Troppi pochi epidemiologi nelle crisi umanitarie: una lacuna critica da colmare. Un nuovo corso organizzato dall'Associazione Italiana di Epidemiologia.

The current humanitarian crises in Ukraine and Gaza, along with the chronic crises, and the climate-related disasters, have exposed the limitations of the humanitarian system. Within these contexts, humanitarian organisations frequently struggle with collecting, analysing, interpreting, and utilising health data, due to the challenging environments in which they operate and funding constraints. It is precisely in these contexts that field epidemiology plays a crucial, but often overlooked role.Field epidemiologists face unique challenges, including rapidly changing conditions, poor-quality data, and biases. Despite these difficulties, accurate epidemiological data are essential for needs assessment, guidance on interventions, and advocacy. Conventional methods often need adaptation for crisis settings, and there are still gaps in measurement.This article discusses the role of epidemiology in such contexts, noting a shortage of trained 'humanitarian epidemiologists' and specialised training as major issues.To address these needs, the Italian Association of Epidemiology organised a course in early 2024 to enhance the epidemiological skills of staff working in humanitarian crises and introduce traditional epidemiologists to crisis-specific challenges. The course covered key concepts and methods of field epidemiology, emphasising the use of secondary health data. Its positive reception underscored the demand for such specialised training.Improving public health information collection and use in humanitarian crises is an ethical and practical necessity. Indeed, investing in field epidemiology and recognising its importance can enhance humanitarian interventions and better serve vulnerable populations.

Deep Learning for Epidemiologists: An Introduction to Neural Networks.

Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g., overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article's scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers.

A Long Way from Steubenville: Environmental Epidemiology in a Rapidly Changing World.

This commentary focuses on research that has long been at the core of environmental epidemiology: studies of the health effects of air pollution. It highlights publications in the American Journal of Epidemiology going back more than 50 years that have contributed to the debate about the validity of this research and its meaning for public policy. Technological advances have greatly expanded the toolbox of environmental epidemiologists in terms of measuring and analyzing complex exposures in large populations. Yet, discussions about biases in estimating air pollution health effects have always been and remain intense. Epidemiologists have brought new methodologies and concepts to this research, alleviating some but not all concerns. Here, the focus is on seminal epidemiologic work that established valid links between air pollution exposures and health outcomes and generated data for environmental policies and prevention. With this commentary, I hope to inspire epidemiologists to address many more of the burning environmental health questions-wildfires included-with a similar scientific doggedness. The rapidly changing conditions of our planet are challenging us to innovate and offer solutions, albeit perhaps a little bit faster this time around.

Sex and Gender Multidimensionality in Epidemiologic Research.

Along with age and race, sex has historically been a core stratification and control variable in epidemiologic research. While in recent decades research guidelines and institutionalized requirements have incorporated an approach differentiating biological sex from social gender, neither sex nor gender is itself a unidimensional construct. The conflation of dimensions within and between sex and gender presents a validity issue wherein proxy measures are used for dimensions of interest, often without explicit acknowledgement or evaluation. Here, individual-level dimensions of sex and gender are outlined as a guide for epidemiologists, and 2 case studies are presented. The first case study demonstrates how unacknowledged use of a sex/gender proxy for a sexed dimension of interest (uterine status) resulted in decades of cancer research misestimating risks, racial disparities, and age trends. The second illustrates how a multidimensional sex and gender framework may be applied to strengthen research on coronavirus disease 2019 incidence, diagnosis, morbidity, and mortality. Considerations are outlined, including: 1) addressing the match between measures and theory, and explicitly acknowledging and evaluating proxy use; 2) improving measurement across dimensions and social ecological levels; 3) incorporating multidimensionality into research objectives; and 4) interpreting sex, gender, and their effects as biopsychosocial.

Council of State and Territorial Epidemiologists/CDC Surveillance Case Definition for Multisystem Inflammatory Syndrome in Children Associated with SARS-CoV-2 Infection - United States.

This report summarizes the evidence and rationale supporting the components of the CSTE/CDC MIS-C surveillance case definition and describes the methods used to develop the definition. These methods included convening MIS-C clinical experts (i.e., consultants): regarding identification of MIS-C and its distinction from other pediatric conditions, a review of available literature comparing MIS-C phenotype with that of pediatric COVID-19 and other hyperinflammatory syndromes, and retrospective application of different criteria to data from MIS-C cases previously reported to CDC.

Why and How Epidemiologists Should Use Mixed Methods.

