epidemiology

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Epidemiology is the study and analysis of the distribution (who, when, and where), patterns and determinants of health and disease conditions in a defined . It is a cornerstone of , and shapes policy decisions and evidence-based practice by identifying risk factors for disease and targets for
preventive healthcare Preventive healthcare, or prophylaxis, consists of measures taken for the purposes of disease prevention.Hugh R. Leavell and E. Gurney Clark as "the science and art of preventing disease, prolonging life, and promoting physical and mental hea ...
. 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, studies, and, to a lesser extent, basic research in the biological sciences. 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 to better understand disease processes, to make efficient use of the data and draw appropriate conclusions, s to better understand proximate and distal causes, and
engineering Engineering is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and buildings. The discipline of engineering encompasses a broad range of more specializ ...

for exposure assessment. ''Epidemiology'', literally meaning "the study of what is upon the people", is derived , 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 Hippocrates of Kos (; grc-gre, Ἱπποκράτης ὁ Κῷος, Hippokrátēs ho Kôios; ), also known as Hippocrates II, was a Greek physician of the Classical Greece, classical period who is considered one of the most outstanding figures ...

, to distinguish between diseases that are "visited upon" a population (epidemic) from those that "reside within" a population (endemic).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. 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, 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 . 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 Hippocrates of Kos (; grc-gre, Ἱπποκράτης ὁ Κῷος, Hippokrátēs ho Kôios; ), also known as Hippocrates II, was a Greek physician of the Classical Greece, classical period who is considered one of the most outstanding figures ...

, known as the father of
medicine Medicine is the science and Praxis (process), practice of caring for a patient, managing the diagnosis, prognosis, Preventive medicine, prevention, therapy, treatment, Palliative care, palliation of their injury or disease, and Health promotion ...

, 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 (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.Merril, Ray M., PhD, MPH. (2010): ''An Introduction to Epidemiology'', Fifth Edition. Chapter 2: "Historic Developments in Epidemiology". Jones and Bartlett Publishing 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).

## Modern era

In the middle of the 16th century, a doctor from named 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 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. During the , 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. 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. His concepts were still being considered in analysing SARS outbreak by WHO in 2004 in the context of traditional Chinese medicine. 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 fever he researched and treated. John Graunt, a
haberdasher In British English, a haberdasher is a business or person who sells small articles for sewing, dressmaking and knitting, such as buttons, ribbons, and zipper, zippers; in the United States, the term refers instead to a retailer who sells men's c ...
and amateur statistician, published ''Natural and Political Observations ... upon the Bills of Mortality'' in 1662. In it, he analysed the mortality rolls in
London London is the capital and List of urban areas in the United Kingdom, largest city of England and the United Kingdom, with a population of just under 9 million. It stands on the River Thames in south-east England at the head of a estuary dow ...

before the Great Plague, presented one of the first
life tables In actuarial science and demography Demography () is the statistics, statistical study of populations, especially human beings. Demographic analysis examines and measures the dimensions and Population dynamics, dynamics of populations; ...
, 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 John Snow (15 March 1813 – 16 June 1858) was an English physician and a leader in the development of anaesthesia and Hygiene#Medical hygiene, medical hygiene. He is considered one of the founders of modern epidemiology, in part because of h ...
is famous for his investigations into the causes of the 19th-century
cholera Cholera is an infection of the small intestine by some strain (biology), strains of the Bacteria, bacterium ''Vibrio cholerae''. Symptoms may range from none, to mild, to severe. The classic symptom is large amounts of watery diarrhea that last ...

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 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 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 due to the prevailing
Miasma Theory The miasma theory (also called the miasmatic theory) is an obsolete medical theory that held that diseases—such as cholera, chlamydia, or the Black Death—were caused by a ''miasma'' (, Ancient Greek Ancient Greek includes the ...
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. 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 Iceland ( is, Ísland; ) is a Nordic countries, Nordic island country in the Atlantic Ocean, North Atlantic Ocean and in the Arctic Ocean. Iceland is the most list of countries and dependencies by population density, sparsely populated coun ...

. Another important pioneer was physician
Ignaz Semmelweis Ignaz Philipp Semmelweis (; hu, Semmelweis Ignác Fülöp ; 1 July 1818 – 13 August 1865) was a Hungarian physician and scientist, who was an early pioneer of antiseptic procedures. Described as the "saviour of mothers", he discovered that t ...

, 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 Joseph Lister, 1st Baron Lister, (5 April 182710 February 1912) was a British surgeon In modern medicine, a surgeon is a medical professional who performs surgery. Although there are different traditions in different times and places, ...
'discovered'
antiseptics An antiseptic (from Greek ἀντί ''anti'', "against" and σηπτικός ''sēptikos'', "putrefactive") is an antimicrobial substance or compound that is applied to living biological tissue, tissue/skin to reduce the possibility of infection ...
in 1865 in light of the work of
Louis Pasteur Louis Pasteur (, ; 27 December 1822 – 28 September 1895) was a French chemist and microbiologist renowned for his discoveries of the principles of vaccination, Fermentation, microbial fermentation and pasteurization, the latter of which wa ...

