Risk Factors Associated with Major Adverse Cardiovascular Events after Ischemic Stroke: A Linked Registry Study.

INTRODUCTION: Survivors of stroke are at risk of experiencing subsequent major adverse cardiovascular events (MACE). We aimed to determine the incidence of, and risk factors for, MACE after first-ever ischemic stroke, by age group (18-64 years vs. ≥65 years). METHODS: Observational cohort study using patient-level data from the Australian Stroke Clinical Registry (2009-2013), linked with hospital administrative data. We included adults with first-ever ischemic stroke who had no previous acute cardiovascular admissions and followed these patients for 2 years post-discharge, or until the first post-stroke MACE event. A Fine-Gray sub-distribution hazard model, accounting for the competing risk of non-cardiovascular death, was used to determine factors for incident post-stroke MACE. RESULTS: Among 5,994 patients with a first-ever ischemic stroke (median age 73 years, 45% female), 17% were admitted for MACE within 2 years (129 events per 1,000 person-years). The median time to first post-stroke MACE was 117 days (89 days if aged <65 years vs. 126 days if aged ≥65 years; p = 0.025). Among patients aged 18-64 years, receiving intravenous thrombolysis (sub-distribution hazard ratio [SHR] 0.51 [95% CI, 0.28-0.92]) or being discharged to inpatient rehabilitation (SHR 0.65 [95% CI, 0.46-0.92]) were associated with a reduced incidence of post-stroke MACE. In those aged ≥65 years, being unable to walk on admission (SHR 1.33 [95% CI 1.15-1.54]), and history of smoking (SHR 1.40 [95% CI 1.14-1.71]) or atrial fibrillation (SHR 1.31 [95% CI 1.14-1.51]) were associated with an increased incidence of post-stroke MACE. Acute management in a large hospital (>300 beds) for the initial stroke event was associated with reduced incidence of post-stroke MACE, irrespective of age group. CONCLUSIONS: MACE is common within 2 years of stroke, with most events occurring within the first year. We have identified important factors to consider when designing interventions to prevent MACE after stroke, particularly among those aged <65 years.

Cost-Effectiveness of a Government Policy to Incentivise Chronic Disease Management following Stroke: A Modelling Study.

INTRODUCTION: Little is known about the cost-effectiveness of government policies that support primary care physicians to provide comprehensive chronic disease management (CDM). This paper aimed to estimate the potential cost-effectiveness of CDM policies over a lifetime for long-time survivors of stroke. METHODS: A Markov model, using three health states (stable, hospitalised, dead), was developed to simulate the costs and benefits of CDM policies over 30 years (with 1-year cycles). Transition probabilities and costs from a health system perspective were obtained from the linkage of data between the Australian Stroke Clinical Registry (cohort n = 12,368, 42% female, median age 70 years, 45% had CDM claims) and government-held hospital, Medicare, and pharmaceutical claims datasets. Quality-adjusted life years (QALYs) were obtained from a comparable cohort (n = 512, 34% female, median age 69.6 years, 52% had CDM claims) linked with Medicare claims and death data. A 3% discount rate was applied to costs in Australian dollars (AUD, 2016) and QALYs beyond 12 months. Probabilistic sensitivity analyses were used to understand uncertainty. RESULTS: Per-person average total lifetime costs were AUD 142,939 and 8.97 QALYs for those with a claim, and AUD 103,889 and 8.98 QALYs for those without a claim. This indicates that these CDM policies were costlier without improving QALYs. The probability of cost-effectiveness of CDM policies was 26.1%, at a willingness-to-pay threshold of AUD 50,000/QALY. CONCLUSION: CDM policies, designed to encourage comprehensive care, are unlikely to be cost-effective for stroke compared to care without CDM. Further research to understand how to deliver such care cost-effectively is needed.

Age and Sex Disparities in Cardiovascular Risk Factor Management prior to Stroke: Linked Registry and General Practice Data.

