Associations between historical redlining and BMI: Potential indirect pathways through neighborhood socioeconomic and environment factors and their associated lifestyle behaviors in CARDIA

It is unknown whether living in neighborhoods historically rated as "high-risk" mortgage by lenders (i.e., "redlining") defined by maps of the Home Owners' Loan Corporation (HOLC) neighborhood ratings may associate with obesity through neighborhood factors and health behaviors, or how such associations might differ by race and gender. We used data from the 1985-86 Coronary Artery Risk Development in Young Adults (CARDIA) study to test whether retrospective cohort associations between redlining and body mass index (BMI) were mediated by paths from census-derived social and economic neighborhood factors, food availability, physical activity (PA) resources to diet and physical activity behaviors. We found that the HOLC ratings (which reflected institutionalized racial sentiment of the time) were associated with higher levels of neighborhood socioeconomic deprivation within all racial and gender groups. For White adults and men, historically redlined neighborhoods were indirectly associated with higher BMI through neighborhood socioeconomic deprivation and lower diet quality. An indirect pathway connecting redlining to BMI was also identified for Black adults, but higher quality diet correlated with higher BMI. While women's dietary quality was not associated with BMI, redlining was directly associated with neighborhood socioeconomic deprivation which was directly associated with higher BMI and lower dietary quality, separately. Food and PA resource availability were inconsistently associated with HOLC ratings. Overall, historically redlined neighborhoods remained socioeconomically deprived 50 years later, which may have contributed to lower diet quality for all race and gender groups. The historic intertwining of race and value, as illustrated by the redlining maps, may have influenced disparities in BMI across race and gender groups. This suggests complex interactions among redlining, race, gender, and BMI.