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January 22, 2026

The effect of air pollution and household environmental indicators on anemia status among under-five children in low-middle income countries: exploring potential machine learning applications on demographic and health surveys.

Key Points

The study aims to evaluate the impact of air pollution and household environmental indicators on anemia in children aged 6-59 months in low-middle income countries.
Linked DHS data from 45 LMICs with satellite-derived PM2.5 and NO2 estimates.
Used modified Poisson regression to analyze associations between pollution, household indicators, and anemia.
Employed machine learning algorithms like logistic regression, random forest, and more for anemia status prediction.
Dataset was split into train/test sets for model performance evaluation.
56% of children studied were anemic, varying from 16% to 81% across countries.
Higher PM2.5 levels increased anemia risk by 26%, while clean cooking fuel, improved water, and sanitation lowered risks by 24%, 3%, and 13%, respectively.
Random forest achieved 68% classification accuracy, 54% specificity, 79% sensitivity, and 74% AUC.
Key predictors included child's age, environmental wet days, location, and PM2.5 levels.

Abstract

Exposure to air pollution has been associated with anemia in children, but little effort has been made in low- and middle-income countries (LMICs) in which the prevalence of anemia is persistently high. This study aimed to assess the effects of air pollution and household environmental indicators on anemia among children aged 6-59 months using machine learning algorithms. The Demographic and Health Survey (DHS) datasets from 45 LMICs were linked with the satellite-derived estimates of annual average particulate matter (PM2.5) and nitrogen dioxide (NO2) based on children's area of residence. The modified Poisson regression model was used to assess the association between exposure to air pollutants, household environmental indicators, and anemia status of children. Machine learning algorithms (MLA) such as logistic regression, Ridge, Lasso, elastic net, Artificial Neural Network, Naïve Bayes, Boosting, and Random Forest were used for predicting the anemia status. We randomly split the dataset into two (train/test), and the model performance was evaluated using sensitivity, specificity and the area under the receiver operating characteristic curve (AU-ROC). The study included 177,251 under-five children, of which 99,290 (56%) were anemic and varied across countries ranging from 16% (Armenia) to 81% (Mali). A child who lived in areas with a PM2.5 concentration above the WHO recommended guidelines has a 26% higher risk of being anemic (aPR = 1.26; 95% CI 1.22-1.30) and a child from households having clean fuel for cooking, improved water, and improved sanitation have 24%, 3%, and 13% lower risk of being anemic. The random forest MLA achieved the best classification accuracy of 68%, specificity of 54%, sensitivity of 79% and AUC of 74%. The MLA is more effective than traditional analytical approaches in predicting anemia status. The RF revealed that the age of the child, wet day of the environment, the location of the child, and PM2.5 are the most important features to predict the anemia status of a child.

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Cite This Study

Fenta et al. (Mon,) studied this question.

synapsesocial.com/papers/6971be50642b1836717e2f06 https://doi.org/https://doi.org/10.1038/s41598-025-33837-3

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