Improving the environmental quality component of the County Health Rankings model

Cluster Analysis Methods to Support Population Health Improvement Among US Counties

CONTEXT

Population health rankings can be a catalyst for the improvement of health by drawing attention to areas in need of relative improvement and summarizing complex information in a manner understood by almost everyone. However, ranks also have unintended consequences, such as being interpreted as "hard truths," where variations may not be significant. There is a need to improve communication about uncertainty in ranks, with accurate interpretation. The most common solutions discussed in the literature have included modeling approaches to minimize statistical noise or borrow strength from covariates. However, the use of complex models can limit communication and implementation, especially for broad audiences.

OBJECTIVES

Explore data-informed grouping (cluster analysis) as an easier-to-understand, empirical technique to account for rank imprecision that can be effectively communicated both numerically and visually.

DESIGN

Cluster analysis, specifically k-means clustering with Wasserstein (earth mover's) distance, was explored as an approach to identify natural and meaningful groupings and gaps in the data distribution for the County Health Rankings' (CHR) health outcomes ranks.

SETTING

County-level health outcomes from the 2022 CHR.

PARTICIPANTS

3082 counties that were ranked in the 2022 CHR.

MAIN OUTCOME MEASURE

Data-informed health groups.

RESULTS

Cluster analysis identified 30 health groupings among counties nationwide, with cluster size ranging from 9 to 184 counties. On average, states had 16 identified clusters, ranging from 3 in Delaware and Hawaii to 27 in Virginia. Number of clusters per state was associated with number of counties per state and population of the state. The method helped address many of the issues that arise from providing rank estimates alone.

CONCLUSIONS

Public health practitioners can use this information to understand uncertainty in ranks, visualize distances between county ranks, have context around which counties are not meaningfully different from one another, and compare county performance to peer counties.

Proximity to Oil Refineries and Risk of Cancer: A Population-Based Analysis

Background

The association between proximity to oil refineries and cancer rate is largely unknown. We sought to compare the rate of cancer (bladder, breast, colon, lung, lymphoma, and prostate) according to proximity to an oil refinery in Texas.

Methods

A total of 6 302 265 persons aged 20 years or older resided within 30 miles of an oil refinery from 2010 to 2014. We used multilevel zero-inflated Poisson regression models to examine the association between proximity to an oil refinery and cancer rate.

Results

We observed that proximity to an oil refinery was associated with a statistically significantly increased risk of incident cancer diagnosis across all cancer types. For example, persons residing within 0-10 (risk ratio [RR] = 1.13, 95% confidence interval [CI] = 1.07 to 1.19) and 11-20 (RR = 1.05, 95% CI = 1.00 to 1.11) miles were statistically significantly more likely to be diagnosed with lymphoma than individuals who lived within 21-30 miles of an oil refinery. We also observed differences in stage of cancer at diagnosis according to proximity to an oil refinery. Moreover, persons residing within 0-10 miles were more likely to be diagnosed with distant metastasis and/or systemic disease than people residing 21-30 miles from an oil refinery. The greatest risk of distant disease was observed in patients diagnosed with bladder cancer living within 0-10 vs 21-30 miles (RR = 1.30, 95% CI = 1.02 to 1.65), respectively.

Conclusions

Proximity to an oil refinery was associated with an increased risk of multiple cancer types. We also observed statistically significantly increased risk of regional and distant/metastatic disease according to proximity to an oil refinery.

Spatial patterns in prostate Cancer-specific mortality in Pennsylvania using Pennsylvania Cancer registry data, 2004-2014

BACKGROUND

Spatial heterogeneity of prostate cancer-specific mortality in Pennsylvania remains unclear. We utilized advanced geospatial survival regressions to examine spatial variation of prostate cancer-specific mortality in PA and evaluate potential effects of individual- and county-level risk factors.

