Jessica Hampton September 2013

1. ACA and the New Individual Segment: Profiling the Uninsured and Non-Group Insured Populations with MEPS 2010 and SAS Survey Procedures Jessica Hampton September 2013

2. Presentation Outline • Introduction • Statement of Purpose • PPACA • MEPS 2010 • Survey Design • Literature Review • Intro to SAS Survey Procedures • Data Preparation • Selected Variables • Derived Variables • Statistical Analysis • PROC SURVEYMEANS • PROC SURVEYFREQ • Profiles (with PROC CORR, PROC SURVEYREG) • CART Models (SPSS) • PROC SURVEYLOGISTIC Models • Conclusions/Recommendations • References

3. Introduction

4. Statement of Purpose • Use MEPS 2010 data (most recent available) • With SAS survey procedures to: • Identify drivers of total and out of pocket medical expenditures for adults 18-65 • Discover significant predictors of uninsured and private non-group insured segments • Profile these populations of interest • Compare mean expenditures across populations with regard to insurance coverage status • Estimate the size of these segments as of 2010 • Why? • Prior to ACA, underwriting practices denied coverage to high risk (high cost) individuals • Individual market is changing • Although underwriters no longer allowed to deny coverage, customer profiles are used to identify desirable characteristics (low risk/low cost) in order to target those individuals in direct marketing campaigns. • Profiles also useful in retention and engagement strategies

5. PPACA/ACA • Patient Protection and Affordable Care Act (March 2010) • Full implementation January 1, 2014 • Guaranteed coverage for people with pre-existing conditions • Minimum standards for coverage • Phases out annual and lifetime maximums • Standardized premiums with regard to gender and prior medical diagnoses • Pricing based on age and smoking status • Smoothes age-based premium differences (younger people will pay more than they used to) • Income-based subsidies (up to 400% poverty level) • Health insurance exchanges for each state to facilitate purchase • Penalizes those who elect not to purchase health insurance • Health insurance sales tax • Dramatically expands the individual segment • Previously uninsured purchase insurance through the exchanges • Others lose their private employer group coverage

6. Medical Expenditures Panel Survey (MEPS) • Administered annually by the U.S. Department of Health and Human Services since 1996 • Agency for Healthcare Research and Quality (AHRQ) • Anonymity protected by removing individual identifiers from the public data files • MEPS 2010 consolidated data file released September 2012 • Multiple components (household, insurance/employer, and medical provider). • Household component (1,911 variables) covers the following topics: • Demographics • Household income • Employment • Diagnosed health conditions • Additional health status issues • Medical expenditures and utilization • Satisfaction with and access to care • Insurance coverage • 18,692 after excluding out of scope, negative person weights, under 18 and 65+ • U.S. civilian, noninstitutionalized population • ~3% out of scope (birth/adoption, death, incarceration, living abroad)

7. MEPS Survey Design Methods • MEPS is a representative but NOT a random sample of the population • Person weights must be used to produce reliable population estimates • Stratification: • By demographic variables such as age, race, sex, income, etc. • Goal is to maximize homogeneity within and heterogeneity between strata • Sometimes used to oversample certain groups under-represented in the general population or with interesting characteristics relevant to study • For example: blacks, Hispanics, and low-income households • Clustering: • By geography in order to reduce survey costs -- not feasible or cost-effective to do a random sample of the entire population of the U.S. • Within-cluster correlation underestimates variance/error -- two families in the same neighborhood are more likely to be similar demographically (for example, similar income) • Desire clusters spatially close for cost effectiveness but as heterogeneous within as possible for reasonable variance. • Multi-stage clustering used in MEPS: • sample of counties >> sample of blocks >> individuals/households surveyed from block sample

8. Survey Design Considerations • If person weights are ignored and one tries to generalize sample findings to the entire population, total numbers, percentages, or means are inflated for the groups that are oversampled and underestimated for others • In regression analysis, ignoring person weights leads to biased coefficient estimates • If sampling strata and cluster variables are ignored, means and coefficient estimates are unaffected, but standard error (or population variance) may be underestimated; that is, the reliability of an estimate may be overestimated • Or when comparing one estimated population mean to another, the difference may appear to be statistically significant when it is not • (Machlin, S., Yu, W., & Zodet, M., 2005)

9. Literature Review

10. Literature Review – KFF 2011 • Most literature focuses on data available prior to 2012 which describes these segments (uninsured/non-group) through 2008 • Kaiser Family Foundation (KFF) 2011 study, “A Profile of Health Insurance Exchange Enrollees,” based on MEPS 2007 • Model simulation of demographic, health, and medical utilization profiles of the people expected to enroll in exchanges by 2019 • Compares exchange population profile to privately insured and uninsured • Estimates the exchange population in 2019 to include: • 16 million formerly uninsured individuals • 5 million formerly employer group insured • 1 million formerly private non-group insured • Exchange population older, with lower education and income levels, more racially diverse than current privately insured population • Expect self-reported worse health but fewer pre-existing medical diagnoses, possibly due to lack of access to care • Expect utilization and expenditures to increase for the previously uninsured once they gain access to care – similar to those of non-group insured • Some higher income will continue purchasing non-group insurance outside of the exchanges – those with lower income will favor exchanges

