historical case study epidemiology

Historical Explorations of Modern Epidemiology pp 1–25 Cite as

Introduction: The Past Continuous of Epidemiology

• Heini Hakosalo 6 ,
• Katariina Parhi 7 &
• Annukka Sailo 6  
• Open Access
• First Online: 11 April 2023

1125 Accesses

Part of the book series: Medicine and Biomedical Sciences in Modern History ((MBSMH))

From early 2020, epidemiological research has been in the public eye like never before, thanks to the COVID-19 pandemic. Epidemiology has been instrumental in recognizing and framing the pandemic, assessing its scale, and shaping the global response. Much to their surprise, epidemiologists have also been drawn into the highly politicized disputes around the pandemic response. But both pandemics and epidemiologists have been around for centuries or, depending on definitions, even for millenia. Apart from shortly outlining the contents of the book, the introductory chapter charts the boundaries of epidemiology with reference to adjacent fields of inquiry, reviews the widely varied views on its emergence as a disipline, and discusses the historiography of epidemiology—which, much like its subject, is extensive but somewhat nebulous.

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From early 2020, epidemiological research has been in the public eye like never before, thanks to COVID-19. Epidemiology has been instrumental in recognizing and framing the pandemic, assessing its scale, and shaping the global response. Much to their surprise, epidemiologists have also been drawn into the highly politicized disputes around the pandemic response. But both pandemics and epidemiologists have been around for centuries or, depending on definitions, even for millennia. In its broadest sense, the term epidemiology refers to the systematic effort to understand disease distribution and to make sense of the unequal ways that disease and death treat different groups of people. Epidemiological knowledge has informed public health measures from quarantines to urban sanitation, from meat inspections to cancer screening, from anti-smoking campaigns to mass vaccinations. Beyond such obviously health-related issues, epidemiological prescriptions have exerted more diffuse but profound influence on the way that people in present-day societies interact with each other, trade, travel, and arrange their working and living spaces. It is indeed difficult to imagine industrialized societies without this kind of knowledge and its many applications. Modern epidemiology has been instrumental in teaching us to think about health in terms of lifetime accumulation of risks, the reduction of which is primarily perceived as an individual responsibility. As an object of historical research, epidemiology is an intriguing and undeniably important, but also an elusive phenomenon.

Historical Explorations of Modern Epidemiology: Patterns, Populations and Pathologies tackles this phenomenon through the lens of ten case studies. The volume asks how epidemiological knowledge has been produced; what kind of intentions, forces, and interests have shaped the development of the epidemiological field of inquiry; and how epidemiological knowledge has been used—in what way, for instance, has it guided, justified, or undermined public health efforts and policies. Far from making the effort defunct, the fact that the boundaries of the field are somewhat hazy and its disciplinary identity not always clear-cut, makes the effort more worthwhile. In this introductory chapter, we chart the boundaries of epidemiology with reference, first, to adjacent fields of inquiry and, second, to some historical turning points. We then move on to discuss the historiography of epidemiology—which, much like its subject, is extensive but somewhat nebulous—and to shortly outline the contents of the book. As the historiographical review makes clear, book-length studies specifically focused on the history of epidemiology are surprisingly rare. This volume is useful for those seeking a fuller understanding of the development of modern and contemporary medicine. For a practicing epidemiologist, a historical view on the development of their science gives an opportunity to take a step back and see the historicity of present-day practices and beliefs. A look at the history of epidemiology offers exciting examples of creative reasoning and discovery and of the uses and misuses of medical knowledge. In the last instance, epidemiology, like most forms of medicine, can be seen as part of the very human effort to come to terms with the vulnerable and finite nature of life.

The tense of the book should be thought of as past continuous: we are concerned with the history of the field up to and including the present, with emphasis on the period after the Second World War. The specific forms that epidemiological inquiries have taken are always related to and predicated upon the culture and society of their time. An inquiry into the historical trajectory of epidemiology during the past hundred years is therefore also an exploration of the development of modern societies. Today, epidemiological research shapes health, social and educational policies, as well as clinical practice, and it profoundly influences the way that people perceive, manage, and monitor their everyday life choices and think about disease and health.

What Do We Study When We Study the History of Epidemiology?

The three terms in the subtitle of the book—patterns, populations, and pathologies—refer to epidemiology’s basic objective, patterning pathologies on populations . Footnote 1 “Pathologies” is here used as a shorthand not only for diseases but also death, disabilities, disease risks, and even social problems. In contradistinction to many other forms of medical inquiry, epidemiology studies diseases as mass phenomena, on population level. The term “patterning” refers to what epidemiologists do when they seek to define the distribution of pathologies in time and place. In patterning pathologies on populations, epidemiologists contribute to the coproduction of both, participating as they do in the definition of pathologies and the construction of populations.

More concretely, epidemiological practices during the past two hundred years have included things like calculation and statistical analysis of frequencies and incidences of death and disease; disease surveillance and outbreak investigation; contact-tracing; and assessment of preventive public health and therapeutic measures, for instance vaccination. After the Second World War, epidemiological research has commonly been conducted by means of cohort and case–control studies, with the objectives of linking exposure to outcome and identifying risk factors of disease (or social ills like poverty). The former method compares incidence rates between exposed and unexposed people and the latter compares prior exposure frequencies in sick and well people. Epidemiological research is observational and inductive and has, especially since the early twentieth century, relied heavily on statistical analysis and mathematical modeling.

Beyond such basic traits and definitions, there is much ambiguity. One way to try and define the contours of the field is to trace its boundaries towards neighboring and overlapping fields. In the case of epidemiology, these boundaries have been historically shifting and permeable. The following discussion will focus on five key adjacent fields: clinical research, biomedical research, statistical analysis, public health research, and social medicine. Each field is characterized by its central objective and its “truth spot,” or the place from which it primarily derives its scientific authority. Footnote 2 The key features are represented schematically in Fig.  1.1 .

Epidemiological field of inquiry and its chief alliances with their key objectives and truth spots

First, epidemiological research is closely related to but distinguishable from clinical medicine (a), which is concerned with manifestations and treatment of disease. Its truth spot is “the clinic,” i.e., the modern hospital that combines treatment with medical teaching and research. There are many historical points of contact between clinical and epidemiological research. For instance, the early nineteenth-century Parisian teaching hospitals were also the home of pioneering cohort studies. Randomized controlled trials were established as the core element of clinical research after the Second World War, with profound consequences for epidemiology. And the emergence of evidence-based medicine in the 1980s again modified the relationship between clinical medicine and epidemiology.

Second, epidemiology is closely associated with but historically and analytically distinguishable from biomedical research (b), an umbrella term for laboratory-based research primarily concerned with the identification of disease mechanisms. The truth spot here is “the laboratory,” which became crucial for epidemiology with the breakthrough of bacteriology during the last quarter of the nineteenth century. Footnote 3 The collaboration between the two fields has taken many forms, including experimental forms. During the past decades, the collaboration has increasingly moved to the molecular level.

Third, statistical analyses and mathematical modeling (c) of disease distribution have been crucially important allies of epidemiology since the mid-nineteenth century. William Farr (1807–1883) is often regarded as the central figure in bringing the two together, and further steps towards mathematization were taken at the beginning of the twentieth century by the biometrician Karl Pearson (1857–1936) and his disciples. During the latter part of the twentieth century, advances in computing and information technologies made statistical analysis and mathematical modeling increasingly effective and central as epidemiological tools. Given the theoretical nature of this work, the truth spot related to this edge can be called “the desk” (although “the PC” might be more accurate today).

The fourth truth spot is “the field,” the locus of epidemiological field work (d), which has historically revolved around various forms of outbreak investigation and case-finding. It has developed in close collaboration with public health and state administration and been aimed at monitoring and controlling disease in populations, for instance by means of identifying and cutting chains of infection.

The fifth adjacent field is closely related to but not identical with epidemiology. Its primary disciplinary manifestation is social medicine (c), and its truth spot would have to be “society” as a whole, as it attains to social improvement by means of identifying and leveling health inequalities. Social medicine in its different historical guises indeed seems like a natural ally to epidemiology, given that the latter is concerned with differential distribution of disease and that these differences often coincide with and deepen social and economic divisions. Few would deny that effective health leveling requires political action, but not everyone thinks that epidemiologists themselves should be politically engaged. Some students of population-level pathologies, from Rudolf Virchow (1821–1902) to Johan Mackenbach, have placed investigation into health inequalities and their social, economic, and political causes at the very core of their epidemiological and public health endeavors and been explicit about the social and political implications and obligations of epidemiology. Footnote 4  The past decades have also witnessed an increased need to go beyond human societies to embrace the health of non-human animals (“one health”) and to study human health in relation to planetary needs and boundaries (“planetary health”). Footnote 5

Epidemiology, the middle pentagon, should be thought of as a mobile and shape-shifting rather than a static thing. The introduction of fresh innovations, new alliances, and novel goals has involved border negotiations and disputes. The main lines of tension within the field have also changed over time. When one or some of the edges have become stronger, concerns have been voiced that epidemiology risks being subsumed and losing its independent identity. During the past decades, for instance, there have been stark tensions between the biomedically and the more socially oriented parts of the field. Socially oriented epidemiologists have criticized what they see as too heavy reliance of epidemiology on biomedicine. While these critics would not deny that the adoption of molecular research techniques can offer added precision, explanatory power, prestige, and opportunities for interdisciplinary collaboration, they also stress that the dominance of the biomedical framework risks blinding epidemiologists to health inequities and the social aspects of disease distribution. Footnote 6

Another, perhaps more common way to try and outline the epidemiological field of inquiry is by charting its development. At first sight, the impression of the elusiveness of the field is enforced by the difficulty of fixing a birth date. Historical overviews usually start from Hippocratic texts, and epidemiological questions no doubt have ancient origins. Footnote 7 But views on the emergence of epidemiology as a discipline , or as a distinct field of scientific inquiry, differ widely, ranging from the seventeenth to the latter part of the twentieth century.

According to Nancy Krieger, “The development of epidemiology as a self-defined scientific discipline […] had its origins in Europe in the seventeenth century,” while Alberto Morabia refers to the “birth of epidemiology in the 18 th century.” Encyclopaedia Britannica states that epidemiology “as a formal science” emerged in the course of the nineteenth century. David Morens, in turn, places “the birth of epidemiology” in Paris in 1819–1832. Krieger—notwithstanding her earlier statement as to the beginnings of epidemiology in the seventeenth century—agrees with Morens that, “By the 1830s, epidemiology had emerged as a self-designated field of inquiry.” Footnote 8 Others regard the decades following the first wave of cholera in Europe (1831–32) as the crucial period of gestation. Morabia thus notes that epidemiology took its “first steps” between the early 1830s and 1850 in London, where the newly founded London Epidemiological Society “assembled scientists, public health practitioners and physicians to unite their efforts in the fight against ‘epidemics.’” Footnote 9 Lisa Wilkinson agrees that “epidemiology came of age” with the founding of the Society in 1850. Footnote 10 Mervyn Susser and Zena Stein give William Farr, who was professionally most active in the 1850s and 1860s, “a major role as a founder of epidemiology in its modern analytic form.” Footnote 11 Olga Amsterdamska, too, believes that “epidemiology as a scientific study of disease in populations claimed an independent disciplinary status already in the mid-nineteenth century.” Footnote 12

The latter part of the nineteenth century also gets some votes. Historians of Victorian Britain have emphasized that epidemiology went through a process of professional consolidation at that time, turning British epidemiology into “a well-established, practice-oriented field.” Footnote 13 According to Jacob Steere-Williams, practices of outbreak investigation were consolidated during the latter part of the nineteenth century, when British epidemiologists also “began to call themselves epidemiologists and started to think in sociological, institutional, and even historical terms about their status as a discipline.” Footnote 14 Amsterdamska, in spite of dating the birth of the discipline in the first part of the nineteenth century, also highlights the importance of the interwar period, “when epidemiology was becoming an academic discipline” and was clearly demarcated from other endeavors. Footnote 15 It was indeed during this period that the first two chairs, at the London School of Hygiene and Tropical Medicine (1921) and Johns Hopkins Medical School (1927), were founded. Footnote 16 But the majority of votes goes to the immediate post-war decades. Luc Berlivet, for instance, asserts that “the contemporary style of epidemiology goes back no further than the late 1940s.” Footnote 17 Other historians and epidemiologists, too, think that epidemiology as a full-fledged academic discipline took shape only after the Second World War. Footnote 18

The timing of the emergence of epidemiology is thus something of a muddle. Even one and the same author sometimes provides two different, temporally quite distinct birth dates. One reason for the confusion is no doubt that exact timing was not these authors’ primary concern. Another possible explanation is that they were in fact speaking about different things, not using any shared criteria for a “mature” discipline. Amalgamating their views, and applying the principle of charity, one might say that the first part of the nineteenth century witnessed the appearance of a demand for and some shared guidelines for producing epidemiological knowledge and the latter part of the century saw definite signs of professionalization. While some criteria of an academic discipline were met during the interwar period (chairs and textbooks), the rest (departments, doctoral programs, textbooks, and specialized periodicals) were at evidence first during the post-war decades.

Historiography of Epidemiology

A Venn diagram of the historical literature relevant for our theme would display a small core element with several larger circles surrounding and partly overlapping it: while surprisingly few studies specifically focus on the history of epidemiology, there are several adjacent clusters of scholarship that are relevant for understanding it. Apart from the few general histories of epidemiology, three major clusters can be distinguished: histories of concepts, theories, techniques, and epistemological issues, often looked through the lens of epidemiological landmark studies; histories of epidemics; and histories of public health and governance.

There are also some notable absences. Epidemiology, as a distinct theme, is often missing from general histories of medicine. Companion Encyclopedia of the History of Medicine contains an entry on epidemiology and the Encyclopedia of Epidemiology one on the history of epidemiology, Footnote 19 but there is neither a section nor an index term for epidemiology in the encyclopedic Medicine in the 20th Century or, perhaps even more surprisingly, in Dorothy Porter’s synoptic Health, Civilization and the State . Footnote 20 One reason for its slight presence in general histories of medicine may be that—notwithstanding well-known foundation stories like that of John Snow and the Broad Street pump—the development of epidemiology does not as easily translate into linear narratives paced with dramatic breakthroughs, discoveries, and innovations as some other branches of medicine. Another reason has already been discussed: it is not always easy to say where epidemiology ends and another field of inquiry begins.

