Verbal autopsy-based cause-specific mortality trends in rural KwaZulu-Natal, South Africa, 2000-2009

Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Somkhele, South Africa. .
Population Health Metrics (Impact Factor: 2.11). 08/2011; 9(1):47. DOI: 10.1186/1478-7954-9-47
Source: PubMed


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).
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.
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.
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.

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    • "According to the report, tuberculosis remains among the top three killers of women [30]. In line with our findings, different studies have also reported the public health burden of tuberculosis and HIV/AIDS among female population in sub-Saharan African countries including Ethiopia [2, 4, 11, 31–33]. These finds signal the need of more efforts to prevent tuberculosis related mortality among females. "
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    • "The basic modes of operation of the six population surveillance sites involved, of which all are members of the Alpha Network and some are members of the INDEPTH Network, have been described previously (14–19). These sites are located on a north–south transect of some 3,500 km through eastern and southern Africa. "
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    Global Health Action 10/2013; 6:22448. DOI:10.3402/gha.v6i0.22448 · 1.93 Impact Factor
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    • "This confirmed the temporal and spatial consistency of the InterVA model for establishing cause-specific mortalities. However, a higher level of agreement between both approaches was observed in studies which utilized data collected by a demographic surveillance system [19,43]. The reason for the poor level of agreement observed for digestive diseases could be the overlapping nature of the clinical signs and symptoms with other diseases, especially HIV/AIDS. "
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    PLoS ONE 09/2013; 8(9):e73463. DOI:10.1371/journal.pone.0073463 · 3.23 Impact Factor
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