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The Development of PRIMA - A Belgian Prediction Model for Patient Encounters at Mass Gatherings
Kris Spaepen1, Winne Ap Haenen2, Ives Hubloue1
1Vrije Universiteit Brussel, Research Group on Emergency and Disaster Medicine, Brussels, Belgium.
Introduction:
Mass gatherings (MGs) grow in frequency around the world. With the intrinsic potential for significant health risks for all involved, MGs pose a challenge for those responsible for the provision of on-site medical care. Belgian law obliges local governments to identify and analyze the risks involving a MG. Though medical risk factors are long known, all too often, resourcing for in-event health services is based on anecdotal and previous experiences.
Problem:
Despite the fast-evolving science on MGs, the lack of reliable tools - based on empirical and analytical approaches - to predict patient presentation rates (PPRs) at MGs remains.
Methods:
A two-step method was followed to develop, update, and support a Plan Risk Manifestation (PRIMA) program. First, a continuous systematic literature review was conducted. Once developed, the model was run using data obtained from Belgian Federal Public Service (FPS; Brussels, Belgium) Health, Food Chain Safety, and Environment (HFCSE); event organizers; and municipalities.
Results:
In total, 231 studies and documents were included to form the program. With the data provided, three variables were computed to run the calculation model to predict the PPR. Three medical risk axes were defined for this model: (1) isolation risk; (2) population risk; and (3) risk at illness. A combined dataset was derived from the prediction of the PRIMA program combined with the actual data obtained after the MG. This proved a solid basis for the calculation model of the PRIMA program.
Conclusion:
Despite that validation is needed, the PRIMA program and its prediction model for PPRs at MGs carries the promise of a general, applicable prediction and risk analysis tool for a multitude of events.
Insights
A new program, PRIMA, offers a reliable tool to predict patient presentation rates at mass gatherings. This helps improve on-site medical care planning for public health events.
Area of Science:
- Public Health
- Emergency Medicine
- Risk Management
Background:
- Mass gatherings (MGs) are increasing globally, posing significant health risks and challenges for on-site medical care.
- Belgian law mandates risk analysis for MGs, yet resource allocation for health services often relies on anecdotal evidence.
- A gap exists in reliable, data-driven tools for predicting patient presentation rates (PPRs) at MGs.
Purpose of the Study:
- To develop and validate a predictive model for patient presentation rates (PPRs) at mass gatherings (MGs).
- To create a reliable tool for risk analysis and resource allocation for health services at MGs.
Main Methods:
- A two-step approach was used to develop the Plan Risk Manifestation (PRIMA) program.
- A continuous systematic literature review informed the model development.
- The model was populated with data from Belgian authorities, event organizers, and municipalities.
Main Results:
- The PRIMA program integrated 231 studies and documents.
- A calculation model was developed using three medical risk axes: isolation, population, and illness risk.
- A combined dataset from PRIMA predictions and actual event data validated the model's basis.
Conclusions:
- The PRIMA program shows promise as a general, applicable tool for predicting PPRs at MGs.
- It offers a data-driven approach to risk analysis and resource planning for mass gathering medical care.
- Further validation is recommended to solidify its application across diverse events.
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