The few book-length general histories of epidemiology have been written by epidemiologists rather than historians. These include, most importantly, Mervyn Susser and Zena Stein’s Eras in Epidemiology: The Evolution of Ideas and Nancy Krieger’s Epidemiology and the People’s Health: Theory and Context . Footnote 21 The former contains what is probably the most well-known periodization of the development of the field. Susser and Stein distinguished three to four periods: the era of sanitary statistics, prior to the 1880s; the era of infectious disease epidemiology, from the 1880s to WWII; and the era of chronic disease epidemiology after WWII. Writing in the 1990s, Susser and Stein believed that the third period was about to end and a new period to start. Footnote 22 They regarded their 3–4 historical phases as paradigms in the Kuhnian sense and saw theories as the driving force of disciplinary development. Footnote 23 Krieger’s temporal scope is similar, starting from Hippocrates and extending to the time of writing. Both books are also heavily focused on British and US developments. Krieger, too, saw theoretical thinking as the motor of scientific development and the thing that gives each period its distinct character. Again like Susser and Stein, she had firm views about the present shortcomings and the preferable future course of the discipline. Krieger, a social epidemiologist, was highly critical of that mix of biomedical and lifestyle perspectives that she saw as dominating current epidemiology. Footnote 24 She wanted to see epidemiology move away from risk factor epidemiology towards a multilevel approach more sensitive to health inequalities. Footnote 25

Historians have usually preferred a more temporally and geographically restricted focus, perhaps because it allows them to investigate in more detail the interconnections between epidemiological knowledge-making and the historically specific social context. The second significant cluster of historical research revolves around the landmark studies of the post-WWII years. These include the randomized controlled trials (RCT) directed by Austin Bradford Hill (1897–1991) under the auspices of the British Medical Research Council to assess the efficacy of the new chemotherapeutic tuberculosis drugs (UK 1946); the National Survey of Health and Development, the first national birth cohort study (UK 1946); the series of studies that explored the link between lung cancer and smoking, conducted in Britain and the US in the 1950s; and the Framingham Heart Study (US 1948). These studies have been extensively studied by historians and are also frequently revisited by practicing epidemiologists.

Landmark studies are regarded as such not only because they delivered important results but also because they introduced methodological innovations and new constitutive concepts. RCT, while not a specifically epidemiological method, has been extremely important for epidemiology both as a tool and as a yardstick, a standard against which epidemiological study designs are often measured. Footnote 26 The 1950s tobacco-cancer studies, mainly using cohort and case–control methods, were crucial for the development of a new understanding of causality and for the disciplinary identity and status of epidemiology. As Mark Parascandola puts it, “the debate over tobacco and lung cancer provided a crucial test for the discipline of epidemiology.” Footnote 27 In the present volume, Nicolas Brault (Chapter 3 ) shows that the nature of epidemiological causality was still very much an issue in the 1970s.

Post-war cohort studies, especially the Framingham Heart Study, center-staged the concept of differential risk, first introduced in print in 1961. Footnote 28 The position of the Framingham Study in the history of post-WWII epidemiology is not unlike that of John Snow’s studies on cholera in the mid-nineteenth century: it is regarded as a turning point in both practice and reasoning. The concept of risk forced epidemiologists to rethink the nature of epidemiological inference. “Risk,” being non-necessary and non-sufficient but still statistically significant, was a new kind of causal factor. The Framingham Study dealt with multiple risk factors, whereas the tobacco-cancer studies focused on the role of one, and the former therefore added complexity to epidemiological explanations. Investigations into the emergence of “risk factor epidemiology” has allowed historians and STS scholars not only to observe a major reconfiguration of epistemological reasoning but also to discuss its repercussions for the relationships between clinical, experimental, and epidemiological research. Footnote 29

Population-level research relies heavily on counting, and studies on the history of calculation and quantification are indeed an important sub-group of the second cluster of scholarship. Studies like Theodor Porter’s The Rise of Statistical Thinking, 1820–1900 and Trust in Numbers: The Pursuit of Objectivity in Science and Public Life , Ian Hacking’s The Taming of Chance , and Alain Desrosières’ The Politics of Large Numbers: A History of Statistical Reasoning , as well as the edited volumes The Road to Medical Statistics (2002), Body Counts: Medical Quantification in Historical and Sociological Perspectives (2005) and Accounting for Health (2021) Footnote 30 have asked how people—especially ill and dead people—have been categorized and counted and how the resulting figures have been interpreted and made use of both in epidemiological research and in administrative and clinical practices. There is a close connection between statistical and health administration, as the state has been a major player in the development of population bills and statistics, registration, and record-keeping.

STS scholars have conducted research on issues that, even if not primarily epidemiological, are nevertheless highly relevant for understanding epidemiological knowledge-making. To take one example, G.C. Bowker and Susan Leigh Star’s Sorting Things Out: Classification and Its Consequences explores how categories—including epidemiological categories—result from continuous negotiations and compromises and how they render some things visible and others invisible. Footnote 31 Particularly since the 1980s, the huge increase in the capacities of data storage and processing techniques has given rise to vast repositories of varied health data. Epidemiology has played a major role in generating, making use of, and legitimizing such data collections. While historical studies tend to terminate prior to the 1980s digital revolution, STS scholars have produced a host of useful studies on more recent data practices. These studies tackle, for instance, the uses and abuses of the big biomedical data and the ethical and technical complexities involved in disease registration, in repurposing cohort data and in depositing biomedical legacy samples in biobanks, as well as the implications of the European General Data Protection Regulation (GDPR) (2018) for health research. Footnote 32 Sociologist Susanne Bauer’s work is particularly useful for understanding the co-construction of administrative infrastructures and epidemiological knowledge in the Nordic countries. Footnote 33

Historical practices of epidemiological representation range from narrative to numerical, from schematic to photographic. Anne Hardy has stressed the strong narrative tradition of epidemiology: “The working epidemiologists of the late nineteenth century and of the first half of the twentieth were trained in a tradition which prized the art of story-telling.” Footnote 34 Jacob Steere-William agrees, while also drawing attention to the role of statistical charts as the fundamental form of visual representation in late nineteenth-century field epidemiology. Footnote 35 The development and uses of epidemiological maps is the topic of Tom Koch’s Disease Maps , Footnote 36 and Lukas Engelmann discusses maps and other forms of visualization in his Mapping AIDS: Visual Histories of an Enduring Epidemic . Footnote 37 A temporally extensive look at forms of visual representations of epidemic diseases is provided by Plague Image and Imagination from Medieval to Modern Times , edited by Christos Lynteris, who has studied the representation of epidemics, and the epidemiologist, in his other works as well. Footnote 38 In this volume, Lukas Engelmann applies tools of visual analysis to the COVID-19 pandemic (Chapter 11 ).

Turning to the third cluster of scholarship, histories of epidemics, we meet an embarrassment of riches. Many studies on epidemics, while not specifically targeting epidemiological knowledge-making, touch upon all the three edges of the triangle disease—control measures—epidemiological knowledge . Not surprisingly, the history of epidemics has shown a strong preference for contagious disease outbreaks, which are dramatic, high-impact events with relatively clear spatial and temporal boundaries and obvious and often wide-ranging social repercussions. Histories of epidemics that shed light on the interaction between disease, epidemiological knowledge-production, and public health measures include, to name just a few, Anne Hardy’s The Epidemic Streets: Infectious Diseases and the Rise of Preventative Medicine, 1856–1900 and Salmonella Infections, Networks of Knowledge, and Public Health in Britain, 1880–1975 , William Coleman’s Yellow Fever in the North: The Methods of Early Epidemiology , François Delaporte’s The History of Yellow Fever , and Jacob Steere-Williams’s The Filth Disease: Typhoid Fever and the Practices of Epidemiology in Victorian England . Footnote 39 Charles Rosenberg discusses the construction of epidemic outbreak narratives in his seminal “What is an epidemic?”. The paper was written in 1989, during the early years of the HIV/AIDS epidemic, and has been a standard reference point also during the COVID-19 pandemic, particularly in the US. Footnote 40 Priscilla Wald’s Contagious: Cultures, Carriers, and the Outbreak Narrative is a more recent and extensive analysis of the forms and variations of “outbreak narratives,” or tales of disease emergence, and an argument for their cultural and political significance.

The history of non-communicable diseases is less well covered but hardly neglected. George Weisz’s Chronic Disease in the Twentieth Century: A History charts the emergence of chronic disease as a major public health problem in the US, with comparative chapters on Britain and France. The history of cancer and cancer research, particularly in the US context, has interested several historians. Robin Wolfe Scheffler’s A Contagious Cause: The American Hunt for Cancer Viruses and the Rise of Molecular Medicine sheds light both on the disease and on the broader category of chronic disease. There is also a growing body of research on “the obesity epidemic,” obesity being an example of a condition that has evolved from a non-pathological (although often negative) trait into a risk factor and further into a disease with epidemic, even pandemic proportions. Footnote 41 Chronic diseases were long studied as a first-world problem, but this imbalance has been addressed lately by publications like Epidemiological Change and Chronic Disease in Sub-Saharan Africa: Social and Historical Perspectives , edited by Megan Vaughan, Kafui Adjaye-Gbewonyo, and Marissa Mika, Improvising Medicine: An African Oncology Ward in an Emerging Cancer Epidemic by Julie Livingston and  Travelling with Sugar: Chronicles of a Global Epidemic  by Amy Moran-Thomas. Footnote 42

Several chapters in this volume also start from specific disease or disease groups. Heini Hakosalo (Chapter 2 ) discusses mid-twentieth-century tuberculosis research as a test ground for later chronic disease approaches, Jan Kuhanen and Markku Hokkanen (Chapter 8 ) trace the history of epidemiological research into sexually transmitted infections in Africa, and Mona Mannevuo (Chapter 10 ) investigates the way that a social problem, unemployment, was framed in quasi-medical terms in Finland in the 2010s.

The fourth cluster of scholarship are histories of public health and the politics of epidemiology. Epidemiological questions, state administration, and politics have been closely linked from the eighteenth century onwards. Public health administration is a well-studied field, especially as concerns Britain. Only a few examples can be named here. Erwin Ackerknecht’s 1948 paper on the nineteenth-century debates on contagion and quarantine is a classic case study on the entanglements of disease, medicine, and political and economic interests. Footnote 43 Anne Hardy’s work, which often moves at the intersection of public health and epidemiology, has already been mentioned. Graham Mooney’s Intrusive Interventions: Public Health, Domestic Space, and Infectious Disease Surveillance in England, 1840–1914 looks at the ways in which health education, surveillance, and monitoring influenced everyday life and the domestic sphere. Alison Bashford’s Quarantine: Local and Global Histories offers a global long-term perspective on quarantine as a way to control disease. Virginia Berridge’s Marketing Health focuses on the way that the risks of smoking found their way into the new public health discourse in the latter part of the twentieth century, and shows that not only health care workers and the state but also pharmaceutical and tobacco industries had a stake in the discourse. Footnote 44 Peder Clark has explored the formation of “health citizenship,” or the relationship between the individual and the state in matters of health, in post-war Britain, showing how social values and political trends of the British class society were reflected in epidemiological research and public health. The role of epidemiological research in shaping health citizenship is also addressed in Placing the Public in Public Health in Post-War Britain , edited by Clark and others. Footnote 45

The power and politics of epidemiology have interested STS scholars, too. A source of inspiration has been the critical tradition of Michel Foucault, Footnote 46 which stresses the less benevolent aspects of counting, surveilling, and monitoring people’s health, seen as forms of “biopower” or as expressions of “governmentality.” The latter notion has been elaborated on by Nikolas Rose, Footnote 47 among others, while another British sociologist, David Armstrong, has critically scrutinized practices of data-production and “surveillance medicine,” chronic disease surveillance, and risk-factor modification in many of his publications, starting with Political Anatomy of the Body: Medical Knowledge in Britain in the Twentieth Century . Footnote 48 The notion of population, so central to epidemiology, has been dissected by Armstrong in several papers and by the historian and STS and gender scholar Michelle Murphy in her influential The Economization of Life . Footnote 49

The colonial roots and ingrained colonialism of epidemiological expertise have been discussed and debated a lot in recent years, often in the context of global organizations and global networks of expertise and business. An example is Rohan Deb Roy’s Malarial Subjects: Empire, Medicine and Nonhumans in British India, 1820–1909 , a history of the pandemic and of colonial medical and epidemiological practices of study and control. Eugene Richardson, a physician and anthropologist with extensive experience of working in Africa and Asia, has criticized public health and epidemiological reasoning and practices for upholding rather than effectively undermining the ubiquitous and persistent global health inequities in his Epidemic Illusions: On the Coloniality of Global Public Health . In a conscious effort to decolonize its own history, a recent report looks at the colonial history of the London School of Hygiene and Tropical Medicine, an institution that has been central both to British and colonial public health research and to historical research into British and colonial public health. Footnote 50 History of psychiatric epidemiology, too, is increasingly often discussed from a global perspective. Footnote 51

As difficult as it is to sum up such a large and heterogenous historiography, we will end our review with three general observations. First, notwithstanding the few cross-disciplinary publications, the three groups with the greatest interest in the history of epidemiology—historians, epidemiologists, STS scholars—have taken relatively little notice of each other, with few cross-references across disciplinary boundaries. Generally speaking, epidemiologists have been primarily interested in pioneers and basic concepts, seeing theoretical advancement as the driving force of disciplinary development, while historians have been more likely to look at changing practices of knowledge-gathering and knowledge-making in historically specific social and political contexts. Second, the historiography of epidemiology is temporally and geographically uneven. The two most thoroughly researched contexts are mid- and late nineteenth-century Britain on the one hand and the post-WWII decade in Britain and the US on the other hand. Because historians have seldom moved beyond the 1980s, the historical implications of the digital revolution for epidemiology are still largely unexplored—a lacuna to an extent compensated for by the rich STS scholarship on health data practices. Third, research into history of epidemiology is heavily focused on Anglo-American developments. Although British and US developments have indeed been central for modern epidemiology, there are plenty of other, divergent national trajectories to explore.

