Epidemiology

For other uses, see Epidemiology (disambiguation).

Epidemiology is the study and analysis of the distribution (who, when, and where), patterns and determinants of health and disease conditions in a defined population.

It is a cornerstone of public health, and shapes policy decisions and evidence-based practice by identifying risk factors for disease and targets for preventive healthcare. Epidemiologists help with study design, collection, and statistical analysis of data, amend interpretation and dissemination of results (including peer review and occasional systematic review). Epidemiology has helped develop methodology used in clinical research, public health studies, and, to a lesser extent, basic research in the biological sciences. ^[1]

Major areas of epidemiological study include disease causation, transmission, outbreak investigation, disease surveillance, environmental epidemiology, forensic epidemiology, occupational epidemiology, screening, biomonitoring, and comparisons of treatment effects such as in clinical trials. Epidemiologists rely on other scientific disciplines like biology to better understand disease processes, statistics to make efficient use of the data and draw appropriate conclusions, social sciences to better understand proximate and distal causes, and engineering for exposure assessment.

Epidemiology, literally meaning "the study of what is upon the people", is derived from Greek epi 'upon, among' demos  'people, district'and logos  'study, word, discourse', suggesting that it applies only to human populations. However, 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 or plant disease epidemiology). ^[2]

The distinction between "epidemic" and "endemic" was first drawn by Hippocrates, ^[3] to distinguish between diseases that are "visited upon" a population (epidemic) from those that "reside within" a population (endemic). ^[4] The term "epidemiology" appears to have first been used to describe the study of epidemics in 1802 by the Spanish physician Joaquín de Villalba  [ es ] in Epidemiología Española. ^[4] Epidemiologists also study the interaction of diseases in a population, a condition known as a syndemic.

The term epidemiology is now widely applied to cover the description and causation of not only epidemic, infectious disease, but of disease in general, including related conditions. Some examples of topics examined through epidemiology include as high blood pressure, mental illness and obesity. Therefore, this epidemiology is based upon how the pattern of the disease causes change in the function of human beings.

History

The Greek physician Hippocrates, taught by Democritus, was known as the father of medicine, ^{[5] [6]} sought a logic to sickness; he is the first person known to have examined the relationships between the occurrence of disease and environmental influences. ^[7] Hippocrates believed sickness of the human body to be caused by an imbalance of the four humors (black bile, yellow bile, blood, and phlegm). The cure to the sickness was to remove or add the humor in question to balance the body. This belief led to the application of bloodletting and dieting in medicine. ^[8] He coined the terms endemic (for diseases usually found in some places but not in others) and epidemic (for diseases that are seen at some times but not others). ^[9]

Modern era

See also: History of emerging infectious diseases

In the middle of the 16th century, a doctor from Verona named Girolamo Fracastoro was the first to propose a theory that the 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 miasma theory (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 Antonie van Leeuwenhoek in 1675 provided visual evidence of living particles consistent with a germ theory of disease.^{[ citation needed ]}

During the Ming dynasty, Wu Youke (1582–1652) developed the idea that some diseases were caused by transmissible agents, which he called Li Qi (戾气 or pestilential factors) when he observed various epidemics rage around him between 1641 and 1644. ^[10] His book Wen Yi Lun (瘟疫论, Treatise on Pestilence/Treatise of Epidemic Diseases) can be regarded as the main etiological work that brought forward the concept. ^[11] His concepts were still being considered in analysing SARS outbreak by WHO in 2004 in the context of traditional Chinese medicine. ^[12]

Another pioneer, Thomas Sydenham (1624–1689), was the first to distinguish the fevers of Londoners in the later 1600s. His theories on cures of fevers met with much resistance from traditional physicians at the time. He was not able to find the initial cause of the smallpox fever he researched and treated. ^[8]

John Graunt, a haberdasher and amateur statistician, published Natural and Political Observations ... upon the Bills of Mortality in 1662. In it, he analysed the mortality rolls in London before the Great Plague, presented one of the first life tables, and reported time trends for many diseases, new and old. He provided statistical evidence for many theories on disease, and also refuted some widespread ideas on them.^{[ citation needed ]}

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

John Snow is famous for his investigations into the causes of the 19th-century cholera epidemics, and is also known as the father of (modern) Epidemiology. ^{[13] [14]} He began with noticing the significantly higher death rates in two areas supplied by Southwark Company. His identification of the Broad Street pump as the cause of the Soho epidemic is considered the classic example of epidemiology. Snow used chlorine in an attempt to clean the water and removed the handle; this ended the outbreak. This has been perceived as a major event in the history of public health and regarded as the founding event of the science of epidemiology, having helped shape public health policies around the world. ^{[15] [16]} However, Snow's research and preventive measures to avoid further outbreaks were not fully accepted or put into practice until after his death due to the prevailing Miasma Theory of the time, a model of disease in which poor air quality was blamed for illness. This was used to rationalize high rates of infection in impoverished areas instead of addressing the underlying issues of poor nutrition and sanitation, and was proven false by his work. ^[17]

Other pioneers include Danish physician Peter Anton Schleisner, who in 1849 related his work on the prevention of the epidemic of neonatal tetanus on the Vestmanna Islands in Iceland. ^{[18] [19]} 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.^{[ citation needed ]}

