering”[tiab] OR type 2 diabet*[tiab] OR antidiabet*[tiab] OR metformin[tiab] OR “lipid regulating”[tiab] OR “blood pressure regulating”[tiab] OR “glucose lowering”[tiab] OR “glucose regulating”[tiab]) AND (“Databases, Factual”[Mesh] OR “Electronic prescribing”[Mesh] OR “Pharmacoepidemiology”[Mesh] OR “Insurance claim review” [Mesh] OR “Electronic health records”[Mesh] OR “Registries”[Mesh] OR medical claim*[tiab] OR pharmacy data*[tiab] OR pharmacy claim*[tiab] OR pharmacy record*[tiab] OR pharmacy administrative data*[tiab] OR refill data*[tiab] OR dispensing data*[tiab] OR dispensing record*[tiab] OR prescription data*[tiab] OR “prescription refill”[tiab] OR prescription claim*[tiab] OR “prescription register”[tiab] OR automated data*[tiab] OR automated pharmacy data*[tiab] OR

121 Chapter 5 computerized data*[tiab] OR computerised data*[tiab] OR “computerized pharmacy”[tiab] OR computerized medical record*[tiab] OR administrative claim*[tiab] OR administrative data*[tiab] OR administrative pharmacy claim*[tiab] OR administrative insurance record*[tiab] OR “health database”[tiab] OR “health care database”[tiab] OR “healthcare database”[tiab] OR health care claim*[tiab] OR healthcare claim*[tiab] OR insurance plan*[tiab] OR refill data record*[tiab] OR refill histor*[tiab] OR medical record*[tiab] OR electronic record*[tiab] OR electronic health record*[tiab] OR electronic data*[tiab] OR electronic medication prescribe*[tiab] OR “electronic prescription”[tiab] OR electronic claim*[tiab] OR “electronic medical records”[tiab] OR “national health insurance”[tiab] OR claims data* [tiab] OR “drug reimbursement register”[tiab] OR reimbursement record*[tiab] OR secondary data*[tiab] OR prescribing data*[tiab])

Search strings in Embase (‘medication compliance’/exp OR ‘drug withdrawal’/exp OR ‘add on therapy’/exp OR complian*:ab,ti OR noncomplian*:ab,ti OR non-complian*:ab,ti OR adher*:ab,ti OR nonadher*:ab,ti OR non-adher*:ab,ti OR comply:ab,ti OR complies:ab,ti OR complying:ab,ti OR concordance:ab,ti OR ‘proportion of days covered’:ab,ti OR ‘medication possession ratio’:ab,ti OR (possession NEXT/1 rate*):ab,ti OR refill:ab,ti OR discontinu*:ab,ti OR continu*:ab,ti OR persist*:ab,ti OR ‘treatment refusal’:ab,ti OR switch*:ab,ti OR addition:ab,ti OR (medication NEXT/1 gap*):ab,ti OR (treatment NEXT/1 gap*):ab,ti OR (day NEXT/1 gap*):ab,ti OR (gaps NEXT/1 refill*):ab,ti OR (daily polypharmacy possession NEXT/1 ratio*):ab,ti OR MPR:ab,ti OR PDC:ab,ti OR ‘medication acquisition’:ab,ti) AND (‘polypharmacy’/exp OR ‘drug combination’/exp OR ‘drug substitution’/exp OR ‘comorbidity’/ exp OR (multiple NEXT/1 therap*):ab,ti OR (multiple NEXT/1 drug*):ab,ti OR (multiple NEXT/1 medication*):ab,ti OR polypharmac*:ab,ti OR ‘overlapping prescription’:ab,ti OR ‘multi drug’:ab,ti OR (combination NEXT/1 therap*):ab,ti OR (pill NEXT/1 combination*):ab,ti OR ‘concurrent medication’:ab,ti OR ‘concurrent use’:ab,ti OR ‘concurrent adherence’:ab,ti) AND (‘multiple chronic conditions’/exp OR ‘dyslipidemia’/exp OR ‘antilipemic agent’/exp OR ‘antihypertensive therapy’/exp OR ‘angiotensin receptor antagonist’/exp OR ‘calcium channel blocking agent’/exp OR ‘ therapy’/exp OR ‘hypertension’/ exp OR ‘non insulin dependent diabetes mellitus’/exp OR ‘oral antidiabetic agent’/ exp OR ‘hydroxymethylglutaryl-coa’:ab,ti OR statin*:ab,ti OR cardiovascular:ab,ti OR hypertens*:ab,ti OR antihypertens*:ab,ti OR anti-hypertens*:ab,ti OR hyperlipid*:ab,ti OR dyslipid*:ab,ti OR lipid-lowering:ab,ti OR antihyperlipid*:ab,ti OR anti-hyperlipid*:ab,ti OR blood pressure-lowering:ab,ti OR (type 2 NEXT/1 diabet*):ab,ti OR antidiabet*:ab,ti OR anti-diabet*:ab,ti OR metformin:ab,ti OR ‘lipid regulating’:ab,ti OR ‘blood pressure regulating’:ab,ti OR ‘glucose lowering’:ab,ti OR ‘glucose regulating’:ab,ti) AND

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(‘drug database’/exp OR ‘electronic prescribing’/exp OR ‘pharmacoepidemiology’/ exp OR ‘electronic health record’/exp OR (medical NEXT/1 claim*):ab,ti OR (pharmacy NEXT/1 data*):ab,ti OR (pharmacy NEXT/1 claim*):ab,ti OR (refill NEXT/1 data*):ab,ti OR (pharmacy administrative NEXT/1 data*):ab,ti OR (pharmacy NEXT/1 record*):ab,ti OR (dispensing NEXT/1 data*):ab,ti OR (dispensing data NEXT/1 record*):ab,ti OR (dispensing NEXT/1 record*):ab,ti OR (prescription NEXT/1 data*):ab,ti OR ’prescription refill’:ab,ti OR (prescription NEXT/1 claim*):ab,ti OR ‘prescription register’:ab,ti OR (automated NEXT/1 data*):ab,ti OR (automated pharmacy NEXT/1 data*):ab,ti OR (computerized NEXT/1 data*):ab,ti OR (computerised NEXT/1 data*):ab,ti OR ‘computerized pharmacy’:ab,ti OR (computerized medical NEXT/1 record*):ab,ti OR (administrative NEXT/1 claim*):ab,ti OR (administrative NEXT/1 data*):ab,ti OR (administrative pharmacy NEXT/1 claim*):ab,ti OR (administrative insurance NEXT/1 record*):ab,ti OR ‘health database’:ab,ti OR ‘health care database’:ab,ti OR ‘healthcare database’:ab,ti OR (health care NEXT/1 claim*):ab,ti OR (healthcare NEXT/1 claim*):ab,ti OR (insurance NEXT/1 plan*):ab,ti OR (refill data NEXT/1 record*):ab,ti OR (refill NEXT/1 histor*):ab,ti OR (medical NEXT/1 record*):ab,ti OR (electronic NEXT/1 record*):ab,ti OR (electronic health NEXT/1 record*):ab,ti OR (electronic NEXT/1 data*):ab,ti OR ‘electronic medication prescribing’:ab,ti OR ‘electronic 5 prescription’:ab,ti OR (electronic NEXT/1 claim*):ab,ti OR ‘electronic medical records’:ab,ti OR ‘national health insurance’:ab,ti OR (claims NEXT/1 data*):ab,ti OR ‘drug reimbursement register’:ab,ti OR (reimbursement NEXT/1 record*):ab,ti OR (secondary NEXT/1 data*):ab,ti OR (prescribing NEXT/1 data*):ab,ti)

Search strings in PsycINFO (DE “Treatment Compliance” OR TX complian* OR TX noncomplian* OR TX “non- complian*” OR TX adher* OR TX nonadher* OR TX “non-adher*” OR TX comply OR TX complies OR TX complying OR TX concordance OR TX “proportion of days covered” OR TX “medication possession ratio” OR TX “possession rate*” OR TX refill OR TX discontinu* OR TX continu* OR TX persist* OR TX “treatment refusal” OR TX switch* OR TX addition OR TX “medication gap*” OR TX “treatment gap*”OR TX “day gap*” OR TX “gaps refill*” OR TX “daily polypharmacy possession ratio*” OR TX MPR OR TX PDC OR TX “medication acquisition”) AND (DE “Polypharmacy” OR DE “Comorbidity” OR TX “multiple therap*” OR TX “multiple drug*” OR TX “multiple medication*” OR TX polypharmac* OR TX “overlapping prescription” OR TX “multi drug” OR TX “combination therap*” OR TX “pill combination*” OR TX “concurrent medication” OR TX “concurrent use” OR TX “concurrent adherence”) AND (DE “Hypertension” OR DE “Antihypertensive Drugs” OR DE “Channel Blockers” OR DE “Diuretics” OR DE “Type 2 Diabetes” OR TX “hydroxymethylglutaryl-coa” OR TX statin* OR TX cardiovascular OR TX hypertens* OR TX antihypertens* OR TX “anti-hypertens*” OR TX hyperlipid* OR TX dyslipid* OR TX “lipid-lowering” OR TX antihyperlipid* OR TX “anti-

123 Chapter 5 hyperlipid*” OR TX “blood pressure-lowering” OR TX “type 2 diabet*” OR TX “antidiabet*” OR TX “anti-diabet*” OR TX metformin OR TX “lipid regulating” OR TX “blood pressure regulating” OR TX “glucose lowering” OR TX “glucose regulating”) AND (DE “Databases” OR TX “medical claim*” OR TX “pharmacy data*” OR TX “pharmacy claim*” OR TX “refill data*” OR TX “pharmacy administrative data*” OR TX “pharmacy record*” OR TX “dispensing data*” OR TX “dispensing data record*” OR TX “dispensing record*” OR TX “prescription data*” OR TX “prescription refill” OR TX “prescription claim*” OR TX “prescription register” OR TX “automated data*” OR TX “automated pharmacy data*” OR TX “computerized data*” OR TX “computerised data*” OR TX “computerized pharmacy” OR TX “computerized medical record*” OR TX “administrative claim*” OR TX “administrative data*” OR TX “administrative pharmacy claim*” OR TX “administrative insurance record*” OR TX “health database” OR TX “health care database” OR TX “healthcare database” OR TX “health care claim*” OR TX “healthcare claim*” OR TX “insurance plan*” OR TX “refill data record*” OR TX “refill histor*” OR TX “medical record*” OR TX “electronic record*” OR TX “electronic health record*” OR TX “electronic data*” OR TX “electronic medication prescribing” OR TX” electronic prescription” OR TX “electronic claim*” OR TX “electronic medical records” OR TX “national health insurance” OR TX “claims data*” OR TX “drug reimbursement register” OR TX “reimbursement record*” OR TX “secondary data*” OR TX “prescribing data*”)

Table S1. Characteristics of studies clearly describing adherence and/or persistence to multiple medications, and Table S2. Characteristics of studies not clearly describing adherence and/or persistence to multiple medications, can be seen in the link below:

https://tinyurl.com/tableS1-S2

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125 Prague, 2019 CHAPTER INTERVENTIONS TO IMPROVE MEDICATION ADHERENCE IN PATIENTS WITH LATENT AND ACTIVE TUBERCULOSIS INFECTION: A SYSTEMATIC 6 REVIEW OF RANDOMIZED CONTROLLED STUDIES

Ivan S. Pradipta Daphne Houtsma Job F.M. van Boven Jan-Willem C. Alffenaar Eelko Hak

Submitted Chapter 6

ABSTRACT

Introduction: Tuberculosis (TB) treatment is long and takes up to 24 months. Non- adherence to medication is a major risk factor for poor treatment outcome. A contemporary comprehensive overview of potential interventions to improve treatment and their effectiveness is needed. We therefore systematically reviewed studies on the effectiveness of various interventions for optimization of medication adherence in TB patients.

Methods: We performed a systematic review using the Medline/PubMed and Cochrane literature databases and included randomized controlled studies. Studies with patients of all ages with either latent tuberculosis infection (LTBI) or active TB were eligible for inclusion in this review. We included studies that analysed single and/or multifactorial interventions to improve medication adherence. We defined adherence rate, completed and defaulted treatment as the primary outcomes, while negative sputum conversion, cured and poor treatment outcomes were secondary outcomes. The quality of the included studies was assessed using the JADAD score. Data were narratively described for LTBI and active TB groups separately.

Results: We identified four and eleven eligible studies with LTBI and active TB patients, respectively. The interventions targeted several aspects, including socio-economic, health-care, patient, and treatment aspects. Not all interventions appeared to significantly improve medication adherence. Several interventions were found effective in improving adherence and outcomes of active TB patients, i.e. DOT by trained community members, SMS combined with TB education, a reinforced counselling method, monthly TB voucher, drug box reminder, and a combination drug box reminder with text messaging. In the LTBI patients, shorter regimens and DOT interventions improved treatment completion effectively. Interestingly, intervention using DOT showed heterogeneous effects on the study outcomes.

Conclusions: Our study showed that interventions to increase TB drug adherence can be effective with varying impact across studies and settings. Since non-adherence factors are patient-specific, personalized interventions that take into account such factors are required to enhance the impact of a program to improve medication adherence in TB patients.

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INTRODUCTION

Tuberculosis (TB) remains an important worldwide health issue. The World Health Organization (WHO) reported that TB is the cause of illness for around 10 million people every year and has been ranked among the top ten causes of death globally.(1) TB, caused by Mycobacterium tuberculosis (M.tb), can be spread easily from patients suffering from pulmonary TB to healthy people by air transmission.(1) Consequently, anti-tuberculosis drug treatment is required for TB patients to cure the disease and prevent disease transmission.

Like in other complex diseases, TB patients have to be treated with several drugs for a long period. According to the WHO guideline, active pulmonary TB patients should take drugs for at least six months,(2) while latent tuberculosis infection (LTBI) patients should take drugs for at least three months.(3) The treatment duration can be extended if TB patients are diagnosed as multi-drug resistant tuberculosis (MDR-TB), a resistance of the pathogen to the most potent anti-tuberculosis medicines (isoniazid and rifampicin). The MDR-TB treatment can be up to 24 months using multiple drugs.(4)

Poor adherence to medication is widely known as a causal factor for increased risk of morbidity, mortality and cost burden.(5,6) A global meta-analysis revealed that non- 6 adherence to treatment is a risk factor for MDR-TB.(7) Furthermore, MDR-TB patients, as compared to drug-susceptible patients, have more frequently poor treatment outcomes. (8,9) Treatment adherence is affected by multiple factors. These factors are divided into five different interacting dimensions, including socio-economic, health care system, condition, therapy, and patient factors.(10) Although studies on adherence in other diseases than TB showed that interventions targeting these factors can significantly improve adherence rates,(11–13) a better understanding of the effects of possible interventions in TB is required. We therefore systematically reviewed the effectiveness of various interventions to improve medication adherence in LTBI and active TB patients.

METHODS

Literature review We performed a systematic review of the randomized controlled trials that were published between January 1, 2003 and April 24, 2018, and reported in the English language. The search period was restricted from 2003 because in that year the influential WHO Adherence report was published and created wide-scale awareness on the issue of non-adherence ever since.(10) Given their increased risk for bias, quasi-experimental, cohort, cross-sectional, case-control, case reports, case series, review articles, and abstract conference were not eligible for inclusion. This systematic review was reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance.(14)

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Study population and interventions To be able to better distinguish between the potential impact of experienced symptoms on extent of adherence, the study population was divided into two different groups, i.e. patients with latent tuberculosis infection (LTBI) and active TB. The status of TB disease should be confirmed by clinical or laboratory examination (e.g., TB symptoms, Mantoux, IGRA, chest radiograph, or other TB examination) and/ or microbiological verification (e.g. smear sputum test, phenotyping drug susceptibility test or polymerase chain reaction). We excluded studies restricted to specific high-risk treatment non-adherence groups, such as TB patients with Human Immunodeficiency Virus (HIV), drug resistant TB, and illicit drug use. Studies that analysed interventions related to improving medication adherence and treatment outcomes were included in this review. The intervention was allowed to target one or multiple factors of adherence such as socio-economic, health care team and system, health condition, therapy, or patient factors. The intervention should have a comparison group to analyse the effect of the intervention.

Study outcomes In terms of the study outcomes, we followed the global definition published by the WHO in 2014. (15) We defined medication adherence as the primary outcome. Of note, adherence consists of three phases: initiation, implementation and persistence.(16) In our assessment, persistence was deemed a synonym terms for “completed treatment” and “defaulted treatment”, respectively. Implementation was deemed similar to “adherence rate”. Treatment completion is defined as successful treatment by the WHO, because it is a combination of cured and completed treatment. Defaulted treatment was defined as an interruption of TB treatment for two or more consecutive months, while adherence rate was identified by the proportion of missing anti-TB dose during treatment period defined. In case of limited data on adherence, cured treatment, negative sputum conversion and poor treatment outcomes were used as the secondary outcomes. Cured treatment was defined as smear- or culture-negative in the last month of treatment and on at least one previous occasion, while completed treatment was defined as a TB patient who completed treatment without evidence of failure, but with no record to show that sputum smear or culture results were positive in the last month of treatment. Negative sputum conversion was defined as the conversion sputum to a negative result, while poor treatment outcome is a combination of defaulted, failed treatment and death outcome. Failed treatment was defined as a positive sputum smear or culture at fifth months after treatment initiation.

Data collection The relevant articles were obtained from the Medline/PubMed and Cochrane databases with specific key-terms. To obtain the relevant articles effectively, we used restriction to the following filters in the Medline/PubMed database, such as clinical trial, comparative study, controlled clinical trial, observational study, randomized controlled trial and humans.

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Applying observational study in the Medline/PubMed filter was intended to anticipate potential RCT studies in the group labelled as observational studies. Key terms for obtaining the articles can be found in the Appendix.

Data extraction and quality assessment Title and abstract of the articles were screened by ISP and DH, then the full-text of the articles were assessed for the eligibility and quality by ISP. Duplicated articles from two databases were removed using Refwork® software. The eligible articles were then reviewed for the relevant information. Information related to year publication, population, type of intervention, comparator group, and study outcome were extracted by ISP. Any disagreement between the reviewers during the screening phase were solved by discussion.

Regarding the quality assessment, we used the JADAD score for assessing the quality of the randomized studies.(17) Three main domains were appraised in the JADAD score system, i.e. randomization, blinding method, and subject withdrawal. The domains were assessed by five questions. For each question, a study could earn 1 point, with a total score of 5 points. The five questions were described as follows: Was the study described as randomized ?; Was the method used to generate sequence of randomization described and appropriate ?; Was the study described as double-blind ?; Was the method of double-blinding appropriately 6 described ?; and was there a description of withdrawals and dropouts?.

Summary measures and synthesis of results The total number and group of patients with any specific outcome for both primary and secondary outcomes were extracted by ISP from studies and summarized in the tables. For the point estimate of the intervention, we used relative risk (RR) for dichotomous outcome data and mean ratio (MR) for continuous outcome data with a 95% confidence interval (95%CI).

RESULTS

Study selection During the search, we found 200 records from the Medline/PubMed database and 186 records from the Cochrane database. We identified 72 duplicate records from the Refwork® software. A total of 314 articles were screened for the title and abstract. This initial screening excluded 268 irrelevant records, then the full-text screening process was continued for 46 records. In the full-text screening, 31 articles were excluded due to different populations (3 articles), different study outcomes (11 articles), and non-randomized study design (17 articles). We finally analysed 15 studies for qualitative synthesis. The flow diagram, literature search, and screening process are presented in Figure 1.

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Figure 1. Flow diagram of the included articles

Study characteristics and interventions In total, 16,029 subjects were included in the analysis. In all, 1,991 LTBI patients and 14,038 active TB patients participated. The minimum number of subjects in the included studies was 89, while the maximum number was 4,154 subjects. The included studies were conducted in both low and high burden TB countries, i.e. Pakistan,(18,19) Australia,(20) Iraq,(21) China,(22,23) Senegal,(24) South Africa,(25–27) Tanzania,(28) Timor Leste,(29) Canada,(30) United States, (27,31) Spain,(27) Hong Kong,(27) and Mexico.(32)

The 15 randomized controlled trials assessed a broad range of adherence management interventions. These included Short Message Service (SMS) intervention,(18,19,21,23,27) DOT administered by a family member,(20) DOT with supervision by a community,(21,27,28) a reinforced counselling method,(24) a trained lay health workers intervention to manage TB case,(25) monthly TB vouchers,(33) a drugs box reminder,(23) a combination text messaging and drugs box reminder,(23) a nutritious intervention,(29) shorter regimen,(30) a peer-

132 Medication adherence interventions based intervention(31) and a behavioural intervention.(32) The characteristics of the included studies are shown in Table 1.

