Validation of a Belgian Prediction Model for Patient Encounters at Music Mass Gatherings

Quantitative Metrics in Mass-Gathering Studies: A Comprehensive Systematic Review

INTRODUCTION

Mass gatherings are events where many people come together at a specific location for a specific purpose, such as concerts, sports events, or religious gatherings, within a certain period of time. In mass-gathering studies, many rates and ratios are used to assess the demand for medical resources. Understanding such metrics is crucial for effective planning and intervention efforts. Therefore, this systematic review aims to investigate the usage of rates and ratios reported in mass-gathering studies.

METHODS

In this systematic review, the PRISMA guidelines were followed. Articles published through December 2023 were searched on Web of Science, Scopus, Cochrane, and PubMed using the specified keywords. Subsequently, articles were screened based on titles, abstracts, and full texts to determine their eligibility for inclusion in the study. Finally, the articles that were related to the study's aim were evaluated.

RESULTS

Out of 745 articles screened, 55 were deemed relevant for inclusion in the study. These included 45 original research articles, three special reports, three case presentations, two brief reports, one short paper, and one field report. A total of 15 metrics were identified, which were subsequently classified into three categories: assessment of population density, assessment of in-event health services, and assessment of out-of-event health services.

CONCLUSION

The findings of this study revealed notable inconsistencies in the reporting of rates and ratios in mass-gathering studies. To address these inconsistencies and to standardize the information reported in mass-gathering studies, a Metrics and Essential Ratios for Gathering Events (MERGE) table was proposed. Future research should promote consistency in terminology and adopt standardized methods for presenting rates and ratios. This would not only enhance comparability but would also contribute to a more nuanced understanding of the dynamics associated with mass gatherings.

How the Italian Formula 1 Grand Prix 2022 Mass Gathering Event Compares to the Arbon Model: A Descriptive Study

OBJECTIVE

To describe the health-care resources implemented during the Italian Formula 1 Grand Prix (F1GP) and to calculate the patient presentation rate (PPR) based on both real data and a prediction model.

METHODS

Observational and descriptive study conducted from September 9 to September 11, 2022, during the Italian F1GP hosted in Monza (Italy). Maurer's formula was applied to decide the number and type of health resources to be allocated. Patient presentation rate (PPR) was computed based on real data (PPR_real) and based on the Arbon formula (PPR_est).

RESULTS

Of 336,000 attendees, n = 263 requested medical assistance with most of them receiving treatment at the advanced medical post, and n = 16 needing transport to the hospital. The PPR_real was 51 for Friday, 78 for Saturday, 134 for Sunday, and 263 when considering the whole event as a single event. The PPR_est resulted in 85 for Friday, 93 for Saturday, 97 for Sunday, and 221 for the total population.

CONCLUSIONS

A careful organization of health-care resources could mitigate the impact of the Italian F1GP on local hospital facilities. The Arbon formula is an acceptable model to predict and estimate the number of patients requesting medical assistance, but further investigation needs to be conducted to implement the model and tailor it to broader categories of MGE.

The Impact of In-Event Health Services at Europe's Largest Electronic Dance Music Festival on Ems and Ed in the Host Community

BACKGROUND

Electronic dance music festivals (EDMF) can cause a significant disruption in the standard operational capacity of emergency medical services (EMS) and hospitals. We determined whether or not the presence of in-event health services (IEHS) can reduce the impact of Europe's largest EDMF on the host community EMS and local emergency departments (EDs).

METHODS

We conducted a pre-post analysis of the impact of Europe's largest EDMF in July 2019, in Boom, Belgium, on the host community EMS and local EDs. Statistical analysis included descriptive statistics, independent t-tests, and χ2 analysis.

RESULTS

Of 400,000 attendees, 12,451 presented to IEHS. Most patients only required in-event first aid, but 120 patients had a potentially life-threatening condition. One hundred fifty-two patients needed to be transported by IEHS to nearby hospitals, resulting in a transport-to-hospital rate of 0.38/1000 attendees. Eighteen patients remained admitted to the hospital for >24 h; one died after arrival in the ED. IEHS limited the overall impact of the MGE on regular EMS and nearby hospitals. No predictive model proved optimal when proposing the optimal number and level of IEHS members.

CONCLUSIONS

This study shows that IEHS at this event limited ambulance usage and mitigated the event's impact on regular emergency medical and health services.

Terrorist Attacks against Concerts and Festivals: A Review of 146 Incidents in the Global Terrorism Database

BACKGROUND

Mass gatherings are vulnerable to terrorist attacks and are considered soft targets with potential to inflict high numbers of casualties. The objective of this study was to identify and characterize all documented terrorist attacks targeted at concerts and festivals reported to the Global Terrorism Database (GTD) over a 50-year period.

METHODS

The GTD was searched for all terrorist attacks against concerts and festivals that occurred world-wide from 1970 through 2019. Analyses were performed on temporal factors, location, target type, attack and weapon type, attacker type, and number of casualties or hostages. Ambiguous incidents were excluded if there was doubt about whether they were exclusively acts of terrorism. Chi-square tests were performed to evaluate trends over time and differences in attack types.

