Sierra Acai Company
Sierra Acai Company
Open 7 Days a Week
MonaVie Independent Distributor
Distributor Training
Shop Online
Enroll Now
Buy Wholesale and maintain an Active status for 2 months and we will refund your $39 Distributor Fee
Friends
Buy Wholesale and maintain an Active status for 2 months and we will refund your $39 Distributor Fee
16-September-2008 16:15:08 - Epidemiology Epidemiology is the study of factors affecting the health and illness of populations, and serves as the foundation and logic of interventions made in the interest of public health and preventive medicine. It is considered a cornerstone methodology of public health research, and is highly regarded in evidence-based medicine for identifying risk factors for disease and determining optimal treatment approaches to clinical practice. In the 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. Contents 1 Etymology 2 History 3 The profession 4 The practice 5 As causal inference 5.1 Bradford-Hill criteria 5.2 Legal interpretation 6 Advocacy 7 Population-based health management 8 Types of studies 8.1 Case series 8.2 Case control studies 8.3 Cohort studies 8.4 Outbreak investigation 9 Validity: precision and bias 9.1 Random error 9.2 Systematic error 9.3 Selection bias 10 Journals 10.1 General journals 10.2 Specialty journals 11 Areas 11.1 By physiology/disease 11.2 By methodological approach 12 See also 13 Footnotes 14 Sources 15 External links 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.1 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 for diseases usually found in some places but not in others and epidemic for disease that are seen at some times but not others.2 One of the earliest theories on the origin of disease was that it was primarily the fault of human luxury. This was expressed by philosophers such as Plato3 and Rousseau,4 and social critics like Jonathan Swift.5 In the medieval Islamic world, physicians discovered the contagious nature of infectious disease. In particular, the Persian physician Avicenna, considered a father of modern medicine,6 in The Canon of Medicine 1020s, discovered the contagious nature of tuberculosis and sexually transmitted disease, and the distribution of disease through water and soil.7 Avicenna stated that bodily secretion is contaminated by foul foreign earthly bodies before being infected.8 He introduced the method of quarantine as a means of limiting the spread of contagious disease.9 He also used the method of risk factor analysis, and proposed the idea of a syndrome in the diagnosis of specific diseases.10 When the Black Death bubonic plague reached Al Andalus in the 14th century, Ibn Khatima hypothesized that infectious diseases are caused by small minute bodies which enter the human body and cause disease. Another 14th century Andalusian-Arabian physician, Ibn al-Khatib 1313-1374, wrote a treatise called On the Plague, in which he stated how infectious disease can be transmitted through bodily contact and through garments, vessels and earrings.8 In the middle of the 16th century, a famous Italian doctor from 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 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. Original map by Dr. John Snow showing the clusters of cholera cases in the London epidemic of 1854 Original map by Dr. John Snow showing the clusters 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 to present one of the first life tables and report time trends for many diseases, new and old. He provided statistical evidence for many theories on disease, and also refuted many widespread ideas on them. Dr. John Snow is famous for 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 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 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. Doctorates include the Doctor of Public Health DrPH, Doctor of Pharmacy PharmD, Doctor of Philosophy PhD, Doctor of Science ScD, or for clinically trained physicians, Doctor of Medicine MD. 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 exposures such as alcohol or smoking, biological agents, stress, or chemicals to mortality or morbidity. Identifying causal relationships between these exposures and outcomes are important aspects of epidemiology. Modern epidemiologist use 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.11 These guidelines are sometimes referred to as the Bradford-Hill criteria, but this makes it seem like it is some sort of checklist. For example, Phillips and Goodman 2004 note that they are often taught or referenced as a checklist for assessing causality, despite this not being Hill's intention 12. Hill himself said None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required sine qua non11. Strength: A small association does not mean that there is not a causal effect.11 Consistency: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.11 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.11 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.11 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.11 Plausibility: A plausible mechanism between cause and effect is helpful but Hill noted that knowledge of the mechanism is limited by current knowledge.11 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 11. Experiment: Occasionally it is possible to appeal to experimental evidence 11. Analogy: The effect of similar factors may be considered11. Legal interpretation Epidemiological studies can only go to prove that an agent could have caused, but not that it did cause, an effect in any particular case: Epidemiology is concerned with the incidence of disease