The correlation between respiratory infectious diseases, air pollution, and meteorological factors in Jinan, China from 2021 to 2023.

OBJECTIVE

To investigate the epidemiological patterns of respiratory infectious diseases in Jinan from 2021 to 2023 and to elucidate the relationship among these diseases, meteorological factors, and air pollution.

METHODS

We conducted a retrospective analysis of outpatient and inpatient data related to respiratory infectious diseases recorded by the Jinan Health Care Development Center from 2021 to 2023. Additionally, we gathered data on outdoor air pollution indicators and meteorological variables from 14 environmental monitoring stations in Jinan. A generalized Poisson regression model for time series analysis was employed to examine the correlation between meteorological factors, air pollution levels, and hospitalization rates for respiratory infectious diseases.

RESULTS

From 2021 to 2023, the daily average concentrations of atmospheric pollutants sulfur dioxide (SO), nitrogen dioxide (NO), and carbon monoxide (CO) adhered to the national air quality standards, while the daily average concentrations of inhalable particulate matter (PM) and fine particulate matter (PM) exceeded the national first-class limits. The daily average concentration of ozone (O) surpassed the national secondary standard, indicating severe O pollution. Regarding respiratory disease patients, the relative risk (RR) values of PM, PM, SO, NO, and CO were highest at a lag of 07 d. Compared to other age groups, PM, SO, NO, and CO had a more significant impact on respiratory disease treatment on children aged 0-12, while PM significantly affected individuals aged 60 and above. In the single pollution model, RR values corresponding to PM, PM, SO, CO, and NO were 1.008, 1.058, 1.224, 1.405, and 1.102 respectively on lag07d. The multi-pollutant model maintained the positive relationship between the total hospitalization frequency of respiratory diseases and CO, NO, SO, PM, and PM.

CONCLUSION

Our study found a strong, positive correlation with a lag effect between total number of hospitalizations for common respiratory diseases and pollutants CO, NO, SO, PM, and PM in the examined sample.