Time series modelling to forecast prehospital EMS demand for diabetic emergencies.

Publisher: BioMed Central Country of Publication: England NLM ID: 101088677 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6963 (Electronic) Linking ISSN: 14726963 NLM ISO Abbreviation: BMC Health Serv Res Subsets: MEDLINE

Original Publication: London : BioMed Central, [2001-

Diabetes Mellitus, Type 1/*therapy ; Diabetes Mellitus, Type 2/*therapy ; Emergency Medical Services/*statistics & numerical data; Aged ; Ambulances/statistics & numerical data ; Emergencies ; Emergency Medical Services/trends ; Female ; Forecasting ; Health Services Needs and Demand/statistics & numerical data ; Humans ; Hyperglycemia/therapy ; Hypoglycemia/therapy ; Male ; Middle Aged ; Seasons ; Victoria

Background: Acute diabetic emergencies are often managed by prehospital Emergency Medical Services (EMS). The projected growth in prevalence of diabetes is likely to result in rising demand for prehospital EMS that are already under pressure. The aims of this study were to model the temporal trends and provide forecasts of prehospital attendances for diabetic emergencies.
Methods: A time series analysis on monthly cases of hypoglycemia and hyperglycemia was conducted using data from the Ambulance Victoria (AV) electronic database between 2009 and 2015. Using the seasonal autoregressive integrated moving average (SARIMA) modelling process, different models were evaluated. The most parsimonious model with the highest accuracy was selected.
Results: Forty-one thousand four hundred fifty-four prehospital diabetic emergencies were attended over a seven-year period with an increase in the annual median monthly caseload between 2009 (484.5) and 2015 (549.5). Hypoglycemia (70%) and people with type 1 diabetes (48%) accounted for most attendances. The SARIMA (0,1,0,12) model provided the best fit, with a MAPE of 4.2% and predicts a monthly caseload of approximately 740 by the end of 2017.
Conclusions: Prehospital EMS demand for diabetic emergencies is increasing. SARIMA time series models are a valuable tool to allow forecasting of future caseload with high accuracy and predict increasing cases of prehospital diabetic emergencies into the future. The model generated by this study may be used by service providers to allow appropriate planning and resource allocation of EMS for diabetic emergencies.

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Keywords: Access/Demand/Utilization of services; Diabetes; Emergency medical services; Time series analysis

Date Created: 20170507 Date Completed: 20171218 Latest Revision: 20220331

20221213

PMC5420132

10.1186/s12913-017-2280-6

28476117