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

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.

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

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

MEPS-HC Survey Design Nationally representative sub-sample of responding households from previous years 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

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

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

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

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

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

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

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

Sources of Expenditure Data for Selected Event Types, 2008 Data SourceHospital Inpatient Stays Office-Based Physician Visits MPC61%23% HC3%17% Partially Imputed--25% Fully Imputed36%35%

Method of Imputation : 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

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

Four Models 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

Model R-Squared 2008 MEPS Model TypeHospital Inpatient StaysOffice-Based Physician Visits Basic Expanded Log transform Square Root Transform.60.66

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

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

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

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

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

Percent Bias Estimate from Proxy Pattern-Mixture Analysis Hospital Inpatient Stays (resp mean=$10,404) Office-Based Physician Visits (resp mean=$194) λ=0 (MAR) 0.13%.01% λ=1 (Heckman) 0.15%.13% λ= (Brown)2.5%2.9%

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)

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

PMM Percent Bias Estimate and FMI Hospital Inpatient StaysOffice-Based Physician Visits λ=0 (MAR) 0.13%.01% λ=1 (Heckman) 0.15%.13% λ= (Brown)2.5%2.9% FMI (adjusted for unequal weights) 17% (11%)

Respondent Means vs Imputed Means Hospital Inpatient StaysOffice-Based Physician Visits Respondent Mean (SE) $10404 ($420) $194 ($4) Mean with imputations (SE without MI) $10,061 ($310) $196 ($2)

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