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

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. To conduct a systematic review to scre...

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Veröffentlicht in:PloS one 2020-06, Vol.15 (6), p.e0234977
Hauptverfasser: Van Remoortel, Hans, Scheers, Hans, De Buck, Emmy, Haenen, Winne, Vandekerckhove, Philippe, Bajpai, Ram Chandra, Mathes, Tim
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Scheers, Hans
De Buck, Emmy
Haenen, Winne
Vandekerckhove, Philippe
Bajpai, Ram Chandra
Mathes, Tim
description 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. 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. 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. We identified 7,036 references and finally included 16 prediction models which were developed (n = 13) or validated (n = 3) in the USA (n = 8), Australia (n = 4), Japan (n = 1), Singapore (n = 1), South Africa (n = 1) and The Netherlands (n = 1), with a combined audience of >48 million people in >1700 mass gatherings. Variables to predict medical usage rates were biomedical (i.e. age, gender, level of competition, training characteristics and type of injury) and environmental predictors (i.e. crowd size, accommodation, weather, free water availability, time of the manifestation and type of the manifestation) (low-certainty evidence). Evidence from 3 studies indicated that using Arbon's or Zeitz' model in other contexts significantly over- or underestimated medical usage rates (from 22% overestimation to 81% underestimation). This systematic review identified multivariable models with biomedical and environmental predictors for medical usage rates at mass gatherings. Since the overall certainty of the evidence is low and the predictive performance is generally poor, proper development and validation of a context-specific model is recommended.
doi_str_mv 10.1371/journal.pone.0234977
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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. 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. 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. We identified 7,036 references and finally included 16 prediction models which were developed (n = 13) or validated (n = 3) in the USA (n = 8), Australia (n = 4), Japan (n = 1), Singapore (n = 1), South Africa (n = 1) and The Netherlands (n = 1), with a combined audience of &gt;48 million people in &gt;1700 mass gatherings. Variables to predict medical usage rates were biomedical (i.e. age, gender, level of competition, training characteristics and type of injury) and environmental predictors (i.e. crowd size, accommodation, weather, free water availability, time of the manifestation and type of the manifestation) (low-certainty evidence). Evidence from 3 studies indicated that using Arbon's or Zeitz' model in other contexts significantly over- or underestimated medical usage rates (from 22% overestimation to 81% underestimation). This systematic review identified multivariable models with biomedical and environmental predictors for medical usage rates at mass gatherings. 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subjects Bias
Biology and Life Sciences
Crowds
Earth Sciences
Environment models
Evidence-based practice
Free water
Health aspects
Mathematical models
Medical care utilization
Medicine
Medicine and Health Sciences
Performance prediction
Physical Sciences
Prediction models
Primary care
Public health
Regression analysis
Regression models
Research and Analysis Methods
Social Sciences
Statistical analysis
Statistical modelling
Statistical models
Studies
Systematic review
Variables
Water availability
Weather
title Prediction modelling studies for medical usage rates in mass gatherings: A systematic review
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