Using a novel strategy to identify the clustered regions of associations between short-term exposure to temperature and mortality and evaluate the inequality of heat- and cold-attributable burdens: A case study in the Sichuan Basin, China

Association between stock market volatility and severe mental disorders: a multi-city time-series study

Background

Stock market volatility was commonly considered as a psychological stressor. However, the association between stock market volatility and severe mental disorders (SMD) has not been investigated.

Methods

Daily numbers of SMD hospital admissions and Shanghai Stock Exchange Composite (SSEC) Index in 7 cities in southwestern China from 2020 to 2023 were collected. A two-stage time-series analysis was conducted to reveal the association between stock volatility and the risk of hospital admission for SMD. Stratified analyses were performed by age, gender, and ICD-10 codes to explore potential high-risk groups.

Results

The association between the SSEC percentage change, SSEC closing price and SMD hospital admissions both exhibit a U-shaped curve. A 1 % decrease in SSEC is associated with a 7.2 % (95 %CI: 4.1 %-10.4 %) increase in the SMD admission, while a 1 % increase in SSEC is associated with a 2.6 % (95 %CI: 0.1 %-5.2 %) increase in the SMD admissions. With 3400 points as the dividing point, every 10-point increase in SSEC closing price is associated with a 9 % (95 %CI: 3.6 %-14.7 %) increase in the SMD hospital admissions, while each 10-point decrease is associated with a 4.4 % (95 %CI: 0.6 %-8.3 %) increase in the SMD hospital admissions. Furthermore, these associations tended to be stronger in the female and non-schizophrenia patients.

Conclusion

These associations indicate that both rise and fall in stock prices increase the risk of SMD. This finding suggests that it is an effective way to take the stock speculation behavior into consideration when managing and treating the SMD patients.

Exploring spatiotemporal pattern in the association between short-term exposure to fine particulate matter and COVID-19 incidence in the continental United States: a Leroux-conditional-autoregression-based strategy

Background

Numerous studies have demonstrated that fine particulate matter (PM2.5) is adversely associated with COVID-19 incidence. However, few studies have explored the spatiotemporal heterogeneity in this association, which is critical for developing cost-effective pollution-related policies for a specific location and epidemic stage, as well as, understanding the temporal change of association between PM2.5 and an emerging infectious disease like COVID-19.

Methods

The outcome was state-level daily COVID-19 cases in 49 native United States between April 1, 2020 and December 31, 2021. The exposure variable was the moving average of PM2.5 with a lag range of 0-14 days. A latest proposed strategy was used to investigate the spatial distribution of PM2.5-COVID-19 association in state level. First, generalized additive models were independently constructed for each state to obtain the rough association estimations, which then were smoothed using a Leroux-prior-based conditional autoregression. Finally, a modified time-varying approach was used to analyze the temporal change of association and explore the potential causes spatiotemporal heterogeneity.

Results

In all states, a positive association between PM2.5 and COVID-19 incidence was observed. Nearly one-third of these states, mainly located in the northeastern and middle-northern United States, exhibited statistically significant. On average, a 1 μg/m3 increase in PM2.5 concentration led to an increase in COVID-19 incidence by 0.92% (95%CI: 0.63-1.23%). A U-shaped temporal change of association was examined, with the strongest association occurring in the end of 2021 and the weakest association occurring in September 1, 2020 and July 1, 2021. Vaccination rate was identified as a significant cause for the association heterogeneity, with a stronger association occurring at a higher vaccination rate.

Conclusion

Short-term exposure to PM2.5 and COVID-19 incidence presented positive association in the United States, which exhibited a significant spatiotemporal heterogeneity with strong association in the eastern and middle regions and with a U-shaped temporal change.