Conditional Wasserstein Generator.

The statistical distance of conditional distributions is an essential element of generating target data given some data as in video prediction. We establish how the statistical distances between two joint distributions are related to those between two conditional distributions for three popular statistical distances: f-divergence, Wasserstein distance, and integral probability metrics. Such characterization plays a crucial role in deriving a tractable form of the objective function to learn a conditional generator. For Wasserstein distance, we show that the distance between joint distributions is an upper bound of the expected distance between conditional distributions, and derive a tractable representation of the upper bound. Based on this theoretical result, we propose a new conditional generator, the conditional Wasserstein generator. Our proposed algorithm can be viewed as an extension of Wasserstein autoencoders (Tolstikhin et al. 2018) to conditional generation or as a Wasserstein counterpart of stochastic video generation (SVG) model by Denton and Fergus (Denton et al. 2018). We apply our algorithm to video prediction and video interpolation. Our experiments demonstrate that the proposed algorithm performs well on benchmark video datasets and produces sharper videos than state-of-the-art methods.

Cluster-specific nonignorably missing, endogenous, and continuous regressors in multilevel model for binary outcome.

In multilevel regression models for observational clustered data, regressors can be correlated with cluster-level error components, namely endogenous, due to omitted cluster-level covariates, measurement error, and simultaneity. When endogeneity is ignored, regression coefficient estimators can be severely biased. To deal with endogeneity, instrument variable methods have been widely used. However, the instrument variable method often requires external instrument variables with certain conditions that cannot be verified empirically. Methods that use the within-cluster variations of the endogenous variable work under the restriction that either the outcome or the endogenous variable has a linear relationship with the cluster-level random effect. We propose a new method for binary outcome when it follows a logistic mixed-effects model and the endogenous variable is normally distributed but not linear in the random effect. The proposed estimator capitalizes on the nested data structure without requiring external instrument variables. We show that the proposed estimator is consistent and asymptotically normal. Furthermore, our method can be applied when the endogenous variable is missing in a cluster-specific nonignorable mechanism, without requiring that the missing mechanism be correctly specified. We evaluate the finite sample performance of the proposed approach via simulation and apply the method to a health care study using a San Diego inpatient dataset. Our study demonstrates that the clustered structure can be exploited to draw valid analysis of multilevel data with correlated effects.

ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI.

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).

Cox model with interval-censored covariate in cohort studies.

In cohort studies the outcome is often time to a particular event, and subjects are followed at regular intervals. Periodic visits may also monitor a secondary irreversible event influencing the event of primary interest, and a significant proportion of subjects develop the secondary event over the period of follow-up. The status of the secondary event serves as a time-varying covariate, but is recorded only at the times of the scheduled visits, generating incomplete time-varying covariates. While information on a typical time-varying covariate is missing for entire follow-up period except the visiting times, the status of the secondary event are unavailable only between visits where the status has changed, thus interval-censored. One may view interval-censored covariate of the secondary event status as missing time-varying covariates, yet missingness is partial since partial information is provided throughout the follow-up period. Current practice of using the latest observed status produces biased estimators, and the existing missing covariate techniques cannot accommodate the special feature of missingness due to interval censoring. To handle interval-censored covariates in the Cox proportional hazards model, we propose an available-data estimator, a doubly robust-type estimator as well as the maximum likelihood estimator via EM algorithm and present their asymptotic properties. We also present practical approaches that are valid. We demonstrate the proposed methods using our motivating example from the Northern Manhattan Study.

Evaluation of a technology-enhanced integrated care model for frail older persons: protocol of the SPEC study, a stepped-wedge cluster randomized trial in nursing homes.

BACKGROUND: Limited evidence exists on the effectiveness of the chronic care model for people with multimorbidity. This study aims to evaluate the effectiveness of an information and communication technology- (ICT-)enhanced integrated care model, called Systems for Person-centered Elder Care (SPEC), for frail older adults at nursing homes. METHODS/DESIGN: SPEC is a prospective stepped-wedge cluster randomized trial conducted at 10 nursing homes in South Korea. Residents aged 65 or older meeting the inclusion/exclusion criteria in all the homes are eligible to participate. The multifaceted SPEC intervention, a geriatric care model guided by the chronic care model, consists of five components: comprehensive geriatric assessment for need/risk profiling, individual need-based care planning, interdisciplinary case conferences, person-centered care coordination, and a cloud-based information and communications technology (ICT) tool supporting the intervention process. The primary outcome is quality of care for older residents using a composite measure of quality indicators from the interRAI LTCF assessment system. Outcome assessors and data analysts will be blinded to group assignment. Secondary outcomes include quality of life, healthcare utilization, and cost. Process evaluation will be also conducted. DISCUSSION: This study is expected to provide important new evidence on the effectiveness, cost-effectiveness, and implementation process of an ICT-supported chronic care model for older persons with multiple chronic illnesses. The SPEC intervention is also unique as the first registered trial implementing an integrated care model using technology to promote person-centered care for frail older nursing home residents in South Korea, where formal LTC was recently introduced. TRIAL REGISTRATION: ISRCTN11972147.