in populations and does not address the question of the cause of an individual's disease. This question, sometimes referred to as specific causation, is beyond the domain of the science of epidemiology. Epidemiology has its limits at the point where an inference is made that the relationship between an agent and a disease is causal general causation and where the magnitude of excess risk attributed to the agent has been determined; that is, epidemiology addresses whether an agent can cause a disease, not whether an agent did cause a specific plaintiff's disease.13 In United States law, epidemiology alone cannot prove that a causal association does not exist in general. Conversely, it can be and is in some circumstances taken by US courts, in an individual case, to justify an inference that a causal association does exist, based upon a balance of probability. Advocacy As a public health discipline, epidemiologic evidence is often used to advocate both personal measures like diet change and corporate measures like removal of junk food advertising, with study findings disseminated to the general public in order to help people to make informed decisions about their health. Often the uncertainties about these findings are not communicated well; news articles often prominently report the latest result of one study with little mention of its limitations, caveats, or context. Epidemiological tools have proved effective in establishing major causes of diseases like cholera and lung cancer but have had problems with more subtle health issues, and several recent epidemiological results on medical treatments for example, on the effects of hormone replacement therapy have been refuted by later randomized controlled trials.14 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.151617 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 article: 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.18 The latter type, more formally described as self-controlled case-series studies, divide individual patient follow-up time into exposed and unexposed periods and use fixed-effects poisson regression processes to compare the incidence rate of a given outcome between exposed and unexposed periods. This technique has been extensively used in the study of adverse reactions to vaccination, and has been shown to provide statistical power comparable to that available in cohort studies. Case control studies 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 that both populations cases and controls may have encountered. A 2x2 table is constructed, displaying exposed cases A, exposed controls B, unexposed cases C and unexposed controls D. The statistic generated to measure association is the odds ratio OR, which is the ratio of the odds of exposure in the cases A/C to the odds of exposure in the controls B/D, i.e. OR = A/C / B/D . ..... Cases 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, which is the probability of disease for a person in the exposed group, Pe = A / A+B over the probability of disease for a person in the unexposed group, Pu = C / C+D, i.e. RR = Pe / Pu. ..... Case Non case Total Exposed A B A+B Unexposed C D C+D As with the OR, a RR greater than 1 shows association, where the conclusion can be read those with the exposure were more likely to develop disease. Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status. Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed. Outbreak investigation For information on investigation of infectious disease outbreaks, please see 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:19 General journals American Journal of Epidemiology Epidemiologic Reviews Epidemiology journal International Journal of Epidemiology Annals of Epidemiology Journal of Epidemiology and Community Health European Journal of Epidemiology Emerging themes in epidemiology Epidemiologic Perspectives and Innovations Eurosurveillance Specialty journals Cancer Epidemiology Biomarkers and Prevention Genetic epidemiology journal Journal of Clinical Epidemiology Paediatric Perinatal Epidemiology Epidemiology and Infection 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 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 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 Footnotes ^ Nutter, Jr., F.W. 1999. Understanding the interrelationships between botanical, human, and veterinary epidemiology: the Ys and Rs of it all. Ecosys Health 5 3: 131-40. doi:10.1046/j.1526-0992.1999.09922.x. ^ Changing Concepts: Background to Epidemiology. Duncan Associates. Retrieved on 2008-02-03. ^ The Republic, by Plato. The Internet Classic Archive. Retrieved on 2008-02-03. ^ A Dissertation on the Origin and Foundation of the Inequality of Mankind. Constitution Society. ^ Swift, Jonathan. Gulliver's Travels: Part IV. A Voyage to the Country of the Houyhnhnms. Retrieved on 2008-02-03. ^ Cesk, Cas Lek 1980. The father of medicine, Avicenna, in our science and culture: Abu Ali ibn Sina 980-1037 in Czech. Becka J. 119 1: 17-23. ^ George Sarton, Introduction to the History of Science. cf. Dr. A. Zahoor and Dr. Z. Haq 1997, Quotations From Famous Historians of Science, Cyberistan. ^ a b Ibrahim B. Syed, Ph.D. 2002. Islamic Medicine: 1000 years ahead of its times, Journal of the Islamic Medical Association 2, p. 2-9. ^ Tschanz, David W. August 2003. Arab Roots of European Medicine. Heart Views 4 2. Qatar: The Gulf Heart Association. ^ Goodman, Lenn Evan 2003. Islamic Humanism. Oxford University Press, 155. ISBN 0195135806. ^ a b c d e f g h i j k Hill, A.B. 1965. The environment and disease: association or causation?. Proceedings of the Royal