Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016

Background As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016. Methods We estimated prevalence and incidence for 328 diseases and injuries and 2982 sequelae, their non-fatal consequences. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between incidence, prevalence, remission, and cause of death rates for each condition. For some causes, we used alternative modelling strategies if incidence or prevalence needed to be derived from other data. YLDs were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae, corrected for comorbidity and aggregated to cause level. We updated the Socio-demographic Index (SDI), a summary indicator of income per capita, years of schooling, and total fertility rate. GBD 2016 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, low back pain, migraine, age-related and other hearing loss, iron-deficiency anaemia, and major depressive disorder were the five leading causes of YLDs in 2016, contributing 57·6 million (95% uncertainty interval [UI] 40·8-75·9 million [7·2%, 6·0-8·3]), 45·1 million (29·0-62·8 million [5·6%, 4·0-7·2]), 36·3 million (25·3-50·9 million [4·5%, 3·8-5·3]), 34·7 million (23·0-49·6 million [4·3%, 3·5-5·2]), and 34·1 million (23·5-46·0 million [4·2%, 3·2-5·3]) of total YLDs, respectively. Age-standardised rates of YLDs for all causes combined decreased between 1990 and 2016 by 2·7% (95% UI 2·3-3·1). Despite mostly stagnant age-standardised rates, the absolute number of YLDs from non-communicable diseases has been growing rapidly across all SDI quintiles, partly because of population growth, but also the ageing of populations. The largest absolute increases in total numbers of YLDs globally were between the ages of 40 and 69 years. Age-standardised YLD rates for all conditions combined were 10·4% (95% UI 9·0-11·8) higher in women than in men. Iron-deficiency anaemia, migraine, Alzheimer’s disease and other dementias, major depressive disorder, anxiety, and all musculoskeletal disorders apart from gout were the main conditions contributing to higher YLD rates in women. Men had higher age-standardised rates of substance use disorders, diabetes, cardiovascular diseases, cancers, and all injuries apart from sexual violence. Globally, we noted much less geographical variation in disability than has been documented for premature mortality. In 2016, there was a less than two times difference in age-standardised YLD rates for all causes between the location with the lowest rate (China, 9201 YLDs per 100 000, 95% UI 6862-11943) and highest rate (Yemen, 14 774 YLDs per 100 000, 11 018-19 228). Interpretation The decrease in death rates since 1990 for most causes has not been matched by a similar decline in age-standardised YLD rates. For many large causes, YLD rates have either been stagnant or have increased for some causes, such as diabetes. As populations are ageing, and the prevalence of disabling disease generally increases steeply with age, health systems will face increasing demand for services that are generally costlier than the interventions that have led to declines in mortality in childhood or for the major causes of mortality in adults. Up-todate information about the trends of disease and how this varies between countries is essential to plan for an adequate health-system response. The GBD study provides a standardised analytical approach for estimating incidence, prevalence, and YLDs by age, sex, cause, year, and location. We aim to use all accessible information on disease occurrence, natural history, and severity that passes minimum inclusion criteria set disease-by-disease (appendix 1, p 33). Our approach is to optimise the comparability of data collected by varying methods or different case definitions; find a consistent set of estimates between data for prevalence, incidence, and causes of death; and predict estimates for locations with sparse or absent data by borrowing information from other locations and using covariates. In this study, we use different methods to reflect the available data and specific epidemiology of each disease. Our main approach is to combine all sources of information for a disease using the Bayesian meta-regression tool DisMod-MR 2.1.16 Subsequently, we use data for severity, the occurrence of particular consequences of diseases, or sequelae, to establish the proportion of prevalent cases experiencing each sequela. Several broad classes of alternative approaches are used within the GBD study. First, for injuries, non-fatal estimates must account for the cause of injury (eg, a fall), the nature of injury (eg, a fracture or head injury), the amount of disability arising in the short term, and permanent disability for a subset of cases. Second, cancers were estimated by assessing the association between mortality and incidence, taking into account the effect on survival of access to, and quality of, treatment for