Abstract
Multimorbidity is a major factor contributing to increased mortality among
people with severe mental illnesses (SMI). Previous studies either focus on
estimating prevalence of a disease in a population without considering rela-
tionships between diseases or ignore heterogeneity of individual patients in
examining disease progression by looking merely at aggregates across a whole
cohort. Here, we present a temporal bipartite network model to jointly rep-
resent detailed information on both individual patients and diseases, which
allows us to systematically characterize disease trajectories from both pa-
tient and disease centric perspectives. We apply this approach to a large set
of longitudinal diagnostic records for patients with SMI collected through
a data linkage between electronic health records from a large UK mental
health hospital and English national hospital administrative database. We
find that the resulting diagnosis networks show disassortative mixing by de-
gree, suggesting that patients affected by a small number of diseases tend to
suffer from prevalent diseases. Factors that determine the network structures
include an individual’s age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more intercon-
nected over the illness duration of SMI, which is largely driven by the process
that patients with similar attributes tend to suffer from the same conditions.
Our analytic approach provides a guide for future patient-centric research on
multimorbidity trajectories and contributes to achieving precision medicine.
people with severe mental illnesses (SMI). Previous studies either focus on
estimating prevalence of a disease in a population without considering rela-
tionships between diseases or ignore heterogeneity of individual patients in
examining disease progression by looking merely at aggregates across a whole
cohort. Here, we present a temporal bipartite network model to jointly rep-
resent detailed information on both individual patients and diseases, which
allows us to systematically characterize disease trajectories from both pa-
tient and disease centric perspectives. We apply this approach to a large set
of longitudinal diagnostic records for patients with SMI collected through
a data linkage between electronic health records from a large UK mental
health hospital and English national hospital administrative database. We
find that the resulting diagnosis networks show disassortative mixing by de-
gree, suggesting that patients affected by a small number of diseases tend to
suffer from prevalent diseases. Factors that determine the network structures
include an individual’s age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more intercon-
nected over the illness duration of SMI, which is largely driven by the process
that patients with similar attributes tend to suffer from the same conditions.
Our analytic approach provides a guide for future patient-centric research on
multimorbidity trajectories and contributes to achieving precision medicine.
| Original language | English |
|---|---|
| Journal | JOURNAL OF BIOMEDICAL INFORMATICS |
| Publication status | Accepted/In press - 10 Feb 2022 |
Keywords
- Multimorbidity
- Severe mental illnesses
- Temporal bipartite network
- Disease trajectories
- EHR data linkage
- Network evolution