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Anirudh V.S. Ruhil Holly Raffle Sara L. Boyd Nicole R. Yandell Ohio University

Using Small Area Estimation Techniques to Provide County-level Estimates for Select Indicators from the OFHS. Anirudh V.S. Ruhil Holly Raffle Sara L. Boyd Nicole R. Yandell Ohio University. Introduction.

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Anirudh V.S. Ruhil Holly Raffle Sara L. Boyd Nicole R. Yandell Ohio University

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  1. Using Small Area Estimation Techniques to ProvideCounty-level Estimates forSelect Indicators from the OFHS Anirudh V.S. Ruhil Holly Raffle Sara L. Boyd Nicole R. Yandell Ohio University

  2. Introduction • Financial and logistical constraints often prevent national and state surveys from interviewing a large enough sample from a small geographical area that will yield accurate estimates from the data. • Small geographic area = county • Policy or programmatic considerations often require reliable estimates for various health indicators at the county level.

  3. Outline of Presentation • What types of geographic estimates are available from the 2008 OFHS? • Why is there a need to further explore county-level estimates for indicators? • How can we generate more robust county-level estimates for indicators? • What county-level indicators based will become available to the public?

  4. What types of estimates are available from the 2008 OFHS? The OFHS was designed to yield accurate geographic estimates for the following designations: • State level • Regional level: Metropolitan counties, Suburban counties, Rural Non-Appalachian counties, and Rural Appalachian counties. • County level estimates have been released, these are based on sample weighting

  5. Why is there a need to further explore county-level estimates for indicators? • The OFHS was not designed to yield county-level estimates. • For this reason, the sample size within some counties may be too small to generate accurate estimates from the data.

  6. OFHS Respondents in Selected Counties Select Metropolitan Counties Select Appalachian Counties

  7. OFHS Respondents ReportingDiabetes Diagnosis in Selected Counties Select Metropolitan Counties Select Appalachian Counties

  8. OFHS Respondents ReportingDiabetes Diagnosis in Select CountiesBy Gender Franklin County (Metro) Morgan County (Appalachian)

  9. OFHS Respondents ReportingDiabetes Diagnosis in Select CountiesBy Age Group Franklin County (Metro) Morgan County (Appalachian)

  10. OFHS Respondents ReportingDiabetes Diagnosis in Select CountiesBy Gender and Age Group Franklin County (Metro) Morgan County (Appalachian)

  11. Why is there a need to further explore county-level estimates for indicators? • County-level estimates for Appalachian counties based upon sample weights will have larger confidence intervals than those for metropolitan counties. • Confidence Interval: Estimate that gives a more accurate impression of the degree of confidence that you can have in your point estimate (often expressed as a range).

  12. County-level Estimates of Diabetes Diagnosis Based on Survey Weights Select Metropolitan Counties Select Appalachian Counties

  13. County-level Estimates of Diabetes DiagnosisBy Gender Based on Survey Weights Franklin County (Metro) Morgan County (Appalachian)

  14. County-level Estimates of Diabetes DiagnosisBy Age Group Based on Survey Weights Franklin County (Metro) Morgan County (Appalachian)

  15. County-level Estimates of Diabetes DiagnosisBy Gender and Age Group Based on Survey Weights Franklin County (Metro) Morgan County (Appalachian)

  16. How can we generate more robust county-level estimates for indicators? • “Small area estimation” (SAE) techniques • The goal of SAE is to develop direct/indirect estimates (e.g., prevalence rates) of health status indicators for smaller geographies • Supplemental data (e.g., US Census Data) vital for SAE

  17. How can we generate robust county-level estimates for indicators? • SAE techniques allow us to “make up” for the small sample in the survey of interest (OFHS) by “borrowing strength” from data collected in the same area at a different time (Census). • In essence, SAE methods fill a gap in available data

  18. Some Examples … • BRFSS County-level Estimates • Vaccination Coverage ‘04-05 Flu • Community-level obesity (MA)

  19. SAE from the 2008 OFHS

  20. Preliminary Estimates

  21. BRFSS Estimates (2005)

  22. Coding Differences OFHS BRFSS Have you ever been told by a doctor that you have diabetes? If “Yes” and respondent is female, ask: “Was this only when you were pregnant?” 1 Yes 2 Yes, but female told only during pregnancy 3 No 4 No, pre-diabetes or borderline diabetes 7 Don’t know / Not sure 9 Refused Have you/Has [FILL IN] ever been told by a doctor or any other health professional that you/he/she had diabetes or sugar diabetes? 01-YES 02-NO 03-BORDERLINE 98-DK 99-REFUSED

  23. Diabetes Prevalence OFHS Current OFHS Calculated as BRFSS

  24. Next Steps … • Continue to refine the estimates for all 13 indicators • Validate analyses in other software (where/when possible) • Spatially smoothed estimates • Apply to BRFSS 2000-2008 data (Several indicators)

  25. Contact Information • Anirudh V.S. Ruhil ruhil@ohio.edu 740-597-1949 • Holly Raffle raffle@ohio.edu 740-797-1710

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