Identifying Bright Spots in Appalachian 

Health: Statistical Analysis

Executive Summary

STATISTICAL 

ANALYSIS

3

This report, Identifying Bright Spots in Appalachian Health: Statistical Analysis, was produced through the 

“Creating a Culture of Health in Appalachia: Disparities and Bright Spots" research initiative funded by the 

Robert Wood Johnson Foundation (RWJF) and the Appalachian Regional Commission (ARC), and 

administered by the Foundation for a Healthy Kentucky. To date, this multi-part health research project has 

produced the following three reports: 

1. Health Disparities in Appalachia (August 2017) measures population health in the Appalachian 

Region and documents disparities between Appalachia and the nation as a whole, as well as 

disparities within the Region. 

2. Identifying Bright Spots in Appalachian Health: Statistical Analysis (July 2018) describes the 

results of the regression analysis used to assess how each of the Appalachian Region’s 420 counties 

scored on 19 different health indicators, and then identifies counties with better-than-expected 

outcomes, given their characteristics and resource levels. Through this process, 42 Appalachian 

counties were classified as Bright Spot counties. 

3. Exploring Bright Spots in Appalachian Health: Case Studies (July 2018) presents in-depth studies 

of 10 of the 42 Bright Spot counties identified through the statistical analysis. This report explores

local perceptions of practices that may be associated with better-than-expected health outcomes, 

and summarizes promising strategies that may be replicable in other communities. 

As described above, this report is the second in the series and is the quantitative companion to the third 

report, Exploring Bright Spots in Appalachian Health: Case Studies. 

The reports offer a basis for understanding and addressing health in the Appalachian Region and for 

identifying factors that support a culture of health in Appalachian communities. They also explore 

activities, programs, and policies that may encourage better-than-expected health outcomes, many of 

which may be replicable in other communities. 

The fourth and final report in the series, expected to be published in late 2018, will provide 

recommendations for practical strategies and activities that build on the findings of the first three reports. 

ABOUT THE APPALACHIAN REGION

The current boundary of the Appalachian Region includes all of West Virginia and parts of 12 other 

states: Alabama, Georgia, Kentucky, Maryland, Mississippi, New York, North Carolina, Ohio, 

Pennsylvania, South Carolina, Tennessee, and Virginia (see Figure 1). The Region covers 205,000 square 

miles and 420 counties, and is home to more than 25 million Americans. Forty-two percent of the 

Region’s population is rural, compared with 20 percent of the national population.
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Figure 1: Map of the Appalachian Region

BRIGHT SPOTS ANALYSIS

Overview

The primary objective of this report is to use regression analysis to identify Bright Spots, or counties in 

the Appalachian Region that have better-than-expected health outcomes given their characteristics and 

resource levels—that is, the socioeconomics, demographics, behaviors, health care facilities, and other 

factors that influence health outcomes. The second objective is to develop a systematic way to match the 

counties identified as Bright Spots with other counties in the Region to facilitate the exchange of ideas 

and lessons learned. The third objective is to identify the factors that appear to have the greatest impact on 

health outcomes—although this statistical analysis will provide suggestions rather than definitive 

conclusions. 

The use of regression models and residual-based analyses is common in health research, though typically 

the focus of these research efforts is on how much the factors included in the model explain the outcome. 

In the Bright Spots analysis, however, the unexplained portions are of the greatest interest—that is, which 

counties are the most “unexpectedly” healthy given their characteristics and resources.

The model in this report bears some similarity to another research approach used to identify positive 

health outcomes that occur despite difficult circumstances. That approach, positive deviance, identifies 

the individuals, groups, and organizations affecting change at the local level, as opposed to macro-level 

policies at the state and national levels. The underlying principle of positive deviance is that by 

identifying individuals and groups that are overcoming challenges affecting a large number of people in a 

given community, researchers can identify specific, simple best practices that can be shared with other 

communities. Many of the best practices uncovered via the positive deviance approach originate from 

within the community and are implementable despite resource constraints. 

Although the Bright Spots model does not fit wholly under the umbrella of positive deviance, this 

approach provided the motivation for our framework and the foundation for exploring counties through 

in-depth, field-based case studies. 
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Methods

The general approach in this analysis assumes we can broadly measure health in a community, compare 

actual outcomes to expected outcomes, and determine whether a community exceeds expectations.

