Opioid Policy Model
阿片类药物政策模型
基本信息
- 批准号:10552015
- 负责人:
- 金额:$ 61.64万
- 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:AccountingAddressAffectAwarenessBuprenorphineCalibrationCaringCenters for Disease Control and Prevention (U.S.)Cessation of lifeCharacteristicsCommunitiesComplexCountyDataDatabasesDeath RateEffectiveness of InterventionsEmergency department visitEnvironmentEnvironmental Risk FactorEpidemicEpidemiologyEvidence based interventionFentanylGeographyGoalsGuidelinesHarm ReductionHealth ProfessionalHeroinImprove AccessIndividualInjectionsInterventionLegalLifeLinkLiteratureMeasuresMethadoneMethodologyModalityModelingMorbidity - disease rateNaloxoneNaltrexoneNorth CarolinaOpioidOutcomeOverdosePathway interactionsPatientsPeer ReviewPersonsPharmaceutical PreparationsPharmacotherapyPhysiciansPoliciesPolicy MakerPopulationPopulation DensityPovertyPreventionPrivacyProbabilityPublic HealthPublishingRecommendationRecoveryResourcesReview LiteratureSourceSpecific qualifier valueStimulantStructureSurveysSystemTimeUncertaintyUnited States Dept. of Health and Human ServicesVariantWorkcostcost effectivecost effective interventioncost effectivenesscost estimatedashboarddrug marketgeographic differencemedication-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.
项目总结/文摘
项目成果
期刊论文数量(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:
$ 61.64万 - 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:
$ 61.64万 - 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:
$ 61.64万 - 项目类别:
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