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.

Comparison of Machine Learning and Conventional Statistical Modeling for Predicting Readmission Following Acute Heart Failure Hospitalization.

INTRODUCTION: Developing accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical models (CSM), but the calibration of such models is unclear. OBJECTIVES: To compare models developed using ML with those developed using CSM to predict 30-day readmission for cardiovascular and non-cardiovascular causes in HF patients. METHODS: We retrospectively enrolled 10,919 patients with HF (> 18 years) discharged alive from a hospital or emergency department (2004-2007) in Ontario, Canada. The study sample was randomly divided into training and validation sets in a 2:1 ratio. CSMs to predict 30-day readmission were developed using Fine-Gray subdistribution hazards regression (treating death as a competing risk), and the ML algorithm employed random survival forests for competing risks (RSF-CR). Models were evaluated in the validation set using both discrimination and calibration metrics. RESULTS: In the validation sample of 3602 patients, RSF-CR (c-statistic=0.620) showed similar discrimination to the Fine-Gray competing risk model (c-statistic=0.621) for 30-day cardiovascular readmission. In contrast, for 30-day non-cardiovascular readmission, the Fine-Gray model (c-statistic=0.641) slightly outperformed the RSF-CR model (c-statistic=0.632). For both outcomes, The Fine-Gray model displayed better calibration than RSF-CR using calibration plots of observed vs. predicted risks across the deciles of predicted risk. CONCLUSIONS: Fine-Gray models had similar discrimination but superior calibration to the RSF-CR model, highlighting the importance of reporting calibration metrics for ML-based prediction models. The discrimination was modest in all readmission prediction models regardless of the methods used.

Comparative Safety Analysis of Nabilone Versus Opioids: A Population-Based Cohort Study.

BACKGROUND: Some have advocated that nabilone be used rather than opioids to manage chronic, noncancer pain, since the former drug may have a better safety profile. OBJECTIVE: We compared the safety of incident nabilone use relative to incident opioid use with respect to multiple clinically important outcomes. DESIGN: A population-based, retrospective cohort study. SETTING: Province of Ontario, Canada. PARTICIPANTS: Persons aged 12 years and older, diagnosed with a musculoskeletal condition within the past 3 years prior to the index date. EXPOSURES: Incident nabilone use, with incident opioid use serving as the reference group. MEASUREMENTS: Within 3 months following the index date, we separately evaluated for pneumonia, motor vehicle accidents, falls or fractures, mental and behavioral disorder due to psychoactive substance use, and all-cause mortality. RESULTS: A total of 18,863 incident nabilone users were propensity score matched to an equal number of opioid users. In the overall matched analysis, incident nabilone users vs. incident opioid users had significantly lower rates of pneumonia (hazard ratio [HR] 0.78, 95% CI 0.63-0.96), falls or fractures (HR 0.56, 95% CI 0.50-0.64), and all-cause mortality (HR 0.79, 95% CI 0.65-0.95), but significantly higher rate of mental or behavioral disorder (HR 2.23, 95% CI 1.45-3.43). There was no significant difference between groups with respect to rate of motor vehicle accidents. LIMITATIONS: Unmeasured confounding may have influenced results. CONCLUSIONS: While usage of nabilone relative to opioids was associated with reduced rates of pneumonia, falls or fractures, and all-cause mortality, it was simultaneously associated with an increased rate of adverse mental health outcomes. This picture of mixed safety results raises concerns with the policy approach of broadly substituting use of opioids with nabilone. FUNDING SOURCE: Ontario Ministry of Health.

Persistent disparities in insulin pump uptake despite a universal pump programme for type 1 diabetes in Ontario, Canada.

