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A longitudinal dataset was used to investigate adult mortality in rural South Africa in order to determine location, trends, high impact determinants and policy implications. Adult (15-59 years) mortality data for the period 1993-2010 were extracted from the health and demographic surveillance system in the rural sub-district of Agincourt. A Bayesian geostatistical frailty survival model was used to quantify significant associations between adult mortality and various multilevel (individual, household and community) variables. It was found that adult mortality significantly increased over time with a reduction observed late in the study period. Non-communicable disease mortality appeared to increase and decrease in parallel with communicable mortality, whilst deaths due to external causes remained constant. Male gender, unemployment, circular (labour) migrant status, age and gender of household heads, partner and/or other household death, low education and low household socio-economic status were identified as significant and highly attributable determinants of adult mortality. Health facility remoteness was a risk for adult mortality and households falling outside a critical buffering zone were identified. Spatial foci of higher adult mortality risk were observed, indicating a strong non-random pattern. Communicable diseases differed from non-communicable diseases with respect to spatial distribution of mortality. Areas with significant excess mortality risk (hot spots) were found to be part of a complex interaction of highly attributable factors that continues to drive differential space-time risk patterns of communicable (HIV/AIDS and tuberculosis) mortality in Agincourt. The impact of HIV mortality and its subsequent lowering due to the introduction of antiretroviral therapy was found to be clearly evident in this rural population.

Original publication





Geospatial health

Publication Date





237 - 249


Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.


Humans, Population Surveillance, Mortality, Cause of Death, Bayes Theorem, Risk Factors, Age Distribution, Sex Distribution, Time Factors, Socioeconomic Factors, Adolescent, Adult, Middle Aged, Rural Population, South Africa, Female, Male, Young Adult, Spatial Analysis