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Background: Undernutrition among under-five children is one of the intractable public health problems in Ethiopia. More recently, Ethiopia faced a rising problem of the double burden of malnutrition—where a mother may be overweight/obese, and a child is stated as having undernutrition (i.e., stunting, wasting, or underweight) under the same roof. The burden of double burden of malnutrition (DBM) and its association with maternal height are not yet fully understood in low-income countries including Ethiopia. The current analysis sought: (a) to determine the prevalence of double burden of malnutrition (i.e., overweight/obese mother paired with her child having one form of undernutrition) and (b) to examine the associations between the double burden of malnutrition and maternal height among mother–child pairs in Ethiopia. Methods: We used population-representative cross-sectional pooled data from four rounds of the Ethiopia Demographic and Health Survey (EDHS), conducted between 2000 and 2016. In our analysis, we included children aged 0–59 months born to mothers aged 15–49 years. A total of 33,454 mother–child pairs from four waves of EDHS were included in this study. The burden of DBM was the primary outcome, while the maternal stature was the exposure of interest. Anthropometric data were collected from children and their mothers. Height-for-age (HFA), weight-for-height (WFH), and weight-for-age (WFA) z-scores < − 2 SD were calculated and classified as stunted, wasting, and underweight, respectively. The association between the double burden of malnutrition and maternal stature was examined using hierarchical multilevel modeling. Results: Overall, the prevalence of the double burden of malnutrition was 1.52% (95% CI 1.39–1.65). The prevalence of overweight/obese mothers and stunted children was 1.31% (95% CI 1.19–1.44), for overweight/obese mothers and wasted children, it was 0.23% (95% CI 0.18–0.28), and for overweight/obese mothers and underweight children, it was 0.58% (95% CI 0.51–0.66). Children whose mothers had tall stature (height ≥ 155.0 cm) were more likely to be in the double burden of malnutrition dyads than children whose mothers’ height ranged from 145 to 155 cm (AOR: 1.37, 95% CI 1.04–1.80). Similarly, the odds of the double burden of malnutrition was 2.98 times higher for children whose mothers had short stature (height < 145.0 cm) (AOR: 2.98, 95% CI 1.52–5.86) compared to those whose mothers had tall stature. Conclusions: The overall prevalence of double burden of malnutrition among mother–child pairs in Ethiopia was less than 2%. Mothers with short stature were more likely to suffer from the double burden of malnutrition. As a result, nutrition interventions targeting households’ level double burden of malnutrition should focus on mothers with short stature to address the nutritional problem of mother and their children, which also has long-term and intergenerational benefits.
This study utilized data from the four consecutive Ethiopia Demographic and Health Survey (EDHS) (2000–2016), a nationally representative cross-sectional household survey [53–56]. Pooled data on mother–child pairs from the EDHS were included in the study, to explore the prevalence of double burden of malnutrition (DBM). This pooled data analysis also increased the study power, which allowed a full exploration of the effect of maternal height on DBM. In the EDHS, ever-married women aged 15–49 years were interviewed for data on women and children (0–59 months). The survey was designed to be representative at both national and regional levels. The EDHS sampling and household listing methods have been described elsewhere [56]. We used anthropometric indices such as height-for-age, weight-for-height, and weight-for-age to evaluate children’s nutritional status below 5 years of age (0–59 months). In addition, the study used the women’s body mass index (BMI) according to WHO cutoff values [57]. Maternal body mass index (BMI) was classified as underweight (< 18.5 kg/ m2), normal (18.5 to < 24.99 kg/m2), or overweight/obesity ≥ 25.0 kg/m2). The EDHS collected data on the nutritional status of children by measuring the weight and height of children under the age of 5 years in all sampled households, regardless of whether their mothers were interviewed in the survey or not. Weight was measured with an electronic mother–infant scale (SECA 878 flat) designed for mobile use [56]. Height was measured with a measuring board (ShorrBoard®). Children younger than 24 months were measured lying down on the board (recumbent length), while standing height was measured for all older children. The three child anthropometric indices used in this study were calculated using growth standards published by the World Health Organization (WHO) in 2006 [58]. The height-for-age index is an indicator of linear growth retardation and cumulative growth deficits in children. Children with height-for-age Z-score below minus two standard deviations (− 2 SD) from the median of the WHO reference population are stunted or chronically malnourished. The weight-for-height index measures body mass in relation to body height or length and describes current nutritional status. Children