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

Correspondence to Dr. David W. Dowdy, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Suite E6531, Baltimore, MD 21205 (e-mail: ude.imhj@1ydwodd).

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Abstract

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.

In many recent commentaries in the epidemiology literature, the focus has been on the role of epidemiologists in not only identifying causes of disease but also in improving population health (1–4). Emerging methods in epidemiology, including agent-based modeling (5) and target trial emulation (6), are likewise increasingly focused on answering questions related to the improvement of health, as well as identifying etiologies of disease. Very few epidemiologists would take issue with the assertion that epidemiology should provide evidence that ultimately aims to advance the health of populations. Little consensus exists, however, regarding how best to translate knowledge from epidemiologic studies into better population health.

Many proposals have advocated for specific improvements to analytic methods (7), education (8), and public health practice (9); however, we still lack a coherent vision for answering the question: If epidemiology aims to improve population health, what activities and skills should epidemiologists prioritize? A holistic discussion of this question is particularly timely, because epidemiologists increasingly face struggles to sustain funding streams, compete for high-quality students, maintain relevance in the public eye, bridge gaps between academic and public health efforts, and improve scientific rigor.

Here, we endeavor to link epidemiologic knowledge and population health outcomes using the lens of translational epidemiology, defined by Szklo (10) as the “effective transfer of new knowledge from epidemiologic studies into the planning of population-wide and individual-level disease control programs and policies.” To help crystallize a vision of translational epidemiology, we conducted a scoping review of existing literature addressing this topic. On the basis of those results, we propose a conceptual framework of translational epidemiology and synthesize recommendations from the reviewed literature toward developing a more translational modern epidemiology.

A SCOPING REVIEW OF TRANSLATIONAL EPIDEMIOLOGY

Methods

Given the exploratory nature of this investigation, we conducted a scoping review of the current literature on translational epidemiology (11–14). As compared with systematic reviews designed to address more specific research questions, scoping reviews are designed to inform exploratory questions by “mapping key concepts, types of evidence, and gaps in research” (12).

Our research question was: How have researchers approached the problem of translating epidemiologic knowledge into improved health outcomes? We considered all published works that took as their primary subject the general translation of epidemiologic research into improved health outcomes. Papers concerned with translation solely of a specific intervention or disease were excluded, as were articles that were not peer reviewed, not published in scientific journals, or not written in English.

We started with an initial document core of 43 de-duplicated publications (41 journal articles and 2 books), generated by requesting articles of relevance to the research question from 8 faculty members in various subdisciplines of epidemiology (Figure ​ (Figure1). 1 ). We then reviewed all publications cited by the 41 journal articles (i.e., “backward snowballing”; n = 1,562 citations) and all publications citing these articles (i.e., “forward snowballing”; n = 1,246 citations), and we held a series of discussions with the explicit aim of structuring the literature according to a few themes (ideally, 3–6) found repeatedly across multiple publications. Removal of duplicate citations left a title pool of 2,088 papers. Sequential abstract (n = 186) and full-text review (n = 83) reduced this pool to a set of 33 documents for detailed indexing by 2 reviewers (M.W. and H.D.L.), using a standardized form (see Web Table 1). The final set of documents found relevant by consensus between the 2 reviewers (n = 12) was added to original 43 publications for the final body of literature for the scoping review (n = 55) (15).

Document flow for a scoping review of papers on translational epidemiology. An initial core set of 43 documents was expanded through forward and backward citation (i.e., “snowballing”). All cited works were then subjected to title, abstract, and full-text review, after which an additional 12 papers were added to the original core to form the final document set.

From our final document review pool, we abstracted publication details, all definitions of translational epidemiology, any conclusions of the authors related to the primary aim of the publication or specific to translational epidemiology, and any recommendations or actionable steps described. Findings were categorized according to thematic overlap, with the aim of condensing conclusions relevant to translational epidemiology into a structured set of 3–6 themes. This process was first performed on the core set of 43 articles, providing structure to the scoping review. Upon generating the final review pool of 55 articles, the list of themes was reviewed and updated accordingly. We synthesized findings into a conceptual framework to provide a visual and textual vision of translational epidemiology. Finally, we formed teams of 2–3 authors according to content expertise to map each theme onto a priority for advancing translational epidemiology and to generate a set of specific recommendations in each thematic area. All terminology relating to themes was generated by the authors without reference to any specific existing framework.

