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Predicting Multidrug-Resistant Gram-Negative Bacterial Colonization and Associated Infection on Hospital Admission: Methodological Issues

Published online by Cambridge University Press:  14 January 2018

Kamyar Mansori ,
Erfan Ayubi  and
Saeid Safiri
Kamyar Mansori
Affiliation:
Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
Erfan Ayubi
Affiliation:
Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Saeid Safiri*
Affiliation:
Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran.
*
Address correspondence to Saeid Safiri, Assistant Professor of Epidemiology, Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran ([email protected]).
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Type
Letters to the Editor
Information
Infection Control & Hospital Epidemiology , Volume 39 , Issue 2 , February 2018 , pp. 241
Copyright
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved 

To the Editor—We read with great interest the article titled “Predicting Multidrug-Resistant Gram-Negative Bacterial Colonization and Associated Infection on Hospital Admission” by Tseng et alReference Tseng, Chen and Yang 1 published in a recent issue of this journal. We would like to congratulate the authors on their valuable work; however, we think some methodological and statistical issues should be considered to avoid misinterpretation.

As shown in the Table 3 of the article, when a predictor meets a univariate criterion of P<.01, the predictor is further considered for multivariable analysis. Here, we are concerned that the authors considered a very conservative P value for univariate screening of candidate predictors. They argued that when a conservative P value (eg, <.01 or <.05) is selected in univariate analysis, only the predictors with relatively large effect will be included in the multivariable analysis. In such a situation, the estimated regression coefficients of selected predictors can have bias away from the null,Reference Steyerberg 2 , Reference Pakzad and Safiri 3 which is known as testimation bias.

Considering a liberal P value (eg, <.10 or <.20) in univariable analysis can effectively compensate for testimation bias.Reference Steyerberg 2 In other words, we can be sure that predictors with relatively large effect (eg, P<.01) and predictors with relatively small effect (eg, .10<P<.20) can be tested in multivariable analysis after univariate screening with, for example, P<.20. In the study,Reference Tseng, Chen and Yang 1 although long-term hemodialysis appear to be an uninteresting predictor for risk of multidrug-resistant gram-negative bacteria (MDR-GNB) colonization in univariable analysis, it may have a significant effect but only in the presence of other predictors.

We acknowledge that the study provides very interesting results, but the estimated associations for predictors of MDR-GNB colonization may be different from those reported in the study due to testimation bias.

ACKNOWLEDGMENTS

Financial support: No financial support was provided relevant to this article.

Potential conflicts of interest: All authors report no conflicts of interest relevant to this article.

References

REFERENCES

1. Tseng, WP, Chen, YC, Yang, BJ, et al. Predicting multidrug-resistant gram-negative bacterial colonization and associated infection on hospital admission. Infect Control Hosp Epidemiol 2017;38:1216–1225.CrossRefGoogle ScholarPubMed
2. Steyerberg, E. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer Science; 2008.Google Scholar
3. Pakzad, R, Safiri, S. Incidence and risk factors for surgical site infection posthysterectomy in a tertiary care center: methodologic issues. Am J Infect Control 2017;45:580–581.CrossRefGoogle Scholar