Indeed, one objective of Historical Explorations of Modern Epidemiology: Patterns, Populations and Pathologies is to add to the temporal, geographical, and cultural diversity of the historiography of epidemiology. Its cases deal with developments also outside the major Euro American centers, from Central and East Africa to the Circumpolar North. The Nordic countries, whose contribution to late twentieth- and twenty-first-century epidemiology has been distinctive and significant, are particularly well represented in the volume. The volume draws attention to the diversity of epidemiological activities and agents. We will meet not only self-identified epidemiologists with specialist training but also medical practitioners addressing epidemiological questions, colonialists, administrators, policymakers, health campaigners, and members of the “populations” studied by epidemiologists. Temporally, the contributions range from the interwar period to the present, with the focus on post-WWII developments. Being a collection of case studies, the volume does not aspire to a comprehensive view of post-war epidemiology. Rather, it highlights the diversity, range, and impact of epidemiological research during this period.

Book Outline

The volume tackles its key question—how epidemiological knowledge has been made and how it has been used—through ten cases, in three parts. The cases in the first part, Patterns , demonstrate how the uneven distribution of disease in populations has been observed, explained, and modeled. The second part, Populations , asks how populations have been constructed in epidemiological research and how the latter has created populations by calculating and categorizing people in terms of time and space but also in terms of age, sex, ethnicity, and living conditions. The third part, Pathologies , explores the role of epidemiology in defining and representing pathological phenomena.

The first empirical chapter (Chapter 2 ) is Heini Hakosalo’s “Patterning Tuberculosis: Interwar Tuberculosis Research as a Bridge between Infectious and Risk Factor Epidemiology.” It argues that pre-WWII tuberculosis research functioned as a crucially important test ground for concepts and methods that would later become elemental for post-WWII risk-factor epidemiology. The chapter is also a reminder that different diseases have the potential to steer epidemiological research in different directions. Tuberculosis, being a public health priority, had a lot of steering power. The disease acquired the role of a “bridging condition” not only because it was both infectious and chronic, but also because of its other qualities. The empirical examples discussed in the chapter derive from the US and the Nordic countries, and the chapter stresses the transnational character of interwar epidemiological concerns and practices.

In Chapter 3 , “The Case–Control Method on Trial: The ‘Bermuda Summit Peace Conference’ (1978),” Nicolas Brault offers a well-focused analysis of methodological and epistemological tensions in epidemiological thought and practice in the 1970s, using the discussions and debates of a specific epidemiological conference as the starting point. The Bermuda conference brought together a group of prominent epidemiologists ostensibly to discuss the use and misuse of the case–control method. Brault shows how disagreements about seemingly technical issues relating to the method were grounded in and fueled by the interests of the pharmaceutical industry and also by constitutive theoretical and epistemological issues with long historical roots. What was at stake was not only the scientific value of the case–control method but also the scientific value of observational epidemiological studies more generally.

Katariina Parhi’s contribution (Chapter 4 ), “The Coexistent Temporalities: Multilayered Ethics in Birth Cohort Studies,” delves into the history of a long-term birth cohort study, Northern Finland Birth Cohorts (NFBC 1966, 1986). The Finnish study was in some respects modeled on the famous British national birth cohort studies (1946, 1958). During its 57 years of existence, NFBC’s social and research environment has undergone many changes. Parhi focuses on one of them, the changing ethical guidelines and legal precepts that steer cohort studies. Using interviews with cohort investigators as her main source material, Parhi shows how they have navigated the temporally multilayered ethical and legal requirements constraining the use of their equally multilayered cohort data. Parhi’s chapter offers a rare glimpse on the everyday choices facing cohort scientists, and also contributes to the history of epidemiological research ethics.

The second part, “Populations,” opens with Paul Weindling’s “The Oxford Nutrition Survey (1941–1950): Its Rise and Fall under Hugh Sinclair” (Chapter 5 ). The chapter focuses on a series of innovative cohort and community studies conducted by the biochemist Hugh Sinclair in mid-twentieth-century Oxford. Weindling provides an intriguing view on the objectives and methods of this little-known researcher, asking why Sinclair’s studies, despite their initial scientific and policy influence, ultimately proved a dead end, leaving Sinclair in possession of a mass of valuable, underused data, but without a solid institutional base. The case offers an opportunity to look at a methodologically exceptionally dynamic period from a novel angle and to ask why some research projects, however innovative and promising, nevertheless fail.

Ida Al Fakir’s contribution, “Spotlighted or Hidden in Plain Sight: Consequences of the Post-War Ban on Ethnic Registration in Sweden” (Chapter 6 ), examines how Swedish population and health researchers and administrators dealt with the controversial issue of registering (or not) the ethnic and racial background of citizens in 1945–1985. Al Fakir discusses the long history of categorizing ethnic minorities and the ways in which administrators and epidemiologists have dealt with the ethical complexities and practical difficulties involved in the official policy of non-registration. Nordic administrative practices are usually considered as exceptionally conducive to epidemiological research, but, as Al Fakir shows, things can be more complicated on the grassroots level of practical research. The chapter relates to a topical broader issue: the potential of health data practices to both undermine and enforce racial and ethnic categories.

In Chapter 7 , “Risk Factor Epidemiology Viewed from Below: Lay Reception of the North Karelia Project (Finland) in the 1970s and early 1980s,” Mikko Jauho deals with a large, well-known community intervention study conducted in a region that was known for its exceptionally high incidence and prevalence of cardiovascular disease. The North Karelia Project can be regarded as a successor of the Framingham Heart Study and the Seven Countries Study. With the help of his unique source material—writings produced by the local people in response to the intervention—Jauho discloses the tensions between the objectives and practices of the investigators on the one hand and the expectations and health beliefs of the participants on the other hand. One lesson drawn by Jauho is that the designers of community interventions neglect the participant voices and viewpoints at their own peril. The chapter also shows how epidemiological concepts and perceptions travel from scientific spaces to local communities, not always without friction.

Jan Kuhanen’s and Markku Hokkanen’s chapter (Chapter 8 ) “From Colonial Medicine to Global Health: Epidemiologies of Sexually Transmitted Infections in East and Central Africa” takes us from the Global North to the Global South. It provides a critical long-term view on research into sexually transmitted diseases in Africa, revealing how lingering colonial practices and mindset influenced the way that the African HIV/AIDS epidemic was framed and tackled, and sometimes overlooked. In the colonial framework, intensive public health and disease control methods were often based on questionable data, produced by means of imported medical techniques and concepts, without sufficient attention to local conditions. Hokkanen and Kuhanen argue that the long history of flawed disease control efforts contributed to the failures of AIDS control in Africa in the 1980s.

The third part, “Pathologies,” opens with Jennifer Fraser’s “Light Pollution: Auroral Displays, Environmental Carcinogens and Epidemiological Imaginings of Inuit Cancer” (Chapter 9 ). The chapter brings together Inuit people, the Arctic environment, celestial health hazards, and a physician with original ideas and epidemiological ambition. Dr Otto Schaefer proposed that aurora borealis had cancer-causing properties that explained the high prevalence of so-called “Inuit cancer” in the high north. Fraser’s skillful analysis shows how this seemingly outlandish notion drew from current trends in atmospheric physics, atomic energy, and environmental sciences. Her chapter also alerts us to the cultural and political factors behind the selective attention that atmospheric health hazards have received in the Arctic.

In Chapter 10 , “Scientized Politics: The Finnish Basic Income Trial as a Quest for Experimental Truth,” Mona Mannevuo discusses an instance where RCT was used as a social policy and planning tool. The Finnish Basic Income Trial (2017–2018) was framed as something novel and groundbreaking, but, as Mannevuo shows, it can in fact be seen as a recent example of the long-established tradition of social engineering, i.e., the effort to rationalize policies by means of science. The trial, which was initially conceived by a private think tank inspired by behavioral economics and the nudge theory, was executed by state administration, welcomed by politicians on left and right, and enthusiastically covered by the media, only to end with inconclusive results. Mannevuo explores the historical roots of the ideas that informed the experiment and critically discusses the dangers of recasting RCT as a universal tool for designing  evidence-based policies.

In Chapter 11 , “Virus-Imagery: A Short History of Pandemic Mis-Representation, HIV to COVID-19,” Lukas Engelmann reminds us that visualization is not just about illustrating but also about constructing epidemiological knowledge. The chapter asks how a particular image of the SARS-Cov-2  virus became “the official and unofficial portrait of the COVID-19 pandemic,” discussing it against the backdrop of the long history of virus visualization. Engelmann compares the visualization of the present pandemic with the images that circulated during the AIDS pandemic in the 1980s and 1990s. He argues that diagrammatic virus visualizations, in all their effectiveness and clarity, are poor representations of the pandemic, being unrelated to the realities with which epidemiologists and patients struggle. Engelmann’s analysis is a significant contribution to the history of epidemiological representation and a topical call for more accurate and multilevel ways of modeling and representing epidemics.

The wording comes close to Nancy Krieger’s definition of epidemiology as “population patterning of health.” Nancy Krieger, Epidemiology and the People’s Health: Theory and Context (Oxford: Oxford University Press, 2014 [2011]), 67. For definitions of epidemiology, see Mervyn Susser and Zena Stein, Eras in Epidemiology: The Evolution of Ideas (Oxford: Oxford University Press, 2009), 3. There is a helpful list of textbook definitions in Krieger (2014), 34–42.

The term has been introduced and employed by Thomas F. Gieryn. See his “City as Truth-Spot: Laboratories and Field-Sites in Urban Studies,” Social Studies of Science 36:1 (2006), 5–38, and Truth-Spots: How Places Make People Believe (Chicago: University of Chicago Press, 2018).

For an analysis of “epidemiologists’ boundary-making endeavors,” especially towards bacteriologists, see Amsterdamska (2005), 17.

For an interesting discussion on variations of the idea that health is a political issue and medicine and politics are therefore mutually interdependent, see J.P. Mackenbach, “Politics Is Nothing but Medicine at a Larger Scale: Reflections on Public Health’s Biggest Idea,” Journal of Epidemiology and Community Health 63:3 (2009), 181–4.

Warwick Anderson and James Dunk, “Planetary Health Histories: Toward New Ecologies of Epidemiology?”, Isis 113:4 (2022), 767–88; James H. Dunk, David S. Jones, Anthony Capon, and Warwick H. Anderson, “Human Health on an Ailing Planet—Historical Perspectives on Our Future”, New England Journal of Medicine 381:8 (2019), 778–82.

Krieger (2014), viii–ix, 126; Susanne Bauer, “Mining Data, Gathering Variables and Recombining Information: The Flexible Architecture of Epidemiological Studies,” Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 39:4 (2008), 415–28, here 417.

On ancient Greek views on epidemics, see David M. Morens, “Epidemiology, History of,” in Encyclopedia of Epidemiology , ed. by Sarah Boslaugh (Thousand Oaks: Sage, 2008), 318–24.

Krieger (2014), 38; Alfredo Morabia, ed., A History of Epidemiologic Methods and Concepts (Basel: Birkhäser Verlag, 2004), 6; Morens (2008), 8, 2–3; “Epidemiology, Medicine,” in Encyclopaedia Britannica , at https://www.britannica.com/science/epidemiology#ref323516 .

Morabia (2004), 3.

Lisa Wilkinson, “Epidemiology,” in Companion Encyclopedia of the History of Medicine II , ed. by W.F. Bynum and Roy Porter (London and New York: Routledge, 1993), 1262–81, here 1262, 1273.

Susser and Stein (2009), 65.

Olga Amsterdamska, “Demarcating Epidemiology,” Science, Technology & Human Values 30:1 (2005), 17–51, here 17.

Amsterdamska (2005), 23.

Jacob Steere-Williams, The Filth Disease: Typhoid Fever and the Practices of Epidemiology in Victorian England (Rochester, NY: University of Rochester Press, 2020), 7. See also Anne Hardy, Salmonella Infections, Networks of Knowledge, and Public Health in Britain, 1880–1975 (Oxford: Oxford University Press, 2015).

Amsterdamska (2005), 18.

Krieger (2014), 103; Susser and Stein (2019), 165; Wilkinson (1993), 1276–7.

Berlivet (2005), 41.

MacMahon and Pugh (1970), 5. See also Susser and Stein (2009), 164, 172–3.

Wilkinson (1993); Morens (2008).

Roger Cooter, ed., Medicine in the Twentieth Century (Harwood Academic Publishers, 2000); Dorothy Porter, Health, Civilization, and the State: A History of Public Health from Ancient to Modern Times (Routledge, 1999); Gert Brieger, “The Historiography of Medicine,” 33, cited by Steere-Williams (2020), 113; Hardy (2015), 9.

See also Morabia (2004) and History of Psychiatric Epidemiology, International Journal of Epidemiology 43, supplement 1 (2014), ed. by Anne Lovell and Ezra Susser.

Susser and Stein (2009), 70–71, 120, 296, 302, 309, 333. The eras are summed up in tables on pages 304 and 321. The chapters where the model is outlined were originally published as independent articles in 1995–1996 and then included in the book.

E.g., Susser and Stein (2009), 16–17, 22, 163. They see no great difference between Kuhnian paradigms and Fleck’s thought styles/communities, thus diverging from the original definitions of these concepts.

Krieger (2014), vii, 3, 30–31, 34 passim.

Krieger (2014), 97.

On the emergence and application of RCT, see e.g., Harry Marks, The Progress of Experiment: Science and Therapeutic Reform in the Unites States, 1900–1990 (Cambridge: Cambridge University Press, 1997); Iain Chalmers, “Statistical Theory Was Not the Reason That Randomization Was Used in the British Research Council’s Clinical Trial of Streptomycin for Pulmonary Tuberculosis,” in Body Counts: Medical Quantification in Historical and Sociological Perspectives , ed. by Gérard Jorland, Annick Opinel and George Weisz (Montreal: McGill-Queen’s University Press, 2005), 309–34. On the 1950s tobacco-cancer debates, see Mark Parascandola, “Epidemiology in Transition: Tobacco and Lung Cancer in the 1950s,” in Jorland, Opinel and Weisz (2005), 226–48.

