What matters most: quantifying an epidemiology of consequence

Innovative epidemiology instruction for promoting population health thinking in occupational therapy doctoral students

PURPOSE

Recent shifts toward population-based health care and research in health science training programs are vital to reducing health disparities, although students need stronger education in this area. This study aimed to determine if innovative epidemiology research instruction is associated with improved science literacy skills, a core objective of population health curricula, for occupational therapy doctoral students.

METHODS

We used the Test of Scientific Literacy Skills (TOSLS) to determine change in student science literacy skills based on change in performance from baseline. Multilevel repeated measures models were run clustering by student and cohort, adjusted for demographics and prior research courses.

RESULTS

The sample included 166 students (91 % female, 57 % Non-Hispanic White, 14 % Hispanic, 19 % Non-Hispanic Black, 10 % Asian; 83 % ≤25 years old). Model estimates showed improvements in science literacy skills (β=0.39 [95 %CI:0.10, 0.67]) relative to baseline. Interaction models showed greater improvements from baseline for students who identified as Hispanic (β=0.82 [95 %CI:0.10, 1.63]) and Non-Hispanic White (β=0.55 [95 %CI:0.16, 0.94]). Additional interaction models did not show differences in the time-TOSLS association by student characteristics.

CONCLUSIONS

Innovative epidemiology instruction may be an appropriate method for supporting health sciences student development of science literacy skills to foster population health thinking, research, and practice.

Critical feminist epidemiology in action: reflections from a multidisciplinary partnership between Mujeres Unidas y Activas and academic researchers

Critical feminist research addresses social inequities, encourages equitable partnerships between researchers and participants, and acknowledges that research can be inherently political. Building upon critical feminist research practices, community-based participatory research, and social and structural epidemiology, we propose the approach of critical feminist epidemiology. A critical feminist epidemiology approach can study community and population health inequities with an eye toward identifying interventions that reduce inequities, through research processes that center the lived experiences of people from minoritized genders. We describe how our interdisciplinary, community-led team used a critical feminist epidemiology approach for an applied public health research project. Mujeres Unidas y Activas, a community organizing nonprofit led by and for Latina and Indigenous immigrant women, partnered with academic researchers to conduct community-led research on how their approach to building community power affected the health and well-being of organization members and their families. Critical feminist epidemiology is a promising approach for conducting research that is grounded in and relevant to the lives of women and gender-expansive people. Building upon social epidemiology and community-based participatory research, critical feminist epidemiology can be a useful research approach to generate novel evidence to inform action toward health equity for communities and populations. This article is part of a Special Collection on Methods in Social Epidemiology.

Big Epidemiology: The Birth, Life, Death, and Resurgence of Diseases on a Global Timescale

Big Epidemiology represents an innovative framework that extends the interdisciplinary approach of Big History to understand disease patterns, causes, and effects across human history on a global scale. This comprehensive methodology integrates epidemiology, genetics, environmental science, sociology, history, and data science to address contemporary and future public health challenges through a broad historical and societal lens. The foundational research agenda involves mapping the historical occurrence of diseases and their impact on societies over time, utilizing archeological findings, biological data, and historical records. By analyzing skeletal remains, ancient DNA, and artifacts, researchers can trace the origins and spread of diseases, such as Yersinia pestis in the Black Death. Historical documents, including chronicles and medical treatises, provide contextual narratives and quantitative data on past disease outbreaks, societal responses, and disruptions. Modern genetic studies reveal the evolution and migration patterns of pathogens and human adaptations to diseases, offering insights into co-evolutionary dynamics. This integrative approach allows for temporal and spatial mapping of disease patterns, linking them to social upheavals, population changes, and economic transformations. Big Epidemiology also examines the roles of environmental changes and socioeconomic factors in disease emergence and re-emergence, incorporating climate science, urban development, and economic history to inform public health strategies. The framework reviews historical and contemporary policy responses to pandemics, aiming to enhance future global health governance. By addressing ethical, legal, and societal implications, Big Epidemiology seeks to ensure responsible and effective epidemiological research and interventions. This approach aims to profoundly impact how we understand, prevent, and respond to diseases, leveraging historical perspectives to enrich modern scientific inquiry and global public health strategies.

Defining Spatial Epidemiology: A Systematic Review and Re-orientation

BACKGROUND

Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk-factor studies but is not yet aligned with epidemiology's current focus on causal inference and intervention.

METHODS

We conducted a systematic review of studies indexed in PubMed that used the term "spatial epidemiolog*" in the title, abstract, or keywords. Excluded articles were not written in English, examined disease in animals, or reported biologic pathogen distribution only. We coded the included papers into five categories (review, demonstration of method, descriptive, analytic, and intervention) and recorded the unit of analysis (i.e., individual vs. ecological). We additionally examined articles coded as analytic ecologic studies using scales for lexical content.