The field of epidemiology's current focus on causal inference follows a quantitative approach and limits research questions to those that are strictly quantifiable. How can epidemiologists study biosociocultural public health problems that they cannot easily quantify? The mixed-methods approach offers a possible solution by incorporating qualitative sociocultural factors as well as the perspective and context from the population under study into quantitative studies. After a pluralist perspective of causal inference, this article provides a guide for epidemiologists interested in applying mixed methods to their observational studies of causal identification and explanation. We begin by reviewing the current paradigms guiding quantitative, qualitative, and mixed methodologies. We then describe applications of convergent and sequential mixed-methods designs to epidemiologic concepts including confounding, mediation, effect modification, measurement, and selection bias. We provide concrete examples of how epidemiologists can use mixed methods to answer research questions of complex bio-socio-cultural health outcomes. We also include a case study of using mixed methods in an observational study design. We describe how mixed methods can enhance how epidemiologists define underlying causal structures. Our alignment of mixed-methods study designs with epidemiologic concepts addresses a major gap in current epidemiology education- how do epidemiologists systematically determine what goes into causal structures?

Losing the Public, for Better or for Worse: A Lesson from John Everett Gordon (1890-1983) and John Rodman Paul (1893-1971).

The Covid-19 pandemic has laid bare a tension around scientific expertise that has major implications for the effectiveness of health systems. Critical engagement with this tension, however, is largely missing from the lessons and programs consolidating in the wake of the emergency. Lacking good frameworks for discussing the tension, the vague term "public trust" has proliferated into a buzzword that stands in for more articulate discussion. The tension between experts and the public is not new, however. It is useful to look back to the 1930s, when health experts identifying as "new epidemiologists" imagined a new modern science of epidemiology that, some believed, would resolve evident failures in public cooperation. Historical analysis of different approaches to the production and use of epidemiological knowledge in these years reveals a debate about power at the heart of epidemiology, and a critical framework for discussing the tension around epidemiological expertise in public health.

Chance, ignorance, and the paradoxes of cancer: Richard Peto on developing preventative strategies under uncertainty.

During the early 1980s both cancer biology and epidemiological methods were being transformed. In 1984 the leading cancer epidemiologist Richard Peto - who, in 1981, had published the landmark Causes of Cancer with Richard Doll - wrote a short chapter on "The need for ignorance in cancer research", in which the worlds of epidemiology and speculative Darwinian biology met. His reflections on how evolutionary theory related to cancer have become known as "Peto's paradox", whilst his articulation of "black box epidemiology" provided the logic of subsequent practice in the field. We reprint this sparkling and prescient example of biologically-informed epidemiological theorising at its best in this issue of the European Journal of Epidemiology, together with four commentaries that focus on different aspects of its rich content. Here were provide some contextual background to the 1984 chapter, and our own speculations regarding various paradoxes in cancer epidemiology. We suggest that one reason for the relative lack of progress in indentifying novel modifiable causes of cancer over the last 40 years may reflect such exposures being ubiquitous within environments, and discuss the lessons for epidemiology that would follow from this.

Views on increased federal access to state and local National Syndromic Surveillance Program data: a nominal group technique study with state and local epidemiologists.

BACKGROUND: US public health authorities use syndromic surveillance to monitor and detect public health threats, conditions, and trends in near real-time. Nearly all US jurisdictions that conduct syndromic surveillance send their data to the National Syndromic Surveillance Program (NSSP), operated by the US. Centers for Disease Control and Prevention. However, current data sharing agreements limit federal access to state and local NSSP data to only multi-state regional aggregations. This limitation was a significant challenge for the national response to COVID-19. This study seeks to understand state and local epidemiologists' views on increased federal access to state NSSP data and identify policy opportunities for public health data modernization. METHODS: In September 2021, we used a virtual, modified nominal group technique with twenty regionally diverse epidemiologists in leadership positions and three individuals representing national public health organizations. Participants individually generated ideas on benefits, concerns, and policy opportunities relating to increased federal access to state and local NSSP data. In small groups, participants clarified and grouped the ideas into broader themes with the assistance of the research team. An web-based survey was used to evaluate and rank the themes using five-point Likert importance questions, top-3 ranking questions, and open-ended response questions. RESULTS: Participants identified five benefit themes for increased federal access to jurisdictional NSSP data, with the most important being improved cross-jurisdiction collaboration (mean Likert = 4.53) and surveillance practice (4.07). Participants identified nine concern themes, with the most important concerns being federal actors using jurisdictional data without notice (4.60) and misinterpretation of data (4.53). Participants identified eleven policy opportunities, with the most important being involving state and local partners in analysis (4.93) and developing communication protocols (4.53). CONCLUSION: These findings identify barriers and opportunities to federal-state-local collaboration critical to current data modernization efforts. Syndromic surveillance considerations warrant data-sharing caution. However, identified policy opportunities share congruence with existing legal agreements, suggesting that syndromic partners are closer to agreement than they might realize. Moreover, several policy opportunities (i.e., including state and local partners in data analysis and developing communication protocols) received consensus support and provide a promising path forward.

