View source for Epidemiology

{{Short description|Study of health and disease within a population}}
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'''Epidemiology''' is the study and analysis of the distribution (who, when, and where), patterns and [[Risk factor (epidemiology)|determinants]] of health and disease conditions in a defined [[population]], and application of this knowledge to prevent diseases.

It is a cornerstone of [[public health]], and shapes policy decisions and [[evidence-based practice]] by identifying [[Risk factor (epidemiology)|risk factor]]s for disease and targets for [[preventive healthcare]]. Epidemiologists help with study design, collection, and [[statistical analysis]] of data, amend interpretation and dissemination of results (including [[peer review]] and occasional [[systematic review]]). Epidemiology has helped develop [[methodology]] used in [[clinical research]], [[public health]] studies, and, to a lesser extent, [[basic research]] in the biological sciences.<ref>{{cite book | first =Miquel | last = Porta |title=A Dictionary of Epidemiology |url= http://global.oup.com/academic/product/a-dictionary-of-epidemiology-9780199976737?cc=us&lang=en |edition=6th |year=2014 |location=New York |publisher=Oxford University Press |isbn= 978-0-19-997673-7 |access-date=16 July 2014}}</ref>

Major areas of epidemiological study include disease causation, [[transmission (medicine)|transmission]], [[outbreak]] investigation, [[disease surveillance]], [[environmental epidemiology]], [[forensic epidemiology]], [[occupational epidemiology]], [[screening (medicine)|screening]], [[biomonitoring]], and comparisons of treatment effects such as in [[clinical trials]]. Epidemiologists rely on other scientific disciplines like [[biology]] to better understand disease processes, [[statistics]] to make efficient use of the data and draw appropriate conclusions, [[social science]]s to better understand proximate and distal causes, and [[engineering]] for [[exposure assessment]].

''Epidemiology'', literally meaning "the study of what is upon the people", is derived {{ety|gre|epi|upon, among||[[Glossary of rhetorical terms#Demos|demos]]|people, district||[[logos]]|study, word, discourse}}, suggesting that it applies only to human populations. However, the term is widely used in studies of zoological populations (veterinary epidemiology), although the term "[[epizoology]]" is available, and it has also been applied to studies of plant populations (botanical or [[plant disease epidemiology]]).<ref>{{cite journal | last=Nutter | first=F.W. Jr. |title= Understanding the interrelationships between botanical, human, and veterinary epidemiology: the Ys and Rs of it all |journal= Ecosystem Health|volume= 5 |issue= 3 |pages=131–40 |year=1999 |doi= 10.1046/j.1526-0992.1999.09922.x}}</ref>

The distinction between "epidemic" and "endemic" was first drawn by [[Hippocrates]],<ref>Hippocrates (~200 BC). ''Airs, Waters, Places''.</ref> to distinguish between diseases that are "visited upon" a population (epidemic) from those that "reside within" a population (endemic).<ref name= "Carol Buck 1998 p3">Carol Buck, Alvaro Llopis; Enrique Nájera; Milton Terris (1998) ''The Challenge of Epidemiology: Issues and Selected Readings''. Scientific Publication No. 505. Pan American Health Organization. Washington, DC. p. 3.</ref> The term "epidemiology" appears to have first been used to describe the study of epidemics in 1802 by the Spanish physician {{Interlanguage link|Joaquín de Villalba|lt=Joaquín de Villalba|es}} in ''Epidemiología Española''.<ref name="Carol Buck 1998 p3" /> Epidemiologists also study the interaction of diseases in a population, a condition known as a [[syndemic]].

The term epidemiology is now widely applied to cover the description and causation of not only epidemic, infectious disease, but of disease in general, including related conditions. Some examples of topics examined through epidemiology include as high blood pressure, mental illness and [[obesity]]. Therefore, this epidemiology is based upon how the pattern of the disease causes change in the function of human beings.

== History ==

The Greek physician [[Hippocrates]], taught by Democritus, was known as the father of [[medicine]],<ref>{{cite book |url=https://books.google.com/books?id=E-OZbEmPSTkC&pg=PA93 |title=A history of epidemiologic methods and concepts |author=Alfredo Morabia |year=2004 |publisher=Birkhäuser |page=93 |isbn=978-3-7643-6818-0}}</ref><ref>[http://samples.jbpub.com/9780763766221/66221_CH02_5398.pdf Historical Developments in Epidemiology] {{Webarchive|url=https://web.archive.org/web/20180219135301/http://samples.jbpub.com/9780763766221/66221_CH02_5398.pdf |date=19 February 2018 }}. Chapter 2. Jones & Bartlett Learning LLC.</ref> sought a logic to sickness; he is the first person known to have examined the relationships between the occurrence of disease and environmental influences.<ref>{{cite book |url=https://books.google.com/books?id=RMDBh6gw1_UC&pg=PA24 |title=Introduction to Epidemiology |author=Ray M. Merrill |year=2010 |publisher=Jones & Bartlett Learning |page=24 |isbn=978-0-7637-6622-1}}</ref> Hippocrates believed sickness of the human body to be caused by an imbalance of the four [[humorism|humors]] (black bile, yellow bile, blood, and phlegm). The cure to the sickness was to remove or add the humor in question to balance the body. This belief led to the application of bloodletting and dieting in medicine.<ref name="Merril, Ray M. 2010">Merril, Ray M., PhD, MPH. (2010): ''An Introduction to Epidemiology'', Fifth Edition. Chapter 2: "Historic Developments in Epidemiology". Jones and Bartlett Publishing</ref> He coined the terms [[Endemic (epidemiology)|''endemic'']] (for diseases usually found in some places but not in others) and ''[[epidemic]]'' (for diseases that are seen at some times but not others).<ref name="hip">{{cite web |title=Changing Concepts: Background to Epidemiology |publisher=Duncan & Associates |url=http://www.duncan-associates.com/changing_concepts.pdf |access-date=3 February 2008 |archive-date=25 July 2011 |archive-url=https://web.archive.org/web/20110725065539/http://www.duncan-associates.com/changing_concepts.pdf |url-status=dead }}</ref>

=== Modern era ===
{{See also|History of emerging infectious diseases}}
In the middle of the 16th century, a doctor from [[Verona]] named [[Girolamo Fracastoro]] was the first to propose a theory that the very small, unseeable, particles that cause disease were alive. They were considered to be able to spread by air, multiply by themselves and to be destroyable by fire. In this way he refuted [[Galen]]'s [[Miasma theory of disease|miasma theory]] (poison gas in sick people). In 1543 he wrote a book ''[[De contagione et contagiosis morbis]]'', in which he was the first to promote personal and environmental [[hygiene]] to prevent disease. The development of a sufficiently powerful microscope by [[Antonie van Leeuwenhoek]] in 1675 provided visual evidence of living particles consistent with a [[germ theory of disease]].{{citation needed|date=June 2022}}

During the [[Ming dynasty]], [[Wu Youke]] (1582–1652) developed the idea that some diseases were caused by transmissible agents, which he called ''Li Qi'' (戾气 or pestilential factors) when he observed various epidemics rage around him between 1641 and 1644.<ref>{{cite book |last1=Joseph |first1=P Byre |title=Encyclopedia of the Black Death |date=2012 |publisher=ABC-CLIO |isbn=978-1598842548 |page=76 |url=https://books.google.com/books?id=AppsDAKOW3QC&pg=PA76 |access-date=24 February 2019}}</ref> His book ''Wen Yi Lun'' (瘟疫论, Treatise on Pestilence/Treatise of Epidemic Diseases) can be regarded as the main etiological work that brought forward the concept.<ref>{{cite book |last1=Guobin |first1=Xu |last2=Yanhui |first2=Chen |last3=Lianhua |first3=Xu |title=Introduction to Chinese Culture: Cultural History, Arts, Festivals and Rituals |year= 2018 |publisher=Springer |isbn=978-9811081569 |page=70 |url=https://books.google.com/books?id=-KFTDwAAQBAJ&pg=PA70 |access-date=24 February 2019}}</ref> His concepts were still being considered in analysing SARS outbreak by WHO in 2004 in the context of traditional Chinese medicine.<ref>{{cite web |title=Report 1: Clinical research on treatment of SARS with integrated Traditional Chinese medicine and Western Medicine |work=SARS: Clinical Trials on Treatment Using a Combination of Traditional Chinese Medicine and Western Medicine |date=2004 |url=http://apps.who.int/medicinedocs/en/d/Js6170e/4.html#Js6170e.4 |publisher=World Health Organization |access-date=24 February 2019 |archive-url=https://web.archive.org/web/20180608111238/http://apps.who.int/medicinedocs/en/d/Js6170e/4.html |archive-date=8 June 2018}}</ref>

Another pioneer, [[Thomas Sydenham]] (1624–1689), was the first to distinguish the fevers of Londoners in the later 1600s. His theories on cures of fevers met with much resistance from traditional physicians at the time. He was not able to find the initial cause of the [[smallpox]] fever he researched and treated.<ref name="Merril, Ray M. 2010"/>

