Opioid Policy Model
阿片类药物政策模型
基本信息
- 批准号:10347344
- 负责人:
- 金额:$ 63.09万
- 依托单位:
- 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 2025-01-31
- 项目状态:未结题
- 来源:
- 关键词:AccountingAddressAffectAwarenessBuprenorphineCaringCenters for Disease Control and Prevention (U.S.)Cessation of lifeCharacteristicsCommunitiesComplexCountyDataDatabasesDeath RateEffectiveness of InterventionsEmergency department visitEnvironmentEnvironmental Risk FactorEpidemicEpidemiologyEvidence based interventionFentanylGeographyGoalsGuidelinesHarm ReductionHealth ProfessionalHeroinImprove AccessIndividualInjectionsInterventionLeadLegalLifeLinkLiteratureLong-Term EffectsMeasuresMethadoneMethodologyModalityModelingMorbidity - disease rateNaloxoneNaltrexoneNorth CarolinaOpioidOutcomeOverdosePathway interactionsPeer ReviewPersonsPharmaceutical PreparationsPharmacotherapyPhysiciansPoliciesPolicy MakerPopulationPopulation DensityPovertyPreventionPrivacyProbabilityPublic HealthPublishingRecommendationRecoveryResourcesReview LiteratureSourceSpecific qualifier valueStimulantStructureSurveysSystemTimeUncertaintyUnited States Dept. of Health and Human ServicesVariantWorkbasecostcost effectivecost effective interventioncost effectivenesscost estimatedashboarddrug marketgeographic differenceindividual patientmedication-assisted treatmentmodels and simulationmortalitymultiple data sourcesmultiple data typesonline communityopioid misuseopioid mortalityopioid overdoseopioid policyopioid useoverdose deathpredicting responseprescription opioidsocial interventionssocial stigma
项目摘要
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.
项目摘要/摘要
在这项研究中,我们将开发基于代理的仿真模型(ABM),以帮助制定者和健康
北卡罗来纳州的专业人士确定了最佳具有成本效益的干预措施,以减少阿片类药物
过量服用(OD)和相关死亡。在NC阿片类动作计划中确定了干预措施,并掩盖
这三个支柱:预防,与护理的联系和减少伤害。
我们的ABM将代表一个个人(例如,患者,医师,经销商等)的社区(例如一个城镇),
模拟建议的干预措施如何影响阿片类药物滥用和其他结果的个人途径(即
OD死亡)。这些途径中各州之间的过渡概率的估计将基于
来自多个来源的数据:北卡罗来纳州仪表板,国家研究和出版文献。模型
将依靠代表性的合成人群,该人口允许多种数据类型(例如,预防,
处理)将在一个模型中概率地连接。
目的1。开发北卡罗来纳州特定的ABM,描述了阿片类药物使用的多种途径
处方惯例,治疗方式和可用性,非法药物市场,预防的背景
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,我们还将研究其他多种发病源。我们将利用现有的
国家模型和代表性的合成人群,以检查空间(社区级)和
预防和治疗干预措施对阿片类药物滥用的时间(短期和长期)影响
ODS。我们将在过去13年中验证北卡罗来纳州数据的模型,并将评估
预测不确定性的来源。
目标2。预测北卡罗来纳州阿片类药物中指定的干预措施的响应
在地方一级计划(例如县)。政策包括减少POS的过度处方,增加
纳洛酮的可用性,提高社区意识以及扩大治疗和康复护理。我们
将估计预测的不确定性,以说明不断变化的政策和环境因素
并将根据NC DHHS的新数据来完善模型。我们将与
专家小组并将向北卡罗来纳州利益相关者传播数据驱动的建议以产生
公共卫生影响。
目标3。估计AIM 2中关键干预措施的成本和成本效益并进行比较
带有现状。对于每种干预措施,我们将与NC DHHS合作,以估算成本和成本
县特征的变化(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,并将为这些问题的短期和长期成本效益提供信息
干预措施。
项目成果
期刊论文数量(0)
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专利数量(0)
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{{ truncateString('GEORGIY BOBASHEV', 18)}}的其他基金
Supplement for Cloud Computing: Opioid Policy Models
云计算的补充:阿片类药物政策模型
- 批准号:
10826888 - 财政年份:2020
- 资助金额:
$ 63.09万 - 项目类别:
Systems Approach to Modeling of Drug Use Recovery
药物使用回收建模的系统方法
- 批准号:
8224973 - 财政年份:2012
- 资助金额:
$ 63.09万 - 项目类别:
Systems Approach to Modeling of Drug Use Recovery
药物使用回收建模的系统方法
- 批准号:
8416409 - 财政年份:2012
- 资助金额:
$ 63.09万 - 项目类别:
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