Dictionary:
ep·i·de·mi·ol·o·gy (ĕp'ĭ-dē'mē-ŏl'ə-jē, -dĕm'ē-)  |
The branch of medicine that deals with the study of the causes, distribution, and control of disease in populations.
[Medieval Latin epidēmia, an epidemic; see epidemic + –LOGY.]
epidemiologic ep'i·de'mi·o·log'ic (-ə-lŏj'ĭk) or ep'i·de'mi·o·log'i·cal (-ĭ-kəl) adj.| 5min Related Video: epidemiology |
| Sci-Tech Encyclopedia: Epidemiology |
The study of the distribution of diseases in populations and of factors that influence the occurrence of disease. Epidemiology examines epidemic (excess) and endemic (always present) diseases; it is based on the observation that most diseases do not occur randomly, but are related to environmental and personal characteristics that vary by place, time, and subgroup of the population. The epidemiologist attempts to determine who is prone to a particular disease; where risk of the disease is highest; when the disease is most likely to occur and its trends over time; what exposure its victims have in common; how much the risk is increased through exposure; and how many cases of the disease could be avoided by eliminating the exposure.
In the course of history, the epidemiologic approach has helped to explain the transmission of communicable diseases, such as cholera and measles, by discovering what exposures or host factors were shared by individuals who became sick. Modern epidemiologists have contributed to an understanding of factors that influence the risk of chronic diseases, particularly cardiovascular diseases and cancer, which account for most deaths in developed countries today. Epidemiology has established the causal association of cigarette smoking with heart disease; shown that acquired immune deficiency syndrome (AIDS) is associated with certain sexual practices; linked menopausal estrogen use to increased risk of endometrial cancer but to decreased risk of osteoporosis; and demonstrated the value of mammography in reducing breast cancer mortality. By identifying personal characteristics and environmental exposures that increase the risk of disease, epidemiologists provide crucial input to risk assessments and contribute to the formulation of public health policy.
Epidemiologic studies, based mainly on human subjects, have the advantage of producing results relevant to people, but the disadvantage of not always allowing perfect control of study conditions. For ethical and practical reasons, many questions cannot be addressed by experimental studies in humans and for which observational studies (or experimental studies using laboratory animals or biomedical models) must suffice. Still, there are circumstances in which experimental studies on human subjects are appropriate, for example, when a new drug or surgical procedure appears promising and the potential benefits outweigh known or suspected risks. See also Disease; Epidemic.
Descriptive epidemiologic studies provide information about the occurrence of disease in a population or its subgroups and trends in the frequency of disease over time. Data sources include death certificates, special disease registries, surveys, and population censuses; the most common measures of disease occurrence are (1) mortality (number of deaths yearly per 1000 of population at risk); (2) incidence (number of new cases yearly per 100,000 of population at risk); and (3) prevalence (number of existing cases at a given time per 100 of population at risk). Descriptive measures are useful for identifying populations and subgroups at high and low risk of disease and for monitoring time trends for specific diseases. They provide the leads for analytic studies designed to investigate factors responsible for such disease profiles.
Analytic epidemiologic studies seek to identify specific factors that increase or decrease the risk of disease and to quantify the associated risk. In observational studies, the researcher does not alter the behavior or exposure of the study subjects, but observes them to learn whether those exposed to different factors differ in disease rates. Alternatively, the researcher attempts to learn what factors distinguish people who have developed a particular disease from those who have not. In experimental studies, the investigator alters the behavior, exposure, or treatment of people to determine the impact of the intervention on the disease. Usually two groups are studied, one that experiences the intervention (the experimental group) and one that does not (the control group). Outcome measures include incidence, mortality, and survival rates in both the intervention and control groups.
| Dental Dictionary: epidemiology |
The science of epidemics and epidemic diseases, which involve the total population rather than the individual. The aim of epidemiology is to determine those factors in the group environment that make the group more or less susceptible to disease.
| Encyclopedia of Public Health: Epidemiology |
Epidemiology is the indispensable basic science of public health. It provides the logical framework for the facts that enable public health officials to identify important public health problems and to delineate their dimensions. Epidemiologic methods are used to define these health problems; to classify, identify, and elucidate their causes; and to plan and evaluate rational control measures.
