Just How Useful Are Health Rankings?

Health differences between rural and non-rural Texas counties based on 2023 County Health Rankings

BACKGROUND

Place matters for health. In Texas, growing rural populations face a variety of structural, social, and economic disparities that position them for potentially worse health outcomes. The current study contributes to understanding rural health disparities in a state-specific context.

METHODS

Using 2023 County Health Rankings data from the University of Wisconsin Population Health Institute, the study analyzes rural/non-rural county differences in Texas across six composite indexed domains of health outcomes (length of life, quality of life) and health factors (health behavior, clinical care, socioeconomic factors, physical environment) with a chi-square test of significance and logistic regression.

RESULTS

Quartile ranking distributions of the six domains differed between rural and non-rural counties. Rural Texas counties were significantly more likely to fall into the bottom quartile(s) in the domains of length of life and clinical care and less likely to fall into the bottom quartile(s) in the domains of quality of life and physical environment. No differences were found in the domains of health behavior and socioeconomic factors. Findings regarding disparities in length of life and clinical care align with other studies examining disease prevalence and the unavailability of many health services in rural Texas. The lack of significant differences in other domains may relate to indicators that are not present in the dataset, given studies that find disparities relating to other underlying factors.

CONCLUSIONS

Texas County Health Rankings data show differences in health outcomes and factors between rural and non-rural counties. Limitations of findings relate to the study's cross-sectional design and parameters of the secondary data source. Ultimately, results can help state health stakeholders, especially those in community or operational contexts with limited resources or access to more detailed health statistics, to use the CHR dataset to consider more relevant local interventions to address rural health disparities.

County health ranking: untangling social determinants of health and other factors associated with short-term bariatric surgery outcomes

BACKGROUND

The complex interplay of the social determinants of health, race/ethnicity, and traditional surgical risk factors on outcomes following metabolic surgery is poorly understood.

OBJECTIVE

To evaluate the relationship between the social determinants of health as measured by county health ranking (CHR) and short-term metabolic surgery outcomes.

SETTING

Five accredited bariatric program sites at a national academic health system.

METHODS

Data were collected from 5 sites of a single health system from 2010 to 2021. Current procedural terminology codes identified primary and revisional cases. Patient characteristics, procedural data, and 30-day occurrences were collected. CHRs for health factors were determined by ZIP Code and stratified into best, middle, and worst terciles. The primary outcome was 30-day complications, readmissions, or reinterventions/reoperations. Logistic regression assessed the correlation between CHR tercile and morbidity.

RESULTS

We analyzed 4,315 primary and 370 revisional metabolic surgery cases. Overall, 64.0%, 27.4%, and 8.6% of patients lived in the best, middle, and worst CHR terciles, respectively. Patients in the middle and worst CHR terciles were more commonly older; non-Hispanic Black or Hispanic; suffered from preexisting chronic obstructive pulmonary disease or hypertension, were dialysis dependence, were on therapeutic anticoagulation, or had inferior vena cava filters. Middle and worst CHR tercile patients were more likely to undergo index sleeve gastrectomy or robotic-assisted surgery and have surgery performed by a self-designated general surgeon. Thirty-day outcomes were similar across CHR terciles. Racial disparity in multiple short-term outcomes persisted despite adjustment for CHR tercile.

CONCLUSION

Higher-risk patients are more likely to be from counties with lower CHRs, but CHR was not independently associated with 30-day outcomes after metabolic surgery.

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.

Empirical evidence supporting the inclusion of multi-axes segregation in assessing US county health

To facilitate community action toward health equity, the County Health Rankings & Roadmaps program (CHR&R) assigns health rankings to US counties. The CHR&R conceptual model considers White-Black and White-non-White dissimilarity values to represent residential segregation as part of the family and social support subcomponent. As the US is greying and becoming more multi-racial-ethnic, the two-group White-centered segregation measures are inadequate to capture segregation among population subgroups in the US. Thus, we evaluate the relevancy of segregation measures that consider multiple racial, ethnic, and age groups in assessing US county health. Besides using the two-group dissimilarity index to measure White-centered racial segregation as conceptualized by CHR&R, the study also uses the multi-group generalized dissimilarity index to measure racial-ethnic-age segregation by counties, employing both aspatial and spatial versions of these measures. These indices are computed for counties using the 2015-2019 American Community Survey data at the census tract level. Descriptive statistics and regressions controlling for sociodemographic factors and healthcare access are used to assess the contributions of individual segregation measures to mortality (life expectancy, years of potential life lost and premature mortality) and morbidity (frequent mental distress, frequent physical distress, and low birth weight) indicators representing county health. Overall, correlations between these indicators and most segregation measures are significant but weak. Regression results show that many segregation measures are not significantly related to mortality indicators, but most are significantly associated with morbidity indicators, with the magnitudes of these associations higher for the multi-group racial-ethnic-age segregation index and its spatial version. Results provide evidence that racial-ethnic-age segregation is associated with county-level morbidity and that spatial measures capturing segregation of multiple population axes should be considered for ranking county health.

