“[Introduction] [..] population-based payment models, as in the Medicare Shared Savings Program or Medicare Advantage (MA) program, can facilitate the resource reallocations necessary to address health care disparities. Risk adjustment is the mechanism by which payment is allocated in these models.
Traditionally, risk adjustment has been conceived and executed purely as a predictive exercise. Regression is used to predict total annual per person spending as a function of demographic and clinical characteristics. A person’s predicted spending is converted to a risk score, which is applied to a base regional rate to determine the prospective payment or benchmark for that person. The more accurately spending is predicted (that is, the better the fit of the regression model), the more closely payment matches spending, thereby equalizing financial risk across providers or plans serving different populations and limiting incentives to attract favorable risks (patients with overpredicted spending) or avoid unfavorable risks (patients with underpredicted spending).
A commonly voiced concern with the transition to population-based payment is that risk adjustment will fail to account for historically marginalized groups’ presumed higher spending, thereby exacerbating health disparities. Framed as solving a prediction problem, social risk adjustment is thus often thought to achieve its goal by adding social risk factors as predictors to standard risk-adjustment models. Various studies have considered the incremental predictiveness of measurable social risk factors and made recommendations about which to add, but this line of research has focused largely on health outcomes or acute care use, as opposed to spending. Implicit in many calls for “improved” risk adjustment is an assumption that social risk factors predict higher spending, that the problem is their omission from predictive models, and that equity-promoting reallocations thus can be motivated by predictive accuracy.
However, attempting to support more equitable care by improving the predictive accuracy of risk adjustment is a fundamentally limited strategy because historical and current levels of spending (the target of prediction) are unlikely to be the desired levels of spending for those populations. People who experience social disadvantage may use less health care and have lower spending than others with the same clinical needs. For example, they may have less income to spend on health care, have less generous insurance coverage, be less aware of their health care needs because of lower educational attainment, face greater barriers to obtaining care (for example, travel and time constraints), or encounter additional barriers from other manifestations of structural or interpersonal racism. The inclusion of markers of social disadvantage in risk-adjustment models may therefore improve predictive accuracy but reduce payments for underserved populations relative to models that omit these markers.
Moreover, current spending for historically marginalized groups may be too low to support equitable care because providers serving those groups may have insufficient resources to improve the quality of care or provide the additional supportive services (for example, case management) necessary to mitigate the adverse impact of social determinants on health care use and effectiveness. Many supportive services are not reflected in fee-for-service spending.
Thus, even if the addition of some social factors to standard risk adjustment results in higher population-based payments for populations with a higher prevalence of those factors, the adjustments merely recover spending levels under fee-for-service that are believed to be too low to cover the costs of reducing disparities. To address disparities, payment must instead be set above current spending (or an accurate prediction thereof).
More generally, the objective of risk adjustment is not solely to predict observed spending accurately. Rather, it is to support the broader social goals of payment reform—to make the health care system more efficient and equitable. With that as the goal, current spending is inherently the wrong target for population-based payments. A reformed system should encourage the desired level and distribution of spending, not entrench the status quo.
That risk adjustment presents trade-offs between fit (the predictive accuracy of a model) and other objectives has been well described. Improving fit inherently weakens the power of incentives in a population-based payment system. As payments (or benchmarks) are adjusted for more markers of health care use (for example, diagnoses) or for use directly (for example, lagged indicators of hospitalization), risk-bearing entities save less from curbing unnecessary or avoidable care (reducing use reduces payment). In the extreme, adjusting for use of each service would achieve perfect fit but revert payment incentives to those of fee-for-service. To some extent, incentives encouraging risk selection (deficient fit) must be tolerated to allow the payment system to control spending.
Likewise, setting population-based payments (or benchmarks) above an accurate prediction of fee-for-service spending for historically disadvantaged groups worsens fit but advances the goal of health equity by mitigating resource disparities that contribute to health disparities and better aligning payment with health care needs (including unmet needs). Deliberately paying above current spending for those groups also protects socially vulnerable patients with underpredicted clinical needs against risk selection and creates incentives for competing providers or plans to attract the underserved with enhanced benefits or services. Several approaches have been developed to set population-based payments at desired rather than accurately predicted levels. Yet concerns about inadequate accounting for social determinants in population-based payment models remain largely framed around the predictive accuracy of standard risk-adjustment methods, often appealing to the promise of advanced prediction tools, such as machine learning and artificial intelligence, in proposed solutions.
To inform payment policy intended to support health equity, in this study we first added individual-level predictors of social disadvantage (race, ethnicity, and educational attainment) to the Hierarchical Condition Categories (HCC) model currently used to risk-adjust payments in Medicare Advantage and benchmarks in the Medicare Shared Savings Program. The results describe existing underpredictions or overpredictions by the HCC model of spending by race, ethnicity, and education. Second, we calculated the associated reallocations across groups achieved by moving from fee-for-service to a population-based payment system under current risk-adjusted methods, which omit these social characteristics as predictors. These reallocations equivalently describe the incentives for a risk-bearing entity to attract people with these characteristics. Third, we compared the HCC-adjusted differences in spending between groups with HCC-adjusted differences in self-reported health status, functional limitations, and access to care to gauge the extent to which reallocations under current risk adjustment are commensurate with addressing evident disparities. Fourth, we compared results when using area-level, instead of individual-level, versions of the same predictors. Finally, we considered the implications of our findings for the targeting and implementation of population-based payment adjustments that depart from predictive accuracy to support health equity.