Results and synthesis

Among the core documents, we identified 4 primary themes in the existing translational epidemiology literature (Table ​ (Table1, 1 , with full details in Web Table 1, available at https://academic.oup.com/aje): 1) foundations of epidemiologic thinking; 2) evidence-based public health or medicine; 3) epidemiologic education; and 4) implementation science. We identified a fifth category, community-engaged research, from the additional 12 snowballed manuscripts. Although distinct, these themes are also complementary, mutually informative, and motivated by common scientific and ethical concerns.

Table 1.

Scoping Review of Translational Epidemiology: Brief Overview

Abbreviation: DAG, directed acyclic graph.

Through iterative discussions, we mapped each theme onto a corresponding priority for advancing the discipline of translational epidemiology, namely:

Engaging science and public health: Asking research questions that are likely to lead to public health impact and linking questions with appropriate methods.

Public health communication: Communication and partnership with other public health stakeholders throughout and after the research process.

Education: Incorporating translational skills and engaging public health practitioners as both teachers and students of epidemiology.

Implementation: Conducting efficient epidemiologic inquiry that considers implementation of interventions, including appropriate metrics for program monitoring and evaluation.

Community engagement: Engaging with members of communities to assess and understand public health needs.

As with the underlying themes, these corresponding priority areas are mutually informative and overlapping. For example, asking appropriate research questions can facilitate productive communication and efficient study design, and effective education may include community engagement, helping epidemiologists and other public health practitioners ask better questions. Although definitions of translational epidemiology were proposed in a few articles, no definition posed by a single article was adopted or expanded in a subsequent article.

Engaging science and public health

Dominant concepts in this literature included consequentialism and external validity of causal inference. Although the term translational epidemiology was rarely used, authors frequently cited improvement of population health outcomes as a key goal of epidemiology; this goal was framed alternatively as forging an epidemiology of consequences (i.e., focusing on outcomes that are important to patients and populations) (2, 16–19), or of retaining appropriate focus on external validity/generalizability (i.e., transporting estimates from epidemiologic studies to more general populations) (4, 19–25). These 2 goals of consequentialism and generalizability are distinct but share a concern that epidemiologic research be relevant to population health. Individual preferences for the types of research questions (e.g., strictly causal vs. descriptive) and analytic approaches remain diverse (26). However, consensus generally exists that essential skills of the translational epidemiologist are the ability to ask research questions that have more direct implications for decision-making and to match such questions with methods that are likely to answer such questions appropriately.

Public health communication

In evidence-based public health, or the “conscientious, explicit, and judicious use of current best evidence” (27, p. 459) in making decisions about the health of populations, frequent use is made of knowledge obtained through public health research. In this context, translational epidemiology has been implicitly depicted as comprising those components of evidence-based public health that are contributed by epidemiologists. The literature on evidence-based public health emphasizes the importance of integrating public health research (including epidemiology) with the efforts of other stakeholders (e.g., implementation, law, media) to improve health outcomes or generate more effective policy (9, 28–38). As such, a critical component of translational epidemiology is communication and partnership with other stakeholders in the broader enterprise of evidence-based public health.

Education

It is suggested in the literature on epidemiologic education that specific skills must be more effectively taught for translational epidemiology to succeed (8, 39, 40). These skills include better understanding of health outcomes, knowledge of the policymaking process, and integrated methods that link research and practice. Standalone curricula on translational epidemiology to attract students and engage them in the translation process were highlighted in some studies; in others, the effective teaching of epidemiologic principles to public health practitioners and other stakeholders, including policymakers, was described as a critical component of the translational effort. Thus, to be effective, translational epidemiology must include a strong component of education, closely linking epidemiologists in training with public health practice, and public health stakeholders with training in epidemiology (as teachers and learners).

Implementation

The field of implementation science (or dissemination and implementation research) is growing, and literature on the overlap of implementation science and epidemiology is emerging (41). Implementation research has been defined in the journal Implementation Science as “the scientific study of methods to promote the systematic uptake of research findings . . . into routine practice, and, hence, to improve the quality and effectiveness of health services and care” (42, p. 1). This definition first clarifies the scope of implementation research, then clearly links it to translation; epidemiologists, therefore, are part of a larger cadre of professionals engaging in implementation research. An important component of implementation science is the iterative process of program monitoring and evaluation (43–49). As the knowledge and methods of implementation science evolve, there is an increased appreciation that epidemiologists must understand the mechanisms by which research findings are implemented into practice, and they must evaluate programs according to their ability to improve health outcomes (50). Also important to translational epidemiology are efficiency in epidemiologic research and program evaluation (i.e., maximizing knowledge gained using limited resources) (44, 51). Translational epidemiology, then, should involve efficiently conducting research with an eye toward implementation of potential health interventions.

