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Using data linkage methodologies to augment healthcare-associated infection surveillance data

Published online by Cambridge University Press:  29 July 2019

Seungwon Lee ,
Paul Ronksley ,
John Conly ,
Stephanie Garies ,
Hude Quan ,
Peter Faris ,
Bing Li  and
Elizabeth Henderson
Seungwon Lee
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
Paul Ronksley
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada O’Brien Institute of Public Health, University of Calgary, Calgary, Alberta, Canada
John Conly
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada Alberta Health Services Infection Prevention and Control, Calgary, Alberta, Canada O’Brien Institute of Public Health, University of Calgary, Calgary, Alberta, Canada The Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada
Stephanie Garies
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada
Hude Quan
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada O’Brien Institute of Public Health, University of Calgary, Calgary, Alberta, Canada
Peter Faris
Affiliation:
Alberta Health Services Research Facilitation Analytics (DIMR), Calgary, Alberta, Canada
Bing Li
Affiliation:
Alberta Health Services Research Facilitation Analytics (DIMR), Calgary, Alberta, Canada
Elizabeth Henderson*
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
*
Author for correspondence: Dr Elizabeth Henderson, Email: ehenders@ucalgary.ca.
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Abstract

Background and objectives:

The landscape of antimicrobial resistance (AMR) surveillance is changing rapidly. The primary objective of this study was to assess the benefit of linking population-based infection prevention and control surveillance data on methicillin-resistant Staphylococcus aureus (MRSA) to hospital discharge abstract data (DAD). We assessed the value of this novel data linkage for the characterization of hospital-acquired (HA) and community-acquired MRSA (CA-MRSA) cases.

Methods:

Incident inpatient MRSA surveillance data for all adults (≥18 years) from 4 acute-care facilities in Calgary, Alberta, between April 1, 2011, and March 31, 2017, were linked to DAD. Personal health number (PHN) and gender were used to identify specific individuals, and specimen collection time-points were used to identify specific hospitalization records. A third common variable on admission date between these databases was used to validate the linkage process. Descriptive statistics were used to characterize HA-MRSA and CA-MRSA cases identified through the linkage process.

Results:

A total of 2,430 surveillance records (94.6%) were successfully linked to the correct hospitalization period. By linking surveillance and administrative data, we were able to identify key differences between patients with HA- and CA-MRSA. These differences are consistent with previously reported findings in the literature. Data linkage to DAD may be a novel tool to enhance and augment the details of base surveillance data.

Conclusion and recommendations:

This is the first Canadian study linking a frontline healthcare-associated infection AMR surveillance database to an administrative population database. This work represents an important methodological step toward complementing traditional AMR surveillance data practices. Data linkage to other data types, such as primary care, emergency, social, and biological data, may be the basis of achieving more precise data focused around AMR.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 40 , Issue 10 , October 2019 , pp. 1144 - 1150
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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Footnotes

PREVIOUS PRESENTATION. This study was presented at the 2018 International Population Data Linkage Conference on September 13, 2018, in Banff, Alberta, Canada.

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