Cox proportional hazards models with left truncation and time-varying coefficient: Application of age at event as outcome in cohort studies.

When analyzing time-to-event cohort data, two different ways of choosing a time scale have been discussed in the literature: time-on-study or age at onset of disease. One advantage of choosing the latter is interpretability of the hazard ratio as a function of age. To handle the analysis of age at onset in a principled manner, we present an analysis of the Cox Proportional Hazards model with time-varying coefficient for left-truncated and right-censored data. In the analysis of Northern Manhattan Study (NOMAS) with age at onset of stroke as outcome, we demonstrate that well-established risk factors may be important only around a certain age span and less established risk factors can have a strong effect in a certain age span.

Calibrated propensity score method for survey nonresponse in cluster sampling.

Weighting adjustment is commonly used in survey sampling to correct for unit nonresponse. In cluster sampling, the missingness indicators are often correlated within clusters and the response mechanism is subject to cluster-specific nonignorable missingness. Based on a parametric working model for the response mechanism that incorporates cluster-specific nonignorable missingness, we propose a method of weighting adjustment. We provide a consistent estimator of the mean or totals in cases where the study variable follows a generalized linear mixed-effects model. The proposed method is robust in the sense that the consistency of the estimator does not require correct specification of the functional forms of the response and outcome models. A consistent variance estimator based on Taylor linearization is also proposed. Numerical results, including a simulation and a real-data application, are presented.

Procalcitonin and Midregional Proatrial Natriuretic Peptide as Markers of Ischemic Stroke: The Northern Manhattan Study.

BACKGROUND AND PURPOSE: Chronic infections and neuroendocrine dysfunction may be risk factors for ischemic stroke (IS). We hypothesized that selected blood biomarkers of infection (procalcitonin [PCT]), hypothalamic-pituitary-axis function (copeptin), and hemodynamic dysfunction (midregional proatrial natriuretic peptide [MRproANP]) are associated with incident IS risk in the multiethnic, urban Northern Manhattan Study (NOMAS) cohort. METHODS: A nested case-control study was performed among initially stroke-free participants. Cases were defined as first IS (n=172). We randomly selected controls among those who did not develop an event (n=344). We calculated Cox proportional hazards models with inverse probability weighting to estimate the association of blood biomarkers with risk of stroke after adjusting for demographic, behavioral, and medical risk factors. RESULTS: Those with PCT and MRproANP, but not copeptin, in the top quartile, compared with the lowest quartile, were associated with IS (for PCT adjusted hazard ratio [HR], 1.9; 95% confidence interval [CI], 1.0-3.8 and for MRproANP adjusted HR, 3.5; 95% CI, 1.6-7.5). The associations of PCT and MRproANP differed by stroke etiology; PCT levels in the top quartile were particularly associated with small vessel stroke (adjusted HR, 5.1; 95% CI, 1.4-18.7) and MRproANP levels with cardioembolic stroke (adjusted HR, 16.3; 95% CI, 3.7-70.9). CONCLUSIONS: Higher levels of PCT, a marker of infection, and MRproANP, a marker for hemodynamic stress, were independently associated with IS risk. PCT was specifically associated with small vessel and MRproANP with cardioembolic stroke risk. Further study is needed to validate these biomarkers and determine their significance in stroke risk prediction and prevention.

Predictors and outcomes of unplanned readmission to a different hospital.

