Identifying gender differences in risk profiles and in opioid treatment outcomes in Los Angeles County.
Identifying gender differences in risk profiles and in opioid treatment outcomes in Los Angeles County.
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DOI：
10.1016/j.evalprogplan.2023.102240
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发表时间：
2023-04
影响因子：
1.6
通讯作者：
Guerrero, Erick G.
中科院分区：
文献类型：
--
作者：
Amaro, Hortensia;Kong, Yinfei;Marsh, Jeanne C.;Khachikian, Tenie;Guerrero, Erick G.
旨在最大限度缩短阿片类药物使用障碍 (OUD) 治疗等待时间并最大限度提高保留率的政策和计划，应对女性和男性客户风险状况的潜在差异。我们使用重要的个人风险因素进行了多组潜在类别分析。我们的样本包括来自加利福尼亚州洛杉矶县 135 个独特药物使用障碍治疗项目的 13,453 次阿片类药物治疗，分四波：2011 年（66 个项目，1035 名客户）、2013 年（77 个项目、3671 名客户）、2015 年（75 个项目、4625 名客户）和 2017 年（69 个项目，4106 名客户）。面临等待时间较长风险的群体包括女性客户、有心理健康问题、接受 OUD 药物治疗、参与刑事司法、接受强制转介、有儿童接受儿童保护服务以及有看护责任的客户。所有有儿童在保护服务中的客户可能会比没有在保护服务中的客户等待更长时间，但女性等待的时间更长。调查结果强调：(a) 接受 OUD 治疗的女性和男性都存在严重的健康和社会问题； (b) 女性和男性客户具有不同的风险状况； (c) 针对风险状况的有针对性的服务可以改善治疗的获取和参与。研究结果对考虑性别风险因素的卫生政策和计划评估以及提供治疗服务的规划具有影响。
Policies and programs that aim to minimize wait time to enter opioid use disorder (OUD) treatment and maximize retention respond to potential differences in female and male clients’ risk profiles. We conducted multigroup latent class analysis using significant individual risk factors. Our sample included 13,453 opioid treatment episodes from 135 unique substance use disorder treatment programs in Los Angeles County, California, in four waves: 2011 (66 programs, 1035 clients), 2013 (77 programs, 3671 clients), 2015 (75 programs, 4625 clients), and 2017 (69 programs, 4106 clients). Groups at risk of waiting longer included clients who were female, had mental health issues, received medication for OUD, had criminal justice involvement, received mandated referrals, had children in child protective services, and had caretaker responsibilities. All clients with children in protective services were likely to wait longer than those not in protective services, but women waited longer. Findings highlight that: (a) women and men in OUD treatment have significant health and social problems; (b) female and male clients have distinct risk profiles; and (c) targeted services responding to risk profiles may improve treatment access and engagement. Findings have implications for health policy and program evaluation and planning in the delivery of treatment services considering gendered risk factors.
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