The Use of Indirect Race and Ethnicity Data in Reducing Health Disparities: A Healthplan Model Diversity Rx October 20, 2010 Peggy Payne, MA, CDE CIGNA

© 2010 CIGNA 2 Mission: To help the people we serve improve their health, well-being and sense of security Customers: 46 million customers worldwide History: 1792Insurance Company of North America 1865Connecticut General Life Insurance Company 1982Connecticut General and INA form CIGNA Team: 25,000 Employees Worldwide Headquarters: Philadelphia, PA MedicalDentalVisionBehavioralVoluntaryDisabilityAccidentLifeInternational About CIGNA

© 2010 CIGNA 3 What are health disparities?  Meaningful differences in the health status (risk, prevalence, outcomes, etc) of one sub- population compared to another  No single operational definition, many factors to consider and many ways to look at the data  We focus on percent differences among racial/ethnic groups using the group with the most favorable rate/outcomes as the point of comparison

© 2010 CIGNA 4 Why are health plans interested in demographic data? To know our customers to provider superior customer service To identify need for customized interventions To develop targeted communications To add meaning to our outcomes

© 2010 CIGNA 5 Spectrum of Difficulty in Defining, Collecting and Understanding Demographic Data Gender, Age  Most universal descriptors  Collect at enrollment Location  Useful for examining regional differences  Mailing address collected at enrollment, not 100% accurate  P.O. boxes limit the precision Race/Ethnicity  Most common descriptor -health disparities  Limited collection; not collected at enrollment  Can be indirectly estimated based on home address and last name Social/Cultural  Income, education, urban/rural, etc.  significant implications on underlying causes and opportunities for intervention, not collected  Can be estimated based on home address, difficult to assess reliability with no ‘true’ data available for validity checks EasyChallenging

© 2010 CIGNA 6 The RAND Model 2  Uses address and last name by combining the race/ethnicity distribution of where a person lives (census block or tract data) with the race/ethnicity distribution of people with the same last name to determine the probability of a person belonging to each OMB category  The estimates only work aggregated for a population and have limited use at individual level. How is it possible to estimate race/ethnicity? 1 Office of Management and Budget (OMB) OMB Race/Ethnicity Categories 1 :  American Indian or Alaskan Native  Asian  Black or African American  Native Hawaiian or Other Pacific Islander  White  Hispanic or Latino

© 2010 CIGNA 7 So what is CIGNA doing with the data?  Analysis –Examining our utilization and HEDIS measures to identify disparities by race/ethnicity. –Using Hispanic/Latino ethnicity as a proxy for Spanish language to estimate volume of Spanish-speaking.  Action –Creating pilot initiatives to address disparities identified. –Training staff to be culturally sensitive. –Mobilizing staff with knowledge of a specific racial/ethnic culture to serve a subject matter experts to guide the development of culturally-sensitive materials and interventions.

© 2010 CIGNA 8 Lessons Learned  Understand the people behind the numbers. Race/ethnicity estimates are just that – estimates.  Measure twice, cut once. Looking at the data from multiple angles will lead to a more informed conclusion and the most effective allocation of resources.  Don’t outsmart your common sense. A large percent difference observed among various groups doesn’t necessarily mean that action is required.

Questions? Peggy Payne Phone “CIGNA” and the “Tree of Life” logo are registered service marks of CIGNA Intellectual Property, Inc., licensed for use by CIGNA Corporation and its operating subsidiaries. All products and services are provided exclusively by such operating subsidiaries, including Connecticut General Life Insurance Company and CIGNA Health and Life Insurance Company and not by CIGNA Corporation.