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Background Previous epidemiological studies on the association between short-term exposure to particulate matter (PM) with hospital admission in major cities in China were limited to shorter study periods or a single hospital. The aim of this ecological study based on a 12.5-year time series was to investigate the association of short-term exposure to PM with aerodynamic diameter ≤ 2.5 μm (PM 2.5 ) and aerodynamic diameter ≤ 10 μm (PM 10 ) with hospital admissions for respiratory diseases. Methods Daily hospital admissions data were from the Shanghai Medical Insurance System for the period January 1, 2008 to July 31, 2020. We estimated the percentage change with its 95% confidence interval (CI) for each 10 μg/m 3  increase in the level of PM 2.5  and PM 10  after adjustment for calendar time, day of the week, public holidays, and meteorological factors applying a generalized additive model with a quasi-Poisson distribution. Results There were 1,960,361 hospital admissions for respiratory diseases in Shanghai during the study period. A 10 μg/m 3  increase in the level of each class of PM was associated with increased total respiratory diseases when the lag time was 0 day (PM 2.5 : 0.755%; 95% CI: 0.422, 1.089%; PM 10 : 0.250%; 95% CI: 0.042, 0.459%). The PM 2.5  and PM 10  levels also had positive associations with admissions for COPD, asthma, and pneumonia. Stratified analyses demonstrated stronger effects in patients more than 45 years old and during the cold season. Total respiratory diseases increased linearly with PM concentration from 0 to 100 μg/m 3 , and increased more slowly at higher PM concentrations. Conclusions This time-series study suggests that short-term exposure to PM increased the risk for hospital admission for respiratory diseases, even at low concentrations. These findings suggest that reducing atmospheric PM concentrations may reduce hospital admissions for respiratory diseases.

Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai