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/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, lipid-, 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.

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.

Cost-Effectiveness of an Individualised Management Program after Stroke: A Trial-Based Economic Evaluation.

INTRODUCTION: Evidence on the cost-effectiveness of comprehensive post-stroke programs is limited. We assessed the cost-effectiveness of an individualised management program (IMP) for stroke or transient ischaemic attack (TIA). METHODS: A cost-utility analysis alongside a randomised controlled trial with a 24-month follow-up, from both societal and health system perspectives, was conducted. Adults with stroke/TIA discharged from hospitals were randomised by primary care practice to receive either usual care (UC) or an IMP in addition to UC (intervention). An IMP included stroke-specific nurse-led education and a specialist review of care plans at baseline, 3 months, and 12 months, and telephone reviews by nurses at 6 months and 18 months. Costs were expressed in 2021 Australian dollars (AUD). Costs and quality-adjusted life years (QALYs) beyond 12 months were discounted by 5%. The probability of cost-effectiveness of the intervention was determined by quantifying 10,000 bootstrapped iterations of incremental costs and QALYs below the threshold of AUD 50,000/QALY. RESULTS: Among the 502 participants (65% male, median age 69 years), 251 (50%) were in the intervention group. From a health system perspective, the incremental cost per QALY gained was AUD 53,175 in the intervention compared to the UC group, and the intervention was cost-effective in 46.7% of iterations. From a societal perspective, the intervention was dominant in 52.7% of iterations, with mean per-person costs of AUD 49,045 and 1.352 QALYs compared to mean per-person costs of AUD 51,394 and 1.324 QALYs in the UC group. The probability of the cost-effectiveness of the intervention, from a societal perspective, was 60.5%. CONCLUSIONS: Care for people with stroke/TIA using an IMP was cost-effective from a societal perspective over 24 months. Economic evaluations of prevention programs need sufficient time horizons and consideration of costs beyond direct healthcare utilisation to demonstrate their value to society.

In Search of a Gold Standard Tool for Assessing Knowledge of Stroke: A Systematic Review.

BACKGROUND: Knowledge of stroke is essential to empower people to reduce their risk of these events. However, valid tools are required for accurate and reliable measurement of stroke knowledge. We aimed to systematically review contemporary stroke knowledge assessment tools and appraise their content validity, feasibility, and measurement properties. METHODS: The protocol was registered in PROSPERO (CRD42023403566). Electronic databases (MEDLINE, PsycInfo, CINAHL, Embase, Scopus, Web of Science) were searched to identify published articles (1 January 2015-1 March 2023), in which stroke knowledge was assessed using a validated tool. Two reviewers independently screened titles and abstracts prior to undertaking full-text review. COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methods guided the appraisal of content validity (relevance, comprehensiveness, comprehensibility), feasibility, and measurement properties. RESULTS: After removing duplicates, the titles and abstracts of 718 articles were screened; 323 reviewed in full; with 42 included (N = 23 unique stroke knowledge tools). For content validity, all tools were relevant, two were comprehensive, and seven were comprehensible. Validation metrics were reported for internal consistency (n = 20 tools), construct validity (n = 17 tools), cross-cultural validity (n = 15 tools), responsiveness (n = 9 tools), reliability (n = 7 tools), structural validity (n = 3 tools), and measurement error (n = 1 tool). The Stroke Knowledge Test met all content validity criteria, with validation data for six measurement properties (n = 3 rated "Sufficient"). CONCLUSION: Assessment of stroke knowledge is not standardised and many tools lacked validated content or measurement properties. The Stroke Knowledge Test was the most comprehensive but requires updating and further validation for endorsement as a gold standard.

The utility of a type 2 diabetes polygenic score in addition to clinical variables for prediction of type 2 diabetes incidence in birth, youth and adult cohorts in an Indigenous study population.