[Study Data and Methods] We analyzed Medicare claims from the period 2012–17 for 20 percent annual random samples of fee-for-service beneficiaries and for respondents to the 2012–17 fee-for-service Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys. [..]
We calculated total annual Medicare spending per beneficiary by summing payments across all services reimbursed by Part A or B. From survey data for CAHPS respondents, we assessed indicators of compromised health or access to care: fair or poor general health status; fair or poor mental health; difficulty with one or more activities of daily living (ADLs); and difficulty accessing routine, urgent, or specialty care in a timely fashion, as defined by a report of never or sometimes receiving care as soon as needed (versus usually or always). [..]
[Limitations] [..] First, we relied on fee-for-service Medicare claims. Although our estimates are informative for understanding how the HCC model allocates payment and creates selection incentives across groups of MA enrollees, the estimates would differ somewhat if they were based on MA data. For example, spending for beneficiaries with less education may be lower in traditional fee-for-service Medicare than in Medicare Advantage, in part because beneficiaries with less education may be less likely to have supplemental insurance in traditional Medicare. [..]
Second, although our analysis showed the extent to which the current (HCC) risk-adjustment system over- or underpredicts fee-for-service spending for historically marginalized groups and communities, it could not determine the socially optimal payment level. That depends on social values, the extent of underspending for the underserved, and the extent to which payment increases would be passed through by providers or plans to populations in need. [..]
Third, the social characteristics we examined were limited to race, ethnicity, and education, selected because they could be ascertained at both individual and block group levels. These are powerful predictors of disadvantage mediated by a range of mechanisms and strongly correlated with other markers; conclusions were similar, for example, in analyses using the Area Deprivation Index. [..]
Finally, although our analysis can inform payment reallocations to support health equity, it did not assess the extent to which reallocated resources reached specific populations, as intended, to improve their care.
[Results] After adjustment for age, sex, enrollment segment, HCCs, and county, total annual Medicare spending per beneficiary was $574 lower for Black beneficiaries and $1,462 lower for Hispanic beneficiaries than for White beneficiaries in the 20 percent samples. These estimates suggest substantial overprediction of fee-for-service spending for Black and Hispanic beneficiaries by the current or standard (HCC) model, which does not include race or ethnicity. In turn, population-based payments set by applying HCC risk scores to a county base rate would redistribute payment away from White beneficiaries (−$198 per beneficiary) toward Black (+$376) and Hispanic (+$1,264) beneficiaries. 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 ACO would have a strong incentive to attract Hispanic and Black residents of that county. Conversely, adding race and ethnicity to the HCC model would lower payments for Black and Hispanic beneficiaries (but would improve the predictive accuracy [fit] of the model).
[..] findings for area-level predictors suggest that moving from fee-for-service to population-based payment under current risk adjustment would result in minimal to modest reallocations toward communities with higher proportions of Black, Hispanic, or less-educated residents and would thus give ACOs (or, by extension, MA plans) minimally to modestly stronger incentives to enter or expand their provider networks in those communities relative to other communities. [..]
[Discussion] In this study of community-dwelling fee-for-service Medicare beneficiaries, Medicare spending was similar or substantially lower for groups at higher risk of experiencing social disadvantage after adjustment for variables in the current HCC risk-adjustment model. That HCC-adjusted spending was not higher for these groups is consistent with the findings of other studies, but it may run counter to expectations. For example, some may extrapolate from evidence of worse risk-adjusted health outcomes for the same groups that social predictors should also predict higher spending. Our findings suggest that adding social factors, particularly race and ethnicity, to the HCC model can entrench health disparities instead of reducing them, by lowering population-based payments to more accurately predicted levels of spending. [..]
HCC-adjusted population payments would increase per beneficiary provider payments for Black and Hispanic beneficiaries by $376–$1,264 (approximately 4–14 percent) above current fee-for-service spending. Although greater increases may be necessary to fully correct underuse and other quality deficits, these are sizable redistributions that require only continued movement away from fee-for-service toward population-based payments. In the case of ACO models, this requires moving away from benchmarks that incorporate historical spending, which reflect underspending for Black and Hispanic beneficiaries, toward a system of risk-adjusted regional rates. It is arguably fortuitous that omission of race and ethnicity from the HCC model results in meaningful implicit reallocations insofar as data on race and ethnicity are imperfect; progress need not await better data. Moreover, if more explicit adjustments were needed, they could face legal challenges [..].
In contrast, risk-adjusted population-based payments using the current model would result in minimal reallocations toward beneficiaries with less than a high school diploma. Thus, additional payment reallocations would be needed to better resource efforts to address education-related disparities, which were larger than racial and ethnic disparities in health and functional status.
[..] because the optimal distribution of payment cannot be determined from a predictive exercise, the process must be iterative. As such, it will be important to monitor disparities to understand the impact of initial reallocations and inform subsequent adjustments of population-based payments.”
Full article, JM McWilliams, G Weinreb, L Ding, CD Ndumele and J Wallace, Health Affairs, 2023.1.9