Epidemiology, "the study of what is upon the people," is derived from the Greek terms epi = upon, among; demos = people, district; logos = study, word, discourse; suggesting that it applies only to human populations. But 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 epidemiology).
The Greek physician Hippocrates is sometimes said to be the father of epidemiology. He is the first person known to have examined the relationships between the occurrence of disease and environmental influences. He coined the terms endemic (for diseases usually found in some places but not in others) and epidemic (for disease that are seen at some times but not others).
One of the earliest theories on the origin of disease was that it was primarily the fault of human luxury. This was expressed by philosophers such as Plato and Rousseau, and social critics like Jonathan Swift.
In the medieval Islamic world, physicians discovered the contagious nature of infectious disease. In particular, the Persian physician Avicenna, considered a "father of modern medicine," in The Canon of Medicine (1020s), discovered the contagious nature of tuberculosis and sexually transmitted disease, and the distribution of disease through water and soil. Avicenna stated that bodily secretion is contaminated by foul foreign earthly bodies before being infected. He introduced the method of quarantine as a means of limiting the spread of contagious disease. He also used the method of risk factor analysis, and proposed the idea of a syndrome in the diagnosis of specific diseases.
When the Black Death (bubonic plague) reached Al Andalus in the 14th century, Ibn Khatima hypothesized that infectious diseases are caused by small "minute bodies" which enter the human body and cause disease. Another 14th century Andalusian-Arabian physician, Ibn al-Khatib (1313–1374), wrote a treatise called On the Plague, in which he stated how infectious disease can be transmitted through bodily contact and "through garments, vessels and earrings."
In the middle of the 16th century, a famous Italian doctor from Florence 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 theory of miasms (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.
John Graunt, a professional haberdasher and serious amateur scientist, published Natural and Political Observations ... upon the Bills of Mortality in 1662. In it, he used analysis of the mortality rolls in London before the Great Plague to present one of the first life tables and report time trends for many diseases, new and old. He provided statistical evidence for many theories on disease, and also refuted many widespread ideas on them.
Dr. John Snow is famous for the suppression of an 1854 outbreak of cholera in London's Soho district. He identified the cause of the outbreak as a public water pump on Broad Street and had the handle removed, thus ending the outbreak. (It has been questioned as to whether the epidemic was already in decline when Snow took action.) This has been perceived as a major event in the history of public health and can be regarded as the founding event of the science of epidemiology.
Other pioneers include Danish physician P. A. Schleisner, who in 1849 related his work on the prevention of the epidemic of tetanus neonatorum on the Vestmanna Islands in Iceland. Another important pioneer was 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 'discovered' antiseptics in 1865 in light of the work of Louis Pasteur.
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 suspicion that tobacco smoking was linked to lung cancer.
To date, few universities offer epidemiology as a course of study at the undergraduate level. Many epidemiologists are physicians, or hold other postgraduate degrees including a Master of Public Health (MPH), Master of Science or Epidemiology (MSc.) Doctorates include the Doctor of Public Health (DrPH), Doctor of Pharmacy (PharmD), Doctor of Philosophy (PhD), Doctor of Science (ScD), or for clinically trained physicians, Doctor of Medicine (MD). In the United Kingdom, the title of 'doctor' is an honorary one conferred to those having attained the professional degrees of Bachelor of Medicine and Surgery (MBBS or MBChB). 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, or the Public Health Agency of Canada.
Epidemiologists employ a range of study designs from the observational to experimental and are 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). Epidemiological studies are aimed, where possible, at revealing unbiased relationships between exposures such as alcohol or smoking, biological agents, stress, or chemicals to mortality or morbidity. Identifying causal relationships between these exposures and outcomes are important aspects of epidemiology. Modern epidemiologists use informatics as a tool.
The term 'epidemiologic triad' is used to describe the intersection of Host, Agent, and Environment in analyzing an outbreak.
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.
It is nearly impossible to say with perfect accuracy how even the most simple physical systems behave beyond the immediate future, much less the complex field of epidemiology, which draws on biology, sociology, mathematics, statistics, anthropology, psychology, and policy; "Correlation does not imply causation" is a common theme for much of the epidemiological literature. For epidemiologists, the key is in the term inference. 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 Rothman and Greenland emphasize that the "one cause - one effect" understanding is a simplistic mis-belief. Most outcomes — whether disease or death — are caused by a chain or web consisting of many component causes.
In 1965 Austin Bradford Hill detailed criteria for assessing evidence of causation. These guidelines are sometimes referred to as the Bradford-Hill criteria, but this makes it seem like it is some sort of checklist. For example, Phillips and Goodman (2004) note that they are often taught or referenced as a checklist for assessing causality, despite this not being Hill's intention . 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".
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:
"Epidemiology is concerned with the 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."
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.
As a public health discipline, epidemiologic evidence is often used to advocate both personal measures like diet change and corporate measures like removal of junk food advertising, with study findings disseminated to the general public in order to help people to make informed decisions about their health. Often the uncertainties about these findings are not communicated well; news articles often prominently report the latest result of one study with little mention of its limitations, caveats, or context. Epidemiological tools have proved effective in establishing major causes of diseases like cholera and lung cancer but have had problems with more subtle health issues, and several recent epidemiological results on medical treatments (for example, on the effects of hormone replacement therapy) have been refuted by later randomized controlled trials.
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:
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.
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.
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:
Case-series may refer to the qualititative 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.
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 to provide statistical power comparable to that available in cohort studies.
Case control studies select subjects based on their disease status. 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 2x2 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 = (A/C) / (B/D) .
If the OR is clearly 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.
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 2x2 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, Pe = A / (A+B) over the probability of disease for a person in the unexposed group, Pu = C / (C+D), i.e. RR = Pe / Pu.
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.
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 accurate 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.
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, unbeknown 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. 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 (eg, 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:
If one or more of the sampled groups does not accurately represent the population they are intended to represent, then the results of that comparison may be misleading.
Selection bias can produce either an overestimation or underestimation of the effect measure. It can also produce an effect when none actually exists.
An example of selection bias is volunteer bias. Volunteers may not be representative of the true population. They may exhibit exposures or outcomes which may differ from nonvolunteers (eg volunteers tend to be healthier or they may seek out the study because they already have a problem with the disease being studied and want free treatment).
Another type of selection bias is caused by non-respondents. For example, women who have been subjected to politically motivated sexual assault may be more fearful of participating in a survey measuring incidents of mass rape than non-victims, leading researchers to underestimate the number of rapes.
To reduce selection bias, you should develop explicit (objective) definitions of exposure and/or disease. You should strive for high participation rates. Have a large sample size and randomly select the respondents so that you have a better chance of truly representing the population.
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