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'''Epidemiology''' is the study and analysis of the [[pattern]]s, [[cause]]s, and effects of [[health]] and [[disease]] conditions in defined [[population]]s. It is the cornerstone of [[public health]], and shapes policy decisions and [[evidence-based practice]] by identifying [[risk factor]]s for disease and targets for [[preventive healthcare]]. Epidemiologists help with study design, collection, and [[statistical analysis]] of data, amend lmaol wet 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 |author=Miquel 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 |accessdate=16 July 2014}}</ref>

Major areas of epidemiological study include disease [[Etiology (medicine)|etiology]], [[transmission (medicine)|transmission]], [[outbreak]] investigation, [[disease surveillance]], [[forensic epidemiology]] and [[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]].

== Etymology ==
''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, Jr. |first= F.W. |title= Understanding the interrelationships between botanical, human, and veterinary epidemiology: the Ys and Rs of it all |journal= Ecosys 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. (~200BC). 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. p3.</ref> The term "epidemiology" appears to have first been used to describe the study of epidemics in 1802 by the Spanish physician Villalba 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 disease, but of disease in general, and even many non-disease, health-related conditions, such as high blood pressure and [[obesity]]. Therefore, this epidemiology is based upon how the pattern of the disease cause changes in the function of everyone.

== History ==

The Greek physician [[Hippocrates]], (See information regarding the African doctor Imhotep, whom Hippocrates called the true father of medicine. [[Imhotep]]) is known as the father of [[medicine]],<ref>{{cite book |url=https://books.google.com/?id=E-OZbEmPSTkC&pg=PA93&dq#v=onepage&q=&f=false |title=A history of epidemiologic methods and concepts |author=Alfredo Morabia |year=2004 |publisher=Birkhäuser |page=93 |isbn=3-7643-6818-7}}</ref><ref>[http://samples.jbpub.com/9780763766221/66221_CH02_5398.pdf Historical Developments in Epidemiology]. 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/?id=RMDBh6gw1_UC&pg=PA24&dq#v=onepage&q=&f=false |title=Introduction to Epidemiology |author=Ray M. Merrill |year=2010 |publisher=Jones & Bartlett Learning |page=24 |isbn=0-7637-6622-4}}</ref> Hippocrates believed sickness of the human body to be caused by an imbalance of the four [[humorism|humors]] (air, fire, water and earth “atoms”). 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. "An Introduction to Epidemiology, Fifth Edition". Chapter 2: Historic Developments in Epidemiology. Jones and Bartlett Publishing, 2010. Web. 17 Sept. 2012.</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 |accessdate= 2008-02-03}}</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 these 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 [[Anton van Leeuwenhoek]] in 1675 provided visual evidence of living particles consistent with a [[germ theory of disease]].

[[Wu Youke]] (1582-1652) developed the concept that some diseases were caused by transmissible agents, which he called liqi (pestilential factors). His book Wenyi Lun (Treatise on Acute Epidemic Febrile Diseases) can be regarded as the main etiological work that brought forward the concept, ultimately attributed to Westerners, of germs as a cause of epidemic diseases (source: http://baike.baidu.com/view/143117.htm). His concepts are still considered in current scientific research in relation to Traditional Chinese Medicine studies (see: http://apps.who.int/medicinedocs/en/d/Js6170e/4.html).

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"/>

[[File:Snow-cholera-map.jpg|thumb|350px|Original map by [[John Snow (physician)|John Snow]] showing the [[cluster (epidemiology)|cluster]]s of cholera cases in the [[1854 Broad Street cholera outbreak|London epidemic of 1854]].]]
[[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.

[[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>[http://www.ph.ucla.edu/epi/snow/fatherofepidemiology.html Doctor John Snow Blames Water Pollution for Cholera Epidemic, by David Vachon] UCLA Department of Epidemlology, School of Public Health May & June, 2005</ref><ref>[http://www.npr.org/templates/story/story.php?storyId=3935461 John Snow, Father of Epidemiology] NPR Talk of the Nation. September 24, 2004</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>
[http://www.ph.ucla.edu/epi/snow/importance.html The Importance of Snow. Gro Harlem Brundtland, M.D., M.P.H.former Director-General, World Health Organization. Geneva, Switzerland Talk, Washington, D.C., 28 October 1998]</ref><ref>[http://www.jsi.com/JSIInternet/About/snow.cfm Dr. John Snow.] 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.