[[John Graunt]], a [[haberdasher]] and amateur statistician, published ''Natural and Political Observations ... upon the Bills of Mortality'' in 1662. In it, he analysed the mortality rolls in [[London]] before the [[Great Plague of London|Great Plague]], presented one of the first [[life tables]], and reported time trends for many diseases, new and old. He provided statistical evidence for many theories on disease, and also refuted some widespread ideas on them.{{citation needed|date=June 2022}}

[[File:Snow-cholera-map.jpg|thumb|350px|Original map by [[John Snow (physician)|John Snow]] showing the [[cluster (epidemiology)|clusters]] of cholera cases in the [[1854 Broad Street cholera outbreak|London epidemic of 1854]]]]
[[John Snow (physician)|John Snow]] is famous for his investigations into the causes of the 19th-century [[cholera]] epidemics, and is also known as the father of (modern) Epidemiology.<ref>{{cite journal |url-status=dead |url=http://www.ph.ucla.edu/epi/snow/fatherofepidemiology.html |title=Doctor John Snow Blames Water Pollution for Cholera Epidemic |first1=David |last1=Vachon |archive-url=https://web.archive.org/web/20111228050259/http://www.ph.ucla.edu/epi/snow/fatherofepidemiology.html |archive-date=28 December 2011 |website= UCLA Department of Epidemiology, School of Public Health |date=May–June 2005}}</ref><ref>"[https://www.npr.org/templates/story/story.php?storyId=3935461 John Snow, Father of Epidemiology]", NPR, ''Talk of the Nation''. 24 September 2004. {{Webarchive|url=https://web.archive.org/web/20170620140913/http://www.npr.org/templates/story/story.php?storyId=3935461 |date=20 June 2017 }}.</ref> He began with noticing the significantly higher death rates in two areas supplied by Southwark Company. His identification of the [[Broadwick Street|Broad Street]] pump as the cause of the Soho epidemic is considered the classic example of epidemiology. Snow used chlorine in an attempt to clean the water and removed the handle; this ended the outbreak. This has been perceived as a major event in the history of [[public health]] and regarded as the founding event of the science of epidemiology, having helped shape public health policies around the world.<ref>{{cite web|url=http://www.ph.ucla.edu/epi/snow/importance.html|title=Importance of Snow|website=Jonathan and Karin Fielding School of Public Health |url-status=dead |archive-url= https://web.archive.org/web/20210617195407/https://www.ph.ucla.edu/epi/snow/importance.html |archive-date= Jun 17, 2021 }}</ref><ref>{{cite web |url-status=dead |url=http://www.jsi.com/JSIInternet/About/snow.cfm |title=Dr. John Snow |archive-url=https://web.archive.org/web/20140616100942/http://www.jsi.com/JSIInternet/About/snow.cfm |archive-date=16 June 2014 |publisher= John Snow, Inc. and JSI Research & Training Institute, Inc.}}</ref> However, Snow's research and preventive measures to avoid further outbreaks were not fully accepted or put into practice until after his death due to the prevailing [[Miasma theory|Miasma Theory]] of the time, a model of disease in which poor air quality was blamed for illness. This was used to rationalize high rates of infection in impoverished areas instead of addressing the underlying issues of poor nutrition and sanitation, and was proven false by his work.<ref>{{Citation|last=Johnson, Steven|title=The ghost map : [the story of London's most terrifying epidemic – and how it changed science, cities, and the modern world]|url=http://worldcat.org/oclc/1062993385|oclc=1062993385|access-date=2020-09-16}}</ref>

Other pioneers include Danish physician [[Peter Anton Schleisner]], who in 1849 related his work on the prevention of the epidemic of [[neonatal tetanus]] on the [[Vestmanna Islands]] in [[Iceland]].<ref>{{cite journal |last1=Olafsson |first1=O. |title=P.A. Schleisner: a pioneer in epidemiology |journal=Journal of Clinical Epidemiology |date=October 1999 |volume=52 |issue=10 |pages=905–907 |doi=10.1016/s0895-4356(99)00096-7 |pmid=10513751 |url=https://pubmed.ncbi.nlm.nih.gov/10513751/ |issn=0895-4356}}</ref> Another important pioneer was [[Hungary|Hungarian]] physician [[Ignaz Semmelweis]], who in 1847 brought down infant mortality at a Vienna hospital by instituting a disinfection procedure. His findings were published in 1850, but his work was ill-received by his colleagues, who discontinued the procedure. Disinfection did not become widely practiced until British surgeon [[Joseph Lister, 1st Baron Lister|Joseph Lister]], aided by his college, chemist [[Thomas Anderson (chemist)|Thomas Anderson]], was able to "discover" [[antiseptics]] in 1865 based on the earlier work of [[Louis Pasteur]].<ref>{{Cite web |last=Hollingham |first=Richard |date=2020-08-20 |title=The pioneering surgeons who cleaned up filthy hospitals |url=https://www.bbc.com/future/article/20200812-the-pioneering-surgeons-who-cleaned-up-filthy-hospitals |access-date=2025-03-11 |website=www.bbc.com |language=en-GB}}</ref>

In the early 20th century, mathematical methods were introduced into epidemiology by [[Ronald Ross]], [[Janet Lane-Claypon]], [[Anderson Gray McKendrick]], and others.<ref>[https://books.google.com/books?id=6DD1FKq6fFoC&q=mathematical+methods+were+introduced+into+epidemiology+20th+century+ross&pg=PA323 Statisticians of the centuries] {{Webarchive|url=https://web.archive.org/web/20220630015959/https://books.google.com/books?id=6DD1FKq6fFoC&pg=PA323#v=onepage&q=mathematical%20methods%20were%20introduced%20into%20epidemiology%2020th%20century%20ross |date=30 June 2022 }}. By C. C. Heyde, Eugene Senet</ref><ref>[http://statprob.com/encyclopedia/AndersonGrayMcKENDRICK.html Anderson Gray McKendrick] {{webarchive|url=https://web.archive.org/web/20110822114404/http://statprob.com/encyclopedia/AndersonGrayMcKENDRICK.html |date=22 August 2011 }}</ref><ref>{{cite web|url=https://oneweb.soton.ac.uk/node/201970|title=Homepage|publisher=Tel: +4423 8059 5000 Fax: +4423 8059 3131 University of Southampton University Road Southampton SO17 1BJ United Kingdom|website=University of Southampton}}{{Dead link|date=May 2023 |bot=InternetArchiveBot |fix-attempted=yes }}</ref><ref>{{cite web |url=http://www.epidemiology.ch/history/papers/SPM%2047(6)%20359-65%20Paneth%20et%20al.%20_%20Part%202.pdf |title=Origins and early development of the case-control study |access-date=31 August 2013 |archive-url=https://web.archive.org/web/20170118055648/http://www.epidemiology.ch/history/papers/SPM%2047(6)%20359-65%20Paneth%20et%20al.%20_%20Part%202.pdf |archive-date=18 January 2017 |url-status=dead |df=dmy-all }}</ref> In a parallel development during the 1920s, German-Swiss pathologist [[Max Askanazy]] and others founded the International Society for Geographical Pathology to systematically investigate the geographical pathology of cancer and other non-infectious diseases across populations in different regions. After World War II, [[Richard Doll]] and other non-pathologists joined the field and advanced methods to study cancer, a disease with patterns and mode of occurrences that could not be suitably studied with the methods developed for epidemics of infectious diseases. Geography pathology eventually combined with infectious disease epidemiology to make the field that is epidemiology today.<ref name="cancer">{{cite journal |author=Mueller LM |title=Cancer in the tropics: geographical pathology and the formation of cancer epidemiology |journal=BioSocieties|pages=512–528 |year=2019  |volume=14 |issue=4 |doi=10.1057/s41292-019-00152-w|hdl=1721.1/128433 |s2cid=181518236 |hdl-access=free }}</ref>

Another breakthrough was the 1954 publication of the results of a [[British Doctors Study]], led by [[Richard Doll]] and [[Austin Bradford Hill]], which lent very strong statistical support to the link between [[tobacco smoking]] and [[lung cancer]].{{cn|date=November 2023}}

In the late 20th century, with the advancement of biomedical sciences, a number of molecular markers in blood, other biospecimens and environment were identified as predictors of development or risk of a certain disease. Epidemiology research to examine the relationship between these [[biomarker]]s analyzed at the molecular level and disease was broadly named "[[molecular epidemiology]]". Specifically, "[[genetic epidemiology]]" has been used for epidemiology of germline genetic variation and disease. Genetic variation is typically determined using DNA from peripheral blood leukocytes.{{citation needed|date=June 2022}}

