“Association between airborne PM2.5 chemical constituents and birth weight—implication of buffer exposure assignment” by Keita Ebisu et al. was selected to be in the Highlights of 2014 special issue of Environmental research Letters.

March 1, 2015

“Association between airborne PM2.5 chemical constituents and birth weight—implication of buffer exposure assignment” by Keita Ebisu et al. was selected to be in the Highlights of 2014 special issue of Environmental research Letters.

Abstract 

Several papers reported associations between airborne fine particulate matter (PM2.5) and birth weight, though findings are inconsistent across studies. Conflicting results might be due to (1) different PM2.5 chemical structure across locations, and (2) various exposure assignment methods across studies even among the studies that use ambient monitors to assess exposure. We investigated associations between birth weight and PM2.5 chemical constituents, considering issues arising from choice of buffer size (i.e. distance between residence and pollution monitor). We estimated the association between each pollutant and term birth weight applying buffers of 5 to 30km in Connecticut (2000–2006), in the New England region of the USA. We also investigated the implication of the choice of buffer size in relation to population characteristics, such as socioeconomic status. Results indicate that some PM2.5 chemical constituents, such as nitrate, are associated with lower birth weight and appear more harmful than other constituents. However, associations vary with buffer size and the implications of different buffer sizes may differ by pollutant. A homogeneous pollutant level within a certain distance is a common assumption in many environmental epidemiology studies, but the validity of this assumption may vary by pollutant. Furthermore, we found that areas close to monitors reflect more minority and lower socioeconomic populations, which implies that different exposure approaches may result in different types of study populations. Our findings demonstrate that choosing an exposure method involves key tradeoffs of the impacts of exposure misclassification, sample size, and population characteristics.