The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults.

Area of Science:

• Gerontology
• Neurology
• Public Health

Background:

• Managing multimorbidity in aging stroke survivors presents significant challenges.
• Standard self-management programs require adaptation for this complex patient population.
• Understanding comorbidity patterns is crucial for effective chronic disease management.

Purpose of the Study:

• To utilize visual analytics to explore complex relationships among comorbidities in aging stroke survivors.
• To inform the pre-adaptation of the Chronic Disease Self-Management Program (CDSMP) for stroke patients.
• To enhance self-management support for older adults with stroke and multiple chronic conditions.

Main Methods:

• Secondary analysis of Medicare claims data (2013-2014) for patients with stroke as an index condition and 90-day hospital readmission.
• Inclusion of 72 distinct comorbidities to identify patient subgroups and co-occurring conditions.
• Application of visual analytics to uncover significant comorbidity patterns and inform intervention adaptation.

Main Results:

• Identification of five significant patient subgroups based on co-occurring comorbidities (e.g., obesity, cancer).
• Development of six customized vignettes based on the identified comorbidity subgroups.
• Demonstration of visual analytics' utility in revealing complex disease interactions.

Conclusions:

• Aging stroke survivors frequently encounter substantial hurdles in managing multiple chronic conditions.
• Visual analytics provides a powerful tool for understanding comorbidity networks in this population.
• Pre-adaptation of self-management programs, informed by visual analytics, can better meet the needs of older adult stroke survivors with multimorbidity.