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PURPOSE OF REVIEW: To critically appraise literature on recent advances and methods using "big data" to evaluate stroke outcomes and associated factors. RECENT FINDINGS: Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.

Background: Identifying those at risk of stroke promotes better targeting of clinical and policy interventions. Aim(s): We aimed to investigate first-ever stroke incidence in an incident cohort of cardiac patients aged 20-84 years over a 5-year period. Method(s): We utilised person-linked hospitalisation and mortality data from Western Australia (1985- 2017). Historical records were reviewed (32-year lookback) to exclude people with previous cardiac disease and stroke. A cohort with incident cardiac conditions during 2012-2017 was identified: coronary heart disease (CHD), atrial fibrillation (AF) and valvular heart disease (VHD). All were followed up for first-ever stroke. Stroke incidence rates were stratified by sex and cardiac condition, and agestandardised using the direct method by 5-year age group. Result(s): The cohort comprised 52,968 patients: 27,947 CHD; 18,194 AF; 3,110 VHD and 3,717 with multiple cardiac conditions. 1,765 (3.3%) first-ever strokes occurred in the 5-year study period: 6.0% in AF, 3.1% in VHD and 1.5% in CHD patients. Older people with multiple cardiac conditions had the highest rates. Incidence rates of first-ever stroke were higher in women than in men (8.5 strokes per 1,000 person-years [95%CI:6.5-10.5] and 6.8 [95%CI:5.6-8.1], respectively). The highest incidence of stroke in each cardiac group was in women with AF (13.1/1000, 95%CI:10.4-15.8), men with VHD (11.0/1000, 95%CI:7.3-14.8) and women with CHD (3.1/1000, 95%CI: 2.5-3.7). Conclusion(s): These data highlight the high incidence of stroke in cardiac patients, particularly those with AF, and the need to target these patients with evidence-based treatments and management to prevent onset of stroke.

Background: Most estimates of stroke incidence among Aboriginal and Torres Strait Islander (hereinafter respectfully referred to as Aboriginal) Australians include limited case numbers and are generally confined to single regions. Aim(s): To measure age-standardised stroke incidence in Aboriginal and non-Aboriginal Australians in Western Australia (WA), South Australia (SA) and the Northern Territory (NT) combined. Method(s): We used whole-population multi-jurisdictional person-linked data from hospital and death datasets to identify stroke admissions and stroke-related deaths (2001-2015). We utilised hospital, emergency department and death datasets to identify Aboriginal people. We identified incident (firstever) strokes in patients aged 20-84 during the study period (2012-2015), using a 10-year lookback period to exclude previous stroke events. Incidence rates for the Aboriginal and non-Aboriginal population per 100,000/year were estimated, age-standardised to the world population aged 20-84. Result(s): We identified 17,986 incident strokes (32.2% fatal) between 2012-2015. Of these, 752 (4.2%) occurred in Aboriginal people (23.1% fatal). Median age at onset (55 years) in Aboriginal people was 16 years younger than non-Aboriginal people (p<0.001). In those aged 20-84 years, the age-standardised stroke incidence was 2.9-fold greater in the Aboriginal population (206/100,000) than the non- Aboriginal population (70/100,000). Disparities were particularly apparent at younger ages (20-54 years), where the age-standardised stroke incidence was 4.4-fold greater in the Aboriginal population (93/100,000) than the non-Aboriginal population (21/100,000). Conclusion(s): In the largest multi-jurisdictional person-linked dataset to date we found significant disparities in stroke incidence between Aboriginal and non-Aboriginal populations, particularly at younger ages. These results will be used to inform region-specific, culturally safe interventions.

Background: Previous research has raised substantial concerns regarding the validity of the International Statistical Classification of Diseases and Related Health Problems (ICD) codes (ICD-10 I05–I09) for rheumatic heart disease (RHD) due to likely misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD. There is currently no validated, quantitative approach for reliable case ascertainment of RHD in administrative hospital data. Methods: A comprehensive dataset of validated Australian RHD cases was compiled and linked to inpatient hospital records with an RHD ICD code (2000–2018, n=7555). A prediction model was developed based on a generalized linear mixed model structure considering an extensive range of demographic and clinical variables. It was validated internally using randomly selected cross-validation samples and externally. Conditional optimal probability cutpoints were calculated, maximising discrimination separately for high-risk versus low-risk populations. Results: The proposed model reduced the false-positive rate (FPR) from acute rheumatic fever (ARF) cases misclassified as RHD from 0.59 to 0.27; similarly for non-rheumatic VHD from 0.77 to 0.22. Overall, the model achieved strong discriminant capacity (AUC: 0.93) and maintained a similar robust performance during external validation (AUC: 0.88). It can also be used when only basic demographic and diagnosis data are available. Conclusion: This paper is the first to show that not only misclassification of non-rheumatic VHD but also of ARF as RHD yields substantial FPRs. Both sources of bias can be successfully addressed with the proposed model which provides an effective solution for reliable RHD case ascertainment from hospital data for epidemiological disease monitoring and policy evaluation.

