Health Care Utilization Prediction Models and Tools During Mass Gathering Events: A Scoping Review

Introduction: The ability to estimate healthcare utilization during mass gathering events can tremendously impact local healthcare resources and the cost of organizing such events. Various prediction models have been proposed in the literature to assist event organizers in forecasting medical service demands. This study will examine the extent and type of evidence regarding prediction modules and variables for healthcare utilization at various mass gatherings (MG) events. Methods: The study included a search of four electronic databases (MEDLINE, EMBASE, Cochrane Library, and SCOPUS). Limits include the literature published on or after January 1, 2003. All retrieved citations looked at prediction models and variables of medical usage at all types of mass gathering events across all demographics, planned or spontaneous, that reported patient presentation rate (PPR) and/or transport to hospital rate (TTHR) at the event. All literature review articles were excluded. Results: This scoping review analyzed 25 studies. Thirteen studies focused on prediction models for medical usage rates in MG, and 12 studies explored predictive variables for PPR in MG. The Arbon model (used in nine studies) was the most frequently examined. The Hartman classification model appeared in seven studies. Plan Risk Manifestations (PRIMA), a Belgian tool, was used in three studies. The Zeitz method and the South Africa Mass Gathering Model (SA-MGM) are featured in two studies. Positively correlated variables with PPR included temperature (most cited, six times), event type (five times), heat index (four times), and crowd size (three times). Other less frequently cited variables (once each) included humidity, venue accessibility, festival format, and age class. Overall, temperature and event type were the most frequently used predictors across studies. Conclusion: Healthcare utilization prediction tools vary widely. Healthcare organizations can help improve and optimize scarce resources during mass gathering events by understanding the variety and nature of these prediction tools.