OBJECTIVES: To examine patient, hospital and market factors and outcomes associated with readmission to a different hospital compared with the same hospital. DESIGN: A population-based, secondary analysis using multilevel causal modeling. SETTING: Acute care hospitals in California in the USA. PARTICIPANTS: In total, 509 775 patients aged 50 or older who were discharged alive from acute care hospitals (index hospitalizations), and 59 566 who had a rehospitalization within 30 days following their index discharge. INTERVENTION: No intervention. MAIN OUTCOME MEASURE(S): Thirty-day unplanned readmissions to a different hospital compared with the same hospital and also the costs and health outcomes of the readmissions. RESULTS: Twenty-one percent of patients with a rehospitalization had a different-hospital readmission. Compared with the same-hospital readmission group, the different-hospital readmission group was more likely to be younger, male and have a lower income. The index hospitals of the different-hospital readmission group were more likely to be smaller, for-profit hospitals, which were also more likely to be located in counties with higher competition. The different-hospital readmission group had higher odds for in-hospital death (8.1 vs. 6.7%; P < 0.0001) and greater readmission hospital costs ($15 671.8 vs. $14 286.4; P < 0.001) than the same-hospital readmission group. CONCLUSIONS: Patient, hospital and market characteristics predicted different-hospital readmissions compared with same-hospital readmissions. Mortality and cost outcomes were worse among patients with different-hospital readmissions. Strategies for better care coordination targeting people at risk for different-hospital readmissions are necessary.

High-sensitivity C-reactive protein and interleukin-6-dominant inflammation and ischemic stroke risk: the northern Manhattan study.

BACKGROUND AND PURPOSE: Interleukin-6 (IL-6) is a proinflammatory cytokine with known autoregulatory feedback mechanisms. We hypothesized that elevated high-sensitivity C-reactive protein (hsCRP) relative to IL-6 confers an increased risk of ischemic stroke (IS), and low hsCRP relative to IL-6 a decreased risk, for individuals in the prospective, multiethnic, population-based Northern Manhattan Study (NOMAS). METHODS: Serum hsCRP and IL-6 were measured in NOMAS participants at baseline. We created a trichotomized predictor based on the dominant biomarker in terms of quartiles: hsCRP-dominant, IL-6-dominant, and codominant groups. Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals for the association between inflammatory biomarker group status and risk of incident IS. RESULTS: Of 3298 participants, both hsCRP and IL-6 were available in 1656 participants (mean follow-up, 7.8 years; 113 incident IS). The hsCRP-dominant group had increased risk of IS (adjusted hazard ratio, 2.62; 95% confidence interval, 1.56-4.41) and the IL-6-dominant group had decreased risk (adjusted hazard ratio, 0.38; 95% confidence interval, 0.18-0.82) when compared with the referent group, after adjusting for potential confounders. Model fit was improved using the inflammation-dominant construct, over either biomarker alone. CONCLUSIONS: In this multiethnic cohort, when hsCRP-quartile was higher than IL-6 quartile, IS risk was increased, and conversely when IL-6 quartiles were elevated relative to hsCRP, IS risk was decreased. Construct validity requires confirmation in other cohorts.

Diabetes predicts long-term disability in an elderly urban cohort: the Northern Manhattan Study.

PURPOSE: There are limited data on vascular predictors of long-term disability in Hispanics. We hypothesized that (1) functional status declines over time and (2) vascular risk factors predict functional decline. METHODS: The Northern Manhattan Study contains a population-based study of 3298 stroke-free individuals aged 40 years or older, followed for median 11 years. The Barthel Index (BI) was assessed annually. Generalized estimating equations and Cox models were adjusted for demographic, medical, and social risk factors. Stroke and myocardial infarction occurring during follow-up were censored in sensitivity analysis. Secondarily, motor and nonmotor domains of the BI were analyzed. RESULTS: Mean age (standard deviation) of the cohort (n = 3298) was 69.2 (10) years, 37% were male, 52% Hispanic, 22% diabetic, and 74% hypertensive. There was a mean annual decline of 1.02 BI points (P < .0001). Predictors of decline in BI included age, female sex, diabetes, depression, and normocholesterolemia. Results did not change with censoring. We found similar predictors of BI for motor and nonmotor domains. CONCLUSION: In this large, population-based, multiethnic study with long-term follow-up, we found a 1% mean decline in function per year that did not change when vascular events were censored. Diabetes predicted functional decline in the absence of clinical vascular events.

Lipoprotein-associated phospholipase A2 is associated with atherosclerotic stroke risk: the Northern Manhattan Study.

