ArticleFrontiers in neurology2023
Network analysis of multimorbidity and health outcomes among persons with spinal cord injury in Canada.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review.Spinal cord · 2026Pooled it
- EGPRN primary care research at a crossroads advancing research on interprofessional collaboration and on integrated, equitable and complex care. An EGPRN Keynote Paper from the Autumn 2025 Plovdiv Conference.The European journal of general practice · 2026Article
- Why family medicine requires a paradigm shift from linear guidelines to complexity science.Family medicine and community health · 2026Article
- Towards an understanding of disturbed sleep phenotypes after traumatic spinal cord injury.Journal of rehabilitation medicine · 2026Observational
- Epigenomic Interactions Between Chronic Pain and Recurrent Pressure Injuries After Spinal Cord Injury.Epigenomes · 2025Article
- Multimorbidity patterns of mental disorders and physical diseases of adults in northeast China: a cross-sectional network analysis.Epidemiology and psychiatric sciences · 2025Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Multimorbidity, defined as the coexistence of two or 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). The study objectives were to (1) apply network models to the 2011-2012 Canadian SCI Community Survey dataset to identify key variables linking the SHCs measured by the Multimorbidity Index-30 (MMI-30) to healthcare utilization (HCU), health status, and quality of life (QoL), (2) create a short form of the MMI-30 based on network analysis, and (3) compare the network-derived MMI to the MMI-30 in persons with TSCI and NTSCI. Methods: Three network models (Gaussian Graphical, Ising, and Mixed Graphical) were created and analyzed using standard network measures (e.g., network centrality). Data analyzed included demographic and injury variables (e.g., age, sex, region of residence, date, injury severity), multimorbidity (using MMI-30), HCU (using the 7-item HCU questionnaire and classified as "felt needed care was not received" [HCU-FNCNR]), health status (using the 12-item Short Form survey [SF-12] Physical and Mental Component Summary [PCS-12 and MCS-12] score), and QoL (using the 11-item Life Satisfaction questionnaire [LiSAT-11] first question and a single item QoL measure). Results: Network analysis of 1,549 participants (TSCI: 1137 and NTSCI: 412) revealed strong connections between the independent nodes (30 SHCs) and the dependent nodes (HCU-FNCNR, PCS-12, MCS-12, LiSAT-11, and the QoL score). Additionally, network models identified that cancer, deep vein thrombosis/pulmonary embolism, diabetes, high blood pressure, and liver disease were isolated. Logistic regression analysis indicated the network-derived MMI-25 correlated with all health outcome measures ( Discussion: The network-derived MMI-25 was comparable to the MMI-30 and was associated with inadequate HCU, lower health status, and poor QoL. The MMI-25 shows promise as a follow-up screening tool to identify persons living with SCI at risk of having poor health outcomes.
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