Despite overall global reductions in childhood stunting, urban inequalities remain large in many settings. Our results from Bangladesh suggest that, during 2000-2018, larger reductions in stunting were achieved among urban poor children than among urban non-poor and rural children. Nevertheless, stunting prevalence remained consistently higher among urban poor children than for urban non-poor children, and also than for rural children.
We identified child demographics (age and sex), health service access (vaccination and institutional delivery), maternal education, maternal nutrition (BMI and stature), exposure to the media, family planning (number of children at home), and household wealth, size, and environment (divisional location and sanitation infrastructure) to be predictive of childhood stunting in Bangladesh. In terms of their absolute magnitude, child’s age group, maternal stature, and household wealth were found to be most important. Stunting rates increase with children’s age [28, 36], given cumulative exposure to poor nutritional conditions over time. Maternal stature likely reflects both genetics and mother’s exposure to poor nutrition in early life [37, 38]. Household wealth has been well established as an important determinant in child undernutrition [38].
Our key stunting determinants at the national level (child’s age, sex, place of birth, and vaccination status; maternal education, BMI, stature, media exposure, and number of children; and household wealth, size, divisional location, and sanitation infrastructure) predicted 84% of the linear growth difference between urban poor and non-poor children. We found that a child’s place of birth, maternal education, maternal BMI, and household wealth were key drivers of the linear growth gap between urban poor and non-poor children. Among the explained factors contributing to decreased intra-urban average HAZ gap between 2000 and 2018, we found that larger absolute improvements for the urban poor than for the urban non-poor in levels of maternal education and maternal BMI (reduction in underweight mothers) were the most important. Changes in relative household wealth played an overall positive but a less prominent role, while progress in child’s institutional birth was greater among the urban non-poor than among the urban poor and thus widened the average HAZ gap. In terms of factors explaining the sizeable linear growth gain among urban poor children between 2000 and 2018, we found that changes in maternal BMI, maternal education, and child’s place of birth were the most important contributors. Shifts in relative wealth status, with an increased share of urban poor households in the poorest national wealth quintile, notably reduced the expected growth improvement for urban poor children during this period.
Despite significant residual disparities, there were noticeable pro-poor improvements in maternal education and maternal BMI during 2000-2018, which helped decrease the intra-urban HAZ gap. We speculate the changes in maternal BMI as an overall marker of improved household food access, which also affect child’s daily food intake and nutritional status. Other studies have remarked on significantly improved food security in Bangladesh during this time—largely resulting from rapid agricultural development and increased rice productivity—which broadly expanded food access in Bangladesh [39, 40]. The rising proportions of overweight mothers among both urban poor and urban non-poor, however, is a matter of growing public health concern, despite its positive association with child linear growth trends. Although obesity prevalence is higher among the urban non-poor, they have also increased rapidly among the urban poor. Whereas food programs expanding staple crops have improved maternal undernutrition, they may also have inadvertently increased obesity through promotion of cheaper but less varied and energy dense diets [13]. Given that stunted growth in children is a determinant of obesity in adulthood [2, 12, 41], the increased stunting risk among urban poor children may also heighten their susceptibility to developing obesity later in life. Thus, promotion of more optimal diets and obesity prevention need increased integration in food programs targeting the urban poor.
While the definitive pathways through which maternal education benefit child nutritional status remain unclear [42,43,44], we theorize that the rise in education and literacy among the urban poor mothers likely facilitated their access to relevant media and nutrition-promoting knowledge. For example, a slum-based study found no direct effect of maternal education on child nutrition after adjusting for maternal knowledge about child health [45]. Improved maternal education could have also led to better employment opportunities for mothers, therefore improving household income linked to nutritional improvements [38, 42]. However, this is unlikely to be the major pathway given prevailing cultural limitations and low female labor force participation in Bangladesh [46]. Furthermore, we found a positive association between maternal employment and lower levels of maternal education in this data, suggesting mothers with less education, and presumably poorer, are more likely to be working than those of higher socio-economic class.
