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Research Methods in Healthcare Epidemiology and Antimicrobial Stewardship—Observational Studies

Part of: Research and Methodology in Infection Control

Published online by Cambridge University Press:  20 June 2016

Graham M. Snyder ,
Heather Young ,
Meera Varman ,
Aaron M. Milstone ,
Anthony D. Harris  and
Silvia Munoz-Price
Graham M. Snyder*
Affiliation:
Beth Israel Deaconess Medical Center, Harvard University, Boston, Massachusetts
Heather Young
Affiliation:
Denver Health Medical Center, University of Colorado Hospital, Denver, Colorado
Meera Varman
Affiliation:
Creighton University School of Medicine, Omaha, Nebraska
Aaron M. Milstone
Affiliation:
Johns Hopkins Medical Institutions, Baltimore, Maryland
Anthony D. Harris
Affiliation:
University of Maryland School of Medicine, Veterans Affairs Maryland Health Care System, Baltimore, Maryland
Silvia Munoz-Price
Affiliation:
Institute for Health and Society and Department of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
*
Address correspondence to Graham Snyder, Mailstop SL-435, 330 Brookline Ave, Boston, MA 02215 ([email protected]).
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Abstract

Observational studies compare outcomes among subjects with and without an exposure of interest, without intervention from study investigators. Observational studies can be designed as a prospective or retrospective cohort study or as a case-control study. In healthcare epidemiology, these observational studies often take advantage of existing healthcare databases, making them more cost-effective than clinical trials and allowing analyses of rare outcomes. This paper addresses the importance of selecting a well-defined study population, highlights key considerations for study design, and offers potential solutions including biostatistical tools that are applicable to observational study designs.

Infect Control Hosp Epidemiol 2016;1–6

Type
SHEA White Papers
Information
Infection Control & Hospital Epidemiology , Volume 37 , Issue 10 , October 2016 , pp. 1141 - 1146
Copyright
© 2016 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1. von Elm, E, Altman, DG, Egger, M, Pocock, SJ, Gotzsche, PC, Vandenbroucke, JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007;147:573–577.Google Scholar
2. Sanderson, S, Tatt, ID, Higgins, JP. Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography. Int J Epidemiol 2007;36:666–676.Google Scholar
3. Hofler, M. Causal inference based on counterfactuals. BMC Med Res Methodol 2005;5:28.Google Scholar
4. Kleinbaum, DG, Kupper, LL, Morgenstern, H. Epidemiologic Research: Principles and Quantitative Methods. Belmont, CA: Lifetime Learning Publications; 1982.Google Scholar
5. Schulz, KF, Grimes, DA. Case-control studies: research in reverse. Lancet 2002;359:431–434.Google Scholar
6. Wacholder, S, McLaughlin, JK, Silverman, DT, Mandel, JS. Selection of controls in case-control studies. I. Principles. Am J Epidemiol 1992;135:1019–1028.Google Scholar
7. Wacholder, S, Silverman, DT, McLaughlin, JK, Mandel, JS. Selection of controls in case-control studies. III. Design options. Am J Epidemiol 1992;135:1042–1050.Google Scholar
8. Wyllie, D, Davies, J. Role of data warehousing in healthcare epidemiology. J Hosp Infect 2015;89:267–270.Google Scholar
9. Harris, AD, Carmeli, Y, Samore, MH, Kaye, KS, Perencevich, E. Impact of severity of illness bias and control group misclassification bias in case-control studies of antimicrobial-resistant organisms. Infect Control Hosp Epidemiol 2005;26:342–345.Google Scholar
10. Dhar, S, Tansek, R, Toftey, EA, et al. Observer bias in hand hygiene compliance reporting. Infect Control Hosp Epidemiol 2010;31:869–870.Google Scholar
11. Nelson, RE, Nelson, SD, Khader, K, et al. The magnitude of time-dependent bias in the estimation of excess length of stay attributable to healthcare-associated infections. Infect Control Hosp Epidemiol 2015;36:1089–1094.Google Scholar
12. Schweizer, ML, Braun, BI, Milstone, AM. Research methods in healthcare epidemiology and antimicrobial stewardship—quasi-experimental designs. Infect Control Hosp Epidemiol DOI: http://dx.doi.org/10.1017/ice.2016.91. Published online April 14, 2016.Google Scholar
13. Rhame, FS, Sudderth, WD. Incidence and prevalence as used in the analysis of the occurrence of nosocomial infections. Am J Epidemiol 1981;113:1–11.Google Scholar
14. Viera, AJ, Garrett, JM. Understanding interobserver agreement: the kappa statistic. Fam Med 2005;37:360–363.Google Scholar
15. Hallgren, KA. Computing inter-rater reliability for observational data: an overview and tutorial. Tutor Quant Methods Psychol 2012;8:23–34.Google Scholar
16. 2014 NHSN Data Quality Guidance and Toolkit for Reporting Facilities: Internal Validation of NHSN Patient Safety Component Data. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/validation/index.html. Published 2014. Accessed January 29, 2016.Google Scholar
17. Piroth, L, Aube, H, Doise, JM, Vincent-Martin, M. Spread of extended-spectrum beta-lactamase-producing Klebsiella pneumoniae: are beta-lactamase inhibitors of therapeutic value? Clin Infect Dis 1998;27:76–80.Google Scholar
18. Harris, AD, Karchmer, TB, Carmeli, Y, Samore, MH. Methodological principles of case-control studies that analyzed risk factors for antibiotic resistance: a systematic review. Clin Infect Dis 2001;32:1055–1061.CrossRefGoogle ScholarPubMed
19. Ong, DS, Jongerden, IP, Buiting, AG, et al. Antibiotic exposure and resistance development in Pseudomonas aeruginosa and Enterobacter species in intensive care units. Crit Care Med 2011;39:2458–2463.CrossRefGoogle ScholarPubMed
20. Wirtschafter, DD, Powers, RJ, Pettit, JS, et al. Nosocomial infection reduction in VLBW infants with a statewide quality-improvement model. Pediatrics 2011;127:419–426.Google Scholar
21. Petticrew, M, Cummins, S, Ferrell, C, et al. Natural experiments: an underused tool for public health? Public Health 2005;119:751–757.Google Scholar
22. Davies, HT, Crombie, IK, Tavakoli, M. When can odds ratios mislead? BMJ 1998;316:989–991.Google Scholar
23. Newgard, CD, Lewis, RJ. Missing data: how to best account for what is not known. JAMA 2015;314:940–941.Google Scholar
24. Muscedere, JG, Day, A, Heyland, DK. Mortality, attributable mortality, and clinical events as end points for clinical trials of ventilator-associated pneumonia and hospital-acquired pneumonia. Clin Infect Dis 2010;51(Suppl 1):S120–S125.CrossRefGoogle ScholarPubMed
25. Schumacher, M, Allignol, A, Beyersmann, J, Binder, N, Wolkewitz, M. Hospital-acquired infections—appropriate statistical treatment is urgently needed! Int J Epidemiol 2013;42:1502–1508.Google Scholar
26. Austin, PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011;46:399–424.Google Scholar