Dictionary of Epidemiology free download

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Published Date:11-07-2017
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About This Dictionary (Advice on how best to use it) Alphabetical Listing The alphabetical listing is a guide to the placement of entries and follows a letter-by-letter sequence of headwords without regard to word spacing, hyphens, or apostrophes. For example: Case classification Case-cohort study Case collateral Case-comparison study Case-control study Case-crossover study Case definition Case fatality rate Headwords Headwords (terms) are in BOLD SMALL CAPITALS. If there are several varieties of a concept, such as bias, all are listed alphabetically under the headwor BIAS;d but when customary epidemiological usage places an adjective before the headword, the alphabetical listing is usually a cross-reference to the entry where the definition is to be found. Thus BIAS, CONFOUNDING leads the reader to the definition under CONFOUNDING BIAS. However, terms on the periphery of epidemiology are mostly located together under the pertinent headword. For example, most of those associated with costs appear under COST. When several logical options were feasible, we avoided the automatic “solution” and chose the one we deemed most practical for the reader’s mind and needs. Compound Phrases Epidemiology has many compound phrases, not all of which have an obvious headword. Thus rare-disease assumption is found under RARE-DISEASE ASSUMPTION, not under disease. Bibliographic Citations In the previous, fifth edition all citations of articles and books made in the definitions and comments on terms were moved to the back of the book, and this is again the case now (see pages 307–343). This decision allows to avoid repetition—most books and articles are cited several times—, and to present the definition and comments without the interference of the full references. xixAbout This Dictionary xx Importantly, most citations are just meant to invite or help the reader follow up on some of the other meanings, dimensions, levels, or implications of the definition and related ideas. They just aim to evoke or suggest further avenues of thought. Bibliographic citations need not be, cannot be, and are not exhaustive. None is meant to endorse the definition or the comment. No citation is meant either to imply that an article or book is the original or most authoritative source on the issue. Many cited books will offer additional ideas on the term and on related terms and concepts; this is particularly the case for definitions of terms on epidemiological methods. Some fundamental, auxiliary, or advanced books are cited either in the term definition (and then appear in the References, pages 307–343) or in the Bibliography (please see pages 301–306). Once again, it would be impossible and illogical to pretend to cite all books that might be relevant to a definition or comment. While we trust that the books, articles, and websites cited are indeed relevant, we are sure that many which are also relevant could not be cited, for obvious reasons. Cross-References Often, a definition—or part of the text commenting a TERM—includes a cross-reference to another term to be found elsewhere in the dictionary: such cross-references are in nonbold small capitals. Cross-references are again just meant to suggest—to invite the reader to take a look elsewhere in the book. If a highly related term is nearby (i.e., because of alphabetic order), the text is unlikely to mention it in nonbold small capitals; the eye will catch it (wide visual scopes: an undervalued strength of the printed book in expert hands). Cross-references need not be exhaustive and could never be so; if they were they’d become a nightmare labyrinth, not a seductive suggestion. They are not exhaustive: a term may have a definition in the book and yet not be marked in nonbold small capitals in the definition of another term; the two most common reasons for that are to avoid a baroque or visually confusing sentence, and, foremost, to elude gardens of forking paths. For the same intellectual, cognitive, aesthetic, and practical reasons, cross-references should never be automatically marked, not even as hyperlinks in a digital medium. To mark all cross-references and to do so automatically would be equally easy as wrong. Other important words and phrases mentioned in a definition are sometimes italicized to draw attention to their special significance in relation to that particular term or to signify the absence of a cross-reference. Definitions and Discussions A brief definition generally appears at the beginning of each entry. This usually follows dictionary conventions and is a statement of the meaning rather than an explanation. An explanation will often follow, and then some remarks, a brief discussion, and, occasionally, illustrative examples and a comment on the provenance of the term. Explicit admonitions and cautionary notes about use and abuse of terms have been kept to a minimum. xxi About This Dictionary Acronyms There is no generally agreed way to pronounce or print acronyms, but some usages are commonly agreed. Thus, for instance, WHO is always spelled out in pronunciation (“W-H-O”), never pronounced “Hoo.” By contrast, AIDS is always pronounced “Aids” and never spelled out. Acronyms can be made up of the initial letters of the words on which they are based, such as NIOSH (National Institute for Occupational Safety and Health, pronounced “Ny-osh”), or on parts of words or abbreviations, such as QUANGO (quasi-autonomous nongovernmental organization). Acronyms can survive after the organization they signify has changed names; for