Standard descriptive analysis was conducted to display the distribution of air pollutants, meteorological factors, and daily hospitalizations for respiratory diseases, while the temporal distribution of air pollutants was presented in line charts. Spearman's correlation analysis was used to identify the correlation between air pollutants and meteorological factors.
The generalized additive model was applied to quantify the association between daily ambient CO concentration and daily hospitalizations for respiratory diseases. Quasi-Poisson regression was applied in the model, as daily hospitalizations tended to display an over-dispersed Poisson distribution. A dichotomous variable for public holidays and a categorical variable for the day of the week (DOW) were incorporated into the model to adjust the variation of daily hospitalizations within holidays and each week. Moreover, smoothing terms were used to fit daily hospitalizations in the models to control long-term and seasonal trends of daily hospitalizations and meteorological effects (14 (link)). According to previous studies (15 (link)–17 (link)), we applied 6 degrees of freedom (df) per year for long-term and seasonal trends, 3 df each for the same day's temperature (Tem0) and relative humidity (Humid0). In brief, the model can be represented as follows:
where E(Yt) represents the estimated daily hospitalizations for respiratory diseases at day t. β represents the log-relative risk of hospitalization associated with a 1 mg/m3 increase in ambient CO concentration. s () is the restricted smoothing spline function for variables with the non-linear association, day indicates the variable of the long-term and seasonal trends, and α is the intercept for the model.
Considering the delayed health effects of air pollutants, we estimated the lag effects of different days in both single-day lag from lag0 to lag7 and multi-day lag from lag0 to lag0–7 (moving average from lag0 to lag7). To improve the comparability of the association between CO exposure and risk of hospitalization for total and specific respiratory diseases, we selected the same CO exposure window for different respiratory diseases in exposure–response relationship analysis, stratified analysis, and sensitivity analysis adjusted for co-pollutants.
Based on the same models that estimated the association between CO exposure and risk of hospitalization for respiratory diseases, the smoothing function with 3 df was used to graphically describe the exposure–response relationship between ambient CO concentration and risk of hospitalization for respiratory diseases. Stratified analysis was conducted to assess the potential effect modification by age (minors/adult/elderly), gender (men/women), and season (warm: May–October, cold: November–April) (18 (link), 19 (link)). To further quantify the potential effect modification, we calculated the significant differences between subgroups based on the widely used method (20 (link)). We also investigated whether the association between CO and hospitalizations was still robust to the adjustment for other co-pollutants including PM2.5, PM10, SO2, NO2, and O3. Dual- and multi-pollutant models were performed in this study.
In this study, two-sided P < 0.05 was considered statistically significant. The generalized additive model was conducted in R 4.0.2 within the “mgcv” package (21 (link)). Effect estimates were presented as percentage changes and 95% confidence intervals (CIs) in daily hospitalizations in relation to each 1 mg/m3 increase in ambient CO concentration.
The generalized additive model was applied to quantify the association between daily ambient CO concentration and daily hospitalizations for respiratory diseases. Quasi-Poisson regression was applied in the model, as daily hospitalizations tended to display an over-dispersed Poisson distribution. A dichotomous variable for public holidays and a categorical variable for the day of the week (DOW) were incorporated into the model to adjust the variation of daily hospitalizations within holidays and each week. Moreover, smoothing terms were used to fit daily hospitalizations in the models to control long-term and seasonal trends of daily hospitalizations and meteorological effects (14 (link)). According to previous studies (15 (link)–17 (link)), we applied 6 degrees of freedom (df) per year for long-term and seasonal trends, 3 df each for the same day's temperature (Tem0) and relative humidity (Humid0). In brief, the model can be represented as follows:
where E(Yt) represents the estimated daily hospitalizations for respiratory diseases at day t. β represents the log-relative risk of hospitalization associated with a 1 mg/m3 increase in ambient CO concentration. s () is the restricted smoothing spline function for variables with the non-linear association, day indicates the variable of the long-term and seasonal trends, and α is the intercept for the model.
Considering the delayed health effects of air pollutants, we estimated the lag effects of different days in both single-day lag from lag0 to lag7 and multi-day lag from lag0 to lag0–7 (moving average from lag0 to lag7). To improve the comparability of the association between CO exposure and risk of hospitalization for total and specific respiratory diseases, we selected the same CO exposure window for different respiratory diseases in exposure–response relationship analysis, stratified analysis, and sensitivity analysis adjusted for co-pollutants.
Based on the same models that estimated the association between CO exposure and risk of hospitalization for respiratory diseases, the smoothing function with 3 df was used to graphically describe the exposure–response relationship between ambient CO concentration and risk of hospitalization for respiratory diseases. Stratified analysis was conducted to assess the potential effect modification by age (minors/adult/elderly), gender (men/women), and season (warm: May–October, cold: November–April) (18 (link), 19 (link)). To further quantify the potential effect modification, we calculated the significant differences between subgroups based on the widely used method (20 (link)). We also investigated whether the association between CO and hospitalizations was still robust to the adjustment for other co-pollutants including PM2.5, PM10, SO2, NO2, and O3. Dual- and multi-pollutant models were performed in this study.
In this study, two-sided P < 0.05 was considered statistically significant. The generalized additive model was conducted in R 4.0.2 within the “mgcv” package (21 (link)). Effect estimates were presented as percentage changes and 95% confidence intervals (CIs) in daily hospitalizations in relation to each 1 mg/m3 increase in ambient CO concentration.
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