Understanding the differential impact of vegetation measures on the association between vegetation and mental health disorders
Bibliographic record
Abstract
Background: Considerable debate exists as to whether vegetation can help achieve better mental health outcomes. Although few studies have attempted to evaluate the health effects of vegetation, a spatial study, which has analyzed the effect of different vegetation measures on the detection of a significant association between vegetation and mental health disorders, is still missing. Furthermore, based on the available literature, there is an absence of studies that have analyzed the age and sex-specific effects of surrounding vegetation on mental health disorders, while adjusting for the overdispersion, spatial autocorrelation and unmeasured covariates in the models. \n \nObjective: The objective of this study is to understand the differential impact of vegetation measures on the association between vegetation and various types of mental health disorders. In doing so, the study also attempted to understand whether there are any age and sex-specific effects of vegetation on mental health disorder cases. \n \nMethods: Remote sensing and machine learning techniques were employed to generate three vegetation indices and one area-based vegetation measure from the Landsat-8 satellite images. The satellite-based indices comprised of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and the soil-adjusted vegetation index (SAVI). The area-based vegetation measure was developed from a Land Use/Land Cover (LULC) model using the Random Forest ensemble classifier. The conventionally used vegetation data was extracted from the Toronto Open Data portal and compared with the variables created from the satellite images. \n \nThe dataset comprising psychotic, non-psychotic, substance use and family, social and occupational-related disorder cases were retrieved from the Ontario Community Health Profiles Partnership database. The dataset also contained the combined mental health disorder cases, which is a total of the four types of mental health disorders. \n \nThe association between vegetation and psychotic and non-psychotic disorders were analyzed using the Poisson lognormal models under a Bayesian framework. Based on the results from the Bayesian models, a single vegetation measure was selected and the association of the vegetation with the combined mental health disorders for males and females in the age groups, 0-19, 20-44, 45-64 and 65+ were analyzed using Bayesian spatial modeling. \n \nResults: Results suggested substantial effects of the type of vegetation measure used to analyze the association between vegetation and mental health disorder cases. Only the vegetation indices, which could capture both the areal extent and health of the vegetation cover, could detect a significant association with the mental health disorder cases. Specifically, EVI and SAVI, which were constructed after adjusting for different urban and environmental disturbances, were able to detect significant and negative associations with the psychotic and non-psychotic disorder cases. \n \nFurthermore, the findings of this study suggested significant age and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. The combined mental health disorder cases for males from the age group 0-19 years and for both males and females from the age group 20-44 years were found to be negatively associated with the vegetation cover. For older adults in the age-groups 45-64 and 65+, only the socioeconomic covariates were found to be significantly associated with the combined mental health disorder cases. \n \nFor each of the Bayesian models analyzed in this study, a substantial influence of the spatially structured and unmeasured covariates was detected. \n \nConclusions: Epidemiological studies must consider both the quantity and quality of people’s exposure to surrounding vegetation cover. Vegetation measures that capture both the areal extent and the health of the surrounding vegetation can help detect the actual relationship between vegetation and the mental health conditions of the people in an area. The study setting (urban, peri-urban and rural) can have a notable influence on the detection of different types of vegetation cover and should always be addressed while selecting a vegetation measure for epidemiological studies. As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, policymakers should consider incorporating more greenspaces and vegetation-covered areas in urban areas, to reduce the future burden of mental health disorders in Canada. The findings of this study can provide critical guidelines to public health researches aiming to understand the exposure of the population to surrounding greenness. The relative risk maps can help devise targeted intervention strategies to reduce mental health burdens in the Toronto area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".