{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:53:49Z","timestamp":1790888029904,"version":"4.1.0"},"reference-count":60,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>District-representative data are rarely collected in the surveys for identifying localised disparities in Bangladesh, and so district-level estimates of undernutrition indicators \u2013 stunting, wasting and underweight \u2013 have remained largely unexplored. This study aims to estimate district-level prevalence of these indicators by employing a multivariate Fay\u2013Herriot (MFH) model which accounts for the underlying correlation among the undernutrition indicators. Direct estimates (DIR) of the target indicators and their variance\u2013covariance matrices calculated from the 2019 Bangladesh Multiple Indicator Cluster Survey microdata have been used as input for developing univariate Fay\u2013Herriot (UFH), bivariate Fay\u2013Herriot (BFH) and MFH models. The comparison of the various model-based estimates and their relative standard errors with the corresponding direct estimates reveals that the MFH estimator provides unbiased estimates with more accuracy than the DIR, UFH and BFH estimators. The MFH model-based district level estimates of stunting, wasting and underweight range between 16 and 43%, 15 and 36%, and 6 and 13% respectively. District level bivariate maps of undernutrition indicators show that districts in north-eastern and south-eastern parts are highly exposed to either form of undernutrition, than the districts in south-western and central parts of the country. In terms of the number of undernourished children, millions of children affected by either form of undernutrition are living in densely populated districts like the capital district Dhaka, though undernutrition indicators (as a proportion) are comparatively lower. These findings can be used to target districts with a concurrence of multiple forms of undernutrition, and in the design of urgent intervention programs to reduce the inequality in child undernutrition at the localised district level.<\/jats:p>","DOI":"10.1515\/ijb-2021-0130","type":"journal-article","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T22:20:28Z","timestamp":1653690028000},"page":"191-215","source":"Crossref","is-referenced-by-count":4,"title":["Multivariate small area modelling of undernutrition prevalence among under-five children in Bangladesh"],"prefix":"10.1515","volume":"19","author":[{"given":"Saurav","family":"Guha","sequence":"first","affiliation":[{"name":"ICAR-Indian Agricultural Statistics Research Institute , New Delhi , India"},{"name":"Health Analytics Network , Pittsburgh , PA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1560-2349","authenticated-orcid":false,"given":"Sumonkanti","family":"Das","sequence":"additional","affiliation":[{"name":"School of Demography , Australian National University , Canberra , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernard","family":"Baffour","sequence":"additional","affiliation":[{"name":"School of Demography , Australian National University , Canberra , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hukum","family":"Chandra","sequence":"additional","affiliation":[{"name":"ICAR-Indian Agricultural Statistics Research Institute , New Delhi , India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"key":"2025121208542897234_j_ijb-2021-0130_ref_001","doi-asserted-by":"crossref","unstructured":"Black, RE, Allen, LH, Bhutta, ZA, Caulfield, LE, De Onis, M, Ezzati, M, et al.. 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