Association of air pollutants and hospital admissions for respiratory diseases in Lanzhou, China, 2014-2019

The aim of this study was to assess the effects of air pollutants on hospital admissions for respiratory disease (RD) by using distributed lag nonlinear model (DLNM) in Lanzhou during 2014-2019. In this study, the dataset of air pollutants, meteorological, and daily hospital admissions for RD in Lanzhou, from January 1st, 2014 to December 31st, 2019, were collected from three national environmental monitoring stations, China meteorological data service center, and three large general hospitals, respectively. A time-series analysis with DLNM was used to estimate the associations between air pollutants and hospital admissions for RD including the stratified analysis of age, gender, and season. The key findings were expressed as the relative risk (RR) with a 95% confidence interval (CI) for single-day and cumulative lag effects (0-7). A total of 90, 942 RD hospitalization cases were identified during the study period. The highest association (RR, 95% CI) of hospital admissions for RD and PM2.5 (1.030, 1.012-1.049), and PM10 (1.009, 1.001-1.015), and NO2 (1.047, 1.024-1.071) were observed at lag 07 for an increase of 10 mu g/m(3) in the concentrations, and CO at lag07 (1.140, 1.052-1.236) for an increase of 1 mg/m(3) in the concentration. We observed that the RR estimates for gaseous pollutants (e.g., CO and NO2) were larger than those of particulate matter (e.g., PM2.5 and PM10). The harmful effects of PM2.5, PM10, NO2, and CO were greater in male, people aged 0-14 group and in the cold season. However, no significant association was observed for SO2, O(3)8h, and total hospital admissions for RD. Therefore, some effective intervention strategies should be taken to strengthen the treatment of the ambient air pollutants, especially gaseous pollutants (e.g., CO and NO2), thereby, reducing the burden of respiratory diseases.

Automated summary

This study assessed the effects of air pollutants on hospital admissions for respiratory disease in Lanzhou using a distributed lag nonlinear model. The results showed a significant association between PM2.5, PM10, NO2, and CO concentrations and respiratory disease hospitalizations, particularly in the cold season and among males and the 0-14 age group.