Edgcomb Juliet Beni, Tseng Chi-Hong, Klomhaus Alexandra M, Seroussi Ariel, Heldt Jonathan P, Ponce Chrislie G, Perez Liliana, Lee Joshua J, Zima Bonnie T
University of California, Los Angeles, Los Angeles, California.
JAACAP Open. 2024 Sep 18;3(3):689-700. doi: 10.1016/j.jaacop.2024.09.001. eCollection 2025 Sep.
To examine the prevalence and correlates of child involuntary mental health detentions through evaluation of legal documentation embedded in medical records and children's electronic health information.
Medical records were analyzed from 3,440 children ages 10 to 17 years with MH-related emergency department visits in a large academic health system over 2 years (2017-2019). Bivariate analyses and random forests were deployed to identify child-, neighborhood-, and systems-level correlates of involuntary MH detentions.
Nearly 1 in 4 (n = 769, 22.4%) visits involved an involuntary detention. Half of detained children (n = 357, 46.4%) arrived on a detainment that was discontinued after MH provider evaluation. Odds of detention were greater among Black (odds ratio 1.33 [95% CI 1.02-1.73]) and publicly insured (odds ratio 1.63 [95% CI 1.37-1.94]) children. Children detained in prehospital settings resided in census tracts with greater social vulnerability scores (χ 13.42, < .001). Machine learning classifiers (area under the curve 0.83, [95% CI 0.81-0.84]) revealed that strongest indicators of detainment included psychiatric chief concern, prior year psychiatric hospitalization, Social Vulnerability Index, and code for suicide or self-harm.
Medical record-embedded legal documentation supports transparency in the use of detentions, which are common and jointly predicted by children's clinical need and social vulnerability.
通过评估病历和儿童电子健康信息中嵌入的法律文件,研究儿童非自愿心理健康拘留的患病率及其相关因素。
对一个大型学术健康系统中2017 - 2019年期间因心理健康问题到急诊科就诊的3440名10至17岁儿童的病历进行分析。采用双变量分析和随机森林方法来确定非自愿心理健康拘留的儿童、社区和系统层面的相关因素。
近四分之一(n = 769,22.4%)的就诊涉及非自愿拘留。被拘留儿童中有一半(n = 357,46.4%)在心理健康服务提供者评估后被取消拘留。黑人儿童(优势比1.33 [95%置信区间1.02 - 1.73])和参加公共保险的儿童(优势比1.63 [95%置信区间1.37 - 1.94])被拘留的几率更高。在院前环境中被拘留的儿童居住在社会脆弱性得分较高的普查区(χ² = 13.42,P <.001)。机器学习分类器(曲线下面积0.83,[95%置信区间0.81 - 0.84])显示,拘留的最强指标包括精神科主要关注点、上一年的精神科住院治疗、社会脆弱性指数以及自杀或自残代码。
病历中嵌入的法律文件有助于提高拘留使用的透明度,拘留情况很常见,且由儿童的临床需求和社会脆弱性共同预测。