Team effectiveness: epidemiologists' perception of collective performance during emergency response.

BACKGROUND: To describe epidemiologists' experience of team dynamics and leadership during emergency response, and explore the utility of the Team Emergency Assessment Measure (TEAM) tool during future public health emergency responses. The TEAM tool included categories for leadership, teamwork, and task management. METHODS: We conducted a cross-sectional survey between October 2019 and February 2020 with the global applied field epidemiology workforce. To validate the TEAM tool for our context, we used exploratory and confirmatory factor analysis. RESULTS: We analysed 166 completed surveys. Respondents included national and international emergency responders with representation of all WHO regions. We were unable to validate the TEAM tool for use with epidemiology teams involved in emergency response, however descriptive analysis provided insight into epidemiology emergency response team performance. We found female responders were less satisfied with response leadership than male counterparts, and national responders were more satisfied across all survey categories compared to international responders. CONCLUSION: Functional teams are a core attribute of effective public health emergency response. Our findings have shown a need for a greater focus on team performance. We recommend development of a fit-for-purpose performance management tool for teams responding to public health emergencies. The importance of building and supporting the development of the national workforce is another important finding of this study.

Enhancing the Applied Epidemiology Workforce: The Impact of the 2021 Epidemiology Capacity Assessment on CSTE's Workforce Priorities.

The Council of State and Territorial Epidemiologists (CSTE) conducted the seventh Epidemiology Capacity Assessment (ECA) from January to April 2021 in state and territorial health departments. The ECA serves to enumerate the applied epidemiology workforce and evaluate workforce capacity across the nation. The results of the ECA demonstrated a need for additional epidemiologists across jurisdictions and challenges of maintaining a trained workforce and improving public health infrastructure. The results of the ECA serve as the foundation for CSTE's workforce priorities, which focus on transforming applied epidemiology by promoting the field as a career opportunity, recruitment, and retention strategies, upskilling the workforce, and enhancing infrastructure. CSTE has outlined current and future workforce priorities, and these priorities contribute to a larger strategy to transform the field and enhance applied epidemiology capacity nationwide. This report describes the programmatic actions taken by CSTE in response to the results of the 2021 ECA.

Increasing Representation of Epidemiologists From Around the World in the Society for Epidemiologic Research: The Case of Mexico.

Mexico has a population of 129 million and is considered one of the most unequal countries in the world, suffering from widespread health disparities. There is a pressing need to strengthen epidemiologic capacity in Mexico, to help solve the complex health problems the country faces and to reduce health inequities. However, the representation of Mexican epidemiologists in the largest epidemiologic society in North America is low, despite the short distance to the United States. In this commentary, we discuss the barriers to higher representation of Mexican epidemiologists within the Society for Epidemiologic Research (SER), including language barriers, costs, and regional necessities. We also discuss opportunities to expand Mexican SER representation and collaboration. Overall, we hope that this is a call towards expanding SER global participation and starting a conversation on a common agenda for epidemiologic research.

Measuring Structural Racism: A Guide for Epidemiologists and Other Health Researchers.

There have been over 100 years of literature discussing the deleterious influence of racism on health. Much of the literature describes racism as a driver of social determinants of health, such as housing, employment, income, and education. More recently, increased attention has been given to measuring the structural nature of a system that advantages one racialized group over others rather than solely relying on individual acknowledgement of racism. Despite these advances, there is still a need for methodological and analytical approaches to complement the aforementioned. This commentary calls on epidemiologists and other health researchers at large to engage the discourse on measuring structural racism. First, we address the conflation between race and racism in epidemiologic research. Next, we offer methodological recommendations (linking of interdisciplinary variables and data sets and leveraging mixed-method and life-course approaches) and analytical recommendations (integration of mixed data, use of multidimensional models) that epidemiologists and other health researchers may consider in health equity research. The goal of this commentary is to inspire the use of up-to-date and theoretically driven approaches to increase discourse among public health researchers on capturing racism as well as to improve evidence of its role as the fundamental cause of racial health inequities.

