Ambulatory Cardiology or General Internal Medicine Assessment Prior to Scheduled Major Vascular Surgery is Associated with Improved Outcomes.

OBJECTIVE: To characterize the association between ambulatory cardiology or general internal medicine (GIM) assessment prior to surgery and outcomes following scheduled major vascular surgery. BACKGROUND: Cardiovascular risk assessment and management prior to high-risk surgery remains an evolving area of care. METHODS: This is population-based retrospective cohort study of all adults who underwent scheduled major vascular surgery in Ontario, Canada, April 1, 2004-March 31, 2019. Patients who had an ambulatory cardiology and/or GIM assessment within 6 months prior to surgery were compared to those who did not. The primary outcome was 30-day mortality. Secondary outcomes included: composite of 30-day mortality, myocardial infarction or stroke; 30-day cardiovascular death; 1-year mortality; composite of 1-year mortality, myocardial infarction or stroke; and 1-year cardiovascular death. Cox proportional hazard regression using inverse probability of treatment weighting (IPTW) was used to mitigate confounding by indication. RESULTS: Among 50,228 patients, 20,484 (40.8%) underwent an ambulatory assessment prior to surgery: 11,074 (54.1%) with cardiology, 8,071 (39.4%) with GIM and 1,339 (6.5%) with both. Compared to patients who did not, those who underwent an assessment had a higher Revised Cardiac Risk Index (N with Index over 2= 4,989[24.4%] vs. 4,587[15.4%], P<0.001) and more frequent pre-operative cardiac testing (N=7,772[37.9%] vs. 6,113[20.6%], P<0.001) but, lower 30-day mortality (N=551[2.7%] vs. 970[3.3%], P<0.001). After application of IPTW, cardiology or GIM assessment prior to surgery remained associated with a lower 30-day mortality (weighted Hazard Ratio [95%CI] = 0.73 [0.65-0.82]) and a lower rate of all secondary outcomes. CONCLUSIONS: Major vascular surgery patients assessed by a cardiology or GIM physician prior to surgery have better outcomes than those who are not. Further research is needed to better understand potential mechanisms of benefit.

The performance of marginal structural models for estimating risk differences and relative risks using weighted univariate generalized linear models.

We used Monte Carlo simulations to compare the performance of marginal structural models (MSMs) based on weighted univariate generalized linear models (GLMs) to estimate risk differences and relative risks for binary outcomes in observational studies. We considered four different sets of weights based on the propensity score: inverse probability of treatment weights with the average treatment effect as the target estimand, weights for estimating the average treatment effect in the treated, matching weights and overlap weights. We considered sample sizes ranging from 500 to 10,000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We examined both the robust variance estimator when using generalized estimating equations with an independent working correlation matrix and a bootstrap variance estimator for estimating the standard error of the risk difference and the log-relative risk. The performance of these methods was compared with that of direct weighting. Both the direct weighting approach and MSMs based on weighted univariate GLMs resulted in the identical estimates of risk differences and relative risks. When sample sizes were small to moderate, the use of an MSM with a bootstrap variance estimator tended to result in the most accurate estimates of standard errors. When sample sizes were large, the direct weighting approach and an MSM with a bootstrap variance estimator tended to produce estimates of standard error with similar accuracy. When using a MSM to estimate risk differences and relative risks, in general it is preferable to use a bootstrap variance estimator than the robust variance estimator. We illustrate the application of the different methods for estimating risks differences and relative risks using an observational study on the effect on mortality of discharge prescribing of a beta-blocker in patients hospitalized with acute myocardial infarction.

The Dynamic Nature of the Socioeconomic Determinants of Cardiovascular Health: A Narrative Review.

