Search

The annual meeting of the Society for Epidemiologic Research (SER) is a major forum for sharing new research and promoting the career development of participants. Because of this, evaluating representation in key presentation formats is critical. For the 3,257 presentations identified at the 2015-2017 SER annual meetings, we evaluated presenter characteristics, including gender, affiliation, subject area, and h-index, and representation in 3 highlighted presentation formats: platform talks (n = 382), invited symposium talks (n = 273), and chairing a concurrent contributed session or symposium (n = 188). Data were abstracted from SER records, abstract booklets, and programs. Gender was assessed using GenderChecker software, and h-index was determined using the Scopus application programming interface. Log-binomial models were adjusted for participant characteristics and conference year. In adjusted models, women were less likely than men to present an invited symposium talk (relative risk = 0.60, 95% confidence interval: 0.45, 0.81) compared with all participants with accepted abstracts. Researchers from US public universities, US government institutions, and international institutions were less likely to present a symposium talk or to chair a concurrent contributed session or symposium than were researchers from US private institutions. The research areas that were most represented in platform talks were epidemiologic methods, social epidemiology, and cardiovascular epidemiology. Our findings suggest differences in representation by gender, affiliation, and subject area after accounting for h-index., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Although every emerging infectious disease occurs in a unique context, the behaviour of previous pandemics offers an insight into the medium- and long-term outcomes of the current threat. Where an informative historical analogue exists, epidemiologists and policymakers should consider how the insights of the past can inform current forecasts and responses.

Interpreting the results of epidemiologic studies calls for objectivity and rigorous scrutiny, acknowledging the limitations that temper the applicability of the findings to public health action. Current trends have posed new challenges to balancing goals of scientific objectivity and validity with public health applications. The ongoing tension between epidemiology's aspirations and capability has several sources: the need to overpromise in research proposals, compromising methodological rigor because of public health importance, defending findings in the face of hostile critics, and appealing to core constituencies who have specific expectations from the research., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Religion and spirituality are important social determinants that drive public health practice. The field of epidemiology has played a vital role in answering long-standing questions about whether religion is causally associated with health and mortality. As epidemiologists spark new conversations (e.g., see Kawachi (Am J Epidemiol. (https://doi.org/10.1093/aje/kwz204)) and Chen and VanderWeele (Am J Epidemiol. 2018;187(11):2355-2364)) about methods (e.g., outcomes-wide analysis) used to establish causal inference between religion and health, epidemiologists need to engage with other aspects of the issue, such as emerging trends and historical predictors. Epidemiologists will need to address 2 key aspects. The first is changing patterns in religious and spiritual identification. Specifically, how do traditional mechanisms (e.g., social support) hold up as explanations for religion-health associations now that more people identify as spiritual but not religious and more people are not attending religious services in physical buildings? The second is incorporation of place into causal inference designs. Specifically, how do we establish causal inference for associations between area-level constructs of the religious environment (e.g., denomination-specific church membership/adherence rates) and individual- and population-level health outcomes?, (© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Over the past century, the field of epidemiology has evolved and adapted to changing public health needs. Challenges include newly emerging public health concerns across broad and diverse content areas, new methods, and vast data sources. We recognize the need to engage and educate the next generation of epidemiologists and prepare them to tackle these issues of the 21st century. In this commentary, we suggest a skeleton framework upon which departments of epidemiology should build their curriculum. We propose domains that include applied epidemiology, biological and social determinants of health, communication, creativity and ability to collaborate and lead, statistical methods, and study design. We believe all students should gain skills across these domains to tackle the challenges posed to us. The aim is to train smart thinkers, not technicians, to embrace challenges and move the expanding field of epidemiology forward., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2020. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Introduction: Determinants and drivers for emergencies, such as political instability, weak health systems, climate change and forcibly displaced populations, are increasing the severity, complexity and frequency of public health emergencies. As emergencies become more complex, it is increasingly important that the required skillset of the emergency response workforce is clearly defined. To enable essential epidemiological activities to be implemented and managed during an emergency, a workforce is required with the right mix of skills, knowledge, experience and local context awareness. This study aims to provide local and international responders with an opportunity to actively contribute to the development of new thinking around emergency response roles and required competencies. In this study, we will develop recommendations using a broad range of evidence to address identified lessons and challenges so that future major emergency responses are culturally and contextually appropriate, and less reliant on long-term international deployments., Method and Analysis: We will conduct a mixed-methods study using an exploratory sequential study design. The integration of four data sources, including key informant interviews, a scoping literature review, survey and semistructured interviews will allow the research questions to be examined in a flexible, semistructured way, from a range of perspectives. The study is unequally weighted, with a qualitative emphasis. We will analyse all activities as individual components, and then together in an integrated analysis. Thematic analysis will be conducted in NVivo V.11 and quantitative analysis will be conducted in Stata V.15., Ethics and Dissemination: All activities have been approved by the Science and Medical Delegated Ethics Review Committee at the Australian National University (protocol numbers 2018-521, 2018-641, 2019-068). Findings will be disseminated through international and local deployment partners, peer-reviewed publication, presentation at international conferences and through social media such as Twitter and Facebook., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Epidemiologic methods have advanced tremendously in the last several decades. As important as they are, even the most sophisticated approaches are unable to provide meaningful answers when the user lacks a clear study question. Yet, instructors have more and more resources on how to conduct studies and analyze data but few resources on how to ask clearly defined study questions that will guide those methods. Training programs have limited time for coursework, and if novel statistical estimation methods become the focus of instruction, programs that go this route may end up underemphasizing the process of asking good study questions, designing robust studies, considering potential biases in the collected data, and appropriately interpreting the results of the analysis. Given the demands for space in curricula, now is an appropriate time to reevaluate what we teach epidemiology doctoral students. We advocate that programs place a renewed focus on asking good study questions and following a comprehensive approach to study design and data analysis in which questions guide the choice of appropriate methods, helping us avoid methods for methods' sake and highlighting when application of a new method can provide the opportunity to answer questions that were intractable with traditional approaches., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post-Modern Epidemiology world., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

