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. 2020 Oct;37(10):605-610.
doi: 10.1136/emermed-2019-208820. Epub 2020 Jun 16.

A data linkage study of suspected seizures in the urgent and emergency care system in the UK

Thomas Hughes-Gooding  1   2 , Jon M Dickson  3 , Colin O'Keeffe  4 , Suzanne M Mason  4
Affiliations

A data linkage study of suspected seizures in the urgent and emergency care system in the UK

Thomas Hughes-Gooding et al. Emerg Med J. 2020 Oct.

Abstract

Introduction: The urgent and emergency care (UEC) system is struggling with increased demand, some of which is clinically unnecessary. Patients suffering suspected seizures commonly present to EDs, but most seizures are self-limiting and have low risk of short-term adverse outcomes. We aimed to investigate the flow of suspected seizure patients through the UEC system using data linkage to facilitate the development of new models of care.

Methods: We used a two-stage process of deterministic linking to perform a cross-sectional analysis of data from adults in a large region in England (population 5.4 million) during 2014. The core dataset comprised a total of 739 436 ambulance emergency incidents, 1 033 778 ED attendances and 362 358 admissions.

Results: A high proportion of cases were successfully linked (86.9% ED-inpatient, 77.7% ED-ambulance). Suspected seizures represented 2.8% of all ambulance service incidents. 61.7% of these incidents led to dispatch of a rapid-response ambulance (8 min) and 72.1% were conveyed to hospital. 37 patients died before being conveyed to hospital and 24 died in the ED (total 61; 0.3%). The inpatient death rate was 0.4%. Suspected seizures represented 0.71% of ED attendances, 89.8% of these arrived by emergency ambulance, 45.4% were admitted and 44.5% of these admissions lasted under 48 hours.

Conclusions: This study confirms previously published data from smaller unlinked datasets, validating the linkage method, and provides new data for suspected seizures. There are significant barriers to realising the full potential of data linkage. Collaborative action is needed to create facilitative governance frameworks and improve data quality and analytical capacity.

Keywords: EDs; emergency care systems; neurology, epilepsy; prehospital; research, methods.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Construction of the core dataset.
Figure 2
Figure 2
Identification and flow of suspected seizures through the UEC system. (A) Shows identification using the ambulance service data and (B) identification using the ED data.
See this image and copyright information in PMC

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