Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratified case-crossover study in China from 2015 to 2019

Abstract

Background Long-term exposure to air pollution has been associated with the onset and progression of kidney diseases, but the association between short-term exposure to air pollution and mortality of kidney diseases has not yet been reported. Methods A nationally representative sample of 101,919 deaths from kidney diseases was collected from the Chinese Center for Disease Control and Prevention from 2015 to 2019. A time-stratified case-crossover study was applied to determine the associations. Satellite-based estimates of air pollution were assigned to each case and control day using a bilinear interpolation approach and geo-coded residential addresses. Conditional logistic regression models were constructed to estimate the associations adjusting for nonlinear splines of temperature and relative humidity. Results Each 10 µg/m3 increment in lag 0–1 mean concentrations of air pollutants was associated with a percent increase in death from kidney disease: 1.33% (95% confidence interval [CI]: 0.57% to 2.1%) for PM1, 0.49% (95% CI: 0.10% to 0.88%) for PM2.5, 0.32% (95% CI: 0.08% to 0.57%) for PM10, 1.26% (95% CI: 0.29% to 2.24%) for NO2, and 2.9% (95% CI: 1.68% to 4.15%) for SO2. Conclusions Our study suggests that short-term exposure to ambient PM1, PM2.5, PM10, NO2, and SO2 might be important environmental risk factors for death due to kidney diseases in China.

Ambient Air Particulate Matter and Hospital Admissions for Chronic Kidney Disease in China: A Nationwide Case-Crossover Study

“…Plenty of studies have demonstrated that ambient air pollution, especially particulate matter (PM), has adverse impacts on almost every organ system including the kidney, a vital organ involved in hemofiltration and toxin excretion in the body . Emerging epidemiological studies suggest that ambient fine particulate matter (PM 2.5 ) and inhalable particles (PM 10 ) exposure is associated with an increased risk of CKD. − However, these studies were limited to health effects on incidence, prevalence, death, or emergency room visits; little attention has been paid to the health effects on hospital admissions.…”

“…We coded them according to the International Statistical Classification of Diseases, Tenth Revision (ICD-10) (Table S1). ,, All CKD subtypes have enough hospital admissions for further analysis. In addition, information about gender, age, admission date, and length of hospital stay (LOS) was also obtained.…”

“…A two-stage time-stratified case-crossover design which was used to explore the associations of PM 2.5 and PM 10 exposure with hospital admissions for total CKD and its major subtypes in in multicity studies based on previous literature. ,, The case day was defined as the date of hospital admission for CKD, and the other 3 or 4 days in the same stratum (the same day of the week, month, year, and city) were defined as the control days. In the first stage, the conditional logistic regression model was applied to evaluate the city-specific associations of daily average PM 2.5 and PM 10 with hospital admissions for total CKD and its subtypes at single lag days (including the current day and previous 1–3 days before admission, denoted as lag0 to lag3) and cumulative lag days (including the moving averages of the current day and previous 1–3 days before admission, denoted as lag01 to lag03) (see details in Section S1).…”

Ambient fine particulate matter chemical composition associated with in-hospital case fatality, hospital expenses, and length of hospital stay among patients with heart failure in China

“…In the second step, bilinear interpolation is used for the purpose of assessing the magnitudes of environmental variables, including PM 2.5 mass and its chemical components, temperature, and relative humidity, at the residential addresses of the patients prior to their hospitalisation. The detailed calculation process can be found elsewhere [ 26 , 27 ]. Briefly, this algorithm improved the spatial precision of environmental variables at particular locations through the calculation of a weighted average derived from the closest four grid points, and the weights were determined by the distance between the case address and the grids of meteorologic variables, with closer distances having higher weights [ 28 , 29 ].…”

Air pollution and stroke hospitalization in the Beibu Gulf Region of China: A case-crossover analysis

“…1 June, 15 June, 22 June, and 29 June) ( Shoraka et al, 2022 ). Therefore, the estimates represent the acute triggering effect of air pollution on the stroke hospitalization ( Cai et al, 2023c , Liu et al, 2022 ). This approach ensured that there are 3 or 4 control days matched to each stroke hospitalization (case day).…”