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Background In the Global Burden of Disease Study 2013 (GBD 2013), knowledge about health and its determinants has been integrated into a comparable framework to inform health policy. Outputs of this analysis are relevant to current policy questions in England and elsewhere, particularly on health inequalities. We use GBD 2013 data on mortality and causes of death, and disease and injury incidence and prevalence to analyse the burden of disease and injury in England as a whole, in English regions, and within each English region by deprivation quintile. We also assess disease and injury burden in England attributable to potentially preventable risk factors. England and the English regions are compared with the remaining constituent countries of the UK and with comparable countries in the European Union (EU) and beyond. Methods We extracted data from the GBD 2013 to compare mortality, causes of death, years of life lost (YLLs), years lived with a disability (YLDs), and disability-adjusted life-years (DALYs) in England, the UK, and 18 other countries (the first 15 EU members [apart from the UK] and Australia, Canada, Norway, and the USA [EU15+]). We extended elements of the analysis to English regions, and subregional areas defined by deprivation quintile (deprivation areas). We used data split by the nine English regions (corresponding to the European boundaries of the Nomenclature for Territorial Statistics level 1 [NUTS 1] regions), and by quintile groups within each English region according to deprivation, thereby making 45 regional deprivation areas. Deprivation quintiles were defined by area of residence ranked at national level by Index of Multiple Deprivation score, 2010. Burden due to various risk factors is described for England using new GBD methodology to estimate independent and overlapping attributable risk for five tiers of behavioural, metabolic, and environmental risk factors. We present results for 306 causes and 2337 sequelae, and 79 risks or risk clusters. Findings Between 1990 and 2013, life expectancy from birth in England increased by 5·4 years (95% uncertainty interval 5·0-5·8) from 75·9 years (75·9-76·0) to 81·3 years (80·9-81·7); gains were greater for men than for women. Rates of age-standardised YLLs reduced by 41·1% (38·3-43·6), whereas DALYs were reduced by 23·8% (20·9-27·1), and YLDs by 1·4% (0·1-2·8). For these measures, England ranked better than the UK and the EU15+ means. Between 1990 and 2013, the range in life expectancy among 45 regional deprivation areas remained 8·2 years for men and decreased from 7·2 years in 1990 to 6·9 years in 2013 for women. In 2013, the leading cause of YLLs was ischaemic heart disease, and the leading cause of DALYs was low back and neck pain. Known risk factors accounted for 39·6% (37·7-41·7) of DALYs; leading behavioural risk factors were suboptimal diet (10·8% [9·1-12·7]) and tobacco (10·7% [9·4-12·0]). Interpretation Health in England is improving although substantial opportunities exist for further reductions in the burden of preventable disease. The gap in mortality rates between men and women has reduced, but marked health inequalities between the least deprived and most deprived areas remain. Declines in mortality have not been matched by similar declines in morbidity, resulting in people living longer with diseases. Health policies must therefore address the causes of ill health as well as those of premature mortality. Systematic action locally and nationally is needed to reduce risk exposures, support healthy behaviours, alleviate the severity of chronic disabling disorders, and mitigate the effects of socioeconomic deprivation. Funding Bill & Melinda Gates Foundation and Public Health England.
Here we use data from the GBD 2013 study of causes of death, disease, and injury incidence and prevalence as well as years lived with disability (YLDs) to analyse the burden of diseases and injuries in England by English region and, within each English region by deprivation quintile (defined nationally). The methods employed in the GBD 2013, including the systematic approach to collating cause of death from different countries, the mapping across different revisions and national variants of the International Classification of Diseases and Injuries (ICD), redistribution of deaths assigned to so-called garbage codes, and the cause of death modelling approach used for each cause, have been described in detail elsewhere.4 The GBD 2013 Collaborators5 describe the data and methods used to estimate incidence, prevalence, and YLDs for 306 causes and 2337 sequelae from 1990 to 2013, a substantial increase from 220 causes and 1160 sequelae in the GBD 2010 analysis.1 This GBD 2013 paper includes a description of the systematic reviews of the published literature, identification of unpublished data sources, efforts to map data to a consistent set of case definitions, and the general approach to Bayesian meta-regression using DisMod-MR 2.0, which allows the estimation of incidence, prevalence, remission, excess mortality, and cause-specific mortality rates that are internally consistent. Details of the method, the likelihood used in estimation, and the source code have been published elsewhere.8, 9 The analysis of risk factor-attributable burden uses the GBD 2013 framework and results.6 Sampling and non-sampling error as well as model uncertainty is propagated by estimating all steps in the calculations 1000 times. 