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      Understanding Child Wasting in Ethiopia: Cross-sectional Analysis of 2019 Ethiopian Demographic and Health Survey Data Using Generalized Linear Latent and Mixed Models

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          Abstract

          Background

          Wasting is an immediate, visible, and life-threatening form of undernutrition in children aged <5 years. Within a short time, wasting causes recurrent sickness, delayed physical and mental growth, impatience, poor feeding, and low body weight. The long-term consequences of wasting and undernutrition are stunting, inability to learn, poor health status, and poor work performance. Wasting remains a public health problem in Ethiopia. According to the World Health Organization, countries have to reduce undernutrition including child wasting to below 5% by 2025. Ethiopia is attempting to attain national and international targets of undernutrition while struggling with many problems.

          Objective

          This study aimed to identify the prevalence and associated factors of wasting to provide information for further renewing policy commitments.

          Methods

          We used community-based, cross-sectional data from the Ethiopian Mini Demographic and Health Survey. The survey was conducted in 9 regions and 2 city administrations. Two-stage cluster sampling was used to recruit study participants. In the first stage, enumerations areas were selected, and 28-35 households per enumeration area were selected in the second stage. Our analysis included 2016 women with children aged <5 years from the 2019 EMDHS data set. We dropped incomplete records and included all women who fulfilled the eligibility criteria. We used multilevel ordinal regression using Generalized Linear Latent and Mixed Models (GLLAMM) and predicted probability with log-likelihood ratio tests. Fulfilling the proportional odds model’s assumption during the application of multilevel ordinary logistic regression was a cumbersome task. GLLAMM enabled us to perform the multilevel proportional odds model using an alternative method.

          Results

          In our analysis, wasting was 7.68% (95% CI 6.56%-8.93%). Around 26.82% of mothers never used antenatal care for their current child. Most mothers (52.2%) did not have formal education, and 86.8% did not have postnatal care for their children. Additionally, half (50.93%) of the mothers have ≥6 household members. Wasting was associated with feeding diverse foods (coefficient 4.90, 95% CI 4.90-4.98), female sex of the household head (–40.40, 95% CI –40.41 to –40.32), home delivery (–35.51, 95% CI –35.55 to –35.47), first (16.66, 95% CI, 16.60-16.72) and second (16.65, 95% CI 16.60-16.70) birth order, female child (–12.65, 95% CI –12.69 to –12.62), and household size of 1 to 3 (10.86, 95% CI 10.80-10.92).

          Conclusions

          According to the target set by World Health Organization for reducing undernutrition in children aged <5 years to below 5% by 2025, child wasting of 7.68% in Ethiopia should spark an immediate reaction from the government and stakeholders. Informed policy decisions, technology-based child-feeding education, and food self-sufficiency support could improve the current challenges. Additional effort is important to improve low maternal education, family planning, awareness of sex preferences, women empowerment, and maternal health services.

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          Most cited references60

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          WHO Child Growth Standards based on length/height, weight and age

          To describe the methods used to construct the WHO Child Growth Standards based on length/height, weight and age, and to present resulting growth charts.
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            Variable selection – A review and recommendations for the practicing statistician

            Abstract Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10–30. This number is often too large to be considered in a statistical model. We provide an overview of various available variable selection methods that are based on significance or information criteria, penalized likelihood, the change‐in‐estimate criterion, background knowledge, or combinations thereof. These methods were usually developed in the context of a linear regression model and then transferred to more generalized linear models or models for censored survival data. Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p‐values or confidence intervals. Therefore, we give pragmatic recommendations for the practicing statistician on application of variable selection methods in general (low‐dimensional) modeling problems and on performing stability investigations and inference. We also propose some quantities based on resampling the entire variable selection process to be routinely reported by software packages offering automated variable selection algorithms.
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              Stunting, Wasting and Underweight in Sub-Saharan Africa: A Systematic Review

              Introduction: Child undernutrition is a major public health problem. One third of all undernourished children globally reside in Sub-Saharan Africa (SSA). The aim of this study was to systematically review studies to determine the factors associated with stunting, wasting and underweight in SSA and contribute to the existing body of evidence needed for the formulation of effective interventions. Methods: This systematic review was conducted using the 2015 Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Five computerized bibliographic databases were searched: Scopus, PubMed, PsycINFO, CINAHL and Embase. The included studies were rated using eight quality-appraisal criteria derived from the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) checklist: sample size, sampling methodology, response rate, outcome measures, statistical analyses, control for confounding, study limitation, and ethical consideration. Results: Of a total of 2810 articles retrieved from the five databases, 49 studies met our inclusion criteria. The most consistent factors associated with childhood stunting, wasting and underweight in SSA were: low mother’s education, increasing child’s age, sex of child (male), wealth index/SES (poor household), prolonged duration of breastfeeding (>12 months), low birth weight, mother’s age (<20 years), source of drinking water (unimproved), low mother’s BMI (<18.5), birth size (small), diarrhoeal episode, low father’s education and place of residence (rural). Conclusions: The factors that predispose a child to undernutrition are multisectoral. To yield a sustainable improvement in child nutrition in SSA, a holistic multi-strategy community-based approach is needed that targets the factors associated with undernutrition, thereby setting the region on the path to achieving the WHO global nutrition target by 2025.
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                Author and article information

                Contributors
                Journal
                JMIR Public Health Surveill
                JMIR Public Health Surveill
                JPH
                JMIR Public Health and Surveillance
                JMIR Publications (Toronto, Canada )
                2369-2960
                2023
                8 February 2023
                : 9
                : e39744
                Affiliations
                [1 ] Department of Health Informatics, School of Public Health College of Medicine and Health Sciences Arba Minch University Arba Minch, Southern Nation Nationalities Peoples' Region Ethiopia
                [2 ] Department of Public Health, School of Public Health College of Medicine and Health Sciences Arba Minch University Arba Minch, Southern Nation Nationalities Peoples' Region Ethiopia
                [3 ] Department of Public Health Arba Minch College of Health Sciences Arba Minch, Southern Nation Nationalities Peoples' Region Ethiopia
                [4 ] Department of Health Informatics, School of Public Health College of Medicine and Health Sciences Dilla University Dilla, Southern Nation Nationalities Peoples' Region Ethiopia
                [5 ] Department of Biomedical Science, School of Medicine College of Medicine and Health Sciences Arba Minch University Arba Minch, Southern Nation Nationalities Peoples' Region Ethiopia
                Author notes
                Corresponding Author: Girma Gilano gilanog@ 123456yahoo.com
                Author information
                https://orcid.org/0000-0002-5847-1425
                https://orcid.org/0000-0003-0887-7803
                https://orcid.org/0000-0003-0126-0165
                https://orcid.org/0000-0002-7497-7565
                https://orcid.org/0000-0002-1091-2164
                https://orcid.org/0000-0002-8309-5986
                https://orcid.org/0000-0002-1950-7819
                Article
                v9i1e39744
                10.2196/39744
                9947770
                36753309
                a8f88949-204a-4ba3-96c2-4bf6f3e29428
                ©Girma Gilano, Samuel Hailegebreal, Sewunet Sako, Firehiwot Haile, Kasarto Gilano, Binyam Tariku Seboka, Kefita Kashala. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 08.02.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

                History
                : 20 May 2022
                : 14 December 2022
                : 17 December 2022
                : 27 December 2022
                Categories
                Original Paper
                Original Paper

                wasting,generalized linear latent and mixed models,gllamm,under-five children,factors,ethiopia

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