Diarrheal disease risk in rural Bangladesh decreases as tubewell density increases: a zero-inflated and geographically weighted analysis. Carrel, M., Escamilla, V., Messina, J., Giebultowicz, S., Winston, J., Yunus, M., Streatfield, P. K., & Emch, M. International journal of health geographics, 10:41, June, 2011. doi abstract bibtex This study investigates the impact of tubewell user density on cholera and shigellosis events in Matlab, Bangladesh between 2002 and 2004. Household-level demographic, health, and water infrastructure data were incorporated into a local geographic information systems (GIS) database. Geographically-weighted regression (GWR) models were constructed to identify spatial variation of relationships across the study area. Zero-inflated negative binomial regression models were run to simultaneously measure the likelihood of increased magnitude of disease events and the likelihood of zero cholera or shigellosis events. The aim of this study was to examine the effect of tubewell density on both the occurrence of diarrheal disease and the magnitude of diarrheal disease incidence. In Matlab, households with greater tubewell density were more likely to report zero cholera or shigellosis events. Results for both cholera and shigellosis GWR models suggest that tubewell density effects are spatially stationary and the use of non-spatial statistical methods is appropriate. Increasing the amount of drinking water available to households through increased density of tubewells contributed to lower reports of cholera and shigellosis events in rural Bangladesh. Our findings demonstrate the importance of tubewell installation and access to groundwater in reducing diarrheal disease events in the developing world.
@Article{Carrel2011,
author = {Carrel, Margaret and Escamilla, Veronica and Messina, Jane and Giebultowicz, Sophia and Winston, Jennifer and Yunus, Mohammad and Streatfield, P. Kim and Emch, Michael},
journal = {International journal of health geographics},
title = {Diarrheal disease risk in rural Bangladesh decreases as tubewell density increases: a zero-inflated and geographically weighted analysis.},
year = {2011},
issn = {1476-072X},
month = jun,
pages = {41},
volume = {10},
abstract = {This study investigates the impact of tubewell user density on cholera and shigellosis events in Matlab, Bangladesh between 2002 and 2004. Household-level demographic, health, and water infrastructure data were incorporated into a local geographic information systems (GIS) database. Geographically-weighted regression (GWR) models were constructed to identify spatial variation of relationships across the study area. Zero-inflated negative binomial regression models were run to simultaneously measure the likelihood of increased magnitude of disease events and the likelihood of zero cholera or shigellosis events. The aim of this study was to examine the effect of tubewell density on both the occurrence of diarrheal disease and the magnitude of diarrheal disease incidence. In Matlab, households with greater tubewell density were more likely to report zero cholera or shigellosis events. Results for both cholera and shigellosis GWR models suggest that tubewell density effects are spatially stationary and the use of non-spatial statistical methods is appropriate. Increasing the amount of drinking water available to households through increased density of tubewells contributed to lower reports of cholera and shigellosis events in rural Bangladesh. Our findings demonstrate the importance of tubewell installation and access to groundwater in reducing diarrheal disease events in the developing world.},
chemicals = {Drinking Water},
citation-subset = {IM},
completed = {2011-12-12},
country = {England},
doi = {10.1186/1476-072X-10-41},
issn-linking = {1476-072X},
keywords = {Bangladesh, epidemiology; Diarrhea, epidemiology; Drinking Water, microbiology; Geography; Humans; Population Surveillance; Regression Analysis; Rural Population; Water Wells},
nlm-id = {101152198},
owner = {NLM},
pii = {1476-072X-10-41},
pmc = {PMC3136404},
pmid = {21676249},
pubmodel = {Electronic},
pubstate = {epublish},
revised = {2018-11-13},
}
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