Parascandola (2005), 226. See also Mark Parascandola, “Scepticism, Statistical Methods, and the Cigarette: A Historical Analysis of a Methodological Debate,” Perspectives in Biology and Medicine 47:2 (2004), 244–61; A.M. Brandt, “The Cigarette, Risk, and American Culture,” Daedalus 119:4 (1990), 155–76.

W.B. Kannel, T.R. Dawber, A. Kagan, N. Revotskie, and J. Stokes, “Factors of Risk in the Development of Coronary Heart Disease—Six-Year Follow-Up Experience. The Framingham Study,” Annals of Internal Medicine 55 (1961), 33–50; Luc Berlivet, “Association and Causation: The Debate on Scientific Status of Risk Factor Epidemiology, 1947–c. 1965,” in Making Health Policy: Networks in Research Policy After 1945 , ed. by Virginia Berridge (Amsterdam: Rodopi, 2005), 43–74.

William G. Rothstein, Public Health and the Risk Factor (Rochester: University of Rochester Press, 2003); Robert A. Aronowitz, Making Sense of Illness: Science, Society, and Disease (Cambridge: Cambridge University Press, 1998); William G. Rothstein, The Coronary Heart Disease Pandemic in the Twentieth Century: Emergence and Decline in Advanced Countries (London: Taylor & Francis, 2018). On the history of the notion of risk and risk factor epidemiology, see also Élodie Giroux, “Enquête de cohorte et analyse multivariée: une analyse épistémologique et historique du rôle fondateur de l’étude de Framingham,” Revue d’épidemiologie et santé publique 56:3 (2008), 177–88; “The Framingham Study and the Constitution of a Restrictive Concept of Risk Factor,” Social History of Medicine 26:1 (2012), 94–112; Luc Berlivet (2005); Gerald M. Oppenheimer, “Profiling Risk: The Emergence of Coronary Heart Disease Epidemiology in the United States (1947–70),” International Journal of Epidemiology 35 (2006), 720–30; “Becoming the Framingham Study 1947–1950,” American Journal of Public Health 95:4 (2005), 602–10; Robert Aronowitz, “The Framingham Heart Study and the Emergence of Risk Factor Approach to Coronary Heart Disease,” Revue d’histoire des sciences 64:2 (2011), 263–95; Sejal S. Patel, “Methods and Management: NIH Administrators, Federal Oversight, and the Framingham Heart Study,” Bulletin of the History of Medicine 86:1 (2012), 94–121.

Alain Desrosières, The Politics of Large Numbers: A History of Statistical Reasoning (Cambridge, MA: Harvard University Press, 1998); Ian Hacking, The Taming of Chance (Cambridge: Cambridge University Press, 1990); Theodore M. Porter, The Rise of Statistical Thinking, 1820–1900 (Princeton: Princeton University Press, 1986); Theodore M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life (Princeton: Princeton University Press, 1995); The Road to Medical Statistics (Amsterdam: Rodopi, 2002), ed. by Eileen Magnello and Anne Hardy. See also George Weisz, “From Clinical Counting to Evidence-Based Medicine,” in Jorland, Opinel and Weisz (2005), 377–93; Theodore M. Porter, “Life Insurance, Medical Testing, and the Management of Mortality,” in Biographies of Scientific Objects , ed. by Lorraine Daston (Chicago: University of Chicago Press, 1999), 226–46.

G.C. Bowker and Susan Leigh Star, Sorting Things Out: Classification and Its Consequences (Cambridge, MA: MIT Press, 1999). On the establishment of global health communication infrastructures, see also Heidi Tworek, “Communicable Disease: Information, Health, and Globalization in the Interwar Period,” The American Historical Review 124:3 (2019): 813–42.

E.g., Robert Mitchell and Catherine Waldby, “National Biobanks: Clinical Labor, Risk Production, and the Creation of Biovalue,” Science, Technology, & Human Values 35:3 (2010), 330–55; Marjut Salokannel, Heta Tarkkala, and Karoliina Snell, “Legacy Samples in Finnish Biobanks. Social and Legal Issues Related to the Transfer of Old Sample Collections into Biobanks,” Human Genetics 138:11–12 (2019), 1287–99; Aaro Tupasela, Karoliina Snell, and J.A. Cañada, “Constructing Populations in Biobanking,” Life Sciences, Society and Policy 11:5 (2015), 1–18; Alison Cool, “Impossible, Unknowable, Accountable: Dramas and Dilemmas of Data Law,” Social Studies of Science 49:4 (2019), 503–30; David Armstrong, “The Social Life of Data Points: Antecedents of Digital Technologies,” Social Studies of Science 49:1 (2019), 102–17; Soraya de Chadarevian and Theodore M. Porter, “Histories of Data and the Database (special Issue),” Historical Studies in the Natural Sciences 48:5 (2018); Sabina Leonelli, Biomedical Knowledge Production in the Age of Big Data . Analysis conducted on behalf of the Swiss Science and Innovation Council SSIC (Schweizerische Eidgenossenschaft, 2017); M. Rückenstein and N.D. Schüll, “The Datafication of Health,” Annual Review of Anthropology 46 (2017), 261–78; K. Hoyer, S. Bauer, and M. Pickersgill, “Datafication and Accountability in Public Health: Introduction to a Special Issue,” Social Studies of Science 49:4 (2019), 459–75.

Susanne Bauer, “Danish Population Registries, the ‘Scandinavian Laboratory,’ and the ‘Epidemiologist’s Dream,’” Science in Context 27:2 (2014), 187–213; Bauer (2008); and Susanne Bauer, “Modeling Population Health: Reflections on the Performativity of Epidemiological Techniques in the Age of Genomics,” Medical Anthropology Quarterly 27:4 (2013), 510–30.

Hardy (2015), 17.

Steere-Williams (2020), 16–17.

Tom Koch, Disease Maps: Epidemics on the Ground (Chicago and London: University of Chicago Press, 2011).

Lukas Engelmann, Mapping AIDS: Visual Histories of an Enduring Epidemic (Cambridge: Cambridge University Press, 2018).

Plague Image and Imagination from Medieval to Modern Times , ed. by Christos Lynteris (London: Palgrave Macmillan, 2021); Christos Lynteris, “The Epidemiologist as Culture Hero: Visualizing Humanity in the Age of ‘the Next Pandemic,’” Visual Anthropology 29:1 (2016), 36–53; Christos Lynteris, Human Extinction and the Pandemic Imaginary (Routledge, 2020); Christos Lynteris,  Visual Plague: The Emergence of Epidemic Photography (Cambridge, MA: MIT Press, 2022).

Anne Hardy, The Epidemic Streets: Infectious Diseases and the Rise of Preventative Medicine, 1856–1900 (Oxford: Clarendon Press, 1993); William Coleman, Yellow Fever in the North: The Methods of Early Epidemiology (Madison: University of Wisconsin Press, 1987); Francois Delaporte, The History of Yellow Fever: An Essay on the Birth of Tropical Medicine (Cambridge, MA: MIT Press, 1991); Steere-Williams (2020); Naomi Rogers, Dirt and Disease: Polio before FDR (New Brunswick: Rutgers University Press, 1992); Dora Vargha, Polio Across the Iron Curtain: Hungary’s Cold War with an Epidemic (Cambridge: Cambridge University Press, 2018); Lukas Engelmann and Christos Lynteris, Sulphuric Utopias: The History of Maritime Fumigation (Cambridge, MA: MIT Press, 2020).

Charles E. Rosenberg, “What Is an Epidemic? AIDS in Historical Perspective,” Daedalus 118:2 (1989), 1–17.

E.g., Nicolas Rasmussen, “Downsizing Obesity: On Ancel Keys, the Origins of BMI, and the Neglect of Excess Weight as a Health Hazard in the United States from the 1950s to 1970s,” Journal of the History of the Behavioral Sciences 55:4 (2019), 299–318; Laura Dawes, Childhood Obesity in America: Biography of an Epidemic (Harvard, MA: Harvard University Press, 2014); Frank B. Hu’s “Introduction to Obesity Epidemiology,” in Obesity Epidemiology , ed. by Frank Hu (Oxford: Oxford University Press, 2008: 5–14), also includes a concise discussion on the history of epidemiological research into obesity.

George Weisz, Chronic Disease in the Twentieth Century: A History (Baltimore: Johns Hopkins University Press, 2014); Robin Wolfe Scheffler, A Contagious Cause: The American Hunt for Cancer Viruses and the Rise of Molecular Medicine (Chicago: University of Chicago Press, 2020); Megan Vaughan, Kafui Adjaye-Gbewonyo, and Marissa Mika, eds., Epidemiological Change and Chronic Disease in Sub-Saharan Africa: Social and Historical Perspectives (London: University College London Press, 2021); Julie Livingston, Improvising Medicine: An African Oncology Ward in an Emerging Cancer Epidemic (Durham, NC: Duke University Press, 2012).

Erwin H. Ackerknecht, “Anticontagionism between 1821 and 1867,” Bulletin of the History of Medicine 22:5 (1948), 562–93.

Graham Mooney, Intrusive Interventions: Public Health, Domestic Space, and Infectious Disease Surveillance in England, 1840–1914 (Woodbridge: Boydell & Brewer, 2015); Alison Bashford, Quarantine: Local and Global Histories (London: Palgrave Macmillan, 2016); Virginia Berridge, Marketing Health: Smoking and the Discourse of Public Health in Britain, 1945–2000 (Oxford: Oxford University Press, 2007); Virginia Berridge, “Science and Policy: The Case of Post-War British Smoking Policy,” in Ashes to Ashes: The History of Smoking and Health , ed. by S. Lock, L.A. Reynolds and E.M. Tanser (Amsterdam: Rodopi, 1998), 143–63; Ilana Löwy and J. Krige, eds., Images of Disease: Science, Public Policy and Health in Post-War Europe (Luxemburg: Office for Official Publications of the European Communities, 2011), 53–72.

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Hakosalo, H., Parhi, K., Sailo, A. (2023). Introduction: The Past Continuous of Epidemiology. In: Hakosalo, H., Parhi, K., Sailo, A. (eds) Historical Explorations of Modern Epidemiology. Medicine and Biomedical Sciences in Modern History. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-20671-9_1

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John Snow and the Birth of Epidemiology

Even though this physician pre-dated germ theory, he was able to track a London outbreak of cholera to one particular water pump.

An 1854 cholera outbreak in London confounded those who thought the disease was caused by miasma, or foul air. Enter John Snow, who had already made a name for himself by administering chloroform to Queen Victoria during childbirth. Snow was skeptical of the reigning miasmatic theory of disease because of his own experiences fighting cholera. Even though he pre-dated germ theory and didn’t know that a bacterium caused cholera, he nonetheless tracked the outbreak of the disease.

According to evolutionary biologist Susan Bandoni Muench, in the mid-nineteenth century, London had a population density greater than Manhattan’s today. Sanitary conditions weren’t particularly good even for the upper classes, while at the bottom rungs at least 100,000 people scavenged rags, bones, coal scraps, and night soil.

During an earlier cholera outbreak in London, Snow wondered how the “blue death”—so called because of the blue tinge of the victims’ skin—spread. In an 1849 monograph, he postulated that cesspools might be spreading human waste to drinking water. This idea was met with scorn. For several years he mapped past incidences of the disease, compared neighborhoods and neighbors, and virtually invented epidemiology.

When the 1854 epidemic hit, killing 700 people in a matter of weeks, Snow was ready. He knocked on doors and interviewed families with cholera. What united all the cases? They got their water at this Broad Street pump in the Soho neighborhood. Snow’s research was reinforced by the Reverend Henry Whitehead, who initially doubted Snow’s thesis. But Whitehead found the same Broad Street pump connection by interviewing those locals who hadn’t gotten cholera; these people hadn’t used that pump.

The water pump’s handle was removed to render it inoperable. Cases of cholera plummeted afterwards. Snow himself noted that the epidemic was probably petering out by then anyway, as epidemics tend to do. But shutting the pump down clearly had an effect on the mortality of the epidemic.

Snow thought of cholera’s spread as analogous to a gas’s diffusion, but in the medium of water, not the air as the miasmatists had it. And Snow really knew his gasses, since he had been experimenting for years on chloroform, ether, ethyl nitrate, carbon disulphide, benzene, and several other potential anesthetics. These experiments were performed on animals and… himself .

Epidemiologist A.R. Mawson suggests that “ extensive and prolonged self-experimentation with anaesthetics over a 9-year period led to Snow’s renal failure, swollen fingers and early death from stroke.” Snow was only 45 when he died. An early bout with tuberculosis and a probable vitamin D deficiency from his vegetarian diet (since the age of 17) wouldn’t have helped.

Snow followed an exemplary lifestyle by today’s standards—he didn’t drink, didn’t eat meat, and exercised vigorously. He even distilled his own water while he lived in the heart of the Soho epidemic. His work ethic seems admirable, too. But he couldn’t know his very work was hazardous to health. Exposure to anesthetic gasses damaged kidneys and livers, as well as the nervous and reproductive systems.

For public health, Snow sacrificed his own.

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The Mystery of the Blue Death

A Case Study in Epidemiology and the History of Science

By Susan Bandoni Muench

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This historical case study describes the story of John Snow’s discovery of water-borne transmission of cholera in 19th-century London. Designed for use in a Global Health class, the case explores cholera outbreaks and their causes as well as models of disease. In addition, the case provides a framework for discussing the nature of science, particularly non-experimental tests of hypotheses, the cultural context of science, and populational thinking. The case could be used in a variety of other contexts, including courses in microbiology and introductory biology for either majors or non-majors. Because it addresses the nature of science, it is also appropriate for courses in the history, philosophy, or sociology of science.

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• Apply terminology and concepts from epidemiology and public health to a case study.
• Explore aspects of the nature of science, including the role of models in hypothesis testing, non-experimental tests of hypotheses, and populational thinking.
• Explore the relationship between science and the surrounding culture, and cultural and class influences on the practice of science.

