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A data linkage study of suspected seizures in the urgent and emergency care system in the UK
  1. Thomas Hughes-Gooding1,2,
  2. Jon M Dickson3,
  3. Colin O'Keeffe4,
  4. Suzanne M Mason4
  1. 1The University of Sheffield Medical School, Sheffield, UK
  2. 2Rotherham General Hospitals NHS Trust, Rotherham, UK
  3. 3The Academic Unit of Primary Medical Care, The University of Sheffield, Sheffield, UK
  4. 4School of Health and Related Research, The University of Sheffield, Sheffield, UK
  1. Correspondence to Dr Thomas Hughes-Gooding, The University of Sheffield Medical School, Sheffield S10 2RX, UK;{at}


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.

  • research, methods
  • prehospital
  • neurology, epilepsy
  • EDs
  • emergency care systems

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  • Handling editor Caroline Leech

  • Twitter @Dr_J_M_Dickson, @ProfSueMason

  • Contributors JMD and SM conceived and planned the study, and both of them supervised TH-G. TH-G analysed the data and wrote the first draft of the Methods and Results. JMD wrote the initial draft of Introduction, Discussion and Abstract sections. CO redrafted Methods section and drafted the appendices. All authors contributed to and approved the final manuscript.

  • Funding This report is independent research funded by the National Institute for Health Research Y&H ARC. Funding reference: IS-CLA-0113-10020.

  • Disclaimer The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

  • Patient consent for publication Not required.

  • Ethics approval Ethical approval for use of the dataset in this research was gained from the NHS Research Ethics Committee (REC reference 14/YH/1139). The NHS Health Research Authority Confidentiality Advisory Group also approved the research (CAG REC reference 14/CAG/1015).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Deidentified, linked participant data forming the core dataset as used in the study available from first author (ORCID 0000-0001-8483-5686) upon reasonable request and with the agreement of other authors. No other data are available.

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