whose Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population are considered thin (wasted), or acutely undernourished. Weight-for-age is a composite index of height-for-age and weight-for-height that accounts for both acute and chronic undernutrition. Children whose weight-for-age Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population are classified as underweight [58]. The primary outcome of this study was DBM, derived from three child anthropometric indices (stunting, wasting, and underweight) and the body mass index (BMI) of their respective mothers. Height-for-age (HAZ), weight-for-height (WHZ), and weight-for-age (WAZ) z-scores below − 2 SD of the WHO Child Growth Standard were used to define stunting, wasting, and underweight, respectively [58]. A child who was either stunted, wasted, or underweight and the mother is over-nourished (overweight/ obese) in the same household was considered as having DBM, as used in past studies [15, 25, 59]. Following previous studies, the binary response variable DBM was measured using “normal” and “DBM” response categories. Additionally, the prevalence of overweight/obese mothers and stunted children, overweight/obese mother and wasted children, and overweight/obese mother and underweight child was estimated. The main exposure of our study was maternal height. We adopted height cutoffs used by previous studies [37, 60, 61], but subdivided them into three categories. Accordingly, we categorized maternal height as: very short (< 145.0 cm), short (145.0 to 154.9 cm) and normal/tall (≥ 155.0 cm). Covariates were considered based on the availability of data and previous literature [15, 25, 47, 62–65]. In this study, we included two levels of confounding variables: individual (i.e., child, maternal, and household factors) and community levels. The individual-level covariates included: child factors (child’s age in months, gender, birth order, birth interval, size of child at birth, diarrhea, fever, and ARI), maternal factors (mother’s age, mother’s education, mother's occupation, ANC visit, anemia status, listening to the radio, and watching television), and household-level covariates (wealth index, household size, type of cooking fuel, toilet facility, source of drinking water, household flooring, and time to get a water source). Lastly, the community-level factors include the place of residence (urban or rural) and contextual region of residence (agrarian, pastoralist, and city administration). All analyses were carried out using STATA/MP version 14.1 (StataCorp, College Station, TX, USA). The survey command (svy) in STATA was used to take into account the sampling design of the survey. Sampling weighting was applied to all descriptive statistics to compensate for the disproportionate allocation of the sample. The weighting technique is explained in full in the EDHS report [56]. Descriptive statistics such as frequencies and percentages were used to present the distribution of all variables. Given the hierarchical nature of the EDHS data, a multilevel binary logistic regression model was fitted to estimate the association between DBM and maternal height. In this model-building process, we first performed an unadjusted bivariable multilevel analysis between DBM and exposure or each of the covariates. Variables in bivariable analysis with a p value < 0.2 were entered in the multilevel multivariable binary logistic regression models. All independent variables associated with the DBM were tested for multicollinearity and there was no evidence of multicollinearity. Following the recommendations of a previous study, five hierarchical models were run [66–68]. Accordingly, five models were fitted: the empty model without any explanatory variable was run to detect the presence of a possible contextual effect (Model I), Model II (Model I + child characteristics), Model III (Model II + mothers characteristics), Model IV (Model III + household characteristics), Model V (Model IV + community-level characteristics) were fitted. In our analysis, all models assumed a random intercept. Model comparisons were done using the deviance information criteria (DIC) and the model with the lowest DIC value was chosen as the best-fitted model for the data. The intraclass correlation coefficient (ICC) was computed for each model to show the amount of variations explained at each level of modeling. The adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p value < 0.05 in the Model V multivariable model were used to declare significant determinants of DBM and its association with maternal height. Finally, after controlling all covariates and exposure variables the mean value estimates were presented (the estimates are obtained after the post-estimation command) using the figure representing the predictive probability for DBM and maternal height. The data used in this study were obtained from the MEASURE DHS Program, and permission for data access was obtained from the measure DHS program through an online request from http://www.dhsprogram.com. The data used for this study were publicly available with no personal identifier. There was no need for ethical clearance as the researcher did not interact with respondents.
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