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Статті в журналах з теми "Applied Epidemiology"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Martinez Navarro, J. F., D. Herrera, and Candi Sanchez Barco. "Applied field epidemiology programme in Spain." Eurosurveillance 6, no. 3 (March 1, 2001): 46–47. http://dx.doi.org/10.2807/esm.06.03.00220-en.

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In 1994, the Instituto de Salud Carlos III (ISCIII; Carlos III Health Institute) of the Spanish Ministry of Health and the Consumer (MSC) created the Programa de Epidemiología Aplicada de Campo (PEAC; Applied Field Epidemiology Programme). The programme is managed by the Centro Nacional de Epidemiología (National Epidemiological Centre) in collaboration with the Escuela Nacional de Sanidad (National School of Health), and supported by General Direction for Health and Consumer of MSC and the Health Councils (Consejerías de Sanidad) of the autonomous regions. The PEAC runs a masters degree programme in applied field epidemiology, in which degrees are conferred by the National School of Health. As PEAC is a national programme, it forms a part of the European Program for Intervention Epidemiology Training (EPIET) and is a member of the Network Training for Epidemiology Public Health Intervention (TEPHINET), the association of 27 regional and national programmes of the acting Intervention Epidemiology Training Programs.

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2

Bundy, Janet K. "Applied epidemiology." American Journal of Infection Control 25, no. 3 (June 1997): 191–92. http://dx.doi.org/10.1016/s0196-6553(97)90002-x.

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3

WYNDER, ERNST L. "APPLIED EPIDEMIOLOGY." American Journal of Epidemiology 121, no. 6 (June 1985): 781–82. http://dx.doi.org/10.1093/oxfordjournals.aje.a114048.

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4

Levterova, Boryana. "APPLIED EPIDEMIOLOGY - HISTORY AND FUTURE." Knowledge International Journal 28, no. 4 (December 10, 2018): 1179–82. http://dx.doi.org/10.35120/kij28041179b.

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Epidemiology is both a science and a fundamental method of public health. This is the science that seeks to link health or disease to various factors affecting the health of human populations. Although epidemiology as a discipline develops after World War II, epidemiological thinking can be traced by Hippocrates through John Graunt, William Farr, John Snow to the present day. Epidemiology's roots are nearly 2500 years old. Hippocrates attempted to explain disease occurrence from a rational rather than a supernatural viewpoint. In his essay entitled “On Airs, Waters, and Places,” Hippocrates suggested that environmental and host factors such as behaviours might influence the development of disease.Another early contributor to epidemiology was John Graunt, a London haberdasher and councilman who published a landmark analysis of mortality data in 1662. This publication was the first to quantify patterns of birth, death, and disease occurrence, noting disparities between males and females, high infant mortality, urban/rural differences, and seasonal variations. In the mid-1800s, an anaesthesiologist John Snow was conducting a series of investigations in London that warrant his being considered the “father of field epidemiology.” Twenty years before the development of the microscope, Snow conducted studies of cholera outbreaks both to discover the cause of disease and to prevent its recurrence.Epidemiology is a philosophy and methodology that can be applied to study and solve a very wide range of health problems. The Art of Epidemiology consists of not only to use different study designs and statistical methods, but when and how to apply the various epidemiological strategies most effectively to respond to specific health issues axes and get the information we need. It is a key element in the formulation of effective public health initiatives to prevent disease and promote community health. Epidemiology studies the factors affecting health and disease of the population and thus serves as a basis for a logical approach to protecting and strengthening public health. The scope of epidemiology has been steadily expanding over recent decades as epidemiologists demonstrate new applications and variations traditional design and training methods. We can expect that the scope of epidemiology will be further expanded in the future as more and more epidemiologists develop innovative strategies and techniques.

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5

Kreis, J. A. "APPLIED EPIDEMIOLOGY TEACHING." Epidemiology 9, Supplement (July 1998): S123. http://dx.doi.org/10.1097/00001648-199807001-00410.

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6

Sherman, David M. "Applied veterinary epidemiology." Small Ruminant Research 4, no. 3 (March 1991): 313–14. http://dx.doi.org/10.1016/0921-4488(91)90155-j.

