Abstract

Background: Mortality1 data are frequently presented at the overall population level, possibly obscuring small-scale variations over time and space and between different population sub-groups.Objective: Analysis of mortality data from the Dikgale Health and Demographic Surveillance System, in rural South Africa, over the period 1996–2007, to identify local clustering of mortality among the eight villages in the observed population.Design: Mortality data and person-time of observation were collected annually in an open-cohort population of approximately 8,000 people over 12 years. Poisson regression modelling and space–time clustering analyses were used to identify possible clustering of mortality.Results: Similar patterns of mortality clustering emerged from Poisson regression and space–time clustering analyses after allowing for age and sex. There was no appreciable clustering of mortality among children under 15 years of age nor in adults 50 years and over. For adults aged 15–49 years, there were substantial clustering effects both in time and in space, with mortality increasing during the period observed and particularly so in some locations, which were nearer to local conurbations. Mortality was relatively lower in the vicinity of the local health centre.Conclusions: Although cause-specific mortality data were not available, the rise in mortality in the 15–49-year age group over time and in areas closer to conurbations strongly suggests that the clustering observed was due to the development of HIV/AIDS-related mortality, as seen similarly elsewhere in South Africa. The HIV/AIDS services offered by the local health centre may have contributed to lower relative mortality around that location.

Highlights

  • Mortality data are frequently presented at the overall population level, possibly obscuring small-scale variations over time and space and between different population sub-groups

  • The aim of this paper is to characterise mortality patterns in this population over the 12-year period up to the end of 2007, and to see whether the application of spaceÁtime clustering analysis can offer additional insights into these patterns compared with overall analyses and regression modelling

  • SpaceÁtime clustering analysis commenced with a spatial-only analysis over the whole time period using SaTScanTM 8.0, allowing high and low incidence clusters with a maximum cluster size of 50% of the overall population, in which the only statistically significant cluster was one covering Ntsima and Maphoto, with a relative risk of 1.74 (p00.010)

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Summary

Introduction

Mortality data are frequently presented at the overall population level, possibly obscuring small-scale variations over time and space and between different population sub-groups. Poisson regression modelling and spaceÁtime clustering analyses were used to identify possible clustering of mortality. Results: Similar patterns of mortality clustering emerged from Poisson regression and spaceÁtime clustering analyses after allowing for age and sex. For adults aged 15Á49 years, there were substantial clustering effects both in time and in space, with mortality increasing during the period observed and so in some locations, which were nearer to local conurbations. Conclusions: cause-specific mortality data were not available, the rise in mortality in the 15Á49-year age group over time and in areas closer to conurbations strongly suggests that the clustering observed was due to the development of HIV/AIDS-related mortality, as seen elsewhere in South Africa. The HIV/ AIDS services offered by the local health centre may have contributed to lower relative mortality around that location

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