Despite decades of social epidemiologic research, health inequities remain pervasive and ubiquitous in Canada and elsewhere. One reason may be our use of socioeconomic measurement, which has often relied on single point-in-time exposures. To explore the extent to which researchers have incorporated dynamic socioeconomic measurement into cardiovascular health outcome evaluations, we performed a narrative review. We estimated the prevalence of socioeconomic longitudinal cardiovascular research studies that identified socioeconomic exposures at 2 or more points in time between the years of 2019 and 2023. We defined cardiovascular outcome studies as those that examined coronary artery disease, myocardial infarction, acute coronary syndrome, stroke, heart failure, cardiac arrhythmias, cardiac death, cardiometabolic factors, transient ischemic attacks, peripheral artery disease, or hypertension. Socioeconomic exposures included individual income, neighbourhood income, intergenerational social mobility, education, occupation, insurance status, and economic security. Seven percent of socioeconomic cardiovascular outcome studies have measured socioeconomic status at 2 or more points in time throughout the follow-up period, hypothesized mechanisms by which dynamic socioeconomic measures affected outcome focused on social mobility, accumulation, and critical period theories. Insights, implications, and future directions are discussed, in which we highlight ways in which postal code data can be better used methodologically as a dynamic socioeconomic measure. Future research must incorporate dynamic socioeconomic measurement to better inform root causes, interventions, and health-system designs if health equity is to be improved.

Comparing Costs of Noninvasive Cardiac Diagnostic Tests-a Population-Based Study.

INTRODUCTION: Noninvasive cardiac diagnostic tests (NITs) for the diagnosis of coronary artery disease have been estimated to cost >$3 billion annually in the United States alone and have recently undergone scrutiny over concerns of overuse. Consequently, comparing costs of different NIT testing strategies is of urgent importance to health care planning. METHODS: We utilized population-based administrative and clinical data from Ontario, Canada, to compare downstream costs between 4 available NIT testing strategies (graded exercise stress testing [GXT], stress echocardiography, cardiac computed tomography angiography [CCTA], and myocardial perfusion imaging [MPI] as well as no testing), among patients evaluated for chest pain. To compare costs among the tested (overall and by testing strategy) and nontested groups, we used a log-gamma generalized linear model to account for the skewed distribution of health care cost data, adjusting for relevant clinical covariates. RESULTS: A total of 2,340,699 patients were included in our cohort, of whom 481,170 (21%) patients received 1 of the 4 NITs. Among patients who received a NIT, 254,492 (53%) received a GXT as their initial test, 154,137 (32%) received MPI, 69,160 (14%) received a stress echo, and 3,381 (<1%) received a CCTA. After adjustment for differences in baseline patient characteristics, receipt of any NIT was associated with an approximate 12% reduction in downstream 1-year mean costs (cost ratio = 0.88; 95% CI, 0.87, 0.89) compared with those without any testing. Comparing the different testing strategies with no testing, both GXT (cost ratio = 0.80; 95% CI, 0.79-0.81) and stress echocardiography (cost ratio = 0.82; 95% CI, 0.81-0.83) were associated with the lower downstream costs, while both MPI (cost ratio = 1.26; 95% CI, 1.25, 1.27) and CCTA (cost ratio = 1.29; 95% CI, 1.23, 1.35) were associated with higher downstream costs. CONCLUSIONS: In a large population-based cohort consisting of >2 million people evaluated for chest pain, we report that receipt of noninvasive testing was associated with a 12% reduction in downstream costs when compared with no testing. Graded exercise stress testing and stress echocardiography were associated with the least downstream costs, whereas CCTA and MPI were associated with higher costs when compared with no testing. These findings may help inform testing decisions in chest pain patients.

Association of Neighborhood-Level Marginalization With Health Care Use and Clinical Outcomes Following Hospital Discharge in Patients Who Underwent Coronary Catheterization for Acute Myocardial Infarction in a Single-Payer Health Care System.

