Changes in health in the countries of the UK and 150 English Local Authority areas 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

BACKGROUND: Previous studies have reported national and regional Global Burden of Disease (GBD) estimates for the UK. Because of substantial variation in health within the UK, action to improve it requires comparable estimates of disease burden and risks at country and local levels. The slowdown in the rate of improvement in life expectancy requires further investigation. We use GBD 2016 data on mortality, causes of death, and disability to analyse the burden of disease in the countries of the UK and within local authorities in England by deprivation quintile. METHODS: We extracted data from the GBD 2016 to estimate years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), and attributable risks from 1990 to 2016 for England, Scotland, Wales, Northern Ireland, the UK, and 150 English Upper-Tier Local Authorities. We estimated the burden of disease by cause of death, condition, year, and sex. We analysed the association between burden of disease and socioeconomic deprivation using the Index of Multiple Deprivation. We present results for all 264 GBD causes of death combined and the leading 20 specific causes, and all 84 GBD risks or risk clusters combined and 17 specific risks or risk clusters. FINDINGS: The leading causes of age-adjusted YLLs in all UK countries in 2016 were ischaemic heart disease, lung cancers, cerebrovascular disease, and chronic obstructive pulmonary disease. Age-standardised rates of YLLs for all causes varied by two times between local areas in England according to levels of socioeconomic deprivation (from 14 274 per 100 000 population [95% uncertainty interval 12 791-15 875] in Blackpool to 6888 [6145-7739] in Wokingham). Some Upper-Tier Local Authorities, particularly those in London, did better than expected for their level of deprivation. Allowing for differences in age structure, more deprived Upper-Tier Local Authorities had higher attributable YLLs for most major risk factors in the GBD. The population attributable fractions for all-cause YLLs for individual major risk factors varied across Upper-Tier Local Authorities. Life expectancy and YLLs have improved more slowly since 2010 in all UK countries compared with 1990-2010. In nine of 150 Upper-Tier Local Authorities, YLLs increased after 2010. For attributable YLLs, the rate of improvement slowed most substantially for cardiovascular disease and breast, colorectal, and lung cancers, and showed little change for Alzheimer's disease and other dementias. Morbidity makes an increasing contribution to overall burden in the UK compared with mortality. The age-standardised UK DALY rate for low back and neck pain (1795 [1258-2356]) was higher than for ischaemic heart disease (1200 [1155-1246]) or lung cancer (660 [642-679]). The leading causes of ill health (measured through YLDs) in the UK in 2016 were low back and neck pain, skin and subcutaneous diseases, migraine, depressive disorders, and sense organ disease. Age-standardised YLD rates varied much less than equivalent YLL rates across the UK, which reflects the relative scarcity of local data on causes of ill health. INTERPRETATION: These estimates at local, regional, and national level will allow policy makers to match resources and priorities to levels of burden and risk factors. Improvement in YLLs and life expectancy slowed notably after 2010, particularly in cardiovascular disease and cancer, and targeted actions are needed if the rate of improvement is to recover. A targeted policy response is also required to address the increasing proportion of burden due to morbidity, such as musculoskeletal problems and depression. Improving the quality and completeness of available data on these causes is an essential component of this response. FUNDING: Bill & Melinda Gates Foundation and Public Health England.

Estimating disease burden using national linked electronic health records: a study using an English population-based cohort.

Background: Electronic health records (EHRs) have the potential to be used to produce detailed disease burden estimates. In this study we created disease estimates using national EHR for three high burden conditions, compared estimates between linked and unlinked datasets and produced stratified estimates by age, sex, ethnicity, socio-economic deprivation and geographical region. Methods: EHRs containing primary care (Clinical Practice Research Datalink), secondary care (Hospital Episode Statistics) and mortality records (Office for National Statistics) were used. We used existing disease phenotyping algorithms to identify cases of cancer (breast, lung, colorectal and prostate), type 1 and 2 diabetes, and lower back pain. We calculated age-standardised incidence of first cancer, point prevalence for diabetes, and primary care consultation prevalence for low back pain. Results: 7.2 million people contributing 45.3 million person-years of active follow-up between 2000-2014 were included. CPRD-HES combined and CPRD-HES-ONS combined lung and bowel cancer incidence estimates by sex were similar to cancer registry estimates. Linked CPRD-HES estimates for combined Type 1 and Type 2 diabetes were consistently higher than those of CPRD alone, with the difference steadily increasing over time from 0.26% (2.99% for CPRD-HES vs. 2.73 for CPRD) in 2002 to 0.58% (6.17% vs. 5.59) in 2013. Low back pain prevalence was highest in the most deprived quintile and when compared to the least deprived quintile the difference in prevalence increased over time between 2000 and 2013, with the largest difference of 27% (558.70 per 10,000 people vs 438.20) in 2013. Conclusions: We use national EHRs to produce estimates of burden of disease to produce detailed estimates by deprivation, ethnicity and geographical region. National EHRs have the potential to improve disease burden estimates at a local and global level and may serve as more automated, timely and precise inputs for policy making and global burden of disease estimation.

