Background Despite improved outcomes associated with ticagrelor compared with clopidogrel in acute coronary syndrome (ACS), many studies have demonstrated slow adoption of ticagrelor in the United States because of its increased cost. Less is known about how ticagrelor is adopted when there is no added cost consideration. Our objectives were to determine patterns of use of ticagrelor, hospital-level adoption of ticagrelor use, and factors associated with its use after ACS in a publicly funded health care system. Methods and Results We conducted a population-based cohort study including patients (≥65 years) hospitalized with their first ACS from April 2014 to March 2018 in Ontario, Canada. We determined temporal trends in ticagrelor use and hospital-level adoption of its use post-ACS discharge. Using hierarchical regression models, we identified significant predictors of ticagrelor use. There were 23 962 patients with ACS (mean age 76.3 years, 59.7% men) hospitalized in 156 hospitals. Overall ticagrelor use increased from 32.6% in 2014/2015 to 51.8% in 2017/2018. There was substantial variation in ticagrelor use post-ACS across hospitals, with hospital-specific prescribing rates ranging from 0% to 83.6%. Lower odds of ticagrelor use was associated with advanced age and the presence of comorbidities. Besides patient factors, being admitted to a rurally located hospital more than halved the odds of being prescribed ticagrelor (odds ratio [OR], 0.49; 95% CI, 0.32-0.77). Being managed by a cardiologist during the index ACS hospitalization was associated with higher odds of having a ticagrelor prescription after ACS (OR, 2.80; 95% CI, 2.36-3.33). Conclusions Ticagrelor use rates varied substantially across hospitals and were strongly associated with physician and hospital factors independent of patient characteristics.

Background

The health effects of smoking marijuana are not well-understood.

Purpose

To examine the association between marijuana use and respiratory symptoms, pulmonary function, and obstructive lung disease among adolescents and adults.

Data sources

PubMed, Embase, PsycINFO, MEDLINE, and the Cochrane Library from 1 January 1973 to 30 April 2018.

Study selection

Observational and interventional studies published in English that reported pulmonary outcomes of adolescents and adults who used marijuana.

Data extraction

Four reviewers independently extracted study characteristics and assessed risk of bias. Three reviewers assessed strength of evidence. Studies of similar design with low or moderate risk of bias and sufficient data were pooled.

Data synthesis

Twenty-two studies were included. A pooled analysis of 2 prospective studies showed that marijuana use was associated with an increased risk for cough (risk ratio [RR], 2.04 [95% CI, 1.02 to 4.06]) and sputum production (RR, 3.84 [CI, 1.62 to 9.07]). Pooled analysis of cross-sectional studies (1 low and 3 moderate risk of bias) showed that marijuana use was associated with cough (RR, 4.37 [CI, 1.71 to 11.19]), sputum production (RR, 3.40 [CI, 1.99 to 5.79]), wheezing (RR, 2.83 [CI, 1.89 to 4.23]), and dyspnea (RR, 1.56 [CI, 1.33 to 1.83]). Data on pulmonary function and obstructive lung disease were insufficient.

Limitation

Few studies were at low risk of bias, marijuana exposure was limited in the population studied, cohorts were young overall, assessment of marijuana exposure was not uniform, and study designs varied.

Conclusion

Low-strength evidence suggests that smoking marijuana is associated with cough, sputum production, and wheezing. Evidence on the association between marijuana use and obstructive lung disease and pulmonary function is insufficient.

Primary funding source

None. (PROSPERO: CRD42017059224).

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.

Background

Evidence on the cardiovascular health effects of cannabis use is limited. We designed a prospective cohort study of older Veterans (66 to 68 years) with coronary artery disease (CAD) to understand the cardiovascular consequences of cannabis use. We describe the cohort construction, baseline characteristics, and health behaviors that were associated with smoking cannabis.

Objective

To understand the cardiovascular consequences of cannabis use.

Design

We designed a prospective cohort study of older Veterans (66 to 68 years) with CAD.

Participants

A total of 1,015 current cannabis smokers and 3,270 non-cannabis smokers with CAD.

Main measures

Using logistic regression, we examined the association of baseline variables with smoking cannabis in the past 30 days.

Results

The current cannabis smokers and non-current smokers were predominantly male (97.2% vs 97.1%, p=0.96). Characteristics associated with recent cannabis use in multivariable analyses included lack of a high school education (odds ratio [OR] 2.15, 95% confidence interval [CI]: 1.10 to 4.19), financial difficulty (OR 1.47, 95% CI: 1.02 to 2.11), tobacco use (OR 3.02, 95% CI: 1.66 to 5.48), current drug use (OR 2.82, 95% CI: 1.06 to 7.46), and prior drug use (OR 2.84, 95% CI: 2.11 to 3.82). In contrast, compared to individuals with 0 to 1 comorbid conditions, those with 5 chronic conditions or more (OR 0.43, 95% CI: 0.27 to 0.70) were less likely to smoke cannabis.

Conclusions

In this older high-risk cohort, smoking cannabis was associated with higher social and behavioral risk, but with fewer chronic health conditions.

Importance

Randomized clinical trials have demonstrated that patients with asymptomatic carotid stenosis are eligible for carotid endarterectomy (CEA) if the 30-day surgical complication rate is less than 3% and the patient's life expectancy is at least 5 years.

Objective

To develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis.

Design, setting, and participants

In this cohort study, veterans 65 years and older who received both carotid imaging and CEA in the Veterans Administration between January 1, 2005, and December 31, 2009 (n = 2325) were followed up for 5 years. Data were analyzed from January 2005 to December 2015. A risk prediction tool (the Carotid Mortality Index [CMI]) based on 23 candidate variables identified in the literature was developed using Veterans Administration and Medicare data. A simpler model based on the number of 4 key comorbidities that were prevalent and strongly associated with 5-year mortality was also developed (any cancer in the past 5 years, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]). Model performance was assessed using measures of discrimination (eg, area under the curve [AUC]) and calibration. Internal validation was performed by correcting for optimism using 500 bootstrapped samples.