example, UNICEF originally stood for United Nations International Children’s Emergency Fund. This UN agency is now called the United Nations Children’s Fund, but the acronym is unchanged. Generally, all the letters of an acronym are upper case, even when not all the letters are initial letters of individual words; so, ANOVA (analysis of variance) is spelled as here, not AnOVa, although Anova is sometimes seen. During the past 20 years the Internet has increasingly allowed to reliably identify the meaning of many acronyms. Hence, in the present edition we suppressed almost all acronyms included in previous editions. Thus, for instance, SER was suppressed; in some previous editions it referred to the Society for Epidemiologic Research ( www.epiresearch.org) . SEER (Surveillance, Epidemiology and End Results, a program of the U.S. National Cancer Institute) was also deleted. Similarly, ACE (American College of Epidemiology ht , tp://acepidemiology.org) was also removed. By the way, with Sander Greenland we also decided against mentioning that ACE also refers to a statistical method called Alternating Conditional Expectations, since we did not wish to burden the dictionary with entries for every special statistical technique. Many such choices had to be made to avoid a too extensive, expensive, and specialized book. We hope we struck a balance most of the time. Acronyms and full names of studies (e.g., NHANES, Framingham Study) were also deleted in this edition. We did retain and update acronyms of guidelines of worldwide importance (e.g., CONSORT; PRISMA; STROBE; TREND). Evolving Language and Changing Usage English and its common, technical, and scientific varieties are rapidly evolving, as now do many societies, cultures, and epistemic communities worldwide—foremost, perhaps, the Internet and the virtual / rglobal eal society. A simple example: the meaning of E-book in the first five editions (from 1983 to 2008: 25 years) became obsolete; it is no longer a method of recording encounters used in primary medical care (but do let me preserve the 55-year-old article for us all: “organized 31 curiosity”). And, of course, a completely new meaning emerged with electronic publishing. In addition to E-book, Hollerith cards, punch card, spreadsheet, and jargon are also examples of words that we deleted to make place for terms more current, relevant, and closely related to epidemiology.About This Dictionary xxii Speaking of which, and of some human leanings that never change much, I am afraid the definition of jargon by Edmond Murphy, reproduced by John Last in the preface of earlier editions of this dictionary, is still worth keeping in mind: “Obscure and/or pretentious language, circumlocutions, invented meanings, and 32 pomposity delighted in for its own sake.” I hope we avoided much of this. Words such as gay, computer, and Internet were still defined in the fourth edition (of 2001), but no longer in the fth fi (2008). We did not remove PLAs (persons living with AIDS). We aimed at respecting all scientific, democratic, and reasonable sensitivities, and we found no major difficulties in doing so while at the same time maximizing precision and minimizing ambiguity. Scope A couple of additional remarks on the scope of this work. Epidemiology continues to expand and bud off subspecialties, each of which has, in part, its own vocabulary, creating its own neologisms. Epidemiology regularly absorbs, transforms, embodies, codes for, and expresses terms from other fields: as a living organism, it actively metabolizes language. We have tried to capture and present the essential terminology of clinical, environmental, social, genetic, and molecular epidemiology as well as core terms from subspecialties of epidemiology such as pediatric epidemiology, pharmacoepidemiology, and some others. Of course, we did not wish to lengthen the book too much, as mentioned. Thus, the reader seeking definitions of highly specialized terms will need to go to the specialty books, articles, and websites. As mentioned in several term entries below, epidemiology studies all types of diseases, health-related events, and health determinants in populations; hence, this dictionary includes just some examples of definitions for a few disease-based branches or subspecialties of epidemiology. Some common everyday words are defined and discussed because they often occur in epidemiological articles with a distinct meaning, because they are particularly relevant to conducting health research, or because their sense is not always clear in these contexts to people whose first language is not English. Words and expressions from disciplines that overlap or interact with epidemiology are defined here for the same reasons. What Is New? Thorough as all past revisions have been, this sixth edition contains the most profound changes that the dictionary ever experienced. The fundamental reason is that a methodological “revolution” is ongoing. It is deeply changing how we conceive epidemiological and clinical research, and how we assess the validity of findings, to say the least. It is having an immense impact on the production of scientific evidence in the health sciences, and on most policies, programs, services, xxiii About This Dictionary and products in which such evidence is used, affecting thousands of institutions, organizations, and companies, and millions of people. There is no quick way to explain this. But a real way to understand what is happening is to read the fundamental changes that this book contains, and if you wish, to keep on reading the suggested references. The new ideas partly or completely change basic concepts such as, for example, risk, rate, risk ratio, attributable