Win-Win: Reconciling Social Epidemiology and Causal Inference

Methodological approaches to structural change: epidemiology and the case for reparations

Scholars, activists, and policymakers have long called for reparations-a process of repair and restitution for harm and injustices done-to descendants of enslaved Africans in the United States as a structural intervention to address historic and ongoing injustices. However, there has been very limited epidemiologic work examining reparations. We explore some of the epidemiologic benefits and challenges of using causal inference frameworks to model reparations as an example of a large-scale, structural intervention that pushes the limits of what is considered "well-defined" and may violate key identification assumptions. Finally, we weigh these methodological limitations with the utility of assessing public health implications of reparations policies and conclude by discussing implications for future epidemiologic research. This article is part of a Special Collection on Methods in Social Epidemiology.

Poverty and Childhood Obesity: Current Evidence and Methodologies for Future Research

PURPOSE OF REVIEW

This narrative review summarizes current knowledge on the link between poverty and childhood obesity, and then describes conventional and modern epidemiologic methods for causal inference that may help provide more robust evidence on how poverty reduction can prevent childhood obesity.

RECENT FINDINGS

Household poverty has been consistently associated with increased risk of childhood obesity across observational studies in industrialized countries. Due to ethical and feasibility limitations, few randomized controlled trials directly test the effect of poverty reduction. A growing number of studies use quasi-experimental methods to study the effects of poverty reduction policies on childhood obesity. These methods include instrumental variables, difference-in-differences, interrupted time series analysis, and regression discontinuity. Other complementary methods such as causal mediation analysis allow us to elucidate the mechanisms of how poverty reduction affects childhood obesity outcomes, while examining heterogeneous treatment effects using cutting-edge machine learning algorithms may further identify subpopulations that benefit the most from poverty interventions. Despite the strong associations between poverty and childhood obesity observed in industrialized countries, current evidence about the causal effect of poverty reduction on childhood obesity is mixed. This is likely due to the complex etiology of childhood obesity and potentially unintended effects of policies. Future studies that leverage advances in causal inference with quasi-experimental approaches will help provide more robust evidence to help guide practitioners and policymakers in ongoing childhood obesity prevention efforts.

Inconsistent consistency: evaluating the well-defined intervention assumption in applied epidemiological research

BACKGROUND

According to textbook guidance, satisfying the well-defined intervention assumption is key for estimating causal effects. However, no studies have systematically evaluated how the assumption is addressed in research. Thus, we reviewed how researchers using g-methods or targeted maximum likelihood estimation (TMLE) interpreted and addressed the well-defined intervention assumption in epidemiological studies.

METHODS

We reviewed observational epidemiological studies that used g-methods or TMLE, were published from 2000-21 in epidemiology journals with the six highest 2020 impact factors and met additional criteria. Among other factors, reviewers assessed if authors of included studies aimed to estimate the effects of hypothetical interventions. Then, among such studies, reviewers assessed whether authors discussed key causal-inference assumptions (e.g. consistency or treatment variation irrelevance), how they interpreted their findings and if they specified well-defined interventions.

RESULTS

Just 20% (29/146) of studies aimed to estimate the effects of hypothetical interventions. Of such intervention-effect studies, almost none (1/29) stated 'how' the exposure would be intervened upon; among those that did not state a 'how', the 'how' mattered for consistency (i.e., for treatment variation irrelevance) in 64% of studies (18/28). Moreover, whereas 79% (23/29) of intervention-effect studies mentioned consistency, just 45% (13/29) interpreted findings as corresponding to the effects of hypothetical interventions. Finally, reviewers determined that just 38% (11/29) of intervention-effect studies had well-defined interventions.

CONCLUSIONS

We found substantial deviations between guidelines regarding meeting the well-defined intervention assumption and researchers' application of the guidelines, with authors of intervention-effect studies rarely critically examining the assumption's validity, let alone specifying well-defined interventions.

