What Do We Know About the Drivers of Health and Equity? A Narrative Review of Graphic Representations.

Purpose: Frameworks can be influential tools for advancing health and equity, guiding population health researchers and practitioners. We reviewed frameworks with graphic representations that address the drivers of both health and equity. Our purpose was to summarize and discuss graphic representations of population health and equity and their implications for research and practice. Methods: We identified publicly available frameworks that were scholarly or practice oriented and met defined inclusion and exclusion criteria. The identified frameworks were then described and coded based on their primary area of focus, key elements included, and drivers of health and equity specified. Results: The variation in purpose, concepts, drivers, underlying theory or scholarly evidence, and accompanying measures was highlighted. Graphic representations developed over the last 20 years exhibited some consistency in the drivers of health; however, there has been little uniformity in depicting the drivers of equity, disparities or interplay among the determinants of health, or transparency in underlying theories of change. Conclusion: We found that current tools do not offer consistency or conceptual clarity on what shapes health and equity. Some variation is expected as it is difficult for any framework to be all things to all people. However, keeping in mind the importance of audience and purpose, the field of population health research and practice should work toward greater clarity on the drivers of health and equity to better guide critical analysis, narrative development, and strategic actions needed to address structural and systemic issues perpetuating health inequities.

Assessment of Factors Contributing to Health Outcomes in the Eight States of the Mississippi Delta Region.

INTRODUCTION: The objective of this observational study was to examine the key contributors to health outcomes and to better understand the health disparities between Delta and non-Delta counties in 8 states in the Mississippi River Delta Region. We hypothesized that a unique set of contributors to health outcomes in the Delta counties could explain the disparities between Delta and non-Delta counties. METHODS: Data were from the 2014 County Health Rankings for counties in 8 states (Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, and Tennessee). We used the Delta Regional Authority definition to identify the 252 Delta counties and 468 non-Delta counties or county equivalents. Information on health factors (eg, health behaviors, clinical care) and outcomes (eg, mortality) were derived from 38 measures from the 2014 County Health Rankings. The contributions of health factors to health outcomes in Delta and non-Delta counties were examined using path analysis. RESULTS: We found similarities between Delta counties and non-Delta counties in the health factors (eg, tobacco use, diet and exercise) that significantly predicted the health outcomes of self-rated health and low birthweight. The most variation was seen in predictors of mortality; however, Delta counties shared 2 of the 3 significant predictors (ie, community safety and income) of mortality with non-Delta counties. On average across all measures, values in the Delta were 16% worse than in the non-Delta and 22% worse than in the rest of the United States. CONCLUSION: The health status of Delta counties is poorer than that of non-Delta counties because the health factors that contribute to health outcomes in the entire region are worse in the Delta counties, not because of a unique set of health predictors.

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.

Relative Contributions of a Set of Health Factors to Selected Health Outcomes.

Although many researchers agree that multiple determinants impact health, there is no consensus regarding the magnitude of the relative contributions of individual health factors to health outcomes. This study presents a method to empirically estimate the relative contributions of health behaviors, clinical care, social and economic factors, and the physical environment to health outcomes using nationally representative county-level data and statistical approaches that account for potential sources of bias. The analyses for this study were conducted in 2014. Data were from the 2010-2013 County Health Rankings & Roadmaps. Data covered 2,996 of 3,141 U.S. counties. Ordinary least squares modeling was used as a baseline model. Multilevel latent growth curve modeling was used to estimate the relative contributions of health factors to health outcomes while accounting for measurement errors and state-specific characteristics. Almost half of the variance of health outcomes was due to state-level variation rather than county-level variation. When adjusted for measurement errors and state-level variation using multilevel latent growth curve modeling, the relative contribution of clinical care decreased and that of social and economic factors increased compared with the baseline model. This study presents how potential sources of bias affected the estimates of the relative contributions of a set of modifiable health factors to health outcomes at the county level. Further verification of these approaches with other data sources could lead to a better understanding of the impact of specific health determinants to health outcomes, and will provide useful information on policy interventions.

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.

Monitoring progress in population health: trends in premature death rates.

INTRODUCTION: Trends in population health outcomes can be monitored to evaluate the performance of population health systems at the national, state, and local levels. The objective of this study was to compare and contrast 4 measures for assessing progress in population health improvement by using age-adjusted premature death rates as a summary measure of the overall health outcomes in the United States and in all 50 states. METHODS: To evaluate the performance of statewide population health systems during the past 20 years, we used 4 measures of age-adjusted premature (<75 years of age) death rates: current rates (2009), baseline trends (1990s), follow-up trends (2000s), and changes in trends from baseline to the follow-up periods (ie, "bending the curve"). RESULTS: Current premature death rates varied by approximately twofold, with the lowest rate in Minnesota (268 deaths per 100,000) and the highest rate in Mississippi (482 deaths per 100,000). Rates improved the most in New York during the baseline period (-3.05% per year) and in New Jersey during the follow-up period (-2.87% per year), whereas Oklahoma ranked last in trends during both periods (-0.30%/y, baseline; +0.18%/y, follow-up). Trends improved the most in Connecticut, bending the curve downward by -1.03%; trends worsened the most in New Mexico, bending the curve upward by 1.21%. DISCUSSION: Current premature death rates, recent trends, and changes in trends vary by state in the United States. Policy makers can use these measures to evaluate the long-term population health impact of broad health care, behavioral, social, and economic investments in population health.

Using empirical Bayes methods to rank counties on population health measures.

University of Wisconsin Population Health Institute has published County Health Rankings (The Rankings) since 2010. These rankings use population-based data to highlight variation in health and encourage health assessment for all US counties. However, the uncertainty of estimates remains a limitation. We sought to quantify the precision of The Rankings for selected measures. We developed hierarchical models for 5 health outcome measures and applied empirical Bayes methods to obtain county rank estimates for a composite health outcome measure. We compared results using models with and without demographic fixed effects to determine whether covariates improved rank precision. Counties whose rank had wide confidence intervals had smaller populations or ranked in the middle of all counties for health outcomes. Incorporating covariates in the models produced narrower intervals, but rank estimates remained imprecise for many counties. Local health officials, especially in smaller population and mid-performing communities, should consider these limitations when interpreting the results of The Rankings.