fraction, bias, selection bias, confounding, residual confounding, interaction, cumulative and density sampling, open population, test hypothesis, null hypothesis, causal null, causal inference, Berkson’s bias, Simpson’s paradox, frequentist statistics, generalizability, representativeness, missing data, standardization, or overadjustment. They are also reflected in recent and new terms as collider, M-bias, causal diagram, backdoor (biasing path), instrumental variable, negative controls, inverse probability weighting, identifiability, transportability, positivity, ignorability, collapsibility, exchangeable, g-estimation, marginal structural models, risk set, immortal time bias, Mendelian randomization, nonmonotonic, counterfactual outcome, potential outcome, sample space, or false discovery rate. The scope of this sixth edition has also broadened with definitions of methods for clinical research, public health practice, genetics, and the social sciences. There are new terms from biostatistics, clinical epidemiology, preventive medicine, health promotion, and behavioral sciences; environmental, life course, and social epidemiology; genetic and molecular epidemiology; health economics; and bioethics. In addition, scientific terms relevant to professionals in clinical medicine, public health, and the other health, life, and social sciences are included. Pledge We have tried to be highly rigorous intellectually and formally, clear, stimulating, systematic, thorough, and to a reasonable extent explicit and transparent. Yet, when necessary, these goals gave precedence to the primary objective: that the dictionary should be accurate, practical, and plural, and that it should encourage critical and creative thinking. I will amend any errors that are brought to my attention, and I will be glad to consider with all respect any suggestions that are rigorous, relevant, constructive, and potentially useful to most readers. M. P. Contributors to the Fourth, Fifth and Sixth Editions Preben Aavitsland John Bailar III Kristiansand, Norway Chicago, Illinois, USA Ibrahim Abdelnour Michael Baker Damascus, Syria Wellington, New Zealand Theo Abelin Mauricio Barreto Berne, Switzerland Salvador, Bahia, Brazil Aman-Oloniyo Abimbola Aluísio J. D. Barros Abuja, Nigeria Pelotas, Brazil Victor Abraira Xavier Basagaña Madrid, Spain Barcelona, Catalonia, Spain Joe Abramson Olga Basso Jerusalem, Israel Aarhus, Denmark Anders Ahlbom Renaldo Battista Stockholm, Sweden Montreal, Quebec, Canada Neal Alexander Robert Beaglehole London, England, UK Auckland, New Zealand Mohamed Farouk Allam Stella Beckman Córdoba, Spain Berkeley, California, USA Álvaro Alonso Solomon Benatar Minneapolis, Minnesota, USA Cape Town, South Africa Jordi Alonso Fernando G. Benavides Barcelona, Catalonia, Spain Barcelona, Catalonia, Spain Douglas Altman Yoav Ben-Shlomo London, England, UK Bristol, England, UK Janet Byron Anderson Roger Bernier Rocky River, Ohio, USA Atlanta, Georgia, USA Kunio Aoki Silvina Berra Nagoya, Japan Córdoba, Argentina Onyebuchi Arah Raj Bhopal Los Angeles, USA Edinburgh, Scotland, UK Haroutune Armenian Nicholas Birkett Baltimore, Maryland, USA, Ottawa, Ontario, Canada and Yerevan, Armenia Jordi Blanch Shabnam Asghari Girona, Catalonia, Spain St. John’s, Canada Dankmar Böhning Mary Jane Ashley Berlin, Germany Toronto, Ontario, Canada Jean-François Boivin Aleksei Baburin Montreal, Quebec, Canada Tallinn, Estonia xxvContributors xxvi Francisco Bolúmar Silvia Declich Madrid, Spain Rome, Italy David Boniface Del De Hart London, England, UK Saginaw, Michigan, USA Knut Borch-Johnsen Julia del Amo Horsholm, Denmark Madrid, Spain Ric Bouvier N. S. Deodhar Kew, Victoria, Australia Pune, India Annette Braunack-Mayer Bob Douglas Adelaide, South Australia, Australia Canberra, ACT, Australia Clive Brown Gerard Dubois Port of Spain, Trinidad and Tobago Amiens, France Ross Brownson John Duffus St. Louis, Missouri, USA Edinburgh, Scotland, UK Jim Butler Kate Dunn Canberra, ACT, Australia Keele, Newcastle, UK Lee Caplan Shah Ebrahim Atlanta, Georgia, USA London, England, UK Iain Chalmers Matthias Egger Oxford, England, UK Bern, Switzerland Yue Chen Emon Elboudwarej Ottawa, Ontario, Canada Berkeley, California, USA Bernard Choi Mark Elwood Ottawa, Ontario, Canada Melbourne, Victoria, Australia Stella Chungong Leon Epstein Geneva, Switzerland Jerusalem, Israel Mike Clarke Caroline H.D. Fall Oxford, England, UK Southampton, England, UK Tammy Clifford Alvan Feinstein London, Ontario, Canada New Haven, Connecticut, USA Alison Cohen Charles du V. Florey Berkeley, California, USA Sidmouth, England, UK Philip Cole Stephen Francis Birmingham, Alabama, USA Berkeley, California, USA Deborah Cook Erica Frank Hamilton, Ontario, Canada Atlanta, Georgia, USA Doug Coyle Rayner Fretzel-Behme Ottawa, Ontario, Canada Bremen, Germany Andrew Creese Gary Friedman Geneva, Switzerland Oakland, California, USA Giovanna Cruz Ana M. Garcia Berkeley, California, USA Valencia, Spain Nancy Czaicki B. Burt Gerstman Berkeley, California, USA San Jose, California, USA George Davey Smith Milena Gianfrancesco Bristol, England, UK Berkeley, California, USA xxvii Contributors Alan Gibbs D’ Arcy Holman Manchester, England, UK Perth, Western Australia, Australia Philippe Grandjean Ernest Hook Odense, Denmark Berkeley, California, USA Nicola Grandy Jeffrey House Paris, France San Francisco, California, USA Sander Greenland John P. A. Ioannidis Los Angeles, California, USA Stanford, California, USA, and Duane Gubler Ioannina, Greece Atlanta, Georgia, USA Masako Iwanaga Charles Guest Tokyo, Japan Canberra, ACT, Australia Konrad Jamrozik Tee Guidotti Perth, Western Australia, Australia Washington, DC, USA Mohsen Janghorbani Gordon