RESULTS

A total of 482 articles met the inclusion criteria, including 76 reviews, 117 demonstrations of methods, 122 descriptive studies, 167 analytic studies, and 0 intervention studies. Demonstration studies were most common from 2006 to 2014, and analytic studies were most common after 2015. Among the analytic ecologic studies, those published in later years used more terms relevant to spatial statistics (incidence rate ratio =1.3; 95% confidence interval [CI] = 1.1, 1.5) and causal inference (incidence rate ratio =1.1; 95% CI = 1.1, 1.2).

CONCLUSIONS

Spatial epidemiology is an important and growing subfield of epidemiology. We suggest a re-orientation to help align its practice with the goals of contemporary epidemiology.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

Global Health Equity Requires Global Equity

Many global health challenges are characterized by the inequitable patterning of their health and economic consequences, which are etched along the lines of pre-existing inequalities in resources, power, and opportunity. These links require us to reconsider how we define global health equity, and what we consider as most consequential in its pursuit. In this article, we discuss the extent to which improving underlying global equity is an essential prerequisite to global health equity. We conclude that if we are to improve global health equity, there is a need to focus more on foundational—rather than proximal—causes of ill health and propose ways in which this can be achieved.

Epidemiology and the Covid-19 pandemic: opportunities to review trajectories and plan for the future

The Covid-19 pandemic has caused substantial changes in various spheres of academic life. Epidemiology can use the experiences accumulated in this period as an opportunity to plan for its future. Facing a pandemic requires the production of explanatory theories about the pandemic process and its unequal manifestation in the population. In this sense, Epidemiology needs to strengthen its scientific foundations and recognize the values and limits of its approaches. Thus, it is essential to strengthen the links with other disciplines. A new teaching model can be produced from pandemic experiences, including transversal contents, such as preparation for responses to natural and technological disasters, like epidemics and pandemics, and scientific communication. The teaching of Epidemiology needs to be contextualized with the bases of Collective Health, reinforcing its commitment to the translation and application of knowledge in order to improve people’s health and lives.

Epidemiologia e pandemia de Covid-19: oportunidades para rever trajetórias e planejar o futuro

A pandemia de Covid-19 provocou modificações substanciais em diversas esferas da vida acadêmica. A Epidemiologia pode utilizar as experiências acumuladas nesse período como oportunidade para planejar seu futuro. O enfrentamento de uma pandemia exige a produção de teorias explicativas sobre o processo pandêmico e sua expressão desigual na população. Nesse sentido, a Epidemiologia necessita fortalecer seus fundamentos científicos e reconhecer os valores e limites de suas abordagens. Nesse caminho, é essencial o fortalecimento dos elos com outras disciplinas. Um novo modelo de ensino pode ser produzido por meio das experiências ao longo da pandemia, integrando conteúdos transversais, como a preparação para a resposta a desastres naturais e de origem tecnológica, incluindo epidemias e pandemias e a comunicação científica. O ensino da Epidemiologia precisa ser contextualizado com as bases da Saúde Coletiva, reforçando seu compromisso com a tradução e a aplicação do conhecimento para a melhoria da saúde e da vida das pessoas.

The need for a complex systems approach in rural health research

On a global scale, many major rural health issues have persisted for decades despite the introduction of new health interventions and public health policies. Although research efforts have generated valuable new knowledge about the aetiology of health, disease and health inequities in rural communities, rural health systems remain to be some of the most deprived and challenged in both the developing and developed world. While the reasons for this are many, a significant factor contributing to the current state of play is the pressing need for methodological innovation and relevant scientific approaches that have the capacity to support the translation of novel solutions into 'real world' rural contexts. Fortunately, complex systems approaches, which have seen an increase in popularity in the wider public health literature, could provide answers to some of the most resilient rural health problems in recent times. The purpose of this article is to promote the value and utility of a complex systems approach in rural health research. We explain the benefits of a complex systems approach and provide a background to the complexity sciences, including the main characteristics of complex systems. Two popular computational methods are described. The next step for rural health research involves exploring how a complex systems approach can help with the identification and evaluation of new and existing solutions to policy-resistant rural health issues. This includes generating awareness around the analytical trade-offs that occur between the use of traditional scientific methods and complex systems approaches.

Sins of inquiry: How to criticize scientific pursuits

Criticism is a staple of the scientific enterprise and of the social epistemology of science. Philosophical discussions of criticism have traditionally focused on its roles in relation to objectivity, confirmation, and theory choice. However, attention to criticism and to criticizability should also inform our thinking about scientific pursuits: the allocation of resources with the aim of developing scientific tools and ideas. In this paper, we offer an account of scientific pursuitworthiness which takes criticizability as its starting point. We call this the apokritic model of pursuit. Its core ideas are that pursuits are practices governed by norms for asking and answering questions, and that criticism arises from the breach of these norms. We illustrate and advertise our approach using examples from institutional grant review, neuroscience, and sociology. We show that the apokritic model can unify several indices of criticizability, that it can account for the importance of criticizing pursuits in scientific practice, and that it can offer ameliorative advice to erstwhile pursuers.