AIM: To evaluate associations between social disadvantage and insulin pump use among adults with type 1 diabetes (T1D) in the context of a universal publicly funded insulin pump programme in Ontario, Canada, and to ascertain whether social disparities in insulin pump programme enrolment have decreased over time. METHODS: Population-based cross-sectional studies were conducted using administrative healthcare data in Ontario, Canada. First, among adults aged older than 18 years diagnosed with T1D before 31 March 2021, logistic regression was used to assess the association between neighbourhood social disadvantage (Ontario marginalization index quintiles) and insulin pump use. Second, among all paediatric and adult applicants to the insulin pump programme from 1 September 2006 to 31 March 2022, ordinal logistic regression was used to evaluate associations between year of insulin pump initiation and social disadvantage. RESULTS: Among 27 453 adults with T1D, 60% used insulin pumps. Greater social disadvantage was associated with lower odds of insulin pump use (adjusted odds ratio [OR] 0.44 [95% confidence interval {CI} 0.39-0.48] for greatest vs. lowest social disadvantage quintile). Among 21 002 paediatric and adult applicants to the insulin pump programme, social disparities in pump use decreased in the first 3 years of the programme, plateaued until 2020, then increased from 2020 to 2022, with no change in the odds of being in a higher social deprivation quintile for 2022 relative to 2007 (OR 1.09 [95% CI 0.83-1.44]). CONCLUSIONS: Despite a universal pump programme for individuals with T1D, disparities by social disadvantage persist. Residual financial and non-financial barriers must be addressed to promote equitable insulin pump uptake.

The effect of number of clusters and magnitude of within-cluster homogeneity in outcomes on the performance of four variance estimators for a marginal multivariable Cox regression model fit to clustered data in the context of observational research.

Researchers often estimate the association between the hazard of a time-to-event outcome and the characteristics of individuals and the clusters in which individuals are nested. Lin and Wei's robust variance estimator is often used with a Cox regression model fit to clustered data. Recently, alternative variance estimators have been proposed: the Fay-Graubard estimator, the Kauermann-Carroll estimator, and the Mancl-DeRouen estimator. Using Monte Carlo simulations, we found that, when fitting a marginal Cox regression model with both individual-level and cluster-level covariates: (i) in the presence of weak to moderate within-cluster homogeneity of outcomes, the Lin-Wei variance estimator can result in estimates of the SE with moderate bias when the number of clusters is fewer than 20-30, while in the presence of strong within-cluster homogeneity, it can result in biased estimation even when the number of clusters is as large as 100; (ii) when the number of clusters was less than approximately 20, the Fay-Graubard variance estimator tended to result in estimates of SE with the lowest bias; (iii) when the number of clusters exceeded approximately 20, the Mancl-DeRouen estimator tended to result in estimated standard errors with the lowest bias; (iv) the Mancl-DeRouen estimator used with a t-distribution tended to result in 95% confidence that had the best performance of the estimators; (v) when the magnitude of within-cluster homogeneity in outcomes was strong or very strong, all methods resulted in confidence intervals with lower than advertised coverage rates even when the number of clusters was very large.

Days alive and out of hospital for adult female and male cardiac surgery patients: a population-based cohort study.

BACKGROUND: Research shows women experience higher mortality than men after cardiac surgery but information on sex-differences during postoperative recovery is limited. Days alive and out of hospital (DAH) combines death, readmission and length of stay, and may better quantify sex-differences during recovery. This main objective is to evaluate (i) how DAH at 30-days varies between sex and surgical procedure, (ii) DAH responsiveness to patient and surgical complexity, and (iii) longer-term prognostic value of DAH. METHODS: We evaluated 111,430 patients (26% female) who underwent one of three types of cardiac surgery (isolated coronary artery bypass [CABG], isolated non-CABG, combination procedures) between 2009 - 2019. Primary outcome was DAH at 30 days (DAH30), secondary outcomes were DAH at 90 days (DAH90) and 180 days (DAH180). Data were stratified by sex and surgical group. Unadjusted and risk-adjusted analyses were conducted to determine the association of DAH with patient-, surgery-, and hospital-level characteristics. Patients were divided into two groups (below and above the 10th percentile) based on the number of days at DAH30. Proportion of patients below the 10th percentile at DAH30 that remained in this group at DAH90 and DAH180 were determined. RESULTS: DAH30 were lower for women compared to men (22 vs. 23 days), and seen across all surgical groups (isolated CABG 23 vs. 24, isolated non-CABG 22 vs. 23, combined surgeries 19 vs. 21 days). Clinical risk factors including multimorbidity, socioeconomic status and surgical complexity were associated with lower DAH30 values, but women showed lower values of DAH30 compared to men for many factors. Among patients in the lowest 10th percentile at DAH30, 80% of both females and males remained in the lowest 10th percentile at 90 days, while 72% of females and 76% males remained in that percentile at 180 days. CONCLUSION: DAH is a responsive outcome to differences in patient and surgical risk factors. Further research is needed to identify new care pathways to reduce disparities in outcomes between male and female patients.