INTRODUCTION: There is limited evidence about the management of cardiovascular risk factors within 12 months before stroke or transient ischaemic attack (TIA) in Australian general practices. We evaluated whether age and sex disparities in cardiovascular risk factor management for primary prevention exist in general practice. METHODS: A retrospective cohort study using data from the Australian Stroke Clinical Registry (2014-2018) linked with general practice data from three Primary Health Networks in Victoria, Australia. We included adults who had ≥2 encounters with a general practitioner within 12 months immediately before the first stroke/TIA. Cardiovascular risk factor management within 12 months before stroke/TIA was evaluated in terms of: assessment of risk factors (blood pressure [BP], serum lipids, blood glucose, body weight); prescription of prevention medications (BP-lowering, lipid-lowering, glucose-lowering, antithrombotic agents); and attainment of risk factor targets. RESULTS: Of 2,880 patients included (median age 76.5 years, 48.4% women), 80.9% were assessed for BP, 49.9% serum lipids, 46.8% blood glucose, and 39.3% body weight. Compared to patients aged 65-84 years, those aged <65 or ≥85 years were less often assessed for risk factors, with women aged ≥85 years assessed for significantly fewer risk factors than their male counterparts. The most prescribed prevention medications were BP-lowering (64.9%) and lipid-lowering agents (42.0%). There were significant sex differences among those aged <65 years (34.7% women vs. 40.2% men) and ≥85 years (34.0% women vs. 44.3% men) for lipid-lowering agents. Risk factor target attainment was generally poorer in men than women, especially among those aged <65 years. CONCLUSION: Age-sex disparity exists in risk factor management for primary prevention in general practice, and this was more pronounced among younger patients and older women.

Using a multi-stakeholder co-design process to develop a health service organisation-wide patient reported outcome measure collection system.

PURPOSE: Limited examples exist of successful Patient Reported Outcome Measure (PROM) implementation across an entire healthcare organisation. The aim of this study was to use a multi-stakeholder co-design process to develop a PROM collection system, which will inform implementation of routine collection of PROMs across an entire healthcare organisation. METHODS: Co-design comprised semi-structured interviews with clinicians (n = 11) and workshops/surveys with consumers (n = 320). The interview guide with clinicians focused on their experience using PROMs, preferences for using PROMs, and facilitators/barriers to using PROMs. Co-design activities specific to consumers focused on: (1) how PROMs will be administered (mode), (2) when PROMs will be administered (timing), (3) who will assist with PROMs collection, and (4) how long a PROM will take to complete. Data were analysed using a manifest qualitative content analysis approach. RESULTS: Core elements identified during the co-design process included: PROMs collection should be consumer-led and administered by someone other than a clinician; collection at discharge from the healthcare organisation and at 3-6 months post discharge would be most suitable for supporting comprehensive assessment; PROMs should be administered using a variety of modes to accommodate the diversity of consumer preferences, with electronic as the default; and the time taken to complete PROMs should be no longer than 5-10 min. CONCLUSION: This study provides new information on the co-design of a healthcare organisation-wide PROM collection system. Implementing a clinician and patient informed strategy for PROMs collection, that meets their preferences across multiple domains, should address known barriers to routine collection.

Healthcare consumer acceptability of routine use of the EQ-5D-5L in clinical care: a cross-sectional survey.

PURPOSE: Patient reported outcome measures, such as the EQ-5D-5L, provide a measure of self-perceived health status or health-related quality of life. Understanding the consumer acceptability of a patient reported outcome measure can help to decide about its implementation across a healthcare organisation and possibly increase the likelihood of its use in clinical care. This study established the acceptability of the EQ-5D-5L from the perspective of clients receiving healthcare, and determined if acceptability varied by client sub-types. METHODS: A cross-sectional survey explored clients' experience of the EQ-5D-5L. Eligible clients were aged ≥ 18 years and completed the EQ-5D-5L on admission and discharge to one of two multi-disciplinary community health services. Likert scale items explored acceptability, and open-ended questions determined if the EQ-5D-5L reflects experience of illness. Associations between acceptability and client characteristics were established using χ2 test. Open-ended questions were analysed using content analysis. RESULTS: Most of the 304 clients (mean age 70 years, SD 16) agreed that the EQ-5D-5L: was easy to use/understand (n = 301, 99%) and useful (n = 289, 95%); improved communication with their therapist (n = 275, 90%); and made them feel more in control of their health (n = 276, 91%). Most clients also agreed that they wished to continue using the EQ-5D-5L (n = 285, 93%). Clients aged ≥ 60 years reported lower acceptability. Clients noted that the EQ-5D-5L did not capture experience of illness related to fatigue, balance/falls, cognition, and sleep. CONCLUSION: The EQ-5D-5L is acceptable for use in care but does not capture all aspects of health relevant to clients, and acceptability varies by subgroup.

The Right to Rehabilitation for People With Dementia: A Codesign Approach to Barriers and Solutions.

