Opioid Overdoses among Medicaid Beneficiaries: Predictors, Outcomes, and State Policy Effects

医疗补助受益人中阿片类药物过量：预测因素、结果和国家政策影响

• 批准号: 10348125
• 负责人: Stephen Crystal
• 金额: $ 79.29万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10348125
• 关键词: Address; Adult; Affect; Alcohols; Benzodiazepines; Caring; Cause of Death; Cessation of life; Characteristics; Clinical; Clinical Management; Clinical Treatment; Communities; Complex; Contracts; Data; Diagnosis; Disease; Event; Goals; Health Policy; Health system; Healthcare Systems; Incidence; Individual; Intervention; Knowledge; Link; Medicaid; Medical; Mental Health; Missouri; Modeling; Natural experiment; New Jersey; Opiate Addiction; Opioid; Outcome; Overdose; Pain management; Patients; Pattern; Play; Policies; Polypharmacy; Population; Predictive Factor; Public Health; Risk; Risk Reduction; Role; Spottings; Statutes and Laws; Substance abuse problem; Suicide; Survivors; System; Treatment outcome; Update; Variant; beneficiary; clinical risk; cohort; comorbidity; disability; dosage; experience; experimental study; heroin use; high risk; high risk population; improved; improved outcome; indexing; medication-assisted treatment; multiple chronic conditions; opioid abuse; opioid epidemic; opioid overdose; opioid use; opioid use disorder; outcome prediction; overdose prevention; overdose risk; prescription opioid; programs; respiratory; response; success; systemic intervention; treatment disparity; treatment services

Reducing opioid overdose is an urgent national goal that needs to be supported with the best available understanding of the clinical, treatment, and health system factors that predict overdoses, and points of intervention for their reduction. For individuals surviving medically treated overdoses, there is a need to better understand the factors affecting risk of fatal overdose, including effects of post-overdose treatment, in order to reduce the death toll in this high-risk population. To inform systemic intervention, there is an urgent need for evidence on the role of state policies in reducing risk and improving outcomes. These issues are particularly vital in the Medicaid population, which contributes disproportionately to the national overdose toll. To build evidence on these vital issues, we will use 45-state Medicaid data from 2001-2019, linked to the National Death Index (NDI), supplemented with regularly updated state data from New Jersey and Missouri, to identify the interacting factors contributing to overdose risk and to outcomes for overdose survivors. Under Aim 1, overdose risk will be examined in national incidence cohorts of Medicaid beneficiaries with new episodes of persistent opioid use, and those with new diagnoses of opioid use disorder (OUD). To analyze both non-fatal and fatal overdoses, we will link Medicaid data with the NDI for 50,000 individuals with new persistent use, and 50,000 individuals with new OUD diagnoses, for each year of the Medicaid data, totaling 950,000 in each cohort over the study period. Under Aim 2, we will examine treatment engagement and survival following non-fatal medically treated overdoses, linking Medicaid and NDI data on these overdoses to identify factors affecting post-overdose treatment and outcomes. Under Aim 3, we will examine the impact of an important natural experiment in state policy: the 2017 enactment of major New Jersey legislation. Changes included new limitations on opioid prescribing; use of pain management contracts; and expanded access to medication assisted treatment (MAT) for opioid use disorder. Substantial increases in Medicaid rates for MAT and other OUD treatment were implemented concurrently. Using analytic models similar to those in Aims 1 and 2, we will examine changes in overdose risk among individuals with new long- term opioid use, and among those with new OUD diagnoses, before and after the implementation of the new legislation, relative to comparison populations in states that did not implement similar policies. These analyses will advance our fundamental understanding of opioid risks and outcomes in the Medicaid population with its distinctive demographic characteristics and clinical challenges. By grounding intervention policy in a more comprehensive understanding of overdose risk, and the complex intersection of disability, multi-morbidity, and treatment services within which overdose events are embedded, results can change the paradigm of overdose prevention to one that is informed by a better understanding of these events and the individuals experiencing them.

减少阿片类药物过量是一个紧急的国家目标，需要提供最好的可用 了解预测过量药物的临床，治疗和卫生系统因素， 减少干预。对于幸存的医学治疗过量的人，有必要改善 了解影响致命过量风险的因素，包括过量后治疗的影响，以便 减少这种高风险人群的死亡人数。为了告知系统性干预，迫切需要 关于国家政策在降低风险和改善预后的作用的证据。这些问题特别是 在医疗补助人群中至关重要，这对国家过量造成过量造成了不成比例的贡献。 为了建立有关这些重要问题的证据，我们将使用2001 - 2019年的45个国家医疗补助数据与 国家死亡指数（NDI），并补充了新泽西州定期更新的状态数据 密苏里州，确定导致过量风险和过量结果的相互作用因素 幸存者。在AIM 1下，将在医疗补助受益人的国家发病率中检查过量风险 随着持续使用阿片类药物的新发作，以及那些具有新诊断的阿片类药物使用障碍（OUD）的事件。到 分析非致命性和致命性过量，我们将与NDI联系50,000名患者的医疗补助数据 新的持续使用，每年医疗补助数据的每一年都有新的OUD诊断的人， 在研究期间，每个队列中总计950,000。在AIM 2下，我们将检查治疗参与 以及非致命性医学治疗过量服药后的生存，将医疗补助和NDI数据联系起来 过量服用以识别影响过量后治疗和结果的因素。在AIM 3下，我们将检查 重要的自然实验在国家政策中的影响：2017年新泽西州主要的制定 立法。更改包括对阿片类药物处方的新限制；使用疼痛管理合同；和 扩大了用于阿片类药物使用障碍的药物辅助治疗（MAT）的机会。大量增加 同时实施了MAT和其他OUD治疗的医疗补助率。使用分析模型 与目标1和2中的相似，我们将检查具有新长期长期的个体中过量风险的变化 术语阿片类药物使用，以及在实施新的OUD诊断的术语 立法，相对于没有执行类似政策的州的比较人口。 这些分析将提高我们对阿片类药物风险和结果的基本理解 医疗补助人口具有独特的人口特征和临床挑战。通过接地 干预政策在对过量风险的更全面理解中，以及复杂的交集 嵌入过量事件的残疾，多元车和治疗服务，结果可以 将预防过量的范式更改为通过对这些事件的更好理解所告知的范式 和经历他们的个人。