Cannabis use has increased, especially in individuals with depression

Recent research supported by the NIDA assessed trends from 2005 to 2017 in the prevalence of cannabis use and perceptions of risk associated with its use in the United States in people with and without depression. Cannabis is the most commonly used psychoactive drug in the United States and globally. Heavy use of cannabis has been associated with psychiatric and substance use comorbidities, disability, and psychosocial and health problems. Previous research indicates that heavy cannabis use is both more common in people with depression and has potentially more negative outcomes compared with those without depression or other mental health conditions. Major depression is the most common mental health condition in the United States, and recent data suggests that the prevalence of depression is also increasing. It not known why there is an increase in cannabis use among people with depression, however, one potential factor may be a difference in risk-taking and risk perception between those with depression and those without. Leveraging data from the National Survey on Drug Use and Health, an annual cross-sectional survey (2005 to 2017; n = 728,691 individuals in the United States, aged ≥ 12 years), the researchers assessed linear time trends of the prevalence of any, daily, and non-daily (past 30 days) cannabis use and perceived risk associated with regular cannabis use among people with and without a depression diagnosis within the past year using logistic regression with the survey year as the predictor. All analyses were adjusted for gender, age, race/ethnicity, and income; and models assessing time trends of cannabis use prevalence were also adjusted for perceived risk. From 2005 to 2017, the prevalence of cannabis usage in the United States increased for all individuals and was approximately twice as common among those with depression compared to those without (e.g., 2017 18.9 percent versus 8.7 percent), and this difference was consistent across socio-demographic groups. However, certain groups appeared more vulnerable to use, such as young adults aged 18–25 where one-fifth (20.3 percent) of those without depression and nearly one-third (29.7 percent) of those with depression reporting current cannabis use. Additionally, more than one-fifth (22.6 percent) of males with depression in the past year reported current cannabis use. The perception of risk associated with regular cannabis use was significantly lower among those with depression versus those without and the perception of risk declined more rapidly among those with depression. These findings provide new clues to a potential modifiable factor of risk perception that may be contributing to the disproportionate use of cannabis and rate of increase in cannabis use among those with depression. These results indicate that it may be beneficial to have interventions that target cannabis-related risk perceptions among persons with depression in order to reduce the prevalence of cannabis use among this high-risk group.

Citation: Pacek LR, Weinberger AH, Zhu J, Goodwin RD, 2019. Rapid increase in the prevalence of cannabis use among people with depression in the United States, 2005–17: the role of differentially changing risk perceptions. Addiction. doi: 10.1111/add.14883.

Exercising 2.5 to 5 hours per week may lower the risk of several cancer types

A recent study supported by the NCI and NIA linked recommended physical activity levels to a decreased risk of seven cancers. In the United States, 1.7 million people are diagnosed with invasive cancer and more than 600,000 people a year die as a result of malignant diseases, highlighting the importance of cancer prevention. Physical activity has been associated with a lower risk of several cancers, however, less is known about the nature of this relationship and amount of exercise required for this effect. Updated U.S. guidelines for activity state that people should aim for 2.5 to 5 hours per week of moderate-intensity activity or 1.25 to 2.5 hours per week of vigorous activity (7.5–15 metabolic equivalent task [MET] hours per week). The researchers in this study determined whether the recommended amounts of physical activity were associated with significantly lower cancer risks and examined the physical activity cancer dose-response relationship across different cancer types. The researchers pooled data from nine prospective cohorts and harmonized the self-reported physical activity measures to examine the relationship to the incidence of 15 types of cancer. Data included five U.S. cohorts, three European cohorts, and one Australian cohort (755,459 participants; median age: 62 years [range: 32–91 years]; 53 percent female) that were followed for 10.1 years, and 50,620 incident cancers were accrued. Multivariable Cox regression was used to estimate adjusted hazard ratios (HRs) and 95 percent confidence intervals of the relationships between physical activity with incidence of the cancer types. Dose-response relationships were modeled with restricted cubic spline functions that compared various activity levels (hours per week) to no physical activity. They found that engaging in recommended amounts of activity (7.5 to 15 MET hours per week) was associated with a statistically significant lower risk of seven of the 15 cancer types studied, with greater reduction with more MET hours. Physical activity was associated with a lower risk of colon cancer in men (8 percent for 7.5 MET hours/week; 14 percent for 15 MET hours per week), breast cancer in women (6–10 percent), endometrial cancer (10–18 percent), kidney cancer (11–17 percent), myeloma (14–19 percent), liver cancer (18–27 percent), and non-Hodgkin’s lymphoma (11–18 percent in women). The shape of the dose-response curves varied by cancer type. The dose-response relationship between physical activity and cancer risk provides new insight into the amount of physical activity needed for a reduced risk of several cancer types. Importantly, this study provides support for the U.S. physical activity guideline for levels of activity recommended for cancer prevention and provides actionable evidence for ongoing and future cancer prevention efforts. Additional research in this area is indicated as there are some limitations of this study including low patient numbers for some cancers, a predominately white study population, and the reliance on self-report measures for physical activity.