Cholera; Vibrio cholera; diarrheal disease; infectious disease; water-borne disease; models of disease; epidemiological methods; experimental design; hypothesis testing; populational thinking; health inequities; John Snow; London

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High school, Undergraduate lower division, Undergraduate upper division, Graduate, Professional (degree program)

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History of science, Scientific method, Social issues, Social justice issues

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Article Contents

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Invited Commentary: When Case-Control Studies Came of Age

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Kenneth J. Rothman, Invited Commentary: When Case-Control Studies Came of Age, American Journal of Epidemiology , Volume 185, Issue 11, 1 June 2017, Pages 1012–1014, https://doi.org/10.1093/aje/kwx074

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In his 1976 paper “Estimability and Estimation in Case-Referent Studies” ( Am J Epidemiol . 1976;103(2):226–235), Miettinen weaved together a patchwork of new ideas into a coherent view of case-control studies. His article spurred theoretical development in epidemiologic methods and became a platform for teaching about some key concepts in epidemiologic study design.

By 1976, when “Estimability and Estimation in Case-Referent Studies” was published ( 1 ), case-control studies had become common, but their theoretical underpinnings had yet to be clearly elucidated. Today, we conceptualize case-control studies as streamlined versions of cohort studies. The case-control design improves efficiency by sampling from the persons or person-time that gave rise to the cases rather than collecting data from all of that experience. The control-group sample allows estimation of the prevalence of exposure and covariates more efficiently than does a complete census of the source population, with only a slight reduction in precision.

A perusal of basic epidemiology texts from 4 decades ago reveals just a glimmer of present-day insights into the theory of case-control study design. In that era, the case-control study design was described in most textbooks as a “retrospective study” or a “case-history study” and considered more of a quick and dirty approach to epidemiologic research than a legitimate study design. For example, in one textbook, the “retrospective study” was relegated to the final pages and described with this theoretical foundation: “Careful consideration must also be given to the selection of a control group; the important principle is that the controls should resemble the cases closely except for the presence of the disease under study” ( 2 , pp. 314–315). Epidemiologists now understand this advice to be incorrect, although it was common then, was often repeated in classrooms, and even today may still have currency among amateur epidemiologists. It is an example of a false analogy: In a cohort study, it would be reasonable to say that an unexposed cohort should resemble the exposed cohort closely except for the presence of the exposure. It was reasoned by analogy that in a case-control study, because the controls were the comparison series for the cases, they should be just like the cases apart from having disease. It took new theoretical insights, largely influenced by Miettinen, to reach the understanding that the control series should not be like the cases; instead, it should be like the population from which the cases arise. Of course, hindsight has great acuity, and I cite this example only to illustrate the conceptual level of understanding about case-control studies that was prevalent when Miettinen wrote this paper.

Although historical examples of case-control studies can be found from before the 20th century, many credit Janet Lane-Claypon as the author of the first formal case-control study, which was published in 1926 ( 3 ). However, it was not until 1950, with the publication of the landmark case-control studies on smoking and lung cancer by Wynder and Graham ( 4 ), Doll and Hill ( 5 ), and Levin et al. ( 6 ), that the case-control design gained impetus. Within a year, Jerome Cornfield ( 7 ) introduced the odds ratio and showed that it was an estimator of the risk ratio in case-control studies. Nonetheless, Cornfield's paper left gaps. For example, it did not distinguish between rates and risks, and it led to the widespread belief that the odds ratio could serve as an estimator of relative risk only if one could assume that the disease is rare. “Relative risk” was an ambiguous term; we now understand that it can refer to the ratio of risks or the ratio of incidence rates (and in some circumstances, the ratio of prevalences). In Cornfield's day, the distinction between risks and rates was largely submerged. It is a historical curiosity, explained by Vandenbroucke ( 8 ), that the distinction (between risk and rate) was well known to William Farr in the mid-19th century but was later forgotten, at least among epidemiologists, until it was reintroduced in the 1970s ( 9 ).

Miettinen's 1976 paper was rich with content that made it a watershed development in the understanding of case-control studies. As the title suggests, Miettinen addressed several measures that might be estimable from a case-control study and discussed how to estimate them. He meticulously distinguished rates from risk and explained why the rare disease assumption discussed by Cornfield was only applicable to a particular type of case-control study in which the cases are ascertained after the end of the entire risk period of interest and controls are sampled from among those who did not become cases, a design nowadays called a cumulative case-control study. In the paper, Miettinen discussed estimation of incidence density ratio (he used the term “incidence density” to describe “incidence rate”), explaining how the rare disease assumption was not needed for case-control studies in which the incidence density ratio was estimated. The paper also addressed how the incidence density ratio could be estimated from a study of either incident or prevalent cases; however, with prevalent cases, it was necessary to assume that the duration of illness was unrelated to the exposure. Another issue discussed was the estimation of the risk ratio (Miettinen's term for this was “cumulative incidence ratio”) and the etiologic fraction, as well as exposure-specific estimation. These ideas laid a strong theoretical foundation for the case-control study.

In Appendix 1, Miettinen also introduced a controversial method for estimating confidence intervals. This was the method of test-based confidence limits, a simple approach that capitalized on the connection between statistical tests of a null hypothesis and the calculation of confidence limits. This procedure was easy to apply but had theoretical drawbacks, because the confidence intervals were found to have coverage levels that departed from nominal levels ( 10 , 11 ). Miettinen argued that the coverage levels were not as important as the proximity of the approximate limits to exact limits ( 12 ); however, with the advent of programmable calculators and personal computers that enabled quick calculation of exact confidence limits, test-based limits ultimately conferred little advantage and did not gain traction either theoretically or practically.

Miettinen did not discuss matched case-control designs in that paper. Presumably the sweep of the paper was broad enough without introducing the issue of matching, which for case-control studies brought considerable added complexity. Nonetheless, the role of matching in case-control studies was something that he had already elaborated in detail in a series of papers that stemmed from his doctoral thesis in biostatistics at the University of Minnesota, which was completed in 1968 ( 13 – 16 ). Later, Greenland and Thomas ( 17 ) noted that his proposed density-sampling design involved time-matching and therefore required taking that matching into account in the analysis to obtain unbiased results.

With his paper, Miettinen introduced the neologism “case-referent studies” in place of the more traditional and popular term case-control studies. As noted above, the term case-control itself was not universally used in the 1970s, but it was rapidly gaining acceptance. Miettinen had used the term case-control in several of his earlier papers, but he switched to case-referent here. Some epidemiologists still use case-referent, but the term case-control is the one more commonly used by epidemiologists today. Similarly, his introduction of the term incidence density for incidence rate is still occasionally used, but most epidemiologists use the term incidence rate.

Not surprisingly, some of Miettinen's observations were anticipated in earlier literature. For example, several authors had used control sampling directly from the population from which the cases arose and thus did not need a rare disease assumption ( 18 , 19 ). However, Miettinen drew ideas together in a concise and elegant elaboration of some key elements of modern epidemiologic theory that is likely to be read by many future generations of epidemiologists.

Author affiliations: RTI Health Solutions, Research Triangle Institute, Waltham, Massachusetts (Kenneth J. Rothman); and Department of Epidemiology, Boston University, Boston, Massachusetts (Kenneth J. Rothman).

Conflict of interest: none declared.

Miettinen O . Estimability and estimation in case-referent studies . Am J Epidemiol . 1976 ; 103 ( 2 ): 226 – 235 .

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Mausner JS , Bahn AK . Epidemiology: An Introductory Text . Philadelphia, PA : WB Saunders Co. ; 1974 .

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Lane-Claypon J . A Further Report on Cancer of the Breast: Reports on Public Health and Medical Subjects . London, UK: Ministry of Health; 1926 .

Wynder EL , Graham EA . Tobacco smoking as a possible etiologic factor in bronchiogenic carcinoma; a study of 684 proved cases . J Am Med Assoc . 1950 ; 143 ( 4 ): 329 – 336 .

Doll R , Hill AB . Smoking and carcinoma of the lung; preliminary report . Br Med J . 1950 ; 2 ( 4682 ): 739 – 748 .

Levin ML , Goldstein H , Gerhardt PR . Cancer and tobacco smoking; preliminary report . J Am Med Assoc . 1950 ; 143 ( 4 ): 336 – 338 .

Cornfield J . A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix . J Natl Cancer Inst . 1951 ; 11 ( 6 ): 1269 – 1275 .

Vandenbroucke JP . On the rediscovery of a distinction . Am J Epidemiol . 1985 ; 121 ( 5 ): 627 – 628 .

Elandt-Johnson R . Definition of rates: some remarks on their use and misuse . Am J Epidemiol . 1975 ; 102 ( 4 ): 267 – 271 .

Halperin M . Re: “estimability and estimation in case-referent studies” . Am J Epidemiol . 1977 ; 105 ( 5 ): 496 – 498 .

Greenland S . A counterexample to the test-based principle of setting confidence limits . Am J Epidemiol . 1984 ; 120 ( 1 ): 4 – 7 .

Miettinen OS . The author replies . Am J Epidemiol . 1977 ; 105 ( 5 ): 498 – 502 .

Miettinen OS . Some Basic Theory for Matching Designs in Nonexperimental Research on Causation [thesis]. Ann Arbor, MI: University of Minnesota; 1968 .

Miettinen OS . The matched pairs design in the case of all-or-none responses . Biometrics . 1968 ; 24 ( 2 ): 339 – 352 .

Miettinen OS . Under- and overmatching in epidemiologic studies . Atti 5? Cong Internaz Igi6ne Med Prev . 1968 ; 1 : 49 – 61 .

Miettinen OS . Individual matching with multiple controls in the case of all-or-none responses . Biometrics . 1969 ; 25 ( 2 ): 339 – 355 .

Greenland S , Thomas DC . On the need for the rare disease assumption in case-control studies . Am J Epidemiol . 1982 ; 116 ( 3 ): 547 – 553 .

Sheehe PR . Dynamic risk analysis in retrospective matched-pair studies of disease . Biometrics . 1962 ; 18 ( 3 ): 323 – 341 .

Thomas DB . Relationship of oral contraceptives to cervical carcinogenesis . Obstet Gynecol . 1972 ; 40 ( 4 ): 508 – 518 .

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Epidemiology

Although lacking the control available to toxicity studies, epidemiology allows for much larger study sizes and is particularly useful for identifying causal factors for population-wide health concerns. Some epidemiological studies are given more credence than their toxicological counterparts because the data are seen as more accurate for ‘real-life’ situations. Also, data collected for completely different reasons can often be incorporated into an epidemiological study. The greatest difference between epidemiology and toxicology, aside from the observational/experimental basis, is the measure of exposure, which, although it would improve any study, is often difficult to incorporate into epidemiological studies because of the historical nature of exposures.

Epidemiology looks at the association between adverse effects seen in humans and a selected potential ‘cause’ of interest, such as the use of or exposure to a chemical, disease agent, radiation, drug, or a medical device. Epidemiology is sometimes simply defined as the study of patterns of health in groups of people. Behind this deceptively simple definition lies a surprisingly diverse science, rich in concepts and methodology. For instance, the group of people might consist of only two people, such as the case of a father suffering from rheumatoid arthritis and his daughter with vertigo. In both father and daughter, the pattern of affected areas was remarkably similar, which might suggest that the distribution of joint lesions in rheumatoid arthritis is genetically determined. At the opposite extreme, studies of the geographic distribution of diseases using national mortality and cancer incidence rates have provided clues about the etiology of several diseases, such as cardiovascular disease and stomach cancer. The patterns of health studied are also wide-ranging and may include the distribution, course, and spread of disease. The term disease also has a loose definition in the context of epidemiology and might include ill-defined conditions, such as organic solvent syndrome and sick-building syndrome, or consist of an indirect measure of impairment, such as biochemical and hematological parameters or lung function measurements.

Epidemiology and toxicology differ in many other ways, but principally in that epidemiology is essentially an observational science, in contrast to the experimental nature of toxicology. The epidemiologist often has to make do with historical data that have been collected for reasons that have nothing to do with epidemiology. Nevertheless, the availability of personnel records, such as lists of new employees and former employees, payrolls and work rosters, and exposure monitoring data collected for compliance purposes has enabled many epidemiological studies to be conducted in the occupational setting. Thus, the epidemiologist has no control over who is exposed to an agent, the levels at which they are exposed to the agent of interest, or the other agents to which they may be exposed. The epidemiologist has great difficulty in ascertaining what exposure has taken place and certainly has no control over lifestyle variables, such as diet and smoking.

Despite the lack of precise data, the epidemiologist has one major advantage over the toxicologist. An epidemiological study documents the actual health experiences of human beings subjected to real-life exposures in an occupational or environmental setting. The view has been expressed that uncertainty in epidemiology studies resulting from exposure estimation may be equal to or less than the uncertainty associated with extrapolation from animals to humans. Regulatory bodies, such as the US Environmental Protection Agency (EPA) are starting to change their attitudes toward epidemiology and recognize that it has a role to play in the process of risk assessment. However, there is also a complementary need for epidemiologists to introduce more rigor into the conduct of their studies and introduce standards akin to the Good Laboratory Practices standards under which animal experiments are performed.

Measurement of Exposure

Epidemiologists have placed much greater emphasis on the measure of response than on the measure of exposure. They claim that this is because most epidemiologists have been trained as physicians and are consequently more oriented toward measuring health outcomes. It is certainly true that a modern textbook of epidemiology says very little about what the epidemiologist should do with exposure assessments. However, this is probably as much a reflection of the historical paucity of quantitative exposure information as a reflection on the background of epidemiologists. Nevertheless, it is surprising how many epidemiological studies do not contain even a basic qualitative assessment of exposure. The contrast between epidemiology and toxicology is never more marked than in the area of estimation of dose response. The toxicologist can carefully control the conditions of exposure to the agent of interest. Moreover, the toxicologist can be sure that the test animals have not come into contact with any other toxic agents. An industrial epidemiologist conducting a study of workers exposed to a hepatotoxin certainly has to control for alcohol intake and possibly for exposure to other hepatotoxins in the work and home environments. Nevertheless, it can be argued that epidemiological studies more accurately measure the effect on human health of ‘real-life’ exposures.