Guyatt Isfahan, Iran Hamilton, Ontario, Canada Tom Jefferson Matti Hakama Camberley, England, UK Tampere, Finland Milos Jenicek Timo Hakulinen Rockwood, Ontario, Canada Helsinki, Finland Rose Kagawa Philip Hall Berkeley, California, USA Winnipeg, Manitoba, Canada Deb Karasek Philip Hannaford Berkeley, California, USA Aberdeen, Scotland, UK Wilfried Karmaus Susan Harris Columbia, South Carolina, USA Boston, Massachusetts, USA Suraj Khanal Maureen Hatch Norfolk, USA New York, New York, USA Mustafa Khogali Brian Haynes Beirut, Lebanon Hamilton, Ontario, Canada Daniel Kim Miguel A. Hernán Boston, Massachusetts, USA Boston, Massachusetts, USA Maurice King Ildefonso Hernández Leeds, England, UK Maó, Menorca, and Alacant, Spain Tord Kjellström Sonia Hernández-Diaz Auckland, New Zealand Boston, Massachusetts, USA Rosemary Korda Andrew Herxheimer Canberra, ACT, Australia Edinburgh, Scotland, UK Daniel Kotz Basil Hetzel Maastricht, Netherlands Adelaide, South Australia, Australia Dan Krewski Alan Hinman Ottawa, Ontario, Canada Decatur, Georgia, USA Nino Künzli Walter Holland Basel, Switzerland London, England, UK Diana Kuh María-Graciela Hollm-Delgado London, England, UK Montreal, Quebec, CanadaContributors xxviii Esa Läärä Jaime Miranda Oulu, Finland Lima, Perú Chandrakant Lahariya, David Moher New Delhi, India Ottawa, Ontario, Canada Stephen Lambert Alfredo Morabia Melbourne, Victoria, Australia New York, New York, USA Henk Lamberts Salah Mostafa Amsterdam, Netherlands Cairo, Egypt Ron Laporte Kiumarss Nasseri Pittsburgh, Pennsylvania, USA Santa Barbara, California, USA John Last Ana Navas-Acien Ottawa, Ontario, Canada Baltimore, Maryland, USA Diana Lauderdale Norman Noah Chicago, Illinois, USA London, England, UK Duk-Hee Lee Patricia O’Campo Daegu, Korea Baltimore, Maryland, USA Abby Lippman Jørn Olsen Montreal, Quebec, Canada Aarhus, Denmark Irvine Loudon Nigel Paneth Oxford, England, UK Ann Arbor, Michigan, USA Tapio Luostarinen Toni Patama Oulu, Finland Helsinki, Finland Shi Luyan Skip Payne Wuhan, China Tiffin, Ohio, USA Outi Lyytikäinen Neil Pearce Helsinki, Finland London, England, UK, and Wellington, Johan Mackenbach New Zealand Rotterdam, Netherlands Susana Perez-Gutthann Ahmed Mandil  Barcelona, Catalonia, Spain Alexandria, Egypt Diana Petitti Arturo Martí-Carvajal Sierra Madre, California, USA Valencia, Venezuela Aileen Plant John McCallum Perth, Western Australia, Australia Canberra, ACT, Australia Miquel Porta Ian McDowell Barcelona, Catalonia, Spain Ottawa, Ontario, Canada Eero Pukkala Robert McKeown Helsinki, Finland Columbia, South Carolina, USA Hedley Quintana Rick McLean Stockholm, Sweden Melbourne, Victoria, Australia Zoran Radovanovic Tony McMichael Belgrade, Yugoslavia Canberra, ACT, Australia Mati Rahu Curtis Meinert Tallinn, Estonia Baltimore, Maryland, USA Gloria Ramirez Ricard Meneu Santiago de Chile, Chile Valencia, Spain Arindam Ray Juan Merlo New Delhi, India Lund, Sweden xxix Contributors Jose Rigau Bob Spasoff Atlanta, Georgia, USA Ottawa, Ontario, Canada Chris Rissell Hans Storm Sydney, New South Wales, Australia Copenhagen, Denmark Stefan Röder David Streiner Leipzig, Germany Hamilton, Ontario, Canada Alfonso Rodriguez-Morales Ezra Susser Pereira, Colombia New York, New York, USA Ken Rothman Mervyn Susser Boston, Massachusetts, USA New York, New York, USA Michael Ryan Kazuo Tajima Geneva, Switzerland Nagoya, Japan Lucie Rychetnik José A. Tapia Sydney, New South Wales, Australia Ann Arbor, Michigan, USA Rodolfo Saracci Michel Thuriaux Lyon, France, and Pisa, Italy Geneva, Switzerland Seppo Sarna Aurelio Tobías Helsinki, Finland Barcelona, Catalonia, Spain David Savitz Bernard Toma New York, New York, USA Maisons-Alfort, France Pathom Sawanpanyalert Karen Trollope-Kumar Bangkok, Thailand Hamilton, Ontario, Canada Sabine Schipf Elena Tschishowa Hamburg, Germany Berlin, Germany Fran Scott Jan Vandenbroucke Hamilton, Ontario, Canada Utrecht, Netherlands Pippa Scott Tyler VanderWeele Bern, Switzerland Boston, Massachusetts, USA Andreu Segura Frank van Hartingsveld Barcelona, Catalonia, Spain Amsterdam, Netherlands Jack Siemiatycki Hector Velasco Laval, Quebec, Canada Baltimore, Maryland, USA, and Gustavo A. Silva Cuernavaca, Mexico Geneve, Sweden Sally Vernon Isabel dos Santos Silva Houston, Texas, USA London, England, UK Cesar G. Victora Chitr Sitthi-Amorn Pelotas, Brazil Bangkok, Thailand Ester Villalonga Björn Smedby Maó, Menorca, Spain, and Uppsala, Sweden Boston, Massachusetts, USA Robbie Snyder Paolo Vineis Berkeley, California, USA London, England, UK Cynthia Sonich-Mullin Anna-Maija Virtala Paris, France Helsinki, Finland Colin Soskolne Vasily Vlassov Edmonton, Alberta, Canada Moscow, RussiaContributors xxx Divya Vohra Don Wigle Berkeley, California, USA Ottawa, Ontario, Canada Douglas L. Weed Allen Wilcox Salt Lake City, Utah, USA Research Triangle Park, North Paul Wesson Carolina, USA Berkeley, California, USA Christopher P. Wild Denise Werker Lyon, France Ottawa, Ontario, Canada Kathleen Winter Claes-Göran Westrin Berkeley, California, USA Uppsala, Sweden Michael Wolfson Amanda Wheeler Ottawa, Ontario, Canada Berkeley, California, USA Caroline Wood Frank White London, England, UK Karachi, Pakistan Hiroshi Yanagawa Kerr White Jiichi, Japan Charlottesville, Virginia, USA Kue Young Martin White Winnipeg, Manitoba, Canada NewcastleUK ,  P. Auke Wiegersma Groningen, NetherlandsReferences 1. Berkman Center for Internet & Society at Harvard University. Internet Monitor 2013. Reflections on the Digital World. Boston, December 11, 2013. ht tp://cyber. law.harvard.edu/publications/2013/reflections_on_the_digital_world 2. http://en.wikipedia.org/wiki/Android_%28operating_system%29Market_share; http://blogs.wsj.com/corporate-intelligence/2014/02/19/facebooks-whatsapp-price- tag-19-billion; http://investor.fb.com/releasedetail.cfm?ReleaseID=821954. 