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.
Introduction: Survivors of stroke are at increased risk of experiencing subsequent major adverse cardiovascular events (MACE). We aimed to determine the incidence of MACE within two years following first-ever ischemic stroke and identify associated factors. Methods: Patient-level data from the Australian Stroke Clinical Registry (2009-2013) were linked with emergency department and hospital admission data. Patient comorbidities were identified in the emergency and admissions data using published algorithms. Adults with no prior history of acute cardiovascular events were followed for two years post-discharge, or until first occurrence of MACE, whichever occurred earlier. Multivariable competing risks regression, accounting for deaths due to non-cardiovascular causes, was used to determine factors associated with MACE post-stroke. Results: Among 5,994 patients with first-ever ischemic stroke (median age 73 years, 45% female), 17% were admitted for MACE (129/100,000 person-years). MACE incidence was greatest in the first year following stroke (157/100,000 person-years). Compared to participants aged ≥65 years, the median time to first MACE was 37 days earlier for those aged <65 years. As there was a significant interaction (p<0.05) between age and other factors in predicting MACE incidence, subsequent analyses were stratified by age group. Being discharged to inpatient rehabilitation (sub-distribution hazard ratio [SHR]: 0.63; 95% Confidence Interval (CI): 0.44-0.90) was associated with reduced risk of MACE in patients aged <65 years only. Whereas, in those aged ≥65 years, MACE was associated with being female (SHR: 1.04; 95% CI: 1.03-1.05), initial stroke severity (SHR: 1.33; 95% CI: 1.15-1.54), smoking (SHR: 1.41; 95% CI: 1.14-1.71), and atrial fibrillation (SHR: 1.31; 95% CI: 1.14-1.51). Being treated in a large hospital (>300 beds) was associated with a lower risk of MACE in those aged <65 (SHR: 0.68; 95% CI: 0.44-0.90) and ≥65 years (SHR: 0.74; 95% CI: 0.62-0.87). Conclusion: MACE presentations are common within two-years of stroke, with most events occurring in the first year. We have identified factors (e.g., use of rehabilitation among younger patients) to consider when designing interventions to prevent MACE after stroke.
Background: Cerebrovascular accident (CVA) is an outdated term for describing stroke as it implies stroke is an accident. We conducted an integrative review to examine the use of CVA in terms of (1) frequency in major medical journals over time; (2) associated publication characteristics (e.g., number of authors, senior author country, topic); and (3) frequency in medical records. Methods: We searched Google Scholar for publications in leading neurology and vascular journals (Quartile 1) across two 5-year periods (1998–2002 and 2018–2022) using the terms “cerebrovascular accident” or “CVA.” Two reviewers independently reviewed full-text publications and recorded the frequency of CVA use. Rates of use (per 1,000 articles/year) were calculated for each journal and time period. Associations of publication characteristics with CVA use were determined using multivariable logistic regression models. In addition, admission and discharge forms in the Auckland Regional Community Stroke Study (ARCOS V) were audited for frequency of use of the term CVA. Results: Of the 1,643 publications retrieved, 1,539 were reviewed in full. Of these, CVA was used ≥1 time in 676 publications, and ≥2 times in 276 publications (129 in 1998–2002; 147 in 2018–2022). The terms CVA and stroke both appeared in 57% of publications where CVA was used ≥2 times in 1998–2002, compared to 65% in 2018–2022. Majority of publications were on the topic of stroke (22% in 1998–2002; 20% in 2018–2022). There were no associations between publication characteristics and the use of CVA. The highest rate of CVA use in 2018–2022 was in Circulation, which had increased over time from 1.3 uses per 1,000 publications in 1998–2002 to 1.8 uses per 1,000 publications in 2018–2022. The largest reduction in the use of CVA was in Neuroepidemiology (2.0 uses per 1,000 publications in 1998–2002 to 0 uses in 2018–2022). The term CVA was identified in 0.2% (17/7,808) of stroke admission and discharge forms audited. Conclusion: We found evidence of changes in the use of CVA in the scientific literature over the past two decades. Editors, authors, and clinicians should avoid the use of the term CVA as it perpetuates the use of an ambiguous and inappropriate term.
Although medication adherence is commonly measured in electronic datasets using the proportion of days covered (PDC), no standardized approach is used to calculate and report this measure. We conducted a scoping review to understand the approaches taken to calculate and report the PDC for cardiovascular medicines to develop improved guidance for researchers using this measure. After prespecifying methods in a registered protocol, we searched Ovid Medline, Embase, Scopus, CINAHL Plus and grey literature (1 July 2012 to 14 December 2020) for articles containing the terms “proportion of days covered” and “cardiovascular medicine”, or synonyms and subject headings. Of the 523 articles identified, 316 were reviewed in full and 76 were included (93% observational studies; 47% from the USA; 2 grey literature articles). In 45 articles (59%), the PDC was measured from the first dispensing/claim date. Good adherence was defined as 80% PDC in 61 articles, 56% of which contained a rationale for selecting this threshold. The following parameters, important for deriving the PDC, were often not reported/unclear: switching (53%), early refills (45%), in‐hospital supplies (45%), presupply (28%) and survival (7%). Of the 46 articles where dosing information was unavailable, 59% reported how doses were imputed. To improve the transparent and systematic reporting of the PDC, we propose the TEN‐SPIDERS tool, covering the following PDC parameters: T hreshold, E ligibility criteria, N umerator and denominator, S urvival, P resupply, I n‐hospital supplies, D osing, E arly R efills, and S witching. Use of this tool will standardize reporting of the PDC to facilitate reliable comparisons of medication adherence estimates between studies.