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7

Guinee, Vincent F. "RE: “APPLIED EPIDEMIOLOGY”." American Journal of Epidemiology 124, no. 1 (July 1986): 157. http://dx.doi.org/10.1093/oxfordjournals.aje.a114364.

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8

Muntaner, Carles, Ross C. Brownson, and Diana B. Petiti. "Applied Epidemiology: Theory to Practice." Journal of Public Health Policy 21, no. 1 (2000): 99. http://dx.doi.org/10.2307/3343476.

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9

Monteiro, N. Z., and S. R. Mazorche. "Fractional Derivatives Applied to Epidemiology." Trends in Computational and Applied Mathematics 22, no. 2 (June 28, 2021): 157–77. http://dx.doi.org/10.5540/tcam.2021.022.02.00157.

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We seek investigate the use of fractional derivatives, both analytically and through simulations. We derivate some models and perform investigations about them, discussing difficulties and differences between classic and fractional models. Also, we analyzed the COVID-19 pandemic using a fractional epidemiological SIR model and performed a numerical analysis using finite differences and implementation in MATLAB.

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10

Turnock, Bernard J. "Applied Epidemiology: Theory to Practice." Medicine& Science in Sports & Exercise 30, no. 8 (August 1998): 1342–43. http://dx.doi.org/10.1097/00005768-199808000-00030.

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11

Pugliese, Gina, and Martin S. Favero. "International Course in Applied Epidemiology." Infection Control & Hospital Epidemiology 19, no. 8 (August 1998): 545. http://dx.doi.org/10.1017/s0899823x00095295.

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12

Battersby, Mark. "Applied Epistemology and Argumentation in Epidemiology." Informal Logic 26, no. 1 (February 10, 2008): 41. http://dx.doi.org/10.22329/il.v26i1.430.

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The general goal is to encourage informal logicians and those interested in applied epistemology to look at epidemiology as a paradigmatic science crucially dependant on argumentation to justify its claims. Three specific goals are: 1. exemplify applied epistemology by looking critically at causal argumentation in epidemiology, 2. show that justification of causal claims in epidemiology is a form of “argument to the best explanation,” 3. show that there could be a symbiotic relationship between epidemiology and work in various applied reasoning disciplines such as argumentation and “applied epistemology.”

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13

Koo, D. "Strategic Workforce Development for Applied Epidemiology." American Journal of Epidemiology 163, suppl_11 (June 1, 2006): S87. http://dx.doi.org/10.1093/aje/163.suppl_11.s87-d.

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14

Thacker, Stephen B., and Joanna Buffington. "Applied epidemiology for the 21st Century." International Journal of Epidemiology 30, no. 2 (April 2001): 320–25. http://dx.doi.org/10.1093/ije/30.2.320.

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15

Montgomery, Scott M. "Applied longitudinal data analysis for epidemiology:." European Journal of Epidemiology 20, no. 2 (February 2005): 201. http://dx.doi.org/10.1007/s10654-004-3142-1.

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16

Savilov, Evgeny D., Svetlana N. Shugaeva, Nikolaj I. Briko, and Sergey I. Kolesnikov. "Risk ― A Basic Concept of Epidemiology." Annals of the Russian academy of medical sciences 74, no. 1 (April 3, 2019): 54–60. http://dx.doi.org/10.15690/vramn1006.

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This article presents the analysis of current scientific understanding of the term «risk» along with theoretical justification of its use in epidemiological studies. Epidemiology commonly uses definitions such as «risk factor», «group of risk», «risk area», and «risk period». However, these definitions were useful only for specific groups or nosoligical infectious diseases. In Noninfectious Pathology the terms had been used exclusively in the applied studies. There is a lack of publications which compile theoretical basics of such fundamental term category. The authors suggest a definition of epidemiologic «risk» which can be used in the epidemiology of both infectious and noninfectious diseases. It is a probability of negative influence on illness (and/or its impact) of specific groups of general population which is defined by external and/or internal factors in specific times and territories. The authors differentiate types of risk and their evaluation measures into categories for used in applied studies of epidemiology. The relationships and the unity of the basic categories of the epidemiologic risk are discussed. The authors conclude that riskology is the main branch of epidemiology and the category of «risk» is the basic paradigm of this science.

