Epidemiology

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Epidemiology is the study of the distribution and determinants of disease in human populations (Rothman and Greenland), and the application of this study to control health problems (Last 2001).

Epidemiology is considered the cornerstone methodology in all of public health research, and is highly regarded in evidence-based clinical medicine for identifying risk factors for disease and determining optimal treatment approaches to clinical practice. Epidemiology is the scientific study of factors affecting the health and illness of individuals and populations, and, in this capacity, it serves as the foundation and logic of interventions made in the interest of the public’s health and preventive medicine.

The acting epidemiologist works on issues from the practical, such as outbreak investigation, environmental exposure, and health promotion, to the theoretical, including the development of statistical, mathematical, philosophical, and biological theory. To this end, epidemiologists employ a range of study designs from the observational to experimental with the purpose of revealing the unbiased relationships between exposures such as nutrition, HIV, stress, or chemicals to outcomes such as disease, wellness, and health indicators.

Epidemiological studies are generally categorized as descriptive, analytic (aiming to examine associations, commonly hypothesized causal relationships), and experimental (a term often equated with clinical or community trials of treatments and other interventions).

Epidemiologists work in a variety of settings. Some epidemiologists work "in the field", i.e., in the community, commonly in a public health service, and are at the forefront of investigating and combatting disease outbreaks.

The term "Epidemiologic triangle" is used to describe the intersection of Host, Agent, and Environment in analyzing an outbreak.

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Etymology

The etymology of "epidemiology" (Greek epi = upon, among; demos = people, district; logos = word, discourse) suggests that it applies only to human populations. But the term is widely used in studies of animal populations ("veterinary epidemiology"), although the term "epizoology" is available, and it has also been applied to studies of plant populations ("botanical epidemiology"); see Nutter 1999. It is also applied to studies of micro organisms (microbial epidemiology)

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Epidemiology as causal inference

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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. This conceptualization of epidemiology is difficult to grasp because our internal notions of causation are often poorly developed, frequently being predicated on the notion of a one-to-one relationship. For example, almost everyone would agree that gravity causes a dropped ball to fall towards the ground, but would most agree that drinking one glass of milk a day will cause weight loss? Even very heavy smokers know that their vice causes lung cancer, but only 10% of life-long smokers will get lung cancer. How can this be?

The answer is complex and involves philosophical notions of causality, induction, deduction, logic and other dense topics. It is nearly impossible to say with perfect accuracy how even the most simple physical systems will behave, much less the complex field of epidemiology that draws on biology, sociology, mathematics, statistics, anthropology, psychology, and policy. However, for the epidemiologist the key is in the term inference. Epidemiologists use gathered data and a broad range of bio-medical and psycho-social 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 exactly how they are causal.

In US law epidemiology alone cannot prove a causal association does not exist in general. Conversely, it can be and is in some circumstances taken by US courts to justify an inference a causal association does exist in an individual case on a balance of probability. However, strictly epidemiology can only go to prove that an agent could have caused but not that, in any particular case, it did cause:-

"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 ." (See page 381 [1])

By way of example, it is often cited that epidemiology has proven there is no link between MMR vaccine and regressive autism. For the United States, this would require a large randomized controlled trial, powered to detect the reported incidence of 1:160. Such a trial would establish that vaccines are (or are not) the cause of regressive Autistic Spectrum Disorders. The trial would need to be randomized ideally into three arms--a control group that receives no vaccinations, another group on the full US Centers for Disease Control immunization schedule, and a third that gets only the MMR vaccine. However, such a study has never been done and currently seems unlikely it ever will be. Accordingly, the matter would have to be established by other means, like cohort studies (where, for example, unvaccinated Amish children could serve as the control group) and/or adverse drug reaction challenge-dechallenge-rechallenge case reports.

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Epidemiology and advocacy

Some epidemiologists feel that their duties include advocacy for the health of populations, bearing in mind the outpost perspective they have over factors that affect a whole population. Strict requirements for scientific accuracy are sometimes relaxed in the course of public health education regarding epidemiologic findings. This of course does not mean that epidemiologists can advocate for whatever positions they please independent of the data, but presentation of results to the general public is sometimes simplified to help change behavior or understanding. For example, consider these two alternative admonishments against smoking:

1. Smoking has been consistently linked to health problems such as lung cancer and coronary heart disease in several large prospective studies, this link has been deemed causal by a complex process of induction, consensus, and modeling.

2. Smoking will kill you.

Although statement one is more accurate, statement two has an air of finality and explicit causation that may help to reduce the rate of smoking, albeit scientifically, philosophically, and morally questionable.

The best public health advocates consider the broader context beyond the epidemiology and public health literature to render judgment on a course of action for a population. In this manner they are employing a different analytical framework than the strict scientific method that is more common in scientific epidemiology. However, it is rare for one person to wield the skills and embody the features required to be a leader in both the scientific and advocacy aspects of public health. Moreover, scientists who stretch the truth in matters of advocacy ultimately risk their own scientific credibility.

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Measures

  1. Measures of occurrence
    1. Incidence measures
      1. Incidence density (also known as Incidence rate) (Szklo & Nieto, 2000)
      2. Hazard rate
      3. Cumulative incidence
    2. Prevalence measures
      1. Point prevalence
      2. Period prevalence
  2. Measures of association
    1. Relative measures
      1. Risk ratio
      2. Rate ratio
      3. Odds ratio
      4. Hazard ratio
    2. Absolute measures
      1. Risk/rate/incidence difference
      2. Attributable risk
        1. Attributable risk in exposed
        2. Percent attributable risk
        3. Levin’s attributable risk


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History of epidemiology

Original map by Dr. John Snow showing the clusters of cholera cases in the London epidemic of 1854
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Original map by Dr. John Snow showing the clusters of cholera cases in the London epidemic of 1854

John Graunt, a professional haberdasher¸and a 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 Vienna hospital by instituting a disinfection procedure. His findings were published in 1850, but his work was ill received by this 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.

In the early 20th century, mathematical methods were introduced into epidemiology by Ronald Ross, Anderson Gray McKendrick and others.

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.

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Areas of Epidemiology

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By Physiology/Disease Area

  • Infectious Disease epidemiology
  • Cardiovascular disease epidemiology
  • Cancer epidemiology
  • Neuroepidemiology
  • Epidemiology of Aging
  • Oral/Dental epidemiology
  • Reproductive epidemiology
  • Obesity/Diabetes epidemiology
  • Renal Epidemiology
  • Injury epidemiology
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By Methodological Approach

  • Nutritional epidemiology
  • Environmental epidemiology
  • Clinical epidemiology
  • Genetic epidemiology
  • Molecular epidemiology
  • Social epidemiology
  • Epi methods development / Biostatistics
  • Meta-analysis
  • Biomarker epidemiology
  • Pharmacoepidemiology
  • Public Health practice epidemiology
  • Surveillance epidemiology (Clinical surveillance)
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References

  • Last JM (2001). "A dictionary of epidemiology", 4th edn, Oxford: Oxford University Press.
  • Morabia, Alfredo. ed. (2004) A History of Epidemiologic Methods and Concepts. Basel, Birkhauser Verlag. Part I.
  • Nutter FW Jr (1999) "Understanding the Interrelationships Between Botanical, Human, and Veterinary Epidemiology: The Ys and Rs of It All. Ecosystem Health 5 (3): 131-140".
  • Szklo MM & Nieto FJ (2002). "Epidemiology: beyond the basics", Aspen Publishers, Inc.
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See also

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External links

Retrieved from "http://en.wikipedia.org/wiki/Epidemiology"
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