Influence of social determinants of health on oncology care quality rankings

139 Background: Adjustment for social determinants of health (SDOH) when assessing provider care quality remains limited. The Oncology Care Model (OCM), for example, includes low-income status/dual eligibility (LIS/DE) as a part of the risk adjustment model for some quality measures, but does not account for any social risk variables in the hospice measure. No measures within the OCM account for additional social risk factors beyond LIS/DE such as patients’ race, rurality, and social deprivation. Additional SDOH adjustment could increase the accuracy of provider quality rankings and better align performance-based payments with true provider quality. Methods: North Carolina Medicare claims from 2015-2017 comprised the data for this study. The year 2015 was used to establish baseline covariates. Episodes were attributed to physician practices’ Tax Identification Number (TIN), lasted 6 months, and were divided into performance years beginning 1/1/2016 and 7/1/2016. Three measures were used: 1) all-cause hospital admissions; 2) all-cause emergency department visits or observation stays; and 3) admission to hospice for 3 days or more among patients who died. SDOH included patient-level race as well as county-level rurality and social deprivation, measured using the social deprivation index (SDI). TIN-level scores with and without expanded SDOH variables were divided into quintiles and compared descriptively as well as using weighted kappa statistics. Results: No SDOH were significantly associated with the hospitalization outcome (P = 0.118-0.944). For the ED measure, Black patients and rural patients were significantly more likely to have an ED visit or observation stay during an episode than white patients and urban patients (P < 0.0001). For the hospice measure, greater SDI values were associated with less hospice use (P < 0.05). Accordingly, including SDOH variables for ED visit/observation stay and hospice measures had a greater impact on TIN rankings than for the hospitalization measure (Table). Conclusions: Because quintile rankings in determine potential shared savings under models like the OCM, differences in rankings due to additional SDOH variables could have a meaningful impact on TIN-level revenue. Additional work is needed to expand the scope of patient-level SDOH variables used for risk adjustment and to explore differences across TINs which contribute to SDOH-sensitive changes in rankings.[Table: see text]