In epidemiologic analytical studies, the primary goal is to obtain a valid and precise estimate of the effect of the exposure of interest on a given outcome in the population under study. A crucial source of violation of the internal validity of a study involves bias arising from confounding, which is always a challenge in observational research, including life course epidemiology. The increasingly popular approach of meta-analyzing individual participant data from several observational studies also brings up to discussion the problem of confounding when combining data from different populations. In this study, we review and discuss the most common sources of confounding in life course epidemiology: (i) confounding by indication, (ii) impact of baseline selection on confounding, (iii) time-varying confounding and (iv) mediator-outcome confounding. We also discuss the issue of addressing confounding in the context of an individual participant data meta-analysis.

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89)., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2019.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Epidemiology has always filled a unique space. It lies squarely at the intersection of the social and biological sciences as well as at the intersection of knowledge generation and the translation of that knowledge into actions. Today, new data sources, new methods, and continued population health problems create opportunities and challenges for epidemiology. In this commentary, 4 areas of opportunity for epidemiology are reviewed: 1) the continued value of precise description; 2) a rigorous yet broad and practical approach to drawing conclusions about causes; 3) embracing methodological diversity; and 4) retaining a strong connection to public health practice and policy., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Epidemiology has long been concerned with understanding the causes of health and disease states so that we can improve the health of populations. Despite broad agreement on this definition of the field, we continue to debate certain core goals of epidemiology: whether epidemiology is a pragmatic science or not, which methods constitute epidemiologic methods, and what our gold-standard thinking should be to understand causation. We suggest that recognizing epidemiology as the quantitative heart of population health science can push these tensions aside and allow us to focus our science on the health of populations and on the processes that shape that health. Seeing epidemiology as the core quantitative health science has implications for the questions we ask, how we organize ourselves as a field, and how we train the next generation of epidemiologists., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The rapid pace of technological advancements and the corresponding societal innovations and adaptations make it difficult to predict how teaching epidemiology will look in the coming decades. We discuss changes in the teaching of epidemiology that are currently unfolding. First, typical epidemiology curricula often lack formal instruction in important components of causal thinking, such as the formulation of well-defined research questions. We address gaps related to causal thinking, communication about our science, and interpretation of study results, and we make suggestions of specific content to close such gaps. Second, digital technology increasingly influences epidemiology instruction. We discuss classroom and online teaching modalities in terms of challenges and advantages., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Epidemiology is the study of epidemics. It is a biological science that includes expertise in many disciplines in social and behavioral sciences. Epidemiology is also a key component of preventive medicine and public health. Unfortunately, over recent years, academic epidemiology has lost its relationship with preventive medicine, as well as much of its focus on epidemics. The new "-omics" technologies to measure risk factors and phenotypes, and advances in genomics (e.g., host susceptibility) consistent with good epidemiology methods will likely enhance epidemiology research. There is a need based on these new technologies to modify training, especially for the first-level doctorate epidemiologist., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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.