Outcomes In the active TB patients, the primary outcomes were treatment completion,(18–20,22,25,26) interrupted rate(18,19,21,24,26) and adherence rate,(23,29) while the secondary outcomes were negative sputum conversion,(21,22,28) cured(21,24,28) and poor treatment outcomes.(23) In the LTBI studies, we observed that treatment completion was the only outcome. Regarding the intervention effect, not all interventions significantly improved drug adherence and treatment outcomes. Several interventions were found effective in improving medication adherence and outcomes of active TB patients, i.e. DOT by trained community members, SMS combined with TB education, a reinforced counselling method, monthly TB voucher, drugs box reminder, a combination drugs box reminder and text messaging. However, only two studies reported adherence rate as the study outcome. (23,29) We identified that a drugs box reminder (MR 0.58; 95%CI 0.42- 0.79) and its combination with text messaging (MR 0.49; 95%CI 0.27-0.88) significantly reduced missing a drug dose among active TB patients,(23) while food incentives were not significantly different from the comparator for the intensive (MR -4.7; 95%CI-0.8 - -8.6) and continuation phase (MR 0; 95%CI -1.7- 1.7) in the active TB patients,(29) see Table 2. 6

In the LTBI patients, shorter regimens and DOT interventions significantly improved treatment completion. We identified that 4 months of daily rifampicin 10 mg/kg was more effective to improve treatment completion than 9 months of daily isoniazid 5 mg/ kg (RR 1.2; 95%CI 1.02-1.4),(30) while DOT intervention was more effective to improve treatment completion than self-administration therapy with monthly monitoring (RR 1.18; 95%CI;1.09- 1.27).(27) In contrast, several interventions, such as self-administration therapy with weekly text message reminders plus monthly monitoring (RR 1.03;95%CI 0.95-1.13),(27) a peer- based intervention (RR 1.06; 95%CI 0.86-1.31),(31) adherence coaching intervention (RR 1.36; 95%CI 0.98-1.88) (32) and self-esteem counselling (RR 1.12; 95%CI 0.78-1.58)(32) did not significantly improve treatment completion in the LTBI patients, see Table 3.

133 Chapter 6 Outcomes completionTreatment (sum of completed treatment and cured treatment) and defaulted treatment completionTreatment (sum of completed treatment and cured treatment) Cured and defaulted treatment; sputum conversion: a negative sputum smear at the fifth month after treatment completionTreatment (sum of completed treatment and cured treatment) and sputum conversion: a negative sputum smear at the sixth month after treatment Control Standard of care Standard supervised but non-directly observed therapy DOT without home visits DOT Intervention Zindagi SMS, a two-way SMS reminder system, sent daily SMS reminders and motivational messages participants to and asked them respond to through SMS or miss calls after taking their medication DOT administered by a family member (FDOT) DOT and home visits from trained members of the Iraqi Federation Women’s (IWF) Regular SMS remind to taking medicine and educate core knowledge about pulmonary TB. The SMS contents: Following a). the doctor’s instructions and taking medicine Reexamining b). timely, sputum and chest periodically,X-ray Covering c). nose and mouth when sneezing or coughing, and doing not spit everywhere Paying d). attention to washing hands, opening a window Ventilated regularly, doing sports more, and improving adhering resistibility,e). regular to treatment, and most of TB patients can be cured Setting TB clinics and hospitals in Karachi, Pakistan clinics in the Two North-Western Health care network, Victoria, Australia 15 TB centers in Iraq Baghdad, Six districts in Anhui Province, China Type participant Adult newly TB patients Adult newly TB patients Adult newly TB patients Adult pulmonary TB patients Study period 2011- 2014 1998- 2000 2001 2014- 2015 Study design RCT RCT RCT RCT . Characteristics of the included articles Authors Mohammed S, et al., 2016(18) MacIntyre CR, et al., 2003(20) Mohan A, et al., 2003(21) XH,Fang et al., 2017(22) Table 1

134 Medication adherence interventions Outcomes Cured and defaulted treatment completionTreatment (sum of completed treatment and cured treatment) completionTreatment (sum of completed treatment and cured treatment) and defaulted treatment completionTreatment (sum of completed treatment and cured treatment) and defaulted treatment Control The usual standard care of TB No intervention healthof lay workers DOT No monthly voucher

6 Intervention Reinforced counselling through improved communication between health personnel and patients, decentralization of treatment, choice of DOT supporter by the patient, and reinforcement of supervision activities health lay workersTrained screen, to refer, report, educate monitor, and motivate TB patients DOT and daily mobile SMS reminders Monthly voucher USD 15 per month Setting Government district health centers in Senegal in theFarms Boland health district, Western Cape, South Africa Khyber Teaching Hospital Peshawar and Emergency Satellite Hospital Nahaqi, Pakistan Primary public health care at Kwazulu-Natal, South of Africa Type participant Newly diagnosed TB patients Adult newly TB patients Adult newly pulmonary and extra- pulmonary TB patients Adult newly TB patients Study period 2003- 2005 200- 2001 2014- 2015 2009- 2010 . Characteristics of the included articles Study design Cluster RCT Cluster RCT RCT Cluster RCT Authors Thiam S, et al., 2007(24) Clarke M, et al., 2005(25) Farooqi RA, et al., 2016(19) Lutge E, et al., 2013(26) Table 1 (Continued)Table

135 Chapter 6 Outcomes Poor adherence: was the percentage patient-months of where at least 20% of doses doses) were (15 missed; poor treatment outcome Sputum conversion: AFB negative sputum after two months of treatment; Cure rate at seven months: AFB- negative sputum at two months, remaining negative at month five and/or at month seven after the start of treatment Control The usual care: treatment monitoring can be self- administered treatment or treatment supervised by family members or treatment supervised by health care workers. The local doctor monitor the treatment Institution- based DOT (IBDOT): Patients visit health facility daily for the first two months Intervention messagingText reminder Drug box reminder Combined (text messaging and drug box reminder) Community-based DOT (CBDOT): daily observed patient by a volunteer for the first two months and the patients a monthly have healthvisit to facility Setting Provinces of Heilongjiang, Jiangsu, Hunan, and Chongqing, China, Kilombero District, Morogoro, Southern Tanzania Type participant Adult newly pulmonary TB patients Adult newly pulmonary TB patients Study period 2009 1999- 2000 . Characteristics of the included articles Study design Cluster RCT Cluster RCT Authors Liu X, et al., 2015(26) Lwilla et al., F, 2003(26) Table 1 (Continued)Table

136 Medication adherence interventions Outcomes completion:Treatment clearance of acid-fast bacilli from the sputum after treatment or the completion of eight months of treatment, or both; adherence treatment:to clinic attendance, DOT, interview and count pill Completed treatment defined tookas 80% or more of doses within 20 weeks for 4RIF or within 43 weeks for 9INH completionTreatment (sum of completed treatment and cured treatment) Control Nutritional advice 9 month of daily isoniazid 5 mg/ kg with SAT monthly monitoring

6 Intervention Nutritious, culturally appropriate daily meal (weeks and 1-8) food package (weeks 9-32) 4 month of daily rifampicin mg/ 10 kg Direct observation treatment with monthlySAT monitoring and text message reminders Setting Three primary care clinics in Dili, Timor-Leste A university- affiliated respiratory hospital, Canada Outpatient tuberculosis clinics in the United States (9 Spainsites), (1 Kong Hong site), and South site), (1 Africa site) (1 Type participant Adult newly pulmonary TB patients Adult LTBI Adult LTBI patients Study period 2005- 2006 2002 2012- 2014 . Characteristics of the included articles Study design RCT RCT Non- inferiority RCT Authors Martins N, et al., 2009(29) Menzies etD, al., 2004(30) Belknap R et al., 2017(27) Table 1 (Continued)Table

137 Chapter 6 Outcomes completionTreatment (sum of completed treatment and cured treatment) completion:Treatment completion of LTBI treatment as taking 180 within pills 270 days Control Self- administered 9-month isoniazid treatment The usual medical care: 300 mg INH per was day prescribed for 6-9 month monthlywith evaluation Intervention intervention:Peer-based peers educated and coached patients on adherence; gave social and emotional support and provided health care and social service system navigation, together with patients and health workers, enhanceto patient-provider communication. The peers were people who had completed treatments or anti-TB andLTBI had attended a 4-week training program that includes role-playing exercise, informational sessions, and observation. Peers met participants by one-on-one at least once a week Usual care plus adherence coaching: monthly case review and discussion about adherence advice and problems Usual care plus self-esteem counselling: monthly meeting about relationship and communication with friends family, and cultural identity enhance to self-esteem Setting The Harlem Hospital Chest in NewClinic USA NY, York, Diego- San Tijuana, Mexico- United States Type participant Adult LTBI patients Adolescents LTBI patients Study period 2002- 2005 1996- 2000 . Characteristics of the included articles Study design RCT RCT Authors Hirsch- Moverman et al., Y, 2013(31) Hovell et al., MF, 2003(32) Table 1 (Continued)Table Information: Randomized RCT, control latent tuberculosis LTBI, trial; infection; directly DOT, observed Self-administration therapy; therapy; SAT, SMS, short message services, AFB, acid-fast bacilli

138 Medication adherence interventions ------Poor treatment ¥ - - - - - (1.14- (1.15- 1.42) 1.37)* Sputum RR 1.26; RR 1.26; conversion - - - - - 1.33) 1.34) (1.14- (1.03- Cured RR 1.2; RR 1.18; treatment ------rate Adherence Adherence Treatment outcomesTreatment - - - rate RR 0.75; RR 0.92; RR 0.43;RR RR 0.83; (0.17-3.24) (0.68-1.24) (0.21-0.89) (0.02-0.34) Interrupted - - RR 1.11; RR 1.96; RR 1.01; RR 1.08; RR 1.00; 6 Treatment Treatment (0.95-1.10) (0.98-1.15) (0.92-1.27) (1.04-1.18) (0.96-1.04) completion 74 42 86 744 190 240 1,093 group Comparator 74 47 87 240 160 778 1,104 group Number participants of Intervention target health care health care health care health care health care health care Patient and Patient and Patient and Patient and Patient and Patient and Health care Intervention SMS and regular education of core knowledge about pulmonary TB Reinforced counselling method LHWs Trained intervention Mobile SMS reminders Intervention SMSTwo-way reminder system with motivational words Family DOT DOT and home visits by trained members of the Iraqi Women’s Federation Effect of the intervention on the treatment outcomes in active tuberculosis patients Authors Mohammed S, et al, 2016(18) MacIntyre CR, et al., 2003(20) Mohan A, et al., 2003(21) XH,Fang et al., 2017(22) Thiam S, et al., 2007(24) Clarke M, et al., 2005(25) Farooqi RA, et al., 2016(19) No 1 2 3 4 5 6 7 Table 2. Table

139 Chapter 6 - - - Poor 1.51) 1.13) 2.20) (0.17, (0.45, (0.33, MR 0.71 MR 0.44 MR 1.00 treatment - - - - - 1.71) Sputum 0.62 (0.23- 0.62 conversion - - - - - 7.88) (0.32- Cured OR 1.58 treatment ** * - - rate -8.6) MR 0 (-0.8 - MR -4.7 MR 0.94 MR 0.49 MR 0.58 (-1.7- 1.7) (-1.7- (0.71-1.24) (0.42, 0.79) (0.42, (0.27, 0.88) (0.27, Adherence Adherence Treatment outcomesTreatment - - - - - rate 0.06 (0.04-0.11) Interrupted - - - - RR 0.98 RR 1.07; Treatment Treatment (1.04-1.11) (0.86- 1.11) completion 129 301 1,984 1,091 group Comparator 221 996 136 992 2,170 1,059 group Number participants of Intervention Socio- target Patient economy healthcare health care Patient and Patient and Intervention Effect of the intervention on the treatment outcomes in active tuberculosis patients Community Community based DOT Food incentive Combination of text messaging and drug box reminder Intervention Monthly voucher USD 15 per month messagingText reminder Drug box reminder Lwilla et al., F, 2003(28) Martins N, et al., 2009(29) Authors Lutge E, et al., 2013(26) Liu X, et al., 2015(23) 11 10 No 8 9 Information: completion Treatment completing is the prescribed doses of drugs (completed and cured treatment); Interrupted treatment a defaulted is or/and interrupted treatment groups that were compared with non-interrupted patient group; Poor treatment is a combination of defaulted, failed treatment and death outcome; Adherence rate a proportion is dose; of missing Sputum anti-TB conversion a conversion is sputum a negative to result; MR, mean ratio; RR, Relative Risk; OR, Odds Ratio; *intensive phase; **Continuation phase; TB, tuberculosis; latent tuberculosis LTBI, infection; directly DOT, observed Self- therapy; SAT, administration therapy; SMS, short message services; AFB, acid-fast bacilli; USD, United States dollar; LHWs, health lay workers. Table 2 (Continued).Table

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Table 3. Effect of the interventions on the treatment outcomes in latent tuberculosis infection (LTBI) patients

No Authors Intervention Intervention Number of participants Study target outcome Intervention Comparator Treatment group group completion (RR; 95%CI) 1 Menzies 4 month of daily Treatment 58 58 1.2; (1.02- D, et al., rifampicin 10 mg/ kg 1.4) 2004(30) 2 Belknap DOT Patient and 337 337 1.18; (1.09- R, et al., health care 1.27) 2017(27) Self-administration Patient and 328 337 1.03; (0.95- therapy with weekly health care 1.13) text message reminders and monthly monitoring 3 Hirsch- Peer-based Patient and 128 122 1.06; (0.86- Moverman intervention health care 1.31) Y, et al., 2013(27) 4 Hovell Usual care plus Patient and 92 96 1.36; (0.98- MF, et al., adherence coaching health care 1.88) 6 2003(27) Usual care plus self- Patient and 98 1.12; (0.78- esteem counselling health care 1.58)

Information: Treatment completion is completing the prescribed doses of drugs (completed and cured treatment); DOT, directly observed therapy; RR, relative risk; 95%CI, 95% confidence interval.

Quality assessment of the included studies A double-blinding method was either not possible or not applied for most of the included studies. This is due to the fact that intervention activities were impossible to blind, such as SMS reminders, DOT, counselling, monthly vouchers, drugs box reminders, food incentive and peer-based intervention. Often, an open-label design was applied. Therefore, none of the included had a maximum JADAD score (5 points). Three points of the JADAD score was the maximum score among the included studies because none of the studies applied the blinding method. Six of the 15 studies had the lowest JADAD score (2 points) due to absence of a description of randomization procedure.(20–22,28,31,32) The other studies had a higher score (3 points) given they appropriately described the randomization method and clearly illustrated the withdrawals of participants.(18,19,23–27,29,30) The risk of bias assessment is presented in Table 4.

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Table 4. The risk of bias assessment for randomized studies using JADAD score

No Author Randomization Description of Double- Description Description Total randomization blind of the of score method blinding participants method withdrawal/ drop-out 1 Mohammed 1 1 0 0 1 3 S, et al, 2016(18) 2 MacIntyre 1 0 0 0 1 2 CR et al., 2003(20) 3 Mohan A, et 1 0 0 0 1 2 al., 2003(21) 4 Belknap R, et 1 1 0 0 1 3 al., 2017(27) 5 Fang XH, et 1 0 0 0 1 2 al., 2017(22) 6 Hirsch- 1 0 0 0 1 2 Moverman Y, et al., 2013(31) 7 Thiam S, et 1 1 0 0 1 3 al., 2007(24) 8 Clarke M, et 1 1 0 0 1 3 al., 2005(25) 9 Farooqi 1 1 0 0 1 3 RA, et al., 2016(19) 10 Hovell MF, et 1 0 0 0 1 2 al., 2003(32) 11 Lutge E, et al., 1 1 0 0 1 3 2013(26) 12 Menzies D, et 1 1 0 0 1 3 al., 2004(30) 13 Liu X, et al., 1 1 0 0 1 3 2015(23) 14 Lwilla F, et al., 1 0 0 0 1 2 2003(28) 15 Martins N, et 1 1 0 0 1 3 al., 2009(29)

Information: JADAD questions: (1) Was the study described as randomized?; (2) Was the method used to generate sequence of randomization described and appropriate?; (3) Was the study described as double-blind?; (4) Was the method of double-blinding described and appropriate?; (5) Was there a description of withdrawals and dropouts?

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DISCUSSION

We observed various interventions that were successful in improving medication adherence and outcomes in TB patients. The interventions targeted several factors of adherence, such as socio-economic, patient, healthcare, and treatment aspects. The effective interventions to improve treatment completion in active TB patients were DOT with daily home visits by community-trained members, SMS reminders combined with TB education, a reinforced counselling method, and a monthly voucher intervention. In the LTBI patients, DOT and a shorter regimen significantly improved treatment completion. We identified that the drugs box reminder or its combination with text messaging reminder significantly improved the medication adherence rate among active TB patients, while no studies were found showing an effective intervention to improve medication adherence rate in LTBI patients. In contrast, we found that some interventions, such as SMS reminders or its combination with motivational messages, family DOT, community-based DOT, involving trained lay health workers in TB management, and food incentives were not significantly different compared with the comparator groups for improving treatment completion and outcomes in active TB patients. Similarly, we identified SMS reminders combined with monthly monitoring, peer- based intervention, coaching adherence and self-esteem counselling were not effective in improving treatment completion in LTBI patients. 6

Interestingly, interventions using DOT showed various effects on the study outcomes. Studies that used community-based DOT interventions showed opposite effect in Iraq(21) and Tanzania.(28) Family DOT(20) and community DOT(28) were not superior in the improvement of treatment outcomes among active TB patients, while institutional DOT significantly improved treatment completion in the LTBI patients. Unfortunately, a meta- analysis of the crude data was impossible because heterogeneities were identified across the included studies with regard to population, intervention and study outcomes. However, a previous meta-analysis stated that the understanding of resources and situations in which DOT can be beneficial is an essential part of successful implementation of DOT.(34) Furthermore, the interaction between DOT providers and TB patients may also influence the effect on medication adherence and outcome. Therefore, the differences of resources, situation and interaction between DOT providers and TB patients may indicate that the effects of interventions can vary across studies and settings.

Generally, the differences in observed effect sizes of the interventions in this review can be explained by several aspects: 1) characteristics of the subjects, 2) measurement method of the adherence (outcome), 3) characteristics of the comparator group, and 4) the quality of the study design and intervention. According to the WHO,(10) adherence is a multidimensional phenomenon that can be determined by the interaction of the five essential factors, i.e., socio-economic, provider-patient/healthcare system, condition-

143 Chapter 6 related, therapy-related, and patient-related. Since the essential causal factors for poor adherence can be individual, assessing the individual non-adherence factors is a critical approach to have effective personalized interventions to increase medication adherence. The most optimal intervention to improve medication adherence should not be “one-size- fits-all”. As an example, an intervention using SMS reminders for taking medicine may not be effective if the individual problem of medication adherence is mainly caused by inaccessibility of the patient to have a qualified medicine.

In terms of outcome measurement, heterogeneity was shown in the included studies. Most of the studies used a treatment completion parameter measured by temporary patient visits or self-reported/medical documentation as the outcome parameter for medication adherence. An implication of this is the possibility of misclassification for the status of medication adherence. The measurement did not represent the daily consumption of the medicines during the treatment phase. Hence, potential information bias is high in the studies that used temporary patient visits or self-reported/medical documentation to assess medication adherence. The accuracy of adherence measurement in TB patients was reported in a systematic review.(35) Methods to measure adherence can be categorized as direct (e.g. DOT, ingestible sensors, drug or metabolites measurements) and indirect (e.g. patient self- report, pill counts, health information system, electronic pill bottles, and SMS). Currently, digital adherence technologies have been developed that offer large potential to measure and improve medication adherence in TB patients.(36) The technology potentially facilitates drug monitoring adherence that provides a more patient-centric approach than the existing DOT.(37) The digital technology for monitoring medication adherence of TB patients was reported in the form of video observed therapy (VOT) and electronic medication monitoring. (33) Considering the accuracy, direct measurement should be preferably used for measuring medication adherence in an interventional study to enhance the validity of the results.

