Opioid Policy Model

项目摘要/摘要 在这项研究中，我们将开发一个基于主体的模拟模型(ABM)，以帮助决策者和卫生 北卡罗来纳州的专业人士确定了减少阿片类药物的成本效益干预的最佳组合 服药过量(OD)和相关死亡。干预措施在NC阿片类药物行动计划和覆盖范围内确定 三大支柱：预防、关爱和减少伤害。 我们的ABM将代表个人(患者、医生、经销商等)的社区(例如，城镇)，以及 模拟拟议的干预措施如何影响阿片类药物滥用的个体途径和其他结果(即， 过量死亡)。这些路径中状态之间的转移概率的估计将基于 数据来自几个来源：北卡罗来纳州仪表板、国家研究和出版的文献。模型 将依赖于允许多种数据类型(例如，预防， 治疗)在一个模型中以概率方式连接。 目的1.开发一种北卡罗来纳州特有的ABM，描述阿片类药物在 处方做法、治疗方式和可获得性、非法药物市场、预防 政策，以及影响导致过量服药死亡的各种途径参数的其他因素。 除了过量服药死亡外，我们还将调查其他多种致病原因。我们将利用现有的 国家模型和具有代表性的综合人口，以审查空间(社区一级)和 预防和治疗干预对阿片类药物滥用的短期和长期影响 消耗臭氧层物质。我们将在北卡罗来纳州过去13年的数据上验证该模型，并将评估 预测不确定性的来源。 目的2.预测对北卡罗来纳州阿片类药物行动中规定的混合干预措施的反应 在地方一级(例如，县)进行规划。政策包括减少POS的过度处方，增加 提供纳洛酮，提高社区意识，扩大治疗和康复护理。我们 将考虑到不断变化的政策和环境因素来估计预测的不确定性 并将根据NC DHHS的新数据对模型进行改进。我们将与 并将向北卡罗来纳州的利益相关者传播数据驱动的建议，以生成 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，并对其进行比较 维持现状。对于每一次干预，我们将与NC DHHS合作估计成本和成本 不同县的特点(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，并将就这些措施的短期和长期成本效益向政策提供信息 干预措施。

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.