the cancer site. Third, we combined the natural history model strategy for HIV/AIDS with the DisMod-MR 2.1 modelling approach for tuberculosis as HIV infection affects outcomes in patients who also have tuberculosis. Fourth, models for malaria, hepatitis, and varicella relied on data of the presence of circulating antibodies or parasites in the blood to predict the incidence of clinical episodes for which we estimate disability. Fifth, neonatal disorders were estimated from birth prevalence data and cohort studies on the risk of death in the first month and the probability of long-term disabling outcomes. Sixth, incidence of rabies, whooping cough, diphtheria, and tetanus was estimated from cause-specific mortality rates and data on the case fatality of acute episodes (appendix 1, p 33). Below we describe these modelling efforts organised into eight sections; the supplementary methods (appendix 1, p 1) presents a single source for additional detail of inputs, analytical processes, outputs, and methods specific to each cause. This study complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) recommendations (appendix 1, p 723).17 The GBD 2016 study was based on a geographic hierarchy that includes 195 countries and territories grouped within 21 regions and seven GBD super-regions (appendix 1, p 726). For this publication, we present subnational estimates in figures and only for Brazil, China, India, and the USA. Details of subnational estimates will be reported in separate publications. Cause-specific estimation in GBD 2016 was done for the years 1990, 1995, 2000, 2006, 2010, and 2016 and interpolated to get a full time series. In view of policy priorities, a subset of results focus on change over the time period 2006–16. Results from GBD 2016 by year and location can be explored further in dynamic data visualisations. In the GBD Study, causes and their sequelae are collectively exhaustive and mutually exclusive and are organised in a hierarchy with five levels. Level 1 contains three broad cause groups: communicable, maternal, neonatal, and nutritional diseases; non-communicable diseases; and injuries. These are broken down into 21 Level 2 causes with further disaggregation into 163 Level 3 causes and 271 Level 4 causes. Sequelae of these causes are represented at Level 5 of the hierarchy. For GBD 2016, we expanded the list of causes of non-fatal outcomes from 310 to 328. This involved the refinement of certain Level 3 causes into new Level 4 causes, including disaggregation of tuberculosis and HIV- tuberculosis into drug-susceptible tuberculosis, multidrug-resistant tuberculosis, extensively drug-resistant tuberculosis, and latent tuberculosis infection. Cardiomyopathy and myocarditis were further refined as alcoholic cardiomyopathy, myocarditis, and other cardiomyopathy. Other leukaemia was added as an additional sub-cause at Level 4. Self-harm was separated into self-harm by firearm and self-harm by other means. The previously named cause grouping “collective violence and legal intervention” was divided into two Level 4 causes: executions and police conflict. New causes of non-fatal outcomes added to the GBD hierarchy for 2016 included Zika virus disease; musculoskeletal, urogenital, and digestive congenital anomalies; Guinea worm disease; and sexual violence. Medication overuse headache was removed as a cause and, instead, characterised as a sequela of migraine and tension-type headache. The first step in non-fatal estimation was the compilation of data sources from systematic data and literature searches conducted by cause. This process resulted in 4043 published studies newly included in GBD 2016, leading to a total of 14 521. Our network of collaborators for GBD 2016 provided 2598 data sources and studies. These were systematically screened, together with sources suggested by country-level experts, surveys located in multinational survey data catalogues, and Ministry of Health and Central Statistical Office websites. We analysed 18 792 sources of epidemiological surveillance data (country-years of disease reporting), up from 14 081 in 2015. All counts reflect our updated counting criteria for GBD 2016. The supplementary methods provides details of data adjustments, correction factors, and standardisations employed in incorporating these different data types (appendix 1, p 18). The number of location-years of hospital inpatient data by cause increased from 1176 in GBD 2015 to 3557 in GBD 2016. This increase can be attributed to the addition of new years of data for some locations, as well as newly incorporated data for 16 countries where we had previously lacked clear information about the population covered. To allow their use in GBD, we first collated information from surveys and hospital administrative records to estimate hospital admission rates per capita for all GBD locations by age and sex, from 1990 to 2016, using DisMod-MR 2.1 (appendix 