First-Year Trajectories of Medical Cannabis Use Among Adults Taking Opioids for Chronic Pain: An Observational Cohort Study

Objective To describe first-year trajectories of medical cannabis use and identify characteristics associated with patterns of use in a cohort of adults using opioids for chronic pain. Design Latent class trajectory analysis of a prospective cohort study using data on the 14-day frequency of medical cannabis use. Setting A large academic medical center and four medical cannabis dispensaries in the New York City metropolitan area. Subjects Adults with chronic pain using opioids and newly certified for medical cannabis in New York between 2018 and 2020. Methods Using latent class trajectory analysis, we identified clusters of participants based on the 14-day frequency of medical cannabis use. We used logistic regression to determine factors associated with cluster membership, including sociodemographic characteristics, pain, substance use, and mental health symptoms. Results Among 99 participants, the mean age was 53 years; 62% were women, and 52% were White. We identified three clusters of medical cannabis use: infrequent use (n = 30, mean use = 1.5 days/14-day period), occasional use (n = 28, mean = 5.7 days/14-day period), and frequent use (n = 41, mean = 12.1 days/14-day period). Within clusters, use patterns did not vary significantly over 52 weeks. Differences were observed in two sociodemographic variables: Frequent (vs infrequent) use was associated with non-Hispanic White race/ethnicity (adjusted odds ratio 4.54, 95% confidence interval 1.49–14.29), while occasional (vs infrequent) use was associated with employment (adjusted odds ratio 13.84, 95% confidence interval 1.21–158.74). Conclusions Three clusters of medical cannabis use patterns emerged and were stable over time. Results suggest that structural factors related to race/ethnicity and employment may be major drivers of medical cannabis use, even among adults certified for its use.

Long-term body mass trajectories and hypertension by sex among Chinese adults: a 24-year open cohort study

Evidence was limited on trajectory of body mass index (BMI) through adulthood and its association with hypertension. We aimed to evaluate their association by sex in large-scale study. Data were obtained from the China Health and Nutrition Survey (CHNS) from 1991 to 2015. Latent class trajectory analysis (LCTA) was used to capture BMI change trajectories. Hazard risks (HRs) were estimated from Cox proportion hazard regression. Among 14,262 participants (mean age, 38.8; 47.8% men), 5138 hypertension occurred (2687 men and 2451 women) occurred during a mean follow-up 9.6 years. Four body mass trajectory groups were identified as BMI loss, stable, moderate and substantial gain. Appropriately half of participants (48.0%) followed 1 of the 2 BMI gain trajectories, where BMI increased at least 3 kg/m2 overtime. Compared with participants with stable BMI, those gaining BMI substantially had higher risk of hypertension by 65% (HR 1.65, 95% CI 1.45–1.86) in male and 83% (HR 1.83, 95% CI 1.58–2.12) in female. The HRs in BMI loss patterns were 0.74 (0.62–0.89) in men and 0.87 (0.75–1.00) in women. Our findings imply that majority of Chinese adults transited up to a higher BMI level during follow-up. Avoiding excessive weight gain and maintaining stable weight might be important for hypertension prevention.

Latent Class Trajectory Analysis of Risk Factors Uncovers Progression to Type 2 Diabetes

We identified trajectories of diabetes risk factors in the Longitudinal Epidemiologic Assessment of Diabetes Risk (LEADR) cohort analyzing 8 years of electronic health records on 1.4 million patients, and investigated associations between trajectories and progression to new onset Type 2 diabetes. Design and Methods: Analyzing LEADR data (2010-2016), we applied Latent Class Trajectory Analysis (LCTA) to classify patterns of risk factor change. There were 824,043 patients with BMIs; 955,128 patients with systolic blood pressures; 957,491 patients with diastolic blood pressures; 300,137 patients with HDLs; 267,553 patients with non-HDL cholesterols; and 297,026 patients with triglycerides. Patients had to have data for all risk factors being assessed. Association between trajectories and incidence of type 2 diabetes for 94,551 patients was assessed using negative binomial regression analysis. Results: Compared to a static BMI trajectory, those with a sustained weight increase (25%+ from starting BMI) were at higher risk of type 2 diabetes over 4.8 years of follow-up (range 2.0 to 8.0 years) (adjusted rate ratios ranged 1.53-1.62, p-value<0.05). Patients with a BMI decrease trajectory (of ~10%), were at reduced risk of diabetes (adjusted rate ratios ranged 0.54-0.74, p-value<.05). BP and lipid trajectories had significant associations with diabetes onset. Conclusions: Regardless of the starting BMI, those who increased their BMI by 25% within two years and maintained the higher weight were significantly at increased risk of type 2 diabetes. Monitoring BMI change and other known risk factor trajectories, BP and lipids, are additional tools for identifying patients at risk for type 2 diabetes.