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住院与出院蓄意自伤患者的自杀风险:使用大型理赔数据集的广义随机森林分析。

Suicide Risk Among Hospitalized Versus Discharged Deliberate Self-Harm Patients: Generalized Random Forest Analysis Using a Large Claims Data Set.

机构信息

Department of Public Health, School of Social Sciences, Humanities, and Arts, University of California Merced, Merced, California.

Department of Applied Mathematics, School of Natural Sciences, University of California Merced, Merced, California.

出版信息

Am J Prev Med. 2022 Apr;62(4):558-566. doi: 10.1016/j.amepre.2021.08.028. Epub 2021 Nov 19.

Abstract

INTRODUCTION

Suicide rates are extremely high among emergency department patients seen for deliberate self-harm. Inpatient hospitalization is often recommended for these patients, but evidence on the suicide prevention impacts of hospitalization is scarce. Confounding by indication and challenges to implementing randomized designs are barriers to advances in this field.

METHODS

Investigators used 2009-2012 statewide data on 57,312 self-harm emergency department patients from California, linked to mortality records. Naive 12-month and 30-day suicide risks were estimated among patients who were hospitalized versus those who were discharged. Then, generalized random forest methods were applied to estimate the average treatment impacts of hospitalization on suicide, conditioning on observable covariates. Associations were calculated separately for sex- and age-specific subgroups. Analyses were conducted in February 2019-August 2021.

RESULTS

In naive analyses, suicide risk was significantly higher in hospitalized than in discharged patients in each subgroup. In 12-month models accounting for the observed covariates through generalized random forest methods, hospitalized male patients had 5.4 more suicides per 1,000 patients (95% CI=3.0, 7.8), hospitalized patients aged 10-29 years had 2.4 more suicides per 1,000 (95% CI=1.1, 3.6), and those aged ≥50 years had 5.8 more suicides per 1,000 (95% CI=0.5, 11.2) than corresponding discharged patients. Hospitalization was not significantly associated with suicide among female patients or patients aged 30-49 years in generalized random forest analyses. Patterns were similar in 30-day generalized random forest models.

CONCLUSIONS

Emergency department personnel intend to hospitalize self-harm patients with high suicide risk; this study suggests that this goal is largely realized. Analyses that control for confounding by observable covariates did not find clear evidence that hospitalization reduces suicide risk and could not rule out the possibility of iatrogenic effects.

摘要

引言

在因故意自残而到急诊科就诊的患者中,自杀率极高。通常建议对这些患者进行住院治疗,但住院对预防自杀的影响的证据很少。混杂因素和实施随机设计的挑战是该领域进展的障碍。

方法

研究人员使用了 2009 年至 2012 年来自加利福尼亚州的 57312 名因自残而到急诊科就诊的患者的全州范围数据,并将其与死亡记录相联系。在未校正的情况下,分别估计了住院与出院患者在 12 个月和 30 天内自杀的风险。然后,应用广义随机森林方法,根据可观察的协变量,估计住院治疗对自杀的平均治疗效果。分别针对性别和年龄特定亚组进行了关联计算。分析于 2019 年 2 月至 2021 年 8 月进行。

结果

在未校正的分析中,每个亚组中住院患者的自杀风险均明显高于出院患者。在通过广义随机森林方法考虑到观察到的协变量的 12 个月模型中,住院的男性患者每 1000 名患者中增加 5.4 例自杀(95%CI=3.0,7.8),10-29 岁的住院患者每 1000 名患者中增加 2.4 例自杀(95%CI=1.1,3.6),≥50 岁的患者每 1000 名患者中增加 5.8 例自杀(95%CI=0.5,11.2),而相应的出院患者则减少。在广义随机森林分析中,女性患者或 30-49 岁的患者的住院与自杀无关。广义随机森林 30 天模型的结果类似。

结论

急诊科工作人员有意将自杀风险高的自残患者住院治疗;本研究表明,这一目标在很大程度上实现了。对可观察协变量进行混杂因素校正的分析并未发现明确证据表明住院治疗降低了自杀风险,也不能排除医源性影响的可能性。

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