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 ResearchCenter 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 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

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 Source Hospital Inpatient Stays

Office-Based Physician Visits

MPC 61% 23%

HC 3% 17%

Partially Imputed -- 25%

Fully Imputed 36% 35%

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

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

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-Squared2008 MEPS

Model Type Hospital Inpatient Stays Office-Based Physician Visits

Basic .54 .61

Expanded .56 .62

Log transform .61 .20

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

)(1 respallrespall XXYY

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 Stays Office-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 Stays Office-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