=== 21st century ===
Since the 2000s, [[Genome-wide association study|genome-wide association studies]] (GWAS) have been commonly performed to identify genetic risk factors for many diseases and health conditions.<ref>{{Cite web |date=17 August 2020 |title=Genome-Wide Association Studies Fact Sheet |url=https://www.genome.gov/about-genomics/fact-sheets/Genome-Wide-Association-Studies-Fact-Sheet |access-date=17 June 2024 |website=National Human Genome Research Institute}}</ref>

While most molecular epidemiology studies are still using conventional disease [[diagnosis]] and classification systems, it is increasingly recognized that disease progression represents inherently heterogeneous processes differing from person to person. Conceptually, each individual has a unique disease process different from any other individual ("the unique disease principle"),<ref>{{cite journal |vauthors=Ogino S, Fuchs CS, Giovannucci E | year = 2012 | title = How many molecular subtypes? Implications of the unique tumor principle in personalized medicine | journal = Expert Rev Mol Diagn | volume = 12 | issue = 6| pages = 621–28 | doi=10.1586/erm.12.46 | pmid=22845482 | pmc=3492839}}</ref><ref>{{cite journal |vauthors=Ogino S, Lochhead P, Chan AT, Nishihara R, Cho E, Wolpin BM, Meyerhardt JA, Meissner A, Schernhammer ES, Fuchs CS, Giovannucci E | year = 2013 | title = Molecular pathological epidemiology of epigenetics: Emerging integrative science to analyze environment, host, and disease | journal = Mod Pathol | volume = 26 | issue = 4| pages = 465–84 | doi=10.1038/modpathol.2012.214 | pmid=23307060 | pmc=3637979}}</ref> considering uniqueness of the [[exposome]] (a totality of endogenous and exogenous / environmental exposures) and its unique influence on molecular pathologic process in each individual. Studies to examine the relationship between an exposure and molecular pathologic signature of disease (particularly [[cancer]]) became increasingly common throughout the 2000s. However, the use of [[molecular pathology]] in epidemiology posed unique challenges, including lack of research guidelines and standardized [[Statistics|statistical]] methodologies, and paucity of interdisciplinary experts and training programs.<ref>{{cite journal |vauthors=Ogino S, King EE, Beck AH, Sherman ME, Milner DA, Giovannucci E | year = 2012 | title = Interdisciplinary education to integrate pathology and epidemiology: Towards molecular and population-level health science | journal = Am J Epidemiol | volume = 176 | issue = 8| pages = 659–67 | doi=10.1093/aje/kws226| pmid = 22935517 | pmc = 3571252}}</ref> Furthermore, the concept of disease heterogeneity appears to conflict with the long-standing premise in epidemiology that individuals with the same disease name have similar etiologies and disease processes. To resolve these issues and advance population health science in the era of molecular [[precision medicine]], "molecular pathology" and "epidemiology" was integrated to create a new interdisciplinary field of "[[molecular pathological epidemiology]]" (MPE),<ref>{{cite journal |vauthors=Ogino S, Stampfer M | year = 2010 | title = Lifestyle factors and microsatellite instability in colorectal cancer: the evolving field of molecular pathological epidemiology | journal = J Natl Cancer Inst | volume = 102 | issue = 6| pages = 365–67 | doi=10.1093/jnci/djq031 | pmid=20208016 | pmc=2841039}}</ref><ref>{{cite journal |vauthors=Ogino S, Chan AT, Fuchs CS, Giovannucci E | year = 2011 | title = Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field | journal = Gut | volume = 60 | issue = 3| pages = 397–411 | doi=10.1136/gut.2010.217182 | pmid=21036793 | pmc=3040598}}</ref> defined as "epidemiology of molecular pathology and heterogeneity of disease". In MPE, investigators analyze the relationships between (A) environmental, dietary, lifestyle and genetic factors; (B) alterations in cellular or extracellular molecules; and (C) evolution and progression of disease. A better understanding of heterogeneity of disease [[pathogenesis]] will further contribute to elucidate [[Etiology|etiologies]] of disease. The MPE approach can be applied to not only neoplastic diseases but also non-neoplastic diseases.<ref>{{cite journal |vauthors=Field AE, Camargo CA, Ogino S | year = 2013 | title = The merits of subtyping obesity: one size does not fit all | journal = JAMA | volume = 310 | issue = 20| pages = 2147–48 | doi=10.1001/jama.2013.281501| pmid = 24189835 }}</ref> The concept and paradigm of MPE have become widespread in the 2010s.<ref>{{cite journal |vauthors=Curtin K, Slattery ML, Samowitz WS | year = 2011 | title = CpG island methylation in colorectal cancer: past, present and future | journal = Pathology Research International | volume = 2011 | page = 902674 | doi = 10.4061/2011/902674 | pmid = 21559209 | pmc = 3090226 | doi-access = free }}</ref><ref>{{cite journal |vauthors=Hughes LA, Khalid-de Bakker CA, Smits KM, den Brandt PA, Jonkers D, Ahuja N, Herman JG, Weijenberg MP, van Engeland M|author-link6=Nita Ahuja|author-link7=James G. Herman | year = 2012 | title = The CpG island methylator phenotype in colorectal cancer: Progress and problems | journal = Biochim Biophys Acta | volume = 1825 | issue = 1| pages = 77–85 | doi=10.1016/j.bbcan.2011.10.005 | pmid=22056543|url=https://cris.maastrichtuniversity.nl/en/publications/64ca3af6-de2b-4150-b52e-0507ac49e51c}}</ref><ref>{{cite journal |vauthors=Ku CS, Cooper DN, Wu M, Roukos DH, Pawitan Y, Soong R, Iacopetta B | year = 2012 | title = Gene discovery in familial cancer syndromes by exome sequencing: prospects for the elucidation of familial colorectal cancer type X. | journal = Mod Pathol | volume = 25 | issue = 8| pages = 1055–68 | doi=10.1038/modpathol.2012.62 | pmid=22522846| doi-access = free }}</ref><ref>{{cite journal |vauthors=Chia WK, Ali R, Toh HC | year = 2012 | title = Aspirin as adjuvant therapy for colorectal cancer-reinterpreting paradigms | journal = Nat Rev Clin Oncol | volume = 9 | issue = 10| pages = 561–70 | doi=10.1038/nrclinonc.2012.137| pmid = 22910681 | s2cid = 7425809 }}</ref><ref>{{cite journal |vauthors=Spitz MR, Caporaso NE, Sellers TA | year = 2012 | title = Integrative cancer epidemiology – the next generation | journal = Cancer Discov | volume = 2 | issue = 12| pages = 1087–90 | doi=10.1158/2159-8290.cd-12-0424 | pmid=23230187 | pmc=3531829}}</ref><ref>{{cite journal |vauthors=Zaidi N, Lupien L, Kuemmerle NB, Kinlaw WB, Swinnen JV, Smans K | year = 2013 | title = Lipogenesis and lipolysis: The pathways exploited by the cancer cells to acquire fatty acids | journal = Prog Lipid Res | volume = 52 | issue = 4| pages = 585–89 | doi=10.1016/j.plipres.2013.08.005| pmc = 4002264 | pmid=24001676}}</ref><ref>{{cite journal |vauthors=Ikramuddin S, Livingston EH | year = 2013 | title = New Insights on Bariatric Surgery Outcomes | journal = JAMA | volume = 310 | issue = 22| pages = 2401–02 | doi=10.1001/jama.2013.280927| pmid = 24189645 }}</ref>{{excessive citations inline|date=March 2023}}

By 2012, it was recognized that many pathogens' [[evolution]] is rapid enough to be highly relevant to epidemiology, and that therefore much could be gained from an interdisciplinary approach to infectious disease integrating epidemiology and [[molecular evolution]] to "inform control strategies, or even patient treatment."<ref>{{cite journal |vauthors=Little TJ, Allen JE, Babayan SA, Matthews KR, Colegrave N | year = 2012 | title = Harnessing evolutionary biology to combat infectious disease | journal = Nature Medicine | volume = 18| issue = 2| pages = 217–20 | doi=10.1038/nm.2572 | pmc=3712261 | pmid=22310693}}</ref><ref>{{cite journal |vauthors=Pybus OG, Fraser C, Rambaut A | year = 2013 | title = Evolutionary epidemiology: preparing for an age of genomic plenty | journal = Phil Trans R Soc B | volume = 368| issue = 1614| pages = 20120193 | doi=10.1098/rstb.2012.0193| pmid = 23382418 | pmc = 3678320}}</ref> Modern epidemiological studies can use advanced statistics and [[machine learning]] to create [[Predictive modelling|predictive models]] as well as to define treatment effects.<ref>{{cite journal|doi=10.1146/annurev-publhealth-040119-094437|doi-access=free|title=Machine Learning in Epidemiology and Health Outcomes Research|year=2020|last1=Wiemken|first1=Timothy L.|last2=Kelley|first2=Robert R.|journal=Annual Review of Public Health|volume=41|pages=21–36|pmid=31577910}}</ref><ref>{{cite journal|doi=10.1093/aje/kwz189|title=What is Machine Learning? A Primer for the Epidemiologist|year=2019|last1=Bi|first1=Qifang|last2=Goodman|first2=Katherine E.|last3=Kaminsky|first3=Joshua|last4=Lessler|first4=Justin|journal=American Journal of Epidemiology|volume=188|issue=12|pages=2222–2239|pmid=31509183}}</ref> There is increasing recognition that a wide range of modern data sources, many not originating from healthcare or epidemiology, can be used for epidemiological study. Such digital epidemiology can include data from internet searching, mobile phone records and retail sales of drugs.{{cn|date=November 2023}}