There were some variations in the comparator group in the included studies, which may also have affected the validity of the findings. We noted that self-administration of treatment without supervision and DOT were the comparator groups in the included studies. Theoretically, the effect of the studied intervention will be higher in the studies that used self-administration without supervision as the comparator group instead of DOT. A previous study showed that DOT was more effective than self-administration treatment (SAT) in the improvement of treatment adherence(38) and DOT intervention was also recommended by WHO for improving treatment adherence in TB patients.(38) It is possible, therefore, that using different comparators to compare two or more intervention studies will lead to an under- or over-estimation.

Another aspect, which may explain the variations in the results of studied interventions, is the quality of the included studies. Among the randomized studies, in six studies the

144 Medication adherence interventions randomization method was unclear.(20–22,28,31,32) The investigators did not describe how the random allocation was conducted. In most of the included studies, blinding was impossible. Since the intervention involved direct activities with the research subjects such as reminders, counselling, education, and incentives, performing a blinding procedure was impossible. In addition, the quality of intervention is also essential. For instance in DOT studies, ability treatment observer to improve medication adherence of TB patients will affect the successful intervention. As previously described, the interaction between treatment observer and TB patients should also be considered to have an improvement in medication adherence.

Several limitations to our review should be acknowledged. First, the review was based on the two databases with restriction to English publications and searching period, hence not all the intervention studies may be covered in this study. However, to the best of our knowledge most studies are published in the English language and recent trials incorporated knowledge from the potential trials before 2003. Second, only a few studies used adherence rate as the study outcome. Most of the study mentioned treatment outcomes (i.e. sputum conversion, completed, cured, defaulted and poor treatment outcome), but did not include sufficient detail on adherence to correlate them. Therefore, the effectiveness of the interventions to improve medication adherence, as reported in the studies, should be carefully interpreted, 6 and clearly high-quality intervention studies should be developed in the future.

CONCLUSIONS

Our review highlighted various interventions that have the potential to improve medication adherence among LTBI and active TB patients. Characteristics of the research subjects, accurate measurement of the adherence, type of the comparator group, robustness of study design and intervention should be considered to develop an effective and unbiased intervention for medication adherence in TB patients. Since non-adherence factors can be individual, interventions that takes into account the individual factors are required to have an effective medication adherence program. Therefore, further intervention studies that consider patient’s problems and develop a personalized approach are required for future development of effective programs on improving medication adherence among TB patients.

Funding This work was supported by the Indonesia Endowment Fund for Education or LPDP in the form of a Ph.D. scholarship to ISP; this funding source had no role in the concept development, study design, data analysis or article preparation.

Conflict of interests All authors have no competing financial or non-financial interests in this work.

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APPENDIX.

Search strategies Medline/ PubMed database: ((Tuberculosis[tiab] or TB[tiab] OR tuberculosis infection[tiab] OR active tuberculosis[tiab] OR latent tuberculosis[tiab] OR pulmonary tuberculosis[tiab] OR extrapulmonary tuberculosis[tiab] OR anti-tuberculosis treatment*[tiab] OR anti-tb treatment*[tiab] OR “Tuberculosis”[Mesh]) AND (Adherence[tiab] OR compliance[tiab] OR nonadherence[tiab] OR non-adherence[tiab] OR concordance[tiab] OR medication adherence[tiab] OR patient adherence[tiab] OR patient compliance[tiab] OR “Medication Adherence”[Mesh]) AND (self- administration[tiab] OR self-administered[tiab] OR DOT*[tiab] OR directly observed*[tiab] OR directly observed therapy[tiab] OR directly observed treatment[tiab] OR incentive*[tiab] OR social support*[tiab] OR patient organization*[tiab] OR education[tiab] OR adherence education[tiab] OR dose frequency[tiab] OR memory aid*[tiab] OR reminder*[tiab] OR reinforcement*[tiab] OR reminder system*[tiab] OR motivation[tiab] OR motivational tool*[tiab] OR home visit*[tiab] OR patient education[tiab] OR counseling[tiab])).

Cochrane database: #1. Tuberculosis[Mesh] #2. ‘tuberculosis’ OR ‘tb’ OR ‘tuberculosis infection’ OR ‘active tuberculosis’ OR ‘latent tuberculosis’ OR ‘pulmonary tuberculosis’ OR ‘extra pulmonary tuberculosis’ OR ‘anti- tuberculosis treatment’ OR ‘anti-tb treatment*’ #3. #1 AND #2 #4. Medication Adherence [Mesh} #5. ‘adherence’ OR ‘compliance’ OR ‘non adherence OR ‘non-adherence’ OR ‘concordance’ OR ‘medication adherence’ OR ‘patient adherence’ OR ‘patient compliance’ #6. #4 AND #5 #7. ‘self-administration’ OR ‘self-administered’ OR ‘DOT*’ OR ‘directly observed*’ OR ‘directly observed therapy’ OR ‘directly observed treatment’ OR ‘incentive*’ OR ‘social support*’ OR ‘patient organization*’ OR ‘education’ OR ‘adherence education’ OR ‘dose frequency’ OR ‘memory aid*’ OR ‘reminder*’ OR ‘reinforcement*’ OR ‘reminder system*’ OR ‘motivation’ OR ‘motivational tool*’ OR ‘home visit*’ OR ‘patient education’ OR ‘counseling’ #8. #3 AND #6 AND #7

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149 Rome, 2020 CHAPTER BARRIERS AND STRATEGIES TO SUCCESSFUL TUBERCULOSIS TREATMENT IN A HIGH-BURDEN TUBERCULOSIS SETTING: A QUALITATIVE STUDY FROM THE 7 PATIENT’S PERSPECTIVE

Ivan S. Pradipta Lusiana R. Idrus Ari Probandari Bony W. Lestari Ajeng Diantini Jan-Willem C. Alffenaar Eelko Hak

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ABSTRACT

Introduction: Previously treated tuberculosis (TB) patients are a widely reported risk factor for multi-drug-resistant tuberculosis. Therefore, identifying patients’ problems during treatment is necessary to control TB, especially in a high-burden setting. We investigated and constructed barriers to successful TB treatment from the patients’ perspective, aiming to identify potential strategies to improve treatment outcome in Indonesia.

Methods: A phenomenological qualitative study was conducted in a province of Indonesia with high TB prevalence. Participants from various backgrounds (i.e. TB patients, physicians, nurses, pharmacists, people responsible for TB at the district health office, TB activist and TB programmers) were subject to in-depth interviews and focus group discussions. All interviews were transcribed verbatim from audio and visual recordings and the respective transcriptions were used for data analysis. Barriers were constructed by interpreting the codes’ pattern and co-occurrence. The information’s trustworthiness and credibility were established using information saturation and participant validation and triangulation approaches. Data were analysed using the Atlas.ti 8.4 software and reported following COREQ 32-items.

Results: We interviewed 62 of the 66 pre-defined participants. We identified 15 barriers and classified them into three themes, i.e. socio-demography and economy; knowledge and perception and TB treatment. We identified five main barriers across all barrier themes, i.e. lack of TB knowledge, stigmatisation, long distance to the health facility, adverse drug reaction and loss of household income.

Conclusion: Effective treatment outcome improvement requires target interventions that can be focused on the five main barriers. A multi-component intervention including TB patients, healthcare providers, broad community and policy makers is required to improve TB treatment success in Indonesia.

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INTRODUCTION

According to the World Health Organisation (WHO), tuberculosis (TB) is one of the top 10 causes of death and the leading cause of single infection.(1) In 2017, the WHO estimated that 10 million people developed TB and that 23% of the world population is at risk of developing active TB.(1) This problem has become more complex with the worldwide spread of drug-resistant TB (DR-TB) pathogens (i.e. TB pathogens resistant to one or more anti-tuberculosis drugs). Previous studies have shown that migration from high- to low-TB prevalence countries contributed for TB’s worldwide development.(2,3) Therefore, identification of TB problems and intervention strategies in high-prevalence countries are essential to control TB at global level.

With a total population of 264 million people, Indonesia is globally ranked third regarding TB burden and one of the top-10 countries with the highest prevalence of multidrug-resistant tuberculosis (MDR-TB).(1) About 842,000 people in Indonesia contracted TB with 16,000 people dying from the disease and an estimated 23,000 becoming DR-TB patients.(1) DR-TB became an important issue in Indonesia, since its associated financial burden can reach USD 2,342 per MDR-TB patient.(4) Generally, Indonesia’s reported economic TB burden is extremely high,(5) with an estimated out-of-pocket health expenditure of about 48%.(1)

TB’s management is an essential disease control factor. A global meta-analysis reported 7 that previously treated TB patients were more prone to develop MDR-TB.(6) This finding is especially relevant for high-burden countries such as Indonesia, where problems affecting successful TB treatment should be a cause of concern.

Qualitative study is a powerful method to identify issues that influence treatment, as they allow the detection of problems that cannot be easily measured by pre-determined information from previous studies.(7) A considerable number of qualitative studies looking into the barriers to successful TB treatment in Indonesia have been published.(8–13) However, to date, no previous studies have either constructed a barrier or identified its core, aiming to develop potential effective strategies to solve the underlying problems. Since the barriers can change over time, qualitative studies must be periodically repeated to provide updated information to TB stakeholders, including policy makers, healthcare providers, TB patients and the wide community. We therefore explored and constructed the barriers to successful TB treatment from the patients’ perspective, aiming to identify current potential strategies to improve treatment outcome in Indonesia.

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METHODS

Context and setting In Indonesia, tuberculosis (TB) care is managed both by the public and private health sectors, accounting from primary to tertiary healthcare facilities. The Community Health Centre (CHC), designated as ‘Puskesmas’, is a backbone TB care facility established as the primary public health sector at the sub-district level. Managed by the local government, CHCs have the responsibility to identify, notify and monitor TB patients within their specific area. TB care is also supported by referral hospitals, although not all referral hospitals have the facilities to support MDR-TB care. MDR-TB is therefore managed in several centralised hospitals in Indonesia. The prevalence and remoteness of TB have been taken into account for the selection of study sites, as they represent the complexity of TB problems and facilities in Indonesia.

This study was performed as part of a qualitative study to engage pharmacists in TB management. West Java, an Indonesian province with 48,684,000 inhabitants in 2018,(14) was selected as the study location. In 2018 West Java had the third highest TB prevalence in Indonesia.(15) Two districts were selected that represent rural and urban area in West Java province. A district near with the capital of Indonesia was selected to represent the urban area, while the rural area was represented by a district which is located about 300 kilometres from the capital of Indonesia.

Study design A qualitative study, with an ontological assumption, was performed from the patient’s perspective, to describe the real nature of the barrier, aiming at the successful TB treatment. According to this assumption, reality was seen through many perspectives. (7) A phenomenological approach was determined, to interpret the individuals’ common meanings of their life experiences. Within this framework, researchers attempted to understand the essence of the patients’ experience in terms of barrier to TB treatment, based on the perspective of participants from various backgrounds.(7)

Purposive sampling was used to select participants with different background, age, gender, remoteness and TB experience. Sixty-six subjects were pre-defined as participants, including TB patients, physicians, nurses, pharmacists, the people responsible for TB from the district health office, TB activist and TB programmers at the CHC level. Since this study investigated the barriers to TB treatment, patients potentially experiencing problems during TB treatment (e.g. defaulted/failed treatment, no smear sputum conversion from the first TB regimen) were selected. Thus, the inclusion criteria were TB patients being treated with a category II or MDR-TB regimen, for at least two months. On the other hand, subjects with a minimum of six months of TB experience were included as non-TB participants. Researchers

154 Barriers and strategies for TB treatment and participants had no prior relationship. Participants were fully aware that the study aimed at improving TB healthcare services in Indonesia.

Interviews were conducted by two researchers: ISP (male) and LRI (female), both with a clinical pharmacy background and interested in tuberculosis research. ISP has received both quantitative and qualitative research training and conducted public health studies involving in-depth interviews, focus group discussions and observational studies.

All participants provided informed consent at the beginning of the interview process. The informed consent form was sent to the participants at least one week in advance, allowing sufficient time to decide whether to join the study. All interviews were performed as face-to-face, in-depth interviews (IDIs) and focus group discussions (FGDs). TB patients underwent face-to-face, IDIs for convenience, due to TB stigma. Similarly, key persons from the local government and non-governmental organisations were also interviewed face- to-face due to time availability. On the other hand, FGDs were employed for healthcare providers (i.e. physicians and pharmacists) and TB programmer/ nurses at the CHC level. Each interview started with general questions using the Indonesian language (Bahasa), then the interviewer explored and expanded the information based on pre-established research questions. For TB patients general questions included ‘Can you tell me about your experience as a TB patient? (What, where, why and how)’ and ‘What are the main TB problems that you have faced before and after your diagnosis?’. In contrast, for non-TB 7 participants, general questions included ‘What are your activities in TB management’; ‘what are the main TB problems from the patient’s and healthcare providers’ perspectives?’ The interview followed several steps according to the interview guide shown in Appendix 1.

Information’s trustworthiness and credibility Information saturation was defined as no emergence of new information relevant to the study’s objective and was used to determine the final number of participants during the interview process. Participants were re-interviewed whenever further clarifications were needed. IDIs were audio-recorded, whereas FGDs were audio-visually-recorded to recognise participant statements within the data analysis. At the end of each interview, interviewers discussed the findings and made notes on essential information requiring further exploration. Information cross-validation among participants was employed to enhance information trustworthiness. Other sources, such as documents, regulations and standard operation procedures, were also explored to increase information credibility. To avoid misinterpretation of the interview, the verbatim was sent to the participants for content and meaning approval. Interviewer-related bias was addressed by continuously discussing and negotiating the content of keywords, broader concepts and units of meanings among the research team.

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Data analysis All interviews in the original language (Bahasa) were transcribed verbatim from audio and visual recordings. All transcripts left out any participant’s identification and were transferred to the Atlas ti version 8.4 for data analysis. The formal analysis process involved four main stages: familiarisation, thematic framework identification, codification and interpretation. Familiarisation aimed to identify a general thematic framework, by creating a segmentation of the transcript’s meaning unit. The meaning unit was identified by the transcript’s sentences that related to the study objective. Once the general thematic framework was created, coding was performed. ISP inductively coded the transcript’s information and the codes were discussed with the second coder (LRI). Identified codes were classified into themes/sub-themes according to the created general thematic. Field notes were also reviewed during the coding and themes/sub-themes development process. Data interpretation was performed by analysing the code’s pattern among the participants. Potential relationships across the codes were investigated through co-occurring codes, which overlapped in a meaning unit. Emerged codes, themes and sub-themes were translated into English and reviewed by other researchers (AP, BWL, AD, JWA, EH). Disagreements were resolved among research team members, through continuous content discussions and negotiations based on the transcripts and field notes. The consolidated criteria for reporting qualitative studies (COREQ)-32 items(16) guideline was followed for reporting this study. An example of the coding process is presented in Appendix 2.

Ethics approval and research permission This study was approved by the ethics committee of Universitas Padjadjaran (No. 333/ UN.6/ Kep/ EC/2019) and by the local governments (No. 070/005/KBL and No. 070.1/134/ DPMPTSP.Set).

Results We obtained consent from 62 of the 66 pre-defined participants. Among the pre-defined participants, four (2 general practices, 1 TB nurse and 1 TB patient) did not participate in the interview process. Lack of back fill for daily clinical work has been the reason of three healthcare staffs in the absence of interview, while 1 TB patient could not be contacted during the research period. We performed a data follow-up for an additional participant (the wife of a TB participant), following her provision of informed consent for further clarification and information exploration by phone. Finally, the study included data from 63 participants and information saturation was achieved from the interviews. An in-depth interview was conducted for 19 participants, while FGDs were conducted for nine groups of 44 participants. The participants’ characteristics are presented in Table 1.

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Table 1. Characteristics of the participants (N=63)

No Characteristics Number 1 Background of the participants (n, %) TB patients Non-MDR-TB patient 5 (7.9) MDR-TB patient 4 (6.3) Health care workers at the community setting Physician of the CHC 8 (12.7) Nurse / TB programmer of the CHC level 10 (15.9) Pharmacist of the CHC 10 (15.9) Community pharmacist 13 (20.6) Health care workers at the hospital setting TB nurse 1 (1.6) Pharmacist 1 (1.6) Pulmonologist 1 (1.6) Internist 1 (1.6) Others Government sector 5 (8.0) Tuberculosis activist 1 (1.6) Patient’s family 1 (1.6) Profesional organization at the district level 2 (3.2) 2 Male gender (n, %) 16 (25.4) 3 Age, in year (mean; min-max) 40.38; 16-66 7 4 Experience in TB, in month (mean; min-max) 89.46; 6-348 5 Type of interview (n, %) In-depth Interview 19 (30.2) Focus Group Discussion 44 (69.8) 6 Duration of the interview, in minute (mean; min-max) 80.80; 4 - 124 7 Area (n, %) Rural 29 (46) Urban 34 (54) 8 Interview’s location (n, %) Health district office 26 (41.3) Community health service 21 (33.3) Professional organization’s office 9 (14.3) Hospital 5 (7.9) Home 1 (1.6) Non-Governmental Organization’ office 1 (1.6)

A total of 184 meaning units were gathered in the analysis. The meaning units were converted into codes and classified into themes and sub-themes. To enhance the findings’ credibility, all emerged codes were confirmed by at least three participant sources, except for the treatment duration code, which was gathered from only two participants—the

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TB programmer at the CHC level and the hospital’s nurse. However, upon reviewing the national guideline(17) we found that the minimum treatment duration for active TB patients is relatively long (6 months). Therefore, the information gathered from the two participants during the interview was supported by the national guideline. Duration of TB treatment was determined as a patient’s barrier to treatment success. The code pattern can be seen in Appendix 3.

We identified three themes: 1) socio-demographic and economic; 2) knowledge and perception; and 3) TB treatment. The socio-demographic and economic theme was constructed from the socio-demography and economic aspects. We constructed the knowledge and perception theme from TB knowledge and perception aspects. Lastly, TB treatment theme was constructed from codes related to TB treatment, such as adverse drug reaction and treatment duration. The classification of themes, sub-themes and codes is described in Figure 1.

Figure 1. Barriers to successful TB treatment from the patient perspective

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Social aspects—stigmatisation and lack of family support Stigmatisation of TB patients was identified from the community, in that the patients felt that the community disapproved of them and feared close contact with them.

‘If I meet people, they look afraid of me.’—Male TB patient, 33 years old.

Surprisingly, perceived stigmatisation arose not only from the general community, but also from close family and healthcare providers.

‘There was a man who rejected his wife due to TB, which may have been incited by his family. The wife was expelled by her mother-in-law, and she was finally divorced.’—TB activist, 32 years old.

‘The stigma exists even in the CHC from healthcare providers. They do not want to inject the medicine. It causes inconvenience and disgrace to the patients.’—General Practitioner’ (GP) CHC, 41 years old.

Participants receiving TB treatment during the study period reported lack of spouse support. For example, one patient was left alone by his wife due to his poor condition. He was asked to move back to his hometown and live separately with his children. 7 ‘My wife assumed that I could not be cured because of the disease severity. That is why she left me.’—MDR-TB patient, 29 years old.

Lack of spouse support was confirmed by the CHC TB programmer upon observing a defaulted patient. This barrier was identified from a husband who prevented his wife from having further TB treatment.

‘During my field observations, a husband said that his wife was already cured and she did not have to come to CHC anymore.’—Nurse/ CHC TB programmer, 40 years old.

Demographic aspects—long distance and difficulties in reaching public transportation Accessibility to the public health facility was the main patient’s barrier in the demographical aspects. Long distance to the public health service added another burden to TB patients while they were receiving a regular injection of category II or MDR-TB regimen.

