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Journal articles on the topic 'Epidemiology Case studies'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Shapiro, Eugene D. "Analytic Strategies in Hospital Epidemiology: Case-Control Studies." Infection Control & Hospital Epidemiology 10, no. 4 (April 1989): 167–69. http://dx.doi.org/10.1086/645993.

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Case-control studies (sometimes referred to as case-referent studies) have long been important in epidemiologic research. This technique has been instrumental in allowing researchers to discover the associations between cigarettes and lung cancer, thromboembolism and oral contraceptives, and stilbesterol and vaginal cancer, just to cite a few examples. The case-control method is also a very useful technique for studies in hospital epidemiology. This article will briefly review this technique, its advantages, and some of its potential pitfalls.

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2

Wecht, Cyril H., and Steven A. Koehler. "Case Studies in Forensic Epidemiology." Journal of Legal Medicine 24, no. 4 (December 2003): 587–94. http://dx.doi.org/10.1080/714044492.

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3

Stang, Andreas, and Karl-Heinz Jöckel. "Appending Epidemiological Studies to Conventional Case–Control Studies (Hybride Case–Control Studies)." European Journal of Epidemiology 19, no. 6 (June 2003): 527–32. http://dx.doi.org/10.1023/b:ejep.0000032380.03554.9f.

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4

K??nzli, Nino, and Christian Schindler. "Case???Crossover Studies." Epidemiology 16, no. 4 (July 2005): 592–93. http://dx.doi.org/10.1097/01.ede.0000165792.14924.f1.

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5

Sullivan, Jeffrey, Lianne Sheppard, Astrid Schreuder, and Joel Kaufman. "Case???Crossover Studies." Epidemiology 16, no. 4 (July 2005): 593. http://dx.doi.org/10.1097/01.ede.0000165815.89253.25.

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6

Sanders, Joseph. "Review of: Case Studies in Forensic Epidemiology." Journal of Forensic Sciences 49, no. 4 (2004): 1. http://dx.doi.org/10.1520/jfs2004086.

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7

Langholz, Bryan. "Case-Control Studies = Odds Ratios." Epidemiology 21, no. 1 (January 2010): 10–12. http://dx.doi.org/10.1097/ede.0b013e3181c308f5.

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8

Wang, Shirley V., Joshua J. Gagne, Robert J. Glynn, and Sebastian Schneeweiss. "Case-crossover Studies of Therapeutics." Epidemiology 24, no. 3 (May 2013): 375–78. http://dx.doi.org/10.1097/ede.0b013e31828ac9cb.

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9

Frombier, D. J. "Case-control studies of screening." Journal of Clinical Epidemiology 41, no. 1 (January 1988): 101. http://dx.doi.org/10.1016/0895-4356(88)90015-7.

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10

CUMMING, ROBERT L., and JENNIFER L. KELSEY. "Case-control studies." International Journal of Epidemiology 18, no. 3 (1989): 725–26. http://dx.doi.org/10.1093/ije/18.3.725.

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11

MAYES, LINDA C., RALPH I. HORWITZ, and ALVAN R. FEINSTEIN. "Case-control studies." International Journal of Epidemiology 18, no. 3 (1989): 726–27. http://dx.doi.org/10.1093/ije/18.3.726.

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12

Thompson, S. G. "Modelling in Matched Case-Control Studies in Epidemiology." Statistician 35, no. 2 (1986): 237. http://dx.doi.org/10.2307/2987528.

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13

Shapiro, Eugene D. "Analytic Strategies in Hospital Epidemiology: Case-Control Studies." Infection Control and Hospital Epidemiology 10, no. 4 (April 1989): 167–69. http://dx.doi.org/10.2307/30144323.

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14

Stang, Andreas, and Karl-Heinz Jöckel. "The Limits of Case-Control Studies." Epidemiology 17, no. 6 (November 2006): 706. http://dx.doi.org/10.1097/01.ede.0000239726.53102.79.

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15

Starr, Jacqueline R., and Barbara McKnight. "Assessing Interaction in Case-Control Studies." Epidemiology 15, no. 4 (July 2004): 422–27. http://dx.doi.org/10.1097/01.ede.0000129508.82783.94.

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16

Langholz, Bryan, and David Richardson. "Are Nested Case-Control Studies Biased?" Epidemiology 20, no. 3 (May 2009): 321–29. http://dx.doi.org/10.1097/ede.0b013e31819e370b.

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17

Olson, Sara H., Lynda F. Voigt, Colin B. Begg, and Noel S. Weiss. "Reporting Participation in Case-Control Studies." Epidemiology 13, no. 2 (March 2002): 123–26. http://dx.doi.org/10.1097/00001648-200203000-00004.

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18

Lichtenstein, Michael J., Cynthia D. Mulrow, and Peter C. Elwood. "Guidelines for reading case-control studies." Journal of Chronic Diseases 40, no. 9 (January 1987): 893–903. http://dx.doi.org/10.1016/0021-9681(87)90190-1.

