Proxy Pattern-Mixture Analysis of Missing Health Expenditure Variables in the Medical Expenditure Panel Survey

1. Proxy Pattern-Mixture Analysis of Missing Health Expenditure Variables in the Medical Expenditure Panel Survey Robert M. Baskin, Samuel H. Zuvekas and Trena M. Ezzati-Rice Division of Statistical Methods and Research Center for Financing, Access and Cost Trends

2. Purpose of Study • Use Fraction of Missing Information (FMI) to evaluate new item imputation methodology in Medical Expenditure Panel Survey (MEPS) • Expenditures for hospitals and office-based physicians from MEPS 2008 will be used.

3. Medical Expenditure Panel Survey Components • HC -- Household Component • MPC -- Medical Provider Component • IC -- Insurance Component

4. What is MEPS-HC Annual Survey of ~15,000 households: Provides national estimates of health care use, expenditures, insurance coverage, sources of payment, access to care and health care quality Permits studies of: • Distribution of expenditures and sources of payment • Role of demographics, family structure, insurance • Expenditures for specific conditions • Trends over time

5. MEPS-HC Survey Design • Nationally representative sub-sample of responding households from previous year’s National Health Interview Survey (NHIS) • Covers civilian non-institutionalized population • Selected from ~ 200/400 NHIS PSUs • Five CAPI interviews cumulate data for 2 consecutive years • Overlapping panels for annual data • Two panels in field concurrently

6. MEPS-HC Core Interview Content • Demographics • Health Status • Conditions • Employment • Health Insurance • Health Care Use & Expenditures

7. Non-response in MEPS • Unit non-response • - weighting adjustment • Item non-response • - imputation • The following ignores unit non-response

8. MEPS-MPC • Survey of medical providers that provided care to MEPS sample persons • Signed permission forms required to contact providers • Purpose is to collect data that can be difficult for HC respondents to report completely or accurately • Charges and payments • Dates of visit, diagnosis and procedure codes • Not designed as independent nationally representative sample of providers

9. Primary Uses of MPC Data • Supplement or replace expenditure data reported in HC • Imputation source • Methodological studies

10. MPC - Targeted Sample • All providers for households with Medicaid recipients • All hospitals and associated physicians • About ½ of office-based physicians • All home health agencies • All pharmacies

11. Linking MPC to HC Data • Probabilistic record linkage approach • Primary variables used: • Date • Event Type • Medical condition(s) • Types of services

12. Final MEPS Expenditure Data • General approach • MPC data used when available • HC data used when no MPC data available • Events with no expenditure data from MPC or HC are imputed • MPC data generally preferred donor

13. Sources of Expenditure Data for Selected Event Types, 2008

14. Method of Imputation • 1996-2007: Weighted Sequential Hotdeck within imputation cells • 2008: Office Based Visits used Predictive Mean Matching (PMM) • 2009: 4 Event Types will use PMM • -Office Based Visits • -Out Patient • -Emergency Room • -In Patient

15. Predictive Mean Matching • For each event type recipients are classified into subgroups based on available predictors of total payments • For each subgroup four models are built based on donor data

16. Four Models • Basic: all predictors in hotdeck • - no transformation • Expanded: add GPCI codes (Medicare geographic payment codes) and chronic conditions (e.g. diabetes) • - no transformation • - log of payments - square root of payments

17. Model R-Squared2008 MEPS

18. Proxy Pattern-Mixture Models • The stated purpose of the study is to use Proxy Pattern-Mixture models to evaluate the effect of missingness on the estimates of mean • - Little (1994) describes analyzing the data based on the pattern of missingness

19. Proxy Pattern-Mixture Models • Likelihood based f(Y, X, M| θ,π)= f(Y, X | M, θ) f(M|π) - Y=dependent variable with missingness - X=covariates - M=missingness indicator

20. Proxy Pattern-Mixture Assumptions • f(Y, X | M, θ) is estimable from respondents • f(M| Y, X, θ) is an increasing function of X + λY λ is assumed to be known – it is not estimable from the data

21. Proxy Pattern-Mixture Assumptions • If f(M| Y, X, θ) is an increasing function of X + λY λ = 0 is equivalent to missing at random λ = 1 is equivalent to Heckman selection λ = ∞ is equivalent to Brown model

22. Proxy Pattern-Mixture Estimate of Bias • If f(M| Y, X θ) is an increasing function of X + λY then the maximum likelihood estimate of the bias in estimating the mean using respondents is given by

23. Percent Bias Estimate from Proxy Pattern-Mixture Analysis

24. Proxy Pattern-Mixture Models and FMI “The FMI due to non-response is estimated by the ratio of between-imputation to total variance under multiple imputation. Traditionally one applies this under the assumption that data are MAR, but we propose its application under the pattern-mixture model where missingness is not necessarily at random.” (from Andridge and Little)

25. FMI vs PPMA • The Pattern Mixture-Model estimates the bias in using the mean of respondents (complete case analysis) • FMI estimates the ‘uncertainty’ in using the mean including imputed values

26. PMM Percent Bias Estimate and FMI

27. Respondent Means vs Imputed Means

28. Summary • Item imputation in MEPS is improved with use of available predictors • Under assumptions for Proxy Pattern-Mixture models MEPS item imputation evaluated well