# Presentation: 947453

## Safety Net Data Collection Strategies

**AHRQ User Liaison Program**
**Washington, D.C.**
**September 24, 2003**

*Supported by a grant from The Robert Wood Johnson Foundation*

**Lynn A. Blewett, Ph.D.**
**State Health Access Data Assistance Center**
**School of Public Health, University of Minnesota**
- _[blewe001@umn.edu](mailto:blewe001@umn.edu)_
- _[www.shadac.org](http://www.shadac.org/)__ _

## Estimating the Number of Uninsured

**Estimates of demand based on state and local estimates of the number of uninsured**
**Most estimates come from household surveys that ask about health insurance coverage **
**Also used in combination with employer surveys that ask about health insurance coverage**

## Estimating the Size of the Uninsured Populations at the Local Level

**Do your own survey **
**	****- To directly measure health insurance coverage and rates of uninsurance**
**Use existing available data to:**
**Develop proxy measures of uninsurance **
**Develop statistical model-based estimates**

## Direct Measures: National Data

**Three surveys provide *****state-level***** estimates of health insurance coverage.**
**Current Population Survey (CPS):  *****Census***
**Medical Expenditure Panel (Employer) Survey – Insurance Component (MEPS-IC):  *****Agency for Healthcare Research and Quality***
**State and Local Area Integrated Telephone Survey (SLAITS):  *****National Center for Health Statistics***

## Direct Measures: State Data

**37 States have developed and fielded their own state household survey to estimate levels of health insurance coverage**
**Many funded by the federal Health Resources Services and Administration State Planning Grant Program**
**Typically have larger sample size and some regional/county estimates of coverage**

## Other National Surveys of Note

**National Survey of America’s Families (NSAF):  *****Urban Institute***
**Uninsurance estimates available for 13 states**
**Public use files available to states for analysis**
**Community Tracking Study: *****Center for Health System Change***
**Uninsurance estimates can be made for the 12 communities in the CTS sample**

## Proxy Measures

**Use an available measure to serve as a proxy for health insurance coverage **
**Example: self-pay variable from hospital administrative records to estimate local levels of uninsurance**

## Model-Based Approach

**Predicts health insurance coverage using one or more variables correlated with health insurance coverage**** **
**Example: correlation between state unemployment and uninsurance and applying this at local level**

## National Model-Based Estimates

**Small-Area Estimation **
**Census and AHRQ are both working on sophisticated models to provide state and local area estimates**
**CPS:  Uninsurance**
**MEPS-IC:  Employment offer and take up rates**

## Overview of Approaches for Estimating Number of

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- <date/time>

| Approach | Principal 
Strength | Principal
Weakness |
| --- | --- | --- |
| Direct Estimation- New Survey | Precision | Cost |
| Proxy Measure | Cost | Bias |
| Model-Based | Predictive Accuracy | Complexity |

- Uninsured At the Local Level

## Conclusions

**Be knowledgeable and aware of different data sources available for your community  **
**Use existing resources and data**** and ****build on existing state and local survey activities**
**Use multiple approaches to maximize information**
**Be flexible as new data become available**** **

## Contact Information

- 2221 University Avenue, Suite 345
- Minneapolis Minnesota 55414
- (612) 624-4802

- Principal Investigator: Lynn Blewett, Ph.D. blewe001@umn.edu
- Co-Principal Investigator:  Kathleen Call, Ph.D. callx001@umn.edu
- Center Director:  Kelli Johnson, M.B.A. johns706@umn.edu
- Senior Research Associate: Timothy Beebe, Ph.D. beebe002@umn.edu
- Research Associate: Michael Davern, Ph.D.  daver004@umn.edu