== Types of studies ==
[[File:Epidemiologic study hierarchy.png|thumb|Epidemiologic study hierarchy]]
{{Main|Study design}}
Epidemiologists employ a range of study designs from the observational to experimental and generally categorized as descriptive (involving the assessment of data covering time, place, and person), analytic (aiming to further examine known associations or hypothesized relationships), and experimental (a term often equated with clinical or community trials of treatments and other interventions). In observational studies, nature is allowed to "take its course", as epidemiologists observe from the sidelines. Conversely, in experimental studies, the epidemiologist is the one in control of all of the factors entering a certain case study.<ref name="Epidemiology 2009">"Principles of Epidemiology." Key Concepts in Public Health. London: Sage UK, 2009. Credo Reference. 1 August 2011. Web. 30 September 2012.</ref> Epidemiological studies are aimed, where possible, at revealing unbiased relationships between [[Exposure Assessment#Exposure|exposures]] such as alcohol or smoking, [[infections|biological agents]], [[stress (medicine)|stress]], or [[Chemical compound|chemicals]] to [[death|mortality]] or [[morbidity]]. The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology. Modern epidemiologists use [[Health informatics|informatics]] and [[infodemiology]]<ref>{{cite journal |last=Eysenbach |first=Gunther |date=May 2011 |title=Infodemiology and Infoveillance |url=https://doi.org/10.1016/j.amepre.2011.02.006 |journal=American Journal of Preventive Medicine |volume=40 |issue=5 |pages=S154–S158 |doi=10.1016/j.amepre.2011.02.006 |pmid=21521589 |issn=0749-3797}}</ref><ref>{{cite journal |last=Eysenbach |first=Gunther |date=2009-03-27 |title=Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet |journal=Journal of Medical Internet Research |language=en |volume=11 |issue=1 |pages=e11 |doi=10.2196/jmir.1157 |doi-access=free |issn=1438-8871 |pmc=2762766 |pmid=19329408}}</ref> as tools.{{citation needed|date=June 2022}}<ref>{{cite journal |last=Wyatt |first=J C |date=2002-11-01 |title=Basic concepts in medical informatics |journal=Journal of Epidemiology & Community Health |volume=56 |issue=11 |pages=808–812 |doi=10.1136/jech.56.11.808 |pmc=1732047 |pmid=12388565}}</ref><ref>{{cite journal |last1=Mackey |first1=Tim |last2=Baur |first2=Cynthia |last3=Eysenbach |first3=Gunther |date=2022-02-14 |title=Advancing Infodemiology in a Digital Intensive Era |journal=JMIR Infodemiology |language=EN |volume=2 |issue=1 |pages=e37115 |doi=10.2196/37115|doi-access=free |pmid=37113802 |pmc=9987192 }}</ref><ref>{{cite journal |last=Mavragani |first=Amaryllis |date=2020-04-28 |title=Infodemiology and Infoveillance: Scoping Review |url=https://www.jmir.org/2020/4/e16206 |journal=Journal of Medical Internet Research |language=EN |volume=22 |issue=4 |pages=e16206 |doi=10.2196/16206|doi-access=free |pmid=32310818 |pmc=7189791 }}</ref>

Observational studies have two components, descriptive and analytical. Descriptive observations pertain to the "who, what, where and when of health-related state occurrence". However, analytical observations deal more with the 'how' of a health-related event.<ref name="Epidemiology 2009"/> [[Experimental epidemiology]] contains three case types: randomized controlled trials (often used for a new medicine or drug testing), field trials (conducted on those at a high risk of contracting a disease), and community trials (research on social originating diseases).<ref name="Epidemiology 2009"/>

The term 'epidemiologic triad' is used to describe the intersection of ''Host'', ''Agent'', and ''Environment'' in analyzing an outbreak.<ref>{{Cite web |date=2023-08-17 |title=Principles of Epidemiology {{!}} Lesson 1 – Section 8 |url=https://archive.cdc.gov/www_cdc_gov/csels/dsepd/ss1978/lesson1/section8.html |access-date=2024-09-12 |website=archive.cdc.gov |language=en-us}}</ref>

=== Case series ===
Case-series may refer to the qualitative study of the experience of a single patient, or small group of patients with a similar diagnosis, or to a statistical factor with the potential to produce illness with periods when they are unexposed.<ref>{{cite journal |last1=Song |first1=Jae W. |last2=Chung |first2=Kevin C. |date=December 2010 |title=Observational Studies: Cohort and Case-Control Studies |journal=Plastic and Reconstructive Surgery |language=en |volume=126 |issue=6 |pages=2234–2242 |doi=10.1097/PRS.0b013e3181f44abc |pmid=20697313 |pmc=2998589 |issn=0032-1052}}</ref>

The former type of study is purely descriptive and cannot be used to make inferences about the general population of patients with that disease. These types of studies, in which an astute clinician identifies an unusual feature of a disease or a patient's history, may lead to a formulation of a new hypothesis. Using the data from the series, analytic studies could be done to investigate possible causal factors. These can include case-control studies or prospective studies. A case-control study would involve matching comparable controls without the disease to the cases in the series. A prospective study would involve following the case series over time to evaluate the disease's natural history.<ref>{{cite book |last1=Hennekens |first1=Charles H. |author2=Julie E. Buring |year=1987 |title=Epidemiology in Medicine |editor=Mayrent, Sherry L. |publisher=Lippincott, Williams and Wilkins |isbn=978-0-316-35636-7 |url-access=registration |url=https://archive.org/details/epidemiologyinme00henn }}</ref>

The latter type, more formally described as [[self-controlled case-series]] studies, divide individual patient follow-up time into exposed and unexposed periods and use fixed-effects Poisson regression processes to compare the incidence rate of a given outcome between exposed and unexposed periods. This technique has been extensively used in the study of adverse reactions to vaccination and has been shown in some circumstances to provide statistical power comparable to that available in cohort studies.{{citation needed|date=June 2022}}

=== Case-control studies ===
[[case-control study|Case-control studies]] select subjects based on their disease status. It is a retrospective study. A group of individuals that are disease positive (the "case" group) is compared with a group of disease negative individuals (the "control" group). The control group should ideally come from the same population that gave rise to the cases. The case-control study looks back through time at potential exposures that both groups (cases and controls) may have encountered. A 2×2 table is constructed, displaying exposed cases (A), exposed controls (B), unexposed cases (C) and unexposed controls (D). The statistic generated to measure association is the [[odds ratio]] (OR),<ref>{{Cite journal |last1=Bewick |first1=Viv |last2=Cheek |first2=Liz |last3=Ball |first3=Jonathan |date=February 2004 |title=Statistics review 8: Qualitative data – tests of association |journal=Critical Care |volume=8 |issue=1 |pages=46–53 |doi=10.1186/cc2428 |doi-access=free |issn=1466-609X |pmid=14975045|pmc=420070 }}</ref> which is the ratio of the odds of exposure in the cases (A/C) to the odds of exposure in the controls (B/D), i.e. OR = (AD/BC).{{citation needed|date=March 2023}}

{| class="wikitable"
|-
!
! Cases
! Controls
|-
| Exposed
| A
| B
|-
| Unexposed
| C
| D
|}

If the OR is significantly greater than 1, then the conclusion is "those with the disease are more likely to have been exposed", whereas if it is close to 1 then the exposure and disease are not likely associated. If the OR is far less than one, then this suggests that the exposure is a protective factor in the causation of the disease.
Case-control studies are usually faster and more cost-effective than [[cohort studies]] but are sensitive to bias (such as [[recall bias]] and [[selection bias]]). The main challenge is to identify the appropriate control group; the distribution of exposure among the control group should be representative of the distribution in the population that gave rise to the cases. This can be achieved by drawing a random sample from the original population at risk. This has as a consequence that the control group can contain people with the disease under study when the disease has a high attack rate in a population.{{citation needed|date=March 2023}}

A major drawback for case control studies is that, in order to be considered to be statistically significant, the minimum number of cases required at the 95% confidence interval is related to the odds ratio by the equation:

:<math>\text{total cases} = A+C = 1.96^2 (1+N) \left(\frac{1}{\ln(OR)}\right)^2 \left(\frac{OR+2\sqrt{OR}+1}{\sqrt{OR}}\right) \approx 15.5 (1+N) \left(\frac{1}{\ln(OR)}\right)^2</math>

where N is the ratio of cases to controls.
As the odds ratio approaches 1, the number of cases required for statistical significance grows towards infinity; rendering case-control studies all but useless for low odds ratios. For instance, for an odds ratio of 1.5 and cases = controls, the table shown above would look like this:

