Association between short-term exposure to ambient air pollutants and the risk of hospital visits for acute upper respiratory tract infections among adults: a time-series study in Ningbo, China.

OBJECTIVES: Acute upper respiratory tract infections (AURTIs) are prevalent in the general population. However, studies on the association of short-term exposure to air pollution with the risk of hospital visits for AURTIs in adults are limited. This study aimed to explore the short-term exposure to air pollutants among Chinese adults living in Ningbo. METHODS: Quasi-Poisson time serious regressions with distributed lag non-linear models (DLNM) were applied to explore the association between ambient air pollution and AURTIs cases. Patients ≥ 18 years who visit three hospitals, being representative for urban, urban-rural junction and rural were included in this retrospective study. RESULTS: In total, 104,441 cases with AURTIs were enrolled in hospital during 2015-2019. The main results showed that particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), nitrogen dioxide (NO2) and nitrogen dioxide (SO2), were positively associated to hospital visits for AURTIs, except for nitrogen dioxide (O3), which was not statistically significant. The largest single-lag effect for PM2.5 at lag 8 days (RR = 1.02, 95%CI: 1.08-1.40), for NO2 at lag 13 days (RR = 1.03, 95%CI: 1.00-1.06) and for SO2 at lag 5 days (RR = 1.27, 95%CI: 1.08-1.48), respectively. In the stratified analysis, females, and young adults (18-60 years) were more vulnerable to PM2.5 and SO2 and the effect was greater in rural areas and urban-rural junction. CONCLUSIONS: Exposure to ambient air pollution was significantly associated with hospital visits for AURTIs. This study provides epidemiological evidence for policymakers to control better air quality and establish an enhanced system of air pollution alerts.

Modeling and Predicting Pulmonary Tuberculosis Incidence and Its Association with Air Pollution and Meteorological Factors Using an ARIMAX Model: An Ecological Study in Ningbo of China.

The autoregressive integrated moving average with exogenous regressors (ARIMAX) modeling studies of pulmonary tuberculosis (PTB) are still rare. This study aims to explore whether incorporating air pollution and meteorological factors can improve the performance of a time series model in predicting PTB. We collected the monthly incidence of PTB, records of six air pollutants and six meteorological factors in Ningbo of China from January 2015 to December 2019. Then, we constructed the ARIMA, univariate ARIMAX, and multivariate ARIMAX models. The ARIMAX model incorporated ambient factors, while the ARIMA model did not. After prewhitening, the cross-correlation analysis showed that PTB incidence was related to air pollution and meteorological factors with a lag effect. Air pollution and meteorological factors also had a correlation. We found that the multivariate ARIMAX model incorporating both the ozone with 0-month lag and the atmospheric pressure with 11-month lag had the best performance for predicting the incidence of PTB in 2019, with the lowest fitted mean absolute percentage error (MAPE) of 2.9097% and test MAPE of 9.2643%. However, ARIMAX has limited improvement in prediction accuracy compared with the ARIMA model. Our study also suggests the role of protecting the environment and reducing pollutants in controlling PTB and other infectious diseases.