Citation: Matthews CE, Moore SC, Arem H, Cook MB, Trabert B, Håkansson N, Larsson SC, Wolk A, Gapstur SM, Lynch BM, Milne RL, Freedman ND, Huang WY, Berrington de Gonzalez A, Kitahara CM, Linet MS, Shiroma EJ, Sandin S, Patel AV, Lee IM. 2019. Amount and Intensity of Leisure-Time Physical Activity and Lower Cancer Risk. J Clin Oncol. doi: 10.1200/JCO.19.02407.

New computer game may predict opioid use relapse

In a recent publication, researchers funded by the NIDA, the Brain and Behavior Research Foundation, the U.S. Fulbright Commission, and the Colombian government adapted tools from computational psychiatry and decision neuroscience to identify changes in decision-making processes in patients with opioid use disorder (OUD) preceding opioid reuse. Opioid addiction is a major public health problem with drug overdose now the leading cause of unintentional death in the United States. Despite the availability of evidence-based treatments, relapse and treatment dropout are common. In order to improve OUD treatment outcomes, studies that identify reuse risk and the mechanisms underlying vulnerabilities for reuse are needed. The researchers studied a cohort of individuals with OUD longitudinally (up to 7 months, 1–15 sessions per person) in a community-based treatment setting. Each time a participating patient came in for weekly or monthly clinic visits, they played the betting game which assesses risky decision-making. As part of the game, patients had the option of accepting a known risk, such as an immediate chip reward worth $5, or gambling on a "riskier" bag of chips with the possibility of either greater reward, as high as $66, or nothing. A cohort of control participants with no history of OUD also played the game weekly (1–5 sessions per person), serving both as a baseline comparison group and an independent sample in which to assess measurement test-retest reliability. Seventy patients (mean age: 45 years; 82.9 percent male) and 55 control subjects (mean age: 42 years; 76.4 percent male) were included. Two individual model-based behavioral markers were derived from the task, capturing a participant’s current tolerance of known risks and ambiguity (a context in which there is limited information about environmental risk). Current anxiety, craving, withdrawal, and nonadherence were assessed via interview and clinic records. Opioid use was determined from random urine tests and self-report. The researchers found that 252 (45.7 percent) of 552 sessions completed with patients directly preceded opioid use events. An increase in ambiguity tolerance was associated with significantly increased odds of future opioid use (within 1–4 weeks; adjusted OR = 1.37; 95 percent CI, 1.07–1.76). This indicated that patients were more tolerant of ambiguous risks prior to use events, which was independent of established risk factors for reuse (e.g., cravings, withdrawal). Known risk tolerance was elevated in patients relative to control participants, however, it was not associated with opioid reuse. This study provides insight on the role of risky decision making in addiction by identifying subtypes of risk that map onto different OUD features. Ambiguity tolerance may track discrete changes underlying ongoing opioid use vulnerability, while known risk tolerance may reflect trait-like features that make patients vulnerable over longer timescales. This study demonstrates that computer games and applications may provide a new, relatively low-cost tool for assessing how a patient is doing during treatment and, when combined with traditional measures, could lead to improved clinical-risk assessment tools to inform clinical care.

Citation: Konova AB, Lopez-Guzman S, Urmanche A, Ross S, Louie K, Rotrosen J, Glimcher PW. 2019. Computational Markers of Risky Decision-making for Identification of Temporal Windows of Vulnerability to Opioid Use in a Real-world Clinical Setting. JAMA Psychiatry. doi: https://doi.org/10.1001/jamapsychiatry.2019.4013