Association of a Positive Drug Screening for Cannabis With Mortality and Hospital Visits Among Veterans Affairs Enrollees Prescribed Opioids.

Importance: Cannabis has been proposed as a therapeutic with potential opioid-sparing properties in chronic pain, and its use could theoretically be associated with decreased amounts of opioids used and decreased risk of mortality among individuals prescribed opioids. Objective: To examine the risks associated with cannabis use among adults prescribed opioid analgesic medications. Design, Setting, and Participants: This cohort study was conducted among individuals aged 18 years and older who had urine drug screening in 2014 to 2019 and received any prescription opioid in the prior 90 days or long-term opioid therapy (LTOT), defined as more than 84 days of the prior 90 days, through the Veterans Affairs health system. Data were analyzed from November 2020 through March 2022. Exposures: Biologically verified cannabis use from a urine drug screen. Main Outcomes and Measures: The main outcomes were 90-day and 180-day all-cause mortality. A composite outcome of all-cause emergency department (ED) visits, all-cause hospitalization, or all-cause mortality was a secondary outcome. Weights based on the propensity score were used to reduce confounding, and hazard ratios [HRs] were estimated using Cox proportional hazards regression models. Analyses were conducted among the overall sample of patients who received any prescription opioid in the prior 90 days and were repeated among those who received LTOT. Analyses were repeated among adults aged 65 years and older. Results: Among 297 620 adults treated with opioids, 30 514 individuals used cannabis (mean [SE] age, 57.8 [10.5] years; 28 784 [94.3%] men) and 267 106 adults did not (mean [SE] age, 62.3 [12.3] years; P < .001; 247 684 [92.7%] men; P < .001). Among all patients, cannabis use was not associated with increased all-cause mortality at 90 days (HR, 1.07; 95% CI, 0.92-1.22) or 180 days (HR, 1.00; 95% CI, 0.90-1.10) but was associated with an increased hazard of the composite outcome at 90 days (HR, 1.05; 95% CI, 1.01-1.07) and 180 days (HR, 1.04; 95% CI, 1.01-1.06). Among 181 096 adults receiving LTOT, cannabis use was not associated with increased risk of all-cause mortality at 90 or 180 days but was associated with an increased hazard of the composite outcome at 90 days (HR, 1.05; 95% CI, 1.02-1.09) and 180 days (HR, 1.05; 95% CI, 1.02-1.09). Among 77 791 adults aged 65 years and older receiving LTOT, cannabis use was associated with increased 90-day mortality (HR, 1.55; 95% CI, 1.17-2.04). Conclusions and Relevance: This study found that cannabis use among adults receiving opioid analgesic medications was not associated with any change in mortality risk but was associated with a small increased risk of adverse outcomes and that short-term risks were higher among older adults receiving LTOT.

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.