Association between area-level socioeconomic deprivation and a cluster of behavioural risk factors: cross-sectional, population-based study.

BACKGROUND: The Commission on Social Determinants of Health has urged governments across the world to promote health equity by reducing the gap between the most and least deprived individuals in society. Some of this gap can be bridged by promoting healthy lifestyles through targeted Public Health policy and interventions. METHODS: Cross-sectional analyses of data on behavioural risk factors, individual socioeconomic factors and neighbourhood deprivation score collected from 26 290 adults aged over 16 years who participated in the 2008 East of England Lifestyle Survey. RESULTS: After adjustment for individual socioeconomic factors, across quintiles of increasing neighbourhood deprivation, participants were more likely to smoke and less likely to consume five portions of fruit and vegetables on five or more days of the week (least deprived versus most deprived quintile: odds ratios for not smoking 0.45 (0.41-0.50); and fruit and vegetable consumption 0.70 (0.64-0.76), P-trend <0.0001). Greater neighbourhood deprivation and lower occupational social class were independently associated with a lower summary healthy lifestyle score (both P-trend <0.0001). CONCLUSIONS: Public health interventions aimed at reducing health inequalities by targeting behavioural risk factors may focus in particular on reducing smoking and increasing fruit and vegetable consumption in more deprived communities.

COPD in England: a comparison of expected, model-based prevalence and observed prevalence from general practice data.

BACKGROUND: Primary care data show that 765 000 people in England have a general practice (GP) diagnosis of chronic obstructive pulmonary disease (COPD). We hypothesized that this underestimates actual prevalence, and compared expected prevalence of COPD for English local authority areas with prevalence of diagnosed COPD. METHODS: Cross-sectional comparison of GP observed and model-based prevalence estimates (using spirometry data without clinical diagnosis) from the Health Survey for England. Local underdiagnosis of COPD was estimated as the ratio of observed to expected cases. We investigated geographical patterns using classical and geographically weighted regression analysis. RESULTS: Both observed and expected prevalence of COPD varied widely between areas. There was evidence of a 'north-south' divide, with both observed and modelled prevalence higher in the north. The ratio of diagnosed to expected prevalence varied from 0.20 to 0.95, with a mean of 0.52. Underdiagnosis was more pronounced in urban areas, and is particularly severe in London. The inclusion of GP numbers in the analysis yielded a stronger regression relationship, suggesting primary care supply affects diagnosis. CONCLUSION: Both observed and modelled COPD prevalence varies considerably across England. Cost-effective case-finding strategies should be evaluated, especially in areas where the ratio of observed to expected cases is low.

Evaluation of the uptake and delivery of the NHS Health Check programme in England, using primary care data from 9.5 million people: a cross-sectional study.

OBJECTIVES: To describe the uptake and outputs of the National Health Service Health Check (NHSHC) programme in England. DESIGN: Observational study. SETTING: National primary care data extracted directly by NHS Digital from 90% of general practices (GP) in England. PARTICIPANTS: Individuals aged 40-74 years, invited to or completing a NHSHC between 2012 and 2017, defined using primary care Read codes. INTERVENTION: The NHSHC, a structured assessment of non-communicable disease risk factors and 10-year cardiovascular disease (CVD) risk, with recommendations for behavioural change support and therapeutic interventions. RESULTS: During the 5-year cycle, 9 694 979 individuals were offered an NHSHC and 5 102 758 (52.6%) took up the offer. There was geographical variation in uptake between local authorities across England ranging from 25.1% to 84.7%. Invitation methods changed over time to incorporate greater digitalisation, opportunistic delivery and delivery by third-party providers.The population offered an NHSHC resembled the English population in ethnicity and deprivation characteristics. Attendees were more likely to be older and women, but were similar in terms of ethnicity and deprivation, compared with non-attendees. Among attendees, risk factor prevalence reflected population survey estimates for England. Where a CVD risk score was documented, 25.9% had a 10-year CVD risk ≥10%, of which 20.3% were prescribed a statin. Advice, information and referrals were coded as delivered to over 2.5 million individuals identified to have risk factors. CONCLUSION: This national analysis of the NHSHC programme, using primary care data from over 9.5 million individuals offered a check, reveals an uptake rate of over 50% and no significant evidence of inequity by ethnicity or deprivation. To maximise the anticipated value of the NHSHC, we suggest continued action is needed to invite more eligible people for a check, reduce geographical variation in uptake, prioritise engagement with non-attendees and promote greater use of evidence-based interventions especially where risk is identified.