‘The distance from my home to this Puskesmas (community health service) is long. I cannot afford the cost of a taxi bike every day.’—TB patient, male 33 years old

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MDR-TB centre unavailability at the district level makes it difficult to access a public health facility. In the study, an MDR-TB patient experienced difficulties to receive diagnosis and treatment for lack of a proper facility. It would take him 3–4 hours to reach the centre using public transportation and he had to spend his own money.

‘Because we want to get fast action, I went to the MDR-TB centre using public transportation. I stayed there for two days because of the distance.’—MDR-TB patient, 33 years old.

Economic aspects—cost of the private/public health service, transportation cost and loss of household income Although the government has claimed free TB service, TB patients who visit private health sectors have to pay for TB care, including diagnosis and treatment. Due to lack of information about free TB programs in the public health sector, some patients with poor living condition are required to spend their own money to purchase medications in the private health sector.

‘Some medicines must be taken regularly. I bought the medicine in a pharmacy every month.’—MDR-TB patient, 33 years old

Since patients within the category II regimen must receive injectable medicines, regular visits to the health facility are required. ­Unfortunately, a transportation subsidy was only provided to MDR-TB patients, which means no transportation subsidy for patients with category II regimen.

‘No transportation subsidies are available for the patient in the category II regimen. They should come to CHC every day. The problem is when we have patients with low economic level.’—CHC’s GP, 50 years old.

Our study revealed that TB patients who visit a public health service spent an additional cost. Participants expressed their experience in spending their own money in a public health service. One of them told us that an additional cost was needed for injectable medicines.

‘Although in this Community Health Centre the medical examination and medicine were free, I spent my own money for the injectable medicine using healthcare staff from another CHC.’—TB patient, 54 years old.

‘The problem is when the patients do not have a health insurance. Sometimes they have to be referred to a hospital for a paid chest X-Ray.’—CHC’s GP, 39 years old.

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Furthermore, we identified a barrier related to TB patients’ household income. Patients must do their work activities to continue their life. Some TB patients stopped treatment because they had to work to fulfil their household income.

‘MDR-TB patients, especially men, face extraordinary challenges. As head of the family, they should be the backbone of family income. Although we educated them, most patients had a defaulted treatment’—TB nurse, 31 years old.

Knowledge aspects—knowledge about the TB program, disease and treatment Insufficient knowledge on the implementation of the free TB program was explicitly identified in TB patients who reached TB care through private health services. The problem arose when TB patients started the treatment in the private health service. They could not afford the medical expenses until the end of the treatment, which led to its discontinuation.

‘Initially, treatment lasted for six months and was mandatory. However, I stopped the medicine because I did not know that the healthcare service was free of charge. I stopped the treatment because of the cost.’—TB patient, 33 years old.

Adding to these problems, lack of knowledge on TB programmes was worsened by the lack of coordination between the private and public health service facilities. Some private health sectors have low motivation to refer TB patients to a public health facility. We 7 identified resistance from health providers to transfer a TB patient to the CHC. Different ingredients and different medicine forms were indicated by participants as reasons to treat TB patients in the private service. This reason seems unacceptable, since the TB regimen was standardised by the national guideline and provided in the public health facilities during study observation.

‘In the middle of the treatment period, I asked the private doctor to provide me a referral letter to a CHC. The doctor answered that my husband could not move to a CHC, since he was being administered a specially compounded medicine made by the doctor himself.’— Wife of TB patient, 45 years old.

An MDR-TB patient who delayed treatment, indicated lack of knowledge and awareness about TB as the cause for delayed treatment.

‘I ignored the signs for six months until I read about TB. After that, I decided to get a medical examination.’—MDR-TB patient, 33 years old

The study also revealed that participants tried to self-medicate with herbal medicine and to purchase anti-TB drugs at the private pharmacies without proper medical examination.

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’Because I felt my symptom was lessen using the previously prescribed medicines, I decided to keep the packaging of the medicines. Whenever I experience the same symptoms, I bought package and buy the medicines directly.’—TB patient, 33 years old.

‘A patient said that ‘I was treated in my village’, but TB treatment only lasted for one week because the patient experienced itchiness. The patient then continued treatment using herbal medicines’—CHC’s GP, 39 years old.

Lack of knowledge regarding adverse reactions and treatment duration were identified by participants. Our study successfully detected a patient who was advised to stop the medicine due to treatment side effects.

‘My family advised me to stop the medicine, due to adverse drug reactions. They said that the medicine worsened my condition.’—MDR-TB patient, 29 years old

Perception—perception of public health services and self-condition There was a negative image regarding the quality of TB medicine in CHC. Some patients believed that qualified medicine should be expensive and not free.

‘There are rich people who do not want to go to CHC because they assume that free of charge medicines have poor quality.’—TB coordinator, 43 years old.

Our observations demonstrated that the CHCs have provided a special line for suspected or diagnosed TB patients to shorten the waiting time and control disease contamination. As a result, the patients can be directly examined by a physician. However, there were some negative comments about the public health service. One interviewee argued that he would not go to the public health service due to his bad experience about waiting time for getting the medical examination.

‘ I asked my husband to visit the nearest CHC, but he said no, because of the long queue of patients.’—wife of TB patient, 45 years old.

Another reported problem regarded physician preference. Some patients prefer to be examined and treated by a famous physician in their area than by a physician in the public health service.

‘They may have a suggestive (placebo) effect when they go to a famous physician or private health facility instead of CHC, so they do not choose CHC.’—TB programmer/ Nurse at CHC, 31 years old.

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Psychological problems emerged in TB patients due to a negative perception of their condition. They felt disgrace, hopelessness and rejection due to their health condition. Another identified story regarded the rejection of a wealthy patient for assuming that TB only affects people with poor living condition.

‘Because of this disease, I fell in disgrace with my neighbours.’—TB patient, 41 years old.

‘I have felt in that position, and I felt tired. It was better to die.’—TB activist, 32 years old.

‘If rich people were getting TB, they seemed to have a rejection.’—Hospital pulmonologist, 45 years old.

TB treatment—treatment duration and adverse drug reactions As widely known, active TB patients should follow treatment for at least six months. Our study identified boredom and low treatment adherence. Furthermore, adverse drug reactions were highly reported from the participants, potentially leading to unsuccessful treatment.

‘The fact that the patient got bored of taking medicines for a long time was a common problem.’—TB programmer/ nurse at CHC, 31 years old. 7 ‘I felt a headache, dizziness, flying and I hallucinated buying a car. It was like a crazy person.’—MDR-TB patient, 16 years old.

Potential relationship We identified several co-occurred codes in a meaning unit, indicating a potential relationship across the codes. We, therefore, constructed the barriers considering the co-occurred data. Data co-occurrence and constructed barriers are presented in Table 2 and Figure 2, respectively.

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Table 2. Co-occurrence of data and potential relationships

No Data co- Meaning unit Interpretation Potential occurrence relationship 1 Lack of ‘I ignored the signs for six months Lack of knowledge Association knowledge until I read about TB. After about TB disease is about TB disease that, I decided to get a medical a cause of delayed AND delayed examination.’—MDR-TB patient, treatment treatment 33 years old 2 Lack of ’Because I felt my symptom Lack of knowledge Association knowledge was lessen using the previously about TB treatment about TB prescribed medicines, I decided is a cause of treatment AND to keep the packaging of the inappropriate inappropriate medicines. Whenever I experience medicine use and medicine use the same symptoms, I bought non-adherence AND non- package and buy the medicines adherence directly.’—TB patient, 33 years old. 3 Lack of ‘Initially, treatment lasted for Lak of knowledge is Association knowledge about six months and was mandatory. a cause of financial TB program However, I stopped the medicine problems AND Financial because I did not know that the problems healthcare service was free of charge. I stopped the treatment because of the cost.’—TB patient, 33 years old. 4 Lack of ‘There are rich people who do Lack of knowledge Association knowledge about not want to go to CHC because about TB programs TB program they assume that free of charge is a cause of negative AND Negative medicines have poor quality.’—TB perception about perception of coordinator, 43 years old. the public health public health service and financial service AND ‘ I asked my husband to visit the problems financial problem nearest CHC, but he said no, because of the long queue of patients.’—wife of TB patient, 45 years old. 5 Lack of ‘I have felt in that position, and I Lack of knowledge Association knowledge felt tired. It was better to die.’—TB about TB disease about TB disease activist, 32 years old. and treatment and treatment is a cause of the AND negative negative perception perception of self- of self-condition condition and psychological problems 6 Distance to ‘The distance from my home to Long distance to a Association health facility this Puskesmas (community health public health facility AND financial service) is long. I cannot afford the is cause of financial problems AND cost of a taxi bike every day.’—TB problems and inaccessible patient 33 years old. inaccessibility to a qualified TB care qualified TB care

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Table 2 (Continued). Co-occurrence of data and potential relationships No Data co- Meaning unit Interpretation Potential occurrence relationship 7 Stigmatisation ‘My wife assumed that I could not Stigmatisation is a Association AND isolation/ be cured because of the disease cause of isolation/ discrimination severity. That is why she left me.’— discrimination and AND lack of MDR-TB patient, 29 years old. low family support family support ‘There was a man who rejected his wife due to TB, which may have been incited by his family. The wife was expelled by her mother-in-law, and she was finally divorced.’—TB activist, 32 years old. 8 Negative ‘Sometimes, I feel inferior and shy A negative Association perception of for going to CHC because of my perception of the self- self-condition disease.’—TB patient, 33 years old, condition is cause of AND inaccessible male. inaccessible qualified qualified TB care TB care 9 Household ‘MDR-TB patients, especially men, Low household Association income AND face extraordinary challenges. As income due to Financial head of the family, they should be TB is a cause of problems AND the backbone of family income. financial problems Unsuccessful TB Although we educated them, and unsuccessful treatment most patients had a defaulted treatment treatment’—TB nurse, 31 years old. 7 10 Adverse drug ‘I felt a headache, dizziness, flying ADR is a cause Association reaction AND and I hallucinated buying a car. It of psychological psychological was like a crazy person.’—MDR-TB problems problems patient, 16 years old. 11 Adverse drug ‘A patient said that ‘I was treated in ADR is a cause of Association reaction AND my village’, but TB treatment only non-adherence/ non-adherence lasted for one week because the persistence and non- patient experienced itchiness. The persistence patient then continued treatment using herbal medicines’—CHC’s GP, 39 years old.

165 Chapter 7 . The constructed barriers successful to tuberculosis treatment from the patient perspectives Figure 2

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DISCUSSION

In this study we highlighted the barriers to successful TB treatment from the patient’s perspective and found that they are related to three themes as follows: 1) socio- demography and economy; 2) knowledge and perception and 3) TB treatment. The socio- demography and economy theme comprises several barriers, such as stigmatisation, lack of family support, long distance to public health service, transportation difficulties, cost of the private and public health service, cost of transportation and loss of household income. The knowledge and perception theme includes lack knowledge about TB (i.e. TB program, diseases and treatment), negative perception of public health service and self-condition. As mentioned above, the TB treatment theme concerns the barriers of adverse drug reaction and long duration of treatment. Since the barriers can be interrelated, we identified five main barriers: lack of TB knowledge, stigmatisation, long distance to the health facility, adverse drug reaction and loss of household income.

Our results are consistent with a previous study that indicates that stigmatisation is one of the barriers experienced by TB patients in Indonesia. Watkins and Plant (2004) stated that people with TB still carry a social stigma from the community in Bali.(8) Unfortunately, the previous study did not provide details regarding the source of the stigma, which can be an essential factor for intervention strategy development. We identified that the stigma originates not only from the community, but also from the close family and healthcare 7 providers.

The stigma originating from close family generates discrimination and isolation in TB patients. In the present study, several patients reported to have been left alone by their close family, without any support in facing the disease. The issue gets more complex when stigmatisation is also identified in the community and workplace, as this can influence the patients’ ability to access qualified TB care and to generate the daily income required for survival. In addition, in Indonesia TB patients do not have social security. Although the government has announced a free TB care programme, our study demonstrated that some costs were still covered by the patients themselves. Some participants mentioned that some costs such as transportation, private services and additional services in public health facilities, had to be covered by themselves.

Interestingly, our study identified a TB patient who went to a different health facility to receive the injectable medicine, which was paid with his own money. Two reasons for this issue were identified: 1) Limited service time for TB patients in CHC, which happens only twice a week, on ‘TB days’; and 2) Existence of health condition-related stigma in health facilities.

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In the present study, the occurrence of health condition-related stigma in health facilities was identified by the fear of TB felt by the healthcare staff. The staff realised that this can affect the patients’ psychological condition, leading to sub-standard of care. Previous studies showed that health condition-related stigma is a barrier to health-seeking behaviour(18), engagement in care(19) and adherence to treatment(20). Consistently, our study also identified this as a main barrier. Several aspects such as lack of family support, isolation, discrimination, psychological problems and inaccessibility to a qualified TB care were observed. All contributed for unsuccessful TB treatment.

Previous studies have clarified the association between knowledge, perception and health- related behaviour.(21) We found that lack of knowledge about the national TB programme and public health facilities potentially cause a negative perception of the public health services, contributing for inaccessibility to qualified TB care, especially for people with low income. We also found that TB patients did not want to go to the public health services due to their perception about the quality of services and medicines. Another finding showed that some patients were not aware that the public health service had a free TB programme. This may lead patients with poor living condition to defaulted treatment due to cost burden, since, in the private health service, patients must spend their own money to receive TB care. It can therefore be suggested that lack of knowledge on TB and its treatment may lead to non-adherence and non-persistence, delayed treatment and a negative perception of self-condition.

Regarding physiological problems, TB patients could be also affected by adverse drug reactions and negative perception of self-condition. For example isoniazid, ethambutol, fluoroquinolones and can induce psychiatric disorders in tuberculosis patients. (22) Moreover, the level of education can also impact the patients’ perception of their own health status.(23) It can be argued that self-condition perception can be driven by the patient’s knowledge of the disease. Therefore, the patient’s knowledge has an important role in the control of physiological problems in TB patients.

Study limitations Several limitations should be acknowledged in the present study. First, the association among barriers was analysed by the emergence of the co-occurred code in a meaning unit. Although, in qualitative studies this is a generally used approach, in our specific context, the association significance should be further investigated by a quantitative study to obtain a robust estimate. Secondly, the present study can only be generalised for areas with similar healthcare system and social, economic, cultural and political context. However, several approaches were used in this study, such as maximising the characteristics of the participants and study location (rural vs urban); using a triangulation method and respondent validation; analysing the code pattern, data co-occurrence and information

168 Barriers and strategies for TB treatment saturation; employing continuous discussions about the obtained data among the research team. We believe that such approaches contribute for the validity and reliability of our study.

Study implications Strategies to improve treatment success among TB patients should focus on the five main barriers: lack of knowledge about TB, stigmatisation, long distance to public health service, occurrence of adverse drug effects and loss of household income.

Improving knowledge on TB disease, programmes and treatment should be achieved both for TB patients and the wider community. Information regarding the free TB service and how people can access TB care in the public service, should be continuously provided to the community. People should be ensured that in public services TB care fulfils adequate standards, so as to minimise their negative perception. Educational programs should be strengthened at the CHC level, aiming to improve TB-related knowledge in TB patients and the community.

Since stigmatisation is part of the issue with TB treatment, educating patients and the community is of crucial importance. TB patients should be counselled on how to deal with potential stigmatisation and psychological problems. Similarly, non-TB patients should be educated on how to adequately act and support TB patients to support treatment success. 7 The existence of a qualified TB counsellor supported by guidance in CHC level may help to address stigmatisation. As mentioned earlier, stigmatisation awareness should also be improved among healthcare providers. Lack of knowledge from healthcare providers may generate a stigma to TB patients.(24) Importantly, healthcare providers should be ensured about their capability and facility to manage TB patients, since the occurrence of stigma can be generated by fear of the disease, lack of awareness, inability to manage the patient and institutional procedures or practices(24–27). TB work teams should therefore implement regular training, standard operating procedures, workload estimations and sufficient healthcare staff. Furthermore, provision of adequate facilities, such as personal protective equipment, standardised TB room, ambulances that ensure patient mobility and regular medical TB check-up for healthcare staff, are necessarily needed to avoid the stigma in health facilities. It was supported by an Indonesian study that suboptimal infrastructures and healthcare staff’s knowledge and motivation as factors of stigmatisation in health facilities.(28) Multi-component interventions including TB patients, healthcare providers, community leaders, the wide community and policy makers, are required to reduce the stigma.(29)

As previously described, long distance to public health facilities is associated with the patient’s treatment cost burden. This burden increases for MDR-TB patients requiring

169 Chapter 7 regular visits to MDR-TB centres. A commitment from the central and local government to fully decentralise MDR-TB care at the district level may solve this issue. A previous meta- analysis reported that a decentralised approach was associated with higher treatment success among MDR-TB patients.(30) This problem may be further reduced by a new fully oral treatment for MDR-TB, by reducing long visit duration, patient inconvenience and unavailability of proper transportation to the referral hospital.

Regarding drug’s adverse effects, access to a drug consultant who supports and educates TB patients may help to minimise this issue. A reasonable approach to tackle this issue could be to involve a pharmacist for direct patient service in TB management, especially at the CHC level. As a result from the limited national guidance,(17) institutions should prepare the pharmacists to be involved in direct patient service. Some arrangements may be required in terms of pharmacists’ involvement, such as availability, TB knowledge and specific service guidelines. Pharmacists may act as treatment supporters who educate, monitor and evaluate medicine use, based on the principles of pharmaceutical care. In fact, it was previously reported that pharmacists’ direct involvement in TB patients’ management improved treatment success.(31,32)

Lastly, social protection schemes, reaching beyond direct medical costs, such as loss of household income, should be a concern for the government. This might tackle issues related to defaulted treatment due to the patient’s decreasing household income. Special attention and priority should be given to patients with MDR-TB and to the lowest income groups, since both groups have the highest potency for economic vulnerability as a result of the disease.(33)

CONCLUSION

This study has identified several barriers to successful TB treatment, from the patient’s perspective, in Indonesia. The barriers were classified into three themes: 1) socio- demography and economy; 2) knowledge and perception; and 3) TB treatment. Our findings indicate that there are five main barriers across those themes, i.e. lack of TB knowledge, stigmatisation, long distance to the health facility, adverse drug reaction and loss of household income. To effectively improve treatment outcome, target interventions should be focused on the five main barriers. Multi-component interventions, including TB patients, healthcare providers and the community and policy makers are required as a strategy to improve TB treatment outcome in Indonesia. Further studies are needed to develop effective strategies involving the five main barriers, to enhance patient-centred care in TB disease.

170 Barriers and strategies for TB treatment

Funding This work was supported by the Indonesia Endowment Fund for Education or LPDP in the form of a Ph.D. scholarship to ISP. This funding source had no role in the concept development, study design, data analysis, or article preparation.

Acknowledgments We thank Dr. Hans Wouters who gave constructive advice in this study. We also thank Mira Miratuljannah, Puti Primadini, Chevy Luviana, and Devi who supported this study during the field work.

Competing interest The authors declare that they have no competing interests.

