Epidemiologic Methods Can Be Applied to Which of the Following Public Health-related Fields

Attribute of health and disease scientific discipline

Epidemiology is the study and analysis of the distribution (who, when, and where), patterns and determinants of health and illness atmospheric condition in defined populations.

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

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

Epidemiology, literally meaning "the report of what is upon the people", is derived from Greek epi 'upon, amid', demos 'people, district', and logos 'study, discussion, discourse', suggesting that it applies simply to human populations. Withal, the term is widely used in studies of zoological populations (veterinary epidemiology), although the term "epizoology" is available, and information technology has as well been practical to studies of plant populations (botanical or institute affliction epidemiology).^[2]

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

The term epidemiology is now widely applied to comprehend 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 loftier blood pressure, mental disease and obesity. Therefore, this epidemiology is based upon how the pattern of the affliction causes change in the function of man beings.

History [edit]

The Greek physician Hippocrates, known equally the male parent of medicine,^{[5] [half dozen]} sought a logic to sickness; he is the kickoff person known to have examined the relationships betwixt the occurrence of disease and environmental influences.^[7] Hippocrates believed sickness of the man torso to be caused by an imbalance of the four humors (black bile, xanthous bile, claret, and phlegm). The cure to the sickness was to remove or add the sense of humor in question to balance the trunk. This belief led to the application of bloodletting and dieting in medicine.^[viii] He coined the terms endemic (for diseases usually found in some places but non in others) and epidemic (for diseases that are seen at some times merely non others).^[9]

Modernistic era [edit]

In the eye of the 16th century, a doctor from Verona named Girolamo Fracastoro was the first to suggest 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 exist destroyable past fire. In this mode he refuted Galen's miasma theory (poison gas in sick people). In 1543 he wrote a volume De contagione et contagiosis morbis, in which he was the first to promote personal and environmental hygiene to forestall disease. The development of a sufficiently powerful microscope by Antonie van Leeuwenhoek in 1675 provided visual evidence of living particles consequent with a germ theory of disease.

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

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

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

John Snow is famous for his investigations into the causes of the 19th-century cholera epidemics, and is also known as the father of (modernistic) epidemiology.^{[13] [14]} He began with noticing the significantly higher decease rates in two areas supplied past Southwark Visitor. His identification of the Broad Street pump equally the cause of the Soho epidemic is considered the archetype example of epidemiology. Snow used chlorine in an endeavor to make clean the water and removed the handle; this ended the outbreak. This has been perceived every bit a major effect in the history of public health and regarded as the founding event of the science of epidemiology, having helped shape public health policies effectually the world.^{[15] [16]} Withal, Snow's enquiry and preventive measures to avert further outbreaks were not fully accustomed or put into practice until subsequently his expiry due to the prevailing Miasma Theory of the fourth dimension, 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 fake by his piece of work.^[17]

Other pioneers include Danish md Peter Anton Schleisner, who in 1849 related his work on the prevention of the epidemic of neonatal tetanus on the Vestmanna Islands in Iceland.^{[18] [19]} Another important pioneer was Hungarian physician Ignaz Semmelweis, who in 1847 brought down infant mortality at a Vienna hospital by instituting a disinfection procedure. His findings were published in 1850, simply his work was sick-received by his colleagues, who discontinued the procedure. Disinfection did non get widely practiced until British surgeon Joseph Lister 'discovered' antiseptics in 1865 in light of the work of Louis Pasteur.

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

Another quantum was the 1954 publication of the results of a British Doctors Study, led past Richard Doll and Austin Bradford Hill, which lent very strong statistical support to the link betwixt tobacco smoking and lung cancer.

In the late 20th century, with the advancement of biomedical sciences, a number of molecular markers in blood, other biospecimens and environment were identified every bit predictors of evolution or risk of a certain illness. Epidemiology research to examine the relationship between these biomarkers analyzed at the molecular level and disease was broadly named "molecular epidemiology". Specifically, "genetic epidemiology" has been used for epidemiology of germline genetic variation and affliction. Genetic variation is typically determined using Deoxyribonucleic acid from peripheral blood leukocytes.

21st century [edit]

Since the 2000s, genome-wide association studies (GWAS) take been usually performed to identify genetic risk factors for many diseases and health conditions.

