揭示多种疾病对Covid-19严重性的隐藏影响:一种潜伏类分析方法
Sedigheh Akhavnnezhad1, Seyedeh Solmaz Talebi2, Ehsan Mosa Farkhani3,4
1Student Research Committee, School of public health, Shahroud University of Medical Sciences, Shahroud, Iran.
BMC public health
|April 4, 2025
概括
在COVID-19患者中,多重发病模式显著影响疾病的严重程度. 识别这些模式,例如高血压,呼吸道和心理健康 (HRMD) 类,对于更好地评估和管理严重的COVID-19结果的风险至关重要.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
背景情况:
- 之前关于COVID-19严重性的研究集中在单一的慢性疾病上.
- 这项研究调查了COVID-19患者多发性疾病的流行率和模式.
- 了解多重发病模式是预测COVID-19严重程度的关键.
研究的目的:
- 为了确定COVID-19患者多病症的流行率和模式.
- 分析多病态模式与COVID-19严重程度之间的关系.
- 为COVID-19的风险评估和临床管理策略提供信息.
主要方法:
- 在伊朗马什哈德对318,502名COVID-19患者 (年龄≥30) 的回顾性研究.
- 使用国际疾病分类识别的潜在疾病.
- 隐藏类分析 (LCA) 用于多病态模式;多变量逻辑回归用于严重程度分析.
主要成果:
- 高血压 (9.5%),代谢障碍 (7.5%) 和高脂血症 (7%) 是最常见的并发症.
- LCA确定了三个类别:没有多重发病率 (83%),HRMD类 (9%) 和代谢性疾病类 (7%).
- HRMD和代谢性疾病类别显示严重COVID-19的几率显著增加 (分别为81%和55%).
结论:
- 确定了多重发病率类别,可以更清楚地了解患者风险分层.
- 考虑多重发病模式,而不仅仅是单个疾病,对于COVID-19风险评估至关重要.
- 这种方法有助于临床决策和资源分配,用于管理COVID-19患者.
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