Invited Commentary: The Need for Repeated Measures and Other Methodological Considerations When Investigating Discrimination as a Contributor to Health

To determine potential measurement error related to the assessment of lifetime discrimination, Van Dyke et al. (Am J Epidemiol. 2022;191(3):370-378) investigated inconsistencies in reporting of racial, socioeconomic status, and sex discrimination over time among Black and White adults enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. The authors found that "ever" reports of discrimination (as assessed by the Experiences of Discrimination Scale) decreased over time and that populations who most experience discrimination (i.e., Black/African-American people, younger persons, persons of low socioeconomic status, and women) were often also the most likely to inconsistently report discrimination over the course of the study period (from 1992 to 2010). The authors have raised an important issue related to the potential underestimation of lifetime discrimination that may depend on when discrimination is assessed, as well as the social identity of individuals surveyed. With implications for health inequities, these findings highlight study design and methodological issues that should be addressed to accurately estimate the true burden discrimination places on health. In this commentary, we further illuminate potential methodological challenges and opportunities to consider when investigating the impact of discrimination on health.

Association of Childhood Adversity With Morbidity and Mortality in US Adults: A Systematic Review

IMPORTANCE

Childhood adversity (CA) is a powerful determinant of long-term physical and mental health that is associated with elevated risk for chronic disease and psychopathology. However, the degree to which CA contributes to mortality as a preventable driver of ill-health and death is unknown.

OBJECTIVE

To estimate the contribution of CA to health behaviors, including smoking and sedentary behavior, as well as the annual mortality attributable to CA in the US through influences on leading causes of death (eg, cardiovascular disease).

EVIDENCE REVIEW

For this systematic review, the PsycINFO and MEDLINE databases were searched on November 15, 2019. The databases were searched for publications from inception (1806 for PsycINFO, 1946 for MEDLINE) to November 15, 2019. Meta-analyses of the associations between CA and morbidity outcomes were included. The population attributable fraction (PAF) was calculated from these associations along with the estimated US prevalence of CA. The PAF was then applied to the number of annual deaths associated with each cause of death to estimate the number of deaths that are attributable to CA. Additionally, the PAF was applied to the incidence of health behaviors to derive the number of cases attributable to CA. Exposure to 1 or more experiences of adversity before the age of 18 years was analyzed, including abuse, neglect, family violence, and economic adversity.

FINDINGS

A total of 19 meta-analyses with 20 654 832 participants were reviewed. Childhood adversity accounted for approximately 439 072 deaths annually in the US, or 15% of the total US mortality in 2019 (2 854 838 deaths), through associations with leading causes of death (including heart disease, cancer, and suicide). In addition, CA was associated with millions of cases of unhealthy behaviors and disease markers, including more than 22 million cases of sexually transmitted infections, 21 million cases of illicit drug use, 19 million cases of elevated inflammation, and more than 10 million cases each of smoking and physical inactivity. The greatest proportion of outcomes attributable to CA were for suicide attempts and sexually transmitted infections, for which adversity accounted for up to 38% and 33%, respectively.

CONCLUSIONS AND RELEVANCE

The results of this systematic review suggest that CA is a leading contributor to morbidity and mortality in the US and may be considered a preventable determinant of mortality. The prevention of CA and the intervention on pathways that link these experiences to elevated disease risk should be considered a critical public health priority.

Large-scale genetic investigation reveals genetic liability to multiple complex traits influencing a higher risk of ADHD

Attention Deficit-Hyperactivity Disorder (ADHD) is a complex psychiatric and neurodevelopmental disorder that develops during childhood and spans into adulthood. ADHD’s aetiology is complex, and evidence about its cause and risk factors is limited. We leveraged genetic data from genome-wide association studies (GWAS) and performed latent causal variable analyses using a hypothesis-free approach to infer causal associations between 1387 complex traits and ADHD. We identified 37 inferred potential causal associations with ADHD risk. Our results reveal that genetic variants associated with iron deficiency anemia (ICD10), obesity, type 2 diabetes, synovitis and tenosynovitis (ICD10), polyarthritis (ICD10), neck or shoulder pain, and substance use in adults display partial genetic causality on ADHD risk in children. Genetic variants associated with ADHD have a partial genetic causality increasing the risk for chronic obstructive pulmonary disease and carpal tunnel syndrome. Protective factors for ADHD risk included genetic variants associated with the likelihood of participating in socially supportive and interactive activities. Our results show that genetic liability to multiple complex traits influences a higher risk for ADHD, highlighting the potential role of cardiometabolic phenotypes and physical pain in ADHD’s aetiology. These findings have the potential to inform future clinical studies and development of interventions.

