Investing in health equity

Health outcome disparities revealed in SDoH pilots

Published March 10, 2020

Numbers make things real for me. I think that’s what keeps me and my team of clinicians and data experts working through 10 social determinants of health (SDoH) pilots with plan sponsors — we want to see and share hard data around how SDoH play a role within employed populations. Our thinking is based on the recognition that each employer is a form of community and has a massive influence on the environments where many of us spend 8 to 10 hours a day.

In other posts, I’ve shared that Aetna is tackling SDoH in a totally different way. We are teaming up with our customers, using analytics to reveal true barriers. Armed with numbers, we can engage multiple stakeholders and initiate change using a value-based model that shows returns. You can read more about the Health Equity framework process and 10 pilots with plan sponsors if you like. Our goal is to take learnings from the pilot studies and apply them to all Aetna customers interested in how addressing SDoH can improve the health of their employees and the financial performance of their medical plan. 

Just what are we seeing in the data?

We see clear health differences when we look at member populations negatively impacted by SDoH compared to nondisadvantaged populations. We often see a distinct difference in the prevalence of chronic conditions, with higher rates of diabetes, hypertension and obesity, among members affected by SDoH. This population also typically receives higher risk scores, which often predict future costs.

We look specifically at employed individuals and their families, so the data sets we explore are those of select large employers. We study the disparities between an affected and nonaffected set of employees working for the same plan sponsor (employer), which naturally controls for industry, plan design, care management model, location and other differences. We use new analytics that simplify the hundreds of metrics we study by grouping them into four broad categories:

• Outcomes — inpatient readmission rates, prevalence of impactable conditions like diabetes, etc.
• Navigation — use of out-of-network care, nonemergent ER use, etc.
• Engagement — adherence to disease management programs, etc.
• Lifestyle — smoking, preventive screenings, etc.

There is good news and bad news in the measurable disparities. The bad news is that there are quantifiable increased costs associated with these disparities. The good news is that we now have insight into these issues, can quantify their impact and can build a path to solutions that will make an important difference for employees and their employers.

What else are we seeing in the data?

High-cost events for disadvantaged populations occur at many times the rate as those for nondisadvantaged populations and are often avoidable. For example, the disadvantaged population more frequently visits the emergency room for conditions or events that, had better care management practices been in place, could have been avoided.

Before developing solutions as part of pilots, we are applying the Health Equity framework to perform root-cause analysis. Originally, we believed that most SDoH-driven disparities would be the result of financial challenges faced by members. But what we have learned from our Aetna colleagues on the Medicaid side of our business is that it’s not always about money. The Health Equity framework shows drivers of employee health disparities classified into three types of root cause:

• Awareness — members don’t know about a program or plan design feature (“are urgent care centers covered by my plan?”)
• Access — members know about a program feature but are unable to use it (“my son’s pediatrician closes at 5:00 but my shift ends at 7:00”)
• Affordability — the care or service is cost prohibitive (“with all the other bills I have this month, I can’t afford to get my prescription filled”)

Clearly, once we find SDoH-driven disparities, we must know the root cause before taking action.

Hopefully you already see the merits of directly addressing SDoH in a data-driven, value-focused manner. I hope to share additional insights around care-seeking behaviors soon.

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