Epidemiology 66997 226068469 2008-07-16T18:09:52Z 70.212.167.19 '''Epidemiology''' is the study of factors affecting the [[health]] and [[illness]] of populations, and serves as the foundation and [[logic]] of interventions made in the interest of [[public health]] and [[preventive medicine]]. It is considered a cornerstone methodology of public health research, and is highly regarded in [[evidence-based medicine]] for identifying risk factors for [[disease]] and determining optimal treatment approaches to clinical practice. In the work of communicable and non-communicable diseases, the work of epidemiologists range from [[outbreak]] investigation to study design, data collection and analysis including the development of statistical models to test hypotheses and the documentation of results for submission to peer-reviewed journals. Epidemiologists may draw on a number of other scientific disciplines such as biology in understanding disease processes and social science disciplines including [[sociology]] and [[philosophy]] in order to better understand proximate and distal risk factors. ==Etymology== Epidemiology, "the study of what is upon the people," is derived from the Greek terms ''epi'' = upon, among; ''demos'' = people, district; ''logos'' = study, word, discourse; suggesting that it applies only to human populations. But the term is widely used in studies of zoological populations (veterinary epidemiology), although the term '[[epizoology]]' is available, and it has also been applied to studies of plant populations (botanical epidemiology).<ref>{{cite journal |last= Nutter, Jr. |first= F.W. |title= Understanding the interrelationships between botanical, human, and veterinary epidemiology: the Ys and Rs of it all |journal= Ecosys Health |volume=5 |issue=3 |pages=131–40 |date=1999 |doi=10.1046/j.1526-0992.1999.09922.x}}</ref> ==History== The Greek physician [[Hippocrates]] is sometimes said to be the father of epidemiology. He is the first person known to have examined the relationships between the occurrence of disease and environmental influences. He coined the terms [[Endemic (epidemiology)|''endemic'']] (for diseases usually found in some places but not in others) and ''[[epidemic]]'' (for disease that are seen at some times but not others).<ref name = hip>{{cite web |title= Changing Concepts: Background to Epidemiology |publisher = Duncan & Associates |url= http://www.duncan-associates.com/changing_concepts.pdf |accessdate= 2008-02-03}}</ref> One of the earliest theories on the origin of disease was that it was primarily the fault of human luxury. This was expressed by philosophers such as [[Plato]]<ref name= plato>{{cite web |title= ''The Republic'', by Plato |publisher=The Internet Classic Archive |url= http://classics.mit.edu/Plato/republic.4.iii.html |accessdate= 2008-02-03}}</ref> and [[Rousseau]],<ref name = rousseau>{{cite web |title= A Dissertation on the Origin and Foundation of the Inequality of Mankind |publisher= Constitution Society |url= http://www.constitution.org/jjr/ineq_03.htm |accessdate= }}</ref> and social critics like Jonathan Swift.<ref name= swift>{{cite web |first= Jonathan |last= Swift |title= Gulliver's Travels: Part IV. A Voyage to the Country of the Houyhnhnms |url= http://www.jaffebros.com/lee/gulliver/bk4/chap4-7.html |accessdate= 2008-02-03}}</ref> In the [[Islamic Golden Age|medieval Islamic world]], [[Islamic medicine| physicians]] discovered the contagious nature of [[infectious disease]]. In particular, the [[Persian people|Persian]] physician [[Avicenna]], considered a "father of modern medicine,"<ref name=Cesk>{{cite journal |last=Cesk |first=Cas Lek |year=1980 |title=The father of medicine, Avicenna, in our science and culture: Abu Ali ibn Sina (980-1037) |journal=Becka J. |volume=119 |issue=1 |pages=17–23 |language=Czech}}</ref> in ''[[The Canon of Medicine]]'' (1020s), discovered the contagious nature of [[tuberculosis]] and [[sexually transmitted disease]], and the distribution of [[disease]] through [[water]] and [[soil]].<ref name=Sarton>[[George Sarton]], ''Introduction to the History of Science''.<br>([[cf.]] Dr. A. Zahoor and Dr. Z. Haq (1997), [http://www.cyberistan.org/islamic/Introl1.html Quotations From Famous Historians of Science], Cyberistan.</ref> Avicenna stated that bodily [[secretion]] is contaminated by [[Bacteria|foul foreign earthly bodies]] before being infected.<ref name=Syed/> He introduced the method of [[quarantine]] as a means of limiting the spread of contagious disease.