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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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. |
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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 >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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0234977</identifier><identifier>PMID: 32574190</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2020-06, Vol.15 (6), p.e0234977</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Van Remoortel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Van Remoortel et al 2020 Van Remoortel et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c669t-63b8eed15c476ce06fc23b234b439fc374b6794d4aca8584f0a76312116c96483</citedby><cites>FETCH-LOGICAL-c669t-63b8eed15c476ce06fc23b234b439fc374b6794d4aca8584f0a76312116c96483</cites><orcidid>0000-0003-1942-1799</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310685/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310685/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids></links><search><contributor>Mathes, Tim</contributor><creatorcontrib>Van Remoortel, Hans</creatorcontrib><creatorcontrib>Scheers, Hans</creatorcontrib><creatorcontrib>De Buck, Emmy</creatorcontrib><creatorcontrib>Haenen, Winne</creatorcontrib><creatorcontrib>Vandekerckhove, Philippe</creatorcontrib><creatorcontrib>Bajpai, Ram Chandra</creatorcontrib><creatorcontrib>Mathes, Tim</creatorcontrib><title>Prediction modelling studies for medical usage rates in mass gatherings: A systematic review</title><title>PloS one</title><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. 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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.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>32574190</pmid><doi>10.1371/journal.pone.0234977</doi><tpages>e0234977</tpages><orcidid>https://orcid.org/0000-0003-1942-1799</orcidid><oa>free_for_read</oa></addata></record> |
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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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