CVD: Primary Care Intelligence Packs CCG: NHS Kernow CCG
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CVD: Primary Care Intelligence Packs CCG: NHS Kernow CCG April 2016 Version 1.0 Contents 1. Introduction 3 2. CVD prevention • The narrative 9 • The data 11 3. Hypertension • The narrative 18 • The data 19 4. Stroke • The narrative 30 • The data 31 5. Diabetes • The narrative 44 • The data 45 6. Kidney • The narrative 52 • The data 53 7. Heart • The narrative 63 • The data 64 8. Outcomes 82 9. Appendix 88 This document is valid only when viewed via the internet. If it is printed into hard copy or saved to another location, you must first check that the version number on your copy matches that of the one online. Printed copies are uncontrolled copies. 2 CVD: Primary Care Intelligence Packs Introduction 3 CVD: Primary Care Intelligence Packs This Intelligence Pack has been compiled by GPs and nurses and pharmacists in the Primary Care CVD Leadership Forum in collaboration with the National Cardiovascular Intelligence Network Matt Kearney Sarit Ghosh Kathryn Griffith George Kassianos Jo Whitmore Matthew Fay Chris Harris Jan Procter-King Yassir Javaid Ivan Benett Ruth Chambers Ahmet Fuat Mike Kirby Peter Green Kamlesh Khunti Helen Williams Quincy Chuhka Sheila McCorkindale Nigel Rowell Ali Morgan Stephen Kirk Sally Christie Clare Hawley Paul Wright Bruce Taylor Mike Knapton John Robson Richard Mendelsohn Chris Arden David Fitzmaurice 4 CVD: Primary Care Intelligence Packs Local intelligence as a tool for clinicians and commissioners to improve outcomes for our patients Why should we use this CVD Intelligence Pack Every year in England there are around 150,000 premature deaths. A quarter of premature deaths are due to cardiovascular disease. Two thirds of premature deaths could be avoided through improved prevention, earlier detection and better treatment. High quality primary care is crucial for improving outcomes in CVD because primary care is where much prevention and most diagnosis and treatment is delivered. This cardiovascular intelligence pack is a powerful resource for stimulating local conversations about quality improvement in primary care. Across a number of vascular conditions, looking at prevention, diagnosis, care and outcomes, the data allows comparison between CCGs and between practices. This is not about performance management because we know that variation can have more than one interpretation. But patients have a right to expect that we will ask challenging questions about how the best practices are achieving the best and what average or below average achievers could do differently and how they could be supported to perform as well as the best. How to use the CVD Intelligence Pack The intelligence pack has several sections – CVD prevention, hypertension, stroke and AF, diabetes, kidney and heart disease and heart failure. Each section has one slide of narrative that makes the case and asks some questions. This is followed by data for a number of indicators, each with benchmarked comparison between CCGs and between practices. Use the pack to identify where there is variation that needs exploring and to start asking challenging questions about where and how quality could be improved. We suggest you then develop a local action plan for quality improvement – this might include establishing communities of practice to build clinical leadership, use of audit tools to get a better understanding of the gaps in care and outcomes, agreeing local protocols and consensus approaches, assessing training and education needs, and exploring new ways of delivering care. 5 CVD: Primary Care Intelligence Packs Data and methods This slide pack compares the clinical commissioning group (CCG) with CCGs in its strategic clinical network (SCN) and England. Where a CCG is in more than one SCN, it has been allocated to the SCN with the greatest geographical or population coverage. The slide pack also compares the CCG to its 10 most similar CCGs in terms of demography, ethnicity and deprivation. For information on the methodology used to calculate the 10 most similar CCGs please go to: http://www.england.nhs.uk/resources/resources-for-ccgs/comm-for-value/ The 10 most similar CCGs to NHS Kernow CCG are: NHS Somerset CCG NHS Cumbria CCG NHS Coastal West Sussex CCG NHS Gloucestershire CCG NHS Dorset CCG NHS West Hampshire CCG NHS Northumberland CCG NHS East Riding of Yorkshire CCG NHS North East Essex CCG NHS Ipswich and East Suffolk CCG The majority of data used in the packs are taken from the 2014/15 Quality and Outcomes Framework (QOF). Where this is not the case, this is indicated in the slide. All GP practices that were included in the 2014/15 QOF are included. Full source data are shown in the appendix. For the majority of indicators, the additional number of people that would be treated if all practices were to achieve as well as the average of the top achieving practices is calculated. This is calculated by taking an average of the intervention rates (i.e. the denominator includes exceptions) for the best 50% of practices in the CCG and applying this rate to all practices in the CCG. Note, this number is not intended to be proof of a realisable improvement; rather it gives an indication of the magnitude of available opportunity. 