If an exposure matrix has been constructed with quantitative estimates of the exposure in each job and time period, then it is a simple matter to estimate cumulative exposure. It is a more difficult process when, as is common, only a qualitative measure of exposure is available (e.g., high, medium, and low).

Even when exposure measurements are available, it may not be sensible to make an assumption that an exposure that occurred 20 years ago is equivalent to the same exposure yesterday. The use of average exposures may also be questionable, and peak exposures may be more relevant in the case of outcomes, such as asthma and chronic bronchitis. Noise is a good example of an exposure that must be carefully characterized and where the simple calculation of a cumulative exposure may be misleading.

Study Designs

This section provides a brief introduction to the most important types of studies conducted by epidemiologists. It is an attempt to briefly describe the principles of the major types of epidemiological studies to provide insight into the reporting of epidemiological studies and the assumptions made by epidemiologists. The next section discusses the similarities and differences between the methodologies of toxicology and epidemiology.

Cohort Studies

Historical cohort study.

When the need arises to study the health status of a group of individuals, there is often a large body of historical data that can be used. If sufficient information exists on individuals exposed in the past to a potential workplace hazard, then it may be possible to undertake a retrospective cohort study. The historical data will have been collected for reasons that have nothing to do with epidemiology. Nevertheless, the availability of personnel records, such as registers of new and former employees, payrolls, work rosters, and individuals' career records, has enabled many epidemiological studies to be conducted, particularly mortality studies.

The principles of a historical cohort study can also be applied to follow a cohort of workers prospectively. This approach is discussed further in the next section, although it should be emphasized that many historical data studies have a prospective element insofar as they are updated after a further follow-up period. The discussion of historical cohort studies in this section concentrates on mortality and cancer incidence studies. However, there is no reason why hearing loss, lung function, or almost any measure of the health status of an individual should not be studied retrospectively if sufficient information is available.

Mortality and cancer incidence studies are unique among retrospective cohort studies in that they can be conducted using national cancer and mortality registers even if there has been no medical surveillance of the work force. A historical cohort study also has the advantages of being cheaper and providing estimates of the potential hazard much earlier than a prospective study. However, historical cohort studies are beset by a variety of problems. Principal among these is the problem of determining which workers have been exposed and, if so, to what degree? In addition, it may be difficult to decide what an appropriate comparison group is. It should also be borne in mind that in epidemiology, unlike animal experimentation, random allocation is not possible and there is no control over the factors that may distort the effects of the exposure of interest, such as smoking and the standard of living. The principles of historical cohort studies are described in the following subsections.

Cohort Definition and Follow-Up Period

A variety of sources of information are used to identify workers exposed to a particular workplace hazard, construct an occupational history, and complete the collection of information necessary for tracing (see the following). It is essential that the cohort be well defined and that criteria for eligibility are strictly followed. This requires that a clear statement be made about membership of the cohort so it is easy to decide whether an employee is a member or not. It is also important that the follow-up period be carefully defined. For instance, it is readily apparent that the follow-up period should not start before exposure has occurred. Furthermore, it is uncommon for the health effect of interest to manifest itself immediately after exposure, and allowance for an appropriate biological induction (or latency) period may need to be made when interpreting the data.

Comparison Subjects

The usual comparison group for many studies is the national population. However, it is known that there are marked regional differences in the mortality rates for many causes of death. Regional mortality rates exist in most industrialized countries but have to be used with caution because they are based on small numbers of deaths and estimated population sizes. In some situations the local rates for certain causes may be highly influenced by the mortality of the patients being studied. Furthermore, it is not always easy to decide what the most appropriate regional rate for comparison purposes is, as many employees may reside in a different region from that in which the plant is situated.

An alternative or additional approach is to establish a cohort of unexposed workers for comparison purposes. However, workers with very low exposures to the workplace hazard often provide similar information.

Analysis and Interpretation

In a cohort study, the first stage in the analysis consists of calculating the number of deaths expected during the follow-up period. To calculate the expected number of deaths for the cohort, the survival experience of the cohort is broken down into individual years of survival, known as ‘person years.’ Each person year is characterized by the age and sex of the cohort member and the time period when survival occurred. The person years are then multiplied by age-, sex-, and time period-specific mortality rates to obtain the expected number of deaths. The ratio between observed and expected deaths is expressed as a standardized mortality ratio (SMR) as follows:

Thus, an SMR of 1.25 represents an excess mortality of 25%. An SMR can be calculated for different causes of death and for subdivision of the person years by factors, such as the level of exposure and time since the first exposure.

Interpretation of cohort studies is not always straightforward; there are a number of selection effects and biases that must be considered. Cohort studies routinely report that the mortality of active workers is less than that of the population as a whole. It is not an unexpected finding because workers usually have to undergo some sort of selection process to become or remain workers. Nevertheless, this selection effect, known as the ‘healthy worker’ effect, can lead to considerable arguments over the interpretation of study results, particularly if the cancer mortality is as expected but the all-cause mortality is much lower than expected. However, even an experimental science, such as toxicology is not without a similar problem of interpretation, namely, the problem of distinguishing between the effects of age and treatment on tumor incidence.

Proportional Mortality Study

There are often situations in which one has no accurate data on the composition of a cohort but does possess a set of death records (or cancer registrations). In these circumstances, a proportional mortality study may sometimes be substituted for a cohort study. In such a mortality study, the proportions of deaths from a specific cause among the study deaths is compared with the proportion of deaths from that cause in a comparison population. The results of a proportional mortality study are expressed in an analogous way to those of the cohort study with follow-up corresponding to the observed deaths from a particular cause; it is possible to calculate an expected number of deaths based on mortality rates for that cause and all causes of death in a comparison group and the total number of deaths in the study. The ratio between observed and expected deaths from a certain cause is expressed as a proportional mortality ratio (PMR) as follows:

Thus, a PMR of 125 for a particular cause of death represents a 25% increase in the proportion of deaths owing to that cause. A proportional mortality study has the advantage of avoiding the expensive and time-consuming establishment and tracing of a cohort but the disadvantage of little or no exposure information.

Prospective Cohort Study

Prospective cohort studies are no different in principle from historical cohort studies in terms of scientific logic, the major differences being timing and methodology. The study starts with a group of apparently healthy individuals whose health and exposure are studied over a period of time. As it is possible to define in advance the information that is to be collected, prospective studies are theoretically more reliable than retrospective studies. However, long periods of observation may be required to obtain results.

Prospective cohort studies or longitudinal studies of continually changing health parameters, such as lung function, hearing loss, blood biochemistry, and hematological measurements, pose different problems from those encountered in mortality and cancer incidence studies. The relationships between changes in the parameters of interest and exposure measurements have to be estimated and, if necessary, a comparison made of changes in the parameters between groups. These relationships may be extremely complicated, compounded by factors such as aging, and difficult to estimate because there may be relatively few measurement points. Furthermore, large errors of measurement in the variables may be present because of factors, such as within-laboratory variation and temporal variation within individuals. Missing observations and withdrawals may also cause problems, particularly if they are dependent on the level and change of the parameter of interest. These problems may make it difficult to interpret and judge the validity of analytical conclusions. Nevertheless, prospective cohort studies provide the best means of measuring changes in health parameters and relating them to exposure.

Case–Control Study

In a case–control study (also known as a case–referent study), two groups of individuals are selected for study, of which one has the disease whose causation is to be studied (the cases) and the other does not (the controls). In the context of the chemical industry, the aim of a case–control study is to evaluate the relevance of past exposure to the development of a disease. This is done by obtaining an indirect estimate of the rate of occurrence of the disease in an exposed and an unexposed group by comparing the frequency of exposure among cases and controls.

Principal Features

Case–control and cohort studies complement each other as types of epidemiological study. In a case–control study, the groups are defined on the basis of the presence or absence of a given disease and, hence, only one disease can be studied at a time. The case–control study compensates for this by providing information on a wide range of exposures that may play a role in the development of the disease. In contrast, a cohort study generally focuses on a single exposure but can be analyzed for multiple disease outcomes. A case–control study is a better way of studying rare diseases because a very large cohort would be required to demonstrate an excess of a rare disease. In contrast, a case–control study is an inefficient way of assessing the effect of an uncommon exposure, when it might be possible to conduct a cohort study of all those exposed.

The complementary strengths and weaknesses of case–control and cohort studies can be used to advantage. Increasingly, mortality studies are being reported that utilize ‘nested’ case–control studies to investigate the association between the exposures of interest and a cause of death for which an excess has been discovered. However, case–control studies have traditionally been held in low regard, largely because they are often poorly conducted and interpreted. There is also a tendency to overinterpret the data and misuse statistical procedures. In addition, there is still considerable debate among leading epidemiologists themselves as to how controls should be selected.

In a case–control study, it is possible to compare the frequencies of exposures in the cases and controls. However, what one is really interested in is a comparison of the frequencies of the disease in the exposed and the unexposed. The latter comparison is usually expressed as a relative risk (RR), which is defined as

It is clearly not possible to calculate the RR directly in a case–control study because exposed and unexposed groups have not been followed so as to determine the rates of occurrence of the disease in the two groups. Nevertheless, it is possible to calculate another statistic, the odds ratio (OR), which, if certain assumptions hold, is a good estimate of the RR. For cases and controls, the exposure odds are simply the odds of being exposed, and the OR is defined as

An OR of 1 indicates that the rate of disease is unaffected by exposure of workers to the agent of interest. An OR >1 indicates an increase in the rate of disease in exposed workers.

Matching is the selection of a comparison group that is, within stated limits, identical with the study group with respect to one or more factors (e.g., age, years of service, and smoking history), which may distort the effect of the exposure of interest. The matching may be done on an individual or group basis. Although matching may be used in all types of study, including follow-up and cross-sectional studies, it is more widely used in case–control studies. It is common to see case–control studies in which each case is matched to as many as three or four controls.

Nested Case–Control Study

In a cohort study, the assessment of exposure for all cohort members may be extremely time consuming and demanding of resources. If an excess of incidence of death has been discovered for a small number of conditions, it may be much more efficient to conduct a case–control study to investigate the effect of exposure. Thus, instead of all members being studied, only the cases and a sample of noncases would be compared with regard to exposure history. Thus, there is no need to investigate the exposure histories of all those who are neither cases nor controls. However, the nesting is only effective if there are a reasonable number of cases and sufficient variation in the exposure of the cohort members.

Other Study Designs

Descriptive studies.

There are large numbers of records in existence that document the health of various groups of people. Mortality statistics are available for many countries and even for certain companies. Similarly, there is a wide range of routine morbidity statistics, in particular, those based on cancer registrations. These health statistics can be used to study differences between geographic regions (e.g., maps of cancer mortality and incidence presented at a recent symposium), occupational groups, and time periods. Investigations based on existing records of the distribution of the disease and of possible causes are known as descriptive studies. It is sometimes possible to identify hazards associated with the development of rare conditions from observation of clustering in occupational or geographical areas.

Cross-Sectional Study

Cross-sectional studies measure the cause (exposure) and the effect (disease) at the same point in time. They compare the rates of diseases or symptoms of an exposed group with an unexposed group. Strictly speaking, the exposure information is ascertained simultaneously with the disease information. In practice, such studies are usually more meaningful from an etiological or causal point of view if the exposure assessment reflects past exposures. Current information is often all that is available but may still be meaningful because of the correlation between current exposure and relevant past exposure.

Cross-sectional studies are widely used to study the health of groups of workers who are exposed to possible hazards but do not undergo regular surveillance. They are particularly suited to the study of subclinical parameters, such as blood biochemistry and hematological values. Cross-sectional studies are also relatively straightforward to conduct in comparison with prospective cohort studies and are generally simpler to interpret.

Intervention Study

Not all epidemiology is observational, and experimental studies have a role to play in evaluating the efficiency of an intervention program to prevent disease (e.g., fluoridation of water). An intervention study at one extreme may closely resemble a clinical trial with individuals randomly selected to receive some form of intervention (e.g., advice on reducing cholesterol levels). However, in some instances it may be a whole community that is selected to form the intervention group. The selection may or may not be random.

Veterinary Epidemiology

Humans are in close association with their pets and other animals (e.g., local wildlife and animals on a farm). Veterinary epidemiology, like human epidemiology, looks at the association between adverse effects and a selected potential ‘cause’ of interest, such as exposure to a chemical or a disease agent. For example, veterinary epidemiology can play a key role in emerging and global disease outbreaks, helping in the understanding and prevention of infections and other emerging diseases, including those transmitted from an animal to other animals, and those possibly transmitted from animals to humans. An example of a veterinary epidemiological study was one investigating the transmission of Salmonella typhimurium from cattle that had received no growth-promoting antibiotics to humans who had direct contact with the sick animals. Another example is severe acute respiratory syndrome (SARS). In the investigation of the origins of the SARS outbreak in China, viruses associated with SARS were isolated from Himalayan palm civets found in a live-animal market in Guangdong, China, and evidence of virus infection was also detected in other animals and humans working at the same market. The detection of these viruses in small, live wild mammals in a retail market helped identify at least one means of the interspecies transmission; that is, infected animals sold in that market to human customers.

Meta-Analysis

The pooling of data from smaller studies to increase potential power of clinical and epidemiological studies has become popular in the last 10 years. Care must be taken, however, to ensure that all studies for which the data are pooled are adequately comparable.

Epidemiological studies can be the most powerful and persuasive tools for establishing the hazards associated with chemical exposures or personal actions (e.g., cigarette smoking). However, because of all the factors discussed previously, such studies also tend to be somewhat insensitive. Unless one can clearly establish the symptoms and signs of a disease for which there is a causal connection, such studies lose the desired specificity.

Analytical Toxicology; Carcinogen Classification Schemes; Carcinogenesis; International Agency for Research on Cancer; Medical Surveillance; National Institute for Occupational Safety and Health.

