Literature Collection

Objectives. To use publicly accessible data from people who post to Twitter to rapidly capture and describe the public's recent experiences with cannabis.Methods. We obtained Twitter posts containing cannabis-related terms from May 1, 2018, to December 31, 2018. We used methods to distinguish between posts from social bots and nonbots. We used text classifiers to identify topics in posts (n = 60 861).Results. Prevalent topics of posts included using cannabis with mentions of cannabis initiation, processed cannabis products, and health and medical with posts suggesting that cannabis could help with cancer, sleep, pain, anxiety, depression, trauma, and posttraumatic stress disorder. Polysubstance use was a common topic with mentions of cocaine, heroin, ecstasy, LSD, meth, mushrooms, and Xanax along with cannabis. Social bots regularly made health claims about cannabis.Conclusions. Findings suggest that processed cannabis products, unsubstantiated health claims about cannabis products, and the co-use of cannabis with legal and illicit substances warrant considerations by public health researchers in the future.

BACKGROUND: As the legalization of recreational cannabis becomes more widespread, its impact on individuals with substance use disorders must be studied. Amidst an ongoing opioid crisis, Canada's legalization of recreational cannabis in October 2018 provides an important setting for investigation. We examined changes to cannabis use patterns in patients receiving medication-assisted treatment (MAT) for opioid use disorder (OUD) following legalization. METHODS: This study includes cross-sectional data from 602 participants recruited 6 months pre-legalization and 788 participants recruited 6 months post-legalization, providing information on cannabis use. Regression analysis was used to estimate the association between legalization and cannabis use patterns. We collected longitudinal urine drug screens (UDSs) detecting cannabis-metabolites for 199 participants recruited pre-legalization and followed prospectively post-legalization. Conditional logistic regression was used to assess the association between legalization and UDS results. RESULTS: Past-month cannabis use was self-reported by 54.8 and 52.3% of participants recruited pre- and post-legalization, respectively. Legalization was not associated with changes in any measured cannabis characteristics: cannabis use (OR 0.91, 95% CI 0.73-1.13), days of use/month (B -0.42, 95% CI - 2.05-1.21), money spent, or cannabis source. There was no association between legalization and prevalence of cannabis use on UDS (OR 1.67, 95% CI 0.93-2.99) or percentage of cannabis-positive UDSs (OR 1.00, 95% CI 0.99-1.01). Participants overwhelmingly reported that legalization would have no impact on their cannabis use (85.7%). CONCLUSIONS: Amongst patients treated for OUD, no significant change in cannabis use was observed following legalization; however, high rates of cannabis use are noted.

IMPORTANCE: Despite the changing legal status of cannabis and the potential impact on health, few health systems routinely screen for cannabis use, and data on the epidemiology of cannabis use, and especially medical cannabis use among primary care patients, are limited. OBJECTIVE: To describe the prevalence of, factors associated with, and reasons for past-3 month cannabis use reported by primary care patients. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used electronic health record data from patients aged 18 years and older who had an annual wellness visit between January 2021 and May 2023 from a primary care clinic within a university-based health system in Los Angeles, California. EXPOSURES: Factors of interest included age, race and ethnicity, sex, employment status, and neighborhood Area Deprivation Index (ADI). MAIN OUTCOMES AND MEASURES: Cannabis use was assessed using the Alcohol Substance Involvement Screening Test (ASSIST). Patients were also asked about reasons for use, symptoms for which they used cannabis, and mode of use. RESULTS: Among the 175 734 patients screened, the median (range) age was 47 (18-102) years; 101 657 (58.0%) were female; 25 278 (15.7%) were Asian, 21 971 (13.7%) were Hispanic, and 51 063 (31.7%) were White. Cannabis use was reported by 29 898 (17.0%), with 10 360 (34.7%) having ASSIST scores indicative of moderate to high risk for cannabis use disorder (CUD). Prevalence of cannabis use was higher among male patients than female patients (14 939 [20.0%] vs 14 916 [14.7%]) and younger patients (18-29 years, 7592 [31.0%]; ≥60 years, 4200 [8.5%]), and lower among those who lived in the most disadvantaged neighborhoods (ADI decile 9-10, 189 [13.8%]; ADI decile 1-2, 12 431 [17.4%]). The most common modes of use included edibles (18 201 [61.6%]), smoking (15 256 [51.7%]), and vaporizing (8555 [29.0%]). While 4375 patients who reported using cannabis (15.6%) did so for medical reasons only, 21 986 patients (75.7%) reported using cannabis to manage symptoms including pain (9196 [31.7%]), stress (14 542 [50.2%]), and sleep (16 221 [56.0%]). The median (IQR) number of symptoms managed was 2 (1-4), which was higher among patients who were at moderate to high risk for CUD (4 [2-6] symptoms). CONCLUSIONS AND RELEVANCE: In this study, cannabis use and risk of CUD were common, and more than three-quarters of patients who reported any cannabis use reported doing so to manage a health-related symptom. These findings suggest that integration of information regarding cannabis use for symptom management could help provide a crucial point-of-care opportunity for clinicians to understand their patients' risk for CUD.