We first identified 19 county-level outcome measures that capture the overall health of a community (see

Table 1). Some examples of these measures include the infant mortality rate, cancer mortality rate, 

percentage of adults who are obese, prevalence of diabetes, and prevalence of depression among 

Medicare beneficiaries. The selected outcomes represent both physical and behavioral health, as well as 

diagnosed and perceived health.

We then identified 29 county-level drivers known to affect individual and community health (see 

Table 2). The drivers were organized into broad categories, such as social determinants, health behaviors, 

and access to health care services. Examples of drivers include median income, percentage of adults with 

some college education, percentage of the population under age 65 who are uninsured, number of primary 

care physicians per 100,000 population, and percentage of adults who smoke. 

A multivariate regression analysis determined the relationship between the 19 health outcome measures 

and the 29 driver measures, producing one expected value for each of the 19 outcome measures for each 

Appalachian county. The expected outcomes were then compared to the actual, observed outcomes for 

each county to identify counties that performed better than expected. In most counties, some of the 19 

outcomes were better than expected and some were worse than expected. Each outcome residual was then 

standardized into a z-score to allow comparison across all outcome measures. We reversed signs on the 

outcomes so that positive z-scores indicated “good health.” 

By using the average degree to which a county’s observed health outcomes exceeded expected values, the 

Bright Spots model identified counties that either did very well on a few outcomes or exceeded 

expectations—perhaps only marginally—across many outcomes.

Because access to resources differs between metropolitan and nonmetropolitan areas, the statistical 

analysis was applied independently to two geographic groups: metropolitan and nonmetropolitan 

counties. Inclusion in a U.S. Census Metropolitan Statistical Area defined a county as metro. However, 

because the metro and nonmetro datasets are distinct, average residuals of the two groups are not 

comparable.

A county whose average of all 19 standardized outcome residuals scored in the top decile in either 

the metropolitan or nonmetropolitan group was classified as a Bright Spot. 

The variation in county location and economic status lends support to the study design—we did not aim 

to identify healthy counties with high levels of resources and the sorts of characteristics that support 

positive health outcomes, but rather counties encompassing a wide range of resource levels and 

characteristics that all managed to find a way to be healthier than expected. Bright Spots are places that 

exceed expectations, regardless of the values of the drivers. This is a strength of the approach, one that 

allowed us to focus on the positive aspects of communities relative to their own characteristics and 

resource levels.
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Table 1: Outcome Measures

Category Measure

Mortality 

Years of potential life lost

per 100,000

Stroke mortality per 

100,000

All cancer mortality per 

100,000

Unintentional injury 

mortality per 100,000

COPD mortality per 

100,000

Heart disease mortality 

per 100,000

Mental 

Health

Average mentally 

unhealthy days per

person per month 

Suicide mortality per 

100,000

Percentage Medicare 

beneficiaries with 

depression

Child 

Health

Percentage of live births 

with low birth weight

(<2500g) 

Infant mortality per 1,000 

births

Chronic 

Disease

Percentage adults with 

diabetes

Medicare heart disease 

hospitalizations per 1,000

Average Hierarchical 

Condition Category 

(HCC) risk score per 

Medicare beneficiary a

Percentage adults with 

obesity (BMI>30)

Average physically 

unhealthy days per 

person per month 

Substance 

Abuse

Percentage residents 

drinking excessively

Poisoning mortality per 

100,000

Opioid prescriptions as 

percentage of Part D 

claims

Notes: a. Unless noted, information on each 

measure is included in the Disparities report 

in this series (Marshall, et al., 2017).

Table 2: Driver Measures

Category Measure

Child Health Teenage births per 1,000

Environment

Full-service restaurants per 1,000 population a

Percentage with access to exercise 

opportunities a

Air pollution (average daily particulate matter,

PM2.5) a

Grocery stores per 1,000 population

Students per teacher (primary and secondary 

school)

Average travel time to work in minutes

Health 

Behaviors

Percentage of adults currently smoking

Percentage of adults not physically active

Chlamydia incidence per 100,000

Health Care 

System and 

Utilization

Primary care physicians per 100,000 

population

Dentists per 100,000 population

Specialty physicians per 100,000 population

Mental health providers per 100,000 

population

Percentage of physicians that e-prescribe

Percentage under 65 who are uninsured

Quality

Percentage of Medicare diabetics with HbA1c

testing

Percentage of Medicare women with recent 

mammogram

Social 

Determinants

Percentage of total population in paid Social 

Assistance jobs a

Income inequality ratio a

Percentage eligible enrolled in SNAP (Food 

Assistance) a

Percentage of households with no car and low 

access to grocery stores a

Percentage of households spending >30% of 

income on housing a

ARC Economic Index

Social association rate per 10,000 population

Percentage receiving disability benefits 

(OASDI and/or SSI)

