Opioid Policy Model

阿片类药物政策模型

• 批准号: 10552015
• 负责人: GEORGIY BOBASHEV
• 金额: $ 61.64万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552015
• 关键词: Accounting; Address; Affect; Awareness; Buprenorphine; Calibration; Caring; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Communities; Complex; County; Data; Databases; Death Rate; Effectiveness of Interventions; Emergency department visit; Environment; Environmental Risk Factor; Epidemic; Epidemiology; Evidence based intervention; Fentanyl; Geography; Goals; Guidelines; Harm Reduction; Health Professional; Heroin; Improve Access; Individual; Injections; Intervention; Legal; Life; Link; Literature; Measures; Methadone; Methodology; Modality; Modeling; Morbidity - disease rate; Naloxone; Naltrexone; North Carolina; Opioid; Outcome; Overdose; Pathway interactions; Patients; Peer Review; Persons; Pharmaceutical Preparations; Pharmacotherapy; Physicians; Policies; Policy Maker; Population; Population Density; Poverty; Prevention; Privacy; Probability; Public Health; Publishing; Recommendation; Recovery; Resources; Review Literature; Source; Specific qualifier value; Stimulant; Structure; Surveys; System; Time; Uncertainty; United States Dept. of Health and Human Services; Variant; Work; cost; cost effective; cost effective intervention; cost effectiveness; cost estimate; dashboard; drug market; geographic difference; medication-assisted treatment; models and simulation; mortality; multiple data sources; multiple data types; online community; opioid misuse; opioid mortality; opioid overdose; opioid policy; opioid use; overdose death; predicting response; prescription opioid; social interventions; social stigma

PROJECT SUMMARY/ABSTRACT In this study we will develop an agent-based simulation model (ABM) to help policy makers and health professionals in North Carolina identify the best mix of cost-effective interventions to reduce opioid overdoses (ODs) and related deaths. Interventions are identified in the NC Opioid Action Plan and cover the Three Pillars: prevention, connection to care, and harm reduction. Our ABM will represent a community (e.g., a town) of individuals (patients, physicians, dealers, etc.), and simulate how proposed interventions affect individual pathways to opioid misuse and other outcomes (i.e., OD death). The estimation of transition probabilities between the states in these pathways will be based on data from several sources: North Carolina dashboard, national studies, and published literature. The model will rely on a representative synthetic population, which allows multiple data types (e.g. prevention, treatment) to be probabilistically connected in one model. Aim 1. To develop a North Carolina-specific ABM that describes multiple pathways of opioid use in the context of prescription practices, treatment modality and availability, the illegal drug market, prevention policies, and other factors affecting the parameters of the various pathways that lead to OD fatalities. Besides OD deaths, we will investigate multiple other sources of morbidity. We will leverage existing national models and a representative synthetic population to examine spatial (community-level) and temporal (short- and long-term) effects of prevention and treatment interventions on opioid misuse and ODs. We will validate the model on North Carolina data from the past 13 years and will evaluate the sources of prediction uncertainty. Aim 2. To predict the response to the mix of interventions specified in the North Carolina Opioid Action Plan at the local level (e.g., county). The policies include reducing the over prescription of POs, increasing naloxone availability, increasing community awareness, and expanding treatment and recovery care. We will estimate the uncertainty of the forecasts accounting for the changing policy and environmental factors and will refine the model on the basis of new data from the NC DHHS. We will discuss the results with the expert panel and will disseminate data-driven recommendations to North Carolina stakeholders to generate public health impact. Aim 3. To estimate the cost and cost-effectiveness of the key interventions in Aim 2 and compare them with the status quo. For each intervention, we will work with the NC DHHS to estimate costs and cost variation by county characteristics (e.g., population density, poverty). The model will address a significant public health problem and will inform policy on the short- and long-term cost-effectiveness of these interventions.

项目总结/摘要 在这项研究中，我们将开发一个基于代理的仿真模型（ABM），以帮助政策制定者和健康 北卡罗来纳州的专业人员确定了减少阿片类药物的最佳成本效益干预组合 过量用药（ODs）和相关死亡。NC阿片类药物行动计划中确定了干预措施， 三大支柱：预防、与护理的联系和减少伤害。 我们的ABM将代表一个社区（例如，一个城镇）的个人（病人，医生，经销商等），和 模拟拟议的干预措施如何影响阿片类药物滥用的个别途径和其他结果（即， OD死亡）。这些途径中状态之间的跃迁概率的估计将基于 数据来自多个来源：北卡罗来纳州仪表板、国家研究和出版文献。模型 将依赖于一个代表性的合成人口，这允许多种数据类型（例如预防， 治疗）在一个模型中概率相关。 目标1.制定一个北卡罗来纳州特定的ABM，描述阿片类药物使用的多种途径， 处方做法的背景、治疗方式和可用性、非法药物市场、预防 政策，以及影响导致OD死亡的各种途径参数的其他因素。 除了OD死亡，我们还将调查其他多种发病原因。我们将利用现有的 国家模型和有代表性的综合人口，以审查空间（社区一级）和 预防和治疗干预措施对类阿片滥用的暂时（短期和长期）影响 耗氧物质我们将在过去13年的北卡罗来纳州数据上验证该模型，并将评估 预测不确定性的来源。 目标2.预测对北卡罗来纳州阿片类药物行动中规定的干预组合的反应 地方一级的计划（例如，郡）。这些政策包括减少PO的过度处方，增加 纳洛酮的可用性，提高社区意识，扩大治疗和康复护理。我们 我会估计预测的不确定性，包括政策和环境因素的变化 并将根据NC DHHS的新数据完善模型。我们将讨论结果与 专家小组，并将传播数据驱动的建议，以北卡罗来纳州利益相关者，以产生 公共卫生影响。 目标3.估计目标2中关键干预措施的成本和成本效益，并进行比较 与现状。对于每项干预措施，我们将与NC DHHS合作，估算成本和费用 通过县特性的变化（例如，人口密度、贫困）。该模型将解决一个重要的 公共卫生问题，并将告知政策的短期和长期的成本效益，这些 干预措施。