The present study assessed the effect of short-term exposure to PM2.5 of daily outpatient visits for respiratory diseases. Electronic records of daily outpatient visits were collected from two large general hospitals in Lanzhou, China from 01 January 2007 to 31 December 2016. Daily air pollution data from the Lanzhou Environmental Monitoring Station and daily meteorological data from the Lanzhou Meteorological Bureau were collected in the same period. A distributed lag non-linear model, based on a gender and age groups, was applied to analyse the exposure-response relationship between the air pollutants, and the daily outpatient visits for respiratory diseases. From 2007 to 2016, the PM2.5 concentrations were associated with an increase in the daily outpatient visits for respiratory diseases in Lanzhou. In addition, a lag effect was observed and this effect was the strongest on day 1. For every 10 μg/m3 increase in the PM2.5 concentration, the daily outpatient visits for respiratory diseases increased by 0.53% (95% CI: 0.22%-0.84%). People aged 18 years or younger were most sensitive to PM2.5, and the influence of PM2.5 was more significant for females than for males. The cumulative effect of the PM2.5 concentration for the number of outpatient visits was greater than its daily effect, and the cumulative effect peaked on day 12. From day 0 to day 14, every 10-μg/m3 increase in the PM2.5 concentration had a statistically significant cumulative effect on the outpatient visits for respiratory diseases among individuals aged 18 years or younger (p < 0.05), reaching a maximum value on day 14 (PM2.5: RR = 1.0213, 95% CI: 1.0128-1.0299).
Keywords: Distributed lag non-linear model; PM(2.5); Respiratory disease; Time-series analysis.
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