Further Reading

• Adami H.O., Berry S.C., Breckenridge C.B., Smith L.L., Swenberg J.A., Trichopoulos D., Weiss N.S., Pastoor T.P. Toxicology and epidemiology: improving the science with a framework for combining toxicological and epidemiological evidence to establish casual inference. Toxicol. Sci. 2011 Aug; 122 (2):223–234. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Descotes J. From clinical to human toxicology: linking animal research and risk assessment in man. Toxicol. Lett. 2003; 140–141 :3–10. [ PubMed ] [ Google Scholar ]
• Dourson M.L., Andersen M.E., Erdreich L.S., MacGregor J.A. Using human data to protect the public’s health. Regul. Toxicol. Pharmacol. 2001; 33 :234–256. [ PubMed ] [ Google Scholar ]
• Lilienfeld D.E., Stolley P.D. third ed. Oxford University Press; Oxford: 1994. Foundations of Epidemiology. [ Google Scholar ]
• Pesch B., Bruning T., Frentzel-Beyme R. Challenges to environmental toxicology and epidemiology: where do we stand and which way do we go? Toxicol. Lett. 2004; 151 :255–266. [ PubMed ] [ Google Scholar ]
• Rothman K.J., Greenland S., Lash T.L. third ed. Lippincott Williams and Wilkins; Philadelphia, PA: 2008. Modern Epidemiology. [ Google Scholar ]
• Salman M.D. Controlling emerging diseases in the 21st century. Prev. Vet. Med. 2004; 16 :177–184. [ PubMed ] [ Google Scholar ]

Relevant Websites

• http://www.niehs.nih.gov – National Institute of Environmental Health Sciences, homepage.
• http://www.cvm.uiuc.edu – The Association for Veterinary Epidemiology and Preventive Medicine (AVEPM).
• http://www.pitt.edu – Toxicology and Epidemiology (Online Supercourse). (More than 9000 faculty from 118 countries have contributed to an online library of more than 700 lectures with quality control and adherence to accepted pedagogic principles. The goal is to improve teaching and research in epidemiology and public health worldwide.)

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2 History of Epidemiology

Learning Objectives

By the end of this chapter, the learner will be able to

Learn about the history of epidemiology, which parallels the history of public health

• Examine the history of epidemiology as a relatively new science
• Illustrate the history of epidemiology with events that not only happened in the traditionally European tradition, but also in the United States, and the Americas.
• Create a history of the field of epidemiology and public health in the United States.

Epidemiology: Classic historic events The following content intends to provide only the most basic historical events in epidemiology. It does not intend to be exhaustive, but at least it will cover the most salient events that influenced the development of what it is known now as the field/science of epidemiology. From ancient civilizations, the time of the Greeks and Romans, and the 17th, 1800s, and 1990s to the 20th and 21st century the main purpose of epidemiology and public health has been the search for determinants that can explain causation and disease, the discovery of treatments and moreover, the prevention and control of disease, and related health problems

Early sanitation efforts, water systems and toilets Then, the Romans, who developed aqueducts and sewage systems to prevent the development of diseases related to the lack of proper sanitation; brought water to their cities improved sanitation, and personal hygiene, which are essential for good health. [2]

It is the Romans who also developed the first rudimentary system of disposition of excretas (or, excreta) before what we know as toilets were invented. [3]

Medieval period: The Black Death (or, Plague) Then, the medieval period with the presence of deadly diseases such as the plague. [4]

The Renaissance With the advent of the sciences and arts during the Renaissance period, the initial work on medicine, and anatomic studies started. Several individuals flourished during those times, bringing new ideas, and discoveries including Girolamo Fracastoro (1478–1553), an Italian physician who introduced the term  ‘ fomites ‘ still in use today in Epidemiology to refer to those inanimate objects that might contribute indirectly to the transmission of an infectious disease. And of course, the anatomical draws and notes done by Leonardo DaVinci were part of the contributions from this era. [5]

In this context, the first systematic methods used to identify causation are attributed to the father of medicine, Hippocrates who in about 400 years of the contemporary era was able to propose that there is a connection among three major elements of nature and health or disease, these are: air, water, and place (as we will see later in the book, place is usually referred as location – where people reside/live); he proposed for example that in some cases the ‘bad air’ can cause disease, or, the ‘quality of the waters’ as he called, and the place in which people live such as high altitudes and other geographical characteristics, especially the soil, and terrain. [7]

Another important person in the history of epidemiology is, John Graunt, a London cloth merchant who lived in the middle of the 17th century. Due to his work, he is considered the first demographer and initiator of the concept of vital statistics. John Graunt   In 1603 in London, Graunt compiled the first register of births and deaths in England, under the name of, the ‘Bills of Mortality’. This is considered, the first systematic recording of deaths known in the history of record keeping and reporting of deaths, and other vital events. Graunt’s intentions were to show that human life conforms certain predictable statistical patterns, he wrote for example that deaths varied by age, sex, who died, of what, and where they died (the location), and when (the time of death). [8] , [9]

In the other in which events are presented here, the next person who made significant contributions to epidemiology and public health is Dr. John Snow – already discussed in another section of the chapter. One of the main remarks is that Dr. John Snow is considered the father of public health.

The history of epidemiology in the U.S. and England

The Parallel of the history of public health in England and the United States: 

As a result (of the Shattuck’s report) in 1866 in New York City for the first time in the United States an organization as the report recommended was created. This is considered by many as the beginning of public health in the U.S. A picture of Dr. Shattuck is shown below:

Seat belts law in the U.S. A similar example can be found in the U.S. when the use of seat belts started as a public health measure to save lives during motor vehicle accidents, in the early 1970s when the measures were implemented, most people did not buckle up; they thought it was optional; but with the pass of time, people started seeing the benefits of seat belt use. [24] When the regulations are enforced, people who drive without a seat belt will get a traffic ticket from the police; and just because getting tickets is not pleasant, but also, just by knowing that the ticket is recompense for not wearing a seat belt, has made the measure very effective. It was probably the best way to make people use (consistently) seat belts while driving; and nowadays, it has become a habit, a norm. [25] .

Seat Belts Have Saved an Estimated 255,000 Lives Since 1975

Image from CDC . Public Domain .

Another parallel between England and the United States is the second wave of immigrants in the U.S.

Most books in epidemiology used the historical events of public health in England in order to draw a parallel with the U.S. history of public health; and this works well until we realize that the history of public health, and epidemiology in the U.S. has to do with one major issue, the wave of immigrants from distant countries, and how these immigrants dealt with the current sanitary conditions, housing, and environment conditions of their time, especially those immigrants who decided to reside in New York; which was not prepared to receive such high numbers of people, especially the unsanitary conditions of the buildings that many of these immigrants were allocated in the city. Crowdedness, lack of proper disposal of excreta, and high levels of cross-contamination of food, and water were typically in an Irish immigrant household (say, apartment) in New York. These immigrants faced a challenging time keeping themselves healthy and alive (especially their children, who in many cases died of dysentery, or any other type of oral-fecal-related disease. [26]

The early regulations started earlier in England, and the English people started hearing about the noxious effects of tobacco use (mainly smoking cigarettes) way before people in the U.S., and it was not until 1957 when this country (the U.S.) became aware of the need to study (do some research) the problem, and considered for the first time to start regulations on its use. In this regard, one of the main public health event on the noxious effects of tobacco is the released of the U.S. Surgeon General’s Advisory Committee on Smoking and Health report (on January 11, 1964), which concluded that cigarette smoking is a cause of lung cancer and laryngeal cancer in men, a probable cause of lung cancer in women, and the most important cause of chronic bronchitis. [29] .

The Framingham Heart Study is the first cohort to study the U.S. population on the issue of heart disease, and its related risk factors. It started in 1948 with a small group of individuals, and it is still collecting information about the white population in the U.S., and heart disease behavioral lifestyle risk factors. The findings of the Framingham Heart Study provided much of the common knowledge that people now have about heart disease, including the effects of exercise, diet, and smoking on cardiovascular disease. [32]

The Bogalusa Heart Study Later in the history of epidemiology in the U.S., there was another major study focused on cardiovascular disease; but this time on the black population, its name the Bogalusa Heart Study, Bogalusa is a city (mainly black population) in Washington Parish, Louisiana, United States. It is the principal city of the Bogalusa Micropolitan Statistical Area, which includes all of Washington Parish and is also part of the larger New Orleans–Metairie–Hammond combined statistical area. [33] See Louisiana Map below:

Bogalusa became well-known because of this study, which began in 1972 being the main investigator, Dr. Gerald S. Berenson, a Bogalusa native and pediatric cardiologist who recognized the need for investigations into the childhood antecedents of adult cardiovascular diseases. [34] I had the privilege to meet Dr. Berenson while doing my public health doctoral studies at Tulane University in New Orleans. [35] . To see additional images of the city of Bogalusa, see below:

The main contributions of the Bogalusa Heart Study among others include the following: adult heart disease, atherosclerosis, coronary heart disease, and essential hypertension begin in childhood. Cardiovascular risk factors can be identified in early life, but the levels of risk factors in childhood are different than those in the adult years. In addition, another great contribution of the Bogalusa Heart Study among others is that it is targeted (collected data) to African American children (while the Framingham targeted mainly adults). [36]

The next step, the San Antonio Heart Study (SAHS) With the past of time, the need to have information (data) about cardiovascular disease among another major ethnic group in the U.S. became one of the major reasons to conduct another famous cohort study on the Latino/Hispanic population in the U.S. The name of the study, is San Antonio Heart Study, San Antonio, Texas. See a picture of San Antonio below:

My personal critique/comments of the San Antonio Heart study is that it lacks representativity of the different U.S. Latino ethnic groups, by being mainly focused on Mexican-American, which constitute a great majority of the population of Latinos in the U.S. and excluding the great numbers of new Latino immigrants from other countries, and regions especially Central America and the Caribbean, the data results cannot be generalized to the rest of the U.S. Latino population in the U.S., but on the positive side, the contribution of the San Antonio Heart Study (SAHS) cannot be denied because it represents pioneering research on two major problems (cardiovascular disease and type II diabetes) of morbidity, and  mortality among the U.S. Latino population.

Key Takeaways

Medical and Public Health Research going wrong in the U.S.,  the case of the Tuskegee syphilis study

According to the CDC, this photograph was taken around 1932, it shows participants in the Tuskegee Syphilis Study. In this picture, an African-American man was being X-rayed, while in the standing position.

At the beginning of the study, the number of participants included 600 African-American men, from this group, 399 had syphilis and 201 did not have the disease. During the time of the study, the investigators told these men that were being treated by “bad blood,” a common term at that time that was used to describe some common diseases such as syphilis, anemia, and fatigue. The study lasted for 40 years, although the men participating in the study, were told that the study would last 6 months. [39]

The Tuskegee syphilis study is an example of human experimentation on the clinical history of a common infectious disease which by that time had treatment already available. The withholding of the treatment exposed the study participants, their families, and generations of African Americans in the South to syphilis. For this reason, this study is frequently cited as an example of what an investigator is not supposed to do in conducting research, especially research on human subjects. The question that usually comes to mind when the author reads about this study is, what can be done to prevent similar situations in the future of research in the U.S.? And the question remains open.

The epidemic of smallpox in Europe and then, later in the United States From the list of plagues that affected the world population in the past, one of the major problems is smallpox, a disease now eradicated . A disease caused by the Variola virus . It caused millions of deaths especially during the Medieval times, although it is believed to have existed for at least 3000 years. [40]

The person who created the first smallpox vaccine was Dr. Edward Jenner,  an English doctor who as part of his observations noted that milkmaids who had gotten cowpox were protected from smallpox, which means that they received immunity (this concept was unknown at his time) from having the disease, this became the base for the development of the smallpox vaccine. Having a vaccine made a tremendous difference in the history of the disease, which basically contributed to its eradication. [41] , [42]

Flu pandemics In general, flu pandemics have been repeated in history every 100 years, and because of that the ‘expected’ and ‘predicted’ pandemic was on flu (and not coronavirus). [44] . Because of its importance in history, the first major pandemic that appears usually in most history books is the flu pandemic.

The flu pandemic of 1918 The 1918 Influenza (flu) Pandemic (also called, the Spanish flu, because of the great mortality in that country at that time). This famous pandemic lasted from 1918-1919 and killed 50 to 100 million persons worldwide. The pandemic had three waves as it is represented in the following image:

The 2009 H1N1 influenza pandemic: a repeat of the 1919 pandemic The specific time of this pandemic is from 2009-2010. The disease was first identified in the United States and eventually was named the 2009 H1N1 influenza. The first two cases of the disease were reported by the Centers for Disease Control and Prevention (CDC) in April 2009, after this, the number of cases grew to 60 million by summer 2010. The pandemic also attacked several other countries in the world, and it was known to prefer the group of people 14-64 years, with those aged 65 and older as the less affected group. [47] , [48] This pandemic should have served as a lesson for the U.S., but a lot of skepticism grew that the possible for another major pandemic was not possible, or, if this would happen then, the country had enough public health resources including vaccines to control the problem, which has been proven wrong with the current coronavirus pandemic making manifest that the U.S. public health system was not prepared for a pandemic.

Summary Although a historical review of events, and individuals, it shows that the history of public health and epidemiology are intertwined. The historical events mentioned in this chapter confirm that epidemiology as a science has made great contributions to medicine and public health, in the search for determinants and the design of effective public health interventions.