BACKGROUND: Lipoprotein-associated phospholipase A2 (LpPLA2) levels are associated with stroke, though whether this extends to all populations and stroke subtypes is unknown. METHODS: Serum samples from stroke-free community participants in the Northern Manhattan Study were assayed for LpPLA2 mass and activity. Participants were followed annually for stroke. Cox-proportional-hazard models were fitted to estimate hazard-ratios and 95% confidence intervals (HR, 95% CI) for the association of LpPLA2 levels with ischemic stroke (IS), after adjusting for demographic and medical risk factors. RESULTS: Serum samples were available in 1946 participants, of whom 151 (7.8%) experienced a first IS during median follow-up 11 years. Mean age was 69 (SD 10), 35.6% were men, 20% non-Hispanic Whites, 22% non-Hispanic Blacks, and 55% Hispanics. LpPLA2 mass and activity levels were not associated with overall IS risk. LpPLA2 mass but not activity levels were associated with strokes due to large artery atherosclerosis (LAA; adjusted HR per SD 1.55, 95% CI 1.17-2.04). There was a dose-response relationship with LAA (compared to first quartile, 2nd quartile HR = 1.43, 95% CI 0.23-8.64; 3rd quartile HR = 4.47, 95% CI 0.93-21.54; 4th quartile HR = 5.07, 95% CI 1.07-24.06). The associations between LpPLA2-mass and LAA-stroke risk differed by race-ethnicity (p = 0.01); LpPLA2-mass was associated with increased risk of LAA among non-Hispanic Whites (adjusted HR per SD 1.44, 95% CI 0.98-2.11), but not other race-ethnic groups. CONCLUSION: LpPLA2-mass levels were associated with risk of atherosclerotic stroke among non-Hispanic White participants, but not in other race-ethnic groups in the cohort. Further study is needed to confirm these race-ethnic differences and the reasons for them.

Infectious burden and cognitive function: the Northern Manhattan Study.

OBJECTIVE: We hypothesized that infectious burden (IB), a composite serologic measure of exposure to common pathogens (i.e., Chlamydia pneumoniae, Helicobacter pylori, cytomegalovirus, and herpes simplex virus 1 and 2) associated with vascular risk in the prospective Northern Manhattan Study (NOMAS), would also be associated with cognition. METHODS: Cognition was assessed using the Mini-Mental State Examination (MMSE) at enrollment and the modified Telephone Interview for Cognitive Status (TICS-m) at annual follow-up visits. Adjusted linear and logistic regressions were used to measure the association between IB index and MMSE. Generalized estimating equation models were used to evaluate associations with TICS-m and its change over time. RESULTS: Serologies and cognitive assessments were available in 1,625 participants of the NOMAS cohort. In unadjusted analyses, higher IB index was associated with worse cognition (change per standard deviation [SD] of IB for MMSE was -0.77, p < 0.0001, and for first measurements of TICS-m was -1.89, p < 0.0001). These effects were attenuated after adjusting for risk factors (for MMSE adjusted change per SD of IB = -0.17, p = 0.06, for TICS-m adjusted change per SD IB = -0.68, p < 0.0001). IB was associated with MMSE ≤24 (compared to MMSE >24, adjusted odds ratio 1.26 per SD of IB, 95% confidence interval 1.06-1.51). IB was not associated with cognitive decline over time. The results were similar when IB was limited to viral serologies only. CONCLUSION: A measure of IB associated with stroke risk and atherosclerosis was independently associated with cognitive performance in this multiethnic cohort. Past infections may contribute to cognitive impairment.

Association between northern Manhattan study global vascular risk score and successful aging.

OBJECTIVES: To examine the association between successful aging without subsequent cognitive decline (SA-ND) and the Northern Manhattan Study (NOMAS) global vascular risk score (GVRS), which is predictive of stroke, myocardial infarction, and vascular death. DESIGN: Prospective cohort study. SETTING: A stroke-free sample of Hispanic, black, and white participants living in the same community enrolled in a magnetic resonance imaging (MRI) substudy of NOMAS, a population-based prospective cohort study. PARTICIPANTS: One thousand two hundred ninety individuals in whom a cognitive screen was administered at baseline and at enrollment in the MRI substudy. MEASUREMENTS: SA-ND was based on disease, disability, and cognitive function. The GVRS includes age, sex, race and ethnicity, waist circumference, alcohol intake, smoking, physical activity, blood pressure, antihypertensive medication use, fasting blood sugar, lipid levels, and peripheral vascular disease. RESULTS: Data at baseline and follow-up were available for 1,162 participants (mean age 70 ± 9; 61% women; 13% white, 16% black, 69% Hispanic; mean GVRS 8.6 ± 0.9). Logistic regression, adjusted for education, socioeconomic status, and follow-up time, showed that the odds of SA-ND were 38% greater for each additional 1-point decrease on the GVRS (odds ratio = 1.38, 95% confidence interval = 1.17-1.61; P < .001). An inverse dose-response was observed between quartiles of GVRS and SA-ND. Greater diastolic blood pressure in participants taking antihypertensive medication and a history of claudication (P = .003) or peripheral arterial disease (P < .001) were inversely associated with SA-ND in the fully adjusted model. CONCLUSION: Potentially modifiable vascular risk factors were independently associated with SA-ND in a multiethnic community-based sample. Improvements in GVRSs could help promote healthy longevity in the aging population.

