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

BACKGROUND: 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 a...

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Published in:The Lancet (British edition) 2013-01, Vol.380 (9859), p.2095-2128
Main Authors: Naghavi, Mohsen, Lim, Stephen, Ahn, Stephanie Y, Alvarado, Miriam, Atkinson, Charles, Barker-Collo, Suzanne, Benjamin, Emelia J, Bikbov, Boris, Birbeck, Gretchen, Blyth, Fiona, Bolliger, Ian, Boufous, Soufiane, Burch, Michael, Carapetis, Jonathan, Chen, Honglei, Chugh, Sumeet S, Colan, Steven D, Colson, K Ellicott, Condon, John, Connor, Myles D, Cooper, Leslie T, Cowie, Benjamin C, Criqui, Michael H, Dabhadkar, Kaustubh C, Dahodwala, Nabila, De Leo, Diego, Delossantos, Allyne, Denenberg, Julie, Des Jarlais, Don C, Driscoll, Tim, Erwin, Patricia J, Ezzati, Majid, Feigin, Valery, Flaxman, Abraham D, Franklin, Richard, Fransen, Marlene, Gabriel, Sherine E, Gillum, Richard F, Gonzalez-Medina, Diego, Haring, Diana, Hay, Roderick J, Hotez, Peter J, Jasrasaria, Rashmi, Jayaraman, Sudha, Kassebaum, Nicholas, Keren, Andre, Knowlton, Lisa Marie, Ohno, Summer Lockett, Mabweijano, Jacqueline, Mallinger, Leslie, March, Lyn, Marks, Guy B, Marks, Robin, Matsumori, Akira, Matzopoulos, Richard, Mayosi, Bongani M, McAnulty, John H, McGrath, John, Mensah, George A, Merriman, Tony R, Michaud, Catherine, Miller, Ted R, Mock, Charles, Mokdad, Ali A, Moran, Andrew, Naldi, Luigi, Nasseri, Kiumarss, Omer, Saad B, Ortblad, Katrina, Osborne, Richard, Pandian, Jeyaraj Durai, Padilla, Rogelio Perez, Perico, Norberto, Phillips, David, Pierce, Kelsey, Pope, C Arden, III, Raju, Murugesan, Remuzzi, Guiseppe, Roberts, Thomas, De León, Felipe Rodriguez, Rosenfeld, Lisa C, Rushton, Lesley, Sacco, Ralph L, Sanman, Ella, Singh, David, Singleton, Jessica, Sliwa, Karen, Smith, Emma, Taylor, Jennifer A, Undurraga, Eduardo A, Vijayakumar, Lakshmi, Wang, Mengru, Wang, Wenzhi, Watt, Kerrianne, Weintraub, Robert, Wilkinson, James D, Yip, Paul, Zheng, Zhi-Jie, Lopez, Alan D, Murray, Christopher JL
Format: Article
Language:eng ; rus
Subjects:
accidents
African trypanosomiasis
Alzheimer disease
bronchi
Chagas disease
death
diabetes mellitus
diarrhea
disability-adjusted life year
disasters
etiology
health policy
hepatitis E
hospitals
kidney diseases
leishmaniasis
liver cirrhosis
liver neoplasms
lung neoplasms
malaria
melanoma
meningitis
model validation
monitoring
mortality
myocardial ischemia
natural history
necropsy
police
population growth
prediction
premature birth
prostatic neoplasms
risk factors
statistical models
stroke
suicide
surveys
tetanus
traffic
Treponema pallidum
tuberculosis
typhoid fever
uncertainty
violence
whooping cough
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Summary:BACKGROUND: 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. METHODS: 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 mortal
ISSN:0140-6736
1474-547X