Comparison of Prediction Models for Use of Medical Resources at Urban Auto-racing Events

Nable, Jose V.; Margolis, Asa M.; Lawner, Benjamin J.; Hirshon, Jon Mark; Perricone, Alexander J.; Galvagno, Samuel M.; Lee, Debra; Millin, Michael G.; Bissell, Richard A.; Alcorta, Richard L.

doi:10.1017/s1049023x14001046

Cited by 14 publications

(19 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), but also difference in public health systems across countries (leading to different emergency care services delivery policies) hinders extrapolation. This limited generalizability was also confirmed by the 3 studies that applied observations from few mass gatherings to the prediction models of Arbon or Zeitz, showing significant under/overestimation of the medical usage rates when using an existing prediction model [28][29][30]. Future development of prediction models should therefore be validated both internally and externally, preferably against big data sets of various types of mass gatherings.…”

Section: Discussionmentioning

confidence: 92%

“…Three studies externally applied prediction models for mass gatherings by comparing the actual number of patient presentations or transports at 3 outdoor electronic dance music manifestations in the USA [28], a US spectator sport manifestations (i.e. automobile race, Baltimore Grand Prix) [29] and a city festival (i.e. Royal Air Show, Adelaide, Australia) [30] with the predicted number by the model developed by Arbon et al [15], by Hartman et al [32] and/or the retrospective (historical) analysis undertaken by Zeitz et al [33].…”

Section: Plos Onementioning

confidence: 99%

See 1 more Smart Citation

Prediction modelling studies for medical usage rates in mass gatherings: A systematic review

et al. 2020

View full text Add to dashboard Cite

Background Mass gathering manifestations attended by large crowds are an increasingly common feature of society. In parallel, an increased number of studies have been conducted that developed and/or validated a model to predict medical usage rates at these manifestations. Aims To conduct a systematic review to screen, analyse and critically appraise those studies that developed or validated a multivariable statistical model to predict medical usage rates at mass gatherings. To identify those biomedical, psychosocial and environmental predictors that are associated with increased medical usage rates and to summarise the predictive performance of the models. Method We searched for relevant prediction modelling studies in six databases. The predictors from multivariable regression models were listed for each medical usage rate outcome (i.e. patient presentation rate (PPR), transfer to hospital rate (TTHR) and the incidence of new injuries). The GRADE methodology (Grades of Recommendation, Assessment, Development and Evaluation) was used to assess the certainty of evidence. Results We identified 7,036 references and finally included 16 prediction models which were developed (n = 13) or validated (n = 3

show abstract

Section: Discussionmentioning

confidence: 92%

Section: Plos Onementioning

confidence: 99%

Prediction modelling studies for medical usage rates in mass gatherings: A systematic review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…, 13 , 16 Most of the reviewed manuscripts highlighted the link with temperature and dehydration contributing to a large number of presentations. In comparison, one study suggested that higher temperatures did not affect patient presentations, but rather reduced them 15 . This could be due to alcohol availability, if the events were indoor or outdoor, and the age demographic of the event.…”

Section: Discussionmentioning

confidence: 98%

Impact of Patients Presenting with Alcohol and/or Drug Intoxication on In-Event Health Care Services at Mass-Gathering Events: An Integrative Literature Review

Bullock

Ranse

Hutton

2018

Prehosp. Disaster med.

View full text Add to dashboard Cite

Substance use and/or intoxication can place a strain on in-event medical services at mass-gathering events. Of the various types of mass-gathering events, music festivals appear to be the most affected by substance use and intoxication. BullockM, RanseJ, HuttonA. Impact of patients presenting with alcohol and/or drug intoxication on in-event health care services at mass-gathering events: an integrative literature review. Prehosp Disaster Med. 2018;33(5):539-542.

show abstract

“…As indicated earlier, the prediction of number of patient encounters during MGs remains complex and somewhat enigmatic as each event has its own characteristics that influence decisions on the deployment of medical resources. 17 Despite these challenges, the results are deemed to hold sufficient confidence because of the different methods the other models used in calculating their predictions. There is a low degree of dependency among the models used.…”

Section: Discussionmentioning

confidence: 99%

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

Spaepen

Haenen

Kaufman

et al. 2020

Prehosp. Disaster med.

View full text Add to dashboard Cite

Introduction: 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. Methods: 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. Results: 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%). Conclusion: 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.

show abstract

Comparison of Prediction Models for Use of Medical Resources at Urban Auto-racing Events

Cited by 14 publications

References 22 publications

Prediction modelling studies for medical usage rates in mass gatherings: A systematic review

Prediction modelling studies for medical usage rates in mass gatherings: A systematic review

Impact of Patients Presenting with Alcohol and/or Drug Intoxication on In-Event Health Care Services at Mass-Gathering Events: An Integrative Literature Review

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

Contact Info

Product

Resources

About