INTRODUCTION: People with dementia of all ages have a human right to equal access to quality health care. Despite evidence regarding its effectiveness, many people living with dementia are unable to access rehabilitation for promoting function and quality of life. Conducted in Australia, this study was designed to (1) explore barriers to access to dementia rehabilitation and (2) identify solutions that improve access to rehabilitation. METHODS: People living with dementia (n = 5) and care partners (n = 8) and health professionals (n = 13) were recruited nationally. Experience-based codesign across three virtual workshops was used to understand barriers and design solutions to improve access to rehabilitation treatments. Socio-ecological analyses, using the Levesque Access to Health care framework, were applied to findings regarding barriers and to aid selection of solutions. RESULTS: There was high attendance (92.3%) across the three workshops. Barriers were identified at a user level (including lack of knowledge, transport, cost and difficulty navigating the health, aged care and disability sectors) and health service level (including health professional low dementia knowledge and negative attitudes, inequitable funding models and non-existent or fragmented services). Solutions focused on widespread dementia education and training, including ensuring that people with dementia and their care partners know about rehabilitation therapies and that health professionals, aged care and disability co-ordinators know how to refer to and deliver rehabilitation interventions. Dementia care navigators, changes to Australia's public funding models and specific dementia rehabilitation programmes were also recommended. CONCLUSIONS: Barriers to accessing rehabilitation for people with dementia exist at multiple levels and will require a whole-community and systems approach to ensure change. PATIENT OR PUBLIC CONTRIBUTION: People with living experience (preferred term by those involved) were involved at two levels within this research. A Chief Investigator living with dementia was involved in the design of the study and writing of the manuscript. People with living experience, care partners and service providers were participants in the codesign process to identify barriers and design potential solutions.

The implementation of a perioperative medicine for older people undergoing surgery service: a qualitative case study.

BACKGROUND: The international scale and spread of evidence-based perioperative medicine for older people undergoing surgery (POPS) services has not yet been fully realised. Implementation science provides a structured approach to understanding factors that act as barriers and facilitators to the implementation of POPS services. In this study, we aimed to identify factors that influence the implementation of POPS services in the UK. METHODS: A qualitative case study at three UK health services was undertaken. The health services differed across contextual factors (population, workforce, size) and stages of POPS service implementation maturity. Semi-structured interviews with purposively sampled clinicians (perioperative medical, nursing, allied health, and pharmacy) and managers (n = 56) were conducted. Data were inductively coded, then thematically analysed using the Consolidated Framework for Implementation Research (CFIR). RESULTS: Fourteen factors across all five CFIR domains were relevant to the implementation of POPS services. Key shared facilitators included stakeholders understanding the rationale of the POPS service, with support from their networks, POPS champions, and POPS clinical leads. We found substantial variation and flexibility in the way that health services responded to these shared facilitators and this was relevant to the implementation of POPS services. CONCLUSIONS: Health services planning to implement a POPS service should use health service-specific strategies to respond flexibly to local factors that are acting as barriers or facilitators to implementation. To support implementation of a POPS service, we recommend health services prioritise understanding local networks, identifying POPS champions, and ensuring that stakeholders understand the rationale for the POPS service. Our study also provides a structure for future research to understand the factors associated with 'unsuccessful' implementation of a POPS service, which can inform ongoing efforts to implement evidence-based perioperative models of care for older people.

Fracture Risk Increases After Stroke or Transient Ischemic Attack and Is Associated With Reduced Quality of Life.

BACKGROUND: Fractures are a serious consequence following stroke, but it is unclear how these events influence health-related quality of life (HRQoL). We aimed to compare annualized rates of fractures before and after stroke or transient ischemic attack (TIA), identify associated factors, and examine the relationship with HRQoL after stroke/TIA. METHODS: Retrospective cohort study using data from the Australian Stroke Clinical Registry (2009-2013) linked with hospital administrative and mortality data. Rates of fractures were assessed in the 1-year period before and after stroke/TIA. Negative binomial regression, with censoring at death, was used to identify factors associated with fractures after stroke/TIA. Respondents provided HRQoL data once between 90 and 180 days after stroke/TIA using the EuroQoL 5-dimensional 3-level instrument. Adjusted logistic regression was used to assess differences in HRQoL at 90 to 180 days by previous fracture. RESULTS: Among 13 594 adult survivors of stroke/TIA (49.7% aged ≥75 years, 45.5% female, 47.9% unable to walk on admission), 618 fractures occurred in the year before stroke/TIA (45 fractures per 1000 person-years) compared with 888 fractures in the year after stroke/TIA (74 fractures per 1000 person-years). This represented a relative increase of 63% (95% CI, 47%-80%). Factors associated with poststroke fractures included being female (incidence rate ratio [IRR], 1.34 [95% CI, 1.05-1.72]), increased age (per 10-year increase, IRR, 1.35 [95% CI, 1.21-1.50]), history of prior fracture(s; IRR, 2.56 [95% CI, 1.77-3.70]), and higher Charlson Comorbidity Scores (per 1-point increase, IRR, 1.18 [95% CI, 1.10-1.27]). Receipt of stroke unit care was associated with fewer poststroke fractures (IRR, 0.67 [95% CI, 0.49-0.93]). HRQoL at 90 to 180 days was worse among patients with prior fracture across the domains of mobility, self-care, usual activities, and pain/discomfort. CONCLUSIONS: Fracture risk increases substantially after stroke/TIA, and a history of these events is associated with poorer HRQoL at 90 to 180 days after stroke/TIA.

