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Browne, M orcid:0000-0002-2668-6229; Unsworth, CA orcid:0000-0001-6430-2823 ; Introduction: It is difficult to determine if, or when, individuals with stroke are ready to undergo on-road fitness-to-drive assessment. The Occupational Therapy – Driver Off Road Assessment Battery was developed to determine client suitability to resume driving. The predictive validity of the Battery needs to be verified for people with stroke. Aim: Examine the predictive validity of the Occupational Therapy – Driver Off Road Assessment Battery for on-road performance among people with stroke. Method: Off-road data were collected from 148 people post stroke on the Battery and the outcome of their on-road assessment was recorded as: fit-to-drive or not fit-to-drive. Results: The majority of participants (76%) were able to resume driving. A classification and regression tree (CART) analysis using four subtests (three cognitive and one physical) from the Battery demonstrated an area under the curve (AUC) of 0.8311. Using a threshold of 0.5, the model correctly predicted 98/112 fit-to-drive (87.5%) and 26/36 people not fit-to-drive (72.2%). Conclusion: The three cognitive subtests from the Occupational Therapy – Driver Off Road Assessment Battery and potentially one of the physical tests have good predictive validity for client fitness-to-drive. These tests can be used to screen client suitability for proceeding to an on-road test following stroke.Implications for Rehabilitation:Following stroke, drivers should be counseled (including consideration of local legislation) concerning return to driving.The Occupational Therapy – Driver Off Road Assessment Battery can be used in the clinic to screen people for suitability to undertake on road assessment.Scores on four of the Occupational Therapy – Driver Off Road Assessment Battery subtests are predictive of resumption of driving following stroke. © 2018 Informa UK Limited, trading as Taylor & Francis Group

Introduction: It is difficult to determine if, or when, individuals with stroke are ready to undergo on-road fitness-to-drive assessment. The Occupational Therapy - Driver Off Road Assessment Battery was developed to determine client suitability to resume driving. The predictive validity of the Battery needs to be verified for people with stroke. Aim: Examine the predictive validity of the Occupational Therapy - Driver Off Road Assessment Battery for on-road performance among people with stroke. Method: Off-road data were collected from 148 people post stroke on the Battery and the outcome of their on-road assessment was recorded as: fit-to-drive or not fit-to-drive. Results: The majority of participants (76%) were able to resume driving. A classification and regression tree (CART) analysis using four subtests (three cognitive and one physical) from the Battery demonstrated an area under the curve (AUC) of 0.8311. Using a threshold of 0.5, the model correctly predicted 98/112 fit-to-drive (87.5%) and 26/36 people not fit-to-drive (72.2%). Conclusion: The three cognitive subtests from the Occupational Therapy - Driver Off Road Assessment Battery and potentially one of the physical tests have good predictive validity for client fitness-to-drive. These tests can be used to screen client suitability for proceeding to an on-road test following stroke. Implications for Rehabilitation: Following stroke, drivers should be counseled (including consideration of local legislation) concerning return to driving. The Occupational Therapy - Driver Off Road Assessment Battery can be used in the clinic to screen people for suitability to undertake on road assessment. Scores on four of the Occupational Therapy - Driver Off Road Assessment Battery subtests are predictive of resumption of driving following stroke.

IntroductionRecent advances in Australia mean that it is possible to link national clinical registries with government held administrative datasets. However, formal evaluations of such activities and the potential impact for research are lacking.
Objectives and ApproachWe aimed to assess the feasibility and accuracy of linking registrants from the Australian Stroke Clinical Registry (AuSCR) with the Medicare enrolment file. Following data custodian and ethics approvals, personal linkage identifiers were submitted to the Australian Institute of Health and Welfare (AIHW). De-identified data from AuSCR and the AIHW were submitted into the Secure Unified Research Environment and merged using project specific person-based IDs. We calculated the proportion of patients linked with the Medicare enrolment file that were present in the associated Medicare and medication dispensing datasets and the proportion with claims after their date of death. Logistic regression was used to identify factors associated with a non-merged patient.
Results17,980 AuSCR registrants (January 2010-July 2014) were submitted for linkage (median age 76 years; 46% female; 67% ischaemic stroke; 16% TIA). Of these, 93% were merged with Medicare (N=16,648) and 95% with subsidised medication dispensing claims data (N=17,079). In those who died, 127 (0.8%) had one or more Medicare claim and 411 (2.4%) had one or more medications dispensed after their death date. Asian born registrants were less likely to be merged with Medicare (adjusted Odds Ratio [aOR]: 0.54; 95% Confidence Interval [CI]: 0.40, 0.72) than Australian born registrants. Those aged ≥85 years were less likely to be merged with Medicare data than those aged <65 years (aOR 0.24; 95% CI: 0.19, 0.29) but were more likely to be merged with dispensing data (aOR: 2.22 (95% CI: 1.73, 2.84).
Conclusion/ImplicationsLinkage between a national clinical quality registry and the Medicare spine is feasible. These linkages will provide novel insights into post-stroke care.