AIMS/HYPOTHESIS: There is limited information on how polygenic scores (PSs), based on variants from genome-wide association studies (GWASs) of type 2 diabetes, add to clinical variables in predicting type 2 diabetes incidence, particularly in non-European-ancestry populations. METHODS: For participants in a longitudinal study in an Indigenous population from the Southwestern USA with high type 2 diabetes prevalence, we analysed ten constructions of PS using publicly available GWAS summary statistics. Type 2 diabetes incidence was examined in three cohorts of individuals without diabetes at baseline. The adult cohort, 2333 participants followed from age ≥20 years, had 640 type 2 diabetes cases. The youth cohort included 2229 participants followed from age 5-19 years (228 cases). The birth cohort included 2894 participants followed from birth (438 cases). We assessed contributions of PSs and clinical variables in predicting type 2 diabetes incidence. RESULTS: Of the ten PS constructions, a PS using 293 genome-wide significant variants from a large type 2 diabetes GWAS meta-analysis in European-ancestry populations performed best. In the adult cohort, the AUC of the receiver operating characteristic curve for clinical variables for prediction of incident type 2 diabetes was 0.728; with the PS, 0.735. The PS's HR was 1.27 per SD (p=1.6 × 10-8; 95% CI 1.17, 1.38). In youth, corresponding AUCs were 0.805 and 0.812, with HR 1.49 (p=4.3 × 10-8; 95% CI 1.29, 1.72). In the birth cohort, AUCs were 0.614 and 0.685, with HR 1.48 (p=2.8 × 10-16; 95% CI 1.35, 1.63). To further assess the potential impact of including PS for assessing individual risk, net reclassification improvement (NRI) was calculated: NRI for the PS was 0.270, 0.268 and 0.362 for adult, youth and birth cohorts, respectively. For comparison, NRI for HbA1c was 0.267 and 0.173 for adult and youth cohorts, respectively. In decision curve analyses across all cohorts, the net benefit of including the PS in addition to clinical variables was most pronounced at moderately stringent threshold probability values for instituting a preventive intervention. CONCLUSIONS/INTERPRETATION: This study demonstrates that a European-derived PS contributes significantly to prediction of type 2 diabetes incidence in addition to information provided by clinical variables in this Indigenous study population. Discriminatory power of the PS was similar to that of other commonly measured clinical variables (e.g. HbA1c). Including type 2 diabetes PS in addition to clinical variables may be clinically beneficial for identifying individuals at higher risk for the disease, especially at younger ages.

A Scoping Review of mHealth Interventions for Secondary Prevention of Stroke: Implications for Policy and Practice.

Secondary prevention is a major priority for those living with stroke and may be improved through the use of mobile Health (mHealth) interventions. While evidence for the effectiveness of mHealth interventions for secondary prevention of stroke is growing, little attention has been given to the translation of these interventions into real-world use. In this review, we aimed to provide an update on the effectiveness of mHealth interventions for secondary prevention of stroke, and investigate their translation into real-world use. Four electronic databases and the gray literature were searched for randomized controlled trials of mHealth interventions for secondary prevention of stroke published between 2010 and 2023. Qualitative and mixed-methods evaluations of the trials were also included. Data were extracted regarding study design, population, mHealth technology involved, the intervention, and outcomes. Principal researchers from these trials were also contacted to obtain further translational information. From 1151 records, 13 randomized controlled trials and 4 evaluations were identified; sample sizes varied widely (median, 56; range, 24-4298). Short message service messages (9/13) and smartphone applications (6/13) were the main technologies used to deliver interventions. Primary outcomes of feasibility of the intervention were achieved in 4 trials, and primary outcomes of changes in risk factors, lifestyle behaviors, and adherence to medication improved in 6 trials. Only 1 trial had a hard end point (ie, stroke recurrence) as a primary outcome, and no significant differences were observed between groups. There was evidence for only 1 intervention being successfully translated into real-world use. Further evidence is required on the clinical effectiveness of mHealth interventions for preventing recurrent stroke, and the associated delivery costs and cost-effectiveness, before adoption into real-world settings.

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.

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.

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.

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature.

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.

Quality of life after stroke: a longitudinal analysis of a cluster randomized trial.

PURPOSE: Health-related quality of life (QoL) is poor after stroke, but may be improved with comprehensive care plans. We aimed to determine the effects of an individualized management program on QoL in people with stroke or transient ischemic attack (TIA), describe changes in QoL over time, and identify variables associated with QoL. METHODS: This was a multicenter, cluster randomized controlled trial with blinded assessment of outcomes and intention-to-treat analysis. Patients with stroke or TIA aged ≥ 18 years were randomized by general practice to receive usual care or an intervention comprising a tailored chronic disease management plan and education. QoL was assessed at baseline and 3, 12, and 24 months after baseline using the Assessment of Quality of Life instrument. Patient responses were converted to utility scores ranging from - 0.04 (worse than death) to 1.00 (good health). Mixed-effects models were used for analyses. RESULTS: Among 563 participants recruited (mean age 68.4 years, 64.5% male), median utility scores ranged from 0.700 to 0.772 at different time points, with no difference observed between intervention and usual care groups. QoL improved significantly from baseline to 3 months (ß = 0.019; P = 0.015) and 12 months (ß = 0.033; P < 0.001), but not from baseline to 24 months (ß = 0.013; P = 0.140) in both groups combined. Older age, females, lower educational attainment, greater handicap, anxiety and depression were longitudinally associated with poor QoL. CONCLUSION: An individualized management program did not improve QoL over 24 months. Those who are older, female, with lower educational attainment, greater anxiety, depression and handicap may require greater support. CLINICAL TRIAL REGISTRATION: https://www.anzctr.org.au . Unique identifier: ACTRN12608000166370.