METHODS

Prostate cancer cases, aged ≥40 years, were identified in the 2004-2014 Pennsylvania Cancer Registry. The 2018 County Health Rankings data and the 2014 U.S. Environmental Protection Agency's Environmental Quality Index were used to extract county-level data. The accelerated failure time models with spatial frailties for geographical correlations were used to assess prostate cancer-specific mortality rates for Pennsylvania and by the Penn State Cancer Institute (PSCI) 28-county catchment area. Secondary assessment based on estimated spatial frailties was conducted to identify potential health and environmental risk factors for mortality.

RESULTS

There were 94,274 cases included. The 5-year survival rate in PA was 82% (95% confidence interval, CI: 81.1-82.8%), with the catchment area having a lower survival rate 81% (95% CI: 79.5-82.6%) compared to the non-catchment area rate of 82.3% (95% CI: 81.4-83.2%). Black men, uninsured, more aggressive prostate cancer, rural and urban Appalachia, positive lymph nodes, and no definitive treatment were associated with lower survival. Several county-level health (i.e., poor physical activity) and environmental factors in air and land (i.e., defoliate chemical applied) were associated with higher mortality rates.

CONCLUSIONS

Spatial variations in prostate cancer-specific mortality rates exist in Pennsylvania with a higher risk in the PSCI's catchment area, in particular, rural-Appalachia. County-level health and environmental factors may contribute to spatial heterogeneity in prostate cancer-specific mortality.

Measuring Subcounty Differences in Population Health Using Hospital and Census-Derived Data Sets: The Missouri ZIP Health Rankings Project

CONTEXT

Measures of population health at the subcounty level are needed to identify areas for focused interventions and to support local health improvement activities.

OBJECTIVE

To extend the County Health Rankings population health measurement model to the ZIP code level using widely available hospital and census-derived data sources.

DESIGN

Retrospective administrative data study.

SETTING

Missouri.

POPULATION

Missouri FY 2012-2014 hospital inpatient, outpatient, and emergency department discharge encounters (N = 36 176 377) and 2015 Nielsen data.

MAIN OUTCOME MEASURES

ZIP code-level health factors and health outcomes indices.

RESULTS

Statistically significant measures of association were observed between the ZIP code-level population health indices and published County Health Rankings indices. Variation within counties was observed in both urban and rural areas. Substantial variation of the derived measures was observed at the ZIP code level with 20 (17.4%) Missouri counties having ZIP codes in both the top and bottom quintiles of health factors and health outcomes. Thirty of the 46 (65.2%) counties in the top 2 county quintiles had ZIP codes in the bottom 2 quintiles.

CONCLUSIONS

This proof-of-concept analysis suggests that readily available hospital and census-derived data can be used to create measures of population health at the subcounty level. These widely available data sources could be used to identify areas of potential need within counties, engage community stakeholders, and target interventions.

More than gangsters and girl scouts: Environmental health perspectives of urban youth

The purpose of this study was to explore environmental health perspectives among urban youth. A total of 12 focus groups with 64 youth were conducted. Youth defined environmental health in a multidimensional manner which integrated aspects of the physical, social, and built environment and concentrated on the neighborhood context. A theme of environmental health resilience factors and sub-themes of safety, trust, engagement, leadership, and representation were identified and described. A second theme of underlying structural drivers of environmental health with sub-themes of equitable opportunities and power inform environmental health. A conceptual model was developed to guide future environmental health research and action.

County Health Factors Associated with Avoidable Deaths from Cardiovascular Disease in the United States, 2006-2010

OBJECTIVE

Many cardiovascular deaths can be avoided through primary prevention to address cardiovascular disease (CVD) risk factors or better access to quality medical care. In this cross-sectional study, we examined the relationship between four county-level health factors and rates of avoidable death from CVD during 2006-2010.