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 web |author=Ólöf Garðarsdóttir |author2=Loftur Guttormsson |title= An isolated case of early medical intervention. The battle against neonatal tetanus in the island of Vestmannaeyjar (Iceland) during the 19th century |publisher=Instituto de Economía y Geografía |date=June 2008 |url=http://ftp.ieg.csic.es/workshop/pdf/olofpaper.pdf |accessdate=2011-04-19}}</ref><ref>{{cite journal |author1=Ólöf Garðarsdóttir |author2=Loftur Guttormsson |title=Public health measures against neonatal tetanus on the island of Vestmannaeyjar (Iceland) during the 19th century |journal=The History of the Family|volume=14 |issue=3 |date=25 August 2009 |pages=266–79 |doi=10.1016/j.hisfam.2009.08.004}}{{Verify source|date=April 2011}}</ref><!-- please check also Daniel E. Vaisey, "An Estimate of Neonatal Tetanus Mortality in Iceland, 1790–1839" European Journal of Population 13 (1997), 62, 67 as cited in Loftur Guttormsson and Ólöf Garðarsdóttir [http://www.ep.liu.se/ej/hygiea/ "The Development of Infant Mortality in Iceland 1800–1920"] (2002) Hygiea Internationalis 3(1) pp. 151–77 --> 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]] 'discovered' [[antiseptics]] in 1865 in light of the work of [[Louis Pasteur]].

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&lpg=PA323&ots=RRTToupz4X&dq=mathematical%20methods%20were%20introduced%20into%20epidemiology%2020th%20century%20ross&pg=PA323#v=onepage&q=mathematical%20methods%20were%20introduced%20into%20epidemiology%2020th%20century%20ross&f=false Statisticians of the centuries]. By C. C. Heyde, Eugene Senet</ref><ref>[http://statprob.com/encyclopedia/AndersonGrayMcKENDRICK.html Anderson Gray McKendrick]</ref><ref>[http://www.economics.soton.ac.uk/staff/aldrich/Hardy%20and%20Magnello.pdf Statistical methods in epidemiology: Karl Pearson, Ronald Ross, Major Greenwood and Austin Bradford Hill, 1900 – 1945. Trust Centre for the History of Medicine at UCL, London]</ref><ref>[http://www.epidemiology.ch/history/papers/SPM%2047(6)%20359-65%20Paneth%20et%20al.%20_%20Part%202.pdf Origins and early development of the case-control study]</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]].

In the late 20th century, with 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. 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.

While most molecular epidemiology studies are still using conventional disease [[diagnosis]] and classification systems, it is increasingly recognized that disease [[evolution]] 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 | author = Ogino S, Fuchs CS, Giovannucci E | year = 2012 | title = How many molecular subtypes? Implications of the unique tumor principle in personalized medicine | url = | journal = Expert Rev Mol Diagn | volume = 12 | issue = | pages = 621–8 | doi=10.1586/erm.12.46 | pmid=22845482 | pmc=3492839}}</ref><ref>{{cite journal | author = 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 | url = | journal = Mod Pathol | volume = 26 | issue = | 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 | author = 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 | url = | journal = Am J Epidemiol | volume = 176 | issue = | pages = 659–67 | doi=10.1093/aje/kws226}}</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 | author = Ogino S, Stampfer M | year = 2010 | title = Lifestyle factors and microsatellite instability in colorectal cancer: the evolving field of molecular pathological epidemiology | url = | journal = J Natl Cancer Inst | volume = 102 | issue = | pages = 365–7 | doi=10.1093/jnci/djq031 | pmid=20208016 | pmc=2841039}}</ref><ref>{{cite journal | author = Ogino S, Chan AT, Fuchs CS, Giovannucci E | year = 2011 | title = Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field | url = | journal = Gut | volume = 60 | issue = | 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 | author = Field AE, Camargo CA, Ogino S | year = 2013 | title = The merits of subtyping obestity: one size does not fit all | url = | journal = JAMA | volume = 310 | issue = | pages = 2147–8 | doi=10.1001/jama.2013.281501}}</ref> The concept and paradigm of MPE have become widespread in the 2010s.<ref>{{cite journal | author = Curtin K, Slattery ML, Samowitz WS | year = 2011 | title = CpG island methylation in colorectal cancer: past, present and future | url = | journal = Pathology Research International | volume = 2011 | issue = | page = 902674 }}</ref><ref>{{cite journal | author = Hughes LA, Khalid-de Bakker CA, Smits KM, den Brandt PA, Jonkers D, Ahuja N, Herman JG, Weijenberg MP, van Engeland M | year = 2012 | title = The CpG island methylator phenotype in colorectal cancer: Progress and problems | url = | journal = Biochim Biophys Acta | volume = 1825 | issue = | pages = 77–85 | doi=10.1016/j.bbcan.2011.10.005}}</ref><ref>{{cite journal | author = 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. | url = | journal = Mod Pathol | volume = 25 | issue = | pages = 1055–68 | doi=10.1038/modpathol.2012.62 | pmid=22522846}}</ref><ref>{{cite journal | author = Chia WK, Ali R, Toh HC | year = 2012 | title = Aspirin as adjuvant therapy for colorectal cancer-reinterpreting paradigms | url = | journal = Nat Rev Clin Oncol | volume = 9 | issue = | pages = 561–70 | doi=10.1038/nrclinonc.2012.137}}</ref><ref>{{cite journal | author = Spitz MR, Caporaso NE, Sellers TA | year = 2012 | title = Integrative cancer epidemiology--the next generation | url = | journal = Cancer Discov | volume = 2 | issue = | pages = 1087–90 | doi=10.1158/2159-8290.cd-12-0424}}</ref><ref>{{cite journal | author = 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 | url = | journal = Prog Lipid Res | volume = 52 | issue = | pages = 585–9 | doi=10.1016/j.plipres.2013.08.005}}</ref><ref>{{cite journal | author = Ikramuddin S, Livingston EH | year = 2013 | title = New Insights on Bariatric Surgery Outcomes | url = | journal = JAMA | volume = 310 | issue = | pages = 2401–2 | doi=10.1001/jama.2013.280927}}</ref>