The implications of outcome truncation in reproductive medicine RCTs: a simulation platform for trialists and simulation study

BACKGROUND

Randomised controlled trials in reproductive medicine are often subject to outcome truncation, where the study outcomes are only defined in a subset of the randomised cohort. Examples include birthweight (measurable only in the subgroup of participants who give birth) and miscarriage (which can only occur in participants who become pregnant). These outcomes are typically analysed by making a comparison between treatment arms within the subgroup (for example, comparing birthweights in the subgroup who gave birth or miscarriages in the subgroup who became pregnant). However, this approach does not represent a randomised comparison when treatment influences the probability of being observed (i.e. survival). The practical implications of this for the design and interpretation of reproductive trials are unclear however.

METHODS

We developed a simulation platform to investigate the implications of outcome truncation for reproductive medicine trials. We used this to perform a simulation study, in which we considered the bias, type 1 error, coverage, and precision of standard statistical analyses for truncated continuous and binary outcomes. Simulation settings were informed by published assisted reproduction trials.

RESULTS

Increasing treatment effect on the intermediate variable, strength of confounding between the intermediate and outcome variables, and the presence of an interaction between treatment and confounder were found to adversely affect performance. However, within parameter ranges we would consider to be more realistic, the adverse effects were generally not drastic. For binary outcomes, the study highlighted that outcome truncation could cause separation in smaller studies, where none or all of the participants in a study arm experience the outcome event. This was found to have severe consequences for inferences.

CONCLUSION

We have provided a simulation platform that can be used by researchers in the design and interpretation of reproductive medicine trials subject to outcome truncation and have used this to conduct a simulation study. The study highlights several key factors which trialists in the field should consider carefully to protect against erroneous inferences. Standard analyses of truncated binary outcomes in small studies may be highly biassed, and it remains to identify suitable approaches for analysing data in this context.

Data, Social Determinants, and Better Decision-making for Health: the 3-D Commission

More than a decade after the World Health Organization Commission on the Social Determinants of Health (SDoH), it is becoming widely accepted that social and economic factors, including but not limited to education, energy, income, race, ethnicity, and housing, are important drivers of health in populations. Despite this understanding, in most contexts, social determinants are not central to local, national, or global decision-making. Greater clarity in conceptualizing social determinants, and more specificity in measuring them, can move us forward towards better incorporating social determinants in decision-making for health. In this paper, first, we summarize the evolution of the social framing of health. Second, we describe how the social determinants are conceptualized and contextualized differently at the global, national, and local levels. With this, we seek to demonstrate the importance of analyzing and understanding SDoH relative to the contexts in which they are experienced. Third, we problematize the gap in data across contexts on different dimensions of social determinants and describe data that could be curated to better understand the influence of social determinants at the local and national levels. Fourth, we describe the necessity of using data to understand social determinants and inform decision-making to improve health. Our overall goal is to provide a path for our collective understanding of the foundational causes of health, facilitated by advances in data access and quality, and realized through improved decision-making.

United Kingdom Biobank (UK Biobank): JACC Focus Seminar 6/8

An increasing number of people are now living with cardiovascular disease (CVD), with concomitant CVD-related hospitalizations, operations, and prescriptions. To ultimately deliver optimal cardiovascular care, access to population-based biobanks with data on multiomics, phenotypes, and lifestyle risk factors are crucial. UK Biobank is a cohort study that incorporated data between 2006 and 2010 from over half a million individuals (40 to 69 years of age) at recruitment from across the United Kingdom. As one of the most accessible, largest, and in-depth cohort studies in the world, UK Biobank continues to enhance the resource with the addition of data from various omics platforms (eg, genomics, metabolomics, proteomics), multimodal imaging, self-reported risk factors and health outcomes, and linkage to electronic health records. The vision of UK Biobank is to allow as many researchers as possible to apply their expertise and imagination to undertake research to prevent, diagnose, and treat a wide range of chronic conditions, including CVD.

Racial Disparities in Coronavirus Disease 2019 (COVID-19) Mortality Are Driven by Unequal Infection Risks

Background

As of 1 November 2020, there have been >230 000 deaths and 9 million confirmed and probable cases attributable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the United States. However, this overwhelming toll has not been distributed equally, with geographic, race/ethnic, age, and socioeconomic disparities in exposure and mortality defining features of the US coronavirus disease 2019 (COVID-19) epidemic.

Methods

We used individual-level COVID-19 incidence and mortality data from the state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks.