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REFERENCES

1. World Health Organization. Global tuberculosis 13. Watkins RE, Rouse CR, Plant AJ. Tuberculosis Report WHO 2018. Vol. 69, WHO report. 2018. treatment delivery in Bali: A qualitative study 2. Pradipta IS, van’t Boveneind-Vrubleuskaya of clinic staff perceptions. Int J Tuberc Lung Dis. N, Akkerman OW, Alffenaar JWC, Hak E. 2004;8(2):218–25. Predictors for treatment outcomes among 14. BPS. Badan Pusat Statistik Provinsi Jawa Barat patients with drug-susceptible tuberculosis in 2018 [Internet]. 2019 [cited 2019 Aug 22]. the Netherlands: a retrospective cohort study. Available from: https://jabar.bps.go.id/quickMap. Clin Microbiol Infect. 2019;25(6):761.e1-761.e7. html 3. Pradipta IS, Van’T Boveneind-Vrubleuskaya N, 15. Kemenkes RI. Riset Kesehatan Dasar Tahun Akkerman OW, Alffenaar JWC, Hak E. Treatment 2018. Kementrian Kesehatan Republik outcomes of drug-resistant tuberculosis in the Indonesia. Jakata; 2018. Netherlands, 2005-2015. Antimicrob Resist 16. Tong A, Sainsbury P, Craig J. Consolidated criteria Infect Control. 2019;8(1):1–12. for reporting qualitative research (COREQ): 4. van den Hof S, Collins D, Hafidz F, Beyene A 32-item checklist for interviews and focus D, Tursynbayeva A, Tiemersma E. The groups. Int J Qual Heal Care. 2007;19(6):349–57. socioeconomic impact of multidrug resistant 17. Ministry of health Republic of Indonesia. tuberculosis on patients: Results from Ethiopia, Regulation of health minister RI no. 67 about Indonesia and Kazakhstan. BMC Infect Dis. management of tuberculosis disease. Ministry 2016;16(1):1–14. of Health, Republic of Indonesia. 2016. 5. Collins D, Hafidz F, Mustikawati D. The economic 18. Scott N, Crane M, Lafontaine M, Seale H, Currow burden of tuberculosis in Indonesia. Int J Tuberc D. Stigma as a barrier to diagnosis of lung cancer: Lung Dis. 2017;21(9):1041–8. patient and general practitioner perspectives. 6. Pradipta IS, Forsman LD, Bruchfeld J, Hak E, Prim Health Care Res Dev. 2015;16(6):618–22. Alffenaar JW. Risk factors of multidrug-resistant 19. Corrigan P. How stigma interferes with mental tuberculosis: A global systematic review and health care. Am Psychol. 2004;59(7):614–25. meta-analysis. J Infect. 2018;77(6):469–78. 20. Dodor EA, Kelly S, Neal K. Health professionals 7. Creswell JW. Qualitative Inquiry & Research as stigmatisers of tuberculosis: Insights from Design. Sage Publications, Inc. 2007. community members and patients with TB in 8. Watkins RE, Plant AJ. Pathways to treatment for an urban district in Ghana. Psychol Heal Med. tuberculosis in Bali: Patient perspectives. Qual 20 09;14(3):301–10. Health Res. 2004;14(5):691–703. 21. Ferrer RA, Klein WMP. Risk perceptions and 9. Rintiswati N, Mahendradhata Y, Suharna, health behavior. Curr Opin Psychol. 2015;5:85–9. Susilawati, Purwanta, Subronto Y, et al. Journeys 22. Yang TW, Park HO, Jang HN, Yang JH, Kim SH, to tuberculosis treatment: A qualitative study of Moon SH, et al. Side effects associated with the patients, families and communities in Jogjakarta, treatment of multidrug-resistant tuberculosis at Indonesia. BMC Public Health. 2009;9(158):1– a tuberculosis referral hospital in South Korea. 10. Medicine (Baltimore). 2017;96(28):e7482. 10. Rutherford ME, Ruslami R, Maharani W, Yulita 23. Kaleta D, Polaǹska K, Dziankowska-Zaborszczyk I, Lovell S, Van Crevel R, et al. Adherence to E, Hanke W, Drygas W. Factors influencing self- isoniazid preventive therapy in Indonesian perception of health status. Cent Eur J Public children: A quantitative and qualitative Health. 2009;17(3):122–7. investigation. BMC Res Notes. 2012;5(7):1–7. 24. Nyblade L, Stockton MA, Giger K, Bond V, 11. Martins N, Grace J, Kelly PM. An ethnographic Ekstrand ML, Lean RM, et al. Stigma in health study of barriers to and enabling factors for facilities: Why it matters and how we can change tuberculosis treatment adherence in Timor it. BMC Med. 2019;17(1):25. Leste. Int J Tuberc Lung Dis. 2008;12(5):532–7. 25. Van Brakel WH. Measuring health-related 12. Dewi C, Barclay L, Passey M, Wilson S. Improving stigma--a literature review. Psychol Health Med. knowledge and behaviours related to the cause, 2006;11(3):307–34. transmission and prevention of Tuberculosis and early case detection: A descriptive study of 26. Chang SH, Cataldo JK. A systematic review of community led Tuberculosis program in Flores, global cultural variations in knowledge, attitudes Indonesia. BMC Public Health. 2016;16(740):1– and health responses to tuberculosis stigma. Int 12. J Tuberc Lung Dis. 2014;18(2):168–73.

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27. Daftary A. HIV and tuberculosis: The 30. Ho J, Byrne AL, Linh NN, Jaramillo E, Fox GJ. construction and management of double stigma. Decentralized care for multidrug-resistant Soc Sci Med. 2012;74(10):1512–9. tuberculosis: A systematic review and 28. Probandari A, Sanjoto H, Mahanani MR, meta-analysis. Bull World Health Organ. Azizatunnisa L, Widayati S. Being safe, feeling 2017;95(8):584–93. safe, and stigmatizing attitude among primary 31. Clark PM, Karagoz T, Apikoglu-Rabus S, Izzettin health care staff in providing multidrug-resistant FV. Effect of pharmacist-led patient education on tuberculosis care in Bantul District, Yogyakarta adherence to tuberculosis treatment. Am J Heal Province, Indonesia. Hum Resour Health. Pharm. 2007;64(5):497–506. 2019;17(1):16. 32. Last JP, Kozakiewicz JM. Development of a 29. Stangl AL, Earnshaw VA, Logie CH, Van Brakel pharmacist-managed latent tuberculosis clinic. W, Simbayi LC, Barré I, et al. The Health Stigma Am J Heal Pharm. 2009;66(17):1522–3. and Discrimination Framework: A global, 33. Tanimura T, Jaramillo E, Weil D, Raviglione M, crosscutting framework to inform research, Lönnroth K. Financial burden for tuberculosis intervention development, and policy on health- patients in low- And middle-income related stigmas. BMC Med. 2019;17(1):31. countries: A systematic review. Eur Respir J. 2014;43(6):1763–75.

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APPENDIX.

Appendix 1. Guideline of the interview There are several steps for conducting an in-depth interview: A. Preparation 1. Ensuring the location of FGD and tools needed (e.g., recorder, camera, stationery, mini-board, circle seating) 2. Ensuring the criteria of the participant and number of participants B. Introduction 1. Giving appreciation for participation in this study 2. Identifying name, age, and background of participant (patients/ non-patients) 3. Explaining the study, informed consent, and purpose of the interview 4. Informing that all interview will be confidentially recorded and used for a scientific purpose only 5. Informing the average of interview duration max. 120 minutes 6. Informing about how the interview will be conducted, including an emphasized statement that the participants may end the interview anytime 7. Giving opportunity for the questions 8. Giving an opportunity to read and sign the consent C. Questions The open-ended questions will be given, and the questions will be asked about the factual condition before the opinion. D. Closing statement 1. Offering additional comment or question related to the interview or study. 2. Giving a thank you statement for the participation in this study

There are several steps for conducting the focus group discussion: A. Preparation 1. Ensuring the location of FGD and tools needed (e.g., recorder, camera, stationery, mini-board, circle seating) 2. Ensuring the criteria of the participant and number of participants B. Introduction 1. Introducing moderator and assistant moderators 2. Explaining the background of study and objective of the FGD 3. Explaining about the guideline of FGD: · No right answer · The FGD will be confidentially recorded and analyzed · Informing about how the interview will be conducted, including an emphasized statement that the participants may end the interview anytime

174 Barriers and strategies for TB treatment

· The participants do not need to agree with other participants but must listen respectfully to others’ views 4. Giving opportunity for the questions 5. Giving an opportunity to read and sign the consent C. Questions The question will start with general questions: “ What are your activities in TB management”; “What are the problems of TB from the patient and healthcare providers perspectives?” D. Closing statement · Confirming some important points · Offering additional comment or question related to the interview or study. · Giving a thank you statement for the participation in this study

Appendix 2. The example of the coding process

Meaning unit Codes Sub-theme Theme ‘The stigma exists even in the CHC from healthcare Stigmatization Social aspect Socio- providers. They do not want to inject the medicine. It demography causes inconvenience and disgrace to the patients.’ and ‘Because we want to get fast action, I went to the Distance Demographical economy MDR-TB centre using public transportation. I stayed aspects there for two days because of the distance.’ ‘Some medicines must be taken regularly. I bought the Cost in the Economical medicine in a pharmacy every month.’ private aspect service 7 ‘Initially, treatment lasted for six months and was Knowledge of Knowledge Knowledge mandatory. However, I stopped the medicine because TB program and I did not know that the healthcare service was free of perception charge. I stopped the treatment because of the cost.’ ‘They may have a suggestive (placebo) effect when Perception of Perception they go to a famous physician or private health facility the physician instead of CHC, so they do not choose CHC.’ ‘I felt a headache, dizziness, flying and I hallucinated Adverse drug Adverse drug TB buying a car. It was like a crazy person.’ reaction reaction treatment ‘The fact that the patient got bored of taking Treatment Treatment medicines for a long time was a common problem.’ duration duration

175 Chapter 7 5 5 3 3 3 5 4 16 20 22 Totals 3 2 0 0 0 0 1 0 2 0 TB Activist 0 0 0 0 1 0 0 0 1 0 TB specialist 3 1 5 0 1 8 0 3 2 3 TB patients 0 1 0 1 0 2 0 1 0 0 Local key persons key government government 0 0 0 0 0 0 0 0 0 0 nurse Hospital’s Hospital’s 0 0 0 0 0 0 0 0 0 0 Hospital Hospital pharmacist 0 0 0 1 0 5 0 0 2 1 pharmacist Community Community 4 1 0 1 0 3 2 0 6 0 / nurse / CHC’s TB Programmer Programmer 2 0 0 0 0 1 0 0 2 0 CHC’ Pharmacist 4 0 0 0 1 3 0 1 5 0 CHC’s Physician Codes Socio, demography, economic Stigmatization support Family Distance Difficulties in public transportation Additional cost in the public services Cost for diagnosis and treatment in the private clinic or hospital Household problem income Transportation cost Knowledge and perception Lack of knowledge about TB disease Lack of knowledge about TB program No 1 2 Appendix 3. Codes’ pattern among the study participants

176 Barriers and strategies for TB treatment 3 5 2 14 42 32 184 Totals 0 0 0 4 3 0 TB Activist 1 0 1 1 3 0 TB specialist 0 0 3 0 8 16 TB patients 1 0 0 2 0 3 Local key persons key government government 0 0 0 0 1 1 nurse Hospital’s Hospital’s 0 0 0 0 0 0 Hospital Hospital pharmacist 7 1 3 2 0 0 12 pharmacist Community Community 3 0 1 2 8 1 / nurse / CHC’s TB Programmer Programmer 3 0 0 0 4 0 CHC’ Pharmacist 2 1 0 2 5 0 CHC’s Physician Codes Lack of knowledge about TB treatment Patient perception of public health service Patient perception of the physician or health care provider Perception on self-condition treatmentTB drug Adverse reaction Long duration of treatment No 3 Total Appendix 3 (Continued). Codes’ pattern among the study participants

177 Rome, 2020 CHAPTER DISCUSSION AND FUTURE PERSPECTIVES 8 Chapter 8

MAIN FINDINGS

Our meta-analytic study (chapter 2) showed that previous TB disease and prior drug treatment are the most influential risk factors for the development of multi-drug resistant tuberculosis (MDR-TB).(1) The study confirmed that drug resistance might occur because of treatment issues in an earlier episode of the disease. Another finding from this meta- analysis supported the essential role of drug treatment because non-adherence to drug regimens was highly associated with the development of MDR-TB. Importantly, the effects of several other risk factors for MDR-TB, such as male gender, being married, urban domicile, homelessness and having a history of imprisonment vary by geographical area. This so-called effect modification highlighted that prognostic studies for MDR-TB should be conducted locally to enable the development of the most effective strategy to control drug-resistant TB (DR-TB).

The Netherlands, as an example of a low burden TB country, showed a high success rate in TB treatment from 2005 to 2015.(2,3) Our database study confirmed that approximately 92% of patients with a diagnosis of drug-susceptible tuberculosis (DS-TB) were successfully treated, while the remainder experienced either defaulted or failed treatment or died due to TB (chapter 3). Multivariable regression analysis revealed that having an age between 18 to 24 years, being homeless or prisoner, and having diabetes predicted unsuccessful treatments, while several other factors, such as older age, combined pulmonary and extra- pulmonary TB, central nervous system or miliary TB, drug addiction and renal insufficiency/ dialysis predicted the severe outcome mortality among adult DS-TB patients. Furthermore, in the Netherlands, the treatment success slightly increased over time and 95% of patients with the diagnosis of DR-TB were cured (chapter 4). The highest proportion of DR-TB cases existed of patients with isoniazid mono- or poly-resistance (68%), followed by patients with MDR-TB (18.9%), pyrazinamide mono-resistance (8.3%), rifampicin mono- or poly- resistance (3.1%), extensively drug-resistance (0.7%), and ethambutol mono-resistance (0.1%). Among all DR-TB cases, patients with MDR-TB, substance abuse and homelessness were more likely to experience unsuccessful treatment, while miliary and central nervous system TB predicted TB mortality. In addition, male gender and substance abuse were more likely to predict poor outcome after MDR-TB treatment. Our study highlighted that the majority of DS-TB and DR-TB cases in the Netherlands were foreign-born and most of the DR-TB patients were diagnosed with primary drug-resistant TB. This situation is substantially different from a high TB burden country such as Indonesia.(4–6)

In a qualitative study, we identified several barriers to successful TB treatment in a high burden TB setting in Indonesia (chapter 7). We identified three central themes from the patient perspective: 1) socio-demography and economy; 2) knowledge and perception; 3) TB treatment. The socio-demography and economy theme comprised barriers such as

180 Discussion and future perspectives stigmatization, lack of family support, long-distance to public health service, transportation difficulties, costs of the private and public health service, cost of transportation and loss of household income. The knowledge and perception theme consisted of barriers such as lack of knowledge about TB (i.e., TB program, diseases and treatment), negative perception of the own patient condition, and negative viewpoint of public health service, while barriers related to adverse drug reaction and long duration of treatment were classified within the TB treatment theme. The results of data analysis demonstrated that all different barriers could be interrelated. In all, the most important barriers that should be prioritized for intervention are part of the following five main barriers: lack of TB knowledge, stigmatization, long- distance to the health facility, loss of household income and adverse drug reaction that can lead to non-adherence.

Medication adherence and persistence are still one of the main concerns for treatment success in chronic diseases, such as tuberculosis. The drug adherence assessments are therefore crucial to improve treatment outcomes. In a systematic review, we included studies on multiple cardio-metabolic medications since such studies that used prescription databases were lacking in the scientific TB area (chapter 5).(7) We demonstrated that adherence to multiple medications is currently measured using several different approaches, namely: 1) “all medication” which is adherence to at least 80% utilization of each medicine; 2) “both medication”, adherence to two medication by calculating the number days when both of medicine were available; 3) “any medication”, adherence to at least 80% utilization of one of the medication; 4) “average medication”, adherence assessed by calculating adherence at the individual level and then calculate the overall average; 5) “highest/lowest medication”, 8 adherence determined by calculating adherence for each medication class and presented by both the “highest” and the “lowest” level as measure of adherence. Regarding persistence to medication, two main approaches were applied: 1) persistence to “all medication”, and 2) “both medication”. In the TB context, the “all medication” approach with an interval- based measurement can be used to measure treatment adherence of anti-TB drugs, because the regimen should be strictly taken by patients within a defined period. However, the prescription system and quality of the database should be considered when measuring drug adherence and persistence in TB patients using a prescription database.

We reviewed available randomized controlled studies on intervention to improve medication adherence in chapter 6. In contrast with methodologies of measurement of drug adherence used with prescription databases (chapter 5), we identified several other different measures in the various interventional studies, such as directly observed therapy, patient self-report (e.g. interview and questionnaire), pill counts, patient attendance and electronic medication monitor (e.g. short message service). We identified that the interventions targeted several aspects, including socio-economic, health-care, patient, and treatment aspects. However, not all interventions appeared to improve medication adherence significantly. Several

181 Chapter 8 interventions were found effective in improving adherence and outcomes of active TB patients, i.e. DOT by trained community members, SMS combined with TB education, a reinforced counseling method, monthly TB voucher, drug box reminder, and a combination drug box reminder with text messaging. In the LTBI patients, shorter regimens and DOT interventions effectively improved treatment completion. Interestingly, intervention using DOT showed different effects on the study outcomes, which indicates that the impact of interventions can vary across studies and settings.

IMPLICATIONS AND FUTURE PERSPECTIVES

Our work demonstrated the important role of the management of TB drug treatment, which may substantially vary across countries and modify the impact on TB care. Therefore, strategies to develop an effective anti-microbial stewardship program for TB disease should consider local issues.

The implication for the Netherlands as a low burden TB country In the Netherlands with relatively high treatment success rates for TB disease (92-95%), management of TB drug treatment is not a major issue, though further small improvement is still possible.(2,3) The current screening strategy to find and treat latent tuberculosis infections (LTBI) and active TB patients should even more focus on migrants since the majority of TB cases are foreign-born, while drug resistant TB cases are dominated by primary drug resistance. Strategies in TB screening among migrants in low TB countries may include pre-entry screening,(8,9) one point-of-entry screening,(10) post-entry screening(9,11–13) and screening at all three points.(14) Pre- or post-entry screening were reviewed as a favorable strategy compared with no-screening in low TB incidence countries.(15) Although entry screening for asylum seekers and people from high endemic TB countries were efficiently implemented in the Netherlands,(16,17) the effectiveness and cost-effectiveness of entry screening strategy are still under debate.(18–20) Such studies are needed to support screening strategies as part of an antimicrobial stewardship program in low TB countries, such as the Netherlands.

We confirmed that hard-to-reach populations (i.e., migrants, homeless people, drug or alcohol abusers, and prisoners) are in most studies identified as the major driver for continuing TB disease in low incidence countries.(2,3) Hence, screening and treatment programs should be focused on this population to be able to achieve the national target. Health care-seeking behavior in these hard-to-reach populations has been reported to be suboptimal in low TB-incidence countries.(21) It is therefore essential to find and treat these high-risk groups to avoid therapeutic failure, further disease transmission and development of MDR-TB.

182 Discussion and future perspectives

Furthermore, our work suggests that increasing awareness among patients that acknowledges and addresses local perceptions can be used to improve the outcome of TB treatment in the hard-to-reach populations.(22,23) Intervention programs, such as family and community education, treatment-supporters, counseling and incentive policy, considering individual culture and belief can be developed as part of an antimicrobial stewardship program that specifically focuses on these hard-to-reach populations in the Netherlands. Moreover, screening and possible chemoprophylaxis for latent TB in dialyzed patients may prevent the activation of TB that can potentially lead to a poor outcome of TB treatment (24,25). The activation of TB is due to the alteration of immune response associated with uremia and dialysis exacerbation that can be a predisposing factor for active TB in dialysis patients (24). Potential drug toxicity due to drug accumulation in patients with insufficient renal function (26) may be tackled by the implementation of therapeutic drug monitoring (TDM). TDM can reduce the incidence of adverse drug reactions and optimize the treatment,(27,28) especially in diabetics and impaired renal patients.(29) To have a more designed care program and treatment, admission of the hard-to-reach populations to modern sanatoria hospitals (TB center) for the first treatment period may further improve the treatment outcome in those groups. Although a recent study showed that LTBI screening among an Eritrean group of asylum seekers is feasible to be implemented in the Netherlands,(30) further study on the cost-effectiveness of screening programs in populations of immigrants (e.g. asylum seekers, legal and illegal immigrants) in the Netherlands is needed. Moreover, since population migration from high endemic TB countries has been an essential factor for developing TB in the Netherlands, cooperation to solve TB problems in the origin countries can be beneficial to further reduce the incidence 8 of TB in the Netherlands.

The implication for Indonesia as a high burden TB country In Indonesia, TB care is managed both by the public and private health sectors, from primary to tertiary healthcare facilities. The Community Health Centre (CHC), designated as ‘Puskesmas’, is a backbone TB care facility established as the primary public health sector at the sub-district level. Managed by the local government at the district level, CHCs have the responsibility to identify, notify and monitor TB patients within their specific area. TB care is also supported by referral hospitals, although not all referral hospitals have the facilities to support MDR-TB care. MDR-TB is therefore managed in several centralized and specialized hospitals in Indonesia.