Community engagement

Definitions of community-engaged research vary but share a focus on community direction of research priorities, intervention design, and implementation (52). Complexities include the difficulty in predicting how research, once translated into interventions, will play out in a given community context (53, 54). Methodologically, community-engaged research can help identify the data and research questions of primary relevance to the community, potentially improving the probability of external validity and providing scientific evidence that is more likely to lead to improved health outcomes in those communities. Therefore, participation and engagement with members of affected communities are also important components of translational epidemiology.

In summary, we identified 5 themes—foundations of epidemiologic thinking, evidence-based public health, epidemiologic education, implementation science/program evaluation, and community-engaged research—through which translational epidemiology has been addressed in the scientific literature. For each theme, we identified a corresponding priority area: engaging science and public health, public health communication, education, implementation, and community engagement. We next construct a conceptual framework of translational epidemiology that is grounded in this literature.

A CONCEPTUAL FRAMEWORK OF TRANSLATIONAL EPIDEMIOLOGY

The 5 areas highlighted by our scoping review comprise a set of skills and activities that help link the field of epidemiology with other disciplines engaged in translating scientific knowledge into improved population health. In Figure ​ Figure2A, 2 A, we present a “linear” view of the role of epidemiology in this process. In this view, epidemiologists identify the outputs of foundational inquiry that are useful to them (presented as diamonds in the figure); apply those theories, methods, and knowledge to the application of research questions; and generate results that may be descriptive, causally framed, or predictive. Other stakeholders must then take these epidemiologic findings (portrayed as pentagons, trapezoids, and right triangles), choose the ones that are useful to them (pentagons, in Figure ​ Figure2A), 2 A), and apply those findings to improve public health. The notable weakness is the lack of linkage between disciplines. Each acts without explicit consideration of how its efforts interact with those of other disciplines, resulting in inefficiency: Most knowledge generated in each step is minimally useful to “downstream” partners. Each discipline must itself identify the most relevant pieces of work from other disciplines, and there exists little feedback from downstream partners to upstream ones. It is not hard to see how such a linear, siloed process could fail to substantively improve population health, despite that being its stated purpose.

A translational view of epidemiology. A) A conceptually linear view of the role of epidemiology in improving population health outcomes. In this admittedly abstracted view, foundational science, epidemiology, and public health stakeholders are independent disciplines, each seeking to identify outputs of the “upstream” disciplines that can be useful but paying relatively little attention to the utility of its outputs for “downstream” disciplines. For example, diamonds might represent statistical methods or philosophical frameworks that prove useful to epidemiologic investigation, and trapezoids might represent epidemiologic findings that are ultimately never used by public health stakeholders. B) A translational view of epidemiology, in which epidemiologists play a more active role in ensuring that knowledge leads to improved health. The distinguishing characteristics of a translational epidemiology are its increased interconnectedness with foundational science and public health stakeholders, feedback at all steps of the process, and increased efficiency of the system in generating improved health outcomes. This goal is achieved by ensuring that the products of each discipline are more useful to other disciplines and more directly delivered to them. A more detailed description of this conceptual framework is provided in the text.

In Figure ​ Figure2B, 2 B, we illustrate how a translational vision of epidemiology could improve the efficiency of this process in generating better health outcomes. In this model, epidemiologists actively engage with members of foundational disciplines and public health stakeholders. These cross-disciplinary linkages are facilitated by activity in the aforementioned 5 priority areas. For example, by engaging science and public health, epidemiologists help foundational disciplines generate more useful outputs (“more diamonds”), deliver those outputs more directly to epidemiologists, and establish feedback loops whereby the results of epidemiologic inquiry and lessons from public health applications can inform more efficient foundational inquiry in the future. By engaging public health stakeholders in the process of epidemiologic education (as both producers and consumers of that education), epidemiologists ensure they generate products more helpful to the improvement of public health (“more pentagons”). By actively engaging with affected communities, epidemiologists learn what questions and outputs are most likely to have the greatest health impact on those communities. By evaluating the implementation of public health programs and communicating results directly to public health stakeholders (including the process of evidence synthesis), epidemiologists ensure the results of epidemiologic inquiry are delivered to public health stakeholders, enabling those stakeholders to appropriately use high-quality epidemiologic outputs to improve the health of individuals and communities. The result of this translational vision of epidemiology is a more interconnected system with stronger links of communication and feedback between each of the involved disciplines that ultimately lead to greater efficiency, greater public faith in the system, and better health outcomes.