In the early 20th century, mathematical methods were introduced into epidemiology by Ronald Ross, Janet Lane-Claypon, Anderson Gray McKendrick, and others. ^{[20] [21] [22] [23]} In a parallel development during the 1920s, German-Swiss pathologist Max Askanazy and others founded the International Society for Geographical Pathology to systematically investigate the geographical pathology of cancer and other non-infectious diseases across populations in different regions. After World War II, Richard Doll and other non-pathologists joined the field and advanced methods to study cancer, a disease with patterns and mode of occurrences that could not be suitably studied with the methods developed for epidemics of infectious diseases. Geography pathology eventually combined with infectious disease epidemiology to make the field that is epidemiology today. ^[24]

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 link between tobacco smoking and lung cancer.^{[ citation needed ]}

In the late 20th century, with the advancement of biomedical sciences, a number of molecular markers in blood, other biospecimens and environment were identified as predictors of development or risk of a certain disease. Epidemiology research to examine the relationship between these biomarkers analyzed at the molecular level and disease was broadly named "molecular epidemiology". Specifically, "genetic epidemiology" has been used for epidemiology of germline genetic variation and disease. Genetic variation is typically determined using DNA from peripheral blood leukocytes.^{[ citation needed ]}

21st century

Since the 2000s, genome-wide association studies (GWAS) have been commonly performed to identify genetic risk factors for many diseases and health conditions. ^[25]

While most molecular epidemiology studies are still using conventional disease diagnosis and classification systems, it is increasingly recognized that disease progression represents inherently heterogeneous processes differing from person to person. Conceptually, each individual has a unique disease process different from any other individual ("the unique disease principle"), ^{[26] [27]} considering uniqueness of the exposome (a totality of endogenous and exogenous / environmental exposures) and its unique influence on molecular pathologic process in each individual. Studies to examine the relationship between an exposure and molecular pathologic signature of disease (particularly cancer) became increasingly common throughout the 2000s. However, the use of molecular pathology in epidemiology posed unique challenges, including lack of research guidelines and standardized statistical methodologies, and paucity of interdisciplinary experts and training programs. ^[28] Furthermore, the concept of disease heterogeneity appears to conflict with the long-standing premise in epidemiology that individuals with the same disease name have similar etiologies and disease processes. To resolve these issues and advance population health science in the era of molecular precision medicine, "molecular pathology" and "epidemiology" was integrated to create a new interdisciplinary field of "molecular pathological epidemiology" (MPE), ^{[29] [30]} defined as "epidemiology of molecular pathology and heterogeneity of disease". In MPE, investigators analyze the relationships between (A) environmental, dietary, lifestyle and genetic factors; (B) alterations in cellular or extracellular molecules; and (C) evolution and progression of disease. A better understanding of heterogeneity of disease pathogenesis will further contribute to elucidate etiologies of disease. The MPE approach can be applied to not only neoplastic diseases but also non-neoplastic diseases. ^[31] The concept and paradigm of MPE have become widespread in the 2010s. ^{[32] [33] [34] [35] [36] [37] [38] [ excessive citations ]}

By 2012, it was recognized that many pathogens' evolution is rapid enough to be highly relevant to epidemiology, and that therefore much could be gained from an interdisciplinary approach to infectious disease integrating epidemiology and molecular evolution to "inform control strategies, or even patient treatment." ^{[39] [40]} Modern epidemiological studies can use advanced statistics and machine learning to create predictive models as well as to define treatment effects. ^{[41] [42]} There is increasing recognition that a wide range of modern data sources, many not originating from healthcare or epidemiology, can be used for epidemiological study. Such digital epidemiology can include data from internet searching, mobile phone records and retail sales of drugs.^{[ citation needed ]}

Types of studies

Epidemiologic study hierarchy

Main article: Study design

Epidemiologists employ a range of study designs from the observational to experimental and generally categorized as descriptive (involving the assessment of data covering time, place, and person), 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). In observational studies, nature is allowed to "take its course", as epidemiologists observe from the sidelines. Conversely, in experimental studies, the epidemiologist is the one in control of all of the factors entering a certain case study. ^[43] 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. The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology. Modern epidemiologists use informatics and infodemiology ^{[44] [45]} as tools.^{[ citation needed ] [46] [47] [48]}

Observational studies have two components, descriptive and analytical. Descriptive observations pertain to the "who, what, where and when of health-related state occurrence". However, analytical observations deal more with the 'how' of a health-related event. ^[43] Experimental epidemiology contains three case types: randomized controlled trials (often used for a new medicine or drug testing), field trials (conducted on those at a high risk of contracting a disease), and community trials (research on social originating diseases). ^[43]

The term 'epidemiologic triad' is used to describe the intersection of Host, Agent, and Environment in analyzing an outbreak. ^[49]

Case series

Case-series may refer to the qualitative study of the experience of a single patient, or small group of patients with a similar diagnosis, or to a statistical factor with the potential to produce illness with periods when they are unexposed. ^[50]

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 a 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. ^[51]

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 in some circumstances to provide statistical power comparable to that available in cohort studies.^{[ citation needed ]}