Receipt of Mental Health Treatment in People Living With Stroke: Associated Factors and Long-Term Outcomes.

BACKGROUND: Untreated poststroke mood problems may influence long-term outcomes. We aimed to investigate factors associated with receiving mental health treatment following stroke and impacts on long-term outcomes. METHODS: Observational cohort study derived from the Australian Stroke Clinical Registry (AuSCR; Queensland and Victorian registrants: 2012-2016) linked with hospital, primary care billing and pharmaceutical dispensing claims data. Data from registrants who completed the AuSCR 3 to 6 month follow-up survey containing a question on anxiety/depression were analyzed. We assessed exposures at 6 to 18 months and outcomes at 18 to 30 months. Factors associated with receiving treatment were determined using staged multivariable multilevel logistic regression models. Cox proportional hazards regression models were used to assess the impact of treatment on outcomes. RESULTS: Among 7214 eligible individuals, 39% reported anxiety/depression at 3 to 6 months following stroke. Of these, 54% received treatment (88% antidepressant medication). Notable factors associated with any mental health treatment receipt included prestroke psychological support (odds ratio [OR], 1.80 [95% CI, 1.37-2.38]) or medication (OR, 17.58 [95% CI, 15.05-20.55]), self-reported anxiety/depression (OR, 2.55 [95% CI, 2.24-2.90]), younger age (OR, 0.98 [95% CI, 0.97-0.98]), and being female (OR, 1.30 [95% CI, 1.13-1.48]). Those who required interpreter services (OR, 0.49 [95% CI, 0.25-0.95]) used a health benefits card (OR, 0.73 [95% CI, 0.59-0.92]) or had continuity of primary care visits (ie, with a consistent physician; OR, 0.78 [95% CI, 0.62-0.99]) were less likely to access mental health services. Among those who reported anxiety/depression, those who received mental health treatment had an increased risk of presenting to hospital (hazard ratio, 1.06 [95% CI, 1.01-1.11]) but no difference in survival (hazard ratio, 0.86 [95% CI, 0.58-1.27]). CONCLUSIONS: Nearly half of the people living with mood problems following stroke did not receive mental health treatment. We have highlighted subgroups who may benefit from targeted mood screening and factors that may improve treatment access.

Factors Associated With Transition From Community to Permanent Residential Aged Care Following Stroke: A Linked Registry Data Study.

BACKGROUND: Understanding factors that influence the transition to permanent residential aged care following a stroke or transient ischemic attack may inform strategies to support people to live at home longer. We aimed to identify the demographic, clinical, and system factors that may influence the transition from living in the community to permanent residential care in the 6 to 18 months following stroke/transient ischemic attack. METHODS: Linked data cohort analysis of adults from Queensland and Victoria aged ≥65 years and registered in the Australian Stroke Clinical Registry (2012-2016) with a clinical diagnosis of stroke/transient ischemic attack and living in the community in the first 6 months post-hospital discharge. Participant data were linked with primary care, pharmaceutical, aged care, death, and hospital data. Multivariable survival analysis was performed to determine demographic, clinical, and system factors associated with the transition to permanent residential care in the 6 to 18 months following stroke, with death modeled as a competing risk. RESULTS: Of 11 176 included registrants (median age, 77.2 years; 44% female), 520 (5%) transitioned to permanent residential care between 6 and 18 months. Factors most associated with transition included the history of urinary tract infections (subhazard ratio [SHR], 1.41 [95% CI, 1.16-1.71]), dementia (SHR, 1.66 [95% CI, 1.14-2.42]), increasing age (65-74 versus 85+ years; SHR, 1.75 [95% CI, 1.31-2.34]), living in regional Australia (SHR, 31 [95% CI, 1.08-1.60]), and aged care service approvals: respite (SHR, 4.54 [95% CI, 3.51-5.85]) and high-level home support (SHR, 1.80 [95% CI, 1.30-2.48]). Protective factors included being dispensed antihypertensive medications (SHR, 0.68 [95% CI, 0.53-0.87]), seeing a cardiologist (SHR, 0.72 [95% CI, 0.57-0.91]) following stroke, and less severe stroke (SHR, 0.71 [95% CI, 0.58-0.88]). CONCLUSIONS: Our findings provide an improved understanding of factors that influence the transition from community to permanent residential care following stroke and can inform future strategies designed to delay this transition.

