Keeping It Political and Powerful: Defining the Structural Determinants of Health.

Policy Points The structural determinants of health are 1) the written and unwritten rules that create, maintain, or eliminate durable and hierarchical patterns of advantage among socially constructed groups in the conditions that affect health, and 2) the manifestation of power relations in that people and groups with more power based on current social structures work-implicitly and explicitly-to maintain their advantage by reinforcing or modifying these rules. This theoretically grounded definition of structural determinants can support a shared analysis of the root causes of health inequities and an embrace of public health's role in shifting power relations and engaging politically, especially in its policy work. Shifting the balance of power relations between socially constructed groups differentiates interventions in the structural determinants of health from those in the social determinants of health.

Discounting Health Grades for Disparity: The 2021 Wisconsin Population Health and Equity Report Card.

We describe updates to the University of Wisconsin Population Health Institute's methodology for a state health report card, first described in Preventing Chronic Disease in 2010, and the considerations that were weighed in making those updates. These methods have been used since 2006 to issue a periodic report entitled Health of Wisconsin Report Card. The report highlights Wisconsin's standing among other states and serves as an example for others seeking to measure and improve their population's health. For 2021, we revisited our approach with an increased emphasis on disparities and health equity, which required many choices about data, analysis, and reporting methods. In this article, we outline the decisions, rationale, and implications of several choices we made in assessing Wisconsin's health by answering several questions, among them: Who is the intended audience and which measures of length (eg, mortality rate, years of potential life lost) and quality of life (eg, self-reported health, quality-adjusted life years) are most relevant to them? Which subgroups should we report disparities about, and which metric is most easily understood? Should disparities be summarized with overall health or reported separately? Although these decisions are applicable to 1 state, the rationale for our choices could be applied to other states, communities, and nations. Consideration of the purpose, audience, and context for health and equity policy making is important in developing report cards and other tools that can improve the health of all people and places.

Evidence Clearinghouses as Tools to Advance Health Equity: What We Know from a Systematic Scan.

Evidence clearinghouses evaluate and summarize literature to help decision-makers prioritize and invest in evidence-informed interventions. Clearinghouses and related practice-oriented tools are continuously evolving; however, it is unclear the extent to which these tools assess and summarize evidence describing an intervention's impact on health equity. We conducted a systematic scan to explore how clearinghouses communicated an intervention's equity impact and reviewed their underlying methods and how they defined and operationalized health equity. In 2021, we identified 18 clearinghouses that were US-focused, web-based registries of interventions that assigned an intervention effectiveness rating for improving community health and the social determinants of health. We reviewed each clearinghouse's website and collected publicly available information about their health equity impact review, review methods, and health equity definitions and values. We conducted a comparative analysis among select clearinghouses using qualitative methods. Among the 18 clearinghouses, fewer than half (only seven) summarized an intervention's potential impact on health equity. Overall, those seven clearinghouses defined and operationalized equity differently, and most lacked transparency in their review methods. Clearinghouses used one or more approaches to communicate findings from their review: summarize study findings on differential impact for subpopulations, curate interventions that reduce health disparities, and/or assign a disparity/equity rating to each intervention. Evidence clearinghouses can enhance equity-focused methods and be transparent in their underlying values to better support the uptake and implementation of evidence-informed interventions to advance health equity. However, clearinghouses are unable to do so without underlying equity-focused empirical evidence.

Theory in Action: Public Health and Community Power Building for Health Equity.

