Abstract
BackgroundMultimorbidity represents a global health challenge, which requires a more global understanding of multimorbidity patterns and trends. However, the majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed.ObjectiveWe attempted to provide a multidimensional approach to understand the full spectrum of chronic disease co-occurrence among general inpatients in southwest China, in order to investigate multimorbidity patterns and temporal trends, and assess their age and sex differences.MethodsWe conducted a retrospective cohort analysis based on 8.8 million hospital discharge records of about 5.0 million individuals of all ages from 2015 to 2019 in a megacity in southwest China. We examined all chronic diagnoses using the ICD-10 (International Classification of Diseases, 10th revision) codes at 3 digits and focused on chronic diseases with ≥1% prevalence for each of the age and sex strata, which resulted in a total of 149 and 145 chronic diseases in males and females, respectively. We constructed multimorbidity networks in the general population based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Then, we divided the networks into communities and assessed their temporal trends.ResultsThe results showed complex interactions among chronic diseases, with more intensive connections among males and inpatients ≥40 years old. A total of 9 chronic diseases were simultaneously classified as central diseases, hubs, and bursts in the multimorbidity networks. Among them, 5 diseases were common to both males and females, including hypertension, chronic ischemic heart disease, cerebral infarction, other cerebrovascular diseases, and atherosclerosis. The earliest leaps (degree leaps ≥6) appeared at a disorder of glycoprotein metabolism that happened at 25-29 years in males, about 15 years earlier than in females. The number of chronic diseases in the community increased over time, but the new entrants did not replace the root of the community.ConclusionsOur multimorbidity network analysis identified specific differences in the co-occurrence of chronic diagnoses by sex and age, which could help in the design of clinical interventions for inpatient multimorbidity.
Highlights
With the recent improvements in clinical interventions, advances in public health, lifestyle changes, and environmental exposures, multimorbidity has been a growing global health challenge [1-3]
The Salton cosine index (SCI) was used to measure the strength of comorbid diseases, and the cutoff of the SCI was determined by assessing the relationship between the Pearson correlation coefficient and SCI (Figure 3B)
For patients above the age of 30 years, the number of edges we found was more significant in the male multimorbidity network
Summary
With the recent improvements in clinical interventions, advances in public health, lifestyle changes, and environmental exposures, multimorbidity has been a growing global health challenge [1-3]. A multidimensional approach is still needed to understand the full spectrum of multimorbidity networks, time trends, and patterns in age and sex, in developing countries or regions [17]. With the enhancement of the storage capacity and accessibility of electronic information systems, digitized clinical record keeping has made routinely collected administrative data of unprecedented depth and variability available to researchers This provides an opportunity for the application of network analysis to extract conceptual insights from large and messy data sets [18-20]. It is difficult to investigate the true extent of multimorbidity associations from these studies because of the differential definition of multimorbidity at the cross-sectional level or over a lifetime period, the difference in the measurement of associations, and the study settings that were mainly dominated by developed countries or regions. The majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed
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