Epidemiology

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

The acting epidemiologist works on issues ranging from the practical, such as outbreak investigation, environmental exposure, and health promotion, to the theoretical, including the development of statistical, mathematical, philosophical, biological, and psychosocial theory. To this end, epidemiologists employ a range of study designs from the observational to experimental, with the purpose of revealing unbiased relationships between exposures such as nutrition, biological agents, stress, or chemicals to outcomes such as disease, wellness and health indicators. Defining the diseases, drawing disease causal chain / chains, and formulation of health strategy are important aspects of epidemiology. Modern epidemiologist use disease informatics as a tool.

Epidemiologic 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 often at the forefront of investigating and combating disease outbreaks. Others work for non-profit organizations, universities, and larger government entities like the Centers for Disease Control and Prevention.

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

[edit] Etymology

The etymology of 'epidemiology' ("the study of what is upon the people" derived from the Greek terms epi = upon, among; demos = people, district; logos = study, word, discourse) suggests 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); see Nutter 1999. It is also applied to studies of micro-organisms (microbial epidemiology)

[edit] Epidemiology as causal inference

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 equal causation," is a common theme to much of the epidemiologic literature. For the epidemiologist, 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 misbelief. Most outcomes — whether disease or death — are caused by a chain or web consisting of many component causes.

[edit] Bradford-Hill guidelines

In 1965 Austin Bradford Hill outlined a series of 9 guidelines for assessing evidence of causation^[1]. 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 ^[2]. 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"^[1].

Strength: A small association does not mean that there is not a causal effect.^[1]
Consistency: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.^[1]
Specificity: Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.^[1]
Temporality: The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay).^[1]
Biological gradient: Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.^[1]
Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).^[1]
Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect. However, Hill noted that "... lack of such [laboratory] evidence cannot nullify the epidemiological associations" ^[1].
Experiment: "Occasionally it is possible to appeal to experimental evidence" ^[1].
Analogy: The effect of similar factors may be considered^[1].

[edit] Legal interpretation of epidemiologic studies

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. Strictly speaking, 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." [2])

[edit] Epidemiology and advocacy

Epidemiology is one of the main resources of public health. In general, most 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. In many cases, epidemiologic evidence has to be disseminated to the general public in order to obtain health benefits, and to help people to make informed decisions about their health. The 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:

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.
Smoking may kill you.

Although the first statement is more accurate, the second statement has an air of finality and explicit causation that may help to reduce more the rate of smoking.

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.

[edit] Types of Studies

Main article: Study design

[edit] Case Series

Case-series describe the experience of a single patient or a group of patients with a similar diagnosis. They are 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. (Hennekens C.H. and Buring, J.E. (1987) ‚ Epidemiology in Medicine.™ Mayrent, S.L (Ed.), Lippincott, Williams and Wilkins)

[edit] Case control studies

Case control studies select subjects based on their disease status. The study population is comprised of individuals that are disease positive. The control group should come from the same population that gave rise to the cases. The case control study looks back through time at potential exposures both populations (cases and controls) may have encountered. A 2x2 table is constructed, displaying exposed cases (A), the exposed controls (B), unexposed cases (C) and the 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). This is equal to (A*D)/(B*C).

.....	Cases	Controls
Exposed	A	B
Unexposed	C	D

If the OR is clearly greater than 1, then the conclusion is "those with the disease are more likely to have been exposed," wherease 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.

[edit] Cohort studies

Cohort studies may be prospective or retrospective and select subjects based on their exposure status. The study subjects should be at risk of the disease 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 disease (or outcome) status. An example of a cohort study would be watching a group of smokers versus nonsmokers through time and measuring incidence of lung cancer. The same 2x2 table is constructed as with the case control study. However, the statistic generated is the Relative Risk (RR), which is the incidence of disease in the exposured group (A/A+B) over the incidence in the unexposed (C/C+D).

