Background
Epidemiological evidence has shown an association between long-term exposure to fine particulate matter (PM2.5) and hypertension and diabetes, but few studies have considered the spatial properties of the samples. This study aimed to investigate the long-term effect of PM2.5 exposure on hypertension and diabetes among middle-aged and elderly people in China based on a spatial study.
Methods
We conducted a national cross-sectional study of the most recently launched wave 4 2018 data of the China Health and Retirement Longitudinal Study (CHARLS) to calculate the prevalence of hypertension and diabetes. The exposure data of annual average PM2.5 concentrations were estimated combined with satellite observations, chemical transport modeling, and ground-based monitoring. A shared component model (SCM) was used to explore the association of PM2.5 with hypertension and diabetes, in which these two diseases borrowed information on spatial variations from each other. Then, we evaluated the effect variations in PM2.5 in different periods and smoking status on changes in outcomes.
Results
The prevalence of hypertension and diabetes was 44.27% and 18.44%, respectively, among 19,529 participants. The annual average PM2.5 concentration in 31 provinces ranged from 4.4 μg/m3 to 51.3 μg/m3 with an average of 27.86 μg/m3 in 2018. Spatial auto-correlations of the prevalence of hypertension and diabetes and PM2.5 concentrations were seen (Moran’s I = 0.336, p = 0.01; Moran’s I = 0.288, p = 0.03; Moran’s I = 0.490, p = 0.01). An interquartile range (IQR: 16.2 μg/m3) increase in PM2.5 concentrations was significantly associated with a higher prevalence of hypertension and diabetes with odds ratios (ORs) of 1.070 [95% credible interval (95% CrI): 1.034, 1.108] and 1.149 (95% CrI: 1.100, 1.200), respectively. Notably, the effect of PM2.5 on both hypertension and diabetes was relatively stronger among non-smokers than smokers.
Conclusion
Our nationwide study demonstrated that long-term exposure to PM2.5 might increase the risk of hypertension and diabetes, and could provide guidance to public policymakers to prevent and control hypertension and diabetes according to the spatial distribution patterns of the above effects in China.
