[show abstract][hide abstract] ABSTRACT: Reliable and timely information on the leading causes of death in populations, and how these are changing, is a crucial input into health policy debates. In the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010), we aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex.
We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and mortuaries. We assessed data quality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes of death. We applied six different modelling strategies to estimate cause-specific mortality trends depending on the strength of the data. For 133 causes and three special aggregates we used the Cause of Death Ensemble model (CODEm) approach, which uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models. We assessed model performance with rigorous out-of-sample testing of prediction error and the validity of 95% UIs. For 13 causes with low observed numbers of deaths, we developed negative binomial models with plausible covariates. For 27 causes for which death is rare, we modelled the higher level cause in the cause hierarchy of the GBD 2010 and then allocated deaths across component causes proportionately, estimated from all available data in the database. For selected causes (African trypanosomiasis, congenital syphilis, whooping cough, measles, typhoid and parathyroid, leishmaniasis, acute hepatitis E, and HIV/AIDS), we used natural history models based on information on incidence, prevalence, and case-fatality. We separately estimated cause fractions by aetiology for diarrhoea, lower respiratory infections, and meningitis, as well as disaggregations by subcause for chronic kidney disease, maternal disorders, cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality shock regressions. For every cause, we estimated 95% UIs that captured both parameter estimation uncertainty and uncertainty due to model specification where CODEm was used. We constrained cause-specific fractions within every age-sex group to sum to total mortality based on draws from the uncertainty distributions.
In 2010, there were 52·8 million deaths globally. At the most aggregate level, communicable, maternal, neonatal, and nutritional causes were 24·9% of deaths worldwide in 2010, down from 15·9 million (34·1%) of 46·5 million in 1990. This decrease was largely due to decreases in mortality from diarrhoeal disease (from 2·5 to 1·4 million), lower respiratory infections (from 3·4 to 2·8 million), neonatal disorders (from 3·1 to 2·2 million), measles (from 0·63 to 0·13 million), and tetanus (from 0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in 1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality also rose by an estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in 2010. Deaths from non-communicable diseases rose by just under 8 million between 1990 and 2010, accounting for two of every three deaths (34·5 million) worldwide by 2010. 8 million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million (19%) were from trachea, bronchus, and lung cancer. Ischaemic heart disease and stroke collectively killed 12·9 million people in 2010, or one in four deaths worldwide, compared with one in five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents (1·3 million in 2010) and a rise in deaths from falls. Ischaemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, lung cancer, and HIV/AIDS were the leading causes of death in 2010. Ischaemic heart disease, lower respiratory infections, stroke, diarrhoeal disease, malaria, and HIV/AIDS were the leading causes of years of life lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990, except for HIV/AIDS and preterm birth complications. YLLs from lower respiratory infections and diarrhoea decreased by 45-54% since 1990; ischaemic heart disease and stroke YLLs increased by 17-28%. Regional variations in leading causes of death were substantial. Communicable, maternal, neonatal, and nutritional causes still accounted for 76% of premature mortality in sub-Saharan Africa in 2010. Age standardised death rates from some key disorders rose (HIV/AIDS, Alzheimer's disease, diabetes mellitus, and chronic kidney disease in particular), but for most diseases, death rates fell in the past two decades; including major vascular diseases, COPD, most forms of cancer, liver cirrhosis, and maternal disorders. For other conditions, notably malaria, prostate cancer, and injuries, little change was noted.
Population growth, increased average age of the world's population, and largely decreasing age-specific, sex-specific, and cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal, and nutritional causes towards non-communicable diseases. Nevertheless, communicable, maternal, neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on this general pattern of the epidemiological transition, marked regional variation exists in many causes, such as interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound epidemiological assessments of the causes of death on a regular basis.
Bill & Melinda Gates Foundation.
The Lancet 12/2013; 380(9859):2095-128. · 39.06 Impact Factor
[show abstract][hide abstract] ABSTRACT: Estimates of under-5 mortality at the national level for countries without high-quality vital registration systems are routinely derived from birth history data in censuses and surveys. Subnational or stratified analyses of under-5 mortality could also be valuable, but the usefulness of under-5 mortality estimates derived from birth histories from relatively small samples of women is not known. We aim to assess the magnitude and direction of error that can be expected for estimates derived from birth histories with small samples of women using various analysis methods.
We perform a data-based simulation study using Demographic and Health Surveys. Surveys are treated as populations with known under-5 mortality, and samples of women are drawn from each population to mimic surveys with small sample sizes. A variety of methods for analyzing complete birth histories and one method for analyzing summary birth histories are used on these samples, and the results are compared to corresponding true under-5 mortality. We quantify the expected magnitude and direction of error by calculating the mean error, mean relative error, mean absolute error, and mean absolute relative error.
