Six paths for the future of social epidemiology

A social acceptability scale: Validation in the context of government measures to curb the COVID-19 pandemic in Senegal

INTRODUCTION

In March 2020, the government of Senegal introduced a curfew, a ban on travel between regions, the closure of markets, and a ban on attending places of worship to contain the spread of the COVID-19 pandemic. As part of research into the response to COVID-19, we developed a scale to measure the social acceptability of these measures.

METHODS

We used Sekhon's theoretical framework of acceptability (TFA) to formulate the content of the scale items. We assessed the homogeneity of the scale using Cronbach's Alpha and average interitem correlations. We measured the dimensional properties of the scale using rating scale models. We tested the sensitivity of the scale to sociodemographic characteristics using mixed linear regressions and rating scale models.

RESULTS

The final scale consisted of seven items corresponding to the constructs of acceptability. Analysis performed on data from 813 individuals showed that the scale has satisfactory statistical properties (Cronbach's α > 0.8, Loevinger's coefficient>0.3, intraclass correlation>0.4).

CONCLUSION

This scale was one of the first to test the TFA. The small number of items was advantageous for use under challenging data collection contexts. Measuring the acceptability of public health interventions with this tool can help in their design and implementation.

A hypothetical intervention to reduce inequities in anxiety for Multiracial people: simulating an intervention on childhood adversity

Multiracial people report higher mean Adverse Childhood Experiences (ACEs) scores and prevalence of anxiety than other racial groups. Studies using statistical interactions to estimate racial differences in ACEs-anxiety associations do not show stronger associations for Multiracial people. Using data from Waves 1 (1995-97) through 4 (2008-09) of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we simulated a stochastic intervention over 1,000 resampled datasets to estimate the race-specific cases averted per 1,000 of anxiety if all racial groups had the same exposure distribution of ACEs as Whites. Simulated cases averted were greatest for the Multiracial group, (median = -4.17 cases per 1,000, 95% CI: -7.42, -1.86). The model also predicted smaller risk reductions for Black participants (-0.76, 95% CI: -1.53, -0.19). CIs around estimates for other racial groups included the null. An intervention to reduce racial disparities in exposure to ACEs could help reduce the inequitable burden of anxiety on the Multiracial population. Stochastic methods support consequentialist approaches to racial health equity, and can encourage greater dialogue between public health researchers, policymakers, and practitioners.

Invited Commentary: Some Social Epidemiologic Lessons From the COVID-19 Pandemic

In their recent article, Dimitris et al. (Am J Epidemiol. 2022;191(6):980-986) presented a series of challenges modern epidemiology has faced during the coronavirus disease 2019 (COVID-19) pandemic, including challenges around the scientific progress, epidemiologic methods, interventions, equity, team science, and training needed to address these issues. Here, 2 social epidemiologists who have been working on COVID-19 inequities reflect on further lessons with an added year of perspective. We focus on 2 key challenges: 1) dominant biomedical individualistic narratives around the production of population health, and 2) the role of profit in policy-making. We articulate a need to consider social epidemiologic approaches, including acknowledging the importance of considering how societal systems lead to health inequities. To address these challenges, future (and current) epidemiologists should be trained in theories of population health distribution and political structures of governance. Last, we close with the need for better investment in public health infrastructure as a crucial step toward achieving population health equity.

The Future of Social Determinants of Health: Looking Upstream to Structural Drivers

Policy Points Policies that redress oppressive social, economic, and political conditions are essential for improving population health and achieving health equity. Efforts to remedy structural oppression and its deleterious effects should account for its multilevel, multifaceted, interconnected, systemic, and intersectional nature. The U.S. Department of Health and Human Services should facilitate the creation and maintenance of a national publicly available, user-friendly data infrastructure on contextual measures of structural oppression. Publicly funded research on social determinants of health should be mandated to (a) analyze health inequities in relation to relevant data on structural conditions and (b) deposit the data in the publicly available data repository.

Conceptualization, Operationalization, and Utilization of Race and Ethnicity in Major Epidemiology Journals, 1995-2018: A Systematic Review

Despite repeated calls by scholars to critically engage with the concepts of race and ethnicity in US epidemiologic research, the incorporation of these social constructs in scholarship may be suboptimal. This study characterizes the conceptualization, operationalization, and utilization of race and ethnicity in US research published in leading journals whose publications shape discourse and norms around race, ethnicity, and health within the field of epidemiology. We systematically reviewed randomly selected articles from prominent epidemiology journals across 5 periods: 1995-1999, 2000-2004, 2005-2009, 2010-2014, and 2015-2018. All original human-subjects research conducted in the United States was eligible for review. Information on definitions, measurement, coding, and use in analysis was extracted. We reviewed 1,050 articles, including 414 (39%) in our analyses. Four studies explicitly defined race and/or ethnicity. Authors rarely made clear delineations between race and ethnicity, often adopting an ethnoracial construct. In the majority of studies across time periods, authors did not state how race and/or ethnicity was measured. Top coding schemes included "Black, White" (race), "Hispanic, non-Hispanic" (ethnicity), and "Black, White, Hispanic" (ethnoracial). Most often, race and ethnicity were deemed "not of interest" in analyses (e.g., control variables). Broadly, disciplinary practices have remained largely the same between 1995 and 2018 and are in need of improvement.