• Yannis T. (2009). The historical origins of the basic concepts of health promotion and education: the role of ancient Greek philosophy and medicine, Health Promotion International, 24 (2), 185–192. From https://doi.org/10.1093/heapro/dap006 ↵
• Deming D. (2020). The Aqueducts and Water Supply of Ancient Rome. Ground water, 58(1), 152–161. https://doi.org/10.1111/gwat.12958 ↵
• Gill, N.S. (August 02, 2019). Roman Baths and Hygiene in Ancient Rome. ThoughtCo. From https://www.thoughtco.com/hygiene-in-ancient-rome-and-baths-119136 ↵
• ‘Black Death,’ Wikipedia, licensed Creative Commons Attribution-ShareAlike License 3.0 URL: https://en.wikipedia.org/wiki/Black_Death ↵
• MNT Editorial Team. (Feb. 8, 2022). What was medieval and Renaissance medicine? Medical News Today (MNT). From https://www.medicalnewstoday.com/articles/323533 ↵
• Poppick, L. (March 30, 2017). Let Us Now Praise the Invention of the Microscope In Smithsonian Magazine. Available at: https://www.smithsonianmag.com/science-nature/what-we-owe-to-the-invention-microscope-180962725/ ↵
• Centers for Disease Control and Prevention (CDC). (n.d.). Lesson 1: Introduction to Epidemiology Section 2: Historical Evolution of Epidemiology. Circa 400 B.C. section. Available at https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section2.html ↵
• Stephan, E. (n.d.). John Graunt (1620-1674), Observations in the Bills of Mortality. From http://www.edstephan.org/Graunt/graunt.html ↵
• Linda Hall Library. (2020, April 24). Scientist of the Day - John Graunt. From https://www.lindahall.org/john-graunt/ ↵
• Kneisl, K. (February 9, 2021). John Snow, A Turning Point In Epidemiology. From https://storymaps.arcgis.com/stories/1913fb6e17cd49c88b801e4c6edb67bf ↵
• University of California Los Angeles (UCLA). (2003). Who is John Snow? UCLA Dept. of Epidemiology, Fielding School of Public Health. From http://www.ph.ucla.edu/epi/snow.html ↵
• No author. (April 23, 2020). "Careless of Cholera": The New Orleans Outbreak of 1848. The Times Picayune. From https://www.pitothouse.org/blog/2020/4/23/careless-of-cholera-the-new-orleans-outbreak-of- 1848 ↵
• No author. (2020). History of Tulane SPHTM, a Timeline. From https://sph.tulane.edu/timeline ↵
• Segre J. A. (2013). What does it take to satisfy Koch's postulates two centuries later? Microbial genomics and Propionibacteria acnes. The Journal of investigative dermatology, 133(9), 2141–2142. From https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3775492/ ↵
• Snow, J. (1855). On the Mode of Communication of Cholera. From http://www.ph.ucla.edu/epi/snow/snowbook_a2.html ↵
• Rogers HR.(1895). Dr. Robert Koch and His Germ Theory of Cholera. JAMA, XXIV(23):903–904. doi:10.1001/jama.1895.02430230037013 ↵
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• Johns Hopkins Bloomberg School of Public Health. (December 20, 2012). Medical Historian Speaks at School about Wade Hampton Frost (web article). From https://publichealth.jhu.edu/2004/frost ↵
• Thacker, SB. (n.d.). Epidemiology and Public Health at CDC. Office of Workplace and Career Development, Office of the Director. From https://www.cdc.gov/mmwr/preview/mmwrhtml/su5502a2.htm ↵
• Report of the Sanitary Commission of Massachusetts 1850. Report available in pdf from https://www.google.com/search?client=firefox-b-1-d&q=the+shattuck+report ↵
• Winkelstein, W. (July 2008). Lemuel Shattuck Architect of American Public Health. Epidemiology, 19(4), 634. From https://journals.lww.com/epidem/Fulltext/2008/07000/Lemuel_Shattuck__Architect_of_American_Public.21.aspx ↵
• Britannica. (n.d.). National developments in the 18th and 19th centuries. From https://www.britannica.com/topic/public-health/National-developments-in-the-18th-and-19th-centuries ↵
• University of California Los Angeles (UCLA). (2003). John Snow and the Broad Street Pump. UCLA Dept. of Epidemiology. From https://www.ph.ucla.edu/epi/snow/snowcricketarticle.html ↵
• Centers for Disease Control and Prevention. (n.d.). Policy Impact: Seat Belts. From https://www.cdc.gov/transportationsafety/seatbeltbrief/index.html ↵
• Dept of Transportation (US), National Highway Traffic Safety Administration (NHTSA). (2010). Traffic Safety Facts: Seat Belt Use in 2010—Overall Results. Washington (DC): NHTSA. From http://www-nrd.nhtsa.dot.gov/Pubs/811378.pdfpdf icon ↵
• Library of Congress. (n.d.). Adaptation and Assimilation. Presentation, Immigration and Relocation in U.S. History. From https://www.loc.gov/classroom-materials/immigration/irish/adaptation-and-assimilation/ ↵
• Markel, H., & Stern, A. M. (2002). The foreignness of germs: the persistent association of immigrants and disease in American society. The Milbank quarterly, 80(4), 757–v. https://doi.org/10.1111/1468-0009.00030 From https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152289/ ↵
• Comments of the author. ↵
• Centers for Disease Control and Prevention. (n.d.). History of the Surgeon General’s Reports on Smoking and Health. From https://www.cdc.gov/tobacco/data_statistics/sgr/history/index.htm ↵
• See the cohort studies section in chapter 5. ↵
• Bacallao Gallestey, J. (n.d). Framingham Heart Study research project, Framingham, Massachusetts, United States. Encyclopedia Britannica. From https://www.britannica.com/event/Framingham-Heart-Studay ↵
• No Author. (n.d.). Framingham Heart Study, Participant cohorts. Available at https://www.framinghamheartstudy.org/participants/participant-cohorts/ ↵
• Bogalusa Louisiana. From Wikipedia, https://en.wikipedia.org/wiki/Bogalusa,_Louisiana ↵
• No Author. (n.d.). Bogalusa Heart Study. Available at: https://www.clersite.org/bogalusaheartstudy/ ↵
• Personal comments from the author. ↵
• Tulane Center for Lifespam Epidemiology Research (CLER). (n.d.). Bogalusa Heart Study. Available at: https://www.clersite.org/bogalusaheartstudy/ ↵
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• CDC. (n.d.). The U.S. Public Health Service Syphilis Study at Tuskegee. From https://www.cdc.gov/tuskegee/index.html ↵
• National Archives of Atlanta. (n.d.). Tuskegee Syphilis Study. From https://www.archives.gov/atlanta/exhibits/item470-exh.html ↵
• World Health Organization (WHO). (No date). Smallpox. From https://www.who.int/health-topics/smallpox#tab=tab_1 ↵
• Riedel S. (2005). Edward Jenner and the history of smallpox and vaccination. Proceedings (Baylor University. Medical Center), 18(1), 21–25. https://doi.org/10.1080/08998280.2005.11928028 ↵
• CDC. (No date). History of Smallpox. From https://www.cdc.gov/smallpox/history/history.html ↵
• Kiger, PJ. (Nov 25, 2019). Did Colonists Give Infected Blankets to Native Americans as Biological Warfare? There’s evidence that British colonists in 18th-century America gave Native Americans smallpox-infected blankets at least once—but did it work? History, 15. From https://www.history.com/news/colonists-native-americans-smallpox-blankets ↵
• Kertscher, T. (April 10, 2020). Fact-check: Has a pandemic occurred every 100 years? PolitiFact. From https://www.statesman.com/story/news/politics/elections/2020/04/10/fact-check-has-pandemic-occurred-every-100-years/984128007/ ↵
• Taubenberger, J. K., & Morens, D. M. (2006). 1918 Influenza: the mother of all pandemics. Emerging infectious diseases, 12(1), 15–22. https://doi.org/10.3201/eid1201.050979 ↵
• Barry, J. M. (2020). The great influenza: The story of the deadliest pandemic in history. Penguin UK. ↵
• CDC. (no date). 2009 H1N1 Pandemic (H1N1pdm09 virus). From https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html ↵
• Mayo Clinic Staff. (Feb 24, 2021). H1N1 flu (swine flu). From https://www.mayoclinic.org/diseases-conditions/swine-flu/symptoms-causes/syc-20378103 ↵
• Branswell, H. (June 11, 2019). The last pandemic was a ‘quiet killer.’ Ten years after swine flu, no one can predict the next one. STAT, Health. From https://www.statnews.com/2019/06/11/h1n1-swine-flu-10-years-later/ ↵
• Osterholm, MT., Kelley, NS., Manske, JM., Ballering, KS., Leighton, TR. (October 2012). CIDRAP Comprehensive Influenza Vaccine Initiative report. University of Minnesota, Academic Health Center. From https://www.cidrap.umn.edu/sites/default/files/public/downloads/ccivi_report.pdf ↵
• Tagliabue,F.,Galassi,L., Mariani,P.(2020). The “Pandemic” of Disinformation in COVID-19. SN Comprehensive Clinical Medicine, 2 (9),1287. From https://doi.org/10.1007/s42399-020-00439-1 ↵
• Grimes, DR. (March 12, 2021). Medical disinformation and the unviable nature of COVID-19 conspiracy theories. PLOS One (Pone). From https://doi.org/10.1371/journal.pone.0245900 ↵
• Dunleavy, Daniel, & Hendricks, Vincent. (2020, September 28). Fast Science, Slow Science: Finding Balance in the Time of COVID-19 and the Age of Misinformation (Version 1). Zenodo. http://doi.org/10.5281/zenodo.4056909 ↵
• Smriti Mallapaty, S. (28 January 2022). Where did Omicron come from? Three key theories The highly transmissible variant emerged with a host of unusual mutations. Now scientists are trying to work out how it evolved. Nature. From https://www.nature.com/articles/d41586-022-00215-2 ↵
• Haidong Wang, H., Paulson, KR., Pease, SA., Watson, S. et al. (April 16, 2022). Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. The Lancet, 399 (10334), 1513. From https://doi.org/10.1016/S0140-6736(21)02796-3 ↵
• No author. (No date). Johns Hopkins University Coronavirus Resource Center. From https://coronavirus.jhu.edu/map.html ↵
• aidong Wang, H., Paulson, KR., Pease, SA., Watson, S. et al. (April 16, 2022). Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. The Lancet, 399 (10334), 1513. From https://doi.org/10.1016/S0140-6736(21)02796-3 ↵

Term commonly used for the disposal of urine and feces.

it refers to inanimate objects that may be contaminated with the infectious agent

the term that refers main to disease etiology (cause)

Considered the father of medicine as a science.

considered the first demographer

He is considered the father of public health.

A map that is developed using GIS software in which data are represented by dots (the pixels in the image).

geographic information system, which is a series of software and computers used to model data that is represented by dot maps - see definition of a dot map in this section of the book.

A choropleth map is a type of thematic map in which a set of pre-defined areas is colored or patterned in proportion to a statistical variable.

International Classification of Diseases, it uses specific coding systems to classify diseases, so, international comparisons can be established.

The act of planning a system for something, or of organizing something in a system - Cambridge Dictionary online: https://dictionary.cambridge.org/

A figure of speech that means, 'similar' or, 'analogous' to another. A comparison.

The term eradicated is used to refer to infectious diseases that do not longer exist due to public health efforts mainly vaccinations.

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Historical parallels between Trump ballot case, RI's Dorr Rebellion | Opinion

Erik Chaput and Russell DeSimone are the co-creators of the Dorr Rebellion Project website.

The study of our nation’s past — from just a few years ago to as far back as the Reconstruction era — continues to shape the contours of American politics. For teachers tasked with engaging the next generation of voters, there are interesting historical parallels from a recent Supreme Court case to one over a century and a half ago stemming from the 1842 Dorr Rebellion in Rhode Island .

In December 2023, the Colorado high court ruled that because former President Donald Trump had, in their understanding, directly aided an insurrection on Jan. 6, 2021, he was ineligible to run for office under Section 3 of the 14th Amendment. Trump appealed. In its ruling on March 4 in Trump v. Anderson, the U.S. Supreme Court put the former president back on the ballot.

Despite the urging of a who’s who list of American historians who wrote amicus curiae briefs outlining the original understanding of Section 3, the court charted a path to preserve the democratic order in a presidential election year. They did not explore connections between the insurrection on Jan. 6 and the Civil War.

More: Maine just barred Trump from the ballot. Will RI's secretary of state follow suit?

A study of Rhode Island’s mini (and bloodless) civil war in 1842 will also help students explore complicated questions relating to the Supreme Court and democracy. For Providence attorney Thomas W. Dorr, the state’s failure to usher in a modern constitution, along with its neglect of voting reform measures, justified forceful action.

In mid-May 1842, Dorr, believing himself to be the duly elected governor, took drastic steps to take control, including an attempt to seize the state arsenal by force of arms. Unlike the insurrection on Jan. 6, however, Dorr and his allies caused no damage to state property nor was anyone killed − though a great deal of that was due to dumb luck.

In June 1844, Dorr was tried and convicted of treason. The Supreme Court eventually weighed in, though not on Dorr’s appeal, but rather on a trespass case stemming from an overzealous militia during the short-lived rebellion.

Luther v. Borden (1849) raised significant issues, ones that Dorr helped to manage early on from his jail cell. These included the possibility of whether or not the Supreme Court could decide between the legality of Dorr’s government and the sitting government that put down his brief insurrection. If the justices retroactively ruled that Dorr was right and his opponents wrong, then the state, which finally adopted a new constitution in 1843, would have been thrown into chaos.

The Luther case was argued in 1848, but the court waited until after the presidential election to hand down its ruling. Dorr wanted the Supreme Court to use the guarantee of the republican government clause in Article IV, Section 4 to justify his actions. Instead, the court, while acknowledging the “unfortunate political differences” in the state, said the guarantee was established by the sitting government sending representatives to Congress and the actions of President John Tyler at the time.

More: 'A dangerous time in America': Political scholar looks at national, world politics

The long-term impact of the ruling was that the guarantee clause became wholly nonjusticiable, something akin to what the court said a few weeks ago about Section 3 of the 14th Amendment. In Luther, the court, while certainly acknowledging the state’s antiquated political culture, did not get at the myriad of ways reform efforts had been squashed in Rhode Island.

On March 4, the Supreme Court refused to discuss the elephant in the room of whether Jan. 6 constituted an insurrection and whether or not a former president played a role. The ruling hinged on whether or not Section 3 required Congress to act first to disqualify a federal candidate before a state could act. The recent past was not discussed.

The issue of the people’s sovereignty continues to arise in moments of intense disagreement. Key participants will continue to frame their words and deeds within the ideology of democracy, exploiting ambiguities, and making the role of the teacher so vital as they set out to help students navigate this turbulent political landscape.