Patients' Health-Related Quality of Life and Use of Medicinal Cannabis: A Cross-Sectional Survey Study.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2025-01-10 DOI:10.1007/s40801-024-00479-2

Frederik Rosenbæk, Sonja Wehberg, Line Bjørnskov Pedersen, Jesper Bo Nielsen, Jens Søndergaard

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Abstract

Background: Studies on medicinal cannabis (MC) have primarily investigated effects on diseases and symptoms, while there is only sparse knowledge on patients' health-related quality of life. Our aim was, firstly, to compare the health-related quality of life of patients (MC users and non-users) within four specified diagnostic indications (multiple sclerosis, paraplegia, neuropathy, and nausea and vomiting after chemotherapy) with that of patients with other diagnostic indications (MC users only) and the adult population (non-users only). Secondly, we estimate the associations between use of MC and health-related quality of life for patients in the four specified diagnostic indications.

Methods: We collected data on quality-adjusted life years (QALYs), using EQ-5D-3L, and patients' self-reported use of MC in a Danish nationwide online survey distributed to 23,846 patients in October 2020. We compared QALY scores of all groups using a two-tailed t-test, listed QALY scores of MC users versus non-users, and investigated associations between QALY score and MC use using unadjusted and adjusted linear regression analyses. Significance level was set to p-value < 0.05.

Results: A total of 9265 patients took part in the survey. All diagnostic indications had a statistically significant lower QALY score than the adult population (0.87). Paraplegia patients had the lowest QALY score, being 0.36 lower, followed by other diagnostic indication (- 0.34), multiple sclerosis (- 0.20), neuropathy (- 0.13), and nausea and vomiting after chemotherapy (- 0.06). MC users had a statistically significant lower QALY score than non-users (0.44 vs 0.74). Users redeeming 1-6 and ≥ 7 MC prescriptions (except for paraplegia patients) had a statistically significant lower QALY score than non-users, ranging between 0.11-0.24 and 0.26-0.32 lower than non-users, accordingly. Although, it should be noted that the number of users was small when stratifying by number of prescriptions.

Conclusion: Patients with either multiple sclerosis, paraplegia, neuropathy, or nausea and vomiting after chemotherapy had a significantly lower health-related quality of life than individuals from the adult population. Users of medicinal cannabis also had a significantly lower health-related quality of life compared with non-users, in all diagnostic indications.

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患者健康相关生活质量与药用大麻的使用：一项横断面调查研究

背景：药用大麻（MC）的研究主要是调查对疾病和症状的影响，而对患者健康相关生活质量的了解很少。首先，我们的目的是比较四种特定诊断指征（多发性硬化症、截瘫、神经病变和化疗后恶心呕吐）内患者（MC使用者和非使用者）与其他诊断指征（仅MC使用者）和成人（仅非使用者）的健康相关生活质量。其次，我们估计在四种特定的诊断指征中使用MC与患者健康相关的生活质量之间的关联。方法：我们使用EQ-5D-3L收集了质量调整生命年（QALYs）的数据，并在2020年10月对23,846名患者进行的丹麦全国在线调查中收集了患者自我报告的MC使用情况。我们使用双尾t检验比较了所有组的QALY得分，列出了MC使用者与非使用者的QALY得分，并使用未调整和调整的线性回归分析调查了QALY得分与MC使用之间的关系。结果：共有9265例患者参与了调查。所有诊断指征的QALY评分均低于成人（0.87）。截瘫患者的QALY评分最低，为0.36，其次是其他诊断指征（- 0.34）、多发性硬化症（- 0.20）、神经病变（- 0.13）和化疗后恶心呕吐（- 0.06）。MC使用者的QALY评分低于非使用者（0.44 vs 0.74）。使用1-6张、≥7张MC处方者（截瘫患者除外）的QALY评分低于非使用者，分别为0.11-0.24、0.26-0.32，差异有统计学意义。但需要注意的是，按处方数量分层时，使用人数较少。结论：化疗后伴有多发性硬化症、截瘫、神经病变或恶心和呕吐的患者的健康相关生活质量明显低于成人。在所有诊断指征中，医用大麻使用者的健康相关生活质量也明显低于非使用者。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-

CiteScore

3.60

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.