Identifying the role of high school in educational inequality: A causal mediation approach

Although the effect of socioeconomic background on children's educational attainment has long been investigated, the extent to which high school mediates this effect has not been assessed from the causal mediation framework. This study investigates the direct effect of family income on educational attainment not mediated by the rank of high school, using data from longitudinal surveys of ninth-grade students in Japan (n = 1,790). Regression with residuals (RWR) analysis reveals that, although high school type mediates the effects of family income on university enrollment and relative educational attainment to some extent (approximately 25% and 33%, respectively), the direct effects of family income are considerable. The result also suggests a positive interaction effect between family income and high school selectivity, implying a boosting advantage pattern rather than a compensatory advantage. I argue that the analytical framework can be applied to many methodological settings in sociological studies.

Three things we learned along the way: lessons for training in psychiatric epidemiology

The coronavirus disease 2019 (COVID-19) pandemic, and its associated mortality, morbidity, and deep social and economic impacts, was a global traumatic stressor that challenged population mental health and our de facto mental health care system in unprecedented ways. Yet, in many respects, this crisis is not new. Psychiatric epidemiologists have recognized for decades the need and unmet need of people in distress and the limits of the public mental health services in the United States. We argue that psychiatric epidemiologists have a critical role to play as we endeavor to address population mental health and draw attention to 3 areas of consideration: elevating population-based solutions; engaging equitably with lived experience; and interrogating recovery. Psychiatric epidemiology has a long history of both responding to and shaping our understanding of the relationships among psychiatric disorders and society through evolving methods and training, and the current sociohistorical moment again suggests that shifts in our practice can strengthen our field and its impact. This article is part of a Special Collection on Mental Health.

Theorizing epidemiology, the stories bodies tell, and embodied truths: a status update on contending 21st c CE epidemiological theories of disease distribution

This critical review considers the status of 21st-century epidemiological theories of disease distribution, updating to 2024 prior analyses published up through 2014, and discusses the implications of these theories for research, practice, and pedagogy. Three key trends stand out: (a) the continued dominance of individualistic biomedical and lifestyle theories; (b) growth and elaboration of social epidemiological alternatives; and (c) the ongoing inattention to epidemiologic theories of disease distribution in the training of epidemiologists and public health professionals and in current efforts to improve the rigor of epidemiological research and causal inference. In a context of growing global political polarization, climate crisis, broader environmental and ecological crises, and stubbornly persistent health inequities within and between nations, producing actionable knowledge relevant to improving the people's health and advancing health justice will require much greater engagement with social epidemiologic theories of disease distribution in research, pedagogy, and practice. At issue is critically engaging with the embodied truths manifested in the stories bodies tell in population patterns of health, disease, and well-being.

Enhancing inferences and conclusions in body image focused non-experimental research via a causal modelling approach: A tutorial

Causal inference is often the goal of psychological research. However, most researchers refrain from drawing causal conclusions based on non-experimental evidence. Despite the challenges associated with producing causal evidence from non-experimental data, it is crucial to address causal questions directly rather than avoiding them. Here we provide a clear, non-technical overview of the fundamental concepts (including the counterfactual framework and related assumptions) and tools that permit causal inference in non-experimental data, intended as a starting point for readers unfamiliar with the literature. Certain tools, such as the target trial framework and causal diagrams, have been developed to assist with the identification and reduction of potential biases in study design and analysis and the interpretation of findings. We apply these concepts and tools to a motivating example from the body image field. We assert that more precise and detailed elucidation of the barriers to causal inference within one's study is arguably a key first step in the enhancement of non-experimental research and future intervention development and evaluation.

Socioeconomic inequalities in cardiovascular disease: a causal perspective

Socioeconomic inequalities in cardiovascular disease (CVD) persist in high-income countries despite marked overall declines in CVD-related morbidity and mortality. After decades of research, the field has struggled to unequivocally answer a crucial question: is the association between low socioeconomic position (SEP) and the development of CVD causal? We review relevant evidence from various study designs and disciplinary perspectives. Traditional observational, family-based and Mendelian randomization studies support the widely accepted view that low SEP causally influences CVD. However, results from quasi-experimental and experimental studies are both limited and equivocal. While more experimental and quasi-experimental studies are needed to aid causal understanding and inform policy, high-quality descriptive studies are also required to document inequalities, investigate their contextual dependence and consider SEP throughout the lifespan; no simple hierarchy of evidence exists for an exposure as complex as SEP. The COVID-19 pandemic illustrates the context-dependent nature of CVD inequalities, with the generation of potentially new causal pathways linking SEP and CVD. The linked goals of understanding the causal nature of SEP and CVD associations, their contextual dependence, and their remediation by policy interventions necessitate a detailed understanding of society, its change over time and the phenotypes of CVD. Interdisciplinary research is therefore key to advancing both causal understanding and policy translation.

