Worthington, OH – 3/4/2014
Prosper Insights & Analytics just released a special analysis of over 15,000 consumers comparing health diseases/conditions to healthcare coverage type in its “Healthcare Coverage Vital Signs Matrix.” Prosper funded and conducted this research to get a true assessment of a healthcare market now in turmoil as a result of the regulatory disruption. The findings will be utilized in deploying predictive models that can be used by all stakeholders including insurance companies, pharmaceutical, government and retailers. This unique modeling will allow industry decision makers to better understand the various consumer segments and to better target their consumer educational messages, marketing channels and risk management. 

Using its InsightCenter™ technology, which easily integrates data sets from various sources, Prosper analyzed data from its proprietary data warehouse of hundreds of databases to identify which coverage groups are more likely to have medical conditions. The Healthcare Coverage Vital Signs Matrix is a high level visualization of where the problem areas are as well as the areas of opportunities. It includes consumers who are on their employer’s plan, a family member’s plan, have insurance directly through a provider, are on a government plan or are uninsured.

According to the findings, consumers with a government healthcare plan (24% of U.S. Adults) have a higher likelihood than other groups to be suffering from a disease or health condition such as arthritis, back pain, heart disease, high cholesterol and obesity. Those who are not insured (16%) are least likely to have a disease/condition, although they report a higher incident rate of depression and obsessive-compulsive disorder. Consumers who are on their employer’s plan (40%) tend to be less likely to be dealing with certain illnesses when compared to the general population.

“Based on our analysis, we found that consumers on Employer Plans are the healthiest,” said Phil Rist, EVP, Prosper. “On the other hand, the ‘not insured’ group is healthier than average—one of the reasons that they don’t have insurance—posing a significant challenge for the Affordable Healthcare Act roll out.”

Click here to download the Healthcare Coverage Vital Signs Matrix. To see coverage in USA Today, click here

About the Healthcare Coverage Vital Signs Matrix
The widths of the columns in the Healthcare Coverage Vital Signs Matrix are representative of the percentage of the population that fall within that health insurance group. Red indicates that the percentage of that health insurance group that suffers from the corresponding disease/condition is at least 10% higher than the percentage of the general population that suffers from the same disease/condition. Blue signifies that the percentage is 10% lower and white indicates that it falls within the expected range +/-10%.

Prosper funded this study independently. It was not underwritten or ‘sponsored’ by any third party.  As such, the in-depth data and predictive models are available for licensing. A complimentary collection of healthcare market insights are available at www.ProsperHealthInsights.com

Prosper Insights & Analytics™
Prosper Insights & Analytics provides advanced business intelligence using sophisticated analytical software to examine big datasets and provide answers to executives via its cloud-based InsightCenter™ platform powered by Prosper Technologies. By integrating a variety of data including economic, behavioral and attitudinal data, Prosper Insights & Analytics delivers insights for executive decision making. Further, it is continually identifying unique insights through analytics to enable marketers to make knowledge-based decisions rather than relying on intuition. To learn more: www.ProsperDiscovery.com

Contact:
Chrissy Wissinger, Director, Communications
chrissy@goProsper.com
Stacie Severs, Client Services & Marketing Director
stacie@goProsper.com
614-846-0146

Prosper Insights & Analytics™ Releases Healthcare Coverage Vital Signs Matrix To Assess U.S. Healthcare Market In Turmoil And Drive Predictive Modeling