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Epidemiology is the study and analysis of the patterns, causes, and effects of health and disease conditions in defined populations. It is the 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.
Major areas of epidemiological study include disease etiology, transmission, outbreak investigation, disease surveillance, forensic epidemiology and 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, meaning "upon, among", demos, meaning "people, district", and logos, meaning "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).
The distinction between "epidemic" and "endemic" was first drawn by Hippocrates, to distinguish between diseases that are "visited upon" a population (epidemic) from those that "reside within" a population (endemic). The term "epidemiology" appears to have first been used to describe the study of epidemics in 1802 by the Spanish physician Villalba in Epidemiología Española. 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 disease, but of disease in general, and even many non-disease, health-related conditions, such as high blood pressure and obesity. Therefore, this epidemiology is based upon how the pattern of the disease cause changes in the function of everyone.
The Greek physician Hippocrates, (See information regarding the African doctor Imhotep, whom Hippocrates called the true father of medicine. Imhotep) is known as the father of medicine, sought a logic to sickness; he is the first person known to have examined the relationships between the occurrence of disease and environmental influences. Hippocrates believed sickness of the human body to be caused by an imbalance of the four humors (air, fire, water and earth “atoms”). 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. 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).
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 these very small, unseeable, particles that cause disease were alive. They were considered to be able to spread by air, multiply by themselves and to be destroyable by fire. In this way he refuted Galen's 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 Anton van Leeuwenhoek in 1675 provided visual evidence of living particles consistent with a germ theory of disease.
Wu Youke (1582-1652) developed the concept that some diseases were caused by transmissible agents, which he called liqi (pestilential factors). His book Wenyi Lun (Treatise on Acute Epidemic Febrile Diseases) can be regarded as the main etiological work that brought forward the concept, ultimately attributed to Westerners, of germs as a cause of epidemic diseases (source: http://baike.baidu.com/view/143117.htm). His concepts are still considered in current scientific research in relation to Traditional Chinese Medicine studies (see: http://apps.who.int/medicinedocs/en/d/Js6170e/4.html).
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.
Original map by John Snow showing the clusters of cholera cases in the London epidemic of 1854.
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.
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. 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. However, Snow’s research and preventive measures to avoid further outbreaks were not fully accepted or put into practice until after his death.
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. 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, Janet Lane-Claypon, 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 link between tobacco smoking and lung cancer.
In the late 20th century, with 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. Since the 2000s, genome-wide association studies (GWAS) have been commonly performed to identify genetic risk factors for many diseases and health conditions.
While most molecular epidemiology studies are still using conventional disease diagnosis and classification systems, it is increasingly recognized that disease evolution 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”), 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. 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), 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. The concept and paradigm of MPE have become widespread in the 2010s.
Types of studies
Main article: Study design
Epidemiologists employ a range of study designs from the observational to experimental and 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). 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. 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 as a tool.
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. Experimental epidemiology contains three case types: randomized controlled trials (often used for new medicine or drug testing), field trials (conducted on those at a high risk of conducting a disease), and community trials (research on social originating diseases).
The term 'epidemiologic triad' is used to describe the intersection of Host, Agent, and Environment in analyzing an outbreak.
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 technique comparing periods during which patients are exposed to some factor with the potential to produce illness with periods when they are unexposed.
The former type of study is purely descriptive and cannot be used to make inferences about the general population of patients with that disease. These types of studies, in which an astute clinician identifies an unusual feature of a disease or a patient's history, may lead to formulation of a new hypothesis. Using the data from the series, analytic studies could be done to investigate possible causal factors. These can include case control studies or prospective studies. A case control study would involve matching comparable controls without the disease to the cases in the series. A prospective study would involve following the case series over time to evaluate the disease's natural history.
The latter type, more formally described as self-controlled case-series studies, divide individual patient follow-up time into exposed and unexposed periods and use fixed-effects Poisson regression processes to compare the incidence rate of a given outcome between exposed and unexposed periods. This technique has been extensively used in the study of adverse reactions to vaccination, and has been shown in some circumstances to provide statistical power comparable to that available in cohort 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), 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).
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.
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:
total cases = (a+c) = (1.96)^2×(1+N)×(1÷ln(OR))^2×((OR+2√OR+1)÷√OR)≈15.5×(1+N)×(1÷ln(OR))^2
where N = the ratio of cases to controls. As the odds ratio approached 1, approaches 0; 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:
For an odds ratio of 1.1: