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Quantifying Sources of Bias in National Healthcare Safety Network Laboratory-Identified Clostridium difficile Infection Rates

Published online by Cambridge University Press:  10 May 2016

Valerie B. Haley ,
A. Gregory DiRienzo ,
Emily C. Lutterloh  and
Rachel L. Stricof
Valerie B. Haley*
Affiliation:
Bureau of Healthcare-Associated Infections, New York State Department of Health, Albany, New York
A. Gregory DiRienzo
Affiliation:
Department of Epidemiology and Biostatistics, State University of New York at Albany, New York
Emily C. Lutterloh
Affiliation:
Bureau of Healthcare-Associated Infections, New York State Department of Health, Albany, New York Department of Epidemiology and Biostatistics, State University of New York at Albany, New York
Rachel L. Stricof
Affiliation:
Council of State and Territorial Epidemiologists, Atlanta, Georgia
*
Bureau of Healthcare-Associated Infections, New York State Department of Health, Corning Tower, Room 523, Albany, NY 12237 ([email protected])
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Abstract

Objective.

To assess the effect of multiple sources of bias on state- and hospital-specific National Healthcare Safety Network (NHSN) laboratory-identified Clostridium difficile infection (CDI) rates.

Design.

Sensitivity analysis.

Setting.

A total of 124 New York hospitals in 2010.

Methods.

New York NHSN CDI events from audited hospitals were matched to New York hospital discharge billing records to obtain additional information on patient age, length of stay, and previous hospital discharges. “Corrected” hospital-onset (HO) CDI rates were calculated after (1) correcting inaccurate case reporting found during audits, (2) incorporating knowledge of laboratory results from outside hospitals, (3) excluding days when patients were not at risk from the denominator of the rates, and (4) adjusting for patient age. Data sets were simulated with each of these sources of bias reintroduced individually and combined. The simulated rates were compared with the corrected rates. Performance (ie, better, worse, or average compared with the state average) was categorized, and misclassification compared with the corrected data set was measured.

Results.

Counting days patients were not at risk in the denominator reduced the state HO rate by 45% and resulted in 8% misclassification. Age adjustment and reporting errors also shifted rates (7% and 6% misclassification, respectively).

Conclusions.

Changing the NHSN protocol to require reporting of age-stratified patient-days and adjusting for patient-days at risk would improve comparability of rates across hospitals. Further research is needed to validate the risk-adjustment model before these data should be used as hospital performance measures.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 35 , Issue 1 , January 2014 , pp. 1 - 7
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2014

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