Results

In crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than for Whites for all groups except Native Americans. Blacks experienced the greatest burden of confirmed and probable COVID-19 (age-standardized incidence, 1626/100 000 population) and mortality (age-standardized mortality rate, 244/100 000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.5 (95% posterior credible interval [CrI], 5.4–5.6) and 6.7 (95% CrI, 6.4–7.1) times higher than Whites, respectively. We found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates.

Conclusions

This work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as Michigan, are driven primarily by variation in household, community, and workplace exposure rather than case-fatality rates.

Defining Core Competencies for Epidemiologists in Academic Settings to Tackle Tomorrow's Health Research Challenges: A Structured, Multinational Effort

Only a few efforts have been made to define core competencies for epidemiologists working in academic settings. Here we describe a multinational effort to define competencies for epidemiologists, who are increasingly facing emerging and potentially disruptive technological and societal health trends in academic research. During a 1.5-year period (2017–2019), we followed an iterative process that aimed to be inclusive and multinational to reflect the various perspectives of a diverse group of epidemiologists. Competencies were developed by a consortium in a consensus-oriented process that spanned 3 main activities: 2 in-person interactive meetings held in Amsterdam, the Netherlands, and Zurich, Switzerland, and an online survey. In total, 93 meeting participants from 16 countries and 173 respondents from 19 countries contributed to the development of 31 competencies. These 31 competencies included 14 on “developing a scientific question” and “study planning,” 12 on “study conduct and analysis,” 3 on “overarching competencies,” and 2 on “communication and translation.” The process described here provides a consensus-based framework for defining and adapting the field. It should initiate a continuous process of thinking about competencies and the implications for teaching epidemiology to ensure that epidemiologists working in academic settings are well prepared for today’s and tomorrow’s health research.

Invited Commentary: Epidemiologic Methods as Applied to Epidemiology Education-Goals and Expectations to Advance and Diversify the Field

In their accompanying article, Brown and Cox (Am J Epidemiol. 2021;190(2):305-312) offer strategies for improving introductory epidemiology instruction in large undergraduate classes using a team-based instructional approach. Effective epidemiology instruction can increase the quality and effectiveness of teaching and learning in our discipline. Recent work has suggested that epidemiology education can also be targeted as a mechanism to fuel equity and progress in the field. In this commentary, I contextualize Brown and Cox's article within the literature on epidemiology instruction for undergraduates. I also explore the status of methods for measuring teaching and learning effectiveness in our field. I revisit what makes epidemiology a unique science and connect this with instructional strategies that have the potential to capitalize on these features. Lastly, I consider whether our field can improve in developing methods of measuring the effectiveness of epidemiology instruction for achieving these aims. Ultimately, as I suggest, increasing the rigor of methods for evaluating epidemiology instruction will serve to diversify, enhance, and advance our discipline.

Early origins of socioeconomic inequalities in chronic inflammation: Evaluating the contributions of low birth weight and short breastfeeding

The United States is characterized by persistent and widening social inequities in a wide range of adult health outcomes. A life course approach challenges us to consider if, and how, these inequities trace back to early life conditions, and chronic inflammation represents a potentially important mechanism through which early environments may have lasting effects on health in adulthood. Low birth weight (LBW) and shorter durations of breastfeeding both predict increased inflammation in adulthood, which is associated with increased risk for cardiovascular disease, metabolic syndrome, and all-cause mortality. Using data from a large representative sample of young adults in the US (National Longitudinal Study of Adolescent to Adult Health (Add Health)), we document the socioeconomic status (SES) gradient in chronic inflammation, as indicated by concentrations of C-reactive protein (CRP). Using a nested set of structural equation models and marginal standardization techniques, we investigate the extent to which this gradient is explained by patterns of LBW and breastfeeding in infancy. Findings reveal a particularly important role for breastfeeding duration: Based on model predictive margins, increasing breastfeeding duration to three or more months corresponds to a flattening of the SES gradient by 80%, and 83% when LBW is eliminated. This study expands current understandings of the consequential role of developmental environments for population health and for addressing health inequities in future generations.

Invited Commentary: Reckoning With the Relationship Between Stressors and Suicide Attempts in a Time of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic presents a unique set of risk exposures for populations, which might lead to an increase in suicide. While large-scale traumatic events are known to increase psychological disorders, thus far the science has not shown a clear link between these events and suicide. In this issue of the Journal, Elbogen et al. (Am J Epidemiol. 2020;189(11):1266-1274) used representative data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) to show that 4 dimensions of financial strain-financial debt/crisis, unemployment, past homelessness, and lower income-are associated with subsequent suicide attempts. There are 3 main lessons we can take from Elbogen et al.: First, with populations facing record-breaking unemployment, economic recession, and reduced wages, we can anticipate an increase in suicide in the wake of the COVID-19 pandemic. Second, these data show the centrality of financial stressors, marking the current moment as distinct from other disasters or large-scale trauma. Third, the data teach us that financial stressors are linked and cumulative. In this way, Elbogen et al. provide a sobering harbinger of the potential effects on suicide of the collective stressors borne by the COVID-19 pandemic and other mass traumatic events that are accompanied by substantial financial stressors.

