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.

Comparative Methodologic and Practical Considerations for Life Expectancy as a Public Health Mortality Measure.

INTRODUCTION: Life expectancy is a public health metric used to assess mortality. We describe life expectancy calculations for US counties and present methodologic considerations compared with years of potential life lost before age 75 (YPLL-75) and premature age-adjusted mortality (PAAM), 2 commonly used length-of-life metrics. METHODS: We used death data from the National Center for Health Statistics for 2015-2017 and other health measures from the 2019 County Health Rankings & Roadmaps. We calculated life expectancy from birth at the county level using an abridged life table and the Chiang method of variance. Studentized residuals identified counties with discordant life expectancy and YPLL-75 or PAAM values. Correlations tested associations of life expectancy with key health measures (eg, smoking, child poverty, uninsured). RESULTS: Among 3073 US counties, life expectancy ranged from 62.4 to 98.0 years, with a mean of 77.4 years. Life expectancy was strongly and negatively correlated with YPLL-75 (r = -0.91) and PAAM (r = -0.95) at the county level. Life expectancy was also associated with other key health metrics, such as smoking, employment, and education rates, where an improvement in the health factor indicated improvement in the respective length-of-life measure. Counties with discordant life expectancy and YPLL-75 or PAAM values had differing age structures. PRACTICE IMPLICATIONS: Commonly used length-of-life metrics in population health settings are differentiated by methodological matters, such as computation complexity, data availability, and differential risk among age groups, especially among the very old or very young. The choice of metric should consider these factors, in addition to practical concerns, such as the communication needs of the audience.

Students authoring molecular case studies as a partial course-based undergraduate research experience (CURE) for lab instruction.

Understanding the relationship between protein structure and function is a core-learning goal in biochemistry. Students often struggle to visualize proteins as three-dimensional objects that interact with other molecules to affect its biochemical consequences. We describe here a partial course-based undergraduate research experiences that has students exploring protein structure and function hands-on while authoring a molecular case study intended for others to use.

Life Expectancy At The US-Mexico Border: Evidence Of Disparities By Place, Race, And Ethnicity.

The mortality experience for the cluster of US counties in the US-Mexico border region has not been well described. We calculated 2016-18 life expectancy for the border region (counties within 100 kilometers of the border), making key comparisons to the US overall and to nonborder counties in border states. Life expectancy from birth for the border region was 81.1 years, which was greater than for the US and for the nonborder counties of border states. However, the disparity in life expectancy between racial/ethnic subgroups in the border region was also greater, within a range of more than thirteen years. Although White, Black, and Asian residents of the border region could expect to live significantly longer than residents of the US and nonborder counties of border states, Hispanic and American Indian residents could not. Understanding the mortality experience via life expectancy can help public health professionals and leaders prioritize efforts to ensure that all border residents have an equal opportunity to live a long, healthy life.

Trends in infants born at low birthweight and disparities by maternal race and education from 2003 to 2018 in the United States.

BACKGROUND: Understanding current levels, as well as past and future trends, of the percentage of infants born at low birthweight (LBW) in the United States is imperative to improving the health of our nation. The purpose of this study, therefore, was to examine recent trends in percentage of LBW, both overall and by maternal race and education subgroups. Studying disparities in percentage of LBW by these subgroups can help to further understand the health needs of the population and can inform policies that can close race and class disparities in poor birth outcomes. METHODS: Trends of percentage of LBW in the U.S. from 2003 to 2018, both overall and by race/ethnicity, and from 2007 to 2018 by education and race by education subgroups were analyzed using CDC WONDER Natality data. Disparities were analyzed using between group variance methods. RESULTS: Percentage of LBW experienced a significant worsening in the most recent 5 years of data, negating nearly a decade of prior improvement. Stark differences were observed by race/ethnicity and by education, with all subgroups experiencing increasing rates in recent years. Disparities also worsened over the course of study. Most notably, all disparities increased significantly from 2014 to 2018, with annual changes near 2-5%. CONCLUSIONS: Recent reversals in progress in percentage of LBW, as well as increasing disparities particularly by race, are troubling. Future study is needed to continue monitoring these trends and analyzing these issues at additional levels. Targets must be set and solutions must be tailored to population subgroups to effectively make progress towards equitable birth outcomes and maternal health.

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.

Deaths of Despair(ity) in Early 21st Century America: The Rise of Mortality and Racial/Ethnic Disparities.