While most molecular epidemiology studies are still using conventional disease diagnosis and nomenclature systems, information technology is increasingly recognized that affliction progression represents inherently heterogeneous processes differing from person to person. Conceptually, each individual has a unique disease process different from whatsoever other individual ("the unique disease principle"),^{[25] [26]} considering uniqueness of the exposome (a totality of endogenous and exogenous / ecology 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 (especially cancer) became increasingly common throughout the 2000s. Yet, the utilise of molecular pathology in epidemiology posed unique challenges, including lack of research guidelines and standardized statistical methodologies, and paucity of interdisciplinary experts and grooming programs.^[27] Furthermore, the concept of affliction heterogeneity appears to conflict with the long-continuing premise in epidemiology that individuals with the same disease proper noun accept like etiologies and disease processes. To resolve these issues and advance population health scientific discipline in the era of molecular precision medicine, "molecular pathology" and "epidemiology" was integrated to create a new interdisciplinary field of "molecular pathological epidemiology" (MPE),^{[28] [29]} defined as "epidemiology of molecular pathology and heterogeneity of affliction". 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 illness. A better understanding of heterogeneity of illness pathogenesis will further contribute to elucidate etiologies of disease. The MPE approach can be applied to not only neoplastic diseases but too non-neoplastic diseases.^[30] The concept and paradigm of MPE accept become widespread in the 2010s.^{[31] [32] [33] [34] [35] [36] [37]}

By 2012, information technology was recognized that many pathogens' evolution is rapid enough to exist highly relevant to epidemiology, and that therefore much could be gained from an interdisciplinary approach to infectious disease integrating epidemiology and molecular evolution to "inform control strategies, or even patient treatment."^{[38] [39]}

Modern epidemiological studies can use avant-garde statistics and machine learning to create predictive models likewise equally to define treatment effects.^{[40] [41]}

Types of studies [edit]

Epidemiologists employ a range of study designs from the observational to experimental and generally categorized equally descriptive (involving the assessment of data covering fourth dimension, identify, 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 command of all of the factors entering a certain case report.^[42] Epidemiological studies are aimed, where possible, at revealing unbiased relationships between exposures such as booze or smoking, biological agents, stress, or chemicals to bloodshed or morbidity. The identification of causal relationships between these exposures and outcomes is an of import attribute of epidemiology. Modern epidemiologists use computer science as a tool.

Observational studies have two components, descriptive and analytical. Descriptive observations pertain to the "who, what, where and when of health-related land occurrence". However, analytical observations deal more than with the 'how' of a health-related result.^[42] Experimental epidemiology contains three case types: randomized controlled trials (often used for new medicine or drug testing), field trials (conducted on those at a loftier risk of contracting a illness), and customs trials (research on social originating diseases).^[42]

The term 'epidemiologic triad' is used to describe the intersection of Host, Agent, and Surroundings in analyzing an outbreak.

Case series [edit]

Example-series may refer to the qualitative written report of the experience of a single patient, or small group of patients with a like diagnosis, or to a statistical factor with the potential to produce affliction with periods when they are unexposed.

The onetime type of study is purely descriptive and cannot be used to make inferences near the full general population of patients with that affliction. These types of studies, in which an astute clinician identifies an unusual characteristic of a disease or a patient'south history, may lead to a conception 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 post-obit the case series over time to evaluate the disease's natural history.^[43]

The latter type, more than formally described as cocky-controlled instance-serial studies, dissever individual patient follow-up time into exposed and unexposed periods and use stock-still-effects Poisson regression processes to compare the incidence rate of a given upshot betwixt exposed and unexposed periods. This technique has been extensively used in the written report 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 [edit]

Example-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 affliction negative individuals (the "control" grouping). The command group should ideally come from the aforementioned population that gave ascent to the cases. The instance-control written report looks back through fourth dimension at potential exposures that both groups (cases and controls) may have encountered. A 2×2 tabular array is constructed, displaying exposed cases (A), exposed controls (B), unexposed cases (C) and unexposed controls (D). The statistic generated to measure out 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 = (Advertizing/BC).

