A long history of discriminatory policies in the United States has created disparities in neighborhood resources that shape ethnoracial health inequities today. To quantify these differences, we organized publicly available data on forty-two variables at the census tract level within nine domains affected by structural racism: built environment, criminal justice, education, employment, housing, income and poverty, social cohesion, transportation, and wealth. Using data from multiple sources at several levels of geography, we developed scores in each domain, as well as a summary score that we call the Structural Racism Effect Index. We examined correlations with life expectancy and other measures of health for this index and other commonly used area-based indices. The Structural Racism Effect Index was more strongly associated with each health outcome than were the other indices. Its domain and summary scores can be used to describe differences in social risk factors, and they provide powerful new tools to guide policies and investments to advance health equity.

Importance: The first MassHealth Social Determinants of Health payment model boosted payments for groups with unstable housing and those living in socioeconomically stressed neighborhoods. Improvements were designed to address previously mispriced subgroups and promote equitable payments to MassHealth accountable care organizations (ACOs). Objective: To develop a model that ensures payments largely follow observed costs for members with complex health and/or social risks. Design, setting, and participants: This cross sectional study used administrative data for members of the Massachusetts Medicaid program MassHealth in 2016 or 2017. Participants included members who were eligible for MassHealth's managed care, aged 0 to 64 years, and enrolled for at least 183 days in 2017. A new total cost of care model was developed and its performance compared with 2 earlier models. All models were fit to 2017 data (most recent available) and validated on 2016 data. Analyses were begun in February 2019 and completed in January 2023. Exposures: Model 1 used age-sex categories, a diagnosis-based morbidity relative risk score (RRS), disability, serious mental illness, substance use disorder, housing problems, and neighborhood stress. Model 2 added an interaction for unstable housing with RRS. Model 3 added rurality and updated diagnosis-based RRS, medication-based RRS, and interactions between sociodemographic characteristics and morbidity. Main outcome and measures: Total 2017 annual cost was modeled and overall model performance (R2) and fair pricing of subgroups evaluated using observed-to-expected (O:E) ratios. Results: Among 1 323 424 members, mean (SD) age was 26.4 (17.9) years, 53.4% were female (46.6% male), and mean (SD) 2017 cost was $5862 ($15 417). The R2 for models 1, 2, and 3 was 52.1%, 51.5%, and 60.3%, respectively. Earlier models overestimated costs for members without behavioral health conditions (O:E ratios 0.94 and 0.93 for models 1 and 2, respectively) and underestimated costs for those with behavioral health conditions (O:E ratio >1.10); model 3 O:E ratios were near 1.00. Model 3 was better calibrated for members with housing problems, those with children, and those with high morbidity scores. It reduced underpayments to ACOs whose members had high medical and social complexity. Absolute and relative model performance were similar in 2016 data. Conclusions and relevance: In this cross-sectional study of data from Massachusetts Medicaid, careful modeling of social and medical risk improved model performance and mitigated underpayments to safety-net systems.

MassHealth’s current risk adjustment method is to measure risk as proportional to a DxCG relative risk score (RRS) calculated from age, sex and diagnoses reported on medical encounters. We call this the BASE model and use it as a tool for examining enrollee differences in health risk and utilization in the fee-for-service part of MassHealth (the PCC plan) and the managed care (MCO) part. We describe large differences in PCC and MCO program enrollees, with the PCC program attracting a much higher proportion of patients entitled to coverage due to disability and/or with severe behavioral health problems, developmental disabilities and complex medical needs. We find limited to negative evidence that the MCO plans are able to either save money or better manage the care of such patients. We also explore ways to better predict health care costs. Although we examine data from 2011 through 2014, we focused on model building in the 2013 data. In addition to the DxCG RRS, our recommended FULL model for predicting cost includes demographics, selected medical factors, and social determinants of health. Moving from the BASE to the FULL model improved total explanatory power (R2) for both PCC enrollees and for MCO enrollees. Applying the FULL model, as fit to the PCC 2013 data, to 2014 data yielded high R2s in the 2014 data (52.3% in PCC 2014 data and 56.6% in MCO 2014 data). These figures are increases in R2 of 25% and 4% respectively, from BASE model predictions. All variables used in the models are shown in Table 2. FULL model predictions make better predictions for subgroups. Children, for example, cost at least 20% more in both the PCC and MCO populations than was predicted by the BASE model (Table 3a, Female and Males in the 0-17 age strata). The FULL model modestly over- reimburses both the PCC and MCO plans for children. We offer advice about how to use all (or most) of the variables in the FULL model to finalize a risk-adjustment strategy for global payments in 2017.