Case-control studies

Case-control studies select subjects based on their disease status. It is a retrospective study. 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 2×2 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), ^[52] 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 = (AD/BC).^{[ citation needed ]}

	Cases	Controls
Exposed	A	B
Unexposed	C	D

If the OR is significantly 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.^{[ citation needed ]}

A major drawback for case control studies is that, in order to be considered to be statistically significant, the minimum number of cases required at the 95% confidence interval is related to the odds ratio by the equation:

${\displaystyle {\text{total cases}}=A+C=1.96^{2}(1+N)\left({\frac {1}{\ln(OR)}}\right)^{2}\left({\frac {OR+2{\sqrt {OR}}+1}{\sqrt {OR}}}\right)\approx 15.5(1+N)\left({\frac {1}{\ln(OR)}}\right)^{2}}$

where N is the ratio of cases to controls. As the odds ratio approaches 1, the number of cases required for statistical significance grows towards infinity; rendering case-control studies all but useless for low odds ratios. For instance, for an odds ratio of 1.5 and cases = controls, the table shown above would look like this:

	Cases	Controls
Exposed	103	84
Unexposed	84	103

For an odds ratio of 1.1:

	Cases	Controls
Exposed	1732	1652
Unexposed	1652	1732

Cohort studies

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 2×2 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, P_e = A / (A + B) over the probability of disease for a person in the unexposed group, P_u = C / (C + D), i.e. RR = P_e / P_u.

.....	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 the 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.

Cohort studies also are limited by the same equation for number of cases as for cohort studies, but, if the base incidence rate in the study population is very low, the number of cases required is reduced by 1⁄2.

Causal inference

Main article: 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.

"Correlation does not imply causation" is a common theme for much of the epidemiological literature. For epidemiologists, the key is in the term inference. Correlation, or at least association between two variables, is a necessary but not sufficient criterion for the inference that one variable causes the other. 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 emphasize that the "one cause – one effect" understanding is a simplistic mis-belief. ^[53] Most outcomes, whether disease or death, are caused by a chain or web consisting of many component causes. ^[54] Causes can be distinguished as necessary, sufficient or probabilistic conditions. If a necessary condition can be identified and controlled (e.g., antibodies to a disease agent, energy in an injury), the harmful outcome can be avoided (Robertson, 2015). One tool regularly used to conceptualize the multicausality associated with disease is the causal pie model. ^[55]

Bradford Hill criteria

Main article: Bradford Hill criteria

In 1965, Austin Bradford Hill proposed a series of considerations to help assess evidence of causation, ^[56] which have come to be commonly known as the "Bradford Hill criteria". In contrast to the explicit intentions of their author, Hill's considerations are now sometimes taught as a checklist to be implemented for assessing causality. ^[57] 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." ^[56]

1. Strength of Association: A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal. ^[56]
2. Consistency of Data: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. ^[56]
3. 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. ^[56]
4. 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). ^[56]
5. 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. ^[56]
6. Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge). ^[56]
7. 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 effect on associations". ^[56]
8. Experiment: "Occasionally it is possible to appeal to experimental evidence". ^[56]
9. Analogy: The effect of similar factors may be considered. ^[56]

Legal interpretation

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 disease, not whether an agent did cause a specific plaintiff's disease. ^[58]

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.

The subdiscipline of forensic epidemiology is directed at the investigation of specific causation of disease or injury in individuals or groups of individuals in instances in which causation is disputed or is unclear, for presentation in legal settings.

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. ^[59]

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. ^{[60] [61] [62]}

Each of these organizations uses 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).

Applied field epidemiology

Applied epidemiology is the practice of using epidemiological methods to protect or improve the health of a population. Applied field epidemiology can include investigating communicable and non-communicable disease outbreaks, mortality and morbidity rates, and nutritional status, among other indicators of health, with the purpose of communicating the results to those who can implement appropriate policies or disease control measures.

Humanitarian context

As the surveillance and reporting of diseases and other health factors become increasingly difficult in humanitarian crisis situations, the methodologies used to report the data are compromised. One study found that less than half (42.4%) of nutrition surveys sampled from humanitarian contexts correctly calculated the prevalence of malnutrition and only one-third (35.3%) of the surveys met the criteria for quality. Among the mortality surveys, only 3.2% met the criteria for quality. As nutritional status and mortality rates help indicate the severity of a crisis, the tracking and reporting of these health factors is crucial.

Vital registries are usually the most effective ways to collect data, but in humanitarian contexts these registries can be non-existent, unreliable, or inaccessible. As such, mortality is often inaccurately measured using either prospective demographic surveillance or retrospective mortality surveys. Prospective demographic surveillance requires much manpower and is difficult to implement in a spread-out population. Retrospective mortality surveys are prone to selection and reporting biases. Other methods are being developed, but are not common practice yet. ^{[63] [64] [65] [66]}

Characterization, validity, and bias

Epidemic wave

The concept of waves in epidemics has implications especially for communicable diseases. A working definition for the term "epidemic wave" is based on two key features: 1) it comprises periods of upward or downward trends, and 2) these increases or decreases must be substantial and sustained over a period of time, in order to distinguish them from minor fluctuations or reporting errors. ^[67] The use of a consistent scientific definition is to provide a consistent language that can be used to communicate about and understand the progression of the COVID-19 pandemic, which would aid healthcare organizations and policymakers in resource planning and allocation.