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17

Myers Griffith, Matthew, Amy Parry, Tambri Housen, Tony Stewart, and Martyn Kirk. "COVID-19 and investment in applied epidemiology." Bulletin of the World Health Organization 100, no. 7 (July 1, 2022): 415–415. http://dx.doi.org/10.2471/blt.22.288687.

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18

Saurel-Cubizolles, M. J. "Applied epidemiology. Theory to practice, 2nd edn." Journal of Epidemiology & Community Health 61, no. 10 (October 1, 2007): 926. http://dx.doi.org/10.1136/jech.2007.059543.

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19

Thoroughman, Doug. "Applied Epidemiology Competencies: Experience in the Field." Public Health Reports 123, no. 1_suppl (January 2008): 8–10. http://dx.doi.org/10.1177/00333549081230s104.

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20

Turnbull, James F., Iain K. Berrill, Darren M. Green, Ryan Kaye, David Morris, Alexander G. Murray, Jorge del-Pozo, and Andrew Shinn. "Applied epidemiology with examples from UK aquaculture." Aquaculture Research 42 (February 2011): 21–27. http://dx.doi.org/10.1111/j.1365-2109.2010.02667.x.

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21

Tillett, H. E., and R. G. Carpenter. "Statistical methods applied in microbiology and epidemiology." Epidemiology and Infection 107, no. 3 (December 1991): 467–78. http://dx.doi.org/10.1017/s0950268800049165.

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Those who work in any area of public health are exposed to large amounts of data and processed information. Some acquaintance with statistical methods can make that exposure more profitable. The objectives of this paper are to describe a suitable framework for governing scientific investigations which involve applied statistics, and to set down guidelines for research workers preparing material for publication. Final sections suggest further reading and give a brief description of some of the statistical software available to assist analysis.

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22

Needleman, Carolyn. "Applied epidemiology and environmental health: Emerging controversies." American Journal of Infection Control 25, no. 3 (June 1997): 262–74. http://dx.doi.org/10.1016/s0196-6553(97)90013-4.

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23

Davey Smith, George. "Post–Modern Epidemiology: When Methods Meet Matter." American Journal of Epidemiology 188, no. 8 (March 16, 2019): 1410–19. http://dx.doi.org/10.1093/aje/kwz064.

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Abstract In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post–Modern Epidemiology world.

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24

Tapia Granados, J. A. "Is epidemiology an applied science or a technology?" American Journal of Public Health 89, no. 4 (April 1999): 599–600. http://dx.doi.org/10.2105/ajph.89.4.599.

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25

Bulsara, Max K. "Book Review: Applied longitudinal data analysis for epidemiology." Statistical Methods in Medical Research 13, no. 4 (August 2004): 339–40. http://dx.doi.org/10.1177/096228020401300407.

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26

Bensyl, Diana M., Michael E. King, and Ashley Greiner. "Applied Epidemiology Training Needs for the Modern Epidemiologist." American Journal of Epidemiology 188, no. 5 (March 16, 2019): 830–35. http://dx.doi.org/10.1093/aje/kwz052.

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27

Caspersen, C. J., A. M. Kriska, and S. R. Dearwater. "Physical activity epidemiology as applied to elderly populations." Baillière's Clinical Rheumatology 8, no. 1 (February 1994): 7–27. http://dx.doi.org/10.1016/s0950-3579(05)80222-5.

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28

Devineni, Bhargavi, and Chiadi U. Onyike. "Young-Onset Dementia Epidemiology Applied to Neuropsychiatry Practice." Psychiatric Clinics of North America 38, no. 2 (June 2015): 233–48. http://dx.doi.org/10.1016/j.psc.2015.02.003.

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29

&NA;. "Applied Longitudinal Data Analysis for Epidemiology, 2nd Edition." Medicine & Science in Sports & Exercise 46, no. 4 (April 2014): 842. http://dx.doi.org/10.1249/mss.0000000000000305.

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30

Lengerich, Eugene J., Jennifer C. Siedlecki, Ross Brownson, Tim E. Aldrich, Katrina Hedberg, Patrick Remington, and Paul Z. Siegel. "Mentorship and Competencies for Applied Chronic Disease Epidemiology." Journal of Public Health Management and Practice 9, no. 4 (July 2003): 275–83. http://dx.doi.org/10.1097/00124784-200307000-00005.

