Substitution of medical cannabis for pharmaceutical agents for pain, anxiety, and sleep.

A prior epidemiological study identified a reduction in opioid overdose deaths in US states that legalized medical cannabis (MC). One theory to explain this phenomenon is a potential substitution effect of MC for opioids. This study evaluated whether this substitution effect of MC for opioids also applies to other psychoactive medications. New England dispensary members ( n = 1,513) completed an online survey about their medical history and MC experiences. Among respondents that regularly used opioids, over three-quarters (76.7%) indicated that they reduced their use since they started MC. This was significantly ( p < 0.0001) greater than the patients that reduced their use of antidepressants (37.6%) or alcohol (42.0%). Approximately two-thirds of patients decreased their use of anti-anxiety (71.8%), migraine (66.7%), and sleep (65.2%) medications following MC which significantly ( p < 0.0001) exceeded the reduction in antidepressants or alcohol use. The patient's spouse, family, and other friends were more likely to know about their MC use than was their primary care provider. In conclusion, a majority of patients reported using less opioids as well as fewer medications to treat anxiety, migraines, and sleep after initiating MC. A smaller portion used less antidepressants or alcohol. Additional research is needed to corroborate these self-reported, retrospective, cross-sectional findings using other data sources.

Chronic pain patients' perspectives of medical cannabis.

Medical cannabis (MC) is used for a variety of conditions including chronic pain. The goal of this report was to provide an in-depth qualitative exploration of patient perspectives on the strengths and limitations of MC. Members of MC dispensaries (N = 984) in New England including two-thirds with a history of chronic pain completed an online survey. In response to "How effective is medical cannabis in treating your symptoms or conditions?," with options of 0% "no relief" to 100% "complete relief," the average was 74.6% ± 0.6. The average amount spent on MC each year was $3064.47 ± 117.60, median = $2320.23, range = $52.14 to $52,140.00. Open-ended responses were coded into themes and subthemes. Analysis of answers to "What is it that you like most about MC?" (N = 2592 responses) identified 10 themes, including health benefits (36.0% of responses, eg, "Changes perception and experience of my chronic pain."), the product (14.2%, eg, "Knowing exactly what strain you are getting"), nonhealth benefits (14.1%), general considerations (10.3%), and medications (7.1%). Responses (N = 1678) to "What is it that you like least about MC?" identified 12 themes, including money (28.4%, eg, "The cost is expensive for someone on a fixed income"), effects (21.7%, eg, "The effects on my lungs"), the view of others (11.4%), access (8.2%), and method of administration (7.1%). These findings provide a patient-centered view on the advantages (eg, efficacy in pain treatment, reduced use of other medications) and disadvantages (eg, economic and stigma) of MC.

Understanding community structure: a data-driven multivariate approach.

Habitat is known to influence community structure yet, because these effects are complex, elucidating these relationships has proven difficult. Multiple aspects of vegetation architecture or plant species composition, for example, may simultaneously affect animal communities and their constituent species. Many traditional statistical approaches (e.g., regression) have difficulty in handling large numbers of collinear variables. On the other hand, multivariate methods, such as ordination, are well suited to handle these large datasets, but they have primarily been used in ecology as descriptive techniques, and less frequently as a data reduction tool for predictor variables in regression. Here, I employ a multivariate approach for variable reduction of both the predictor and response variables to investigate the influences of vegetation architecture and plant species on community composition in spiders using multiple regression. This allows retention of the information in the original dataset while producing statistically tractable variables for use in further analyses. I used nonmetric multidimensional scaling to reduce the number of variables for predictor (habitat architecture and plant species) and response (spider species) data matrices, and used these new variables in multiple regression analyses. These axes can be interpreted based on their correlations with the original variables, allowing for recovery of biologically meaningful information from regressions. Consequently, the important variables are determined by the data themselves, rather than by a priori assumptions of the researcher. Contrary to expectations based on previous work in spiders and other animals, plant species composition explained more variation in spider communities than did habitat architecture, and was also a stronger predictor of other community structure variables (overall abundance, species richness, and species diversity). I discuss possible ecological explanations for these results, and the advantages of the proposed method.