<ref name=Tschanz>{{cite journal |first=David W. |last=Tschanz |month=August |year=2003 |title=Arab Roots of European Medicine |journal=Heart Views |volume=4 |issue=2 |publisher=The Gulf Heart Association |location=Qatar |url=http://www.hmc.org.qa/hmc/heartviews/H-V-v4%20N2/9.htm}}</ref> He also used the method of [[risk factor]] analysis, and proposed the idea of a [[syndrome]] in the [[diagnosis]] of specific diseases.<ref name=Goodman>{{cite book |first=Lenn Evan |last=Goodman |year=2003 |title=Islamic Humanism |pages=155 |publisher=Oxford University Press |isbn=0195135806}}</ref> When the Black Death ([[bubonic plague]]) reached Al Andalus in the 14th century, Ibn Khatima hypothesized that infectious diseases are caused by small "minute bodies" which enter the human body and cause disease. Another 14th century Andalusian-Arabian physician, [[Islamic medicine#Bacteriology, epidemiology, microbiology|Ibn al-Khatib]] (1313&ndash;1374), wrote a treatise called ''On the Plague'', in which he stated how infectious disease can be transmitted through bodily contact and "through garments, vessels and earrings."<ref name=Syed>Ibrahim B. Syed, Ph.D. (2002). "Islamic Medicine: 1000 years ahead of its times", ''[[The Islamic Medical Association of North America|Journal of the Islamic Medical Association]]'' '''2''', p. 2-9.</ref> In the middle of the 16th century, a famous Italian doctor from [[Florence]] named [[Girolamo Fracastoro]] was the first to propose a theory that these very small, unseeable, particles that cause disease were alive. They were considered to be able to spread by air, multiply by themselves and to be destroyable by fire. In this way he refuted [[Galen]]'s theory of [[Miasma theory of disease|miasms]] (poison gas in sick people). In [[1543]] he wrote a book ''[[De contagione et contagiosis morbis]]'', in which he was the first to promote personal and environmental [[hygiene]] to prevent disease. The development of a sufficiently powerful microscope by [[Anton van Leeuwenhoek]] in [[1675]] provided visual evidence of living particles consistent with a [[germ theory of disease]]. [[Image:Snow-cholera-map.jpg|thumb|right|300px|Original map by Dr. John Snow showing the [[cluster (epidemiology)|cluster]]s of cholera cases in the London epidemic of 1854]] [[John Graunt]], a professional [[haberdasher]] and serious amateur scientist, published ''Natural and Political Observations ... upon the Bills of Mortality'' in [[1662]]. In it, he used analysis of the mortality rolls in [[London]] before the [[Great Plague of London|Great Plague]] to present one of the first [[life tables]] and report time trends for many diseases, new and old. He provided statistical evidence for many theories on disease, and also refuted many widespread ideas on them. [[John Snow (physician)|Dr. John Snow]] is famous for the suppression of an [[1854]] outbreak of [[cholera]] in London's [[Soho]] district. He identified the cause of the outbreak as a public water pump on [[Broadwick Street|Broad Street]] and had the handle removed, thus ending the outbreak. (It has been questioned as to whether the epidemic was already in decline when Snow took action.) This has been perceived as a major event in the history of [[public health]] and can be regarded as the founding event of the science of epidemiology. Other pioneers include Danish physician [[P. A. Schleisner]], who in [[1849]] related his work on the prevention of the epidemic of [[tetanus]] neonatorum on the [[Vestmanna Islands]] in [[Iceland]]. Another important pioneer was [[Hungary|Hungarian]] physician [[Ignaz Semmelweis]], who in [[1847]] brought down infant mortality at a Vienna hospital by instituting a disinfection procedure. His findings were published in [[1850]], but his work was ill received by his colleagues, who discontinued the procedure. Disinfection did not become widely practiced until British surgeon [[Joseph Lister]] 'discovered' antiseptics in [[1865]] in light of the work of [[Louis Pasteur]]. In the early 20th century, mathematical methods were introduced into epidemiology by [[Ronald Ross]], [[Anderson Gray McKendrick]] and others. Another breakthrough was the [[1954]] publication of the results of a [[British Doctors Study]], led by [[Richard Doll]] and [[Austin Bradford Hill]], which lent very strong statistical support to the suspicion that [[tobacco smoking]] was linked to [[lung cancer]]. *[[History of emerging infectious diseases]] == The