Background And Aims
Prescription of multiple classes of medications (antihypertensive, antithrombotic and lipid-lowering) is recommended in clinical guidelines for ischemic stroke (IS) and transient ischemic attack (TIA) to prevent further vascular events. We aimed to determine the association between optimal combination medication treatment (supply of all three classes, “optimal treatment”) and survival after IS/TIA.
Methods
Cohort of patients with first-ever IS/TIA from the Australian Stroke Clinical Registry (2010–2014) linked with pharmaceutical claims data. We excluded patients who died within 30 days of admission. Cox regression was used to determine associations between optimal treatment and 1-year (from day 30 to 395) survival using landmark methods, adjusting for socio-demographics (age, sex, socioeconomic position) and clinical characteristics (stroke type, discharge destination).
Results
Among 8136 survivors with first-ever IS/TIA satisfying inclusion criteria (45% female, median age 74 years), 75.5% received ≥1 medication class, and 34.0% had optimal treatment. Patients with optimal treatment (N = 2765) were more often aged ≥75 years (51.3% vs 44.3%; p < 0.001), discharged directly home (65.8% vs 49.5%; p < 0.001) and experienced a less severe stroke (53.2% vs 43.5%; p < 0.001), than those without optimal treatment. Compared to no medication, treatment with two medications was associated with a 42% lesser risk of death (95%CI: 27–55%); and optimal treatment, a 68% lesser risk of death (95% CI: 49–65%). Survival was similar between those with one or no medication.
Conclusions
Patients with stroke/TIA who received optimal combination medication treatment within 30-days of admission had greater one-year-survival. Further research is required to understand reasons for sub-optimal medication treatment.
Background and Purpose: Although a target of 80% medication adherence is commonly cited, it is unclear whether greater adherence improves survival after stroke or transient ischemic attack (TIA). We investigated associations between medication adherence during the first year postdischarge, and mortality up to 3 years, to provide evidence-based targets for medication adherence. Methods: Retrospective cohort study of 1-year survivors of first-ever stroke or TIA, aged ≥18 years, from the Australian Stroke Clinical Registry (July 2010–June 2014) linked with nationwide prescription refill and mortality data (until August 2017). Adherence to antihypertensive agents, statins, and nonaspirin antithrombotic medications was based on the proportion of days covered from discharge until 1 year. Cox regression with restricted cubic splines was used to investigate nonlinear relationships between medication adherence and all-cause mortality (to 3 years postdischarge). Models were adjusted for age, sex, socioeconomic position, stroke factors, primary care factors, and concomitant medication use. Results: Among 8363 one-year survivors of first-ever stroke or TIA (44% aged ≥75 years, 44% female, 18% TIA), 75% were supplied antihypertensive agents. In patients without intracerebral hemorrhage (N=7446), 84% were supplied statins, and 65% were supplied nonaspirin antithrombotic medications. Median adherence was ≈90% for each medication group. Between 1% and 100% adherence, greater adherence to statins or antihypertensive agents, but not nonaspirin antithrombotic agents, was associated with improved survival. When restricted to linear regions above 60% adherence, each 10% increase in adherence was associated with a reduction in all-cause mortality of 13% for antihypertensive agents (hazard ratio, 0.87 [95% CI, 0.81–0.95]), 13% for statins (hazard ratio, 0.87 [95% CI, 0.80–0.95]), and 15% for nonaspirin antithrombotic agents (hazard ratio, 0.85 [95% CI, 0.79–0.93]). Conclusions: Greater levels of medication adherence after stroke or TIA are associated with improved survival, even among patients with near-perfect adherence. Interventions to improve medication adherence are needed to maximize survival poststroke.
Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.
Methods
The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.
Findings
The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.
Interpretation
Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere.
ObjectiveTo determine the effectiveness of government policies supporting coordinated multidisciplinary primary care (MDC) in improving long-term survival following stroke or Transient Ischaemic Attack (TIA). ApproachWe used the target trial framework for observational data to assess the average population effect of primary care MDC policies. The cohort comprised patients from the Australian Stroke Clinical Registry (January 2012-June 2015) linked with (i) Australian Medicare claims to define exposures (MDC claims in the 6-18 months post-stroke); (ii) hospital, pharmaceutical and aged care datasets for additional covariates; (iii) National Death Registry for survival outcomes (19-30 months post-stroke). Level of impairment was classified by latent class analysis using EQ-5D-3L questionnaire data obtained 90-180 days post-stroke. Multilevel survival analysis with inverse probability treatment weights was applied. ResultsAmong 7,255 people with stroke (42% female, median age 71 years, 24% TIA, level of impairment: 39% minimal, 32% moderate, 29% severe), 29% had a Medicare claim for MDC (23% minimal, 31% moderate, 39% severe). Mortality was reduced in those receiving a claim (vs non-receipt) in the minimal (adjusted Hazard Ratio (aHR): 0.50, 95%CI:0.27, 0.91) and severe (aHR: 0.65, 95%CI:0.46, 0.91) impairment groups, but not the moderate impairment group (aHR: 1.31, 95%CI:0.86, 1.99). Group differences in allied health services claimed during MDC were observed: secondary prevention (14% minimal vs 10% severe impairment), rehabilitation (21% minimal vs 25% severe impairment). ImplicationsDrawing causal inferences from linked observational data demonstrated the population effectiveness of primary care MDC policies, in improving survival following stroke/TIA, with variation by impairment class.
<b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> 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). <b><i>Results:</i></b> 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). <b><i>Conclusions:</i></b> 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.
<b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Discussion/Conclusion:</i></b> 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.
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