All methods are prone to high levels of error at the smallest sample size with no method performing better than 73% error on average when the sample contains 10 women. There is a high degree of variation in performance between the methods at each sample size, with methods that contain considerable pooling of information generally performing better overall. Additional stratified analyses suggest that performance varies for most methods according to the true level of mortality and the time prior to survey. This is particularly true of the summary birth history method as well as complete birth history methods that contain considerable pooling of information across time.
Performance of all birth history analysis methods is extremely poor when used on very small samples of women, both in terms of magnitude of expected error and bias in the estimates. Even with larger samples there is no clear best method to choose for analyzing birth history data. The methods that perform best overall are the same methods where performance is noticeably different at different levels of mortality and lengths of time prior to survey. At the same time, methods that perform more uniformly across levels of mortality and lengths of time prior to survey also tend to be among the worst performing overall.
Population Health Metrics 07/2013; 11(1):13. · 2.11 Impact Factor
[show abstract][hide abstract] ABSTRACT: Selection bias is common in clinic-based HIV surveillance. Clinics located in HIV hotspots are often the first to be chosen and monitored, while clinics in less prevalent areas are added to the surveillance system later on. Consequently, the estimated HIV prevalence based on clinic data is substantially distorted, with markedly higher HIV prevalence in the earlier periods and trends that reveal much more dramatic declines than actually occur.
Using simulations, we compare and contrast the performance of the various approaches and models for handling selection bias in clinic-based HIV surveillance. In particular, we compare the application of complete-case analysis and multiple imputation (MI). Several models are considered for each of the approaches. We demonstrate the application of the methods through sentinel surveillance data collected between 2002 and 2008 from India.
Simulations suggested that selection bias, if not handled properly, can lead to biased estimates of HIV prevalence trends and inaccurate evaluation of program impact. Complete-case analysis and MI differed considerably in their ability to handle selection bias. In scenarios where HIV prevalence remained constant over time (i.e. beta = 0), the estimated beta^1 derived from MI tended to be biased downward. Depending on the imputation model used, the estimated bias ranged from -1.883 to -0.048 in logit prevalence. Furthermore, as the level of selection bias intensified, the extent of bias also increased. In contrast, the estimates yielded by complete-case analysis were relatively unbiased and stable across the various scenarios. The estimated bias ranged from -0.002 to 0.002 in logit prevalence.
Given that selection bias is common in clinic-based HIV surveillance, when analyzing data from such sources appropriate adjustment methods need to be applied. The results in this paper suggest that indiscriminant application of imputation models can lead to biased results.
Population Health Metrics 07/2013; 11(1):12. · 2.11 Impact Factor
[show abstract][hide abstract] ABSTRACT: Importance Understanding the major health problems in the United States and how they are changing over time is critical for informing national health policy.
Objectives To measure the burden of diseases, injuries, and leading risk factors in the United States from 1990 to 2010 and to compare these measurements with those of the 34 countries in the Organisation for Economic Co-operation and Development (OECD) countries.
Design We used the systematic analysis of descriptive epidemiology of 291 diseases and injuries, 1160 sequelae of these diseases and injuries, and 67 risk factors or clusters of risk factors from 1990 to 2010 for 187 countries developed for the Global Burden of Disease 2010 Study to describe the health status of the United States and to compare US health outcomes with those of 34 OECD countries. Years of life lost due to premature mortality (YLLs) were computed by multiplying the number of deaths at each age by a reference life expectancy at that age. Years lived with disability (YLDs) were calculated by multiplying prevalence (based on systematic reviews) by the disability weight (based on population-based surveys) for each sequela; disability in this study refers to any short- or long-term loss of health. Disability-adjusted life-years (DALYs) were estimated as the sum of YLDs and YLLs. Deaths and DALYs related to risk factors were based on systematic reviews and meta-analyses of exposure data and relative risks for risk-outcome pairs. Healthy life expectancy (HALE) was used to summarize overall population health, accounting for both length of life and levels of ill health experienced at different ages.