BACKGROUND: Canadian data suggest that patients of lower socioeconomic status with acute myocardial infarction receive less beneficial therapy and have worse clinical outcomes, raising questions regarding care disparities even in universal health care systems. We assessed the contemporary association of marginalization with clinical outcomes and health services use. METHODS: Using clinical and administrative databases in Ontario, Canada, we conducted a population-based study of patients aged ≥65 years hospitalized for their first acute myocardial infarction between April 1, 2010 and March 1, 2019. Patients receiving cardiac catheterization and surviving 7 days postdischarge were included. Our primary exposure was neighborhood-level marginalization, a multidimensional socioeconomic status metric. Neighborhoods were categorized by quintile from Q1 (least marginalized) to Q5 (most marginalized). Our primary outcome was all-cause mortality. A proportional hazards regression model with a robust variance estimator was used to quantify the association of marginalization with outcomes, adjusting for risk factors, comorbidities, disease severity, and regional cardiologist supply. RESULTS: Among 53 841 patients (median age, 75 years; 39.1% female) from 20 640 neighborhoods, crude 1- and 3-year mortality rates were 7.7% and 17.2%, respectively. Patients in Q5 had no significant difference in 1-year mortality (hazard ratio [HR], 1.08 [95% CI, 0.95-1.22]), but greater mortality over 3 years (HR, 1.13 [95% CI, 1.03-1.22]) compared with Q1. Over 1 year, we observed differences between Q1 and Q5 in visits to primary care physicians (Q1, 96.7%; Q5, 93.7%) and cardiologists (Q1, 82.6%; Q5, 72.6%), as well as diagnostic testing. There were no differences in secondary prevention medications dispensed or medication adherence at 1 year. CONCLUSIONS: In older patients with acute myocardial infarction who survived to hospital discharge, those residing in the most marginalized neighborhoods had a greater long-term risk of mortality, less specialist care, and fewer diagnostic tests. Yet, there were no differences across socioeconomic status in prescription medication use and adherence.

Attributable Costs of Stroke in Ontario, Canada and Their Variation by Stroke Type and Social Determinants of Health.

BACKGROUND: Estimates of attributable costs of stroke are scarce, as most prior studies do not account for the baseline health care costs in people at risk of stroke. We estimated the attributable costs of stroke in a universal health care setting and their variation across stroke types and several social determinants of health. METHODS: We undertook a population-based administrative database-derived matched retrospective cohort study in Ontario, Canada. Community-dwelling adults aged ≥40 years with a stroke between 2003 and 2018 were matched (1:1) on demographics and comorbidities with controls without stroke. Using a difference-in-differences approach, we estimated the mean 1-year direct health care costs attributable to stroke from a public health care payer perspective, accounting for censoring with a weighted available sample estimator. We described health sector-specific costs and reported variation across stroke type and social determinants of health. RESULTS: The mean 1-year attributable costs of stroke were Canadian dollars 33 522 (95% CI, $33 231-$33 813), with higher costs for intracerebral hemorrhage ($40 244; $39 193-$41 294) than ischemic stroke ($32 547; $32 252-$32 843). Most of these costs were incurred in acute care hospitals ($15 693) and rehabilitation facilities ($7215). Compared with all patients with stroke, the mean attributable costs were higher among immigrants ($40 554; $39 316-$41 793), those aged <65 years ($35 175; $34 533-$35 818), and those residing in low-income neighborhoods ($34 687; $34 054-$35 320) and lower among rural residents ($29 047; $28 362-$29 731). CONCLUSIONS: Our findings of high attributable costs of stroke, especially in immigrants, younger patients, and residents of low-income neighborhoods, can be used to evaluate potential health care cost savings associated with different primary stroke prevention strategies.

Variance estimation of the risk difference when using propensity-score matching and weighting with time-to-event outcomes.