The Excess Costs of Hospitalization for Acute Stroke in People With Communication Impairment: A Stroke123 Data Linkage Substudy.

OBJECTIVE: To describe the costs of hospital care for acute stroke for patients with aphasia or dysarthria. DESIGN: Observational study from the Stroke123 project. SETTING: Data from patients admitted with stroke (2009-2013) from 22 hospitals in Queensland participating in the Australian Stroke Clinical Registry (AuSCR) were linked to administrative datasets. PARTICIPANTS: Communication impairments were identified using International Classification of Diseases, 10th Revision, Australian Modification codes. Overall, 1043 of 4195 (25%) patients were identified with aphasia (49% were women; median age 78 years; 83% with ischemic stroke), and 1005 (24%) with dysarthria (42% were women; median age 76 years; 85% with ischemic stroke). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Linked patient-level, hospital clinical costing related to the stroke, were adjusted to 2013/2014 Australian dollars (AU$, US$ conversion x 0.691) using recommended national price indices and multivariable regression analysis with clustering by hospital performed. RESULTS: Compared with patients without aphasia, the median hospital costs/patient were greater for those with aphasia for medical (aphasia AU$2273 vs AU$1727, P<.001), nursing (aphasia AU$3829 vs AU$2748, P<.001) and allied health services (aphasia AU$1138 vs AU$720, P<.001). Similarly, costs were greater for patients with dysarthria compared with those without dysarthria. Adjusted median total costs were AU$2882 greater for patients with aphasia compared with patients without aphasia (95% confidence interval, AU$1880-3884), and AU$843 greater for patients with dysarthria compared with those without dysarthria (95% confidence interval, AU$-301 to 1987). CONCLUSIONS: People with communication impairment after stroke incur greater hospital costs, in particular for medical, allied health, and nursing resources.

Targeted care navigation to reduce hospital readmissions in 'at-risk' patients.

BACKGROUND: Care navigation is commonly used to reduce preventable hospitalisation. The use of Electronic Health Record-derived algorithms may enable better targeting of this intervention for greater impact. AIMS: To evaluate if community-based Targeted Care Navigation, supported by an Electronic Health Record-derived readmission risk algorithm, is associated with reduced rehospitalisation. METHODS: A propensity score matching cohort (5 comparison to 1 intervention cohort ratio) study was conducted in an 850-bed Victorian public metropolitan health service, Australia, from May to November 2017. Admitted acute care patients with a non-surgical condition, identified as at-risk of hospital readmission using an Electronic Health Record-derived readmission risk algorithm provide by the state health department, were eligible. Targeted Care Navigation involved telephone follow-up support provided for 30 days post-discharge by a registered nurse. The hazard ratio for hospital readmission was calculated at 30, 60 and 90 days post-discharge using multivariable Cox Proportional Hazards regression. RESULTS: Sixty-five recipients received care navigation and were matched to 262 people who did not receive care navigation. Excellent matching was achieved with standardised differences between groups being <0.1 for all 11 variables included in the propensity score, including the readmission risk score. The Targeted Care Navigation group had a significantly reduced hazard of readmission at 30 days (hazard ratio 0.34; 95% confidence interval: 0.12, 0.94) compared with the comparison group. The effect size was reduced at 60 and 90 days post-discharge. CONCLUSION: We provide preliminary evidence that Targeted Care Navigation supported by an Electronic Health Record-derived readmission risk algorithm may reduce 30-day hospital readmissions.

A landscape assessment of the use of patient reported outcome measures in research, quality improvement and clinical care across a healthcare organisation.

BACKGROUND: Patient reported outcome measures (PROMs) can be used by healthcare organisations to inform improvements in service delivery. However, routine collection of PROMs is difficult to achieve across an entire healthcare organisation. An understanding of the use of PROMs within an organisation can provide valuable insights on the purpose, scope and practical considerations of PROMs collection, which can inform implementation of PROMs. METHODS: We used multiple research methods to assess the use of PROMs in research projects, data registries and clinical care across a healthcare organisation from January 2014 to April 2021. The methods included an audit of ethics applications approved by the organisation's human research ethics committee and registries which the health organisation had contributed data to; a literature review of peer-reviewed journal articles reporting on research projects conducted at the organisation; and a survey of health professionals use of PROMs in research projects, data registries and clinical care. The scope of PROMs was determined by classifying PROMs as either 'specific' to a particular disease and/or condition, or as a 'generic' measure with further classification based on the health domains they measured, using the World Health Organization International Classification Framework. Practical considerations included mode and timing of PROMs administration. Data were described using frequency and proportion. RESULTS: PROMs were used by 22% of research projects (n = 144/666), 68% of data registries (n = 13/19), and 76% of clinical specialties in their clinical care (n = 16/21). Disease specific PROMs were most commonly used: 83% of research projects (n = 130/144), 69% of clinical registries (n = 9/13), and 75% of clinical specialties (n = 12/16). Greater than 80% of research projects, clinical registries and clinical specialties measured health domains relating to both body impairments and participation in daily life activities. The most commonly used generic PROM was the EQ-5D (research projects n = 56/144, 39%; data registries n = 5/13, 38%; clinical specialties n = 4/16, 25%). PROMs used in clinical care were mostly paper-based (n = 47/55, 85%). CONCLUSIONS: We have elicited information on the use of PROMs to inform a health organisation wide implementation strategy. Future work will determine clinician and patient acceptability of the EQ-5D, and co-design a system for the collection of PROMs.