3. Evans JP, Mesliin EM, Marteau TM, et al. Genomics: deflating the genomic bubble. Science 2011; 331: 861–862. 4. Porta M, Hernández-Aguado I, Lumbreras B, et al. “Omics” research, monetization of intellectual property and fragmentation of knowledge: can clinical epidemiology strengthen integrative research? J Clin Epidemiol 2007; 60: 1220–1225. 5. Trouble at the lab: unreliable research. The Economist 2013 (Oct 1 ht9).  tp://www. economist.com/node/21588057/comments. 6. Norman G. 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Porta M. Do we really need “real” epidemiological scientific meetings?. Eur J Epidemiol 2003; 18: 101–103. 15. Amsterdamska O. Demarcating epidemiology. Science Technology & Human Values 2005; 30: 17–51. 16. Rothman KJ, Greenland S, Lash TL, eds. Modern Epidemiology. 3rd. ed. Philadel phia: Lippincott-Raven; 2008. xxxiReferences xxxii 17. Miettinen OS. Theoretical Epidemiology. Principles of Occurrence Research in Medicine. New York: Wiley; 1985. 18. Greenland S, ed. Evolution of Epidemiologic Ideas. Annotated Readings on Concepts and Methods. Chestnut Hill, MA: Epidemiology Resources; 1987. 19. Susser M, Stein Z. Eras in Epidemiology. The Evolution of Ideas. New York: Oxford University Press; 2009. 20. Almeida-Filho N. La Ciencia Tímida. Ensayos de Deconstrucción de la Epidemiología. Buenos Aires: Lugar; 2000. 21. Morabia A. A History of Epidemiologic Methods and Concepts. Basel: Birkhäuser / Springer; 2004. 22. Holland WW, Olsen J, du V Florey C, eds. The Development of Modern Epidemiology. Personal Reports from Those Who Were There. New York: Oxford University Press; 2007. 23. Porta M, Alvarez-Dardet C. Epidemiology: bridges over (and across) roaring levels Editorial. J Epidemiol Community Health 1998; 52: 605. 24. Bolúmar F, Porta M. Epidemiologic methods: beyond clinical medicine, beyond -epi demiology. Eur J Epidemiol 2004; 19: 733–735. 25. Fine P, Goldacre B, Haines A. Epidemiology—a science for the people. Lancet 2013; 381: 1249–1252. 26. Geneletti SG, Gallo V, Porta M, et al. Assessing causal relationships in geno - mics: From Bradford-Hill criteria to complex gene-environment interactions and directed acyclic graphs. Emerg Themes Epidemiol 2011; 8: ht 5. tp://www.ete-online. com/content/8/1/5 27. Rockström J, Steffen W, Noone K, et al. A safe operating space for humanity. Nature 2009; 461: 472–475. 28. Feinberg AP. Phenotypic plasticity and the epigenetics of human disease. Nature 2007; 447: 433–440. 29. Gluckman PD, Hanson MA, Bateson P, et al. Towards a new developmental synthe - sis: adaptive developmental plasticity and human disease. Lancet 2009; 373: 1654–1657 . 30. http://www.termcat.cat/en . 31. Eimerl TS. Organized curiosity. A practical approach to the problem of keeping r - e cords for research purposes in general practice. J Coll Gen Pract 1960; 3: 246-252; and J Coll Gen Pract 1961; 4: 628-636. 32. Murphy EA. The Logic of Medicine. Baltimore: Johns Hopkins University Press; 1976. p. 16. A tementaab The process of reducing or minimizing public health or other types of dangers and nuisances, usually supported by regulation or legislation; e.g., noise abatement, pollution abatement. abC aoappr Ch “Abstinence, Be faithful, use Condoms.” ABC strategies are promoted to combat, foremost, infection with HIV and the HIV / AIDS pandemic, as well as other sexually transmitted diseases. Pragmatic sex education policies that aim at balancing abstinence-only sex education by including education about safe sex and birth control methods. Excessive emphasis on ABC strategies may marginalize broader, integrated programs. See also CNN approach. tionabor tear The estimated annual number of abortions per 1000 women of reproductive age (usually defined as ages 15–44). tionabor tioar The estimated number of abortions per 100 live births in a given year. absCaiss The distance along the horizontal coordinatex , or axis, of a point P from the vertical or y axis of a graph. See also axis; graph; ordinate. absenteeism Habitual failure to appear for work or other regular duty. Contrast with sickness absence and presenteeism. absolute C effe t The effect of an exposure (expressed as the difference between rates, 1-3 proportions, means), of the outcome, etc., as opposed to the ratio of these measures . See also risk difference. absolute tyverpo level Income level below which a minimum nutritionally adequate diet 4 plus essential nonfood requirements is not affordable The amount of income a person, . family, or group needs to purchase an absolute amount of the basic necessities of life. See also relative poverty level. absolute tear See rate. absolute risk () ar The probability of an event (usually adverse, but it may also be beneficial) in a closed population over a specified time interval. The number of events 1,5-8 in a group divided by the total number of subjects in that group T.his usage presumes the population is a cohort. AR is not a synonym of attributable fraction, excess risk, or risk difference. See also risk. absolute risk C in serea (ari) The absolute risk of adverse events in the exposed or treatment group (ART) minus the absolute risk of events in the control group (ARC): ARI = ART–ARC. Same as risk difference. Also, the proportion of treated persons who experience an adverse event minus the proportion of untreated persons who experience the event. See also number needed to harm. 1Absolute risk reduction (ARR) 2 absolute risk C redu tion (ARR) The absolute risk of events in the control group (ARC) minus the absolute risk of events in the exposed or treatment