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

Aspect of health and disease science

Epidemiology is the written report and analysis of the distribution (who, when, and where), patterns and determinants of health and disease weather 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 assistance with written report pattern, collection, and statistical assay of data, improve estimation and broadcasting of results (including peer review and occasional systematic review). Epidemiology has helped develop methodology used in clinical research, public wellness studies, and, to a bottom extent, bones inquiry in the biological sciences.^[ane]

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

Epidemiology, literally pregnant "the study of what is upon the people", is derived from Greek epi 'upon, among', demos 'people, district', and logos 'written report, word, discourse', suggesting that it applies just to man populations. Even so, the term is widely used in studies of zoological populations (veterinarian epidemiology), although the term "epizoology" is available, and it has besides been practical to studies of plant populations (botanical or plant disease epidemiology).^[2]

The stardom betwixt "epidemic" and "owned" was first fatigued by Hippocrates,^[3] to distinguish between diseases that are "visited upon" a population (epidemic) from those that "reside inside" a population (endemic).^[4] The term "epidemiology" appears to have first been used to draw the study of epidemics in 1802 past the Spanish physician Villalba in Epidemiología Española.^[iv] Epidemiologists also study the interaction of diseases in a population, a status known as a syndemic.

The term epidemiology is now widely applied to cover the description and causation of non simply epidemic, infectious affliction, but of disease in full general, including related conditions. Some examples of topics examined through epidemiology include as high blood pressure, mental affliction and obesity. Therefore, this epidemiology is based upon how the pattern of the disease causes change in the part of human beings.

History [edit]

The Greek physician Hippocrates, known as the father of medicine,^{[5] [six]} sought a logic to sickness; he is the kickoff person known to take examined the relationships between the occurrence of illness and ecology influences.^[7] Hippocrates believed sickness of the human body to exist acquired by an imbalance of the four humors (black bile, yellow bile, blood, and phlegm). The cure to the sickness was to remove or add the sense of humor in question to balance the trunk. This belief led to the application of bloodletting and dieting in medicine.^[eight] He coined the terms endemic (for diseases usually constitute in some places only not in others) and epidemic (for diseases that are seen at some times but non others).^[9]

Modern era [edit]

In the middle of the 16th century, a doctor from Verona named Girolamo Fracastoro was the first to propose a theory that these very pocket-sized, unseeable, particles that cause disease were alive. They were considered to exist able to spread past air, multiply by themselves and to be destroyable by fire. In this way he refuted Galen's miasma theory (toxicant gas in ill people). In 1543 he wrote a book De contagione et contagiosis morbis, in which he was the first to promote personal and environmental hygiene to prevent disease. The development of a sufficiently powerful microscope by Antonie van Leeuwenhoek in 1675 provided visual evidence of living particles consistent with a germ theory of disease.

During the Ming Dynasty, Wu Youke (1582–1652) developed the idea that some diseases were caused by transmissible agents, which he called Li Qi (戾气 or pestilential factors) when he observed various epidemics rage around him between 1641 and 1644.^[10] His book Wen Yi Lun (瘟疫论，Treatise on Pestilence/Treatise of Epidemic Diseases) tin can be regarded as the main etiological piece of 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 showtime to distinguish the fevers of Londoners in the afterwards 1600s. His theories on cures of fevers met with much resistance from traditional physicians at the time. He was non able to find the initial cause of the smallpox fever he researched and treated.^[8]

John Graunt, a haberdasher and amateur statistician, published Natural and Political Observations ... upon the Bills of Mortality in 1662. In it, he analysed the mortality rolls in London before the Bully Plague, presented one of the get-go life tables, and reported time trends for many diseases, new and one-time. 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 besides known as the father of (modern) epidemiology.^{[xiii] [14]} He began with noticing the significantly college death rates in two areas supplied past Southwark Visitor. His identification of the Broad Street pump as the crusade of the Soho epidemic is considered the archetype example of epidemiology. Snow used chlorine in an endeavour to clean the h2o and removed the handle; this concluded the outbreak. This has been perceived as a major consequence in the history of public health and regarded as the founding consequence of the science of epidemiology, having helped shape public health policies around the world.^{[15] [16]} However, Snowfall'due south research and preventive measures to avert further outbreaks were not fully accepted or put into exercise until afterward his death due to the prevailing Miasma Theory of the time, a model of disease in which poor air quality was blamed for illness. This was used to rationalize high rates of infection in impoverished areas instead of addressing the underlying issues of poor nutrition and sanitation, and was proven false by his work.^[17]

