Spatially varying associations between a small-area index of livability and mental health outcomes in New York City: A geographically weighted Poisson regression approach.

Area-level livability indices are commonly used tools for policymakers to evaluate and improve built and social environments, but their relationship to mental health is not well-understood. In this study, we used geographically weighted Poisson regression (GWPR) to measure spatially varying associations between neighborhood livability and poor mental health across New York City (NYC) census tracts. A small-area livability index was previously constructed with publicly available data for 2101 census tracts in NYC using principal components analysis applied to 38 variables across six domains: socioeconomic, housing, resources, environment, safety, and accessibility & transportation. The outcome was cases of self-reported mental health not good for ≥14 days in past month among adults (CDC 500 Cities - 2016). GWPR models measured spatially varying associations of poor mental health cases, offset by adult population size, with the overall livability score and, separately, with each of the domain sub-scores. Estimated risk ratios were mapped to identify areas in NYC where livability domains acted as protective/risk factors for poor mental health, depending on the location. Socioeconomic livability was a geographically consistent protective factor (negative associations across all census tracts), while the overall livability score and other domain sub-scores showed geographically varying associations with poor mental health (associations ranging from negative to positive). Geographic areas showing protective associations between livability and poor mental health highlight opportunities to improve mental health outcomes through targeted domain-specific interventions. In areas where elements of livability were positively associated with poor mental health, tackling mental health disparities may require more multifaceted approaches.