Results US life expectancy for both sexes combined increased from 75.2 years in 1990 to 78.2 years in 2010; during the same period, HALE increased from 65.8 years to 68.1 years. The diseases and injuries with the largest number of YLLs in 2010 were ischemic heart disease, lung cancer, stroke, chronic obstructive pulmonary disease, and road injury. Age-standardized YLL rates increased for Alzheimer disease, drug use disorders, chronic kidney disease, kidney cancer, and falls. The diseases with the largest number of YLDs in 2010 were low back pain, major depressive disorder, other musculoskeletal disorders, neck pain, and anxiety disorders. As the US population has aged, YLDs have comprised a larger share of DALYs than have YLLs. The leading risk factors related to DALYs were dietary risks, tobacco smoking, high body mass index, high blood pressure, high fasting plasma glucose, physical inactivity, and alcohol use. Among 34 OECD countries between 1990 and 2010, the US rank for the age-standardized death rate changed from 18th to 27th, for the age-standardized YLL rate from 23rd to 28th, for the age-standardized YLD rate from 5th to 6th, for life expectancy at birth from 20th to 27th, and for HALE from 14th to 26th.
Conclusions and Relevance From 1990 to 2010, the United States made substantial progress in improving health. Life expectancy at birth and HALE increased, all-cause death rates at all ages decreased, and age-specific rates of years lived with disability remained stable. However, morbidity and chronic disability now account for nearly half of the US health burden, and improvements in population health in the United States have not kept pace with advances in population health in other wealthy nations.
The United States spends the most per capita on health care across all countries,1- 2 lacks universal health coverage, and lags behind other high-income countries for life expectancy3 and many other health outcome measures.4 High costs with mediocre population health outcomes at the national level are compounded by marked disparities across communities, socioeconomic groups, and race and ethnicity groups.5- 6 Although overall life expectancy has slowly risen, the increase has been slower than for many other high-income countries.3 In addition, in some US counties, life expectancy has decreased in the past 2 decades, particularly for women.7- 8 Decades of health policy and legislative initiatives have been directed at these challenges; a recent example is the Patient Protection and Affordable Care Act, which is intended to address issues of access, efficiency, and quality of care and to bring greater emphasis to population health outcomes.9 There have also been calls for initiatives to address determinants of poor health outside the health sector including enhanced tobacco control initiatives,10- 12 the food supply,13- 15 physical environment,16- 17 and socioeconomic inequalities.18
With increasing focus on population health outcomes that can be achieved through better public health, multisectoral action, and medical care, it is critical to determine which diseases, injuries, and risk factors are related to the greatest losses of health and how these risk factors and health outcomes are changing over time. The Global Burden of Disease (GBD) framework19 provides a coherent set of concepts, definitions, and methods to do this. The GBD uses multiple metrics to quantify the relationship of diseases, injuries, and risk factors with health outcomes, each providing different perspectives. Burden of disease studies using earlier variants of this approach have been published for the United States for 199620- 22 and for Los Angeles County, California.23 In addition, 12 major risk factors have also been compared for 2005.24
In this report, we use the GBD Study 2010 to identify the leading diseases, injuries, and risk factors associated with the burden of disease in the United States, to determine how these health burdens have changed over the last 2 decades, and to compare the United States with other Organisation for Economic Co-operation and Development (OECD) countries.
JAMA The Journal of the American Medical Association 07/2013; · 29.98 Impact Factor
[show abstract][hide abstract] ABSTRACT: Realizar un análisis comparativo del desempeño (benchmarking) de las unidades subnacionales en un sistema de salud descentralizado es importante para favorecer la rendición de cuentas, monitorear el progreso, identificar los factores que determinan tanto el éxito como el fracaso, y crear una cultura basada en la evidencia. Desde 2001, la Secretaría de Salud de México se ha dedicado a desarrollar esta tarea basándose en el concepto de cobertura efectiva promovido por la Organización Mundial de la Salud (OMS), que la define como la fracción de ganancia potencial en salud que el sistema de salud podría aportar, con los servicios que actualmente ofrece. Usando los sistemas de información en salud, que incluyen encuestas de salud representativas a nivel estado, registros vitales y registros de egresos hospitalarios, se ha monitoreado la prestación de 14 intervenciones para mejorar la salud entre 2005 y 2006. La cobertura efectiva en general va desde 54% en Chiapas hasta 65% en el Distrito Federal. La cobertura efectiva para intervenciones en salud materno-infantil es mayor que para las intervenciones que abordan otros problemas de salud del adulto. La cobertura efectiva para el quintil de ingresos más bajo es de 52%, comparada con 61% para el quintil de ingresos más alto. La cobertura efectiva guarda especial relación con el gasto público en salud per cápita en todos los estados, y esta relación es más estrecha con las intervenciones ajenas a la salud materno-infantil que con las que tienen que ver directamente con ella. También se observan variaciones considerables en la cobertura efectiva en niveles de gasto similares. Asimismo, se discuten algunas implicaciones para el desarrollo que debiera seguir el sistema de información en salud en México. Este enfoque alienta a quienes toman decisiones a concentrarse en brindar servicios de calidad y no sólo en ofrecer la disponibilidad del servicio. El cálculo de la cobertura efectiva es una herramienta clave para la rectoría del sistema de salud. Al adoptar este enfoque, otros países podrán elegir intervenciones con base en criterios de accesibilidad, efecto en la salud de la población, efecto en desigualdades de salud y en la capacidad para medir dichos efectos. Para alcanzar el éxito en este tipo de análisis comparativo del desempeño a nivel subnacional, las instituciones nacionales que lo lleven a cabo deberán contar con autoridad, habilidades técnicas, recursos e independencia suficientes.