Historical Development of Epidemiology
In ancient times, epidemics and plagues were terrifying natural phenomena that cried out for a more rational explanation than that they were due to the wrath of god or the machinations of evil spirits. Hippocrates (c. 460–377 B.C.E.) described many kinds of epidemics and in On Airs, Waters, Places and other writings. He offered empirical insights into environmental and behavioral factors that might be associated with certain kinds of disease. Although doctors and others engaged in the healing arts did not clearly understand the concept of contagion until several hundred years later, Fracastorius (c. 1478–1553) identified several ways that infections can be transmitted—by direct contact, by what we now call droplet spread, and by contaminated clothing.
The science of epidemiology took root with empirical observations of epidemics and other causes of death. John Graunt (1620–1674), in London, complied the first mortality tables on England's bills of mortality. Statistical analyses of deaths due to childbed fever by Ignaz Semmelweiss (1818–1865) in Vienna in the early nineteenth century and of tuberculosis by Pierre Charles Alexandre Louis (1787–1872) in Paris demonstrated the power of numbers. In London, in 1848 and 1854, meticulous, logical examination of the facts and figures about cholera epidemics by John Snow (1813–1858) revealed the mode of communication of this deadly epidemic disease. Snow is regarded as the founder of modern epidemiology because of his use of such careful methods.
Until early in the twentieth century almost all epidemiology focused on communicable diseases, although Percivall Pott's (1714–1788) observations on cancer of the scrotum in chimney sweeps and James Lind's dietary experiment with fresh fruit to prevent scurvy (1975) were precursors of modern noncommunicable disease epidemiology and clinical trials, respectively. The use of epidemiology in studies of coronary heart disease and cancer in large-scale trials of many new preventive and therapeutic regimens, in nationwide surveys of health status, and in evaluation of health services came to the fore in the second half of the twentieth century. In the final quarter of the twentieth century, powerful computers, information technology, and more rigorous methodological approaches transformed epidemiology and made it a mandatory feature of clinical science as well as the most fundamental basic science of public health.
Definition and Scope
The word "epidemiology" was coined in the mid– nineteenth century to describe the scientific study of epidemics. Its meaning has expanded over the years, and present-day epidemiology encompasses the study of all varieties of illness and injury as they affect defined groups of people. In 1983 a committee representing the International Epidemiological Association defined epidemiology as "the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control of health problems." Study includes observation, surveillance, hypothesis-testing research projects, analysis of epidemiologic and other kinds of data, and certain other kinds of experiments. Distribution includes analysis of data according to the time scale over which events occur, the places where the events occur, and the categories of persons to whom they occur. Determinants are all the physical, biological, behavioral, social, and cultural factors that influence health. Health-related states or events include diseases, causes of death, behaviors such as the use of tobacco, reactions to preventive regimens, and provision and use of health services. Specified populations are those with identifiable characteristics such as known numbers and age groups. The ultimate aim and purpose of epidemiology—to promote, protect, and restore good health—is manifested in the "application of this study to control health problems."
Epidemiologists attempt to identify, measure, count, and control diseases, injuries, and causes of untimely death; and to relate these events to the associated inherited, environmental, and behavioral factors that cause or contribute to them. One of the great intellectual challenges of epidemiology is to dissect these factors and unravel their connections in order to identify exactly what is ultimately responsible for a particular disease or health problem.
Relationship to Other Sciences and Technologies
The information used by epidemiologists comes from a diverse array of sources; draws on a wide range of sciences and technologies; and calls on the expertise of technologists and other people engaged in many kinds of crafts. Some connections are obvious—those with vital statistics, biostatistics, microbiology, immunology, and chemistry; with every clinical specialty from pediatrics to geriatrics and palliative care, and from family practice to hematology and neurosurgery. Other obvious connections are to the social and behavioral sciences, and, less obviously, to animal husbandry, wildlife biology, agricultural science, physics, atmospheric sciences, oceanography, engineering, town planning, education, law enforcement, communications technology, and the media. Epidemiology may be the most ecumenical of all the sciences. Probably no other branch of biomedical science has so many connections to such a wide range of other human activities.
