On the Need to Revitalize Descriptive Epidemiology.

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.

Code Review as a Simple Trick to Enhance Reproducibility, Accelerate Learning, and Improve the Quality of Your Team's Research.

Programming for data wrangling and statistical analysis is an essential technical tool of modern epidemiology, yet many epidemiologists receive limited formal training in strategies to optimize the quality of our code. In complex projects, coding mistakes are easy to make, even for skilled practitioners. Such mistakes can lead to invalid research claims that reduce the credibility of the field. Code review is a straightforward technique used by the software industry to reduce the likelihood of coding bugs. The systematic implementation of code review in epidemiologic research projects could not only improve science but also decrease stress, accelerate learning, contribute to team building, and codify best practices. In the present article, we argue for the importance of code review and provide some recommendations for successful implementation for 1) the research laboratory, 2) the code author (the initial programmer), and 3) the code reviewer. We outline a feasible strategy for implementation of code review, though other successful implementation processes are possible to accommodate the resources and workflows of different research groups, including other practices to improve code quality. Code review isn't always glamorous, but it is critically important for science and reproducibility. Humans are fallible; that's why we need code review.

Will Podcasting and Social Media Replace Journals and Traditional Science Communication? No, but...

The digital world in which we live is changing rapidly. The evolving media environment is having a direct impact on traditional forms of communication and knowledge translation in public health and epidemiology. Openly accessible digital media can be used to reach a broader and more diverse audience of trainees, scientists, and the lay public than can traditional forms of scientific communication. The new digital landscape for delivering content is vast, and new platforms are continuously being added. In this article, we focus on several, including Twitter and podcasting, and discuss their relevance to epidemiology and science communication. We highlight 3 key reasons why we think epidemiologists should be engaging with these mediums: 1) science communication, 2) career advancement, and 3) development of a community and public service. Other positive and negative consequences of engaging in these forms of new media are also discussed. The authors of this commentary are all engaged in social media and podcasting for scientific communication, and we reflect on our experiences with these mediums as tools to advance the field of epidemiology.

National Psoriasis Foundation COVID-19 Task Force Guidance for Management of Psoriatic Disease During the Pandemic: Version 1.

OBJECTIVE: To provide guidance about management of psoriatic disease during the coronavirus disease 2019 (COVID-19) pandemic. STUDY DESIGN: A task force (TF) of 18 physician voting members with expertise in dermatology, rheumatology, epidemiology, infectious diseases, and critical care was convened. The TF was supplemented by nonvoting members, which included fellows and National Psoriasis Foundation (NPF) staff. Clinical questions relevant to the psoriatic disease community were informed by questions received by the NPF. A Delphi process was conducted. RESULTS: The TF approved 22 guidance statements. The average of the votes was within the category of agreement for all statements. All guidance statements proposed were recommended, 9 with high consensus and 13 with moderate consensus. LIMITATIONS: The evidence behind many guidance statements is limited in quality. CONCLUSION: These statements provide guidance for the management of patients with psoriatic disease on topics ranging from how the disease and its treatments impact COVID-19 risk and outcome, how medical care can be optimized during the pandemic, what patients should do to lower their risk of getting infected with severe acute respiratory syndrome coronavirus 2 and what they should do if they develop COVID-19. The guidance is intended to be a living document that will be updated by the TF as data emerge.

The Future of Observational Epidemiology: Improving Data and Design to Align With Population Health.

Improvements in data resources and computational power provide important opportunities to ensure the continued relevance and growth of observational epidemiology. To achieve that promise, rigorous statistical analyses are important but not sufficient. We must prioritize articulating relevant research questions and developing strong study designs. Relevance depends on designing observational research so it delivers actionable clinical or population health evidence. Expanding data sources, including administrative records and data from emerging technologies such as sensors, can potentially be leveraged to improve study design, statistical power, measurement, and availability of evidence on diverse populations. With these advantages, particularly evidence on the heterogeneity of treatment effects, observational research can better guide design of randomized trials. Evidence on the heterogeneity of treatment effects is also essential to extend the evidence from randomized trials beyond the narrow range of settings and populations for which trials have been conducted. Machine learning tools will likely grow in importance in observational epidemiology in coming years, although we need careful attention to the appropriate uses of prediction models. Despite the potential of these innovations, they will only be useful if embedded in theoretical frameworks motivated by applied clinical and population health questions.