Background: The evaluation of imprecision is a key dimension of the grading of the confidence in the estimate. Grading of Recommendations Assessment, Development and Evaluation (GRADE) gives recommendations on how to downgrade evidence for imprecision, but authors vary in their use. Trial Sequential Analysis (TSA) has been advocated for a more reliable assessment of imprecision. We aimed to evaluate reporting of and adherence to GRADE and to compare the assessment of imprecision of intervention effects assessed by GRADE and TSA in Cochrane systematic reviews., Methods: In this cross-sectional study, we included 100 Cochrane reviews irrespective of type of intervention with a key dichotomous outcome meta-analyzed and assessed by GRADE. The methods and results sections of each review were assessed for adequacy of imprecision evaluation. We re-analyzed imprecision following the GRADE Handbook and the TSA Manual., Results: Overall, only 13.0% of reviews stated the criteria they applied to assess imprecision. The most common dimensions were the 95% width of the confidence intervals and the optimal information size. Review authors downgraded 48.0% of key outcomes due to imprecision. When imprecision was re-analyzed following the GRADE Handbook, 64% of outcomes were downgraded. Agreement between review authors' assessment and assessment by the authors of this study was moderate (kappa 0.43, 95% confidence interval [CI] 0.23 to 0.58). TSA downgraded 69.0% outcomes due to imprecision. Agreement between review authors' GRADE assessment and TSA, irrespective of downgrading levels, was moderate (kappa 0.43, 95% CI 0.21 to 0.57). Agreement between our GRADE assessment following the Handbook and TSA was substantial (kappa 0.66, 95% CI 0.49 to 0.79)., Conclusions: In a sample of Cochrane reviews, methods for assessing imprecision were rarely reported. GRADE according to Handbook guidelines and TSA led to more severe judgment of imprecision rather than GRADE adopted by reviews' authors. Cochrane initiatives to improve adherence to GRADE Handbook are warranted. TSA may transparently assist in such development.

More than ever, competent field epidemiologists are needed worldwide. As known, new, and resurgent communicable diseases increase their global impact, the International Health Regulations and the Global Health Security Agenda call for sufficient field epidemiologic capacity in every country to rapidly detect, respond to, and contain public health emergencies, thereby ensuring global health security. To build this capacity, for >35 years the US Centers for Disease Control and Prevention has worked with countries around the globe to develop Field Epidemiology Training Programs (FETPs). FETP trainees conduct surveillance activities and outbreak investigations in service to ministry of health programs to prevent and control infectious diseases of global health importance such as polio, cholera, tuberculosis, HIV/AIDS, malaria, and emerging zoonotic infectious diseases. FETP graduates often rise to positions of leadership to direct such programs. By training competent epidemiologists to manage public health events locally and support public health systems nationally, health security is enhanced globally.

