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新冠疫情期间医疗工作与焦虑和抑郁的关联:结构化主题建模研究

Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study.

作者信息

Malgaroli Matteo, Tseng Emily, Hull Thomas D, Jennings Emma, Choudhury Tanzeem K, Simon Naomi M

机构信息

Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY, United States.

Ann S Bowers College of Computing and Information Science, Cornell University, Ithaca, NY, United States.

出版信息

JMIR AI. 2023 Oct 24;2:e47223. doi: 10.2196/47223.

Abstract

BACKGROUND

Stressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying the factors most tied to HCWs' psychological challenges is crucial to addressing HCWs' mental health needs effectively, now and for future large-scale events.

OBJECTIVE

In this study, we used natural language processing methods to examine deidentified psychotherapy transcripts from telemedicine treatment during the initial wave of COVID-19 in the United States. Psychotherapy was delivered by licensed therapists while HCWs were managing increased clinical demands and elevated hospitalization rates, in addition to population-level social distancing measures and infection risks. Our goal was to identify specific concerns emerging in treatment for HCWs and to compare differences with matched non-HCW patients from the general population.

METHODS

We conducted a case-control study with a sample of 820 HCWs and 820 non-HCW matched controls who received digitally delivered psychotherapy in 49 US states in the spring of 2020 during the first US wave of the COVID-19 pandemic. Depression was measured during the initial assessment using the Patient Health Questionnaire-9, and anxiety was measured using the General Anxiety Disorder-7 questionnaire. Structural topic models (STMs) were used to determine treatment topics from deidentified transcripts from the first 3 weeks of treatment. STM effect estimators were also used to examine topic prevalence in patients with moderate to severe anxiety and depression.

RESULTS

The median treatment enrollment date was April 15, 2020 (IQR March 31 to April 27, 2020) for HCWs and April 19, 2020 (IQR April 5 to April 27, 2020) for matched controls. STM analysis of deidentified transcripts identified 4 treatment topics centered on health care and 5 on mental health for HCWs. For controls, 3 STM topics on pandemic-related disruptions and 5 on mental health were identified. Several STM treatment topics were significantly associated with moderate to severe anxiety and depression, including working on the hospital unit (topic prevalence 0.035, 95% CI 0.022-0.048; P<.001), mood disturbances (prevalence 0.014, 95% CI 0.002-0.026; P=.03), and sleep disturbances (prevalence 0.016, 95% CI 0.002-0.030; P=.02). No significant associations emerged between pandemic-related topics and moderate to severe anxiety and depression for non-HCW controls.

CONCLUSIONS

The study provides large-scale quantitative evidence that during the initial wave of the COVID-19 pandemic, HCWs faced unique work-related challenges and stressors associated with anxiety and depression, which required dedicated treatment efforts. The study further demonstrates how natural language processing methods have the potential to surface clinically relevant markers of distress while preserving patient privacy.

摘要

背景

在新冠疫情期间,医护人员面临的压力多种多样,抑郁和焦虑程度较高,同时还存在护理缺口。识别与医护人员心理挑战最相关的因素,对于有效满足医护人员当前及未来大规模事件中的心理健康需求至关重要。

目的

在本研究中,我们使用自然语言处理方法,检查了美国新冠疫情第一波期间远程医疗治疗中身份不明的心理治疗记录。心理治疗由持牌治疗师提供,而医护人员除了要应对人群层面的社交距离措施和感染风险外,还要处理增加的临床需求和升高的住院率。我们的目标是确定医护人员治疗中出现的具体问题,并与来自普通人群的匹配非医护人员患者进行差异比较。

方法

我们进行了一项病例对照研究,样本包括820名医护人员和820名匹配的非医护人员对照,他们于2020年春季在美国新冠疫情第一波期间,在49个州接受了数字形式的心理治疗。在初始评估中,使用患者健康问卷-9测量抑郁,使用广泛性焦虑障碍-7问卷测量焦虑。使用结构主题模型(STM)从治疗前3周身份不明的记录中确定治疗主题。STM效应估计器还用于检查中度至重度焦虑和抑郁患者中的主题流行情况。

结果

医护人员的治疗登记日期中位数为2020年4月15日(四分位间距为2020年3月31日至4月27日),匹配对照为2020年4月19日(四分位间距为2020年4月5日至4月27日)。对身份不明记录的STM分析确定了4个以医疗保健为中心的治疗主题和5个针对医护人员的心理健康主题。对于对照,确定了3个与大流行相关干扰的STM主题和5个心理健康主题。几个STM治疗主题与中度至重度焦虑和抑郁显著相关,包括在医院病房工作(主题流行率0.035,95%置信区间0.022 - 0.048;P <.001)、情绪障碍(流行率0.014,95%置信区间0.002 - 0.026;P =.03)和睡眠障碍(流行率0.016,95%置信区间0.002 - 0.030;P =.02)。对于非医护人员对照,与大流行相关的主题与中度至重度焦虑和抑郁之间未出现显著关联。

结论

该研究提供了大规模定量证据,表明在新冠疫情第一波期间,医护人员面临与焦虑和抑郁相关的独特工作相关挑战和压力源,这需要专门的治疗措施。该研究进一步证明了自然语言处理方法如何有潜力在保护患者隐私的同时,揭示临床上相关的痛苦指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f684/11041488/3d0ef6315d17/ai_v2i1e47223_fig1.jpg

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