Increased institutional births contributed to linear growth gains among urban poor children during 2000-2018, but the pace of progress in this area for the urban poor lagged significantly behind that of the non-poor and widened the intra-urban HAZ gap. In 2018, a large majority (67%) of births among the urban poor still took place at home. Child’s institutional birth—and corresponding link with antenatal care—is not only important for increasing chances of safe delivery, but often marks the first connection with the health system for the child that eases subsequent health service utilization, including nutritional counselling, vaccinations, and care for sickness and infections, which can all help downstream in improving child nutritional status. The increase in institutional deliveries among the urban poor was likely aided by the large-scale expansion of NGO-run health facilities that focused on maternal and child health and specifically targeted the urban poor [47, 48]. However, both demand- and supply-side barriers remain in adequate promotion of facility-based births among the urban poor with continually expanding urbanization [47, 49].
Evidence across countries and in Bangladesh points to increased household wealth—implying household’s ability to afford better commodities and services related to improved child nutrition—as a key driver of improved linear growth outcomes [37,38,39]. Our pooled data showed household wealth index as a key predictor of the intra-urban HAZ gap. However, our measurements of asset index and poverty cut-offs do not allow reliable interpretations about changes in urban wealth inequality during this period. Nevertheless, our analysis did not show a worsening urban wealth gap over time, which would have widened the intra-urban HAZ gap. Between 2000 and 2018, there was a decreased concentration of urban non-poor households in richest national wealth quintile (possibly due to previously poor households rising above poverty lines), alongside an increased concentration of urban poor households in the poorest national wealth quintile (potentially because of large growth in urban population and influxes of ‘new’ urban poor from rural areas). The net effect of these relative distributional changes in asset quintile helped reduce the intra-urban HAZ gap during this period. However, the rise in share of urban poor households in poorer wealth quintiles in our analysis decreased the average linear growth among urban poor children.
Our analysis of explanatory factors driving the linear growth gap between urban poor and non-poor children is generally comparable with a previous finding at the national level, which suggested household wealth, maternal education, maternal nutrition, and health service access as major contributors to socio-economic inequities in child linear growth status [23]. However, our finding of a reduced intra-urban child linear growth gap during 2000-2018 departs from the earlier national level findings of an increased difference in absolute stunting prevalence between poorest and richest wealth quintiles [23]. Yet, it is generally consistent with slum-based findings that saw a reduced—albeit marginally—intra-urban gap between slum- and non-slum children in mean HAZ during 2006-2013 [19, 50].
Our results also showed a significant improvement in average linear growth over time among urban poor children, consistent with trends at the national level as well as among slum children [19, 39]. Increased household wealth and maternal education were commonly the most important drivers explaining these linear growth improvements [19, 23, 28, 39, 40]. Progress in these factors were attributed to broad economic and social development [39, 40], including in slums, which saw improvements in living conditions over time [24, 29, 50]. In comparison, our findings attributed improved linear growth among urban poor children to primarily changes in maternal BMI, maternal education, and institutional deliveries, with a large residual of unexplained factors. Although our study did not review absolute changes in household wealth of urban poor, increased levels of maternal BMI could imply improvements in household food access stemming from general household economic progress and increased purchasing power. Taken as such, our findings among the urban poor are also consistent with existing global evidence on key drivers of national declines in stunting prevalence, which include improvements in household wealth, parental education, and access to reproductive health services [37].
Limitations
Our study has several limitations: First, our asset-based definition of urban poor may underestimate the multi-dimensional aspects of urban poverty, including which is slum- or neighborhood-based. Compared to non-slum urban poor children, urban poor children in slums may be subject to increased spatial or residential risk of infection and undernutrition, which was not directly accounted for in this study. Second, the BDHS asset-based wealth index is distinct from HIES consumption-based measure of poverty. Therefore, applying the HIES poverty estimates directly to the BDHS sample may over- or under-estimate the quantification of the urban poor below official poverty lines, although there is comparability between asset- and expenditure-based indices in health inequality measurements [51]. Third, our empirical models were limited in terms of variable numbers and specifications, which may not reflect lived realities of urban poor, and we may thus miss or underestimate some important effects. Our focus on national-level predictors may also overlook variations by residence or poverty status in subgroup-level analyses. Additionally, we excluded community-level factors and interactions among variables that are also important in explaining variations [52]. Lastly, given its observational design, the associations shown in this study are subject to confounding bias and do not suggest causal interpretation.