Validation of the Passive Surveillance Stroke Severity Score in Three Canadian Provinces.

BACKGROUND: Stroke outcomes research requires risk-adjustment for stroke severity, but this measure is often unavailable. The Passive Surveillance Stroke SeVerity (PaSSV) score is an administrative data-based stroke severity measure that was developed in Ontario, Canada. We assessed the geographical and temporal external validity of PaSSV in British Columbia (BC), Nova Scotia (NS) and Ontario, Canada. METHODS: We used linked administrative data in each province to identify adult patients with ischemic stroke or intracerebral hemorrhage between 2014-2019 and calculated their PaSSV score. We used Cox proportional hazards models to evaluate the association between the PaSSV score and the hazard of death over 30 days and the cause-specific hazard of admission to long-term care over 365 days. We assessed the models' discriminative values using Uno's c-statistic, comparing models with versus without PaSSV. RESULTS: We included 86,142 patients (n = 18,387 in BC, n = 65,082 in Ontario, n = 2,673 in NS). The mean and median PaSSV were similar across provinces. A higher PaSSV score, representing lower stroke severity, was associated with a lower hazard of death (hazard ratio and 95% confidence intervals 0.70 [0.68, 0.71] in BC, 0.69 [0.68, 0.69] in Ontario, 0.72 [0.68, 0.75] in NS) and admission to long-term care (0.77 [0.76, 0.79] in BC, 0.84 [0.83, 0.85] in Ontario, 0.86 [0.79, 0.93] in NS). Including PaSSV in the multivariable models increased the c-statistics compared to models without this variable. CONCLUSION: PaSSV has geographical and temporal validity, making it useful for risk-adjustment in stroke outcomes research, including in multi-jurisdiction analyses.

Accounting for Competing Risks in Clinical Research.

This JAMA Guide to Statistics and Methods article discusses accounting for competing risks in clinical research.

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.

Minimizing confounding in comparative observational studies with time-to-event outcomes: An extensive comparison of covariate balancing methods using Monte Carlo simulation.

Observational studies are frequently used in clinical research to estimate the effects of treatments or exposures on outcomes. To reduce the effects of confounding when estimating treatment effects, covariate balancing methods are frequently implemented. This study evaluated, using extensive Monte Carlo simulation, several methods of covariate balancing, and two methods for propensity score estimation, for estimating the average treatment effect on the treated using a hazard ratio from a Cox proportional hazards model. With respect to minimizing bias and maximizing accuracy (as measured by the mean square error) of the treatment effect, the average treatment effect on the treated weighting, fine stratification, and optimal full matching with a conventional logistic regression model for the propensity score performed best across all simulated conditions. Other methods performed well in specific circumstances, such as pair matching when sample sizes were large (n = 5000) and the proportion treated was < 0.25. Statistical power was generally higher for weighting methods than matching methods, and Type I error rates were at or below the nominal level for balancing methods with unbiased treatment effect estimates. There was also a decreasing effective sample size with an increasing number of strata, therefore for stratification-based weighting methods, it may be important to consider fewer strata. Generally, we recommend methods that performed well in our simulations, although the identification of methods that performed well is necessarily limited by the specific features of our simulation. The methods are illustrated using a real-world example comparing beta blockers and angiotensin-converting enzyme inhibitors among hypertensive patients at risk for incident stroke.

Derivation and internal validation of the multivariate toxigenic C. difficile diarrhea model and risk score for emergency room and hospitalized patients with diarrhea.