Validities

Different fields in epidemiology have different levels of validity. One way to assess the validity of findings is the ratio of false-positives (claimed effects that are not correct) to false-negatives (studies which fail to support a true effect). In genetic epidemiology, candidate-gene studies may produce over 100 false-positive findings for each false-negative. By contrast genome-wide association appear close to the reverse, with only one false positive for every 100 or more false-negatives. ^[68] This ratio has improved over time in genetic epidemiology, as the field has adopted stringent criteria. By contrast, other epidemiological fields have not required such rigorous reporting and are much less reliable as a result. ^[68]

Random error

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 errors 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 a random error in all sampling procedures – sampling error.^{[ citation needed ]}

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 precise 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.

Systematic error

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, unknown 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. The measuring device could be precise but not accurate. 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 (e.g., 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

Selection bias occurs when study subjects are selected or become part of the study as a result of a third, unmeasured variable which is associated with both the exposure and outcome of interest. ^[69] For instance, it has repeatedly been noted that cigarette smokers and non smokers tend to differ in their study participation rates. (Sackett D cites the example of Seltzer et al., in which 85% of non smokers and 67% of smokers returned mailed questionnaires.) ^[70] Such a difference in response will not lead to bias if it is not also associated with a systematic difference in outcome between the two response groups.

Information bias

Information bias is bias arising from systematic error in the assessment of a variable. ^[71] An example of this is recall bias. A typical example is again provided by Sackett in his discussion of a study examining the effect of specific exposures on fetal health: "in questioning mothers whose recent pregnancies had ended in fetal death or malformation (cases) and a matched group of mothers whose pregnancies ended normally (controls) it was found that 28% of the former, but only 20% of the latter, reported exposure to drugs which could not be substantiated either in earlier prospective interviews or in other health records". ^[70] In this example, recall bias probably occurred as a result of women who had had miscarriages having an apparent tendency to better recall and therefore report previous exposures.

Confounding

Confounding has traditionally been defined as bias arising from the co-occurrence or mixing of effects of extraneous factors, referred to as confounders, with the main effect(s) of interest. ^{[71] [72]} A more recent definition of confounding invokes the notion of counterfactual effects. ^[72] According to this view, when one observes an outcome of interest, say Y=1 (as opposed to Y=0), in a given population A which is entirely exposed (i.e. exposure X = 1 for every unit of the population) the risk of this event will be R_A1. The counterfactual or unobserved risk R_A0 corresponds to the risk which would have been observed if these same individuals had been unexposed (i.e. X = 0 for every unit of the population). The true effect of exposure therefore is: R_A1 − R_A0 (if one is interested in risk differences) or R_A1/R_A0 (if one is interested in relative risk). Since the counterfactual risk R_A0 is unobservable we approximate it using a second population B and we actually measure the following relations: R_A1 − R_B0 or R_A1/R_B0. In this situation, confounding occurs when R_A0 ≠ R_B0. ^[72] (NB: Example assumes binary outcome and exposure variables.)

Some epidemiologists prefer to think of confounding separately from common categorizations of bias since, unlike selection and information bias, confounding stems from real causal effects. ^[69]

The profession

Few universities have offered epidemiology as a course of study at the undergraduate level.^{[ citation needed ]} An undergraduate program exists at Johns Hopkins University in which students who major in public health can take graduate-level courses—including epidemiology—during their senior year at the Bloomberg School of Public Health. ^[73] In addition to its master's and doctoral degrees in epidemiology, the University of Michigan School of Public Health has offered undergraduate degree programs since 2017 that include coursework in epidemiology. ^{[74] [75]}

Although epidemiologic research is conducted by individuals from diverse disciplines, variable levels of training in epidemiologic methods are provided during pharmacy, medical, veterinary, social work, podiatry, nursing, physical therapy, and clinical psychology doctoral programs in addition to the formal training master's and doctoral students in public health fields receive.

As public health practitioners, epidemiologists work in a number of different settings. Some epidemiologists work "in the field" (i.e., in the community; commonly^{[ according to whom? ]} in a public health service), and are often at the forefront of investigating and combating disease outbreaks.^{[ citation needed ]} Others work for non-profit organizations, universities, hospitals, or larger government entities (e.g., state and local health departments in the United States), ministries of health, Doctors without Borders, the Centers for Disease Control and Prevention (CDC), the Health Protection Agency, the World Health Organization (WHO), or the Public Health Agency of Canada. Epidemiologists can also work in for-profit organizations (e.g., pharmaceutical and medical device companies) in groups such as market research or clinical development.

