Supplement for Cloud Computing: Opioid Policy Models

云计算的补充：阿片类药物政策模型

基本信息

批准号：
10826888
负责人：
GEORGIY BOBASHEV
金额：
$ 23.64万
依托单位：
RESEARCH TRIANGLE INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10826888
关键词：
Address Administrative Supplement Affect Benchmarking Buprenorphine Calibration Caring Cessation of life Cloud Computing Collaborations Communities Complex Computer software Consumption County Data Science Development Disease Environment Experimental Designs Funding Future Geographic Locations Geography Goals Grant Guidelines Harm Reduction Health Personnel Health Professional Health Services Accessibility Hospitals Individual Insurance Coverage Intervention Law Enforcement Location Modeling Nature North Carolina Opioid Outcome Overdose Paper Pathway interactions Patients Performance Pharmaceutical Preparations Pharmacy facility Physicians Policies Policy Maker Population Positioning Attribute Prevention Price Public Health Public Policy Pythons Research Research Personnel Research Project Grants Research Support Resources Running Rural Rural Appalachia Services Side Speed Testing Time Travel Uncertainty United States United States Dept. of Health and Human Services United States National Institutes of Health Validation access disparities advanced analytics analytical method cloud based computer infrastructure cost cost effective intervention cost effectiveness design effectiveness evaluation experimental study improved interest intervention effect model design models and simulation multiple datasets opioid misuse opioid overdose opioid policy overdose death overdose risk parent grant racial disparity scale up service providers simulation statistics tool urban area

项目摘要

PROJECT SUMMARY/ABSTRACT In our study, Opioid Policy Modeling in North Carolina (5R01DA04799), we developed an agent-based simulation model (ABM) to inform policy makers and health professionals in North Carolina about local and state-wide interventions to reduce opioid overdoses 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 represents a community (e.g., a county, town) of networked individuals (e.g., patients, physicians, dealers), and simulates how proposed interventions affect individual pathways to opioid misuse and other outcomes (i.e., OD death). The model relies on a synthetic population representing every individual in the United States, which allows information from multiple datasets to be probabilistically connected in one model. Synthetic individuals are positioned in geospatial context that includes the locations of hospitals, other treatment facilities, and service providers. Cloud infrastructure will allow us to speed up realistic simulations and consider more scenario options than are considered on a current “in-house” computational infrastructure. It will enable us to develop a meta- model summarizing a multitude of scenarios. Benefits of cloud computing will affect geospatial calibration, validation, and scenario simulations.

项目概要/摘要在我们的研究“北卡罗来纳州阿片类药物政策建模”(5R01DA04799) 中，我们开发了一种基于代理的方法模拟模型 (ABM) 向北卡罗来纳州的政策制定者和卫生专业人员通报当地和 NC 确定了减少阿片类药物过量和相关死亡的全州干预措施。阿片类药物行动计划涵盖三大支柱：预防、护理联系和减少伤害。我们的 ABM 代表一个由网络个人（例如患者、医生、经销商），并模拟拟议的干预措施如何影响阿片类药物滥用和其他该模型依赖于代表每个个体的合成群体。美国，允许将多个数据集的信息概率性地连接到一个数据集中模型中的合成个体被定位在地理空间环境中，其中包括医院的位置，其他处理设施和服务提供商。云基础设施将使我们能够加快现实模拟的速度，并考虑更多的场景选项被考虑在当前的“内部”计算基础设施上，这将使我们能够开发一个元- 总结了多种场景的模型将影响地理空间校准，验证和场景模拟。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

National polydrug use patterns among people who misuse prescription opioids and people who use heroin. Results from the National Household Survey on Drug Use and Health.

滥用处方阿片类药物和海洛因滥用者的全国多种药物使用模式。

DOI：
发表时间：
2022-09-01
期刊：
影响因子：
4.2
作者：
Bobashev, Georgiy V;Warren, Lauren K
通讯作者：
Warren, Lauren K

Trends in Opioid Misuse Among Individuals Aged 12 to 21 Years in the US.

美国 12 至 21 岁人群中阿片类药物滥用的趋势。

DOI：
发表时间：
2023-06-01
期刊：
影响因子：
13.8
作者：
Warren, Lauren Klein;Adams, Joella;Bobashev, Georgiy
通讯作者：
Bobashev, Georgiy

Using Named Entity Recognition to Identify Substances Used in the Self-medication of Opioid Withdrawal: Natural Language Processing Study of Reddit Data.

使用命名实体识别来识别阿片类药物戒断自我治疗中使用的物质：Reddit 数据的自然语言处理研究。

DOI：
发表时间：
2022-03-30
期刊：
影响因子：
2.2
作者：
Preiss, Alexander;Baumgartner, Peter;Edlund, Mark J;Bobashev, Georgiy V
通讯作者：
Bobashev, Georgiy V

Examining buprenorphine diversion through a harm reduction lens: an agent-based modeling study.

通过减少危害的视角检查丁丙诺啡的转移：基于代理的建模研究。

DOI：
发表时间：
2023-10-17
期刊：
影响因子：
4.4
作者：
Adams, Joëlla W;Duprey, Michael;Khan, Sazid;Cance, Jessica;Rice, Donald P;Bobashev, Georgiy
通讯作者：
Bobashev, Georgiy