CONTEXT: Within the field of public health, there is growing awareness of how complex social conditions shape health outcomes and the role that power plays in driving health inequities. Despite public health frameworks lifting up the need to tackle power imbalances to advance equity, there is little guidance on how to accomplish this as an integral part of health promotion. OBJECTIVE: This article addresses the need for public health professionals to better understand power and identifies opportunities for shifting power to achieve more equitable outcomes. First, it defines power and community power building. Next, it reviews a pragmatic theoretical framework that organizes power into 3 faces: (1) exercising influence in formal decision-making processes; (2) organizing the decision-making environment; and (3) shaping worldviews about social issues. Finally, it connects each face of power to community power-building practices using concrete examples. IMPLEMENTATION: This article highlights real-world case examples to demonstrate how theory translates to action by describing how public health practitioners in government, academic, and nonprofit settings incorporate the 3 faces of power into their work. The case examples illustrate how public health organizations and practitioners can partner with those most impacted by inequities to help shape decision making, agenda setting, and worldviews to influence policy and practice toward more equitable outcomes. DISCUSSION: The public health field can learn from and build on these innovative examples to establish new practices, scale up promising approaches, and evaluate what works to shift power for the greater good.

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.

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.

Generating Subcounty Health Data Products: Methods and Recommendations From a Multistate Pilot Initiative.

BACKGROUND: County Health Rankings & Roadmaps (CHR&R) makes data on health determinants and outcomes available at the county level, but health data at subcounty levels are needed. Three pilot projects in California, Missouri, and New York explored multiple approaches for defining measures and producing data at subcounty geographic and demographic levels based on the CHR&R model. This article summarizes the collective technical and implementation considerations from the projects, challenges inherent in analyzing subcounty health data, and lessons learned to inform future subcounty health data projects. METHODS: The research teams used 12 data sources to produce 40 subcounty measures that replicate or approximate county-level measures from the CHR&R model. Using varying technical methods, the pilot projects followed similar stages: (1) conceptual development of data sources and measures; (2) analysis and presentation of small-area and subpopulation measures for public health, health care, and lay audiences; and (3) positioning the subcounty data initiatives for growth and sustainability. Unique technical considerations, such as degree of data suppression or data stability, arose during the project implementation. A compendium of technical resources, including samples of automated programs for analyzing and reporting subcounty data, was also developed. RESULTS: The teams summarized the common themes shared by all projects as well as unique technical considerations arising during the project implementation. Furthermore, technical challenges and implementation challenges involved in subcounty data analyses are discussed. Lessons learned and proposed recommendations for prospective analysts of subcounty data are provided on the basis of project experiences, successes, and challenges. CONCLUSIONS: This multistate pilot project offers 3 successful approaches for creating and disseminating subcounty data products to communities. Subcounty data often are more difficult to obtain than county-level data and require additional considerations such as estimate stability, validating accuracy, and protecting individual confidentiality. We encourage future projects to further refine techniques for addressing these critical considerations.

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.

How to Be an Informed Consumer of Evidence Ratings: It's in the Details.

What are evidence-based strategies and how can public health practitioners find evidence without conducting extensive literature reviews? We developed an inventory of clearinghouses and other resources that disseminate research on evidence of effectiveness. We examined differences in evidence classification among 6 evidence clearinghouses that rate the effectiveness of community-level strategies to address determinants of health. Most evidence clearinghouses clearly defined their scope, but only a few clearinghouses explicitly defined the types of strategies they assess (eg, programs, policies, practices). The term "evidence-based" was widely used, but definitions and standards were inconsistent across organizations and disciplines. Evidence clearinghouses varied in the way they used evidence rating classifications and criteria for assigning ratings. Attention to detail is important. The criteria for the top rating of some evidence clearinghouses, for example, require a more thorough literature review with more robust results than the criteria for the top rating of others. In addition, some clearinghouses report only on strategies considered to be evidence-based, whereas others also report on strategies that have no effect, mixed evidence, or no qualifying studies, demonstrating that a listing of a strategy by an evidence clearinghouse does not necessarily mean that it is effective. We conclude by providing guidance for users of evidence clearinghouses about how to interpret and effectively apply rating criteria across platforms: look closely at the details of how clearinghouses assign their ratings and be aware of similarities and differences when you are aligning potential strategies with your local priorities. We encourage communities to balance evidence with local needs, resources, and culture in strategy selection and funding decisions.

