Back

Assessing the Complex Relationship Between Urban Tree Canopy and Self-Reported Health in a Deep South State: The Role of Socioeconomic Factors

Shirley, S.; Dey, R.; Sen, B.; Baker, E.; Malone, L.

2025-11-14 public and global health
10.1101/2025.11.12.25340089 medRxiv
Show abstract

AbstractUrban tree canopy, or the proportion of city land surface covered by trees as viewed from above, has been studied as an ecological resource with various physical and mental health benefits. At the same time, disparities in both the availability and quality of trees by neighborhood disadvantage have been noted, and the relationship between tree canopy and health is sometimes ambiguous. This study employs observational, secondary data to study the associations between urban tree canopy and self-reported mental and physical health in Alabama, a state with a history of redlining that contributed to neighborhood disadvantage and environmental degradation. Multivariate linear regression models were estimated to explore the relationship between self-reported poor mental and physical health and tree canopy aggregated at the census-tract level in Alabama, USA. A stepwise modeling framework initially revealed a negative correlation between poor mental health and less extensive tree canopy. However, this relationship was suppressed when lack of insurance, which had a strong correlation with poverty, was included. It is likely that in areas of higher poverty, more extensive tree canopy may even exacerbate poor health. It seems likely that, for tree canopy to benefit health and well-being, it is important to consider the quality of the neighborhood trees and to address concerns the community may have regarding maintenance and safety issues around neighborhood trees.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.