Beyond Geography: Social Quality Environments and Health

The concept of social quality has garnered increasing attention as a composite indicator of the well-being of societies as well as individuals embedded within them. Prior research suggests four domains of social quality: socio-economic security, social cohesion, social inclusion, and social empowerment, based on the assumption that these domains influence health and well-being. In this paper, we investigate whether and to what extent social quality environments defined with reference to the cross-cutting social quality domains reliably predict various types of health, using data collected in a municipality in Switzerland as part of a participatory action research project. We found that social inclusion had the highest predictive power for mental health and functional health, while economic security had the highest predictive power for physical capacity and overall self-rated health. Results indicate interaction among various domains of social quality for a subset of health measures. Findings suggest that environments defined as combinations of social quality domains effectively distinguish between population segments with varying levels of health. Social quality represents a promising avenue for policy and intervention development, particularly from the social determinants of health perspective, as it jointly captures the multiple domains of social well-being relevant to population health.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11205-023-03073-1.

Overcoming structural barriers to sharing power with communities in global health research priority-setting: Lessons from the Participation for Local Action project in Karnataka, India

Community engagement is gaining prominence in global health research. But communities rarely have a say in the agendas or conduct of the very health research projects that aim to help them. This paper provides new evidence on how to share power in priority-setting in ways that seek to overcome structural constraints created by the funding environment. The five strategies were identified through case study research on the Participation for Local Action project in Karnataka, India. That project was carried out by researchers in partnership with the Zilla Budakattu Girijana Abhivrudhhi Sangha, an indigenous community development organisation representing the Solega people. The paper describes each identified strategy for sharing power in priority-setting, followed by a report of the pitfalls and challenges that arose when implementing it. Thus, the study also demonstrates that even where actions and strategies are used to address power imbalances, pitfalls will arise that need to be navigated. Given those challenges, considerations to reflect upon before employing the identified strategies are suggested. Ultimately, the paper aims to communicate strategies for sharing power during and after priority-setting and lessons on how to implement them effectively that can be used by global health researchers in the current funding environment.

Eliminating racial disparities in dementia risk by equalizing education quality: A sensitivity analysis

BACKGROUND

Higher risk of dementia among racial/ethnic minorities compared to White populations in the U.S. has been attributed to life-course exposures to adverse conditions such as lower educational attainment, but most studies have not considered additional disparities in education quality. We sought to determine the extent to which disparities in dementia would be reduced had different racial groups received the same quality of education, with no change to present disparities in educational attainment.

METHODS

We conducted a literature review to assess whether and how measures of educational attainment and quality are utilized in the development of norms for standard cognitive screening measures. In a separate search of the literature, we identified estimates of relationships between race, education quality and dementia; and calculated the adjusted association between race and dementia had education quality been equalized between Black and White participants.

RESULTS

Most norms for cognitive measures included educational attainment, but few addressed quality. Our search identified relevant parameter estimates: 44.3% of Black participants and 10.5% of White participants had "limited literacy" (<9th grade reading level, a potential marker of poor education quality), which was associated with a 53% greater hazard of dementia compared with "adequate literacy" (≥ 9th grade reading level) after adjusting for educational attainment. Applying these parameters to a hazard ratio of 1.37 (95%CI: 1.12,1.67) for the risk of dementia comparing Black to White participants, we obtained an adjusted hazard ratio of 1.17 (0.96,1.43), a 54% reduction.

DISCUSSION

Present studies are limited in their consideration of education quality. Our work using available measures from the literature suggests that if education quality were equalized across groups by race, without changing disparities in attainment, racial disparities in dementia would be reduced by about half. Future work should seek to consistently incorporate education quality in order to better understand the sources of disparities.

Advancing the Social Epidemiology Mission of the American Journal of Epidemiology

Social epidemiology is concerned with how social forces influence population health. Rather than focusing on a single disease (as in cancer or cardiovascular epidemiology) or a single type of exposure (e.g., nutritional epidemiology), social epidemiology encompasses all the social and economic determinants of health, both historical and contemporary. These include features of social and physical environments, the network of relationships in a society, and the institutions, politics, policies, norms and cultures that shape all of these forces. This commentary presents the perspective of several editors at the Journal with expertise in social epidemiology. We articulate our thinking to encourage submissions to the Journal that: 1) expand knowledge of emerging and underresearched social determinants of population health; 2) advance new empirical evidence on the determinants of health inequities and solutions to advance health equity; 3) generate evidence to inform the translation of research on social determinants of health into public health impact; 4) contribute to innovation in methods to improve the rigor and relevance of social epidemiology; and 5) encourage critical self-reflection on the direction, challenges, successes, and failures of the field.

A new era: improving use of sociodemographic constructs in the analysis of pediatric cohort study data

Given the diversity of sex, gender identity, race, ethnicity, and socioeconomic position (SEP) in children across the United States, it is incumbent upon pediatric and epidemiologic researchers to conduct their work in ways that promote inclusivity, understanding and reduction in inequities. Current child health research often utilizes an approach of "convenience" in how data related to these constructs are collected, categorized, and included in models; the field needs to be more systematic and thoughtful in its approach to understand how sociodemographics affect child health. We offer suggestions for improving the discourse around sex, gender identity, race, ethnicity, and SEP in child health research. We explain how analytic models should be driven by a conceptual framework grounding the choices of variables that are included in analyses, without the automatic "adjusting for" all sociodemographic constructs. We propose to leverage newly available data from large multi-cohort consortia as unique opportunities to improve the current standards for analyzing and reporting core sociodemographic constructs. Improving the characterization and interpretation of child health studies with regards to core sociodemographic constructs is critical for optimizing child health and reducing inequities in the health and well-being of all children across the United States. IMPACT: Current child health research often utilizes an approach of "convenience" in how data related to sex, race/ethnicity, and SEP are collected, categorized, and included in models. We offer suggestions for how scholars can improve the discourse around sex, gender identity, race, ethnicity, and SEP in child health research. We explain how analytic models should be driven by a conceptual framework grounding the choices of variables that are included in analyses. We propose to leverage newly available large cohort consortia of child health studies as opportunities to improve the current standards for analyzing and reporting core sociodemographic constructs.

