Background: Urinary incontinence is highly prevalent in older adults, imposing substantial physical, psychological, and economic burdens. Stigma and unmet care needs, particularly psychosocial support, are widespread and interact negatively, significantly compromising quality of life. However, the complex interrelationships among stigma, care needs, and quality of life remain unclear. This study employs network analysis to elucidate these intricate connections, identify key intervention targets, and inform precise strategies to improve outcomes for this population.
Purpose: To explore quality-of-life-based heterogeneous subgroups among older adults with urinary incontinence, analyze network structural characteristics of stigmatization and care needs across subgroups, and identify key intervention targets.
Methods: This cross-sectional study evaluated 448 older adults with urinary incontinence at three tertiary hospitals in Guangdong Province from December 2023 to February 2024. Latent profile analysis based on Urinary Incontinence Quality of Life scores was used for stratification. Network analysis constructed models of stigma and care needs for each subgroup. Node centrality and bridging centrality were calculated. Network comparison tests and computer-simulated intervention analyses evaluated intergroup differences and intervention effects.
Results: The study identified three subgroups: Low Distress Group (15.8%), Moderate Distress Group (63.5%), and High Burden Group (20.7%). Their network structures differed significantly, with Internalized Stigma and Health Education Needs forming the strongest connection and key bridges between systems. Computer simulations pinpointed the optimal intervention targets for each subgroup, such as Health Education Needs for the Moderate Distress group.
Conclusion: Older adults with urinary incontinence exhibit heterogeneous subgroups in quality of life, with distinct network structures and core drivers of stigma and care needs across subgroups. This study identifies core intervention targets for subpopulations, thereby providing both a theoretical basis for developing personalized precision care strategies and practical pathways for implementation.
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