RESULTS

In total, 146 terrorist attacks were identified. In addition to musical concerts, festivals included religious, cultural, community, and food festivals. With 53 incidents, South Asia was the most heavily hit region of the world, followed by the Middle East & North Africa with 25 attacks. Bombings and explosions were the most common attack types. The attacks targeted attendees, pilgrims, politicians, or police/military members who secured the concerts and festivals.

CONCLUSION

This analysis of the GTD, which identified terrorist attacks aimed at concerts and festivals over a 50-year period, demonstrates that the threat is significant, and not only in world regions where terrorism is more prevalent or local conflicts are going on. The findings of this study may help to create or enhance contingency plans.

Predicting medical usage rate at mass gathering events in Belgium: development and validation of a nonlinear multivariable regression model

Background

Every year, volunteers of the Belgian Red Cross provide onsite medical care at more than 8000 mass gathering events and other manifestations. Today standardized planning tools for optimal preventive medical resource use during these events are lacking. This study aimed to develop and validate a prediction model of patient presentation rate (PPR) and transfer to hospital rate (TTHR) at mass gatherings in Belgium.

Methods

More than 200,000 medical interventions from 2006 to 2018 were pooled in a database. We used a subset of 28 different mass gatherings (194 unique events) to develop a nonlinear prediction model. Using regression trees, we identified potential predictors for PPR and TTHR at these mass gatherings. The additional effect of ambient temperature was studied by linear regression analysis. Finally, we validated the prediction models using two other subsets of the database.

Results

The regression tree for PPR consisted of 7 splits, with mass gathering category as the most important predictor variable. Other predictor variables were attendance, number of days, and age class. Ambient temperature was positively associated with PPR at outdoor events in summer. Calibration of the model revealed an R² of 0.68 (95% confidence interval 0.60–0.75). For TTHR, the most determining predictor variables were mass gathering category and predicted PPR (R² = 0.48). External validation indicated limited predictive value for other events (R² = 0.02 for PPR; R² = 0.03 for TTHR).

Conclusions

Our nonlinear model performed well in predicting PPR at the events used to build the model on, but had poor predictive value for other mass gatherings. The mass gathering categories “outdoor music” and “sports event” warrant further splitting in subcategories, and variables such as attendance, temperature and resource deployment need to be better recorded in the future to optimize prediction of medical usage rates, and hence, of resources needed for onsite emergency medical care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12580-8.

Validation of a Belgian Prediction Model for Patient Encounters at Football Mass Gatherings

BACKGROUND

To validate the Belgian Plan Risk Manifestations (PRIMA) model, actual patient presentation rates (PPRs) from Belgium's largest football stadium were compared with predictions provided by existing models and the Belgian PRIMA model.

METHODS

Actual patient presentations gathered from 41 football games (2010-2019) played at the King Baudouin Stadium (Brussels, Belgium) were compared with predictions by existing models and the PRIMA model. All attendees who sought medical help from in-event health services (IEHS) in the stadium or called 1-1-2 within the closed perimeter around the stadium were included. Data were analyzed by ANOVA, Pearson correlation tests, and Wilcoxon singed-rank test.

RESULTS

A total of 1,630,549 people attended the matches, with 626 people needing first aid. Both the PRIMA and the Hartman model over-estimated the number of patient encounters for each occasion. The Arbon model under-estimated patient encounters for 9.75% (95% CI, 0.49-19.01) of the events. When comparing deviations in predictions between the PRIMA model to the other models, there was a significant difference in the mean deviation (Arbon: Z = -5.566, P <.001, r = -.61; Hartman: Z = -4.245, P <.001, r = .47).

CONCLUSION

When comparing the predicted patient encounters, only the Arbon model under-predicted patient presentations, but the Hartman and the PRIMA models consistently over-predicted. Because of continuous over-prediction, the PRIMA model showed significant differences in mean deviation of predicted PPR. The results of this study suggest that the PRIMA model can be used during planning for domestic and international football matches played at the King Baudouin Stadium, but more data and further research are needed.

Validation of a Belgian Prediction Model for Patient Encounters at Belgium's Largest Public Cultural Mass Gathering

OBJECTIVE

To compare actual patient presentation rates from Belgium's largest public open-air cultural festival with predictions provided by existing models and the Belgian Plan Risk Manifestations model.

METHODS

Retrospectively, actual patient presentation rates gathered from the Ghent Festivities (Belgium) during 2013-2019 were compared to predicted patient presentation rates by the Arbon, Hartman, and PRIMA models.

RESULTS

During 7 editions, 8673000 people visited the Ghent Festivities; 9146 sought medical assistance resulting in a mean patient presentation rate (PPR) of 1.05. The PRIMA model overestimated the number of patient encounters for each occasion. The other models had a high rate of underprediction. When comparing deviations in predictions between the PRIMA model to the other models, there is a significant difference in the mean deviation (Arbon: T = 0.000, P < 0.0001, r = -0.8701; Hartman: T = 0.000, P < 0.0001, r = -0.869).

CONCLUSION

Despite the differences between the predictions of all 3 models, our results suggest that the PRIMA model is a valid tool to predict patient presentations to IEHS during public cultural MG. However, to substantiate the PRIMA model even further, more research is needed to further validate the model for a broad range of MG.