TY - JOUR
T1 - Spatial risk distribution and determinants of E. coli contamination in household drinking water
T2 - a case study of Bangladesh
AU - Khan, Jahidur Rahman
AU - Bakar, K. Shuvo
N1 - Publisher Copyright:
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/5/3
Y1 - 2020/5/3
N2 - The Escherichia coli (E. coli) contamination in the household (HH) drinking water is often a public health concern. Very few studies explore the associated factors and spatial risk modeling together for E. coli contamination in Bangladesh, this research gap motivates to explore this fact further by utilizing Bangladesh Multiple Indicator Cluster Survey (MICS) 2012–13 data. A Bayesian spatial ordered logit model was used to examine the associated factors and spatial risks of the E. coli contamination. The results show that 62% of HH water samples were contaminated with E. coli. After controlling for different factors, a high level of E. coli contamination was observed among HHs who had access to non-improved water sources. Moreover, no significant rural-urban difference was observed. The spatial prediction of the high-risk contamination was prominent in districts like Dhaka and Bandarban. The study findings can provide insights into the planning of policy activities in Bangladesh.
AB - The Escherichia coli (E. coli) contamination in the household (HH) drinking water is often a public health concern. Very few studies explore the associated factors and spatial risk modeling together for E. coli contamination in Bangladesh, this research gap motivates to explore this fact further by utilizing Bangladesh Multiple Indicator Cluster Survey (MICS) 2012–13 data. A Bayesian spatial ordered logit model was used to examine the associated factors and spatial risks of the E. coli contamination. The results show that 62% of HH water samples were contaminated with E. coli. After controlling for different factors, a high level of E. coli contamination was observed among HHs who had access to non-improved water sources. Moreover, no significant rural-urban difference was observed. The spatial prediction of the high-risk contamination was prominent in districts like Dhaka and Bandarban. The study findings can provide insights into the planning of policy activities in Bangladesh.
KW - Bayesian spatial model
KW - E. coli contamination
KW - spatial risk
UR - http://www.scopus.com/inward/record.url?scp=85063570724&partnerID=8YFLogxK
U2 - 10.1080/09603123.2019.1593328
DO - 10.1080/09603123.2019.1593328
M3 - Article
SN - 0960-3123
VL - 30
SP - 268
EP - 283
JO - International Journal of Environmental Health Research
JF - International Journal of Environmental Health Research
IS - 3
ER -