BACKGROUND: The advent of the HIV pandemic and the more recent prevention and therapeutic interventions have resulted in extensive and rapid changes in cause-specific mortality rates in sub-Saharan Africa, and there is demand for timely and accurate cause-specific mortality data to steer public health responses and to evaluate the outcome of interventions. The objective of this study is to describe cause-specific mortality trends based on verbal autopsies conducted on all deaths in a rural population in KwaZulu-Natal, South Africa, over a 10-year period (2000-2009). METHODS: The study used population-based mortality data collected by a demographic surveillance system on all resident and nonresident members of 12,000 households. Cause of death was determined by verbal autopsy based on the standard INDEPTH/WHO verbal autopsy questionnaire. Cause of death was assigned by physician review and the Bayesian-based InterVA program. RESULTS: There were 11,281 deaths over 784,274 person-years of observation of 125,658 individuals between Jan. 1, 2000 and Dec. 31, 2009. The cause-specific mortality fractions (CSMF) for the population as a whole were: HIV-related (including tuberculosis), 50%; other communicable diseases, 6%; noncommunicable lifestyle-related conditions, 15%; other noncommunicable diseases, 2%; maternal, perinatal, nutritional, and congenital causes, 1%; injury, 8%; indeterminate causes, 18%. Over the course of the 10 years of observation, the CSMF of HIV-related causes declined from a high of 56% in 2002 to a low of 39% in 2009 with the largest decline starting in 2004 following the introduction of an antiretroviral treatment program into the population. The all-cause age-standardized mortality rate (SMR) declined over the same period from a high of 174 (95% confidence interval [CI]: 165, 183) deaths per 10,000 person-years observed (PYO) in 2003 to a low of 116 (95% CI: 109, 123) in 2009. The decline in the SMR is predominantly due to a decline in the HIV-related SMR, which declined in the same period from 96 (95% CI: 89, 102) to 45 (95% CI: 40, 49) deaths per 10,000 PYO.There was substantial agreement (79% kappa = 0.68 (95% CI: 0.67, 0.69)) between physician coding and InterVA coding at the burden of disease group level. CONCLUSIONS: Verbal autopsy based methods enabled the timely measurement of changing trends in cause-specific mortality to provide policymakers with the much-needed information to allocate resources to appropriate health interventions.
BACKGROUND: The advent of the HIV pandemic and the more recent prevention and therapeutic interventions have resulted in extensive and rapid changes in cause-specific mortality rates in sub-Saharan Africa, and there is demand for timely and accurate cause-specific mortality data to steer public health responses and to evaluate the outcome of interventions. The objective of this study is to describe cause-specific mortality trends based on verbal autopsies conducted on all deaths in a rural population in KwaZulu-Natal, South Africa, over a 10-year period (2000-2009). METHODS: The study used population-based mortality data collected by a demographic surveillance system on all resident and nonresident members of 12,000 households. Cause of death was determined by verbal autopsy based on the standard INDEPTH/WHO verbal autopsy questionnaire. Cause of death was assigned by physician review and the Bayesian-based InterVA program. RESULTS: There were 11,281 deaths over 784,274 person-years of observation of 125,658 individuals between Jan. 1, 2000 and Dec. 31, 2009. The cause-specific mortality fractions (CSMF) for the population as a whole were: HIV-related (including tuberculosis), 50%; other communicable diseases, 6%; noncommunicable lifestyle-related conditions, 15%; other noncommunicable diseases, 2%; maternal, perinatal, nutritional, and congenital causes, 1%; injury, 8%; indeterminate causes, 18%. Over the course of the 10 years of observation, the CSMF of HIV-related causes declined from a high of 56% in 2002 to a low of 39% in 2009 with the largest decline starting in 2004 following the introduction of an antiretroviral treatment program into the population. The all-cause age-standardized mortality rate (SMR) declined over the same period from a high of 174 (95% confidence interval [CI]: 165, 183) deaths per 10,000 person-years observed (PYO) in 2003 to a low of 116 (95% CI: 109, 123) in 2009. The decline in the SMR is predominantly due to a decline in the HIV-related SMR, which declined in the same period from 96 (95% CI: 89, 102) to 45 (95% CI: 40, 49) deaths per 10,000 PYO.There was substantial agreement (79% kappa = 0.68 (95% CI: 0.67, 0.69)) between physician coding and InterVA coding at the burden of disease group level. CONCLUSIONS: Verbal autopsy based methods enabled the timely measurement of changing trends in cause-specific mortality to provide policymakers with the much-needed information to allocate resources to appropriate health interventions.
Authors: Bongani M Mayosi; Alan J Flisher; Umesh G Lalloo; Freddy Sitas; Stephen M Tollman; Debbie Bradshaw Journal: Lancet Date: 2009-08-24 Impact factor: 79.321
Authors: Abraham J Herbst; Graham S Cooke; Till Bärnighausen; Angelique KanyKany; Frank Tanser; Marie-Louise Newell Journal: Bull World Health Organ Date: 2009-10 Impact factor: 9.408
Authors: Frank Tanser; Victoria Hosegood; Till Bärnighausen; Kobus Herbst; Makandwe Nyirenda; William Muhwava; Colin Newell; Johannes Viljoen; Tinofa Mutevedzi; Marie-Louise Newell Journal: Int J Epidemiol Date: 2007-11-12 Impact factor: 7.196
Authors: Kobus Herbst; Matthew Law; Pascal Geldsetzer; Frank Tanser; Guy Harling; Till Bärnighausen Journal: Curr Opin HIV AIDS Date: 2015-11 Impact factor: 4.283
Authors: Jané Joubert; Debbie Bradshaw; Chodziwadziwa Kabudula; Chalapati Rao; Kathleen Kahn; Paul Mee; Stephen Tollman; Alan D Lopez; Theo Vos Journal: Int J Epidemiol Date: 2014-08-21 Impact factor: 7.196
Authors: Penelope A Phillips-Howard; Frank O Odhiambo; Mary Hamel; Kubaje Adazu; Marta Ackers; Anne M van Eijk; Vincent Orimba; Anja van't Hoog; Caryl Beynon; John Vulule; Mark A Bellis; Laurence Slutsker; Kevin deCock; Robert Breiman; Kayla F Laserson Journal: PLoS One Date: 2012-11-05 Impact factor: 3.240
Authors: Makandwe Nyirenda; Somnath Chatterji; Jane Falkingham; Portia Mutevedzi; Victoria Hosegood; Maria Evandrou; Paul Kowal; Marie-Louise Newell Journal: BMC Public Health Date: 2012-04-02 Impact factor: 3.295
Authors: Jané Joubert; Chalapati Rao; Debbie Bradshaw; Rob E Dorrington; Theo Vos; Alan D Lopez Journal: Glob Health Action Date: 2012-12-27 Impact factor: 2.640
Authors: Peter Byass; Daniel Chandramohan; Samuel J Clark; Lucia D'Ambruoso; Edward Fottrell; Wendy J Graham; Abraham J Herbst; Abraham Hodgson; Sennen Hounton; Kathleen Kahn; Anand Krishnan; Jordana Leitao; Frank Odhiambo; Osman A Sankoh; Stephen M Tollman Journal: Glob Health Action Date: 2012-09-03 Impact factor: 2.640