The aim of this project is to produce a systematic history of epidemiology and of the development of epidemiological techniques from the mid seventeenth to the mid twentieth century. The work of Thomas Sydenham (1624-89), in identifying disease entities, and of John Graunt (1620-74) in using quantitative methods for the study of plague, is generally recognized as initiating a population approach to problems of disease. After that, epidemiology struggled to achieve recognition as a scientific discipline. It was only at the turn of the nineteenth century, with chronic diseases such as cancer and cardiovascular diseases emerging as major killers, and with the development of Pearsonian and Fisherian statistics, that new epidemiological methods began to be developed and applied in a systematic way. The project will adopt a case-study approach, focusing on specific diseases and the methodological developments they originally stimulated. It will provide rigorous, evidence-based answers to questions such as: Why weren't medical issues studied using group comparisons before the 17th century? Why was the development of new epidemiological techniques sporadic before 1900? Why did these early developments only have a limited practical impact? Why was there an acceleration in the development and practical application of epidemiologic methods after circa 1900, and even more so after 1945? Why have cancer and heart disease issues been most useful for allowing epidemiology to demonstrate the validity of its methods and to directly impact social policy and population health? For each case-study, the project is to: 1. Systematically screen the historical, epidemiological and biostatistical literature for relevant information about the historical context and the evolution of the methods and concepts, which constitute epidemiology today;and 2. Document the medical knowledge accrued through the use of epidemiologic methods. The project also comprises the interview in audio-visual, internet conferences of senior epidemiologists to determine what they think is important about the history of their discipline and what epidemiologists-in-training should know. All this work will serve to write, revise, and publis a book entitled, "The Greatest Benefit to Prevention and Medical Treatment: Origin and Evolution of Epidemiology."
This first comprehensive history of epidemiology will fill a gap in the field and provide new inroads to understanding the evolution of this science and its impact on prevention and treatment, and more generally on everyday life.
|Morabia, Alfredo (2015) Mervyn Susser, the last of the three American classical epidemiology tenors. Ann Epidemiol 25:140-2|
|Morabia, Alfredo (2014) Mervyn Susser and the logic of scientific discovery. Paediatr Perinat Epidemiol 28:476-8|
|Mooney, Stephen J; Knox, Justin; Morabia, Alfredo (2014) The Thompson-McFadden Commission and Joseph Goldberger: contrasting 2 historical investigations of pellagra in cotton mill villages in South Carolina. Am J Epidemiol 180:235-44|
|Snoep, Jaapjan D; Morabia, Alfredo; Hernández-Díaz, Sonia et al. (2014) Commentary: A structural approach to Berkson's fallacy and a guide to a history of opinions about it. Int J Epidemiol 43:515-21|
|Morabia, Alfredo (2014) Invited commentary: do-it-yourself modern epidemiology--at last! Am J Epidemiol 180:669-72|
|Morabia, Alfredo (2013) The new "snippets from the past" and a new section about "epidemiology in history". Am J Epidemiol 177:490-1|
|Morabia, Alfredo (2013) Snippets from the past: the evolution of Wade Hampton Frost's epidemiology as viewed from the American Journal of Hygiene/Epidemiology. Am J Epidemiol 178:1013-9|
|Morabia, Alfredo; Rubenstein, Beth; Victora, Cesar G (2013) Epidemiology and public health in 1906 England: Arthur Newsholme's methodological innovation to study breastfeeding and fatal diarrhea. Am J Public Health 103:e17-22|
|Morabia, Alfredo (2013) Epidemiology's 350th Anniversary: 1662-2012. Epidemiology 24:179-83|
|Morabia, Alfredo (2013) Hume, Mill, Hill, and the sui generis epidemiologic approach to causal inference. Am J Epidemiol 178:1526-32|
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