Other pioneers include Danish physician Peter Anton Schleisner, who in 1849 related his work on the prevention of the epidemic of neonatal tetanus on the Vestmanna Islands in Iceland.^{[18] [19]} Another of import pioneer was Hungarian physician Ignaz Semmelweis, who in 1847 brought down babe mortality at a Vienna hospital by instituting a disinfection process. His findings were published in 1850, but his work was ill-received by his colleagues, who discontinued the procedure. Disinfection did non go 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 Society for Geographical Pathology to systematically investigate the geographical pathology of cancer and other not-infectious diseases across populations in different regions. After World War 2, Richard Doll and other not-pathologists joined the field and advanced methods to study cancer, a affliction with patterns and way of occurrences that could not be suitably studied with the methods developed for epidemics of infectious diseases. Geography pathology somewhen combined with infectious disease epidemiology to brand the field that is epidemiology today.^[24]

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

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

21st century [edit]

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

While most molecular epidemiology studies are still using conventional affliction diagnosis and classification systems, it is increasingly recognized that disease progression represents inherently heterogeneous processes differing from person to person. Conceptually, each individual has a unique disease process different from whatever other individual ("the unique affliction principle"),^{[25] [26]} 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 betwixt an exposure and molecular pathologic signature of affliction (specially cancer) became increasingly mutual throughout the 2000s. Nevertheless, the use of molecular pathology in epidemiology posed unique challenges, including lack of enquiry guidelines and standardized statistical methodologies, and paucity of interdisciplinary experts and preparation programs.^[27] Furthermore, the concept of illness heterogeneity appears to disharmonize with the long-continuing premise in epidemiology that individuals with the same illness proper name have similar etiologies and affliction processes. To resolve these problems and advance population health science in the era of molecular precision medicine, "molecular pathology" and "epidemiology" was integrated to create a new interdisciplinary field of "molecular pathological epidemiology" (MPE),^{[28] [29]} defined every bit "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) development and progression of affliction. A improve understanding of heterogeneity of disease pathogenesis will further contribute to elucidate etiologies of disease. The MPE approach tin exist applied to not simply neoplastic diseases simply as well non-neoplastic diseases.^[30] The concept and image of MPE have go widespread in the 2010s.^{[31] [32] [33] [34] [35] [36] [37]}

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

Modern epidemiological studies tin utilise advanced statistics and machine learning to create predictive models as well every bit to define treatment effects.^{[forty] [41]}

Types of studies [edit]

Epidemiologists employ a range of report 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 inbound a certain case written report.^[42] Epidemiological studies are aimed, where possible, at revealing unbiased relationships between exposures such as alcohol 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. Modernistic epidemiologists utilize informatics every bit a tool.

Observational studies take ii components, descriptive and analytical. Descriptive observations pertain to the "who, what, where and when of health-related country occurrence". Notwithstanding, analytical observations deal more with the 'how' of a health-related upshot.^[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 high take a chance of contracting a disease), and community trials (enquiry on social originating diseases).^[42]

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

Instance series [edit]

Case-series may refer to the qualitative report of the experience of a unmarried patient, or pocket-sized 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 almost the general population of patients with that disease. These types of studies, in which an astute clinician identifies an unusual characteristic of a disease or a patient's history, may atomic number 82 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 instance-command 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.^[43]

The latter blazon, more formally described every bit self-controlled case-serial studies, divide individual patient follow-upwards fourth dimension into exposed and unexposed periods and use stock-still-furnishings 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 written report of adverse reactions to vaccination and has been shown in some circumstances to provide statistical ability comparable to that available in cohort studies.

