# Presentation: 950464

## Ron Prevost
U.S. Census Bureau
FSCPE 
September 27, 2006

**U.S. Census Bureau**
**FSCPE **
**September 27, 2006**

**Health-Related Administrative ****Records Research ****& ****Daytime Population Estimates**

## Overview

- Small Area Health Insurance Estimates
- Medicaid Undercount Study Description
- Preliminary Results & Next Steps
- Related Research
- Benefits of Integrated Data Sets
- Policy Challenges
- Conclusions
- Daytime Population Estimates

## U.S. Census Bureau’s Small Area Health Insurance Estimates (SAHIE) Program Overview

- Produces a consistent set of estimates of health insurance coverage for all counties
- Have published estimates for counties and states by age (under age 18, and total) with confidence intervals
- Investigating model improvements
- Expanding age categories

## Small Area Health Insurance Measures

## Small Area Health Insurance Measures

## U.S. Census Bureau’s Small Area Health Insurance Estimates (SAHIE) Program Overview

- Health insurance coverage estimates are created by combining survey data with population estimates and administrative records
- Race, ethnicity, age, sex and income categories are being investigated for counties and states
- State-level estimates such as uninsured Black or African American children under age 18 <= 200% of poverty
- County-level estimates such as uninsured children under age 18  <= 200% of poverty
- This project is partially funded by the Centers for Disease Control and Prevention’s (CDC’s) National Breast and Cervical Cancer Early Detection Program

## U.S. Census Bureau’s Small Area Health Insurance Estimates (SAHIE) Program Overview

- Forthcoming health insurance coverage estimates in 2007
- Updated county- and state-level estimates by age
- State-level estimates by race, ethnicity, age, sex, and income categories
- Depending on future funding, the SAHIE program plans to produce county- and state-level model-based estimates as an annual series

## Medicaid Undercount Project Collaborators and Co-Authors

- Project Lead:  Dr. Michael Davern, State Health Access Data Assistance Center, UMN
- Collaborators:
- US Census Bureau Collaborators:
- Sally Obenski
- Ron Prevost
- Dean Michael Resnick
- Marc Roemer
- Office of the Assistant Secretary for Planning and Evaluation:
- Rob Stewart
- George Greenberg
- Kate Bloniarz
- Coauthors:
- Centers for Medicare and Medicaid Services
- Dave Baugh
- Gary Ciborowski
- State Health Access Data Assistance Center
- Kathleen Thiede Call
- Gestur Davidson
- Lynn Blewett
- Rand Corporation
- Jacob Klerman

## What is the Medicaid Undercount?

- Survey estimates of Medicaid enrollment are well below administrative data enrollment figures
- Preliminary numbers for CY 2000:
- Current Population Survey (CPS) estimated 25M
- The Medicaid Statistical Information System (MSIS) estimated 38.8M
- There is a substantial undercount in the CPS relative to MSIS (in this case 64%)

## Why Do We Care?

- Survey estimates are important to policy research
- Used for policy simulations by federal and state governments
- Only source for the number of uninsured
- Only source of the Medicaid eligible, but uninsured population
- Used in the SCHIP funding formula
- The undercount calls the validity of survey estimates into question
- Study is intended to understand causes

## What Could Explain the Undercount?

- Universe differences between MSIS and CPS survey data
- Measurement error
- Administrative and survey data processing, editing, and imputation
- Survey sample coverage error and survey non-response bias

## Issues to be Addressed

**Universe Differences**
- Persons included in MSIS but not in CPS
- Persons living in “group quarters”
- Persons who do not have a usual residence
**Persons receiving Medicaid in 2+ states**
**What is “meaningful” health insurance coverage**
- Persons with restricted Medicaid benefits
- Persons with Medicaid coverage for one or few months
**CPS respondent knowledge**
- Because of plan names, enrollees in Medicaid prepaid plans may not know they have Medicaid coverage

## Issues to be Addressed

**CPS responses and respondent recall**
- Medicaid enrollees who did not use Medicaid services may not consider themselves covered by Medicaid
- Proxy responses for other members of a household may be incorrect, especially for non-family members or multiple families
- Persons with other insurance and/or personal liability, in addition to Medicaid, may respond incorrectly
- Respondents may confuse Medicare and Medicaid benefits
**Possible bias**
- Non-response may be higher for poorer populations
- Persons who did not receive Medicaid benefits during the month in which they were CPS respondents may not report Medicaid

