Can Air Quality Data Predict Stunting Prevalence?

Abstract One of the objectives outlined in the Sustainable Development Goals (SDGs) is to promote good health and well-being, particularly concerning the issue of stunting, which refers to children under five years of age who fall below two standard deviations of the normal height. Indonesia continues to face challenges in reducing the prevalence of stunting, which was recorded at 21.5% in 2023, with a target of lowering it to below 20%. Numerous studies have explored various factors contributing to stunting, including poverty, nutrition, sanitation, family background, and socio-economic conditions. However, there has been a lack of research examining the connection between air quality and stunting. This paper aims to investigate the relationship between stunting and the aerosol index as a measure of air quality. For this modeling study, we utilized data from the Indonesian Family Life Survey (IFLS) spanning from 1993 to 2015, analyzing a sample of 18357 children aged 0–5 years and correlating this data with aerosol indexes obtained from NASA's Total Ozone Mapping Spectrometer and Moderate Resolution Imaging Spectroradiometer (TOMS/MODIS). A logistic regression model was employed to assess the correlation. The key finding is a counter-intuitive inverse association: a one-unit increase in the Aerosol Optical Depth (AOD) index is linked to a significant decrease of 1.42 in the odds of stunting ( p < 0.01). However, further analysis suggests this association likely reflects AOD's role as a spatial proxy for socioeconomic development and urbanization—factors that strongly reduce stunting—rather than a direct biological effect of aerosols. This underscores the need for location-specific health policies and cautions against interpreting satellite-derived pollution indices without considering regional development contexts. From a policy standpoint, reducing stunting remains paramount, but this must be pursued alongside, not at the expense of, air quality improvements. Graphical Abstract