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Maximising data value and avoiding data waste: a validation study in stroke research
Author(s) -
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
Publication year - 2019
Publication title -
medical journal of australia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.904
H-Index - 131
eISSN - 1326-5377
pISSN - 0025-729X
DOI - 10.5694/mja2.12029
Subject(s) - medicine , concordance , record linkage , emergency medicine , medical emergency , national death index , coroner , cohort , emergency department , stroke (engine) , data quality , hazard ratio , poison control , injury prevention , confidence interval , population , environmental health , mechanical engineering , metric (unit) , operations management , psychiatry , engineering , economics
Objectives To determine the feasibility of linking data from the Australian Stroke Clinical Registry (Au SCR ), 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 Au SCR in‐hospital death data for predicting NDI registrations. Results Data for 16 214 patients registered in the Au SCR 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 Au SCR and hospital admissions data exceeded 99% for sex, age, in‐hospital death (each κ = 0.99), and Indigenous status ( κ = 0.83). Of 1498 registrants identified in the Au SCR as dying in hospital, 1440 (96%) were also recorded by the NDI as dying in hospital. In‐hospital death in Au SCR 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.