== Types of studies ==
{{Main|Study design}}
Epidemiologists employ a range of study designs from the observational to experimental and generally categorized as descriptive, 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 Aug. 2011. Web. 30 Sept. 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]] as a tool.

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 new medicine or drug testing), field trials (conducted on those at a high risk of conducting 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.

=== 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 technique comparing periods during which patients are exposed to some factor with the potential to produce illness with periods when they are unexposed.

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 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. (Ed.) |publisher=Lippincott, Williams and Wilkins |isbn=978-0-316-35636-7}}</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.

=== 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), 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).

{| 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.

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:

total cases = (a+c) = (1.96)^2×(1+N)×(1÷ln(OR))^2×((OR+2√OR+1)÷√OR)≈15.5×(1+N)×(1÷ln(OR))^2

where N = the ratio of cases to controls.
As the odds ratio approached 1, approaches 0; 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 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;½.

=== Outbreak investigation ===
: ''For information on investigation of [[infectious disease]] outbreaks, please see [[Outbreak#Outbreak investigation|outbreak investigation]].''

== 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 is  a necessary but not sufficient criteria for inference of causation. 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.{{citation needed|date=June 2015}} Most outcomes, whether disease or death, are caused by a chain or web consisting of many component causes (Rothman et al., 2008). 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).

=== 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}}</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 and Innovations |volume=1 |issue=3 |url=http://www.epi-perspectives.com/content/1/1/3 |date=October 2004 |pmid=15507128 |pmc=524370 |doi=10.1186/1742-5573-1-3 |pages=3}}</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''': 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''': 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 a 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 |format=PDF |accessdate= 2008-02-03}}</ref></blockquote>

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|accessdate=16 December 2011|author1=Neil Myburgh|author2=Debra Jackson}}</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 |conferenceurl=http://www.riskanalytica.com/sites/riskanalytica.com/files/Canadian%20Cancer%20Abstract%2010%20June%202005.pdf |conference=1st International Cancer Control Congress |date=October 2005}}</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 |conferenceurl= http://2006.confex.com/uicc/uicc/techprogram/P7935.HTM |url=http://www.riskanalytica.com/?q=node/73 |format=PDF}}</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}}</ref>

Each of these organizations use 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).

== Validity: precision and bias ==
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). To take the field of genetic epidemiology, candidate-gene studies produced 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. | title = The False-positive to False-negative Ratio in Epidemiologic Studies | journal = Epidemiology | volume = 22 | issue = 4 | pages = 450–456 | year = 2011 | pmid = 21490505 | doi = 10.1097/EDE.0b013e31821b506e }}</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 error 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 random error in all sampling procedures. This is called [[sampling error]].

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] 24</ref> It is important to note that 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|date=2002|publisher=[[Oxford University Press]]|location=Oxford|isbn=0195135547}}</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.

==== 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 | author = Greenland S, Morgenstern H | year = 2001 | title = Confounding in Health Research | url = http://www.annualreviews.org/doi/full/10.1146/annurev.publhealth.22.1.189 | journal = Annu. Rev. Publ. Health | volume = 22 | issue = | pages = 189–212 | doi=10.1146/annurev.publhealth.22.1.189}}</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. | last3 = Robins | first3 = J. M. | title = A structural approach to selection bias | journal = Epidemiology (Cambridge, Mass.) | volume = 15 | issue = 5 | pages = 615–625 | year = 2004 | pmid = 15308962 | doi=10.1097/01.ede.0000135174.63482.43}}</ref>