Prospective application of theoretical implementation frameworks to improve health care in hospitals - a systematic review.

BACKGROUND: Health Service implementation projects are often guided by theoretical implementation frameworks. Little is known about the effectiveness of these frameworks to facilitate change in processes of care and patient outcomes within the inpatient setting. The aim of this review was to assess the effectiveness of the application of theoretical implementation frameworks in inpatient healthcare settings to change processes of care and associated patient outcomes. METHOD: We conducted a search in CINAHL, MEDLINE, EMBASE, PsycINFO, EMCARE and Cochrane Library databases from 1st January 1995 to 15th June 2021. Two reviewers independently applied inclusion and exclusion criteria to potentially eligible studies. Eligible studies: implemented evidence-based care into an in-patient setting using a theoretical implementation framework applied prospectively; used a prospective study design; presented process of care or patient outcomes; and were published in English. We extracted theoretical implementation frameworks and study design against the Workgroup for Intervention Development and Evaluation Research (WIDER) Checklist and implementation strategies mapped to the Cochrane Effective Practice and Organisation of Care (EPOC) taxonomy. We summarised all interventions using the Template for Intervention Description and Replication (TIDieR) checklist. We appraised study quality using the Item bank on risk of bias and precision of observational studies and the revised Cochrane risk of bias tool for cluster randomised trials. We extracted process of care and patient outcomes and described descriptively. We conducted meta-analysis for process of care and patient outcomes with reference to framework category. RESULTS: Twenty-five studies met the inclusion criteria. Twenty-one used a pre-post (no comparison), two a pre-post with a comparison, and two a cluster randomised trial design. Eleven theoretical implementation frameworks were prospectively applied: six process models; five determinant frameworks; and one classic theory. Four studies used two theoretical implementation frameworks. No authors reported their justification for selecting a particular framework and implementation strategies were generally poorly described. No consensus was reached for a preferred framework or subset of frameworks based on meta-analysis results. CONCLUSIONS: Rather than the ongoing development of new implementation frameworks, a more consistent approach to framework selection and strengthening of existing approaches is recommended to further develop the implementation evidence base. TRIAL REGISTRATION: CRD42019119429.

Does a History of Cancer Influence the Effectiveness of Statins on Outcomes After Stroke?

BACKGROUND: Evidence is growing on anticancer effects of statins. We investigated whether the effectiveness of treatment with statins after ischemic stroke on mortality is influenced by a history of cancer. METHODS: Analyses of 90-day survivors of ischemic stroke (2012-2016; 45 hospitals) using linked registry and administrative data. Dispense of statins within 90 days postdischarge was determined from pharmaceutical records. Participants were followed from 91 days postdischarge until death or June 30, 2018. History of cancer was determined from hospital data. Propensity score-adjusted Cox proportional hazards regression model was used to determine the association between being dispensed statins and survival. The influence of history of cancer on this association was assessed based on the concepts of (1) statistical interaction and (2) biological interaction using 3 indices: relative excess risk due to interaction>0, attributable proportion due to interaction >0, or synergy index >1. RESULTS: Among 9948 eligible participants (median age=72 years, 42% female), there were 1463 deaths. In adjusted analyses, there was no statistical interaction between being dispensed statins and history of cancer on mortality (P=0.156). However, being dispensed statins had a significant positive biological interaction with having a history of cancer on mortality: relative excess risk due to interaction, 2.80 (95% CI, 1.56-5.05), attributable proportion due to interaction, 0.45 (95% CI, 0.23-0.66), and synergy index, 2.14 (95% CI, 1.32-3.49). CONCLUSIONS: Treatment with statins after ischemic stroke may confer additional survival benefits for people who also have had cancer.

Factors Associated with Receiving a Discharge Care Plan After Stroke in Australia: A Linked Registry Study.

