DEDUCTIBLE EXPENDITURE Deductible REVENUE EXPENDITURE CAPITAL ALLOWANCES.
Proxy Pattern-Mixture Analysis of Missing Health Expenditure Variables in the Medical Expenditure...
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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
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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.
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Medical Expenditure Panel Survey Components
HC -- Household Component
MPC -- Medical Provider Component
IC -- Insurance Component
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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
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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
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MEPS-HC Core Interview Content
Demographics Health Status Conditions Employment Health Insurance Health Care Use & Expenditures
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Non-response in MEPS
Unit non-response - weighting adjustment Item non-response - imputation The following ignores unit non-response
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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
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Primary Uses of MPC Data Supplement or replace expenditure data
reported in HC
Imputation source
Methodological studies
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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
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Linking MPC to HC Data
Probabilistic record linkage approach Primary variables used:
Date Event Type Medical condition(s) Types of services
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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
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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%
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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%
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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)
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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
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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%)
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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)
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Summary
Item imputation in MEPS is improved with use of available predictors
Under assumptions for Proxy Pattern-Mixture models MEPS item imputation evaluated well