{| class="wikitable"
|-
!
! Cases
! Controls
|-
| Exposed
| 103
| 84
|-
| Unexposed
| 84
| 103
|}

For an odds ratio of 1.1:

{| class="wikitable"
|-
!
! Cases
! Controls
|-
| Exposed
| 1732
| 1652
|-
| Unexposed
| 1652
| 1732
|}

=== Cohort studies ===
[[Cohort studies]] select subjects based on their exposure status. The study subjects should be at risk of the outcome under investigation at the beginning of the cohort study; this usually means that they should be disease free when the cohort study starts. The cohort is followed through time to assess their later outcome status. An example of a cohort study would be the investigation of a cohort of smokers and non-smokers over time to estimate the incidence of lung cancer. The same 2×2 table is constructed as with the case control study. However, the point estimate generated is the [[relative risk]] (RR), which is the probability of disease for a person in the exposed group, ''P''<sub>e</sub>&nbsp;=&nbsp;''A''&nbsp;/&nbsp;(''A''&nbsp;+&nbsp;''B'') over the probability of disease for a person in the unexposed group, ''P''<sub>''u''</sub>&nbsp;=&nbsp;''C''&nbsp;/&nbsp;(''C''&nbsp;+&nbsp;''D''), i.e. ''RR''&nbsp;=&nbsp;''P''<sub>e</sub>&nbsp;/&nbsp;''P''<sub>u</sub>.

{| class="wikitable"
|-
! .....
! Case
! Non-case
! Total
|-
| Exposed
| ''A''
| ''B''
| (''A''&nbsp;+&nbsp;''B'')
|-
| Unexposed
| ''C''
| ''D''
| (''C''&nbsp;+&nbsp;''D'')
|}

As with the OR, a RR greater than 1 shows association, where the conclusion can be read "those with the exposure were more likely to develop the disease."

Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status. Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed.

Cohort studies also are limited by the same equation for number of cases as for cohort studies, but, if the base incidence rate in the study population is very low, the number of cases required is reduced by&nbsp;{{frac|1|2}}.

== Causal inference ==
{{main|Causal inference}}
Although epidemiology is sometimes viewed as a collection of statistical tools used to elucidate the associations of exposures to health outcomes, a deeper understanding of this science is that of discovering ''causal'' relationships.

"[[Correlation does not imply causation]]" is a common theme for much of the epidemiological literature. For epidemiologists, the key is in the term [[inference]]. Correlation, or at least association between two variables, is a necessary but not sufficient criterion for the inference that one variable causes the other. Epidemiologists use gathered data and a broad range of biomedical and psychosocial theories in an iterative way to generate or expand theory, to test hypotheses, and to make educated, informed assertions about which relationships are causal, and about exactly how they are causal.

Epidemiologists emphasize that the "'''one cause – one effect'''" understanding is a simplistic mis-belief.<ref>{{cite journal |last=Woodward |first=James |date=2010 |title=Causation in biology: stability, specificity, and the choice of levels of explanation. |journal=Biology & Philosophy |volume=25 |issue=3 |pages=287–318 |doi=10.1007/s10539-010-9200-z |s2cid=42625229 |url=http://philsci-archive.pitt.edu/4813/1/09.doc |via=SpringerLink}}</ref> Most outcomes, whether disease or death, are caused by a chain or web consisting of many component causes.<ref>{{cite book|title=Modern Epidemiology|last=Rothman|first=Kenneth J.|publisher=Little, Brown and Company|year=1986|isbn=978-0-316-75776-8|location=Boston/Toronto|url-access=registration|url=https://archive.org/details/modernepidemiolo0000roth}}</ref> Causes can be distinguished as necessary, sufficient or probabilistic conditions. If a necessary condition can be identified and controlled (e.g., antibodies to a disease agent, energy in an injury), the harmful outcome can be avoided (Robertson, 2015). One tool regularly used to conceptualize the multicausality associated with disease is the [[causal pie model]].<ref>{{cite book|last=Rothman|first=Kenneth J.|url=https://www.worldcat.org/oclc/750986180|title=Epidemiology : An introduction|date=2012|publisher=Oxford University Press|isbn=978-0-19-975455-7|edition=2nd|location=New York, NY|pages=24|oclc=750986180}}</ref>

=== Bradford Hill criteria ===
{{Main|Bradford Hill criteria}}
In 1965, [[Austin Bradford Hill]] proposed a series of considerations to help assess evidence of causation,<ref name="bh65">{{cite journal |last=Hill |first=Austin Bradford |year=1965 |title=The Environment and Disease: Association or Causation? |journal=[[Proceedings of the Royal Society of Medicine]] |volume=58 |pages=295–300 |url=http://www.edwardtufte.com/tufte/hill |pmid=14283879 |pmc=1898525 |issue=5 |doi=10.1177/003591576505800503 |archive-date=19 February 2021 |access-date=14 March 2006 |archive-url=https://web.archive.org/web/20210219104245/https://www.edwardtufte.com/tufte/hill |url-status=dead }}</ref> which have come to be commonly known as the "[[Bradford Hill criteria]]". In contrast to the explicit intentions of their author, Hill's considerations are now sometimes taught as a checklist to be implemented for assessing causality.<ref>{{cite journal |last1=Phillips |first1=Carl V. |author2=Karen J. Goodman |title=The missed lessons of Sir Austin Bradford Hill |journal=Epidemiologic Perspectives & Innovations |volume=1 |issue=3 |date=October 2004 |pmid=15507128 |pmc=524370 |doi=10.1186/1742-5573-1-3 |pages=3 |doi-access=free }}</ref> Hill himself said "None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required ''sine qua non''."<ref name="bh65" />

# '''Strength of Association''': A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal.<ref name="bh65" />
# '''Consistency of Data''': Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.<ref name="bh65" />
# '''Specificity''': Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.<ref name="bh65" />
# '''Temporality''': The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay).<ref name="bh65" />
# '''Biological gradient''': Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.<ref name="bh65" />
# '''Plausibility''': A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).<ref name="bh65" />
# '''Coherence''': Coherence between epidemiological and laboratory findings increases the likelihood of an effect. However, Hill noted that "... lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations".<ref name="bh65" />
# '''Experiment''': "Occasionally it is possible to appeal to experimental evidence".<ref name="bh65" />
# '''Analogy''': The effect of similar factors may be considered.<ref name="bh65" />

=== Legal interpretation ===
[[Epidemiological study|Epidemiological studies]] can only go to prove that an agent could have caused, but not that it did cause, an effect in any particular case:

{{blockquote|Epidemiology is concerned with the [[Incidence (epidemiology)|incidence]] of disease in populations and does not address the question of the cause of an individual's disease. This question, sometimes referred to as specific causation, is beyond the domain of the science of epidemiology. Epidemiology has its limits at the point where an inference is made that the relationship between an agent and a disease is causal (general causation) and where the magnitude of excess risk attributed to the agent has been determined; that is, epidemiology addresses whether an agent can cause disease, not whether an agent did cause a specific plaintiff's disease.<ref name="green">{{cite book |last1= Green |first1= Michael D. |author2= D. Michal Freedman, and Leon Gordis |title= Reference Guide on Epidemiology |publisher= Federal Judicial Centre |url= http://www.fjc.gov/public/pdf.nsf/lookup/sciman06.pdf/$file/sciman06.pdf |access-date= 3 February 2008 |url-status= dead |archive-url= https://web.archive.org/web/20080227143925/http://www.fjc.gov/public/pdf.nsf/lookup/sciman06.pdf/$file/sciman06.pdf |archive-date= 27 February 2008 |df= dmy-all }}</ref>}}

In United States law, epidemiology alone cannot prove that a causal association does not exist in general. Conversely, it can be (and is in some circumstances) taken by US courts, in an individual case, to justify an inference that a causal association does exist, based upon a balance of [[probability]].

The subdiscipline of forensic epidemiology is directed at the investigation of specific causation of disease or injury in individuals or groups of individuals in instances in which causation is disputed or is unclear, for presentation in legal settings.

== Population-based health management ==
Epidemiological practice and the results of epidemiological analysis make a significant contribution to emerging population-based health management frameworks.

Population-based health management encompasses the ability to:
* Assess the health states and health needs of a target population;
* Implement and evaluate interventions that are designed to improve the health of that population; and
* Efficiently and effectively provide care for members of that population in a way that is consistent with the community's cultural, policy and health resource values.

