Epidemiology: Difference between revisions

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=== Case Control Study===
=== Case Control Study===
Case control studies or '''case-referent design''' are easier to conduct and much less expensive and time consuming.
Case control studies (aka'''case-referent design''' and '''retrospective studies''') are easier to conduct and much less expensive and time consuming.


In this type of epidemiological study, specific patients with a targeted disease are identified. Previous exposure to a suspected '''etiological factor'''<ref> Note British spelling, aetiological</ref> (i.e. the cause) is compared with '''controls''' (people who have not developed the disease). Case control studies allow for the estimation of possibilities (odds ratios) but do not ascertain possible causes ('''attributable risks'''). Possible factors that may effect the outcome of the study, the incidence of the disease or other factors that may lead to the wrong conclusion ('''confounding factors''') are taken into account by measuring them and adjusting the statistical analysis of the study.
In this type of epidemiological study, specific patients with a targeted disease are first identified and then compared with those who do not have the disease.  


Thus statistical adjustment could include matching cases and controls for confounding factors, individually or in groups with similar characteristics. However, matching does not on its own eliminate confounding (as may be the case in a cohort study) and there is still a need for statistical adjustment.<ref>[http://www.bmj.com/epidem/epid.8.html Case-control and cross sectional studies] British Medical Journal</ref>
Patients with previous exposure to a suspected '''etiological factor'''<ref> Note British spelling, aetiological</ref> (i.e. the cause) are compared with '''controls''' (people who have not developed the disease). Case control studies allow for the estimation of odds ratios (comparison of possibilities of exposure or developing a disease between patients and controls) but do not ascertain possible causes ('''attributable risks'''). Possible factors that may effect the outcome of the study, the incidence of the disease or other factors that may lead to the wrong conclusion ('''confounding factors''') are taken into account by measuring them and adjusting the statistical analysis of the study.
 
Thus statistical adjustment could include matching cases and controls for confounding factors, individually or in groups with similar characteristics. However, matching does not on its own eliminate confounding (as may be the case in a cohort study) and there is still a need for statistical adjustment.<ref>[http://www.bmj.com/epidem/epid.8.html Case-control and cross sectional studies] British Medical Journal</ref><ref>[http://www.acponline.org/srf/abstracts/glossary.htm Study Design] Glossary of Common Research Terms, American College of Physicians</ref>


===Occupational Epidemiological Study===
===Occupational Epidemiological Study===

Revision as of 20:08, 2 August 2007

Epidemiology is the study of disease in human or animal populations. It includes the study of patterns, circumstances, causes and control using statistical determinations of incidence, frequency, prevalence and distribution.


History

In London in September of 1854 during a cholera epidemic (five hundred fatal attacks in ten days), Dr. John Snow recorded and plotted deaths caused by cholera on a map of central London. He correlated these with the location of water pumps used by the public, marking them with crosses

From this map of scattered dots and crosses he was able to see that the overwhelming majority of deaths occurred near a water pump on Broad Street in a district called Golden Square in central London. The water pump was the source of water for these residents. He had the handle of the pump removed. The number of cases of cholera diminished until the epidemic ended.

John Snow had engaged in numerous studies of cholera researching distribution, prevalence and incidence prior to the Broad Street pump epidemic. He had studied the transmission of cholera for some time and was usually able to trace it to water sources that had been contaminated. However, in the Broad Street epidemic he was able to ascertain that while some of the fatal cases had not actually drunk any water from the Broad Street pump, those who drank water from pumps in nearby areas had had few if any cases of cholera, many of which were connected to the Board Street pump through having drunk water at coffee shops, dining rooms and pubs that mixed water with spirits. In this way he was able to isolate the Broad Street pump water as the source of the contamination.

By plotting the incidence of cases and interviewing the people in the community he was able to single out the specific water well involved in the epidemic. [1]

Determining Cause

Epidemiologists consider three primary criteria in determining the cause of a disease, temporality, consistency and dose-response.

Temporality refers to the exposure of the possible cause prior to the occurrence of the disease. In other words, if the cholera victims in Broad Street epidemic had contracted cholera before having drunk water from that well, the well itself would have been eliminated as a source of the contaminate. Cholera became evident after they had drunk water from the Broad Street well. A similar example would be lung cancer. If a victim were to develop cancer prior to being exposed to cigarette smoke, then the tobacco smoke would be eliminated as a cause in that case.

Consistency implies that the same effect associated with the same possible cause is demonstrated in a variety of studies. In other words, a single study showing a possible association between cause and disease is not sufficient to make a link between the two. In Dr. Snow’s Broad Street epidemic study he was able to show that the various groups (families, places of employment, nearby sub districts) could either show no connection to the Broad Street well in those groups who had a small number of cholera cases or none at all, and a definite connection to the Broad Street well in those groups that did have a large number of cases.

