Adding Social Factors to Risk Adjustment Not Enough to Reach Health Equity, Paper Says

Population-based payment models can help promote health equity, but only if risk adjustment supports increased resource allocation to historically marginalized groups. A study published in Health Affairs suggests that payment adjustments factoring in more than predictive accuracy alone are needed to reduce disparities in population-based payment models.

Unlike fee-for-service (FFS) systems, which distribute spending based on service use, population-based payment systems such as the Medicare Shared Savings Program or Medicare Advantage program distribute spending based on population characteristics. In these models, payment allocation is determined by risk adjustment, which has historically been strictly predictive in nature. A regression model predicts annual per-person spending based on demographic and clinical characteristics, with more accurate predictions equalizing the financial risks for providers or plans working with distinct populations.

Improving health equity through population-based payment plans likely requires more than increasing predictive accuracy alone, however. In marginalized populations, historical and current spending levels most likely do not reflect optimal levels of spending, as those who experience social disadvantages may use less resources than others with the same health care needs. Current spending in these populations may also be too low to support improvements in disparities because providers may not have the resources needed to improve care quality, according to the study published Monday, which aimed to guide the reallocation of funds to advance health equity by analyzing current risk adjustments for the community-dwelling Medicare population.

Adding social factors to standard risk adjustment may still not cover the costs required to reduce health care disparities, the researchers found. Instead, setting payment above current spending could support health care system efficiency and health equity more effectively than accurate spending prediction in population-based models.

Researchers assessed Medicare claims for 20% annual random samples of FFS beneficiaries between 2012 and 2017 and for individuals who responded to FFS Medicare Consumer Assessment of Healthcare Providers and Systems surveys between 2012 and 2017. Both populations had similar sociodemographic characteristics.

In the Medicare 20% sample, the total annual spending per beneficiary was $574 lower for Black beneficiaries and $1462 lower for Hispanic beneficiaries compared with White beneficiaries after adjusting for age, sex, enrollment segment, Hierarchical Condition Categories (HCCs), and county. This suggests that FFS spending for Black and Hispanic beneficiaries is significantly overpredicted in the current standard HCC model that does not include race or ethnicity in spending prediction.

Population-based payments determined by applying HCC risk scores to a county base rate would move $198 per beneficiary away from White individuals, redistributing payment by adding $376 toward Black beneficiaries and $1264 to Hispanic beneficiaries. Although adding race and ethnicity to the HCC model would improve its predictive accuracy, it would lower the payments that accountable care organizations (ACOs) receive for Black and Hispanic beneficiaries compared with the standard model.

“These payment reallocations equivalently quantify the relative selection incentives that an ACO receiving such risk-adjusted population-based payments would face, on average, in a given county,” the authors wrote. There would be a stronger incentive to provide care for Hispanic and Black residents in the HCC model that does not factor in race and ethnicity, although the predictive accuracy would be lower in that model.

While HCC-adjusted spending was lower in Black and Hispanic beneficiaries compared with White beneficiaries, Black and Hispanic individuals reported worse overall health, mental health, and difficulty with daily activities than White beneficiaries.

Taken together, the findings suggest that incorporating race, ethnicity, and other social factors into the standard HCC model improves predictive accuracy but may not have the desired effect of improving health equity. Rather, they may solidify current disparities by lowering population-based payments and therefore allocating less resources to marginalized populations.

“By departing from predictive accuracy as the singular goal of risk adjustment, a population-based payment system that set payments above current levels of FFS spending for groups with greater deficits in health care access or quality (and set payments below current spending for others) would create incentives for providers or plans to attract those groups and help address resource disparities that contribute to health care disparities,” the authors concluded.

Reference

McWilliams JM, Weinreb G, Ding L, Ndumele CD, Wallace J. Risk adjustment and promoting health equity in population-based payment: concepts and evidence. Health Aff (Millwood). 2023;42(1):105-114. doi:10.1377/hlthaff.2022.00916