Percentage of adults with some college

education

Percentage of households with income below 

poverty line

Median household income
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The Bright Spot Counties 

Appalachian counties with an average standardized residual score in the top decile (10 percent) in either 

the metropolitan or the nonmetropolitan groups were identified as Bright Spots. Thus, the model 

identified 42 Appalachian Bright Spot counties: 15 in the metropolitan group and 27 in the 

nonmetropolitan group. The 42 counties in the top decile represent the best of the better than expected. In 

fact, scores for 202 counties were better than expected; the Bright Spots are simply those counties with 

scores in the top ten percent in their respective groups. 

Bright Spot counties are located in all five Appalachian subregions and represent the diversity of 

communities across the Appalachian Region (see Figure 2). 

Figure 2: Map of the Bright Spot Counties in Appalachia 

Table 3 lists the metropolitan Bright Spot counties, their average standardized residual score, and the 

outcome with the highest residual, which reflects the greatest over performance relative to available 

resources. Table 4 shows the same information for nonmetropolitan Bright Spot counties. 

The higher the residual score, the more a county outperformed its expectations. The standardized residual 

scores represent standard deviations. For example, outcomes in a county with an average residual score of 

0.47 were, on average, 0.47 standard deviations above the expected outcomes. 

GEORGIA

SOUTH 

CAROLINA

NORTH CAROLINA

ALABAMA

MISSISSIPPI

TENNESSEE

KENTUCKY

OHIO PENNSYLVANIA

MARYLAND

VIRGINIA

NEW YORK

WEST

VIRGINIA

Northern

Southern

Central

South Central

North Central

0 100 50

Miles

Bright Spot Counties

Rest of Region

Bright Spots
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Table 3: Metropolitan Appalachian Bright Spot Counties, Ranked by Average Outcome Residual

Rank County State

Average 

Standardized 

Residual Score a

Highest Individual Residual b

1 Wirt West Virginia 0.47 Injury mortality 1.58

2 Clay West Virginia 0.40 Heart disease mortality 1.51

3 Henderson North Carolina 0.35 % obese adults 0.98

4 Hale Alabama 0.35 Depression prevalence 1.10

5 Sequatchie Tennessee 0.31 Poisoning mortality 1.22

6 Floyd Virginia 0.30 COPD mortality 1.08

7 Sullivan Tennessee 0.30 Poisoning mortality 1.23

8 Marshall Mississippi 0.30 % opioid Rx claims 1.58

9 Madison North Carolina 0.29 % obese adults 1.26

10 Whitfield Georgia 0.29 Depression prevalence 0.97

11 Tioga New York 0.27 Stroke mortality 0.87

12 Schoharie New York 0.25 Average HCC risk score 0.83

13 Beaver Pennsylvania 0.25 Average HCC risk score 1.00

14 Jefferson Tennessee 0.24 Average HCC risk score 1.06

15 Catoosa Georgia 0.24 Stroke mortality 0.90

Notes: a. Average residual score for the regression analysis involving 152 Appalachian metro counties

b. Highest of the 19 standardized residual outcome scores for each county.
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Table 4: Nonmetropolitan Appalachian Bright Spot Counties, Ranked by Average Outcome 

