What question did this study set out to answer?

This study investigates how different aspects of social networks affect the delay in reaching hospitals after stroke symptoms onset in patients with acute ischemic stroke.

January 25, 2026

Association Between Social Networks and Prehospital Delay in Care of Patients With Acute Ischemic Stroke in China

Key Points

This study investigates how different aspects of social networks affect the delay in reaching hospitals after stroke symptoms onset in patients with acute ischemic stroke.
Conducted a cross-sectional survey of AIS patients in two hospitals in Qingdao, China.
Used validated survey tools to assess five social network characteristics.
Analyzed prehospital delay as a binary outcome (>3 hours) using binary logistic regression.
Adjusted for clinical and demographic variables in the analysis.
Approximately 64.0% of participants experienced prehospital delay (>3 hours).
Larger effective network size and higher kin proportion were linked to reduced odds of delay.
Higher betweenness centrality, greater network constraint, and better network efficiency were associated with increased odds of delay.

Abstract

BACKGROUND: Prompt hospital arrival is critical for acute ischemic stroke (AIS) treatment. This study aimed to examine the association between social network structure and prehospital delay among patients with AIS in China. METHODS: A cross-sectional survey using a validated instrument was conducted among patients with AIS admitted to 2 general hospitals in Qingdao, China, between October 2020 and May 2021. Key inclusion criteria were confirmed AIS diagnosis, age ≥18 years, and ability to complete the survey. Exclusion criteria included severe cognitive impairment or aphasia. We assessed 5 social network characteristics using egocentric network methodology: effective network size (number of nonredundant contacts), kin proportion (percentage of relatives), betweenness centrality (role as a bridge in the network), network constraint (limitations in forming new connections), and network efficiency (ability to access resources through contacts). Prehospital delay was defined as a binary outcome (arrival to hospital >3 hours after symptom onset). Binary logistic regression was used to analyze associations between network features and delay, adjusting for clinical and demographic variables. RESULTS: Among 422 participants (mean age 65.0±11.7 years, 71.0% male), 270 (64.0%) experienced prehospital delay (>3 hours). Adjusted logistic regression revealed that while a larger effective network size (odds ratio OR, 0.39 95% CI, 0.24–0.64) and a higher kin proportion (OR, 0.21 95% CI, 0.05–0.78) were associated with reduced odds of delay, higher betweenness centrality (OR, 1.16 95% CI, 1.03–1.31), greater network constraint (OR, 1.02 95% CI, 1.00–1.03), and higher network efficiency (OR, 1.04 95% CI, 1.02–1.06) were associated with increased odds. CONCLUSIONS: Social network structure significantly influences prehospital delay in patients with AIS. Interventions focused on social network expansion and strengthening kin involvement may reduce delays and improve stroke outcomes.

Read Full Paperexternally

Demander à l'IA

Bookmark

View Full Paper