Adherence to cardioprotective medications following myocardial infarction (MI) is commonly assessed using a binary threshold of 80%. We investigated the relationship between medication adherence as a continuous measure and outcomes in MI survivors using restricted cubic splines (RCS). We identified all patients aged >=65 years hospitalised for MI from 2003-2008 who survived one-year post-discharge (n = 5938). Adherence to statins, beta-blockers, renin angiotensin system inhibitors (RASI) and clopidogrel was calculated using proportion of days covered to one-year post-discharge (landmark date). Outcomes were 1-year all-cause death and major adverse cardiac events (MACE) after the landmark date. Adherence-outcome associations were estimated from RCS Cox regression models. RCS analyses indicated decreasing risk for both outcomes above 60% adherence for statins, RASI and clopidogrel, with each 10% increase in adherence associated with a 13.9%, 12.1% and 18.0% decrease respectively in adjusted risk of all-cause death (all p<0.02). Similar results were observed for MACE (all p<0.03). Beta-blockers had no effect on outcomes at any level of adherence. In MI survivors, increasing adherence to statins, RASI, and clopidogrel, but not beta blockers, is associated with a decreasing risk of death/MACE with no adherence threshold beyond 60%. Medication adherence should be considered as a continuous measure in outcomes analyses.

D Bond-Smith,1 R Seth,1 N de Klerk,1,2 L Nedkoff,1 M Anderson,3 J Hung,1 J Cannon,1,2 K Griffiths,4,5 JM Katzenellenbogen1,2 1School of Population and Global Health, The University of Western Australia, Perth, Australia; 2Telethon Kids Institute, Perth, Australia; 3Queensland Health, Brisbane, Australia; 4Centre for Big Data Research, The University of New South Wales, Sydney, Australia; 5Menzies School of Health Research, Charles Darwin University, Darwin, AustraliaCorrespondence: D Bond-Smith Email daniela.bond-smith@uwa.edu.auBackground: Previous research has raised substantial concerns regarding the validity of the International Statistical Classification of Diseases and Related Health Problems (ICD) codes (ICD-10 I05–I09) for rheumatic heart disease (RHD) due to likely misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD. There is currently no validated, quantitative approach for reliable case ascertainment of RHD in administrative hospital data.Methods: A comprehensive dataset of validated Australian RHD cases was compiled and linked to inpatient hospital records with an RHD ICD code (2000– 2018, n=7555). A prediction model was developed based on a generalized linear mixed model structure considering an extensive range of demographic and clinical variables. It was validated internally using randomly selected cross-validation samples and externally. Conditional optimal probability cutpoints were calculated, maximising discrimination separately for high-risk versus low-risk populations.Results: The proposed model reduced the false-positive rate (FPR) from acute rheumatic fever (ARF) cases misclassified as RHD from 0.59 to 0.27; similarly for non-rheumatic VHD from 0.77 to 0.22. Overall, the model achieved strong discriminant capacity (AUC: 0.93) and maintained a similar robust performance during external validation (AUC: 0.88). It can also be used when only basic demographic and diagnosis data are available.Conclusio

Background This paper reports the quantitative outcomes of a mixed-methods pilot study of the characteristics and demographics of chiropractic practices and patients in Western Australia. Methods This was a mixed-methods data transformation model (qualitative to quantitative) pilot study. A non-random sample of chiropractic practices across Western Australia was recruited and data collected anonymously from consecutive new patients using an online platform. Data covered practice and patient demographics and characteristics, alongside quality of life measures. A descriptive quantitative analysis characterised the sample, and the patient population was stratified by main reason for presentation to compare characteristics according to the presence of secondary complaints. Odds ratios were calculated to estimate the odds of a secondary complaint for various combinations of main complaints, from univariate logistic regression models. Results Of the 539 registered practitioners in WA in July 2014, 33 agreed to participate, from 20 different practices. Ten participating practices provided data on 325 adult new patients. The recruited practices (metropolitan n = 8, regional n = 2) had a positive response rate of 79.7 % (n = 301 metropolitan and n = 24 regional patients), mean age 36.3 years (range 18–74) (53.2 % female). Spinal problems were reported as the main reason for consultation by 67 % and as secondary reasons by 77.2 % of patients. People presented primarily for health maintenance or a general health check in 11.4 %, and as a secondary reason 14.8 %. There were 30 % of people below societal norms for the SF-12 Physical Component Score (mean 47.19, 95 % CI; 46.27–48.19) and 86 % for the Mental Component Score (mean 36.64, 95 % CI; 35.93-37.65), Pain Impact Questionnaire mean scores were 54.60 (95 % CI; 53.32–55.88). Conclusions Patients presented to chiropractors in Western Australia with a fairly wide range of conditions, but primarily spinal and musculoskeletal-r

Background Practice-based data collection can offer insight into the nature of chiropractic practice and contribute to resolving the conundrum of the chiropractic profession’s role in contemporary healthcare, subsequently informing care service policy. However, there is little formal data available about chiropractic practice to inform decision-makers about the nature and role of chiropractic within the context of a modern multidisciplinary healthcare context in Australia, particularly at a local and regional level. Methods This was a mixed-methods data transformation model (qualitative to quantitative) pilot study the purpose of which was to provide a critique of the research design and collect data from a selected sample of chiropractic practices in Western Australia, with a view to offer recommendations related to the design, feasibility and implementation of a future confirmatory study. Results A narrative critique of the research methods of this pilot study is offered in this paper covering: (a) practice and patient recruitment, (b) enrollment of patients, (c) data collection methods, (d) acceptability of the study methods, (e) sample size calculations, and (f) design critique. Conclusions The result of this critique provides a sensible sample size estimate and recommendations as to the design and implementation of a future confirmatory study. Furthermore, we believe that a confirmatory study is not only feasible, but indeed necessary, with a view to offer meaningful insight into chiropractic practice in Western Australia.