Epidemiology

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Epidemiology is the branch of demography that studies patterns of disease in human or animal populations.^[1][2] It includes the study of patterns, circumstances, causes and control using statistical determinations of incidence, frequency, prevalence and distribution.

History[edit]

In London, in September of 1854, during an intense 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.^[3]

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 making it impossible to access water from the well. 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 Broad 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. ^[4]

Determining Cause[edit]

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 adjacent to the well and drank its 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. ^[5]

Epidemiological studies[edit]

Cohort study[edit]

For more information, see: 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.^[6]

Case Control Study[edit]

For more information, see: Case-control study.

Case control studies (also called case-referent design) 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^[7] (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.^[8][9]

Cross-Sectional Study[edit]

For more information, see: Cross sectional study.

Cross sectional studies basically ask two questions of a population: What is their current health? What exposure have they had to possible causes? Cross section studies measure health outcomes and determinants at a specific time or for a short period. They compare groups in terms of their current health and exposure status and assesses their similarities. These studies are designed to discover relationships between cause and effect, to expose possible etiological factors. The prevalence of cataracts and the intake of vitamins have been studied in this way.

Cross sectional studies are relatively easy to execute because there is no need to wait for a health outcome to occur or estimate levels of exposure to risk factors in the past. Their main disadvantage is that a specific cause can't be easily inferred, because only current health and exposure are being studied. ^[10]

Ecological studies[edit]

Ecological studies compare different communities.

Geographical comparisons[edit]

Geographical comparisons are studies that compare populations in different regions. A comparison of general populations in the north and the south of England for example, or northern Europe and southern Europe, may demonstrate differences in the incidence of a disease. Allowance must be made for confounding factors such as age and sex, or dietary habits and occupation.

Time trends[edit]

Time trend studies are useful when a disease shows fluctuations with time. Melanoma manifests a greater frequency during the time of year when there is more sunlight. Influenza shows a greater frequency during those times of the year when weather usually forces people inside for longer periods. On the other hand, lung cancer and coronary diseases are disorders that develop over long periods of time and seasonal changes seem to have a low impact on their incidence.

Migrant groups[edit]

Migrant populations bring existing maladies with them that may not be as common amongst the resident population. Their new location may also change the prevalence of diseases and disorders than would be typical in the country of origin. A decrease in stomach cancer amongst Japanese immigrants or an increase in psychosis amongst Norwegian immigrants may be evident when compared with their comparable groups at home that did not migrate.

Occupational and social classes[edit]

Occupational or social groups often have very clear differences. Miners and welders have a high incidence of pulmonary diseases when compared to those outside these occupations. The impoverished show much higher rates of infant mortality.^[11]

Analyzing studies[edit]

For more information, see: Statistics.

Confounding factors[edit]

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.

Confounding factors can be illustrated from Dr. Snow's study of the Broad Street epidemic. What if, for example, the group of people who had cholera had only recently arrived from another area? What if those not affected included a large number of people who had only just arrived. In both cases this would make the two groups very disimilar and there could be other factors involved that would need to be identified. In either case 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 linking 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 false link between coffee and cancer will be made because a confounding factor will not have been eliminated. ^[12]

References[edit]

See also[edit]

• Demography
• Statistics