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Published in: BMC Medical Informatics and Decision Making 1/2017

Open Access 01-12-2017 | Research article

Using record linkage to validate notification and laboratory data for a more accurate assessment of notifiable infectious diseases

Authors: Faye J. Lim, Christopher C. Blyth, Avram Levy, Parveen Fathima, Nicholas de Klerk, Carolien Giele, Hannah C. Moore

Published in: BMC Medical Informatics and Decision Making | Issue 1/2017

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Abstract

Background

Infectious disease burden is commonly assessed using notification data. Using retrospective record linkage in Western Australia, we described how well notification data captures laboratory detections of influenza, pertussis and invasive pneumococcal disease (IPD).

Methods

We linked data from the Western Australian Notifiable Infectious Diseases Database (WANIDD) and the PathWest Laboratory Database (PathWest) pertaining to the Triple I birth cohort, born in Western Australia in 1996–2012. These were combined to calculate the number of unique cases captured in each dataset alone or in both datasets. To assess the impact of under-ascertainment, we compared incidence rates calculated using WANIDD data alone and using combined data.

Results

Overall, there were 5550 influenza, 513 IPD (2001–2012) and 4434 pertussis cases (2000–2012). Approximately 2% of pertussis and IPD cases and 7% of influenza cases were solely recorded in PathWest. Notification of influenza and pertussis cases to WANIDD improved over time. Overall incidence rates of influenza in children aged <5 years using both datasets was 10% higher than using WANIDD data alone (IRR = 1.1, 95% CI = 1.1–1.2).

Conclusions

This is the first time WANIDD data have been validated against routinely collected laboratory data. We anticipated all cases would be captured in WANIDD but found additional laboratory-confirmed cases that were not notified. Studies investigating pathogen-specific infectious disease would benefit from using multiple data sources.
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Metadata
Title
Using record linkage to validate notification and laboratory data for a more accurate assessment of notifiable infectious diseases
Authors
Faye J. Lim
Christopher C. Blyth
Avram Levy
Parveen Fathima
Nicholas de Klerk
Carolien Giele
Hannah C. Moore
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2017
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-017-0484-7

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