95% uncertainty intervals (UIs) are presented by the 2·5 and 97·5 centile values. Another new feature of the GBD 2013 study is the systematic aggregation of the burden attributed to five tiers of risk factors: the first tier is all GBD risks combined; the second tier consists of three large categories of metabolic, behavioural, and environmental and occupational risks; the third tier contains single risks, such as high blood pressure, and risk clusters, such as child and maternal under-nutrition or air pollution; the fourth tier includes single risks within such clusters, such as vitamin A deficiency or household air pollution; and the fifth tier is for individual occupational carcinogens or the division of childhood underweight into stunting, underweight, and wasting. At each level of the hierarchy, a decision is made whether the combined effects are independent and can be added, whether they are joint effects best represented by multiplication, or whether they share common pathways for which mediation needs to be taken into account. For each aggregation, the proportion of the effect shared with another risk or combination of risks can thus be made explicit, using modified Venn diagrams that show the overlaps between metabolic, behavioural, and environmental and occupational risks. Here we focus on specific issues related to the analysis of causes of death, injury incidence and prevalence, and risk factor prevalence in England, the nine English regions, and 45 subregional areas defined by deprivation quintile (deprivation areas). Estimates of disease burden have been created for the nine English regions, as defined by the former government office regions in England, and correspond to the European boundaries of the Nomenclature for Territorial Statistics level 1 (NUTS 1) regions. In a further refinement, all English lower super output areas, relatively homogeneous areas containing about 1600 people on average,10 were ranked nationally using the Index of Multiple Deprivation (IMD-2010) and allocated to quintiles. The IMD-2010 is a composite measure estimated at a small geographical area and includes seven domains: income, employment, health and disability, education, skills and training, barriers to housing and services, living environment, and crime.11 The health and disability domain of the IMD-2010 contributes 13·5% to the score and encompasses four measures: years of potential life lost, comparative illness and disability ratio, rate of emergency admissions to hospital, and proportion of adults younger than 60 years who have mood or anxiety disorders. Although inevitably partially correlated with health, exclusion of the health component from an earlier version of the IMD has been shown to make little difference to ranking of areas by deprivation in practice.12 The lower super output areas in each quintile were then reallocated to their region, thereby dividing each of the nine English regions into five deprivation groups, or 45 regional deprivation areas in total. As the lower super output areas at each level of deprivation are unevenly distributed among the English regions, there will be a greater share of the regional population living in the most deprived and least deprived (nationally) areas in each English region. Thus, the proportion of the population in the most deprived group ranges from 7·3% in South East England to 32·8% in North West England. For the least deprived group, this proportion ranges from 7·9% in Greater London to 34·8% in South East England. The complete breakdown of these proportions is provided in the appendix (p 4). Within each English region, the most deprived area is referred to as deprivation level 1, and the least deprived area as deprivation level 5. Mortality data for England from 1990 to 2012, available from the Office for National Statistics, were split into regional and deprivation groups based on the postcode of residence. Where a postcode for the deceased had not been provided, these deaths were discarded from the analysis of England mortality because no residence in England was assumed. These records make up less than 0·3% of all mortality records. Each death was assigned to a lower super output area, deprivation group, and English region on the basis of the person’s postcode. GBD estimation of disease prevalence and incidence also makes use of social, cultural, economic, and environmental covariates; some covariate and morbidity data were available at the level of English region. The source data for England used at each level are provided in the appendix p 5; regional level covariates that have been included are listed in the appendix p 44. As outlined in the GBD 2013 report about