Ruba Sajdeya

Introduction: The number of people using medical cannabis has increased in the United States, but little is known about the prevalence or risk factors for Cannabis Use Disorder (CUD) in this population. We sought to estimate the prevalence of CUD and to identify individual characteristics and cannabis use patterns associated with CUD in a statewide sample of adults certified to use medical cannabis. Methods: The sample included 632 adults (median age 45, 63% female, 75% non-Hispanic White) certified and currently using medical cannabis in Florida. Participants were recruited from medical cannabis clinics and completed an online cross-sectional survey in 2022. The primary outcome, based on the 8-item Cannabis Use Disorder Identification Test-Revised (CUDIT-R), was categorized as possible CUD (CUDIT-R score ≥12), hazardous use (score ≥8), or non-hazardous use (1 year of daily marijuana use (1–5 years aOR 2.2 95% CI: 1.2–5.2; 6+ years aOR 3.1 95% CI: 1.5–7.2), lower preference for oral concentrates (aOR 0.29, 95% CI: 0.10–0.70), and self-reported recreational reasons for marijuana use (aOR 5.1, 95% 1.5–17.3). Possible CUD was not associated with sex, race/ethnicity, mental health conditions, or preferred THC percentage. Hazardous use was associated with >1 year of daily marijuana use, recreational reasons for use, younger age at marijuana use, greater preference for marijuana flower, and anxiety diagnosis. Few (4%) reported concern about addiction of dependence. Discussion: About 1 in 5 adults certified to use medical cannabis in Florida had possible CUD, and over half (54%) had hazardous use. Possible CUD was associated with younger age, prolonged daily use, lower preference for oral concentrates, and recreational motives for marijuana use.

Background: Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. Objective: This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. Methods: In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium’s data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Results: Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. Conclusions: The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

Introduction: Medical cannabis (MC) is available upon certification for one of several qualifying conditions in Florida, USA. Previous studies suggested that some people seek cannabis for medical conditions/symptoms beyond those legally permitted. Yet, data remains limited on patient motives for seeking MC and their experiences around its impact on their health. We aimed to compare reported qualifying conditions for MC certification with the most frequently self-reported reasons for using MC while assessing the alignment between the two and understand the perceived impacts of MC on self-reported conditions and symptoms. Methods: We conducted a cross-sectional study using survey data from the Medical Marijuana & Me (M3) Databank of individuals receiving MC in Florida, USA, in 2022. Participants were recruited via convenience sampling from nine MC clinics/clinic networks across Florida and were asked to fill out an online survey. The study measures included sociodemographic variables, self-reported health conditions, self-reported main reasons for using MC, self-reported qualifying conditions for MC certification, and self-reported perceived impact of MC on health conditions. We cross-tabulated reported qualifying conditions and reasons for MC use and reported the perceived impact per condition. Results: A total of 632 participants completed the survey, of whom 396 (62.66%) were female, and 471 (74.53%) were non-Hispanic White. The median (IQR) age was 45 (35, 58). The most frequently reported qualifying conditions were post-traumatic stress disorder (PTSD) (n=187, 29.59%), a condition not on the qualifying conditions list (n=175, 27.69%), medical conditions of the same kind/comparable to those listed (n=140, 22.15%), and chronic nonmalignant pain (n=62, 25.63%). The top ten most frequently reported reasons for using MC were anxiety (n=383, 60.60%), chronic pain (n=278, 43.99%), depression (n=252, 39.87%), PTSD (n=220, 34.81%), headaches/migraine (n=134, 21.20%), fibromyalgia (n=67, 10.60%), attention-deficit/hyperactivity disorder (ADHD) (n=59, 9.34%), bipolar disorder (n=53, 8.39%), high blood pressure (n=41, 6.49%), and cancer, (n=18,2.85%). Of respondents, 70-90% with each qualifying condition reported it as one of the main reasons for using MC. Most respondents reported improvement of anxiety (n=430/451, 95.34%), depression (n=381/392, 97.20%), chronic pain (n=305/310, 98.39%), insomnia/ sleeping problems (n=225/295, 86.44%), PTSD (n=247/270, 91.48%), headaches/ migraine (n=172/218, 78.90%), ADHD (n=82/123, 66.67%), bipolar disorder (n=79/89, 88.76%), and fibromyalgia (n=77/82, 93.90%). Most respondents were unsure/reported no change in blood pressure (n=93/162, 57.41%). A small percentage reported perceived worsening impacts on their conditions. Conclusion: Qualifying conditions and self-reported reasons for using MC aligned for most respondents. Yet, a notable proportion of respondents sought MC for broader treatment effects beyond those delineated by the officially recognized qualifying conditions in Florida, USA. Most patients perceived positive effects, including those with limited available evidence on efficacy.

