Understanding the short- and long-term effects of the COVID-19 pandemic on the overdose crisis
了解 COVID-19 大流行对药物过量危机的短期和长期影响
基本信息
- 批准号:10739492
- 负责人:
- 金额:$ 103.88万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-30 至 2028-07-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAdoptionAdverse drug effectAffectBlack PopulationsBlack raceBuffersCOVID-19COVID-19 impactCOVID-19 pandemicCOVID-19 pandemic effectsCessation of lifeCharacteristicsCommunitiesConflict (Psychology)ConsumptionContainmentCountryCountyDiagnosisDisastersDrug usageDrug userEconomic PolicyEconomically Deprived PopulationEconomicsEthnic OriginEventFentanylFutureHarm ReductionHealthHealth ServicesHealth Services AccessibilityHeterogeneityHispanic PopulationsHomeHospitalizationHousingIncidenceIncomeIndividualInequalityInequityInvestigationKnowledgeMeasuresMediatingMedicaidMethadoneModelingNaloxoneNatural DisastersOutcomeOverdosePathway interactionsPatientsPatternPharmaceutical PreparationsPoliciesPoliticsPopulationPovertyPrevalencePreventionPublic HealthPublic PolicyRaceRelapseResearchResearch DesignRiskRoleSARS-CoV-2 infectionServicesSocial PoliciesSocial isolationSourceStructureSubgroupTestingUnemploymentUrban CommunityVulnerable PopulationsWagesWingWorkplaceagedclimate changecohortdrug marketexperiencefuture pandemicimprovedinsightlong term consequences of COVID-19manmass shootingmortalityopioid use disorderoverdose riskpandemic diseasepandemic impactpreventprogramsresidential segregationresponsesocialsocial disparitiestheorieswaiver
项目摘要
Overdose has risen sharply during the COVID-19 pandemic, highlighting an urgent need to understand the
impact of disasters on overdose. We need research to identify the types of policy measures that can prevent a
similar increase in overdose in future disasters. We also need to understand why certain communities are
particularly vulnerable to experiencing rises in overdose during and after a disaster, so that we can optimally
target prevention and disaster response efforts. We propose to use Big Events Theory as a framework to: 1)
study the COVID-19 pandemic and its effects on overdose and related outcomes; 2) identify policy responses
to COVID-19 that affected individual-level overdose risk; and 3) examine how the pandemic's impact on
overdose risk varied across communities and populations. We hypothesize that COVID-19 containment
policies (e.g., workplace closings) contributed to social isolation, increasing the risk of consuming drugs alone
and limiting access to treatment and harm reduction services, thus increasing overdose risk. In contrast, public
health (e.g., take-home methadone waivers) and economic support policy responses to COVID-19 (e.g.,
housing eviction moratoria) may have eased access to health services for people who use drugs (PWUD) and
alleviated economic difficulties arising from the pandemic, blocking health and economic pathways through
which the pandemic could increase overdose risk. We hypothesize that communities with more structural
sources of despair (e.g., high rates of unemployment, poverty), and those that concurrently experienced other
major societal crises (natural disasters, political conflict, mass shootings) were more vulnerable to the effects
of COVID-19 on overdose. In contrast, social and economic policies enacted before the pandemic to protect
vulnerable populations (e.g., higher state Medicaid income threshold) may have reduced the impact of the
pandemic on overdose risk. Our Aims are to: (1) Determine how individual overdose risk in localities
(overall and by race/ethnicity) changed over 2019–2025 after county-level elevations in COVID-19 health
burden (hospitalizations, deaths). (2) Determine which county- and state-level policies targeting COVID-19
infection containment, public health of PWUDs, and economic support during the pandemic mediated
relations between changes in county-level COVID-19 burden and changes in overdose incidence, overall and
by race/ethnicity. (3) Identify which community conditions affected the strength of the relationships
between changes in county-level COVID-19 burden and individual overdose risk, overall and by race/ethnicity,
including: a) pre-existing community structural characteristics (e.g., poverty rate); b) pre-existing policies to
protect vulnerable populations (e.g., state Medicaid program income threshold); and c) intersecting crises. We
will build a multi-center cohort of ~2.4 million patients aged 18+ in six PCORnet® networks across the country
to track the impact of community COVID-19 burden on individual overdose risk. Our study findings will inform
the response to the current overdose crisis and to future disasters.
