Association of low birth weight with undernutrition in preschool-aged children in Malawi

The current study utilized a population-based cross-sectional data taken from the Malawi Demographic and Health Survey (MDHS) 2015–16 which was conducted between October 2015 and February 2016. The procedures used in this study can be found in details elsewhere [14]. In brief, the MDHS was designed to yield a nationally representative sample at the national level, residence level (urban-rural) and the regional level). The survey employed a stratified two-stage cluster design. In the first stage, the country was stratified depending upon the place of residence as well as subnational administrative regions to construct stratum. Using the probability proportional to size technique, a certain number of enumeration areas (EAs) from each stratum are drawn. The population size was drawn from Census files. In the second stage, a fixed number of households were selected from EAs using the systematic random sampling technique. In the 2015–16 MDHS, a total of 27,516 households were selected for the sample, of which 26,564 were occupied. Of the occupied households, 26,361 were successfully interviewed, for a response rate of 99%. Secondly, in the interviewed households, 25,146 eligible women were identified for individual interviews. However, interviews were completed with 24,562 women, for a response rate of 98%.

The survey collected weight and height of preschool-aged children in a sub-sample of one-third of the households, regardless of whether their mothers got interviewed or not. Thus, a total of 6033 children under age 5 were eligible for height and weight measurements. The analysis of height-for-age indices include 94% of eligible children with complete and valid height measurement and age data. Analysis of weight-for-height indices includes 95% of eligible children with complete and valid height and weight measurements. Analysis of weight-for-age indices includes 96% of eligible children with complete and valid weight measurement and age data. Thus, the final sample analyzed by the MDHS was 5786. An electronic SECA 878 flat scale was used to collect the weight measurements. Whilst a Shorr Board® measuring board was used to estimate the height measurements [14]. Recumbent lengths were measured for children less than 2 years old while standing heights were measured for children aged 24 months or older [14]. The data on height and weight were used to calculate several measures of childhood nutritional status, namely; height-for-age, weight-for-height, and weight-for-age. The socio-demographic information was collected from women of reproductive age (15–49 years) who had children below 5 years prior to the survey by means of personal interviews.

The current study selected children of age group 6–59 months. Children who were missing measurements on birth weight, weight and height were excluded from the analysis. Children whose households were not sampled for height and weight measurements as well as who had missing data on other covariates were also excluded. In addition, this study only included children whose caregivers had been interviewed. After applying the inclusion and exclusion criteria, which included age restriction, 4047 children under 5 years old were analyzed in this study.

Childhood undernutrition was the dependent variable of the present study, and the variable was further disintegrated into three indices, namely stunting (low-height-for-age), underweight (low-weight-for-age) and, wasting (low-weight-for-height) [14, 19]. Each of these indices is expressed as Z-score from the median of the WHO Child Growth Standards reference population [26]. Particularly, children whose Z-scores for any of these three indices were below minus two standard deviations (−2SD) from the median of the WHO’s reference population were considered as stunted, wasted or underweight, respectively [26]. Stunting reflects the linear growth achieved during the pre- and post-natal periods with its deficits indicating long-term, cumulative effects of inadequate nutrition and/or health [27]. Wasting indicates acute or current malnutrition that is the consequence of the poor dietary intake or frequent occurrence of infectious disease [28]. Underweight is influenced by the height and weight of the child and thus it’s a combination of stunting and wasting [28].

The main dependent variable of the current study was birth weight – expressed in grams (g). According to the WHO and UNICEF, birth weight is defined as the first weight of the live newborn or fetus collected soon after birth [29, 30]. The MDHS 2015–16 reported that birth weight was recorded from either a written record (child’s health cards) or mother’s recall. Thus, to limit recall bias, the present study included only children who had valid birth weight recorded in their health cards. All children who did not have valid birth weight recorded in their health cards, children who were not weighed, whose delivery occur in homes or other places were excluded. Using the guidelines set by WHO and UNICEF, the birth weight was categorized as LBW or normal birth weight. LBW was defined as birth weight below 2500 g i.e. up to and including 2499 g. On the other hand, greater than or equal 2500 g was classified as normal birth weight [30].

It is known that child undernutrition is the result of multiple and interrelated factors. Therefore, apart from the LBW various control variables were included in the present study following careful review of the relevant literature [3, 31], and were grouped as child biological factors, child health factors, maternal/household factors, household environmental factors, health service and seeking behavior related factors, and community level factors (Table 1). Child biological factors include child’s sex (male/female) child’s age (< 12 months /12–23 months/24–35 months /35–47 months/48–59), type of birth (single births/multiple births), preceding birth interval (< 24 months/12–47 months/48+ months). Child health factors recent diarrhea (no/yes) and recent fever (no/yes). Maternal factors include, women’s age (15–19 years/20–24 years/25–29 years/ 30–34 years/35–49 years), women’s education (none/primary/secondary education and higher), body mass index (underweight/ optimal weight/ overweight/or obesity), wealth index (poorest/ poorer/ middle/richer/ richest), number of under-5-year children in the household (≤1/2–3/4+), and amount of media exposure (0/1/2/3). Household environmental factors include the source of drinking water (unimproved/ improved) and sanitation facility (unimproved/improved). Health Service utilization factors include the place of delivery (home and other/hospital/institution) and perceived distance to the health facility (not perceived to be a problem or perceived problem). Community characteristics include place of residence (rural/urban) and region (northern/central/ southern). The household wealth index was generated using a statistical method well-known as principal components analysis (PCA) by the Monitoring and Evaluation to Assess and Use Results Demographic and Health Surveys (MEASURE DHS) [32]. Amount of media exposure was derived from three items namely frequency of reading a newspaper, watching television, and or listening to radio at once a week. The MDHS asked respondents the following questions; (1) Do you read a newspaper or magazine at least once a week, less than once a week or not at all? (2) Do you listen to the radio at least once a week, less than once a week or not at all? And (3) Do you watch television at least once a week, less than once a week or not at all? [14]. Then, the amount of media exposure was calculated by summing up the reported frequency of each media if an activity was performed at least once a week. Furthermore, diarrhea was defined as the passage of three or more loose or liquid stools during a 24-h period while fever was determined by self-reports made by caregivers about the symptoms that had occurred within 2 weeks prior to the survey [14]. Specifically, caregivers were asked; has (NAME) been ill with a fever at any time in the last 2 weeks? And has (NAME) had diarrhea in the last 2 weeks?

