Transcript What Kind of Care Are We Buying?

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 officebased 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:
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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
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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
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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:
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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%
--
25%
36%
35%
Partially Imputed
Fully Imputed
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-Squared
2008 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
Y all  Yresp


( X all  X resp )
  1
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 PatternMixture models MEPS item imputation
evaluated well