Maximising data value and avoiding data waste: a validation study in stroke research

Monique F. Kilkenny, Joosup Kim, Nadine E. Andrew, Vijaya Sundararajan, Amanda G. Thrift, Judith M. Katzenellenbogen, Felicity Flack, Melina Gattellari, James H. Boyd, Phil Anderson, Natasha Lannin, Mark Sipthorp, Ying Chen, Trisha Johnston, Craig S. Anderson, Sandy Middleton, Geoffrey A. Donnan, Dominique A. Cadilhac

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Objectives: To determine the feasibility of linking data from the Australian Stroke Clinical Registry (AuSCR), the National Death Index (NDI), and state-managed databases for hospital admissions and emergency presentations; to evaluate data completeness and concordance between datasets for common variables.

Design, setting, participants: Cohort design; probabilistic/deterministic data linkage of merged records for patients treated in hospital for stroke or transient ischaemic attack from New South Wales, Queensland, Victoria, and Western Australia.

Main outcome measures: Descriptive statistics for data matching success; concordance of demographic variables common to linked databases; sensitivity and specificity of AuSCR in-hospital death data for predicting NDI registrations.

Results: Data for 16 214 patients registered in the AuSCR during 2009-2013 were linked with one or more state datasets: 15 482 matches (95%) with hospital admissions data, and 12 902 matches (80%) with emergency department presentations data were made. Concordance of AuSCR and hospital admissions data exceeded 99% for sex, age, in-hospital death (each k = 0.99), and Indigenous status (k = 0.83). Of 1498 registrants identified in the AuSCR as dying in hospital, 1440 (96%) were also recorded by the NDI as dying in hospital. In-hospital death in AuSCR data had 98.7% sensitivity and 99.6% specificity for predicting in-hospital death in the NDI.

Conclusion: We report the first linkage of data from an Australian national clinical quality disease registry with routinely collected data from several national and state government health datasets. Data linkage enriches the clinical registry dataset and provides additional information beyond that for the acute care setting and quality of life at follow-up, allowing clinical outcomes for people with stroke (mortality and hospital contacts) to be more comprehensively assessed.

Original languageEnglish
Pages (from-to)27-31
Number of pages5
JournalMedical Journal of Australia
Volume210
Issue number1
DOIs
Publication statusPublished - 14 Jan 2019

Cite this

Kilkenny, M. F., Kim, J., Andrew, N. E., Sundararajan, V., Thrift, A. G., Katzenellenbogen, J. M., ... Cadilhac, D. A. (2019). Maximising data value and avoiding data waste: a validation study in stroke research. Medical Journal of Australia, 210(1), 27-31. https://doi.org/10.5694/mja2.12029
Kilkenny, Monique F. ; Kim, Joosup ; Andrew, Nadine E. ; Sundararajan, Vijaya ; Thrift, Amanda G. ; Katzenellenbogen, Judith M. ; Flack, Felicity ; Gattellari, Melina ; Boyd, James H. ; Anderson, Phil ; Lannin, Natasha ; Sipthorp, Mark ; Chen, Ying ; Johnston, Trisha ; Anderson, Craig S. ; Middleton, Sandy ; Donnan, Geoffrey A. ; Cadilhac, Dominique A. / Maximising data value and avoiding data waste : a validation study in stroke research. In: Medical Journal of Australia. 2019 ; Vol. 210, No. 1. pp. 27-31.
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abstract = "Objectives: To determine the feasibility of linking data from the Australian Stroke Clinical Registry (AuSCR), the National Death Index (NDI), and state-managed databases for hospital admissions and emergency presentations; to evaluate data completeness and concordance between datasets for common variables.Design, setting, participants: Cohort design; probabilistic/deterministic data linkage of merged records for patients treated in hospital for stroke or transient ischaemic attack from New South Wales, Queensland, Victoria, and Western Australia.Main outcome measures: Descriptive statistics for data matching success; concordance of demographic variables common to linked databases; sensitivity and specificity of AuSCR in-hospital death data for predicting NDI registrations.Results: Data for 16 214 patients registered in the AuSCR during 2009-2013 were linked with one or more state datasets: 15 482 matches (95{\%}) with hospital admissions data, and 12 902 matches (80{\%}) with emergency department presentations data were made. Concordance of AuSCR and hospital admissions data exceeded 99{\%} for sex, age, in-hospital death (each k = 0.99), and Indigenous status (k = 0.83). Of 1498 registrants identified in the AuSCR as dying in hospital, 1440 (96{\%}) were also recorded by the NDI as dying in hospital. In-hospital death in AuSCR data had 98.7{\%} sensitivity and 99.6{\%} specificity for predicting in-hospital death in the NDI.Conclusion: We report the first linkage of data from an Australian national clinical quality disease registry with routinely collected data from several national and state government health datasets. Data linkage enriches the clinical registry dataset and provides additional information beyond that for the acute care setting and quality of life at follow-up, allowing clinical outcomes for people with stroke (mortality and hospital contacts) to be more comprehensively assessed.",
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Kilkenny, MF, Kim, J, Andrew, NE, Sundararajan, V, Thrift, AG, Katzenellenbogen, JM, Flack, F, Gattellari, M, Boyd, JH, Anderson, P, Lannin, N, Sipthorp, M, Chen, Y, Johnston, T, Anderson, CS, Middleton, S, Donnan, GA & Cadilhac, DA 2019, 'Maximising data value and avoiding data waste: a validation study in stroke research' Medical Journal of Australia, vol. 210, no. 1, pp. 27-31. https://doi.org/10.5694/mja2.12029