profession == To date, few [[universities]] offer epidemiology as a course of study at the undergraduate level. Many epidemiologists are [[physicians]], or hold other postgraduate degrees including a [[Master of Public Health]] (MPH), [[Master of Science]] or Epidemiology (MSc.) [[Doctorate]]s include the [[Doctor of Public Health]] (DrPH), [[Doctor of Philosophy]] (PhD), [[Doctor of Science]] (ScD), or for clinically trained physicians, [[Doctor of Medicine]] (MD). In the United Kingdom, the title of 'doctor' is an honorary one conferred to those having attained the professional degrees of [[Bachelor of Medicine and Surgery]] (MBBS or MBChB). As public health/health protection practitioners, epidemiologists work in a number of different settings. Some epidemiologists work 'in the field', i.e., in the community, commonly in a public health/health protection service and are often at the forefront of investigating and combating disease outbreaks. Others work for non-profit organizations, universities, hospitals and larger government entities such as the [[Centers for Disease Control and Prevention]] (CDC), the [[Health Protection Agency]], or the [[Public Health Agency of Canada]]. == The practice == Epidemiologists employ a range of study designs from the observational to experimental and are generally categorized as descriptive, analytic (aiming to further examine known associations or hypothesized relationships), and experimental (a term often equated with clinical or community trials of treatments and other interventions). Epidemiological studies are aimed, where possible, at revealing unbiased relationships between [[Exposure Assessment#Exposure|exposures]] such as alcohol or smoking, [[infections|biological agents]], [[stress (medicine)|stress]], or [[Chemical compound|chemicals]] to [[death|mortality]] or [[morbidity]]. Identifying causal relationships between these exposures and outcomes are important aspects of epidemiology. Modern epidemiologist use [[disease informatics]] as a tool. The term 'epidemiologic triad' is used to describe the intersection of ''Host'', ''Agent'', and ''Environment'' in analyzing an outbreak. ==As 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. It is nearly impossible to say with perfect accuracy how even the most simple physical systems behave beyond the immediate future, much less the complex field of epidemiology, which draws on [[biology]], [[sociology]], [[mathematics]], [[statistics]], [[anthropology]], [[psychology]], and [[policy]]; "[[Correlation does not imply causation]]" is a common theme for much of the epidemiological literature. For epidemiologists, the key is in the term [[inference]]. Epidemiologists use gathered data and a broad range of biomedical and psychosocial theories in an iterative way to generate or expand theory, to test hypotheses, and to make educated, informed assertions about which relationships are causal, and about exactly how they are causal. Epidemiologists Rothman and Greenland emphasize that the "'''one cause - one effect'''" understanding is a simplistic mis-belief. Most outcomes — whether disease or death — are caused by a chain or web consisting of many component causes. ===Bradford-Hill criteria=== In 1965 [[Austin Bradford Hill]] detailed criteria for assessing evidence of causation.<ref name="bh65">{{cite journal |last=Hill |first=A.B. |year=1965 |title=The environment and disease: association or causation? |journal=Proceedings of the Royal Society of Medicine |volume=58 |pages=295–300 |url=http://www.edwardtufte.com/tufte/hill}}</ref> These guidelines are sometimes referred to as the ''Bradford-Hill criteria'', but this makes it seem like it is some sort of checklist. For example, Phillips and Goodman (2004) note that they are often taught or referenced as a checklist for assessing causality, despite this not being Hill's intention <ref>{{cite journal |last=Phillips |first=Carl V. |coauthors=Karen J. Goodman |year=2004 |title=The missed lessons of Sir Austin Bradford Hill |journal=Epidemiologic Perspectives and Innovations |volume=1 |issue=3 |url=http://www.epi-perspectives.com/content/1/1/3 |month=October |doi=10.1186/1742-5573-1-3 |pages=3}}</ref>. 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"<ref name="bh65"/>. #'''Strength''': A small association does not mean that there is not a causal effect.<ref name="bh65"/> #'''Consistency''': Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.<ref name="bh65"/> #'''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.