Integrating Sex/Gender into Environmental Health Research: Development of a Conceptual Framework

There is a growing awareness about the need to comprehensively integrate sex and gender into health research in order to enhance the validity and significance of research results. An in-depth consideration of differential exposures and vulnerability is lacking, especially within environmental risk assessment. Thus, the interdisciplinary team of the collaborative research project INGER (integrating gender into environmental health research) aimed to develop a multidimensional sex/gender concept as a theoretically grounded starting point for the operationalization of sex and gender in quantitative (environmental) health research. The iterative development process was based on gender theoretical and health science approaches and was inspired by previously published concepts or models of sex- and gender-related dimensions. The INGER sex/gender concept fulfills the four theoretically established prerequisites for comprehensively investigating sex and gender aspects in population health research: multidimensionality, variety, embodiment, and intersectionality. The theoretical foundation of INGER's multidimensional sex/gender concept will be laid out, as well as recent sex/gender conceptualization developments in health sciences. In conclusion, by building upon the latest state of research of several disciplines, the conceptual framework will significantly contribute to integrating gender theoretical concepts into (environmental) health research, improving the validity of research and, thus, supporting the promotion of health equity in the long term.

Can populations be healthy? Perspectives from Georges Canguilhem and Geoffrey Rose

Canguilhem criticized the concept of "public health": health and disease are concepts that only apply to individuals, taken as organic totalities. Their extension to a different level of organization is purely metaphorical. The importance assumed by epidemiology in the construction of our knowledge of the normal and the pathological does, however, call for reflection on the role and the status of the population level of organization in our approach to health phenomena. The entanglement of the biological and the social in human life and in contemporary societies justifies this level of analysis for better understanding the complexity and the interaction of health determinants both at the level of individuals and their interactions and at that of the population. But is this population level just a useful level of analysis that makes it possible to bring to light the social determinants of health at the individual level, or does it rest instead on characteristics of the population that are irreducible to individual characteristics, but which are nevertheless important for understanding and taking action with respect to both population and individual health? Defending this second alternative, I show how the epidemiological point of view, and in particular that of social epidemiology, leads us to rethink the possibility of a concept of "population health" that is not reducible to the sum of individual instances of health.

Epidemiologists’ ambivalence towards the epigenetics of social adversity

This article studies how social epidemiologists get involved in research carried out on rodent models to explore the biological pathways underpinning exposure to social adversity in early life. We analyze their interdisciplinary exchanges with biologists in a social epigenetics project—i.e., in the experimental study of molecular alterations following social exposures. We argue that social epidemiologists are ambivalent regarding the use of non-human animal models on two levels: first, in terms of whether such models provide scientific evidence useful to social epidemiology, and second, regarding whether such models help promote their conception of public health. While they maintain expectations towards rodent experiments by elevating their functional value over their representational potential, they fear that their research will contribute to a public health approach that focuses on individual responsibility rather than the social causes of health inequalities. This interdisciplinary project demonstrates the difficulties encountered when research in social epigenetics engages with the complexities of laboratory experiments and social environments, as well as the conflicting sociopolitical projects stemming from such research.

Operationalization of intersectionality in physical activity and sport research: A systematic scoping review

Participation in and opportunities for physical activity (PA) and sports (PA inclusively hereafter) are known to vary across individuals with different social positions. Intersectionality theory may help us to better understand the complex processes of multiple interlocking systems of oppression and privilege shaped by intersections of individuals’ social categories. The objectives of this systematic scoping review were (1) to summarize the findings of articles examining PA claimed operationalization of intersectionality and (2) to identify the scope and gaps pertaining to the operationalization of intersectionality in PA research. A search was conducted in September 2019 in seven electronic databases (e.g., SPORTDiscus, Scopus, Web of Science) for relevant research articles written in English. Key search terms included “intersectionality” AND “physical activity” OR “sport”. Database searches, data screening and extraction, and narrative synthesis were conducted between September 2019 and May 2020. Of 16564 articles identified, 45 articles were included in this review. The majority of included articles used qualitative methods (n = 41), with two quantitative and two mixed-methods articles. The most frequently observed intersectional social position was sex/gender + race/ethnicity (n = 11), followed by sex/gender + race/ethnicity + sexuality (n = 6) and sex/gender + race/ethnicity + religion (n = 6). Most qualitative studies (n = 38) explicitly claimed operationalization of intersectionality as a key theoretical framework, and over half of these studies (n = 27) implicitly used intra-categorical intersectionality. Two quantitative studies were identified which examined a number of intersections simultaneously using inter-categorical intersectionality. Complex processes of individual and social-structural level factors that drive inequalities in PA opportunities and participation could be better elucidated with the operationalization of intersectionality theory. Intersectionality theory may serve as a useful framework in both qualitative and quantitative investigations. Advancement in quantitative intersectionality is critical in order to produce knowledge that could inform more inclusive PA promotion efforts.

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Intra-categorical intersectionality is commonly used in most qualitative investigations.

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Inter-categorical intersectionality is used in quantitative research.

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Utilizing multiplicative statistical models may advance quantitative intersectionality.

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Investigating varying axes of marginalization beyond sex/gender + race/ethnicity is important.

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Intersectionality is useful in better understanding disparities in physical activity.

An atlas of health inequalities and health disparities research: "How is this all getting done in silos, and why?"