11. Literature Review – KFF 2010 • 2010 study by KFF: “Comparison of Expenditures in Nongroup and Employer Sponsored Insurance: 2004-2007 • Recent focus on the non-group market prompted by healthcare reform, which would expand the non-group market • DiJulio and Claxton, study authors, use combined data from MEPS 2004-2007 for the nonelderly adult population • Non-group insured have lower premiums, higher out-of-pocket expenses and better (self-reported) health than the private employer group segment • Implies some combination of more cost-sharing, higher deductible levels, and/or less coverage for non-group • If lower income people are entering the group market, they may not be able to afford the high out-of-pocket costs • Plans may need higher premiums if the uninsured population is not as healthy as the non-group segment (DiJulio, B. & Claxton, G., 2010)

12. Literature Review – KFF 2008 • 2008 Kaiser study, prior to ACA being signed into law: “How Non-Group Health Coverage Varies with Income” • Based on combined data from MEPS 2000-2003 for nonelderly adults • Finds that even higher income people may prefer to remain uninsured than to buy in the non-group market if they have no coverage offered through an employer • At 4x the poverty level, 25% purchase non-group • At 10x the poverty level, only 50% purchase non-group coverage • Although people are more likely to purchase non-group plans at higher income levels, study concludes that the non-group insurance market is unattractive to consumers • Insurers either need to improve their product or the government needs to subsidize such plans even for higher incomes in order to encourage participation

13. Literature Review – MEPS Statistical Brief 2009 • A MEPS statistical brief from 2009 examines trends in group and non-group private coverage • 1996-2007, nonelderly adult population • 65% had private group coverage at some time in 2007 • Percentage private group has remained similar since 1996, with increasing overall numbers corresponding to population increase • Non-group coverage has declined in the same time period, falling from 6% to 4% (Cohen, J.W. & Rhoades, J.A., 2009).

14. Literature Review – O’Neill & O’Neill 2009 • O’Neill and O’Neill (2009) conducted an analysis of the characteristics of the uninsured population • Employment Policies Institute study finds that a large portion of the uninsured are “voluntarily uninsured” • Percentage varies by state from as low as 27% to as high as 55% • Defined as nonelderly adults at or exceeding 2.5 times the poverty level • The media tends to portray the uninsured population as being in very poor health and without options, but the authors say this portrayal is exaggerated and does not present a full picture • Uninsured population has very different demographic characteristics from the privately insured: • young • low education levels • immigrants • lower medical utilization/expenditure levels • Higher mortality rates (about 3% higher than those of the privately insured segment after controlling for other risk factors such as smoking) among uninsured • Lack of health insurance coverage not the major contributing factor – other disadvantages associated with poor health, such as lack of education (O’Neill & O’Neill, 2009)

15. Literature Review – Summary • Private non-group coverage declining over time • Non-group market unpopular and overpriced • Non-group insured have lower premiums, higher out-of-pocket expenses and better (self-reported) health than the private employer group segment • Large portion of the uninsured are “voluntarily uninsured” • Uninsured population has very different demographic characteristics from the privately insured • Exchange population characteristics will be driven by large influx of formerly uninsured • Lower education and income levels, more racial diversity than current privately insured population • Fewer pre-existing medical diagnoses • Conflicting information on whether uninsured population is actually healthier or not

16. SAS Survey Procedures

17. SAS Survey Procedures • Intended for use with sample designs that may include unequal person weights, clustering, and stratification. • PROC SURVEYMEANS estimates population totals, percentages, and means. Includes estimated variance, confidence intervals, and descriptive statistics. • PROC SURVEYFREQ produces frequency tables, population estimates, percentages, and standard error. • PROC SURVEYREG estimates regression coefficients by generalized least squares. • PROC SURVEYLOGISTIC fits logistic regression models for discrete response (categorical) survey data by maximum likelihood. • PROC SURVEYMEANS and PROC SURVEYREG available starting with SAS version 8. • PROC SURVEYFREQ and PROC SURVEYLOGISTIC available starting with version 9. • PROC SURVEYSELECT for sampling which will not be used in this project

18. PROC SURVEYMEANS Syntax PROCSURVEYMEANS DATA=PQI.MEPS_2010; STRATA VARSTR; CLUSTER VARPSU; WEIGHT PERWT10F; DOMAIN INSCOV10; VAR TOTEXP10 TOTSLF10; RUN;

19. PROC SURVEYMEANS Output

20. PROC SURVEYFREQ Syntax PROCSURVEYFREQ DATA=PQI.MEPS_2010; STRATA VARSTR; CLUSTER VARPSU; WEIGHT PERWT10F; TABLES PRIEU10 PRING10 INSCOV10; RUN;

