Tree Canopy and Self-Reported Mental and Physical Health in Urban Alabama: A Census Tract-Level Ecological Study - Simona N. Shirley; Bisakha Sen; Ye Liu; Rajarshi Dey; Elizabeth H. Baker; Laurie A. Malone (International Journal of Environmental Research and Public Health, 2026)
Background: Urban tree canopy, the proportion of a city’s land surface covered by trees, has been linked to physical and mental health benefits. This association is understudied in the U.S. Deep South despite its high burden of poor health and environmental inequity. This study examined associations between census tract-level tree canopy and self-reported mental and physical health in urban Alabama. Methods: We linked tract-level tree canopy estimates for Alabama metropolitan statistical areas with self-reported health and contextual measures from publicly available data, retaining tree canopy as the exposure of interest. A bootstrap-stabilized penalized variable-selection approach identified covariates from socioeconomic, demographic, housing, transportation, environmental, and health-related measures; we then mapped spatial distributions and fit spatial error multivariable models. Results: A higher proportion of land surface covered by trees was associated with a lower age-adjusted prevalence of adults who report 14 or more days during the past 30 days during which their mental health was not good (estimate: −0.0110; p < 0.001). A higher proportion of land surface covered by trees was also associated with a lower age-adjusted prevalence of adults who report 14 or more days during the past 30 days during which their physical health was not good (estimate: −0.0045; p = 0.002). Spatial error parameters persisted in both models, indicating residual spatial patterning not fully explained by canopy or the selected covariates. Conclusions: The findings extend the urban tree canopy literature to an understudied setting and demonstrate how publicly available tract-level data, combined with machine learning-guided covariate selection and spatial modeling, can generate actionable hypotheses for environmental health and equity research.