ArticleInternational journal of environmental research and public health2021
Understanding the Differential Impact of Vegetation Measures on Modeling the Association between Vegetation and Psychotic and Non-Psychotic Disorders in Toronto, Canada.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Current methodologies of greenspace exposure and mental health research-a scoping review.Frontiers in public health · 2024Article
- Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes.Sensors (Basel, Switzerland) · 2023Article
- Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study.JMIR public health and surveillance · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Considerable debate exists on whether exposure to vegetation cover is associated with better mental health outcomes. Past studies could not accurately capture people's exposure to surrounding vegetation and heavily relied on non-spatial models, where the spatial autocorrelation and latent covariates could not be adjusted. Therefore, a suite of five different vegetation measures was used to separately analyze the association between vegetation cover and the number of psychotic and non-psychotic disorder cases in the neighborhoods of Toronto, Canada. Three satellite-based and two area-based vegetation measures were used to analyze these associations using Poisson lognormal models under a Bayesian framework. Healthy vegetation cover was found to be negatively associated with both psychotic and non-psychotic disorders. Results suggest that the satellite-based indices, which can measure both the density and health of vegetation cover and are also adjusted for urban and environmental perturbations, could be better alternatives to simple ratio- and area-based measures for understanding the effect of vegetation on mental health. A strong dominance of spatially structured latent covariates was found in the models, highlighting the importance of adopting a spatial approach. This study can provide critical guidelines for selecting appropriate vegetation measures and developing spatial models for future population-based epidemiological research.
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