Research on health inequalities and health disparities has grown exponentially since the 1960s, but this expansion has not been matched by an associated sense of progress. Criticisms include claims that too much research addresses well-trodden questions and that the field has failed to gain public and policy traction. Qualitative studies have found researchers partly attribute these challenges to fragmentation resulting from disciplinary and methodological differences. Yet, empirical investigation ('research on research') is limited. This study addresses this gap, employing mixed-methods to examine, at scale, how and why this field is defined by insular research clusters. First, bibliometric analysis identifies and visualizes the 250 most-connected authors. Next, an algorithm was used to identify clustering via citation links between authors. We used researcher profiling to ascertain authors' geographical and institutional locations and disciplinary training, examining how this mapped onto clusters. Finally, causes of siloing were investigated via semi-structured interviews with 45 researchers. The resulting 'atlas' of health inequalities and health disparities research identifies eight clusters of authors with varying degrees of connectedness. No single factor neatly describes observed fragmentation, health equity scholars exhibit a diverse disciplinary backgrounds, and geographical, institutional, and historical factors appear to intersect to explain siloed citation patterns. While the configuration of research activity within clusters potentially helps render questions scientifically manageable, it affirms perceptions of the field as fragmented. We draw on Thomas Kuhn and Sheila Jasanoff to position results within theoretical pictures of scientific progress. Newcomers to the field can use our findings to orient themselves within the many streams of health equity scholarship, and existing health equity scholars can use the atlas to move beyond existing geo-disciplinary networks. However, although stronger cross-cluster engagement would be likely to improve insights, the complex nexus of factors underlying the field's structure will likely make this challenging in practice.

Religion, Spirituality, and Health: New Considerations for Epidemiology

Religion and spirituality are important social determinants that drive public health practice. The field of epidemiology has played a vital role in answering long-standing questions about whether religion is causally associated with health and mortality. As epidemiologists spark new conversations (e.g., see Kawachi (Am J Epidemiol. (https://doi.org/10.1093/aje/kwz204)) and Chen and VanderWeele (Am J Epidemiol. 2018;187(11):2355-2364)) about methods (e.g., outcomes-wide analysis) used to establish causal inference between religion and health, epidemiologists need to engage with other aspects of the issue, such as emerging trends and historical predictors. Epidemiologists will need to address 2 key aspects. The first is changing patterns in religious and spiritual identification. Specifically, how do traditional mechanisms (e.g., social support) hold up as explanations for religion-health associations now that more people identify as spiritual but not religious and more people are not attending religious services in physical buildings? The second is incorporation of place into causal inference designs. Specifically, how do we establish causal inference for associations between area-level constructs of the religious environment (e.g., denomination-specific church membership/adherence rates) and individual- and population-level health outcomes?

A definition of the causal effect of a political party's nominee on the U.S. general presidential election using counterfactual response types

The electability of the candidates for the 2020 Democratic U.S. presidential nomination was frequently debated. Arguments regarding a candidate's electability often claimed that they would affect the general election by changing the behavior of a certain subset of eligible voters. For example, is it more important electorally that a candidate drive turnout or swing voting? As lay consumers of political opinion, we were having difficulty weighing these questions from a strategic standpoint. Although candidate electability is a nebulous term that might be interpreted in various ways, one interpretation of the term is a population-based causal question: What would the effect of the Democratic nominee be on the presidential election result? Population-based causal questions are commonly studied in epidemiology. To aid interpretation of electability arguments, we frame the question through a counterfactual model used in epidemiology. Specifically, we define the causal effect by characterizing the population of eligible voters into nine counterfactual response types. The definition clarifies our ability to interpret arguments regarding the electability of the candidates. For example, the causal effect can be subdivided into three parts: the effect of the nominee on (1) Democratic turnout, (2) Republican turnout, and (3) swing voting. We show using notation that the third part has twice the weight as the other two. The definition follows intuition. However, we hope its formalization using counterfactual response types may foster interdisciplinary communication.

Participatory model building for suicide prevention in Canada

Background

Suicide is a behaviour that results from a complex interplay of factors, including biological, psychological, social, cultural, and environmental factors, among others. A participatory model building workshop was conducted with fifteen employees working in suicide prevention at a federal public health organization to develop a conceptual model illustrating the interconnections between such factors. Through this process, knowledge emerged from participants and consensus building occurred, leading to the development of a conceptual model that is useful to organize and communicate the complex interrelationships between factors related to suicide.

Methods

A model building script was developed for the facilitators to lead the participants through a series of group and individual activities that were designed to elicit participants' implicit models of risk and protective factors for suicide in Canada. Participants were divided into three groups and tasked with drawing the relationships between factors associated with suicide over a simplified suicide process model. Participants were also tasked with listing prevention levers that are in use in Canada and/or described in the scientific literature.

Results

Through the workshop, risk and prevention factors and prevention levers were listed and a conceptual model was drafted. Several "lessons learned" which could improve future workshops were generated through reflection on the process.

Conclusions

This workshop yielded a helpful conceptual model contextualising upstream factors that can be used to better understand suicide prevention efforts in Canada.

Fundamental social causes of inequalities in colorectal cancer mortality: A study of behavioral and medical mechanisms

BACKGROUND

Fundamental cause theory posits that social conditions strongly influence the risk of health risks. This study identifies risk mechanisms that social conditions associated with socioeconomic status (SES) and race/ethnicity shape in the production of colorectal cancer (CRC) mortality.

METHODS

Two large datasets in the United States examining behavioral and medical preventive factors (N = 4.63-million people) were merged with population-level mortality data observing 761,100 CRC deaths among 3.31-billion person-years of observation to examine trends in CRC mortality from 1999-2012. Analyses examined the changing role of medical preventions and health behaviors in catalyzing SES and racial/ethnic inequalities in CRC mortality.

RESULTS

Lower SES as well as Black, Hispanic, Asian/Pacific Islander, and Native American race/ethnicity were associated with decreased access to age-appropriate screening and/or increased prevalence of behavioral risk factors. Analyses further revealed that SES and racial/ethnic inequalities were partially determined by differences in engagement in two preventive factors: use of colonoscopy, and participation in physical activity.