Background: Many factors have been associated with the risk of toxigenic C. difficile diarrhea (TCdD). This study derived and internally validated a multivariate model for estimating the risk of TCdD in patients with diarrhea using readily available clinical factors. Methods: A random sample of 3,050 symptomatic emergency department or hospitalized patients undergoing testing for toxigenic C. difficile at a single teaching hospital between 2014 and 2018 was created. Unformed stool samples positive for both glutamate dehydrogenase antigen by enzyme immunoassay and tcdB gene by polymerase chain reaction were classified as TCdD positive. The TCdD Model was created using logistic regression and was modified to the TCdD Risk Score to facilitate its use. Results: 8.1% of patients were TCdD positive. TCdD risk increased with abdominal pain (adjusted odds ratio 1.3; 95% CI, 1.0-1.8), previous C. difficile diarrhea (2.5, 1.1-6.1), and prior antibiotic exposure, especially when sampled in the emergency department (4.2, 2.5-7.0) versus the hospital (1.7, 1.3-2.3). TCdD risk also increased when testing occurred earlier during the hospitalization encounter, when age and white cell count increased concurrently, and with decreased eosinophil count. In internal validation, the TCdD Model had moderate discrimination (optimism-corrected C-statistic 0.65, 0.62-0.68) and good calibration (optimism-corrected Integrated Calibration Index [ICI] 0.017, 0.001-0.022). Performance decreased slightly for the TCdD Risk Score (C-statistic 0.63, 0.62-0.63; ICI 0.038, 0.004-0.038). Conclusions: TCdD risk can be predicted using readily available clinical risk factors with modest accuracy.

Timing of follow-up visits after hospital discharge for COPD: Application of a new method.

RATIONALE: A common strategy to reduce COPD readmissions is to encourage patient follow-up with a physician within 1 to 2 weeks of discharge, yet evidence confirming its benefit is lacking. We used a new study design called target randomized trial emulation to determine the impact of follow-up visit timing on patient outcomes. METHODS: All Ontario residents aged 35 or older discharged from a COPD hospitalization were identified using health administrative data and randomly assigned to those who received and did not receive physician visit follow-up by within seven days. They were followed to all-cause emergency department visits, readmissions or death. Targeted randomized trial emulation was used to adjust for differences between the groups. COPD emergency department visits, readmissions or death was also considered. RESULTS: There were 94,034 patients hospitalized with COPD, of whom 73.5% had a physician visit within 30 days of discharge. Adjusted hazard ratio for all-cause readmission, emergency department visits or death for people with a visit within seven days post discharge was 1.03 (95% Confidence Interval [CI]: 1.01-1.05) and remained around 1 for subsequent days; adjusted hazard ratio for the composite COPD events was 0.97 (95% CI 0.95-1.00) and remained significantly lower than 1 for subsequent days. CONCLUSION: While a physician visit after discharge was found to reduce COPD events, a specific time period when a physician visit was most beneficial was not found. This suggests that follow-up visits should not occur at a predetermined time but be based on factors such as anticipated medical need.

Risk Prediction Scores for Type 2 Diabetes Microvascular and Cardiovascular Complications Derived and Validated With Real-world Data From 2 Provinces: The DIabeteS COmplications (DISCO) Risk Scores.

OBJECTIVES: Existing tools to predict the risk of complications among people with type 2 diabetes poorly discriminate high- from low-risk patients. Our aim in this study was to develop risk prediction scores for major type 2 diabetes complications using real-world clinical care data, and to externally validate these risk scores in a different jurisdiction. METHODS: Using health-care administrative data and electronic medical records data, risk scores were derived using data from 25,088 people with type 2 diabetes from the Canadian province of Ontario, followed between 2002 and 2017. Scores were developed for major clinically important microvascular events (treatment for retinopathy, foot ulcer, incident end-stage renal disease), cardiovascular disease events (acute myocardial infarction, heart failure, stroke, amputation), and mortality (cardiovascular, noncardiovascular, all-cause). They were then externally validated using the independent data of 11,416 people with type 2 diabetes from the province of Manitoba. RESULTS: The 10 derived risk scores had moderate to excellent discrimination in the independent validation cohort, ranging from 0.705 to 0.977. Their calibration to predict 5-year risk was excellent across most levels of predicted risk, albeit with some displaying underestimation at the highest levels of predicted risk. CONCLUSIONS: The DIabeteS COmplications (DISCO) risk scores for major type 2 diabetes complications were derived and externally validated using contemporary real-world clinical data. As a result, they may be more accurate than other risk prediction scores derived using randomized trial data. The use of more accurate risk scores in clinical practice will help improve personalization of clinical care for patients with type 2 diabetes.

