Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition model
Objective The study contextualises the spatial heterogeneity and associated drivers of open defecation (OD) in India.Design The present study involved a secondary cross-sectional survey data from the fifth round of the National Family Health Survey conducted during 2019–2021 in India. We mapped the...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2023-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/13/7/e072507.full |
| _version_ | 1797745406773297152 |
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| author | Avijit Roy Margubur Rahaman Mihir Adhikary Nanigopal Kapasia Pradip Chouhan Kailash Chandra Das |
| author_facet | Avijit Roy Margubur Rahaman Mihir Adhikary Nanigopal Kapasia Pradip Chouhan Kailash Chandra Das |
| author_sort | Avijit Roy |
| collection | DOAJ |
| description | Objective The study contextualises the spatial heterogeneity and associated drivers of open defecation (OD) in India.Design The present study involved a secondary cross-sectional survey data from the fifth round of the National Family Health Survey conducted during 2019–2021 in India. We mapped the spatial heterogeneity of OD practices using LISA clustering techniques and assessed the critical drivers of OD using multivariate regression models. Fairlie decomposition model was used to identify the factors responsible for developing OD hot spots and cold spots.Setting and participants The study was conducted in India and included 636 699 sampled households within 36 states and union territories covering 707 districts of India.Primary and secondary outcome measures The outcome measure was the prevalence of OD.Results The prevalence of OD was almost 20%, with hot spots primarily located in the north-central belts of the country. The rural–urban (26% vs 6%), illiterate-higher educated (32% vs 4%) and poor-rich (52% vs 2%) gaps in OD were very high. The odds of OD were 2.7 and 1.9 times higher in rural areas and households without water supply service on premises compared with their counterparts. The spatial error model identified households with an illiterate head (coefficient=0.50, p=0.001) as the leading spatially linked predictor of OD, followed by the poorest (coefficient=0.31, p=0.001) and the Hindu (coefficient=0.10, p=0.001). The high-high and low-low cluster inequality in OD was 38%, with household wealth quintile (67%) found to be the most significant contributing factor, followed by religion (22.8%) and level of education (6%).Conclusion The practice of OD is concentrated in the north-central belt of India and is particularly among the poor, illiterate and socially backward groups. Policy measures should be taken to improve sanitation practices, particularly in high-focus districts and among vulnerable groups, by adopting multispectral and multisectoral approaches. |
| first_indexed | 2024-03-12T15:22:52Z |
| format | Article |
| id | doaj.art-73629536e6da4158bb9877d3c073a35e |
| institution | Directory Open Access Journal |
| issn | 2044-6055 |
| language | English |
| last_indexed | 2024-03-12T15:22:52Z |
| publishDate | 2023-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj.art-73629536e6da4158bb9877d3c073a35e2023-08-11T00:05:06ZengBMJ Publishing GroupBMJ Open2044-60552023-07-0113710.1136/bmjopen-2023-072507Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition modelAvijit Roy0Margubur Rahaman1Mihir Adhikary2Nanigopal Kapasia3Pradip Chouhan4Kailash Chandra Das51 Department of Geography, Malda College, Malda, West Bengal, India2 Department of Migration & Urban Studies, International Institute for Population Sciences, Mumbai, Maharashtra, India3 Department of Public Health & Mortality Studies, International Institute for Population Sciences, Mumbai, India1 Department of Geography, Malda College, Malda, West Bengal, India4 Department of Geogrpahy, University of Gour Banga, Malda, West Bengal, India2 Department of Migration & Urban Studies, International Institute for Population Sciences, Mumbai, Maharashtra, IndiaObjective The study contextualises the spatial heterogeneity and associated drivers of open defecation (OD) in India.Design The present study involved a secondary cross-sectional survey data from the fifth round of the National Family Health Survey conducted during 2019–2021 in India. We mapped the spatial heterogeneity of OD practices using LISA clustering techniques and assessed the critical drivers of OD using multivariate regression models. Fairlie decomposition model was used to identify the factors responsible for developing OD hot spots and cold spots.Setting and participants The study was conducted in India and included 636 699 sampled households within 36 states and union territories covering 707 districts of India.Primary and secondary outcome measures The outcome measure was the prevalence of OD.Results The prevalence of OD was almost 20%, with hot spots primarily located in the north-central belts of the country. The rural–urban (26% vs 6%), illiterate-higher educated (32% vs 4%) and poor-rich (52% vs 2%) gaps in OD were very high. The odds of OD were 2.7 and 1.9 times higher in rural areas and households without water supply service on premises compared with their counterparts. The spatial error model identified households with an illiterate head (coefficient=0.50, p=0.001) as the leading spatially linked predictor of OD, followed by the poorest (coefficient=0.31, p=0.001) and the Hindu (coefficient=0.10, p=0.001). The high-high and low-low cluster inequality in OD was 38%, with household wealth quintile (67%) found to be the most significant contributing factor, followed by religion (22.8%) and level of education (6%).Conclusion The practice of OD is concentrated in the north-central belt of India and is particularly among the poor, illiterate and socially backward groups. Policy measures should be taken to improve sanitation practices, particularly in high-focus districts and among vulnerable groups, by adopting multispectral and multisectoral approaches.https://bmjopen.bmj.com/content/13/7/e072507.full |
| spellingShingle | Avijit Roy Margubur Rahaman Mihir Adhikary Nanigopal Kapasia Pradip Chouhan Kailash Chandra Das Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition model BMJ Open |
| title | Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition model |
| title_full | Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition model |
| title_fullStr | Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition model |
| title_full_unstemmed | Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition model |
| title_short | Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition model |
| title_sort | unveiling the spatial divide in open defecation practices across india an application of spatial regression and fairlie decomposition model |
| url | https://bmjopen.bmj.com/content/13/7/e072507.full |
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