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 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.

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?

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

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.

OFHS Respondents in Selected Counties Select Metropolitan Counties CountyN% Cuyahoga4, Franklin3, Hamilton2, Lucas1, Montgomery1, … ALL Metro Counties22, Select Appalachian Counties CountyN% Adams Carroll Guernsey Jackson Morgan Ross Washington … ALL Appalachian Counties11,

OFHS Respondents Reporting Diabetes Diagnosis in Selected Counties Select Metropolitan Counties CountyYES%N Cuyahoga ,103 Franklin ,118 Hamilton ,266 Lucas ,857 Montgomery ,770 Select Appalachian Counties CountyYES% N Adams Carroll Guernsey Jackson Morgan Ross Washington

OFHS Respondents Reporting Diabetes Diagnosis in Select Counties By Gender Franklin County (Metro) GenderYES%N Male ,179 Female ,938 Morgan County (Appalachian) GenderYES%N Male Female

OFHS Respondents Reporting Diabetes Diagnosis in Select Counties By Age Group Franklin County (Metro) Age GroupYES%N % % % % % %654 Morgan County (Appalachian) Age GroupYES%N % % % % % %87

OFHS Respondents Reporting Diabetes Diagnosis in Select Counties By Gender and Age Group Franklin County (Metro) CategoryYES%N Male Male Male Male Male Male Morgan County (Appalachian) CategoryYES%N Male Male Male Male Male Male

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).

County-level Estimates of Diabetes Diagnosis Based on Survey Weights Select Metropolitan Counties County%SE (%) LCL 90% UCL 90% Cuyahoga Franklin Hamilton Lucas Montgomery Select Appalachian Counties County%SE (%) LCL 90% UCL 90% Adams Carroll Guernsey Jackson Morgan Ross Washington

County-level Estimates of Diabetes Diagnosis By Gender Based on Survey Weights Franklin County (Metro) Gender%SE (%)LCL 90% UCL 90% Male Female Morgan County (Appalachian) Gender%SE (%)LCL 90% UCL 90% Male Female

County-level Estimates of Diabetes Diagnosis By Age Group Based on Survey Weights Franklin County (Metro) Age Group %SE (%)LCL 90% UCL 90% Morgan County (Appalachian) Age Group %SE (%)LCL 90% UCL 90% 18-24No Observations

County-level Estimates of Diabetes Diagnosis By Gender and Age Group Based on Survey Weights Franklin County (Metro) Category%SE (%) LCL 90% UCL 90% Male Male Male Male Male Male Morgan County (Appalachian) Category%SE (%) LCL 90% UCL 90% Male 18-24No Observations Male 25-34No Observations Male 35-44No Observations Male Male Male

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

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

Some Examples … BRFSS County-level EstimatesEstimates Vaccination Coverage ‘04-05 Flu Community-level obesity (MA)

SAE from the 2008 OFHS Selected Indicators – Adults Only 1.High Blood Pressure (Hypertension) 2.Heart Attack (Myocardial Infarction) 3.Coronary Heart Disease (Coronary Artery Disease, Angina, etc.) 4.Stroke 5.Congestive Heart Failure 6.Diabetes (Sugar Diabetes) 7.Cancer 8.Obesity (based on BMI) 9.Individuals reporting to have NOT filled an Rx due to cost 10.Individuals reporting inability to pay medical bills 11.Of those reporting inability to pay medical bills, inability to pay for basic needs 12.Of those reporting inability to pay medical bills, drained savings due to medical bills 13.Of those reporting inability to pay medical bills, incurred debt due to medical bills

Preliminary Estimates CountySamplePopulationEstimateSAE Vinton23510, %18.05% Monroe23211, %15.73% Noble26111, %15.63% Morgan31911, %17.21% Montgomery1,770412, %17.60% Hamilton2,266634, %13.54% Franklin3,118849, %14.36% Cuyahoga4,103988, %14.02%

BRFSS Estimates (2005)

Coding Differences OFHS 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 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

Diabetes Prevalence OFHS Current DiabetesN% No 43, Yes 7, OFHS Calculated as BRFSS DiabetesN% No 44, Yes 6,

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 data (Several indicators)

Contact Information Anirudh V.S. Ruhil Holly Raffle