OBJECTIVE: To determine the distribution of under-five deaths in Dodowa Health and Demographic Surveillance Area (DHDSA) and to identify possible clustering of deaths. METHODS: Data from the Dodowa Health and Demographic Surveillance System (DHDSS) were used for the analysis. These data covered a population of about 89 371 in 371 communities in seven area councils from 2005-2006. Under-five crude mortality rates were calculated for each community and area council. The central feature command in ArcGIS 9.2 was used to locate the centroid of each community from a shapefile of housing structures of communities. A spatial scan statistic was used to identify and test for clusters of under-five deaths. Data on socioeconomic indicators and insecticide treated net (ITN) ownership were analysed to determine the status of the clustered communities. RESULTS: Although several clusters of high under-five mortality were identified, only two were significant in two area councils: one cluster comprising three communities and another involving one community. Analysis of probable risk factors indicates that the single community which formed a significant cluster in Osuwem area council was seriously disadvantaged. About 71% of its households were in the poorest quintile, no household had ITN, electricity connection, good source of water or health insurance. The three communities that formed the significant cluster in Ningo area council, however, fared well in almost all indicators. CONCLUSION: The identified significant clustering of under-five mortality demands further studies to investigate the causes of the clustering, especially the Ningo area council.
OBJECTIVE: To determine the distribution of under-five deaths in Dodowa Health and Demographic Surveillance Area (DHDSA) and to identify possible clustering of deaths. METHODS: Data from the Dodowa Health and Demographic Surveillance System (DHDSS) were used for the analysis. These data covered a population of about 89 371 in 371 communities in seven area councils from 2005-2006. Under-five crude mortality rates were calculated for each community and area council. The central feature command in ArcGIS 9.2 was used to locate the centroid of each community from a shapefile of housing structures of communities. A spatial scan statistic was used to identify and test for clusters of under-five deaths. Data on socioeconomic indicators and insecticide treated net (ITN) ownership were analysed to determine the status of the clustered communities. RESULTS: Although several clusters of high under-five mortality were identified, only two were significant in two area councils: one cluster comprising three communities and another involving one community. Analysis of probable risk factors indicates that the single community which formed a significant cluster in Osuwem area council was seriously disadvantaged. About 71% of its households were in the poorest quintile, no household had ITN, electricity connection, good source of water or health insurance. The three communities that formed the significant cluster in Ningo area council, however, fared well in almost all indicators. CONCLUSION: The identified significant clustering of under-five mortality demands further studies to investigate the causes of the clustering, especially the Ningo area council.
Authors: Raphael E Arku; James E Bennett; Marcia C Castro; Kofi Agyeman-Duah; Samilia E Mintah; James H Ware; Philomena Nyarko; John D Spengler; Samuel Agyei-Mensah; Majid Ezzati Journal: PLoS Med Date: 2016-06-21 Impact factor: 11.069