6 CVD: Primary Care Intelligence Packs Why Does Variation Matter? A key observation about benchmarking data The variation that exists between is that it does not tell us why there is variation. Some of demographically similar CCGs and the variation may be explained by population or case mix and some may be unwarranted – we will not know between practices illustrates the unless we investigate. local potential to improve care and outcomes for our patients Benchmarking may not be conclusive. Its strength lies not in the answers it provides but in the questions it generates for CCGs and practices. Benchmarking is helpful because it highlights variation. For example: 1. How much variation is there in detection, management, Of course it has long been acknowledged that exception reporting and outcomes? some variation is inevitable in the healthcare and 2. How many people would benefit if average performers outcomes experienced by patients. improved to the level of the best performers? But John Wennberg, who has championed 3. How many people would benefit if the lowest performers research into clinical variation over four decades matched the achievement of the average? and who founded the pioneering Dartmouth Atlas 4. What are better performers doing differently in the way of Health Care, concluded that much variation is they provide services in order to achieve better outcomes? unwarranted – i.e. it cannot be explained on the 5. How can the CCG support low and average performers to basis of illness, medical evidence, or patient help them match the achievement of the best? preference but is accounted for by the willingness 6. How can we build clinical leadership to drive quality and ability of doctors to offer treatment. improvement? There are legitimate reasons for exception-reporting. But ……. Excepting patients from indicators puts them at risk of not receiving optimal care and of having worse outcomes. It is also likely to increase health inequalities. The substantial variation seen in exception reporting for some indicators suggests that some practices are more effective than others at reaching their whole population. Benchmarking exception reporting allows us to identify the practices that need support to implement the strategies adopted by low excepting practices. CVD Prevention 8 CVD: Primary Care Intelligence Packs CVD Prevention “The NHS needs a radical upgrade in prevention if it is to be The size of the prevention problem sustainable” • 2/3 of people are obese or overweight 5 year Forward View 2014 • 1/3 of people are physically inactive • 20% of people smoke but this rises to over 50% in some communities • Evidence based interventions are effective in This is because England faces an epidemic of largely tackling these behavioural risk factors preventable non-communicable diseases, such as heart disease and stroke, cancer, Type 2 diabetes and liver disease. • Thousands of people in every CCG have undiagnosed or undertreated physiological Dietary risks risk factors such as hypertension, atrial Tobacco smoke High body-mass index fibrillation, chronic kidney disease, diabetes High systolic blood pressure Alcohol and drug use HIV/AIDS and tuberculosis and non-diabetic hyperglycaemia High fasting plasma glucose Diarrhea, lower respiratory & other common infectious diseases High total cholesterol Neglected tropical diseases & malaria Maternal disorders Low glomerular filtration rate Neonatal disorders Nutritional deficiencies Low physical activity Other communicable, maternal, neonatal, & nutritional diseases Neoplasms Occupational risks Cardiovascular diseases Air pollution Chronic respiratory diseases Cirrhosis Low bone mineral density Digestive diseases Neurological disorders Child and maternal malnutrition Mental & substance use disorders Diabetes, urogenital, blood, & endocrine diseases The NHS Health Check is a systematic Sexual abuse and violence Musculoskeletal disorders Other environmental risks Other non-communicable diseases Transport injuries Unsafe sex Unintentional injuries approach to identifying local people at high risk Self-harm and interpersonal violence Unsafe water/ sanitation/ handwashing Forces of nature, war, & legal intervention of CVD, offering behaviour change support and 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% Percent of total disability-adjusted life-years (DALYs) early detection of hypertension, CKD, diabetes The Global Burden of Disease Study (next slide) shows us that and pre-diabetes. Modelling suggests that high the leading causes of premature mortality include diet, tobacco, uptake will lead to substantial reductions in obesity, raised blood pressure, physical inactivity and raised premature mortality. cholesterol. The radical upgrade in prevention needs Question: What proportion of our local eligible population-level approaches. But it also needs ongoing population is receiving the NHS Health Check behaviour change support and medical treatment for individuals and how effective is their follow-up management during their frequent contacts with primary care.