{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:31:26Z","timestamp":1776364286274,"version":"3.51.2"},"reference-count":83,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"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":"\n Background<\/jats:title>\n Low- and middle-income countries (LMICs) account for a large share of global infant deaths, but there is a lack of evidence on the pooled estimate of infant mortality and its predictors in LMICs. Therefore, this study aimed to assess the pooled incidence of infant mortality and its associated factors in LMICs.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We used clustered data extracted from the recent Demographic and Health Surveys (DHS 2018-DHS 2024) of all LMICs. A total of 1,404,826weighted numbers of recent live births were included in the study. A lognormal shared gamma frailty model was employed. We used the Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood values for model comparison. An adjusted time ratio (\n \n \n \n \n \n \n \u03d5<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ) with a 95% confidence interval (CI) in the final model was used to select variables that had a significant association with time to infant death. The data were analyzed via R software version 4.3.1.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n A total of 1,404,826 live births were included in the final analysis. By the end of the follow-up period, 72,569 infants (5.17%, 95% CI: 5.13\u20135.21) had died before their first birthday. The pooled estimate of the IMR in LMICs was 39 per 1000 live births (95% CI: 32.68\u201344.95). Maternal education, family size\u2009\u2265\u20095, being a multiparous mother, being delivered at health facilities, being a female infant, immediate initiation of breast feeding, living in Europe & Central Asia, and living in West & East Asia were significantly associated with a lower risk of infant death. Conversely, maternal age 25\u201334, maternal age 35\u201349, unimproved toilet facilities, poor and middle wealth indices, maternal age at birth \u226419, birth interval of <18 and 18\u201323 months, multiple births, 2\n nd<\/jats:sup>\n birth order, small birth size, low and medium Human Development Index (HDI), low and medium literacy rate, low-income and lower-middle income countries, rural residence, living in West Africa, South & Central Africa, and South Asia were significantly associated with a higher risk of infant mortality.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The infant mortality rate (IMR) in LMICs remains high compared with that in WHO targets and shows significant regional variation. West Africa and South Asia had the highest pooled estimate of infant deaths. Variables such as maternal age, education, wealth index, age at first birth, parity, family size, child sex, birth interval, multiple pregnancy, birth order number, perceived child size at birth, place of delivery, residence, country\u2019s literacy rate, income group, and HDI value were identified as significant predictors of time to infant death. Therefore, public health interventions that enhance health facility delivery, optimal birth spacing, maternal education, and immediate breastfeeding are crucial to reduce the incidence of infant mortality in LMICs.<\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0347023","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T17:28:36Z","timestamp":1776360516000},"page":"e0347023","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pooled incidence and predictors of infant mortality in low- and middle-income countries using gamma shared frailty model: Insights for achieving the Sustainable Development Goals"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5777-1269","authenticated-orcid":true,"given":"Dejen Kahsay","family":"Asgedom","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Habtamu Solomon","family":"Demeke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Etsay Woldu","family":"Anbesu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ausman Ahmed","family":"Mohammed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2692-7394","authenticated-orcid":true,"given":"Simachew Getaneh","family":"Endalamew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bewuketu","family":"Terefe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6988-9471","authenticated-orcid":true,"given":"Solomon Keflie","family":"Assefa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asefa Asimasu","family":"Taddese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"issue":"5","key":"pone.0347023.ref001","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1136\/jech.57.5.344","article-title":"Infant mortality rate as an indicator of population health","volume":"57","author":"DD Reidpath","year":"2003","journal-title":"J Epidemiol Community Health"},{"key":"pone.0347023.ref002","unstructured":"UNICEF. 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