Music Genre as a Predictor of Resource Utilization at Outdoor Music Concerts

Westrol, Michael S.; Koneru, Susmith; McIntyre, Norah; Caruso, Andrew T.; Arshad, Faizan; Merlin, Mark A.

doi:10.1017/s1049023x17000085

Cited by 22 publications

(39 citation statements)

References 22 publications

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“…A decreased number of transfers to hospital is well-associated with the provision of on-site medical care by Emergency Medical Services (EMS) and personnel in the field. [10][11][12] And while the science of MG medicine is fast evolving, literature on models predicting patient encounters is scarce, 9 and except for Belgium's Red Cross Flanders' (Flanders, Belgium) Medical Triage and Registration Informatics System (MedTRIS), 13 minimum data sets (MDS) for research and evaluation of MGs are lacking. Models most cited in MG literature are the models of Arbon 5,14 and Hartman.…”

Section: Introductionmentioning

confidence: 99%

The Development of PRIMA - A Belgian Prediction Model for Patient Encounters at Mass Gatherings

Spaepen

Haenen

Hubloue

2020

Prehosp. Disaster med.

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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.

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Section: Introductionmentioning

confidence: 99%

The Development of PRIMA - A Belgian Prediction Model for Patient Encounters at Mass Gatherings

Spaepen

Haenen

Hubloue

2020

Prehosp. Disaster med.

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“…The biomedical and environmental multivariable factors predicting the transfer to hospital rate are depicted in Fig 3. Humidity, temperature or the heat index (�32.2˚C) were positively associated to the TTHR [15,24,26]. In one multivariable regression model developed with data from auto race events, mean temperature, precipitation and type of the manifestation (practice day vs race day) were not predictive for TTHR [21].…”

Section: Factors That Predict Transfer To Hospital (Rate)mentioning

confidence: 91%

“…One study conducted in the USA found that the presence of air conditioning in a mixture of indoor mass gatherings (sport spectator manifestations, concerts, public exhibitions) was linked to a lower patient presentation rate [17]. In five studies, the following climatological parameters were not statistical significantly associated with patient presentation (rate): humidity, %sunshine and wind speed in 1 study [27], temperature in 2 studies [16,27] and precipitation in 3 studies [18,21,24].…”

Section: Factors That Predict Patient Presentation (Rate)mentioning

confidence: 98%

“…The following predictor variables were included in these models: weather conditions (in 8 models), crowd size (in 4 models), type of the manifestation (in 8 models), venue accommodation (in 4 models), time of the manifestation (in 3 models), free water availability (in 1 model), https://doi.org/10.1371/journal.pone.0234977.g001 Weather conditions were found to be a significant factor to predict patient presentation (rate): humidity [15,26], temperature [21], heat index (i.e. a combination of air temperature and relative humidity) [18,24] and dew point [27] were positively associated with the number or rate of patient presentation at first aid posts. In one of our included studies, temperature (i.e.…”

Section: Factors That Predict Patient Presentation (Rate)mentioning

confidence: 99%

“…Two Australian studies found that manifestations organised during both day and night resulted in a higher PPR compared to manifestations organized during the day or night [15,26]. One model to predict PPR at 403 music concerts found that day of the week was not a statistical significant predictor [24].…”

Section: Plos Onementioning

confidence: 99%

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

et al. 2020

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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

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Medical care at a mass gathering music festival

Maleczek

Rubi

Fohringer³

et al. 2021

Wien Klin Wochenschr

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Summary Background Knowledge about longitudinal changes in epidemiological data at mass gathering events is sparse. The goal of this study was to determine and compare the type, severity and frequency of illnesses at a large music festival over 7 consecutive years (2011–2017). Methods Prospectively collected data from the rescue operation protocols of an Austrian music festival were retrieved and analyzed. Patient presentation rates (PPR) and transport to hospital rates (TTHR) were calculated and compared between years. Linear regression was used to investigate the association between (a) total number of visitors and number of patient presentations, and (b) environmental factors and temperature related medical emergencies. A descriptive analysis of pertinent medical logistics management was performed. Results The median (minimum to maximum) PPR and TTHR were 12.01 (9.33 in 2016 to 20.86 in 2011) and 0.57 (0.40 in 2017 to 1.06 in 2013) per 1000 visitors, respectively. In linear regression models, no significant associations were found between the number of visitors and either the total number of patient presentations, NACA 1–2 or NACA 3–5 classified emergencies. Environmental temperature had a significant impact on heat related patient presentations (p < 0.001). Conclusion There were significant differences and a high variance in both PPR and TTHR over the years. Contrary to our expectations, the number of visitors did not predict the number of patient presentations. Ambient temperature was associated with the number of heat related emergencies but not with the number of cold related emergencies. Prevention strategies, such as the removal of insect nests, resulted in significantly fewer insect related emergencies.

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Music Genre as a Predictor of Resource Utilization at Outdoor Music Concerts

Cited by 22 publications

References 22 publications

The Development of PRIMA - A Belgian Prediction Model for Patient Encounters at Mass Gatherings

The Development of PRIMA - A Belgian Prediction Model for Patient Encounters at Mass Gatherings

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

Medical care at a mass gathering music festival

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