1, p 7). We then used inpatient data by cause from locations with unclear denominators as cause fractions of the all-cause inpatient admission rates. Three adjustment factors were derived from USA health insurance claims data on more than 80 million person-years of coverage. The first factor corrected for multiple inpatient episodes for the same cause in an individual. The second adjustment was to include secondary diagnostic fields. The third adjustment was to include any mention of a cause in inpatient or outpatient episodes of care as opposed to inpatient episodes with a primary diagnosis only. This new method of predicting prevalence or incidence from inpatient data allowed us to use these sources for 16 more causes than in 2015. The supplementary methods provides a detailed description of our process for inpatient data (appendix 1, p 11). To provide a summary view on data availability, the number of causes at the most detailed level for which we have any prevalence or incidence data from 1980 to 2016 by location is presented in the appendix (appendix 1, p 722). An online search tool is available to view all data sources that were used in the estimation process for each cause. Non-fatal diseases were modelled using DisMod-MR 2.1, a statistical method that synthesises sparse and heterogeneous epidemiological data for non-fatal outcomes. Estimation occurred at five levels: global, super-region, region, country, and subnational locations, with results from a higher level providing guidance for the analysis at a lower geographical level (appendix 1, p 18). Custom models were created where DisMod-MR 2.1 does not capture the complexity of the disease, or if incidence and prevalence needed to be calculated from other data. Further details of these custom models can be found in the supplementary methods (appendix 1, p 18). Prevalence was estimated for nine impairments, disorders that are sequelae of multiple diseases and for which there were better data available to estimate the overall occurrence than for each underlying cause: anaemia, intellectual disability, epilepsy, hearing loss, vision loss, heart failure, infertility, pelvic inflammatory disease, and Guillain-Barré syndrome. The methods for estimating YLDs from a number of diseases changed substantially for GBD 2016. We improved our estimation of mortality-to-incidence ratios for cancers to better reflect lower survival probabilities in low-income and middle-income locations based on each location’s Socio-demographic Index (SDI) value. As a consequence, our prevalence and YLD estimates were lower in those locations but did not change much for higher-SDI locations. We made major changes to our modelling of tuberculosis. First, we made explicit estimates of latent tuberculosis infection from tuberculin skin testing data and the risk of developing active tuberculosis by induration size. Second, we predicted mortality-to-incidence ratios in locations with high data-quality ratings (4-star or 5-star using a system developed for the GBD 2016 causes of death estimation)18 and SDI as a covariate. We anchored the lower end of the SDI scale with a datapoint from an untreated cohort of pulmonary tuberculosis cases in the 1960s, half of whom had died after five years.18, 19 Third, we estimated incidence from these mortality-to-incidence ratios in all locations except those with higher reported notifications. Fourth, we modelled these incidence estimates as well as the prevalence data from surveys in low-income and middle-income countries and cause-specific mortality rates among the proportion of the population with latent infection in DisMod-MR 2.1. Fifth, we estimated the proportions of tuberculosis cases with multidrug-resistant tuberculosis or extensively drug-resistant tuberculosis from notification and survey data and included an increased risk of multidrug-resistant tuberculosis in HIV/AIDS-infected patients with tuberculosis from a meta-analysis.20 In our measles estimation strategy, we included the coverage of measles-containing vaccine second-dose (MCV2) rather than just the coverage of the primary vaccine as a covariate. As relatively few countries in sub-Saharan Africa have introduced MCV2, the estimated incidence for those locations is notably higher compared with previous estimates. For 214 causes at Level 4 of the GBD hierarchy, sequelae were defined in terms of severity, usually graded as mild, moderate, or severe outcomes. We followed the same approach as in GBD 2015. For Zika virus disease, we included sequelae for those with symptomatic acute infection, a small proportion with Guillain-Barré syndrome, and the number of neonates with congenital Zika virus disease as reported to the Pan American Health Organization (PAHO). For sexual violence, we estimated YLDs associated with concurrent physical injuries and the short-term psychological outcomes following sexual violence. A more substantial change in