INTRODUCTION: Recent media coverage and research have emphasized increasing mortality rates for middle-aged white Americans. A concern is that this has shifted focus away from the health burden of other population subgroups. This cross-sectional study compares the magnitude of racial/ethnic mortality disparities across age groups and investigates how changing mortality trends have affected these disparities. METHODS: Mortality data from 2007 to 2016 by race/ethnicity and age were obtained from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database in 2018‒2019. Absolute and relative racial/ethnic mortality disparities by age groups were determined by calculating between-group variance and mortality rate-adjusted between-group variance, respectively. Trends in disparities were analyzed using joinpoint regression modeling. Annual percentage change in rate-adjusted between-group variance was calculated for each trend segment as well as the relative contribution of each racial/ethnic group to the change. RESULTS: The largest relative and absolute disparities were found in the youngest and oldest age groups, respectively. Trend analysis detected an inflection point between 2009 and 2012 for most age groups where a period of decreasing disparities changed to one of increasing disparities. Three quarters of the decreasing disparities in Period 1 were resultant of lowering mortality among the black subgroup. During Period 2, the increase in child disparities were due to increased mortality among blacks, whereas increased adult disparities were due to increased mortality among whites shifting the overall mean away from subgroups with lower rates. CONCLUSIONS: Racial/ethnic mortality disparities persist and are widening for some age groups. It is imperative to maintain focus on the age groups where those with historically poorer health are contributing most to the increase.

Ranking composite Cancer Burden Indices for geographic regions: point and interval estimates.

PURPOSE: To develop a composite Cancer Burden Index and produce 95% confidence intervals (CIs) as measures of uncertainties for the index. METHODS: The Kentucky Cancer Registry has developed a cancer burden Rank Sum Index (RSI) to guide statewide comprehensive cancer control activities. However, lack of interval estimates for RSI limits its applications. RSI also weights individual measures with little inherent variability equally as ones with large variability. To address these issues, a Modified Sum Index (MSI) was developed to take into account of magnitudes of observed values. A simulation approach was used to generate individual and simultaneous 95% CIs for the rank MSI. An uncertainty measure was also calculated. RESULTS: At the Area Development Districts (ADDs) level, the ranks of the RSI and the MSI were almost identical, while larger variation was found at the county level. The widths of the CIs at the ADD level were considerably shorter than those at the county level. CONCLUSION: The measures developed for estimating composite cancer burden indices and the simulated CIs provide valuable information to guide cancer prevention and control effort. Caution should be taken when interpreting ranks from small population geographic units where the CIs for the ranks overlap considerably.

Wisconsin Versus Minnesota: A Border Battle for the Healthiest State.

BACKGROUND: Measuring and ranking the health of counties helps raise awareness of health disparities based on where people live. Recently, there has been increasing interest in comparing the health of counties across state lines, to potentially measure the impact of local and state-level policies. METHODS: The counties in Minnesota (n = 87) and Wisconsin (n = 72) were combined into a single 2-state region, and all 159 counties were ranked according to the County Health Rankings methods, with summary ranks for health outcomes and health factors. Multivariable regression analysis was then used to examine the potential impact of state-based programs and policies on health outcomes. RESULTS: Minnesota was healthier overall than Wisconsin, with lower rates of premature death and better quality of life. Minnesota also performed better than Wisconsin for all 9 health behavior measures, 4 of 7 clinical care measures, 7 of 8 social and economic factors, and 3 of 5 physical environment measures. Furthermore, counties in Wisconsin were more likely to have lower (worse) ranks than counties in Minnesota for both health outcomes and health factors, as well as for the subcategories that make up these summary ranks. Regression analysis showed that Minnesota's better health status was explained primarily by healthier behaviors and more desirable social and economic factors. CONCLUSIONS: Minnesota's better health outcomes are largely explained by better social, economic, and behavioral factors. These findings suggest a need for examination of policies and strategies that may be influencing the observed differences across these 2 states.

Correlates of physiological and psychological stress among parents of childhood cancer and brain tumor survivors.

