This study aims to test the hypothesis that prenatal exposure to ambient PM2.5 is negatively associated with height-for-age and stunting in East African children under five. We combine existing health, sociodemographic, and environmental datasets at the individual, household, and area levels for children in six countries within the East African Community (EAC) region. We apply multilevel linear regression modeling to these data to quantify the crude and adjusted exposure-response relationship of modeled prenatal ambient PM2.5 exposure on height-for-age and stunting.
4. DiscussionUsing multiple waves of nationally representative DHS data for children under five in six East African countries, our results show a robust negative exposure-response relationship between prenatal ambient PM2.5 exposure and height-for-age Z-scores and a positive prenatal PM2.5 exposure-response relationship for stunting. Additionally, our analysis found that the overall average prenatal ambient PM2.5 exposure (25.83 μg/m3) far exceeded the WHO annual guideline for ambient PM2.5 (5 μg/m3 [59]).From the fully adjusted models, we observe significant adverse effects on height for age in the study population (β range: −0.095 to −0.069 SD, Table 4). Our findings suggest an adverse shift in the population mean and distribution of height-for-age Z-scores due to prenatal PM2.5 exposure. Such population-level impacts imply that more children are likely to fall into the clinically relevant classification of stunting due to elevated PM2.5 levels. Indeed, the fully adjusted logistic regression model (Table 5) showed that the highest quartile of prenatal PM2.5 exposure is associated with a 12% higher risk of stunting compared to the lowest exposure group. The elevated risk of stunting from elevated prenatal ambient PM2.5 exposure underscores the clinical significance of our results.The population attributable fraction (PAF) is a useful metric for evaluating the relative contribution that a specific risk factor has on important population health indicators. In our study, we estimated that 2.8% and 2.5% of stunting in East Africa is attributed to the highest quartiles of exposure to prenatal PM2.5 and postnatal PM2.5, respectively. The estimated PAF for stunting due to exposure to polluting cooking fuel is 7.2% in our study. Together, the highest quartiles of prenatal and postnatal PM2.5 exposures, along with cooking with polluting fuel, attribute to up to 12.5% of stunting risk in East Africa. This finding further highlights the need for health policy and health promotion to emphasize air pollution mitigation, for both ambient and household sources, and to achieve the global target to reduce childhood stunting.
Our results compare favorably with data from the limited number of studies that have investigated the exposure-response relationship between ambient prenatal PM2.5 exposure and childhood height-for-age and stunting using DHS data [16,22]. Spears et al. [16,22] found in India that a 10 μg/m3 increase in PM2.5 was associated with a 0.005 standard deviation reduction in child height [16,22]. Their effect estimate is an order of magnitude lower than what we observed in our study. This effect size difference may be explained by various study design differences, such as our use of entire-pregnancy exposure estimates or more spatially resolved exposure estimates. For example, the India study used the nearest monitoring data to reflect exposure for an entire city, which can contribute to significant exposure misclassification that would bias effects downward. Differences in population vulnerability to PM2.5 effects may also explain these differences.In Bangladesh, Goyal et al. found that children prenatally exposed to levels of entire-pregnancy PM2.5 in the highest quartile were 1.13 times more at risk of stunting than children prenatally exposed to PM2.5 levels [16,22]. In our study, we observed a similar effect size, where children in the highest quartile were up to 1.12 times more at risk of stunting compared to the lowest quartile of exposure. This Bangladesh study used similar ACAG PM2.5 estimates. However, the ACAG estimates were only available annually at the time of their study. In contrast, our study used updated model estimates of monthly PM2.5 concentrations, which likely reduced exposure misclassification for our study. Moreover, we estimated postnatal exposure to control for any possible residual confounding that may be attributed to ambient PM2.5 exposure after the prenatal period.Our estimates for PAF also compares favorably with recent country-level PAF estimates for ambient PM2.5 on risk of low birth weight (LBW) in Burundi, Ethiopia, Kenya, Rwanda, Tanzania, and Uganda [19]. Ghosh et al. (2021) estimated that a median PAF of 2.04% of LBW risk is attributable to prenatal ambient PM2.5 exposure in these countries, while our study estimates a prenatal ambient PM2.5 PAF of 2.7% for stunting [19].Our findings are notably consistent with the existing literature concerning other child-growth risk factors. The bivariate and adjusted models showed that children residing in rural areas were likelier to have lower