Residual

Rank County State

Average 

Standardized 

Residual Score a

Highest Individual Residual b

1 Wayne Kentucky 0.72 Stroke mortality 1.79

2 Noxubee Mississippi 0.58 COPD mortality 2.19

3 Calhoun West Virginia 0.58 Injury mortality 2.02

4 Grant West Virginia 0.49 Cancer mortality 1.88

5 McCreary Kentucky 0.45 Poisoning mortality 1.94

6 Potter Pennsylvania 0.45 Heart disease mortality 1.44

7 Taylor West Virginia 0.42 Heart disease hospitalizations 1.20

8 Rockbridge Virginia 0.41 Heart disease hospitalizations 1.37

9 Pulaski Kentucky 0.40 Poisoning mortality 1.64

10 Green Kentucky 0.40 YPLL 1.38

11 Lee Virginia 0.40 Poisoning mortality 2.29

12 Russell Kentucky 0.40 Heart disease hospitalizations 1.68

13 Bledsoe Tennessee 0.39 Cancer mortality 1.88

14 Grayson Virginia 0.39 Injury mortality 1.83

15 Hardy West Virginia 0.38 % opioid Rx claims 1.21

16 Johnson Tennessee 0.38 Poisoning mortality 1.52

17 Lincoln Kentucky 0.37 % obese adults 1.37

18 Meigs Tennessee 0.36 % opioid Rx claims 2.17

19 Pendleton West Virginia 0.36 Poisoning mortality 1.48

20 Choctaw Mississippi 0.35 Cancer mortality 1.69

21 Adair Kentucky 0.35 Injury mortality 1.57

22 Lewis Kentucky 0.34 Depression prevalence 1.78

23 Roane West Virginia 0.33 Heart disease hospitalizations 1.35

24 Monroe Tennessee 0.32 COPD mortality 1.18

25 Alleghany North 

Carolina 0.31 YPLL 1.18

26 Chickasaw Mississippi 0.31 Stroke mortality 1.61

27 Morgan Kentucky 0.28 Injury mortality 0.92

Notes: a. Average residual score for the regression analysis involving 268 Appalachian nonmetro counties

b. Highest of the 19 standardized residual outcome scores for each county 
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KEY FINDINGS AND OBSERVATIONS 

Bright Spot Patterns and Clusters

The Bright Spots are not distributed evenly among the Appalachian states—Kentucky and Mississippi 

have proportionately more Bright Spot counties than other states. 

On the other hand, the model did not identify any Bright Spot counties in Ohio, a state with 32 

Appalachian counties. The other two states with no identified Bright Spot counties, South Carolina and 

Maryland, have only a few Appalachian counties: six and three, respectively. The absence of Bright Spots 

in these two states may be the result of small sample sizes, whereas the Ohio result suggests a pattern of 

lower-than-expected outcomes. 

Several Bright Spot counties appear in geographic clusters, suggesting that factors leading to better-thanexpected health may prevail across broad, multicounty areas. Clustering suggests the presence of some 

common factor that has improved the health of the cluster. The unit of analysis, the county, may be a 

proxy for a larger “community.” These communities may be in the service area of a particularly effective 

program, health care provider, or other resource. Alternatively, other factors, such as environment, local 

culture, and tradition, may also support a culture of health. 

Correlation of Specific Outcomes with Overall Health 

Our approach allows us to broadly measure health in a community and determine whether that 

community exceeds expectations. We developed the average standardized outcome residual for this 

purpose, as it captures the degree to which a county’s outcomes exceeded expectations. However, it is 

important to keep in mind that the average standardized residual does not represent the entire composition 

of a county’s health status. For individual outcomes, even among counties identified as Bright Spots, 

there were still lower-than-expected values. These results suggest that community health cannot be 

painted with one broad brushstroke; rather, it is more accurately represented as a multidimensional 

combination of many different aspects of health. 

One key aspect was to model the actual value of outcomes, rather than incorporate scales or indices. With 

this approach, we were able to find certain individual outcome measures that were more highly correlated 

with overall good health outcomes. Three of the 19 health outcome measures were consistently better 

than expected in the Bright Spot counties: 

• Premature mortality;

• Unintentional injury mortality; and, 

• Poisoning mortality.

Premature mortality (YPLL) had the highest correlation with the average standardized residual, 

supporting its use as a comprehensive measure of community health. Further, outcomes such as injury 

mortality and poisoning mortality were highly correlated with average standardized outcome residuals in 

the top-performing counties. Bright Spot counties—those in the top decile of average outcome 

residuals—tended to have better-than-expected poisoning mortality rates.

Unintentional injury was the fourth-leading cause of death in the United States in 2014, and includes 

deaths due to car accidents, falls, and poisoning. Poisoning mortality includes deaths due to overdose. 
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Among the ten counties with the lowest average standardized residuals in both the metropolitan and 

nonmetropolitan groups—20 counties altogether—only one county performed better than expected on 

poisoning mortality; many others had much higher poisoning mortality rates than expected. This suggests 

that poisoning mortality—and by extension, substance abuse—may have an important link to overall 

health for all counties.