.....	Case	Non case	Total
Exposed	A	B	(A+B)
Unexposed	C	D	(C+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 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.

[edit] Outbreak Investigation

For information on investigation of infectious disease outbreaks, please see outbreak investigation.

[edit] Measures

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
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
Other measures
1. Virulence and Infectivity
2. Mortality rate and Morbidity
3. Case fatality
4. Sensitivity (tests) and Specificity (tests)

[edit] History of epidemiology

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

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.

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.

[edit] Epidemiology Journals

A ranked list of journals: Impact Factors of leading epidemiology journals

[edit] General Epidemiology Journals

[edit] Specialty Epidemiology Journals

[edit] Areas of epidemiology

[edit] 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
Psychiatric epidemiology
Veterinary epidemiology
Epidemiology of zoonosis
Respiratory Epidemiology
Pediatric Epidemiology
Perinatal Epidemiology
Sport Epidemiology

[edit] By methodological approach

Environmental epidemiology
Clinical epidemiology
Genetic epidemiology
Molecular epidemiology
Nutritional epidemiology
Social epidemiology
Lifecourse epidemiology
Epi methods development / Biostatistics
Meta-analysis
Spatial epidemiology
Biomarker epidemiology
Pharmacoepidemiology
Primary care epidemiology
Infection control and hospital epidemiology
Public Health practice epidemiology
Surveillance epidemiology (Clinical surveillance)
Disease Informatics

[edit] See also

[edit] External links

Epidemiologic.org Epidemiologic Inquiry online weblog for epidemiology researchers
Epidemiology Forum A discussion and forum community for epidemiology to foster debates and collaborations in epidemiology
Epidemiology Job Board and Epidemiology Fellowship Board Regularly updated bulletin boards for the latest jobs, academic positions, and fellowships in epidemiology and public health
The Collection of Biostatistics Research Archive
Statistical Applications in Genetics and Molecular Biology
The International Journal of Biostatistics
BMJ - Epidemiology for the Uninitiated' (fourth edition), D. Coggon, PHD, DM, FRCP, FFOM, Geoffrey Rose DM, DSC, FRCP, FFPHM, DJP Barker, PHD, MD, FRCP, FFPHM, FRCOG, British Medical Journal
Molecular, Environmental, Genetic and Analytic Epidemiology at The University of Melbourne
CRED.be - Center for Research on the Epidemiology of Disasters (CRED), Université catholique de Louvain, Brussels, Belgium
Epidem.com - Epidemiology (peer reviewed scientific journal that publishes original research on epidemiologic topics)
EpiMonitor.net - 'EpiMonitor.net: The domain of epidemiology in the online world' (comprehensive list of links to associations, agencies, bulletins, etc.), EpiMonitor
NIH.gov - 'Epidemiology' (textbook chapter), Philip S. Brachman, Medical Microbiology (fourth edition), US National Center for Biotechnology Information

UTMB.edu - 'Epidemiology' (plain format chapter), Philip S. Brachman, Medical Microbiology

SCKCen.be - 'Radiation Epidemiology', Belgian Nuclear Research Centre, Mol, Belgium
UNC.edu - 'The North Carolina Center for Public Health Preparedness Training Website' (on-line training for epidemiology and related topics)
epidemiology discussion forum - Forum for discussing study designs and analysis methods.

[edit] References

^ ^a ^b ^c ^d ^e ^f ^g ^h ⁱ ^j ^k Hill AB. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58, 295-300. [1]
^ Phillips, CV & Goodman KJ. (2004). The missed lessons of Sir Austin Bradford Hill. Epidemiologic Perspectives and Innovations, 1:3.

Clayton, David and Michel Hills (1993) Statistical Models in Epidemiology Oxford University Press. ISBN 0-19-852221-5

A thorough introduction to the statistical analysis of epidemiological data, focussing on survival rates - their estimation, analysis and comparison.

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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Categories: Epidemiology | Demography | Public health