A Future for Observational Epidemiology: Clarity, Credibility, Transparency.

Observational studies are ambiguous, difficult, and necessary for epidemiology. Presently, there are concerns that the evidence produced by most observational studies in epidemiology is not credible and contributes to research waste. I argue that observational epidemiology could be improved by focusing greater attention on 1) defining questions that make clear whether the inferential goal is descriptive or causal; 2) greater utilization of quantitative bias analysis and alternative research designs that aim to decrease the strength of assumptions needed to estimate causal effects; and 3) promoting, experimenting with, and perhaps institutionalizing both reproducible research standards and replication studies to evaluate the fragility of study findings in epidemiology. Greater clarity, credibility, and transparency in observational epidemiology will help to provide reliable evidence that can serve as a basis for making decisions about clinical or population-health interventions.

On Being an Epidemiologist.

This commentary addresses being an epidemiologist at a time when the field of epidemiology again faces a paradigm shift as the tools for research on human health draw increasingly on emerging technologies-various omics and new methods for collecting individual data at high intensity-and on new methods for carrying out research through administrative and health care data bases linked to biobanks. At the same time, epidemiologists in public health practice continue to face the usual array of challenges, but with the threat of global issues as well. The profession of epidemiologist is not monolithic; it embraces a broad range of activities and professional venues, reflecting the many contexts where epidemiologists do their work, the nature of the problems addressed, and the span and application of their findings. Obtaining data from people and populations comes with serious ethical obligations related to privacy and confidentiality of individuals and respect for the populations studied. Beyond generating and analyzing data, epidemiologists engage in guiding action through the evidence they have created with the ultimate goal of advancing health. This commentary addresses "mega-trends" that will affect the profession in the decades to come.

Epidemiology: Back to the Future.

In 2018, the Society for Epidemiologic Research and its partner journal, the American Journal of Epidemiology, assembled a working group to develop a set of papers devoted to the "future of epidemiology." These 14 papers covered a wide range of topic areas and perspectives, from thoughts on our profession, teaching, and methods to critical areas of substantive research. The authors of those papers considered current challenges and future opportunities for research and education. In light of past commentaries, 4 papers also include reflections on the discipline at present and in the future.

Applied Epidemiology Training Needs for the Modern Epidemiologist.

Applied epidemiology training occurs throughout an epidemiologist's career, beginning with academic instruction before workforce entry, continuing as professional development while working, and culminating with mentoring the next generation. Epidemiologists need ongoing training on advancements in the field and relevant topics (e.g., informatics, laboratory science, emerging topics) to maintain and improve their skills. Even epidemiologists with advanced skills often want training on methodologic innovations or to practice a skill. Effective applied epidemiology training includes blended learning components of instruction that incorporate hands-on experiences such as simulations and experiential learning, allowing for real-time workflows and incorporation of feedback. To prepare epidemiologists for the future, public health training courses in applied epidemiology must consider the evolution in public health toward a focus on including informatics, technologic innovation, molecular epidemiology, multidisciplinary teams, delivery of population health services, and global health security. Supporting efforts by epidemiologists to increase their skills as part of their career paths ensures a strong workforce that able to tackle public health issues. We explore how to meet current training challenges for the epidemiology workforce, especially given limited resources, based on research and our experience in workforce development across federal agencies and state/local health departments, as well as with international governments and organizations.

The pressing need for standardization in epidemiologic studies of PCOS across the globe.

