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Background: In Ethiopia, child undernutrition and anemia are major public health concerns, resulting in increased childhood morbidity and mortality. Despite progress made to reduce the prevalence of malnutrition (especially stunting) from 50% in 2000 to 38% in 2016, little is known about the magnitude and risk factors for concurrent nutritional deficiencies in Ethiopia. Methods: Analysis for this study was based on a total sample of 9218 children aged 6–59 months drawn from the Ethiopian Demographic and Health Survey (EDHS) conducted in the year 2016. The study used two outcome variables: Multiple nutrition deficit index formed by combining stunting, underweight, wasting and anemia status; and a concurrent stunting and anemia (CAS) index. Two mixed effect regression models, Poisson and Logistic, were used to identify the key risk factors of the two outcome variables, respectively. Results: The proportion of children with stunting (length-for-age), underweight (weight-for-age) and wasting children (weight-for-length) was 38%, 25.2% and 9.4%, respectively. About 58% of the children had anemia. The prevalence of children with concurrent stunting and anemia children was 24.8%. Our results showed that the risks of multiple nutritional problems were determined by a range of individual, household and behavioral factors including: sex of the child, age of the child, birth order, parity, parental education, religion, household wealth index and type of family structure. The proximate variables (hygiene and sanitation score, feeding practice, and child health service utilization score) were also found to exert a strong influence on the risk of multiple nutritional deficiencies. The likelihood of co-occurrence of stunting and anemia was determined by certain individual and household factors, including sex of the child, age of the child, maternal education, household asset based wealth, religion and household hygiene and sanitation. Conclusions: This study underscores the importance of improving parental education, household wealth, hygiene and sanitation conditions, promoting feeding practice and child health service utilization. Also, any nutrition sensitive and specific intervention should consider a child’s characteristics such as his/her age, gender and birth order.
The most recent estimate of the World Bank report [17] indicates that Ethiopia has a population of 109 million, making it the second-most populous nation in Africa after Nigeria [17]. According to the report, the country is one of the poorest, with an annual per capita income of $790 [17]. Administratively, Ethiopia is a Federal Democratic Republic with nine autonomous Regional States, each divided into zones, districts and sub-districts/ kebeles [18]. Agriculture has been the main driver for the fast-growing Ethiopian economy, responsible for 85% of total employment [13]. Although the rapid economic growth is attributed to the enhancing productivity of agriculture, particularly of crop production but chronic malnutrition (stunting) of children remains unacceptably high. Considering the new Sustainable Development Goals (SDGs), nutrition has been recognized as a major need for sustainable development [13]. The government of Ethiopia has developed various development plans and strategies to increase food security, improve nutrition and reduce poverty [18–20]. The National Nutrition Program II targeted implementation of both nutrition-sensitive and non-nutrition sensitive interventions to significantly improve maternal and child nutrition in the country. We used data from the Ethiopian Demographic and Health Surveys (EDHS) for 2016. The 2016 survey is one of a series of nationally representative samples, conducted for the fourth time since 2000. The EDHS are cross-sectional data containing comparable household and individual information about sociodemographic characteristics and health indicators such as maternal and child health and nutrition. The EDHS surveys have been carried out nationally by the Central Statistical Agency (CSA) under the guidance of the Ministry of Health (MOH). The data were extracted from the children’s file containing entries for that under-5. Infants below six months of age were excluded since EDHS did not collect data on hemoglobin level for this age group. A total of 9218 children aged 6–59 months was extracted from the dataset for final analysis. As the data were well imputed by the Central Statistics Authority (CSA) of Ethiopia and ICF (the data owners), the overall missing values were limited to 5.8%. The rows with the missing values were excluded from the entire analysis. The EDHS surveys are well-established, nationally representative data. They are respected global initiatives conducted with appropriate permission from the Ethiopian government and informed consent from subjects. ICF International (U.S.) and the Central Statistics Authority (Ethiopia) granted permission for the use of EDHS. Ethical approval was also received by the University of Saskatchewan, Behavioral Research Ethics Committee. The Ethiopian Demographic and Health Surveys collected information on the health and nutritional status of children. Categorization of undernutrition of children was done using height-for-age (HAZ), weight-for-age (WAZ) and