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/文摘