Aduhelm, the Newly Approved Medication for Alzheimer Disease: What Epidemiologists Can Learn and What Epidemiology Can Offer.

Alzheimer disease (AD) is a progressive disorder common among older adults and culminating in profound cognitive impairments and high mortality risk. The US Food and Drug Administration (FDA) recently provided accelerated approval for Aduhelm, a medication for AD treatment. Aduhelm (Biogen Inc., Cambridge, Massachusetts) has been described as the first disease-modifying treatment for AD but has not been demonstrated to improve patients' cognitive or functional outcomes. In this commentary, we describe why Aduhelm approval was controversial and aspects of the current evidence of special pertinence to epidemiologists. The FDA decision was based primarily on 2 randomized controlled trials (RCTs), both terminated early, with conflicting findings about the cognitive benefits of Aduhelm. Both RCTs showed important adverse effects of the medication. The FDA cited the documented reduction in brain amyloid, an AD biomarker hypothesized as a surrogate outcome, to justify accelerated approval. Despite lack of racial/ethnic diversity in the RCT participants, concerns about health disparities have been invoked to argue for public funding of this expensive medication. The Centers for Medicare and Medicaid Services recently made a "Coverage with Evidence Development" determination for Aduhelm and similar medications. We end by describing how innovative study designs could accelerate postapproval research and evaluate the proposed surrogate outcomes.

What Has the Pandemic Revealed about the Shortcomings of Modern Epidemiology? What Can We Fix or Do Better?

In this commentary, we discuss themes that emerged from our symposium about what modern epidemiology as a science may learn from the COVID-19 pandemic. We reflect on the successes and limitations of this discipline from multiple perspectives, including from junior and senior epidemiologists and scientists on the front lines of generating evidence for the COVID-19 pandemic response in Wuhan, China, to Ontario, Canada. These themes include the role of the traditional scientific process in a public health emergency; epidemiologic methods and data that are critical for an effective pandemic response; the interventions that epidemiologists recommended and interventions that we may explore in the future; inequitable impacts of the COVID-19 pandemic contrasted with homogeneity in the epidemiologist workforce; effective and honest communication of uncertainty; trust and collaboration; and the extent to which these themes are currently reflected in our training programs and discipline. We look forward to insights from field epidemiologists directly involved in the ongoing response to the COVID-19 pandemic and further reflection from epidemiologists throughout our discipline.

Working: The Role of Occupational Epidemiology.

The coronavirus disease 2019 (COVID-19) pandemic has redemonstrated the importance of work as a determinant of health. During the pandemic, extant disparities were accentuated as the workforce was divided into the roughly 50% who could safely work from home and those who could not. With the spotlight on work, one might wonder where all the occupational epidemiologists have gone. To answer, we point to diminished research support and more limited workplace access that have led many epidemiologists to shift away from a focus on workers toward other vulnerable populations. Here we build on the renewed interest in work as a driver of health and inequality during the pandemic to highlight contributions of occupational epidemiology to public health. We consider: 1) etiological studies of chronic disease based on employment records to define cohorts and reconstruct long-term exposure; 2) studies of hypothetical interventions that are particularly appropriate for evaluating potential regulations to reduce workplace exposures; and 3) studies of disparities that take advantage of work as a potential source of social stratification and economic opportunity. As we have learned during the COVID-19 pandemic, workplaces can become venues for public health messaging and delivering interventions to enumerated populations of adults. By starting with COVID-19 prevention policies for the workplace, we have a chance to better protect public health.

Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist's Toolbox.

Propensity score weighting and outcome regression are popular ways to adjust for observed confounders in epidemiologic research. Here, we provide an introduction to matching methods, which serve the same purpose but can offer advantages in robustness and performance. A key difference between matching and weighting methods is that matching methods do not directly rely on the propensity score and so are less sensitive to its misspecification or to the presence of extreme values. Matching methods offer many options for customization, which allow a researcher to incorporate substantive knowledge and carefully manage bias/variance trade-offs in estimating the effects of nonrandomized exposures. We review these options and their implications, provide guidance for their use, and compare matching methods with weighting methods. Because of their potential advantages over other methods, matching methods should have their place in an epidemiologist's methodological toolbox.

The Case for Case-Cohort: An Applied Epidemiologist's Guide to Reframing Case-Cohort Studies to Improve Usability and Flexibility.