<ref name="bh65"/> #'''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).<ref name="bh65"/> #'''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.<ref name="bh65"/> #'''Plausibility''': A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).<ref name="bh65"/> #'''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 affect on associations" <ref name="bh65"/>. #'''Experiment''': "Occasionally it is possible to appeal to experimental evidence" <ref name="bh65"/>. #'''Analogy''': The effect of similar factors may be considered<ref name="bh65"/>. ===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: <blockquote>"Epidemiology is concerned with the [[Incidence (epidemiology)|incidence]] of disease in populations and does not address the question of the cause of an individual’s disease. This question, sometimes referred to as specific causation, is beyond the domain of the science of epidemiology. Epidemiology has its limits at the point where an inference is made that the relationship between an agent and a disease is causal (general causation) and where the magnitude of excess risk attributed to the agent has been determined; that is, epidemiology addresses whether an agent can cause a disease, not whether an agent did cause a specific plaintiff’s disease."<ref name= green>{{cite book |last= Green |first= Michael D. |coauthors = D. Michal Freedman, and Leon Gordis |title= Reference Guide on Epidemiology |publisher= Federal Judicial Centre |url= http://www.fjc.gov/public/pdf.nsf/lookup/sciman06.pdf/$file/sciman06.pdf |format=PDF |accessdate= 2008-02-03}}</ref></blockquote> 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]]. ==Advocacy== As a [[public health]] discipline, epidemiologic evidence is often used to [[Advocacy|advocate]] both personal measures like diet change and corporate measures like removal of [[junk food]] advertising, with study findings disseminated to the general public in order to help people to make informed decisions about their health. Often the uncertainties about these findings are not communicated well; news articles often prominently report the latest result of one study with little mention of its limitations, caveats, or context. Epidemiological tools have proved effective in establishing major causes of diseases like [[cholera]] and [[lung cancer]] but have had problems with more subtle health issues, and several recent epidemiological results on medical treatments (for example, on the effects of [[hormone replacement therapy]]) have been refuted by later [[randomized controlled trial]]s.<ref>{{cite news |title= Do we really know what makes us healthy? |last= Taubes |first=Gary |work= New York Times |date=2007-09-16 |url=http://www.nytimes.com/2007/09/16/magazine/16epidemiology-t.html |accessdate=2007-09-18}}</ref> ==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.<ref>{{cite conference |last=Smetanin |first=P. |coauthors=P. Kobak |title=Interdisciplinary Cancer Risk Management: Canadian Life and Economic Impacts |conferenceurl=http://www.cancercontrol2005.com |conference=1st International Cancer Control Congress |month=October |year=2005}}</ref><ref>{{cite conference |last=Smetanin |first=P. |coauthors=P. Kobak |title=A Population-Based Risk Management Framework for Cancer Control |conference=The International Union Against Cancer Conference |month=July |year=2006 |conferenceurl= http://www.2006conferences.org/u-index.php |url=http://www.riskanalytica.com/Library/Papers/Population%20Based%20Risk%20Management%20Framework%20for%20Cancer%20Control.pdf |format=PDF}}</ref><ref>{{cite conference |last=Smetanin |first=P. |coauthors=P. Kobak |title=Selected Canadian Life and Economic Forecast Impacts of Lung Cancer |conference=11th World Conference on Lung Cancer |month=July |year=2005 |url=http://www.riskanalytica.com/Library/Papers/Canadian%20Lung%20Cancer%20Abstract%20Jan%202005.pdf |format=PDF}}</ref> Each of these organizations use a population-based health management framework called Life at Risk that combines epidemiological quantitative analysis with demographics, health agency operational research and economics to perform: * ''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). ==Types of studies== {{main|Study design}} ===Case series=== Case-series may refer to the qualititative study of the experience of a single patient, or small group of patients with a similar diagnosis, or to a statistical technique comparing periods during which