group (ART): ARR = ARC–ART. The negative of the risk difference. Also, the proportion of untreated persons who experience an adverse event minus the proportion of treated persons who experience this event. The reciprocal of the ARR is the number needed to treat (NNT). The ARR is one measure of the strength of an association. It varies with the underlying risk of an event; e.g., it becomes smaller when event rates are low. The ARR is higher and the NNT lower 9,10 in groups with higher absolute risks See also . Hill’s considerations for causation; measure of association; probability of causation; relative risk reduction. a CCtedaeler ailuref -time model A model for survival analysis that models the relation between exposure (or treatment) and survival time. For example, if the probability of being alive at time t is S (t) under no exposure, then the probability for exposed 0 individuals is St ()= St (/ γ) 0 where γ quantifies how much the survival expands (or contracts) under exposure. Thus, if γ is 0.25, the survival time would be four times longer than under conditions of nonexposure. Contrast with proportional hazards model, which models the relation 1,7,11 between exposure and the hazard. a CCableept risk Risk that appears tolerable to some group. Risk that has minimal or 12-14 long-term detrimental effects or for which the benefits outweigh the hazar See ds. also clinical decision analysis; health technology assessment. a CCanC ept e amplings (Syn: stop-or-go sampling) Sampling method that requires division of the “universe” population into groups or batches as they pass a specified time point (e.g., age) followed by sampling of individuals within the sampled groups. 15 a CCess Potential and actual utilization of health care services. The usage of health services, influenced by predisposing factors (e.g., age, health beliefs), enabling factors (e.g., family support, health insurance), and need (perceived and actual need for health 16-19 care services). The perceptions and experiences of people as to their ease in reaching health services or health facilities in terms of location, time, and ease of approach. See also gatekeeper; health equity; sickness “career.” a CCyessor oirvreser A reservoir that contributes to the maintenance of a pathogen in 20-22 nature but is not the primary reservoir for such agent. See also reservoir of infection. a CCident An unanticipated event—commonly leading to injury or other harm—in traffic, the workplace, or a domestic, recreational, or other setting. The primary event in a sequence that leads ultimately to injury if that event is genuinely not predictable. Epidemiological studies have demonstrated that the risk of accidents is often predictable and that many accidents and disasters are preventable. a CCtionumula of risk The extent of cumulative damage to biological systems as the number, duration, or severity of exposures increase, and as body systems age and become less able to repair damage. The notion that life course exposures or insults gradually accumulate through episodes of illness and injury, adverse environmental and social conditions, and health damaging behaviors. Exposures increasing risk of disease may be 3 Additive model 13,23,24 independent or clustered. See also developmental and life course epidemiology; thrifty phenotype hypothesis. a CCaur Cy 1. The degree to which a measurement or an estimate based on measurements represents the true value of the attribute that is being measured. Relative lack of error. In 1,6,25,26 statistics, accuracy is sometimes measured as the mean squared error. See also discriminatory accuracy; measurement, terminology of; precision; validity, measurement. 2. The ability of a diagnostic test to correctly classify the presence or absence of the target disorder. The diagnostic accuracy of a test is usually expressed by its sensitivity and specificity. a CanC aintuq e orknetw The internal and external connections, relationships, and dynamics of a group of persons in regular or sporadic contact or communication, among whom transmission of knowledge, infectious agents, healthy and toxic habits behavior , , and values is common and whose social interaction often has as well individual and public 27,28 health implications. See also context; network; transmission of infection. a Cderiuq ifedonummi Cnei Cy emordnys (sdia ) (Syn: acquired immune deficiency syndrome) The late clinical stage of infection with human immunodeficiency virus (HIV), recognized as a distinct syndrome in 1981. The opportunistic or indicator diseases associated with AIDS include certain protozoan and helminth infections, fungal infections, bacterial infections, viral infections, and some types of cancer. The role of AIDS as an indicator in surveillance has diminished since the advent of highly active antiretroviral therapy (HAART). a Ctive life C expe C ant y See disability-free life expectancy. a Ctivities of yaild living () adl sC ale A scale devised to score physical ability/disability, used to measure outcomes of interventions for various chronic, disabling conditions, such 29 as arthritis. It is based on scores for responses to questions about mobility, self-care, 30 grooming, etc. Refinements or variations of the ADL scale have been developed. a Ctivity setting The places, events, routines, and patterns that structure the experience of everyday life; e.g., a classroom, a neighborhood resident meeting, a commuter train, family meals, a waiting room in a hospital. A unit through which culture and community 31 are propagated across time. See also behavior setting; context. a Carialtu tear See force of mortality. a Carialtu ablet See life table. a Cute 1. Referring to a health effect: of sudden onset, often brief; not necessarily clinically severe. 