Association between out-patient visits and air pollution in Chiang Mai, Thailand: Lessons from a unique situation involving a large data set showing high seasonal levels of air pollution

Abstract

Chiang Mai is one of the most known cities of Northern Thailand, representative for various cities in the East and South-East Asian region exhibiting seasonal smog crises. While a few studies have attempted to address smog crises effects on human health in that geographic region, research in this regard is still in its infancy. We exploited a unique situation based on two factors: large pollutant concentration variations due to the Chiang Mai smog crises and a relatively large sample of out-patient visits. About 216,000 out-patient visits in the area of Chiang Mai during the period of 2011 to 2014 for upper (J30-J39) and lower (J44) respiratory tract diseases were evaluated with respect to associations with particulate matter (PM10), ozone (O3), and nitrogen dioxide (NO2) concentrations using single-pollutant and multiplepollutants Poisson regression models. All three pollutants were found to be associated with visits due to upper respiratory tract diseases (with relative risks RR = 1.023 at cumulative lag 05, 95% CI: 1.021-1.025, per 10 μg/m3 PM10 increase, RR = 1.123 at lag 05, 95% CI: 1.118-1.129, per 10 ppb O3 increase, and RR = 1.110 at lag 05, 95% CI: 1.102-1.119, per 10 ppb NO2increase). Likewise, all three pollutants were found to be associated with visits due to lower respiratory tract diseases (with RR = 1.016 at lag 06, 95% CI: 1.015-1.017, per 10 μg/m3 PM10 increase, RR = 1.073 at lag 06, 95% CI: 1.070-1.076, per 10 ppb O3 increase, and RR = 1.046 at lag 06, 95% CI: 1.040-1.051, per 10 ppb NO2increase). Multipollutants modeling analysis identified O3 as a relatively independent risk factor and PM10- NO2pollutants models as promising two-pollutants models. Overall, these results demonstrate the adverse effects of all three air pollutants on respiratory morbidity and call for air pollution reduction and control.