The Canadian Women's Heart Health Alliance ATLAS on the Epidemiology, Diagnosis, and Management of Cardiovascular Disease in Women - Chapter 7: Sex, Gender, and the Social Determinants of Health

Women vs men have major differences in terms of risk-factor profiles, social and environmental factors, clinical presentation, diagnosis, and treatment of cardiovascular disease. Women are more likely than men to experience health issues that are complex and multifactorial, often relating to disparities in access to care, risk-factor prevalence, sex-based biological differences, gender-related factors, and sociocultural factors. Furthermore, awareness of the intersectional nature and relationship of sociocultural determinants of health, including sex and gender factors, that influence access to care and health outcomes for women with cardiovascular disease remains elusive. This review summarizes literature that reports on under-recognized sex- and gender-related risk factors that intersect with psychosocial, economic, and cultural factors in the diagnosis, treatment, and outcomes of women's cardiovascular health.

Co-occurring psychiatric disorders and disparities in buprenorphine utilization in opioid use disorder: An analysis of insurance claims

Background

As the overdose crisis continues in the U.S. and Canada, opioid use disorder (OUD) treatment outcomes for people with co-occurring psychiatric disorders are not well characterized. Our objective was to examine the influence of co-occurring psychiatric disorders on buprenorphine initiation and discontinuation.

Methods

This retrospective cohort study used multi-state administrative claims data in the U.S. to evaluate rates of buprenorphine initiation (relative to psychosocial treatment without medication) in a cohort of 236,198 people with OUD entering treatment, both with and without co-occurring psychiatric disorders, grouping by psychiatric disorder subtype (mood, psychotic, and anxiety-and-related disorders). Among people initiating buprenorphine, we assessed the influence of co-occurring psychiatric disorders on buprenorphine retention. We used multivariable Poisson regression to estimate buprenorphine initiation and Cox regression to estimate time to discontinuation, adjusting for all 3 classes of co-occurring disorders simultaneously and adjusting for baseline demographic and clinical characteristics.

Results

Buprenorphine initiation occurred in 29.3 % of those with co-occurring anxiety-and-related disorders, compared to 25.9 % and 17.5 % in people with mood and psychotic disorders. Mood (adjusted-risk-ratio[aRR] = 0.82[95 % CI = 0.82-0.83]) and psychotic disorders (aRR = 0.95[0.94-0.96]) were associated with decreased initiation (versus psychosocial treatment), in contrast to greater initiation in the anxiety disorders cohort (aRR = 1.06[1.05-1.06]). We observed an increase in buprenorphine discontinuation associated with mood (adjusted-hazard-ratio[aHR] = 1.20[1.17-1.24]) and anxiety disorders (aHR = 1.12[1.09-1.14]), in contrast to no association between psychotic disorders and buprenorphine discontinuation.

Conclusions

We observed underutilization of buprenorphine among people with co-occurring mood and psychotic disorders, as well as high buprenorphine discontinuation across anxiety, mood, and psychotic disorders.