An extremely high economic burden due to TB was reported in Indonesia and an antimicrobial stewardship program may reduce these costs.(31) Our qualitative study suggests that such programs should cover several activities, such as promoting TB knowledge and government’s programs, reducing TB stigma in the society and health facility, providing better access to a qualified TB diagnostic facility and treatment as well

183 Chapter 8 as facilitating social protection for TB patients, notably for those with the lowest income groups. Several strategies are required to develop an optimal program, including engaging leadership commitment, strengthening CHC for TB case detection and treatment, increasing public-private mix (PPM), and re-defining the pharmacist’s role in TB management.

Engaging leadership commitment as part of an Antimicrobial Stewardship Program The governmental system of Indonesia has been shifting from a centralized government to a decentralized government since 1999.(32) The local governments at the district level have more authority to manage their area than earlier in the decentralized era. It enables the authority of local governments at the district level to operationalize national policies, such as TB policies. We identified several problems concerning such policies, such as lack of health care staff working in a TB team and the absence of efficient facilities for TB diagnosis and treatment at the district level. We also observed that collaborations between CHC and private sectors (e.g., private clinics, hospitals and pharmacies) are not optimal and an MDR-TB center is not always available at the district level which hampers access of patients to such qualified TB care. From the patient perspective, we identified that defaulted treatment occurred in the low socio-economic groups due to the loss of household income.

The principal of the local government at the district level, who is supervised by the mayor (regent), has an essential role in the TB management at the district level. Accordingly, leadership commitment and awareness for TB elimination should not only be part of the central government but also by local governments to be able to achieve the national and global target of TB elimination. The leaders should be committed to providing proper facilities, human resources, finance, information technology and regulation to establish qualified TB care. Decentralized MDR-TB treatment at the district level may improve the accessibility of MDR-TB patients to have qualified TB care.(33) Considering sub-standard TB management(34) and incompliant TB guidelines in the private health facilities,(35) implementation of strong regulations and cooperation between local government and the private sectors at the district level should be performed to fight TB together in the same framework. Social protection schemes, reaching beyond direct medical costs, such as loss of household income, should be a concern for the government. This might tackle issues related to defaulted treatment due to the patient’s decreasing household income, especially for patients with the lowest income. As a large democratic country, the role of non-governmental TB actors (such as non-governmental organizations, academics, and TB communities) will be important to advocate leadership commitment for TB elimination in Indonesia. Therefore, synergism programs involving local government, private health sectors and TB actors should be developed to explore and solve the existing problems.

184 Discussion and future perspectives

Strengthening the capacity of the community health center for TB case detection and treatment Strengthening public health facilities is essential. According to our qualitative study, human resources and facilities at the CHC level should be improved. According to the national TB guideline, the TB program at the CHC level should be operated by at least three TB officers, i.e., one general practitioner, one nurse and one laboratory analyst.(36) However, we observed that the workload of the TB officer is high. The TB officers should manage all TB programs, such as case detection, diagnosis, drug services, TB coordination for Public- Private partnership, and reporting. The tasks of TB officers are burdened with operating other non-TB programs at CHC level.

We identified a lack of laboratory facilities for sputum tests as well as limited room for TB examination, counseling and treatment in some CHCs. An analyst for sputum tests is not always available in the CHC. This might lead to diagnostic problems at the CHC level. Regarding a TB room, we observed that rooms for TB diagnosis and treatment showed a lack of space and inadequate protective equipment as well as ventilation in some CHCs. To avoid disease transmission in the health facility and provide an optimal TB care, TB officers should be supported with proper equipment and environment, such as protective equipment and examination for TB officers (e.g., special mask, routine TB test), patient environment (e.g., privacy and isolation room), and staff environment (space for both clinical and administrative work).

Increasing public-private partnership programs 8 The global framework for TB elimination emphasizes the need for collaboration between public and private parties, also called public-private mix (PPM). Developing networks between public and private sectors in TB management should be continuously developed as an effort to manage TB cases in Indonesia. Although a PPM initiative has been implemented within some sectors, our observation is that the engagement should be strengthened especially within private clinics. The observed unwillingness of some private clinics to refer or report TB patients to the TB officer at CHC/district level leads to loss to follow up of TB patients. Deviations in diagnosis and treatment procedures in some private sectors from the national guideline procedures were also identified in our interview with TB officers. Potential lost to follow up commonly occurs in patients who use private services because few private sectors have adequate resources to follow up defaulted patients. Consequently, many TB patients are under-reported and defaulted treatment occurs due to high medical costs.

As previously described, a strong local government regulation followed by adequate socialization and implementation of the regulation as well as strict law enforcement is required to engage private sectors in TB management. Punishment schemes in the form

185 Chapter 8 of deregistering private practitioners or revoking the license may be effective in engaging private sectors for working together under national guidance. Such regulation option was proposed by Healy and Brathwaite (2006) and Moke et al. (2010), who suggested that deregistration or revoking a license can be used as a policy when noncompliance to the standard TB diagnostic and treatment guidelines persist in the private sectors.(37,38) To improve treatment outcomes, broadening the public-private partnership can be performed with community pharmacists to support TB case detection and treatment.

Re-defining the pharmacist’s role in the TB management Pharmacists are health care professionals who are responsible for the rational use of medicines. Nowadays, the new paradigm of pharmacy not only focusses on the drugs but also on the patient outcome, preferably in a personalized way. Consequently, drug-related issues in TB can be explored and targeted by pharmacists as part of their activities to ensure the rational use of medicine. However, the current regulation states that there is no direct service to the TB patients provided by pharmacists. The pharmacist is only involved in the non-direct service, such as management of TB logistics and medicine.(36) On the other hand, a massive program to involve pharmacists in the society has been launched by the Ministry of Health, Indonesia, in close collaboration with the Indonesian Pharmacist Association. There is a national program called “GEMA CERMAT” that attempts to encourage and support pharmacists in the conduct of direct services to patients or society to improve the rational use of medicine. Selected pharmacists are appointed as “agents of change” who should be a role model for direct involvement of pharmacists into the society or patients for improving the rational use of medicine. Within the TB context, it is now an opportunity to develop a national TB stewardship program that involves pharmacists in TB areas.

Referral activities of community pharmacists may be implemented as a function to refer suspected TB patients from private pharmacies to the CHC. Currently, the referral function of the community pharmacist does not work effectively because of the lack of TB awareness among pharmacists and the absence of pharmacist guidelines for the referral function. Our field notes made during focus group discussions with pharmacists (chapter 7) demonstrated a lack of awareness about TB disease from private pharmacists due to only few diagnosed TB patients who visit their pharmacies. TB patients do not visit the pharmacy because a TB diagnosis via CHCs results in receiving free medications. The community pharmacists in our study felt that TB is a low prevalence disease and nothing to be worried about. This is in contrast with the fact that the pharmacy is the typical facility for the initial health care seeking of suspected TB patients in Indonesia. (39) Therefore, the involvement of community pharmacists in stewardship programs has the potential to increase TB case detection in Indonesia.

186 Discussion and future perspectives

Regarding the referral concept, a pharmacist should be able to screen suspected TB patients when disease symptoms are presented in the private pharmacies and to refer the suspected TB patient to CHC for further examination. The referral of suspected TB patients will be more effective if there is excellent communication between community pharmacists and TB officers at the CHC. The pharmacist may schedule for the medical examination at the nearest CHC of the suspected TB patient. In case the suspected patient does not attend for the medical examination at the CHC, a TB officer will follow up the patient using contact details registered by the pharmacist. Hence, an active role of pharmacists and TB officers is needed to increase case detection and improve treatment in Indonesia. This concept may also raise awareness of the pharmacists for not dispensing anti-TB drugs directly without a medical prescription. The flow diagram of the pharmacist’s referral activity is further depicted in Figure 1.

8

Figure 1. Flow diagram of the referral function of the suspected TB patient from a pharmacy to a community health center.

As mentioned earlier, drug-related problems are one of the major issues in TB treatment. Adverse drug reactions and inappropriate drugs are among the main factors of defaulted treatment. Community pharmacists at the private pharmacy can act as a treatment supporter. As a treatment supporter, community pharmacists can provide drug consultations and information as well as contribute to directly observed treatment (DOT) for TB patients. Furthermore, community pharmacists can be involved in promoting the rational use of anti-tuberculosis drugs in society. A drug promotion program, a stewardship program for medication and health education in the TB area, can be performed by community pharmacies coordinated by CHC’s pharmacist at the sub-district level. The program can be developed to improve awareness and knowledge about TB disease, treatment and program. The educational program can also include a topic about how people can support TB patients

187 Chapter 8 for successful treatment that may be beneficial in reducing TB stigma in society, improving treatment outcome and controlling DR-TB.

A pre-condition is required to engage community pharmacists in TB management. A structured training program to increase awareness and knowledge about TB disease, treatment and program should be delivered to the community pharmacists. A pharmacist’s guideline in TB management will support their involvement in TB management. An incentive strategy may be needed as a complementary program to attract and motivate community pharmacists participating in TB management. The incentive can be the credit points from professional organizations for continuing pharmacist licenses or the combination with financial compensation. Importantly, involving pharmacists as a part of the TB team at CHC level is necessarily required. The CHC’s pharmacist will coordinate activities of TB management that are related to community pharmacists at the sub-district level. Therefore, the availability of a trained pharmacist in a CHC is crucial to have a successful stewardship program. An explorative intervention study should be performed to analyze the process and possible impact of the implementation.

The role of pharmacists in the management of TB disease was studied in another setting. A study in India showed that involving community pharmacists in referring suspected TB patients to health facilities improves an earlier detection/case finding of TB patients. (40,41) Moreover, educational programs and drug monitoring provided by a pharmacist demonstrated a significant improvement of adherence to TB treatment among TB and MDR-TB patients.(42,43) Other pharmacist’s activities in TB program could be employed, such as drug assessment for the appropriateness of TB treatment and comorbidity treatment, drug monitoring (proposal to monitor parameters and therapeutic drug monitoring for drug adjustment), promotion of treatment adherence (assessment knowledge and beliefs from patients, patient education, monitoring adherence by pill count, urine check or therapeutic drug monitoring/TDM), medication review, and participation in direct observed treatment (DOT) programs.(40,42,44,45) Such activities may all be developed as part of an antimicrobial stewardship program for TB in Indonesia.

Improving treatment adherence in TB disease Treatment adherence is one of the critical factors for successful TB treatment. Our systematic review described several approaches to monitor adherence and persistence of multiple medications in cardiometabolic disease using prescription databases (chapter 5). Monitoring TB treatment adherence using a prescription database can be applied and may include large numbers of patients. The so-called “all medication” measurement with an interval-based approach may be used to measure treatment adherence to anti- tuberculosis drugs. The “all medication” measurement is selected because all TB drugs are equally important and must be taken by the patients to avoid drug resistance and poor

188 Discussion and future perspectives treatment outcome. The interval-based approach is more accurate than the prescription- based approach because the period of TB medication treatment has been standardized in guidelines. For instance, active DS-TB cases should take anti-TB drugs for a 6-month treatment period. However, the assessment of treatment persistence can be challenging because the duration of anti-TB drug treatment periods is shorter than most cardio- metabolic treatments. Difficulty in assessing treatment persistence of anti TB drugs will be faced in an area that can prescribe medicines for a maximum 3-month treatment in one time of dispensing. As an example, the persistent gap is only one gap and it cannot be interpreted for the persistence of medication use for 6 month TB treatment regimen. In the case of MDR-TB, measurement of treatment adherence may be more effective using daily records of patient visits in TB facility, since the patients use injections.

Our study suggests that the prescription system and quality of prescription databases in particular areas should be considered to measure adherence. Quality of prescription databases should be assessed in terms of volume, variety, velocity, veracity and validity. We analyzed that direct monitoring is more accurate than non-direct monitoring (e.g., using a prescription database) for measuring adherence in daily practice. Furthermore, interventional strategies for improving treatment adherence should be developed incorporating local issues. We identified at least four essential factors to have a successful intervention for improvement of medication adherence in patients with TB disease and assessment: 1) characteristics of the research subject, 2) accurate measurement of treatment adherence as the study outcome, 3) type of the comparator group, 4) robustness of study design for minimizing potential bias. Since non-adherence factors can be individual, 8 a personalized intervention that takes into account the individual factors are required to have an effective medication adherence program.

To sum up, strategies to control DR-TB by developing an antimicrobial stewardship program should consider local issues. An effective screening strategy and treatment program for immigrants and hard-to-reach populations in low burden countries as the Netherlands should be developed to achieve the national and global targets for TB elimination. In high burden countries such as Indonesia, TB treatment outcomes can only be improved by the development and implementation of an integrated antimicrobial stewardship program. Since delayed treatment has been found as a factor for unsuccessful TB treatment in Indonesia, TB case detection should be part of such a program. Several strategies are required to have an optimal antimicrobial stewardship program in Indonesia, including engaging leadership commitment, strengthening CHC as a health facility for case detection and TB treatment, increasing public-private partnership, and re-defining the pharmacist’s role in TB management.

189 Chapter 8

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191 Titlis, 2019 ADDENDUM SUMMARY NEDERLANDSE SAMENVATTING RINGKASAN ACKNOWLEDGMENTS ABOUT THE AUTHOR Ph.D. PORTOFOLIO Addendum

SUMMARY

Nowadays, tuberculosis (TB) is one of the top 10 causes of death. The World Health Organization (WHO) estimated that 10 million people developed TB and 1.3 million patients died because of TB in 2017. The problem has further worsened due to the increase of multidrug-resistant tuberculosis (MDR-TB). The treatment success rate of MDR-TB was reported to be as low as 55%. A history of previous TB treatment is one of the main risk factors for MDR-TB. It highlights that emerging problems associated with TB drug treatment management should be primarily controlled to avoid the development of MDR-TB. Therefore, an effective antimicrobial stewardship program is essentially needed to achieve a global target for TB elimination in 2035. Unfortunately, comprehensive information on the current situation of high-risk groups for therapeutic failure and drug-related problems in TB disease is lacking. Updated studies and novel strategies are required to develop effective antimicrobial stewardship programs in TB disease. By applying drug utilization and pharmacoepidemiological studies, this thesis, therefore, aimed to identify effective strategies for improving TB treatment outcomes as part of drug resistance control in TB disease with the specific objectives were to analyze: 1) high-risk groups for therapeutic failure among TB patients, 2) potential interventions to improve treatment adherence in TB patients, 3) treatment barriers and potential strategies to successful TB treatment.

In chapter 2, a global meta-analytic study was conducted to identify risk factors of MDR- TB. Our study showed that previous TB disease and prior drug treatment are the most influential factors for the development of MDR-TB. Another finding from this meta-analysis was that non-adherence to drug regimens was highly associated with the development of MDR-TB. Importantly, the effects of several other risk factors for MDR-TB, such as male gender, being married, urban domicile, homelessness and having a history of imprisonment vary by geographical area. This so-called effect modification highlighted that prognostic studies for MDR-TB should be conducted locally to enable the development of the most effective strategy to control DR-TB.

In chapter 3, predictors for unsuccessful TB treatment among drug-susceptible TB (DS- TB) patients in the Netherlands were analyzed. A retrospective cohort study that involved 5,674 DS-TB patients showed that having an age between 18 to 24 years, being homeless or prisoner, and having diabetes predicted unsuccessful treatments, while several other factors, such as older age, combined pulmonary and extra-pulmonary TB, central nervous system or miliary TB, drug addiction and renal insufficiency/dialysis predicted mortality among adult DS-TB patients. Our study confirmed that approximately 92% of patients with a diagnosis of DS-TB were successfully treated, while the remainder experienced either defaulted or failed treatment or died due to TB.

194 Summary

In chapter 4, the current situation of the high-risk DR-TB patients in the Netherlands is presented. The highest proportion of DR-TB cases existed of patients with isoniazid mono- or poly-resistance (68%), followed by patients with MDR-TB (18.9%), pyrazinamide mono- resistance (8.3%), rifampicin mono- or poly-resistance (3.1%), extensively drug-resistance (0.7%), and ethambutol mono-resistance (0.1%). Among all DR-TB cases, patients with MDR-TB, substance abuse and homelessness were more likely to experience unsuccessful treatment, while miliary and central nervous system TB predicted TB mortality. Also, male gender and substance abuse were more likely to predict poor outcome after MDR-TB treatment. Our study demonstrated that the majority of DR-TB cases in the Netherlands were foreign-born and most of the DR-TB patients were diagnosed with primary drug- resistant TB. This situation is different from a high TB burden country such as Indonesia.

As previously studied, treatment adherence is a risk factor of MDR-TB. Accordingly, a systematic review of the measurement of adherence and persistence to multiple medications is described in chapter 5. Using a pharmacoepidemiological approach, we analyzed studies on multiple cardio-metabolic medications since such studies that used prescription databases were lacking in the scientific TB area. Our study demonstrated that adherence to multiple medications is currently measured using several different approaches, namely: 1) “All medication”, 2) “Both medication”, 3) “Any medication”, 4) “Average medication”, 5) “Highest/lowest medication”. Regarding persistence to medication, two main approaches were applied: 1) persistence to “all medication”, and 2) “both medication”. In the TB context, the “all medication” approach with an interval-based measurement can be used to measure medication adherence of anti-TB drugs, because the regimen should be strictly taken by patients within a defined period. However, the prescription system and quality of the database should be taken into consideration when measuring drug adherence and persistence in TB patients using a prescription database.

In chapter 6, a systematic review of the randomized controlled studies on intervention to improve medication adherence in patients with latent and active TB infection is presented. The interventions targeted several aspects, including socio-economic, health-care, patient, and treatment aspects. However, not all interventions appeared to significantly improve medication adherence. Several interventions were found effective in improving adherence and outcomes of active TB patients, i.e. DOT by trained community members, SMS combined with TB education, a reinforced counseling method, monthly TB voucher, drug box reminder, and a combination drug box reminder with text messaging. In the LTBI patients, shorter regimens and DOT interventions effectively improved treatment completion. Interestingly, interventions using DOT showed heterogeneous effects on the study outcomes that indicated the interventions can be effective with a varying impact across studies and settings. Since non-adherence factors are patient-specific, personalized

195 Addendum interventions that take into account such factors are required to enhance the impact of a program to improve medication adherence in TB patients.

In chapter 7, a phenomenological qualitative study about barriers to successful TB treatment was performed in a province of Indonesia with high TB prevalence. We identified three barriers from the patient’s perspective: 1) socio-demography (i.e. stigmatization, lack of family support, long-distance to public health service, transportation difficulties, costs of the private and public health service, cost of transportation and loss of household income and economy); 2) knowledge and perception (i.e. lack of knowledge about TB disease and program, negative perception of the own patient condition, and negative viewpoint of public health service); 3) TB treatment (i.e. adverse drug reaction and long duration of treatment). The results of data analysis demonstrated that all different barriers could be interrelated. In all, the most important barriers that should be prioritized for intervention are part of the following five main barriers: lack of TB knowledge, stigmatization, long-distance to the health facility, loss of household income and adverse drug reaction.

In the general discussion (chapter 8), we proposed recommendations for developing an effective antimicrobial stewardship program in TB disease. We identified that strategies to control DR-TB should include local issues. In low burden countries, such as the Netherlands, an effective screening strategy and treatment program for immigrants and hard-to-reach populations should be developed to have an effective antimicrobial stewardship program. However, in high burden countries, such as Indonesia, management of TB treatment is crucial and it can only be improved by the development and implementation of an integrated antimicrobial stewardship program. Since delayed treatment has been found as a factor for unsuccessful TB treatment in Indonesia, TB case detection should be part of such a program. Several strategies are required to have an optimal antimicrobial stewardship program in Indonesia, including engaging leadership commitment, strengthening community health center for case detection and TB treatment, increasing public-private partnership, and re-defining the pharmacist’s role in TB management. In terms of treatment adherence, our study suggests that the prescription system and quality of prescription databases in particular areas should be considered to measure medication adherence in TB patients adequately. Furthermore, since non-adherence factors can be individual, interventions with a personalized approach are required for the future development of effective programs on improving medication adherence among TB patients.