OBJECTIVE: First, we sought to determine if parents of children with cancer or a brain tumor had greater stress compared to parents of healthy children and to evaluate the correlates of stress among parents of children with cancer or brain tumors. Second, we sought to examine the relationship between perceived stress and symptoms of stress and how that relationship may differ for parents of children with cancer. METHODS: In-person, interviewer-assisted surveys were administered to 73 case dyads (children with cancer or a brain tumor and their parents) and 133 comparison dyads (children without health problems and their parents from a community sample). Descriptive analyses and multivariable logistic regressions were performed for case-comparison and case-only analyses to distinguish correlates of parental stress. RESULTS: Parents of children with cancer exhibited higher levels of physiological symptoms of stress than parents of healthy children. Poor sleep quality and greater social stress (negative social interactions) were significant correlates of increased levels of stress in parents of children with cancer (odds ratio 4.23, 95% confidence interval 1.15-15.60; and odds ratio 1.07, 95% confidence interval 1.00-1.14, respectively). A subset of parents reported symptoms of stress but not perceived stress, and this discordance was more pronounced among cancer caregivers. CONCLUSIONS: Implementation of screening tools that include symptoms of stress may help clinicians to comprehensively identify parents of children with cancer who are in need of additional services. Targeted stress-reduction interventions that address sleep quality and negative social interactions may mitigate the deleterious effects of caregiving, improving the psychosocial well-being of both parents and children with cancer.

Progress in reducing premature deaths in Wisconsin counties, 2000-2010.

BACKGROUND: Measuring trends in a county's premature death rate is a straightforward method that can be used to assess a county's progress in improving the health of the population. METHODS: Age-adjusted premature death rate data from Wisconsin Interactive Statistics on Health for persons less than 75 years of age were collected for the years 2000-2010. Overall 10-year percent change was calculated, compared, and ranked for all Wisconsin counties during this time period. Progress was assessed as excellent (25.0% or greater decline), very good (20.0%-24.9% decline), good (10.0%-19.9% decline), fair (0.0%-9.9% decline), or poor (any increase). RESULTS: Overall, premature death rates in counties declined by 16.8% over the 10-year period 2000-2010 in Wisconsin. Trends varied by county, with 8, 15, 37, 9, and 3 counties having excellent, very good, good, fair, and poor progress, respectively. The most improvement was seen in Kewaunee County (decreasing 38.3%) and the least progress in Lafayette County (increasing 4.8%). Trends in premature death rates were not related to the county's initial death rate, population, rurality, or income. CONCLUSIONS: Although premature death rates declined overall in Wisconsin during the 2000s, this progress varied across counties and was not related to baseline mortality rates or other county characteristics.

Family influence: key to fruit and vegetable consumption among fourth- and fifth-grade students.

OBJECTIVE: To assess social and familial environmental influences on fruit and vegetable (FV) consumption of fourth- and fifth-graders living in a culturally diverse, urban setting. DESIGN: In 2006, students from 9 fourth- and fifth-grade classrooms from a public school in the Washington-Baltimore Metropolitan Region were recruited as part of the Food Stamp Nutrition Education's initiative to increase FV consumption among school-aged children in low-income schools. This intervention was conducted in partnership with the school. PARTICIPANTS: Ninety-three fourth- and fifth-grade students and their parents. MAIN OUTCOME MEASURE: The average daily FV consumption of elementary school students was the dependent variable. Home engagement (eg, student participation in menu planning and food selection at home), parental modeling, school environment, peer influences, and individual characteristics were the independent variables. ANALYSIS: Multiple regression analysis. RESULTS: Family and home environment factors explained more than 50% of the variance in students' FV consumption. CONCLUSIONS AND IMPLICATIONS: Interventions designed to influence school-aged children's FV consumption should target family members and consider incorporating strategies to reach families and promote meal planning activities with the children in their homes.

NMR-based microbial metabolomics and the temperature-dependent coral pathogen Vibrio coralliilyticus.

Coral bleaching occurs when the symbioses between coral animals and their zooxanthellae is disrupted, either as part of a natural cycle or as the result of unusual events. The bacterium Vibrio coralliilyticus (type strain ATCC BAA-450) has been linked to coral disease globally (for example in the Mediterranean, Red Sea, Indian Ocean, and Great Barrier Reef) and like many other Vibrio species exhibits a temperature-dependent pathogenicity. The temperature-dependence of V. corallillyticus in regard to its metabolome was investigated. Nuclear magnetic resonance (NMR) spectra were obtained of methanol-water extracts of intracellula rmetabolites (endometabolome) from multiple samples of the bacteria cultured into late stationary phase at 27 degrees C (virulent form) and 24 degrees C (avirulent form). The spectra were subjected to principal components analysis (PCA), and significant temperature-based separations in PC1, PC2, and PC3 dimensions were observed. Betaine, succinate, and glutamate were identified as metabolites that caused the greatest temperature-based separations in the PC scores plots. With increasing temperature, betaine was shown to be down regulated, while succinate and glutamate were up regulated.