The disproportionate impact of COVID-19 on underserved communities underscored the need for systemic change and renewed efforts to reduce health disparities in people with social risks. This post describes a partnership between Inovalon and Humana to develop a new health equity composite quality measure to identify disadvantaged populations with the largest care disparities and determine strategic initiatives faster and with more precision.
An extensive body of research has documented how social and structural inequities lead to poor health outcomes. Healthcare organizations, life science companies, state and local governments, and federal policy makers are taking decisive action to identify disparities and address access and care gaps, but the data and required analytic approaches remain difficult to navigate for many stakeholders.
Building a health equity summary measure using existing quality measures
The Humana study began by evaluating a wide range of existing quality measures focused on receipt of preventative care, appropriate treatment, access to care, and medication adherence. Of 29 measures within this scope, the project team selected eight for inclusion in the health equity composite, based on several considerations:
- The size of the disparity across racial/ethnic groups.
- The clinical importance of the quality measure to patient health.
- The size of the population at risk within the Medicare population (i.e., number of members in the denominator).
- The ability of plans and providers to influence the measure.
The selected 8 health equity measures include three medication adherence measures, breast and colorectal cancer preventive screenings, eye exams for people with diabetes, influenza vaccinations, and member healthcare engagement (based on having at least one primary care visit within the previous year). Higher rates indicate better outcomes.
The supporting data for the study included:
- A large representative national sample from Inovalon’s proprietary de-identified real-world data to provide national benchmarks for comparison to other plan performance.
- 100% Medicare Fee-For-Service (FFS) Parts A, B, and D claims data accessed through a CMS research data use agreement (DUA) to provide national benchmarks for comparison.
- Humana plan medical, pharmacy, and enrollment data to calculate stratified measure rates for Humana members.
The composite measure reflects the rate of member engagement within the Humana population versus the Medicare Advantage (MA) and fee-for-service (FFS) populations, stratified by multi-dimensional social risk characteristics (e.g., White duals vs Black duals; White non-duals vs. Black non-duals). Dual eligibility for Medicare and Medicaid was used as a proxy for low income.
How Medicare FFS and MA compared on the new equity measure
- FFS and MA populations had similar means on the health equity composite measure (64.9% FFS vs 65.1% MA).
- However, FFS enrollees experienced much larger disparities: 24 percentage points between the sub-populations, with White non-duals having the highest rates (70.6%) and Black duals having the lowest (46.8%).
- There was a smaller (14 percentage point) disparity across MA plans.
- Black dual-eligible MA members also had the lowest rates (59.9%), but the rate was 13.1 percentage points higher than Black duals in FFS.
- The study found Humana had a slightly higher rate on the health equity composite (72.8%), with a smaller disparity of 9 percentage points.
Where do we go from here?
While using a health equity summary measure represents progress in better understanding health disparities from a multi-dimensional perspective within member populations, there a need for more accurate and complete data on race and ethnicity, household income, education levels, language proficiency, access to transportation, and other SDOH that affect health outcomes and access to care. For example, using dual eligible status as a proxy for low income helps uncover one aspect of disparities, but can miss people with low incomes who are not enrolled in Medicaid. These members can in fact have worse outcomes than those who have the additional coverage from Medicaid.
It is also important to understand the complex interactions of multiple SDOH factors and how they contribute to inequitable health outcomes. The stratification of measures by dual status within race/ethnic groups revealed disparity gaps that would not be uncovered if measures were stratified by each social risk factor independently.
Health equity moving forward
As evidenced by the large disparity gaps in this study, there is still much work to be done to achieve health equity– which is defined by the World Health Organization as attaining the highest level of health and well-being for all people. Healthcare organizations are doubling down on their commitment to improve patient access to care and treatment outcomes across populations with social risks. Researchers and data aggregators are introducing new technology, methods, and data resources to support health plans and providers in better meeting the clinical and social needs of all patients.
CMS recently announced the addition of a Health Equity Index to the Medicare Advantage Five Star Rating system, with a goal to incentivize contracts to perform well for socially at-risk beneficiaries. Initial stratifications will include beneficiaries entitled to Medicare under age 65 for disability or who are dually eligible for Medicaid and/or the low-income subsidy. Additional stratifications will be added over time as data become available (e.g., race/ethnicity, income, language proficiency, and education). Adopting new tools such as the new CMS Health Equity Index or the Humana Health Equity composite measure can help uncover the root causes driving the largest health disparities.