Racial and Ethnic Inequities in Health: Examining the Contributions of the American Journal of Epidemiology to Advancing the Science

The perverseness of racial and ethnic inequities in the United States continues to implore the investigation of their causes. While there have been improvements in the health of the US population, these improvements have not been equally distributed. To commemorate the 100th anniversary of the American Journal of Epidemiology, in this commentary, we aim to highlight the Journal's contributions to: 1) the definition and use of race and ethnicity in research, and 2) understanding racial and ethnic inequities, both empirically and methodologically, over the past decade. We commend the Journal for its contributions and for spearheading many of the challenges related to measuring and interpreting racial and ethnic data for the past 20 years. We identify 3 additional areas in which the Journal could make further impact to address racial and ethnic inequities: 1) devote a section in every issue of the Journal to scientific papers that make substantive epidemiologic or methodological contributions to racial and ethnic inequities in health; 2) update the Journal's guidelines for authors to include justifying the use of race and ethnicity; and 3) diversify the field of epidemiology by bringing a new cadre of scholars from minoritized racial and ethnic groups who represent the most affected communities into the research process.

Producing Change to Understand the Social Determinants of Health: The Promise of Experiments for Social Epidemiology

In this commentary, invited for the 100th anniversary of the Journal, we discuss the addition of randomized experiments, along with natural experiments that emulate randomized trials using observational data, as designs in the social epidemiologist's toolbox. These approaches transform the way we define and ask questions about social exposures. They compel us to ask questions about how well-defined interventions change a social exposure that might lead to changes in health. As such, experiments are of unique public health and policy significance. We argue that they are a powerful approach to advance our understanding of how well-defined changes in social exposures impact health, and how credible social policy reforms may be instrumental to address health inequalities. We focus on two research designs. The first is a "pure" randomized controlled trial (RCT) in which the investigator defines and randomly assigns the intervention. The second is a natural experiment, which exploits the fact that policies or interventions in the real world often involve an element of random assignment, emulating an RCT. To give the reader our bottom line: While acknowledging their limits, we continue to be very excited about the promise of RCTs and natural experiments to advance social epidemiology.

Childhood socioeconomic position and cardiometabolic risk in young adulthood- the impact of mental health

BACKGROUND

Low socioeconomic position in childhood is associated with greater cardiometabolic disease risk later in life. The aim of the current study is to examine the mediating impact of mental health on the association between childhood socioeconomic position and cardiometabolic disease risk in young adulthood.

METHODS

We used a combination of national registers, longitudinal questionnaire-data and clinical measurements from a sub-sample (N = 259) of a Danish youth cohort. Childhood socioeconomic position was indicated by the educational level of the mother and the father at age 14. Mental health was measured by four different symptom scales at four age-points (age 15, 18, 21 and 28), and combined into one global score. Cardiometabolic disease risk was measured by nine biomarkers at age 28-30 and combined into one global score by sample-specific z-scores. We conducted analyses within the causal inference framework and evaluated the associations using nested counterfactuals.

RESULTS

We found an inverse association between childhood socioeconomic position and cardiometabolic disease risk in young adulthood. The proportion of the association which was mediated by mental health was 10 (95% CI: -4; 24) % and 12 (95% CI: -4; 28) % using educational level of the mother and the father as indicator, respectively.

CONCLUSIONS

Accumulated poorer mental health in childhood, youth and early adulthood partially explained the association between low childhood socioeconomic position and increased cardiometabolic disease risk in young adulthood. The results of the causal inference analyses rely on the underlying assumptions and correct depiction of the DAG. Since these are not all testable, we cannot exclude violations that potentially could bias the estimates. If the findings can be replicated, this would support a causal relationship and direct potentials for intervention. However, the findings point to a potential for intervention in young age in order to impede the translation of childhood social stratification into later cardiometabolic disease risk disparities.

Racial and Ethnic Disparities in the Incidence of High-Impact Chronic Pain Among Primary Care Patients with Acute Low Back Pain: A Cohort Study

OBJECTIVE

We assessed whether race or ethnicity was associated with the incidence of high-impact chronic low back pain (cLBP) among adults consulting a primary care provider for acute low back pain (aLBP).

METHODS

In this secondary analysis of a prospective cohort study, patients with aLBP were identified through screening at seventy-seven primary care practices from four geographic regions. Incidence of high-impact cLBP was defined as the subset of patients with cLBP and at least moderate disability on Oswestry Disability Index [ODI >30]) at 6 months. General linear mixed models provided adjusted estimates of association between race/ethnicity and high-impact cLBP.