COVID-19

An April 2020 University of Southern California article noted that, "The coronavirus epidemic... thrust epidemiology – the study of the incidence, distribution and control of disease in a population – to the forefront of scientific disciplines across the globe and even made temporary celebrities out of some of its practitioners." ^[76]

See also

• Medicine portal

• Age adjustment  – Technique used to compare populations with different age profiles
• Caerphilly Heart Disease Study  – Medical research project
• Centre for Research on the Epidemiology of Disasters (CRED)
• Centro Studi GISED
• Circulation plan
• Contact tracing  – Finding and identifying people in contact with someone with an infectious disease
• Critical community size  – Minimum size of a closed population within which a pathogen can persist indefinitely
• Disease cluster  – Large incidence of a medical condition in a particular location or time frame
• Disease diffusion mapping  – Map of disease risk for a region
• Compartmental models in epidemiology  – Type of mathematical model used for infectious diseases
• Epidemiological method  – Scientific method in the specific field
• Epidemiological transition  – A term in demography
• European Centre for Disease Prevention and Control  – Agency of the European Union
• Hispanic paradox  – Epidemiological finding
• International Society for Pharmacoepidemiology
• Job-exposure matrix  – means of estimating a person's history of occupational exposurePages displaying wikidata descriptions as a fallback
• Mathematical modelling of infectious disease  – Using mathematical models to understand infectious disease transmissionPages displaying short descriptions of redirect targets
• Mendelian randomization  – Statistical method in genetic epidemiology
• Occupational epidemiology  – Epidemiology of workplaces diseases
• Predictive analytics  – Statistical techniques analyzing facts to make predictions about unknown events
• Society for Occupational Health Psychology  – American occupational health psychology organization
• Population groups in biomedicine  – Health based on racial identity
• Spatial epidemiology  – subfield of health geography focused on the study of the spatial distribution of health outcomesPages displaying wikidata descriptions as a fallback
• Study of Health in Pomerania
• Targeted immunization strategies
• Urban planning  – Technical and political process of land use and urban design
• Whitehall Study  – Health study of British civil servants
• Zoonosis  – Disease that can be transmitted from other species to humans