Win-Win: Reconciling Social Epidemiology and Causal Inference

Social epidemiology is concerned with the health effects of forces that are "above the skin." Although causal inference should be a key goal for social epidemiology, social epidemiology and quantitative causal inference have been seemingly at odds over the years. This does not have to be the case and, in fact, both fields stand to gain through a closer engagement of social epidemiology with formal causal inference approaches. We discuss the misconceptions that have led to an uneasy relationship between these 2 fields, propose a way forward that illustrates how the 2 areas can come together to inform causal questions, and discuss the implications of this approach. We argue that quantitative causal inference in social epidemiology is an opportunity to do better science that matters, a win-win for both fields.

Perspectives on the Future of Epidemiology: A Framework for Training

Over the past century, the field of epidemiology has evolved and adapted to changing public health needs. Challenges include newly emerging public health concerns across broad and diverse content areas, new methods, and vast data sources. We recognize the need to engage and educate the next generation of epidemiologists and prepare them to tackle these issues of the 21st century. In this commentary, we suggest a skeleton framework upon which departments of epidemiology should build their curriculum. We propose domains that include applied epidemiology, biological and social determinants of health, communication, creativity and ability to collaborate and lead, statistical methods, and study design. We believe all students should gain skills across these domains to tackle the challenges posed to us. The aim is to train smart thinkers, not technicians, to embrace challenges and move the expanding field of epidemiology forward.

Association of Socioeconomic Status and Comorbidities with Racial Disparities during Kidney Transplant Evaluation

BACKGROUND AND OBJECTIVES

Black patients referred for kidney transplantation have surpassed many obstacles but likely face continued racial disparities before transplant. The mechanisms that underlie these disparities are unclear. We determined the contributions of socioeconomic status (SES) and comorbidities as mediators to disparities in listing and transplant.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We studied a cohort (n=1452 black; n=1561 white) of patients with kidney failure who were referred for and started the transplant process (2009-2018). We estimated the direct and indirect effects of SES (self-reported income, education, and employment) and medical comorbidities (self-reported and chart-abstracted) as mediators of racial disparities in listing using Cox proportional hazards analysis with inverse odds ratio weighting. Among the 983 black and 1085 white candidates actively listed, we estimated the direct and indirect effects of SES and comorbidities as mediators of racial disparities on receipt of transplant using Poisson regression with inverse odds ratio weighting.

RESULTS

Within the first year, 876 (60%) black and 1028 (66%) white patients were waitlisted. The relative risk of listing for black compared with white patients was 0.76 (95% confidence interval [95% CI], 0.69 to 0.83); after adjustment for SES and comorbidity, the relative risk was 0.90 (95% CI, 0.83 to 0.97). The proportion of the racial disparity in listing was explained by SES by 36% (95% CI, 26% to 57%), comorbidity by 44% (95% CI, 35% to 61%), and SES with comorbidity by 58% (95% CI, 44% to 85%). There were 409 (42%) black and 496 (45%) white listed candidates transplanted, with a median duration of follow-up of 3.9 (interquartile range, 1.2-7.1) and 2.8 (interquartile range, 0.8-6.3) years, respectively. The incidence rate ratio for black versus white candidates was 0.87 (95% CI, 0.79 to 0.96); SES and comorbidity did not explain the racial disparity.

CONCLUSIONS

SES and comorbidity partially mediated racial disparities in listing but not for transplant.

From Epidemiologic Knowledge to Improved Health: A Vision for Translational Epidemiology

Epidemiology should aim to improve population health; however, no consensus exists regarding the activities and skills that should be prioritized to achieve this goal. We performed a scoping review of articles addressing the translation of epidemiologic knowledge into improved population health outcomes. We identified 5 themes in the translational epidemiology literature: foundations of epidemiologic thinking, evidence-based public health or medicine, epidemiologic education, implementation science, and community-engaged research (including literature on community-based participatory research). We then identified 5 priority areas for advancing translational epidemiology: 1) scientific engagement with public health; 2) public health communication; 3) epidemiologic education; 4) epidemiology and implementation; and 5) community involvement. Using these priority areas as a starting point, we developed a conceptual framework of translational epidemiology that emphasizes interconnectedness and feedback among epidemiology, foundational science, and public health stakeholders. We also identified 2-5 representative principles in each priority area that could serve as the basis for advancing a vision of translational epidemiology. We believe an emphasis on translational epidemiology can help the broader field to increase the efficiency of translating epidemiologic knowledge into improved health outcomes and to achieve its goal of improving population health.