DISCUSSION

Social inequalities were not completely determined by behavioral risk factors. Nevertheless, a more equitable distribution of preventive medicines has the potential to reduce both the risk of, and social inequalities in, CRC mortality.

Intersectionality-based quantitative health research and sex/gender sensitivity: a scoping review

Background

The implementation of a theoretical intersectionality framework into quantitative data analyses is gaining increasing interest in health research. The substantive foundation of intersectionality was established in the U.S., based on the claim of black feminists to broaden the scope of contemporary gender studies by considering the intersection between sex/gender and race/ethnicity more firmly. The aim of our scoping review with particular emphasis on sex/gender was to assess how intersectionality-informed studies in epidemiological research considered different social dimensions in their multivariable and multivariate analyses.

Methods

Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR), we conducted a literature review in PubMed. Three distinct health-related fields were brought into focus: diabetes representing a frequent chronic disease, smoking as a wide-spread behavioural health determinant and physical activity as a central target for health promotion. Initially, we compared which and how different social dimensions were accounted for and how inter-categorical and intersectionality-informed analyses were conducted. Further, we assessed sex/gender sensitivity by comparing operationalisation of sex/gender, how sex/gender theories were used and which central theoretical sex/gender concepts were referred to when aiming at explanation of (intersectional) sex/gender differences.

Results

Our results suggest, that intersectionality-based analyses within the three selected health-related fields are mainly conducted in the U.S. and focused on the intersection between sex/gender and race/ethnicity by using them jointly as subgrouping variables and as parts of interaction terms in regression analyses. Income and education as proxies for social class as well as age are mainly used for adjustment in quantitative analyses. Other approaches for calculating interactions (i.a. synergy-index, CART-analysis) are an exception. Even though sex/gender was considered in every included study and Gender was the most frequent theoretical sex/gender concept referred to when theoretically explaining sex/gender differences, it was exclusively operationalised as binary and solution-linked sex/gender variables were hardly considered in quantitative analyses.

Conclusion

The systematic integration of solution-linked variables indicating modifiable aspects of sex/gender-related living conditions and disadvantages could improve sex/gender sensitivity as part of intersectionality-based quantitative data analysis in health research.

Bridging the epidemiology-policy divide: A consequential and evidence-based framework to optimize population health

Epidemiology is the scientific cornerstone of public health. Its traditional role has been to test scientific hypotheses on causal relationships of exposures with health outcomes, the results of which should in turn be synthesized and lead to evidence-based recommendations and the formation of policy. However, the messy truth is that the path from epidemiology to policy is frequently not a perfectly rational, linear one, and the choices of which scientific hypotheses are pursued and the ways in which they are tested, evaluated, and translated into policies do not occur systematically. One avenue for bridging this divide is widespread adoption and implementation of a consequential, evidence-based framework-whereby we can systematically facilitate the translation of epidemiology into policies and interventions to optimize population health. This paper describes the roadmap for a seven-step, outcomes-based consequential approach, that includes priority-setting of problems at both the federal and regional/state levels, and that proposes to strengthen alignment of public and private research funding and journals with these priorities. Over the long term, implementing this framework should help to bridge the divide between epidemiology and policy and optimize the use of increasingly constrained resources to reduce disease burden and promote the nation's health.

Towards a People's Social Epidemiology: Envisioning a More Inclusive and Equitable Future for Social Epi Research and Practice in the 21st Century

Social epidemiology has made critical contributions to understanding population health. However, translation of social epidemiology science into action remains a challenge, raising concerns about the impacts of the field beyond academia. With so much focus on issues related to social position, discrimination, racism, power, and privilege, there has been surprisingly little deliberation about the extent and value of social inclusion and equity within the field itself. Indeed, the challenge of translation/action might be more readily met through re-envisioning the role of the people within the research/practice enterprise-reimagining what "social" could, or even should, mean for the future of the field. A potential path forward rests at the nexus of social epidemiology, community-based participatory research (CBPR), and information and communication technology (ICT). Here, we draw from social epidemiology, CBPR, and ICT literatures to introduce A People's Social Epi-a multi-tiered framework for guiding social epidemiology in becoming more inclusive, equitable, and actionable for 21st century practice. In presenting this framework, we suggest the value of taking participatory, collaborative approaches anchored in CBPR and ICT principles and technological affordances-especially within the context of place-based and environmental research. We believe that such approaches present opportunities to create a social epidemiology that is of, with, and by the people-not simply about them. In this spirit, we suggest 10 ICT tools to "socialize" social epidemiology and outline 10 ways to move towards A People's Social Epi in practice.

Applied Epidemiology Training Needs for the Modern Epidemiologist

Applied epidemiology training occurs throughout an epidemiologist's career, beginning with academic instruction before workforce entry, continuing as professional development while working, and culminating with mentoring the next generation. Epidemiologists need ongoing training on advancements in the field and relevant topics (e.g., informatics, laboratory science, emerging topics) to maintain and improve their skills. Even epidemiologists with advanced skills often want training on methodologic innovations or to practice a skill. Effective applied epidemiology training includes blended learning components of instruction that incorporate hands-on experiences such as simulations and experiential learning, allowing for real-time workflows and incorporation of feedback. To prepare epidemiologists for the future, public health training courses in applied epidemiology must consider the evolution in public health toward a focus on including informatics, technologic innovation, molecular epidemiology, multidisciplinary teams, delivery of population health services, and global health security. Supporting efforts by epidemiologists to increase their skills as part of their career paths ensures a strong workforce that able to tackle public health issues. We explore how to meet current training challenges for the epidemiology workforce, especially given limited resources, based on research and our experience in workforce development across federal agencies and state/local health departments, as well as with international governments and organizations.