global mortality and cause of death,4 vital registration data covering the years 1980 to 2012 were analysed at the regional level. Registration of deaths that happen in England is a legal requirement, and because registration is necessary before disposal of the body, mortality data are assumed to be complete.1, 13, 14 We reclassified causes of death for deaths assigned to causes that cannot or should not be an underlying cause of death, so-called garbage codes.15, 16 Standard GBD 2013 redistribution of garbage-code algorithms was applied.17 Although data were available for all years between 1980 and 2012, to deal with stochastic variation at the regional level, while following the GBD 2013 methods, we modelled causes of death using cause of death ensemble modelling (CODEm).4, 18 CODEm has been used extensively to model causes of death; an ensemble model is developed by testing the performance of a wide array of models (mixed effects or space–time Gaussian process regression), different measures of mortality (rates or cause fractions) and varying combinations of covariates (drawing on a database created for GBD of more than 200 diverse characteristics for countries over time, such as gross domestic product, level of education, dietary factors, use of health-service, and environmental statistics), and by selecting the models with best out-of-sample performance. For example, GBD suicide estimates for the UK are lower than those produced by the Office for National Statistics by almost a quarter. The Office for National Statistics estimates include all deaths coded as suicide (ICD-10 X60–X84) and deaths coded as due to injury and poisoning of undetermined intent (ICD Y10–34),19 whereas GBD uses a redistribution approach, coding only a proportion of undetermined intent deaths as suicide. The GBD places disease categories within a four-level cause hierarchy. The first level divides causes into communicable diseases, non-communicable diseases, and injuries; the second level consists of major disease or injury groups, such as cardiovascular diseases or transport injuries; the third level (at which most results are reported) further subdivides causes into disease or injury types, such as cerebrovascular disease or road injuries; and a final fourth level subdivides those disease types where appropriate. Further details can be found in the supplementary appendix of the GBD 2013 Mortality and Cause of Death report.4 Tabulations of deaths by cause were generated by deprivation area within each English region. Where the causes of deaths were identified as garbage codes, these were reclassified using the GBD 2013 algorithms. Owing to small sample size in some age–sex–cause groups, we sought to smooth stochastic variation over time. To estimate causes of death by age, sex, and year for a deprivation area within each English region, we first computed the fraction of deaths for a cause–sex–age–year in each deprivation area. To minimise the effect of stochastic fluctuations on the results, we used a 3-year moving average for age groups over 15 years, and a 5-year moving average for age groups under 15 years. We chose a longer time period for the moving average for childhood age groups because these data are most prone to fluctuations due to small numbers of annual deaths. Moving-average deprivation-area fractions within a cause–age–sex–year group were rescaled so that the sum of cause fractions equalled 100%. These deprivation-area fractions were multiplied by the regional level final estimates of death counts for an age–sex–year group for a given cause to generate estimates of final death counts for each deprivation area. Death counts were divided by deprivation area population to generate deprivation-level cause–age–sex–year rates. A list of sources used for the analysis of non-fatal health outcomes in England organised by disease is provided in the appendix (p 5). These sources include studies extracted from the published literature through the GBD systematic reviews as well as extractions from surveys, such as the Health Survey for England,20 and administrative sources, such as NHS hospital discharge data. We also used new data from the UK-based Cognitive Function and Ageing Studies.21 Most disease sequelae have been modelled in GBD 2013 using a Bayesian meta-regression method, DisMod-MR 2.0, in which each English region has been analysed as a distinct geographic unit. A prior for the Bayesian meta-regression is calculated for each English region using the data for all countries in western Europe, with random effects on countries and English regions and fixed effects that vary by the disease being modelled. To analyse injuries, we made use of both the external cause of injury and the nature of injury. As detailed elsewhere,5 we used survey and hospital activity data to estimate incidence of injuries for which hospital admission was necessary and injuries for which hospital admission was not necessary. Hospital data dual-coded to nature of injury and external cause of injury were used to estimate the fraction of each injury with