Abstract
Background: Despite significant reduction of childhood mortality in Bangladesh, large spatial variations persist. Identification of lower level spatial units with higher concentrations of deaths can be useful for strengthening services in these areas. This paper reports findings from a spatial analysis of deaths in Chakaria, a rural subdistrict, where a Health and Demographic Surveillance System has been in place since 1999. Chakaria is an INDEPTH member site.Methods: An analysis was done of 339 deaths among nearly 24,500 children under the age of five during 2005–2008. One ward, the lowest level of administrative units, was the unit of spatial analysis. Data from 24 wards were analyzed. The Discrete Poisson Probability Model was used to identify the clustering of deaths.Results: Deaths were concentrated within 12 wards located in the low-lying deltaic flood plains of the Chakaria HDSS area. The risk of death in the low-lying areas was statistically, significantly higher, 1.5 times, than the non-low-lying areas (p<0.02).Conclusion: Spatial analysis can be a useful tool for identifying high-risk mortality areas. An understanding of the risk factors prevalent in the low-lying areas can help design effective interventions to reduce mortality in these areas.
Highlights
Despite significant reduction of childhood mortality in Bangladesh, large spatial variations persist
The cluster had a relative risk of 1.5; that is, a child residing in this cluster is 1.5 times more likely to die than a child who resides outside this cluster (Table 2, Fig. 2)
A temporal analysis scanning for high rates using the Discrete Poisson Probability Model for the period 2005Á 2008 was employed
Summary
Despite significant reduction of childhood mortality in Bangladesh, large spatial variations persist. Identification of lower level spatial units with higher concentrations of deaths can be useful for strengthening services in these areas. This paper reports findings from a spatial analysis of deaths in Chakaria, a rural subdistrict, where a Health and Demographic Surveillance System has been in place since 1999. The lowest level of administrative units, was the unit of spatial analysis. Results: Deaths were concentrated within 12 wards located in the low-lying deltaic flood plains of the Chakaria HDSS area. The risk of death in the low-lying areas was statistically significantly higher, 1.5 times, than the non-low-lying areas (p B0.02). Conclusion: Spatial analysis can be a useful tool for identifying high-risk mortality areas. An understanding of the risk factors prevalent in the low-lying areas can help design effective interventions to reduce mortality in these areas
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