Past Research Projects

2021 Research Grant Projects

The 2021 County Health Rankings Research Grants funded seven projects that focused on the structural determinants of health and innovative ways to source, measure and refine CHR&R data. 

Incorporating structural racism measures into CHR&R
(University of Wisconsin)

The University of Wisconsin examined measures of structural racism — such as residential segregation and mortgage discrimination — and their relationship to maternal and child health disparities. Researchers found consistent connections between structural racism and adverse maternal, child and health outcomes for Black individuals.

An intersectional approach to Family & Social Support measures 
(George Mason University)

Researchers from George Mason University evaluated new measures to represent household and social support within counties, including multigenerational households and segregation. They found that multigenerational households had unexpected patterns and relationships to health outcomes and that older minority adults experienced the highest levels of segregation.

Considering climate change and health equity 
(University of Kansas)

The University of Kansas looked at how climate change indicators such as drought, extreme heat, extreme cold and natural disasters, interacted with racial inequity to predict patterns of health outcomes in communities. The results revealed significant impacts on population health and equity.

Examining methods for disparity index calculation 
(Utah State University)

Utah State University expanded on disparity index research to look at disparities by sex, race and geography at the household level. Results showed that choices inherent to disparity measurement varied in importance, but that it was essential to account for diversity within geography.

Factoring in incorporation status 
(University of Texas at Arlington)

Researchers from the University of Texas at Arlington conceptualized the impact of county governance on health and racial/ethnic inequities, then compared findings in unincorporated areas to municipal areas. They discovered that counties with more unincorporated areas had poorer health factors and outcomes and greater health disparities.

A detailed look at neighborhood spatial-racial differences 
(University of Wisconsin)

A research team from the University of Wisconsin looked at the race/ethnicity of residents in different neighborhoods and how the way resources were allocated in each location contributed to inequity in neighborhoods. They showed that wealthy, predominantly white neighborhoods had more activity space and better access to resources.

Building shared knowledge, civic practice, and social solidarity 
(Center for Practical Bioethics)

The Center for Practical Bioethics created a democratic deliberation toolkit to advance health equity. The toolkit was designed to create connection and dialogue around civics and to foster social solidarity across race, class and geography. 

2019 Research Grant Projects 

The 2019 County Health Rankings Research Grants funded three projects, with a focus on research to advance methods and measurement for community health improvement and equity, and to increase the beneficial impact of Rankings’ data in communities 

Statistical modeling for measures with limited data
(Drexel University) 

Statistical modeling can help improve the quality of data for measures where there is not much data available, whether due to rare events or small populations. The Drexel team of grantees sought to explore the utility of spatial Bayesian models to improve the precision of small area estimates used by the County Health Rankings. 

Calculating an accurate size and scale of disparities in large counties

To be able to address health inequities in U.S. communities, you first need an accurate picture of the size and scale of the disparities by race/ethnicity and class that exist in key health metrics. The team at the UCLA Fielding School of Public Health used their Health Equity Metric (HEM) to calculate the HEM for 400 of the nation’s largest counties, laying the groundwork for future evaluation opportunities to find areas of disparity for increased investment.  

Identifying expected county performance given social and economic circumstances
(Rutgers University) 

The team at Rutgers used statistical modeling to determine where a county’s health outcomes fall relative to their peers, they were able to quantify how well a county is expected to perform given its social and economic circumstances. This research can be helpful in deciding where to allocate community resources by identifying areas in which a county may be either overperforming or underperforming relative to what is expected. 


2017 Research Grant Projects

The 2017 County Health Rankings Research Grants funded four innovative projects. These grants serve an important role in strengthening our Rankings methods and increasing their strategic use and impacts. 

Considering health and social services spending
(Arizona State University)

During our last round of research grants, Arizona State University researchers found that higher spending for certain social services led to relative over-performance in health rankings when compared to a community’s wealth – and that additional spending led to improved health rankings four years later. Following up on those findings, ASU’s 2017 project examined the feasibility – and characterized the effects – of incorporating detailed measures for health and social services spending into our Rankings.

A detailed look at our methodology
(Harvard University)

Researchers from Harvard University looked at the methodology behind our Rankings to review the infrastructure and design of how the Rankings are calculated. The review looked at our framework, weighting scheme, and other attributes for possible improvements.

Factoring in the physical environment
(Rutgers University)

The Rankings incorporate a number of measures to evaluate the physical environment – from air and water quality to the built environment. A team from Rutgers University developed a variety of physical environment measures from new data sources, and explored whether measures piloted in New Jersey could be scaled nationwide.

Evaluating education
(University of South Carolina)

Building off of the educational and social measures used in the Rankings, researchers from the University of South Carolina evaluated new county-level measures related to school quality, academic achievement, and other education-related topics. The project also explored relationships between these measures and health outcomes.

2015 Research Grant Projects

The 2015 County Health Rankings Research Grants culminated in five innovative projects looking at how to make the most of community data to understand and address what drives local health:

Extending the Rankings to the ZIP code level
(Washington University in St. Louis and the Missouri Hospital Association)

Reliable data for a wide variety of health metrics are available nationally, for states, and (of course) for counties, but smaller areas such as ZIP codes is another story. Washington University in St. Louis and the Missouri Hospital Association developed local measures of population health for Missouri counties using data available at the ZIP code level. Data collected by hospitals as well as market research data by Nielsen Claritas were combined and calculated in alignment with the CHR approach. When scaled up, these ZIP code rankings correlated with the County Health Rankings and show a path to refine the health snapshot at the local level.

Does wealth sync with health?
(Arizona State University)

Arizona State University researchers using the Rankings data found that while community wealth does correlate with health, it is offset in some cases by the ways counties choose to invest, such as targeting resources to public health initiatives/policies and community health centers. The implications of this show that spending among existing public services may dramatically impact a community’s health.

Mining data to support actionable decisions
(New York Academy of Medicine)

Using the Rankings data, the New York Academy of Medicine brought “big data” procedures (machine learning data mining techniques) to identify clusters of counties with different levels of health outcomes based on complex interactions of various health factors that may be related to these outcomes. These analyses offer a straightforward method which may help local county/city officials better design and calibrate actionable and tractable areas to target policies for local health improvement.

The Rankings' impact on policy decisions
(Drexel University)

Whether through local health officers or media, news of a county’s ranking can spur change in the form of laws or regulations. But how does that happen? Drexel University’s research explored how the Rankings are used by communities, finding that rankings are useful for educating and raising awareness and are key for local health departments and groups with limited resources working to target their health improvement efforts. Another finding points to Rankings data as a helpful tool for engaging political officials on the need for broader action in partnership with those outside of healthcare when it comes to directing policy decisions that influence community health.

The role of economic stress in well-being
(Washington State University and Clemson University, Healthways)

Washington State University and Clemson University in collaboration with Healthways investigated the degree to which county-level factors influence the effect of economic stressors, such as job insecurity or living from one paycheck to the next, on individual well-being outcomes as measured by the nationally representative Gallup-Healthways Well-being Index. Financial and professional concerns have a big impact on psychological well-being and can reveal health impacts beyond just employment statistics. Initial findings suggest that in healthier counties, the effects of income-related stress are diminished while the effects of employment-related stress are magnified in such contexts. This might mean, for example, that in a healthier community experiencing job instability may affect a person’s social contacts or sense of identity and cause more stress compared to a person in a less healthy community.