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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.

Nearly every introductory epidemiology course begins with a focus on person, place, and time, the key components of descriptive epidemiology. And yet in our experience, introductory epidemiology courses were the last time we spent any significant amount of training time focused on descriptive epidemiology. This gave us the impression that descriptive epidemiology does not suffer from bias and is less impactful than causal epidemiology. Descriptive epidemiology may also suffer from a lack of prestige in academia and may be more difficult to fund. We believe this does a disservice to the field and slows progress towards goals of improving population health and ensuring equity in health. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak and subsequent coronavirus disease 2019 pandemic have highlighted the importance of descriptive epidemiology in responding to serious public health crises. In this commentary, we make the case for renewed focus on the importance of descriptive epidemiology in the epidemiology curriculum using SARS-CoV-2 as a motivating example. The framework for error we use in etiological research can be applied in descriptive research to focus on both systematic and random error. We use the current pandemic to illustrate differences between causal and descriptive epidemiology and areas where descriptive epidemiology can have an important impact.

In response to the threat posed by the coronavirus disease 2019 (COVID-19) pandemic, many universities are encouraging or requiring online instruction. Teaching an epidemiology course online is different in many respects from teaching in person. In this article, we review specific approaches and strategies related to teaching epidemiology online during the pandemic and beyond, including a discussion of options for course format, grading and assessment approaches, pandemic-related contingencies, and the use of technology. Throughout this article we present practical, epidemiology-specific teaching examples. Moreover, we also examine 1) how the lessons learned about the practice of epidemiology during the pandemic can be integrated into the didactic content of epidemiology training programs and 2) whether epidemiologic pedagogy and teaching strategies should change in the long term, beyond the COVID-19 pandemic. The pandemic has served to heighten our awareness of concerns related to student health and safety, as well as issues of accessibility, equity, and inclusion. Our goal is to present a practical overview connecting pandemic-era online teaching with thoughts about the future of epidemiologic instruction.

In recent years, increasing attention has been paid to problems of external validity, specifically to methodological approaches for both quantitative generalizability and transportability of study results. However, most approaches to these issues have considered external validity separately from internal validity. Here we argue that considering either internal or external validity in isolation may be problematic. Further, we argue that a joint measure of the validity of an effect estimate with respect to a specific population of interest may be more useful: We call this proposed measure target validity. In this work, we introduce and formally define target bias as the total difference between the true causal effect in the target population and the estimated causal effect in the study sample, and target validity as target bias = 0. We illustrate this measure with a series of examples and show how this measure may help us to think more clearly about comparisons between experimental and nonexperimental research results. Specifically, we show that even perfect internal validity does not ensure that a causal effect will be unbiased in a specific target population.

Accurate, routine measurement of recent illicit substance use is challenging. The Johns Hopkins Human Immunodeficiency Virus Clinical Cohort (Baltimore, Maryland) collects 2 imperfect but routine measurements of recent substance use: medical record review and self-interview. We used Bayesian latent class modeling to estimate sensitivity and specificity of each measurement as well as prevalence of substance use among 2,064 patients engaged in care during 2007-2015. Sensitivity of medical record review was higher than sensitivity of self-interview for cocaine and heroin use; posterior estimates ranged from 44% to 76% for cocaine use and from 39% to 67% for heroin use, depending on model assumptions and priors. In contrast, sensitivity of self-interview was higher than sensitivity of medical record review for any alcohol use, hazardous alcohol use, and cigarette smoking. Posterior estimates of sensitivity of self-interview were generally above 80%, 85%, and 87% for each substance, respectively. Specificity was high for all measurements. From one model, we estimated prevalence of substance use in the cohort to be 12.5% for cocaine, 9.3% for heroin, 48.5% for alcohol, 21.4% for hazardous alcohol, and 55.4% for cigarettes. Prevalence estimates from other models were generally comparable. Measurement error of substance use is nontrivial and should be accounted for in subsequent analyses.

Depression affects up to 30% of human immunodeficiency virus (HIV)-infected individuals. We estimated joint effects of antiretroviral therapy (ART) initiation and depressive symptoms on time to death using a joint marginal structural model and data from a cohort of HIV-infected women from the Women's Interagency HIV Study (conducted in the United States) from 1998–2011. Among 848 women contributing 6,721 years of follow-up, 194 participants died during follow-up, resulting in a crude mortality rate of 2.9 per 100 women-years. Cumulative mortality curves indicated greatest mortality for women who reported depressive symptoms and had not initiated ART. The hazard ratio for depressive symptoms was 3.38 (95% confidence interval (CI): 2.15, 5.33) and for ART was 0.47 (95% CI: 0.31, 0.70). Using a reference category of women without depressive symptoms who had initiated ART, the hazard ratio for women with depressive symptoms who had initiated ART was 3.60 (95% CI: 2.02, 6.43). For women without depressive symptoms who had not started ART, the hazard ratio was 2.36 (95% CI: 1.16, 4.81). Among women reporting depressive symptoms who had not started ART, the hazard ratio was 7.47 (95% CI: 3.91, 14.3). We found a protective effect of ART initiation on mortality, as well as a harmful effect of depressive symptoms, in a cohort of HIV-infected women.