References

Citations

1. Porta M (2014). A Dictionary of Epidemiology (6th ed.). New York: Oxford University Press. ISBN   978-0-19-997673-7 . Retrieved 16 July 2014.
2. Nutter F Jr (1999). "Understanding the interrelationships between botanical, human, and veterinary epidemiology: the Ys and Rs of it all". Ecosystem Health. 5 (3): 131–40. doi:10.1046/j.1526-0992.1999.09922.x.
3. Hippocrates (~200 BC). Airs, Waters, Places.
4. Carol Buck, Alvaro Llopis; Enrique Nájera; Milton Terris (1998) The Challenge of Epidemiology: Issues and Selected Readings. Scientific Publication No. 505. Pan American Health Organization. Washington, DC. p. 3.
5. Alfredo Morabia (2004). A history of epidemiologic methods and concepts. Birkhäuser. p. 93. ISBN   978-3-7643-6818-0.
6. Historical Developments in Epidemiology Archived 19 February 2018 at the Wayback Machine . Chapter 2. Jones & Bartlett Learning LLC.
7. Ray M. Merrill (2010). Introduction to Epidemiology. Jones & Bartlett Learning. p. 24. ISBN   978-0-7637-6622-1.
8. Merril, Ray M., PhD, MPH. (2010): An Introduction to Epidemiology, Fifth Edition. Chapter 2: "Historic Developments in Epidemiology". Jones and Bartlett Publishing
9. "Changing Concepts: Background to Epidemiology" (PDF). Duncan & Associates. Archived from the original (PDF) on 25 July 2011. Retrieved 3 February 2008.
10. Joseph PB (2012). Encyclopedia of the Black Death. ABC-CLIO. p. 76. ISBN   978-1598842548 . Retrieved 24 February 2019.
11. Guobin X, Yanhui C, Lianhua X (2018). Introduction to Chinese Culture: Cultural History, Arts, Festivals and Rituals. Springer. p. 70. ISBN   978-9811081569 . Retrieved 24 February 2019.
12. "Report 1: Clinical research on treatment of SARS with integrated Traditional Chinese medicine and Western Medicine". SARS: Clinical Trials on Treatment Using a Combination of Traditional Chinese Medicine and Western Medicine. World Health Organization. 2004. Archived from the original on 8 June 2018. Retrieved 24 February 2019.
13. Vachon D (May–June 2005). "Doctor John Snow Blames Water Pollution for Cholera Epidemic". UCLA Department of Epidemiology, School of Public Health. Archived from the original on 28 December 2011.
14. "John Snow, Father of Epidemiology", NPR, Talk of the Nation. 24 September 2004. Archived 20 June 2017 at the Wayback Machine .
15. "Importance of Snow". Jonathan and Karin Fielding School of Public Health. Archived from the original on 17 June 2021.
16. "Dr. John Snow". John Snow, Inc. and JSI Research & Training Institute, Inc. Archived from the original on 16 June 2014.
17. Johnson, Steven, The ghost map : [the story of London's most terrifying epidemic – and how it changed science, cities, and the modern world], OCLC   1062993385 , retrieved 16 September 2020
18. Krishna, Kr (May 2019). "Education Consultancy". Krishna.
19. Ólöf Garðarsdóttir, Loftur Guttormsson (25 August 2009). "Public health measures against neonatal tetanus on the island of Vestmannaeyjar (Iceland) during the 19th century". The History of the Family. 14 (3): 266–79. doi:10.1016/j.hisfam.2009.08.004. S2CID   72505045.^{[ verification needed ]}
20. Statisticians of the centuries Archived 30 June 2022 at the Wayback Machine . By C. C. Heyde, Eugene Senet
21. Anderson Gray McKendrick Archived 22 August 2011 at the Wayback Machine
22. "Homepage". University of Southampton. Tel: [phone] Fax: [phone] University of Southampton University Road Southampton SO17 1BJ United Kingdom.^{[ permanent dead link ‍]}
23. "Origins and early development of the case-control study" (PDF). Archived from the original (PDF) on 18 January 2017. Retrieved 31 August 2013.
24. Mueller LM (2019). "Cancer in the tropics: geographical pathology and the formation of cancer epidemiology". BioSocieties. 14 (4): 512–528. doi:10.1057/s41292-019-00152-w. hdl: 1721.1/128433 . S2CID   181518236.
25. "Genome-Wide Association Studies Fact Sheet". National Human Genome Research Institute. 17 August 2020. Retrieved 17 June 2024.
26. Ogino S, Fuchs CS, Giovannucci E (2012). "How many molecular subtypes? Implications of the unique tumor principle in personalized medicine". Expert Rev Mol Diagn. 12 (6): 621–28. doi:10.1586/erm.12.46. PMC   3492839 . PMID   22845482.
27. Ogino S, Lochhead P, Chan AT, Nishihara R, Cho E, Wolpin BM, Meyerhardt JA, Meissner A, Schernhammer ES, Fuchs CS, Giovannucci E (2013). "Molecular pathological epidemiology of epigenetics: Emerging integrative science to analyze environment, host, and disease". Mod Pathol. 26 (4): 465–84. doi:10.1038/modpathol.2012.214. PMC   3637979 . PMID   23307060.
28. Ogino S, King EE, Beck AH, Sherman ME, Milner DA, Giovannucci E (2012). "Interdisciplinary education to integrate pathology and epidemiology: Towards molecular and population-level health science". Am J Epidemiol. 176 (8): 659–67. doi:10.1093/aje/kws226. PMC   3571252 . PMID   22935517.
29. Ogino S, Stampfer M (2010). "Lifestyle factors and microsatellite instability in colorectal cancer: the evolving field of molecular pathological epidemiology". J Natl Cancer Inst. 102 (6): 365–67. doi:10.1093/jnci/djq031. PMC   2841039 . PMID   20208016.
30. Ogino S, Chan AT, Fuchs CS, Giovannucci E (2011). "Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field". Gut. 60 (3): 397–411. doi:10.1136/gut.2010.217182. PMC   3040598 . PMID   21036793.
31. Field AE, Camargo CA, Ogino S (2013). "The merits of subtyping obesity: one size does not fit all". JAMA. 310 (20): 2147–48. doi:10.1001/jama.2013.281501. PMID   24189835.
32. Curtin K, Slattery ML, Samowitz WS (2011). "CpG island methylation in colorectal cancer: past, present and future". Pathology Research International. 2011: 902674. doi: 10.4061/2011/902674 . PMC   3090226 . PMID   21559209.
33. Hughes LA, Khalid-de Bakker CA, Smits KM, den Brandt PA, Jonkers D, Ahuja N, Herman JG, Weijenberg MP, van Engeland M (2012). "The CpG island methylator phenotype in colorectal cancer: Progress and problems". Biochim Biophys Acta. 1825 (1): 77–85. doi:10.1016/j.bbcan.2011.10.005. PMID   22056543.
34. Ku CS, Cooper DN, Wu M, Roukos DH, Pawitan Y, Soong R, Iacopetta B (2012). "Gene discovery in familial cancer syndromes by exome sequencing: prospects for the elucidation of familial colorectal cancer type X." Mod Pathol. 25 (8): 1055–68. doi: 10.1038/modpathol.2012.62 . PMID   22522846.
35. Chia WK, Ali R, Toh HC (2012). "Aspirin as adjuvant therapy for colorectal cancer-reinterpreting paradigms". Nat Rev Clin Oncol. 9 (10): 561–70. doi:10.1038/nrclinonc.2012.137. PMID   22910681. S2CID   7425809.
36. Spitz MR, Caporaso NE, Sellers TA (2012). "Integrative cancer epidemiology – the next generation". Cancer Discov. 2 (12): 1087–90. doi:10.1158/2159-8290.cd-12-0424. PMC   3531829 . PMID   23230187.
37. Zaidi N, Lupien L, Kuemmerle NB, Kinlaw WB, Swinnen JV, Smans K (2013). "Lipogenesis and lipolysis: The pathways exploited by the cancer cells to acquire fatty acids". Prog Lipid Res. 52 (4): 585–89. doi:10.1016/j.plipres.2013.08.005. PMC   4002264 . PMID   24001676.
38. Ikramuddin S, Livingston EH (2013). "New Insights on Bariatric Surgery Outcomes". JAMA. 310 (22): 2401–02. doi:10.1001/jama.2013.280927. PMID   24189645.