different types of disabling sequelae. Cohort studies from four countries were used to estimate the probability of long-term disability for each type of injury.6, 22, 23 DisMod-MR 2.0 was used to estimate the prevalence of injury in each birth cohort on the basis of long-term disability arising from past incidence. Given the absence of data on injury incidence before 1980, we assumed that age-specific incidence in cohorts before 1980 was equal to the rate in 1980. Following the GBD 2013 methods, prevalence of individuals in each sequela was multiplied by the disability weight for the corresponding health state to calculate YLDs for the particular sequela. The sum of all the YLDs for relevant sequelae is the overall YLD for each disease. We based disability weights on the responses by the general public to questions about which health state of randomly chosen pairs represents a higher state of health. GBD 2013 disability weights were based on the pooled analysis of 60 890 responses from household surveys done in a wide range of settings (USA, Peru, Tanzania, Bangladesh, Indonesia, Italy, Hungary, Sweden, and the Netherlands, and an open access internet survey) to allow them to be generalised to the global population.22 We analysed the disability weight surveys to generate 235 health state weights on a scale of 0·0 (perfect health) to 1·0 (like death). Each of the 2337 sequelae in the study are mapped to a particular health state and its associated disability weight. Results showed little variation by country of survey or level of education of respondents, justifying the use of a single set of disability weights for all countries and time periods. YLDs for deprivation areas have been estimated from regional level results. For causes where substantial mortality exists, we have assumed that the pattern of disease prevalence mirrors the pattern of mortality in an age–sex group. For causes where there is minimal mortality and no available data, we have assumed that YLD rates for an age–sex group are constant across deprivation levels in an English region. The threshold used to define minimal mortality is if the ratio of years of life lost (YLLs) to YLDs was less than 0·15. The GBD 2013 global age-standard population was used to compute age-standardised rates.4 We used multiple-decrement life tables to compute the contribution of changes in cause-specific mortality to changes in life expectancy for each English region and deprivation area from 1990 to 2013.24, 25 We computed YLLs by multiplying numbers of deaths from each cause in each age group by the reference life expectancy at the average age of death for those who die in the age group, as in the GBD 2010.9, 13, 16, 26, 27 The reference life expectancy at birth is 86·02 years and is based on the lowest age-specific death rates observed in all countries with populations greater than 5 million in 2010. We compared English regions and England as a whole with 18 other comparator nations (the first 15 members of the EU [apart from the UK], Australia, Canada, Norway, and the USA; EU15+). This set of countries has been used in previous benchmarking analyses for the UK.1 To describe the proportional share of the variation for deprivation group and English region, ANOVA was carried out at regional level and, for each deprivation area, within English regions. We did the analysis separately for each outcome measure (death, YLLs, YLDs, disability-adjusted life years [DALYs], life expectancy), and for specific years (1990, 1995, 2000, 2005, 2010, 2013). Models with main effects for region and deprivation, and of the interaction of English region and deprivation, were fitted to the data. Decomposition of variance was estimated using mixed-effects linear regression employing Gelman’s methods.28 UIs for the decomposition of variance were based on two steps. First, we took 1000 draws from the standard error of the random effect across English regions and the random effect across deprivation levels. Second, we computed the fraction of variance explained by English region and deprivation level based on each of these draws. Table 1 shows the proportion of the variance due to deprivation and due to English region (with 95% UIs). Decomposition of variance in deaths, YLLs, YLDs, DALYs, and life expectancy for men and women, separately, into contributions from level of deprivation and English region for 1990, 1995, 2000, 2005, 2010, and 2013 Data are percentage variance (95% uncertainty interval). YLLs=years of life lost. YLDs=years lived with disability. DALYs=disability-adjusted life-years. Deprivation=deprivation area. Region=English region. The GBD 2013 database development, methods improvement, and global analysis is primarily funded by the Bill & Melinda Gates Foundation, which had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Public Health England contributed to the interpretation of data, the writing of the report, and the decision to submit the paper for publication. The corresponding author had full access to all the data in the study and had final responsibility to submit the paper.