Background: Discharge planning is recommended to optimise the transition from acute care to home for patients admitted with stroke. Despite this guideline recommendation, many patients do not receive a discharge care plan. Also, there is limited evidence on factors influencing the provision of discharge care plan post-stroke. We evaluated patient, clinical and system factors associated with receiving a care plan on discharge from hospital back to the community after stroke. Methods: This was an observational cohort study of patients with acute stroke who were discharged to the community between 2009-2013, using data from the Australian Stroke Clinical Registry linked to hospital administrative data. For this analysis, we used merged dataset containing information on patient demographics, clinical characteristics, and receipt of acute care processes. Multivariable logistic regression models were used to determine factors associated with receiving a discharge care plan. Results: Among 7812 eligible patients (39 hospitals, median age 73 years, 44.7% female, 56.9% ischaemic stroke), 47% received a care plan at discharge. The odds of receiving a discharge care plan increased over time (odds ratio [OR] 1.39 per year, 95% CI 1.37-1.48), and varied between hospitals. Factors associated with receiving a discharge care plan included greater socioeconomic position (OR 1.18, 95% CI 1.02-1.38), diagnosis of ischaemic stroke (OR 1.18, 95% CI 1.05-1.33), greater stroke severity (OR 1.15, 95% CI 1.01-1.31), or being discharged on antihypertensive medication (OR 3.07, 95% CI 2.69-3.50). In contrast, factors associated with a reduced odds of receiving a discharge care plan included being aged 85+ years (vs < 85 years; OR 0.79, 95% CI 0.64-0.96), discharged on a weekend (OR 0.56, 95% CI 0.46-0.67), discharged to residential aged care (OR 0.48, 95% CI 0.39-0.60), or being treated in a large hospital ( > 300 beds; OR 0.30, 95% CI 0.10-0.92). Conclusions: Implementing practices to target people who are older, discharged to residential aged care, or discharged on a weekend may improve discharge planning and post-discharge care after stroke.

Are Medicare Funded Multidisciplinary Care Policies Being Claimed in accordance to Rehabilitation Needs in Patients with Stroke?

Background: Australian Primary Care Practitioners are incentivised through Medicare funded policies to provide chronic disease management and facilitate multidisciplinary care. Little is known about how these policies are claimed in the long-term management of stroke. The objective of this study was to describe the use of funded primary care policies for people with stroke by impairment status. Methods: Linked Australian Stroke Clinical Registry (2010-2014) and Medicare data from adults with 90-180 days post-stroke EQ-5D health status survey data and admitted to one of 26 participating Australian hospitals were analysed. Medicare item claims for Primary Care Practitioner led chronic disease management and multidisciplinary care coordination policies, during the 18 months following stroke are described. Registrants were classified into impairment groups using their EQ-5D dimension responses through Latent Class Analysis. Associations between impairment and use of relevant primary care policies were explored using multivariable regression. Results: 5432 registrants were included (median age 74 years, 44% female, 86% ischaemic), 39% had a chronic disease management claim and 39% a multidisciplinary care coordination claim. Three latent classes emerged representing minimal, moderate and severe impairment. Compared to minimal, those with severe impairment were least likely to receive chronic disease management (adjusted Odds Ratio (aOR): 0.61, 95% Confidence Interval (CI): 0.49, 0.75) but were most likely to receive multidisciplinary care coordination. Podiatry was the commonest allied health service prescribed, regardless of impairment. Conclusions: Less than half of people living with stroke had a claim for primary care initiated chronic disease management, with mixed access for those with severe impairments.

Optimal Measures for Primary Care Physician Encounters after Stroke and Association with Survival: A Data Linkage Study.

BACKGROUND AND PURPOSE: Primary care physicians (PCPs) provide ongoing management after stroke. However, little is known about how best to measure physician encounters with reference to longer term outcomes. We aimed to compare methods for measuring regularity and continuity of PCP encounters, based on survival following stroke using linked healthcare data. METHODS: Data from the Australian Stroke Clinical Registry (2010-2014) were linked with Australian Medicare claims from 2009 to 2016. Physician encounters were ascertained within 18 months of discharge for stroke. We calculated three separate measures of continuity of encounters (consistency of visits with primary physician) and three for regularity of encounters (distribution of service utilization over time). Indices were compared based on 1-year survival using multivariable Cox regression models. The best performing measures of regularity and continuity, based on model fit, were combined into a composite "optimal care" variable. RESULTS: Among 10,728 registrants (43% female, 69% aged ≥65 years), the median number of encounters was 17. The measures most associated with survival (hazard ratio [95% confidence interval], Akaike information criterion [AIC], and Bayesian information criterion [BIC]) were the Continuity of Care Index (COCI, as a measure of continuity; 0.88 [0.76-1.02], p = 0.099, AIC = 13,746, BIC = 13,855) and our persistence measure of regularity (encounter at least every 6 months; 0.80 [0.67-0.95], p = 0.011, AIC = 13,742, BIC = 13,852). Our composite measure, persistent plus COCI ≥80% (24% of registrants; 0.80 [0.68-0.94], p = 0.008, AIC = 13,742, BIC = 13,851), performed marginally better than our persistence measure alone. CONCLUSIONS: Our persistence measure of regularity or composite measure may be useful when measuring physician encounters following stroke.