Rates
The basis of all epidemiology is the comparison of groups of people. For these comparisons to be valid, it is necessary to convert raw numbers into rates. A rate is a fraction—the upper part (the numerator) is the number of people affected by the problem, event, or condition of interest; the lower part (the denominator) is the number of persons in the population who are at risk of experiencing the problem, event, or condition. Because the events normally continue over a long period, often indefinitely, rates are expressed in relation to a specified time. Since fractions are awkward to deal with, there is commonly a multiplier, and the rate, as shown in the following formula, is expressed in terms of so many per thousand, per hundred thousand, etc., in a specified time, usually a year, though shorter periods are used when circumstances warrant it:
In practice there are many variations in the ways rates are expressed, but the basic elements of events, population at risk, and time are common to all.
Rates have many uses. By comparing rates, epidemiologists can examine the experience of particular groups of people at specified times, in different cities, countries, or occupational groups. The observed differences are the basis for inferences about the reasons for these differences, and are used to test hypotheses about these reasons, possibly about the putative cause of a particular kind of cancer, for instance. In addition to the absolute requirement, for validity, of basing all comparisons on rates, another important use is in calculating the risks to individuals and groups of experiencing an event such as a heart attack, the occurrence of cancer, or traffic injury. Comparisons are often rendered invalid, or relatively unreliable, by differences among the populations being compared—often because of failure to allow for various kinds of biases and confounding factors. A common problem stems from differences in the age composition of populations that are being compared. This problem is overcome by the procedure of age-adjustment. Another problem is that there may be important qualitative differences, such as health or employment status, between groups that are being compared.
The terms "incidence" and "prevalence" are often confused. Incidence refers to the number of new cases, events, or deaths, that occur in a specified time, usually one year. Prevalence refers to the total number of events or cases, both new and long-term, that are present at a particular point in time. Prevalence is therefore expressed as a number, not a rate, as there is no time dimension involved.
Investigating Epidemics
An epidemic is the occurrence of a number of cases of a disease clearly in excess of normal expectation. This is usually a large number when the disease is one of the common infectious fevers, but even a single case of a dangerous contagious disease, such as typhoid, that has long been absent from a community should suffice to activate the highest level of epidemic surveillance and control measures. The occurrence of a small number of cases of a rare variety of cancer, closely clustered in time and space, may also signal an epidemic. Observational and analytic epidemiology blend in the investigation of epidemics. The investigation demands meticulous attention to detail in collecting information about all the cases of the condition, including mild and inconspicuous cases as well as those with florid manifestations, and must include details about all possible associated factors, such as dietary intake (this is especially important in outbreaks of food poisoning), occupation, living conditions, and unusual recent experiences. Particular attention is paid to the index case—the first identified case of a condition. In most infectious disease epidemics, this could be the case that introduced the infection into the affected community. Information is also gathered about healthy people in the same community, aimed at discovering why they have not been affected. Laboratory tests are used to confirm the diagnosis, identify the pathogenic organism, toxic chemical, or other agent that caused the disease; and to measure immunological responses among both sick and healthy people. Analyzing all this information often clarifies the nature and cause of an epidemic and points the way to appropriate control measures.
Investigating epidemics can be tedious because it needs to be so painstaking, even, seemingly, a boring routine task. But often it is as exciting as detective fiction. For example, an epidemic of typhoid in Aberdeen, Scotland, was traced eventually to a contaminated can of processed beef from Argentina. The can had been cooled in a river adjacent to the canning works. As the pressure inside the can fell when it cooled, a partial vacuum was created and typhoid bacilli in raw sewage in the water were sucked into the can through a minute hole.
Identifying the existence of an epidemic sometimes requires unusual vigilance and an ability to make connections among seemingly isolated events. An epidemic of lethal pneumonia among members of the American Legion who attended a convention in Philadelphia in 1976 and then returned to their hometowns before becoming ill, would not have come to light without rigorous scrutiny on the part of epidemic intelligence service officers of the Centers for Disease Control. Subsequent investigations led to the identification of Legionnaire's disease.
Techniques of molecular biology, notably DNA typing and the identification of biomarkers, have immensely enhanced the precision of epidemic investigation. It is now possible to trace the exact passage of an infectious agent such as the gonococcus or HIV (human immunodeficiency virus) as it is transmitted by direct contact from one individual to another among a group of people; or to show that coughing by a passenger with open pulmonary tuberculosis on a crowded airline flight can cause primary tuberculous infection of other passengers in the same compartment of that flight; or to determine how certain cancer-causing agents actually induce cancer. Books and articles in the popular press, notably the accounts by the journalist Berton Roueché in the New Yorker, and on some TV programs have communicated the excitement and challenge of epidemic investigations.
