Credit scores reflect financial history and economic resources, and may directly impact disease risk or frame risk-related behaviors. Credit scores may be useful for summarizing the resources individuals and communities bring to obesity prevention and management, but no studies have examined how credit scores reflect neighborhood characteristics and the resulting risk of obesity. Our objective was to 1) describe neighborhood-level characteristics associated with neighborhood-level credit score and 2) evaluate the association between credit score and body mass index (BMI).


A cross-sectional, random digit dialing telephone survey was conducted in 2015 to collect sociodemographic and health outcome data from residents of Philadelphia, PA. One credit bureau’s projection of average household credit score in 2015 was mapped to the zipcode+4 in which participants lived, as were census-derived neighborhood characteristics. Neighborhoods were compared by FICO credit score categories (Excellent/Very Good, Good/Fair, and Poor/Very Bad) using Kruskall-Wallis tests. Multilevel regression models estimated the association of neighborhood-level credit score with individual BMI, adjusting for participant age, race, income, and education and neighborhood income, housing value, and age.


There were 2038 participants for whom zipcode+4 could be mapped and BMI data were available. Mean BMI was 29 (SD= 6.8), and average credit score was 671 (SD= 58). Compared to neighborhoods with poor/very bad credit (n=833), those with very good/excellent credit (n= 219) and with good/fair credit (n=986) had older populations (p<0.0001), higher income (p=0.0001), and higher home value (p=0.0001). A 50-point increase in neighborhood-level credit score was associated with a decrease in BMI of 0.73 [95% CI: -1.2, -0.28], independent of individual and neighborhood socioeconomic factors.


Neighborhood-level credit score may be another method to classify neighborhoods with heightened risk of obesity.