Closing the gap: using prevalence models for long-term conditions in the United Kingdom.

Until now, most healthcare policies have been focused on the relatively small number of patients with long-term conditions who suffer from a high disease burden, and less attention has been directed toward detecting patients with undiagnosed long-term conditions. In the United Kingdom, population-based data on the prevalence of disease detected in primary care are now available. Prevalence models for diabetes, coronary heart disease, hypertension, and chronic obstructive pulmonary disease provide survey-based estimates for small populations based on local data on known risk factors. These suggest that a significant proportion of prevalent disease remains undiagnosed. Other models are being developed. More systematic case finding to close disease prevalence gaps may be cost-effective, but case-finding strategies need to be tested and current and new models require validation.

An alternative approach to quantifying and addressing inequity in healthcare provision: access to surgery for lung cancer in the east of England.

STUDY OBJECTIVE: Equitable access to healthcare services should be monitored routinely. This study compares provision of surgery for non-small cell lung cancer in the east of England with incidence of non-small cell lung cancer. In addition to conventional comparisons, process control charts are used to identify areas in which access seems to be significantly different from average. DESIGN: Ecological comparison of surgery rates for non-small cell lung cancer between 1998 and 2000 and incidence of non-small cell lung cancer over the same time period. SETTING: Population of Norfolk, Suffolk, Cambridgeshire. PARTICIPANTS: The denominator was the resident population. Numerators were 4092 deaths from non-small cell lung cancer and 387 surgical procedures for lung cancer. MAIN RESULTS: Incidence of non-small cell lung cancer by primary care trust (PCT) does not correlate with surgical procedure rate, in men r = 0.37 (95% confidence intervals -0.14 to 0.72), in women r = 0.07 (95% confidence intervals -0.43 to 0.53). Control charts indicate that the surgery rate is significantly different from average in three PCTs, high in one and low in two others. The optimum surgery rate is unclear but raising it from 9% to a theoretical level of 15% would mean no PCTs have above average rates while six PCTs have rates that are significantly low. CONCLUSIONS: There does not seem to be equity of access to surgery for patients with non-small cell lung cancer in the east of England. Control charts can help both to identify areas where access is particularly high or low, and also to monitor performance against a theoretical optimum surgery rate.

Modelling bias in combining small area prevalence estimates from multiple surveys.

Combining information from multiple surveys can improve the quality of small area estimates. Customary approaches, such as the multiple-frame and statistical matching methods, require individual level data, whereas in practice often only multiple aggregate estimates are available. Commercial surveys usually produce such estimates without clear description of the methodology that is used. In this context, bias modelling is crucial, and we propose a series of Bayesian hierarchical models which allow for additive biases. Some of these models can also be fitted in a classical context, by using a mixed effects framework. We apply these methods to obtain estimates of smoking prevalence in local authorities across the east of England from seven surveys. All the surveys provide smoking prevalence estimates and confidence intervals at the local authority level, but they vary by time, sample size and transparency of methodology. Our models adjust for the biases in commercial surveys but incorporate information from all the sources to provide more accurate and precise estimates.

Public health indicators.

An indicator is a measure used to express the behaviour of a system or part of a system. Indicators are widely used in the public sector, and there is widespread use of indicators for performance management of public health. In this paper, we define some of the terms used in relation to indicators. We outline some of the most important issues around selection and construction of indicators, and we include criteria for developing or assessing indicators. Use of inappropriate indicators can be misleading and can result in negative consequences for public health, and we point out the potential for pitfalls. Some misinterpretation of indicators could be avoided by use of better methods of presentation than the familiar league table. We use the example of a funnel plot to show a method of summarising indicator data which avoids ranking, and allows rapid identification of areas functioning outside normal limits.