The polycystic ovary syndrome (PCOS) is a common and important complex endocrine metabolic disorder affecting women mainly in the reproductive age. The prevalence of the disorder varies depending on the epidemiologic design and criterion used to study the disease. This variation in methodology and subsequent effect on epidemiologic estimate makes it difficult to compare prevalences and phenotypes across geographical areas and assess the effect of cultural and racial variations on PCOS phenotypes. Overall, there is an urgent need for a globally accepted standardized protocol for epidemiologic studies of PCOS, which will maximize the comparability of studies around the globe. To address this issue the Androgen Excess and PCOS Society, Inc. has designated an expert Task Force to draft recommendations to guide epidemiologic research worldwide. Once completed, the use of such recommendations will enable epidemiologists to the effects of geographical and cultural variations of PCOS prevalence and assist in determining the phenotype-genotype associations in the disorder. Further, it will assist in developing informed, and thus effective, public health policy. In essence, the need to standardize epidemiologic studies across the globe is pressing and urgent.

Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors.

BACKGROUND: Multiple imputation by chained equations (MICE) requires specifying a suitable conditional imputation model for each incomplete variable and then iteratively imputes the missing values. In the presence of missing not at random (MNAR) outcomes, valid statistical inference often requires joint models for missing observations and their indicators of missingness. In this study, we derived an imputation model for missing binary data with MNAR mechanism from Heckman's model using a one-step maximum likelihood estimator. We applied this approach to improve a previously developed approach for MNAR continuous outcomes using Heckman's model and a two-step estimator. These models allow us to use a MICE process and can thus also handle missing at random (MAR) predictors in the same MICE process. METHODS: We simulated 1000 datasets of 500 cases. We generated the following missing data mechanisms on 30% of the outcomes: MAR mechanism, weak MNAR mechanism, and strong MNAR mechanism. We then resimulated the first three cases and added an additional 30% of MAR data on a predictor, resulting in 50% of complete cases. We evaluated and compared the performance of the developed approach to that of a complete case approach and classical Heckman's model estimates. RESULTS: With MNAR outcomes, only methods using Heckman's model were unbiased, and with a MAR predictor, the developed imputation approach outperformed all the other approaches. CONCLUSIONS: In the presence of MAR predictors, we proposed a simple approach to address MNAR binary or continuous outcomes under a Heckman assumption in a MICE procedure.

Conceptualizing ethnicity in alcohol and drug research: Epidemiology meets social theory.

Since the 1990s, social scientists have rejected notions of ethnicity as something static and discrete, instead highlighting the context-dependent and fluid nature of multiple identities. In spite of these developments, researchers within the substance use fields continue to assess ethnic group categories in ways that suggest little critical reflection in terms of the validity of the measurements themselves, nor the social, bureaucratic, and political decisions shaping standard measures of ethnicity. This paper highlights these considerations, while also acknowledging the role of socially-delineated ethnic categorizations in documenting health inequities and social injustices. We call on researchers in alcohol and drugs research to critically appraise their use of ethnic categorizations, querying how to best measure ethnicity within their own studies in ways that are justified beyond simplified explanations of social convention and that "do no harm" in terms of perpetuating racism and obscuring the roots causes of social and health problems related to alcohol and drugs.

Frontline Field Epidemiology Training Programs as a Strategy to Improve Disease Surveillance and Response.

Since 1980, Field Epidemiology Training Programs (FETPs) have trained highly qualified field epidemiologists to work for ministries of health (MOH) around the world. However, the 2013-2015 Ebola epidemic in West Africa, which primarily affected Guinea, Liberia, and Sierra Leone, demonstrated a lack of field epidemiologists at the local levels. Trained epidemiologists at these levels could have detected the Ebola outbreak earlier. In 2015, the US Centers for Disease Control and Prevention (CDC) launched FETP-Frontline, a 3-month field training program targeting local MOH staff in 24 countries to augment local public health capacity. As of December 2016, FETP-Frontline has trained 1,354 graduates in 24 countries. FETP-Frontline enhances global health security by training local public health staff to improve surveillance quality in their jurisdictions, which can be a valuable strategy to strengthen the capacity of countries to more rapidly detect, respond to, and contain public health emergencies at the source.

