With the Patient Protection and Affordable Care Act’s (ACA’s) ninth open enrollment period (OEP) set to launch in less than a month, the ACA Marketplaces are seeing record enrollment numbers with more generous subsidies, new carrier competition, and a relatively stable rating environment. At the same time, there is uncertainty with the trajectory of the COVID-19 pandemic and medical costs trending upward as the economy recovers, albeit at an uneven pace. These trends have made for a challenging rate review process in the 47 states plus the District of Columbia (D.C.) that conduct their own ACA rate reviews of carrier-proposed rates using federal review standards. State announcements of 2022 rates have trickled out at a slower pace than in prior years, and it is likely that many states will not publish their approved rates until the beginning of open enrollment. As always, state rate results vary widely and, even within states, there often are substantial variations among carriers and across different regions in geographically diverse states. With these caveats, this expert perspective highlights some observations about the factors that are impacting rate changes this year and the kind of variations that exist among states.
Risk Adjustment Based on Social Factors: State Approaches to Filling Data Gaps
Colin Planalp, State Health Access Data Assistance Center at the University of Minnesota
As state policymakers increasingly rely on value-based payment arrangements to reduce health care costs while ensuring quality, there also has been a growing, related focus on how social factors impact health—a concept commonly known as “social determinants of health.” Health-related social factors include not only health care but also issues such as food insecurity, housing instability, and transportation barriers. These factors can influence health status and pose challenges to making equitable improvements in health outcomes.
Efforts to address health-related social risks through health care systems—by screening for social risks and referring patients to public assistance or community resources, for instance—require health care providers to expend additional resources, making it harder for them to contain costs. There is concern that health care payment and delivery reforms that do not address health-related social risks could further disadvantage people who already experience health inequities. Because provider payments are tied to quality performance, and patients with one or more social risk factors are associated with poor health outcomes, providers may be incentivized to limit health care services to high-need populations, further exacerbating health care disparities. To address this tension and mitigate the risk that providers could be unfairly penalized based on the higher costs of addressing their patients’ social needs or for quality performance that is hampered by their patients’ social risk factors, some states have developed risk adjustment methodologies that take patients’ social risk factors into account. However, because data on social risk factors typically are not collected from patients in a systematic and consistent way, obtaining the necessary data to inform a social risk-adjustment model is no small challenge.
This issue brief examines examples from two state Medicaid programs and one nonprofit quality measurement and reporting organization of the data sources they use to identify patients’ social risk factors when risk-adjusting payments or quality measure performance. Within the brief, we will examine both their approaches to risk adjustment based on social risk factors and how each entity filled their gaps in data on social risk factors. To inform this issue brief, we reviewed publicly available documentation and articles on the three profiled examples of risk adjustment based on social risk factors. We also conducted supplemental interviews with Medicaid staff from Minnesota’s Department of Human Services and staff from Minnesota Community Measurement. As noted above, states will need to be mindful of the limitations of these data sources to prevent further exacerbating health care disparities.