IntroductionThe growing burden of chronic diseases means some governments have been providing financial incentives for multidisciplinary care and self-management support delivered within primary care. Currently, population-based evaluations of the effectiveness of these policies are lacking.
AimTo outline the methodological approach for our study that is designed to evaluate the effectiveness (including cost) of primary care policies for chronic diseases in Australia using stroke as a case study.
MethodsPerson-level linkages will be undertaken between registrants from the Australian Stroke Clinical Registry (AuSCR) and (i) Government-held Medicare Australia claims data, to identify receipt or not of chronic disease management and care coordination primary care items; (ii) state government-held hospital data, to define outcomes; and (iii) government-held pharmaceutical and aged care claims data, to define covariates. N=1500 randomly selected AuSCR registrants will be sent surveys to obtain patient experience information. In Australia, unique identifiers are unavailable. Therefore, personal-identifiers will be submitted to government data linkage units. Researchers will merge the de-identified datasets for analysis using a project identifier. An economic evaluation will also be undertaken.
AnalysisThe index event will be the first stroke recorded in the AuSCR. Multivariable competing risks Poisson regression for multiple events, adjusted by a propensity score, will be used to test for differences in the rates of hospital presentations and medication adherence for different care (policy) types. Our estimated sample size of 25,000 patients will provide 80% estimated power (α >0.05) to detect a 6-8% difference in rates. The incremental costs per Quality-adjusted life years gained of community-based care following the acute event will be estimated from a health sector perspective.
ConclusionCompletion of this study will provide a novel and comprehensive evaluation of the effectiveness and cost-effectiveness of Australian primary care policies. Its success will enable us to highlight the value of data-linkage for this type of research.

Background Stroke is a high-cost condition. Detailed patient-level assessments of the costs of care received and outcomes achieved provide useful information for organisation and optimisation of the health system. Objectives To describe the costs of hospital care for stroke and transient ischaemic attack (TIA) and investigate factors associated with costs. Methods Retrospective cohort study using data from the Australian Stroke Clinical Registry (AuSCR) collected between 2009 and 2013 linked to hospital administrative data and clinical costing data in Queensland. Clinical costing data include standardised assignment of costs from hospitals that contribute to the National Hospital Costing programme. Patient-level costs for each hospital admission were described according to the demographic, clinical and treatment characteristics of patients. Multivariable median regression with clustering by hospital was used to determine factors associated with greater costs. Results Among 22 hospitals, clinical costing data were available for 3909 of 5522 patient admissions in the AuSCR (71%). Compared to those without clinical costing data, patients with clinical costing data were more often aged <65 years (30% with cost data vs 24% without cost data, p < 0.001) and male (56% with cost data vs 49% without cost data, p < 0.001). Median cost of an acute episode was $7945 (interquartile range $4176 to $14970) and the median length of stay was 5 days (interquartile range 2 to 10 days). The most expensive cost buckets were related to medical ( n = 3897, median cost $1577), nursing ( n = 3908, median cost $2478) and critical care ( n = 434, median cost $3064). Factors associated with greater total costs were a diagnosis of intracerebral haemorrhage, greater socioeconomic position, in-hospital stroke and prior history of stroke. Conclusion Medical and nursing costs were incurred by most patients admitted with stroke or TIA, and were relatively more expensive on average than other cost buckets such as imaging and allied health. Implications Scaling this data linkage to national data collections may provide valuable insights into activity-based funding at public hospitals. Regular report of these costs should be encouraged to optimise economic evaluations.

ObjectivesData linkage can provide sufficient breadth and size of data, to draw reliable estimates of effectiveness, at a population level using real-world data. We compared differences in survival and hospital presentations following stroke or transient ischaemic attack (TIA), based on whether a Medicare funded chronic disease management plan was claimed.
ApproachA population-based, comparative effectiveness study of Victorian and Queensland Australian Stroke Clinical Registrants (January 2012-June 2015), using the emulated target trial approach, was performed. Chronic disease management items were identified from Medicare claims in the 6-18 months post-stroke (exposure period). Data on covariates for model adjustment were obtained from hospital, pharmacy and aged care datasets. Outcomes at 19-30 months post-stroke were determined using the national death registry and state hospital data. Cox regression, adjusted using propensity score methods with inverse probability treatment weights was used to determine the effect of receipt of chronic disease management claims on outcomes.
ResultsOf 28,775 AuSCR registrants, 27,435 (95.3%) were linked across the Medicare, pharmacy and hospital datasets. Following exclusions, 11,574 registrants from 42 hospitals (42% female, median age 70 years, 27% TIA) were eligible for the study. Overall, 45% of participants received chronic disease management during the exposure period. After propensity score weighting, there was excellent balance between groups across 35 baseline variables (standardised differences <0.1). Receipt of chronic disease management (vs non-receipt) was associated with a 30% reduced hazard of death (adjusted Hazard Ratio [aHR]: 0.70, 95%CI: 0.57, 0.87) but a 17% increase in hospital presentations (aHR: 1.17, 95%CI: 1.12, 1.23). Variation was observed between planned (aHR: 1.21, 95%CI: 1.10, 1.33) and unplanned (aHR: 1.15, 95%CI: 1.07, 1.23) presentations.
ConclusionWe provide an evaluation of the effectiveness of Medicare funded chronic disease management within "real world" healthcare provision, thereby demonstrating the value of linked population data in health services research. Further work is underway to examine causal mechanisms.

