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

基本信息

  • 批准号:
    10521949
  • 负责人:
  • 金额:
    $ 70.94万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-30 至 2027-06-30
  • 项目状态:
    未结题

项目摘要

The goal of this project is to prevent and reduce deaths and injuries due to opioids in the United States by determining the best combination of state and local harm reduction and drug paraphernalia laws needed to reduce overdose rates and other opioid-related harms. To do this, we will: 1) conduct original review of laws on relevant harm reduction and drug paraphernalia laws in the 836 municipalities with >50,000 people and associated counties; 2) conduct biannual surveys on implementation of harm reduction laws and drug paraphernalia laws by law enforcement; 3) create an extensive national dataset by merging data on state and local harm reduction and drug paraphernalia laws; implementation of laws by law enforcement; EMS and fatality data; and information on local harm reduction resources, and socioeconomic indicators; 4) use the merged dataset to determine which combinations of state and local laws have resulted in the biggest decreases in overdoses and related harms; and (5) determine which local characteristics enhanced those effective combinations of policies. Overdose deaths in the United States increased more than six-fold since 2001, and now account for more loss of life than high blood pressure, AIDS, and pneumonia. States, cities, and counties are combating this epidemic by passing laws to reduce overdoses, and by investing in access to harm reduction services. But these efforts are often undertaken in isolation and without considering how the different state and local laws interact or how local factors like enforcement of laws and access to harm reduction services influence their effectiveness. This project will help answer those questions by using large data and powerful analytics to bring together all the evidence on this complicated topic. At the end of the project, we will be able to anwer the following questions: What combinations of state and local harm reduction and drug paraphernalia laws most effectively prevent and reduce opioid deaths and injuries in the United States? And how can we best support local efforts to ensure that those effective combinations have the greatest impact?
该项目的目标是预防和减少美国阿片类药物造成的死亡和伤害, 确定州和地方减少危害和药物用具法律的最佳组合, 减少过量服用率和其他阿片类药物相关的危害。为了做到这一点,我们将:1)对有关法律进行原始审查, 在836个人口超过50,000的城市实施相关的减少危害和吸毒用具法, 2)每年两次对减少危害法律和药物管制的执行情况进行调查 3)通过合并关于州和州政府的数据, 当地减少危害和药物用具法;执法部门实施法律; EMS和死亡率 数据;以及关于当地减少危害资源和社会经济指标的信息; 4)使用合并的 数据集,以确定哪些州和地方法律的组合导致了最大的下降, 过量和相关的危害;(5)确定哪些当地特征增强了这些有效的 政策的组合。自2001年以来,美国的过量死亡人数增加了六倍多, 比高血压、艾滋病和肺炎造成的死亡还要多。州、市和县 我们正在通过法律来减少过量用药,并投资于减少伤害的途径, 服务但这些努力往往是孤立进行的,没有考虑到不同的国家和 当地法律相互作用,或当地因素(如执法和获得减少伤害服务)如何影响 他们的有效性。该项目将通过使用大数据和强大的分析来帮助回答这些问题, 把这个复杂话题的所有证据都收集起来。在项目结束时,我们将能够回答 以下问题:州和地方减少危害和毒品用具法律的什么组合最 有效预防和减少美国阿片类药物的死亡和伤害?我们如何才能最好地支持 当地努力确保这些有效的组合产生最大的影响?

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Magdalena Cerda其他文献

Magdalena Cerda的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Magdalena Cerda', 18)}}的其他基金

A comparative evaluation of overdose prevention programs in New York City and Rhode Island
纽约市和罗德岛州药物过量预防计划的比较评估
  • 批准号:
    10629749
  • 财政年份:
    2023
  • 资助金额:
    $ 70.94万
  • 项目类别:
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
  • 批准号:
    10739492
  • 财政年份:
    2023
  • 资助金额:
    $ 70.94万
  • 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:
    $ 70.94万
  • 项目类别:
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:
    $ 70.94万
  • 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:
    $ 70.94万
  • 项目类别:
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:
    $ 70.94万
  • 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:
    $ 70.94万
  • 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:
    $ 70.94万
  • 项目类别:
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:
    10208128
  • 财政年份:
    2019
  • 资助金额:
    $ 70.94万
  • 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:
    10173211
  • 财政年份:
    2019
  • 资助金额:
    $ 70.94万
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了