Did you ever hear about epidemiology in Germany? Starting from an epidemiological desert the discipline has grown remarkably, especially during the last 10-15 years: research institutes have been established, research funding has improved, multiple curriculae in Epidemiology and Public Health are offered. This increase has been quite steep, and now the epidemiological infrastructure is much better. Several medium-sized and even big population cohorts are ongoing, and the number and quality of publications from German epidemiologists has reached a respectable level. My own career in epidemiology started in the field of environmental health. After German reunification I concentrated for many years on environmental problems in East Germany and observed the health benefits after improvement of the situation. Later, I concentrated on population-based cohorts in newborns (GINI/LISA) and adults (KORA, German National Cohort), and on biobanking. This Essay describes the development in Germany after worldwar 2, illustrated by examples of research results and build-up of epidemiological infractructures worth mentioning.

An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

The emergence and re-emergence of infectious diseases highlights the need to have well-trained field epidemiologists who will be at the forefront in the fight against these diseases, especially during an outbreak. Training for outbreak investigation is most effective when participants can develop their competencies in a practical exercise. To that end, this case study was based on a measles outbreak investigation conducted in Liberia during October 2015 by Liberia Frontline Field Epidemiology Training Program (FETP) residents, simulating steps to perform outbreak investigation in a real-life situation as a field epidemiologist. This case study is ideally suited to reinforce principles and skills already covered in a classroom lecture or in background reading by providing a practical training beyond the scope of theoretical learning. It is primarily intended for training novice public health practitioners who should be able to complete the exercises in approximately 3 hours., Competing Interests: The authors declare no competing interest.

This case study is adapted from events that occurred along the Sierra Leone and Guinea land border during the 2014-2016 Ebola epidemic in West Africa. The response activities involved Sierra Leone and Guinea officials, along with assistance from U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organisation (WHO). This case study builds upon an understanding of basic surveillance systems and outbreak response activities. Through this exercise, students will understand how to incorporate communication and coordination into surveillance and response efforts with counterparts across the border in neighbouring countries. This integration is important to reduce the spread of communicable diseases between neighbouring countries. The time required to complete this case study is 2-3 hours., Competing Interests: The authors declare no competing interest.

Cholera is among the re-emerging diseases in Kenya. Beginning in December 2014, a persistent outbreak occurred involving 29 out of the 47 countries. Homa Bay County in Western Kenya was among the first counties to report cholera cases from January to April 2015. This case study is based on an outbreak investigation conducted by FELTP residents in Homa Bay County in February 2015. It simulates an outbreak investigation including laboratory confirmation, active case finding, descriptive epidemiology and implementation of control measures. This case study is designed for the training of basic level field epidemiology trainees or any other health care workers working in public health-related fields. It can be administered in 2-3 hours. Used as adjunct training material, the case study provides the trainees with competencies in investigating an outbreak in preparation for the actual real-life experience of such outbreaks., Competing Interests: The authors declare no competing interest.

The laboratory plays a major role in surveillance, including confirming the start and end of an outbreak. Knowing the causative agent for an outbreak informs the development of response strategies and management plans for a public health event. However, issues and challenges may arise that limit the effectiveness or efficiency of laboratories in surveillance. This case study applies a systematic approach to analyse gaps in laboratory surveillance, thereby improving the ability to mitigate these gaps. Although this case study concentrates on factors resulting in poor feedback from the laboratory, practise of this general approach to problem analysis will confer skills required in analysing most public health issues. This case study was developed based on a report submitted by the district surveillance officer in Grand Bassa County, Liberia, as a resident of the Liberian Frontline Field Epidemiology Training Program in 2016. This case study will serve as a training tool to reinforce lectures on surveillance problem analysis using the fishbone approach. It is designed for public health training in a classroom setting and can be completed within 2 hours 30 minutes., Competing Interests: The authors declare no competing interest.

