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ABSTRACT
The main objective of this spatial epidemiologic research is to gain greater insights into the geographic dimension displayed by the different duration of mentally unhealthy days (MUDs) across U.S. counties. Mentally unhealthy days (MUDs) are studied in entire cross counties for year of 2014. 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. 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.
Acknowledgments
The authors would like to thank the anonymous reviewers for their comments and suggestions in advance to improve the article and is grateful for the constructive suggestions made by the reviewers. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The contents of this publication are solely the responsibility of the authors and do not necessarily represent CDC official views.
Notes
1. The environmental resilience in BRIC is somewhat limited in scope with some indicators related to absorptive capacities of coastal surges and freshwater flooding, and the remaining indicators representing efficiency with which a community uses natural resources. Our environmental factors that would impact mental health go beyond the limited scope. Moreover, in the first round of analyses, we included this indicator and results from CSGLM indicated that this indicator was positively correlated with the dependent variable, which ran counter to our hypothesis. The later spatial error model suggested that this was an insignificant factor. Due to both lack of theoretical grounding and statistical insignificance, we decided not to include it in our models.
2. We compared the composites of five community resilience indicators with the five covariates we identified as factors of mental health and did not find a substantial amount of overlap.