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Mixed Effects of Neighborhood Revitalization on Residents’ Cardiometabolic Health

      Introduction

      Despite the growing recognition of the importance of neighborhood conditions for cardiometabolic health, causal relationships have been difficult to establish owing to a reliance on cross-sectional designs and selection bias. This is the first natural experiment to examine the impact of neighborhood revitalization on cardiometabolic outcomes in residents from 2 predominantly African American neighborhoods, one of which has experienced significant revitalization (intervention), whereas the other has not (comparison).

      Methods

      The sample included 532 adults (95% African American, 80% female, mean age=58.9 years) from 2 sociodemographically similar, low-income neighborhoods in Pittsburgh, PA, with preintervention and postintervention measures (2016 and 2018) of BMI, diastolic and systolic blood pressure, HbA1c, and high-density lipoprotein cholesterol and covariates. Data were collected in 2016 and 2018 and analyzed in 2020.

      Results

      Difference-in-difference analyses showed significant improvement in high-density lipoprotein cholesterol in intervention residents relative to that in the comparison neighborhood (β=3.88, 95% CI=0.47, 7.29). There was also a significant difference-in-difference estimate in diastolic blood pressure (β=3.00, 95% CI=0.57, 5.43), with residents of the intervention neighborhood showing a greater increase in diastolic blood pressure than those in the comparison neighborhood. No statistically significant differences were found for other outcomes.

      Conclusions

      Investing in disadvantaged neighborhoods has been suggested as a strategy to reduce health disparities. Using a natural experiment, findings suggest that improving neighborhood conditions may have a mixed impact on certain aspects of cardiometabolic health. Findings underscore the importance of examining the upstream causes of health disparities using rigorous designs and longer follow-up periods that provide more powerful tests of causality.
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