Social and behavioral determinants of MOUD utilization and opioid overdose

MMOD使用和阿片类药物过量的社会和行为决定因素 在美国，每天有超过192人死于阿片类药物过量(OOD)，这是意外死亡的主要原因 在成年人中。退伍军人尤其容易患OOD，他们的发病率是非退伍军人的两倍。 由于他们阿片类药物使用障碍(OUD)的患病率较高。对Moud(Moud)的药物治疗是金 阿片使用障碍的标准治疗(OUD)。然而，在退伍军人健康管理局 在退伍军人事务部(退伍军人事务部)和非退伍军人事务部医院，缺乏获得Moud护理的机会。复利 很难获得，许多接受丁丙诺啡治疗的患者在1年内停止治疗，这是一个不理想的指标 治疗滞留。 健康的社会决定因素是人们出生、生活、工作和年龄的条件。不利的社会 健康的决定因素包括工作不安全、住房不安全、财务不安全、粮食不安全以及 法律、社会/家庭、交通和暴力问题。加上不良的行为健康因素 如物质使用和精神健康障碍(如严重抑郁症)、不良社会和行为 健康决定因素(SBDH)与较差的健康相关，并且同时存在不良SBDH可能有 加重了对健康的负面影响。这项建议的首要目标是了解SBDH， 影响Moud获取和结果的生物变量、临床环境和临床因素以及 开发Moud结果的预测模型。

Social and behavioral determinants of MOUD utilization and opioid overdose In the US, over 192 people die each day due to opioid overdose (OOD), the leading cause of accidental death among adults. Veterans are especially vulnerable to OOD, experiencing rates double those of non-Veterans, due to their higher prevalence of opioid use disorder (OUD). Medication treatment for OUD (MOUD) is the gold standard treatment for opioid use disorder (OUD). However, in the Veterans Health Administration of the Department of Veterans Affairs (VA) and non-VA hospitals, access to MOUD care is lacking. Compounding poor access, many patients who receive buprenorphine discontinue within 1 year, an indicator of suboptimal treatment retention. Social determinants of health are the conditions in which people are born, live, work, and age. Adverse social determinants of health include job insecurity, housing insecurity, financial insecurity, food insecurity, as well as legal, social/familial, transportation, and violence problems. Together with adverse behavioral health factors such as substance use and mental health disorders (e.g., major depression), adverse social and behavioral determinants of health (SBDH) are associated with poorer health, and concurrent adverse SBDH can have compounding negative health consequences. The overarching goal of this proposal is to understand SBDH, biological variables, clinical settings and clinical factors that impact MOUD access and outcomes and to develop predictive models for MOUD outcomes.