Pre-hospital delay and its influencing factors in patients with acute ischemic stroke: a cross-sectional study based on the health ecology model.

Background and objective: The treatment of acute ischemic stroke (AIS) is time-dependent, and pre-hospital delays remain a significant barrier to effective stroke management worldwide. Previous studies have often focused on isolated factors; however, the problem is multifaceted. This study lies in applying the Health Ecology Model as a comprehensive framework to systematically investigate the multifaceted factors (Intrapersonal, Interpersonal, Community, and Policy environment levels) associated with pre-hospital delay among AIS patients. Methods: A cross-sectional study was conducted, we consecutively enrolled 439 AIS patients admitted to the stroke center of a tertiary hospital in Guangzhou between January and December 2024. Data were collected through structured questionnaires and medical records, specifically aligned with the constructs of the Health Ecology Model. Measures included Intrapersonal level: Individual characteristics (e.g., number of stroke occurrences, symptoms at onset, mode of onset) and Behavioral and psychological factors (e.g., number of physical examination, Health literacy). Interpersonal level: family function (Family APGAR Index) and social support (Social Support Rating Scale). Community level: Living and working conditions (e.g., Employment status, Present residence). Policy environment level (e.g., Payment method, Awareness to call 120 for stroke emergency). Pre-hospital delay was defined as an onset-to-door time>6 h. Multivariable logistic regression identified independent influencing factors. Results: The pre-hospital delay rate was 54.44%. Significantly, factors from multiple levels of the Health Ecology Model were independently associated with delay: Lower stroke awareness(OR = 5.414, 95% CI 2.291-12.794), Perception of symptom severity (OR = 31.798, 95% CI 13.119-77.077), limb weakness /numbness (OR = 3.661, 95% CI 1.221-10.979), lower health literacy (OR = 1.064, 95% CI 1.041-1.088), poorer family function (OR = 1.545, 95% CI 1.138-2.097), lower social support (OR = 1.466, 95% CI 1.322-1.627), and stroke onset during the night (OR = 0.160, 95% CI 0.064-0.401),which increased the odds of delay. Conclusion: Pre-hospital delay is highly prevalent and is influenced by a complex interplay of factors across individual, family, and systemic levels, as elucidated by the Health Ecology Model. Our findings highlight the critical need to move beyond patient education alone and implement integrated, multi-level interventions. Public health campaigns should target both patients and their families to improve symptom recognition and health literacy. Concurrently, healthcare systems must be optimized, for instance by addressing barriers to after-hours care and strengthening pre-hospital pathways to ensure rapid triage to comprehensive stroke centers, ultimately improving access to timely revascularization therapies.