Examining Disease Multimorbidity in U.S. Hospital Visits Before and During COVID-19 Pandemic: A Graph Analytics Approach

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Karthik Srinivasan, Jinhang Jiang
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引用次数: 0

Abstract

Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted by identifying patterns in diseases recorded in hospital visits over time. Disease multimorbidity or simultaneous occurrence of multiple diseases is a growing global public health challenge as populations age and long-term conditions become more prevalent. We propose a graph analytics framework for analyzing disease multimorbidity in hospital visits. Within the framework, we propose a graph model to explain multimorbidity as a function of prevalence, category, and chronic nature of the underlying disease. We apply our model to examine and compare multimorbidity patterns in public hospitals in Arizona, U.S., during five six-month time periods before and during the pandemic. We observe that while multimorbidity increased by 34.26% and 41.04% during peak pandemic for mental disorders and respiratory disorders respectively, the gradients for endocrine diseases and circulatory disorders were not significant. Multimorbidity for acute conditions is observed to be decreasing during the pandemic while multimorbidity for chronic conditions remains unchanged. Our graph analytics framework provides guidelines for empirical analysis of disease multimorbidity using electronic health records. The patterns identified using our proposed graph model informs future research and healthcare policy makers for pre-emptive decision making.
新冠肺炎大流行前和期间美国医院就诊的疾病多发病率研究:图形分析方法
通过识别医院就诊记录中的疾病模式,可以预防COVID-19大流行对卫生保健系统的持久影响。随着人口老龄化和长期疾病变得更加普遍,疾病多发病或多种疾病同时发生是一个日益严峻的全球公共卫生挑战。我们提出了一个图表分析框架,分析疾病的多发病在医院就诊。在这个框架内,我们提出了一个图表模型来解释多重发病率作为患病率、类别和潜在疾病的慢性性质的函数。我们应用我们的模型来检查和比较美国亚利桑那州公立医院在大流行之前和期间的五个六个月期间的多病模式。我们观察到,精神疾病和呼吸疾病的多病率在流行高峰期间分别增加了34.26%和41.04%,而内分泌疾病和循环系统疾病的多病率梯度不显著。据观察,在大流行期间,急性疾病的多重发病率正在下降,而慢性疾病的多重发病率保持不变。我们的图表分析框架为使用电子健康记录对疾病多发病进行实证分析提供了指导方针。使用我们提出的图模型确定的模式为未来的研究和医疗保健政策制定者提供了先发制人的决策。
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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来源期刊
ACM Transactions on Management Information Systems
ACM Transactions on Management Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
6.30
自引率
20.00%
发文量
60
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