Dose-response correlates increased exposure to a cause to increased risk of disease—the greater the exposure the greater the chance of getting sick. Passing through the Golden Square district on occasion as opposed to actually living there would provide an epidemiologist the means to distinguish between comparable groups and establish the source or the mode of contamination. Those who did not live in the Golden Square area but merely passed through were not effected. Those who stopped and drank from the well were in a number of cases effected. Those who lived and drank from the water showed by far the greatest incidence of the disease. By evaluating exposure to a possible cause the epidemiologist can narrow down the source of the disease.

In tobacco-cancer studies, the period of exposure is a primary criterion for ascertaining tobacco as a possible cause. Those who may encounter tobacco smoke on occasion demonstrate a very low incidence of lung cancer when compared to those people who smoke heavily on a day-to-day basis. [2]

Epidemiological studies

The four most common types of epidemiological studies:

  • Cohort Study
  • Case Control Study
  • Occupational Epidemiological Study
  • Cross-Sectional Study

Cohort study

A cohort study is a longitudinal study that tracks a group of healthy people over time who are exposed to different doses of a suspected cause of a disease. It then assesses what happens to their health over time. The advantage of a cohort study is that shows the health of the study group before and after exposure. In this way it is an aid in establishing the effects of exposure and thereby helps to demonstrate cause and effect (causality). It also has the advantage of showing that exposure precedes the effect and therefore less biased in that it evaluates exposure before health status is known.

Cohort studies are very expensive and must be conducted over long periods of time. They are usually employed for relatively common diseases.

One very large study currently ongoing is the 1970 British Cohort Study (BCS70) which tracks all people born in a one-week period in April, 1970 in England, Scotland and Wales. The study targets an original population of 17,200 people. Large scale enquiries covering various parameters have been made for five, ten, sixteen, twenty-six and twenty-nine years of age.[3]

Case Control Study

Case control studies (akacase-referent design and retrospective studies) are easier to conduct and much less expensive and time consuming.

In this type of epidemiological study, specific patients with a targeted disease are first identified and then compared with those who do not have the disease.

Patients with previous exposure to a suspected etiological factor[4] (i.e. the cause) are compared with controls (people who have not developed the disease). Case control studies allow for the estimation of odds ratios (comparison of possibilities of exposure or developing a disease between patients and controls) but do not ascertain possible causes (attributable risks). Possible factors that may effect the outcome of the study, the incidence of the disease or other factors that may lead to the wrong conclusion (confounding factors) are taken into account by measuring them and adjusting the statistical analysis of the study.

Thus statistical adjustment could include matching cases and controls for confounding factors, individually or in groups with similar characteristics. However, matching does not on its own eliminate confounding (as may be the case in a cohort study) and there is still a need for statistical adjustment.[5][6]

Occupational Epidemiological Study

Cross-Sectional Study

Confounding factors

Confounding factors are possible factors that may effect the outcome of the study, the incidence of the disease or other factors that may lead to the wrong conclusion. They are taken into account by measuring them and adjusting the statistical analysis of the study.[7][8][9][10].

Allowance is made for potential confounding factors by measuring them and making appropriate adjustments in the analysis. In the Broad Street epidemic, what if the group of people who had cholera had only recently arrived from another area? It would have been necessary to ascertain their previous water source and thereby eliminate the possibility that they had contracted cholera from a local source. What if those not affected included a large number of people who had only just arrived. and had not been exposed for a sufficient period of time? It would be necessary to ascertain whether or not the groups being studied and compared had been drinking water from local wells for a necessary length of time prior to the outbreak of cholera. Dr. Snow did in fact include a group of people who worked at a local brewery. None of them had contracted cholera. The proprietor stated that his employees had a certain quota of beer and did not in his estimation drink the local water. In this way, Dr. Snow was able to eliminate a confounding factor and narrow the study to those who relied on the Broad Street well for water for a sufficient period of time.

Another example might be a study of cancer and coffee drinking. It may be that the people who drink coffee have a higher incidence of smoking than those who do not drink coffee. If this difference is not taken into account, the possible connection between smoking and cancer will be missed and a confounding factor will not have been eliminated. [11]


References

  1. Instances of communication of cholera through the medium of polluted water in the neighbourhood of Broad Street, Golden Square John Snow, M.D. (1855). On the Mode of Communication of Cholera. London: John Churchill, New Burlington Street, England
  2. Starting a Study Daniel Wartenberg, 
Doug Ramsey, John Warner, and Doris Ober (2000) Epidemiology for journalists. Facsnet
  3. British cohort study Centre for Longitudinal Studies
  4. Note British spelling, aetiological
  5. Case-control and cross sectional studies British Medical Journal
  6. Study Design Glossary of Common Research Terms, American College of Physicians
  7. Confounding factor Green Facts
  8. Confounding factor Howard S. Hoffman, Professor Emeritus of Psychology, Bryn Mawr College. Internet glossary of statistical terms
  9. Confounding Daniel Wartenberg, Doug Ramsey John Warner, Doris Ober (2000) Problems in Conducting Epidemiological Studies. Epidemiology for journalists. Facsnet
  10. What are confounding factors and how do they affect studies? Stats at George Mason University
  11. What is a confounding factor? State of California, Environmental Health Investigations Branch

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