39. Little TJ, Allen JE, Babayan SA, Matthews KR, Colegrave N (2012). "Harnessing evolutionary biology to combat infectious disease". Nature Medicine. 18 (2): 217–20. doi:10.1038/nm.2572. PMC   3712261 . PMID   22310693.
40. Pybus OG, Fraser C, Rambaut A (2013). "Evolutionary epidemiology: preparing for an age of genomic plenty". Phil Trans R Soc B. 368 (1614): 20120193. doi:10.1098/rstb.2012.0193. PMC   3678320 . PMID   23382418.
41. Wiemken TL, Kelley RR (2020). "Machine Learning in Epidemiology and Health Outcomes Research". Annual Review of Public Health. 41: 21–36. doi: 10.1146/annurev-publhealth-040119-094437 . PMID   31577910.
42. Bi Q, Goodman KE, Kaminsky J, Lessler J (2019). "What is Machine Learning? A Primer for the Epidemiologist". American Journal of Epidemiology. 188 (12): 2222–2239. doi:10.1093/aje/kwz189. PMID   31509183.
43. "Principles of Epidemiology." Key Concepts in Public Health. London: Sage UK, 2009. Credo Reference. 1 August 2011. Web. 30 September 2012.
44. Eysenbach G (May 2011). "Infodemiology and Infoveillance". American Journal of Preventive Medicine. 40 (5): S154–S158. doi:10.1016/j.amepre.2011.02.006. ISSN   0749-3797. PMID   21521589.
45. Eysenbach G (27 March 2009). "Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet". Journal of Medical Internet Research. 11 (1): e11. doi: 10.2196/jmir.1157 . ISSN   1438-8871. PMC   2762766 . PMID   19329408.
46. Wyatt JC (1 November 2002). "Basic concepts in medical informatics". Journal of Epidemiology & Community Health. 56 (11): 808–812. doi:10.1136/jech.56.11.808. PMC   1732047 . PMID   12388565.
47. Mackey T, Baur C, Eysenbach G (14 February 2022). "Advancing Infodemiology in a Digital Intensive Era". JMIR Infodemiology. 2 (1): e37115. doi: 10.2196/37115 . PMC   9987192 . PMID   37113802.
48. Mavragani A (28 April 2020). "Infodemiology and Infoveillance: Scoping Review". Journal of Medical Internet Research. 22 (4): e16206. doi: 10.2196/16206 . PMC   7189791 . PMID   32310818.
49. "Principles of Epidemiology | Lesson 1 – Section 8". archive.cdc.gov. 17 August 2023. Retrieved 12 September 2024.
50. Song JW, Chung KC (December 2010). "Observational Studies: Cohort and Case-Control Studies". Plastic and Reconstructive Surgery. 126 (6): 2234–2242. doi:10.1097/PRS.0b013e3181f44abc. ISSN   0032-1052. PMC   2998589 . PMID   20697313.
51. Hennekens CH, Julie E. Buring (1987). Mayrent, Sherry L. (ed.). Epidemiology in Medicine . Lippincott, Williams and Wilkins. ISBN   978-0-316-35636-7.
52. Bewick V, Cheek L, Ball J (February 2004). "Statistics review 8: Qualitative data – tests of association". Critical Care. 8 (1): 46–53. doi: 10.1186/cc2428 . ISSN   1466-609X. PMC   420070 . PMID   14975045.
53. Woodward J (2010). "Causation in biology: stability, specificity, and the choice of levels of explanation". Biology & Philosophy. 25 (3): 287–318. doi:10.1007/s10539-010-9200-z. S2CID   42625229 – via SpringerLink.
54. Rothman KJ (1986). Modern Epidemiology . Boston/Toronto: Little, Brown and Company. ISBN   978-0-316-75776-8.
55. Rothman KJ (2012). Epidemiology : An introduction (2nd ed.). New York, NY: Oxford University Press. p. 24. ISBN   978-0-19-975455-7. OCLC   750986180.
56. Hill AB (1965). "The Environment and Disease: Association or Causation?". Proceedings of the Royal Society of Medicine . 58 (5): 295–300. doi:10.1177/003591576505800503. PMC   1898525 . PMID   14283879.
57. Phillips CV, Karen J. Goodman (October 2004). "The missed lessons of Sir Austin Bradford Hill". Epidemiologic Perspectives & Innovations. 1 (3): 3. doi: 10.1186/1742-5573-1-3 . PMC   524370 . PMID   15507128.
58. Green MD, D. Michal Freedman, and Leon Gordis. Reference Guide on Epidemiology (PDF). Federal Judicial Centre. Archived from the original (PDF) on 27 February 2008. Retrieved 3 February 2008.
59. Neil Myburgh, Debra Jackson. "Measuring Health and Disease I: Introduction to Epidemiology". Archived from the original on 1 August 2011. Retrieved 16 December 2011.
60. Smetanin P, P. Kobak (October 2005). Interdisciplinary Cancer Risk Management: Canadian Life and Economic Impacts (PDF). 1st International Cancer Control Congress. Archived from the original (PDF) on 2 February 2014. Retrieved 2 August 2013.
61. Smetanin P, P. Kobak (July 2006). A Population-Based Risk Management Framework for Cancer Control. The International Union Against Cancer Conference. Archived from the original (PDF) on 2 February 2014.
62. Smetanin P, P. Kobak (July 2005). Selected Canadian Life and Economic Forecast Impacts of Lung Cancer. 11th World Conference on Lung Cancer. Archived from the original (PDF) on 2 February 2014.
63. WHO, "Health topics: Epidemiology." Archived 9 May 2020 at the Wayback Machine Accessed: 30 October 2017.
64. Miquel Porta. A Dictionary of Epidemiology. http://global.oup.com/academic/product/a-dictionary-of-epidemiology-9780199976737?cc=us&lang=en Archived 11 July 2017 at the Wayback Machine 6th edition, New York, 2014 Oxford University Press ISBN   978-0-19-997673-7 Accessed: 30 October 2017.
65. Prudhon, C & Spiegel, P. "A review of methodology and analysis of nutrition and mortality surveys conducted in humanitarian emergencies from October 1993 to April 2004" Emerging Themes in Epidemiology 2007, 4:10. http://www.ete-online.com/content/4/1/10 Archived 23 October 2015 at the Wayback Machine Accessed: 30 October 2017.
66. Roberts, B et al. "A new method to estimate mortality in crisis-affected and resource-poor settings: validation study." International Journal of Epidemiology 2010; 39:1584–96. Accessed: 30 October 2017.
67. Zhang Stephen X, Marioli Francisco Arroyo, Gao Renfei, Wang Senhu (2021). "When is an epidemic an epidemic?". Risk Management and Healthcare Policy. 14: 3775–3782. doi: 10.2147/RMHP.S326051 . PMC   8448159 . PMID   34548826.
68. Ioannidis JP, Tarone R, McLaughlin JK (2011). "The False-positive to False-negative Ratio in Epidemiologic Studies". Epidemiology. 22 (4): 450–56. doi: 10.1097/EDE.0b013e31821b506e . PMID   21490505. S2CID   42756884.
69. Hernán MA, Hernández-Díaz S, Robins JM (2004). "A structural approach to selection bias". Epidemiology. 15 (5): 615–25. doi: 10.1097/01.ede.0000135174.63482.43 . PMID   15308962. S2CID   1373077.
70. Archived 29 August 2017 at the Wayback Machine 24
71. Rothman K (2002). Epidemiology: An Introduction . Oxford: Oxford University Press. ISBN   978-0195135541.
72. Greenland S, Morgenstern H (2001). "Confounding in Health Research". Annu. Rev. Public Health. 22: 189–212. doi:10.1146/annurev.publhealth.22.1.189. PMID   11274518. S2CID   4647751.
73. "Public Health Studies". Public Health Studies at Johns Hopkins. 6 June 2013. Retrieved 13 April 2017.
74. Meer J (4 September 2017). "New school year marks beginning of undergraduate public health program". The Michigan Daily. Retrieved 9 July 2024.
75. Jerolimov A (16 January 2024). "Undergrad Public Health Program expands for fall". The Michigan Daily. Retrieved 9 July 2024.
76. Hiro B. "Ask the Expert: The Epidemiology of COVID-19". SCUSM. Retrieved 11 June 2020.

