Mary-Jane and her patients: sociodemographic and clinical characteristics of HIV-positive individuals using medical marijuana and antiretroviral agents
Bibliographic record
Abstract
Although it is known that marijuana is used for medical purposes by HIV-positive individuals, the extent and determinants of this usage has not been characterized. We are reporting on the clinical and demographic characteristics of individuals who report using medical marijuana and antiretroviral agents. The analysis was based upon HIV-positive men and women who had ever taken anti-HIV therapy, were 18 years and older, and who completed the 1998–1999 annual participant survey of the British Columbia Centre for Excellence's Drug Treatment Programme between 1 October 1998 and 30 September 1999. The Centre distributes antiretroviral medications to HIV-positive individuals meeting specific criteria, and is the only free source of antiretroviral medications in British Columbia. The survey elicits information about participants’ socioeconomic status, clinical status, and current and past use of HIV-related medications, adverse side-effects, and complementary therapies. The survey contains the Comell–Radimer scale, which is a previously validated scale used to measure the prevalence of food and hunger insecurity at both individual and household levels [1]. Statistical comparisons were conducted using distribution-free methods. Categorical variables and ordinal and skewed continuous variables were compared with the Mantel–Haenszel and the Wilcoxon rank sum test, respectively. Stepwise logistic regression was used to identify patient characteristics independently associated with medical marijuana use. All reported P values are two-sided. A total of 1099 HIV-positive individuals completed the annual 1998–1999 participant survey between 1 October 1998 and 30 September 1999. Of these, 977 individuals (89%) responded to the question regarding medical marijuana use. Of these, 141 (14%) were currently using medical manjuana. Medical marijuana users were more likely to be men (97 versus 92%, P = 0.024) and younger (median age 39 versus 41 years, P = 0.008) than non-medical marijuana users. There were no statistically significant differences between the two groups with respect to clinical status, including a history of an AIDS-defining event, CD4 cell count, HIV-RNA viral load, the number of antiretroviral agents being taken, the length of time on antiretroviral agents, or being on multiple (more than three) antiretroviral drugs. However, nearly half of the 52 side-effects noted in the questionnaire were statistically associated with marijuana use. Each of the gastrointestinal symptoms was statistically significant (P < 0.050), as was the gastrointestinal category as a group (P < 0.001). Painful side-effects classified as either ‘muscle and skeletal problems’ (aches in joints or muscles) or ‘peripheral nervous system problems’ (circumoral parathesia, peripheral neuropathy) were all statistically significant. Urinary problems as a category were statistically significant (P = 0.019), as were some of the individual characteristics within that category, such as kidney stones, being told by one's doctor that one's kidneys are not functioning properly, and abdominal pain caused by kidney spasm (P < 0.010). Three levels of food and hunger issues were statistically significantly associated with marijuana use (P < 0.050). These were household food insecurity (P < 0.001), individual food insecurity (P = 0.019), and individual hunger (P = 0.037). As shown in Table 1 the multivariate analysis of factors associated with medical marijuana use indicated that being male [adjusted odds ratio (AOR) 3.53; 95% confidence interval (Cl) 1.24–10.42], experiencing peripheral neuropathy (AOR 2.12; 95% Cl 1.42–3.15), experiencing gastrointestinal side-effects (AOR 1.77; 95% Cl 1.17–2.67), being food and hunger insecure (AOR 1.51; 95% Cl 1.02–2.22), and younger age (by one year increment) (AOR 0.96; 95% Cl 0.94–0.98) were independently and positively associated with medical marijuana use. Interactions were investigated between all univariately significant results, and no statistically significant interactions were found.Table 1: Multivariate logistic regression analysis of sociodemographic and clinical factors associated with medical marijuana use among 977 HIV-positive participants ever on anti-HIV therapy. There are several implications to our findings. First, this is among the first studies to determine a prevalence rate for the use of medical marijuana among HIV-positive individuals, and suggests that medical marijuana is widely used in this community. Second, our data strongly suggests that individuals in this cohort are using marijuana to manage pharmaceutical side-effects, and in particular gastrointestinal symptoms and peripheral neuropathy. Although it has been reported elsewhere that individuals use marijuana as a pain management strategy [2–4], it has not yet been reported that HIV-positive individuals use marijuana to manage peripheral neuropathy. There is evidence of a plausible biological mechanism in this regard [5–7]. Third, marijuana users report greater food and hunger insecurity. This suggests that poverty may be an important limiting factor in the ability of individuals to manage side-effects and symptoms appropriately. The mechanism of how this phenomenon functions needs to be better elucidated; however, these data suggest that marijuana users do not have enough to eat. There are limitations to this study. First, our study population may not be representative of all program participants on antiretroviral agents. In particular, we found that individuals in our study were older, more likely to be men, to have AIDS at baseline, and had a higher CD4 cell count than other participants in the program who did not respond to the participant survey. Second, there is no adequate control group. It is known that among HIV-negative gay and bisexual men under the age of 30 years in Vancouver, Canada, 60–70% report using marijuana for recreational purposes [8], suggesting that individuals in our cohort reporting medical marijuana use are not using it recreationally. In summary, our data indicate that among HIV-positive individuals on anti-HIV medications in British Columbia, approximately 15% are using marijuana for medical purposes. These individuals are more likely to be men, younger, experiencing side-effects, and experiencing issues of hunger as a consequence of poverty. Paula Braitsteinab Tamil Kendallb Keith Chana Evan Wooda Julio S. G. Montanerac Michael V. O'Shaughnessyad Robert S. Hoggae
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".