Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Abstract

Background: Multimorbidity 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.

Objective: We 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.

Methods: We 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.

Results: The 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.

Conclusions: Our 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.

Keywords: administrative data; longitudinal study; multimorbidity pattern; multimorbidity prevalence; network analysis; regional research; temporal trend.

Figure 1

Study workflow. SCI: Salton cosine index. Dis: chronic disease.

Figure 2

Characteristics of the study population. (A) Selection flow of the study population with multimorbidity. (B) Age and sex distribution for 1.8 million unique inpatients with multimorbidity. (C) Age- and sex-specific percentages of inpatients with multimorbidity among 5.0 million unique inpatients. (D) Age- and sex-specific mean numbers of chronic diseases among 1.8 million unique inpatients with multimorbidity.

Figure 3

The properties of sex-specific phenotypic multimorbidity networks. (A) Scatter plot between the relative risk (RR) and the Pearson correlation coefficient of disease pairs; due to the interest of tightly interconnected diseases, we excluded mutually exclusive disease pairs with RR <1 or correlation <0. (B) The cutoff of the Sclton cosine index (SCI) where the numbers of significant disease pairs are equal in networks using the Pearson correlation coefficient and SCI. (C) Degree (k) distributions for sex-specific multimorbidity networks using the SCI. (D) and (E) The numbers of connected nodes and edges in each multimorbidity network across age strata and by sex. (F) Box plot of the SCI across age strata and by sex. The width of the box is proportion to the number of edges in each strata’s network.

Figure 4

Multimorbidity networks, central diseases, hubs, and connectivity trajectories. Age-adjusted multimorbidity network in females (A) and males (B). Nodes represent chronic diseases (ICD-10 [International Classification of Diseases, 10th revision] codes at 3 digits), such that the node size is proportional to the disease prevalence among multimorbidity patients and its color identifies the ICD-10 category. Link weights are proportional to the magnitudes of the cosine index. (C) The central diseases in each age strata by sex. Diseases with the top 10 percentiles for PageRank in each strata were identified as central diseases. (D) The hubs in each age strata and by sex. Nodes with the top 10 percentiles for hubs in each strata were identified as hubs. The age-based trajectories of the degree (k) of bursts in females (E) and males (F). The triangles indicate degree leaps ≥6 through the consecutive age groups. The disease with at least two such degree leaps was defined as a burst, which means the bursts of disease associations leading to multimorbidity.

Figure 5

Sex-specific connectivity (cumulative of the node-average Sclton cosine index [SCI]) across age groups. All nodes (A), central diseases vs noncentral diseases (B), hubs vs nonhubs (C), and bursts vs nonbursts (D).

Figure 6

Temporal trends of communities in the female (A) and male (B) multimorbidity networks. By conducting networks year by year and comparing across time, we were able to obtain the temporal trends of the communities. The root, defined as the node with the highest eigenvector centrality within the community, is labeled using ICD-10 (International Classification of Diseases, 10th revision) at 3 digits. Similarity over time is assessed using the Pearson correlation coefficient for communities obtained in consecutive years, and unsignificant (P>.05) similarities are excluded. The “n” value is the number of chronic diseases consistent in the consecutive years.