Case-control studies [edit]

Case-control studies select subjects based on their disease status. It is a retrospective study. A group of individuals that are affliction positive (the "case" group) is compared with a group of disease negative individuals (the "command" group). The control group should ideally come from the aforementioned population that gave rise to the cases. The case-command study looks back through fourth dimension at potential exposures that both groups (cases and controls) may have encountered. A 2×2 table is constructed, displaying exposed cases (A), exposed controls (B), unexposed cases (C) and unexposed controls (D). The statistic generated to measure association is the odds ratio (OR), which is the ratio of the odds of exposure in the cases (A/C) to the odds of exposure in the controls (B/D), i.east. OR = (AD/BC).

	Cases	Controls
Exposed	A	B
Unexposed	C	D

If the OR is significantly greater than i, then the conclusion is "those with the illness are more likely to take been exposed," whereas if information technology is close to one then the exposure and illness are not likely associated. If the OR is far less than one, then this suggests that the exposure is a protective cistron in the causation of the illness. Case-control studies are usually faster and more cost-effective than cohort studies but are sensitive to bias (such as call up bias and selection bias). The primary claiming 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 take a chance. This has as a consequence that the command group can contain people with the illness under study when the disease has a high assail rate in a population.

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

${\displaystyle {\text{full cases}}=A+C=1.96^{ii}(1+N)\left({\frac {i}{\ln(OR)}}\correct)^{2}\left({\frac {OR+ii{\sqrt {OR}}+ane}{\sqrt {OR}}}\right)\approx 15.5(i+North)\left({\frac {i}{\ln(OR)}}\correct)^{ii}}$

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

	Cases	Controls
Exposed	103	84
Unexposed	84	103

For an odds ratio of 1.one:

	Cases	Controls
Exposed	1732	1652
Unexposed	1652	1732

Accomplice studies [edit]

Cohort studies select subjects based on their exposure status. The study subjects should exist at adventure of the outcome nether investigation at the beginning of the cohort study; this usually ways that they should be disease gratuitous when the cohort study starts. The cohort is followed through time to assess their afterwards outcome status. An example of a cohort written report would be the investigation of a accomplice of smokers and not-smokers over time to judge the incidence of lung cancer. The same two×2 table is constructed equally with the case control study. However, the point estimate generated is the relative risk (RR), which is the probability of affliction for a person in the exposed group, P _e =A / (A +B) over the probability of affliction for a person in the unexposed group, P _u  =C / (C +D), i.e. RR =P _{due east} /P _u.

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

As with the OR, a RR greater than 1 shows association, where the determination can be 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, as the OR is just an interpretation of the RR, since true incidence cannot be calculated in a case control report where subjects are selected based on affliction status. Temporality can be established in a prospective study, and confounders are more hands controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-upwards based on the long time period over which the cohort is followed.

Cohort studies also are express past the aforementioned equation for number of cases as for cohort studies, just, if the base incidence rate in the written report population is very low, 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 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 betwixt two variables, is a necessary simply non sufficient criterion for inference that i variable causes the other. Epidemiologists use gathered data and a wide 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 "ane cause – one effect" understanding is a simplistic mis-belief.^{[ commendation needed ]} Nigh outcomes, whether disease or death, are acquired past a concatenation or web consisting of many component causes.^[44] Causes can be distinguished as necessary, sufficient or probabilistic conditions. If a necessary condition can exist identified and controlled (east.g., antibodies to a disease amanuensis, free energy in an injury), the harmful outcome can be avoided (Robertson, 2015). 1 tool regularly used to anticipate the multicausality associated with illness is the causal pie model.^[45]

Bradford Colina criteria [edit]

In 1965, Austin Bradford Hill proposed a serial of considerations to help assess testify of causation,^[46] which have come up to exist usually known equally the "Bradford Colina criteria". In contrast to the explicit intentions of their author, Colina'south 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 cause-and-effect hypothesis and none tin can be required sine qua non."^[46]