Assessment of Area-Level Disease Control and Surveillance Vulnerabilities: An Application to Visceral Leishmaniasis in Brazil

The large number of activities contributing to zoonoses surveillance and control capability, on both human and animal domains, and their likely heterogeneous implementation across administrative units make assessment and comparisons of capability performance between such units a complex task. Such comparisons are important to identify gaps in capability development, which could lead to clusters of vulnerable areas, and to rank and subsequently prioritize resource allocation toward the least capable administrative units. Area-level preparedness is a multidimensional entity and, to the best of our knowledge, there is no consensus on a single comprehensive indicator, or combination of indicators, in a summary metric. We use Bayesian spatial factor analysis models to jointly estimate and rank disease control and surveillance capabilities against visceral leishmaniasis (VL) at the municipality level in Brazil. The latent level of joint capability is informed by four variables at each municipality, three reflecting efforts to monitor and control the disease in humans, and one variable informing surveillance capability on the reservoir, the domestic dog. Because of the large volume of missing data, we applied imputation techniques to allow production of comprehensive rankings. We were able to show the application of these models to this sparse dataset and present a ranked list of municipalities based on their overall VL capability. We discuss improvements to our models, and additional applications.

Adolescent Behavioral Cancer Prevention in the United States: Creating a Composite Variable and Ranking States' Performance

Preventive behaviors established during adolescence can reduce cancer throughout the life span. Understanding the combinations of multiple behaviors, and how these behaviors vary across states, is important for identifying where additional interventions are needed. Using data on 2011-2015 vaccination, energy balance, and substance use from national surveys, we created state-level composite scores for adolescent cancer prevention. Hierarchical Bayesian linear mixed models were used to predict estimates for states with no data on select behaviors. We used a Monte Carlo procedure with 100,000 simulations to generate states' ranks and 95% confidence intervals. Across states, hepatitis B vaccination was 84.3% to 97.1%, and human papillomavirus vaccination was 41.8% to 78.0% for girls and 19.0% to 59.3% for boys. For energy balance, 20.2% to 34.6% of adolescents met guidelines for physical activity, 4.1% to 15.8% for fruit and vegetable consumption, and 66.4% to 82.0% for healthy weight. For substance use, 82.5% to 93.5% reported abstaining from binge alcohol use, 84.3% to 95.4% from cigarette smoking, and 62.9% to 92.8% from marijuana use. (1) Rhode Island, (2) Colorado, (4) Hawaii and New Hampshire (tied), and (5) Vermont performed the best for adolescent cancer prevention, and (47) Missouri, (48) Arkansas, Mississippi, and South Carolina (tied), and (51) Kentucky performed the worst. However, 95% CIs around ranks often overlapped, indicating lack of statistical differences. Adolescent cancer prevention behaviors clustered into a composite index. States varied on their performance on this index, especially for states at the high and low extremes, but most states did not differ statistically. These findings can inform decision makers about where and how to intervene to improve cancer prevention among adolescents.

Ranking States on Coverage of Cancer-Preventing Vaccines Among Adolescents: The Influence of Imprecision

OBJECTIVES

Identifying the best and worst states for coverage of cancer-preventing vaccines (hepatitis B [HepB] and human papillomavirus [HPV]) may guide public health officials in developing programs, such as promotion campaigns. However, acknowledging the imprecision of coverage and ranks is important for avoiding overinterpretation. The objective of this study was to examine states' vaccination coverage and ranks, as well as the imprecision of these estimates, to inform public health decision making.

METHODS

We used data on coverage of HepB and HPV vaccines among adolescents aged 13-17 from the 2011-2015 National Immunization Survey-Teen (n = 103 729 from 50 US states and Washington, DC). We calculated coverage, 95% confidence intervals (CIs), and ranks for vaccination coverage in each state, and we generated simultaneous 95% CIs for ranks using a Monte Carlo method with 100 000 simulations.

RESULTS

Across years, HepB vaccination coverage was 92.2% (95% CI, 91.8%-92.5%; states' range, 84.3% in West Virginia to 97.0% in Connecticut). HPV vaccination coverage was 57.4% (95% CI, 56.6%-58.2%; range, 41.8% in Kansas to 78.0% in Rhode Island) for girls and 31.0% (95% CI, 30.3%-31.8%; range, 19.0% in Utah to 59.3% in Rhode Island) for boys. States with the highest and lowest ranks generally had narrow 95% CIs; for example, Rhode Island was ranked first (95% CI, 1-1) and Kansas was ranked 51st (95% CI, 49-51) for girls' HPV vaccination. However, states with intermediate ranks had wider and more imprecise 95% CIs; for example, New York was 26th for girls' HPV vaccination coverage, but its 95% CI included ranks 18-35.

CONCLUSIONS

States' ranks of coverage of cancer-preventing vaccines were imprecise, especially for states in the middle of the range; thus, performance rankings presented without measures of imprecision could be overinterpreted. However, ranks can highlight high-performing and low-performing states to target for further research and vaccination promotion programming.

Uses of Population Health Rankings in Local Policy Contexts: A Multisite Case Study

Population health rankings are a common strategy to spur evidence-informed health policy making, but little is known about their uses or impacts. The study aims were to (1) understand how and why the County Health Rankings (CH-Rankings) are used in local policy contexts, (2) identify factors that influence CH-Rankings utilization, and (3) explore potentially negative impacts of the CH-Rankings. Forty-four interviews were conducted with health organization officials and public policy makers in 15 purposively selected counties. The CH-Rankings were used instrumentally to inform internal planning decisions, conceptually to educate the public and policy makers about determinants of population health, and politically to advance organizational agendas. Factors related to organizational capacity, county political ideology, and county rank influenced if, how, and why the CH-Rankings were used. The CH-Rankings sometimes had the negative impacts of promoting potentially ineffective interventions in politically conservative counties and prompting negative media coverage in some counties with poor rank.