In our previous post, we provided a working definition of Social Determinants of Health (SDoH) as “the non-medical obstacles that prevent people from being as healthy as they can be”. We also noted that SDoH’s were associated with health outcomes, but that they didn’t explain rates of negative health outcomes as well as needed, if they are to become part of health organization operations. We hypothesized that there are important intervening factors which we have termed “Frames of Reference” and “Engagement”.
We define Frames of Reference as “internalized concepts and experiences that predict health behavior”. Frames of Reference may depend on the person’s upbringing, their circle of friends, education, and experiences with health care.
In our terminology, “Engagement” reflects the degree to which the individual makes use of the health care resources that are available to them from their insurer, their doctors, and society at large.
SDoH’s have become important concepts in many health care and insurance settings. As insurers and providers are pressed to control costs and unnecessary utilization, these factors seem to play a critical role in understanding how to manage population health and how to deploy resources.
To understand SDoH better, Deft Research fielded its own survey to measure SDoH and the intervening factors – Frames of Reference and Engagement. The study captured 675 responses from persons with Individual and Family Plans (IFP), Medicaid, or no health insurance (uninsured). All respondents were between the ages of 19 and 64. The average income of the sample is very modest – about $41,000 or 236% of FPL.
Our purpose was to learn so that future research would be of greater value to our clients. The first iteration of new research addressing this subject will be published soon under the title, “Individual and Family Plans — Member Experience and Engagement”. Stay tuned for more on that study as it nears publication.
Our survey included the following:
Our analysis led to the development of five groups of people, based on how their responses to the domains described above clustered together. Membership in one of the groups means that the individual’s responses demonstrated a pattern like the responses of other individuals in that group; they were substantially different from the responses of members of other groups.
For this blog, we are focusing on one specific group of respondents. And so we don’t trigger any unintended associations, we’re just going to refer to this group as “Group 1”.
Group 1 is of interest to insurers and providers because of higher reported rates of using emergency rooms, ambulances, and in-patient hospitalizations. About one-in-seven individuals in this group (14%) has heart disease — more than twice the rate of the overall study sample. Individuals in Group 1 also reported diagnoses of diabetes and depression at higher rates than others.
One of our findings is different from many SDoH texts which suggest that the problem with high-cost healthcare utilization arises primarily from low-income populations. We find that Group 1 is not particularly poor. Their average household income is $47,800, or 262% of the Federal poverty level. On average, Group 1 is eligible for some premium subsidy under the ACA, but not eligible for cost sharing reductions. Group 1 obtains insurance primarily from IFP plans (40%) or goes without (40%). Medicaid is not an option for most.
Group 1 is the most racially/ethnically mixed of the groups. About half are White. One third are African American, one sixth are Hispanic, one eleventh are Asian/Pacific Islander, one in fifty is Native American. The average age of Group 1 respondents is 31.
Group 1 presents a challenge to those who would use social data to identify sub-populations for health interventions. Under many methods, this group would not be targeted because its income is too high.
But Group 1 would be considered “adverse selection” by a health plan. Whether having heart disease or not, about two-thirds of the group used an emergency room last year, 40% used an ambulance, and half were admitted into a hospital overnight. The problem does not appear to be lack of access to primary care. No doubt, it is true that some groups have greater hurdles when accessing care, but this study does not support that for Group 1 – the number of their reported annual visits to a doctor’s office is close to average for the overall population. The problem is not co-morbidities; Group 1 has the same number of health problems as the average for the population, and fewer than other groups.
Having health insurance is a key to understanding emergency room use. Within Group 1, those with no insurance were twice as likely as those with either IFP or Medicaid to use emergency rooms. But, even Group 1’s insured have higher than average rates of utilization of these high-cost health care services.
The question might be asked, “Why is this inefficient and probably low-quality health care a feature of Group 1 lives?”. We can point to insurance and the costs of care, but there may be a better way to orient our thinking. Asking, “How might we nudge Group 1 toward health services usage that is better for them and would cost less?” could create a more productive direction. The intervening factors of Frames of Reference and Engagement are helpful.
Group 1’s Frames of Reference
Frames of Reference: At their core, Group 1 knows about health, but views themselves as failures in the effort to be healthy. The members of Group 1 tend to have family and friends who talk about healthy practices regarding exercise and nutrition, but they do not themselves follow those practices.
They tend toward fatalism; for instance, they are likely to believe that people’s abilities are static and unchanging over time. They tend to doubt that exercise really affects their health. They are twice as likely as average, but only at a rate of 10%, to think that their health is not their own responsibility.
Those in Group 1 are more likely than other groups to have a varying trust of medical science. For example, they tend to believe that immunizations for measles, mumps, etcetera are risky for people, but, in contrast, they believe in the effectiveness of cholesterol medications.
Overall, individuals in Group 1 are twice as likely to describe their own health as “poor” compared to others their own age.
Group 1’s Engagement
Engagement: There are several areas where Group 1 members are engaged in health resources available to them. The group tends to like insurers’ websites and use online patient portals about twice as much as the average for the sample. They use mobile apps and wearable devices. They report that they are likely to switch to insurance plans that offer gym discounts, other wellness programs not associated with a gym, and mental health counseling. These individuals were more likely to seek customer service using technology than through a traditional telephone call.
Individuals in Group 1 reported that in the recent past, they have researched provider costs and doctor ratings – and they report reading health magazines.
Overall, Group 1, feels like they know their way around the health system and have relevant experience in navigation and decision making.
Conclusion
Group 1 creates a challenging picture for population health managers. They frequently use the ER, ambulances, and hospitals, and consequently are costing more, creating debts that will be a problem to collect, and probably receiving poor quality care. Yet they are not the disenfranchised, very low-income persons often pictured as candidates for health interventions.
Frames of Reference and Engagement measures provide keys to reaching Group 1. At the core of their frame of reference is the understanding that they are failing to keep themselves healthy. Messages to Group 1 will likely bear stronger meaning if they use this theme. The group is more likely to engage through websites, apps, and other technology than they are to seek traditional telephone interactions. For this group, addressing the inefficient use of health services may start with the theme and channels identified here.
For more information please contact us at info@deftresearch.com.