Epidemiologic Methods
The application of several analytic methods of epidemiologic study has contributed substantially to scientists' understanding of disease causation, and therefore to control and prevention of many conditions of great public health importance. The available methods are observational epidemiology (the empirical study of naturally occurring events), analytic study, and, under carefully defined conditions and with all due ethical safeguards, human experimentation.
Observational Epidemiology. This method begins with surveillance of populations, using vital and health statistics—including analysis of death rates arranged by age, sex, locality, and cause of death. Other information is derived from notified cases of infectious diseases of public health importance, from registries of cancer or other diseases, and from hospital discharge statistics. Since 1957, the National Center for Health Statistics has conducted continuously a National Health Survey that has carried observational epidemiology to new levels of comprehensiveness.
It is often possible to make imaginative use of many other kinds of available information about defined population groups. Schools and many employers keep records of absences due to sickness, sometimes with reasons for these absences. Police and other law enforcement agencies keep records of calls to settle domestic disputes and of damage due to vandalism, which are useful indicators of social pathologies associated with local variations in the frequency of domestic violence, alcohol abuse, and broken families. All such sources of information combine to make it possible for epidemiologists and public health specialists to produce a multidimensional "community diagnosis." Serial measurements can indicate whether things are improving or getting worse, and in which ways these trends are moving for each of different indicators ranging from adolescent smoking behavior to reasons for long-term disability among the elderly.
Analytic Observational Studies. The possibilities of observational epidemiology are considerable, but not limitless. They are powerfully reinforced by analytic studies. The two main analytic methods are the case-control study and the cohort study.
Careful questioning of patients has enabled many doctors to make inferences about the influence of past experience on present disease. Percivall Pott, an eighteenth-century British physician, observed that cancer of the scrotum occurred among former chimney sweeps, and correctly inferred that it was associated with the accumulation of tar in the skin creases. Two hundred years later, in 1940, Norman Gregg, an ophthalmologist in Sydney, Australia, similarly inferred correctly that the cases he was seeing of congenital cataract must be associated with rubella (German measles), which their mothers had had during early pregnancy.
The case-control study is a systematic extension of routine medical history taking, in which the past histories of patients (the cases) suffering from the condition of interest are compared to the past histories of persons (the controls) who do not have the condition of interest, but who otherwise resemble the cases in such particulars as age and sex. Analysis of data about a series of cases and controls may show differences that are statistically significant. Sometimes only small numbers of cases are required to demonstrate significant differences between cases and controls. This makes the case-control study a suitable way to search for causes of rare conditions. For example, the discovery that a very rare form of liver cancer was strongly associated with occupational exposure to vinyl chloride required only four cases, and the fact that expectant mothers' use of artificial estrogens during early pregnancy can cause cancer of the vagina many years later in their daughters was based on a case-control study of eight cases. Although case-control studies can be flawed by the presence of biases that are often difficult or even impossible to eliminate, they are a valuable method of investigation because they can be done rapidly and at relatively little expense. The findings can be confirmed or refuted by more rigorous research methods such as cohort studies.
A cohort study is conducted by identifying individuals in a defined population who are exposed to varying levels of known or suspected risk for the condition of interest, such as cancer of the lung or coronary heart disease. The population is observed over a certain period, and the death and disease incidence rates among those exposed to varying and known levels of risk are compared. Cohort studies require large numbers, commonly many thousands, and prolonged observation, commonly years or even decades. They are therefore expensive, requiring a large and dedicated staff and maintenance of detailed records of very large numbers of people, only a small proportion of whom will ultimately fall ill and die of the condition of interest. Some cohort studies have become famous. The people of Framingham, Massachusetts, have been the subjects of cohort studies of coronary heart disease since 1948. In 1951, Richard Doll and Austin Bradford Hill began a cohort study of lung cancer in relation to tobacco smoking in a cohort of about 40,000 male British doctors. Later phases of this study have expanded to include risk factors for coronary heart disease and other chronic conditions; and by the late 1990s this study had yielded dramatic evidence of the relationship of tobacco smoking to cancers of many kinds—and to coronary heart disease, chronic obstructive lung disease, and various other life-shortening chronic diseases.