Background: Cannabis use is associated with higher intravenous anesthetic administration. Similar data regarding inhalational anesthetics are limited. With rising cannabis use prevalence, understanding any potential relationship with inhalational anesthetic dosing is crucial. Average intraoperative isoflurane or sevoflurane minimum alveolar concentration equivalents between older adults with and without cannabis use were compared. Methods: The electronic health records of 22,476 surgical patients 65 yr or older at the University of Florida Health System between 2018 and 2020 were reviewed. The primary exposure was cannabis use within 60 days of surgery, determined via (1) a previously published natural language processing algorithm applied to unstructured notes and (2) structured data, including International Classification of Diseases codes for cannabis use disorders and poisoning by cannabis, laboratory cannabinoids screening results, and RxNorm codes. The primary outcome was the intraoperative time-weighted average of isoflurane or sevoflurane minimum alveolar concentration equivalents at 1-min resolution. No a priori minimally clinically important difference was established. Patients demonstrating cannabis use were matched 4:1 to non–cannabis use controls using a propensity score. Results: Among 5,118 meeting inclusion criteria, 1,340 patients (268 cannabis users and 1,072 nonusers) remained after propensity score matching. The median and interquartile range age was 69 (67 to 73) yr; 872 (65.0%) were male, and 1,143 (85.3%) were non-Hispanic White. The median (interquartile range) anesthesia duration was 175 (118 to 268) min. After matching, all baseline characteristics were well-balanced by exposure. Cannabis users had statistically significantly higher average minimum alveolar concentrations than nonusers (mean ± SD, 0.58 ± 0.23 vs. 0.54 ± 0.22, respectively; mean difference, 0.04; 95% confidence limits, 0.01 to 0.06; P = 0.020). Conclusion: Cannabis use was associated with administering statistically significantly higher inhalational anesthetic minimum alveolar concentration equivalents in older adults, but the clinical significance of this difference is unclear. These data do not support the hypothesis that cannabis users require clinically meaningfully higher inhalational anesthetics doses. Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New:

Introduction Cannabis use is increasing among older adults, but its impact on postoperative pain outcomes remains unclear in this population. We examined the association between cannabis use and postoperative pain levels and opioid doses within 24 hours of surgery. Methods We conducted a propensity score-matched retrospective cohort study using electronic health records data of 22 476 older surgical patients with at least 24-hour hospital stays at University of Florida Health between 2018 and 2020. Of the original cohort, 2577 patients were eligible for propensity-score matching (1:3 cannabis user: non-user). Cannabis use status was determined via natural language processing of clinical notes within 60 days of surgery and structured data. The primary outcomes were average Defense and Veterans Pain Rating Scale (DVPRS) score and total oral morphine equivalents (OME) within 24 hours of surgery. Results 504 patients were included (126 cannabis users and 378 non-users). The median (IQR) age was 69 (65–72) years; 295 (58.53%) were male, and 442 (87.70%) were non-Hispanic white. Baseline characteristics were well balanced. Cannabis users had significantly higher average DVPRS scores (median (IQR): 4.68 (2.71–5.96) vs 3.88 (2.33, 5.17); difference=0.80; 95% confidence limit (CL), 0.19 to 1.36; p=0.01) and total OME (median (IQR): 42.50 (15.00–60.00) mg vs 30.00 (7.50–60.00) mg; difference=12.5 mg; 95% CL, 3.80 mg to 21.20 mg; p=0.02) than non-users within 24 hours of surgery. Discussion This study showed that cannabis use in older adults was associated with increased postoperative pain levels and opioid doses.