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.

Teaching on the Continuum: Epidemiology Education From High School Through Graduate School

Epidemiology education is increasingly recognized as a core science necessary for career preparation throughout the health sector, and graduate epidemiology instruction is continually being reevaluated to ensure students receive appropriate training. Recent work has also focused on the potential for epidemiology to be formally incorporated as a stand-alone discipline in undergraduate education and even integrated into wide-scale high-school science learning. As epidemiology educators, however, we face a tremendous challenge in that we should appreciate differences in students' instructional needs and goals (e.g., concepts and skills) at each educational level. In this article we propose an epidemiology learning continuum for students from high school through graduate school. We call for a student-centered instructional approach to best hone learners' grasp of concepts and skills. Furthermore, we propose scaffolded learning to help epidemiology students to develop more advanced insights and abilities as they progress in the field. This approach will not only best serve the discipline but also is well-aligned with the Association of Schools and Programs of Public Health's "Framing the Future" initiative for public health education for the 21st century.

Epidemiology at the Heart of Population Health Science

Epidemiology has long been concerned with understanding the causes of health and disease states so that we can improve the health of populations. Despite broad agreement on this definition of the field, we continue to debate certain core goals of epidemiology: whether epidemiology is a pragmatic science or not, which methods constitute epidemiologic methods, and what our gold-standard thinking should be to understand causation. We suggest that recognizing epidemiology as the quantitative heart of population health science can push these tensions aside and allow us to focus our science on the health of populations and on the processes that shape that health. Seeing epidemiology as the core quantitative health science has implications for the questions we ask, how we organize ourselves as a field, and how we train the next generation of epidemiologists.

The Future of Teaching Epidemiology

The rapid pace of technological advancements and the corresponding societal innovations and adaptations make it difficult to predict how teaching epidemiology will look in the coming decades. We discuss changes in the teaching of epidemiology that are currently unfolding. First, typical epidemiology curricula often lack formal instruction in important components of causal thinking, such as the formulation of well-defined research questions. We address gaps related to causal thinking, communication about our science, and interpretation of study results, and we make suggestions of specific content to close such gaps. Second, digital technology increasingly influences epidemiology instruction. We discuss classroom and online teaching modalities in terms of challenges and advantages.

Sampling and Sampling Frames in Big Data Epidemiology

Purpose of Review

The 'big data' revolution affords the opportunity to reuse administrative datasets for public health research. While such datasets offer dramatically increased statistical power compared with conventional primary data collection, typically at much lower cost, their use also raises substantial inferential challenges. In particular, it can be difficult to make population inferences because the sampling frames for many administrative datasets are undefined. We reviewed options for accounting for sampling in big data epidemiology.

Recent Findings

We identified three common strategies for accounting for sampling when the data available were not collected from a deliberately constructed sample: 1) explicitly reconstruct the sampling frame, 2) test the potential impacts of sampling using sensitivity analyses, and 3) limit inference to sample.

Summary

Inference from big data can be challenging because the impacts of sampling are unclear. Attention to sampling frames can minimize risks of bias.

Commonly Initiated Opioids and Risk of Fracture Hospitalizations in United States Nursing Homes

OBJECTIVES

The aim of this study was to estimate the comparative safety of initiating commonly used opioids among older, long-stay United States nursing home residents with fracture hospitalizations.

METHODS

We conducted a new-user retrospective cohort study of nursing home residents initiating short-acting oxycodone, hydrocodone, or tramadol by merging the 2011-2013 Minimum Data Set 3.0 to Medicare hospitalization and pharmacy claims. Residents (≥ 65 years, no cancer or hospice use) contributed treatment episodes (> 120 days with no prior opioid claims) and were followed for 180 days until incident fracture hospitalization (hip, femur, humerus, pelvis, radius/ulna), death (competing risk), treatment changes (e.g., discontinuation), or administrative censoring. Competing risks models with inverse probability of treatment weighting were used to estimate subdistribution hazard ratios (HR_SD) and 95% confidence intervals (CI).

RESULTS

Overall, 110,862 residents contributed 134,432 treatment episodes: 14,373 oxycodone; 69,182 hydrocodone; and 50,877 tramadol initiators. The incidences of fracture hospitalizations per 100 person-years were 9.4 (95% CI 7.5-11.7) for oxycodone, 7.9 (95% CI 7.1-8.8) for hydrocodone, and 5.0 (95% CI 4.3-5.7) for tramadol initiators. In weighted models, oxycodone initiators had a similar rate of fractures to hydrocodone initiators (HR_SD 1.08, 95% CI 0.79-1.48). Tramadol initiators had lower fracture rates than hydrocodone initiators (HR_SD 0.67, 95% CI 0.56-0.80).