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19

Freeman, Jonathan. "Quantitative Epidemiology." Infection Control & Hospital Epidemiology 17, no. 4 (April 1996): 249–55. http://dx.doi.org/10.1017/s0195941700003842.

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AbstractWe provide guidance for new practitioners in the vocabulary of modern epidemiology and the application of quantitative methods. Most hospital epidemiology involves surveillance (observational) data that were not part of a planned experiment, so the rubric and logic of controlled experimental studies cannot be applied. Forms of incidence and prevalence often are confused. The names “cohort study” and “case-control study” are unfortunate, as cohort studies rarely involve cohorts and case-control studies allow no active control by the investigator. Either type of study can be prospective or retrospective. Results of studies with discrete outcomes (infected or not, lived or died) often are represented best by a form of the risk ratio with 95% confidence intervals. The potential distorting effects of selection bias, misclassification, and confounding need to be considered.

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20

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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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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21

Marshall, Roger J. "Misclassification of Exposure in Case-Control Studies." Epidemiology 5, no. 3 (May 1994): 309–14. http://dx.doi.org/10.1097/00001648-199405000-00009.

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22

Kubota, Kiyoshi, and Akira Wakana. "Sample-size Formula for Case-cohort Studies." Epidemiology 22, no. 2 (March 2011): 279. http://dx.doi.org/10.1097/ede.0b013e3182087650.

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23

Suissa, Samy, Sophie DellʼAniello, Sarah Vahey, and Christel Renoux. "Time-window Bias in Case-control Studies." Epidemiology 22, no. 2 (March 2011): 228–31. http://dx.doi.org/10.1097/ede.0b013e3182093a0f.

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24

Karp, Igor. "Irrelevance of Noncollapsibility in Case-Control Studies." Epidemiology 24, no. 1 (January 2013): 173–74. http://dx.doi.org/10.1097/ede.0b013e318277f03d.

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25

Becker, Stan. "Sample size determination in case-control studies." Journal of Chronic Diseases 40, no. 12 (January 1987): 1141–43. http://dx.doi.org/10.1016/0021-9681(87)90082-8.

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26

Lui, Kung-Jong. "Sample size determination in case-control studies." Journal of Clinical Epidemiology 44, no. 6 (January 1991): 609–11. http://dx.doi.org/10.1016/0895-4356(91)90225-x.

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27

BLITZER, PETER H., CHUNG-CHENG HSIEH, and OLLI S. MIETTINEN. "POWER CALCULATION IN MATCHED CASE-REFERENT STUDIES." American Journal of Epidemiology 124, no. 5 (November 1986): 836–42. http://dx.doi.org/10.1093/oxfordjournals.aje.a114460.

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28

Kuritz, Stephen J., and J. Richard Landis. "RE: “EXPOSURE OPPORTUNITY IN CASE-CONTROL STUDIES”." American Journal of Epidemiology 125, no. 6 (June 1987): 1095. http://dx.doi.org/10.1093/oxfordjournals.aje.a114629.

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29

Wacholder, Sholom, Joseph K. McLaughlin, Debra T. Silverman, and Jack S. Mandel. "Selection of Controls in Case-Control Studies." American Journal of Epidemiology 135, no. 9 (May 1, 1992): 1019–28. http://dx.doi.org/10.1093/oxfordjournals.aje.a116396.

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30

Ramsey, Scott David, and Ruth Etzioni. "Case–control studies of cancer surveillance procedures." Journal of Clinical Epidemiology 64, no. 1 (January 2011): 44. http://dx.doi.org/10.1016/j.jclinepi.2010.07.011.

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31

GREENLAND, SANDER. "Control-Initiated Case-Control Studies." International Journal of Epidemiology 14, no. 1 (1985): 130–34. http://dx.doi.org/10.1093/ije/14.1.130.

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32

MOSS, S. M. "Case-Control Studies of Screening." International Journal of Epidemiology 20, no. 1 (1991): 1–6. http://dx.doi.org/10.1093/ije/20.1.1.

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33

Katusic, S. K., W. J. Barbaresi, R. C. Colligan, A. L. Weaver, C. L. Leibson, and S. J. Jacobsen. "Case definition in epidemiologic studies of AD/HD." Annals of Epidemiology 14, no. 8 (September 2004): 614. http://dx.doi.org/10.1016/j.annepidem.2004.07.061.

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34

Katusic, Slavica K., William J. Barbaresi, Robert C. Colligan, Amy L. Weaver, Cynthia L. Leibson, and Steven J. Jacobsen. "Case Definition in Epidemiologic Studies of AD/HD." Annals of Epidemiology 15, no. 6 (June 2005): 430–37. http://dx.doi.org/10.1016/j.annepidem.2004.12.004.

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35

Etzel, Ruth A. "Case Studies of Reanalysis of Environmental Epidemiologic Data." Epidemiology 17, Suppl (November 2006): S42. http://dx.doi.org/10.1097/00001648-200611001-00071.

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36

Pasternack, Bernard S. "SEQUENTIAL TESTS FOR COHORT AND CASE-CONTROL STUDIES." Epidemiology 6, no. 1 (January 1995): 95. http://dx.doi.org/10.1097/00001648-199501000-00028.