196 Nederlandse samenvatting

NEDERLANDSE SAMENVATTING

Tegenwoordig is TB een van de top-10 doodsoorzaken. De Wereld Gezondheidsorganisatie (WHO) schat dat 10 miljoen mensen TB kregen en dat 1.3 miljoen patiënten zijn overleden aan de gevolgen van TB in 2017. Het probleem is verergerd door de toename van multiresistente tuberculose (MDR-TB). Het gemiddelde succespercentage van de behandeling van MDR-TB is volgens onderzoek slechts 55%. Een voorgeschiedenis van eerdere TB behandelingen is een van de grootste risicofactoren voor MDR-TB. Dit geeft aan dat problemen die geassocieerd zijn met medicamenteuze TB behandeling zouden moeten worden opgelost om de ontwikkeling van MDR-TB te voorkomen. Daarom is een effectief antimicrobieel leiderschapsprogramma nodig om de mondiale doelstelling van TB eliminatie in 2035 te behalen. Helaas ontbreekt het aan volledige informatie over de huidige situatie van hoog-risico groepen met betrekking tot therapeutisch falen en medicijn- gerelateerde problemen bij TB. Vernieuwde studies en innovatieve strategieën zijn nodig om effectieve programma’s voor TB te ontwikkelen. Met behulp van geneesmiddelengebruik en farmaco-epidemiologische onderzoeken worden effectieve strategieën geïdentificeerd om TB behandelresultaten te verbeteren. Het onderzoek maakt daarmee deel uit van de resistentiecontrole van TB met de specifieke doelstellingen om: 1) hoog-risico groepen te identificeren op therapeutisch falen bij TB patiënten; 2) potentiële maatregelen te onderzoeken om de therapietrouw in TB patiënten te vergroten; 3) behandelingsobstakels en potentiële strategieën voor succesvolle TB behandelingen te onderzoeken.

In hoofdstuk 2 verrichten we een wereldwijd meta-analyse onderzoek om de risicofactoren van MDR-TB vast te stellen. Ons onderzoek wijst uit dat voorgaande TB besmetting en medicamenteuze behandeling de meest invloedrijke risicofactoren zijn voor de ontwikkeling van MDR-TB. Een andere bevinding uit deze meta-analyse bevestigt dat het niet navolgen van medicatievoorschriften ook sterk geassocieerd is met de ontwikkeling van MDR- TB. Belangrijk is dat de effecten van verscheidene andere risicofactoren voor MDR-TB, zoals het mannelijk geslacht, getrouwd zijn, stedelijke verblijfplaats, dakloosheid en een gevangenisstraf hebben uitgezeten, verschillen per geografische locatie. Deze zogenoemde effectmodificatie benadrukt dat voorspellende studies voor MDR-TB lokaal moeten worden verricht om de meest effectieve strategieën voor DR-TB bestrijding te ontwikkelen.

In hoofdstuk 3 analyseren we voorspellers voor onsuccesvolle TB behandeling onder antibiotica-gevoelige TB (DS-TB) patiënten in Nederland. Een retrospectief cohortonderzoek waarbij 5674 DS-TB patiënten betrokken waren, toonde aan dat de volgende factoren een onsuccesvolle behandeling voorspelden: tussen de 18 en 24 jaar oud zijn, dakloos zijn of in de gevangenis zitten, en diabetes hebben. Verscheidene andere factoren, zoals op hogere leeftijd zijn, gecombineerde longtuberculose en extrapulmonale tuberculose hebben, centraal zenuwstelsel of miliaire TB, drugsverslaving en een gestoorde

197 Addendum nierfunctie/dialyse voorspelden de ernstige uitkomst sterfte onder volwassen DS-TB patiënten. Ons onderzoek bevestigt dat ongeveer 92% van de patiënten met een diagnose van DS-TB een succesvolle behandeling onderging, terwijl de overige patiënten een falende behandeling hadden, of overleden aan TB.

In hoofdstuk 4 presenteren we de huidige situatie van de hoog-risico DR-TB patiënten in Nederland. Het grootste deel van DR-TB patiënten bestaat out patiënten met isoniazid mono- of poly-resistentie (68%), gevolgd door patiënten met MDR-TB (18.9%), pyrazinamide mono-resistentie (8.3%), rifampicin mono- of poly-resistentie (3.1%), uitgebreide antibiotica- resistentie (0.7%), en ethambutol mono-resistentie (0.1%). Van alle DR-TB patiënten hebben degenen met MDR-TB, drugsgebruik en dakloosheid de meeste kans op een onsuccesvolle behandeling, terwijl patiënten met miliaire TB en TB van het centraal zenuwstelsel de grootste kans hadden hieraan te overlijden. Daarbij komt dat mannen en drugsgebruikers meer kans liepen op een slechte uitkomst na een MDR-TB behandeling. Ons onderzoek toont aan dat de meerderheid van Nederlandse DR-TB gevallen in het buitenland geboren was en de meeste DR-TB patiënten gediagnostiseerd waren met primaire geneesmiddel resistente TB. Deze situatie verschilt met een land als Indonesië waar de TB ziektelast hoger ligt.

Zoals eerder onderzocht, blijkt dat therapietrouw een risicofactor is bij MDR-TB. Daarom beschrijft hoofdstuk 5 een systematisch overzicht van het meten van het houden aan geneesmiddelvoorschriften bij meerdere medicaties. Door de farmaco-epidemiologische aanpak te gebruiken, hebben we verschillende onderzoeken geanalyseerd met betrekking tot verscheidene hart-medicaties, omdat dit soort onderzoek dat gebruik maakt van prescriptiedatabases ontbrak op wetenschappelijk TB gebied. Ons onderzoek demonstreert dat therapietrouw op dit moment gemeten wordt op verschillende manieren, namelijk: 1) “Alle medicatie”; 2) “Beide medicaties”; 3) “Elke medicatie”; 4) “Gemiddelde medicatie”; 5) “Hoogste/laagste medicatie”. Als het gaat om het houden aan meerdere medicatievoorschriften, dan kunnen twee methodes worden gebruikt: 1) het houden aan “alle medicaties”, en 2) “beide medicaties”. In de TB context kan de “alle medicatie” aanpak met een interval-gebaseerde meting gebruikt worden om therapietrouw van anti-TB geneesmiddelen te meten, omdat de kuur alleen binnen bepaalde tijd gevolgd mag worden door de patiënt. Echter, er moet rekening gehouden worden met het voorschrijfsysteem en de kwaliteit van de database als men therapietrouw meet in TB.

In hoofdstuk 6 presenteren we een systematisch overzicht van de gerandomiseerde en gecontroleerde onderzoeken naar interventies om therapietrouw bij patiënten met latente en actieve TB infectie te verbeteren. De interventies richtten zich op meerdere onderdelen, zoals socio-economische-, gezondheidszorg-, patiënt- en behandelingsaspecten. Echter, niet alle interventies bleken significante verbeteringen

198 Nederlandse samenvatting te veroorzaken in de therapietrouw. Verscheidene interventies waren effectief in het verbeteren van therapietrouw als ook de behandelingsuitkomsten bij actieve TB patiënten zoals: DOT door getrainde gezondheidswerkers, SMS gecombineerd met TB onderwijs, een verscherpte begeleidingsmethode, maandelijkse TB voucher, geneesmiddeldoos herinnering, en een combinatie van geneesmiddel herinnering met sms berichtjes. Onder de latente TB patiënten verbeterde de voltooiing van de behandeling door kortere kuren en DOT interventies. Opvallend genoeg bleek dat interventie met DOT een heterogeen effect toonde in de onderzoeksresultaten, wat laat zien dat de interventies effectief kunnen zijn met wisselende invloed tussen verschillende onderzoeken en omgevingen. Omdat therapietrouw patiëntgebonden is, is gepersonaliseerde interventie nodig waarbij met dat soort factoren rekening gehouden wordt om zo de impact van het programma te vergroten en de therapietrouw in TB patiënten te verbeteren.

In hoofdstuk 7 onderzochten we met behulp van een fenomenologisch kwalitatieve studie de belemmeringen voor een succesvolle TB behandeling in een provincie in Indonesië waar TB veel voorkomt. We vonden drie centrale thema’s vanuit het perspectief van de patiënt: 1) socio-demografie (met name stigmatisering, gebrek aan steun vanuit de familie, reisafstand tot een gezondheidscentrum, mobiliteitsproblemen, prijs van private en openbare gezondheidszorg, prijs van vervoer, en daling van het huishoudelijk inkomen en de economie); 2) kennis en gewaarwording (gebrek aan kennis over TB ziekte en programma, negatief beeld van de toestand van de patiënt, en een negatief beeld van de gezondheidszorg); 3) TB behandeling (medicatiebijwerkingen en de lange duur van de behandeling). De resultaten van de data-analyse laten zien dat alle verschillende hindernissen gerelateerd kunnen zijn. Uiteindelijk maken de belangrijkste belemmeringen deel uit van de volgende vijf groepen: gebrekkige kennis van TB, stigmatisering, grote afstand tot gezondheidscentrum, daling van het huishoudelijk inkomen en bijwerkingen.

Hoofdstuk 8 beschrijft een algemene discussie om aanbevelingen voor te stellen voor het ontwikkelen van een effectief antimicrobieel leiderschapsprogramma bij TB. We vonden dat er rekening moet worden gehouden met lokale omstandigheden bij het bepalen van strategieën om DR-TB te controleren. In landen met een lage prevalentie van TB zoals Nederland, moet een effectieve screeningsstrategie ontwikkeld worden en een behandelingsprogramma voor immigranten en moeilijk te bereiken bevolkingsgroepen worden opgezet, om een dergelijk programma te creëren. In landen met een hoge prevalentie van TB, zoals Indonesië, is de management van TB behandeling cruciaal en dit kan alleen maar verbeterd worden door de ontwikkeling en toepassing van een geïntegreerd antimicrobieel leiderschapsprogramma. Omdat uitgestelde behandeling een rol speelt in de onsuccesvolle behandeling van TB in Indonesië, zou TB opsporing een rol moeten spelen in zo’n programma. Verscheidene strategieën zijn nodig om een optimaal programma te creëren in Indonesië, inclusief betrokken leiderschap, het verbeteren van

199 Addendum openbare gezondheidscentra voor het detecteren en behandelen van TB, meer openbare en private samenwerking, en het herdefiniëren van de apothekersrol in het TB beleid. Als het gaat om therapietrouw, toont ons onderzoek aan dat de prescriptiesystemen en de kwaliteit van de apothekersdatabase in bepaalde gebieden in acht moeten worden genomen om de therapietrouw van TB patiënten te kunnen meten. Bovendien moeten interventies worden ontwikkeld voor een gepersonaliseerde aanpak, omdat therapietrouw grotendeels gebaseerd is op individuele factoren. Dit is nodig voor de verdere ontwikkeling van effectieve programma’s om therapietrouw onder TB patiënten te verbeteren.

200 Ringkasan

RINGKASAN

Saat ini penyakit tuberkulosis (TB) merupakan satu dari sepuluh jenis penyakit yang menyebabkan kematian tertinggi di dunia. Badan kesehatan dunia memperkirakan pada tahun 2017 terdapat 10 juta orang terinfeksi TB dan 1,3 juta orang meninggal dunia dikarenakan TB. Permasalahan tersebut semakin diperparah dengan adanya peningkatan kejadian tuberkulosis resistensi multipel obat (TB-RMO) dengan kesuksesan terapi yang masih relatif rendah yaitu hanya berkisar 55%. Riwayat pengobatan TB sebelumnya pada pasien TB merupakan satu dari faktor risiko utama kejadian-kejadian TB-RMO. Hal tersebut menunjukkan bahwa perlunya pengendalian permasalahan TB yang berkaitan dengan pengobatan TB untuk mengatasi kejadian TB-RMO. Atas dasar tersebut, diperlukan sebuah program pengelolaan antibiotik / Antimicrobial Stewardship Program (ASP) yang efektif untuk mencapai target global dalam mengeliminasi TB pada tahun 2035. Akan tetapi, informasi terkini mengenai kelompok berisiko tinggi terhadap kegagalan terapi dan permasalahan- permasalahan mengenai pengobatan TB masih belum banyak diketahui, sehingga diperlukan studi dan strategi terbaru untuk mengembangkan ASP yang efektif. Berdasarkan uraian tersebut, melalui penerapan studi penggunaan obat dan farmakoepidemiologi, disertasi ini bertujuan untuk mengidentifikasi strategi yang efektif dalam meningkatkan luaran terapi TB dengan menganalisis beberapa hal, yaitu: 1) kelompok berisiko terhadap kegagalan terapi TB, 2) intervensi yang berpotensi dalam meningkatkan kepatuhan terapi obat anti-TB, 3) faktor-faktor penghalang dalam kesuksesan pengobatan TB, serta strategi-strategi yang berpotensi untuk meningkatkan kesuksesan terapi TB.

Sebuah studi meta-analisis dengan cakupan global di uraikan pada bab 2. Studi ini bertujuan untuk mengidentifikasi faktor risiko TB-RMO. Kami mengidentifikasi bahwa riwayat penyakit dan pengobatan TB merupakan faktor yang sangat mempengaruhi berkembangnya TB-RMO. Temuan lain menunjukkan bahwa ketidakpatuhan terhadap pengobatan TB berkaitan erat dengan berkembangnya TB-RMO. Hal penting lainnya adalah adanya perbedaan beberapa faktor risiko TB-RMO yang dipengaruhi oleh lokasi penelitian. Beberapa faktor tersebut yaitu jenis kelamin, status pernikahan, lokasi domisili, status kepemilikan tempat tinggal dan status riwayat sebagai tahanan. Adanya perbedaan faktor risiko tersebut menunjukkan bahwa identifikasi terhadap faktor risiko TB-RMO perlu dilakukan pada cakupan daerah tertentu untuk dapat mengembangkan strategi yang paling efektif dalam mengendalikan Tuberkulosis Resistensi Obat (TB-RO).

Pada bab 3 dipaparkan studi mengenai faktor prediksi terhadap kegagalan pengobatan TB pada pasien Tuberkulosis Sensitif Obat (TB-SO) di Belanda. Sebuah studi retrospektif- kohort yang melibatkan 5.674 pasien TB-SO menunjukkan bahwa memiliki umur 18 hingga 24 tahun, status sebagai tuna wisma dan tahanan, atau memiliki penyakit diabetes merupakan faktor prediksi terhadap kegagalan pengobatan TB. Selain itu kami

201 Addendum mengidentifikasi beberapa faktor lainnya, seperti pasien-pasien dengan umur lanjut usia, kombinasi penyakit TB paru dan ekstra paru, penyakit TB sistem saraf pusat atau milier, kecanduan zat adiktif, dan gangguan ginjal/ dialisis, merupakan faktor-faktor prediksi kematian pada kelompok pasien TB-SO dewasa di Belanda. Studi kami juga mengkonfirmasi bahwa 92% pasien dewasa yang didiagnosis TB-SO telah secara sukses diobati, namun sisanya mengalami penghentian, kegagalan terapi atau meninggal dunia dikarenakan TB.

Pada bab 4, kami memaparkan situasi terkini mengenai kelompok berisiko pasien TB-RO di Belanda. Persentase tertinggi kasus TB-RO di Belanda terdapat pada kelompok pasien dengan mono- atau poli-resistensi TB (68%), kemudian diikuti oleh kelompok-kelompok pasien dengan TB-RMO (18,9%), mono-resistensi pirazinamid (8.3%), mono- atau poli- resistensi rifampisin (3,1%), TB resistensi obat extensif / TB-XDR (0.7%) dan mono-resistensi pirazinamid (0.1%). Secara keseluruhan, pasien dengan status TB-RMO, kecanduan zat adiktif dan tuna wisma memiliki kecenderungan mengalami kegagalan terapi TB-RO, sementara pasien TB-RO dengan diagnosis TB milier dan susunan saraf pusat memiliki kecenderungan meninggal dunia dikarenakan TB. Selain itu juga studi ini menunjukkan bahwa kelompok pasien dengan jenis kelamin laki-laki dan kelompok pasien dengan status kecanduan zat adiktif memiliki kecenderungan mendapatkan luaran terapi yang buruk. Studi kami mengkonfirmasi bahwa mayoritas kasus TB-RO di Belanda merupakan pasien-pasien dengan tempat kelahiran dari luar Belanda dan jenis TB-RO primer merupakan jenis kasus resistensi obat TB yang mendominasi di Belanda. Hal ini sangat berbeda situasinya dengan negara dengan angka kejadian TB tinggi seperti Indonesia.

Seperti yang telah dianalisis sebelumnya, ketidakpatuhan pengobatan TB merupakan salah satu faktor yang mempengaruhi kejadian TB-RMO. Berdasarkan hal tersebut, pada bab 5 kami memaparkan sebuah review sistematik yang membahas mengenai penilaian kepatuhan dan kesinambungan (persistence) penggunaan multipel obat. Dengan menggunakan pendekatan farmakoepidemiologi, kami menganalisis studi-studi yang terkait dengan penggunaan multipel obat kardio-metabolik. Pemilihan obat kardio- metabolik dikarenakan minimnya studi di area TB yang menggunakan bank data peresepan. Studi kami menunjukkan bahwa kepatuhan penggunaan multipel obat dianalisis dengan beberapa pengukuran yang berbeda, yaitu pengukuran: 1) “keseluruhan obat”, 2) “kedua-duanya obat”, 3) “salah satu obat”, 4) “rata-rata obat”, 5) “penggunaan obat yang tertinggi/terendah. Berkaitan dengan kesinambungan (persistence) terhadap pengobatan, kami mengidentifikasi dua pendekatan pengukuran yang berbeda, yaitu kesinambungan penggunaan “obat keseluruhan” dan “kedua-duanya obat”. Selain itu juga kami menganalisis bahwa kepatuhan penggunaan obat anti TB dapat diukur melalui penilaian yang menggunakan “keseluruhan obat” dengan sistem penilaian berdasarkan interval waktu. Hal tersebut dikarenakan regimen TB telah dianjurkan untuk selalu diminum dalam periode yang telah ditetapkan. Meskipun demikian, sistem peresapan dan kualitas bank data peresepan perlu diperhatikan

202 Ringkasan ketika akan melakukan penilaian kepatuhan dan kesinambungan penggunaan obat dengan menggunakan bank data peresepan.

Pada bab 6 kami memaparkan sebuah review sistematik terhadap studi-studi intervensi untuk meningkatkan kepatuhan penggunaan obat TB. Studi-studi intervensi dengan desain Randomized Controlled Trial (RCT) tersebut menargetkan beberapa aspek, yaitu sosio-ekonomi, pelayanan kesehatan, pasien dan pengobatan. Akan tetapi, tidak semua intervensi menunjukkan secara signifikan dalam memperbaiki kepatuhan penggunaan obat. Beberapa intervensi yang diketahui memiliki efektivitas dalam meningkatkan kepatuhan penggunaan obat dan memperbaiki luaran terapi pada kelompok pasien TB aktif, antara lain Directly Observed Treatment (DOT) yang dilakukan oleh anggota masyarakat yang terlatih, Short Message Service (SMS) yang dikombinasikan dengan edukasi TB, penguatan metode konseling, penggunaan voucer belanja, alat pengingat yang terintegrasi dengan kotak obat, dan kombinasi alat pengingat kotak obat dengan pesan teks. Pada kelompok pasien dengan infeksi TB laten, intervensi yang menggunakan regimen singkat atau intervensi DOT menunjukkan secara efektif dalam meningkatkan penggunaan obat hingga akhir periode pengobatan. Hal yang menarik adalah intervensi yang menggunakan DOT menunjukkan hasil yang bervariasi, hal tersebut menggarisbawahi bahwa intervensi dapat memiliki dampak yang berbeda di berbagai lokasi dan studi. Oleh karena itu diperlukan intervensi yang bersifat personal untuk meningkatkan kepatuhan penggunaan obat pada pasien TB mengingat faktor ketidakpatuhan pengobatan dapat bersifat individual.

