Editing Epidemiology (section)

== 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}}.