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

AI Article Synopsis

  • - The study aimed to estimate health state utility values (HSUVs) for opioid use disorder (OUD) by analyzing data from 1,777 individuals, using various quality of life measures.
  • - Significant reductions in HSUVs were linked to factors like extra-medical opioid use, injecting behaviors, having HIV, and age, while the impact of OUD treatment medications was not statistically significant.
  • - The findings indicated that withdrawal symptoms significantly impacted HSUVs, with higher symptom severity leading to lower utility values, highlighting the importance of considering patient input in this type of research.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452457PMC
http://dx.doi.org/10.1007/s11136-024-03729-6DOI Listing

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