Economic growth and mortality: do social protection policies matter?

Background

In the 20th century, periods of macroeconomic growth have been associated with increases in population mortality. Factors that cause or mitigate this association are not well understood. Evidence suggests that social policy may buffer the deleterious impact of economic growth. We sought to explore associations between changing unemployment (as a proxy for economic change) and trends in mortality over 30 years in the context of varying social protection expenditures.

Methods

We model change in all-cause mortality in 21 OECD (Organization for Economic Cooperation and Development) countries from 1980 to 2010. Data from the Comparative Welfare States Data Set and the WHO Mortality Database were used. A decrease in the unemployment rate was used as a proxy for economic growth and age-adjusted mortality rates as the outcome. Social protection expenditure was measured as percentage of gross domestic product expended.

Results

A 1% decrease in unemployment (i.e. the proxy for economic growth) was associated with a 0.24% increase in the overall mortality rate (95% confidence interval: 0.07;0.42) in countries with no changes in social protection. Reductions in social protection expenditure strengthened this association between unemployment and mortality. The magnitude of the association was diminished over time.

Conclusions

Our results are consistent with the hypothesis that social protection policies that accompany economic growth can mitigate its potential deleterious effects on health. Further research should identify specific policies that are most effective.

Social factors matter in cancer risk and survivorship

Greater attention to social factors, such as race/ethnicity, socioeconomic position, and others, are needed across the cancer continuum, including breast cancer, given differences in tumor biology and genetic variants have not completely explained the persistent Black/White breast cancer mortality disparity. In this commentary, we use examples in breast cancer risk assessment and survivorship to demonstrate how the failure to appropriately incorporate social factors into the design, recruitment, and analysis of research studies has resulted in missed opportunities to reduce persistent cancer disparities. The conclusion offers recommendations for how to better document and use information on social factors in cancer research and care by (1) increasing education and awareness about the importance of inclusion of social factors in clinical research; (2) improving testing and documentation of social factors by incorporating them into journal guidelines and reporting stratified results; and (3) including social factors to refine extant tools that assess cancer risk and assign cancer care. Implementing the recommended changes would enable more effective design and implementation of interventions and work toward eliminating cancer disparities by accounting for the social and environmental contexts in which cancer patients live and are treated.

Social epidemiology for the 21st century

Social epidemiology, as defined by the textbook of the same name (Berkman et al., 2014) is "that branch of epidemiology concerned with the way that social structures, institutions, and relationships influence health" (p. 2). As our Special Issue commemorates the 50th anniversary of Social Science & Medicine, it is worth noting that the Social Epidemiology office within the journal has existed only for a fraction of that time (fifteen years). So what has been learned in the fifteen years since the establishment of the new office? In this commentary, we spotlight some of the achievements, substantive topics, and future trends in the research papers that we have featured in our Section of the journal.

Social Quality and Health: Examining Individual and Neighbourhood Contextual Effects Using a Multilevel Modelling Approach

Social quality focusses on the nature of 'the social', arguing that people are realised as social beings through interacting with a range of collectives, both from the formal world of systems and the informal lifeworld. Four conditional factors are necessary for this to occur, which at the same time are assumed to influence health and well-being: socio-economic security, social cohesion, social inclusion and social empowerment. In this paper we test the utility of social quality in explaining self-rated health as a response to arguments that the social determinants of health (SDH) framework often lacks a theoretical basis. We use multilevel models to analyse national English and Welsh data (the Citizenship Survey) to test for both individual- and neighbour-level affects. Our key findings are that (1) neighbourhood contextual (cross-level) effects are present with respect to collective action, personal trust, cross-cutting ties, income sufficiency, and income security; (2) measures of national, community and personal identity as indicators of social cohesion show clear associations with health alongside more common measures such as trust; (3) the security aspects of socioeconomic determinants are especially important (housing security, income sufficiency, and income security); (4) social rights, including institutional rights but especially civil rights have effects of particularly large magnitude. Social quality offers a theoretically-driven perspective on the SDH which has important policy implications and suggests a number of promising avenues for future research.

Housing mobility and adolescent mental health: The role of substance use, social networks, and family mental health in the Moving to Opportunity Study

The Moving to Opportunity (MTO) experiment was a housing mobility program begun in the mid-nineties that relocated volunteer low income families from public housing to rental units in higher opportunity neighborhoods in 5 US cities, using the Section 8 affordable housing voucher program. Compared to the control group who stayed behind in public housing, the MTO voucher group exhibited a harmful main effect for boys’ mental health, and a beneficial main effect for girls’ mental health. But no studies have examined how this social experiment caused these puzzling, opposite gender effects. The present study tests potential mediating mechanisms of the MTO voucher experiment on adolescent mental health (n=2829, aged 12–19 in 2001–2002). Using Inverse Odds Ratio Weighting causal mediation, we tested whether adolescent substance use comorbidity, social networks, or family mental health acted as potential mediators. Our results document that comorbid substance use (e.g. past 30 day alcohol use, cigarette use, and number of substances used) significantly partially mediated the effect of MTO on boys’ behavior problems, resulting in -13% to -18% percent change in the total effect. The social connectedness domain was a marginally significant mediator for boys’ psychological distress. Yet no tested variables mediated MTO's beneficial effects on girls’ psychological distress. Confounding sensitivity analyses suggest that the indirect effect of substance use for mediating boys’ behavior problems was robust, but social connectedness for mediating boys’ psychological distress was not robust. Understanding how housing mobility policies achieve their effects may inform etiology of neighborhoods as upstream causes of health, and inform enhancement of future affordable housing programs.

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It is unclear why a large housing voucher experiment affected youth mental health.

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We tested mediators to understand the effects of this policy relevant exposure.