weight-for-height (WHZ) SDs from WHO, also known as z-scores to determine stunting, under-weight, and wasting, respectively [2, 21]. Anemia status was defined by hemoglobin < 11 g/dL [10], and the measure was adjusted for altitude to account for most Ethiopians living at high altitudes where hemoglobin levels are normally higher than at sea level, making true anemia difficult to detect [10]. The present study used two different outcome variables: the number of each of the four possible nutritional problems and the presence of concurrent stunting and anemia (CAS). In the primary analysis, a coding of 1 was used if a child had any of the three anthropometric deficits (stunting, underweight, wasting) or anemia, and “0” if the child experienced none of the four nutritional problems. For the secondary analysis, CAS was the outcome variable. For the CAS, 1 was coded if a child was both anemic and stunted at the same time, and 0 otherwise. The selection of the explanatory variables was made based on the review of literature, availability of the variable in the data set, and statistical plausibility. The factors influencing multiple anthropometric deficit and CAS were broadly classified as maternal and child characteristics (maternal education, autonomy, maternal parity, maternal age, child’s age, child’s sex, child’s birth order); household factors (the type of family structure, religion, household wealth ); child care practices (feeding practices, child health service utilization score, hygiene and sanitation practice score); and community-level variables ( mean maternal education and wealth at cluster level, and type of residence). Scores were constructed for some of the potential predictors by combining different variables. For instance, the hygiene and sanitation score was measured by combining responses of household ownership of facilities that ensure hygienic separation of human excreta from human contact (which include flush or pour-flush toilet/latrine, piped sewer system, septic tank, pit latrine, Ventilated Improved pit (VIP) latrine, pit latrine with slab and composting toilet ) [22], hand washing and access to drinking water. The value for the hygiene and sanitation score ranged between 0 and 6. The child health service utilization score was constructed from six dichotomous responses (Antenatal Care/ANC, delivery care, postnatal care, vitamin A, iron supplementation and deworming pills), each coded as 0 or 1. Adding these values for each respondent yielded a score ranging between 0 and 6. The diet diversity score (DDS) was measured based on the consumption of the seven food groups (0 = no, yes = 1) according to the WHO’s IYCF guidelines [23]. These food groups are: (i) grains, roots, and tubers; (ii) flesh foods (meat, fish, poultry and liver/organ meats); (iii) legumes and nuts; (iv) vitamin A rich fruits and vegetables; (v) eggs vi) dairy products (milk, yogurt, cheese); (vii) other fruits and vegetables [23].The DDS score was obtained by summing up the binary responses, and it ranges from 0 to 7, where a higher score represents the higher level of diet diversification. Household wealth was used as a proxy to household income and was estimated in the DHS with an asset-based index that combined information about ownership of consumer goods and housing quality. It was sorted into three categories for purposes of analysis: poorer, middle, and richer. Similarly, maternal autonomy was measured based on five responses related to her decision making on important household purchases, childcare and mobility. The remaining explanatory variables (such as sex and age of the child, family structure, breastfeeding, and frequency of access to media) were used as coded in the original data. We analyzed the data using STATA version 12 [24]. All analyses were weighted for the sampling probabilities and considered the stratification and clustering nature of the data. Descriptive analysis was used to examine the characteristics of the study sample. The DHS data are clustered, i.e., mothers are nested within households, and households are nested within clusters. As such, mothers within the same cluster may be more like each other than mothers in the rest of the clusters. This violates the assumption of independence of observations across the clusters, and hence, limits the use of conventional regression [25]. For the present analysis, the enumeration areas/EAs were used for clustering women respondents. Mixed-effects Poisson regression was used (for the count outcome variable) and mixed-effect logistic regression model (for CAS) to test the effect sizes of individual, household, and community factors. Multicollinearity between the potential predictors was checked using tolerance test, variance inflation factors. To achieve a parsimonious model, a bivariate analysis was first conducted, and all potential predictors which were statistically associated with the outcomes with a p-value < 0.20 were subsequently included in the multivariable analysis. The Akaike Information Criterion (AIC) was used as model selection criteria. In the final model, a p-value of < 0.05 was used to define statistical significance. The model fit was checked using the ratio of Deviance and Degree of Freedom (DF), i.e., Deviance/ DF [26].
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