Modern population-based health management is complex, requiring a multiple set of skills (medical, political, technological, mathematical, etc.) of which epidemiological practice and analysis is a core component, that is unified with management science to provide efficient and effective health care and health guidance to a population. This task requires the forward-looking ability of modern risk management approaches that transform health risk factors, incidence, prevalence and mortality statistics (derived from epidemiological analysis) into management metrics that not only guide how a health system responds to current population health issues but also how a health system can be managed to better respond to future potential population health issues.<ref>{{cite web|title=Measuring Health and Disease I: Introduction to Epidemiology |url=http://open.umich.edu/education/med/oernetwork/public-health/epidemiology/intro-epidemiology/2010 |access-date=16 December 2011 |author1=Neil Myburgh |author2=Debra Jackson |url-status=dead |archive-url=https://web.archive.org/web/20110801204104/https://open.umich.edu/education/med/oernetwork/public-health/epidemiology/intro-epidemiology/2010 |archive-date=1 August 2011 }}</ref>

Examples of organizations that use population-based health management that leverage the work and results of epidemiological practice include Canadian Strategy for Cancer Control, Health Canada Tobacco Control Programs, Rick Hansen Foundation, Canadian Tobacco Control Research Initiative.<ref>{{cite conference |last1=Smetanin |first1=P. |author2=P. Kobak |title=Interdisciplinary Cancer Risk Management: Canadian Life and Economic Impacts |url=http://www.riskanalytica.com/sites/riskanalytica.com/files/Canadian%20Cancer%20Abstract%2010%20June%202005.pdf |conference=1st International Cancer Control Congress |date=October 2005 |access-date=2 August 2013 |archive-date=2 February 2014 |archive-url=https://web.archive.org/web/20140202111313/http://www.riskanalytica.com/sites/riskanalytica.com/files/Canadian%20Cancer%20Abstract%2010%20June%202005.pdf |url-status=dead }}</ref><ref>{{cite conference |last1=Smetanin |first1=P. |author2=P. Kobak |title=A Population-Based Risk Management Framework for Cancer Control |conference=The International Union Against Cancer Conference |date=July 2006 |conference-url=http://2006.confex.com/uicc/uicc/techprogram/P7935.HTM |url=http://www.riskanalytica.com/?q=node/73 |format=PDF |url-status=dead |archive-url=https://web.archive.org/web/20140202111153/http://www.riskanalytica.com/?q=node%2F73 |archive-date=2 February 2014 |df=dmy-all }}</ref><ref>{{cite conference |last1=Smetanin |first1=P. |author2=P. Kobak |title=Selected Canadian Life and Economic Forecast Impacts of Lung Cancer |conference=11th World Conference on Lung Cancer |date=July 2005 |url=http://www.riskanalytica.com/?q=node/70 |format=PDF |url-status=dead |archive-url=https://web.archive.org/web/20140202111300/http://www.riskanalytica.com/?q=node%2F70 |archive-date=2 February 2014 |df=dmy-all }}</ref>

Each of these organizations uses a population-based health management framework called Life at Risk that combines epidemiological quantitative analysis with demographics, health agency operational research and economics to perform:
* ''Population Life Impacts Simulations'': Measurement of the future potential impact of disease upon the population with respect to new disease cases, prevalence, premature death as well as potential years of life lost from disability and death;
* ''Labour Force Life Impacts Simulations'': Measurement of the future potential impact of disease upon the labour force with respect to new disease cases, prevalence, premature death and potential years of life lost from disability and death;
* ''Economic Impacts of Disease Simulations'': Measurement of the future potential impact of disease upon private sector disposable income impacts (wages, corporate profits, private health care costs) and public sector disposable income impacts (personal income tax, corporate income tax, consumption taxes, [[publicly funded health care]] costs).

== Applied field epidemiology ==

Applied epidemiology is the practice of using epidemiological methods to protect or improve the health of a population. Applied field epidemiology can include investigating communicable and non-communicable disease outbreaks, mortality and morbidity rates, and nutritional status, among other indicators of health, with the purpose of communicating the results to those who can implement appropriate policies or disease control measures.

=== Humanitarian context ===

As the surveillance and reporting of diseases and other health factors become increasingly difficult in humanitarian crisis situations, the methodologies used to report the data are compromised. One study found that less than half (42.4%) of nutrition surveys sampled from humanitarian contexts correctly calculated the prevalence of malnutrition and only one-third (35.3%) of the surveys met the criteria for quality. Among the mortality surveys, only 3.2% met the criteria for quality. As nutritional status and mortality rates help indicate the severity of a crisis, the tracking and reporting of these health factors is crucial.

Vital registries are usually the most effective ways to collect data, but in humanitarian contexts these registries can be non-existent, unreliable, or inaccessible. As such, mortality is often inaccurately measured using either prospective demographic surveillance or retrospective mortality surveys. Prospective demographic surveillance requires much manpower and is difficult to implement in a spread-out population. Retrospective mortality surveys are prone to selection and reporting biases. Other methods are being developed, but are not common practice yet.<ref>WHO, [https://www.who.int/topics/epidemiology/en "Health topics: Epidemiology."] {{Webarchive|url=https://web.archive.org/web/20200509180559/http://www9.who.int/topics/epidemiology/en/ |date=9 May 2020 }}  Accessed: 30 October 2017.</ref><ref>Miquel Porta.  A Dictionary of Epidemiology. http://global.oup.com/academic/product/a-dictionary-of-epidemiology-9780199976737?cc=us&lang=en {{Webarchive|url=https://web.archive.org/web/20170711233713/https://global.oup.com/academic/product/a-dictionary-of-epidemiology-9780199976737?cc=us&lang=en |date=11 July 2017 }} 6th edition, New York, 2014 Oxford University Press {{ISBN|978-0-19-997673-7}} Accessed: 30 October 2017.</ref><ref>Prudhon, C & Spiegel, P. "A review of methodology and analysis of nutrition and mortality surveys conducted in humanitarian emergencies from October 1993 to April 2004" Emerging Themes in Epidemiology 2007, 4:10. http://www.ete-online.com/content/4/1/10 {{Webarchive|url=https://web.archive.org/web/20151023194726/http://www.ete-online.com/content/4/1/10 |date=23 October 2015 }} Accessed: 30 October 2017.</ref><ref>Roberts, B et al. "A new method to estimate mortality in crisis-affected and resource-poor settings: validation study." ''International Journal of Epidemiology'' 2010; 39:1584–96. Accessed: 30 October 2017.</ref>

== Characterization, validity, and bias ==
===Epidemic wave===
The concept of waves in epidemics has implications especially for [[infection|communicable diseases]]. A working definition for the term "epidemic wave" is based on two key features: 1) it comprises periods of upward or downward trends, and 2) these increases or decreases must be substantial and sustained over a period of time, in order to distinguish them from minor fluctuations or reporting errors.<ref name=Zhang>{{cite journal | author = Zhang Stephen X | author2 = Marioli Francisco Arroyo | author3 = Gao Renfei | author4 = Wang Senhu| title = When is an epidemic an epidemic? | journal = Risk Management and Healthcare Policy  | volume = 14 | pages =  3775–3782  | date = 2021 | doi = 10.2147/RMHP.S326051 | pmid = 34548826 | pmc = 8448159 | doi-access = free }}</ref> The use of a consistent scientific definition is to provide a consistent language that can be used to communicate about and understand the progression of the COVID-19 pandemic, which would aid healthcare organizations and policymakers in resource planning and allocation.

===Validities===
Different fields in epidemiology have different levels of validity. One way to assess the validity of findings is the ratio of false-positives (claimed effects that are not correct) to false-negatives (studies which fail to support a true effect). In [[genetic epidemiology]], candidate-gene studies may produce over 100 false-positive findings for each false-negative. By contrast genome-wide association appear close to the reverse, with only one false positive for every 100 or more false-negatives.<ref name="Ioannides2011">{{cite journal | last1 = Ioannidis | first1 = J. P. A. | last2 = Tarone | first2 = R. | last3 = McLaughlin | first3 = J. K. | s2cid = 42756884 | title = The False-positive to False-negative Ratio in Epidemiologic Studies | journal = Epidemiology | volume = 22 | issue = 4 | pages = 450–56 | year = 2011 | pmid = 21490505 | doi = 10.1097/EDE.0b013e31821b506e | doi-access = free }}</ref> This ratio has improved over time in genetic epidemiology, as the field has adopted stringent criteria. By contrast, other epidemiological fields have not required such rigorous reporting and are much less reliable as a result.<ref name="Ioannides2011"/>

=== Random error ===
Random error is the result of fluctuations around a true value because of sampling variability. Random error is just that: random. It can occur during data collection, coding, transfer, or analysis. Examples of random errors include poorly worded questions, a misunderstanding in interpreting an individual answer from a particular respondent, or a typographical error during coding. Random error affects measurement in a transient, inconsistent manner and it is impossible to correct for random error. There is a random error in all sampling procedures {{ndash}} [[sampling error]].{{cn|date=July 2023}}

Precision in epidemiological variables is a measure of random error. Precision is also inversely related to random error, so that to reduce random error is to increase precision. Confidence intervals are computed to demonstrate the precision of relative risk estimates. The narrower the confidence interval, the more precise the relative risk estimate.

