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The Case-Case-Control Study Design: Addressing the Limitations of Risk Factor Studies for Antimicrobial Resistance

Published online by Cambridge University Press:  21 June 2016

Keith S. Kaye ,
Anthony D. Harris ,
Matthew Samore  and
Yehuda Carmeli
Keith S. Kaye*
Affiliation:
Department of Medicine, Duke University Medical Center, Durham, North Carolina
Anthony D. Harris
Affiliation:
University of Maryland School of Medicine, College Park, and the Veterans Affairs Maryland Health Care System, Baltimore, Maryland
Matthew Samore
Affiliation:
University of Utah, Salt Lake City, Utah
Yehuda Carmeli
Affiliation:
Tel Aviv Medical Center, Tel Aviv, Israel
*
Box 3152, Durham, NC 27710[email protected]
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Abstract

Objective:

There are significant limitations of the standard case-control study design for identifying risk factors for resistant organisms. The objective of this study was to develop a study design to overcome these limitations.

Design:

Theoretical analysis of different types of study designs that can be used in risk factor studies for resistant organisms.

Results:

We developed the case-case-control study design, which uses two separate case-control analyses within a single study. The first analysis compares patients infected with resistant bacteria (resistant cases) with control-patients without infection caused by the target organism, who are therefore representative of the source population; and the second analysis compares patients infected with the susceptible phenotype of the target organism (susceptible cases) with the same controlpatients without infection caused by the target organism. These two analyses provide risk models for (1) isolation of the resistant phenotype of the target organism as compared with the source population and (2) isolation of the susceptible phenotype of the organism as compared with the source population. When these two risk models are compared and contrasted, risk factors specifically associated with isolation of the resistant phenotype can be identified.

Conclusions:

The case-case-control study design is an effective method for identifying risk factors for antimicrobial-resistant pathogens. Although the case-case-control study design has limitations, it is, in our opinion, more informative and less flawed than the standard case-control study design.

Type
Orginal Articles
Information
Infection Control & Hospital Epidemiology , Volume 26 , Issue 4 , April 2005 , pp. 346 - 351
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2005