Substantial empirical evidence of the contribution of social and behavioral factors to functional status and the onset and progression of disease has accumulated over the past few decades. Traditionally, research and interventions on social and behavioral determinants of health have largely been the purview of public health which has focused on disease prevention and maintenance of the public’s health. Health care systems, in contrast, have focused primarily on the treatment of disease in individual patients, and, until recently, social determinants of health have not been linked to clinical practice or health care delivery systems. Electronic health records (EHRs) provide crucial information to providers treating individual patients, to health systems about the health of populations, and to researchers about the determinants of health and the effectiveness of treatments. The Health Information Technology for Economic and Clinical Health Act and the Patient Protection and Affordable Care Act place new importance on the widespread adoption and meaningful use of EHRs.The IOM was asked to form a committee to identify domains and measures that capture the social determinants of health to inform the development of recommendations for meaningful use of EHRs. In its Phase 1 report, the committee identifies the social and behavioral domains that are the best candidates to be considered in all EHRs; specifies criteria that should be used in deciding which domains should be included; and identifies any domains that should be included for specific populations or settings defined by age, socioeconomic status, race/ethnicity, disease, or other characteristics.

AIMS: The specific and multifaceted service needs of young people have driven the development of youth-specific integrated primary mental healthcare models, such as the internationally pioneering headspace services in Australia. Although these services were designed for early intervention, they often need to cater for young people with severe conditions and complex needs, creating challenges in service planning and resource allocation. There is, however, a lack of understanding and consensus on the definition of complexity in such clinical settings. METHODS: This retrospective study involved analysis of headspace's clinical minimum data set from young people accessing services in Australia between 1 July 2018 and 30 June 2019. Based on consultations with experts, complexity factors were mapped from a range of demographic information, symptom severity, diagnoses, illness stage, primary presenting issues and service engagement patterns. Consensus clustering was used to identify complexity subgroups based on identified factors. Multinomial logistic regression was then used to evaluate whether these complexity subgroups were associated with other risk factors. RESULTS: A total of 81,622 episodes of care from 76,021 young people across 113 services were analysed. Around 20% of young people clustered into a 'high complexity' group, presenting with a variety of complexity factors, including severe disorders, a trauma history and psychosocial impairments. Two moderate complexity groups were identified representing 'distress complexity' and 'psychosocial complexity' (about 20% each). Compared with the 'distress complexity' group, young people in the 'psychosocial complexity' group presented with a higher proportion of education, employment and housing issues in addition to psychological distress, and had lower levels of service engagement. The distribution of complexity profiles also varied across different headspace services. CONCLUSIONS: The proposed data-driven complexity model offers valuable insights for clinical planning and resource allocation. The identified groups highlight the importance of adopting a holistic and multidisciplinary approach to address the diverse factors contributing to clinical complexity. The large number of young people presenting with moderate-to-high complexity to headspace early intervention services emphasises the need for systemic change in youth mental healthcare to ensure the availability of appropriate and timely support for all young people.

BACKGROUND: Care coordination is commonly employed to assist individuals with mental health challenges [MHCs]. However, its implementation in mental health contexts is inconsistent. The term, 'care coordination' is also used interchangeably with integrated care and case management. This review aims to (1) consolidate the literature on how, and in what contexts, care coordination has been used to help adults with MHCs access care from more than one service and (2) describe the challenges and benefits of implementing care coordination for adults with MHCs from the perspective of service providers, care coordinators and service users. METHODS: This is a scoping review that adopted methodological aspects of Arksey and O'Malley and those proposed by the Joanna Briggs Institute. RESULTS: Care coordination has been employed in a range of clinical and non-clinical settings. Eligibility for care coordination was predominantly restricted to individuals at high risk of deterioration or those unable to access multiple services independently. Care coordinators worked individually or as part of a team and were mostly mental health nurses or social workers. Care coordination was reported to include both clinical and non-clinical tasks. Clinical tasks included medication management, preparing coordinated treatment plans and implementing crisis triage. Non-clinical tasks included acceptance of referrals, identification of service user needs, developing a plan for service involvement, implementation of the said plan, and monitoring of outcomes. Benefits of care coordination included improved access to services, reduced consumer distress, and self-harming behaviour, a team approach to care, decrease in psychiatric hospitalisations, emergency room visits and arrests, and better interservice collaboration. Challenges to care coordination included continuing unmet needs, lack of service availability and health insurance, unclear processes causing confusion, difficulties in engaging with some service users, administrative complications, large consumer load and staff shortages, incompatibility of technology between systems, insufficient funding and limited community support agencies. CONCLUSION: A better understanding of care coordination is needed that includes indications, eligibility criteria, coordination tasks, expected outcomes, as well as organizational and service system requirements.