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.

Joinpoint Trend Analysis of Infant Mortality Disparities in Wisconsin, 1999-2016.

OBJECTIVES: To address shortcomings of previous research exploring trends in racial, educational, and race by educational disparities in infant mortality rates (IMRs) by using nonlinear methods to compare improvement within and between disparity domains. METHODS: We used joinpoint regression modeling to perform a cross-sectional analysis of IMR trends from linked birth and death certificates in Wisconsin between 1999 and 2016. RESULTS: In the race and education domains, IMR decreased by 1.9% per year for infants of White mothers and 1.1% per year for infants of less-educated mothers. Further analysis showed these IMR reductions to be among infants of White mothers with more education (-0.6%/year) and Black mothers with less education (-2.0%/year). CONCLUSIONS: As previously reported, gaps in IMR by race and education in Wisconsin appear to be closing; however, only the change by education is statistically significant. Evidence suggests the racial divide in IMR might soon widen after years of progress in reducing IMR among infants of Black mothers. Public Health Implications. Those advancing strategies to address IMR disparities should pursue data and methods that provide the most accurate and refined information about the challenges that persist and progress that has been realized.

Separate and Sick: Residential Segregation and the Health of Children and Youth in Metropolitan Statistical Areas.

The purpose of this study was to better understand residential segregation and child/youth health by examining the relationship between a measure of Black-White residential segregation, the index of dissimilarity, and a suite of child and youth health measures in 235 U.S. metropolitan statistical areas (MSAs). MSAs are urban areas with a population of 50,000 or more and adjacent communities that share a high degree of economic and social integration. MSAs are defined by the Office of Management and Budget. Health-related measures included child mortality (CDC WONDER), teen births (NCHS natality data), children in poverty (SAIPE program), and disconnected youth (Measure of America). Simple linear regression and two-level hierarchical linear regression models, controlling for income, total population, % Black, and census region, examined the association between segregation and Black health, White health, and Black-White disparities in health. As segregation increased, Black children and youth had worse health across all four measures, regardless of MSA total and Black population size. White children and youth in small MSAs with large Black populations had worse levels of disconnected youth and teen births with increasing segregation, but no associations were found for White children and youth in other MSAs. Segregation worsened Black-White health disparities across all four measures, regardless of MSA total and Black population size. Segregation adversely affects the health of Black children in all MSAs and White children in smaller MSAs with large Black populations, and these effects are seen in measures that span all of childhood. Residential segregation may be an important target to consider in efforts to improve neighborhood conditions that influence the health of families and children.

The association between neighborhood economic hardship, the retail food environment, fast food intake, and obesity: findings from the Survey of the Health of Wisconsin.

BACKGROUND: Neighborhood-level characteristics such as economic hardship and the retail food environment are assumed to be correlated and to influence consumers' dietary behavior and health status, but few studies have investigated these different relationships comprehensively in a single study. This work aims to investigate the association between neighborhood-level economic hardship, the retail food environment, fast food consumption, and obesity prevalence. METHODS: Linking data from the population-based Survey of the Health of Wisconsin (SHOW, n = 1,570, 2008-10) and a commercially available business database, the Wisconsin Retail Food Environment Index (WRFEI) was defined as the mean distance from each participating household to the three closest supermarkets divided by the mean distance to the three closest convenience stores or fast food restaurants. Based on US census data, neighborhood-level economic hardship was defined by the Economic Hardship Index (EHI). Relationships were analyzed using multivariate linear and logistic regression models. RESULTS: SHOW residents living in neighborhoods with the highest economic hardship faced a less favorable retail food environment (WRFEI = 2.53) than residents from neighborhoods with the lowest economic hardship (WRFEI = 1.77; p-trend < 0.01). We found no consistent or significant associations between the WRFEI and obesity and only a weak borderline-significant association between access to fast food restaurants and self-reported fast food consumption (≥ 2 times/week, OR = 0.59-0.62, p = 0.05-0.09) in urban residents. Participants reporting higher frequency of fast food consumption (≥ 2 times vs. <2 times per week) were more likely to be obese (OR = 1.35, p = 0.06). CONCLUSION: This study indicates that neighborhood-level economic hardship is associated with an unfavorable retail food environment. However inconsistent or non-significant relationships between the retail food environment, fast food consumption, and obesity were observed. More research is needed to enhance methodological approaches to assess the retail food environment and to understand the complex relationship between neighborhood characteristics, health behaviors, and health outcomes.