estimating severity was applied to stroke. A systematic review was done to collect data on modified Rankin scores, a measure of neurological disability.21 Levels of Rankin score were analysed in DisMod-MR 2.1 and mapped to the existing GBD health state lay descriptions for mild, moderate, and severe motor impairment from stroke, and, separately, the proportion of stroke patients with moderate-to-severe motor impairment who also experienced cognitive impairment. For GBD 2016 we used the same disability weights as in GBD 2013 and GBD 2015; the supplementary methods provides a complete listing of lay descriptions of all 235 health states used in GBD 2016 (appendix 1, p 799). We estimated comorbidity by simulating 40 000 individuals in every location-age-sex-year combination as exposed to the independent probability, based on the prevalence of the sequelae included in GBD 2016. In simulants with two or more sequelae, we assumed a multiplicative function to combine disability weights and then distributed the reduced combined weight proportionately among all comorbid sequelae. Averaging these adjusted values across all simulants with a particular sequela gave the adjusted value of YLDs. There was no change in the approach compared with GBD 2015. All computations in GBD were done 1000 times, every time drawing from the distribution of the sampling error of data inputs, the uncertainty of data corrections for measurement errors, the uncertainty in coefficients from model fit (eg, in DisMod-MR 2.1), and the uncertainty of severity distributions and disability weights. Uncertainty bounds for a quantity of interest were defined by the 25th and 975th value of the ordered 1000 estimate values. If there was a change in disease estimates between locations or over time that was in the same direction in more than 950 of the 1000 samples we report it as significant. Age-standardised prevalence YLD rates were calculated based on the GBD reference population.22 The GBD cause hierarchy is comprehensive and includes 35 residual disease categories to capture YLDs from conditions for which we do not currently make separate estimates. For 22 of these residual categories, we made explicit epidemiological estimates of prevalence and incidence, and define sequelae based on the most common diseases in the Level 2 or 3 cause group and severity distributions from the Medical Expenditure Panel Survey (MEPS).23 For 13 residual categories, we had no epidemiological data and estimated YLDs from the ratio of YLDs to YLLs from explicitly modelled diseases in the cause category, assuming that relative to each death, the number of YLDs was similar to that of other diseases at the same level of the GBD hierarchy (appendix 1, p 29). SDI is a summary measure that places all GBD locations on a spectrum of socioeconomic development.24 The SDI was developed for GBD 2015 to provide a comparable metric of overall development. This was achieved by using an equal weighting of lag-distributed income per capita, average years of education in the population over age 15 years, and total fertility rate.22 For GBD 2016, we modified the estimation of SDI by taking into consideration that SDI scales were subject to change based on increasing geographic units and an extended time period of analysis, affecting the interpretability across GBD iterations. We redefined the values of zero and one for each component of the index: zero now represents the level below which we have not observed GDP per capita or educational attainment or above which we have not observed the total fertility rate in known datasets. Maximum scores for educational attainment and Lagged Distributed Income represent a plateau in the relationship between each of the two components and life expectancy or under-5 mortality rates, suggesting no additional benefit. Analogously, the maximum score for total fertility rate represents the minimum level at which the relationship with the selected health outcomes plateaued. An SDI value was generated for each location and year as the geometric mean of each component score. Five SDI quintiles, high, high-middle, middle, low-middle, and low, were selected based on 2016 values of SDI; additional details are available in the supplementary methods (appendix 1).22 A Gaussian process regression was used to evaluate the average relationship for each age-sex-cause group, for cause-specific YLD rates on SDI at Levels 1, 2, and 3 of the GBD cause hierarchy using data from 1990 to 2016. These rates were used as the expected values for cause-specific YLD rates at a given level of SDI. Additional detail on this analysis is available in the supplementary methods (appendix 1, p 30) and in previous GBD publications.18 The funder of the study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report. All authors had full access to the data in the study and had final responsibility for the decision to submit for publication.