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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.
We used the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data for this analysis. This data were collected for the second EMDHS in 2019. In Ethiopia, there are 4 administrative levels (Ethiopia or federal, regions, zones, and woredas). The 9 regions are further divided into city administrations (Addis Ababa and Dire Dawa), agrarian regions (Tigray; Amhara; Oromia; Benishangul-Gumuz; Southern Nations, Nationalities, and People’s Region; Gambela; and Harari), and pastoralists regions (Afar and Somali). We obtained data for 2016 eligible women with children aged <5 years from the 2019 EMDHS data set, downloaded from the MEASURE program web address, and extracted data elements necessary for this analysis. EMDHS 2019 used a sampling frame of all census enumeration areas (EAs) created for the 2019 Ethiopia Population and Housing Census (PHC) prepared by the Central Statistical Agency. There was a complete list of 149,093 EAs created for the 2019 PHC. An EA is a geographic area covering an average of 131 households. The sampling frame contained information about the EA location, type of residence (urban or rural), and an estimated number of residential houses focusing on key indicators for this survey. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of EAs were selected independently in each stratum in 2 stages. Finally, 305 EAs (93 in urban and 212 in rural areas) were selected with a probability proportional to EA size (based on the 2019 PHC frame) and with an independent selection in each sampling stratum. Either all women aged 15-49 years who were permanent residents of the selected households or visitors who slept the night before the survey were eligible for interview. The height and weight of children aged 0-59 months were collected, and women aged 15-49 years were interviewed using the Woman’s Questionnaire [37]. The outcome variable of this study was wasting in children aged <5 years: if a child’s weight for height is below 2 SDs from the expected weight-for-height median identified by the WHO for boys and girls [26]. In this study, we classified children as normal (when the weight-for-height z score is between –2 SDs to 2 SDs) [28]; moderate wasting (when the weight-for-height z score is between –3 SDs to –2 SDs); and severe wasting (when the weight-for-height z score is below –3 SDs), depending on the references from WHO 2006 guideline [38]. We selected the following independent variables based on performance in previous evidence [17,23,25,26,30,32,33,39] and the availability of variables in the 2019 EMDHS data set. Age (mother and child), sex of the child, mother’s educational status, head of household, wealth index, religion, residence, antenatal care, place of delivery, postnatal care, breastfeeding, anemia status of the mother, anemia status of the child, dietary diversity score, husband or partner’s educational level, and birth order were independent variables. Antenatal care visits were presented in groups of none, 1-3, or 4+. A mother may have no visits, 1-3 visits, or over 4 visits according to WHO. The anemia state of the mother was defined as the percentage of women aged 15-49 years with mild, moderate, or severe anemia or with any anemia. It is the number of not pregnant women whose hemoglobin count is less than 12.0 grams per deciliter (g/dL) plus the number of pregnant women whose count is less than 11.0 g/dL. The anemia state of the child was defined as the percentage of children aged 6-59 months with mild, moderate, or severe anemia or with any anemia. This is when the hemoglobin count of a child is less than 11 grams per deciliter (g/dL). The dietary diversity score measured children aged <5 years who consumed a minimum of 5 of the 8 food groups (grains, roots and tubers, legumes and nuts, dairy products, meat [fish, poultry, and liver/organ meats], eggs, vitamin A–rich fruits and vegetables, other fruits and vegetables, and breast milk) in the past 24 hours. Households were the primary unit selected for interview in the Ethiopian Demographic and Health Survey (DHS). The definition of a household is a person or group of related or unrelated persons who live together in the same dwelling unit(s), who acknowledge one male or female adult as the head of the household, who share the same housekeeping arrangements, and who are considered a single unit. The questionnaire included queries concerning the household’s ownership of several consumer items such as television and car; dwelling characteristics such as flooring material, type of drinking water source, toilet facilities; and other characteristics related to wealth status. Each household asset for which information was collected is assigned a weight or factor score generated through principal components analysis. The resulting asset scores were standardized about a standard normal distribution with a mean of 0 and an SD of 1. These standardized scores were then used to create the breakpoints that define wealth quintiles as lowest, second, middle, fourth, and highest [40]. We used frequencies, weighted frequencies, means, SDs, and percentages or proportions to describe child wasting. Our data set contained many factors, so we checked multi-colinearity using the mean variance inflation factor (1.31), which was within an acceptable range. Before applying different models for analysis, we cleaned the data per the study criteria in Stata software (version 15.0; StataCorp) and weighted considering sampling weight, primary sampling unit, and strata before analyzing. Since wasting has normal, moderate, and severe categories, we applied multilevel ordinary logistic regression to fit the data. We conducted a bivariate analysis to identify candidate variables for multivariate analysis and selected variables with a P value <.20 [40,41] for the multivariable model. In current statistical analyses, studies conduct preanalysis filters to select variables for final models at a P value <.25 [42] and commonly at P<.20 [43]. We declared the final association at a P value of <.05. We presented the outputs of the model using coefficients and 95% CI. The data used in this analysis were hierarchical, which we could not analyze using binary logistic regression. Multilevel logistic regression was also not applicable since the response variable contained more than 2 categories. Therefore, we applied a special type of ordinal logistic regression (Generalized Linear Latent and Mixed Model [GLLAMM]) to account for the parallel planes and proportional odds assumptions. This model has been used to analyze clustered data [44]. We executed a mixed-effects ordinal logistic regression in a multilevel proportional odds model using GLLAMM. We used adaptive quadrature to estimate deviance and log-likelihood [45-47]. After fitting the full model, we also estimated posterior means and SDs of the latent variable. The marginal test gave us the expected response regarding the prior distribution of the latent variables so that we were able to look at the “marginal” or population-averaged effects of covariates [44,48]. This study used secondary data from demographic and health survey data files. Initially, the authors formally requested access to the data sets from the MEASURE DHS team by completing the web-based request form [49]. Accordingly, permission to access the data and the letter of authorization were obtained from ICF International. Therefore, for this study, consent to participate is not applicable. We kept all data confidential, and no effort was made to identify households or individuals. The Ethiopian Health Nutrition and Research Institute Review Board and the National Research Ethics Review Committee at the Ministry of Science and Technology of Ethiopia approved EMDHS 2019. The original informed consent allowed the free deidentified secondary analysis without additional consent. The authors also confirmed that all methods were carried out with relevant guidelines and regulations. The authors also ensured the study data were anonymous or deidentified for the confidentiality and privacy of the participants. According to the original consent, there was no compensation for this cross-sectional data acquisition.
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