Spatial heterogeneity in the association between particulate matter air pollution and low birth weight in South Korea

As many studies showed the spatial heterogeneity in the association between particulate matter (PM) air pollution and low birth weight (LBW), few studies focused on the variation of local associations at the national scale and related areal characteristics. This study aimed to explore different approaches to estimating local effects of PM with an aerodynamic diameter ≤10 μm (PM₁₀) on LBW across 235 districts in South Korea, to investigate the spatial pattern of local associations, and to examine the relationship with local socio-demographic and environmental characteristics. LBW was identified in 5,692,650 mothers from birth certificate data for 2001-2013. We estimated individual annual-average concentrations of PM₁₀ at centroids of mothers' residential districts by using a previously-validated prediction model. Then, we estimated district-specific odds ratios of LBW for PM₁₀ using modified geographically weighted logistic regression. Here, we applied four approaches with different neighborhood definitions: the distance-based approach within 20- and 40-km bandwidth and the hybrid approach replacing with adjacent districts for urban districts <100 km². In addition, we compared district-specific socioeconomic indicators and emission estimates across three groups of districts that showed significantly positive, no, and significantly negative associations. Medians of district-specific estimates of four approaches were similar to the global estimate and between each other. However, their variability differed with some unreasonably high estimates when a small distance was applied as the neighborhood definition, although spatial pattern was generally similar among the four. The hybrid approach based on the different neighborhood definition by urban and rural areas provided stable risk estimates. Higher risk districts in rural areas were found in more socioeconomically-deprived areas, whereas urban areas showed higher risk districts when their air pollution emissions were higher. Our approach and findings will help identify high risk areas and enhance understanding of geographic determinants.