Sources

• Clayton, David and Michael Hills (1993) Statistical Models in Epidemiology Oxford University Press. ISBN   0-19-852221-5
• Miquel Porta, editor (2014) "A dictionary of epidemiology", 6th edn, New York: Oxford University Press. A Dictionary of Epidemiology Archived 11 July 2017 at the Wayback Machine
• Morabia, Alfredo, editor. (2004) A History of Epidemiologic Methods and Concepts. Basel, Birkhauser Verlag. Part I. A History of Epidemiologic Methods and Concepts Archived 30 June 2022 at the Wayback Machine A History of Epidemiologic Methods and Concepts Archived 3 July 2011 at the Wayback Machine
• 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, 12–15 July 2006, Washington DC.
• Szklo M, Nieto FJ (2002). "Epidemiology: beyond the basics", Aspen Publishers.
• Robertson LS (2015). Injury Epidemiology: Fourth Edition. Free online at nanlee.net
• Rothman K., Sander Greenland, Lash T., editors (2008). "Modern Epidemiology", 3rd Edition, Lippincott Williams & Wilkins. ISBN   0-7817-5564-6, 978-0-7817-5564-1
• Olsen J, Christensen K, Murray J, Ekbom A. An Introduction to Epidemiology for Health Professionals. New York: Springer Science+Business Media; 2010 ISBN   978-1-4419-1497-2

External links

• The Health Protection Agency Archived 29 January 2007 at the Wayback Machine
• The Collection of Biostatistics Research Archive Archived 24 October 2021 at the Wayback Machine
• European Epidemiological Federation
• 'Epidemiology for the Uninitiated' Archived 21 March 2019 at the Wayback Machine by D. Coggon, G. Rose, D.J.P. Barker, British Medical Journal
• Epidem.com Archived 24 September 2001 at the Wayback Machine – Epidemiology (peer-reviewed scientific journal that publishes original research on epidemiologic topics)
• 'Epidemiology' Archived 29 April 2021 at the Wayback Machine – In: Philip S. Brachman, Medical Microbiology (fourth edition), US National Center for Biotechnology Information
• Monash Virtual Laboratory – Simulations of epidemic spread across a landscape
• Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health Archived 12 August 2009 at the Wayback Machine
• Centre for Research on the Epidemiology of Disasters Archived 15 March 2010 at the Wayback Machine  –A WHO collaborating centre
• People's Epidemiology Library
• Epidemiology of COVID-19 outbreak Archived 28 March 2020 at the Wayback Machine