Shiftwork, Sleep Habits, and Metabolic Disparities: Results from the Survey of the Health of Wisconsin.

BACKGROUND: With the expanding demand for a 24-hour society, the prevalence of sleep deprivation and other sleep-related health problems is increasing. Shiftwork is an occupational health risk of growing significance because of its high prevalence and because of its potential role as a determinant of socioeconomic-related health disparities. AIMS: The aim of this study was to examine the associations of shiftwork with overweight status and type 2 diabetes, and explore whether a history of sleep problems mediates or modifies these associations. PARTICIPANTS AND METHODS: A cross-sectional study was conducted among 1,593 participants in the Survey of the Health of Wisconsin (2008-12) who were employed and reported work characteristics (traditional schedule or shiftwork) and sleep habits and history of sleep problems (insomnia, insufficient sleep, wake time sleepiness). Objective measures of body mass index (BMI) and type 2 diabetes were used. RESULTS: Shiftworkers were more overweight than traditional-schedule workers (83% vs. 71% with BMI≥25) and reported more sleep problems, such as insomnia symptoms (24% vs. 16%), insufficient sleep (53% vs.43%), and sleepiness (32% vs. 24%). The associations between shiftwork and being overweight or diabetic were stronger among those reporting insufficient sleep, but the interaction was not statistically significant. CONCLUSIONS: Shiftworkers face disparities in metabolic health, particularly those with insufficient sleep. Improved understanding of the relationship between sleep and metabolic states can inform healthcare providers' and employers' efforts to screen high-risk individuals and intervene with workplace wellness initiatives to address these disparities.

Advancing efforts to achieve health equity: equity metrics for health impact assessment practice.

Equity is a core value of Health Impact Assessment (HIA). Many compelling moral, economic, and health arguments exist for prioritizing and incorporating equity considerations in HIA practice. Decision-makers, stakeholders, and HIA practitioners see the value of HIAs in uncovering the impacts of policy and planning decisions on various population subgroups, developing and prioritizing specific actions that promote or protect health equity, and using the process to empower marginalized communities. There have been several HIA frameworks developed to guide the inclusion of equity considerations. However, the field lacks clear indicators for measuring whether an HIA advanced equity. This article describes the development of a set of equity metrics that aim to guide and evaluate progress toward equity in HIA practice. These metrics also intend to further push the field to deepen its practice and commitment to equity in each phase of an HIA. Over the course of a year, the Society of Practitioners of Health Impact Assessment (SOPHIA) Equity Working Group took part in a consensus process to develop these process and outcome metrics. The metrics were piloted, reviewed, and refined based on feedback from reviewers. The Equity Metrics are comprised of 23 measures of equity organized into four outcomes: (1) the HIA process and products focused on equity; (2) the HIA process built the capacity and ability of communities facing health inequities to engage in future HIAs and in decision-making more generally; (3) the HIA resulted in a shift in power benefiting communities facing inequities; and (4) the HIA contributed to changes that reduced health inequities and inequities in the social and environmental determinants of health. The metrics are comprised of a measurement scale, examples of high scoring activities, potential data sources, and example interview questions to gather data and guide evaluators on scoring each metric.

Primary care practitioners survey of non-alcoholic fatty liver disease.