CONCLUSIONS

The lower rate of fractures that we documented among tramadol initiators compared with hydrocodone initiators is consistent, albeit attenuated compared with prior studies among community-dwelling older adults. However, overall fracture rates were lower than in community settings, potentially due to the limited risk of falling in this population with limited mobility.

Elevated Serum Total Bilirubin Concentrations Are Negatively Associated with Diabetic Retinopathy among the Chinese Northeastern Population

OBJECTIVE

To evaluate the association between serum total bilirubin concentration (STBC) and diabetic retinopathy (DR) among the Chinese northeastern population.

METHODS

A cross-sectional study was conducted in Liaoning between January 2015 and May 2017.

RESULTS

A total of 742 subjects (419 men and 323 women) with type 2 diabetes mellitus (DM) who visited an ophthalmic clinic were included in this study. The mean age of the subjects was 59.55 ± 10.63 years, and 43.5% of the subjects were women. The mean of DM duration was 11.01 ± 7.35 years. STBC were negatively correlated with DM duration, urea nitrogen, serum creatinine, uric acid, and urine microalbumin. After adjusting for confounding factors, as a continuous variable, STBC was inversely associated with the risk of DR in total subjects (OR: 0.95, 95% CI: 0.93-0.99). When STBC was used as a tertiary variable, compared with the first tertile, the OR in the third tertile was 0.37 (95% CI: 0.22-0.64) in total subjects.

CONCLUSION

Our results demonstrate that a significant negative association was found between STBC and DR. STBC might be an early clinical marker for predicting the occurrence of DR.

Patterning of individual heterogeneity in body mass index: evidence from 57 low- and middle-income countries

Modeling variation at population level has become increasingly valued, but no clear application exists for modeling differential variation in health between individuals within a given population. We applied Goldstein's method (in: Everrit, Howell (eds) Encyclopedia of statistics in behavioral science, Wiley, Hoboken, 2005) to model individual heterogeneity in body mass index (BMI) as a function of basic sociodemographic characteristics, each independently and jointly. Our analytic sample consisted of 643,315 non-pregnant women aged 15-49 years pooled from the latest Demographic Health Surveys (rounds V, VI, or VII; years 2005-2014) across 57 low- and middle-income countries. Individual variability in BMI ranged from 9.8 (95% CI: 9.8, 9.9) for the youngest to 23.2 (95% CI: 22.9, 23.5) for the oldest age group; 14.2 (95% CI: 14.1, 14.3) for those with no formal education to 19.7 (95% CI: 19.5, 19.9) for those who have completed higher education; and 13.6 (95% CI: 13.5, 13.7) for the poorest quintile to 20.1 (95% CI: 20.0, 20.2) for the wealthiest quintile group. Moreover, variability in BMI by age was also different for different socioeconomic groups. Empirically testing the fundamental assumption of constant variance and identifying groups with systematically large differentials in health experiences have important implications for reducing health disparity.

A Social Work Approach to Policy: Implications for Population Health

The substantial disparities in health and poorer outcomes in the United States relative to peer nations suggest the need to refocus health policy. Through direct contact with the most vulnerable segments of the population, social workers have developed an approach to policy that recognizes the importance of the social environment, the value of social relationships, and the significance of value-driven policymaking. This approach could be used to reorient health, health care, and social policies. Accordingly, social workers can be allies to public health professionals in efforts to eliminate disparities and improve population health.

New frontiers for environmental epidemiology in a changing world

BACKGROUND

In the next 25years, transformative changes, in particular the rapid pace of technological development and data availability, will require environmental epidemiologists to prioritize what should (rather than could) be done to most effectively improve population health.

OBJECTIVES

In this essay, we map out key driving forces that will shape environmental epidemiology in the next 25years. We also identify how the field should adapt to best take advantage of coming opportunities and prepare for challenges.

DISCUSSION

Future environmental epidemiologists will face a world shaped by longer lifespans but also larger burdens of chronic health conditions; shifting populations by region and into urban areas; and global environmental change. Rapidly evolving technologies, particularly in sensors and OMICs, will present opportunities for the field. How should it respond? We argue, the field best adapts to a changing world by focusing on healthy aging; evidence gaps, especially in susceptible populations and low-income countries; and by developing approaches to better handle complexity and more formalized analysis.

CONCLUSIONS

Environmental epidemiology informing disease prevention will continue to be valuable. However, the field must adapt to remain relevant. In particular, the field must ensure that public health importance drives research questions, while seizing the opportunities presented by new technologies. Environmental epidemiologists of the future will require different, refined skills to work effectively across disciplines, ask the right questions, and implement appropriate study designs in a data-rich world.