Network analysis of multimorbidity and health outcomes among persons with spinal cord injury in Canada - Fallah, Nader; Hong, Heather A.; Wang, Di; Humphreys, Suzanne; Parsons, Jessica; Walden, Kristen; Street, John; Charest-Morin, Raphaele; Cheng, Christiana L.; Cheung, Candice J.; Noonan, Vanessa K. (Frontiers in Neurology, 2024)
Introduction: Multimorbidity, defined as the coexistence of two/more health conditions, is common in persons with spinal cord injury (SCI). Network analysis is a powerful tool to visualize and examine the relationship within complex systems. We utilized network analysis to explore the relationship between 30 secondary health conditions (SHCs) and health outcomes in persons with traumatic (TSCI) and non-traumatic SCI (NTSCI). Specifically the objectives of this paper were to: 1) apply three network models to the 2011-2012 Canadian SCI Community Survey dataset (1) to identify key variables important in each network for 30 SHCs measured by the Multimorbidity Index-30 (MMI-30) for five health outcomes, 2) create a short form of the MMI-30 using network analysis, and 3) compare the network-derived MMI to the MMI-30 in persons with TSCI and NTSCI to predict outcomes. Methods: Three network models (Gaussian Graphical, Ising, and Mixed Graphical) were created and analyzed using standard network measures (i.e. network estimations, centrality, and robustness). Data analyzed included demographic and injury variables (e.g. age, sex, region of residence, date, injury/severity), the MMI-30 and five health outcomes [healthcare utilization (HCU), health status (i.e. Short Form-12 Physical and Mental Component Summary (SF-12 PCS & MCS) score), life satisfaction, and quality of life (QoL)]. The network nodes represented the presence/absence of 30 SHCs and the five health outcomes. Results: Network analysis of 1549 SCICS participants (TSCI: 1137 and NTSCI: 412) revealed strong connections between independent (30 SHCs) and dependent variables (HCU, health status using SF-12 PCS & MCS, life satisfaction, and QoL). Additionally, network models identified that cancer, deep vein thrombosis, diabetes, high blood pressure, and liver disease were isolated and not associated with the other variables. Logistic regression analysis indicated the network-derived MMI-25 correlated with all five health outcomes (P<0.001) and was comparable to the MMI-30. Discussion: The network-derived MMI-25 was comparable to the MMI-30 and was associated with increased HCU, lower health status, lower life satisfaction, and poor QoL. The MMI-25 shows promise as a screening tool to identify persons with SCI at risk of having poor health outcomes. Next steps include undertaking psychometric studies using a longitudinal study design.