With better analytics, we’re learning more about how the U.S. population uses health care resources. Last week, we looked at the “compression” in spending – with a very small percentage of consumers using a very large proportion of health care resources (see From 5:95 To 1:99).
What the emerging data also reveals is that much of the disparity in the use of health care resources is due to a disparity among the low- and high-income populations. Low-income consumers are generally high utilizers of acute care services – with studies showing that low-income consumers have higher rates of potentially preventable hospitalizations (see Neighborhood Socioeconomic Disadvantage & 30-Day Rehospitalization: A Retrospective Cohort Study and Socioeconomic Status & Readmissions: Evidence From An Urban Teaching Hospital); and consumers who lack housing stability and food security have higher rates of emergency department use and hospitalizations (see Housing Instability and Food Insecurity as Barriers to Health Care Among Low-Income Americans). Low income consumers also generally have higher rates of smoking and obesity, and lack access to preventative care, all of which contributes to higher health care spending (see Health Care in the Two Americas: Findings from the Scorecard on State Health System Performance for Low-Income Populations, 2013).); consumers living in poverty have higher readmission rates (see
But this higher utilization of acute care resources doesn’t translate into longer life expectancy. Life expectancy for low income consumers is much shorter – and this gap in life expectancy generated a lot of buzz with the release of a new study published in The Journal of the American Medical Association. The study, The Association Between Income and Life Expectancy in the United States, 2001-2014, had three major findings:
- The gap in life expectancy between the richest 1% of the population and the poorest 1% was 14.6 years for men and 10.1 years for women.
- This disparity has increased over time – life expectancy increased by 2.34 years for men and 2.91 years for women in the richest top 5% of the population studied between 2001 and 2014, on the other hand, life expectancy increased only 0.32 years for men and 0.04 years for women among the poorest 5% during the same time period.
- The study found that while life expectancy remained steady for people with high-incomes regardless of geography, among low-income people, life expectancy varied greatly depending on their location.
We’re also learning more about the causes of this discrepancy in life expectancy. The authors of the HealthAffairs Health Policy Brief, Relative Contributions of Health Determinants to Health Outcomes, found that the percentage of early deaths could be largely attributed to personal behaviors (40%), followed by social circumstances (15%), environment (5%), genetics (30%), and medical care (10%). These estimates are similar to those in the 2010 paper, County Health Rankings Working Paper – Different Perspectives For Assigning Weights To Determinants Of Health, in which 30% of the differential was explained by behaviors, 40% to social circumstances, 10% to environment, and 20% to medical care. (For more on our recent coverage of the role of the social determinants of health, see Tending To The Social Determinants Of Health – Or Not).
The effects of poverty can not only be measured in terms of life expectancy and health care utilization, they also have an impact on mental health. The 2014 report from the World Health Organization (WHO), Social Determinants Of Mental Health, found that “certain population subgroups are at higher risk of mental disorders because of greater exposure and vulnerability to unfavorable social, economic, and environmental circumstances, interrelated with gender.” Specifically, the WHO found that in a review of 115 studies over 70% reported positive associations between poverty measures and common mental disorders.
What does all of this mean for executives of health plans and provider organizations? First, poverty does matter in developing rates in pay-for-performance initiatives. Better analytics about the characteristics of the population (beyond the typical sex/age/race demographics) are the key to better estimation of the future demand for health care services. This is where the organizations with the best “big data” will have a distinct advantage (see Big Data For Dummies and Big Data In Action).
Second, the availability of social support services to address the social determinants of health matter – and the ability of your organization’s care coordinators to link consumers to those services is a strategic advantage. There is a reason why health care spending of low income individuals varies greatly by where they live – it is the availability of social services that are critical to keeping them out of the health care system.
The changes of the past five years in health care underwriting – behavioral health parity, no preexisting condition clauses, no lifetime limits – has changed the game for health plans and the provider organizations that comprise their service delivery system. The market advantage will go to those organizations that understand the “metrics” of their covered population in ways that have not been common for health care organizations in the past. The market advantage will be to those organizations that understand when and how to link consumers to the “non-health care” supports and services that allow them to maintain their health outside of the treatment system.
I think this expanded responsibility for the “whole person” is good public policy and good for consumers. But, it raises the bar on the need for “big data” to link information from multiple health and social service systems – and to develop new models that coordinate medical, behavioral, and social services at the consumer level. It gives new meaning to “personalized medicine.”
For more, check out our latest Market Intelligence Report on social determinants of health – specifically income assistance: What Services Are Available Through The TANF Program & What Is U.S. Spending On The Program?: An OPEN MINDS Market Intelligence Report. And for even more, join us on June 8 at The 2016 OPEN MINDS Strategy & Innovation Institute for the session, “What Are The Challenges Of Innovation In Serving Complex Consumers? A Town Hall Discussion On Overcoming The Barriers To Change,” featuring OPEN MINDS senior associate Joseph P. Naughton-Travers; Kenneth R. Weingardt, Ph.D., Scientific Director, Center for Behavioral Intervention Technologies & Professor, Northwestern University; Bruce C. Nisbet, LMSW, DFNAP, President & CEO, Spectrum Human Services & Health Home Partners of WNY, LLC; and Peter O’Neill, Associate Director of Reimbursement and Health Policy, Neuronetics, Inc.