Society of Medicine 58: 295-300. ^ Phillips, Carl V.; Karen J. Goodman October 2004. The missed lessons of Sir Austin Bradford Hill. Epidemiologic Perspectives and Innovations 1 3: 3. doi:10.1186/1742-5573-1-3. ^ Green, Michael D.; D. Michal Freedman, and Leon Gordis. Reference Guide on Epidemiology PDF, Federal Judicial Centre. Retrieved on 2008-02-03. ^ Taubes, Gary 2007-09-16. Do we really know what makes us healthy?, New York Times. Retrieved on 2007-09-18. ^ Smetanin, P.; P. Kobak October 2005. Interdisciplinary Cancer Risk Management: Canadian Life and Economic Impacts in 1st International Cancer Control Congress.. ^ Smetanin, P.; P. Kobak July 2006. A Population-Based Risk Management Framework for Cancer Control PDF in The International Union Against Cancer Conference.. ^ Smetanin, P.; P. Kobak July 2005. Selected Canadian Life and Economic Forecast Impacts of Lung Cancer PDF in 11th World Conference on Lung Cancer.. ^ Hennekens, Charles H.; Julie E. Buring 1987. in Mayrent, Sherry L. Ed.: Epidemiology in Medicine. Lippincott, Williams and Wilkins. ISBN 978-0316356367. ^ Epidemiologic Inquiry: Impact Factors of leading epidemiology journals. Epidemiologic.org. Retrieved on 2008-02-03. Sources 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 12-15, 2006 in Washington DC. Szklo MM Nieto FJ 2002. Epidemiology: beyond the basics, Aspen Publishers, Inc. External links The Health Protection Agency The Collection of Biostatistics Research Archive Statistical Applications in Genetics and Molecular Biology The International Journal of Biostatistics Analytical Epidemiology BMJ - Epidemiology for the Uninitiated' fourth ion, D. Coggon, PHD, DM, FRCP, FFOM, Geoffrey Rose DM, DSC, FRCP, FFPHM, DJP Barker, PHD, MD, FRCP, FFPHM, FRCOG, British Medical Journal Epidem.com - Epidemiology peer reviewed scientific journal that publishes original research on epidemiologic topics NIH.gov - 'Epidemiology' textbook chapter, Philip S. Brachman, Medical Microbiology fourth ion, US National Center for Biotechnology Information UTMB.edu - 'Epidemiology' plain format chapter, Philip S. Brachman, Medical Microbiology Monash Virtual Laboratory - Simulations of epidemic spread across a landscape v d e Biomedical research: Study designs / Design of experiments Overview Clinical trial Academic clinical trials Controlled study EBM I to II-1; A to B Randomized controlled trial Blind experiment, Open-label trial Observational study EBM II-2 to II-3; B to C Cross-sectional study vs. Longitudinal study Cohort study Retrospective cohort study, Prospective cohort study Case-control study Nested case-control study Case series Case study/Case report Epidemiology/ methods occurrence: Incidence Cumulative incidence - Prevalence Point prevalence, Period prevalence association: Relative risk - Odds ratio - Hazard ratio - Attributable risk other: Virulence - Infectivity - Mortality rate - Morbidity - Case fatality - Sensitivity and specificity v d e Influenza Influenza Research - Vaccine - Avian influenza - Treatment - Genome sequencing - Season Influenza viruses Orthomyxoviridae - Influenza A - Influenza B - Influenza C Subtypes of Influenza A virus H1N1 - H1N2 - H2N2 - H3N1 - H3N2 - H3N8 - H5N1 - H5N2 - H5N3 - H5N8 - H5N9 - H7N1 - H7N2 - H7N3 - H7N4 - H7N7 - H9N2 - H10N7 H5N1 Genetic structure - Transmission and infection - Global spread - Clinical Trials - Human mortality Antiviral drugs Arbidol - adamantane derivatives Amantadine, Rimantadine - neuraminidase inhibitors Oseltamivir, Peramivir, Zanamivir Experimental Peramivir Influenza vaccines FluMist - Fluzone Influenza pandemics Asian Flu - Hong Kong Flu - Spanish flu - Fujian flu - Pandemic Severity Index Outbreaks of Avian influenza Croatia 2005 - India 2006 - UK 2007 - West Bengal 2008 Influenza in non-human mammals Canine influenza - Equine influenza 2007 Australian outbreak - Swine flu Retrieved from http://en..org/wiki/Epidemiology Categories: Epidemiology | Demography | Public health Views Article Discussion this page History Personal tools Log in / create account Navigation Main page Contents Featured content Current events Random article Search Go Search Interaction Community portal Recent changes Contact Donate to Help Toolbox What links here Related changes Upload file Special pages Printable version Permanent link Cite this page Languages العربية Bosanski Català Česky Dansk Deutsch Ελληνικά Español Esperanto Ù?ارسی Français Hrvatski Bahasa Indonesia Italiano עברית Lietuvių Magyar Nederlands ‪Norsk bokmÃ¥l‬ Polski Português Română РуÑ?Ñ?кий SlovenÄ?ina SlovenÅ¡Ä?ina СрпÑ?ки / Srpski Srpskohrvatski / СрпÑ?кохрватÑ?ки Suomi Svenska தமிழà¯? ไทย Tiếng Việt УкраїнÑ?ька Walon ä¸æ–‡ This page was last modified on 15 August 2008, at 17:02
39 Reasons to Drink Acai Juice Every Day
What is MonaVie - Watch the 8-minute video
Discovering MonaVie Video
The Power of You Video
Effects of MonaVie Active on Antioxidant Capacity in Humans
Log into your Wholesale MonaVie Account
So many of us do not eat a balanced diet, get enough sleep, have too much stress, or are impacted with toxins and pollutants. Drinking 2 ounces of MonaVie twice a day will help your body detoxify as well as build your immune system. Its the smartest thing you can do for yourself, so start today. Buying MonaVie through our company guarantees you support 7 days a week and, if you would like to share MonaVie with your family and friends we will guide you from start to finish.
1. Click on Enroll Now (30 - 55% off retail price)
2. Pay $39 for your Wholesale ID number.
3. NO minimum order required.
4. MonaVie is delivered to your door in 3 to 5 days.