IntroductionSurvivors of stroke have complex needs from ongoing disabilities and have increased risk of cardiovascular diseases. The societal costs are therefore substantial. Person-level longitudinal data on the longer-term hospital utilizations of patients with stroke in Australia, and the factors that may influence usage in this setting, are rarely reported.
Objectives and ApproachWe used person-level linkages between the Australian Stroke Clinical Registry (AuSCR: 2009-2013) and hospital admission and Emergency Department (ED) data from four states to examine determinants of hospital utilisation following stroke. The index event was the first event recorded in AuSCR. The rate of hospital contacts/person/year was calculated from contacts 30-365 days post-discharge. Disability was determined from responses to EQ-5D-3L data collected at 90-180 days post-stroke. Comorbidities were identified using ICD-10 discharge diagnosis codes (5 year look back including the index event). Negative binomial regression was used adjusting for patient clustering by hospital and pre-stroke contacts and stratified by disability.
ResultsAmong 10,082 adults with acute stroke (55% male, median age 74 years, 81% ischaemic, 14% hemorrhagic, 5% undetermined, 44% with disability) from 39 hospitals, 57% had a hospital admission or ED contact in the first 30-365 days post-hospital discharge, with median contacts/person/year post-stroke of 1.09 (Q1, Q3: 0, 3.27) compared to a pre-contact rate of 0 (Q1, Q3: 0, 2.18). The strongest associations with subsequent hospital contacts were prior contacts (IRR:1.10, 95%CI:1.07, 1.13), not able to walk on admission (stroke severity) (IRR:1.19, 95%CI 1.07, 1.31) and having a higher comorbidity index score (IRR:1.18, 95%CI:1.14, 1.22). Within stratified cohorts younger age was associated with increased contacts in those with disability (
Conclusion/ImplicationsIn a large linked cohort of patients we have demonstrated the substantial ongoing burden that stroke imposes on hospital systems, particularly regarding survivors with other comorbidities and younger survivors with disability. Knowledge of disability and comorbidity burden may assist with targeting community and hospital interventions to reduce post-stroke hospital usage.

IntroductionRecent advances in data linkage infrastructure in Australia mean that data can be linked based on various identifiers across datasets. In a first for Australia, we tested the feasibility of linking data between a clinical quality disease registry with Australian and state government health data across multiple jurisdictions.
Objectives and ApproachTo determine whether high quality linked data for stroke can be obtained using a non-government managed registry (Australian Stroke Clinical Registry, AuSCR), national death registry data (Australian government), and hospital admission and emergency presentation data (state governments) to assess the accuracy of consistent variables across the different datasets. We used a cohort design with probabilistic data linkage to merge patient-level records. Descriptive statistics presented for matching concordance and Cohen's kappa for concordance across demographic variables. The sensitivity and specificity of in-hospital deaths collected in the AuSCR was assessed against national death registrations.
ResultsThere were 16,214 registrants in the study cohort. Their identifiers in the AuSCR from 2009-2013 were linked with death, emergency department and hospital discharge data from April 2004 to December 2016. In total, 99% of the AuSCR registrants were linked to one or more datasets; 98\% were linked with emergency presentation (80%) and/or admission (95%) data. Linkage to national death registrations identified 4,183 death; 1440 of these were identified as in-hospital deaths in both data sets demonstrating that in-hospital death classification in AuSCR had a 98.7% sensitivity and 99.6% specificity. Concordance between common demographic variables was excellent (kappa 0.84 for aboriginal status and kappa 0.99 for sex).
Conclusion/ImplicationsThe majority of AuSCR registrants were accurately linked to the Australian and state government datasets. Linkage quality was excellent and there was high concordance between common variables. The ability to reliably merge the datasets assures future comprehensive analyses of stroke care, ongoing health care resource utilisation and patient outcomes.

OBJECTIVE: To describe the challenges of obtaining state and nationally held data for linkage to a non-government national clinical registry. METHODS: We reviewed processes negotiated to achieve linkage between the Australian Stroke Clinical Registry (AuSCR), the National Death Index, and state held hospital data. Minutes from working group meetings, national workshop meetings, and documented communications with health department staff were reviewed and summarised. RESULTS: Time from first application to receipt of data was more than two years for most state data-sets. Several challenges were unique to linkages involving identifiable data from a non-government clinical registry. Concerns about consent, the re-identification of data, duality of data custodian roles and data ownership were raised. Requirements involved the development of data flow methods, separating roles and multiple governance and ethics approvals. Approval to link death data presented the fewest barriers. CONCLUSION: To our knowledge, this is the first time in Australia that person-level data from a clinical quality registry has been linked to hospital and mortality data across multiple Australian jurisdictions. Implications for Public Health: The administrative load of obtaining linked data makes projects such as this burdensome but not impossible. An improved national centralised strategy for data linkage in Australia is urgently needed.

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.