It is possible to get results from a cohort study without waiting many years, if detailed information about exposure to risk factors at some time in the past is available in sufficient detail for a population of sufficient size. A method that permits reliable linking of past and present medical and other relevant records, such as a record linkage system, facilitates this approach. Record linkage is the process of relating information from two or more sets of records—compiled years apart and sometimes by different agencies—about the same individuals. A prerequisite is a way to identify individuals with a high degree of precision, such as a unique numbering system, or a system combining a sequence of digits for birthdate, birthplace, and sex; with alphabet letters or a phonetic code used for other details, such as the individual's mother's maiden name. Obviously the logistics of all this make it a costly method, but the yield can justify the expense. This method, known as an historical cohort study, has demonstrated the relationship of childhood cancer and developmental anomalies to prenatal maternal exposure to small diagnostic doses of X-rays. Record linkage and historical cohort studies have also demonstrated a relationship between birthweight and the occurrence of cardiovascular disease in middle age.
Experimental Epidemiology. In the 1920s, experimental epidemiology meant observing the passage of infectious pathogens in colonies of rodents, but such experiments are rarely necessary, and the meaning of the term has changed. Experiments in which the investigator studies the effects of intentional alteration or intervention in the course of a disease are now done on humans rather than experimental animals, usually using a randomized controlled-trial design.
The randomized controlled trial (RCT) is a form of human experimentation in which the subjects, usually patients, are randomly allocated to receive either a standard accepted therapeutic or preventive regimen, or an experimental regimen. The purpose of random allocation is to eliminate or minimize bias in the selection of subjects. This greatly enhances the validity of the results. Preferably, the subjects and those observing the trial's results should be unaware of which subjects are receiving the experimental and control regimens, thus eliminating the power of suggestion as a factor influencing the response of individuals to the regimen. There are very important ethical constraints on the conduct of randomized controlled trials. The only ethically acceptable justification for conducting a randomized controlled trial is uncertainty about which of the available regimens is the best, a state of affairs known as "equipoise." It is absolutely essential to obtain the genuinely informed consent of all human subjects on whom a trial is conducted.
Clinical Epidemiology and Evidence-Based Medicine
In the final quarter of the twentieth century, physicians in clinical practice discovered the value of epidemiologic methods in enhancing the efficacy of treatment regimens, mainly through rigorous attention to the nature and quality of the evidence on which clinical decisions are based. Evidencebased medicine then moved into public health practice, where it is illuminating decisions about many aspects of public health practice, such as the most effective way to deploy public health nurses in a local health department.
Other Recent Advances
Epidemiology made spectacular progress in several other directions in the 1990s. One was in the application of molecular biology, resulting in what is sometimes called molecular epidemiology. Other advances have been made in genetic epidemiology, where the meeting of molecular genetics with public health, occupational and environmental health, and infant and child health has produced both exciting stories of great progress and difficult ethical and moral problems. What are scientists and physicians to do, for instance, with the newfound knowledge and technical capability to identify defective genes, especially genes that, in interaction with some environmental circumstances, can disqualify certain individuals from particular occupations and can render others ineligible for life insurance? Such dilemmas presage a testing time for society's values.
Another set of new challenges face epidemiologists who specialize in studies of risk management. The global environment is changing as the burden of greenhouse gases increases and leads to a rise in average global ambient temperatures, and remote sensing and climate models enable us to predict the likely future distribution of vector-borne diseases such as malaria, dengue, and schistosomiasis. A new realm of risk factor analysis is thus emerging, based on future health scenarios that incorporate climate models and— in the most sophisticated applications—include sets of models for future patterns of biodiversity, human settlements, and economic and industrial dynamics. In these ways epidemiologists are helping to plan the public health services that will be needed in the future.
(SEE ALSO: Case-Control Study, Cohort Study, Cross-Sectional Study; Epidemiologic Transition; Graunt, John; Hippocrates of Cos; Mortality Rates; Notifiable Diseases; Pott, Percivall; Rates; Rates: Age-Adjusted; Record Linkage; Semmelweiss, Ignaz; Snow, John; Vital Statistics; and other articles on specific diseases mentioned herein)
Bibliography
Ashton, J., ed. (1994). The Epidemiological Imagination. Buckingham, UK: Open University Press.
Beaglehole, R.; Bonita, R.; and Kjellström, T. (1993). Basic Epidemiology. Geneva: World Health Organization.
Buck, C.; Llopis, A.; Nájera, E.; and Terris, M., eds. (1988). The Challenge of Epidemiology. Washington, DC: Pan American Health Organization.
