{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:59:05Z","timestamp":1791223145590,"version":"4.1.0"},"reference-count":57,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Infant mortality refers to the death of an infant before their first birthday. In 2021, approximately 3.8 million infants died worldwide. While interventions have reduced infant mortality rate (IMR) globally, Sub-Saharan Africa (SSA), particularly East Africa, still faces high IMR. Despite many studies, evidence regarding on the impact of spatial effects remain limited. This study aimed to incorporate spatial random effects to identify factors associated with infant mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Secondary data analysis was conducted using a total weighted sample of 101,532 infants from DHS data collected between 2015 and 2022 in East Africa. STATA version 14 was used for data cleaning, and R version 4.3.1 was used for data analysis. A Bayesian spatial frailty analysis model was fitted, and convergence was checked using trace plot. The model goodness of fit was assessed using Cox-snell residual plot.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The IMR was 39.83 per 1000 live births (95% CI: 35.81\u201344.27). Breastfeeding initiation time after 24 hours (HR\u2009=\u20094.033, 95% CrI: 3.869\u20134.207), not having antenatal care (ANC) follow-up (HR\u2009=\u20091.534, 95% CrI: 1.259\u20131.869), maternal age between 15 and 24 years (HR\u2009=\u20091.256, 95% CrI: 1.11\u20131.411), low birth weight (HR\u2009=\u20091.575, 95% CrI: 1.388\u20131.770), plurality (HR\u2009=\u20094.0, 95% CrI: 3.334\u20134.746), parity more than ten (HR\u2009=\u20092.173, 95% CrI\u20131.4413.125), parity between five and ten (HR\u2009=\u20091.264, 95% CrI: 1.075\u20131.472), being a male child (HR\u2009=\u20091.276, 95% CrI: 1.149\u20131.411), and maternal employment status (HR\u2009=\u20090.740, 95% CrI: 0.606\u20130.909) were factors associated with infant mortality. High frailty was detected in northern and southwestern Malawi and in the western regions of Mozambique, Zambia, and Burundi.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The pooled IMR was higher than the global estimate of IMR. Infant mortality was associated with maternal, infant, and reproductive factors. In addition, high spatial frailty was observed in some areas, suggesting the presence of unmeasured regional factors related to geographic location. These findings call attention to the need for policies that focus on strengthening antenatal care coverage, promoting early initiation of breastfeeding, improving maternal socioeconomic empowerment, and prioritizing high-frailty areas through resource allocation to reduce infant mortality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0357712","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:42:51Z","timestamp":1791222171000},"page":"e0357712","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Infant mortality rate and time to death determinants in East Africa: A Bayesian spatial frailty analysis of DHS data (2015\u20132022)"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0966-9312","authenticated-orcid":true,"given":"Tigist Kifle","family":"Tsegaw","sequence":"first","affiliation":[{"name":"University of Gondar","place":["Gondar, Ethiopia"],"department":["Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rediet","family":"Eristu","sequence":"additional","affiliation":[{"name":"University of Gondar","place":["Gondar, Ethiopia"],"department":["Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dessie Abebaw","family":"Angaw","sequence":"additional","affiliation":[{"name":"University of Gondar","place":["Gondar, Ethiopia"],"department":["Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"pone.0357712.ref001","author":"Communicable Disease Control"},{"issue":"10063","key":"pone.0357712.ref002","doi-asserted-by":"crossref","first-page":"3027","DOI":"10.1016\/S0140-6736(16)31593-8","article-title":"Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the Sustainable Development Goals","volume":"388","author":"L Liu","year":"2016","journal-title":"Lancet"},{"key":"pone.0357712.ref003","author":"World Health Organization"},{"key":"pone.0357712.ref004","author":"The United Nations Inter-agency Group for Child Mortality Estimation"},{"key":"pone.0357712.ref005","unstructured":"World Health Organization. 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