Abstract:Objective To investigate the correlation and lag effect between atmospheric particulate matter and the risk of hospitalization for chronic kidney disease (CKD). Methods The daily hospitalization data for CKD in 9 hospitals in Urumqi from Jan. 1, 2019, to Dec. 31, 2020, and the air pollution and meteorological data during the same period were collected. The relationship between PM2.5 and PM10 concentrations and CKD incidence was analyzed after controlling for long-term trends, meteorological factors, and potential confounding factors such as the “day of the week effect” by using the generalized additive model (GAM). The effects of single-day lag of 0-7 d (lag0-lag7) and cumulative lag of 0-7 d (lag01-lag07) were analyzed, and subgroup analyses were conducted for gender, age, and season. On the basis of the single pollutant model, other pollutants were included (at most 2 pollutants were included at a time), and a double pollutant model was constructed to evaluate the stability of the model. Results For every 10 μg/m3 increase in PM2.5 concentration, the highest risk of CKD hospitalization occured when lagged alone at lag2 (relative risk ［RR］ ＝1.014, 95% confidence interval ［CI］ 1.006-1.023) and lagged cumulatively at lag04 (RR＝1.018, 95% CI 1.007-1.029). For every 10 μg/m3 increase in PM10 concentration, the risk of CKD hospitalization was highest when lagged alone at lag0 and lagged cumulatively at lag07 (RR＝1.012, 95% CI 1.007-1.017; RR＝1.024, 95% CI 1.016-1.032). In gender stratification, for every 10 μg/m3 increase in PM2.5 concentration, the cumulative lag at lag04 indicated that males had the highest risk of CKD hospitalization (RR＝1.023, 95% CI 1.008-1.038); for every 10 μg/m3 increase in PM10 concentration, the highest risk of CKD hospitalization was observed in males when lagged alone at lag0 (RR＝1.013, 95% CI 1.006-1.020), and in females when lagged alone at lag1 (RR＝1.013, 95% CI 1.006-1.020). In age stratification, for every 10 μg/m3 increase in PM2.5 concentration, the risk of CKD hospitalization was highest in people 65 years old with single-day lag at lag3 and cumulative lag at lag04 (RR＝1.016, 95% CI 1.007-1.026; RR＝1.022, 95% CI 1.010-1.035); for every 10 μg/m3 increase in PM10 concentration, the cumulative lag at lag07 indicated that individuals aged＜65 years old and ≥65 years old had the highest risk of CKD hospitalization (RR＝1.027, 95% CI 1.017-1.037; RR＝1.015, 95% CI 1.001-1.028). In seasonal stratification, for every 10 μg/m3 increase in PM2.5 concentration during the cold season, the risk of CKD hospitalization was highest when lagged alone at lag3 and lagged cumulatively at lag07 (RR＝1.020, 95% CI 1.011-1.029; RR＝1.025, 95% CI 1.011-1.038). For every 10 μg/m3 increase in PM10 concentration during the cold season, the risk of CKD hospitalization was highest when lagged alone at lag2 (RR＝1.013, 95% CI 1.007-1.019). For every 10 μg/m3 increase in PM10 concentration during the warm season, the risk of CKD hospitalization was highest when lagged alone at lag7 (RR＝1.015, 95% CI 1.006-1.024). In the dual pollutant model, the effects of PM2.5 adjusting PM10, SO2, O3 and CO, and PM10 adjusting NO2, SO2, O3, and CO on the risk of CKD hospitalization were still significant (P＜0.05). Conclusion The increase in atmospheric particulate matter concentrations of PM2.5 and PM10 can lead to an increased risk of CKD, and there is a lag effect. Men, people under the age of 65 years old, and those in cold seasons (heating periods) are more sensitive to exposure to PM2.5 and PM10.