When research questions require the use of precious samples, expensive assays or equipment, or labor-intensive data collection or analysis, nested case-control or case-cohort sampling of observational cohort study participants can often reduce costs. These study designs have similar statistical precision for addressing a singular research question, but case-cohort studies have broader efficiency and superior flexibility. Despite this, case-cohort designs are comparatively underutilized in the epidemiologic literature. Recent advances in statistical methods and software have made analyses of case-cohort data easier to implement, and advances from casual inference, such as inverse probability of sampling weights, have allowed the case-cohort design to be used with a variety of target parameters and populations. To provide an accessible link to this technical literature, we give a conceptual overview of case-cohort study analysis with inverse probability of sampling weights. We show how this general analytic approach can be leveraged to more efficiently study subgroups of interest or disease subtypes or to examine associations independent of case status. A brief discussion of how this framework could be extended to incorporate other related methodologic applications further demonstrates the broad cost-effectiveness and adaptability of case-cohort methods for a variety of modern epidemiologic applications in resource-limited settings.

Ethical challenges and moral distress among field epidemiologists.

BACKGROUND: As 'disease detectives' and directors of public health programs, field epidemiologists play essential roles in protecting public health. Although ethical issues receive considerable attention in medical and research settings, less is known about ethical challenges faced by field epidemiologists in public health programs. Similarly, little is known about moral distress among field epidemiologists, i.e., situations in which they are constrained from acting on what they know to be morally right. Moral distress is strongly associated with empathy fatigue, burnout, reduced job retention, and disengagement. To better understand ethics training needs for field epidemiologists, in February 2019, members of TEPHIConnect, an online and mobile networking platform for Field Epidemiology Training Program (FETP) alumni, were invited to participate in an anonymous survey about ethical challenges and moral distress. RESULTS: Among 126 respondents from 54 countries, leading causes of ethical dilemmas included inadequate informed consent (61%), inequitable allocation of resources (49%), and conflicts of interest (43%). These occur primarily in settings of disease outbreaks (60%); research (55%); and public health programs at the state, province, or national level (45%) or community level (43%). Work-related moral distress was reported by 91% of respondents, including 26% who experience it "frequently" or "almost always." Field epidemiologists working in low- and low-middle income countries were more likely to report moral distress "frequently" or "almost always" than those in higher-income countries (33.0% vs 9.1%, P = 0.006). The most common perceived contributors to moral distress included excessive stress and work demands (30%) and inadequate support from leaders (25%). CONCLUSIONS: Field epidemiologists face significant work-related ethical challenges, which are endemic to public health and political systems. A substantial proportion of field epidemiologists also experience some degree of moral distress, often in association with these challenges. These findings indicate an unmet need among field epidemiologists for support in navigating ethical challenges, as well as for resources to address the human and professional consequences of moral distress.

Use of Job Classifications, Career Ladders, and the Applied Epidemiology Competencies to Support Recruitment, Retention, and Practice of State Health Department Epidemiologists.

CONTEXT: State health departments report that recruitment and retention of qualified epidemiologists is a significant challenge to ensuring epidemiology capacity to support essential public health services. OBJECTIVE: To collect information on the use of epidemiology job classifications, career ladders, and professional competencies in state health departments to inform workforce development activities that improve epidemiology capacity. DESIGN: Electronic survey of the designated state epidemiologist. SETTING: Fifty state health departments and the District of Columbia. PARTICIPANTS: State epidemiologists working in state health departments. MAIN OUTCOME MEASURES: Use and perceived benefit of epidemiology job classifications, career ladders, and professional competencies. RESULTS: All 50 states and the District of Columbia responded to the survey. Most state health departments reported having epidemiology-specific job classifications (n = 44, 90%) and career ladders (n = 36, 71%) in place. State epidemiologists strongly agreed or agreed that having an epidemiology-specific classification positively contributed to recruitment (n = 37, 84%) and retention (n = 29, 66%) of epidemiologists in their agency. State epidemiologists strongly agreed or agreed that having an epidemiology-specific career ladder positively contributed to recruitment (n = 24, 69%) and retention (n = 23, 66%) of epidemiologists in their agency. Only 10 (29%) state epidemiologists reported using the applied epidemiology competencies to develop or revise their jurisdiction's epidemiology career ladder. CONCLUSIONS: State health departments should implement well-maintained epidemiology-specific job classifications and career ladders that are based on current epidemiology competencies. Career ladders should be supported with opportunities for competency-based training to support career progression.