Salud publica de Mexico 05/2013; · 0.94 Impact Factor
[show abstract][hide abstract] ABSTRACT: The Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010) constitutes an unprecedented collaboration of 488 scientists from 303 institutions in 50 countries, focusing on describing the state of health around the world using a uniform method. Results for the world and 21 regions for 1990 and 2010 have been reported for 291 diseases and injuries, 1160 sequelae of these causes, and 67 risk factors or clusters of risk factors. 1–7 The burden of each disease, injury, or risk factor has been quantifi ed in terms of deaths, years of life lost due to premature mortality (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs). Although only global and regional results have been reported so far, the underlying unit of analysis for GBD 2010 was 187 countries. Age-specifi c mortality was analysed for each country for each year from 1970 to 2010. Causes of death were estimated for each country from 1990 to 2010 with country-specifi c data and models. Disease and injury sequelae were estimated in most cases with a Bayesian meta-regression method (DisMod-MR) that includes estimation of systematic diff erences in incidence, prevalence, or excess mortality between countries within regions. 5 Systematic analysis of risk factor exposure, excess health risks associated with each risk–outcome pair, and counterfactual minimum risk levels of exposure were used to compute attributable burden. On the basis of these analyses, GBD 2010 provides a complete assessment of the burden of diseases, injuries, and risk factors for 187 countries including quantifi cation of uncertainty in the estimates for 1990 and 2010, albeit with important limitations because of the scarcity of data for some outcomes in some countries and the need to use a range of statistical models to generate estimates. The availability of standardised estimates for each of the 187 countries over time provides an unprecedented opportunity to undertake comparative assessments, to benchmark country performance in control of critical diseases, injuries, and risks, and to stimulate evidence-based action. Most of the scientists in the GBD 2010 collaboration volunteered their own time or raised their own funds to participate. 8 A key motivation for them was the opportunity to publish more detailed analyses of data, methods, and results for specifi c diseases, injuries, and risk factors. Many reports are in submission or in preparation and provide more detail for specifi c diseases, injuries, risk factors, and countries. 9 Although we expect that these reports will be important contributions to the scientifi c literature, we recognise that country results from the GBD are a global public good that could be a useful or even critical input into a more informed national, regional, and global dialogue about health challenges. Already, governments of several developed and developing countries have approached us seeking access to more detailed results. Because we believe that the dissemination and rapid availability of the detailed results is a moral imperative, we are providing global access to these details on March 5, 2013, through a series of online visualisations. To allow suffi cient time for members of the GBD 2010 collaboration to report their own research fi ndings, we will defer dissemination of public-use datasets of the underlying results presented in the visualisations until Sept 1, 2013. In this way, we believe that we can provide global access to these important results while at the same time respecting the intellectual investment of the collaboration's mem-bers. Nonetheless, anticipating that some governments might wish to have immediate access to more detailed information as an input to national policy dialogue, we have provided and will continue to provide detailed national disease burden results on request. We also encourage use of the visualisations or snapshots of their images for teaching, communication, and other educational purposes. Alongside the reporting of global and regional results in The Lancet, fi ve data visualisations were made available in December, 2012. For visualisation of country-level data, the Institute for Health Metrics and Evaluation (IHME) has developed new visualisations with expanded scope and functionality, which are being launched on March 5. Data visualisations can make complex information accessible and interpretable without advanced statistical or epidemiological training. The primary purpose of these visualisations is to allow health specialists, policy makers, the media, donors, and the general public to explore the patterns of health in diff erent age and sex groups, countries, and time periods. Providing information on patterns of health to this broad audience could enhance the scope and quality of national, regional, and global dialogue about the main For data visualisations see
The Lancet 03/2013; 381(9871):965-70. · 39.06 Impact Factor