Last, J. M., ed. (2000). A Dictionary of Epidemiology, 4th edition. New York: Oxford University Press.
Rothman, K. J., and Greenland, S., eds. (1998). Modern Epidemiology, 2nd edition. Philadelphia: Lippincott-Raven.
Roueché, B. (1954). Eleven Blue Men, and Other Narratives of Medical Detection. Boston: Little, Brown & Co.
— JOHN M. LAST
| Britannica Concise Encyclopedia: epidemiology |
For more information on epidemiology, visit Britannica.com.
| Columbia Encyclopedia: epidemiology |
| Intelligence Encyclopedia: Epidemiology |
Epidemiology is the study of the various factors that influence the occurrence, distribution, prevention, and control of disease, injury, and other health-related events in a defined human population. By the application of various analytical techniques including mathematical analysis of the data, the probable cause of an infectious out-break can be pinpointed. This connection between epidemiology and infection makes microorganisms an important facet of epidemiology, and gives epidemiologists a vital link in emergency planning for public health response to a biological attack.
Molecular epidemiology has been used to trace the cause of bacterial, viral, and parasitic diseases. This knowledge is valuable in developing a strategy to prevent further outbreaks of the microbial illness, since the probable source of a disease can be identified.
Furthermore, in the era of biological weapons use by individuals, organizations, and governments, epidemiological studies of the effect of exposure to infectious microbes has become more urgently important. Knowledge of the effect of a bioweapon on the battlefield may not extend to the civilian population that might also be secondarily affected by the weapons. Thus, epidemiology is an important tool in identifying and tracing the course of an infection.
Molecular and genetic basis of epidemiology. Genetic epidemiology studies could result in data that would enable forensic investigators to rapidly identify bioterrorism or biological warfare agents specifically engineered or vectored to affect certain subgroups within a larger population.
Molecular epidemiology arises from varied scientific disciplines, including genetics, epidemiology and statistics. The strategies involved in genetic epidemiology encompass population studies and family studies. Sophisticated mathematical tools are now involved, and computer technology is playing a predominant role in the development of the discipline. Multidisciplinary collaboration is crucial to understanding the role of genetic and environmental factors in disease processes.
Much information can come from molecular epidemiology even if the exact genetic cause of the malady is not known. For example, the identification of a malady in generations of related people can trace the genetic characteristic, and even help identify the original source of the trait. This approach is commonly referred to as genetic screening. The knowledge of why a particular malady appears in certain people, or why such people are more prone to a microbial infection than other members of the population, can reveal much about the nature of the disease in the absence of the actual gene whose defect causes the disease.
Differences in response to pathogens is often a complex interplay of various environmental and genetic factors that require sophisticated analytical tools and techniques to identify. Aided by advances in computer technology, scientists develop complex mathematical formulas for the analysis of epidemiological models, the description of the transmission of the disease, and genetic-environmental interactions. Sophisticated mathematical techniques are now used for assessing classification, diagnosis, prognosis and treatment of many diseases.
Population studies provide data that greatly impact public health programs and emergency responses. By means of several statistical tools, genetic epidemiologic studies evaluate risk factors, inheritance and possible models of inheritance. Different kinds of studies are based upon the number of people who participate and the method of sample collection (i.e., at the time of an outbreak or after an outbreak has occurred). A challenge for the investigator is to achieve a result able to be applied with as low a bias as possible to the general population. In other words, the goal of an epidemiological study of an infectious outbreak is to make the results from a few individuals applicable to the whole population.
A fundamental underpinning of infectious epidemiology is the confirmation that a disease outbreak has occurred. Once this is done, the disease is followed with time. The pattern of appearance of cases of the disease can be tracked by developing what is known as an epidemic curve. This information is vital in distinguishing a natural outbreak from a deliberate and hostile act, for example. In a natural outbreak the number of cases increases over time to a peak, after which the cases subside as immunity develops in the population. A deliberate release of organisms will be evident as a sudden appearance of a large number of cases at the same time.
Tracking diseases with technology. Many illnesses of epidemiological concern are caused by microorganisms. Examples include hemorrhagic fevers such as that caused by the Ebola virus. The determination of the nature of illness outbreaks due to these and other microorganisms involve microbiological and immunological techniques.