== The profession ==
To date, few [[universities]] offer epidemiology as a course of study at the undergraduate level. Many epidemiologists are [[physicians]], or hold graduate degrees such as a [[Master of Public Health]] (MPH), [[Master of Science]] of Epidemiology (MSc.). [[Doctorate]]s include the [[Doctor of Public Health]] (DrPH), [[Doctor of Pharmacy]] (PharmD), [[Doctor of Philosophy]] (PhD), [[Doctor of Science]] (ScD), [[Doctor of Social Work]] (DSW), [[Doctor of Clinical Practice]] (DClinP), [[Doctor of Podiatric Medicine]] (DPM), [[Doctor of Veterinary Medicine]] (DVM), [[Doctor of Nursing Practice]] (DNP), [[Doctor of Physical Therapy]] (DPT), or for clinically trained physicians, [[Doctor of Medicine]] (MD) or [[Bachelor of Medicine and Surgery]] (MBBS or MBChB) and [[Doctor of Osteopathic Medicine]] (DO).

As public health/health protection practitioners, epidemiologists work in a number of different settings. Some epidemiologists work 'in the field'; i.e., in the community, commonly in a public health/health protection service and are often at the forefront of investigating and combating disease outbreaks. Others work for non-profit organizations, universities, hospitals and larger government entities such as 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 such as pharmaceutical and medical device companies in groups such as market research or clinical development.

== See also ==
{{Portal|Health and fitness}}
{{too many see alsos|date=November 2014}}
{{div col|colwidth=20em|small=yes}}
* [[Age adjustment]]
* [[Caerphilly Heart Disease Study]]
* [[Centers for Disease Control and Prevention]] in the United States
* [[Centre for Research on the Epidemiology of Disasters (CRED)]]
* [[Critical community size]]
* [[Demographic Transition]]
* [[Disease diffusion mapping]]
* [[Epidemic model]]
* [[Epidemiological methods]]
* [[Epidemiological Transition]]
* [[European Centre for Disease Prevention and Control]]
* [[Hispanic paradox]]
* [[International Society for Pharmacoepidemiology]]
* [[Mathematical modelling in epidemiology]]
* [[Mendelian randomization]]
* [[Modifiable Areal Unit Problem]]
* [[Natural history of disease]]
* [[Population groups in biomedicine]]
* [[Study of Health in Pomerania]]
* [[Syndemic]]
* [[Targeted Immunization Strategies]]
* [[Thousand Families Study, Newcastle upon Tyne]]
* [[Umeå Centre for Global Health Research]]
* [[Whitehall Study]]
{{div col end}}

== References ==
{{Research help|Med}}

=== Notes ===
{{Reflist|colwidth=30em}}

=== Bibliography ===
{{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]
* Morabia, Alfredo, editor. (2004) A History of Epidemiologic Methods and Concepts. Basel, Birkhauser Verlag. Part I. [https://books.google.com/books?id=Hgnnhu1ym-8C&dq=Morabia,+Alfredo.+ed.+(2004)+A+History+of+Epidemiologic+Methods&printsec=frontcover&source=bn&hl=es&ei=U4ARSvbaEJGUjAew8LnCBg&sa=X&oi=book_result&ct=result&resnum=4] [http://www.springer.com/public+health/book/978-3-7643-6818-0]
* 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, July 12–15, 2006 in Washington DC.
* Szklo M, Nieto FJ (2002). "Epidemiology: beyond the basics", Aspen Publishers.
* Robertson LS (2015). Injury Epidemiology: Fourth Edition. Free online at www.nanlee.net
* Rothman K, [[Sander Greenland]], Lash T, editors (2008). "Modern Epidemiology", 3rd Edition, Lippincott Williams & Wilkins. ISBN 0-7817-5564-6, ISBN 978-0-7817-5564-1
* [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.] e-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]
* [http://www.biostatsresearch.com/repository/ The Collection of Biostatistics Research Archive]
* [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'] by D. Coggon, G. Rose, D.J.P. Barker, ''[[British Medical Journal]]''
* [http://www.epidem.com Epidem.com] – ''[[Epidemiology (journal)|Epidemiology]]'' (peer reviewed scientific journal that publishes original research on epidemiologic topics)
* [http://www.ncbi.nlm.nih.gov/books/NBK7993/ 'Epidemiology'] – In: Philip S. Brachman, ''[[Medical Microbiology]]'' (fourth edition), US [[National Center for Biotechnology Information]]
* [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]
* [http://www.cred.be Centre for Research on the Epidemiology of Disasters]{{spaced ndash}}A [[World Health Organization|WHO]] collaborating centre
* [http://www.epidemiology.ch/history/PeopleEpidemiologyLibrary.html People's Epidemiology Library]
{{Refend}}

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