Teen Pregnancy and Risk of Premature Mortality.

Importance: Unintentional injury, suicide, and homicide are leading causes of death among young females. Teen pregnancy may be a marker of adverse life experiences. Objective: To evaluate the risk of premature mortality from 12 years of age onward in association with number of teen pregnancies and age at pregnancy. Design, Setting, and Participants: This population-based cohort study was conducted among all females alive at 12 years of age from April 1, 1991, to March 31, 2021, in Ontario, Canada (the most populous province, which has universal health care and data collection). The study period ended March 31, 2022. Exposures: The main exposure was number of teen pregnancies between 12 and 19 years of age (0, 1, or ≥2). Secondary exposures included how the teen pregnancy ended (birth or miscarriage vs induced abortion) and age at first teen pregnancy. Main Outcomes and Measures: The main outcome was all-cause mortality starting at 12 years of age. Hazard ratios (HRs) were adjusted for year of birth, comorbidities at 9 to 11 years of age, and area-level education, income level, and rurality. Results: Of 2 242 929 teenagers, 163 124 (7.3%) experienced a pregnancy at a median age of 18 years (IQR, 17-19 years). Of those with a teen pregnancy, 60 037 (36.8%) ended in a birth (of which 59 485 [99.1%] were live births), and 106 135 (65.1%) ended in induced abortion. The median age at the end of follow-up was 25 years (IQR, 18-32 years) for those without a teen pregnancy and 31 years (IQR, 25-36 years) for those with a teen pregnancy. There were 6030 deaths (1.9 per 10 000 person-years [95% CI, 1.9-2.0 per 10 000 person-years]) among those without a teen pregnancy, 701 deaths (4.1 per 10 000 person-years [95% CI, 3.8-4.5 per 10 000 person-years]) among those with 1 teen pregnancy, and 345 deaths (6.1 per 10 000 person-years [95% CI, 5.5-6.8 per 10 000 person-years]) among those with 2 or more teen pregnancies; adjusted HRs (AHRs) were 1.51 (95% CI, 1.39-1.63) for those with 1 pregnancy and 2.14 (95% CI, 1.92-2.39) for those with 2 or more pregnancies. Comparing those with vs without a teen pregnancy, the AHR for premature death was 1.25 (95% CI, 1.12-1.40) from noninjury, 2.06 (95% CI, 1.75-2.43) from unintentional injury, and 2.02 (95% CI, 1.54-2.65) from intentional injury. Conclusions and Relevance: In this population-based cohort study of 2.2 million female teenagers, teen pregnancy was associated with future premature mortality. It should be assessed whether supports for female teenagers who experience a pregnancy can enhance the prevention of subsequent premature mortality in young and middle adulthood.

Association between surgery and rate of incident dementia in older adults: A population-based retrospective cohort study.