Various routes can spread infections (i.e., contact, air borne, insect borne, food and water intake, etc.). Likewise, the route of entry of an infectious microbe can also vary from microbe to microbe.
If an outbreak is recognized early enough, samples of the suspected cause as well as samples from the afflicted (i.e., sputum, feces) can be gathered for analysis. The analysis will depend on the symptoms. For example, in the case of a food poisoning, symptoms such as the rapid development of cramping, nausea with vomiting, and diarrhea after eating a hamburger would be grounds to consider Escherichia coli O157:H7 as the culprit. Analyses would likely include the examination for other known microbes associated with food poisoning (i.e., Salmonella) in order to save time in identifying the organism.
Analysis can involve the use of conventional laboratory techniques (e.g., use of nonselective and selective growth media to detect bacteria). As well, more recent technological innovations can be employed. An example is the use of antibodies to a known microorganism that are complexed with a fluorescent particle. The binding of the antibody to the microbes can be detected by the examination of a sample using fluorescence microscopy or flow cytometry. Molecular techniques such as the polymerase chain reaction are employed to detect genetic material from a target organism. However, the expense of the techniques such as PCR tends to limit its use to more of a confirmatory role, rather than as an initial tool of an investigation. A considerable research effort is ongoing at U.S. National Laboratories to develop quicker, less expensive, and more portable PCR equipment that can be used by inspectors and investigators.
Another epidemiological tool is the determination of the antibiotic susceptibility and resistance of bacteria.
Such laboratory techniques can be combined with other techniques to provide information related to the spread of an outbreak. For example, microbiological data can be combined with geographic information systems (GIS). GIS information has helped pinpoint the source of outbreaks. In addition to geographic based information, epidemiologists will use information including the weather on the days preceding an outbreak, mass transit travel schedules and schedules of mass-participation events that occurred around the time of an outbreak to try and establish a pattern of movement or behavior to those who have been affected by the outbreak. Use of credit cards and bank debit cards can also help piece together the movements of those who subsequently became infected.
Reconstructing the movements of people is especially important when the outbreak is an infectious disease. The occurrence of the disease over time can yield information as to the source of an outbreak. For example, the appearance of a few cases at first with the number of cases increasing over time to a peak is indicative of a natural outbreak. The number of cases usually begins to subside as the population develops immunity to the infection (e.g., influenza). However, if a large number of cases occur in the same area at the same time, the source of the infection might not be natural. Examples include a food poisoning or a bioterrorist action.
Epidemiologists were among the first scientists to effectively utilize the Internet and email capabilities to effectively communicate regarding disease outbreaks. The International Society for Infectious Diseases sponsors PROMED, the global email based electronic reporting system for outbreaks of emerging infectious diseases and toxins, is open to all sources.
Further Reading
Books
Trestrail, John H. Forensic Epidemiology. Loue, Sana, 1999.
Periodicals
Epidemiology Program Office, CDC. "CDC's 50th Anniversary: History of CDC." Morbidity and Mortality Weekly Report no. 45 (1996): 525–30.
Electronic
Centers for Disease Control and Prevention. "About CDC." November 2, 2002. <http://www.cdc.gov/aboutcdc.htm> (28 December 2002).
International Society for Infectious Diseases. ProMED-mail. May, 2003. <http://www.promedmail.org/pls/askus/f?p=2400:1000'>(May 12, 2003).
| Veterinary Dictionary: epidemiologist |
An expert in epidemiology.
| Wikipedia: Epidemiology |
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. In the study of communicable and non-communicable diseases, the work of epidemiologists ranges from outbreak investigation to study design, data collection and analysis including the development of statistical models to test hypotheses and the documentation of results for submission to peer-reviewed journals. Epidemiologists rely on a number of other scientific disciplines, such as biology (to better understand disease processes), Geographic Information Science (to store data and map disease patterns) and social science disciplines (to better understand proximate and distal risk factors).
Contents |
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).[1]
The Greek physician Hippocrates is sometimes said to be the uncle 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).[2]
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[3] and Rousseau,[4] and social critics like Jonathan Swift.[5]
In the medieval Islamic world, physicians discovered the contagious nature of infectious disease. In particular, the Persian physician Avicenna, considered a "father of modern medicine,"[6] 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.[7] Avicenna stated that bodily secretion is contaminated by foul foreign earthly bodies before being infected.[8] He introduced the method of quarantine as a means of limiting the spread of contagious disease.[9] He also used the method of risk factor analysis, and proposed the idea of a syndrome in the diagnosis of specific diseases.[10]
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."[8]
In the middle of the 16th century, a famous Italian 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 theory of miasms (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.