Observational studies are increasingly being used in medicine to estimate the effects of treatments or exposures on outcomes. To minimize the potential for confounding when estimating treatment effects, propensity score methods are frequently implemented. Often outcomes are the time to event. While it is common to report the treatment effect as a relative effect, such as the hazard ratio, reporting the effect using an absolute measure of effect is also important. One commonly used absolute measure of effect is the risk difference or difference in probability of the occurrence of an event within a specified duration of follow-up between a treatment and comparison group. We first describe methods for point and variance estimation of the risk difference when using weighting or matching based on the propensity score when outcomes are time-to-event. Next, we conducted Monte Carlo simulations to compare the relative performance of these methods with respect to bias of the point estimate, accuracy of variance estimates, and coverage of estimated confidence intervals. The results of the simulation generally support the use of weighting methods (untrimmed ATT weights and IPTW) or caliper matching when the prevalence of treatment is low for point estimation. For standard error estimation the simulation results support the use of weighted robust standard errors, bootstrap methods, or matching with a naïve standard error (i.e., Greenwood method). The methods considered in the article are illustrated using a real-world example in which we estimate the effect of discharge prescribing of statins on patients hospitalized for acute myocardial infarction.

Differences in target estimands between different propensity score-based weights.

PURPOSE: Propensity score weighting is a popular approach for estimating treatment effects using observational data. Different sets of propensity score-based weights have been proposed, including inverse probability of treatment weights whose target estimand is the average treatment effect, weights whose target estimand is the average treatment effect in the treated (ATT), and, more recently, matching weights, overlap weights, and entropy weights. These latter three sets of weights focus on estimating the effect of treatment in those subjects for whom there is clinical equipoise. We conducted a series of simulations to explore differences in the value of the target estimands for these five sets of weights when the difference in means is the measure of treatment effect. METHODS: We considered 648 scenarios defined by different values of the prevalence of treatment, the c-statistic of the propensity score model, the correlation between the linear predictors for treatment selection and the outcome, and by the magnitude of the interaction between treatment status and the linear predictor for the outcome in the absence of treatment. RESULTS: We found that, when the prevalence of treatment was low or high and the c-statistic of the propensity score model was moderate to high, that matching weights, overlap weights, and entropy weights had target estimands that differed meaningfully from the target estimand of the ATE weights. CONCLUSIONS: Researchers using matching weights, overlap weights, and entropy weights should not assume that the estimated treatment effect is comparable to the ATE.

Language discordance as a marker of disparities in cerebrovascular risk and stroke outcomes: A multi-center Canadian study.

Background: Differences in ischemic stroke outcomes occur in those with limited English proficiency. These health disparities might arise when a patient's spoken language is discordant from the primary language utilized by the health system. Language concordance is an understudied concept. We examined whether language concordance is associated with differences in vascular risk or post-stroke functional outcomes, depression, obstructive sleep apnea and cognitive impairment. Methods: This was a multi-center observational cross-sectional cohort study. Patients with ischemic stroke/transient ischemic attack (TIA) were consecutively recruited across eight regional stroke centers in Ontario, Canada (2012 - 2018). Participants were language concordant (LC) if they spoke English as their native language, ESL if they used English as a second language, or language discordant (LD) if non-English speaking and requiring translation. Results: 8156 screened patients. 6,556 met inclusion criteria: 5067 LC, 1207 ESL and 282 LD. Compared to LC patients: (i) ESL had increased odds of diabetes (OR = 1.28, p = 0.002), dyslipidemia (OR = 1.20, p = 0.007), and hypertension (OR = 1.37, p<0.001) (ii) LD speaking patients had an increased odds of having dyslipidemia (OR = 1.35, p = 0.034), hypertension (OR = 1.37, p<0.001), and worse functional outcome (OR = 1.66, p<0.0001). ESL (OR = 1.88, p<0.0001) and LD (OR = 1.71, p<0.0001) patients were more likely to have lower cognitive scores. No associations were noted with obstructive sleep apnea (OSA) or depression. Conclusions: Measuring language concordance in stroke/TIA reveals differences in neurovascular risk and functional outcome among patients with limited proficiency in the primary language of their health system. Lower cognitive scores must be interpreted with caution as they may be influenced by translation and/or greater vascular risk. Language concordance is a simple, readily available marker to identify those at risk of worse functional outcome. Stroke systems and practitioners must now study why these differences exist and devise adaptive care models, treatments and education strategies to mitigate barriers influenced by language discordance.

