Developing the evidence base for overdose policies: a multilevel analysis of NHBS

摘要：服药过量(OD)疫情是21世纪美国最严重的危机之一，而注射毒品的人 毒品(PWID)是其震中。消耗臭氧层物质是PWID的主要死亡原因，PWID的数量为 正在崛起。然而，我们经常错过为PWID的干预开发证据基础的机会： 许多州都在颁布法律来对抗阿片类药物的流行(处方药监测方案 [PDMPS]，纳洛酮使用法，好心人法)，以及一个高影响力的研究机构现在正在 分析这些法律和其他卫生政策(医疗补助扩展、医用大麻法律[MML])、 影响普通人群中的过量服用药物。然而，到目前为止，这项研究忽略了PWID，一种非常脆弱的 对这些法律的反应可能与普通民众不同的人群。 越来越多的关于地点特征(例如，社区贫困率)和OD的研究也 被忽视的PWID。然而，以地点为基础的暴露的范围和影响对于PWID可能不同。 NIDA优先考虑产生科学证据来指导政策。在政策制定者面临这场健康危机之际， 然而，他们正在起草法律，几乎没有证据表明它们对PWID的可能影响。同样，缺乏 关于位置特征对PWID中OD的影响的科学证据切断了整个竞技场 以地点为基础的干预措施，已被证明对PWID中的其他健康结果有效。 在风险环境模型和公共卫生法研究原则的指导下，我们将整合疾控中心 全国艾滋病行为监测(NHBS)数据在美国20多个大都市统计地区的约38,800名PWID (MSA)在2009、2012、2015和2018年，利用关于州法律和MSA特征的现有数据， 县，以及这些PWID居住的邮政编码。这个丰富的数据库将使我们能够实现三个目标：目标1： 应用多水平方法分析PDMPS、Medicaid Expansion和MML与自身的关系 在PWID中报告的药物使用、药物过量危险因素和(对于PDMP和医疗补助)药物使用障碍的治疗。 我们将分析关系是否因(A)PWID种族/民族、性别、年龄和艾滋病毒状况而有所不同；以及(B) 邮政编码的特点和PWID居住的县。目的2.应用多水平方法分析 纳洛酮使用法和好心人法与纳洛酮使用和旁观者的关系 对禁食药物的反应，并了解关系是否因(A)PWID种族/族裔、性别、年龄和艾滋病毒状况而异；以及 (B)邮政编码的特点和PWID居住的县。目标3.使用多层次方法分析 PWID邮政编码、县和MSA的特征与自我报告的OD、OD风险因素、 和物质使用障碍的治疗；并测试关系是否因PWID种族/民族、性别、年龄和 艾滋病毒状况。每个目标还将使用多层结构方程模型来分析连接暴露的路径 为结果干杯。结果将提供一些第一批证据，以帮助制定法律和基于地点的干预。 在残疾人中抗击吸毒的行动，这是一个不断增长和被忽视的人口，处于吸毒疫情的中心。

Abstract: The overdose (OD) epidemic is one of the worst crises of the 21st century US, and people who inject drugs (PWID) are at its epicenter. ODs are the leading cause of death for PWID, and the number of PWID is rising. We are, though, routinely missing opportunities to develop the evidence base for interventions for PWID:  Many states are enacting laws to combat the opioid epidemic (Prescription Drug Monitoring Programs [PDMPs], Naloxone Access Laws, Good Samaritan Laws), and a high-impact body of research is now analyzing how these laws, and other health policies (Medicaid Expansion, Medical Marijuana Laws [MMLs]), affect ODs in the general population. To date, though, this research has ignored PWID, a highly vulnerable population that may respond differently to these laws than members of the general population.  The growing body of research on place characteristics (e.g., neighborhood poverty rates) and ODs has also neglected PWID. The range and impact of place-based exposures, however, may differ for PWID. NIDA prioritizes generating scientific evidence to guide policy. As policymakers confront this health crisis, however, they are crafting laws with scant evidence about their possible effects on PWID. Likewise, the lack of scientific evidence about the effects of place characteristics on ODs among PWID cuts off whole arenas of place-based interventions, interventions that have proven effective for other health outcomes among PWID. Guided by the Risk Environment Model and Public Health Law Research principles, we will integrate CDC National HIV Behavioral Surveillance (NHBS) data on ~38,800 PWID in 20+ US metropolitan statistical areas (MSAs) in 2009, 2012, 2015, and 2018 with existing data on state laws and on characteristics of the MSAs, counties, and ZIP codes where these PWID live. This rich database will allow us to achieve three aims: Aim 1: Apply multilevel methods to analyze relationships of PDMPs, Medicaid Expansion, and MMLs to self- reported ODs, OD risk factors, and (for PDMPs and Medicaid) substance use disorder treatment among PWID. We will analyze if relationships vary by (a) PWID race/ethnicity, gender, age, and HIV status; and (b) characteristics of the ZIP codes and counties where PWID live. Aim 2. Apply multilevel methods to analyze relationships of Naloxone Access Laws and Good Samaritan Laws to naloxone access and bystander responses to ODs, and learn if relationships vary by (a) PWID race/ethnicity, gender, age, and HIV status; and (b) characteristics of ZIP codes and counties where PWID live. Aim 3. Use multilevel methods to analyze relationships of characteristics of PWID ZIP codes, counties, and MSAs to self-reported ODs, OD risk factors, and substance use disorder treatment; and test if relationships vary by PWID race/ethnicity, gender, age, and HIV status. Each Aim will also use multilevel structural equation models to analyze pathways linking exposures to outcomes. Results will provide some of the first evidence to help develop laws and place-based interven- tions to combat ODs among PWID, a growing and neglected population at the epicenter of the OD epidemic.