Understanding the Differential Impact of Vegetation Measures on Modeling the Association between Vegetation and Psychotic and Non-Psychotic Disorders in Toronto, Canada

Abu Yousuf Md Abdullah, Jane Law, Zahid A. Butt and Christopher M. Perlman
Additional contact information
Abu Yousuf Md Abdullah: School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Jane Law: School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Zahid A. Butt: School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Christopher M. Perlman: School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada

IJERPH, 2021, vol. 18, issue 9, 1-25

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.

Keywords: vegetation; mental health; spatial modeling; psychotic; non-psychotic; enhanced vegetation index; normalized difference vegetation index; soil-adjusted vegetation index; Bayesian; random forest (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/9/4713/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/9/4713/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:9:p:4713-:d:545575

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().