Many benefits managers are data-hungry. They know that intuition may lead to insight but also that probability and not mere possibility requires evidence. So, they pour over charts and tables from their TPAs, PBMs and advisors, looking for relationships between variables like health plan cost, usage, medical conditions, places of service and the differential effectiveness of providers. Often, the data analysis focuses on plan member cohorts: are our costs coming from one plant or division more than others? Is there a greater prevalence of chronic disease in certain geographical areas? Benefits managers are always trying to understand why things are the way they are, so that they can take action where it will have the greatest impact on cost and usage. In this search for meaningful correlations, there is good reason for benefits managers to use the old phrase, “follow the money,” in a way that most probably have not.
We have pointed out a number of times in the past that the top 5% (claims cost) of plan members tend to cost plans well over 50% of overall plan cost. The top 6% to 10% tend to generate around 80% of total plan costs. Usually, we track these claimants by their conditions. We call these claimants “the outliers” because their situations tend to be very different than those of the other 90% to 95% of plan members. They are often taking many medications, seeing many doctors, incurring frequent ER visits, and more.
These outliers are cancer patients, heart patients, transplant recipients, back surgery recipients, premature infants, etc. Common wisdom then, heads down a path toward wellness programs to promote early detection and treatment (i.e. preventive screenings, cholesterol meds, blood pressure meds, diabetes treatment, etc.), because “we don’t know who the outliers are going to be next year.”
But did we ever think to consider the income or education levels of our outliers? Did we ever compare claim trends to income levels? A study published in the American Journal of Public Health reveals that income matters in healthcare – and well beyond the fact that rich people are generally healthier than poor people. It turns out that the relationship between income and health is gradient: income and health are connected stepwise at every level of the economic ladder. Middle-class Americans are healthier than those living in or near poverty, but they are less healthy than the upper class. Even wealthy Americans are less healthy, on average, than those Americans with even higher incomes.
The following table shows a sampling of the connection between disease prevalence (percent who have the disease) and several family income ranges.
|Coronary Heart Disease||8.1%||6.5%||6.3%||5.3%||4.9%|
The foregoing data are arresting because they point to a group of plan members that managers may not have thought to target – group that may well be the locus of cost outliers. Of course, it is not difficult to analyze medical and drug plan data by employee income. We know that employee income no longer equals household income, but it is probably closely correlated. As another old phrase goes, your results may vary, especially with smaller covered populations where random variation may hold sway. And, to save time and money, benefit managers may be best served to rely on public health information to form their plan and population health management strategies.
The following table shows that there is a second connection involving income levels that indicates reduced worker productivity. The table shows a sampling of the percentage of people at varying income levels who find it difficult-to-impossible to perform basic activities that are common in many workplaces.
|Activities that are very difficult or impossible to perform||<$35k||$35k-$50k||$50k-$75k||$75k-$100k||$100k+|
|Walking ¼ mile||12.5||7.0||5.5||4.1||3.9|
|Climbing 10 steps||9.6||4.9||3.7||2.7||2.8|
|Stooping, bending or kneeling||14.4||9.5||7.4||5.1||4.7|
|Lifting or carrying 10 pounds||8.4||3.8||2.6||2.2||2.1|
Since the study focuses on adults, it may include those not working – including retired, disabled, and/or otherwise unemployed individuals with low “incomes.” But we are confident that the point exists: there is a connection between the prevalence of disease and income levels. Additionally, there is a connection between the ability to perform basic physical activities frequently required in the workplace and income levels. Importantly, these connections are not just between the distant poles of the rich and the poor, but also at varying income levels between rich and poor. These disparities likewise exist among education levels. And, where there might be a reverse correlation between health status and income levels, the same is much less likely to be true with respect to education levels.
The connections between income, disease prevalence and physical abilities create plan management opportunities. Does this mean that plan managers need to provide lower paid employees and their family members with better access to health care services? Not so fast! The same study points out that, “there is wide and growing consensus that, in general, the impact of medical care on health is likely to be limited relative to the impacts of social and physical environments.”
If traditional, broad-based wellness programs don’t work and expanded access to health care services doesn’t help, what can employers do? Now this is where creativity may be required. What can employers do to improve the social and physical environments of their lower paid workers? What can they do to help them move to a new social and physical environment, to help them increase their income levels, to help them become more educated, and more physically active?
Wouldn’t this be discriminatory to target programs to these employees? Yes – and in a positive way. Done properly, people who need it often will accept a helping hand to get to a better place in life.
“Importantly…statistically significant linkages were found between median household income and mortality…This result highlights the main point of this study – that there is an interplay, apparent at the county level, between patterns of health factors and income.”
David A. Kindig, MD, PhD
Emeritus Professor of Population Health Sciences
University of Wisconsin School of Medicine