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.

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.

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).

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.

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.

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.

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).

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.

A Belgian predictive medical resource tool, Plan Risk Manifestations (PRIMA), for the prediction of the number of patient encounters at mass gatherings (MGs) has recently been developed, in addition to the existing models of Arbon and Hartman. This study presents the results of the validation process for the PRIMA model for music MGs.

A retrospective study was conducted using data gathered from music MGs in the province of Antwerp (Belgium) during the period of 2012-2016. Data from 87 music MGs were used for the study. The forecast of medical resources for these events was determined by entering the characteristics of individual events into the Arbon, Hartman, and PRIMA models. In order to determine if the PRIMA model is under- or over-predictive, the data gathered were retrospectively compared to the predicted number of resources needed using the aforementioned models. Statistical analysis included means, medians, and interquartile ranges (IQRs). Nonparametric related samples test (Wilcoxon Samples Signed Rank Test) for comparison of the median in deviations in predictions of patient presentation rates (PPRs) was performed using SPSS version 23 (IBM Corp.; Armonk, New York USA). Confidence interval levels were set at 95% and results were deemed statistically significant at P <.05. This triple comparison was used to determine the overall performance of all three models.

All three models had an acceptable rate of over-prediction of number of patient encounters ([Arbon 25.29%; 95% CI, 30.91-43.74]; [Hartman 29.89%; 95% CI, 57.10-68.90]; and [PRIMA 19.54%; 95% CI, 57.80-76.20]). But all models also had a high rate of under-prediction of number of patient encounters ([Arbon 74.71%; 95% CI, 453.31-752.52]; [Hartman 70.11%; 95% CI, 546.90-873.77]; and [PRIMA 78.16%; 95% CI, 288.91-464.89]). Only the PRIMA model succeeded in the correct prediction of the number of patient encounters on two occasions (2.3%).

Results of this study are in-line with existing literature. When comparing the predicted patient encounters, all three models had high rates of under-prediction and moderate rates of over-prediction. When comparing mean deviations, the PRIMA model had the lowest mean deviation of all predicted PPRs. Belgian events of the types included in the presented data may use the PRIMA model with confidence to predict PPRs and estimate the in-event health services (IEHS) requirements.

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.

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.

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.

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.

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.

Recerntly, the role of the healthcare network, defined as a set of hospitals linked by patient transfers, has been increasingly considered in the control of antimicrobial resistance. Here, we investigate the potential impact of nursing homes on the spread of antimicrobial-resistant pathogens across the healthcare network and its importance for control strategies.

Based on patient transfer data, we designed a network model representing the Dutch healthcare system of hospitals and nursing homes. We simulated the spread of an antimicrobial-resistant pathogen across the healthcare network, and we modeled transmission within institutions using a stochastic susceptible–infected–susceptible (SIS) epidemic model. Transmission between institutions followed transfers. We identified the contribution of nursing homes to the dispersal of the pathogen by comparing simulations of the network with and without nursing homes.

Our results strongly suggest that nursing homes in the Netherlands have the potential to drive and sustain epidemics across the healthcare network. Even when the daily probability of transmission in nursing homes is much lower than in hospitals, transmission of resistance can be more effective because of the much longer length of stay of patients in nursing homes.

If an antimicrobial-resistant pathogen emerges that spreads easily within nursing homes, control efforts aimed at hospitals may no longer be effective in preventing nationwide outbreaks. It is important to consider nursing homes in planning regional and national infection control and in implementing surveillance systems that monitor the spread of antimicrobial resistance.

Infect Control Hosp Epidemiol 2016;37:761–767