{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T10:23:32Z","timestamp":1769077412769,"version":"3.49.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Malnutrition remains one of the most pressing health challenges, particularly in developing nations across Africa, placing a significant burden on both public health agencies and the affected countries. In countries such as Gabon, Gambia, Mauritania, Liberia, and Nigeria, the burden of malnutrition exacerbates public health systems, strains economic resources, and hinders sustainable development, underscoring the urgent need for coordinated efforts at national and international levels. This study aimed to determine the scope and underlying factors contributing to the elevated incidence of malnutrition in West African countries, specifically Gabon, Gambia, Liberia, Mauritania, and Nigeria.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>This study employed a quantile regression model to examine the determinants of malnutrition at various quantiles of interest across the Western African countries under consideration to facilitate focused policy measures and intervention strategies aimed at decreasing the prevalence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For the lower quantiles (0.1 and 0.25), which indicate severe malnutrition, significant variables included the child\u2019s weight [quantile\u202f=\u202f0.1, 95% CI(0.0063, 0.0103), quantile\u202f=\u202f0.25, 95% CI(0.0054, 0.0107)], mother\u2019s education level [No education: quantile\u202f=\u202f0.1, 95% CI(\u221249.7471, \u221232.1376), quantile\u202f=\u202f0.25, 95% CI(\u221238.1513, \u221222.4438) Primary: quantile\u202f=\u202f0.1, 95% CI(\u221224.8095, \u22125.7693), quantile\u202f=\u202f0.25, 95% CI(\u221219.5273, \u22126.3424) Higher: quantile\u202f=\u202f0.1, 95% CI(5.6499, 40.3274), quantile\u202f=\u202f0.25, 95% CI(21.8158, 40.278)], drinking water source [Natural Sources: quantile\u202f=\u202f0.1, 95% CI(0.6877, 24.384),Piped: quantile\u202f=\u202f0.1, 95% CI(25.578, 45.2368), quantile\u202f=\u202f0.25, 95% CI(22.2782, 34.8212), Bottle\/Sachet: quantile\u202f=\u202f0.25, 95% CI(3.438, 98.1675)], toilet type [Flush: quantile\u202f=\u202f0.25, 95% CI(2.2598, 18.3457),Other: quantile\u202f=\u202f0.1, 95% CI(8.7863, 24.504), quantile\u202f=\u202f0.25, 95% CI(7.0995, 20.1119)], household wealth index [Poorest: quantile\u202f=\u202f0.1, 95% CI(\u221252.5112, \u221216.9197), quantile\u202f=\u202f0.25, 95% CI(\u221248.3804, \u221223.0633),Poorer: quantile\u202f=\u202f0.1, 95% CI(\u221238.8744, \u22124.7586), quantile\u202f=\u202f0.25, 95% CI(\u221234.6993, \u22129.1766), Middle: quantile\u202f=\u202f0.25, 95% CI(\u221228.9491, \u22126.5834)], health care visits [No: quantile\u202f=\u202f0.1, 95% CI(\u221219.293, \u22123.6393), quantile\u202f=\u202f0.25, 95% CI(\u221217.2342, \u22125.6411)], consumption of fortified foods and tubers [No: quantile\u202f=\u202f0.1, 95% CI(\u221236.3898, \u221212.0378), quantile\u202f=\u202f0.25, 95% CI(\u221217.8127, \u22121.2374)], anemia status [Anemic: quantile\u202f=\u202f0.1, 95% CI(\u221215.9326, \u22121.1929), quantile\u202f=\u202f0.25, 95% CI(\u221212.3361, \u22121.5516)], mosquito net usage [No: quantile\u202f=\u202f0.1, 95% CI(\u221222.0323, \u22120.8033), quantile\u202f=\u202f0.25, 95% CI(\u221213.8107, 1.1366)], child\u2019s age [0 to 12\u202fmonths: quantile\u202f=\u202f0.1, 95% CI(81.6424, 105.7155), quantile\u202f=\u202f0.25, 95% CI(61.4817, 78.5194),12 to 24\u202fmonths: quantile\u202f=\u202f0.1, 95% CI(0.5592, 24.933), 24 to 36\u202fmonths: quantile\u202f=\u202f0.1, 95% CI(7.9128, 40.2828)] and gender [Female: quantile\u202f=\u202f0.1, 95% CI(4.5351, 17.9783), quantile\u202f=\u202f0.25, 95% CI(5.0076, 15.4735)], and recent fever [No: quantile\u202f=\u202f0.1, 95% CI(11.5663, 29.5984), quantile\u202f=\u202f0.25, 95% CI(7.0313, 20.8918)]. Residence type was significant for the 0.25 quantile but not the 0.1 quantile [Rural: quantile\u202f=\u202f0.25, 95% CI(\u221214.7051, \u22122.1455)]. At higher quantiles (0.75, 0.85, 0.9, and 0.95), factors such as the use of mosquito nets, formula feeding, and access to piped water remain significant, while socioeconomic determinants like maternal education and wealth index lose their influence. Common variables across all quantiles were mother\u2019s age, child\u2019s age (0 to 12\u202fmonths), child\u2019s gender, and recent fever.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>These findings underscore the critical role of primary health care interventions in identifying and managing malnutrition, particularly among lower quantiles where severe malnutrition dominates. High-risk groups, such as teenagers and low-income mothers, should receive targeted support, including prenatal classes and counseling. Community caregivers can monitor at-risk individuals and ensure timely referrals, while collaborations with nonprofits can improve access to food and supplements. Promoting community food gardens, clean water access, and public workshops can further aid prevention and education efforts.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1520191","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T04:10:46Z","timestamp":1748232646000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantile regression application to identify key determinants of malnutrition in five West African countries of Gabon, Gambia, Liberia, Mauritania, and Nigeria"],"prefix":"10.3389","volume":"13","author":[{"given":"Reshav","family":"Beni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaun","family":"Ramroop","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faustin","family":"Habyarimana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"key":"ref1","year":"2023"},{"key":"ref2","year":"2023"},{"key":"ref3","year":"2023"},{"key":"ref4","year":"2024"},{"key":"ref5","year":"2022"},{"key":"ref6","year":"2022"},{"key":"ref7","author":"Akinmoyeje","year":"2024"},{"key":"ref8","author":"Lwanda","year":"2023"},{"key":"ref9","year":"2024"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1159\/000371618","article-title":"The epidemiology of global micronutrient deficiencies","volume":"66","author":"Bailey","year":"2015","journal-title":"Ann Nutr Metab"},{"key":"ref11","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2458-13-S3-S23","article-title":"Treatment of severe and moderate acute malnutrition in low- and middle-income settings: a systematic review, meta-analysis and Delphi process","volume":"13","author":"Lenters","year":"2013","journal-title":"BMC Public Health"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1186\/s12889-020-08782-7","article-title":"Simultaneous quantile regression and determinants of under-five severe chronic malnutrition in Ghana","volume":"20","author":"Aheto","year":"2020","journal-title":"BMC Public Health"},{"key":"ref14","article-title":"Bayesian spatial quantile interval model with application to childhood malnutrition in Ethiopia","author":"Shibeshi","year":"2021","journal-title":"Res. 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