1. Strength of Association: A pocket-size association does non mean that at that place is non a causal effect, though the larger the association, the more probable 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 probable if a very specific population at a specific site and disease with no other probable explanation. The more than specific an clan between a cistron and an effect is, the bigger the probability of a causal relationship.^[46]
4. Temporality: The effect has to occur later on the cause (and if there is an expected filibuster between the cause and expected effect, then the outcome must occur after that delay).^[46]
5. Biological gradient: Greater exposure should generally pb to greater incidence of the outcome. However, in some cases, the mere presence of the factor tin trigger the effect. In other cases, an changed proportion is observed: greater exposure leads to lower incidence.^[46]
6. Plausibility: A plausible mechanism between cause and issue is helpful (just Loma noted that knowledge of the machinery is limited by current noesis).^[46]
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect. Nonetheless, Hill noted that "... lack of such [laboratory] evidence cannot nullify the epidemiological event on associations".^[46]
8. Experiment: "Occasionally it is possible to appeal to experimental testify".^[46]
9. Analogy: The effect of similar factors may exist considered.^[46]

Legal interpretation [edit]

Epidemiological studies tin can only become to prove that an agent could take acquired, just not that it did cause, an effect in any particular instance:

Epidemiology is concerned with the incidence of disease in populations and does not accost the question of the crusade of an private's disease. This question, sometimes referred to equally specific causation, is beyond the domain of the science of epidemiology. Epidemiology has its limits at the bespeak where an inference is fabricated that the human relationship between an agent and a disease is causal (general causation) and where the magnitude of excess risk attributed to the amanuensis has been determined; that is, epidemiology addresses whether an agent can cause a affliction, not whether an agent did crusade a specific plaintiff'south disease.^[48]

In The states constabulary, epidemiology lonely cannot prove that a causal association does non exist in general. Conversely, it tin be (and is in some circumstances) taken by Usa courts, in an individual example, to justify an inference that a causal association does exist, based upon a remainder 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 practise and the results of epidemiological analysis make a meaning contribution to emerging population-based health direction frameworks.

Population-based health management encompasses the power to:

• Assess the health states and health needs of a target population;
• Implement and evaluate interventions that are designed to ameliorate the health of that population; and
• Efficiently and effectively provide treat members of that population in a mode that is consequent with the customs's cultural, policy and wellness resource values.

Mod population-based health management is circuitous, requiring a multiple set of skills (medical, political, technological, mathematical, etc.) of which epidemiological exercise and analysis is a core component, that is unified with management science to provide efficient and effective wellness care and health guidance to a population. This task requires the forrard-looking ability of modern risk direction approaches that transform health adventure factors, incidence, prevalence and mortality statistics (derived from epidemiological assay) into management metrics that not only guide how a wellness organisation responds to current population wellness issues simply as well how a health system can be managed to improve reply to hereafter 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, Wellness Canada Tobacco Control Programs, Rick Hansen Foundation, Canadian Tobacco Control Research Initiative.^{[l] [51] [52]}

Each of these organizations uses a population-based health direction framework chosen Life at Risk that combines epidemiological quantitative assay with demographics, health bureau operational inquiry and economic science 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 decease as well as potential years of life lost from disability and death;
• Labour Force Life Impacts Simulations: Measurement of the future potential affect 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 Illness Simulations: Measurement of the future potential touch of affliction upon private sector dispensable income impacts (wages, corporate profits, individual health intendance costs) and public sector dispensable income impacts (personal income tax, corporate income revenue enhancement, consumption taxes, publicly funded health care costs).

Applied field epidemiology [edit]

Applied epidemiology is the practice of using epidemiological methods to protect or better the wellness of a population. Practical field epidemiology tin include investigating communicable and non-communicable disease outbreaks, bloodshed 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 [edit]

Equally the surveillance and reporting of diseases and other health factors go increasingly difficult in humanitarian crisis situations, the methodologies used to report the data are compromised. One report establish that less than one-half (42.4%) of nutrition surveys sampled from humanitarian contexts correctly calculated the prevalence of malnutrition and but one-third (35.3%) of the surveys met the criteria for quality. Among the mortality surveys, only three.2% met the criteria for quality. As nutritional status and bloodshed rates help signal the severity of a crunch, the tracking and reporting of these health factors is crucial.

