Record linkage is feasible with non‐identifiable trauma and rehabilitation datasets

Abstract Objectives: 1) Describe probabilistic linkage (PL) for road trauma and rehabilitation records in New South Wales (NSW) Australia. 2) Determine the accuracy of linkage for these records. Methods: Data were extracted from the NSW Trauma Registry for all road trauma admissions for the years 20...

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Main Authors: Jane Wu, Steven G. Faux, Ian Harris, Christopher J. Poulos, Tara Alexander
Format: Article
Language:English
Published: Elsevier 2016-06-01
Series:Australian and New Zealand Journal of Public Health
Subjects:
Online Access:https://doi.org/10.1111/1753-6405.12510
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author Jane Wu
Steven G. Faux
Ian Harris
Christopher J. Poulos
Tara Alexander
author_facet Jane Wu
Steven G. Faux
Ian Harris
Christopher J. Poulos
Tara Alexander
author_sort Jane Wu
collection DOAJ
description Abstract Objectives: 1) Describe probabilistic linkage (PL) for road trauma and rehabilitation records in New South Wales (NSW) Australia. 2) Determine the accuracy of linkage for these records. Methods: Data were extracted from the NSW Trauma Registry for all road trauma admissions for the years 2009–2012 and from Australasian Rehabilitation Outcomes Centre for January 2009 to June 2013. PL was performed using: age; sex; residential postcode; and date of acute discharge = date of admission to rehabilitation. False matches were cases that linked but were not true matches; they were determined by manual review. Reasons for incomplete linkages were explored. The benefits and limitations of the linked study dataset are described. Results: Of 3,256 road trauma records, 683 were matched to rehabilitation records. Using the field of ‘discharge destination’ from the trauma records, 265 patients with unmatched records were discharged to inpatient rehabilitation (missed matches). This gave an overall 72% linkage rate (or sensitivity) using PL. There were 16 cases of false matches, giving a specificity of 99%. Conclusion: It was feasible to use PL to link road trauma and rehabilitation datasets in the absence of identifiers. However, this needed to be combined with careful manual review before the linked dataset could be used to make inferences on trauma rehabilitation outcomes. Implication: PL may be a cost‐effective way to capture inpatient rehabilitation outcomes of multi‐trauma patients.
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spelling doaj.art-93dce7ba70434237abb7b77c1c049cb22023-09-03T00:04:40ZengElsevierAustralian and New Zealand Journal of Public Health1326-02001753-64052016-06-0140324524910.1111/1753-6405.12510Record linkage is feasible with non‐identifiable trauma and rehabilitation datasetsJane Wu0Steven G. Faux1Ian Harris2Christopher J. Poulos3Tara Alexander4St. Vincent's Hospital New South WalesSt. Vincent's Hospital New South WalesLiverpool Hospital New South WalesSchool of Public Health and Community Medicine University of New South WalesAustralian Health Services Research Institute University of Wollongong New South WalesAbstract Objectives: 1) Describe probabilistic linkage (PL) for road trauma and rehabilitation records in New South Wales (NSW) Australia. 2) Determine the accuracy of linkage for these records. Methods: Data were extracted from the NSW Trauma Registry for all road trauma admissions for the years 2009–2012 and from Australasian Rehabilitation Outcomes Centre for January 2009 to June 2013. PL was performed using: age; sex; residential postcode; and date of acute discharge = date of admission to rehabilitation. False matches were cases that linked but were not true matches; they were determined by manual review. Reasons for incomplete linkages were explored. The benefits and limitations of the linked study dataset are described. Results: Of 3,256 road trauma records, 683 were matched to rehabilitation records. Using the field of ‘discharge destination’ from the trauma records, 265 patients with unmatched records were discharged to inpatient rehabilitation (missed matches). This gave an overall 72% linkage rate (or sensitivity) using PL. There were 16 cases of false matches, giving a specificity of 99%. Conclusion: It was feasible to use PL to link road trauma and rehabilitation datasets in the absence of identifiers. However, this needed to be combined with careful manual review before the linked dataset could be used to make inferences on trauma rehabilitation outcomes. Implication: PL may be a cost‐effective way to capture inpatient rehabilitation outcomes of multi‐trauma patients.https://doi.org/10.1111/1753-6405.12510record linkageprobabilistic linkagetraumarehabilitation
spellingShingle Jane Wu
Steven G. Faux
Ian Harris
Christopher J. Poulos
Tara Alexander
Record linkage is feasible with non‐identifiable trauma and rehabilitation datasets
Australian and New Zealand Journal of Public Health
record linkage
probabilistic linkage
trauma
rehabilitation
title Record linkage is feasible with non‐identifiable trauma and rehabilitation datasets
title_full Record linkage is feasible with non‐identifiable trauma and rehabilitation datasets
title_fullStr Record linkage is feasible with non‐identifiable trauma and rehabilitation datasets
title_full_unstemmed Record linkage is feasible with non‐identifiable trauma and rehabilitation datasets
title_short Record linkage is feasible with non‐identifiable trauma and rehabilitation datasets
title_sort record linkage is feasible with non identifiable trauma and rehabilitation datasets
topic record linkage
probabilistic linkage
trauma
rehabilitation
url https://doi.org/10.1111/1753-6405.12510
work_keys_str_mv AT janewu recordlinkageisfeasiblewithnonidentifiabletraumaandrehabilitationdatasets
AT stevengfaux recordlinkageisfeasiblewithnonidentifiabletraumaandrehabilitationdatasets
AT ianharris recordlinkageisfeasiblewithnonidentifiabletraumaandrehabilitationdatasets
AT christopherjpoulos recordlinkageisfeasiblewithnonidentifiabletraumaandrehabilitationdatasets
AT taraalexander recordlinkageisfeasiblewithnonidentifiabletraumaandrehabilitationdatasets