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

Traditional epidemiologic approaches allow us to compare counterfactual outcomes under 2 exposure distributions, usually 100% exposed and 100% unexposed. However, to estimate the population health effect of a proposed intervention, one may wish to compare factual outcomes under the observed exposure distribution to counterfactual outcomes under the exposure distribution produced by an intervention. Here, we used inverse probability weights to compare the 5-year mortality risk under observed antiretroviral therapy treatment plans to the 5-year mortality risk that would had been observed under an intervention in which all patients initiated therapy immediately upon entry into care among patients positive for human immunodeficiency virus in the US Centers for AIDS Research Network of Integrated Clinical Systems multisite cohort study between 1998 and 2013. Therapy-naïve patients (n = 14,700) were followed from entry into care until death, loss to follow-up, or censoring at 5 years or on December 31, 2013. The 5-year cumulative incidence of mortality was 11.65% under observed treatment plans and 10.10% under the intervention, yielding a risk difference of -1.57% (95% confidence interval: -3.08, -0.06). Comparing outcomes under the intervention with outcomes under observed treatment plans provides meaningful information about the potential consequences of new US guidelines to treat all patients with human immunodeficiency virus regardless of CD4 cell count under actual clinical conditions.

Decision-making requires choosing from treatments on the basis of correctly estimated outcome distributions under each treatment. In the absence of randomized trials, 2 possible approaches are the parametric g-formula and agent-based models (ABMs). The g-formula has been used exclusively to estimate effects in the population from which data were collected, whereas ABMs are commonly used to estimate effects in multiple populations, necessitating stronger assumptions. Here, we describe potential biases that arise when ABM assumptions do not hold. To do so, we estimated 12-month mortality risk in simulated populations differing in prevalence of an unknown common cause of mortality and a time-varying confounder. The ABM and g-formula correctly estimated mortality and causal effects when all inputs were from the target population. However, whenever any inputs came from another population, the ABM gave biased estimates of mortality-and often of causal effects even when the true effect was null. In the absence of unmeasured confounding and model misspecification, both methods produce valid causal inferences for a given population when all inputs are from that population. However, ABMs may result in bias when extrapolated to populations that differ on the distribution of unmeasured outcome determinants, even when the causal network linking variables is identical.

Among persons with human immunodeficiency virus (HIV) infection, illegal drug use and hazardous alcohol use are hypothesized to be strong risk factors for failure to achieve or maintain a suppressed HIV viral load, but accurate quantification of this association is difficult because of challenges involved in measuring substance use as part of routine clinical care. We estimated the associations of recent cocaine use, opioid/heroin use, and hazardous alcohol use with unsuppressed viral load among 1,554 persons receiving care at the John G. Bartlett Specialty Practice (Baltimore, Maryland) between 2013 and 2017. We accounted for measurement error in substance use using Bayesian models and prior estimates of the sensitivity and specificity of 2 imperfect measures of substance use derived from a previous analysis in this cohort. The prevalence difference for unsuppressed viral load associated with recent cocaine use was 11.3% (95% credible interval (CrI): 6.4, 17.0); that associated with recent opioid/heroin use was 13.2% (95% CrI: 6.6, 20.7); and that associated with recent hazardous alcohol use was 8.5% (95% CrI: 3.2, 14.4). Failure to account for measurement error resulted in clinically meaningful underestimates of the prevalence difference. Time-varying substance use is prevalent and difficult to measure in routine care; here we demonstrate a method that improves the utility of imperfect data by accounting for measurement error.

Increasingly, the statistical and epidemiologic literature is focusing beyond issues of internal validity and turning its attention to questions of external validity. Here, we discuss some of the challenges of transporting a causal effect from a randomized trial to a specific target population. We present an inverse odds weighting approach that can easily operationalize transportability. We derive these weights in closed form and illustrate their use with a simple numerical example. We discuss how the conditions required for the identification of internally valid causal effects are translated to apply to the identification of externally valid causal effects. Estimating effects in target populations is an important goal, especially for policy or clinical decisions. Researchers and policy-makers should therefore consider use of statistical techniques such as inverse odds of sampling weights, which under careful assumptions can transport effect estimates from study samples to target populations.