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We apply innovative, flexible mediation methods and bias sensitivity analyses.

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Substance use was a robust mediator of housing mobility on boys’ behavior problems.

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Mediators of opposite-gender housing mobility effects may be gender-specific.

Participatory epidemiology: the contribution of participatory research to epidemiology

Background

Epidemiology has contributed in many ways to identifying various risk factors for disease and to promoting population health. However, there is a continuing debate about the ability of epidemiology not only to describe, but also to provide results which can be better translated into public health practice. It has been proposed that participatory research approaches be applied to epidemiology as a way to bridge this gap between description and action. A systematic account of what constitutes participatory epidemiology practice has, however, been lacking.

Methods

A scoping review was carried out focused on the question of what constitutes participatory approaches to epidemiology for the purpose of demonstrating their potential for advancing epidemiologic research. Relevant databases were searched, including both the published and non-published (grey) literature. The 102 identified sources were analyzed in terms of comparing common epidemiologic approaches to participatory counterparts regarding central aspects of the research process. Exemplary studies applying participatory approaches were examined more closely.

Results

A highly diverse, interdisciplinary body of literature was synthesized, resulting in a framework comprised of seven aspects of the research process: research goal, research question, population, context, data synthesis, research management, and dissemination of findings. The framework specifies how participatory approaches not only differ from, but also how they can enhance common approaches in epidemiology. Finally, recommendations for the further development of participatory approaches are given. These include: enhancing data collection, data analysis, and data validation; advancing capacity building for research at the local level; and developing data synthesis.

Conclusion

The proposed framework provides a basis for systematically developing the emergent science of participatory epidemiology.

Intersectionality and risk for ischemic heart disease in Sweden: Categorical and anti-categorical approaches

Intersectionality theory can contribute to epidemiology and public health by furthering understanding of power dynamics driving production of health disparities, and increasing knowledge about heterogeneities within, and overlap between, social categories. Drawing on McCall, we relate the first of these potential contributions to categorical intersectionality and the second to anti-categorical intersectionality. Both approaches are used in study of risk of ischemic heart disease (IHD), based on register data on 3.6 million adults residing in Sweden by 2010, followed for three years. Categorical intersectionality is here coupled with between-group differences in average risk calculation, as we use intersectional categorizations while estimating odds ratios through logistic regressions. The anti-categorical approach is operationalized through measurement of discriminatory accuracy (DA), i.e., capacity to accurately categorize individuals with or without a certain outcome, through computation of the area under the curve (AUC). Our results show substantial differences in average risk between intersectional groupings. The DA of social categorizations is found to be low, however, due to outcome variability within and overlap between categories. We argue that measures of DA should be used for proper interpretation of differences in average risk between social (or any other) categories. Tension between average between-group risk and the DA of categorizations, which can be related to categorical and anti-categorical intersectional analyses, should be made explicit and discussed to a larger degree in epidemiology and public health.

Gender inequalities in mental wellbeing in 26 European countries: do welfare regimes matter?

BACKGROUND

Nature and extent of welfare regimes and social policies are important determinants of health and health inequalities. This study examines the association of gender and mental wellbeing in European countries and investigates whether type of welfare regime plays a role in this association.

METHOD

Data of 19 366 women and 14 338 men of the third round of the European Quality of Life Survey (2011-12) was used to analyse mental wellbeing, assessed by the World Health Organization 5-Mental Wellbeing Index. Multilevel logistic regression analyses were performed to analyse the association between gender and good mental wellbeing first at country-level, and secondly the between country variation was analysed and welfare regimes were included as explanatory variables.

RESULTS

We observed cross-national variation in good mental wellbeing. At country levels gender inequalities in good mental wellbeing were observed in 7 out of 26 countries. In analyses considering all countries together gender inequalities in good mental wellbeing were identified independent of further individual socio-demographic variables and independent of the welfare regimes that people lived in [women vs. men: OR = 0.76; (95% CI = 0.71-0.81)]. Gender inequalities in good mental wellbeing were not modified by welfare regimes.

CONCLUSION

There are cross-national differences in good mental wellbeing between European countries. Gender inequalities with a lower prevalence of good mental wellbeing among women are common in European countries. This study suggests that welfare regimes do not modify these gender inequalities in mental wellbeing.

Are neighborhoods causal? Complications arising from the 'stickiness' of ZNA

Are neighborhoods causal? The answer remains elusive. Armed with new multilevel methods, enthusiasm for neighborhoods research surged at the turn of the century. However, a wave of skepticism has arisen based on the difficulty of drawing causal inferences from observational studies in which selection to neighborhoods is non-random. Researchers have sought answers from experimental and quasi-experimental studies of movers vs. stayers. We develop two related concepts in this essay in the hopes of shedding light on this problem. First, the inceptive environment into which persons are born (which we term ZNA for Zip code Nativity Area) exerts a potentially powerful causal impact on health. Detecting that causal effect is challenging for reasons similar that obtain in other fields (including genetics). Second, we explicate the problem of neighborhood 'stickiness' in terms of the persistence of neighborhood treatment assignment, and argue that under-appreciation of stickiness has led to systematic bias in causal estimates of neighborhoods proportional to the degree of stickiness. In sticky contexts, failure to account for the lasting influences of ZNA by adjusting for intermediate individual socioeconomic and health variables on the causal pathway can result in neighborhood effects estimates that are biased toward the null. We follow with an example drawn from evidence of neighborhood 'stickiness' and obesity. The stickiness of ZNA cautions us that experimental evidence may be insufficient or misleading as a solution to causal inference problems in neighborhood research.

Educational Inequalities in Health Behaviors at Midlife: Is There a Role for Early-life Cognition?

