Long-term exposure to ambient air pollution and incidence of depression: A population-based cohort study in China.

BACKGROUND: Long-term exposure to ambient air pollution was linked to depression incidence, although the results were limited and inconsistent. OBJECTIVES: To investigate the effects of long-term air pollution exposure on depression risk prospectively in China. METHODS: The present study used data from Yinzhou Cohort on adults without depression at baseline, and followed up until April 2020. Two-year moving average concentrations of particulate matter with a diameter ≤ 2.5 µm (PM2.5), ≤10 µm (PM10) and nitrogen dioxide (NO2) were measured using land-use regression (LUR) models for each participant. Depression cases were ascertained using the Health Information System (HIS) of the local health administration by linking the unique identifiers. We conducted Cox regression models with time-varying exposures to estimate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of depression with each pollutant, after adjusting for a sequence of individual covariates as demographic characteristics, lifestyles, and comorbidity. Besides, physical activity, baseline potential depressive symptoms, cancer status, COVID-19 pandemic, different outcome definitions and air pollution exposure windows were considered in sensitivity analyses. RESULTS: Among the 30,712 adults with a mean age of 62.22 ± 11.25, 1024 incident depression cases were identified over totaling 98,619 person-years of observation. Interquartile range increments of the air pollutants were associated with increased risks of depression, and the corresponding HRs were 1.59 (95%CI: 1.46, 1.72) for PM2.5, 1.49 (95%CI: 1.35, 1.64) for PM10 and 1.58 (95%CI: 1.42, 1.77) for NO2. Subgroup analyses suggested that participants without taking any protective measures towards air pollution were more susceptible. The results remained robust in all sensitivity analyses. CONCLUSIONS: Long-term exposure to ambient air pollution was identified as a risk factor for depression onset. Strategies to reduce air pollution are necessary to decrease the disease burden of depression.

Effect of COVID-19 on hospital visits in Ningbo, China: an interrupted time-series analysis.

OBJECTIVE: Unprecedented rigorous public health measures were implemented during the coronavirus disease 2019 (COVID-19) epidemic, but it is still unclear how the intervention influenced hospital visits for different types of diseases. We aimed to evaluate the impact of the intervention on hospital visits in Yinzhou District, Ningbo, Zhejiang province, China. METHODS: We conducted an interrupted time-series analysis from 1 January 2017 to 6 September 2020 based on the Yinzhou Health Information System in Ningbo, Zhejiang province. The beginning of the intervention was on 23 January 2020, and thus, there were 160 weeks before the intervention and 32 weeks after the implementation of the intervention. Level changes between expected and observed hospital visits in the post-intervention period were estimated using quasi-Poisson regression models. RESULTS: Compared with the expected level, there was an estimated decrease of -22.60% (95% confidence interval (CI): -27.53%, -17.36%) in the observed total hospital visits following the intervention. Observed hospital visits for diseases of the respiratory system were found to be decreased dramatically (-62.25%; 95% CI: -65.62%, -58.60%). However, observed hospital visits for certain diseases were estimated to be increased, including diseases of the nervous system (+11.17%; 95% CI: +3.21%, +19.74%); diseases of pregnancy, childbirth and the puerperium (+27.01%; 95% CI: +17.89%, +36.85%); certain conditions originating in the perinatal period (+45.05%; 95% CI: +30.24%, +61.56%); and congenital malformation deformations and chromosomal abnormalities (+35.50%; 95% CI: +21.24%, +51.45%). CONCLUSIONS: Our findings provided scientific evidence that cause-specific hospital visits evolve differently following the intervention during the COVID-19 epidemic.

Associations between air pollution and COVID-19 epidemic during quarantine period in China.

The coronavirus disease (COVID-19) has become a global public health threaten. A series of strict prevention and control measures were implemented in China, contributing to the improvement of air quality. In this study, we described the trend of air pollutant concentrations and the incidence of COVID-19 during the epidemic and applied generalized additive models (GAMs) to assess the association between short-term exposure to air pollution and daily confirmed cases of COVID-19 in 235 Chinese cities. Disease progression based on both onset and report dates as well as control measures as potential confounding were considered in the analyses. We found that stringent prevention and control measures intending to mitigate the spread of COVID-19, contributed to a significant decline in the concentrations of air pollutants except ozone (O3). Significant positive associations of short-term exposure to air pollutants, including particulate matter with diameters ≤2.5 µm (PM2.5), particulate matter with diameters ≤10 µm (PM10), and nitrogen dioxide (NO2) with daily new confirmed cases were observed during the epidemic. Per interquartile range (IQR) increase in PM2.5 (lag0-15), PM10 (lag0-15), and NO2 (lag0-20) were associated with a 7% [95% confidence interval (CI): (4-9)], 6% [95% CI: (3-8)], and 19% [95% CI: (13-24)] increase in the counts of daily onset cases, respectively. Our results suggest that there is a statistically significant association between ambient air pollution and the spread of COVID-19. Thus, the quarantine measures can not only cut off the transmission of virus, but also retard the spread by improving ambient air quality, which might provide implications for the prevention and control of COVID-19.