patients are exposed to some factor with the potential to produce illness with periods when they are unexposed. The former type of study is purely descriptive and cannot be used to make inferences about the general population of patients with that disease. These types of studies, in which an astute clinician identifies an unusual feature of a disease or a patient's history, may lead to formulation of a new hypothesis. Using the data from the series, analytic studies could be done to investigate possible causal factors. These can include case control studies or prospective studies. A case control study would involve matching comparable controls without the disease to the cases in the series. A prospective study would involve following the case series over time to evaluate the disease’s natural history.<ref>{{cite book |last=Hennekens |first=Charles H. |coauthors=Julie E. Buring |year=1987 |title=Epidemiology in Medicine |editor=Mayrent, Sherry L. (Ed.) |publisher=Lippincott, Williams and Wilkins |isbn=978-0316356367}}</ref> The latter type, more formally described as self-controlled case-series studies, divide individual patient follow-up time into exposed and unexposed periods and use fixed-effects poisson regression processes to compare the incidence rate of a given outcome between exposed and unexposed periods. This technique has been extensively used in the study of adverse reactions to vaccination, and has been shown to provide statistical power comparable to that available in cohort studies. ===Case control studies=== Case control studies select subjects based on their disease status. The study population is comprised of individuals that are disease positive. The control group should come from the same population that gave rise to the cases. The case control study looks back through time at potential exposures both populations (cases and controls) may have encountered. A 2x2 table is constructed, displaying exposed cases (A), the exposed controls (B), unexposed cases (C) and the 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). This is equal to (A*D)/(B*C). {| class="wikitable" |- ! ..... ! Cases high ! Controls |- | Exposed low | A | B |- | Unexposed | C prevalence | D |} If the OR is clearly greater than 1, then the conclusion is "those with the disease are more likely to have been exposed," whereas if it is close to 1 then the exposure and disease are not likely associated. If the OR is far less than one, then this suggests that the exposure is a protective factor in the causation of the disease. Case control studies are usually faster and more cost effective than [[cohort studies]], but are sensitive to bias (such as recall bias and selection bias). The main challenge is to identify the appropriate control group; the distribution of exposure among the control group should be representative of the distribution in the population that gave rise to the cases. This can be achieved by drawing a random sample from the original population at risk. This has as a consequence that the control group can contain people with the disease under study when the disease has a high attack rate in a population. ===Cohort studies=== Cohort studies select subjects based on their exposure status. The study subjects should be at risk of the outcome under investigation at the beginning of the cohort study; this usually means that they should be disease free when the cohort study starts. The cohort is followed through time to assess their later outcome status. An example of a cohort study would be the investigation of a cohort of smokers and non-smokers over time to estimate the incidence of lung cancer. The same 2x2 table is constructed as with the case control study. However, the point estimate generated is the Relative Risk (RR) [What is Relative Risk? How is it measured? How can values be interpreted? Link to statistical analysis? Explanation needed], which is the incidence of disease in the exposed group (A/A+B) over the incidence in the unexposed (C/C+D). {| class="wikitable" |- ! ..... ! Case ! Non case ! Total |- | Exposed | A | B | (A+B) |- | Unexposed | C | D | (C+D) |} As with the OR, a RR greater than 1 shows association, where the conclusion can be read "those with the exposure were more likely to develop disease." Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status. Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed. ===Outbreak investigation=== :''For information on investigation of [[infectious disease]] outbreaks, please see [[Outbreak#Outbreak Investigation|outbreak investigation]].'' ==Validity: precision and bias== ===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 error include: poorly worded questions, a misunderstanding in interpreting an individual answer from a particular respondent, or a typographical error during coding. Random error affects measurement in a transient, inconsistent manner and it is impossible to correct for random error. There is random error in all sampling procedures. This is called [[sampling error]]. Precision in epidemiological variables is a measure of random error. Precision is also inversely related to random error, so that to reduce random error is to increase precision. Confidence intervals are computed to demonstrate the precision of relative risk estimates. The narrower the confidence interval, the more precise the relative risk estimate. There are two basic ways to reduce random error in an [[epidemiological study]]. The first is to increase the sample size of the study. In other words, add more subjects to your study. The second is to reduce the variability in measurement in the study. This might be accomplished by using a more accurate 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, unbeknown to you, the [[pulse oximeter]] you are using is set incorrectly and adds two points to the true value each time a measurement is taken. Because the error happens in every instance, it is systematic. Conclusions you draw based on that data will still be incorrect. But the error can be reproduced in the future (eg, by using the same mis-set instrument). A mistake in coding that affects *all* responses for that particular question is another example of a systematic error. The validity of a study is dependent on the degree of systematic error. Validity is usually separated into two components: *[[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]] is one of three types of bias that threatens the internal validity of a study. Selection bias is an inaccurate measure of effect which results from a systematic difference in the relation between exposure and disease between those who are in the study and those who should be in the study. If one or more of the sampled groups does not accurately represent the population they are intended to represent, then the results of that comparison may be misleading. Selection bias can produce either an overestimation or underestimation of the effect measure. It can also produce an effect when none actually exists. An example of selection bias is volunteer bias. Volunteers may not be representative of the true population. They may exhibit exposures or outcomes which may differ from nonvolunteers (eg volunteers tend to be healthier or they may seek out the study because they already have a problem with the disease being studied and want free treatment). Another type of selection bias is caused by non-respondents. For example, women who have been subjected to politically motivated sexual assault may be more fearful of participating in a survey measuring incidents of mass rape than non-victims, leading researchers to underestimate the number of rapes. To reduce selection bias, you should develop explicit (objective) definitions of exposure and/or disease. You should strive for high participation rates. Have a large sample size and randomly select the respondents so that you have a better chance of truly representing the population. ==Journals== A ranked list of journals:<ref name = journals>{{cite web |title = Epidemiologic Inquiry: Impact Factors of leading epidemiology journals |publisher= Epidemiologic.org |url= http://www.epidemiologic.org/2006/10/impact-factors-of-epidemiology-and.html |accessdate= 2008-02-03}}</ref> ===General journals=== * [http://aje.oxfordjournals.org American Journal of Epidemiology] * [http://epirev.oxfordjournals.org Epidemiologic Reviews] * [http://www.epidem.com Epidemiology] * [http://ije.oxfordjournals.org International Journal of Epidemiology] * [http://www.elsevier.com/wps/find/journaldescription.cws_home/505746/description Annals of Epidemiology] * [http://jech.bmj.com Journal of Epidemiology and Community Health] * [http://www.springer.com/10654 European Journal of Epidemiology] * [http://www.ete-online.com Emerging Themes in Epidemiology] * [http://www.epi-perspectives.com Epidemiologic Perspectives and Innovations] * [http://www.eurosurveillance.org Eurosurveillance] ===Specialty journals=== * [http://cebp.aacrjournals.org