摘要：Background: Detailed information regarding the spatial and/or spatial-temporal distribution of mortality is required for the efficient implementation and targeting of public health interventions. Objectives: Identify high risk clusters of mortality within the Agincourt subdistrict for targeting of public health interventions, and highlight areas for further research. Design: Mortality data were extracted from the Agincourt health and socio-demographic surveillance system (HDSS) for the period 1992-2007. Mortality rates by age group and time were calculated assuming a Poisson distribution and using precise person-time contribution estimates. A spatial scan statistic (Kulldorff) was used to test for clusters of age group specific all-cause and cause-specific mortality both in space and time. Results: Many statistically significant clusters of higher all-cause and cause-specific mortality were identified both in space and time. Specific areas were consistently identified as high risk areas; namely, the east/southeast and upper east central regions. This corresponds to areas with higher mortality due to communicable causes (especially HIV/TB and diarrheal disease) and indicates a non-random element to the distribution of potential underlying causative factors e.g. settlements comprising former Mozambican refugees in east/southeast of the site, corresponding higher poverty areas, South African villages with higher HIV prevalence, etc. Clusters of older adult mortality were also observed indicating potential non-random distribution of noncommunicable disease mortality. Conclusion: This study has highlighted distinct clusters of all-cause and cause-specific mortality within the Agincourt subdistrict. It is a first step in prioritizing areas for further, more detailed research as well as for future public health follow-on efforts such as targeting of vertical prevention of HIV/TB and antiretroviral rollout in significant child and adult mortality clusters; and assessment and provision of adequate water and sanitation in the child mortality clusters particularly in the south-east where diarrheal mortality appears high. Underlying causative factors need to be identified and accurately quantified. Other questions for more detailed research are discussed. Keywords: all-cause mortality; demographic surveillance; clustering; spatial-temporal (Published: 30 August 2010) Citation: Global Health Action Supplement 1, 2010. DOI: 10.3402/gha.v3i0.5225