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References

1.National Nosocomial Infections Surveillance System. National Nosocomial Infections Surveillance (NNIS) System report: data summary from January 1992-April 2000, issued June 2000. Am J Infect Control 2000;28:429–448.Google Scholar
2.Chambers, HEThe changing epidemiology of Staphylococcus aureus? Emerg Infect Dis 2001;7:178–182.Google Scholar
3.Warren, DK, Kollef, MH, Seiler, SM, Fridkin, SK, Fraser, VJ. The epidemiology of vancomycin-resistant Enterococcus colonization in a medical intensive care unit. Infect Control Hosp Epidemiol 2003;24:257–263.CrossRefGoogle Scholar
4.Muto, CA, Jernigan, JA, Ostrowsky, BE, et al.SHEA guideline for preventing nosocomial transmission of multidrug-resistant strains of Staphylococcus aureus and Enterococcus. Infect Control Hosp Epidemiol 2003;24:362–386.CrossRefGoogle ScholarPubMed
5.Puzniak, LA, Leet, T, Mayfield, J, Kollef, M, Mundy, LM. To gown or not to gown: the effect on acquisition of vancomycin-resistant enterococci. Clin Infect Dis 2002;35:18–25.CrossRefGoogle ScholarPubMed
6.Calfee, DP, Giannetta, ET, Durbin, LJ, Germanson, TP, Farr, BM. Control of endemic vancomycin-resistant Enterococcus among inpatients at a university hospital. Clin Infect Dis 2003;37:326–332.Google Scholar
7.Carmeli, Y, Castro, J, Eliopoulos, GM, Samore, MH. Clinical isolation and resistance patterns of and superinfection with 10 nosocomial pathogens after treatment with ceftriaxone versus ampicillin-sulbac-tam. Antimicrob Agents Chemother 2001;45:275–279.Google Scholar
8.Harris, AD. Control group selection is an important but neglected issue in studies of antibiotic resistance. Ann Intern Med 2000;132:925.CrossRefGoogle ScholarPubMed
9.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
10.Rothman, K, Sander, G. Modern Epidemiology, ed. 2. Philadelphia: Lippincott-Raven; 1998:97-99, 150–159.Google Scholar
11.Wacholder, S, Silverman, DT, McLaughlin, JK, Mandel, JS. Selection of controls in case-control studies: II. Types of controls. Am J Epidemiol 1992;135:1029–1041.Google Scholar
12.Kaye, KS, Harris, AD, Gold, H, Carmeli, Y. Risk factors for recovery of ampicillin-sulbactam-resistant Escherichia coli in hospitalized patients. Antimicrob Agents Chemother 2000;44:1004–1009.CrossRefGoogle ScholarPubMed
13.Carmeli, Y, Samore, MH, Huskins, C. The association between antecedent vancomycin treatment and hospital-acquired vancomycin-resistant enterococci: a meta-analysis. Arch Intern Med 1999;159:2461–2468.Google Scholar
14.Harris, AD, Samore, MH, Lipsitch, M, Kaye, KS, Perencevich, E, Carmeli, Y. Control-group selection importance in studies of antimicrobial resistance: examples applied to Pseudomonas aeruginosa, enterococci, and Escherichia coli. Clin Infect Dis 2002;34:1558–1563.Google Scholar
15.Harris, AD, Perencevich, E, Roghmann, MC, Morris, G, Kaye, KS, Johnson, JA. Risk factors for piperacillin-tazobactam-resistant Pseudomonas aeruginosa among hospitalized patients. Antimicrob Agents Chemother 2002;46:854–858.CrossRefGoogle ScholarPubMed
16.Weber, SG, Gold, HS, Hooper, DC, Karchmer, AW, Carmeli, Y. Fluoroquinolones and the risk for methicillin-resistant Staphylococcus aureus in hospitalized patients. Emerg Infect Dis 2003;9:1415–1422.Google Scholar
17.Tacconelli, E, D'Agata, EM, Karchmer, AW. Epidemiological comparison of true methicillin-resistant and methicillin-susceptible coagulase-neg-ative staphylococcal bacteremia at hospital admission. Clin Infect Dis 2003;37:644–649.Google Scholar
18.Harris, AD, Smith, D, Johnson, JA, Bradham, DD, Roghmann, MC. Risk factors for imipenem-resistant Pseudomonas aeruginosa among hospitalized patients. Clin Infect Dis 2002;34:340–345.CrossRefGoogle ScholarPubMed
19.Horan, TC, Emori, TG. Definitions of key terms used in the NNIS System. Am J Infect Control 1997;25:112–116.CrossRefGoogle ScholarPubMed
20.Carmeli, Y, Troillet, N, Eliopoulos, GM, Samore, MH. Emergence of antibiotic-resistant Pseudomonas aeruginosa: comparison of risks associated with different antipseudomonal agents. Antimicrob Agents Chemother 1999;43:1379–1382.Google Scholar
21.Kaye, KS, Cosgrove, S, Harris, A, Eliopoulos, GM, Carmeli, Y. Risk factors for emergence of resistance to broad-spectrum cephalosporins among Enterobacter spp. Antimicrob Agents Chemother 2001;45:2628–2630.Google Scholar
22.Katz, MH. Multivariable analysis: a primer for readers of medical research. Ann Intern Med 2003;138:644–650.Google Scholar
23.Harris, AD, Carmeli, Y, Samore, MH, Kaye, KS, Perencevich, E. Impact of severity of illness and control group misclassification bias in case-control studies of antimicrobial-resistant organisms. Infect Control Hosp Epidemiol 2005;26:342–345.Google Scholar
24.Hamilton, L. Interpreting multinomial logistic regression. Stata Technical Bulletin 1993;13:24–28.Google Scholar
25.Greene, W. Econometric Analysis, ed. 4. Upper Saddle River, NJ: Prentice-Hall; 2000.Google Scholar