## Medicaid Undercount Study (1) 

**Develop Validated National CMS Enrollment File**
- Determine coverage & validation differences between MSIS and MEDB
- Determine characteristics of MSIS, MEDB, & Dual Eligible individuals
**Conduct National Medicaid to CPS Person Match**
- Determine why Medicaid and CPS differ so widely on enrollment status
- Build suite of tables detailing explanatory factors & characteristics

## Medicaid Undercount Study (2) 

**Complete State Database Work**
- Determine effect of including State Medicaid Files on validation
- Conduct frame coverage study using MAF/MAFARF/CPS/SS01/ and Medicaid addresses
- Conduct state person coverage study to examine if same explanatory factors apply as in national
**Conduct National Medicaid to NHIS Person Coverage Study**** **
**Results of each phase documented in working papers**

## Preliminary Explanations

- Universe differences
- Enforce CPS group quarter definitions on MSIS where we have administrative data address information
- Look for duplicate persons in different states or same state
- Measurement error
- Link CPS respondents to MSIS data for CY 2000 to examine survey reports of enrollees
- Understand the covariates of misreporting

## Building a Common Universe

- Group quarters, dead, not a valid record, in two states

- Not a valid record

- In CPS universe and in MSIS universe

## MSIS Linkage to CPS

- Removed MSIS dual eligible cases defined as a “group quarter” by Census
- Ran the 2000 MSIS data through Census Bureau’s Person-ID validation system
- A record is “valid” if in the appropriate format and demographic data is consistent
- Removed duplicate valid records
- Removed those MSIS enrollees not enrolled in “full benefits”

## Matching the CPS Universe

- Number of MSIS Medicaid Records in 2000:
- 	44.3 M (total MSIS records)
- 	- 1.5 M (records in more than one state 		or group quarter)
-    _- 4.0 M_ (partial Medicaid benefits)
-    38.8 M  (the target Medicaid total)

## Sample Loss in Linkage

- MSIS
- 9% of all MSIS records did not have a valid record and were not eligible to be linked to the CPS
- CPS
-  6.1% (respondents’ records not validated)
- +  21.5% (respondents refused to have their      ______	data linked)
- 27.6% (total not eligible to be linked to MSIS)

## Validated Records in MSIS 

## Matched CPS-MSIS Respondents with Reported Data Only

- 12,341 CPS person records matched into the MSIS
- 1,906 records had imputed or edited CPS data (15.5% of total).
- Focusing on only those with explicitly reported data:
- 60%  (responded they had Medicaid)
- 	   9%  (responded some other type of public coverage 	     but not Medicaid)
- 17%   (responded some type of private coverage, but 	      not Medicaid)
- _15%_   (responded they were uninsured)
- 101% (over 100% due to rounding)

## Factors Associated with Measurement Error

- Length of time enrolled in Medicaid
- Recency of enrollment in Medicaid
- Poverty status impacts Medicaid reporting but does not impact the percent reporting they are uninsured
- Adults 18-44 are less likely to report Medicaid enrollment
- Adults 18-44 more likely to report being uninsured
- Overall CPS rate of those with Medicaid reporting that they are uninsured is higher than other studies
- Overall CPS rate of those with Medicaid reporting Medicaid is lower than other studies

## Work Remaining (1)

- Phase III: Measure Universe Differences:
- Use 7 Medicaid state files with name and address information to understand the impact of MSIS non-validation (one of the states is CA)
- Use enhanced MSIS data to further analyze the CPS sample frame coverage
- Phase IV:  Assess Measurement Error:
- Compare measurement error in the CPS to the National Health Interview Survey (NHIS) by linking the NHIS to the MSIS
- Compare measurement error in CPS to state survey experiments

## Work Remaining (2)

- Administrative and survey data processing, editing and imputation
- Evaluate how well the CPS edits and imputations work at both the micro level and the overall macro level
- Evaluate additional state-level Medicaid data
- Survey sample coverage error and survey nonresponse bias
- Link the address data from the 7 states to the Census Bureau’s Master Address File to determine sample coverage problems
- Assess whether those addresses with a Medicaid enrollee are more likely to not participate in Census Bureau surveys