Maximising data value and avoiding data waste : a validation study in stroke research. / Kilkenny, Monique F.; Kim, Joosup; Andrew, Nadine E.; Sundararajan, Vijaya; Thrift, Amanda G.; Katzenellenbogen, Judith M.; Flack, Felicity; Gattellari, Melina; Boyd, James H.; Anderson, Phil; Lannin, Natasha; Sipthorp, Mark; Chen, Ying; Johnston, Trisha; Anderson, Craig S.; Middleton, Sandy; Donnan, Geoffrey A.; Cadilhac, Dominique A.

In: Medical Journal of Australia, Vol. 210, No. 1, 14.01.2019, p. 27-31.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Maximising data value and avoiding data waste

T2 - a validation study in stroke research

AU - Kilkenny, Monique F.

AU - Kim, Joosup

AU - Andrew, Nadine E.

AU - Sundararajan, Vijaya

AU - Thrift, Amanda G.

AU - Katzenellenbogen, Judith M.

AU - Flack, Felicity

AU - Gattellari, Melina

AU - Boyd, James H.

AU - Anderson, Phil

AU - Lannin, Natasha

AU - Sipthorp, Mark

AU - Chen, Ying

AU - Johnston, Trisha

AU - Anderson, Craig S.

AU - Middleton, Sandy

AU - Donnan, Geoffrey A.

AU - Cadilhac, Dominique A.

PY - 2019/1/14

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N2 - Objectives: To determine the feasibility of linking data from the Australian Stroke Clinical Registry (AuSCR), the National Death Index (NDI), and state-managed databases for hospital admissions and emergency presentations; to evaluate data completeness and concordance between datasets for common variables.Design, setting, participants: Cohort design; probabilistic/deterministic data linkage of merged records for patients treated in hospital for stroke or transient ischaemic attack from New South Wales, Queensland, Victoria, and Western Australia.Main outcome measures: Descriptive statistics for data matching success; concordance of demographic variables common to linked databases; sensitivity and specificity of AuSCR in-hospital death data for predicting NDI registrations.Results: Data for 16 214 patients registered in the AuSCR during 2009-2013 were linked with one or more state datasets: 15 482 matches (95%) with hospital admissions data, and 12 902 matches (80%) with emergency department presentations data were made. Concordance of AuSCR and hospital admissions data exceeded 99% for sex, age, in-hospital death (each k = 0.99), and Indigenous status (k = 0.83). Of 1498 registrants identified in the AuSCR as dying in hospital, 1440 (96%) were also recorded by the NDI as dying in hospital. In-hospital death in AuSCR data had 98.7% sensitivity and 99.6% specificity for predicting in-hospital death in the NDI.Conclusion: We report the first linkage of data from an Australian national clinical quality disease registry with routinely collected data from several national and state government health datasets. Data linkage enriches the clinical registry dataset and provides additional information beyond that for the acute care setting and quality of life at follow-up, allowing clinical outcomes for people with stroke (mortality and hospital contacts) to be more comprehensively assessed.

AB - Objectives: To determine the feasibility of linking data from the Australian Stroke Clinical Registry (AuSCR), the National Death Index (NDI), and state-managed databases for hospital admissions and emergency presentations; to evaluate data completeness and concordance between datasets for common variables.Design, setting, participants: Cohort design; probabilistic/deterministic data linkage of merged records for patients treated in hospital for stroke or transient ischaemic attack from New South Wales, Queensland, Victoria, and Western Australia.Main outcome measures: Descriptive statistics for data matching success; concordance of demographic variables common to linked databases; sensitivity and specificity of AuSCR in-hospital death data for predicting NDI registrations.Results: Data for 16 214 patients registered in the AuSCR during 2009-2013 were linked with one or more state datasets: 15 482 matches (95%) with hospital admissions data, and 12 902 matches (80%) with emergency department presentations data were made. Concordance of AuSCR and hospital admissions data exceeded 99% for sex, age, in-hospital death (each k = 0.99), and Indigenous status (k = 0.83). Of 1498 registrants identified in the AuSCR as dying in hospital, 1440 (96%) were also recorded by the NDI as dying in hospital. In-hospital death in AuSCR data had 98.7% sensitivity and 99.6% specificity for predicting in-hospital death in the NDI.Conclusion: We report the first linkage of data from an Australian national clinical quality disease registry with routinely collected data from several national and state government health datasets. Data linkage enriches the clinical registry dataset and provides additional information beyond that for the acute care setting and quality of life at follow-up, allowing clinical outcomes for people with stroke (mortality and hospital contacts) to be more comprehensively assessed.

KW - NATIONAL DEATH INDEX

KW - QUALITY

KW - LINKAGE

U2 - 10.5694/mja2.12029

DO - 10.5694/mja2.12029

M3 - Article

VL - 210

SP - 27

EP - 31

JO - Medical Journal Australia

JF - Medical Journal Australia

SN - 0025-729X

IS - 1

ER -