Transcript of "Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010"
Global and regional mortality for235 causes of death from 1990 to2010Assessing premature and avoidable mortalityRafael Lozano, MD, on behalf of 189 authors ofthe paper“Global and regional mortality from 235 causes of death for 20age groups in 1990 and 2010: a systematic analysis for theGlobal Burden of Disease Study 2010”
Background• Causes of death (CoD) is one of the most fundamental metrics for population health.• Trends in CoD provide an important summary of whether society is or is not making progress in reducing burden of premature mortality and especially avoidable mortality.• Usually CoD assessments show success and failures of Health Information Systems and provide directions of how to improve them.• GBD 1990 was the first comprehensive study to present the global leading causes of death. 2
Global cause of death assessment: mainissues• The universe of data• Efforts to assess and enhance quality and comparability of data• The statistical modeling strategy• Causes of death constrained to sum to all cause mortality 3
The universe of CoD dataWe attempted to identify all Type Site years Countries Vital Registration 2,798 130available data on causes of Verbal Autopsy 486 66death for 187 countries from Cancer registries 2,715 931980 to 2010 Police Reports 1,129 122• We used 9 different sources Surveys/Census 1,564 82 of CoD data Maternal Mortality Surveillance 83 8• We collected data on around Deaths in health Facilities 21 9 Burial and Mortuary 32 11 600 million deaths in the last Country−years of vital registration, 1980−2010 30 years• Data available varies by disease: o More on maternal, cancer, injuries o Less on NTD, diarrhea and LRI pathogens 4
Assessment and enhancement of dataquality and comparability1. Assessment of completeness Percent garbage from ICD vital registration2. Causes of death mapping3. Redistribution of misclassified causes of death4. Age and age-sex splitting5. Smoothing for stochastic variation due to small numbers6. Outlier detection 5
Modeling causes of death1. Causes of death ensemble modeling, CODEm (133 causes), including all major causes except HIV. CODEm selects models and ensembles of models based on out-of-sample performance.2. Negative binomial (12 causes).3. Fixed proportion models (27 causes).4. Disaggregation by pathogens or sub-causes (36 causes).5. Natural history models (8 causes).6. Mortality shock regressions (2 causes). 6
Combining results: CoDCorrect algorithm• Because we developed single-cause models, it was imperative as a final step to ensure that individual cause estimates summed to the all-cause mortality estimate for every age-sex- country-year group.• This is one of the innovations of this study: o Implemented taking into account uncertainty in every cause of death model outcome o We proportionately rescaled every cause such that the sum of the cause-specific estimates equaled the number of deaths from all causes generated from the demographic analysis (by country, year, age, and sex). o We applied CoDCorrect in a hierarchical way. 7
Shift of causes of death in the last 20 years 1990 2010 Injuries Injuries 10% 9% Comm/Mater /Neonat/Nutr Comm/Mater/ 25% Neonat/Nutr 34% Non Communicable Non Communicable Diseases Diseases 57% 65% 46.5 million 52.7 million 8
Death decomposition analysis by changes inpopulation 75% 50% 25% 0% -25% -50% -75% All Causes Com/Mat NCD Injuries Neo/Nut % change 1990-2000 % change due to change in rates % change due to pop ageing % change due to pop growth 9
Percentage of global deaths for female andmale individuals in 2010 by cause and age Males Females
Years oflife lostputs moreemphasisonleadingcauses inchildren 12
Percentage of YLLs for all ages and both sexescombined by cause and region in 1990 and 2010 1990 2010
YLL top 25 leading causes across 21 regions, 2010 14
Main findings• The shifting pattern of the number of deaths by cause across time, regions, and age groups is consistent with the three key drivers of change.• Our estimates of 235 causes of deaths are internally consistent by age, sex, region, and year, but could be: o Different from other publications (higher or lower), o Similar to other estimates, or o New data for global health.• Causes of “millionaires” deaths o HIV, LRI, diarrhea, malaria, TB (preterm, hepatitis) o IHD, stroke, lung cancer, diabetes, cirrhosis (CKD) o Road injuries (suicide). 15
Limitations• Data on CoD even in settings with medical certification might not always accurately capture the UCD.• Redistribution of misclassified deaths could be improved with more empirical data.• Verbal autopsy data are more accurate for some causes and less for others.• For some cause models only a weak covariate selection was possible.• The use of negative binomial, fixed proportion models, and natural history are in principle related to the lack of quality of data.• UI are good for CODEm results but weak for other modeling strategies.• When expert opinion and data diverge, we tended to follow the available data. 16
Final remarks• GBD 2010 is the most comprehensive and systematic analysis of causes of death undertaken to date.• Adding time trends and quantifying the uncertainty differentiate GBD 2010 from similar studies in the past.• GBD 2010 is an asset for Global Health: o More data then ever before o New methods for improving the quality of data and the modeling.• GBD 2010 has demonstrated that public health priorities everywhere are changing, or soon will be. 17
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