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37

Möller, Jette, Anne-Charlotte Hessén-Söderman, and Johan Hallqvist. "Differential Misclassification of Exposure in Case-Crossover Studies." Epidemiology 15, no. 5 (September 2004): 589–96. http://dx.doi.org/10.1097/01.ede.0000135177.10332.42.

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38

Mittleman, Murray A. "Optimal Referent Selection Strategies in Case-Crossover Studies." Epidemiology 16, no. 6 (November 2005): 715–16. http://dx.doi.org/10.1097/01.ede.0000183170.92955.25.

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39

Bernstein, Leslie. "Control Recruitment in Population-Based Case???Control Studies." Epidemiology 17, no. 3 (May 2006): 255–57. http://dx.doi.org/10.1097/01.ede.0000209440.94875.42.

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40

Weiss, Noel S., and Clara Bodelon. "Interview-Based Case-Control Studies of Screening Efficacy." Epidemiology 19, no. 2 (March 2008): 265–67. http://dx.doi.org/10.1097/ede.0b013e3181632c8c.

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41

Saha-Chaudhuri, Paramita, David M. Umbach, and Clarice R. Weinberg. "Pooled Exposure Assessment for Matched Case-control Studies." Epidemiology 22, no. 5 (September 2011): 704–12. http://dx.doi.org/10.1097/ede.0b013e318227af1a.

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42

Schlesselman, James J. "Valid selection of subjects in case-control studies." Journal of Chronic Diseases 38, no. 7 (January 1985): 549–50. http://dx.doi.org/10.1016/0021-9681(85)90040-2.

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43

Knottnerus, J. A. "Subject selection in hospital-based case-control studies." Journal of Chronic Diseases 40, no. 2 (January 1987): 183–85. http://dx.doi.org/10.1016/0021-9681(87)90071-3.

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44

Liao, Shu-Fen, and Wen-Chung Lee. "Weighing the Causal Pies in Case-Control Studies." Annals of Epidemiology 20, no. 7 (July 2010): 568–73. http://dx.doi.org/10.1016/j.annepidem.2010.04.003.

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45

Brooks, J. Paul. "Challenges for case-control studies with microbiome data." Annals of Epidemiology 26, no. 5 (May 2016): 336–41. http://dx.doi.org/10.1016/j.annepidem.2016.03.009.

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46

Nurminen, Markku. "Assessment of excess risks in case-base studies." Journal of Clinical Epidemiology 45, no. 10 (October 1992): 1081–92. http://dx.doi.org/10.1016/0895-4356(92)90148-g.

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47

Støer, Nathalie C., Haakon E. Meyer, and Sven Ove Samuelsen. "Reuse of Controls in Nested Case-Control Studies." Epidemiology 25, no. 2 (March 2014): 315–17. http://dx.doi.org/10.1097/ede.0000000000000057.

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48

Cohen, Marsha M. "Using administrative data for case-control studies: The case of the papanicolaou smear." Annals of Epidemiology 3, no. 1 (January 1993): 93–98. http://dx.doi.org/10.1016/1047-2797(93)90015-v.

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49

Lin, Xinjiang, Shouping Chen, and Amatul Bushra Akhi. "A Comprehensive Study on Epidemiology Case Studies Using Computational Analysis." Computational Intelligence and Neuroscience 2022 (September 7, 2022): 1–8. http://dx.doi.org/10.1155/2022/6508866.

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Health-related issues and occurrences with regard to a particular population are the subject of an epidemiology study. This paper presents the results of a retrospective epidemiological investigation on 15922 hospitalized hand trauma patients from Central China between 2011 and 2020. Gender, age, onset season, injury mechanism, injury environment, injury location, and clinical characteristics are among the characteristics of the data gathered. The study is using computational analysis to draw inferences from the case studies collected in the databases of the hospitals. The types and characteristics of occupational injuries at home and outdoor are compared and analyzed. The purpose of the study is to present the findings from recent case studies for future reference and to recommend useful roles for the industrial sector in the care of patients with hand trauma in order to lower occupational harm. The injuries of preschool children are also analyzed. The incidence rate of hand injuries in infants has been increasing year by year which is directly related to the inefficient growth of children in rural areas. The data are collected from hospitals, then the data analytical tools are applied to draw conclusions. The suggested model is intelligently learned through the application of computational techniques, which are also used to suggest treatments to trauma victims. According to this study, males are more likely than females to sustain hand trauma; occupational injuries are more common than living injuries; males between the ages of 20 and 50 are at an increased risk of suffering an occupational injury. This study showed that the proportion of hand trauma in preschool children was higher (12.27%), and the 2-3-year-old group was the main injury target of preschool children (45.70%). The accidental injuries of newborns and young children can be reduced by government assistance, social support, and tighter monitoring.

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50

Ashby, Deborah, Jane L. Hutton, and Magnus A. McGee. "Simple Bayesian Analyses for Case-Control Studies in Cancer Epidemiology." Statistician 42, no. 4 (1993): 385. http://dx.doi.org/10.2307/2348472.

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