Day and night blood pressure variability among older persons in South-Western Nigeria.

Background: Hypertension is the largest contributor to the global burden of disease. Emerging risk factors for cardiovascular disease include blood pressure variability (BPV), but evidence on BPV is lacking among older Nigerians. We reported BPV in a cohort of older persons at the University College Hospital (UCH), Ibadan. Methods: We conducted a retrospective cohort study of respondents aged >50 years within the Ibadan Ambulatory Blood Pressure Registry at the UCH, Ibadan, Nigeria. Socio-demographic characteristics, lifestyle habits and anthropometric measurements were obtained. Results: Among 639 respondents, 332 (52.0%) were female. The blood pressure (BP) variables were strongly associated with age. Compared with younger age groups, mean diastolic BP (DBP) was less at an older age, whereas mean pulse pressure was greater. During the wake-up and sleep periods, mean DBP and mean arterial BP were less with each increasing age category, whereas mean pulse pressure was larger with each increasing age category. BP dipping, systolic, diastolic and mean arterial BP decreased with age. Overall, timed BPV increased significantly with increasing age. The prevalence of white-coat hypertension was greater among older participants than younger participants. Most respondents in the 50-59 years' age group were non-dippers (55.8%), whereas 33.7% of older respondents were reverse-dippers. Conclusion: Older persons experienced a greater abnormal circadian blood variation and greater BPV than younger people. In Nigeria, follow-up data are needed to determine the prognostic significance of these data in this population.

Quality of Care and One-Year Outcomes in Patients with Diabetes Hospitalised for Stroke or TIA: A Linked Registry Study.

OBJECTIVES: To evaluate key quality indicators for acute care and one-year outcomes following acute ischaemic stroke (IS), intracerebral haemorrhage (ICH), or transient ischaemic attack (TIA) by diabetes status. MATERIALS AND METHODS: Observational cohort study (2009-2013) using linked data from the Australian Stroke Clinical Registry and hospital records. Diabetes was ascertained through review of hospital records. Multilevel regression models were used to evaluate the association between diabetes and outcomes, including discharge destination, and mortality and hospital readmissions within one-year of stroke/TIA. RESULTS: Among 14,132 patients (median age 76 years, 46% female), 22% had diabetes. Compared to patients without diabetes, those with diabetes were equally likely to receive stroke unit care, but were more often discharged on antihypertensive agents (79% vs. 68%) or with a care plan (50% vs. 47%). In patients with TIA, although 86% returned directly home after acute care, those with diabetes more often had a different discharge destination than those without diabetes. Diabetes was associated with greater all-cause mortality (hazard ratio 1.13, 95% CI 1.04-1.23) in patients with IS/ICH; and with both greater all-cause (1.81, CI 1.35-2.43) and CVD mortality (1.75, CI 1.06-2.91) in patients with TIA. Similarly, diabetes was associated with greater rates of all-cause readmission in both patients with IS/ICH and TIA. CONCLUSIONS: Despite good adherence to best care standards for acute stroke/TIA, patients with comorbid diabetes had worse outcomes at one-year than those without comorbid diabetes. Associations of diabetes with poorer outcomes were more pronounced in patients with TIA than those with IS/ICH.

Weight tracking in childhood and adolescence and type 2 diabetes risk.

