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Novel Approaches to Healthcare-Associated Infection Surveillance Validation Using Cohen's Kappa

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      Background

      The Infection Prevention and Control Department (IPC) at an acute care pediatric facility is required, by state law, to report all healthcare-associated infections (HAI) using the National Healthcare Safety Network (NHSN) surveillance definitions. Due to the definitions being subjective, interpretation could impact data integrity. A novel surveillance validation program was created in 2018 as an internal approach for validation and continuous surveillance education. The aim of this project is to assess the quality assurance program for consistently identifying HAIs between Infection Preventionists (IP) across IPC.

      Methods

      Validators, who are expert IPs, investigated randomly sampled infection events that were already investigated by an IP in the department. They performed chart review and documented in a data collection tool if the infections were reportable to NHSN with supporting rationale. Discrepancies were identified and cases were adjudicated. IPC utilized a statistical method, Cohen's Kappa, to determine inter-rater reliability between the validator response and original documentation monthly. Inter-rater reliability was examined per month and plotted to show trends in surveillance competency.

      Results

      One hundred and thirty-one infections were chosen for validation between July 2020 and April 2021. Cohen's kappa for two raters was calculated for each month. The median and interquartile range for the monthly kappa values were 1.00 [0.83 - 1.00] which is well above the values of 0.60 - 0.80 considered to have substantial agreement. The minimum and maximum kappa values were 0.76 and 1.00 respectively.

      Conclusions

      Overall, this study confirms that the use of Cohen's Kappa is a unique asset to the department's surveillance validation program and allows us to assess inter-rater reliability continually and easily. IPC programs could benefit from statistical analyses in evaluating data integrity and management.
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