A total of 4047 children under 5 years were sampled and analyzed in this study. Baseline characteristics of the study population are presented in Table 1. Overall, approximately 39% of children were stunted, while 12 and 2% were underweight and wasted respectively. In terms of birth weight, a majority of children (about 90%) were born with normal and above weight. With regards to child biological and health-related factors, more than half (51%) of the children were female and a majority of them (97%) were the products of single births while 41% of children born between 24 and 47 month intervals. Furthermore, about 22 and 32% of children had diarrhea and fever episodes in the last 2 weeks respectively. In terms of maternal and household characteristics, approximately 29% of respondents were distributed in the age group 20–24 years, two-thirds had primary school education and a majority (75%), had a normal body mass index. Additionally, only 1% of the participants were exposed to all forms of media (newspaper, radio, and Television). With regards to household environmental and health service utilization factors, a majority of participants 84%, had access to an improved water source while nearly 80% had improved sanitation facilities. Similarly, a majority of participants (> 90%) had institutional delivery, whilst 59% had perceived the distance to the nearest health facility to a big problem. In terms of community characteristics, 46% of respondents were southern region dwellers and 92% were rural dwellers.

Table 1, also presents the prevalence of stunting, underweight, and wasting by sociodemographic characteristics. The prevalence of stunting was observed to be significantly high in children with a low birth weight, male children, in children aged 36–47 months, in children who were products of multiple births, in children whose proceeding birth intervals were less than 24 months, inn children whose mothers were 35–49 years, in children whose mothers had no formal education, in children whose mothers had BMI less than 18.5 kg/m², in children who were residing in the poorest households, in children whose households had ≥4 under-five children, in children whose households were not exposed to mass media, form in children whose households had no access to improved water sources, in children from rural areas, and in children from central region.

With respect to underweight, the prevalence was observed to be significant high in children with a low birthweight, in children aged 36–47 months, in children whose mothers had no formal education, in children whose mothers had BMI less than 18.5 kg/m2, in children who were residing in the poorest households, in children whose households had ≥4 under-five children, in children whose households were not exposed to mass media, and in children who delivered occurred in homes and other places.

In terms of wasting, the prevalence was observed to be significant high in children with a low birthweight, in children aged 6–11 months, in children with an episode of fever in the last 2 weeks, in children with an episode of diarrhea in the last 2 weeks, in children whose mothers had BMI less than 18.5 kg/m2, in children whose households had ≥4 under-five children, and in children from rural areas.

Tables 2, 3 and 4 present the association of the LBW with childhood undernutrition. The unadjusted models (Model 1) showed that compared to children who had normal birth weight those with LBW had an increased odds of being stunted (Crude odds ratio [CrOR]:1.72; 95% confidence interval [CI]: 1.35–2.20), of being underweight (CrOR: 2.30; 95% CI: 1.68–3.14), and, of being wasted (CrOR: 2.42; 95% CI: 1.38–4.25). The associations remain unchanged even after adjustments for the child’s biological factors (Model 2), child’s health factors (Model 3), maternal or household factors (Model 4), household environmental factors (Model 5), and health service utilization (Model 6) though with varying strength of odd ratios. The full models (Model 7) showed that the odds increased by 55% for being stunted (adjusted odds ratio [aOR]: 1.55; 95% CI: 1.91–2.01), 16% for being underweight (aOR: 2.16; 95% CI: 1.56–2.99), and 51% for being wasted (aOR: 2.53; 95% CI: 1.40–4.57) in children who had LBW compared to those children who were born with normal weight.

The strength of the present study is the use of the nationally representative dataset which means the results can be generalized at Malawian setting. However, the potential limitations are (1) the use of cross-sectional study design, thus cannot permit us to draw causal inferences and (2) the use of secondary analysis did not permit the inclusion of relevant factors to control for poor pregnancy conditions.

This study is one of its kind to investigate the independent association of birth weight with childhood undernutrition in Malawi. In particular, the risk of growth and development impairment in the early childhood years were found to be higher in children with LBW than in those with normal birth weights even after controlling for other known risk factors for childhood undernutrition in models. The finding of the current study seems to be an alarm call for all policymakers involved in the development of childhood nutrition policies in Malawi. Thus, the nutrition events throughout the life cycle should not be neglected. Pregnant women should be encouraged to follow good health habits such adequate intake of good nutritious diets, preconception weight should be monitored, they should be discouraged from smoking, and they should be treated for anemia, malaria, and any intrauterine infection so as to minimize LBW deliveries.