Literature Collection

BACKGROUND: Peer support has been extensively studied in specific areas of community-based primary care such as mental health, substance use, HIV, homelessness, and Indigenous health. These programs are often built on the assumption that peers must share similar social identities or lived experiences of disease to be effective. However, it remains unclear how peers can be integrated in general primary care setting that serves people with a diversity of health conditions and social backgrounds. METHODS: A participatory qualitative study was conducted between 2020 and 2022 to explore the feasibility, acceptability, and perceived effects of the integration of a peer support worker in a primary care setting in Montreal, Canada. A thematic analysis was performed based on semi-structured interviews (n = 18) with patients, relatives, clinicians, and a peer support worker. FINDINGS: Findings show that peers connect with patients through sharing their own hardships and how they overcame them, rather than sharing similar health or social conditions. Peers provide social support and coaching beyond the care trajectory and link identified needs with available resources in the community, bridging the gap between health and social care. Primary care clinicians benefit from peer support work, as it helps overcome therapeutic impasses and facilitates communication of patient needs. However, integrating a peer into a primary care team can be challenging due to clinicians' understanding of the nature and limits of peer support work, financial compensation, and the absence of a formal status within healthcare system. CONCLUSION: Our results show that to establish a relationship of trust, a peer does not need to share similar health or social conditions. Instead, they leverage their experiential knowledge, strengths, and abilities to create meaningful relationships and reliable connections that bridge the gap between health and social care. This, in turn, instills patients with hope for a better life, empowers them to take an active role in their own care, and helps them achieve life goals beyond healthcare. Finally, integrating peers in primary care contributes in overcoming obstacles to prevention and care, reduce distrust of institutions, prioritize needs, and help patients navigate the complexities of healthcare services.

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.