AIMS/HYPOTHESIS: The aim of this work was to examine the associations of average weight and weight velocity in three growth periods from birth through adolescence with type 2 diabetes incidence. METHODS: Child participants were selected from a 43 year longitudinal study of American Indians to represent three growth periods: pre-adolescence (birth to ~8 years); early adolescence (~8 to ~13 years); and late adolescence (~13 to ~18 years). Age-, sex- and height-standardised weight z score mean and weight z score velocity (change/year) were computed for each period. Participants were followed for up to 25 years from the end of each growth period until they developed diabetes. Associations of weight z score mean or weight z score velocity with diabetes incidence were determined with sex-, birth date- and maternal diabetes-adjusted Poisson regression models. RESULTS: Among 2100 participants representing the pre-adolescence growth period, 1558 representing the early adolescence period and 1418 representing the late adolescence period, there were 290, 315 and 380 incident diabetes cases, respectively. During the first 10 years of follow-up, the diabetes incidence rate ratio (95% CI) was 1.72 (1.40, 2.11)/SD of log10 weight z score mean in pre-adolescence, 2.09 (1.68, 2.60)/SD of log10 weight z score mean in early adolescence and 1.85 (1.58, 2.17)/SD of log10 weight z score mean in late adolescence. The diabetes incidence rate ratio (95% CI) was 1.79 (1.49, 2.17)/SD of log10 weight z score velocity in pre-adolescence, 1.13 (0.91, 1.41)/SD of log10 weight z score velocity in early adolescence and 1.29 (1.09, 1.51)/SD of log10 weight z score velocity in late adolescence. There were strong correlations in the weight z score means and weak correlations in the weight z score velocities between successive periods. CONCLUSIONS/INTERPRETATION: Higher weight and accelerated weight gain in all growth periods associate with increased type 2 diabetes risk. Importantly, higher weight and greater weight velocity during pre-adolescence jointly associate with the highest type 2 diabetes risk. Graphical abstract.

Provider Preference for Growth Charts in Tracking Children with Obesity.

A web-based survey of pediatric care providers revealed differences in their preference for clinical charts that monitor growth in children with obesity. These findings are attributed to pediatric specialty training. Very few providers believe the currently available Centers for Disease Control and Prevention 2000 body mass index-for-age charts adequately track growth in children with obesity.

Birthweight and early-onset type 2 diabetes in American Indians: differential effects in adolescents and young adults and additive effects of genotype, BMI and maternal diabetes.

AIMS/HYPOTHESIS: The aim of this work was to estimate the impact of birthweight on early-onset (age <40 years) type 2 diabetes. METHODS: A longitudinal study of American Indians, aged ≥5 years, was conducted from 1965 to 2007. Participants who had a recorded birthweight were followed until they developed diabetes or their last examination before the age of 40 years, whichever came first. Age- and sex-adjusted diabetes incidence rates were computed and Poisson regression was used to model the effect of birthweight on diabetes incidence, adjusted for sex, BMI, a type 2 diabetes susceptibility genetic risk score (GRS) and maternal covariates. RESULTS: Among 3039 participants, there were 652 incident diabetes cases over a median follow-up of 14.3 years. Diabetes incidence increased with age and was greater in the lowest and highest quintiles of birthweight. Adjusted for covariates, the effect of birthweight on diabetes varied over time, with a non-linear effect at 10-19 years (p < 0.001) and a negative linear effect at older age intervals (20-29 years, p < 0.001; 30-39 years, p = 0.003). Higher GRS, greater BMI and maternal diabetes had additive but not interactive effects on the association between birthweight and diabetes incidence. CONCLUSIONS/INTERPRETATION: In this high-risk population, both low and high birthweights were associated with increased type 2 diabetes risk in adolescence (age 10-19 years) but only low birthweight was associated with increased risk in young adulthood (20-39 years). Higher type 2 diabetes GRS, greater BMI and maternal diabetes added to the risk of early-onset diabetes.

Community-Based Intervention to Improve Cardiometabolic Targets in Patients With Stroke: A Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Many guidelines for secondary prevention of stroke focus on controlling cardiometabolic risk factors. We investigated the effectiveness of a management program for attaining cardiometabolic targets in survivors of stroke/transient ischemic attack. METHODS: Randomized controlled trial of survivors of stroke/transient ischemic attack aged ≥18 years. General practices were randomized to usual care (control) or an intervention comprising specialist review of care plans and nurse education in addition to usual care. The outcome is attainment of pre-defined cardiometabolic targets based on Australian guidelines. Multivariable regression was undertaken to determine efficacy and identify factors associated with attaining targets. RESULTS: Overall, 283 subjects were randomized to the intervention and 280 to controls. Although we found no between-group difference in overall cardiometabolic targets achieved at 12 months, the intervention group more often achieved control of low-density lipoprotein cholesterol (odds ratio, 1.97; 95% confidence interval, 1.18-3.29) than controls. At 24 months, no between-group differences were observed. Medication adherence was ≥80% at follow-up, but uptake of lifestyle/behavioral habits was poor. Older age, being male, being married/living with partner, and having greater functional ability or a history of diabetes mellitus were associated with attaining targets. CONCLUSIONS: The intervention in this largely negative trial only had a detectable effect on attaining target for lipids but not for other factors at 12 months or any factor at 24 months. This limited effect may be attributable to inadequate uptake of behavioral/lifestyle interventions, highlighting the need for new or better approaches to achieve meaningful behavioral change. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: ACTRN12608000166370.