Literature DB >> 23184922

Ambulance demand: random events or predicable patterns?

Kate Cantwell1, Paul Dietze, Amee E Morgans, Karen Smith.   

Abstract

BACKGROUND: Occupational, social and recreational routines follow temporal patterns, as does the onset of certain acute medical diseases and injuries. It is not known if the temporal nature of injury and disease transfers into patterns that can be observed in ambulance demand. This review examines eligible study findings that reported temporal (time of day, day of week and seasonal) patterns in ambulance demand.
METHODS: Electronic searches of Medline and Cumulative Index of Nursing and Allied Health Literature were conducted for papers published between 1980 and 2011. In addition, hand searching was conducted for unpublished government and ambulance service documents and reports for the same period.
RESULTS: 38 studies examined temporal patterns in ambulance demand. Six studies reported trends in overall workload and 32 studies reported trends in a subset of ambulance demand, either as a specific case type or demographic group. Temporal patterns in overall demand were consistent between jurisdictions for time of day but varied for day of week and season. When analysed by case type, all jurisdictions reported similar time of day patterns, most jurisdictions had similar day of week patterns except for out-of-hospital cardiac arrest and similar seasonal patterns, except for trauma. Temporal patterns in case types were influenced by age and gender.
CONCLUSIONS: Temporal patterns are present in ambulance demand and importantly these populations are distinct from those found in hospital datasets suggesting that variation in ambulance demand should not be inferred from hospital data alone. Case types seem to have similar temporal patterns across jurisdictions; thus, research where demand is broken down into case types would be generalisable to many ambulance services. This type of research can lead to improvements in ambulance service deliverables.

Entities:  

Keywords:  Emergency Ambulance Systems, Systems; Paramedics; Pre-Hospital; Prehospital Care; Research, Epidemiology

Mesh:

Year:  2012        PMID: 23184922     DOI: 10.1136/emermed-2012-201852

Source DB:  PubMed          Journal:  Emerg Med J        ISSN: 1472-0205            Impact factor:   2.740


  6 in total

1.  A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data.

Authors:  Tim Spelman; Orla Gray; Robyn Lucas; Helmut Butzkueven
Journal:  J Vis Exp       Date:  2015-12-09       Impact factor: 1.355

2.  Weather and Temporal Factors Associated with Use of Emergency Medical Services.

Authors:  Sriram Ramgopal; Jennifer Dunnick; Sylvia Owusu-Ansah; Nalyn Siripong; David D Salcido; Christian Martin-Gill
Journal:  Prehosp Emerg Care       Date:  2019-04-05       Impact factor: 3.077

Review 3.  Developing an integrated emergency medical services in a low-income country like Nepal: a concept paper.

Authors:  Deepak Bhandari; Nabin Krishna Yadav
Journal:  Int J Emerg Med       Date:  2020-02-07

4.  Diverting less urgent utilizers of emergency medical services to primary care: is it feasible? Patient and morbidity characteristics from a cross-sectional multicenter study of self-referring respiratory emergency department consulters.

Authors:  Felix Holzinger; Sarah Oslislo; Rebecca Resendiz Cantu; Martin Möckel; Christoph Heintze
Journal:  BMC Res Notes       Date:  2021-03-24

Review 5.  Traumatic stress and the circadian system: neurobiology, timing and treatment of posttraumatic chronodisruption.

Authors:  Agorastos Agorastos; Miranda Olff
Journal:  Eur J Psychotraumatol       Date:  2020-11-27

6.  Ambulance attendance for substance and/or alcohol use in a pandemic: Interrupted time series analysis of incidents.

Authors:  Rachael Mason; Amanda Roberts; Robert Spaight; Debbie Shaw; Gregory Adam Whitley; Todd E Hogue; Aloysius Niroshan Siriwardena; Jim Rogers; Graham R Law
Journal:  Drug Alcohol Rev       Date:  2022-03-01
  6 in total

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