Table 5.

Multivariable meta-regression results of the modification effects of city level characteristics on the associations between concurrent day PM_2.5 levels (lag 0) and daily hospital admissions for cardiovascular disease in 184 cities in China, 2014-17

Variables	Percentage change (%; 95% CI)	P value
Annual average PM2.5 levels (10 μg/m3)	−0.057 (−0.116 to 0.003)	0.06
Annual standard deviation of PM2.5 concentrations (10 μg/m3)*	−0.151 (−0.330 to 0.027)	0.10
Annual average temperature (°C)	0.038 (0.011 to 0.065)	0.006
Annual average relative humidity (%)†	0.013 (0.004 to 0.022)	0.006
GDP per capita (¥10 000)	0.005 (−0.052 to 0.062)	0.87
Average age (year)	0.012 (−0.019 to 0.044)	0.44
Smoking rate (%)	−0.023 (−0.066 to 0.019)	0.27
Coverage rate by UEBMI (%)	−0.007 (−0.015 to 0.001)	0.10
Annual average number of days with PM2.5 >35 μg/m3‡	―	0.71
Annual average number of days with PM2.5 >75 μg/m3‡	―	0.56

^¥10 000 (£1169; $1456; €1377). PM_2.5=particulate matter with aerodynamic diameter ≤2.5 μm; GDP=gross domestic product; UEBMI=urban employee basic medical insurance. The primary meta-regression model was multivariable including annual average PM_2.5 levels, temperature, GDP per capita, average age of people enrolled in UEBMI, smoking rate, and population coverage rate by UEBMI.

^*Percentage change of the annual standard deviation of PM_2.5 concentrations was adjusted for all the variables in the primary meta-regression model.

^†Percentage change of the relative humidity was adjusted for all the variables in the primary meta-regression model except for temperature, owing to the collinearity between the two variables.

^‡Annual average number of days with PM_2.5 greater than 35 μg/m³ and 75 μg/m³ was adjusted separately for all the variables in the primary meta-regression model, owing to the collinearity between the two variables. Percentage changes and their 95% confidence intervals for the two variables were not presented because the coefficients were too small.