Interleukin 6 plasma concentration associates with cognitive decline: the northern Manhattan study.

BACKGROUND: Interleukin 6 (IL-6) is an inflammatory cytokine that has been associated with vascular disease and cognitive impairment, but few studies have examined these relationships in population-based studies that include Hispanic and Black people who often have a greater prevalence of vascular risk factors and are at an elevated risk of dementia than Whites. We examined relative elevations of plasma IL-6 concentrations in relation to cognitive decline in a stroke-free racially/ethnically diverse community-based sample from Northern Manhattan. METHODS: We used mixed effects models to measure the effect of IL-6 on change in performance on the modified Telephone Interview for Cognitive Status (TICS-m) measured annually in our cohort, adjusting for sociodemographic and vascular risk factors. RESULTS: There were 1,224 participants with IL-6 levels (median 1.5 pg/ml, interquartile range 0.83-2.57 pg/ml) and TICS-m data available (mean = 31.6 points, SD 6.5). The mean age was 71 (SD 9.3; 64% women, 59% Hispanic, 19% Black, 19% White) with 3,406 person-years and a median 3.0 years of follow-up (interquartile range 1.1-4.0 years). Participants with IL-6 levels above the median showed greater cognitive decline on the TICS-m compared to those with levels below the median, adjusting for sociodemographic and vascular factors (ß = -0.17 points/year, p = 0.02). Decline on the TICS-m among participants with IL-6 above the median differed by age (p for interaction <0.001). There was no interaction by race/ethnicity, vascular risk factors, C-reactive protein, apolipoprotein ε4 allele status, or the metabolic syndrome among nondiabetics. CONCLUSIONS: IL-6 associated with cognitive decline among older participants in this racially/ethnically diverse sample independent of other vascular risk factors and C-reactive protein.

Tumour necrosis factor receptor 1 and mortality in a multi-ethnic cohort: the Northern Manhattan Study.

OBJECTIVE: to study the association between soluble tumour necrosis factor receptor 1 (sTNFR1) levels and mortality in the population-based Northern Manhattan Study (NOMAS). METHODS: NOMAS is a multi-ethnic, community-based cohort study with mean 8.4 years of follow-up. sTNFR1 was measured using ELISA. Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals (HR, 95% CI) for the association of sTNFR1 with risk of all-cause mortality after adjusting for relevant confounders. RESULTS: sTNFR1 measurements were available in 1,862 participants (mean age 69.2 ± 10.2 years) with 512 all-cause deaths. Median sTNFR1 was 2.28 ng/ml. Those with sTNFR1 levels in the highest quartile (Q4), compared with those with sTNFR1 in the lowest quartile (Q1), were at an increased risk of all-cause mortality (adjusted HR: 1.8, 95% CI: 1.4-2.4) and non-vascular mortality (adjusted HR: 2.5, 95% CI: 1.5-3.6), but not vascular mortality (adjusted HR: 1.3, 95% CI: 0.9-1.9). There were interactions between sTNFR1 quartiles and medical insurance-status [likelihood ratio test (LRT) with 3 degrees of freedom, Pinteraction = 0.02] and alcohol consumption (LRT with 3 degrees of freedom, Pinteraction < 0.01) for all-cause mortality. In participants with no insurance or Medicaid, those with sTNFR1 in the top quartile had nearly a threefold increased risk of total mortality than the lowest quartile (adjusted HR: 2.9, 95% CI: 1.9-4.4). CONCLUSION: in this multi-ethnic cohort, sTNFR1 was associated with all-cause and non-vascular mortality, particularly among those of a lower socioeconomic status.

A marginal approach to reduced-rank penalized spline smoothing with application to multilevel functional data.

