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

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 κ = 0.99), and Indigenous status (κ = 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.

Sex differences in quality of life after stroke were explained by patient factors, not clinical care: evidence from the Australian Stroke Clinical Registry

“…Information on risk factors for stroke and comorbidities were only available up to 2013, obtained from administrative hospital records through a parallel data linkage project (Stroke123) [15]. These were based on the International Statistical Classification of Diseases and Related Health Problems 10th Revision codes associated with emergency department presentations and hospital admissions using available data for 5 years prior to, and including, the stroke event registered in the AuSCR.…”

Sex Differences in Causes of Death After Stroke: Evidence from a National, Prospective Registry

“…Through the Stroke123 data linkage project, 13 information on risk factors for stroke and comorbidities was obtained from administrative hospital records. These were based on the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) codes using a ''look-back period'' of 5 years before and including the stroke event.…”

Protocol for evaluation of enhanced models of primary care in the management of stroke and other chronic disease (PRECISE)

“…[23] Hospital data will be linked by each of the state data linkage units. Prior data linkage accuracy between AuSCR registrants and these hospital admission datasets was 95% for Victoria using stepwise deterministic methods and 98% for Queensland using probabilistic matching [24]. In Australia, unique identifiers are unavailable.…”