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迫的国家目标，需要得到最好的支持。 了解预测过量的临床，治疗和卫生系统因素，以及 干预，以减少。对于药物治疗过量幸存的个人，需要更好地 了解影响致死性药物过量风险的因素，包括药物过量后治疗的影响， 减少这一高危人群的死亡人数。为了提供系统性干预信息，迫切需要 国家政策在减少风险和改善结果方面的作用的证据。这些问题尤其 在医疗补助人群中至关重要，这对全国过量死亡人数的贡献不成比例。 为了在这些重要问题上建立证据，我们将使用2001-2019年45个州的医疗补助数据， 国家死亡指数（NDI），辅以定期更新的新泽西州数据， 密苏里州，以确定导致过量风险和过量结局的相互作用因素 幸存者根据目标1，将在国家医疗补助受益人的发病率队列中检查过量风险 持续使用阿片类药物的新发作，以及新诊断为阿片类药物使用障碍（OUD）的患者。到 分析非致命性和致命性过量，我们将链接医疗补助数据与NDI为50，000人， 新的持续使用，和50,000个人与新的OUD诊断，每年的医疗补助数据， 在研究期间，每个队列总共有95万人。在目标2下，我们将研究治疗参与 和非致命性药物治疗过量后的生存率，将医疗补助和NDI数据联系起来， 确定影响过量后治疗和结果的因素。在目标3下，我们将研究 州政策中一项重要的自然实验的影响：2017年颁布的主要新泽西 立法变化包括对阿片类药物处方的新限制;使用疼痛管理合同;以及 扩大阿片类药物使用障碍的药物辅助治疗（MAT）。大幅增加 MAT和其他OUD治疗的医疗补助率同时实施。使用分析模型 与目标1和目标2相似，我们将研究新的长期- 长期阿片类药物使用，以及在新的OUD诊断中，在实施新的 立法，相对于没有实施类似政策的国家的比较人口。 这些分析将促进我们对阿片类药物风险和结果的基本理解， 医疗补助人群具有其独特的人口统计学特征和临床挑战。通过接地 干预政策，更全面地了解过量的风险，和复杂的交叉点， 残疾、多种发病率和治疗服务，其中包含过量事件，结果可以 改变过量预防的模式，以更好地了解这些事件 和经历这些的个体。