. In the early 20th century, mathematical methods were introduced into epidemiology by
Ronald Ross Sir Ronald Ross (13 May 1857 – 16 September 1932) was a British medical doctor who received the Nobel Prize for Physiology or Medicine in 1902 for his work on the transmission of malaria, becoming the first British Nobel laureate, and the f ...

, Janet Lane-Claypon, Anderson Gray McKendrick, and others. In a parallel development during the 1920s, German-Swiss pathologist
Max Askanazy Max Askanazy (24 February 1865, Stallupönen, East Prussia East Prussia ; german: Ostpreißen, label=Low Prussian dialect, Low Prussian; pl, Prusy Wschodnie; lt, Rytų Prūsija was a Provinces of Prussia, province of the Kingdom of Prussi ...
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, 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. Another breakthrough was the 1954 publication of the results of a British Doctors Study, led by and , which lent very strong statistical support to the link between
tobacco smoking Tobacco smoking is the practice of burning tobacco and ingesting the resulting tobacco smoke, smoke. The smoke may be inhaled, as is done with cigarettes, or simply released from the mouth, as is generally done with tobacco pipes, pipes and cig ...

and
lung cancer Lung cancer, also known as lung carcinoma (since about 98–99% of all lung cancers are carcinomas), is a malignant lung tumor characterized by uncontrolled cell growth in tissue (biology), tissues of the lung. Lung carcinomas derive from tran ...

. 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
biomarker In biomedical contexts, a biomarker, or biological marker, is a measurable wikt:indicator, indicator of some biological state or condition. Biomarkers are often measured and evaluated using blood, urine, or soft tissues to examine normal biologic ...
s analyzed at the molecular level and disease was broadly named "
molecular epidemiology Molecular epidemiology is a branch of epidemiology Epidemiology is the study and analysis of the distribution (who, when, and where), patterns and risk factor, determinants of health and disease conditions in a defined population. It is a corne ...
". Specifically, "
genetic epidemiology Genetic epidemiology is the study of the role of genetics, genetic factors in determining health and disease in families and in populations, and the interplay of such genetic factors with environmental factors. Genetic epidemiology seeks to derive ...
" has been used for epidemiology of germline genetic variation and disease. Genetic variation is typically determined using DNA from peripheral blood leukocytes.

## 21st century

Since the 2000s,
genome-wide association studies In genomics, a genome-wide association study (GWA study, or GWAS), also known as whole genome association study (WGA study, or WGAS), is an observational study of a genome-wide set of Single-nucleotide polymorphism, genetic variants in different i ...
(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 Diagnosis is the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different academic discipline, disciplines, with variations in the use of logic, analytics, and experience, to determine "causality, cause a ...
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"), 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 Cancer is a group of diseases involving Cell growth#Disorders, abnormal cell growth with the potential to Invasion (cancer), invade or Metastasis, spread to other parts of the body. These contrast with benign tumors, which do not spread. Poss ...

) became increasingly common throughout the 2000s. However, the use of
molecular pathology Molecular pathology is an emerging discipline within pathology which is focused in the study and diagnosis of disease through the examination of molecules within organs, tissues or bodily fluids. Molecular pathology shares some aspects of practice ...
in epidemiology posed unique challenges, including lack of research guidelines and standardized
statistical Statistics (from German language, German: ''wikt:Statistik#German, Statistik'', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of ...

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 Precision, precise or precisely may refer to: Science, and technology, and mathematics Mathematics and computing (general) * Accuracy and precision, measurement deviation from true value and its scatter * Significant figures, the number of digit ...
, "molecular pathology" and "epidemiology" was integrated to create a new interdisciplinary field of "
molecular pathological epidemiology Molecular pathological epidemiology (MPE, also molecular pathologic epidemiology) is a Discipline (specialism), discipline combining epidemiology and pathology. It is defined as "epidemiology of molecular pathology and heterogeneity of disease". Pat ...
" (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 Pathogenesis is the process by which a disease or Disease#Disorder, disorder develops. It can include factors which contribute not only to the onset of the disease or disorder, but also to its progression and maintenance. The word comes from Anci ...

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. By 2012, it was recognized that many pathogens'
evolution Evolution is change in the heredity, heritable Phenotypic trait, characteristics of biological populations over successive generations. These characteristics are the Gene expression, expressions of genes, which are passed on from parent to ...

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 Molecular evolution is the process of change in the sequence composition of cell (biology), cellular molecules such as DNA, RNA, and proteins across generations. The field of molecular evolution uses principles of evolutionary biology and popula ...
to "inform control strategies, or even patient treatment." Modern epidemiological studies can use advanced statistics and
machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...

to create predictive models as well as to define treatment effects.