Introduction: Florida’s medical cannabis (marijuana) program is among the largest in the United States. Smokable cannabis forms were not legally available in this program until 2019, and five years after other forms of cannabis were available. This study assessed changes in Δ-9 tetrahydrocannabinol (THC) dispensed per patient following legalization of smokable cannabis in Florida. Materials and Methods: This quasi-experimental study used data from the Florida Department of Health Office of Medical Marijuana Use Reports on THC dispensing from April 6, 2018, through March 13, 2020. Certified medical cannabis user during the study period was included. The exposure was the dispensed amount of THC from legalized smokable forms of medical cannabis (statute identified as SB182), effective as of March 2019. Changes in level and trend of average milligram (mg) of dispensed THC per certified patient with 95% confidence intervals (CIs), before and after SB182, were calculated by fitting a generalized least squares linear model and allowing a 17-week phase-in period. Results: The number of certified patients increased by 24.8% from 197,107 (March 22, 2019) to 246,079 (July 19, 2019) and to 325,868 by March 13, 2020. Assuming that a 20% THC concentration in smokable products, there was a significant level increase in the mean weekly dispensed THC amount per certified patient of 138.45 mg (95% CI: 102.69–174.20), translating to a 42.18% increase (95% CI: 33.14–50.28), from the pre-policy period. We noted a continuous increase of 5.62 mg per certified patient per week (95% CI: 4.35–6.89) throughout the 35 weeks following the policy, when compared with the period before. Assuming 10% THC concentration in smokable products, we observed a significant level increase of 35.10 mg (95% CI: 5.31–64.88), corresponding to an increase of 10.70% (95% CI: 1.70–18.89), and a trend increase of 2.23 mg per certified patient per week (95% CI: 1.18–3.29). Discussion: The expansion of the Florida medical cannabis program to include smokable cannabis forms was associated with a significant increase in the mean amount of weekly dispensed THC per certified patient. Findings suggest that the dispensed amount of THC after legalization of smokable medical cannabis far exceeds the maximum recommended daily dose, based on extrapolation from oral cannabis product dosing recommendations from one expert consensus statement, raising questions about the safety, and need for consumer education.

BACKGROUND Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. OBJECTIVE This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. METHODS In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium’s data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. RESULTS Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. CONCLUSIONS The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) and Deep Learning (DL) techniques to identify and classify documentation of suicidal behaviors in patients with Alzheimer’s disease and related dementia (ADRD). We utilized MIMIC-III and MIMIC-IV datasets and identified ADRD patients and subsequently those with suicide ideation using relevant International Classification of Diseases (ICD) codes. We used cosine similarity with ScAN (Suicide Attempt and Ideation Events Dataset) to calculate semantic similarity scores of ScAN with extracted notes from MIMIC for the clinical notes. The notes were sorted based on these scores, and manual review and categorization into eight suicidal behavior categories were performed. The data were further analyzed using conventional ML and DL models, with manual annotation as a reference. The tested classifiers achieved classification results close to human performance with up to 98% precision and 98% recall of suicidal ideation in the ADRD patient population. Our NLP model effectively reproduced human annotation of suicidal ideation within the MIMIC dataset. These results establish a foundation for identifying and categorizing documentation related to suicidal ideation within ADRD population, contributing to the advancement of NLP techniques in healthcare for extracting and classifying clinical concepts, particularly focusing on suicidal ideation among patients with ADRD. Our study showcased the capability of a robust NLP algorithm to accurately identify and classify documentation of suicidal behaviors in ADRD patients.