1. Part 2: Defining Geographic Areas Frank Porell
Data and Methodology
Part 2: Defining Geographic Areas
Frank Porell

2. Geographic Definitions of Communities
The simplest and most practical way to define communities for healthy aging data reports is to use jurisdictional cities and towns, and neighborhoods within large cities.
When secondary health data sources, such as the BRFSS, are considered, the issue is not so simple.
We examine the factors that ought to be considered and
explain how the geographic communities were defined for reporting healthy aging indicators

3. Population Homogeneity is Desirable in Defining Geographic Communities
Smaller relatively homogenous geographic areas are desirable because residential neighborhoods are commonly characterized by a high degree of segmentation by socioeconomic status and race.
Potential biases can arise from “averaging” data for subareas of larger geographic areas whose residents have very different health status.

4. Large Samples are Desirable for Estimation of Population Health Indicators
Larger geographic areas are desirable because health indicators derived from larger data samples will be more reliable- “population averages” will be less sensitive to unusual “outlier” values of a small number of residents.
With larger data samples changes in health indicators over time are more likely to reflect real trends rather than random sampling variation.

5. Can a single set of geographic communities have both of these desirable features?
It is not generally possible because of the size distribution of cities and towns and the relatively small number of respondents in most health surveys.
There are generally only a handful of larger, more densely populated cities and many more smaller, more sparsely-populated, towns in any state.
Most health surveys have relatively few respondents living in any particular city or town. There were fewer than 800 BRFSS respondents from Manchester 60 years or older in 3 years of the survey ( ).

6. How do you balance the goals of homogeneous geographic areas with the sample size limitations of secondary data sources ?
Multiple nested tiers of indicators and geographic communities
Tiers depend on:
the sample size of the data source
the prevalence of the outcome measured
Indicators are estimated for larger geographic areas when:
data sources have small sample sizes
outcomes are relatively rare among older persons (e.g., HIV diagnosis)

7. Nested Tiers Approach
Smaller lower tier towns are spatially nested within larger higher tier geographic areas
Subsets of indicators are estimated using different geographic areas. Some indicators estimated for individual towns, others indicators are estimated for larger geographic areas.
The same values of higher-tier indicators are reported for all smaller constituent towns that make up larger higher-tier geographic areas.

8. Nested Tiers Approach
We believe that this nested tier approach offer a practical approach that balances the objectives of defining homogenous geographic communities with the realities of limited secondary data sources.
The nested-tiers approach is described in more detail in the next series of slides.

9. Hierarchical Structure of Tiered Geographic Areas for Community Health Aging Profiles
Region1
Area 1
Town 1
Town 2
Area 2
Town 3
Town 4
Area 3
Region 2
Greater Region 1
Town 5
Area 4
Town 6

10. Tiers of Geographic Areas
Tier 1: 244 Cities/ Towns/ Neighborhoods
American Community Survey (ACS)
Tier 2: 154 Medicare High Prevalence Geographic Areas
Medicare Beneficiary Summary File
Tier 3: 69 Medicare Low Prevalence Geographic Areas
Tier 4: 28 BRFSS Geographic Areas
Behavioral Risk Factor Surveillance System

11. Reported Values of Upper Tier Indicators for Cities and Towns
Given the hierarchical structure of the defined geographic tiers, all Tier 1 cities, towns, and neighborhoods which make up a higher-tier geographic area are assigned the same higher-tier indicator estimate.
The next three slides display hypothetical examples of this reporting approach

12. All Tier 1 communities nested within the same Tier 4 area have the same Tier 4 indicator value reported
Tier 4
Tier 3
Tier 2
Tier 1
Region1
Area 1
38.5 %
Area 2
38.5%
Area 3
Region 2
Area 4

13. All Tier 1 communities nested within the same Tier 3 area have the same Tier 3 indicator value reported
Tier 4
Tier 3
Tier 2
Tier 1
42.2%
Area 1
42.2 %
Area 2
17.3%
Area 3
Greater Region 1
Area 4

14. All Tier 1 communities nested within Tier 2 have the same Tier 2 indicator value reported
Region1
12.7%
12.7 %
15.2%
14.1%
Region 2
Greater Region 1
13.3%

15. The End