在COVID-19大流行期间,过量用药急剧增加,突显出迫切需要了解
灾难对过量的影响。我们需要进行研究,以确定可以防止
在未来的灾难中,过量服用药物的人数也会增加。我们还需要了解为什么某些社区
特别容易在灾难期间和灾难之后经历过量的上升,因此我们可以最佳地
有针对性地开展预防和救灾工作。我们建议使用大事件理论作为框架:1)
研究COVID-19大流行及其对过量用药和相关结果的影响; 2)确定政策应对措施
对COVID-19的影响,影响个人层面的过量风险;以及3)研究大流行如何影响
过量用药风险因社区和人群而异。我们假设COVID-19的控制
策略(例如,工作场所关闭)造成了社会孤立,增加了单独消费毒品的风险
以及限制获得治疗和减少伤害服务,从而增加了过量用药的风险。相反,公共
健康(例如,带回家的美沙酮豁免)和经济支持COVID-19的政策反应(例如,
暂停住房驱逐)可能会使吸毒者获得医疗服务变得容易,
缓解了大流行带来的经济困难,阻碍了健康和经济途径,
大流行可能会增加过量服用的风险。我们假设,结构性更强的社区
绝望的来源(例如,高失业率,贫困),以及那些同时经历其他
重大社会危机(自然灾害、政治冲突、大规模枪击事件)更容易受到影响
死于COVID-19相比之下,在大流行之前制定的社会和经济政策是为了保护
弱势群体(例如,更高的州医疗补助收入门槛)可能会减少
大流行的过量风险。我们的目的是:(1)确定个人过量的风险,在地方
2019-2025年,COVID-19健康状况县级升高后,
负担(住院、死亡)。(2)确定针对COVID-19的县级和州级政策
传染控制、残疾人的公共卫生和大流行期间的经济支持
县级COVID-19负担变化与用药过量发生率变化之间的关系,总体和
种族/民族。(3)确定哪些社区条件影响了关系的强度
县级COVID-19负担和个体用药过量风险的变化之间的关系,总体和按种族/民族,
包括:a)预先存在的社区结构特征(例如,(B)现有政策,
保护弱势群体(例如,州医疗补助计划收入门槛);和c)交叉危机。我们
将在全国六个PCORnet®网络中建立一个约240万名18岁以上患者的多中心队列
追踪社区COVID-19负担对个人过量风险的影响。我们的研究结果将告诉
对当前过量用药危机和未来灾难的反应。
项目成果
期刊论文数量(0)
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会议论文数量(0)
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Magdalena Cerda其他文献
Magdalena Cerda的其他文献
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{{ truncateString('Magdalena Cerda', 18)}}的其他基金
A comparative evaluation of overdose prevention programs in New York City and Rhode Island
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- 批准号:
10629749 - 财政年份:2023
- 资助金额:
$ 103.88万 - 项目类别:
Large Data Spatiotemporal Modeling of Optimal Combinations of Interventions to Reduce Opioid Harm in the United States
美国减少阿片类药物危害的最佳干预措施组合的大数据时空建模
- 批准号:
10708823 - 财政年份:2022
- 资助金额:
$ 103.88万 - 项目类别:
Large Data Spatiotemporal Modeling of Optimal Combinations of Interventions to Reduce Opioid Harm in the United States
美国减少阿片类药物危害的最佳干预措施组合的大数据时空建模
- 批准号:
10521949 - 财政年份:2022
- 资助金额:
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Examining the synergistic effects of cannabis and prescription opioid policies on chronic pain, opioid prescribing, and opioid overdose
检查大麻和处方阿片类药物政策对慢性疼痛、阿片类药物处方和阿片类药物过量的协同作用
- 批准号:
10055772 - 财政年份:2019
- 资助金额:
$ 103.88万 - 项目类别:
Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial
使用预测分析降低药物相关死亡率:一项随机、全州范围的社区干预试验
- 批准号:
10026087 - 财政年份:2019
- 资助金额:
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Examining the synergistic effects of cannabis and prescription opioid policies on chronic pain, opioid prescribing, and opioid overdose
检查大麻和处方阿片类药物政策对慢性疼痛、阿片类药物处方和阿片类药物过量的协同作用
- 批准号:
9987897 - 财政年份:2019
- 资助金额:
$ 103.88万 - 项目类别:
Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial
使用预测分析降低药物相关死亡率:一项随机、全州范围的社区干预试验
- 批准号:
10220922 - 财政年份:2019
- 资助金额:
$ 103.88万 - 项目类别:
Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial
使用预测分析降低药物相关死亡率:一项随机、全州范围的社区干预试验
- 批准号:
9817054 - 财政年份:2019
- 资助金额:
$ 103.88万 - 项目类别:
Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial
使用预测分析降低药物相关死亡率:一项随机、全州范围的社区干预试验
- 批准号:
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- 资助金额:
$ 103.88万 - 项目类别:
Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial
使用预测分析降低药物相关死亡率:一项随机、全州范围的社区干预试验
- 批准号:
10554963 - 财政年份:2019
- 资助金额:
$ 103.88万 - 项目类别:
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