Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Nov 8, 2021
Date Accepted: May 25, 2022
The Age-Sex Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study
ABSTRACT
Background:
Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with the rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents.
Objective:
This study aims to analyze the spatial association between vegetation cover and the age and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada.
Methods:
We employed remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an enhanced vegetation index was used to detect urban vegetation, and the Bayesian Poisson lognormal models were implemented to study the association between vegetation and the mental health disorder cases of males and females in the 0-19, 20-44, 45-64 and 65+ years age groups, after controlling for marginalization and unmeasured (latent) spatial and non-spatial covariates at the neighborhood level.
Results:
The results suggest that even after adjusting for marginalization, significant age and sex-specific effects of vegetation exist on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males from the age group 0-19 years (-7.009; 95% CI: -13.130, -0.980) and for both males (-4.544; 95% CI: -8.224, -0.895) and females (-3.513; 95% CI: -6.289, -0.681) aged 20-44 years. However, for older adults in the age-groups 45-64 and 65+, only the marginalization covariates were significantly associated with the mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions > 0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature.
Conclusions:
As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being.
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