A new paper I co-authored with Dr. Hoehun Ha is published in the International Journal of Environmental Health Research. In this paper, we examined potential factors that could affect community-level mental health across the United States. Here is the the summary:
"Mental health studies have underscored the hazardous conditions of each phase of life, from youth and pre-adulthood through adulthood in the United States. This situation calls for increased public awareness of the mental health issue and better understanding of the significant factors associated with mental health hazard. The main objective of this spatial epidemiological research is to gain greater insights into the geographic dimension displayed by the different duration of mentally unhealthy days (MUDs) across U.S. counties. Using Behavioural Risk Factor Surveillance System (BRFSS) data in 2014, we examine main factors of mental health hazard including health behaviour, clinical care, socioeconomic and physical environment, demographic, community resilience, and extreme climatic conditions. In this study, we take complex design factors such as clustering, stratification and sample weight in the BRFSS data into account by using Complex Samples General Linear Model (CSGLM). Then, spatial regression models, spatial lag and error models, are applied to examine spatial dependencies and heteroscedasticity. Econometric analysis underscores that all categories of air pollution, community resilience, and sunlight variables tested are significant push factors of mentally unhealthy days (MUDs) duration. Results of the geographic analyses indicate that counties with lower air pollution (PM2.5), higher community resilience (social, economic, infrastructure, and institutional resilience), and higher sunlight exposure had significantly lower average number of MUDs reported in the past 30 days. These findings suggest that policy makers should take air pollution, community resilience, and sunlight exposure into account when designing environmental and health policies and allocating resources to more effectively manage mental health problems."
The following figure is from this paper (Ha and Shao 2019)
"Mental health studies have underscored the hazardous conditions of each phase of life, from youth and pre-adulthood through adulthood in the United States. This situation calls for increased public awareness of the mental health issue and better understanding of the significant factors associated with mental health hazard. The main objective of this spatial epidemiological research is to gain greater insights into the geographic dimension displayed by the different duration of mentally unhealthy days (MUDs) across U.S. counties. Using Behavioural Risk Factor Surveillance System (BRFSS) data in 2014, we examine main factors of mental health hazard including health behaviour, clinical care, socioeconomic and physical environment, demographic, community resilience, and extreme climatic conditions. In this study, we take complex design factors such as clustering, stratification and sample weight in the BRFSS data into account by using Complex Samples General Linear Model (CSGLM). Then, spatial regression models, spatial lag and error models, are applied to examine spatial dependencies and heteroscedasticity. Econometric analysis underscores that all categories of air pollution, community resilience, and sunlight variables tested are significant push factors of mentally unhealthy days (MUDs) duration. Results of the geographic analyses indicate that counties with lower air pollution (PM2.5), higher community resilience (social, economic, infrastructure, and institutional resilience), and higher sunlight exposure had significantly lower average number of MUDs reported in the past 30 days. These findings suggest that policy makers should take air pollution, community resilience, and sunlight exposure into account when designing environmental and health policies and allocating resources to more effectively manage mental health problems."
The following figure is from this paper (Ha and Shao 2019)
Figure 1. County-level annual average number of age-adjusted mentally unhealthy days (MUDs) reported in the past 30 days, 2014 (source: Ha and Shao, forthcoming)
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