Evaluation of the Effectiveness of Buprenorphine-Naloxone on Opioid Overdose and Death among Insured Patients with Opioid Use Disorder in the United States.

Tianyu Sun, Natallia Katenka, Stephen Kogut, Jeffrey Bratberg, Josiah Rich, Ashley Buchanan
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Abstract

Opioid use disorder (OUD) is a chronic disease requiring long-term treatment and is associated with opioid overdose and increased risk of mortality. However, existing randomized clinical trials focused on short-term treatment engagement and detoxification rather than overdose or mortality risk due to limited follow-up time and ethical considerations. We used a hypothetical trial framework to conduct a retrospective cohort study to assess the effectiveness of time-varying buprenorphine-naloxone on opioid overdose and death. We identified 58,835 insured adult patients with OUD diagnosis in the US, 2010-2017. We fit a marginal structural model using inverse probability weighting methods to account for measured baseline and time-varying confounders, as well as selection bias due to possibly differential loss-to-follow-up. We found that receipt of buprenorphine-naloxone was associated with reduced risk of opioid overdose (hazard ratio (HR) = 0.66, 95% confidence interval (CI): 0.49, 0.91), death (HR = 0.24, 95% CI: 0.08, 0.75), and overdose or death (HR = 0.58, 95% CI: 0.40, 0.84). The E-value for death was 7.8, which was larger than the upper 95% CI of the association between each measured baseline variable and all-cause death, which implies that the unmeasured confounding itself may not explain away the estimated effect of treatment on the endpoint of all-cause mortality.

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丁丙诺啡-纳洛酮对美国参保阿片类药物使用障碍患者阿片类药物过量和死亡的疗效评价
阿片类药物使用障碍(OUD)是一种需要长期治疗的慢性病,与阿片类药物过量和死亡风险增加有关。然而,由于随访时间有限和伦理考虑,现有的随机临床试验侧重于短期治疗参与和解毒,而不是过量或死亡风险。我们采用假设的试验框架进行回顾性队列研究,以评估时变丁丙诺啡-纳洛酮对阿片类药物过量和死亡的有效性。2010-2017年,我们在美国确定了58,835名患有OUD诊断的参保成年患者。我们使用逆概率加权方法拟合一个边际结构模型,以考虑测量的基线和时变混杂因素,以及可能由于差异损失而导致的选择偏差。我们发现丁丙诺啡-纳洛酮与阿片类药物过量(HR = 0.66, 95%可信区间(CI): 0.49, 0.91)、死亡(HR = 0.24, 95% CI: 0.08, 0.75)和过量或死亡(HR = 0.58, 95% CI: 0.40, 0.84)的风险降低相关。死亡的e值为7.8,大于每个测量的基线变量与全因死亡之间关联的95% CI上限,这意味着未测量的混杂因素本身可能无法解释治疗对全因死亡率终点的估计影响。
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