County Health Rankings and the Cult of the Imperfect

Modeling health and well-being measures using ZIP code spatial neighborhood patterns

Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states.

The Sarcoma-Specific Instrument to Longitudinally Assess Health-Related Outcomes of the Routine Care Cycle

Patient-based health related quality of life (HRQoL) measurements are associated with an improvement in quality of care and outcomes. For a complex disease such as sarcoma, there is no disease-specific questionnaire available which covers all clinically relevant dimensions. Herein, we report on the development of an electronically implemented, sarcoma-specific instrument to assess health-related outcomes, which encompasses a combination of generic questionnaires tailored to the respective disease and treatment status covering the entire longitudinal care cycle. An interoperable digital platform was designed to provide a node between patients and physicians and to integrate the sarcoma-specific HRQoL instrument with patient and physician-based quality indicators to allow longitudinal structured real-world-time data evidence analytics. This approach enables the prediction modeling of disease, and by attributing cost tags to quality indicators, treatment effectiveness for a given disease will be directly correlated with financial expenses, which may ultimately lead to a more sustainable healthcare system.

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

IMPORTANCE

Although social determinants of health (SDOH) are important factors in health inequities, they have not been explicitly associated with COVID-19 mortality rates across racial and ethnic groups and rural, suburban, and urban contexts.

OBJECTIVES

To explore the spatial and racial disparities in county-level COVID-19 mortality rates during the first year of the pandemic.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data for all US counties in 50 states and the District of Columbia for the first full year of the COVID-19 pandemic (January 22, 2020, to February 28, 2021). Counties with a high concentration of a single racial and ethnic population and a high level of COVID-19 mortality rate were identified as concentrated longitudinal-impact counties. The SDOH that may be associated with mortality rate across these counties and in urban, suburban, and rural contexts were examined. The 3 largest racial and ethnic groups in the US were selected: Black or African American, Hispanic or Latinx, and non-Hispanic White populations.

EXPOSURES

County-level characteristics and community health factors (eg, income inequality, uninsured rate, primary care physicians, preventable hospital stays, severe housing problems rate, and access to broadband internet) associated with COVID-19 mortality.

MAIN OUTCOMES AND MEASURES

Data on county-level COVID-19 mortality rates (deaths per 100 000 population) reported by the US Centers for Disease Control and Prevention were analyzed. Four indexes were used to measure multiple dimensions of SDOH: socioeconomic advantage index, limited mobility index, urban core opportunity index, and mixed immigrant cohesion and accessibility index. Spatial regression models were used to examine the associations between SDOH and county-level COVID-19 mortality rate.

RESULTS

Of the 3142 counties included in the study, 531 were identified as concentrated longitudinal-impact counties. Of these counties, 347 (11.0%) had a large Black or African American population compared with other counties, 198 (6.3%) had a large Hispanic or Latinx population compared with other counties, and 33 (1.1%) had a large non-Hispanic White population compared with other counties. A total of 489 254 COVID-19-related deaths were reported. Most concentrated longitudinal-impact counties with a large Black or African American population compared with other counties were spread across urban, suburban, and rural areas and experienced numerous disadvantages, including higher income inequality (297 of 347 [85.6%]) and more preventable hospital stays (281 of 347 [81.0%]). Most concentrated longitudinal-impact counties with a large Hispanic or Latinx population compared with other counties were located in urban areas (114 of 198 [57.6%]), and 130 (65.7%) of these counties had a high percentage of people who lacked health insurance. Most concentrated longitudinal-impact counties with a large non-Hispanic White population compared with other counties were in rural areas (23 of 33 [69.7%]), included a large group of older adults (26 of 33 [78.8%]), and had limited access to quality health care (24 of 33 [72.7%]). In urban areas, the mixed immigrant cohesion and accessibility index was inversely associated with COVID-19 mortality (coefficient [SE], -23.38 [6.06]; P < .001), indicating that mortality rates in urban areas were associated with immigrant communities with traditional family structures, multiple accessibility stressors, and housing overcrowding. Higher COVID-19 mortality rates were also associated with preventable hospital stays in rural areas (coefficient [SE], 0.008 [0.002]; P < .001) and higher socioeconomic status vulnerability in suburban areas (coefficient [SE], -21.60 [3.55]; P < .001). Across all community types, places with limited internet access had higher mortality rates, especially in urban areas (coefficient [SE], 5.83 [0.81]; P < .001).