# Types of studies

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."Principles of Epidemiology." Key Concepts in Public Health. London: Sage UK, 2009. Credo Reference. 1 August 2011. Web. 30 September 2012. Epidemiological studies are aimed, where possible, at revealing unbiased relationships between such as alcohol or smoking, biological agents, stress, or
chemicals A chemical substance is a form of matter having constant chemical composition and characteristic properties. Some references add that chemical substance cannot be separated into its constituent Chemical element, elements by physical separation m ...
to or
morbidity A disease is a particular abnormal condition that negatively affects the structure or function (biology), function of all or part of an organism, and that is not immediately due to any external injury. Diseases are often known to be medica ...
. The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology. Modern epidemiologists use
informatics Informatics is the study of computational systems, especially those for data storage and Information retrieval, retrieval. According to ACM ''Europe and'' ''Informatics Europe'', informatics is synonymous with computer science and computing as a ...
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 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). The term 'epidemiologic triad' is used to describe the intersection of ''Host'', ''Agent'', and ''Environment'' in analyzing an outbreak.

## 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. 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. 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

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 An odds ratio (OR) is a statistic that quantifies the strength of the Association (statistics), association between two events, A and B. The odds ratio is defined as the ratio of the odds of A in the presence of B and the odds of A in the absence o ...
(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 A cohort study is a particular form of longitudinal study that samples a Cohort (statistics), cohort (a group of people who share a defining characteristic, typically those who experienced a common event in a selected period, such as birth or gra ...
but are sensitive to bias (such as
recall bias Recall may refer to: * Recall (bugle call), a signal to stop * Recall (information retrieval), a statistical measure * ''ReCALL'' (journal), an academic journal about computer-assisted language learning * Recall (memory) Recall in memory re ...
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: :$\text = A+C = 1.96^2 \left(1+N\right) \left\left(\frac\right\right)^2 \left\left(\frac\right\right) \approx 15.5 \left(1+N\right) \left\left(\frac\right\right)^2$ 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: For an odds ratio of 1.1:

## 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. 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 .

# 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. Most outcomes, whether disease or death, are caused by a chain or web consisting of many component causes. 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.

In 1965, proposed a series of considerations to help assess evidence of causation, 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. Hill himself said "None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required ''sine qua non''." # Strength 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. # Consistency of Data: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. # Specificity: Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship. # Temporality: The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay). # Biological gradient: Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence. # Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge). # Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect. However, Hill noted that "... lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations". # Experiment: "Occasionally it is possible to appeal to experimental evidence". # Analogy: The effect of similar factors may be considered.

## Legal interpretation

Epidemiological study, 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: 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. Examples of organizations that use population-based health management that leverage the work and results of epidemiological practice include Canadian Strategy for Cancer Control, Health Canada Tobacco Control Programs, Rick Hansen Foundation, Canadian Tobacco Control Research Initiative. Each of these organizations 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.

# Validity: precision and bias

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). To take the field of genetic epidemiology, candidate-gene studies produced 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. 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.

## 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. 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.

## 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 Accuracy and precision, 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. 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.)
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It is important to note that 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 (epidemiology), Information bias is bias arising from systematic error in the assessment of a variable. 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". 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. A more recent definition of confounding invokes the notion of ''counterfactual'' effects. 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. (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.

# The profession

Few universities have offered epidemiology as a course of study at the undergraduate level. One notable undergraduate program exists at Johns Hopkins University, where students who major in public health can take graduate-level courses, including epidemiology, during their senior year at the Bloomberg School of Public Health. Although epidemiologic research is conducted by individuals from diverse disciplines, including clinically trained professionals such as physicians, formal training is available through Masters or Doctoral programs including Master of Public Health (MPH), Master of Science of Epidemiology (MSc.), Doctor of Public Health (DrPH), Doctor of Pharmacy (PharmD), Doctor of Philosophy (PhD), Doctor of Science (ScD). Many other graduate programs, e.g., Doctor of Social Work (DSW), Doctor of Clinical Practice (DClinP), Doctor of Podiatric Medicine (DPM), Doctor of Veterinary Medicine (DVM), Doctor of Nursing Practice (DNP), Doctor of Physical Therapy (DPT), or for clinically trained physicians, Doctor of Medicine (MD) or Bachelor of Medicine and Surgery (MBBS or MBChB) and Doctor of Osteopathic Medicine (DO), include some training in epidemiologic research or related topics, but this training is generally substantially less than offered in training programs focused on epidemiology or public health. Reflecting the strong historical tie between epidemiology and medicine, formal training programs may be set in either schools of public health and medical schools. 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 state and local health departments, various Ministries of Health, Médecins Sans Frontières, 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 such as 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."

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# References

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* David Clayton, Clayton, David and Michael Hills (1993) ''Statistical Models in Epidemiology'' Oxford University Press. * Miquel Porta, editor (2014) "A dictionary of epidemiology", 6th edn, New York: Oxford University Press
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* Morabia, Alfredo, editor. (2004) A History of Epidemiologic Methods and Concepts. Basel, Birkhauser Verlag. Part I
A History of Epidemiologic Methods and ConceptsA History of Epidemiologic Methods and Concepts
* 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.
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The Health Protection Agency

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by D. Coggon, G. Rose, D.J.P. Barker, ''British Medical Journal''
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'Epidemiology'
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