BACKGROUND: The risk of incident dementia after surgery in older adults is unclear. The study objective was to examine the rate of incident dementia among older adults after elective surgery compared with a matched nonsurgical control group. METHODS: We conducted a population-based, propensity-matched retrospective cohort study using data from linked administrative databases in Ontario, Canada. All community-dwelling individuals aged 66 years and older who underwent one of five major elective surgeries between April 1, 2007 and March 31, 2011 were included. Each surgical patient was matched 1:1 on surgical specialty of the surgeon at consultation, age, sex, fiscal year of entry, and propensity score with a patient who attended an outpatient visit with a surgeon of the same surgical specialty but did not undergo surgery. Patients were followed for up to 5 years after cohort entry for the occurrence of a new dementia diagnosis, defined from administrative data. Cause-specific hazard models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the association between surgery and the hazard of incident dementia. Subgroup and sensitivity analyses were performed. RESULTS: A total of 27,878 individuals (13,939 matched pairs) were included in the analysis. A total of 640 (4.6%) individuals in the surgical group and 965 (6.9%) individuals in the control group developed dementia over the 5-year follow-up period. Individuals who underwent surgery had a reduced rate of incident dementia compared with their matched nonsurgical controls (HR 0.88; 95% CI 0.80-0.97; p = 0.01). This association was persistent in most subgroups and after sensitivity analyses. CONCLUSIONS: Elective surgery did not increase the rate of incident dementia when compared with matched nonsurgical controls. This could be an important consideration for patients and surgeons when elective surgery is considered.

Wait-times Benchmarks for risk-based prioritization in Transcatheter Aortic Valve Implantation: a simulation study.

BACKGROUND: Demand for transcatheter aortic valve implantation (TAVI) has increased in the last decade, resulting in prolonged wait-times and undesirable health outcomes in many health systems. Risk-based prioritization and wait-times benchmarks can improve equitable access to patients. METHODS: We used simulation models to follow-up a synthetic population of 50,000 individuals from referral to completion of TAVI. Based on their risk of adverse events, patients could be classified as "low-", "medium-" and "high-risk", and shorter wait-times were assigned for the higher risk groups. We assessed the impacts of the size and wait-times for each risk group on waitlist mortality, hospitalization and urgent TAVIs. All scenarios had the same resource constraints, allowing us to explore the trade-offs between faster access for prioritized patients and deferred access for non-prioritized groups. RESULTS: Increasing the proportion of patients categorized as high-risk, and providing more rapid access to the higher-risk groups achieved the greatest reductions in mortality, hospitalizations and urgent TAVIs (relative reductions of up to 29%, 23% and 38%, respectively). However, this occurs at the expense of excessive wait-times in the non-prioritized low-risk group (up to 25 weeks). We propose wait-times of up to 3 weeks for high-risk patients and 7 weeks for medium-risk patients. CONCLUSIONS: Prioritizing higher-risk patients with faster access leads to better health outcomes, however this also results in unacceptably long wait-times for the non-prioritized groups in settings with limited capacity. Decision-makers must be aware of these implications when developing and implementing waitlist prioritization strategies.

Disparities in diabetes processes of care among people experiencing homelessness: An opportunity for intervention.

AIMS: To compare processes of diabetes care by homeless status. METHODS: A population-based propensity matched cohort study was conducted in Ontario, Canada. People with diabetes were identified in administrative healthcare data between April 2006 and March 2019. Those with a documented history of homelessness were matched to non-homeless controls. Data on processes of care measures included glucose monitoring tests, screening for microvascular complications, and physician follow-up. Differences in processes of care were compared by homeless status using proportions, risk ratios, and rate ratios. RESULTS: Of the 1,076,437 people with diabetes, 5219 matched pairs were identified. Homelessness was associated with fewer tests for glycated hemoglobin (RR = 0.63; 95 %CI: 0.60-0.67), LDL cholesterol (RR = 0.80; 95 %CI: 0.78-0.82), serum creatinine (RR = 0.94; 95 %CI: 0.92-0.97), urine protein quantification (RR = 0.62; 95 %CI: 0.59-0.66), and eye examinations (RR = 0.74; 95 %CI: 0.71-0.77). People with a history of homelessness were less likely to use primary care for diabetes management (RR = 0.62; 95 %CI: 0.59-0.66) or specialist care (RR = 0.87; 95 %CI: 0.83-0.91) compared to non-homeless controls. CONCLUSIONS: Disparities in diabetes care are evident for people with a history of homelessness and contribute to excess morbidity in this population. These data provide an impetus for investment in tailored interventions to improve healthcare equity and prevent long-term complications.