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- Main article: Epidemiology of mental health disorders
- Main article: Psychiatric illness and increased mortality
- Main article: Epidemiology of drug use
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 work of communicable and non-communicable disease epidemiologists ranges from outbreak investigation, to study design, data collection and analysis including the development of statistical models to test hypotheses and the 'writing-up' of results for submission to peer reviewed journals. Epidemiologists may draw on a number of other scientific disciplines such as biology in understanding disease processes and social science disciplines including sociology and philosophy in order to better understand proximate and distal risk factors.
- 1 Etymology
- 2 History
- 3 The profession
- 4 The practice
- 5 As causal inference
- 6 Advocacy
- 7 Population-based health management
- 8 Types of studies
- 9 Criticisms
- 10 Journals
- 11 Areas
- 12 Methods
- 13 See also
- 14 Footnotes
- 15 Sources
- 16 External links
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, Muslim 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, stated that bodily secretion is contaminated by foul foreign earthly bodies before being infected, introduced the method of quarantine as a means of limiting the spread of contagious disease, the method of risk factor analysis, and 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 one who proposed a theory that very small, unseeable, live particles cause diseases. They were considered to be able to spread by air, multiply by themselves and to be destroyable by fire. In such a way he refuted Galen's theory of miasms (poison gas in sick people). In 1543 he wrote a book "De contagione et contagiosis morbis". At that time, based of his theory, he was the first to promote personal and environmental hygiene. This theory could not have been proven until the development of the first microscope by Anton van Leeuwenhoek in 1675.
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.
- History of emerging infectious diseases
To date, few Universities offer epidemiology as a course of study at undergraduate level. Many epidemiologists, therefore, are physicians or hold other postgraduate degrees including a Master of Public Health (MPH), Master of Science or Epidemiology (MSc.) Other higher degrees confer the title of Doctor such as a Doctor of Public Health (DrPH), Doctor of Philosophy (PhD), Doctor of Science (ScD) or for those clinically trained, Doctor of Medicine (MD). In the United Kingdom, the title of 'doctor' is a 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), 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 epidemiologist use disease informatics as a tool.
The term 'epidemiologic triangle' is used to describe the intersection of Host, Agent, and Environment in analyzing an outbreak.
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 imply causation," is a common theme to 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".
- Strength: A small association does not mean that there is not a causal effect.
- Consistency: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
- 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.
- 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).
- 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.
- Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).
- 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 affect on associations" .
- Experiment: "Occasionally it is possible to appeal to experimental evidence" .
- Analogy: The effect of similar factors may be considered.
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.
Population-based health management
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:
- assess the health states and health needs of a target population;
- implement and evaluate interventions that are designed to improve the health of that population; and
- efficiently and effectively provide care for members of that population in a way that is consistent with the community’s cultural, policy and health resource values.
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:
- Population Life Impacts Simulations: Measurement of the future potential impact of disease upon the population with respect to new disease cases, prevalence, premature death as well as potential years of life lost from disability and death;
- Labour Force Life Impacts Simulations: Measurement of the future potential impact of disease upon the labour force with respect to new disease cases, prevalence, premature death and potential years of life lost from disability and death;
- Economic Impacts of Disease Simulations: Measurement of the future potential impact of disease upon private sector disposable income impacts (wages, corporate profits, private health care costs) and public sector disposable income impacts (personal income tax, corporate income tax, consumption taxes, publicly funded health care costs).
Types of studies
- Main article: Study design
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.
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).
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) [What is Relative Risk? How is it measured? How can values be interpreted? Link to statistical analysis? Explanation needed], which is the incidence of disease in the exposed group (A/A+B) over the incidence in the unexposed (C/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.
- For information on investigation of infectious disease outbreaks, please see outbreak investigation.
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 we increase the sample size, increase the number of measurements, or purchase a more precise measuring tool, we are usually driving up the costs of the study. 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:
- Internal validity is dependent on the amount of error in measurements, including exposure, disease, and the associations between these variables. Good internal validity implies a lack of error in measurement and suggests that inferences may be drawn at least as they pertain to the subjects under study.
- External validity pertains to the process of generalizing the findings of the study to the population from which the sample was drawn (or even beyond that population to a more universal statement). This requires an understanding of which conditions are relevant (or irrelevant) to the generalization. Internal validity is clearly a prerequisite for external validity.
Selection bias is one of three types of bias that threatens the internal validity of a study. Selection bias is an inaccurate measure of effect which results from a systematic difference in the relation between exposure and disease between those who are in the study and those who should be in the study.
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.
A ranked list of journals:
- American Journal of Epidemiology
- Epidemiologic Reviews
- International Journal of Epidemiology
- Annals of Epidemiology
- Journal of Epidemiology and Community Health
- European Journal of Epidemiology
- Emerging Themes in Epidemiology
- Epidemiologic Perspectives and Innovations
- Cancer Epidemiology Biomarkers and Prevention
- Genetic Epidemiology
- Journal of Clinical Epidemiology
- Paediatric Perinatal Epidemiology
- Epidemiology and Infection
- Pharmacoepidemiology and Drug Safety
- Infectious disease epidemiology
- Cardiovascular disease epidemiology
- Cancer epidemiology
- Epidemiology of Aging
- Oral/Dental epidemiology
- Reproductive epidemiology
- Obesity/diabetes epidemiology
- Renal epidemiology
- Injury epidemiology
- Veterinary epidemiology
- Epidemiology of zoonosis
- Respiratory Epidemiology
- Pediatric Epidemiology
- Cognitive epidemiology
- Conflict epidemiology
- Environmental epidemiology
- Genetic epidemiology
- Molecular epidemiology
- Nutritional epidemiology
- Social epidemiology
- Lifecourse epidemiology
- Spatial epidemiology
- Primary care epidemiology
- Psychiatric epidemiology
- Public Health practice epidemiology
- Biomarker epidemiology
- Disease Informatics
- Epi methods development / Biostatistics
- Surveillance epidemiology (Clinical surveillance)
- Age adjustment
- Centers for Disease Control and Prevention in the United States
- Epidemiological methods
- Epi Info software program
- OpenEpi software program
- Hispanic paradox
- Important publications in epidemiology
- Mathematical modelling in epidemiology
- Mendelian randomization
- Risk factors
- Study design
- Whitehall Study
- Epidemiological Transition
- Demographic Transition
- International Society for Pharmacoepidemiology
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- Clayton, David and Michael 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.
- Smetanin P., Kobak P., Moyer C., Maley O (2005) “The Risk Management of Tobacco Control Research Policy Programs” The World Conference on Tobacco OR Health Conference, July 12-15, 2006 in Washington DC.
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- The Health Protection Agency
- 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
- Epidem.com - Epidemiology (peer reviewed scientific journal that publishes original research on epidemiologic topics)
- 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
- Monash Virtual Laboratory - Simulations of epidemic spread across a landscape
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