Epidemiological method: Difference between revisions

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{{short description|Scientific method in the specific field}}

The science of epidemiology has matured significantly from the times of [[Hippocrates]], [[Ignaz Semmelweis|Semmelweis]] and [[John Snow (physician)|John Snow]]. The techniques for gathering and analyzing epidemiological data vary depending on the type of disease being monitored but each study will have overarching similarities.<ref>[[Miquel Porta]] (2014) ''[http://global.oup.com/academic/product/a-dictionary-of-epidemiology-9780199976737?cc=us&lang=en A dictionary of epidemiology]'', 6th edn, New York: Oxford University Press. {{ISBN|9780199976737}}.</ref>

==Outline of the process of an epidemiological study==

# Establish that a problem exists

#* Full epidemiological studies are expensive and laborious undertakings. Before any study is started, a case must be made for the importance of the research.

# Confirm the homogeneity of the events

#* Any conclusions drawn from inhomogeneous cases will be suspicious. All events or occurrences of the disease must be true cases of the disease.

# Collect all the events

#* It is important to collect as much information as possible about each event in order to inspect a large number of possible risk factors. The events may be collected from varied methods of [[Epidemiology#Types of studies|epidemiological study]] or from censuses or hospital records.

#* The events can be characterized by [[incidence (epidemiology)|Incidence]] rates and [[prevalence]] rates.

#* Often, occurrence of a single disease entity is set as an event.

#* Given inherent [[Homogeneity and heterogeneity|heterogeneous]] nature of any given disease (i.e., the unique disease principle <ref name="pmid23307060">{{cite journal | vauthors = Ogino S, Lochhead P, Chan AT, Nishihara R, Cho E, Wolpin BM, Meyerhardt AJJA, Meissner A, Schernhammer ES, Fuchs CS, Giovannucci E. | title = Molecular pathological epidemiology of epigenetics: emerging integrative science to analyze environment, host, and disease. Mod| Patholjournal = Modern Pathology | volume = 2013;26:465-484 | issue = 4 | pages = 465–84 | date = April 2013 | pmid = 23307060 | pmc = 3637979 | doi = 10.1038/modpathol.2012.214 }}</ref>), a single disease entity may be treated as disease subtypes.<ref name="pmid20949563">{{cite journal | vauthors = Begg CB. | title = A strategy for distinguishing optimal cancer subtypes. Int| Jjournal = International Journal of Cancer 2011;| volume = 129: 931-937.</ref><ref>Begg| CB,issue Zabor= EC.4 Detecting| andpages Exploiting= Etiologic931–7 Heterogeneity| indate Epidemiologic= Studies.August Am2011 J| Epidemiolpmid 2012;= 176:20949563 512-518| pmc = 3043163 | doi = 10.1002/ijc.25714 }}</ref> This framework is well conceptualized in the interdisciplinary field of [[molecular pathological epidemiology]] (MPE).<ref name="pmid20208016">{{cite journal | vauthors = Ogino S, Stampfer M. | title = Lifestyle factors and microsatellite instability in colorectal cancer: the evolving field of molecular pathological epidemiology. J| Natljournal = Journal of the National Cancer InstInstitute | volume = 2010;102:365-367 | issue = 6 | pages = 365–7 | date = March 2010 | pmid = 20208016 | pmc = 2841039 | doi = 10.1093/jnci/djq031 }}</ref><ref name="pmid21036793">{{cite journal | vauthors = Ogino S, Chan AT, Fuchs CS, Giovannucci E. | title = Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field. | journal = Gut 2011;| volume = 60:397-411 | issue = 3 | pages = 397–411 | date = March 2011 | pmid = 21036793 | pmc = 3040598 | doi = 10.1136/gut.2010.217182 }}</ref>

# Characterize the events as to epidemiological factors

## Predisposing factors

##* Non-environmental factors that increase the likelihood of getting a disease. Genetic history, age, and gender are examples.

## Enabling/disabling factors

##* Factors relating to the environment that either increase or decrease the likelihood of disease. Exercise and good diet are examples of disabling factors. A weakened immune system and poor nutrition are examples of enabling factors.

## Precipitation factors

##* This factor is the most important in that it identifies the source of exposure. It may be a germ, toxin or gene.

## Reinforcing factors

##* These are factors that compound the likelihood of getting a disease. They may include repeated exposure or excessive [[Stress (biology)|environmental stresses]].

# Look for patterns and trends

#* Here one looks for similarities in the cases which may identify major risk factors for contracting the disease. [[Outbreak#Types|Epidemic curves]] may be used to identify such risk factors.

# Formulate a hypothesis

#* If a trend has been observed in the cases, the researcher may postulate as to the nature of the relationship between the potential disease-causing agent and the disease.