An essential component of a public health surveillance system is its ability to detect priority diseases which fall within the mandate of public health officials at all levels. Early detection, reporting and response to public health events help to reduce the burden of mortality and morbidity on communities. Analysis of reliable surveillance data provides relevant information which can enable implementation of timely and appropriate public health interventions. To ensure that a resilient system is in place, the World Health Organization (WHO) has provided guidelines for detection, reporting and response to public health events in the Integrated Disease Surveillance and Response (IDSR) strategy. This case study provides training on detection, reporting and analysis of priority diseases for routine public health surveillance in Liberia and highlights potential errors and challenges which can hinder effective surveillance. Table-top exercises and group discussion lead participants through a simulated verification and analyses of summary case reports in the role of the District Surveillance Officer. This case study is intended for public health training in a classroom setting and can be accomplished within 2 hours 30 minutes. The target audience include residents in Frontline Epidemiology Training Programs (FETP-Frontline), Field Epidemiology and Laboratory Training Programs (FELTPs), and others who are interested in this topic., Competing Interests: The authors declare no competing interest.

This case study was written based on events of an outbreak investigation of an unfamiliar disease in Ethiopia during October-December 2012. Ethiopia did not have reports of similar cases in the 50 years prior to this outbreak. In this case study, we recapitulate and analyse this outbreak investigation based on data gathered from the community, health facility, and laboratory systems. It can be used to teach: 1) the outbreak investigation process; 2) selection of appropriate epidemiological design for the investigation process, 3) basic statistical analysis of surveillance data, and 4) principals of disease control. The target audiences for this case study are officials working in public health and public health trainees. It will take at most 3.5 hours to complete this case study. At the end of the case study, participants should be able to apply the principals of outbreak investigation and use surveillance data to respond to an outbreak in their country-specific context., Competing Interests: The authors declare no competing interest.

This case study is based on a real-life outbreak investigation undertaken in Mozambique in 1981. This case study describes and promotes one particular approach to unknown disease outbreak investigation. Investigational procedures, however, may vary depending on location and outbreak. It is anticipated that the epidemiologist investigating an unknown disease outbreak will work within the framework of a "multidisciplinary investigation team". It is through the collaborative efforts of this team, with each member playing a critical role, that outbreak investigations are successfully completed. Some aspects of the original outbreak and investigation have, however, been altered to assist in meeting the desired teaching objectives and to allow completion of the case study in less than 3 hours., Competing Interests: The authors declare no competing interest.

The government of Zimbabwe began providing antiretroviral therapy (ART) to People Living with HIV/AIDS (PLHIV) in public institutions in 2004. In Midlands province two clinics constituted the most active HIV care service points, with patients being followed up through a comprehensive patient monitoring and tracking system which captured specific patient variables and outcomes over time. The data from 2006 to 2011 were subjected to analysis to answer specific research questions and this case study is based on that analysis. The goal of this case study is to build participants' capacity to undertake secondary data analysis and interpretation using a dataset for HIV antiretroviral therapy in Zimbabwe and to draw conclusions which inform recommendations. Case studies in applied epidemiology allow students to practice applying epidemiologic skills in the classroom to address real-world public health problems. Case studies as a vital component of an applied epidemiology curriculum are instrumental in reinforcing principles and skills covered in lectures or in background reading. The target audience includes Field Epidemiology and Laboratory Training Programs (FELTPs), university students, district health executives, and health information officers., Competing Interests: The authors declare no competing interest.

Epidemiology is the study of occurrence, distribution and determinants of health-related events, including the application of that knowledge to the prevention and control of health problems. However, epidemiological studies, in most cases, have limited their research questions to determinants of health outcomes. Research related to the application of knowledge for prevention and control of diseases have been neglected. In this comment, we present a description of how population attributable fraction estimates can provide important elements for planning of prevention and control of diseases in Brazil. RESUMO Epidemiologia é o estudo da ocorrência, distribuição e determinantes de eventos relacionados à saúde da população, incluindo a aplicação desse conhecimento para a prevenção e o controle dos problemas de saúde. Entretanto, estudos epidemiológicos, na maioria das vezes, têm limitado suas perguntas de pesquisa aos fatores determinantes de desfechos em saúde. Pesquisas relacionadas à aplicação do conhecimento para ações de prevenção e controle de doenças têm sido negligenciadas. Nesse comentário, apresentamos uma descrição de como as estimativas de fração atribuível populacional podem fornecer importantes elementos para planejamento de ações de prevenção e controle de doenças no Brasil.