	Cases	Controls
Exposed	A	B
Unexposed	C	D

If the OR is significantly greater than i, so the conclusion is "those with the disease are more likely to have been exposed," whereas if information technology is shut to one then the exposure and disease are not likely associated. If the OR is far less than i, and so this suggests that the exposure is a protective gene in the causation of the disease. Case-command studies are usually faster and more cost-effective than cohort studies merely are sensitive to bias (such as recall bias and selection bias). The main challenge is to identify the advisable control grouping; 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 accomplished by drawing a random sample from the original population at take a chance. This has as a consequence that the command grouping can contain people with the disease under study when the disease has a high assault rate in a population.

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

${\displaystyle {\text{total cases}}=A+C=ane.96^{ii}(1+Northward)\left({\frac {1}{\ln(OR)}}\right)^{ii}\left({\frac {OR+two{\sqrt {OR}}+i}{\sqrt {OR}}}\right)\approx 15.five(one+Due north)\left({\frac {1}{\ln(OR)}}\right)^{2}}$

where Due north 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 only useless for low odds ratios. For instance, for an odds ratio of ane.5 and cases = controls, the table shown in a higher place would look similar this:

	Cases	Controls
Exposed	103	84
Unexposed	84	103

For an odds ratio of i.ane:

	Cases	Controls
Exposed	1732	1652
Unexposed	1652	1732

Accomplice studies [edit]

Cohort studies select subjects based on their exposure status. The written report subjects should be at hazard of the consequence under investigation at the beginning of the cohort written report; this commonly means that they should be illness free when the cohort study starts. The cohort is followed through time to appraise their afterward outcome status. An case 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 command report. 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.eastward. RR =P _e /P _u.

.....	Instance	Non-case	Total
Exposed	A	B	(A +B)
Unexposed	C	D	(C +D)

As with the OR, a RR greater than one shows association, where the conclusion can exist read "those with the exposure were more probable to develop disease."

Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, equally the OR is just an interpretation of the RR, since true incidence cannot be calculated in a case command written report where subjects are selected based on disease condition. Temporality can be established in a prospective report, and confounders are more easily controlled for. Notwithstanding, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long fourth dimension catamenia over which the cohort is followed.

Cohort studies also are limited past the same equation for number of cases as for cohort studies, but, if the base of operations incidence rate in the study population is very depression, the number of cases required is reduced by ½.

Causal inference [edit]

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 scientific discipline is that of discovering causal relationships.

"Correlation does non imply causation" is a mutual theme for much of the epidemiological literature. For epidemiologists, the key is in the term inference. Correlation, or at to the lowest degree association between two variables, is a necessary just not sufficient criterion for inference that one variable causes the other. Epidemiologists use gathered information and a broad range of biomedical and psychosocial theories in an iterative fashion to generate or aggrandize theory, to examination hypotheses, and to make educated, informed assertions about which relationships are causal, and virtually exactly how they are causal.

Epidemiologists emphasize that the "one cause – one effect" understanding is a simplistic mis-belief.^{[ citation needed ]} Most outcomes, whether disease or death, are caused by a concatenation or spider web consisting of many component causes.^[44] Causes tin be distinguished equally necessary, sufficient or probabilistic conditions. If a necessary condition can be identified and controlled (east.g., antibodies to a disease agent, energy in an injury), the harmful outcome can be avoided (Robertson, 2015). 1 tool regularly used to conceptualize the multicausality associated with disease is the causal pie model.^[45]

Bradford Colina criteria [edit]

In 1965, Austin Bradford Hill proposed a series of considerations to help assess evidence of causation,^[46] which accept come up to be ordinarily 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.^[47] Hill himself said "None of my nine viewpoints can bring indisputable evidence for or against the crusade-and-outcome hypothesis and none tin be required sine qua non."^[46]

1. Forcefulness of Association: A minor association does non mean that there is not a causal effect, though the larger the clan, the more likely that it is causal.^[46]
2. Consistency of Data: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.^[46]
3. Specificity: Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.^[46]
4. Temporality: The result has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the outcome must occur after that filibuster).^[46]
5. Biological slope: Greater exposure should generally lead to greater incidence of the effect. Yet, in some cases, the mere presence of the factor can trigger the event. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.^[46]
6. Plausibility: A plausible mechanism between crusade and effect is helpful (just Hill noted that knowledge of the mechanism is express by current knowledge).^[46]
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect. All the same, Loma noted that "... lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations".^[46]
8. Experiment: "Occasionally it is possible to appeal to experimental evidence".^[46]
9. Illustration: The effect of similar factors may be considered.^[46]

Legal estimation [edit]

