The structure of depressive symptoms using CES-D and ZDS in outpatients in a general hospital in Lima, Peru

Jair R. Jara-Fernández, Nieves Gutiérrez-Kolotvina, Jhoselyn Milagros Flores-Egocheaga, Paulo Ruíz-Grosso, Johann M. Vega-Dienstmaier
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Abstract

Background

Depression represents one of the leading causes of disability due to illness worldwide. Previous studies have demonstrated the significant heterogeneity of the diagnosis of depression, making it necessary to develop new diagnostic approaches. Network analysis is a perspective that considers symptoms as constituents of the psychiatric disorder itself. The objective was to determine the structure of depressive symptoms using the CES-D and ZDS depression scales.

Methods

Cross-sectional study of secondary analysis of 194 patients using the CES-D and ZDS scales. Correlation matrices and regularised partial correlation networks were constructed from the database. Centrality measures were estimated, and a network stability analysis was performed.

Results

On the CES-D scale, the most central item was “Sad”; while on the ZDS scale, the most central items were “Sad” and “Live”. On the CES-D scale, the connection between “Enjoy” and “Happy” was the strongest. On the ZDS scale, the strongest connection was between the items “Live” with “Useful”. The item “Morning” was the least connected on the ZDS.

Conclusions

The most central symptom from the CES-D scale was sadness, while from the ZDS scale, was sadness and anhedonia.

使用 CES-D 和 ZDS 分析秘鲁利马一家综合医院门诊患者的抑郁症状结构。
背景:抑郁症是全球因病致残的主要原因之一。以往的研究表明,抑郁症的诊断具有显著的异质性,因此有必要开发新的诊断方法。网络分析从一个角度将症状视为精神障碍本身的组成部分。本研究旨在使用 CES-D 和 ZDS 抑郁量表确定抑郁症状的结构:使用 CES-D 和 ZDS 量表对 194 名患者进行二次分析的横断面研究。从数据库中构建了相关矩阵和正则化部分相关网络。对中心度进行了估算,并进行了网络稳定性分析:在 CES-D 量表中,最中心的项目是 "悲伤";而在 ZDS 量表中,最中心的项目是 "悲伤 "和 "生活"。在 CES-D 量表中,"享受 "和 "快乐 "之间的联系最强。在 ZDS 量表中,"生活 "与 "有用 "之间的联系最强。结论:结论:CES-D 量表中最主要的症状是悲伤,而 ZDS 量表中最主要的症状是悲伤和失乐症。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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