COVID-19 住院病例的潜在异质性:采用聚类加权法分析死亡率

Pub Date : 2024-02-13 DOI:10.1111/anzs.12407
Paolo Berta, Salvatore Ingrassia, Giorgio Vittadini, Daniele Spinelli
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引用次数: 0

摘要

COVID-19 大流行造成了前所未有的超额死亡率。自 2020 年以来,许多研究重点关注 COVID-19 未存活患者的特征。从统计学的角度来看,受 COVID-19 影响的人群具有很大的异质性,要识别受多种当代特征影响而死亡的亚人群极其困难。在本文中,我们提出了一种基于聚类加权模型的极为灵活的方法,该方法允许我们识别在住院时具有相似特征以及相似死亡率的潜在患者群体。我们将重点放在意大利的重灾区之一,并利用大流行第一波住院治疗的行政数据研究了受 COVID-19 影响的患者群体的异质性。研究结果表明,基于模型的聚类方法对于了解接受医院治疗并在住院期间死亡的 COVID-19 患者的复杂性至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Latent heterogeneity in COVID-19 hospitalisations: a cluster-weighted approach to analyse mortality

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Latent heterogeneity in COVID-19 hospitalisations: a cluster-weighted approach to analyse mortality

The COVID-19 pandemic caused an unprecedented excess mortality. Since 2020, many studies have focussed on the characteristics of COVID-19 patients who did not survive. From the statistical point of view, what seems to dominate is the large heterogeneity of the populations affected by COVID-19 and the extreme difficulty in identifying subpopulations who died affected by a plurality of contemporary characteristics. In this paper, we propose an extremely flexible approach based on a cluster-weighted model, which allows us to identify latent groups of patients sharing similar characteristics at the moment of hospitalisation as well as a similar mortality. We focus on one of the hardest hit areas in Italy and study the heterogeneity in the population of patients affected by COVID-19 using administrative data on hospitalisations in the first wave of the pandemic. Results highlighted that a model-based clustering approach is essential to understand the complexity of the COVID-19 patients treated by hospitals and who die during hospitalisation.

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