Epidemiological studies can but go to prove that an agent could accept caused, simply 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 affliction. This question, sometimes referred to equally 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 betwixt an agent and a affliction is causal (general causation) and where the magnitude of excess chance attributed to the agent has been determined; that is, epidemiology addresses whether an amanuensis can cause a illness, not whether an agent did cause a specific plaintiff's affliction.^[48]

In United States law, epidemiology alone cannot prove that a causal association does not be in general. Conversely, it tin be (and is in some circumstances) taken by US courts, in an private case, to justify an inference that a causal clan does exist, based upon a rest 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 [edit]

Epidemiological practice and the results of epidemiological analysis make a significant contribution to emerging population-based wellness management frameworks.

Population-based health management encompasses the ability to:

• Appraise the health states and wellness needs of a target population;
• Implement and evaluate interventions that are designed to meliorate the health of that population; and
• Efficiently and finer provide intendance for members of that population in a manner that is consistent with the customs's cultural, policy and health resource values.

Modern population-based wellness management is complex, requiring a multiple set of skills (medical, political, technological, mathematical, etc.) of which epidemiological do and analysis is a core component, that is unified with direction science to provide efficient and effective health care and wellness guidance to a population. This task requires the forward-looking ability of modern risk direction approaches that transform wellness risk factors, incidence, prevalence and mortality statistics (derived from epidemiological analysis) into management metrics that not simply guide how a health arrangement responds to current population health issues but also how a health arrangement tin can be managed to better respond to future potential population wellness issues.^[49]

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 Command Programs, Rick Hansen Foundation, Canadian Tobacco Control Research Initiative.^{[l] [51] [52]}

Each of these organizations uses a population-based health management framework chosen Life at Risk that combines epidemiological quantitative analysis with demographics, health agency operational inquiry and economics to perform:

• Population Life Impacts Simulations: Measurement of the time to come potential impact of disease upon the population with respect to new disease cases, prevalence, premature death likewise as potential years of life lost from disability and decease;
• Labour Force Life Impacts Simulations: Measurement of the future potential touch on of disease upon the labour force with respect to new illness cases, prevalence, premature death and potential years of life lost from disability and death;
• Economic Impacts of Disease Simulations: Measurement of the futurity potential impact of disease upon private sector disposable income impacts (wages, corporate profits, individual health intendance costs) and public sector disposable income impacts (personal income tax, corporate income taxation, consumption taxes, publicly funded health care costs).

Practical field epidemiology [edit]

Applied epidemiology is the practice of using epidemiological methods to protect or meliorate 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 advisable policies or disease control measures.

Humanitarian context [edit]

As the surveillance and reporting of diseases and other wellness factors get increasingly hard in humanitarian crisis situations, the methodologies used to report the data are compromised. One study constitute that less than half (42.4%) of nutrition surveys sampled from humanitarian contexts correctly calculated the prevalence of malnutrition and simply one-3rd (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 assistance signal the severity of a crunch, the tracking and reporting of these health factors is crucial.

Vital registries are usually the most effective ways to collect information, just in humanitarian contexts these registries tin be non-existent, unreliable, or inaccessible. Every bit 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 option and reporting biases. Other methods are existence adult, simply are not common practice yet.^{[53] [54] [55] [56]}

Validity: precision and bias [edit]

Different fields in epidemiology take different levels of validity. One style to assess the validity of findings is the ratio of imitation-positives (claimed furnishings that are non right) to false-negatives (studies which neglect to support a true effect). To accept the field of genetic epidemiology, candidate-gene studies produced over 100 false-positive findings for each false-negative. By contrast genome-wide association announced close to the opposite, with only one false positive for every 100 or more false-negatives.^[57] This ratio has improved over time in genetic epidemiology as the field has adopted stringent criteria. By dissimilarity, other epidemiological fields have not required such rigorous reporting and are much less reliable every bit a issue.^[57]

Random error [edit]

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 fault during coding. Random error affects measurement in a transient, inconsistent manner and it is incommunicable to correct for random error.

There is random error in all sampling procedures. This is chosen sampling error.

Precision in epidemiological variables is a measure out of random fault. Precision is also inversely related to random error, and then that to reduce random fault is to increment precision. Confidence intervals are computed to demonstrate the precision of relative take a chance estimates. The narrower the confidence interval, the more precise the relative gamble judge.