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31

Camann, D. E., B. Curwin, A. E. Bond, MCR Alavanja, D. P. Sandier, D. T. Mage, and W. C. Steen. "DERMAL EXPOSURE TO GROUNDBOOM-APPLIED PESTICIDES*." Epidemiology 9, Supplement (July 1998): S80. http://dx.doi.org/10.1097/00001648-199807001-00239.

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32

Bürger, Raimund, Gerardo Chowell, and Leidy Yissedt Lara-Díaz. "Measuring differences between phenomenological growth models applied to epidemiology." Mathematical Biosciences 334 (April 2021): 108558. http://dx.doi.org/10.1016/j.mbs.2021.108558.

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33

Braitman, Leonard E. "Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide." Annals of Internal Medicine 140, no. 8 (April 20, 2004): 676. http://dx.doi.org/10.7326/0003-4819-140-8-200404200-00042.

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34

Hu, X. Joan. "Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide." Journal of the American Statistical Association 99, no. 468 (December 2004): 1207–8. http://dx.doi.org/10.1198/jasa.2004.s366.

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35

Schill, W. "Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide." Biometrics 60, no. 1 (March 2004): 291–92. http://dx.doi.org/10.1111/j.0006-341x.2004.172_7.x.

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36

D, Dr Montag. "Participatory research and qualitative methods applied to environmental epidemiology." Environmental Epidemiology 3 (October 2019): 104. http://dx.doi.org/10.1097/01.ee9.0000606848.78253.50.

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37

McKeown, R. E. "Ethics in epidemiology and public health II. Applied terms." Journal of Epidemiology & Community Health 56, no. 10 (October 1, 2002): 739–41. http://dx.doi.org/10.1136/jech.56.10.739.

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38

Crutcher, James M. "Applied Epidemiology Competencies: Perspectives of a State Health Officer." Public Health Reports 123, no. 1_suppl (January 2008): 11–12. http://dx.doi.org/10.1177/00333549081230s105.

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39

Birkhead, Guthrie S., Jac Davies, Kathleen Miner, Jennifer Lemmings, and Denise Koo. "Developing Competencies for Applied Epidemiology: From Process to Product." Public Health Reports 123, no. 1_suppl (January 2008): 67–118. http://dx.doi.org/10.1177/00333549081230s114.

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40

Monis, P. T., and R. H. andrews. "Molecular epidemiology: assumptions and limitations of commonly applied methods." International Journal for Parasitology 28, no. 6 (June 1998): 981–87. http://dx.doi.org/10.1016/s0020-7519(98)00042-3.

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41

CASPERSEN, CARL J., PATRICIA A. NIXON, and ROBERT H. DuRANT. "13 Physical Activity Epidemiology Applied to Children and Adolescents." Exercise and Sport Sciences Reviews 26 (1998): 341???404. http://dx.doi.org/10.1249/00003677-199800260-00015.

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42

Fayram, Elizabeth S., and Laura Anderko. "Applied Epidemiology for Public Health and Community-Based Nurses." Journal of Continuing Education in Nursing 40, no. 8 (August 1, 2009): 361–66. http://dx.doi.org/10.3928/00220124-20090723-05.

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43

Pfaller, Michael A. "Molecular Epidemiology in the Care of Patients." Archives of Pathology & Laboratory Medicine 123, no. 11 (November 1, 1999): 1007–10. http://dx.doi.org/10.5858/1999-123-1007-meitco.

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Анотація:

