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Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India.
BMC Public Health 2018 November 24
BACKGROUND: Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modifiable determinants of diarrhea while controlling for the spatial dependence in the data.
METHODS: An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran's I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Spatial regression was used to identify the modifiable factors associated with the prevalence of diarrhea. The study comprised of the prevalence of diarrhea among the children below the age of five years (U-5 s) across different states in India. The determinants of diarrhea were obtained using spatial lag models. The software used were GeoDa 1.6.6 and QGIS 2.0.
RESULTS: The presence of spatial autocorrelation in DLHS-3 and NFHS-4 (Moron's I = 0.577 and 0.369 respectively) enforces the usage of geographical properties while modeling the diarrhea data. The geographic clustering of high-prevalence districts was observed in the state of UP consistently. The spatial pattern of the percentage of children with diarrhea was persistently associated with the household with a sanitation facility (%) (p = 0.023 and p = 0.011). Compared to the diarrhea cases in the period 2007-2008, no much reduction was observed in the period 2015-2016. The prevalence of diarrhea and percentage of household with sanitation were ranging between 0.1-33.8% and 1.3-96.1% in the period 2007-2008 and 0.6-29.1% and 10.4-92.0% in the period 2015-2016 respectively. The least and highest prevalence of diarrhea being consistently from Assam and UP respectively.
CONCLUSION: Despite improvements in controlling spread of diarrheal disease, the burden remains high. Focus on widespread diarrheal disease control strategy by addressing the social determinants of health like basic sanitation is crucial to reduce the burden of diarrhea among U-5 s in India. The identification of hotspots will aid in the planning of control strategies for goal setting in the targeted regions.
METHODS: An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran's I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Spatial regression was used to identify the modifiable factors associated with the prevalence of diarrhea. The study comprised of the prevalence of diarrhea among the children below the age of five years (U-5 s) across different states in India. The determinants of diarrhea were obtained using spatial lag models. The software used were GeoDa 1.6.6 and QGIS 2.0.
RESULTS: The presence of spatial autocorrelation in DLHS-3 and NFHS-4 (Moron's I = 0.577 and 0.369 respectively) enforces the usage of geographical properties while modeling the diarrhea data. The geographic clustering of high-prevalence districts was observed in the state of UP consistently. The spatial pattern of the percentage of children with diarrhea was persistently associated with the household with a sanitation facility (%) (p = 0.023 and p = 0.011). Compared to the diarrhea cases in the period 2007-2008, no much reduction was observed in the period 2015-2016. The prevalence of diarrhea and percentage of household with sanitation were ranging between 0.1-33.8% and 1.3-96.1% in the period 2007-2008 and 0.6-29.1% and 10.4-92.0% in the period 2015-2016 respectively. The least and highest prevalence of diarrhea being consistently from Assam and UP respectively.
CONCLUSION: Despite improvements in controlling spread of diarrheal disease, the burden remains high. Focus on widespread diarrheal disease control strategy by addressing the social determinants of health like basic sanitation is crucial to reduce the burden of diarrhea among U-5 s in India. The identification of hotspots will aid in the planning of control strategies for goal setting in the targeted regions.
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