Pada bab 7 kami memaparkan sebuah studi kualitatif dengan pendekatan fenomenologi yang membahas mengenai faktor-faktor penghalang terhadap kesuksesan terapi TB di sebuah provinsi di Indonesia. Kami mengidentifikasi tiga tema yang berkaitan dengan faktor penghalang kesuksesan pengobatan TB dari perspektif pasien, antara lain: 1) sosio-ekonomi (adanya stigma, minimnya dukungan keluarga, jarak yang jauh menuju fasilitas pelayanan kesehatan primer publik, kesulitan transportasi, beban biaya di pelayanan kesehatan, beban biaya transportasi dan hilangnya pendapatan keluarga), 2) pengetahuan dan persepsi (minimnya pengetahuan pasien mengenai penyakit dan program TB, persepsi negatif pasien terhadap kondisi diri sendiri, dan pandangan negatif pasien terhadap pelayanan kesehatan publik, 3) Pengobatan TB (adanya efek samping obat dan panjangnya masa terapi). Hasil dari analisis data tersebut menunjukkan bahwa faktor-faktor penghalang yang telah teridentifikasi dapat saling berhubungan, sehingga secara keseluruhan, setidaknya terdapat lima faktor penghalang utama dalam kesuksesan pengobatan TB, antara lain minimnya pengetahuan pasien mengenai TB, adanya stigma, jarak yang relatif jauh menuju fasilitas kesehatan, hilangnya pendapatan keluarga dan adanya kejadian efek samping obat.

Pada bab 8, kami mengajukan beberapa rekomendasi untuk pengembangan ASP yang efektif pada penyakit TB. Kami mengidentifikasi bahwa strategi-strategi untuk mengendalikan

203 Addendum

TB-RO perlu mempertimbangkan masalah-masalah lokal. Di negara dengan prevalensi TB yang rendah, diperlukan sebuah strategi yang efektif yang terkait dengan program skrining dan pengobatan TB bagi imigran dan populasi-populasi yang sulit dijangkau. Akan tetapi, pada negara-negara dengan prevalensi TB yang tinggi, seperti Indonesia, manajemen pengobatan TB merupakan hal yang sangat penting dan hal tersebut perlu dilakukan dan diterapkan melalui ASP yang terintegrasi. Selain itu, penemuan kasus TB juga perlu menjadi bagian dari kegiatan ASP, mengingat keterlambatan terapi TB merupakan salah satu faktor terhadap ketidaksuksesan pengobatan TB di Indonesia. Beberapa strategi yang diperlukan untuk menciptakan ASP yang optimal di Indonesia, diantarnya adalah pelibatan komitmen pemimpin lokal dan nasional, penguatan pusat pelayanan kesehatan primer untuk deteksi kasus dan pengobatan TB, peningkatan kolaborasi antara sektor publik dan swasta, serta peningkatan peran apoteker dalam manajemen TB. Berkaitan dengan kepatuhan penggunaan obat, studi kami menyarankan bahwa sistem peresepan dan kualitas bank data peresepan di suatu daerah perlu dipertimbangkan jika akan menggunakan bank data peresepan sebagai alat untuk menilai kepatuhan penggunaan obat pada pasien TB. Selain itu, intervensi melalui pendekatan personal (personalized approach) dengan mempertimbangkan faktor-faktor individual sangat diperlukan untuk pengembangan program peningkatan kepatuhan penggunaan obat pada pasien-pasien TB.

204 Acknowledgement

ACKNOWLEDGMENTS

A thousand miles was travelled to have a fantastic journey in my four years of Ph.D. trajectory. Without any supports from many kind people, my Ph.D. promotion cannot be real. Therefore, I would like to express my gratitude to all people and institutions who supported me during my Ph.D. time.

Firstly, I would like thank to the Indonesia Endowment Fund for Education or LPDP who sponsored me studying at the University of Groningen, the Netherlands. “Aku pasti kembali !”

I would like sincerely thank my promoters, Prof. Eelko Hak and Prof. Jan-Willem Alffenaar.

Dear Eelko, thank you for allowing me to do my Ph.D. in our unit. I still remember when I met you for the first time at the International Conference on Pharmacoepidemiology, 2014, Taipei. You allowed me to develop my idea and connected with another expert to enrich my research content. I learned a lot from you on how you manage your Ph.D. students. You offered me many independencies, but still under your supervision to have qualified works and get in time finishing my Ph.D. projects. Your fast-thinking inspired me to do smart and effective works. It also motivated me to make decisions bravely when facing with some research problems.

Dear Jan-Willem, thank you for supervising me during my Ph.D. trajectory. I am fortunate to have you as my supervisor. Your creative idea helps me to conduct my research projects. You also connected me with collaborators who support my project, so I can learn how to manage a network. I learned a lot from you on how to be a good listener and problem solver in research projects. Your quick response makes the distance of Sydney-Groningen is close. I always remember your words that as a scientist, we must have high aims. It helped me to publish our work in the top 10% journal and motivated me to use my best effort in every single work.

The reading committee, Prof. T.S. Van der Werf, Prof. K. Taxis and Prof. F.G.J Cobelens, who spent their time to review and gave constructive advice for my thesis.

All my collaborators: Dr. Judith Bruchfeld and Dr. Lina D. Forsman (Karolinska Institute); Prof. Petra Denig, Dr. Onno W. Akkerman, Dr. Job FM. Van Boven, Daphne Houtsma, M.Sc (UMCG, Groningen); dr. Natasha van’t Boveneind-Vrubleuskaya (GGD, the Hague); Dr. Ari Probandari (UNS, Solo); Prof. Ajeng Diantini, Bonny Wiem Lestari, M.Sc (UNPAD, Bandung). Thank you for all your support and insight during my studies. Also, mbak Lusiana R. Idrus and Sofa D. Alfian, who help me a lot for conducting and reporting some studies in this thesis. I also extend my gratitude to Mira Miratuljannah, Pepi, Puti Primadini, Chevy Luviana, and Ibu Devi Aulia, who helped and supported me during my field study.

205 Addendum

My roommates, Yuan, Akbar and mbak Sylvi. Thank you for the fruitful discussions and sharing experiences. I still remember when we struggled in performing data handling assignments and systematic review studies. I will miss that moment where we can work together in our room. Your hard work ensured me that you all will be a successful person in the future.

All the current and former member of PTE2 and PEGET: Prof. Bob Wilfert, Prof. Katja Taxis, Prof. Marteen J. Postma, Prof. Puijenbroek, Dr. Nynke, Pepijn, Marcy, Neily, Jurjen, Pieter, kaka Ury, mba Tia, mba Lusi, Qi, Wandy, Eva, mas Didik, mas Khairul, mba Afifah, Sofa, Christian, Yuan, Akbar, mbak Sylvi, Fajri, mbak Ira, Abraham, Tanja, Simon, Taichi, Aizati, Lan, Thang, Linda, Helen, Atiqul, Monik, Jens, Bert and Anya. Thank you for your support and help during my studies. I also thank Jannie and Jugo for their care in supporting administration things and providing an excellent computer with double screens.

The Faculty members of Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Prof. Anas Subarnas, Prof. Ajeng Diantini, Prof. Keri Lestari, Prof. Jutti Levita, Prof. Sri adi Sumiwi, Prof. Ahmad Muhtadi and Dr. Eli Halimah, who always support and encourage me until this stage. Also, the “young and dangerous gang”, Auliya, teh Moly, teh Ellin, teh Eli, teh Rizki, Rano, Arif, Imam, Sofa, Dika and Adi priyanto, thank you for supporting me all the time. For bang Rizky Abdulah, thanks for being a mentor and discussion partner; I hope you can still guide us patiently.

For the awesome guys in my Ph.D. courses, Omar Faroque and Bart Hiemstra. You helped me to understand about statistic and prediction model. I wish you all the best for your future carrier.

A sweet BBBO family who always help each other’s: mas Amak- mba Putri, mas Khairul- mba Retno, mas Joko- mba Uchi, and om Agung- tante Inna, mba Icha- mas Erdi and Riswandy- Cici. We should continue our group meeting and make more massive barbeque and “lutisan” in Indonesia. I also thank om Sem Likumahua and tante Sarah Hawun for being part of Beren 43, thank you for your patience during your stay in Beren. Renata and Arkan will miss you.

All Indonesian friends in the DeGromist, PPIG and LPDP: mas Chalis- mba Jean, Nazmudin- Anis, Azka Muji- Aidina, Adhyat- Nuri, Afif, Guntur, Ali- Liany, Fajar- Monik, mas Romi- mba Arlina, bude Nunung, mas Ega- mba Irma, mas Ega- mba titis, Habibi- Ma’wa, mba Zamrotul, mas Latif- Septi, mas Panji Jamaah, pak Yopi- mba Dewi, kang Tatang, mas Surya- mba Yassaroh, mas Krisna- mba Icha, Umar Djalin, mas Ristiono- mba Afifah, Adityo, mas Lana- mba Arum, Dimas- Anya, mas rifqi Rohman, Bhimo, mas ust Naufal, Novita Rudiany, Masyita, Ucon, Salva, Sofa- Yudi, Zaki- Nadya, pak lurah Thomas, Prety, Alfian, pak Petrus, Adjie Pratama, mba Rosyta, mba Tania, pak Asmoro- bu Rini om Deka, Marina Ika, Ghozi,

206 Acknowledgement and bli Kadek. You all make my Ph.D. days so colorful with sharing ideas, experiences, foods and opinions. As a communal person, your friendship makes me alive. I hope we still keep in touch in the future and share our happy stories.

Tim Zwagastra, a kind person who advise me many things related to Groningen. I always remember when you help me arguing with a housing provider at the beginning of my study. After that, I try to apply your lesson that debate is primarily needed in the Netherlands. Thanks for helping me and other Indonesian students

Finally, I would like to express my deepest gratitude to my lovely family, who continually stand for me every single time.

Dear Dita Fitria, I know that it is not easy to accompany me in the new chapter of our journey in the Netherlands. Your patience, care and sacrifice realize me that I am the luckiest person to have you in my side. I am glad that you are thoughtful and so much fun to be around with, but I am most glad that you are my wife. I always believe that behind every great man is a truly amazing wife. Thank you for understanding me always.

Renata and Arkan, thank you for accompanying Abi in Groningen. Your smile and happiness raise me up from many difficult situations. I hope you will understand that life is a dynamic process. Don’t be afraid of all challenges, because it will help you to understand the meaning of life.

Dear mama Lilik Sabdaningtyas and papa Annukman Sulaiman, thankful words are not enough to express my gratitude for your unconditional love and support. I always remember that your sacrifice to allow me to stay far away in Java is amazing. Since that, I fully realize that your blessing led me at this stage. I’m thankful for your love and care. May your days be stuffed with health, happiness and love.

Dear mama Diah Herawaty and papa Faturachman, thank you for always supporting and understanding me in every situation. I’m thankful for your love and care to me, Dita, Renata and Arkan. May your days be stuffed with health, happiness and love.

My brothers and sisters, Citra Agung Budhi Dharma- Nirmala Tina; Danny Rachdiana, Dimas Firmanudin- Heni Suherman and also all my nephews and nieces Raya, Lady, Malik, Radif and Arine. Thank you for always being part of me and taking care of our parents. Wishing your days with God’s amazing love and blessings.

Last but not least, I am grateful to all people who gave their contribution to my Ph.D. projects that cannot be listed one by one in this acknowledgment. Thank you very much!.

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ABOUT THE AUTHOR

Ivan Surya Pradipta (1983) was born in Yogyakarta, Indonesia. After finishing high school in Bandar Lampung, Indonesia, he began studying pharmacy at the Faculty of Pharmacy, Universitas Padjadjaran, Indonesia in 2001. He had finished the bachelor’s degree in Pharmacy, then continued his professional degree as a Pharmacist at the same University in 2006. He holds a master’s degree in clinical pharmacy with cum-laude from the Faculty of Pharmacy, Universitas Gadjah Mada, Indonesia, in 2009. After that, He continued working as a lecturer and researcher at the Departement of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Indonesia. As a researcher, he has an interest in the topic of antimicrobial use and resistance. In February 2016, He started a Ph.D. trajectory under the supervision of Prof. Eelko Hak and Prof. Jan-Willem Alffenaar at the Unit of Pharmaco-Therapy, -Epidemiology and -Economics (PTE2), University of Groningen, the Netherlands. His Ph.D. project focused on the strategy to improve treatment outcomes and control drug resistance of tuberculosis, with the funding from the Indonesia Endowment Fund for Education or LPDP. During his Ph.D. trajectory, He actively presented his study in international conferences and successfully published his studies in the top 10% scientific journal. He also obtained a travel grant from the International Union Against Tuberculosis and Lung Disease to present his studies in The 49th Union World Conference on Lung Health. After finishing Ph.D., he will continue his career as a lecturer and researcher at the Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Indonesia.

Ivan lives in Bandung, Indonesia, with his lovely wife (Dita) and two children (Renata and Arkan).

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PHD PORTFOLIO

Name : Ivan Surya Pradipta Unit of PhD : Unit of Pharmaco-Therapy, -Epidemiology and –Economics, Department of Pharmacy, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, the Netherlands. PhD period : 2016-2020 Promotors : Prof. Eelko Hak Prof. Jan-Willem Alffenaar

Courses/ Workshops Year Workload (hours/ ECTS) Internal • Presentation skills 2016 0.5 • Academic writing 2016 2 • Data handling and pharmacoepidemiology in practice 2016 5 • Health technology assessment: quality-of-life and patient-reported 2016 2.5 outcome measures (PROMs) • Systematic review and meta-analysis 2016 1 • Scientific integrity 2016 2.5 • Multivariate analyses: how to handle three variables 2016 1 • Working with questionnaires in patient-related research 2016 1 • Pharmacoepidemiology UK 2016 5 • Epidemiology and applied statistics 2016 3 • Managing your PhD 2016 2 • Medical statistics 2017 3 • Study design in clinical epidemiology 2017 4 • Science writing 2017 2 • Advance pharmacoepidemiology 2017 5 • Critical appraisal of literature 2018 1.5 • Advanced clinical epidemiology 2018 3 • Publishing in English 2018 3 • Introduction to qualitative research methods 2018 2 External • European Society for Clinical Microbiology and Infectious Diseases 2016 1 (ESCMID): post-graduate technical workshop on better methods for clinical studies in infectious diseases and clinical microbiology. Seville, Spain. • Erasmus Summer Program: marker and prediction research. Rotterdam, 2017 0.7 the Netherlands. • ESCMID: antimicrobial stewardship program, principle and practice. 2017 2 Istanbul, Turkey. • USAID- CCR ARI (Center for collaborative research on acute 2019 1 respiratory infection), Faculty of Medicine, Universitas Padjadjaran: Applied clinical trial training, Bandung, Indonesia. • European Consortium for Political Research (ECPR): qualitative data 2019 2 analysis, concept and approaches. Budapest, Hungary.

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Journal referee • Pharmacology and Clinical Pharmacy Research 2016 • The Journal of Infection in Developing Countries 2018 • Medical Journal Indonesia 2018 • Indonesian Journal of Clinical Pharmacy 2016-2019 • BMC Infectious Diseases 2019 • BMC Systematic review 2019 • Therapeutics and Clinical Risk Management 2019 • The American Journal of Tropical Medicine and Hygiene 2019 • International Journal of Integrated Health Sciences 2019 • The Lancet Infectious Diseases 2020 Professional organization • International Society for Pharmacoepidemiology (ISPE), Membership 2013- now Identificaton number : 30080 • European Society for Clinical Microbiology & Infectious Disease 2016- now (ESCMID), member ID: 133948 • International Union Against Tuberculosis and Lung Disease. Member 2018- now ID: CU-0780309

Scientific journals Pradipta IS, Van’t Boveneind-Vrubleuskaya N, Akkerman OW, Alffenaar JC, Hak E. Treatment outcomes of drug-resistant tuberculosis in the Netherlands, 2005-2015. Antimicrob Resist Infect Control. 2019 Jul 12;8:115.

Pradipta IS, Van’t Boveneind-Vrubleuskaya N, Akkerman OW, Alffenaar JWC, Hak E. Predictors for treatment outcomes among patients with drug-susceptible tuberculosis in the Netherlands: a retrospective cohort study. Clin Microbiol Infect. 2019 Jun;25(6):761. e1-761.e7.

Pradipta IS, Forsman LD, Bruchfeld J, Hak E, Alffenaar JW. Risk factors of multidrug- resistant tuberculosis: A global systematic review and meta-analysis. J Infect. 2018 Dec;77(6):469-478.

Pradipta IS, Houtsma D, Van Boven JFM, Alffenaar JWC, Hak E. Interventions to improve medication adherence in patients with latent and active tuberculosis infection: a systematic review of randomized controlled studies. (Submitted)

Pradipta IS, Idrus LR, Probandari A, Lestari BW, Diantini A, Alffenaar JWC, Hak E. Barriers and strategies to successful tuberculosis treatment in a high-burden tuberculosis setting: a qualitative study from the patient’s perspective. (Submitted)

Alfian SD, Pradipta IS, Hak E, Denig P. A systematic review finds inconsistency in the measures used to estimate adherence and persistence to multiple cardiometabolic medications. J Clin Epidemiol. 2019 Apr;108:44-53.

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Jannah MM, Pradipta IS, Santoso P, Puspitasari IM. Association between DOTS program and the outcome of previous therapy in MDR-TB patients: a case study in Tasikmalaya district, West Java, Indonesia. J Adv Pharm Edu Res. 2019 Jan; 9(1):69-71.

Destiani DP, Milanda T, Susilawati S, Suwantika AA, Pradipta IS, et al. Cost-effectiveness analysis of ceftazidime-levofloxacin and cefotaxime‑erythromycin as empirical antibiotic combinations in respiratory infection-induced sepsis. Asian J Pharm Clin Res. 2017 Jan; 10 (s1): 119-121

Barliana MI, Amalya SN, Pradipta IS, Alfian SD, Kusuma AS, Milanda T, Abdulah R. DNA methyltransferase 3A gene polymorphism contributes to daily life stress susceptibility. Psychol Res Behav Manag. 2017 Dec 15;10:395-401.

Chimeh RA, Gafar F, Pradipta IS, Akkerman OW, Hak E, Alffenaar JWC, Van Boven JFM. Clinical and economic impact of non-adherence to medication in patients with drug- susceptible tuberculosis: a systematic review. Int J Tuberc Lung Dis. 2019.(In press)

Scientific conferences Pradipta IS, Van’t Boveneind-Vrubleuskaya N, Akkerman OW, Alffenaar JW, Hak E . Predictors for treatment outcomes among drug-resistant tuberculosis patients in the Netherlands: A retrospective cohort study. The 35th International Conference on Pharmacoepidemiology & Therapeutic Risk Management. Philadelphia, the United States. August 24-28, 2019. (poster presentation)

Pradipta IS, Van’t Boveneind-Vrubleuskaya N, Akkerman OW, Alffenaar JW, Hak E. Development of a multivariable prediction model for poor outcome of tuberculosis treatment among drug-resistant tuberculosis patients in the Netherlands. The 49th Union World Conference on Lung Health. The Hague, The Netherlands, 24-27 October 2018. (oral presentation)

Pradipta IS, Van’t Boveneind-Vrubleuskaya N, Akkerman OW, Alffenaar JW, Hak E. Predictors for Treatment Outcomes among Patients with Drug-susceptible Tuberculosis in the Netherlands. The 49th Union World Conference on Lung Health. The Hague, The Netherlands, 24-27 October 2018. (oral presentation)

Pradipta IS, Van’t Boveneind-Vrubleuskaya N, Akkerman OW, Alffenaar JW, Hak E. Development and validation of a prediction model for poor outcome of tuberculosis treatment: a nationwide, register-based retrospective cohort study from the Netherlands. The 34th International Conference on Pharmacoepidemiology & Therapeutic Risk Management. Prague, Czech Republic. August 22-26, 2018. (poster presentation)

Alfian SD, Pradipta IS, Hak E, Denig P. A systematic review of measures for calculating adherence and persistence to multiple medications from prescription data. The 34th International Conference on Pharmacoepidemiology & Therapeutic Risk Management. Prague, Czech Republic. August 22-26, 2018. (poster presentation)

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