From the Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA (TYL); Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA (TYL, MSJ); Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY (KMK); Heinz College, Carnegie Mellon University, Pittsburgh, PA (JPC); Massachusetts General Hospital Institute for Technology Assessment, Harvard Medical School, Boston, MA (EJS, MSJ); and Department of Population Health, New York University School of Medicine, New York, NY (MC).
J Addict Med. 2024;18(6):705-710. doi: 10.1097/ADM.0000000000001375. Epub 2024 Sep 2.
The United States faces an ongoing drug overdose crisis, but accurate information on the prevalence of opioid use disorder (OUD) remains limited. A recent analysis by Keyes et al used a multiplier approach with drug poisoning mortality data to estimate OUD prevalence. Although insightful, this approach made stringent and partly inconsistent assumptions in interpreting mortality data, particularly synthetic opioid (SO)-involved and non-opioid-involved mortality. We revise that approach and resulting estimates to resolve inconsistencies and examine several alternative assumptions.
We examine 4 adjustments to Keyes and colleagues' estimation approach: (A) revising how the equations account for SO effects on mortality, (B) incorporating fentanyl prevalence data to inform estimates of SO lethality, (C) using opioid-involved drug poisoning data to estimate a plausible range for OUD prevalence, and (D) adjusting mortality data to account for underreporting of opioid involvement.
Revising the estimation equation and SO lethality effect (adj. A and B) while using Keyes and colleagues' original assumption that people with OUD account for all fatal drug poisonings yields slightly higher estimates, with OUD population reaching 9.3 million in 2016 before declining to 7.6 million by 2019. Using only opioid-involved drug poisoning data (adj. C and D) provides a lower range, peaking at 6.4 million in 2014-2015 and declining to 3.8 million in 2019.
The revised estimation equation presented is feasible and addresses limitations of the earlier method and hence should be used in future estimations. Alternative assumptions around drug poisoning data can also provide a plausible range of estimates for OUD population.
美国正面临持续的药物过量危机,但有关阿片类药物使用障碍(OUD)流行率的准确信息仍然有限。Keyes 等人最近的一项分析使用乘数方法和药物中毒死亡率数据来估计 OUD 的流行率。虽然这种方法具有洞察力,但在解释死亡率数据时,它提出了严格且部分不一致的假设,尤其是涉及合成阿片(SO)和不涉及阿片的死亡率。我们修正了该方法和由此产生的估计,以解决不一致问题,并研究了几种替代假设。
我们检验了对 Keyes 及其同事的估计方法的 4 种调整:(A)修正方程如何解释 SO 对死亡率的影响,(B)纳入芬太尼流行数据以了解 SO 致死率的估计,(C)使用涉及阿片类药物的药物中毒数据来估计 OUD 流行率的合理范围,以及(D)调整死亡率数据以说明阿片类药物参与情况的漏报。
在使用 Keyes 和同事最初的假设,即所有致命药物中毒事件都与 OUD 有关的情况下,修正估计方程和 SO 致死率效应(调整 A 和 B)会得出略高的估计值,2016 年 OUD 人群达到 930 万,到 2019 年降至 760 万。仅使用涉及阿片类药物的药物中毒数据(调整 C 和 D)提供了较低的范围,在 2014-2015 年达到峰值 640 万,到 2019 年降至 380 万。
提出的修正估计方程是可行的,解决了早期方法的局限性,因此应在未来的估计中使用。药物中毒数据的替代假设也可以为 OUD 人群提供合理的估计范围。