RESULTS

We identified 9,088 patients with aLBP (81.3% White; 14.3% Black; 4.4% Hispanic). Black/Hispanic patients compared to White patients, were younger and more likely to be female, obese, have Medicaid insurance, worse disability on ODI, and were at higher risk of persistent disability on STarT Back Tool (all P < .0001). At 6 months, more Black and Hispanic patients reported high-impact cLBP (30% and 25%, respectively) compared to White patients (15%, P < .0001, n = 5,035). After adjusting for measured differences in socioeconomic and back-related risk factors, compared to White patients, the increased odds of high-impact cLBP remained statistically significant for Black but not Hispanic patients (adjusted odds ration [aOR] = 1.40, 95% confidence interval [CI]: 1.05-1.87 and aOR = 1.25, 95%CI: 0.83-1.90, respectively).

CONCLUSIONS

We observed an increased incidence of high-impact cLBP among Black and Hispanic patients compared to White patients. This disparity was partly explained by racial/ethnic differences in socioeconomic and back-related risk factors. Interventions that target these factors to reduce pain-related disparities should be evaluated.

CLINICALTRIALS.GOV IDENTIFIER

NCT02647658.

Structural racism theory, measurement, and methods: A scoping review

Introduction

Epidemiologic and public health interest in structural racism has grown dramatically, producing both increasingly sophisticated questions, methods, and findings, coupled with concerns of atheoretical and ahistorical approaches that often leave the actual production of health or disease ambiguous. This trajectory raises concerns as investigators adopt the term "structural racism" without engaging with theories and scholars with a long history in this area. This scoping review aims to build upon recent work by identifying current themes about the incorporation of structural racism into (social) epidemiologic research and practice with respect to theory, measurement, and practices and methods for trainees and public health researchers who are not already deeply grounded in this work.

Methods

This review uses methodological framework and includes peer-review articles written in English published between January 2000-August 2022.

Results

A search of Google Scholar, manual collection, and referenced lists identified a total of 235 articles; 138 met the inclusion criteria after duplicates were removed. Results were extracted by, and organized into, three broad sections: theory, construct measurement, and study practice and methods, with several themes summarized in each section.

Discussion

This review concludes with a summary of recommendations derived from our scoping review and a call to action echoing previous literature to resist an uncritical and superficial adoption of "structural racism" without attention to already existing scholarship and recommendations put forth by experts in the field.

Chronic Pain and Psychiatric Conditions

Introduction

Chronic pain is a common condition with high socioeconomic and public health burden. A wide range of psychiatric conditions are often comorbid with chronic pain and chronic pain conditions, negatively impacting successful treatment of either condition. The psychiatric condition receiving most attention in the past with regard to chronic pain comorbidity has been major depressive disorder, despite the fact that many other psychiatric conditions also demonstrate epidemiological and genetic overlap with chronic pain. Further understanding potential mechanisms involved in psychiatric and chronic pain comorbidity could lead to new treatment strategies both for each type of disorder in isolation and in scenarios of comorbidity.

Methods

This article provides an overview of relationships between DSM-5 psychiatric diagnoses and chronic pain, with particular focus on PTSD, ADHD, and BPD, disorders which are less commonly studied in conjunction with chronic pain. We also discuss potential mechanisms that may drive comorbidity, and present new findings on the genetic overlap of chronic pain and ADHD, and chronic pain and BPD using linkage disequilibrium score regression analyses.

Results

Almost all psychiatric conditions listed in the DSM-5 are associated with increased rates of chronic pain. ADHD and BPD are significantly genetically correlated with chronic pain. Psychiatric conditions aside from major depression are often under-researched with respect to their relationship with chronic pain.

Conclusion

Further understanding relationships between psychiatric conditions other than major depression (such as ADHD, BPD, and PTSD as exemplified here) and chronic pain can positively impact understanding of these disorders, and treatment of both psychiatric conditions and chronic pain.

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.

