Abstract

BACKGROUND AND AIM: A few studies from high-income countries suggested that traffic-related air pollutants (e.g. nitrogen oxides, elemental carbon) were associated with morbidity and mortality from idiopathic pulmonary fibrosis (IPF), a severe respiratory disease but is relatively less studied in relation to air pollution. Air quality in Beijing has been progressively improving since the 2013 Clean Air Act. We compiled data five years before and after 2013, and ran time-series analyses for 2008-2012 and 2013-2017 separately to investigate acute effects of ambient air pollution on IPF hospitalization risk in Beijing. METHODS: Daily counts of IPF hospitalizations were obtained from Beijing Public Health Information Center for the period 2008-2017 while daily city-wide average concentrations of each air pollutant (PM2.5, NO2, Ozone, SO2) were obtained from a single monitoring station set up in the US Embassy for 2008-2012 (for PM2.5 only) and from 35 municipal monitoring stations for 2013-2017. The associations between daily IPF hospitalization and average concentration of each pollutant were analyzed with a generalized additive model (GAM) estimating Poisson distribution. RESULTS:Across Beijing, daily 24-hour mean PM2.5 concentration during 2008-2012 and 2013-2017 was 88.9 and 76.7 μg/m3 respectively. During 2008-2012, relative risk (RR) of IPF hospitalization at lag0 per interquartile range (IQR) increase (83 μg/m3) in PM2.5 was 1.062 (95%CI: 1.025-1.101). The RR was reduced at lag1, lag2 and lag3 but remained significantly positive. In contrast, during 2013-2017, the RR was 1.049 (95%CI: 1.024-1.074) at lag0, but no significant associations were seen for all other lags. No associations were seen for NO2 whilst significant associations were observed with both SO2 and cool-season ozone. CONCLUSIONS:Despite improvement in overall air quality, acute exposure to high-level air pollution is a risk factor for IPF hospitalization in Beijing. Air quality policy should be continuously enforced and carefully monitored to protect public health in the long-term. KEYWORDS: air pollution, respiratory disease, time-series, LMIC

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