Main outcome and measure

Five-year mortality.

Results

Among 2325 veterans, the mean (SD) age was 73.74 (5.92) years. The cohort was predominantly male (98.8%) and of white race/ethnicity (94.4%). Overall, 29.5% (n = 687) of patients died within 5 years of CEA. On the basis of a backward selection algorithm, 9 patient characteristics were selected (age, chronic kidney disease, diabetes, chronic obstructive pulmonary disease, any cancer diagnosis in the past 5 years, congestive heart failure, atrial fibrillation, remote stroke or transient ischemic attack, and body mass index) for the final logistic model, which yielded an optimism-corrected AUC of 0.687 for the CMI. The 4C model had slightly worse discrimination (AUC, 0.657) compared with the CMI model; however, the calibration curve was similar to the full model in most of the range of predicted probabilities.

Conclusions and relevance

According to results of this study, use of the CMI or the simpler 4C model may improve patient selection for CEA among patients with asymptomatic carotid stenosis.

Studies of comparative mRNA booster effectiveness among high-risk populations can inform mRNA booster-specific guidelines. The study emulated a target trial of COVID-19 vaccinated U.S. Veterans who received three doses of either mRNA-1273 or BNT162b2 vaccines. Participants were followed for up to 32 weeks between July 1, 2021 to May 30, 2022. Non-overlapping populations were average and high risk; high-risk sub-groups were age ≥65 years, high-risk co-morbid conditions, and immunocompromising conditions. Of 1,703,189 participants, 10.9 per 10,000 persons died or were hospitalized with COVID-19 pneumonia over 32 weeks (95% CI: 10.2, 11.8). Although relative risks of death or hospitalization with COVID-19 pneumonia were similar across at-risk groups, absolute risk varied when comparing three doses of BNT162b2 with mRNA-1273 (BNT162b2 minus mRNA-1273) between average-risk and high-risk populations, confirmed by the presence of additive interaction. The risk difference of death or hospitalization with COVID-19 pneumonia for high-risk populations was 2.2 (0.9, 3.6). Effects were not modified by predominant viral variant. In this work, the risk of death or hospitalization with COVID-19 pneumonia over 32 weeks was lower among high-risk populations who received three doses of mRNA-1273 vaccine instead of BNT162b2 vaccine; no difference was found among the average-risk population and age >65 sub-group.

Background

Among patients with cancer, prior research suggests that patients with mental illness may have reduced survival. The objective was to assess the impact of psychiatric utilisation (PU) prior to cancer diagnosis on survival outcomes.

Methods

All residents of Ontario diagnosed with one of the top 10 malignancies (1997-2014) were included. The primary exposure was psychiatric utilisation gradient (PUG) score in 5 years prior to cancer: 0: none, 1: outpatient, 2: emergency department, 3: hospital admission. A multivariable, cause-specific hazard model was used to assess the effect of PUG score on cancer-specific mortality (CSM), and a Cox proportional hazard model for effect on all-cause mortality (ACM).

Results

A toal of 676,125 patients were included: 359,465 (53.2%) with PUG 0, 304,559 (45.0%) PUG 1, 7901 (1.2%) PUG 2, and 4200 (0.6%) PUG 3. Increasing PUG score was independently associated with worse CSM, with an effect gradient across the intensity of pre-diagnosis PU (vs PUG 0): PUG 1 h 1.05 (95% CI 1.04-1.06), PUG 2 h 1.36 (95% CI 1.30-1.42), and PUG 3 h 1.73 (95% CI 1.63-1.84). Increasing PUG score was also associated with worse ACM.

Conclusions

Pre-cancer diagnosis PU is independently associated with worse CSM and ACM following diagnosis among patients with solid organ malignancies.

Background

Previous studies have demonstrated an association between a diagnosis of cancer and the risk of suicide; however, they failed to account for psychiatric care before a cancer diagnosis, which may confound this relationship. The objective of this study was to assess the effect of a cancer diagnosis on the risk of suicide, accounting for prediagnosis psychiatric care utilization.

Methods

All adult residents of Ontario, Canada who were diagnosed with cancer (1 of prostate, breast, colorectal, melanoma, lung, bladder, endometrial, thyroid, kidney, or oral cancer) between 1997 and 2014 were identified. Noncancer controls were matched 4:1 based on sociodemographics, including a psychiatric utilization gradient (PUG) score (with 0 indicating none; 1, outpatient; 2, emergency department; and 3, hospital admission). A marginal, cause-specific hazard model was used to assess the effect of cancer on the risk of suicidal death.

Results

Among 676,470 patients with cancer and 2,152,682 matched noncancer controls, there were 8.2 and 11.4 suicides per 1000 person-years of follow-up, respectively. Patients with cancer had an overall higher risk of suicidal death compared with matched patients without cancer (hazard ratio, 1.34; 95% CI, 1.22-1.48). This effect was pronounced in the first 50 months after cancer diagnosis (hazard ratio, 1.60; 95% CI, 1.42-1.81); patients with cancer did not demonstrate an increased risk thereafter. Among individuals with a PUG score 0 or 1, those with cancer were significantly more likely to die of suicide compared with controls. There was no difference in suicide risk between patients with cancer and controls for those who had a PUG score of 2 or 3.

Conclusions

A cancer diagnosis is associated with increased risk of death from suicide compared with the general population even after accounting for precancer diagnosis psychiatric care utilization. The specific factors underlying the observed associations remain to be elucidated.