small area health insurance estimates (sahie) program joanna turner, robin fisher, david waddington,...
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Small Area Health Insurance Small Area Health Insurance Estimates (SAHIE) ProgramEstimates (SAHIE) Program
Joanna Turner, Robin Fisher, David Waddington, and Rick Denby
U.S. Census BureauOctober 6, 2004
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Motivation for Estimates of the Uninsured
Broad interest in health insurance coverage issues
Not a question on the decennial census or on the American Community Survey (ACS)
State Children’s Health Insurance Program (SCHIP)
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Small Area Income and Poverty Estimates (SAIPE)
Model-based estimates of median household income and poverty for states, counties, and school districts
Uses survey data, administrative records, and decennial census data
Estimates evaluated favorably by National Academy of Sciences (NAS) panel
Estimates used by Department of Education and by Department of Health and Human Services
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SAIPE (2)
School district estimates are now produced annually
This fall will release estimates for 2001 and 2002, taking a full year off the lag time
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Small Area Health Insurance Estimates (SAHIE)
Extend SAIPE knowledge and methodologies to the area of health insurance coverage
Estimates for all states and countiesEstimates for various age groupsEstimates of mean squared error
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Experimental Estimates
State• Number of uninsured children ages 0-18 in
households with income 200% of the federal poverty threshold in 1999 (SCHIP)
County • Total number of uninsured in 1999 and
2000
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Survey Estimates
Current Population Survey’s (CPS) Annual Social and Economic Supplement (ASEC)
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Covariates
Tax data Medicaid Statistical Information
System (MSIS): enrollment Food Stamp Program: number of
participants Census 2000 and Demographic
Population Estimates
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Variable SelectionCounty Total Insured
IRS proportions between multiples of federal poverty threshold:
• (100%, 130%)• (200%, 300%)
Proportion enrolled in Medicaid:
• Children ages 0-18• Adults ages 35-64• Hispanics• Blacks
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Variable Selection (2)County Total Insured
Census 2000 and Demographic Population Estimates
• Total population• Proportion
Hispanic
Food Stamps• Number of
participants
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Role for Population Estimates
Covariates
Multiply by our rates to get numbers
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Review and Advice
Census Advisory CommitteeState Health Access Data Assistance
Center (SHADAC)• Will help with validation study using states’
surveysFederal State Cooperative Program for
Local Population Estimates (FSCPE) State Data Centers (SDC)
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Census Advisory Committee ’02on the State Plan
Inclusion of Race/Ethnicity• Explore further the relationship between
Hispanic ethnicity and insurance status
Bayesian methods ok? What priors?• Be explicit about modeling assumptions
and do research regarding robustness to the assumptions
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Census Advisory Committee ’02on the State Plan (2)
Can we use data of uneven availability/reliability?• Yes, unless the jurisdictions have
incentive to not report.
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Further Reading
“Health Insurance Estimates for States” (2002) by Robin Fisher and Jennifer Campbell
“Health Insurance Estimates for Counties” (2003) by Robin Fisher and Joanna Turner
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Further Reading (2)
“Small Area Estimation of Health Insurance Coverage from the Current Population Survey’s Annual Social and Economic Supplement and the Survey of Income and Program Participation” (2004) by Robin Fisher and Joanna Turner• Available in November, 2004
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SAIPE website
http://www.census.gov/hhes/www/saipe.html
SAHIE website coming in spring 2005
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Statistical Models
Models• Find a simple relationship between
covariates and the variable we want to predict
• Random effects regression• Still subject to improvement
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Model
True log insured rate
• Could do ordinary least squares regression if we could observe Yi
ididii uXXY ...110
constant)( iuV
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Model (2)
CPS ASEC log insured
•
• We want to estimate Yi
iii Yy -1/2
i size) sample ASEC (CPS)V(
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Notes on Log Insured Rate
Insured rates• Rather than numbers
Take advantage of the correlation between CPS ASEC insured and universe
• Rather than uninsured Few counties have zero insured Some predictors measure aspect of
insured
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Notes on Log Insured Rate (2)
Logs• Make variances more homogeneous
Not essential, but makes the estimation less sensitive to variances
Can be important for places with low insurance rates
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Bayesian Analysis
End result is the distribution of the model variables given the data
Estimates: means under this distribution• Minimizes mean squared error
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Bayesian Analysis (2)
Benefits• Can calculate variances of the estimates
exactly• Can calculate means on the linear scale
exactly• Easy to interpret results• Easy to build in constraints
Insured rates must be in [0,1]
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Model Fitting
Use regression methods for exploratory analysis
Bayesian methods • Posterior Predictive P-values (PPP-values)• Bayesian residuals
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Results
Overall fit is good (PPP-values for mean, variance, and goodness of fit)• Variance model needs work
Mean posterior coefficient of variation for CPS ASEC uninsured: 7.0%
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CPS ASEC Model Based Estimates Uninsured Rates: 1999
Lightest Gray < 8% to Darkest Gray > 20%Source U.S. Census Bureau
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Validation
Other data• From states
Surveys Model-based estimates
• Other national surveys National Health Interview Survey (NHIS) Survey of Income and Program Participation
(SIPP)
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Validation (2)
Problems• Different definitions of insured• Unknown standard errors of other
estimates• How do we know if we are validated?
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Other Research
Why is Hispanicity so important?Different approaches to MedicaidOther surveys, e.g. SIPPVariance modelsImprovement of regression model
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Questions for FSCPE
Would you like a chance to comment on the estimates before we move to producing them on a regular basis?
Are you aware of data appropriate for validation of these estimates?
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Questions for FSCPE (2)
What if there are administrative records available for some areas but not others?
Is the inclusion of proportion Hispanic problematic?
