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Mixed Effects of Neighborhood Revitalization on Residents' Cardiometabolic Health
Wendy M Troxel1, Andy Bogart2, Stephanie Brooks Holliday3
1Behavior and Policy Sciences Division, RAND Corporation, Pittsburgh, Pennsylvania.
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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