The iterative bisection procedure: a useful tool for determining parameter values in data-generating processes in Monte Carlo simulations.

BACKGROUND: Data-generating processes are key to the design of Monte Carlo simulations. It is important for investigators to be able to simulate data with specific characteristics. METHODS: We described an iterative bisection procedure that can be used to determine the numeric values of parameters of a data-generating process to produce simulated samples with specified characteristics. We illustrated the application of the procedure in four different scenarios: (i) simulating binary outcome data from a logistic model such that the prevalence of the outcome is equal to a specified value; (ii) simulating binary outcome data from a logistic model based on treatment status and baseline covariates so that the simulated outcomes have a specified treatment relative risk; (iii) simulating binary outcome data from a logistic model so that the model c-statistic has a specified value; (iv) simulating time-to-event outcome data from a Cox proportional hazards model so that treatment induces a specified marginal or population-average hazard ratio. RESULTS: In each of the four scenarios the bisection procedure converged rapidly and identified parameter values that resulted in the simulated data having the desired characteristics. CONCLUSION: An iterative bisection procedure can be used to identify numeric values for parameters in data-generating processes to generate data with specified characteristics.

Impute-then-exclude versus exclude-then-impute: Lessons when imputing a variable used both in cohort creation and as an independent variable in the analysis model.

We examined the setting in which a variable that is subject to missingness is used both as an inclusion/exclusion criterion for creating the analytic sample and subsequently as the primary exposure in the analysis model that is of scientific interest. An example is cancer stage, where patients with stage IV cancer are often excluded from the analytic sample, and cancer stage (I to III) is an exposure variable in the analysis model. We considered two analytic strategies. The first strategy, referred to as "exclude-then-impute," excludes subjects for whom the observed value of the target variable is equal to the specified value and then uses multiple imputation to complete the data in the resultant sample. The second strategy, referred to as "impute-then-exclude," first uses multiple imputation to complete the data and then excludes subjects based on the observed or filled-in values in the completed samples. Monte Carlo simulations were used to compare five methods (one based on "exclude-then-impute" and four based on "impute-then-exclude") along with the use of a complete case analysis. We considered both missing completely at random and missing at random missing data mechanisms. We found that an impute-then-exclude strategy using substantive model compatible fully conditional specification tended to have superior performance across 72 different scenarios. We illustrated the application of these methods using empirical data on patients hospitalized with heart failure when heart failure subtype was used for cohort creation (excluding subjects with heart failure with preserved ejection fraction) and was also an exposure in the analysis model.

The failure of four bootstrap procedures for estimating confidence intervals for predicted-to-expected ratios for hospital profiling.

BACKGROUND: Healthcare provider profiling involves the comparison of outcomes between patients cared for by different healthcare providers. An important component of provider profiling is risk-adjustment so that providers that care for sicker patients are not unfairly penalized. One method for provider profiling entails using random effects logistic regression models to compute provider-specific predicted-to-expected ratios. These ratios compare the predicted number of deaths at a given provider given the case-mix of its patients with the expected number of deaths had those patients been treated at an average provider. Despite the utility of this metric in provider profiling, methods have not been described to estimate confidence intervals for these ratios. The objective of the current study was to evaluate the performance of four bootstrap procedures for estimating 95% confidence intervals for predicted-to-expected ratios. METHODS: We used Monte Carlo simulations to evaluate four bootstrap procedures: the naïve bootstrap, a within cluster-bootstrap, the parametric multilevel bootstrap, and a novel cluster-specific parametric bootstrap. The parameters of the data-generating process were informed by empirical analyses of patients hospitalized with acute myocardial infarction. Three factors were varied in the simulations: the number of subjects per cluster, the intraclass correlation coefficient for the binary outcome, and the prevalence of the outcome. We examined coverage rates of both normal-theory bootstrap confidence intervals and bootstrap percentile intervals. RESULTS: In general, all four bootstrap procedures resulted in inaccurate estimates of the standard error of cluster-specific predicted-to-expected ratios. Similarly, all four bootstrap procedures resulted in 95% confidence intervals whose empirical coverage rates were different from the advertised rate. In many scenarios the empirical coverage rates were substantially lower than the advertised rate. CONCLUSION: Existing bootstrap procedures should not be used to compute confidence intervals for predicted-to-expected ratios when conducting provider profiling.

