Health equity under the microscope

This piece was originally published in the April 2022 issue of Policy & Practice magazine.

Health inequity remains a significant problem in the United States. While COVID-19 did not create the inequity, the pandemic dramatically exposed and compounded the disparities. Addressing the needs of populations historically disinvested is the prerequisite for a fair and thriving society. There is growing recognition that the status quo is unacceptable and increasingly urgent calls for change at the federal and state levels.

The root causes of health inequity are challenging to address. In addition to being couched in neutral language or binary terms of “right vs. wrong” or “good vs. bad,” the root causes are laden with hidden judgments against people who are already disadvantaged. Simply because a policy or process exists does not mean that it is right or just.

Consider a common managed care process: prior authorization. When a patient needs specialized services, their doctor may need to confirm that the health plan is willing to pay for these services. If the insurance company rejects the request, the patient has the right to appeal that decision with the support of the physician. When a person with Medicaid appeals a decision, they may also have the option to request Continuation of Benefits. Checking that box on the paperwork helps ensure that their services continue while the health plan reviews the appeal.

But amid complex legal language and explanations for the denial, patients may not recognize their right to continued care. Some may not even understand what “Continuation of Benefits” means.

This is one of many examples where equity-focused analytics can help. To explore the question — Is this policy further marginalizing certain populations? — equity-focused analytics would pull data to measure differences in appeal decision times when Continuation of Benefits is selected. This exercise also would help identify whether certain groups or demographics are less likely to select Continuation of Benefits and therefore less likely to have equitable access to care.

If there are differences, what might account for them? When we examine patients who do not select Continuation of Benefits, is there a correlation based on their primary language? Are there differences based on patients’ disability groups, race/ethnicity or age? If the answer to any of those questions is “yes,” there is a strong case that this policy is imposing barriers to continuous care and needs to be changed or removed.

As the Continuation of Benefits example illustrates, equity-focused analytics can help reveal where a policy is causing unintended consequences. They also can enable states to take a more targeted view, revealing where seemingly unrelated variables are fueling inequity.

From our work in this space, we have identified several factors that contribute to success.

• Start with questions, not assumptions. Rather than focusing on disparities within a certain program or organization, start with a question you want to explore (for example, What are the social status variables that predict variation in asthma risks and social exposures, such as stress and violence, based on race/ethnicity and/or socioeconomic status?). Use data from across and beyond the state ecosystem to answer it. See how answers to your first question shift your perspective and prompt further inquiry.
• Stay transparent. States need to be highly transparent with agency staff about what they are doing, why they are doing it and how it is performing. Staff represent the front line of a state agency. From that vantage point, they have unique access to input from the people they serve. That feedback can help both in evaluating current policies and in implementing new, more equitable ones.
• Mirror the community. The goal of any health equity initiative is to improve services and outcomes for underserved communities. Invite representatives from underserved communities to the table as you discuss challenges and design solutions. And don’t stop with individuals who work at local community or religious organizations. Make sure that the state workers at the table reflect these communities, as well.
• Identify (and evaluate) potential solutions. Beyond studying problems, equity-focused analytics can help surface potential solutions. Which levers might a state pull to improve the social factors that affect equity? Where might policy changes or financial investments improve access to medical care? Equity-focused analytics can also help in testing and finetuning solutions. Are policy and/or program changes supporting the desired improvements? Where might adjustments be made to drive even better results?

Achieving health equity is essential to a strong, vibrant and just society. Solving the myriad of problems that have created and perpetuated inequity will not be simple or easy. But it can and must be done. Actively listening, analyzing data in new ways and then translating insights into action can support and sustain progress toward that goal.