Analyzing quality of life among people with opioid use disorder from the National Institute on Drug Abuse Data Share initiative: implications for decision making.

Autor: Patton T; Division of Infectious Diseases & Global Public Health, UC San Diego, 9500 Gilman Dr., La Jolla, San Diego, CA, 92093, USA. tepatton@health.ucsd.edu., Boehnke JR; School of Health Sciences, University of Dundee, Nethergate, Dundee, DD1 4HN, UK., Goyal R; Division of Infectious Diseases & Global Public Health, UC San Diego, 9500 Gilman Dr., La Jolla, San Diego, CA, 92093, USA., Manca A; Centre for Health Economics, University of York, Heslington, York, YO10 5DD, UK., Marienfeld C; UC San Diego Health Psychiatry, 8950 Villa La Jolla Drive, La Jolla, CA, 92037, USA., Martin NK; Division of Infectious Diseases & Global Public Health, UC San Diego, 9500 Gilman Dr., La Jolla, San Diego, CA, 92093, USA., Nosyk B; Faculty of Health Sciences, Simon Fraser University, Blusson Hall, Room 11300, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada., Borquez A; Division of Infectious Diseases & Global Public Health, UC San Diego, 9500 Gilman Dr., La Jolla, San Diego, CA, 92093, USA.
Jazyk: angličtina
Zdroj: Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation [Qual Life Res] 2024 Oct; Vol. 33 (10), pp. 2783-2796. Date of Electronic Publication: 2024 Aug 08.
DOI: 10.1007/s11136-024-03729-6
Abstrakt: Purpose: We aimed to estimate health state utility values (HSUVs) for the key health states found in opioid use disorder (OUD) cost-effectiveness models in the published literature.
Methods: Data obtained from six trials representing 1,777 individuals with OUD. We implemented mapping algorithms to harmonize data from different measures of quality of life (the SF-12 Versions 1 and 2 and the EQ-5D-3 L). We performed a regression analysis to quantify the relationship between HSUVs and the following variables: days of extra-medical opioid use in the past 30 days, injecting behaviors, treatment with medications for OUD, HIV status, and age. A secondary analysis explored the impact of opioid withdrawal symptoms.
Results: There were statistically significant reductions in HSUVs associated with extra-medical opioid use (-0.002 (95% CI [-0.003,-0.0001]) to -0.003 (95% CI [-0.005,-0.002]) per additional day of heroin or other opiate use, respectively), drug injecting compared to not injecting (-0.043 (95% CI [-0.079,-0.006])), HIV-positive diagnosis compared to no diagnosis (-0.074 (95% CI [-0.143,-0.005])), and age (-0.001 per year (95% CI [-0.003,-0.0002])). Parameters associated with medications for OUD treatment were not statistically significant after controlling for extra-medical opioid use (0.0131 (95% CI [-0.0479,0.0769])), in line with prior studies. The secondary analysis revealed that withdrawal symptoms are a fundamental driver of HSUVs, with predictions of 0.817 (95% CI [0.768, 0.858]), 0.705 (95% CI [0.607, 0.786]), and 0.367 (95% CI [0.180, 0.575]) for moderate, severe, and worst level of symptoms, respectively.
Conclusion: We observed HSUVs for OUD that were higher than those from previous studies that had been conducted without input from people living with the condition.
(© 2024. The Author(s).)
Databáze: MEDLINE
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