Education is a fundamental cause of social inequalities in health because it influences the distribution of resources, including money, knowledge, power, prestige, and beneficial social connections, that can be used in situ to influence health. Recent studies have highlighted early-life cognition as commonly indicating the propensity for educational attainment and determining health and age of mortality. Health behaviors provide a plausible mechanism linking both education and cognition to later-life health and mortality. We examine the role of education and cognition in predicting smoking, heavy drinking, and physical inactivity at midlife using data from the Wisconsin Longitudinal Study (N = 10,317), National Survey of Health and Development (N = 5,362), and National Childhood Development Study (N = 16,782). Adolescent cognition was associated with education but was inconsistently associated with health behaviors. Education, however, was robustly associated with improved health behaviors after adjusting for cognition. Analyses highlight structural inequalities over individual capabilities when studying health behaviors.

Systematic assessment of the correlations of household income with infectious, biochemical, physiological, and environmental factors in the United States, 1999-2006

A fuller understanding of the social epidemiology of disease requires an extended description of the relationships between social factors and health indicators in a systematic manner. In the present study, we investigated the correlations between income and 330 indicators of physiological, biochemical, and environmental health in participants in the US National Health and Nutrition Examination Survey (NHANES) (1999-2006). We combined data from 3 survey waves (n = 249-23,649 for various indicators) to search for linear and nonlinear (quadratic) correlates of income, and we validated significant (P < 0.00015) correlations in an independent testing data set (n = 255-7,855). We validated 66 out of 330 factors, including infectious (e.g., hepatitis A), biochemical (e.g., carotenoids, high-density lipoprotein cholesterol), physiological (e.g., upper leg length), and environmental (e.g., lead, cotinine) measures. We found only a modest amount of association modification by age, race/ethnicity, and gender, and there was no association modification for blacks. The present study is descriptive, not causal. We have shown in our systematic investigation the crucial place income has in relation to health risk factors. Future research can use these correlations to better inform theory and studies of pathways to disease, as well as utilize these findings to understand when confounding by income is most likely to introduce bias.

Stochastic mediation contrasts in epidemiologic research: interpregnancy interval and the educational disparity in preterm delivery

Low maternal education is consistently associated with increased risk of preterm delivery (PTD). The interpregnancy interval (IPI), defined as the time between the date of a previous birth and the conception date of the index pregnancy, may mediate this relationship. We estimated controlled direct effects to assess whether hypothetical interventions designed to increase IPIs would reduce the educational disparity in PTD. We introduce a technique for estimating controlled direct effects under interventions that set only some persons in the population to a specific mediator value. We used data from 847,618 singleton livebirths occurring in Quebec, Canada, between 1989 and 2010. Compared with mothers with some university education (≥14 years), mothers with less than high school (<11 years), high school (11 years), and some college (12-13 years) had excess PTD risks of 2.6% (95% confidence interval (CI): 2.4, 2.8), 1.5% (95% CI: 1.4, 1.7), and 1.0% (95% CI: 0.9, 1.1), respectively. Risk differences under an intervention corresponding to the Healthy People 2020 objective of reducing the number of mothers with IPIs less than 18 months by 3% were no different from those for the total relationship. Our results suggest that interventions designed to increase the length of short IPIs will yield no important change in the PTD disparity by maternal educational level.

Social epidemiology of diabetes and associated conditions

Research focusing on the social determinants of diabetes has focused on individual-level factors such as health behaviors, socioeconomic status, and depression. Fewer studies that incorporate a broader consideration of the multiple contexts or organizational levels (eg, family, health care setting, neighborhood) within which individuals are embedded exist in the mainstream diabetes literature. Such an approach would enhance our understanding of this complex disease, and thus, future avenues of research should consider the following: (1) a life-course approach, which examines the influence of early life exposures on the development of diabetes; (2) aiming to understand the biological mechanisms of social determinants of diabetes; and (3) implementing interventions on multiple levels. Integrating this multilevel and life-course approach will require transdisciplinary science that brings together highly specialized expertise from multiple disciplines. Broadening the study of social determinants is a necessary step toward improving the prevention and treatment of type 2 diabetes.

Invited commentary: Off-roading with social epidemiology--exploration, causation, translation

Population health improvements are the most relevant yardstick against which to evaluate the success of social epidemiology. In coming years, social epidemiology must increasingly emphasize research that facilitates translation into health improvements, with continued focus on macro-level social determinants of health. Given the evidence that the effects of social interventions often differ across population subgroups, systematic and transparent exploration of the heterogeneity of health determinants across populations will help inform effective interventions. This research should consider both biological and social risk factors and effect modifiers. We also recommend that social epidemiologists take advantage of recent revolutionary improvements in data availability and computing power to examine new hypotheses and expand our repertoire of study designs. Better data and computing power should facilitate underused analytic approaches, such as instrumental variables, simulation studies and models of complex systems, and sensitivity analyses of model biases. Many data-driven machine-learning approaches are also now computationally feasible and likely to improve both prediction models and causal inference in social epidemiology. Finally, we emphasize the importance of specifying exposures corresponding with realistic interventions and policy options. Effect estimates for directly modifiable, clearly defined health determinants are most relevant for building translational social epidemiology to reduce disparities and improve population health.

Invited commentary: Paths and pathologies of social epidemiology

The article by professors Galea and Link in this issue of the Journal (Am J Epidemiol. 2013;178(6):843-849) is an important contribution to the field of social epidemiology. Their 6 paths provide ample fodder for reflection, debate, and advancement. Although I agree with the thrust and spirit of their recommendations, I argue that if social epidemiology is to advance and become not just more popular but also more useful, we social epidemiologists need to first address our disciplinary pathologies. Among other things, we must embrace macro-to-micro transitions, understand and act on the principles of effect identification, conduct many more experiments, and train students to be not researchers but scientists.