Conceptualizing, Contextualizing, and Operationalizing Race in Quantitative Health Sciences Research

Differences in health outcomes across racial groups are among the most commonly reported findings in health disparities research. Often, these studies do not explicitly connect observed disparities to mechanisms of systemic racism that drive adverse health outcomes among racialized and other marginalized groups in the United States. Without this connection, investigators inadvertently support harmful narratives of biologic essentialism or cultural inferiority that pathologize racial identities and inhibit health equity. This paper outlines pitfalls in the conceptualization, contextualization, and operationalization of race in quantitative population health research and provides recommendations on how to appropriately engage in scientific inquiry aimed at understanding racial health inequities. Race should not be used as a measure of biologic difference, but rather as a proxy for exposure to systemic racism. Future studies should go beyond this proxy use and directly measure racism and its health impacts.VISUAL ABSTRACTAppeared as Annals "Online First" article.

Pre-trauma predictors of severe psychiatric comorbidity 5 years following traumatic experiences

BACKGROUND

A minority of persons who have traumatic experiences go on to develop post-traumatic stress disorder (PTSD), leading to interest in who is at risk for psychopathology after these experiences. Complicating this effort is the observation that post-traumatic psychopathology is heterogeneous. The goal of this nested case-control study was to identify pre-trauma predictors of severe post-traumatic psychiatric comorbidity, using data from Danish registries.

METHODS

The source population for this study was the population of Denmark from 1994 through 2016. Cases had received three or more psychiatric diagnoses (across all ICD-10 categories) within 5 years of a traumatic experience (n = 20 361); controls were sampled from the parent cohort using risk-set sampling (n = 81 444). Analyses were repeated in samples stratified by pre-trauma psychiatric diagnoses. We used machine learning methods (classification and regression trees and random forest) to determine the important predictors of severe post-trauma psychiatric comorbidity from among hundreds of pre-trauma predictor variables spanning demographic and social variables, psychiatric and somatic diagnoses and filled medication prescriptions.

RESULTS

In the full sample, pre-trauma psychiatric diagnoses (e.g. stress disorders, alcohol-related disorders, personality disorders) were the most important predictors of severe post-trauma psychiatric comorbidity. Among persons with no pre-trauma psychiatric diagnoses, demographic and social variables (e.g. marital status), type of trauma, medications used primarily to treat psychiatric symptomatology, anti-inflammatory medications and gastrointestinal distress were important to prediction. Results among persons with pre-trauma psychiatric diagnoses were consistent with the overall sample.

CONCLUSIONS

This study builds on the understanding of pre-trauma factors that predict psychopathology following traumatic experiences, by examining a broad range of predictors of post-trauma psychopathology and comorbidity beyond PTSD.

Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries

Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might offer opportunities for prevention. This case-cohort study used machine learning to examine sex-specific risk profiles for suicide attempts in Danish nationwide registry data. Cases were all persons who made a nonfatal suicide attempt between 1995 and 2015 (n = 22,974); the subcohort was a 5% random sample of the population at risk on January 1, 1995 (n = 265,183). We developed sex-stratified classification trees and random forests using 1,458 predictors, including demographic factors, family histories, psychiatric and physical health diagnoses, surgery, and prescribed medications. We found that substance use disorders/treatment, prescribed psychiatric medications, previous poisoning diagnoses, and stress disorders were important factors for predicting suicide attempts among men and women. Individuals in the top 5% of predicted risk accounted for 44.7% of all suicide attempts among men and 43.2% of all attempts among women. Our findings illuminate novel risk factors and interactions that are most predictive of nonfatal suicide attempts, while consistency between our findings and previous work in this area adds to the call to move machine learning suicide research toward the examination of high-risk subpopulations.