Multilevel functional data is collected in many biomedical studies. For example, in a study of the effect of Nimodipine on patients with subarachnoid hemorrhage (SAH), patients underwent multiple 4-hour treatment cycles. Within each treatment cycle, subjects' vital signs were reported every 10 minutes. This data has a natural multilevel structure with treatment cycles nested within subjects and measurements nested within cycles. Most literature on nonparametric analysis of such multilevel functional data focus on conditional approaches using functional mixed effects models. However, parameters obtained from the conditional models do not have direct interpretations as population average effects. When population effects are of interest, we may employ marginal regression models. In this work, we propose marginal approaches to fit multilevel functional data through penalized spline generalized estimating equation (penalized spline GEE). The procedure is effective for modeling multilevel correlated generalized outcomes as well as continuous outcomes without suffering from numerical difficulties. We provide a variance estimator robust to misspecification of correlation structure. We investigate the large sample properties of the penalized spline GEE estimator with multilevel continuous data and show that the asymptotics falls into two categories. In the small knots scenario, the estimated mean function is asymptotically efficient when the true correlation function is used and the asymptotic bias does not depend on the working correlation matrix. In the large knots scenario, both the asymptotic bias and variance depend on the working correlation. We propose a new method to select the smoothing parameter for penalized spline GEE based on an estimate of the asymptotic mean squared error (MSE). We conduct extensive simulation studies to examine property of the proposed estimator under different correlation structures and sensitivity of the variance estimation to the choice of smoothing parameter. Finally, we apply the methods to the SAH study to evaluate a recent debate on discontinuing the use of Nimodipine in the clinical community.

Daytime sleepiness and risk of stroke and vascular disease: findings from the Northern Manhattan Study (NOMAS).

BACKGROUND: Recent studies have suggested that poor quality and diminished quantity of sleep may be independently linked to vascular events although prospective and multiethnic studies are limited. This study aimed to explore the relationship between daytime sleepiness and the risk of ischemic stroke and vascular events in an elderly, multiethnic prospective cohort. METHODS AND RESULTS: As part of the Northern Manhattan Study, the Epworth Sleepiness Scale was collected during the 2004 annual follow-up. Daytime sleepiness was trichotomized using previously reported cut points of no dozing, some dozing, and significant dozing. Subjects were followed annually for a mean of 5.1 years. Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals for stroke, myocardial infarction, and death outcomes. We obtained the Epworth Sleepiness Scale on 2088 community residents. The mean age was 73.5 ± 9.3 years; 64% were women; 17% were white, 20% black, 60% Hispanic, and 3% were other. Over 44% of the cohort reported no daytime dozing, 47% some dozing, and 9% significant daytime dozing. Compared with those reporting no daytime dozing, individuals reporting significant dozing had an increased risk of ischemic stroke (hazard ratio, 2.74 [95% confidence interval, 1.38-5.43]), all stroke (3.00 [1.57-5.73]), the combination of ischemic stroke, myocardial infarction, and vascular death (2.38 [1.50-3.78]), and all vascular events (2.48 [1.57-3.91]), after adjusting for medical comorbidities. CONCLUSIONS: Daytime sleepiness is an independent risk factor for stroke and other vascular events. These findings suggest the importance of screening for sleep problems at the primary care level.

Trajectory of functional decline before and after ischemic stroke: the Northern Manhattan Study.

BACKGROUND AND PURPOSE: Previous research in our cohort showed a delayed decline in functional status after first ischemic stroke. We compared the long-term trajectory of functional status before and after ischemic stroke. METHODS: The Northern Manhattan Study contains a prospective, population-based study of stroke-free individuals age ≥40 years, followed for a median of 11 years. The Barthel index (BI), a commonly used measure of activities of daily living, was assessed annually. Generalized estimating equations were used to assess functional decline over time before stroke and beginning 6 months after stroke. Follow-up was censored at the time of recurrent stroke. RESULTS: Among 3298 participants, 210 participants had an ischemic stroke during follow-up and had poststroke BI assessed. Mean age (±SD) was 77±9 years, 38% were men, 52% were Hispanic, 37% had diabetes, and 31% had coronary artery disease. There was no difference in rate of functional decline over time before and after stroke (P=0.51), with a decline of 0.96 BI points per year before stroke (P<0.0001) and 1.24 BI points after stroke (P=0.001). However, when stratified by insurance status, among those with Medicaid or no insurance, in a fully adjusted model, there was a difference in slope before and after stroke (P=0.04), with a decline of 0.58 BI points per year before stroke (P=0.02) and 1.94 BI points after stroke (P=0.001). CONCLUSIONS: In this large, prospective, population-based study with long-term follow-up, there was a significantly steeper decline in functional status after ischemic stroke compared with before stroke among those with Medicaid or no insurance, after adjusting for confounders.