Vital registries are usually the most constructive means to collect data, only in humanitarian contexts these registries tin can be not-existent, unreliable, or inaccessible. As such, bloodshed is often inaccurately measured using either prospective demographic surveillance or retrospective mortality surveys. Prospective demographic surveillance requires much manpower and is hard to implement in a spread-out population. Retrospective mortality surveys are decumbent to selection and reporting biases. Other methods are beingness developed, just are not common practice still.^{[53] [54] [55] [56]}

Validity: precision and bias [edit]

Different fields in epidemiology have unlike levels of validity. I way to assess the validity of findings is the ratio of simulated-positives (claimed effects that are not correct) to faux-negatives (studies which neglect to support a truthful 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 clan appear shut to the reverse, with merely 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 contrast, other epidemiological fields have not required such rigorous reporting and are much less reliable every bit a outcome.^[57]

Random error [edit]

Random mistake is the result of fluctuations around a true value because of sampling variability. Random error is just that: random. It can occur during information drove, coding, transfer, or assay. Examples of random fault include: poorly worded questions, a misunderstanding in interpreting an private answer from a particular respondent, or a typographical mistake during coding. Random fault affects measurement in a transient, inconsistent mode and it is impossible to correct for random error.

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

Precision in epidemiological variables is a measure out of random fault. Precision is likewise inversely related to random error, so that to reduce random mistake is to increase precision. Confidence intervals are computed to demonstrate the precision of relative risk estimates. The narrower the conviction interval, the more precise the relative run a risk estimate.

In that location are two basic ways to reduce random error in an epidemiological study. The beginning is to increase the sample size of the written report. In other words, add more subjects to your study. The 2d is to reduce the variability in measurement in the study. This might be achieved by using a more than precise measuring device or by increasing the number of measurements.

Annotation, that if sample size or number of measurements are increased, or a more precise measuring tool is purchased, the costs of the study are ordinarily increased. There is normally an uneasy balance between the need for adequate precision and the practical issue of study toll.

Systematic error [edit]

A systematic error or bias occurs when there is a departure between the truthful 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 set up incorrectly and adds two points to the true value each time a measurement is taken. The measuring device could be precise merely not accurate. Because the error happens in every instance, it is systematic. Conclusions you draw based on that data will still be incorrect. Only the error can be reproduced in the future (e.g., by using the same mis-set musical instrument).

A mistake in coding that affects all responses for that particular question is another instance of a systematic error.

The validity of a report is dependent on the caste 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 betwixt these variables. Good internal validity implies a lack of mistake in measurement and suggests that inferences may be drawn at least as they pertain to the subjects under report.
• External validity pertains to the process 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 weather condition are relevant (or irrelevant) to the generalization. Internal validity is clearly a prerequisite for external validity.

Pick bias [edit]

Selection bias occurs when study subjects are selected or get office of the report as a result of a third, unmeasured variable which is associated with both the exposure and outcome of involvement.^[58] For instance, it has repeatedly been noted that cigarette smokers and non smokers tend to differ in their report 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] It is of import to note that such a divergence in response will not lead to bias if it is not likewise associated with a systematic deviation in upshot between the two response groups.

Data bias [edit]

Information bias is bias arising from systematic error in the assessment of a variable.^[60] An example of this is recall bias. A typical example is again provided by Sackett in his discussion of a report 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 sometime, merely only 20% of the latter, reported exposure to drugs which could non exist substantiated either in earlier prospective interviews or in other health records".^[59] In this example, recall bias probably occurred equally a outcome of women who had had miscarriages having an credible tendency to better recall and therefore written report previous exposures.

