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一种用于估计美国注射毒品使用和相关感染综合征对健康结果影响的多病模型。

A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States.

机构信息

Medical Practice Evaluation Center and Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.

Harvard Medical School, Boston, MA, USA.

出版信息

BMC Health Serv Res. 2023 Jul 17;23(1):760. doi: 10.1186/s12913-023-09773-1.

Abstract

BACKGROUND

Fatal drug overdoses and serious injection-related infections are rising in the US. Multiple concurrent infections in people who inject drugs (PWID) exacerbate poor health outcomes, but little is known about how the synergy among infections compounds clinical outcomes and costs. Injection drug use (IDU) converges multiple epidemics into a syndemic in the US, including opioid use and HIV. Estimated rates of new injection-related infections in the US are limited due to widely varying estimates of the number of PWID in the US, and in the absence of clinical trials and nationally representative longitudinal observational studies of PWID, simulation models provide important insights to policymakers for informed decisions.

METHODS

We developed and validated a MultimorbiditY model to Reduce Infections Associated with Drug use (MYRIAD). This microsimulation model of drug use and associated infections (HIV, hepatitis C virus [HCV], and severe bacterial infections) uses inputs derived from published data to estimate national level trends in the US. We used Latin hypercube sampling to calibrate model output against published data from 2015 to 2019 for fatal opioid overdose rates. We internally validated the model for HIV and HCV incidence and bacterial infection hospitalization rates among PWID. We identified best fitting parameter sets that met pre-established goodness-of-fit targets using the Pearson's chi-square test. We externally validated the model by comparing model output to published fatal opioid overdose rates from 2020.

RESULTS

Out of 100 sample parameter sets for opioid use, the model produced 3 sets with well-fitting results to key calibration targets for fatal opioid overdose rates with Pearson's chi-square test ranging from 1.56E-5 to 2.65E-5, and 2 sets that met validation targets. The model produced well-fitting results within validation targets for HIV and HCV incidence and serious bacterial infection hospitalization rates. From 2015 to 2019, the model estimated 120,000 injection-related overdose deaths, 17,000 new HIV infections, and 144,000 new HCV infections among PWID.

CONCLUSIONS

This multimorbidity microsimulation model, populated with data from national surveillance data and published literature, accurately replicated fatal opioid overdose, incidence of HIV and HCV, and serious bacterial infections hospitalization rates. The MYRIAD model of IDU could be an important tool to assess clinical and economic outcomes related to IDU behavior and infections with serious morbidity and mortality for PWID.

摘要

背景

在美国,致命的药物过量和严重的注射相关感染正在上升。注射毒品者(PWID)中的多种并发感染使健康状况恶化,但人们对感染之间的协同作用如何使临床结果和成本恶化知之甚少。在美国,注射药物使用(IDU)将多种流行疫情汇聚成一种综合流行疫情,包括阿片类药物使用和艾滋病毒。由于美国 PWID 的数量存在广泛差异,因此美国新的注射相关感染的估计率受到限制,而且由于缺乏 PWID 的临床试验和全国代表性纵向观察研究,模拟模型为决策者提供了重要的见解,以便做出明智的决策。

方法

我们开发并验证了一种用于减少与药物使用相关感染的多病态模型(MultimorbiditY model to Reduce Infections Associated with Drug use,MYRIAD)。该药物使用和相关感染(艾滋病毒、丙型肝炎病毒[HCV]和严重细菌感染)的微观模拟模型使用来自已发表数据的输入来估计美国的国家趋势。我们使用拉丁超立方抽样来根据 2015 年至 2019 年致命阿片类药物过量率的已发表数据对模型输出进行校准。我们对内验证了该模型在 PWID 中 HIV 和 HCV 发病率和细菌感染住院率方面的准确性。我们使用 Pearson's chi-square 检验确定了满足预先设定的拟合度目标的最佳拟合参数集。我们通过将模型输出与 2020 年已发表的致命阿片类药物过量率进行比较来对外验证模型。

结果

在阿片类药物使用的 100 个样本参数集中,有 3 个参数集通过 Pearson's chi-square 检验得到了很好的拟合结果,其致命阿片类药物过量率的关键校准目标范围为 1.56E-5 至 2.65E-5,有 2 个参数集满足了验证目标。该模型在 HIV 和 HCV 发病率和严重细菌感染住院率的验证目标内产生了很好的拟合结果。2015 年至 2019 年,该模型估计有 12 万名 PWID 因注射相关药物过量而死亡,1.7 万名新感染艾滋病毒,14.4 万名新感染 HCV。

结论

该多病态微观模拟模型利用来自国家监测数据和已发表文献的数据进行了填充,准确复制了致命阿片类药物过量、艾滋病毒和 HCV 发病率以及严重细菌感染住院率。IDU 的 MYRIAD 模型可以成为一种重要工具,用于评估与 IDU 行为相关的临床和经济结果以及 PWID 的严重发病率和死亡率感染。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c763/10353126/2a56f1960c67/12913_2023_9773_Figa_HTML.jpg

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