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Do Publicly Funded Neighborhood Investments Impact Individual-Level Health-Related Outcomes? A Longitudinal Study of Two Neighborhoods in Pittsburgh, PA from 2011 to 2018.

Rebecca B SmithMatthew D BairdGerald P HunterBonnie Ghosh-DastidarAndrea S RichardsonJonathan H CantorTamara Dubowitz
Published in: Housing policy debate (2024)
Research examining the relationship between a neighborhood's built-environment and resident health or health-related outcomes has largely either focused on static characteristics using a cross-sectional research design or focuses on the neighborhood in its entirety. Such an approach makes it difficult to understand how specific dynamic neighborhood characteristics are associated with individual well-being. In this analysis, we use longitudinal data from the Pittsburgh Research on Neighborhood Change and Health (PHRESH) studies to assess the relationship between publicly funded neighborhood investments occurring across seven years (2011-2018) on five health-related outcomes: food insecurity, stress, perceived neighborhood safety, neighborhood satisfaction, and dietary quality. We additionally utilize this dataset to determine whether the distance between an individual's place of residence and the investment, as measured at the neighborhood, 1 mile, and ½ mile level, effects the magnitude of associations. Using individual and year fixed effects models, we find that when measured at the neighborhood level, a one standard deviation increase in investments (about $130 million dollars) is associated with decreased food insecurity (-0.294 sd), increased safety (0.231 sd), and increased neighborhood satisfaction (0.201 sd) among adults who remain in the study for at least two waves of data collection. We also analyze specific investment types and find that commercial investments are largely driving the changes in food insecurity, safety, and neighborhood satisfaction, while business investments are correlated with the decrease in stress. We find no relationship between investments and dietary quality.
Keyphrases
  • physical activity
  • public health
  • healthcare
  • type diabetes
  • skeletal muscle
  • electronic health record
  • machine learning
  • insulin resistance
  • cross sectional
  • artificial intelligence