Particulate Matters (PM2.5, PM10) and the risk of depression: analysis of the Korean Longitudinal Study of Aging (KLoSA), 2016-2020 in South Korea

Abstract

Abstract Background There is a growing concern that particulate matter (PM) such as PM2.5 and PM10 has contributed to exacerbating psychological disorders, particularly depression. However, little is known about the roles of these air pollutants on depression in elderly. Therefore, this study aimed to examine the association between PM2.5 and PM10, and depression in the elderly population in South Korea. Methods We used panel survey data, the Korean Longitudinal Study of Aging (KLoSA), administered by the Labor Institute during the study period of 2016, 2018, and 2020 covering 217 districts in South Korea (n = 7674). Annual district-specific PM2.5 and PM10 concentrations were calculated for the study period from the monthly prediction concentrations produced by a machine-learning-based ensemble model (cross-validated R2: 0.87), then linked to the people matching with year and their residential district. We constructed a generalized estimating equation (GEE) model with a logit link to identify the associations between each of the long-term PM2.5 and PM10 exposures and depression (CES-D 10) after adjusting for individual and regional factors as confounders. Results In single-pollutant models, we found that long-term 10 μg/m3 increments in PM2.5 (OR 2.83, 95% CI 2.18–3.67) and PM10 (OR 2.36, 95% CI 1.99–2.79) were associated with an increased risk of depression in the elderly. Associations were consistent after adjusting for other air pollutants (NO2, O3, and CO) in two-pollutant models. In addition, the impacts substantially differed by regions grouped by the tertile of the population density, for which the risks of particulate matters on depression were substantial in the middle- or high-density population regions in contrast to the low-density population regions. Conclusions Long-term exposure to PM2.5 and PM10 was associated with a higher risk of developing depression in elderly people. The impact was modified by the population density level of the region where they reside.