Challenges of translating epidemiologic research: An application to rheumatic and musculoskeletal disorders

Translation of research into public health policy is featured in common definitions of epidemiology, as an end result of scientific discovery on disease occurrence and causes. This dual nature of epidemiology, which brings together discovery and its use, seems to imply two main dimensions by which to judge epidemiologic research: technical or field-specific quality and societal value. This paper uses our research on the epidemiology of rheumatic and musculoskeletal disorders as a starting point to discuss the interface between these dimensions, exploring a number of conceptual, practical and ethical challenges that epidemiologists increasingly need to address when aiming for research translation. Those include not only the appraisal of the technical quality of research, which is familiar to researchers, but also the judgement on the usefulness and actual use of knowledge, as well as the assessment of the legitimacy of research based on translation potential. Several challenges lie ahead, but interdisciplinary conceptual and technical developments have the potential to guide future epidemiologic research of consequence. Approaches that recognize complexity and formalize the involvement of stakeholders in the research process within transparent frameworks open promising avenues for an effective translation of epidemiologic research projected into the future.

Complex systems models for causal inference in social epidemiology

Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.

Data Disaggregation: A Research Tool to Identify Health Inequities

The U.S. population is increasingly diverse, with less than half of all children identifying as non-Hispanic White. It is imperative health care providers and researchers further their awareness of how race and structural racism contribute to pervasive, systemic health disparities among children and their families so that steps can be taken to alleviate health inequities. This paper examines data disaggregation, on the basis of race and ethnicity, as a research tool to examine the heterogeneity of the pediatric population, identifies strategies for conducting research with disaggregated data, and provides examples of research in which data disaggregation has increased knowledge of racial and ethnic inequities in pediatric health and health care delivery.

Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators

Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. First, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Second, the complexity of such questions is invariably such that there are no well-defined mediator interventions (i.e. actual treatments, programs, etc.) for which data exist on the relevant populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data, that is, on mediator measures such as depression symptoms for which the actual interventions that one might implement to alter them are not well defined. We propose a novel framework that addresses these challenges by defining mediation effects that map to a target trial of hypothetical interventions targeting multiple mediators for which we simulate the effects. Specifically, we specify a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical interventions that would shift the mediators' distributions (separately under various interdependence assumptions, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, simulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions, which reflects the lower level of evidence that is expected with ill-defined interventions. Application to the self-harm example in the Victorian Adolescent Health Cohort Study illustrates the value of our proposal for informing the design and evaluation of actual interventions in the future.

The Decline of the Experimental Paradigm During the COVID-19 Pandemic: A Template for the Future

The current Coronavirus Disease 2019 (COVID-19) pandemic has exerted an unprecedented impact across the globe. As a consequence of this overwhelming catastrophe, long-established prevailing medical and scientific paradigms have been disrupted. The response of the scientific community, medical journals, media, and some politicians has been far from ideal. The present manuscript discusses the failure of the scientific enterprise in its initiatives to address the COVID-19 outbreak as a consequence of the disarray attributable to haste and urgency. To enhance conveying our message, this manuscript is organized into 3 interrelated sections: 1) the accelerated pace of publications coupled with a dysfunctional review process; 2) failure of the clinical trial enterprise; 3) propagation of misleading information by the media. In response we propose a template comprising a focus on randomized controlled clinical trials, and an insistence on responsible journal publication, and enumeration of policies to deal with social media-propagated news. We conclude with a reconsideration of the appropriate role of academic medicine and journals.

The Epidemiologic Toolbox: Identifying, Honing, and Using the Right Tools for the Job

There has been much debate about the relative emphasis of the field of epidemiology on causal inference. We believe this debate does short shrift to the breadth of the field. Epidemiologists answer myriad questions that are not causal and hypothesize about and investigate causal relationships without estimating causal effects. Descriptive studies face significant and often overlooked inferential and interpretational challenges; we briefly articulate some of them and argue that a more detailed treatment of biases that affect single-sample estimation problems would benefit all types of epidemiologic studies. Lumping all questions about causality creates ambiguity about the utility of different conceptual models and causal frameworks; 2 distinct types of causal questions include 1) hypothesis generation and theorization about causal structures and 2) hypothesis-driven causal effect estimation. The potential outcomes framework and causal graph theory help efficiently and reliably guide epidemiologic studies designed to estimate a causal effect to best leverage prior data, avoid cognitive fallacies, minimize biases, and understand heterogeneity in treatment effects. Appropriate matching of theoretical frameworks to research questions can increase the rigor of epidemiologic research and increase the utility of such research to improve public health.