Association between traffic-related air pollution and anxiety hospitalizations in a coastal Chinese city: are there potentially susceptible groups?

Abstract

Motor vehicle exhaust emissions have become the main source of urban air pollution in China, but few studies have explored the association of short-term exposure to traffic-related air pollutants (TRAPs) with anxiety disorders. Thus, we used an overdispersed, generalized additive model (GAM) to investigate the association between TRAPs and hospital admissions (HAs) for anxiety in Qingdao, a coastal Chinese city with high vehicle ownership. In addition, stratified analyses were performed by gender, age, season and hospitalization frequency (first admission and readmission). A positive association between TRAPs and HAs for anxiety was observed. Both inhalable particulate matter (PM10) and nitrogen dioxide (NO2) showed significant effects at lag 3 in the single-day lag structure, and each 10 μg/m3 increase in the concentrations was significantly associated with increases of 0.88% [95% confidence interval (CI): 0.04%, 1.72%] for PM10 and 2.74% (0.45%, 5.08%) for NO2 on anxiety hospitalizations. For fine particulate matter (PM2.5) and carbon monoxide (CO), the strongest effects were found at lag05 and lag04 [2.67% (0.77%, 4.62%) and 0.19% (0.04%, 0.34%), respectively] in the multiday lag structure. The estimates of PM2.5 were relatively robust after adjusting for other pollutants in the two-pollutant model. Stratified analyses indicated that the associations were stronger in females and younger individuals (<45 in age) than in males and elderly individuals (≥45 in age). Furthermore, the effects of PM2.5 and CO were most obvious during the cold season. Regarding hospitalization frequency, only PM2.5 was found to have a significant effect in the first-admission group. The results showed that short-term exposure to TRAPs, especially to PM2.5, was significantly associated with the increased risk of daily HAs for anxiety, which can help clinicians and policymakers better understand the effects of TRAPs to implement targeted interventions.