METHODS

We defined avoidable deaths from CVD as deaths among U.S. residents younger than 75 years of age caused by the following underlying conditions, using International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes: ischemic heart disease (I20-I25), chronic rheumatic heart disease (I05-I09), hypertensive disease (I10-I15), or cerebrovascular disease (I60-I69). We stratified county-level death rates by race (non-Hispanic white or non-Hispanic black) and age-standardized them to the 2000 U.S. standard population. We used County Health Rankings data to rank county-level z scores corresponding to four health factors: health behavior, clinical care, social and economic factors, and physical environment. We used Poisson rate ratios (RRs) and 95% confidence intervals (CIs) to compare rates of avoidable death from CVD by health-factor quartile.

RESULTS

In a comparison of worst-ranked and best-ranked counties, social and economic factors had the strongest association with rates of avoidable death per 100,000 population from CVD for the total population (RR=1.49; 95% CI 1.39, 1.60) and for each racial/ethnic group (non-Hispanic white: RR=1.37; 95% CI 1.29, 1.45; non-Hispanic black: RR=1.54; 95% CI 1.42, 1.67). Among the non-Hispanic white population, health behaviors had the next strongest association, followed by clinical care. Among the non-Hispanic black population, we observed a significant association with clinical care and physical environment in a comparison of worst-ranked and best-ranked counties.

CONCLUSION

Social and economic factors have the strongest association with rates of avoidable death from CVD by county, which reinforces the importance of social and economic interventions to address geographic disparities in avoidable deaths from CVD.

County Health Rankings: Relationships Between Determinant Factors and Health Outcomes

INTRODUCTION

The County Health Rankings (CHR) provides data for nearly every county in the U.S. on four modifiable groups of health factors, including healthy behaviors, clinical care, physical environment, and socioeconomic conditions, and on health outcomes such as length and quality of life. The purpose of this study was to empirically estimate the strength of association between these health factors and health outcomes and to describe the performance of the CHR model factor weightings by state.

METHODS

Data for the current study were from the 2015 CHR. Thirty-five measures for 45 states were compiled into four health factors composite scores and one health outcomes composite score. The relative contributions of health factors to health outcomes were estimated using hierarchical linear regression modeling in March 2015. County population size; rural/urban status; and gender, race, and age distributions were included as control variables.

RESULTS

Overall, the relative contributions of socioeconomic factors, health behaviors, clinical care, and the physical environment to the health outcomes composite score were 47%, 34%, 16%, and 3%, respectively. Although the CHR model performed better in some states than others, these results provide broad empirical support for the CHR model and weightings.

CONCLUSIONS

This paper further provides a framework by which to prioritize health-related investments, and a call to action for healthcare providers and the schools that educate them. Realizing the greatest improvements in population health will require addressing the social and economic determinants of health.

The County Health Rankings: rationale and methods

Background

Annually since 2010, the University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation have produced the County Health Rankings—a “population health checkup” for the nation’s over 3,000 counties. The purpose of this paper is to review the background and rationale for the Rankings, explain in detail the methods we use to create the health rankings in each state, and discuss the strengths and limitations associated with ranking the health of communities.

Methods

We base the Rankings on a conceptual model of population health that includes both health outcomes (mortality and morbidity) and health factors (health behaviors, clinical care, social and economic factors, and the physical environment). Data for over 30 measures available at the county level are assembled from a number of national sources. Z-scores are calculated for each measure, multiplied by their assigned weights, and summed to create composite measure scores. Composite scores are then ordered and counties are ranked from best to worst health within each state.

Results

Health outcomes and related health factors vary significantly within states, with over two-fold differences between the least healthy counties versus the healthiest counties for measures such as premature mortality, teen birth rates, and percent of children living in poverty. Ranking within each state depicts disparities that are not apparent when counties are ranked across the entire nation.

Discussion

The County Health Rankings can be used to clearly demonstrate differences in health by place, raise awareness of the many factors that influence health, and stimulate community health improvement efforts. The Rankings draws upon the human instinct to compete by facilitating comparisons between neighboring or peer counties within states. Since no population health model, or rankings based off such models, will ever perfectly describe the health of its population, we encourage users to look to local sources of data to understand more about the health of their community.