BACKGROUND: The rising incidence of non alcoholic fatty liver disease (NAFLD) mirrors the epidemics of obesity and metabolic syndrome. Primary care practitioners (PCPs) are central to management of patients with NAFLD, but data on knowledge and attitudes of PCPs towards NAFLD are lacking. MATERIAL AND METHODS: We conducted a statewide, stratified survey of 250 PCPs to examine knowledge, practices and attitudes regarding NAFLD and the barriers to providing care for this condition. RESULTS: NAFLD was perceived as an important health problem by 83% of PCPs. Eighty five percent of PCPs underestimated the population prevalence of NAFLD. Although the association of NAFLD with metabolic syndrome was identified by 91% of PCPs, only 46% screened diabetic obese patients for NAFLD. Only 27% of PCPs referred NAFLD patients to a hepatologist for evaluation. PCPs who reported seeing more than 5 NAFLD patients annually, referred to hepatology less frequently (P = 0.01). The majority of PCPs (58%) recommended weight loss and a calorie restriction. Only 8% of PCPs would recommend Vitamin E. The major perceived barrier in managing NAFLD was lack of confidence in understanding of the disease (58% of PCPs). DISCUSSION: An overwhelming majority of PCPs perceived NAFLD as an important health issue in their practice. However, screening rates for NAFLD among obese diabetics were low. A major barrier to managing these patients was self-reported lack of knowledge about NAFLD. Development of guidelines should emphasize strategies for screening vulnerable populations (obese, diabetics), evidence based management and barriers to providing care.

Maternal exposure to polybrominated and polychlorinated biphenyls: infant birth weight and gestational age.

Understanding the influence of maternal exposures on gestational age and birth weight is essential given that pre-term and/or low birth weight infants are at risk for increased mortality and morbidity. We performed a retrospective analysis of a cohort exposed to polybrominated biphenyls (PBB) through accidental contamination of cattle feed and polychlorinated biphenyls (PCB) through residual contamination in the geographic region. Our study population consisted of 444 mothers and their 899 infants born between 1975 and 1997. Using restricted maximum likelihood estimation, no significant association was found between estimated maternal serum PBB at conception or enrollment PCB levels and gestational age or infant birth weight in unadjusted models or in models that adjusted for maternal age, smoking, parity, infant gender, and decade of birth. For enrollment maternal serum PBB, no association was observed for gestational age. However, a negative association with high levels of enrollment maternal serum PBB and birth weight was suggested. We also examined the birth weight and gestational age among offspring of women with the highest (10%) PBB or PCB exposure, and observed no significant association. Because brominated compounds are currently used in consumer products and therefore, are increasingly prevalent in the environment, additional research is needed to better understand the potential relationship between in utero exposure to brominated compounds and adverse health outcomes.

Estimating dietary consumption patterns among children: a comparison between cross-sectional and longitudinal study designs.

Estimating dietary intake for children is an essential component of conducting pesticide exposure assessments given the fact that children are predominantly exposed to certain pesticides, such as organophosphorus pesticide, through dietary intake. Different study designs and their respective sampling methodology utilized to estimate food consumption patterns can significantly alter the parameter estimates and the variability in the values obtained. This study investigated the impacts of study design on overall estimates of dietary intake by applying the temporal sampling characteristics used in cross-sectional approaches, as in The Continuing Survey of Food for Intakes by Individuals (CSFII), to food consumption data collected in a longitudinal manner via a bootstrap sampling technique. We examined the precision of time-averaged dietary intake estimates under various sampling schemes and explored the contribution of seasonality toward the dietary patterns. A comparison between the estimates of food consumption obtained from the bootstrap replicates and the longitudinal study estimates indicate that variability is significantly decreased when employing a longitudinal study design. Moreover, both between and within-subject variability decreases when individuals are followed over an increasing number of days. Finally, within the longitudinal study cohort, we observed a seasonal component to dietary intake for fruits and grains. Our findings suggest that longitudinal dietary surveys offer substantial improvements for exposure assessment compared to a standard cross-sectional design.