Cancer Epidemiology Biomarkers and Prevention] * [http://www3.interscience.wiley.com/cgi-bin/jhome/35841 Genetic Epidemiology] * [http://www.elsevier.com/wps/find/journaldescription.cws_home/525472/description Journal of Clinical Epidemiology] * [http://www.blackwellpublishing.com/journal.asp?ref=0269-5022 Paediatric Perinatal Epidemiology] * [http://journals.cambridge.org/action/displayJournal?jid=HYG Epidemiology and Infection] * [http://eu.wiley.com/WileyCDA/WileyTitle/productCd-PDS.html Pharmacoepidemiology and Drug Safety] ==Areas== ===By physiology/disease=== * Infectious disease epidemiology * [[Cardiovascular]] disease epidemiology * [[Cancer]] epidemiology * [[Neuroepidemiology]] * Epidemiology of Aging * Oral/Dental epidemiology * Reproductive epidemiology * [[Obesity]]/[[diabetes]] epidemiology * Renal epidemiology * Injury epidemiology * Psychiatric epidemiology * [[Veterinary]] epidemiology * Epidemiology of [[zoonosis]] * Respiratory Epidemiology * Pediatric Epidemiology * [[Quantitative parasitology]] ===By methodological approach=== * [[Environmental epidemiology]] * [[Economic epidemiology]] * [[Clinical epidemiology]] * [[Conflict epidemiology]] * [[Genetic epidemiology]] * [[Molecular epidemiology]] * [[Nutritional epidemiology]] * [[Social epidemiology]] * Lifecourse epidemiology * Epi methods development / [[Biostatistics]] * [[Meta-analysis]] * [[Spatial epidemiology]] * [[Tele-epidemiology]] * Biomarker epidemiology * Pharmacoepidemiology * Primary care epidemiology * [[Infection control and hospital epidemiology]] * Public Health practice epidemiology * [[Surveillance]] epidemiology ([[Clinical surveillance]]) * [[Disease Informatics]] ==See also== <div style="-moz-column-count:3; column-count:3;"> * [[Age adjustment]] * [[Biostatistics]] * [[Centers for Disease Control and Prevention]] in the United States * [[Centre for Research on the Epidemiology of Disasters (CRED)]] * [[European Centre for Disease Prevention and Control]] * [[E-epidemiology]] * [[Epidemiological methods]] * [[Epi Info]] software program * [[OpenEpi]] software program * [[Hispanic paradox]] * [[List of publications in biology#Epidemiology|Important publications in epidemiology]] * [[Mathematical modelling in epidemiology]] * [[Mendelian randomization]] * [[Study design]] * [[Thousand Families Study, Newcastle upon Tyne]] * [[Whitehall Study]] * [[Epidemiological Transition]] * [[Demographic Transition]] * [[International Society for Pharmacoepidemiology]] * [[palaeoepidemiology]] </div> ==Footnotes== {{reflist}} ==Sources== * [[David Clayton|Clayton, David]] and Michael Hills (1993) ''Statistical Models in Epidemiology'' Oxford University Press. ISBN 0-19-852221-5 :: A thorough introduction to the statistical analysis of epidemiological data, focussing on survival rates - their estimation, analysis and comparison. * Last JM (2001). "A dictionary of epidemiology", 4th edn, Oxford: Oxford University Press. * Morabia, Alfredo. ed. (2004) A History of Epidemiologic Methods and Concepts. Basel, Birkhauser Verlag. Part I. * 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, July 12&ndash;15, 2006 in Washington DC. * Szklo MM & Nieto FJ (2002). "Epidemiology: beyond the basics", Aspen Publishers, Inc. ==External links== * [http://www.hpa.org.uk The Health Protection Agency] * [http://www.biostatsresearch.com/repository/ The Collection of Biostatistics Research Archive] * [http://www.bepress.com/sagmb/ Statistical Applications in Genetics and Molecular Biology] * [http://www.bepress.com/ijb/ The International Journal of Biostatistics] * [http://rairaunak.blogspot.com/search/label/Analytical%20epidemiology Analytical Epidemiology] * [http://bmj.bmjjournals.com/epidem/epid.html BMJ] - Epidemiology for the Uninitiated' (fourth edition), D. Coggon, PHD, DM, FRCP, FFOM, Geoffrey Rose DM, DSC, FRCP, FFPHM, DJP Barker, PHD, MD, FRCP, FFPHM, FRCOG, ''[[British Medical Journal]]'' * [http://www.epidem.com Epidem.com] - ''[[Epidemiology (journal)|Epidemiology]]'' (peer reviewed scientific journal that publishes original research on epidemiologic topics) * [http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=mmed.chapter.631 NIH.gov] - 'Epidemiology' (textbook chapter), Philip S. Brachman, ''[[Medical Microbiology]]'' (fourth edition), US [[National Center for Biotechnology Information]] :* [http://gsbs.utmb.edu/microbook/ UTMB.edu] - 'Epidemiology' (plain format chapter), Philip S. 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