Investigating the Role of Area Deprivation Index in Observed Differences in CT-Based Body Composition by Race

Differences in CT-based body composition (BC) have been observed by race. We sought to investigate whether indices reporting census block group-level disadvantage, Area Deprivation Index (ADI) and Social Vulnerability Index (SVI), age, gender, and clinical factors could explain race-based differences in BC.

The first abdominal CT examinations for patients in Durham County at a single institution in 2020 were analyzed using a fully automated and open-source deep learning BC analysis workflow to generate cross-sectional areas for skeletal muscle (SMA), subcutaneous fat (SFA), and visceral fat (VFA). Patient-level demographic and clinical data were gathered from the electronic health record. State ADI ranking and SVI values were linked to each patient. Univariable and multivariable models were created to assess the association of demographics, ADI, SVI, and other relevant clinical factors with SMA, SFA, and VFA.

In all, 5,311 patients (mean age, 57.4 years; 55.5% female; 46.5% Black; 39.5% White; 10.3% Hispanic) were included. At univariable analysis, race, ADI, SVI, gender, body mass index, weight, and height were significantly associated with all body compartments (SMA, SFA, and VFA, all P < .05). At multivariable analyses adjusted for patient characteristics and clinical comorbidities, race remained a significant predictor, whereas ADI did not. SVI was significant in a multivariable model with SMA.

The results of this retrospective study suggest that neighborhood indices are insufficient proxies for the socio-economic and environmental factors likely driving race-based differences in CT-based BC. Future research should analyze individual census tract variables and patient level data to better understand this relationship.