Determinants of Under-nutrition among Children under Five Years of Age in Ethiopia

Abstract

Background: Ethiopia is one of the developing countries where child under-nutrition is prevalent. Prior studies employed three anthropometric indicators for identifying factors of children’s under-nutrition. This study aimed at identifying factors of child under-nutrition using a single composite index of anthropometric indicator. Methods: Data from Ethiopia’s Demographic and Health Survey 2016 was the base for studying under-nutrition in a sample of 9494 children below 59 months. A single composite index of under-nutrition was created from three anthropometric indices through principal component analysis recoded into an ordinal outcome. In line with WHO 2006 Child Growth Standards, the three anthropometric indices involve z-score of height-for-age (stunting), weight-for-height (wasting) and weight-for-age (underweight). To identify significant determinants of under-nutrition , partial proportional odds model was fitted and its relative performance compared with some other ordinal regression models. Results: The single composite index of anthropometric indicators showed that 49.0% (19.8% moderately and 29.2% severely) of sampled children were undernourished. In the Brant-test of proportional odds model, the null hypothesis that the model parameters equal across categories was rejected. Compared to ordinal regression models that do not involve parallel regression assumption, and Akaike information criterion, partial proportional odds model showed an improved fit. A child with mother of body mass index less than 18.5 kg, from poorest family, a husband without education and male to be in a severe under-nutrition status was 1.4, 1.8 1.2 and 1.2 times more likely to be in worse under-nutrition status compared to its reference group respectively. Conclusion: The authors conclude that the fitted partial proportional odds model indicated that age and sex of the child, maternal education, region, source of drinking water, number of under five children, mother’s body mass index, wealth index, anemic status, multiple birth, fever before two months of survey, mother’s age at first birth, and husband’s education were significantly associated with child under-nutrition. Thus, it is argued that interventions focus on improving household wealth index, food security, educating mothers and their spouses, improving maternal nutritional status, and increasing mothers’ health care access.

Multivariate logistic regression analysis on the association between anthropometric indicators of under-five children in Nigeria: NDHS 2018

“…Females children were less likely to be stunted, underweight, and wasted compared to males. As the level of maternal education increased, the probability of children to be stunted and underweight was lower, which was consistent with study findings in Ethiopia [21].…”

Perbedaan Status Gizi Balita Berdasarkan Tahun Pengamatan di Posyandu Kecubung Wilayah Kerja Puskesmas Ngaglik II Sleman.

“…Upaya yang dapat dilakukan untuk menanggulangi kekurangan gizi pada balita adalah peningkatan indeks kekayaan rumah tangga, ketahanan pangan, pendidikan ibu dan ayah, meningkatkan status gizi ibu, dan meningkatkan akses perawatan kesehatan ibu (6).…”

“…Hasil penelitian sebelumnya menyatakan bahwa 49% mengalami kekurangan gizi dan anak dengan IMT ibu kurang dari 18,5 kg berasal dari keluarga miskin (6). Penelitian sebelumnya menunjukkan bahwa wilayah tinggal, usia ibu, usia, dan jenis kelamin anak berhubungan dengan status gizi anak (9).…”

“…Status gizi kurang dapat disebabkan beberapa faktor seperti pendidikan ibu, kondisi lingkungan seperti pengelolaan air minum, kondisi penyakit. Hasil penelitian sebelumnya menunjukkan bahwa faktor yang berkaitan dengan status gizi adalah usia dan jenis kelamin anak, pendidikan ibu, wilayah, sumber air minum, jumlah balita, massa tubuh ibu, indeks kekayaan, status anemia anak, kelahiran kembar, demam anak sebelum 2 bulan, usia ibu saat pertama melahirkan, dan pendidikan suami (6). Penelitian sebelumnya juga menunjukkan bahwa anak dengan ibu yang memiliki pendidikan menengah akan lebih rendah untuk mengalami stunting dan berat badan kurang jika dibandingkan dengan anakanak yang ibunya tidak memiliki pendidikan formal (4).…”

Facteurs de risque de l’anémie des enfants en RDC : Approche de Régression logistique ordinale du Modèle du Poids d’échantillonnage des données complexes de l’EDS-RDC II

“…Le Modèle de Régression logistique généralisée des variables dépendantes ordinales (PPOM) assouplit les contraintes des variables qui violent l'hypothèse de Modèle des Cotes Proportionnelles (POM). Sur la base de l'option PPOM avec AUTOFIT, une série de tests Wald sont également utilisés pour vérifier l'hypothèse de proportionnalité pour toutes les catégories de chaque variable explicative et enfin tester toutes les catégories des variables explicatives qui réussissent le test Wald en utilisant le Test Wald global (20) (21).…”