Bootstrap vs asymptotic variance estimation when using propensity score weighting with continuous and binary outcomes.

We used Monte Carlo simulations to compare the performance of asymptotic variance estimators to that of the bootstrap when estimating standard errors of differences in means, risk differences, and relative risks using propensity score weighting. We considered four different sets of weights: conventional inverse probability of treatment weights with the average treatment effect (ATE) as the target estimand, weights for estimating the average treatment effect in the treated (ATT), matching weights, and overlap weights. We considered sample sizes ranging from 250 to 10 000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We found that, when using ATE weights and sample sizes were ≤ 1000, then the use of the bootstrap resulted in estimates of SE that were more accurate than the asymptotic estimates. A similar finding was observed when using ATT weights and sample sizes were ≤ 1000 and the prevalence of treatment was moderate to high. When using matching weights and overlap weights, both the asymptotic estimator and the bootstrap resulted in accurate estimates of SE across all sample sizes and prevalences of treatment. Even when using the bootstrap with ATE weights, empirical coverage rates of confidence intervals were suboptimal when sample sizes were low to moderate and the prevalence of treatment was either very low or very high. A similar finding was observed when using the bootstrap with ATT weights when sample sizes were low to moderate and the prevalence of treatment was very high.

Follow-up after post-partum psychiatric emergency department visits: an equity-focused population-based study in Canada.

BACKGROUND: Emergency department visits for a psychiatric reason in the post-partum period represent an acute need for mental health care at a crucial time, but little is known about the extent of timely outpatient follow-up after these visits or how individual and intersecting social determinants of health influence this outcome. This study aimed to examine outpatient mental health care follow-up by a physician in the 30 days after an individual attended the emergency department for a psychiatric reason in the post-partum period and understand how social determinants of health affect who receives follow-up care. METHODS: In this population-based cohort study, routinely collected health data from Ontario, Canada were accessed through ICES to identify all post-partum individuals whose sex was listed as female on their health card and who had attended an emergency department in Ontario before the COVID-19 pandemic for a psychiatric reason. Individuals admitted to hospital at the time of the emergency department visit, who died during the visit, or who left without being seen were excluded from the study. Ethnicity data for individuals were not collected. The primary outcome was the proportion of individuals with any outpatient physician (psychiatrist or family physician) visit for a mental health reason within 30 days of the index emergency department visit. Family physician mental health visits were identified using a validated algorithm for Ontario Health Insurance Plan-billed visits and mental health diagnostic codes for community health centre visits. We examined the associations between social determinants of health (age, neighbourhood income, community size, immigration, neighbourhood ethnic diversity) and who received an outpatient mental health visit. We used modified Poisson regression adjusting for the other social determinants of health, clinical, and health services characteristics to examine independent associations with follow-up, and conditional inference trees to explore how social determinants of health intersect with each other and with clinical and health services characteristics in relation to follow-up. FINDINGS: We analysed data collected between April 1, 2008, and March 10, 2020, after exclusions we identified 12 158 people who had attended the emergency department for a psychiatric reason in the post-partum period (mean age 26·9 years [SD 6·2]; range 13-47); 9848 individuals lived in an urban area, among these 1518 (15·5%) were immigrants and 2587 (26·3%) lived in areas with high ethnic diversity. 5442 (44·8%) of 12 158 individuals received 30-day follow-up. In modified Poisson regression models, younger age, lower neighbourhood income, smaller community size, and being an immigrant were associated with a lower likelihood of follow-up. In the CTREE, similar variables were important, with several intersections between social determinants of health and between social determinants of health and other variables. INTERPRETATION: Fewer than half of emergency department visits for a psychiatric reason in the post-partum period were followed by timely outpatient care, with social-determinants-of-health-based disparities in access to care. Improvements in equitable access to post-emergency department mental health care are urgently needed in this high-risk post-partum population. FUNDING: Department of Psychiatry, University of Toronto; Canadian Institutes of Health Research.