Invited Commentary: Continuing to Loosen the Constraints on Epidemiology in an Age of Change-A Comment on McMichael's "Prisoners of the Proximate".

Published in 1999, McMichael's "Prisoners of the Proximate: Loosening the Constraints on Epidemiology in an Age of Change" (Am J Epidemiol. 1999;149(10):887-897) outlined an exciting vision for epidemiology as the field that that can help us better understand the drivers of population health so that we may intervene, paving the way for healthier populations. McMichael's paper remains today what it was when it was first published: clear, thoughtful, provocative, and usefully prescriptive in its call to action. McMichael identified 4 constraints facing epidemiology. The field has risen to this challenge and addressed some of these constraints. I discuss how successful (or not) we have been in addressing each of these 4 challenges.

[Ways of improvement of military epidemiology as a diagnosis and prophylaxis branch of military medicine].

Ways of improvement of military epidemiology as a diagnosis and prophylaxis branch of military medicine. The prospects of development of epidemiological science, training systems and improvement of practice in preventive medicine based on the integration process are considered. The authors emphasize a final recognition of epidemiology as a single diagnostic and preventive discipline the subjects of which are manifestations of epidemic diseases regardless aetiology. An integration of specialists in prophylaxis requires correction of the system of military-medical education and improvement of preventive medical support subsystem of (naval forces). The prospects of inclusion in the educational and scientific processes of the epidemiology of non-communicable diseases, improve the interaction of different structures of military medical services in the field of infectious disease prevention personnel.

[Good Practice of Secondary Data Analysis (GPS): guidelines and recommendations]. / Gute Praxis Sekundärdatenanalyse (GPS): Leitlinien und Empfehlungen.

In 2005, the Working Group for the Survey and Utilisation of Secondary Data (AGENS) of the German Society for Social Medicine and Prevention (DGSMP) and the German Society for Epidemiology (DGEpi) first published "Good Practice in Secondary Data Analysis (GPS)" formulating a standard for conducting secondary data analyses. GPS is intended as a guide for planning and conducting analyses and can provide a basis for contracts between data owners. The domain of these guidelines does not only include data routinely gathered by statutory health insurance funds and further statutory social insurance funds, but all forms of secondary data. The 11 guidelines range from ethical principles and study planning through quality assurance measures and data preparation to data privacy, contractual conditions and responsible communication of analytical results. They are complemented by explanations and practical assistance in the form of recommendations. GPS targets all persons directing their attention to secondary data, their analysis and interpretation from a scientific point of view and by employing scientific methods. This includes data owners. Furthermore, GPS is suitable to assess scientific publications regarding their quality by authors, referees and readers. In 2008, the first version of GPS was evaluated and revised by members of AGENS and the Epidemiological Methods Working Group of DGEpi, DGSMP and GMDS including other epidemiological experts and had then been accredited as implementation regulations of Good Epidemiological Practice (GEP). Since 2012, this third version of GPS is on hand and available for downloading from the DGEpi website at no charge. Especially linguistic specifications have been integrated into the current revision; its internal consistency was increased. With regards to contents, further recommendations concerning the guideline on data privacy have been added. On the basis of future developments in science and data privacy, further revisions will follow.

[Sanitary and epidemiological supply for the Russian Army during the First World War (1914-1918)].

At the beginning of the First World War the most typical diseases in the Russian Army were typhoid, typhus, diphtheria, cholera, smallpox and other infectious diseases. At the beginning of the First World War the level of infectious morbidity was significantly low, but further increased and pandemic risk arose. Servicemen were mostly ill with typhus, relapsing fever, flux, cholera, smallpox and typhoid. The highest mortality rate was registered in patients with cholera, typhus and typhoid. According the prewar deployment program of the Russian Army anti-epidemiologic facilities were established. By the end of war were established 110 sanitary-and-hygienic and 90 disinfection units. However, organization of anti-epidemiologic security was unsatisfactory. Due to lack of specialists and equipment anti-epidemiologic facilities of units were under strength. Commanders of sanitary units and sanitary service had not enough resources for operational service in the Forces and facilities of rear area.