Real-World Effectiveness of Lipid-Lowering Medications on Outcomes after Stroke: Potential Implications of the New-User Design.

INTRODUCTION: Observational studies are increasingly being used to provide evidence on the real-world effectiveness of medications for preventing vascular diseases, such as stroke. We investigated whether the real-world effectiveness of treatment with lipid-lowering medications after ischemic stroke is affected by prevalent-user bias. METHODS: An observational cohort study of 90-day survivors of ischemic stroke using person-level data from the Australian Stroke Clinical Registry (2012-2016; 45 hospitals) linked to administrative (pharmaceutical, hospital, death) records. The use of, and adherence to (proportion of days covered <80% [poor adherence] vs. ≥80% [good adherence]), lipid-lowering medications within 90 days post-discharge was determined from pharmaceutical records. Users were further classified as prevalent (continuing) or new users, based on dispensing within 90 days prior to stroke. A propensity score-adjusted Cox regression was used to evaluate the effectiveness of lipid-lowering medications on outcomes (all-cause mortality, all-cause and cardiovascular disease readmission) within the subsequent year. Analyses were undertaken using prevalent-user (all users vs. nonusers) and new-user designs (new users vs. nonusers). RESULTS: Of 11,217 eligible patients (median age 72 years, 42% female), 9,294 (83%) used lipid-lowering medications within 90 days post-discharge, including 5,479 new users. In both prevalent-user and new-user designs, nonusers (vs. users) had significantly greater rates of mortality (hazard ratio [HR] 2.35, 95% CI: 1.89-2.92) or all-cause readmissions (HR 1.22, 95% CI: 1.05-1.40) but not cardiovascular disease readmission. In contrast, associations between having poor (vs. good) adherence on outcomes were stronger among new users than all users. Among new users, having poor adherence was associated with greater rates of mortality (HR 1.48, 95% CI: 1.12-1.96), all-cause readmission (HR 1.14, 95% CI: 1.02-1.27), and cardiovascular disease readmission (HR 1.20, 95% CI: 1.01-1.42). CONCLUSIONS: The real-world effectiveness of treatment with lipid-lowering medications after stroke is attenuated when evaluated based on prevalent-user rather than new-user design. These findings may have implications for designing studies on the real-world effectiveness of secondary prevention medications.

Treatment with Multiple Therapeutic Classes of Medication Is Associated with Survival after Stroke.

INTRODUCTION: Treatment with several therapeutic classes of medication is recommended for secondary prevention of stroke. We analyzed the associations between the number of classes of prevention medications supplied within 90 days after discharge for ischemic stroke (IS)/transient ischemic attack (TIA) and survival. METHODS: This is a retrospective cohort study of adults with first-ever IS/TIA (2010-2014) from the Australian Stroke Clinical Registry individually linked with data from national pharmaceutical and Medicare claims. Exposure was the number of classes of recommended medications, i.e., blood pressure-lowering, antithrombotic, or lipid-lowering agents, supplied to patients within 90 days after discharge for IS/TIA. The longitudinal association between the number of classes of medications and survival was evaluated with Cox proportional hazards regression models using the landmark approach. A landmark date of 90 days after hospital discharge was used to separate exposure and outcome periods, and only patients who survived until this date were included. RESULTS: Of 8,429 patients (43% female, median age 74 years, 80% IS), 607 (7%) died in the year following 90 days after discharge. Overall, 56% of patients were supplied all 3 classes of medications, 28% 2 classes of medications, 11% 1 class of medications, and 5% no class of medications. Compared to patients supplied all 3 medication classes, adjusted hazard ratios for all-cause mortality ranged from 1.43 (95% confidence interval [CI]: 1.18-1.72) in those supplied 2 medication classes to 2.04 (95% CI: 1.44-2.88) in those supplied with no medication class. DISCUSSION/CONCLUSION: Treatment with all 3 classes of guideline-recommended medications within 90 days after discharge was associated with better survival. Ongoing efforts are required to ensure optimal pharmacological intervention for secondary prevention of stroke.