TRANSLATIONAL EPIDEMIOLOGY: PRIORITIES IN CRAFTING A WAY FORWARD

To develop a list of priorities for strengthening the enterprise of translational epidemiology, we return to the results of our scoping review. We identified 5 themes from the literature on translational epidemiology and mapped those areas onto 5 key priorities. Table ​ Table2 2 lists these priority areas, expanding each into a statement of a key skill or activity to be emphasized and representative principles that have been highlighted in the reviewed literature.

Table 2.

Priorities for Advancing an Effective Translational Epidemiology

Formulate research questions in a way that distinguishes among description, explanation, prediction, and impact.

The measures used to communicate epidemiologic findings to decision-makers should be meaningful/helpful in answering questions at hand.

Epidemiologic results should be communicated to other stakeholders in a manner that appropriately reflects the magnitude and the certainty of the findings.

Implications of epidemiologic studies should be communicated in the context of what they add to the existing knowledge base.

When communicating the results of epidemiologic studies, it is important to describe the setting in which the study was performed and the degree to which results may or may not generalize to other settings.

Integrate theoretical and applied epidemiologic skills and thinking in teaching traditional epidemiologic principles.

Encourage diverse multidisciplinary collaborations among trainees from different departments/disciplines reflective of translational continuum from discovery to improved population health.

Structure methods courses such that scientific engagement with public health, communication, implementation, and community involvement are a standard part of the curriculum.

Prioritize research questions and inferences that inform strategies for implementation of interventions.

Epidemiologists should understand and study implementation as a process that is likely to occur in stages, rather than an event; corresponding methods should be emphasized.

Formal structures are necessary (e.g., faculty and funding incentives) that support implementation research within the field of epidemiology.

Epidemiologic research questions should reflect the needs of target populations and their corresponding communities.

Epidemiologists should include communities of interest in their research process, receiving and integrating routine feedback from local communities and their people.

To maintain community participation, feedback, and partnership, researchers should provide systematic structure (e.g., funding, scheduled events, dissemination of results) for engaging communities in the research process.

With respect to scientific engagement with public health, manuscripts included in our scoping review highlighted the importance of epidemiologists recognizing the settings in which public health action is likely and clarifying the specific role of any particular research question (i.e., description vs. explanation vs. prediction vs. impact). Descriptive research is useful for assessing public health burden and allocating public health resources. Explanation of how public health interventions do or do not achieve their aims can be used to improve interventions. Prediction can be used to forecast public health needs or support clinical decision-making as precision medicine matures. Impact evaluation can be used to predict and evaluate the intended (and unintended) consequences of specific public health actions. Focusing on actionable questions and providing greater clarity about each study’s purpose and target of inference (e.g., population) are likely to increase stakeholder engagement and uptake of epidemiologic research findings. Such engagement, in turn, can inform the design of more impactful studies.

In terms of public health communication, principles identified in our scoping review included selection of outcome measures more amenable to communication, conveying the importance and certainty of findings, contextualizing results (in terms of existing knowledge and generalizability), and simplifying communication. Examples of effective communication included 1) using actionable metrics (e.g., the increasing importance of the number needed to treat in evidence-based medicine); 2) using language to alert nonexpert stakeholders when effects are either highly uncertain or small in absolute magnitude; 3) conducting value-of-information analysis and decision analysis to depict how new results might or might not help cross existing decision thresholds for intervention; 4) wording the results of epidemiologic investigations in ways likely to be applicable and interpretable to target audiences, especially those outside the field of epidemiology; and 5) highlighting the importance of similar studies in other settings, understanding that many nonexpert audiences may assume generalizability.

Regarding epidemiologic education, leading principles from the scoping review included integration of theoretical and applied approaches, fostering collaboration across disciplines in the educational process, formalizing translational elements in epidemiology curricula, and promoting general awareness of the translational process. Particular emphasis was placed on core epidemiologic methods courses, arguing that the teaching of such skills as communication, implementation, and asking actionable research questions should be integrated with the teaching of standard methods such as management of confounding, bias and missing data. Importantly, many core epidemiologic curricula lack substantive capacity to expand; as such, translational content might be added in other ways (e.g., summary lectures that synthesize material using a translational narrative) that could enhance and integrate students’ appreciation of core concepts and the translational process, without adding courses. Ultimately, these priorities speak to the interdisciplinary nature of epidemiologic education and the need to incorporate not only a breadth of experience among the trainees of epidemiology but also a specific, deeper understanding of how epidemiology as a field interacts with other disciplines.