Applying Propensity Score Methods in Clinical Research in Neurology.

Propensity score-based analysis is increasingly being used in observational studies to estimate the effects of treatments, interventions, and exposures. We introduce the concept of the propensity score and how it can be used in observational research. We describe 4 different ways of using the propensity score: matching on the propensity score, inverse probability of treatment weighting using the propensity score, stratification on the propensity score, and covariate adjustment on the propensity score (with a focus on the first 2). We provide recommendations for the use and reporting of propensity score methods for the conduct of observational studies in neurologic research.

Examining the association of social risk with heart failure readmission in the Veterans Health Administration.

BACKGROUND: Previous research has found that social risk factors are associated with an increased risk of 30-day readmission. We aimed to assess the association of 5 social risk factors (living alone, lack of social support, marginal housing, substance abuse, and low income) with 30-day Heart Failure (HF) hospital readmissions within the Veterans Health Affairs (VA) and the impact of their inclusion on hospital readmission model performance. METHODS: We performed a retrospective cohort study using chart review and VA and Centers for Medicare and Medicaid Services (CMS) administrative data from a random sample of 1,500 elderly (≥ 65 years) Veterans hospitalized for HF in 2012. Using logistic regression, we examined whether any of the social risk factors were associated with 30-day readmission after adjusting for age alone and clinical variables used by CMS in its 30-day risk stratified readmission model. The impact of these five social risk factors on readmission model performance was assessed by comparing c-statistics, likelihood ratio tests, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The prevalence varied among the 5 risk factors; low income (47 % vs. 47 %), lives alone (18 % vs. 19 %), substance abuse (14 % vs. 16 %), lacks social support (2 % vs. <1 %), and marginal housing (< 1 % vs. 3 %) among readmitted and non-readmitted patients, respectively. Controlling for clinical factors contained in CMS readmission models, a lack of social support was found to be associated with an increased risk of 30-day readmission (OR 4.8, 95 %CI 1.35-17.88), while marginal housing was noted to decrease readmission risk (OR 0.21, 95 %CI 0.03-0.87). Living alone (OR: 0.9, 95 %CI 0.64-1.26), substance abuse (OR 0.91, 95 %CI 0.67-1.22), and having low income (OR 1.01, 95 %CI 0.77-1.31) had no association with HF readmissions. Adding the five social risk factors to a CMS-based model (age and comorbid conditions; c-statistic 0.62) did not improve model performance (c-statistic: 0.62). CONCLUSIONS: While a lack of social support was associated with 30-day readmission in the VA, its prevalence was low. Moreover, the inclusion of some social risk factors did not improve readmission model performance. In an integrated healthcare system like the VA, social risk factors may have a limited effect on 30-day readmission outcomes.

Association of Neighborhood-Level Material Deprivation With Health Care Costs and Outcome After Stroke.