There are two basic ways to reduce random error in an epidemiological written report. The first is to increment the sample size of the written report. In other words, add together 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 written report are usually increased. In that location is usually an uneasy balance between the need for adequate precision and the practical issue of written report cost.

Systematic error [edit]

A systematic error or bias occurs when there is a difference betwixt the true value (in the population) and the observed value (in the study) from whatever cause other than sampling variability. An example of systematic error is if, unknown to you, the pulse oximeter you are using is gear up incorrectly and adds two points to the truthful value each fourth dimension a measurement is taken. The measuring device could be precise but not accurate. Because the error happens in every instance, it is systematic. Conclusions yous draw based on that data will however be incorrect. Only the error can exist reproduced in the future (e.g., by using the same mis-ready instrument).

A error in coding that affects all responses for that particular question is some other example of a systematic error.

The validity of a written report is dependent on the degree of systematic fault. 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. Skillful internal validity implies a lack of mistake in measurement and suggests that inferences may be fatigued at least equally they pertain to the subjects under study.
• External validity pertains to the procedure of generalizing the findings of the study to the population from which the sample was drawn (or even across that population to a more universal statement). This requires an understanding of which conditions are relevant (or irrelevant) to the generalization. Internal validity is conspicuously a prerequisite for external validity.

Selection bias [edit]

Pick bias occurs when study subjects are selected or go part of the written report as a consequence of a third, unmeasured variable which is associated with both the exposure and outcome of interest.^[58] 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.)^[59] Information technology is important to note that such a difference in response will not lead to bias if it is non too associated with a systematic difference in outcome between the two response groups.

Data bias [edit]

Data bias is bias arising from systematic error in the assessment of a variable.^[60] An example of this is think bias. A typical instance is again provided by Sackett in his discussion of a written report examining the event 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 commonly (controls) information technology was found that 28% of the former, but only 20% of the latter, reported exposure to drugs which could non be substantiated either in earlier prospective interviews or in other health records".^[59] In this example, recollect bias probably occurred every bit a result of women who had had miscarriages having an credible tendency to better recall and therefore report previous exposures.

Confounding [edit]

Confounding has traditionally been defined as bias arising from the co-occurrence or mixing of effects of extraneous factors, referred to every bit confounders, with the primary effect(s) of interest.^{[threescore] [61]} A more contempo definition of confounding invokes the notion of counterfactual effects.^[61] According to this view, when 1 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 10 = one for every unit of measurement of the population) the risk of this event will exist R _A1. The counterfactual or unobserved risk R _A0 corresponds to the risk which would take been observed if these same individuals had been unexposed (i.e. X = 0 for every unit of measurement 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 1 is interested in relative risk). Since the counterfactual adventure R _A0 is unobservable we approximate it using a second population B and nosotros actually measure the following relations: R _A1 −R _B0 or R _A1/R _B0. In this situation, confounding occurs when R _A0 ≠R _B0.^[61] (NB: Example assumes binary result and exposure variables.)

Some epidemiologists prefer to recollect of confounding separately from common categorizations of bias since, dissimilar pick and data bias, confounding stems from existent causal furnishings.^[58]

The profession [edit]

Few universities have offered epidemiology equally a course of report at the undergraduate level. One notable undergraduate program exists at Johns Hopkins University, where students who major in public health tin take graduate level courses, including epidemiology, during their senior yr at the Bloomberg Schoolhouse of Public Health.^[62]

Although epidemiologic research is conducted by individuals from diverse disciplines, including clinically trained professionals such as physicians, formal grooming is available through Masters or Doctoral programs including Principal of Public Health (MPH), Master of Science of Epidemiology (MSc.), Doctor of Public Health (DrPH), Medico of Pharmacy (PharmD), Doctor of Philosophy (PhD), Doctor of Science (ScD). Many other graduate programs, e.m., Dr. of Social Work (DSW), Doc of Clinical Practice (DClinP), Md of Podiatric Medicine (DPM), Doc of Veterinary Medicine (DVM), Md of Nursing Exercise (DNP), Doc of Physical Therapy (DPT), or for clinically trained physicians, Medico of Medicine (MD) or Bachelor of Medicine and Surgery (MBBS or MBChB) and Doctor of Osteopathic Medicine (Do), include some training in epidemiologic inquiry or related topics, just this training is generally essentially less than offered in grooming programs focused on epidemiology or public wellness. 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 unlike settings. Some epidemiologists piece of 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 not-profit organizations, universities, hospitals and larger government entities such as country and local health departments, various Ministries of Health, Doctors without Borders, the Centers for Disease Command and Prevention (CDC), the Health Protection Agency, the Globe Wellness Organization (WHO), or the Public Wellness Agency of Canada. Epidemiologists can too work in for-turn a profit organizations such as pharmaceutical and medical device companies in groups such as market research or clinical development.