Coverage of implementation science and program evaluation in the scoping review included principles of prioritizing implementation-relevant questions and inferences, incorporating iterative process-based thinking (including grounded theory, implementation design, and cycles of refinement) into the intellectual framework of epidemiology, and providing structures within the field of epidemiology to support implementation research. Traditionally, epidemiology has focused primarily on causal reasoning and estimation of unbiased measures of association or effect, with less emphasis on processes such as intervention design, program monitoring and evaluation, and dissemination. An effective translational epidemiology would result in more iterative, implementation-focused thinking among epidemiologists, increased development of epidemiologic methods that reflect the implementation process (e.g., pragmatic and adaptive study designs, with corresponding analytical approaches), and professional incentives for epidemiologists who spend time and effort to ensure their findings are effectively implemented into public health practice.

Articles in the scoping review highlighted 3 principles for community engagement with epidemiology, namely the framing of research questions to reflect community needs, ongoing inclusion of community members throughout the research cycle, and development of formal structures and incentives to foster community engagement. Authors emphasized the importance of not simply referencing community engagement but also considering effective engagement of target communities to be a core epidemiologic skill (and incorporating methods of community engagement into educational efforts). It is important to recognize that engaged communities may try to influence the outcomes of epidemiologic research (thus impeding translation), but it is equally important to understand that failure to engage communities may result in research findings that are not meaningful. This is arguably best done in an iterative process whereby communities are educated about the implications of existing research findings and thereby engaged in the process of planning research. Without understanding the sociohistorical and health landscape of affected communities and incorporating those realities into the research process—from asking questions to disseminating results—it is unlikely that epidemiology can meaningfully improve the health of specific community members.

A NOTE OF CAUTION

To this point, we have laid out a vision for translational epidemiology, including themes from the existing literature, a conceptual framework, and priorities for taking the field forward. In doing so, we note that translational epidemiology is not, and should not be, for everyone. There remains an important role for epidemiologists who advance methods and generate knowledge without an explicit eye toward translation, as health implications are often not known at the time of knowledge generation. Well-described dangers also exist when mixing science, policy, and advocacy (55); it is arguably impossible for epidemiologists to remain both fully objective (i.e., open to all courses of action) and fully engaged in translation (i.e., generally choosing 1 course of action). To effectively advance a vision translational epidemiology, we must recognize and grapple with these tensions, understanding that, with each research question, each epidemiologist must choose for herself or himself a location on the spectrum between pure science and pure translation. It is equally important to provide supporting structures for those who wish to pursue translational epidemiology as for those epidemiologists who prefer other paths.

CONCLUSION

In summary, we have used a scoping review and subsequent synthesis of the literature to develop a conceptual framework of translational epidemiology. This framework attempts to shift the view of epidemiology away from that of an isolated discipline that exists in a linear process from knowledge to health, and toward a vision of an interconnected and dynamic process that emphasizes linkages between epidemiology, foundational science, and public health stakeholders. In our scoping review, we identified 5 key themes and priority areas associated with translational epidemiology: 1) scientific engagement with public health, emphasized in articles on the foundations of epidemiology; 2) public health communication, a key element of evidence-based public health; 3) epidemiologic education; 4) epidemiology and implementation (including program monitoring and evaluation); and 5) community engagement. In each of these domains, we have highlighted 2–5 representative principles that could serve as the basis for advancing a vision of translational epidemiology, with the ultimate hope of increasing the efficiency of the current system of translating scientific knowledge into improved health outcomes. We believe an emphasis on translational epidemiology as a key component of the broader epidemiologic endeavor can help achieve this goal.

Supplementary Material

Dowdy_Web_Material_Final_kwz085

ACKNOWLEDGMENT

Author affiliations: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland (Michael Windle, Hojoon D. Lee, Sarah T. Cherng, Catherine R. Lesko, Colleen Hanrahan, John W. Jackson, Mara McAdams-DeMarco, Stephan Ehrhardt, Stefan D. Baral, Gypsyamber D’Souza, David W. Dowdy); and Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland (John W. Jackson).

M.W. and H.D.L. contributed equally to this work.

This work was funded in part by the Translational Epidemiology Initiative of the Department of Epidemiology at Johns Hopkins Bloomberg School of Public Health.

Conflict of interest: none declared.

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