OBJECTIVE: To determine the association between material deprivation and direct healthcare costs and clinical outcomes following stroke in the context of a publicly funded universal healthcare system. METHODS: In this population-based cohort study of patients with ischemic and hemorrhagic stroke admitted to hospital between 2008 and 2017 in Ontario, Canada, we used linked administrative data to identify the cohort, predictor variables, and outcomes. The exposure was a five-level neighborhood material deprivation index. The primary outcome was direct healthcare costs incurred by the public payer in the first year. Secondary outcomes were death and admission to long-term care. RESULTS: Among 90,289 patients with stroke, the mean (standard deviation) per-person costs increased with increasing material deprivation, from $50,602 ($55,582) in the least deprived quintile to $56,292 ($59,721) in the most deprived quintile (unadjusted relative cost ratio and 95% confidence intervals 1.11 [1.08,1.13] and adjusted relative cost ratio 1.07 [1.05,1.10] for least compared to most deprived quintile). People in the most deprived quintile had higher mortality within one year compared to the least deprived quintile (adjusted hazard ratio (HR) 1.07 [1.03,1.12]) as well as within three years (adjusted HR 1.09 [1.05,1.13]). Admission to long-term care increased incrementally with material deprivation and those in the most deprived quintile had an adjusted HR of 1.33 [1.24,1.43]) compared to those in the least deprived quintile. CONCLUSION: Material deprivation is a risk factor for increased costs and poor outcomes after stroke. Interventions targeting health inequities due to social determinants of health are needed. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that the neighborhood-level material deprivation predicts direct healthcare costs.

Estimating adjusted risk differences by multiply-imputing missing control binary potential outcomes following propensity score-matching.

We describe a new method to combine propensity-score matching with regression adjustment in treatment-control studies when outcomes are binary by multiply imputing potential outcomes under control for the matched treated subjects. This enables the estimation of clinically meaningful measures of effect such as the risk difference. We used Monte Carlo simulation to explore the effect of the number of imputed potential outcomes under control for the matched treated subjects on inferences about the risk difference. We found that imputing potential outcomes under control (either single imputation or multiple imputation) resulted in a substantial reduction in bias compared with what was achieved using conventional nearest neighbor matching alone. Increasing the number of imputed potential outcomes under control resulted in more efficient estimation, with more efficient estimation of the estimated risk difference when increasing the number of the imputed potential outcomes. The greatest relative increase in efficiency was achieved by imputing five potential outcomes; once 20 outcomes under control were imputed for each matched treated subject, further improvements in efficiency were negligible. We also examined the effect of the number of these imputed potential outcomes on: (i) estimated standard errors; (ii) mean squared error; (iii) coverage of estimated confidence intervals. We illustrate the application of the method by estimating the effect on the risk of death within 1 year of prescribing beta-blockers to patients discharged from hospital with a diagnosis of heart failure.

Sex differences in direct healthcare costs following stroke: a population-based cohort study.

BACKGROUND: The economic burden of stroke on the healthcare system has been previously described, but sex differences in healthcare costs have not been well characterized. We described the direct person-level healthcare cost in men and women as well as the various health settings in which costs were incurred following stroke. METHODS: In this population-based cohort study of patients admitted to hospital with stroke between 2008 and 2017 in Ontario, Canada, we used linked administrative data to calculate direct person-level costs in Canadian dollars in the one-year following stroke. We used a generalized linear model with a gamma distribution and a log link function to compare costs in women and men with and without adjustment for baseline clinical differences. We also assessed for an interaction between age and sex using restricted cubic splines to model the association of age with costs. RESULTS: We identified 101,252 patients (49% were women, median age [Q1-Q3] was 76 years [65-84]). Unadjusted costs following stroke were higher in women compared to men (mean ± standard deviation cost was $54,012 ± 54,766 for women versus $52,829 ± 59,955 for men, and median cost was $36,703 [$16,496-$72,227] for women versus $32,903 [$15,485-$66,007] for men). However, after adjustment, women had 3% lower costs compared to men (relative cost ratio and 95% confidence interval 0.97 [0.96,0.98]). The lower cost in women compared to men was most prominent among people aged over 85 years (p for interaction = 0.03). Women incurred lower costs than men in outpatient care and rehabilitation, but higher costs in complex continuing care, long-term care, and home care. CONCLUSIONS: Patterns of resource utilization and direct medical costs were different between men and women after stroke. Our findings inform public payers of the drivers of costs following stroke and suggest the need for sex-based cost-effectiveness evaluation of stroke interventions with consideration of costs in all care settings.