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Space and time clustering of mortality in rural South Africa (Agincourt HDSS), 1992–2007

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 p...

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Detalles Bibliográficos
Autores principales: Sartorius, Benn, Kahn, Kathleen, Vounatsou, Penelope, Collinson, Mark A., Tollman, Stephen M.
Formato: Texto
Lenguaje:English
Publicado: CoAction Publishing 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938122/
https://www.ncbi.nlm.nih.gov/pubmed/20838482
http://dx.doi.org/10.3402/gha.v3i0.5225
Descripción
Sumario: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/south- east and upper east central regions. This corresponds to areas with higher mortality due to communicable causes (especially HIV/TB and diarrhoeal disease) and indicates a non-random element to the distribution of potential underlying causative factors e.g. settlements comprising former. Mozambican refugees in east/south-east 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 non-communicable 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 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.