1762. The Adjusted Ranking Metric (ARM) and its use in Composite Measures for HAI prevention in the National Healthcare Safety Network (NHSN)
Session: Oral Abstract Session: Healthcare Epidemiology: You Can try this at Home - Innovative Epi Abstracts
Saturday, October 6, 2018: 10:45 AM
Room: S 157

The Adjusted Ranking Metric (ARM) and its use in Composite Measures for HAI prevention in the National Healthcare Safety Network (NHSN)

Mathew R. P. Sapiano1, Jonathan R. Edwards1, Daniel Pollock1

Affiliations:     1. Centers for Disease Control and Prevention, Atlanta, GA, USA

Background: The National Healthcare Safety Network (NHSN), developed and used by the Centers for Disease Control and Prevention (CDC) for surveillance of healthcare-associated infections (HAIs), provides benchmark measures, such as  standardized infection ratio (SIRs), that CDC and its partners in healthcare and public health use for prevention purposes. NHSN provides benchmarks for each HAI measure separately, but a composite HAI measure could provide a more rounded assessment of HAI problems and prevention opportunities.

Methods: Several issues must be addressed to produce a sound HAI composite measure, the most of which is that the SIR can be inaccurate for facilities with low HAI exposure (e.g., low device days, operative procedure volume).  We remedy this issue with the Adjusted Ranking Metric (ARM), a new measure that reliability-adjusts the SIR using a Bayesian mixed effects model.  The ARM is particularly useful in the production of a composite measure because ARMs are well-suited to comparison between facilities.  The composite was therefore produced by applying adjustments to the ARMs to account for (1) differences between exposure to separate HAI types within facilities and (2) differences in frequency and severity between HAIs.  The composite is calculated for six HAIs based on 2015 data. 

Results: Case studies of three facilities (Table) show that the new composite measure provides a meaningful measure of overall facility performance that is less prone to the biases that afflict simple combinations of SIRs.

Conclusion: We introduce a framework for calculating a composite HAI measure that is flexible, customizable and transparent.  The current implementation of the framework is intended to assist in prevention efforts and can be easily modified to include cost weights, if desired.  Flexibility in weighting the HAIs provides an opportunity for different stakeholders to customize the composite measure to their own needs. 

Table. Example of SIRs, ARMs for six HAIs with composite measures for three sample facilities.

Mathew R. P. Sapiano, PhD, Jonathan R. Edwards, MStat and Daniel Pollock, MD, Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, GA


M. R. P. Sapiano, None

J. R. Edwards, None

D. Pollock, None

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