Seven High-Impact Drivers

The results of this analysis suggest that the following seven drivers predicted the most variation in the 19 

health outcomes (the direction generally associated with better health is shown in parentheses): 

• Median income (higher); 

• ARC Economic Index value (lower); 

• Poverty rate (lower);

• Percentage of adults that smoke (lower);

• Percentage of adults that are physically inactive (lower);

• Percentage of the population receiving disability payments (lower); and, 

• Teen birth rates (lower). 

These seven drivers were better predictors of health outcomes in the Bright Spot counties than drivers 

describing the supply of health resources, such as the supply of primary care physicians or the supply of 

specialty physicians. 

These findings suggest that focusing on improvements in these seven drivers may lead to the 

greatest overall impact on health in a community.

Notably, a county’s teen birth rate emerged as a key driver of community health across most of the 

outcomes. Teen pregnancy serves as a marker for economic opportunity in the community, captures 

“risky behavior” among teenagers, including unprotected sex, which is often associated with substance 

use (Salas-Wright, Vaughn, Ugalde, & Todic, 2015), and can have long-lasting effects on young parents. 

The teen birth rate serves as a marker for life course outcomes: daughters of teenage mothers are more 

likely to become teenage mothers themselves (Albert, 2002). Practical strategies aimed at limiting teen 

birth rates, such as comprehensive, medically accurate sex education courses and other public health 

interventions, may have long-lasting, positive effects.

Matching Bright Spot Counties to Other Counties

Because the purpose of the research was to identify Bright Spots, explore best practices—or aspects of 

local culture in those communities that may be associated with better-than-expected health outcomes—

and ultimately share those features with other communities, we calculated a measure to determine the 

similarity between each Appalachian county and each Bright Spot county. 

Based on demographics, resources, and community characteristics, we used Euclidean distance analysis 

to create a propensity score, reflecting the similarity between the Bright Spot counties and other 

Appalachian counties. Table 13 in Appendix C shows the closest match for each county in Appalachia to 

one of the Bright Spot counties selected for case studies. Table 14 in Appendix C then shows each

Appalachian county’s best match out of the 42 Bright Spots identified through the statistical analysis. 
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Opportunity to Live Healthy

Research shows that positive health behaviors consistently have large, statistically significant 

relationships to good health outcomes (National Institutes of Health, 2015). Results in this report support 

and amplify this finding. In this study, the drivers that described behaviors, such as the percentage of 

adults who smoke, the percentage of adults who are physically inactive, and the teen birth rate, were more 

highly correlated with good health outcomes than drivers quantifying the supply of health resources. Our 

findings suggest that traditional public health initiatives should accompany efforts to develop community 

health infrastructure. For example, funding for community health workers trained to communicate chronic 

disease prevention behaviors might reach deeper into community values and have a greater impact on 

population health than the supply of additional providers alone.

Overall, this study supports an emerging body of literature that attests to the association between positive 

population health outcomes and a community’s social, economic, and environmental factors.

NEXT STEPS: CASE STUDIES

From the 42 Bright Spots, we selected ten counties for in-depth, field-based investigations. The ten case 

study counties represent the diversity of communities in the Appalachian Region. Table 5 shows the ten 

case study counties, which include two counties in each of the five Appalachian subregions, an even 

distribution between metro and nonmetro counties, and three of ARC’s five economic status 

classifications.

Table 5: Characteristics of Selected Case Study Sites

County State Subregion Metro /

Nonmetro

Average 

Outcome 

Residuala

2014 

Population

Economic 

Statusb

Wirt WV North Central Metro 0.47 5,810 At-Risk

Hale AL Southern Metro 0.35 15,393 Distressed

Sequatchie TN South Central Metro 0.31 14,431 Transitional

Tioga NY Northern Metro 0.27 50,464 Transitional

Madison NC South Central Metro 0.29 20,951 At-Risk

Wayne KY Central Nonmetro 0.72 20,728 Distressed

Noxubee MS Southern Nonmetro 0.58 11,240 Distressed

Grant WV North Central Nonmetro 0.49 11,829 Transitional

McCreary KY Central Nonmetro 0.45 18,073 Distressed

Potter PA Northern Nonmetro 0.45 17,451 Transitional

Sources: see Table 1 in Appendix B and Tables 3 and 5 in Appendix C

a. Average outcome residuals are not comparable between metro and nonmetro groups

b. ARC economic designation, fiscal year 2017

The findings from the case studies are discussed in the third report in this series, Exploring Bright 

Spots in Appalachian Health: Case Studies.