## Preliminary Results

- We have presented preliminary results that are subject to change after further investigation
- At the moment we conclude that survey measurement error is playing the most significant role in producing the undercount
- Some Medicaid enrollees answer that they have other types of coverage and some answer that they are uninsured
- The overall goal of the project is to improve the CPS for supporting health policy analysis
- Especially refining estimates of the uninsured

## Emerging Survey Improvements

- Use integrated data sets for insight into major discrepancies between survey and federal program data
- Food Stamps, SSI, Medicaid enrollment all suffer from about a substantive discrepancy with current survey numbers
- Provides two views of “truth”
- Informs
- Program administration (statistical evaluations)
- Policy development
- Program performance measures
- Researching integrated data for reengineering SIPP

**Notes:**
AR as basis for tract-level survey controls
Two approaches use variations of this method
AR in model-assisted estimation
Working with POP to move from filer based to person based migration estimates 

Finally, the new business line....began with the MD FS project 

## Related Research Examples 

**Maryland Food Stamp Study**
**SS01 recipiency about 50% of Maryland Food Stamp Recipients**
**Able to explain 85 % of the discrepancy**
**Misreporting -- 63 % of discrepancy**
**Child Care Subsidy Study**
**Develop an eligibility model for identifying cohort in SS01 who are/should be receiving Child Care Subsidy**
**Researchers at RDC examining effects of CCS on employment and self support**

## High-Value Data Sources for Potential Future Integration

- State food stamp and WIC files integrated with NHANES, MEPS, and CPS Food Security Supplement
- SSA’s SS-5 that includes relationships not found in the NUMIDENT
- Federal Health Insurance Administrative Data Files
- State Medicaid files for inclusion in Medicaid Undercount Study

**Notes:**
Includes Indian Health Service Data, VA Health Insurance, Tri-Care etc.

## Value of Integrated Data Sets

- Provides more robust and accurate picture
- Builds on strengths of both views while controlling for their weaknesses
- Provides better statistics for input into simulations for predictions and funds distribution
- As the demand for data increases and budgets decrease data re-use many be the only cost-effective option

## Policy Challenges 

- Communicating the benefits vs. privacy concerns
- Need for interagency teams to ensure accurate results
- Interagency agreements and mission
- “Ownership” of the integrated data sets
- Growth of possible disclosure risks
- Need for longitudinal data bases in order to find an anonomyzed person at an address at a point in time

## Daytime Population Estimates Feasibility Assessment

- Purpose
- Research our capacity to provide assistance to a wide range of stakeholders for disaster planning, evacuation, and recovery operations
- Approach
- Population present in a geographic area during mid-day and mid-week business hours
- Operationalized by producing an experimental set of state, county, and census tract estimates for April 1, 2005
- Relationship to daytime population measures from Census 2000, the Intercensal Estimates Program, and LEHD

**Notes:**
SONS – Spill of National Significance

Residence Rules – Usual residence – not seasonal
Operationalized as 9-12 during the day.

## Daytime Population Estimates Feasibility Assessment (2)

- Prototype Areas
- Illinois, Missouri – SONS07 New Madrid Exercise
- North Carolina, South Carolina  - Hurricanes
- Concept
- Stock vs. Flow
- Population at risk > risk factors > estimates
- Population components
- Persons in school (children and adults)
- Children in daycare
- Adults at work
- Group Quarters
- Persons at short-term medical facilities
- Persons at home

**Notes:**
Phase 1  November 2006

Phase 2 May 2007

## Daytime Population Estimates Feasibility Assessment (3)

- Not in scope
- Seasonal population
- Population temporarily located in hotels, resorts, etc.
- Transportation use
- Service and Retail use
- Phases
- I.  Basic feasibility with go/no-go decision
- II. Assessment of methods and results

## Conclusions

- Integrated data architectures are the future of American statistics
- As the demand for data increases and budgets decrease data re-use many be the only cost-effective option
- Technical and policy related challenges must be addressed
- This approach will support evidence-based public policy research and decisions.

## Contact Information

- Ronald C. Prevost
- Assistant Division Chief for Data Management
- Data Integration Division
- U.S. Census Bureau
- Washington D.C. 20233-8100
- Email: _[Ronald.C.Prevost@Census.Gov](mailto:Ronald.C.Prevost@Census.Gov)_
- Phone: (301) 763-2264
- Cell:     (202) 641-2246