height-for-age and higher stunting than their urban counterparts. This is consistent with other studies identifying rurality as a risk factor for childhood growth impairment [60,61,62]. Like other studies, maternal education and wealth index was also significantly associated with better growth outcomes in our study [63,64,65]. The use of polluting fuel was significantly negatively associated with height-for-age in our study, which is also consistent with other studies that have found strong associations between childhood growth and biomass fuel for cooking [35,37].The results on seasonal trends showed that prenatal PM2.5 exposure varied based on being born in the wet or dry season. Noticeably, the dry seasons had higher levels of PM2.5, and the wet seasons had lower levels of PM2.5. Previous studies also show that PM2.5 depends on seasonally varying factors, including weather conditions. A study conducted in Uganda found that higher monthly precipitation was significantly predictive of lower PM2.5 levels [66]. This finding suggests that infants born during the dry season face disproportionately higher levels of prenatal ambient PM2.5 exposures in our study area. However, there was no clear association between seasonality (wet versus dry) and height-for-age.Additionally, there were differences in air pollution between countries. These differences may be partly explained by the economic development differences between countries in our study. According to the World Bank, the three countries below the population mean PM2.5 levels: Kenya, Tanzania, and Ethiopia, had the highest gross domestic product in 2021 [67,68].There are several limitations to this study. There is a risk of exposure misclassification in our study attributed to maternal factors during pregnancy. These maternal factors could include moving between homes during pregnancy or gestation periods that did not go to full term. For instance, our PM2.5 exposure estimation assumed that the mother lived in the same house during the nine months preceding the DHS household survey, which may not be the case for some mothers. In addition, other pollutants potentially affect the outcome of height-for-age, such as sulfur dioxide, nitrogen dioxide, and ozone [69,70]. These co-pollutants were not included in the analysis as covariates because the data was unavailable for the region. Also, our study did not explore the composition of PM25. Ambient PM2.5 air pollution is composed of different types of chemicals, which our study could not assess.Furthermore, we used calculated PM2.5 exposures with primarily satellite-driven estimates, as ground-level data was unavailable for much of the study area to validate predictions. However, the reported R2 from the ACAG cross-validated model was 0.90–0.92, suggesting reliable estimates [50]. When going through the inclusion criteria, several individuals were excluded (N = 63,284) from the analysis. However, we assume that these unmatched observations are missing randomly, which should not introduce selection bias in the study. Additionally, even though we used the DAG to identify the variables to be included in the models, residual confounding could still be present in the model and should be considered when interpreting the results of the estimated exposure-response relationships between prenatal and postnatal PM2.5.The strengths of this study include analyzing nationally representative data for six East African countries using several waves of the Demographic Health Surveys (DHS). This approach enabled us to spatially link births reported in the DHS with monthly PM2.5 values and estimate PM2.5 prenatal exposures and effects at the individual level after controlling for multiple relevant confounding variables. Using GAMs in a hierarchical modeling framework allowed us to further account for non-linear temporal confounders, the hierarchical structure of the DHS survey itself, and height-for-age outcome. Our study also leveraged newly available high spatial and temporal resolution data on PM2.5 estimates provided on a global scale. While previous studies using DHS data in Bangladesh and India relied on long-term annual global PM2.5 values and month-of-birth PM2.5 measurement data, we analyzed PM2.5 global estimates available at a monthly scale and a prediction model that improved from previous versions. Therefore, we could derive entire-pregnancy exposure estimates incorporating between-season variability of PM2.5, which previous studies did not include. Therefore, our approach to exposure estimation should entail lower exposure misclassification and, thus, more refined PM2.5 effect estimates than the India and Bangladesh studies. Another significant strength of our analysis is the use of multiple models with different risk factors that allowed us to interrogate the robustness of prenatal PM2.5 effect estimates. For instance, we could control for postnatal exposures that could confound the relationship between prenatal exposures and height for age. Indeed, adjusting for postnatal PM2.5 resulted in attenuation in the prenatal effect estimate for height-for-age.
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