2. Referring to an exposure:  brief, intense, or short-term; sometimes specifically referring to a brief exposure of high intensity. Contrast with chronic. tionaaptad 13,32,33 1. The process by which organisms surmount environmental challenges. See also resilience. 2. A heritable component of the phenotype that confers an advantage in survival and reproductive success. additive model A model in which the combined effect of several factors on an outcome measure (such as a risk or rate) is the sum of the effects that would be produced by each of the factors in the absence of the others. For example, if factor X adds x to risk in the absence of Y, and factor Y adds y to risk in the absence of X, an additive model Adherence 4 1,7,8,34 states that the two factors together will add ( x + y) to risk. See also interaction; linear model; mathematical model; multiplicative model. C adheren e Health-related behavior that adheres to the recommendations of a physician, other health care provider, or investigator in a research project. The wor adherence d aims to avoid the authoritarian connotations of compliance, formerly used to describe 1,6,9,35 this behavior. Concordance is another alternative. tmentadjus A summarizing procedure for a statistical measure in which the effects of differences in composition of the populations being compared have been minimized 5,24,36-38 by statistical methods. Examples are adjustment by standardization, by stratification, by regression analysis, by inverse-probability weighting, by 1,2 g-estimation, by propensity scores, or by some combination of these techniques. Adjustment is often performed on an effect measure, commonly because of differing distributions of age, sex, education or other known risk factors in the populations being 39-42,797 compared. See also standardization. tadul aliter Cy tear The percentage of persons 15 years of age and over who can read and 4,43 write. versead thheal event Any unfavorable and unintended sign, symptom, disease, or other relevant health event associated with the use of a medical product or procedure, or that 6,26,44,45 occurred during a research study, regardless of the causal relationship See . also association. versead rea Ction An undesirable or unwanted consequence of a preventive dia , gnostic test, or therapeutic procedure. See also side effect. versead C sele tion A phenomenon of major theoretical concern in health insurance markets, which occurs when people with health-related characteristics different from those of the average person can choose the amount of health insurance they purchase. Individuals who expect high health care costs and utilization differentially tend to prefer more generous and expensive insurance plans, whereas individuals who expect 46 low costs and utilization choose more moderate plans Insur . ers may end up with clients who are costlier than expected; they may thus raise the premium above what the 7 low- and average-risk people are willing to pa See also y. asymmetry of information. ogyaetiol , C ogiaetiol al See etiology. gea The duration of time that a person has lived, conventionally measured in completed years of life. The WHO recommends that age should be defined by completed units of time, counting the day of birth as zero. In epidemiology age is a common independent variable, as well as a confounder. It may be important to distinguish “biological age” from chronological age; for example, milestones of human growth and development 24 such as onset of puberty or closure of epiphyses are delayed by malnutrition. See also cohort analysis. gea C dependen y tioar See dependency ratio. C gena y tionshiprela A conceptual approach to describe the doctor-patient relationship, in which the physician acts as agent for the patient (or other client). Such relationship arises because of the asymmetry of information between the doctor, who possesses superior medical information, and the patient, who possesses superior information on her preferences (e.g., on treatment options). A doctor working as a perfect agent would make the same decision as the patient would were the patient to be party to the same clinical expertise as the doctor. In many systems doctors are expected to act not only for the “patient,” but also for “society” (e.g., for other patients, an organization, 47 taxpayers). See also supplier induced demand. 5 Aging of the population genta (of sedisea ) A factor (e.g., microorganism, chemical substance, form of radiation, mechanical, behavioral, social agent or process) whose presence, excessive presence, or (in deficiency diseases) relative absence is essential for the occurrence of a disease. A disease may have a single agent, a number of independent alternative agents (at least one of which must be present), or a complex of two or more factors whose combined presence is essential for or contributes to the development of the disease or other outcome. See also causality; necessary cause. gea -period -Ctohor sisyanal See cohort analysis. gea -sex amidyrp See population pyramid. ega -xes retsiger A list of all clients or patients of a medical practice or service, classified by age (birthdate) and sex; it provides denominators for calculating age- and sex-specific rates. gea -C spe ifiC tilityfer tear The number of live births occurring during a specified period to women of a specified age group divided by the number of person-years lived during that period by women of that age group. When an age-specific fertility rate is calculated for a calendar year, the number of live births to women of the specified age is usually divided by the midyear population of women of that