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 his investigations into the causes of the 19th Century Cholera epidemics. 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. He used chlorine in an attempt to clean the water 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.
To date, few universities offer epidemiology as a course of study at the undergraduate level. Many epidemiologists are physicians, or hold other postgraduate degrees including a Master of Public Health (MPH), Master of Science or Epidemiology (MSc.). Doctorates include the Doctor of Public Health (DrPH), Doctor of Pharmacy (PharmD), Doctor of Philosophy (PhD), Doctor of Science (ScD), or for clinically trained physicians, Doctor of Medicine (MD) and Doctor of Veterinary Medicine (DVM) . In the United Kingdom, the title of 'doctor' is by long custom used to refer to general medical practitioners, whose professional degrees are usually those 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), the Health Protection Agency, The World Health Organisation (WHO), 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. The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology. Modern epidemiologists use informatics as a tool.
The term 'epidemiologic triad' is used to describe the intersection of Host, Agent, and Environment in analyzing an outbreak.
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 for 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.[11] 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.[12] 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".[11]
A useful mnemonic for remembering these criteria is 'ACCESS PTB'.
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."[13]
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.[14]
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:
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.[15][16][17]
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:
Case-series may refer to the qualititative 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.[18]
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 to provide statistical power comparable to that available in cohort studies.
Case control studies select subjects based on their disease status. 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 2x2 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 = (A/C) / (B/D) .
| ..... | Cases | Controls |
|---|---|---|
| Exposed | A | B |
| Unexposed | C | D |
If the OR is clearly greater than 1, then the conclusion is "those with the disease are more likely to have been exposed," 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), which is the probability of disease for a person in the exposed group, Pe = A / (A+B) over the probability of disease for a person in the unexposed group, Pu = C / (C+D), i.e. RR = Pe / Pu.
| ..... | Case | Non case | Total |
|---|---|---|---|
| Exposed | A | B | (A+B) |
| Unexposed | C | D | (C+D) |
As with the OR, a RR greater than 1 shows association, where the conclusion can be read "those with the exposure were more likely to develop disease."
Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status. Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed.
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 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.
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. 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 (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:
Selection bias is one of three types of bias that threatens the 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 list of journals:[19]
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| Translations: Epidemiology |
Dansk (Danish)
n. - læren om epidemiske sygdomme
Nederlands (Dutch)
epidemiologie (leer van oorzaken etc. van ziekten)
Français (French)
n. - (Méd) épidémiologie
Deutsch (German)
n. - Epidemiologie, (Seuchenlehre)
Ελληνική (Greek)
n. - (ιατρ.) επιδημιολογία
Italiano (Italian)
epidemiologia
Português (Portuguese)
n. - epidemiologia (f) (Med.)
Русский (Russian)
эпидемиология
Español (Spanish)
n. - epidemiología
Svenska (Swedish)
n. - epidemiologi
中文（简体）(Chinese (Simplified))
传染病学, 流行病学
中文（繁體）(Chinese (Traditional))
n. - 傳染病學, 流行病學
العربيه (Arabic)
(الاسم) علم الأوبئه
עברית (Hebrew)
n. - תורת המגיפות, אפידמיולוגיה
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 | Dictionary. The American Heritage® Dictionary of the English Language, Fourth Edition Copyright © 2007, 2000 by Houghton Mifflin Company. Updated in 2007. Published by Houghton Mifflin Company. All rights reserved. Read more |
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| Sci-Tech Encyclopedia. McGraw-Hill Encyclopedia of Science and Technology. Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved. Read more |
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| Encyclopedia of Public Health. Encyclopedia of Public Health. Copyright © 2002 by The Gale Group, Inc. All rights reserved. Read more |
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| Britannica Concise Encyclopedia. Britannica Concise Encyclopedia. © 2006 Encyclopædia Britannica, Inc. All rights reserved. Read more |
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| Columbia Encyclopedia. The Columbia Electronic Encyclopedia, Sixth Edition Copyright © 2003, Columbia University Press. Licensed from Columbia University Press. All rights reserved. www.cc.columbia.edu/cu/cup/ Read more |
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