# Test the hypothesis

#* Because epidemiological studies can rarely be conducted in a laboratory the results are often polluted by uncontrollable variations in the cases. This often makes the results difficult to interpret. Two methods have evolved to assess the strength of the relationship between the disease causing agent and the disease.

#* [[Koch's postulates]] were the first criteria developed for epidemiological relationships. Because they only work well for highly contagious bacteria and toxins, this method is largely out of favor.

#* [[Epidemiology#Bradford Hill criteria|Bradford-Hill Criteria]] are the current standards for epidemiological relationships. A relationship may fill all, some, or none of the criteria and still be true.

# Publish the results.<ref>{{cite book | last1 = Austin, | first1 = Donald F., and| S.last2 B.= Werner | first2 = S. Benson | name-list-style = vanc | title = Epidemiology for the health sciences: a primer on epidemiologicalepidemiologic concepts and their uses. Springfield,| Ill:date C.= 1982 | publisher = Charles C. Thomas | ___location = Springfield, 1974Ill. Print.| isbn = 978-0-398-02949-4 | edition = 7th }}</ref>

== Measures ==

Epidemiologists are famous for their use of rates. Each measure serves to characterize the disease giving valuable information about contagiousness, incubation period, duration, and mortality of the disease.{{cn|date=August 2022}}

=== Measures of occurrence ===

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### [[Attributable risk in exposed]]

### [[Percent attributable risk]]

### [[Levin’sLevin's attributable risk]]

=== Other measures ===

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# [[Sensitivity (tests)]] and [[Specificity (tests)]]

==CriticismLimitations==

Epidemiological (and other observational) studies typically highlight ''associations'' between exposures and outcomes, rather than causation. While some consider this a limitation of observational research, epidemiological models of causation (e.g. Bradford Hill criteria)<ref>{{cite journal | vauthors = Fedak KM, Bernal A, Capshaw ZA, Gross S | title = Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology | journal = Emerging Themes in Epidemiology | volume = 12 | pages = 14 | date = 2015-09-30 | pmid = 26425136 | pmc = 4589117 | doi = 10.1186/s12982-015-0037-4 | doi-access = free }}</ref> contend that an entire body of evidence is needed before determining if an association is truly causal.<ref>{{cite web | url = http://sphweb.bumc.bu.edu/otlt/MPH-Modules/EP/EP713_Causality/EP713_Causality_print.html | title = Causal Inference | publisher = Boston University School of Public Health |access-date=2018-04-01}}</ref> Moreover, many research questions are impossible to study in experimental settings, due to concerns around ethics and study validity. For example, the link between [[cigarette]] smoke and lung cancer was uncovered largely through observational research; however research ethics would certainly prohibit conducting a randomized trial of cigarette smoking once it had already been identified as a potential health threat.{{cn|date=August 2022}}

Epidemiological studies show association not causation.<ref name=NYT022015>{{cite news|author1=Nina Teicholz|title=The Government’s Bad Diet Advice|url=https://www.nytimes.com/2015/02/21/opinion/when-the-government-tells-you-what-to-eat.html|accessdate=February 21, 2015|work=The New York Times|date=February 20, 2015|format=op-ed|quote=Nina Teicholz is the author of “The Big Fat Surprise: Why Butter, Meat and Cheese Belong in a Healthy Diet.”}}</ref>

==See also==

* [[Study{{annotated link|Clinical study design]]}}

* {{annotated link|Epi Info}}

* [[{{annotated link|Epidemiology]]}}

* [[OpenEpi]]

* {{annotated link|Molecular pathological epidemiology}}

* [[Epi Info]]

* {{annotated link|OpenEpi}}

* [[{{annotated link|Sanitary epidemiological reconnaissance]]}}

* [[Molecular Pathological Epidemiology]]

== References ==

{{Reflist}}

==External links==

* [https://web.archive.org/web/20061217105946/http://www.epidemiologic.org/ Epidemiologic.org] Epidemiologic Inquiry online weblog for epidemiology researchers

* [https://web.archive.org/web/20070228224146/http://www.epidemiologic.org/forum/ Epidemiology Forum] A discussion and forum community for epi analysis support and fostering questions, debates, and collaborations in epidemiology

* [httphttps://wwwweb.archive.org/web/20030623032830/http://cebm.net/toolbox.asp The Centre for Evidence Based Medicine] at Oxford maintains an on-line "Toolbox" of [[evidence-based medicine]] methods.

* [http://www.epimonitor.net/index.htm Epimonitor] has a comprehensive list of links to associations, agencies, bulletins, etc.

* [http://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated Epidemiology for the Uninitiated] On line text, with easy explanations.

* [http://www.sph.unc.edu/nccphp/training/ North Carolina Center for Public Health Preparedness Training] On line training classes for epidemiology and related topics.

* [https://web.archive.org/web/20120323082628/http://www.epidemiology.ch/history/betaversion.htm People's Epidemiology Library]

==References==

{{reflist}}

{{Medical research studies}}