Urban-regional disparities in mental health signals in Australia during the COVID-19 pandemic: a study via Twitter data and machine learning models

This study establishes a novel empirical framework using machine learning techniques to measure the urban-regional disparity of the public's mental health signals in Australia during the pandemic, and to examine the interrelationships amongst mental health, demographic and socioeconomic profiles of neighbourhoods, health risks and healthcare access. Our results show that the public's mental health signals in capital cities were better than those in regional areas. The negative mental health signals in capital cities are associated with a lower level of income, more crowded living space, a lower level of healthcare availability and more difficulties in healthcare access.

Impact of long-term exposure to PM2.5 and temperature on coronavirus disease mortality: observed trends in France.

BACKGROUND: The outbreak of coronavirus disease (COVID-19) began in Wuhan, China in December 2019 and was declared a global pandemic on 11 March 2020. This study aimed to assess the effects of temperature and long-term exposure to air pollution on the COVID-19 mortality rate at the sub-national level in France. METHODS: This cross-sectional study considered different periods of the COVID-19 pandemic from May to December 2020. It included 96 departments (or NUTS 3) in mainland France. Data on long-term exposure to particulate matter (PM2.5), annual mean temperature, health services, health risk, and socio-spatial factors were used as covariates in negative binomial regression analysis to assess their influence on the COVID-19 mortality rate. All data were obtained from open-access sources. RESULTS: The cumulative COVID-19 mortality rate by department increased during the study period in metropolitan France-from 19.8/100,000 inhabitants (standard deviation (SD): 20.1) on 1 May 2020, to 65.4/100,000 inhabitants (SD: 39.4) on 31 December 2020. The rate was the highest in the departments where the annual average of long-term exposure to PM2.5 was high. The negative binomial regression models showed that a 1 µg/m3 increase in the annual average PM2.5 concentration was associated with a statistically significant increase in the COVID-19 mortality rate, corresponding to 24.4%, 25.8%, 26.4%, 26.7%, 27.1%, 25.8%, and 15.1% in May, June, July, August, September, October, and November, respectively. This association was no longer significant on 1 and 31 December 2020. The association between temperature and the COVID-19 mortality rate was only significant on 1 November, 1 December, and 31 December 2020. An increase of 1 °C in the average temperature was associated with a decrease in the COVID-19-mortality rate, corresponding to 9.7%, 13.3%, and 14.5% on 1 November, 1 December, and 31 December 2020, respectively. CONCLUSION: This study found significant associations between the COVID-19 mortality rate and long-term exposure to air pollution and temperature. However, these associations tended to decrease with the persistence of the pandemic and massive spread of the disease across the entire country.