using small area estimation techniques to provide county-level estimates for select indicators from...
DESCRIPTION
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?TRANSCRIPT
Using Small Area Estimation Techniques to Provide
County-level Estimates forSelect Indicators from the OFHS
Anirudh V.S. RuhilHolly RaffleSara 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 CountiesCounty N %
Cuyahoga 4,103 8.05
Franklin 3,118 6.12
Hamilton 2,266 4.45
Lucas 1,857 3.65
Montgomery 1,770 3.47
…ALL MetroCounties 22,818 44.79
Select Appalachian CountiesCounty N %
Adams 490 0.96
Carroll 303 0.59
Guernsey 290 0.57
Jackson 307 0.60
Morgan 319 0.63
Ross 365 0.72
Washington 378 0.74
…ALL AppalachianCounties 11,434 22.42
OFHS Respondents ReportingDiabetes Diagnosis in Selected Counties
Select Metropolitan CountiesCounty YES % N
Cuyahoga 572 13.94 4,103
Franklin 446 14.30 3,118
Hamilton 302 13.33 2,266
Lucas 307 16.53 1,857
Montgomery 320 18.08 1,770
Select Appalachian CountiesCounty YES % N
Adams 95 19.39 490
Carroll 38 12.54 303
Guernsey 46 15.86 290
Jackson 60 19.54 307
Morgan 62 19.44 319
Ross 64 17.53 365
Washington 52 13.76 378
OFHS Respondents ReportingDiabetes Diagnosis in Select Counties
By GenderFranklin County (Metro)Gender YES % N
Male 143 12.13 1,179
Female 303 15.63 1,938
Morgan County (Appalachian)Gender YES % N
Male 25 12.55 116
Female 37 18.23 203
OFHS Respondents ReportingDiabetes Diagnosis in Select Counties
By Age GroupFranklin County (Metro)
Age Group YES % N
18-24 2 1.02% 196
25-34 18 4.09% 440
35-44 55 9.79% 562
45-54 97 14.67% 661
54-64 121 20.00% 605
65+ 153 23.39% 654
Morgan County (Appalachian)Age Group YES % N
18-24 0 0.00% 16
25-34 4 10.81% 37
35-44 3 7.14% 42
45-54 12 15.38% 78
54-64 20 33.90% 59
65+ 23 26.44% 87
OFHS Respondents ReportingDiabetes Diagnosis in Select Counties
By Gender and Age GroupFranklin County (Metro)
Category YES % N
Male 18-24 1 1.18 85
Male 25-34 3 1.84 163
Male 35-44 15 6.36 236
Male 45-54 39 14.03 278
Male 55-64 38 16.96 224
Male 65+ 47 24.35 193
Morgan County (Appalachian)Category YES % N
Male 18-24 0 0.00 4
Male 25-34 0 0.00 10
Male 35-44 0 0.00 13
Male 45-54 8 22.86 35
Male 55-64 7 35.00 20
Male 65+ 10 29.41 34
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 CountiesCounty % SE
(%) LCL 90%
UCL90%
Cuyahoga 10.41 0.57 9.47 11.35
Franklin 11.46 0.68 10.34 12.58
Hamilton 10.59 0.75 9.36 11.82
Lucas 10.73 0.92 9.22 12.24
Montgomery 14.24 1.03 12.55 15.93
Select Appalachian CountiesCounty % SE
(%)LCL 90%
UCL90%
Adams 18.16 4.09 11.44 24.88
Carroll 10.35 2.86 5.65 15.06
Guernsey 9.99 1.88 6.89 13.09
Jackson 21.77 4.30 14.69 28.84
Morgan 9.90 2.17 6.32 13.47
Ross 13.13 2.35 9.27 16.99
Washington 11.57 2.04 8.21 14.94
County-level Estimates of Diabetes DiagnosisBy Gender Based on Survey Weights
Franklin County (Metro)Gender % SE (%) LCL
90%UCL90%
Male 9.22 0.95 7.65 10.79Female 13.48 0.96 11.91 15.06
Morgan County (Appalachian)Gender % SE (%) LCL
90%UCL90%
Male 8.76 2.98 3.86 13.67Female 11.32 3.03 6.33 16.30
County-level Estimates of Diabetes DiagnosisBy Age Group Based on Survey Weights
Franklin County (Metro)AgeGroup
% SE (%) LCL90%
UCL90%
18-24 1.45 1.02 -0.22 3.1225-34 4.11 1.10 2.31 5.9234-44 7.85 1.26 5.77 9.9345-54 14.41 1.76 11.52 17.3155-64 19.31 2.05 15.94 22.6865+ 24.10 2.26 20.38 27.83
Morgan County (Appalachian)AgeGroup
% SE (%) LCL90%
UCL90%
18-24 No Observations
25-34 6.37 4.09 -0.36 13.1034-44 3.58 2.31 -0.22 7.3745-54 18.95 6.51 8.24 29.6655-64 34.17 8.83 19.65 48.6865+ 20.65 5.27 11.98 29.32
County-level Estimates of Diabetes DiagnosisBy Gender and Age Group Based on Survey Weights
Franklin County (Metro)Category % SE
(%)LCL90%
UCL90%
Male 18-24 1.52 1.51 -0.96 4.00
Male 25-34 1.89 1.17 -0.04 3.81
Male 35-44 5.66 1.64 2.95 8.36
Male 45-54 13.84 2.58 9.60 18.08
Male 55-64 16.37 3.07 11.33 21.42
Male 65+ 22.15 3.89 15.76 28.54
Morgan County (Appalachian)Category % SE
(%)LCL90%
UCL90%
Male 18-24 No Observations
Male 25-34 No Observations
Male 35-44 No Observations
Male 45-54 19.82 8.34 6.09 33.54
Male 55-64 38.39 14.80 14.05 62.73
Male 65+ 22.05 8.51 8.05 36.05
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 Estimates • Vaccination Coverage ‘04-05 Flu• Community-level obesity (MA)
SAE from the 2008 OFHSSelected 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 EstimatesCounty Sample Population Estimate SAE
Vinton 235 10,111 19.78% 18.05%
Monroe 232 11,282 8.44% 15.73%
Noble 261 11,542 14.37% 15.63%
Morgan 319 11,593 9.90% 17.21%
Montgomery 1,770 412,891 14.24% 17.60%
Hamilton 2,266 634,420 10.59% 13.54%
Franklin 3,118 849,775 11.46% 14.36%
Cuyahoga 4,103 988,741 10.41% 14.02%
BRFSS Estimates (2005)
Coding Differences
OFHSHave 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
Diabetes Prevalence
OFHS CurrentDiabetes N %
No 43,390 85.17Yes 7,554 14.83
OFHS Calculated as BRFSSDiabetes N %
No 44,496 87.34Yes 6,448 12.66
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)