Abstract Several different epidemiologic typing methods have been applied in studies of microbial pathogens. These methods include the more traditional nonmolecular approaches as well as the more sophisticated molecular typing methods. Application of traditional epidemiologic typing methods, such as antibiogram, serotyping, biotyping, and phage typing, have occasionally been useful in describing the epidemiology of infectious diseases. However, these methods have generally been considered to be too variable, labor intensive, and slow to be of practical value in epidemiologic investigations. In response to these limitations, several techniques have been adopted from the molecular biology field for use as epidemiologic typing methods and have been applied in studies of bacteria, fungi, viruses, and protozoa. The most widely used molecular typing methods are the DNA-based methods, such as plasmid profiling, restriction endonuclease analysis of plasmid and genomic DNA, Southern hybridization analysis using specific DNA probes, and chromosomal DNA profiling using either pulsed-field gel electrophoresis or polymerase chain reaction–based methods. The various molecular typing methods may be applied to the investigation of outbreaks of infections or may be used in the context of epidemiologic surveillance. For outbreak investigation, typing methods are used to compare isolates from a suspected outbreak to delineate clonally related and unrelated strains with the goal of short-term control of transmission. In the context of epidemiologic surveillance, molecular typing methods may be used to monitor geographic spread and prevalence shifts of epidemic and endemic clones with the goal of long-term evaluation of preventive strategies or for the detection and monitoring of emerging and reemerging infections. The specific typing method selected may vary with the task at hand; however, the typing studies must always be used to supplement, rather than replace, careful epidemiologic investigation.

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44

Edelman, Natalie. "Towards a critical epidemiology approach for applied sexual health research." Journal of Health Psychology 23, no. 2 (December 5, 2017): 161–74. http://dx.doi.org/10.1177/1359105317743768.

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Анотація:

Critical approaches may benefit epidemiological studies of sexual health. This article proposes a critical approach, reconcilable with social epidemiological enquiry. Key aims of critical epidemiology for sexual health are identified, from which three criticisms of practice emerge: (1) lack of attention to socio-cultural contexts, (2) construction of ‘risk’ as residing in the individual and (3) enactment of public health agendas which privilege and pathologise certain behaviours. These reflect and construct an apolitical understanding of population health. This article proposes features of a critical epidemiology that represent a morally driven re-envisioning of the focus, analysis and interpretation of epidemiological studies of sexual health.

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45

Hopper, J. L. "The Epidemiology of Genetic Epidemiology." Acta geneticae medicae et gemellologiae: twin research 41, no. 4 (October 1992): 261–73. http://dx.doi.org/10.1017/s0001566000002129.

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Анотація:

AbstractFamilial aggregation for disease is important; strong familial risk factors must exist even if the increased risk to a relative of an affected individual is modest. It is in practice difficult, however, to conduct studies in genetic epidemiology which conform to strict epidemiological principles. For twin studies there are two major questions: Are twins ‘no different’ from the population on which inference is to be made? Are study twins ‘no different’ to twins in the population? The importance of each question of bias depends on the scientific question, the trait(s) studied, and sampling issues. The strength of the twin design is its ability to refute the null hypothesis that genetic factors do not explain variation in a trait. Following the Popperian paradigm, alternate hypotheses should be considered in depth (both theoretically and empirically), with a design and sample size sufficient to exclude not just naive explanations. More sophisticated statistical techniques are now being applied, so the philosophy, assumptions, and limitations of statistical modelling must be appreciated. The concept of ‘heritability’ has, in the past, been misunderstood and misused. New advances in DNA technology promise to revolutionise epidemiological thinking, and so case-control-pedigree designs may well become standard tools. The strengths and limitations of studies based on related individuals as the sampling unit are discussed.

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46

Liao, Xiaomei, Donna Spiegelman, and Raymond J. Carroll. "Regression Calibration Is Valid When Properly Applied." Epidemiology 24, no. 3 (May 2013): 466–67. http://dx.doi.org/10.1097/ede.0b013e31828b284b.

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47

Guo, Ying, and Roderick J. Little. "Regression Calibration Is Valid When Properly Applied." Epidemiology 24, no. 3 (May 2013): 467–68. http://dx.doi.org/10.1097/ede.0b013e31828b2a16.

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48

Daley, Ellen, and Erika Thompson. "Disseminating Epidemiological Studies through Applied Doctoral Training." Annals of Epidemiology 27, no. 8 (August 2017): 524. http://dx.doi.org/10.1016/j.annepidem.2017.07.085.

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49

Perkins, N. "Applied Veterinary Epidemiology and the Control of Disease in Populations." Australian Veterinary Journal 78, no. 8 (August 2000): 570. http://dx.doi.org/10.1111/j.1751-0813.2000.tb11905.x.

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50

White, M. E. "Partnerships in International Applied Epidemiology Training and Service, 1975-2001." American Journal of Epidemiology 154, no. 11 (December 1, 2001): 993–99. http://dx.doi.org/10.1093/aje/154.11.993.

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