There are two basic ways to reduce random error in an [[epidemiological study]]. The first is to increase the sample size of the study. In other words, add more subjects to your study. The second is to reduce the variability in measurement in the study. This might be accomplished by using a more precise measuring device or by increasing the number of measurements.

Note, that if sample size or number of measurements are increased, or a more precise measuring tool is purchased, the costs of the study are usually increased. There is usually an uneasy balance between the need for adequate precision and the practical issue of study cost.

=== Systematic error ===
A systematic error or bias occurs when there is a difference between the true value (in the population) and the observed value (in the study) from any cause other than sampling variability. An example of systematic error is if, unknown to you, the [[pulse oximeter]] you are using is set incorrectly and adds two points to the true value each time a measurement is taken. The measuring device could be [[Accuracy and precision|precise but not accurate]]. Because the error happens in every instance, it is systematic. Conclusions you draw based on that data will still be incorrect. But the error can be reproduced in the future (e.g., by using the same mis-set instrument).

A mistake in coding that affects ''all'' responses for that particular question is another example of a systematic error.

The validity of a study is dependent on the degree of systematic error. Validity is usually separated into two components:
* [[Internal validity]] is dependent on the amount of error in measurements, including exposure, disease, and the associations between these variables. Good internal validity implies a lack of error in measurement and suggests that inferences may be drawn at least as they pertain to the subjects under study.
* [[External validity]] pertains to the process of generalizing the findings of the study to the population from which the sample was drawn (or even beyond that population to a more universal statement). This requires an understanding of which conditions are relevant (or irrelevant) to the generalization. Internal validity is clearly a prerequisite for external validity.

==== Selection bias ====
[[Selection bias]] occurs when study subjects are selected or become part of the study as a result of a third, unmeasured variable which is associated with both the exposure and outcome of interest.<ref name="Hernán2004"/> For instance, it has repeatedly been noted that cigarette smokers and non smokers tend to differ in their study participation rates. (Sackett D cites the example of Seltzer et al., in which 85% of non smokers and 67% of smokers returned mailed questionnaires.)<ref name="Sackett D. Bias in analytic research. J Chron Dis 1979; vol. 32:51–63.">[http://www.epidemiology.ch/history/PDF%20bg/Sackett%20DL%201979%20bias%20in%20analytic%20research.pdf] {{Webarchive|url=https://web.archive.org/web/20170829193522/http://epidemiology.ch/history/PDF%20bg/Sackett%20DL%201979%20bias%20in%20analytic%20research.pdf|date=29 August 2017}} 24</ref> Such a difference in response will not lead to bias if it is not also associated with a systematic difference in outcome between the two response groups.

==== Information bias ====
[[Information bias (epidemiology)|Information bias]] is bias arising from systematic error in the assessment of a variable.<ref name=Rothman2002>{{cite book|last1=Rothman|first1=K.|title=Epidemiology: An Introduction|url=https://archive.org/details/epidemiology00kenn|url-access=registration|date=2002|publisher=[[Oxford University Press]]|location=Oxford|isbn=978-0195135541}}</ref> An example of this is recall bias. A typical example is again provided by Sackett in his discussion of a study examining the effect of specific exposures on fetal health: "in questioning mothers whose recent pregnancies had ended in fetal death or malformation (cases) and a matched group of mothers whose pregnancies ended normally (controls) it was found that 28% of the former, but only 20% of the latter, reported exposure to drugs which could not be substantiated either in earlier prospective interviews or in other health records".<ref name="Sackett D. Bias in analytic research. J Chron Dis 1979; vol. 32:51–63." /> In this example, recall bias probably occurred as a result of women who had had miscarriages having an apparent tendency to better recall and therefore report previous exposures.

==== Design-related bias ====
Next to sample- and variable-related bias, bias can also arise from an imperfect study design. One example is immortal time bias, where during study period, there is some interval during which the outcome event cannot occur (making these individual "immortal").<ref>{{Cite journal |last1=Yadav |first1=Kabir |last2=Lewis |first2=Roger J. |date=2021-02-16 |title=Immortal Time Bias in Observational Studies |url=https://jamanetwork.com/journals/jama/article-abstract/2776315 |journal=JAMA |volume=325 |issue=7 |pages=686–687 |doi=10.1001/jama.2020.9151 |pmid=33591334 |issn=0098-7484|url-access=subscription }}</ref><ref>{{Cite journal |last1=Lévesque |first1=Linda E. |last2=Hanley |first2=James A. |last3=Kezouh |first3=Abbas |last4=Suissa |first4=Samy |date=2010-03-12 |title=Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes |url=https://www.bmj.com/content/340/bmj.b5087 |journal=BMJ |language=en |volume=340 |pages=b5087 |doi=10.1136/bmj.b5087 |issn=0959-8138 |pmid=20228141|url-access=subscription }}</ref>

==== Confounding ====
[[Confounding]] has traditionally been defined as bias arising from the co-occurrence or mixing of effects of extraneous factors, referred to as confounders, with the main effect(s) of interest.<ref name=Rothman2002/><ref name=Greenland>{{cite journal |vauthors=Greenland S, Morgenstern H | s2cid = 4647751 | year = 2001 | title = Confounding in Health Research | journal = Annu. Rev. Public Health | volume = 22 | pages = 189–212 | doi=10.1146/annurev.publhealth.22.1.189| pmid = 11274518 | doi-access =  }}</ref> A more recent definition of confounding invokes the notion of ''counterfactual'' effects.<ref name=Greenland/> According to this view, when one observes an outcome of interest, say Y=1 (as opposed to Y=0), in a given population A which is entirely exposed (i.e. exposure ''X''&nbsp;=&nbsp;1 for every unit of the population) the risk of this event will be ''R''<sub>A1</sub>. The counterfactual or unobserved risk ''R''<sub>A0</sub> corresponds to the risk which would have been observed if these same individuals had been unexposed (i.e. ''X''&nbsp;=&nbsp;0 for every unit of the population). The true effect of exposure therefore is: ''R''<sub>A1</sub>&nbsp;−&nbsp;''R''<sub>A0</sub> (if one is interested in risk differences) or ''R''<sub>A1</sub>/''R''<sub>A0</sub> (if one is interested in relative risk). Since the counterfactual risk ''R''<sub>A0</sub> is unobservable we approximate it using a second population B and we actually measure the following relations: ''R''<sub>A1</sub>&nbsp;−&nbsp;''R''<sub>B0</sub> or ''R''<sub>A1</sub>/''R''<sub>B0</sub>. In this situation, confounding occurs when ''R''<sub>A0</sub>&nbsp;≠&nbsp;''R''<sub>B0</sub>.<ref name=Greenland/> (NB: Example assumes binary outcome and exposure variables.)

Some epidemiologists prefer to think of confounding separately from common categorizations of bias since, unlike selection and information bias, confounding stems from real causal effects.<ref name="Hernán2004">{{cite journal |last1=Hernán |first1=M. A. |last2=Hernández-Díaz |first2=S. |author-link2=Sonia Hernández-Díaz |last3=Robins |first3=J. M. |year=2004 |title=A structural approach to selection bias |journal=Epidemiology |volume=15 |issue=5 |pages=615–25 |doi=10.1097/01.ede.0000135174.63482.43 |pmid=15308962 |s2cid=1373077 |doi-access=free}}</ref>

== The profession ==
Few universities have offered epidemiology as a course of study at the undergraduate level.{{Citation needed|date=July 2024|reason=This claim is unsourced and likely incorrect. For example, I personally know that more than a "few universities" offer undergraduate-level courses in epidemiology.}} An undergraduate program exists at [[Johns Hopkins University]] in which students who major in public health can take graduate-level courses—including epidemiology—during their senior year at the [[Bloomberg School of Public Health]].<ref>{{cite web|title=Public Health Studies|url=http://krieger.jhu.edu/publichealth/|website=Public Health Studies at Johns Hopkins|date=6 June 2013 |access-date=13 April 2017}}</ref> In addition to its master's and doctoral degrees in epidemiology, the [[University of Michigan School of Public Health]] has offered undergraduate degree programs since 2017 that include coursework in epidemiology.<ref>{{Cite web |last=Meer |first=Jennifer |date=2017-09-04 |title=New school year marks beginning of undergraduate public health program |url=http://www.michigandaily.com/news/academics/sph-new-program/ |access-date=2024-07-09 |website=The Michigan Daily |language=en-US}}</ref><ref>{{Cite web |last=Jerolimov |first=Anna |date=2024-01-16 |title=Undergrad Public Health Program expands for fall |url=http://www.michigandaily.com/news/undergraduate-public-health-program-expands-for-fall-2024/ |access-date=2024-07-09 |website=The Michigan Daily |language=en-US}}</ref> 

Although epidemiologic research is conducted by individuals from diverse disciplines, variable levels of training in epidemiologic methods are provided during [[Pharmacy school|pharmacy]], [[Medical education|medical]], [[Veterinary education|veterinary]], [[Social work#Theoretical models and practices|social work]], [[Podiatric medical school|podiatry]], [[Public health nursing|nursing]], [[Doctor of Physical Therapy#Professional degree (entry-level)|physical therapy]], and [[Psychologist#United States|clinical psychology]] doctoral programs in addition to the formal training master's and doctoral students in public health fields receive. 

