Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia.
School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia.
Int J Environ Res Public Health. 2020 Jan 16;17(2):596. doi: 10.3390/ijerph17020596.
The prevalence of chronic disease comorbidity has increased worldwide. Comorbidity-i.e., the presence of multiple chronic diseases-is associated with adverse health outcomes in terms of mobility and quality of life as well as financial burden. Understanding the progression of comorbidities can provide valuable insights towards the prevention and better management of chronic diseases. Administrative data can be used in this regard as they contain semantic information on patients' health conditions. Most studies in this field are focused on understanding the progression of one chronic disease rather than multiple diseases. This study aims to understand the progression of two chronic diseases in the Australian health context. It specifically focuses on the comorbidity progression of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM), as the prevalence of these chronic diseases in Australians is high. A research framework is proposed to understand and represent the progression of CVD in patients with T2DM using graph theory and social network analysis techniques. Two study cohorts (i.e., patients with both T2DM and CVD and patients with only T2DM) were selected from an administrative dataset obtained from an Australian health insurance company. Two baseline disease networks were constructed from these two selected cohorts. A final disease network from two baseline disease networks was then generated by weight adjustments in a normalized way. The prevalence of renal failure, fluid and electrolyte disorders, hypertension and obesity was significantly higher in patients with both CVD and T2DM than patients with only T2DM. This showed that these chronic diseases occurred frequently during the progression of CVD in patients with T2DM. The proposed network-based model may potentially help the healthcare provider to understand high-risk diseases and the progression patterns between the recurrence of T2DM and CVD. Also, the framework could be useful for stakeholders including governments and private health insurers to adopt appropriate preventive health management programs for patients at a high risk of developing multiple chronic diseases.
慢性病合并症的患病率在全球范围内有所增加。合并症，即多种慢性病的同时存在，会对患者的行动能力和生活质量以及经济负担产生不良影响。了解合并症的进展可以为预防和更好地管理慢性病提供有价值的见解。在这方面可以使用行政数据，因为它们包含有关患者健康状况的语义信息。该领域的大多数研究都侧重于了解一种慢性病的进展，而不是多种疾病。本研究旨在了解澳大利亚健康背景下两种慢性病的进展。它特别关注 2 型糖尿病（T2DM）患者心血管疾病（CVD）的合并症进展，因为这些慢性病在澳大利亚的患病率很高。提出了一个研究框架，以使用图论和社会网络分析技术来理解和表示 T2DM 患者 CVD 的进展。从一家澳大利亚医疗保险公司获得的行政数据集中选择了两个研究队列（即同时患有 T2DM 和 CVD 的患者以及仅患有 T2DM 的患者）。从这两个选定的队列中构建了两个基线疾病网络。然后，通过归一化方式对两个基线疾病网络的权重进行调整，生成最终的疾病网络。同时患有 CVD 和 T2DM 的患者比仅患有 T2DM 的患者更常出现肾衰竭、体液和电解质紊乱、高血压和肥胖症，这表明这些慢性病在 T2DM 患者 CVD 进展过程中经常发生。所提出的基于网络的模型可能有助于医疗保健提供者了解高危疾病以及 T2DM 和 CVD 复发之间的进展模式。此外，该框架对于包括政府和私人健康保险公司在内的利益相关者来说可能是有用的，因为它可以为有发展多种慢性病高风险的患者制定适当的预防保健管理计划。