Misreckoning [edit]

Confounding has traditionally been divers as bias arising from the co-occurrence or mixing of effects of extraneous factors, referred to as confounders, with the chief result(s) of interest.^{[60] [61]} A more contempo definition of confounding invokes the notion of counterfactual effects.^[61] According to this view, when one observes an consequence of involvement, say Y=ane (as opposed to Y=0), in a given population A which is entirely exposed (i.eastward. exposure X = 1 for every unit of the population) the risk of this outcome will be R _A1. The counterfactual or unobserved adventure R _A0 corresponds to the risk which would take been observed if these same individuals had been unexposed (i.due east. Ten = 0 for every unit of the population). The truthful 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 take chances). Since the counterfactual risk R _A0 is unobservable we approximate it using a 2d population B and we actually mensurate the following relations: R _A1 −R _B0 or R _A1/R _B0. In this state of affairs, misreckoning occurs when R _A0 ≠R _B0.^[61] (NB: Case assumes binary outcome and exposure variables.)

Some epidemiologists prefer to think of misreckoning separately from common categorizations of bias since, unlike pick and data bias, confounding stems from real causal effects.^[58]

The profession [edit]

Few universities have offered epidemiology every bit a course of written report at the undergraduate level. Ane notable undergraduate program exists at Johns Hopkins University, where students who major in public health tin can take graduate level courses, including epidemiology, during their senior year at the Bloomberg School of Public Health.^[62]

Although epidemiologic inquiry 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), Chief of Scientific discipline of Epidemiology (MSc.), Medico of Public Health (DrPH), Doctor of Pharmacy (PharmD), Doctor of Philosophy (PhD), Md of Science (ScD). Many other graduate programs, e.thou., Dr. of Social Piece of work (DSW), Doctor of Clinical Practice (DClinP), Doctor of Podiatric Medicine (DPM), Doctor of Veterinary Medicine (DVM), Dr. of Nursing Practice (DNP), Physician of Concrete Therapy (DPT), or for clinically trained physicians, Doctor of Medicine (MD) or Available of Medicine and Surgery (MBBS or MBChB) and Doctor of Osteopathic Medicine (Practise), include some preparation 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.

Every bit public health/health protection practitioners, epidemiologists piece of work in a number of different settings. Some epidemiologists work 'in the field'; i.e., in the customs, commonly in a public health/wellness protection service, and are often at the forefront of investigating and combating affliction outbreaks. Others work for non-turn a profit organizations, universities, hospitals and larger authorities entities such as country and local health departments, various Ministries of Health, Doctors without Borders, the Centers for Affliction Control and Prevention (CDC), the Health Protection Agency, the World Health Organization (WHO), or the Public Wellness Agency of Canada. Epidemiologists can also work in for-profit organizations such as pharmaceutical and medical device companies in groups such as market place enquiry or clinical development.

COVID-xix [edit]

An Apr 2022 Academy of Southern California article noted that "The coronavirus epidemic... thrust epidemiology – the study of the incidence, distribution and command of disease in a population – to the forefront of scientific disciplines across the globe and even fabricated temporary celebrities out of some of its practitioners."^[63]

See also [edit]

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

References [edit]

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Sources [edit]

• Clayton, David and Michael Hills (1993) Statistical Models in Epidemiology Oxford Academy Printing. 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. [3] [4]
• 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 Briefing, 12–15 July 2006, Washington DC.
• Szklo Grand, Nieto FJ (2002). "Epidemiology: across the basics", Aspen Publishers.
• Robertson LS (2015). Injury Epidemiology: Fourth Edition. Gratuitous online at nanlee.cyberspace
• Rothman K., Sander Greenland, Lash T., editors (2008). "Modern Epidemiology", 3rd Edition, Lippincott Williams & Wilkins. ISBN 0-7817-5564-half dozen, 978-0-7817-5564-1
• Olsen J, Christensen K, Murray J, Ekbom A. An Introduction to Epidemiology for Health Professionals. New York: Springer Science+Business Media; 2010 ISBN 978-1-4419-1497-2

External links [edit]

• The Wellness Protection Bureau
• The Collection of Biostatistics Research Annal
• European Epidemiological Federation
• 'Epidemiology for the Uninitiated' by D. Coggon, Yard. Rose, D.J.P. Barker, British Medical Periodical
• 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 landscape
• Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health
• Centre for Research on the Epidemiology of Disasters – A WHO collaborating center
• People's Epidemiology Library
• Epidemiology of COVID-19 outbreak

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

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