age. gea -C spe ifiC tear A rate for a specified age group. The numerator and denominator refer to the same age group. Example: number of deaths among residents n age 25− 34 in an area in a year Age-specific death rate age (25−= 34) × 100,0 000 average (for midyear) populatio d n age 25− 34 in the area in that year The multiplier (usually 100,000 or 1 million) is chosen to produce a rate that can be expressed as a convenient number. gea tionardizaandts A procedure for adjusting rates (e.g., death rates) designed to minimize the effects of differences in age composition in comparing rates for different 1,3,26 populations. See also adjustment; standardization. tionagggrea sbia (Syn: ecological bias) See aggregative fallacy; ecological fallacy. teagggrea C veillansur e The surveillance of a disease or health event by collecting summary data on groups of cases (e.g., general practitioners taking part in surveillance schemes are asked to report the number of cases of specified diseases seen over a specified 48,49 period of time). tiveagggrea allaf Cy An erroneous application to individuals of a causal relationship observed at the group level. A type of ecological fallacy (sometimes just a synonym) 3,5,50 and an antonym of the atomistic fallacy. ginga of the tionpopula An increase over time in the proportion of older persons in a defined population. It does not necessarily imply an increase in life expectancy or that people are living longer than they used to. In the past, the principal cause of aging of populations has been a decline in the birthrate: in the absence of a rise in the death rate at higher ages, when fewer children are born than in prior years, the proportion of older persons in the population increases. Nowadays, in developed societies, little further mortality reduction can occur in the first parts of life; thus, reductions in mortality that occur in the third and fourth quarters of life are leading to a rise in the proportion of older persons. See also demographic transition.“Agnostic” analysis 6 “C tignosa ” sisyanal In genome-wide association studies (GWAS ), massive testing of multiple genetic markers without necessarily considering available knowledge on gene function, clinical or biological plausibility, coherence, or other prior evidence that some of such variants may be clinically more important than others, or more likely to 51-55 have some association with the clinical or physiological outcomes of interest. See also association study; candidate gene; data mining; exploratory study. greementa See kappa index. aids See acquired immunodeficiency syndrome. airborne C infe tion An infection whose agent is transmitted by particles, dust, dr or oplet nuclei suspended in the air. The infective agent may be transmitted by a patient 56 or carrier in airborne droplets expelled during coughing and sneezing See also . transmission of infection. algorithm Any systematic process that consists of an ordered sequence of steps with each step depending on the outcome of the previous one. The term is commonly used to describe a structured process—for instance, relating to computer programming or 38 health planning See also . decision tree. algorithm , CC lini al An explicit description of steps to be taken in patient care in specified circumstances. This approach makes use of branching logic and of all pertinent data, both about the patient and from epidemiological and other sources, to arrive at decisions that yield maximum benefit and minimum risk. allele Alternative forms of a gene occupying the same locus on a chromosome. Each of 54,57 the different states found at a polymorphic site. oC all tiona sbia An error in the estimate of an effect caused by failure to implement valid procedures for random allocation of subjects to intervention and control groups in a clinical trial or in another type of randomized study (randomized field trials, 58 randomized community trials). oC all tiona ConCealment Concealing the result of the random allocation between two or 1,6 more arms of a study. See also allocation bias; blinded study. sisatosall The adaptive processes that actively maintain homeostasis through physiological 59 or behavioural changes. 59 tiC atosall adol The long-term cost of handling stress The cumulative biological bur . den or physiological consequences exacted on the body through repeated attempts to adapt to life’s demands in the environment. When responses to these demands occur outside of normal ranges, “wear and tear” on the regulatory system is thought to occur, resulting 60 in accumulation of allostatic load. a lma -a at d C e tionalar See health care; health for all; primary health care. alpha orerr See error, type i. alpha level (Syn:  α-level) In statistical hypothesis testing, a prespecified cutoff point α used to judge whether a result is “statistically significant” or not. Typically, if the P value for the test hypothesis is below α (P α), the result will be declared “statistically significant” and the hypothesis will be rejected. An α of 0.05 is used routinely, but this usage is purely customary; in the original theory of hypothesis testing α was supposed , to be chosen based on the cost of errors, with α representing the maximum acceptable 1 probability of Type I error (incorrect rejection). See also error, type i. ambient Surrounding; pertaining to the environment in which events are observed. sisyanal of C arianv e (avano ) A statistical technique that isolates and assesses the contribution of categorical independent variables to the variance of the mean of a continuous dependent variable. The observations are classified according to their categories for

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