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Child wasting continues to be a major public health concern in South Asia, having a prevalence above the emergency threshold. This paper aimed to identify factors associated with wasting among children aged 0–23 months, 24–59 months, and 0–59 months in South Asia. A weighted sample of 564,518 children aged 0–59 months from the most recent demographic and health surveys (2014–2018) of five countries in South Asia was combined. Multiple logistic regression analyses that adjusted for clustering and sampling weights were used to examine associated factors. Wasting prevalence was higher for children aged 0–23 months (25%) as compared to 24–59 months (18%), with variations in prevalence across the South Asian countries. The most common factor associated with child wasting was maternal BMI [adjusted odds ratio (AOR) for 0–23 months = 2.02; 95% CI: (1.52, 2.68); AOR for 24–59 months = 2.54; 95% CI: (1.83, 3.54); AOR for 0–59 months = 2.18; 95% CI: (1.72, 2.77)]. Other factors included maternal height and age, household wealth index, birth interval and order, children born at home, and access to antenatal visits. Study findings suggest need for nutrition specific and sensitive interventions focused on women, as well as adolescents and children under 2 years of age.
This study utilized datasets from the most recent 2014–2018 demographic health survey (DHS) conducted in countries within the South Asia region, including Bangladesh, India, Nepal, Maldives, and Pakistan. Data for other South Asian countries were not available through DHS due to the following reasons: Afghanistan does not collect anthropometric data for children under 5 years of age, data for Bhutan is unavailable on DHS, and finally, data for Sri Lanka have restricted access and are not publicly available for research purposes. The DHS is a nationally representative survey that collects data on mortality, fertility, family planning, and maternal and child health [20]. The DHS programme uses standardized methods in their surveys to ensure uniformity of results from different countries. These surveys were comparable, given the standardized nature of the data collection methods and instruments [20]. Data were obtained from a password-enabled Measure DHS website [21]. Information was collected from eligible women, that is, all women aged 15–49 years who were either permanent residents in the households or visitors present in the households on the night before the survey. Child health information was collected from the mother based on the youngest child aged less than five years, with response rates that ranged from 96% to 99%. Detailed information on the sampling design and questionnaire used is provided in the respective country-specific Measure DHS reports [21]. Our analyses were restricted to 564,518 children aged 0–59 months for five South Asian countries. The outcome variable was wasting (low weight-for-height). Wasting measures body mass in relation to height and describes current nutritional status. Based on the 2007 WHO growth reference, children with weight-for-height Z-scores below minus two standard deviations (-2 SD) below the mean of WHO child growth standards are considered wasted or acutely malnourished while children with Z-scores below minus three standard deviations (-3 SD) below the mean of WHO child growth standards are considered severely wasted [22,23]. The choice of confounding factors used in this study was informed by the UNICEF framework [7]. The confounding factors were organised into three groups: (i) Immediate factors: dietary diversity score and child’s disease occurrence (episodes of diarrhoea and fever in the last two weeks); feeding practices, such as currently breastfeeding and duration of breastfeeding; vitamin A supplementation; vaccination coverage; and child’s age and sex. (ii) Underlying factors: including mother’s characteristics, such as age; age at birth; height; BMI; marital status; birth order and interval; maternal and paternal education; women’s power over household earnings, household decision-making, and health care autonomy. Household factors: pooled household wealth index, access to source of water, and type of toilet, which was categorised into improved and unimproved sources. Access and Utilisation of services: healthcare utilisation factors, such as place and mode of delivery; combined birth rank (the position of the youngest under-five child in the family) and birth interval (the interval between births; that is, whether there were no previous births, births less 24 months prior, or births more than or equal to 24 months prior); delivery assistance; antenatal clinic visits (ANC); and access to media services, such as listening to the radio, watching television, and reading newspapers or magazines. (iii) Basic factors: such as country and place of residence (urban or rural). In order to reduce collinearity, we combined place of birth and mode of delivery and birth order and birth interval. The combined mode of delivery and place of birth was divided into three categories as delivered at home, delivered at a health facility with non-caesarean section, and delivered at a health facility with a caesarean section while the combined birth order and the birth interval was classified as birth rank and birth interval because of the collinearity between birth order and birth interval, which is consistent with previous studies [24,25]. Maternal height was divided in the five following categories: <145 cm, 145–149.9 cm, 150–154.9 cm, 155–159.9 cm, and ≥160 cm, with <145 cm defined as short maternal height [26]. The household wealth index factor score (hv271) was constructed by DHS for each country. For each country, the hv271 variable used that principal component’s statistical procedure, which was used to determine the weights for the wealth index based on information collected about 22 household assets and facilities and produce the standardised scores (z-scores) and factor coefficient scores (factor loadings) of wealth indicators. The household wealth index factor score for the pooled dataset was constructed using the ‘hv271’ variable. The household wealth index factor score (hv271) was separated into categories, as the bottom 20% of households were arbitrarily referred to as the poorest households and the top 20% as the richest households, and was divided into poorest, poor, middle, rich, and richest. Dietary diversity (DD) was calculated by summarizing the 7 food groups consumed during the last 24 h. These foods are grains, roots, and tubers; legumes and nuts; milk/dairy products; flesh foods (meat, fish, poultry and liver/organ meats); vitamin-A rich fruits and vegetables; other fruits and vegetables; and eggs. The children were categorised into two groups, namely, the child had ≥4 food groups and the child had <4 food groups [27]. To examine factors associated with wasting among children aged 0–23 months, 24–59 months, and children 0–59 months, the dependent variables were expressed as a binary outcome, i.e., category 1 for wasting (<−2SD) and otherwise category 0. For the combined five South Asian countries, a population-level weight, unique country-specific clustering, and strata were created to avoid the effect of countries with a large population (such as India with over 1.4 billion people in 2017 [28], offsetting countries with a small population (such as the Maldives with about 437,535 people in 2017) [29]. Population-level weights were used for survey (svy) tabulation that adjusted for a unique country-specific stratum, and clustering was used to determine frequency tabulations to describe the characteristics of the study population and descriptive analysis for estimating 95% confidence intervals (CI) around prevalence estimates for wasting among children aged 0–23 months, 24–59 months, and 0–59 months in each country. Univariate logistic regression analyses were used and presented as unadjusted OR (95% CI) for each confounding variable, while multivariate logistic regression was used to identify independent factors associated with wasting among children aged 0–23 months, 24–59 months, and 0–59 months after adjusting for clustering and sampling weights. In the multivariate logistic regression analyses, three-stage modelling was employed. In the first stage, the immediate factors were entered into the first stage model (Model 1), and a manually executed elimination method was used to determine factors associated with wasting at p < 0.05. The significant factors in the first stage model were then added to underlying factors, which was the second stage model (Model 2); this was then followed by manually executed elimination procedure. The significant factors for both Models 1 and 2 were added to basic factors in the third model (Model 3). We used a similar approach for basic factors in (Model 3). Factors associated with wasting among children aged 0–23 months, 24–59 months, and 0–59 months were presented as adjusted OR (95% CI) for the variables retained in the final modelling step. These results are presented in Tables 2–4 in the results section. These analyses were performed using Stata ‘svy’ commands that allow for adjustments of country-specific stratum and population-level weights. STATA V.14.1 (STATA Corporation, College Station, TX, USA, 2015) was employed for all the study analyses.
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