As public health practitioners, epidemiologists work in a number of different settings. Some epidemiologists work "in the field" (i.e., in the community; commonly{{According to whom|date=July 2024}} in a public health service), and are often at the forefront of investigating and combating disease outbreaks.{{Citation needed|date=July 2024|reason=Why are epidemiologists working in the field often at the forefront of infectious disease outbreaks? I mean... I know the reason why, but the average reader might not.}} Others work for non-profit organizations, universities, hospitals, or larger government entities (e.g., state and local health departments in the United States), ministries of health, [[Médecins Sans Frontières|Doctors without Borders]], the [[Centers for Disease Control and Prevention]] (CDC), the [[Health Protection Agency]], the [[World Health Organization]] (WHO), or the [[Public Health Agency of Canada]]. Epidemiologists can also work in for-profit organizations (e.g., pharmaceutical and medical device companies) in groups such as market research or clinical development.

===COVID-19===
An April 2020 [[University of Southern California]] article noted that, "The [[coronavirus epidemic]]... thrust epidemiology – the study of the incidence, distribution and control of disease in a population – to the forefront of scientific disciplines across the globe and even made temporary celebrities out of some of its practitioners."<ref>{{cite web |last1=Hiro |first1=Brian |title=Ask the Expert: The Epidemiology of COVID-19 |url=https://news.csusm.edu/ask-the-expert-deborah-morton/ |publisher=SCUSM |access-date=11 June 2020}}</ref>

== See also ==
{{Portal|Medicine}}
{{div col}}
* {{annotated link|Age adjustment}}
* {{annotated link|Caerphilly Heart Disease Study}}
* {{annotated link|Centre for Research on the Epidemiology of Disasters |abbreviation=CRED}}
* {{annotated link|Centro Studi GISED}}
* {{annotated link|Circulation plan}}
* {{annotated link|Contact tracing}}
* {{annotated link|Critical community size}}
* {{annotated link|Disease cluster}}
* {{annotated link|Disease diffusion mapping}}
* {{annotated link|Compartmental models in epidemiology}}
* {{annotated link|Epidemiological method}}
* {{annotated link|Epidemiological transition}}
* {{annotated link|European Centre for Disease Prevention and Control}}
* {{annotated link|Hispanic paradox}}
* {{annotated link|International Society for Pharmacoepidemiology}}
* {{annotated link|Job-exposure matrix}}
* {{annotated link|Mathematical modelling of infectious disease}}
* {{annotated link|Mendelian randomization}}
* {{annotated link|Occupational epidemiology}}
* {{annotated link|Predictive analytics}}
* {{annotated link|Society for Occupational Health Psychology|''Society for Occupational Health Psychology''}}
* {{annotated link|Race and health|Population groups in biomedicine}}
* {{annotated link|Spatial epidemiology}}
* {{annotated link|Study of Health in Pomerania}}
* {{annotated link|Targeted immunization strategies}}
* {{annotated link|Urban planning}}
* {{annotated link|Whitehall Study}}
* {{annotated link|Zoonosis}}
{{div col end}}

== References ==
=== Citations ===
{{Reflist}}

=== Sources ===
{{refbegin}}
* [[David Clayton|Clayton, David]] and Michael Hills (1993) ''Statistical Models in Epidemiology'' Oxford University Press. {{ISBN|0-19-852221-5}}<!-- A thorough introduction to the statistical analysis of epidemiological data, focussing on survival rates - their estimation, analysis and comparison.-->
* [[Miquel Porta]], editor (2014) "A dictionary of epidemiology", 6th edn, New York: Oxford University Press. [http://global.oup.com/academic/product/a-dictionary-of-epidemiology-9780199976737?cc=us&lang=en A Dictionary of Epidemiology] {{Webarchive|url=https://web.archive.org/web/20170711233713/https://global.oup.com/academic/product/a-dictionary-of-epidemiology-9780199976737?cc=us&lang=en |date=11 July 2017 }}
* Morabia, Alfredo, editor. (2004) A History of Epidemiologic Methods and Concepts. Basel, Birkhauser Verlag. Part I. [https://books.google.com/books?id=Hgnnhu1ym-8C A History of Epidemiologic Methods and Concepts] {{Webarchive|url=https://web.archive.org/web/20220630015958/https://books.google.com/books?id=Hgnnhu1ym-8C&printsec=frontcover |date=30 June 2022 }} [https://www.springer.com/public+health/book/978-3-7643-6818-0 A History of Epidemiologic Methods and Concepts] {{Webarchive|url=https://web.archive.org/web/20110703131648/http://www.springer.com/public+health/book/978-3-7643-6818-0 |date=3 July 2011 }}
* Smetanin P, Kobak P, Moyer C, Maley O (2005). "The Risk Management of Tobacco Control Research Policy Programs" The World Conference on Tobacco OR Health Conference, 12–15 July 2006, Washington DC.
* Szklo M, Nieto FJ (2002). "Epidemiology: beyond the basics", Aspen Publishers.
* Robertson LS (2015). Injury Epidemiology: Fourth Edition. Free online at <code>nanlee.net</code>
* Rothman K., [[Sander Greenland]], Lash T., editors (2008). "Modern Epidemiology", 3rd Edition, Lippincott Williams & Wilkins. {{ISBN|0-7817-5564-6|978-0-7817-5564-1}}
* [https://web.archive.org/web/20130518044530/https://skydrive.live.com/?cid=ec4d1867f6389ec0&id=EC4D1867F6389EC0%21183 Olsen J, Christensen K, Murray J, Ekbom A. An Introduction to Epidemiology for Health Professionals. New York: Springer Science+Business Media; 2010] {{ISBN|978-1-4419-1497-2}}
{{refend}}

== External links ==
{{commons category|Epidemiology}}
{{Wiktionary|epidemiology}}
{{Library resources box
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{{Refbegin}}
* [http://www.hpa.org.uk The Health Protection Agency] {{Webarchive|url=https://web.archive.org/web/20070129123642/http://www.hpa.org.uk/ |date=29 January 2007 }}
* [https://biostats.bepress.com/ The Collection of Biostatistics Research Archive] {{Webarchive|url=https://web.archive.org/web/20211024171703/https://biostats.bepress.com/ |date=24 October 2021 }}
* [https://web.archive.org/web/20110726171127/http://www.iea-europe.org/index.htm European Epidemiological Federation]
* [http://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated 'Epidemiology for the Uninitiated'] {{Webarchive|url=https://web.archive.org/web/20190321191234/https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated |date=21 March 2019 }} by D. Coggon, G. Rose, D.J.P. Barker, ''[[British Medical Journal]]''
* [http://www.epidem.com Epidem.com] {{Webarchive|url=https://web.archive.org/web/20010924091113/http://epidem.com/ |date=24 September 2001 }} – ''[[Epidemiology (journal)|Epidemiology]]'' (peer-reviewed scientific journal that publishes original research on epidemiologic topics)
* [https://www.ncbi.nlm.nih.gov/books/NBK7993/ 'Epidemiology'] {{Webarchive|url=https://web.archive.org/web/20210429152443/https://www.ncbi.nlm.nih.gov/books/NBK7993/ |date=29 April 2021 }} – In: Philip S. Brachman, ''[[Medical Microbiology]]'' (fourth edition), US [[National Center for Biotechnology Information]]
* [https://web.archive.org/web/20071104183725/http://vlab.infotech.monash.edu.au/simulations/cellular-automata/epidemic/ Monash Virtual Laboratory] – Simulations of epidemic spread across a landscape
* [http://dceg.cancer.gov/ Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health] {{Webarchive|url=https://web.archive.org/web/20090812223649/http://dceg.cancer.gov/ |date=12 August 2009 }}
* [http://www.cred.be Centre for Research on the Epidemiology of Disasters] {{Webarchive|url=https://web.archive.org/web/20100315094506/http://www.cred.be/ |date=15 March 2010 }}{{spaced ndash}}A [[World Health Organization|WHO]] collaborating centre
* [https://web.archive.org/web/20180405101243/http://www.epidemiology.ch/history/PeopleEpidemiologyLibrary.html People's Epidemiology Library]
* [https://www.ncbi.nlm.nih.gov/pubmed/32113704 Epidemiology of COVID-19 outbreak] {{Webarchive|url=https://web.archive.org/web/20200328061221/https://www.ncbi.nlm.nih.gov/pubmed/32113704 |date=28 March 2020 }}
{{Refend}}

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