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AbstractThe epidemiologist Bradford Hill famously argued that in epidemiology, specificity of association (roughly, the fact that an environmental or behavioral risk factor is associated with just one or at most a few medical outcomes) is strong evidence of causation. Prominent epidemiologists have dismissed Hill's claim on the ground that it relies on a dubious `one-cause one effect' model of disease causation. The paper examines this methodological controversy, and argues that specificity considerations do have a useful role to play in causal inference in epidemiology. More precisely, I argue that specificity considerations help solve a pervasive inferential problem in contemporary epidemiology: the problem of determining whether an exposure-outcome correlation might be due to confounding by a social factor. This examination of specificity has interesting consequences for our understanding of the methodology of epidemiology. It highlights how the methodology of epidemiology relies on local tools designed to address specific inference problems peculiar to the discipline, and shows that observational causal inference in epidemiology can proceed with little prior knowledge of the causal structure of the phenomenon investigated. I also argue that specificity of association cannot (despite claims to the contrary) be entirely explained in terms of Woodward's well-known concept of "one-to-one" causal specificity. This is because specificity as understood by epidemiologists depends on whether an exposure (or outcome) is associated with a `heterogeneous' set of variables. This dimension of heterogeneity is not captured in Woodward's notion, but is crucial for understanding the evidential import of specificity of association.

This course was developed by the Centers for Disease Control and Prevention (CDC) as a self-study course. Continuing education is available for certified public health educators, nurses, physicians, pharmacists, veterinarians, and public health professionals. This course covers basic epidemiology principles, concepts, and procedures useful in the surveillance and investigation of health-related states or events. It is designed for federal, state, and local government health professionals and private sector health professionals who are responsible for disease surveillance or investigation. A basic understanding of the practices of public health and biostatistics is recommended. The course materials consist of six lessons. Each lesson presents instructional text interspersed with relevant exercises that apply and test knowledge and skills gained. ; Lesson One: Introduction to epidemiology -- Lesson Two: Summarizing data -- Lesson Three: Measures of risk -- Lesson Four: Displaying Public health data -- Lesson Five: Public health surveillance -- Lesson Six: Investigating an outbreak - Glossary ; Also available via the World Wide Web as an Acrobat .pdf file (6.01 MB, 511 p.). ; Includes bibliographical references.

The book presents a basic introduction to epidemiology from the perspective of economics, using economic modeling to better understand and describe how infectious disease spreads. Three main elements are introduced: epidemiology, social network analysis, and the economics needed to model the behavior of individuals in the presence of infectious disease. The book aims to provide a starting point for discussion between medical professionals, social scientists and public health officials, the three groups interested in the spread of disease

AbstractThis paper primarily argues that Epidemiology is Ecosystem Science. It will not only explore this notion in detail but will also relate it to the argument that Classical Chinese Medicine was/is Ecosystem Science. Ecosystem Science (as instantiated by Epidemiology) and Ecosystem Science (as instantiated by Classical Chinese Medicine) share these characteristics: (a) they do not subscribe to the monogenic conception of disease; (b) they involve multi variables; (c) the model of causality presupposed is multi-factorial as well as non-linear.

AbstractAnxiety is ubiquitous, but anxiety disorders and their distribution can be studied following the tenets of medical epidemiology. The differential diagnosis of anxiety disorders is a most taxing discipline. This chapter presents the problems involved and several realistic solutions.

ObjectivesPrecision Medicine (PM) uses advanced Machine Learning (ML) techniques and big data to develop personalized treatments, but healthcare still relies on traditional statistical procedures not targeted on individuals. This study investigates the impact of ML on epidemiology.MethodsA quantitative analysis of the articles in PubMed for the years 2000–2019 was conducted to investigate the use of statistical methods and ML in epidemiology. Using structural topic modelling, two groups of topics were identified and analysed over time: topics closer to the clinical side of epidemiology and topics closer to the population side.ResultsThe curve of the prevalence of topics associated with population epidemiology basically corresponds to the curve of the relative statistical methods, while the more dynamic curve of clinical epidemiology broadly reproduces the trend of algorithmic methods.ConclusionThe findings suggest that a renewed separation between clinical epidemiology and population epidemiology is emerging, with clinical epidemiology taking more advantage of recent developments in algorithmic techniques and moving closer to bioinformatics, whereas population epidemiology seems to be slower in this innovation.

"The field of infectious disease epidemiology has been front-and-center for the past few years. With the COVID-19 pandemic, an opportunity arose to provide "armchair epidemiologists" with a deeper understanding of the population-level impacts of infectious diseases, and the tools available to control them. This field has its own unique culture and set of tools and rules. We will utilize a whole new vocabulary in this book, from how to consider transmission-with the idea of a disease reproductive rate-to how disease is dispersed or clustered, and to how to design a study. As the reader will see, infectious disease epidemiologists seek to learn who is sick, why they are sick, and when and where they became sick"--

▪ Abstract  Over the past decade anthropologists and epidemiologists have begun to move beyond the "benign neglect" that characterized their prior relationship. Some of the most important collaborations between these disciplines concern themes of culture change and stress, social stratification, and the unpacking of other social and cultural variables. Anthropologists have criticized and expanded epidemiological notions of risk and vulnerability. Multidisciplinary teams of anthropologists and epidemiologists have constructed new measures and used multiple methods to increase the validity of their results. Disputes about classification have also linked the two disciplines. Collaborative projects between anthropologists and epidemiologists are leading to more nuanced and accurate descriptions of human behavior and more appropriate and effective interventions. Epidemiological techniques and ideas are also being used for anthropological ends, because disease often spreads along the framework of social structure. These many forms of collaboration create the foundations of a cultural epidemiology.