21. PROC SURVEYFREQ Output

22. PROC SURVEYREG Syntax PROC SURVEYREG DATA=PQI.MEPS_2010; STRATA VARSTR; CLUSTER VARPSU; WEIGHT PERWT10F; MODEL &TARGET=&&VAR&I /SOLUTION; ODS OUTPUT PARAMETERESTIMATES=PARAMETER_EST FITSTATISTICS=FIT; RUN;

23. PROC SURVEYLOGISTIC Syntax PROCSURVEYLOGISTIC DATA=SASUSER.MEPS_2010; STRATA VARSTR; CLUSTER VARPSU; WEIGHT PERWT10F; MODEL TOTEXP_HIGH(EVENT='1')=AGE10X MARRIED--HISPANX POVLEV10--PHYACT53 OBESE--ADSMOK42 ADINSA42--LOCATN_ER; ODS OUTPUT PARAMETERESTIMATES=WORK.PARAM; RUN;

24. PROC SURVEYLOGISTIC/REG Output Default output (similar to PROC LOGISTIC and PROC REG): • fit statistics (AIC, Schwartz’s criterion, R-square) • chi-squared tests of the global null hypothesis • degrees of freedom • coefficient estimates • standard error of coefficient estimates and p-values • odds ratio point estimates • 95% Wald confidence intervals Does not include: • Option for stepwise selection • chi-squared test of residuals/tabled residuals (assumptions of normality and equal variance do not apply) • influential obs/outliers (person weights)

25. Data Preparation

26. Selected Variables - Demographic

27. Selected Variables - Income

28. Selected Variables - Diagnoses

29. Selected Variables – Behavioral Risks/Preventive Care

30. Selected Variables – Expenditures/Utilization

31. Selected Variables – Insurance Coverage

32. Selected Variables – Derived/Transformed

33. Data Overview

34. Summarized Output from PROC SURVEYMEANS/FREQ • N for Private Non-Group = 397 • Private Non-Group continues decline (from 4% in 2007 – MEPS Statistical Brief, 2009) • Additional 247 with 65+ included • Total adult nonelderly US population ~ 191 million • Any Private/Only Public/Uninsured add up to 100% of total • Mean total expenditures $3,751.61 • Confidence Intervals overlapping for public/uninsured OOP means, but look at OOP as percent of total expenditures

35. Population Size Estimates • Using PROC SURVEYFREQ • Private Insurance largest Group • Any Private/Only Public/Uninsured add up to 100% of total • Private Non-Group/ Private Empl Group subsets of Any Private

36. Mean Expenditures • Using PROC SURVEYMEANS • Public has largest total expenditures • Uninsured has lowest total expenditures, followed by Private Non-Group • Private Non-Group pays the most out-of-pocket in absolute dollars

37. OOP Expenditures – Percent of Total • Using PROC SURVEYMEANS • Private Non-Group and Uninsured pay the most out of pocket as a percent of their total expenditures

38. Profiles

39. Building Profiles • Three approaches • Unweighted PROC CORR • PROC CORR with person weights • “PROC SURVEYCORR” macro with PROC SURVEYREG: • Uses all survey design variables (strata/cluster/weight) • Iteratively runs simple regression models for each predictor variable • Builds table with r-squared, r, and p-values • Sorted by r • See NESUG paper/presentation for more about this approach • Similar results for all three approaches

40. Profile – Expenditures Note: All profiles show characteristics ranked roughly by size of correlation and significance level.

41. Profile – Non-Group Insured Population Note: All profiles show characteristics ranked roughly by size of correlation and significance level.

42. Profile – Uninsured Population Note: All profiles show characteristics ranked roughly by size of correlation and significance level.

43. Profile – Private Employer-Based Insurance Population Note: All profiles show characteristics ranked roughly by size of correlation and significance level.

44. Profile – Publicly Insured Population Note: All profiles show characteristics ranked roughly by size of correlation and significance level.

45. By Population – Number of Diagnoses • Using PROC SURVEYMEANS • Public has highest mean # chronic conditions • Uninsured has lowest # chronic conditions • Private Non-Group second most healthy

46. By Population – Age • Using PROC SURVEYMEANS • Private non-group is oldest (but also relatively healthy for age – see previous slide) • Uninsured is youngest • Underwriting for ACA allowed only based on Age and Smoking status

47. By Population – Education Levels • Using PROC SURVEYMEANS • Public least educated, followed by Uninsured • Private Non-Group most educated

48. By Population – % of Poverty Level • Using PROC SURVEYMEANS • Private Group at highest % of the poverty level (over 500%) • Subsidies for ACA up to 400% poverty level

49. By Population – Income Variables • Using PROC SURVEYMEANS • Private Group has highest income • Skewed high because mean, not median • Private Non-Group has highest total income compared to wage income – oldest segment w/ possible early retirees or more income from investments/pensions

50. By Population – Ethnicity • Using PROC SURVEYFREQ • Uninsured highest percentage of Hispanic (~ 35%)