A Spatial Variation Analysis of In-Hospital Stroke Mortality Based on Integrated Pre-Hospital and Hospital Data in Mashhad, Iran

Abstract

Background: Despite significant advances in the quality and delivery of specialized stroke care, there still persist remarkable spatial variations in emergency medical services (EMS) transport delays, stroke incidence, and its outcomes. Therefore, it is very important to investigate the possible geographical variations of in-hospital stroke mortality and to identify its associated factors. Methods: This historical cohort study included suspected stroke cases transferred to Ghaem Hospital of Mashhad by the EMS from March 2018 to March 2019. Using emergency mission IDs, the pre-hospital emergency data were integrated with the patient medical records in the hospital. We used the Bayesian approach for estimating the model parameters. Results: Out of 301 patients (142 (47.2%) females vs. 159 (52.8%) males) with a final diagnosis of stroke, 61 (20.3%) cases had in-hospital mortality. Results from Bayesian spatial log-logistic proportional odds (PO) model showed that age (PO = 1.07), access rate to EMS (PO = 0.78), arrival time (evening shift vs. day shift, PO = 0.09), and sequelae variables (PO = 9.20) had a significant association with the odds of in-hospital stroke mortality (P< 0.05). Furthermore, the odds of in-hospital stroke mortality were higher in central urban areas compared to suburban areas. Conclusion: Marked regional variations were found in the odds of in-hospital stroke mortality in Mashhad. There was a direct association between age and odds of in-hospital stroke mortality. Hence, the prognosis of in-hospital stroke mortality could be improved by better control of hypertension, prevention of the occurrence of sequelae, increasing the access rate to EMS, and optimizing shift work schedule.