In order to solve public health problems posed by the epidemiology of risk factors centered on the individual and neglecting the causal processes linking the risk factors with the health outcomes, Mervyn Susser proposed a multilevel epidemiology called eco-epidemiology, addressing the interdependence of individuals and their connection with molecular, individual, societal, environmental levels of organization participating in the causal disease processes. The aim of this epidemiology is to integrate more than a level of organization in design, analysis and interpretation of health problems. After presenting the main criticisms of risk-factor epidemiology focused on the individual, we will try to show how eco-epidemiology and its development could help to understand the need for a broader and integrative epidemiology, in which studies designed to identify risk factors would be balanced by studies designed to answer other questions equally vital to public health., (© 2016 médecine/sciences – Inserm.)

A century's worth of efforts to better understand the epidemiology of tuberculosis (TB) and to develop new vaccines, drugs, preventive interventions, and case-finding approaches have provided important insights and helped to advance the field of epidemiology as a whole. Wade Hampton Frost developed methods for cohort analysis that formed the early basis for adjustment of confounding variables. The streptomycin trial in the United Kingdom in the 1940s introduced random allocation for participants to either the treatment or control group, ensuring blinded treatment assignment and comparable treatment groups, which is now a key element in randomized clinical trials. Research into the bacille Calmette-Guérin vaccine demonstrated the importance of comparative analyses, potential difficulties in generalizability to populations not under study, and the role of meta-analysis for discrepant data-approaches now strongly recommended prior to implementing any novel public health intervention. George Comstock's work on preventive therapy for TB demonstrated the use of epidemiologic methods to evaluate interventions on a population level. Finally, studies from the Consortium to Respond Effectively to the AIDS/TB Epidemic focused on the evaluation of real-world effectiveness and of targeting of high-risk subpopulations. In this article, we discuss how TB research in each of these domains has helped to advance epidemiologic thinking and methodology over the past 100 years., (© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Twenty-five years ago, on the 75th anniversary of the Johns Hopkins Bloomberg School of Public Health, I noted that epidemiologic research was moving away from the traditional approaches used to investigate "epidemics" and their close relationship with preventive medicine. Twenty-five years later, the role of epidemiology as an important contribution to human population research, preventive medicine, and public health is under substantial pressure because of the emphasis on "big data," phenomenology, and personalized medical therapies. Epidemiology is the study of epidemics. The primary role of epidemiology is to identify the epidemics and parameters of interest of host, agent, and environment and to generate and test hypotheses in search of causal pathways. Almost all diseases have a specific distribution in relation to time, place, and person and specific "causes" with high effect sizes. Epidemiology then uses such information to develop interventions and test (through clinical trials and natural experiments) their efficacy and effectiveness. Epidemiology is dependent on new technologies to evaluate improved measurements of host (genomics), epigenetics, identification of agents (metabolomics, proteomics), new technology to evaluate both physical and social environment, and modern methods of data collection. Epidemiology does poorly in studying anything other than epidemics and collections of numerators and denominators without specific hypotheses even with improved statistical methodologies., (© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In this article, I provide a perspective on the tobacco epidemic and epidemiology, describing the impact of the tobacco-caused disease epidemic on the field of epidemiology. Although there is an enormous body of epidemiologic evidence on the associations of smoking with health, little systematic attention has been given to how decades of research have affected epidemiology and its practice. I address the many advances that resulted from epidemiologic research on smoking and health, such as demonstration of the utility of observational designs and important parameters (the odds ratio and the population attributable risk), guidelines for causal inference, and systematic review approaches. I also cover unintended and adverse consequences for the field, including the strategy of doubt creation and the recruitment of epidemiologists by the tobacco industry to serve its mission. The paradigm of evidence-based action for addressing noncommunicable diseases began with the need to address the epidemic of tobacco-caused disease, an imperative for action documented by epidemiologic research., (© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)