COVID-nineteen [edit]

An April 2022 University of Southern California article noted that "The coronavirus epidemic... thrust epidemiology – the report of the incidence, distribution and command 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."^[63]

See too [edit]

• Historic period adjustment – Technique used to compare populations with different historic period profiles
• Caerphilly Middle Illness Study
• Eye for Research on the Epidemiology of Disasters  (CRED)
• Centro Studi GISED
• Circulation plan
• Contact tracing – Finding and identifying people in contact with someone with an communicable diseases
• Critical community size – Minimum size of a closed population within which a pathogen can persist indefinitely
• Disease cluster
• Disease diffusion mapping
• Compartmental models in epidemiology – Type of mathematical model used for infectious diseases
• Epidemiological method – Scientific method in the specific field
• Epidemiological transition
• European Centre for Disease Prevention and Control – Agency of the European Union
• Hispanic paradox
• International Society for Pharmacoepidemiology
• Mathematical modelling of infectious disease – Using mathematical models to empathise infectious disease transmission
• Mendelian randomization – Statistical method in genetic epidemiology
• Occupational epidemiology
• Predictive analytics – Statistical techniques analyzing facts to make predictions about unknown events
• Gild for Occupational Health Psychology
• Population groups in biomedicine
• Spatial epidemiology
• Report of Wellness in Pomerania
• Targeted immunization strategies
• Urban planning – Technical and political process concerned with the use of land and design of the urban environment
• Whitehall Report
• Zoonosis – Illness that tin exist transmitted from other species to humans

References [edit]

Citations [edit]

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Sources [edit]

• Clayton, David and Michael Hills (1993) Statistical Models in Epidemiology Oxford University Press. ISBN 0-19-852221-5
• Miquel Porta, editor (2014) "A lexicon of epidemiology", 6th edn, New York: Oxford University Press. [2]
• Morabia, Alfredo, editor. (2004) A History of Epidemiologic Methods and Concepts. Basel, Birkhauser Verlag. Part I. [iii] [four]
• Smetanin P, Kobak P, Moyer C, Maley O (2005). "The Run a risk Management of Tobacco Control Research Policy Programs" The World Conference on Tobacco OR Wellness Conference, 12–15 July 2006, Washington DC.
• Szklo G, Nieto FJ (2002). "Epidemiology: beyond the basics", Aspen Publishers.
• Robertson LS (2015). Injury Epidemiology: Fourth Edition. Gratuitous online at nanlee.cyberspace
• Rothman K., Sander Greenland, Lash T., editors (2008). "Modernistic Epidemiology", 3rd Edition, Lippincott Williams & Wilkins. ISBN 0-7817-5564-6, 978-0-7817-5564-1
• Olsen J, Christensen K, Murray J, Ekbom A. An Introduction to Epidemiology for Health Professionals. New York: Springer Science+Business Media; 2010 ISBN 978-i-4419-1497-2

External links [edit]

• The Health Protection Bureau
• The Collection of Biostatistics Research Archive
• European Epidemiological Federation
• 'Epidemiology for the Uninitiated' by D. Coggon, G. Rose, D.J.P. Barker, British Medical Journal
• Epidem.com – Epidemiology (peer reviewed scientific journal that publishes original research on epidemiologic topics)
• 'Epidemiology' – In: Philip S. Brachman, Medical Microbiology (fourth edition), US National Center for Biotechnology Information
• Monash Virtual Laboratory – Simulations of epidemic spread across a mural
• Division of Cancer Epidemiology and Genetics, National Cancer Constitute, National Institutes of Health
• Centre for Enquiry on the Epidemiology of Disasters – A WHO collaborating centre
• People'southward Epidemiology Library
• Epidemiology of COVID-19 outbreak

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Source: https://en.wikipedia.org/wiki/Epidemiology

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