CONCLUSIONS AND RELEVANCE

This cross-sectional study found an association between different SDOH measures and COVID-19 mortality that varied across racial and ethnic groups and community types. Future research is needed that explores the different dimensions and regional patterns of SDOH to address health inequity and guide policies and programs.

Healthy Aging Data Reports: Measures of Community Health to Identify Disparities and Spur Age-Friendly Progress

BACKGROUND AND OBJECTIVES

This translational research had two aims. First, to analyze and translate data from multiple original data sources to provide accurate, unbiased local community and statewide information about healthy aging. Second, to work with stakeholders to use the tools to identify disparities in healthy aging and to support their efforts to advance healthy aging.

RESEARCH DESIGN AND METHODS

Data sources from the Centers for Medicare and Medicaid Services, Behavioral Risk Factor Surveillance System, U.S. Census American Community Survey, and other sources were analyzed using small area estimation techniques to determine age/gender adjusted local community rates in Connecticut (CT), Massachusetts (MA), New Hampshire (NH), and Rhode Island (RI).

RESULTS

State level analyses revealed gender and racial/ethnic disparities in healthy aging. A factor analysis identified 4 dimensions of community population healthy aging/morbidity: serious complex chronic disease, indolent conditions, physical disability, and psychological disability.

DISCUSSION AND IMPLICATIONS

Healthy Aging Data Reports now exist for MA (2014, 2015, 2018), NH (2019), RI (2016, 2020) and CT (2021) and demonstrate differences in health by place. Each report includes community profiles for every city, town, and some urban neighborhoods with more than 170-197 indicators. The reports include maps of the statewide distribution of rates, an infographic, highlights report with state-specific multivariate analyses, and 18 interactive web-maps, 18 regional interactive web-maps, and technical documentation about data sources and methods. Overall, the research has identified variations in healthy aging and provided tools to track change over time to support age-friendly efforts in the region.

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.

State Prevalence and Ranks of Adolescent Substance Use: Implications for Cancer Prevention

Introduction

This study statistically ranked states’ performance on adolescent substance use related to cancer risk (past-month cigarette smoking, binge alcohol drinking, and marijuana use).

Methods

Data came from 69,200 adolescent participants (50 states and the District of Columbia) in the National Survey on Drug Use and Health (NSDUH) and 450,050 adolescent participants (47 states) in the Youth Risk Behavior Surveillance System (YRBSS). Adolescents were aged 14 to 17 years. For 2011–2015, we estimated and ranked states’ prevalence of adolescent substance use. We calculated the ranks’ 95% confidence intervals (CIs) using a Monte Carlo method with 100,000 simulations. Spearman correlations examined consistency of ranks.

Results

Across states, the prevalence of cigarette smoking was 4.5% to 14.3% in NSDUH and 4.7% to 18.5% in YRBSS. Utah had the lowest prevalence (NSDUH: rank = 51 [95% CI, 47–51]; YRBSS: rank = 47 [95% CI, 46–47]), and states’ ranks across surveys were correlated (r = 0.66, P < .001). The prevalence of binge alcohol drinking was 5.9% to 14.3% (NSDUH) and 7.1% to 21.7% (YRBSS). Utah had the lowest prevalence (NSDUH: rank = 50 [95% CI, 40–51]; YRBSS: rank = 47 [95% CI, 47–47]), but ranks across surveys were weakly correlated (r = 0.38, P = .01). The prevalence of marijuana use was 6.3% to 18.7% (NSDUH) and 8.2% to 27.1% (YRBSS). Utah had the lowest prevalence of marijuana use (NSDUH: rank = 50 [95% CI = 33–51]; YRBSS: rank= 46 [95% CI, 46–46]), and ranks across surveys were correlated (r = 0.70, P < .001). Wide CIs for states ranked in the middle of each distribution obscured statistical differences among them.

Conclusion

Variability emerged across adolescent substance use behaviors and surveys (perhaps because of administration differences). Most states showed statistically equivalent performance on adolescent substance use. Adolescents in all states would benefit from efforts to reduce substance use, to prevent against lifelong morbidity.

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.