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

Introduction:Mass gatherings attended by large crowds are an increasingly common feature of society. In parallel, an increased number of studies have been conducted to identify those variables that are associated with increased medical usage rates.Aim:To identify studies that developed and/or validated a statistical regression model predicting patient presentation rate (PPR) or transfer to hospital rate (TTHR) at mass gatherings.Methods:Prediction modeling studies from 6 databases were retained following systematic searching. Predictors for PPR and/or TTHR that were included in a multivariate regression model were selected for analysis. The GRADE methodology (Grades of Recommendation, Assessment, Development, and Evaluation) was used to assess the quality of evidence.Results:We identified 11 prediction modeling studies with a combined audience of >32 million people in >1500 mass gatherings. Eight cross-sectional studies developed a prediction model in a mixed audience of (spectator) sports events, music concerts, and public exhibitions. Statistically significant variables (p<0.05) to predict PPR and/or TTHR were as follows: accommodation (seated, boundaries, indoor/outdoor, maximum capacity, venue access), type of event, weather conditions (humidity, dew point, heat index), crowd size, day vs night, demographic variables (age/gender), sports event distance, level of competition, free water availability, and specific TTHR-predictive factors (injury status: number of patient presentations, type of injury). The quality of the evidence was considered as low. Three studies externally validated their model against existing models. Two validation studies showed a large underestimation of the predicted patients presentations or transports to hospital (67-81%) whereas one study overestimated these outcomes by 10-28%.Discussion:This systematic review identified a comprehensive list of relevant predictors which should be measured to develop and validate future models to predict medical usage at mass gatherings. This will further scientifically underpin more effective pre-event planning and resource provision.

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Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies

Journal of Critical Care ◽

10.1016/j.jcrc.2021.10.001 ◽

2022 ◽

Vol 67 ◽

pp. 44-56

Author(s):

Tuomas Frondelius ◽

Irina Atkova ◽

Jouko Miettunen ◽

Jordi Rello ◽

Miia M. Jansson

Keyword(s):

Systematic Review ◽

Prediction Models ◽

Meta Analysis ◽

Ventilator Associated Pneumonia ◽

Modelling Studies ◽

Prediction Modelling ◽

Prognostic Prediction

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A systematic review of early modelling studies of Ebola virus disease in West Africa – CORRIGENDUM

Epidemiology and Infection ◽

10.1017/s0950268817000759 ◽

2017 ◽

Vol 145 (9) ◽

pp. 1961-1961

Author(s):

Z. S. Y. WONG ◽

C. M. BUI ◽

A. A. CHUGHTAI ◽

C. R. MACINTYRE

Keyword(s):

Systematic Review ◽

West Africa ◽

Ebola Virus ◽

Virus Disease ◽

Ebola Virus Disease ◽

Modelling Studies

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Statistical modelling studies examining the dimensional structure of psychopathology experienced by adults with intellectual disabilities: Systematic review

Research in Developmental Disabilities ◽

10.1016/j.ridd.2016.01.018 ◽

2016 ◽

Vol 53-54 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

C.A. Melville ◽

P.C.D. Johnson ◽

E. Smiley ◽

N. Simpson ◽

A. McConnachie ◽

...

Keyword(s):

Systematic Review ◽

Intellectual Disabilities ◽

Statistical Modelling ◽

Dimensional Structure ◽

Modelling Studies ◽

Adults With Intellectual Disabilities

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Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

PLoS ONE ◽

10.1371/journal.pone.0136181 ◽

2015 ◽

Vol 10 (9) ◽

pp. e0136181 ◽

Cited By ~ 77

Author(s):

Eugene Y. H. Tang ◽

Stephanie L. Harrison ◽

Linda Errington ◽

Mark F. Gordon ◽

Pieter Jelle Visser ◽

...

Keyword(s):

Systematic Review ◽

Risk Prediction ◽

Dementia Risk ◽

Prediction Modelling

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The use and reporting of airline passenger data for infectious disease modelling: a systematic review

Eurosurveillance ◽

10.2807/1560-7917.es.2019.24.31.1800216 ◽

2019 ◽

Vol 24 (31) ◽

Cited By ~ 3

Author(s):

Margaux Marie Isabelle Meslé ◽

Ian Melvyn Hall ◽

Robert Matthew Christley ◽

Steve Leach ◽

Jonathan Michael Read

Keyword(s):

Infectious Disease ◽

Systematic Review ◽

Airline Industry ◽

Web Of Science ◽

Data Sources ◽

Health Policies ◽

Public Health Policies ◽

Airline Passenger ◽

Modelling Studies ◽

Access Data

Background A variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources. Aim We conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology. Methods Articles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies’ reproducibility assessed. Results We identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible. Limitations By limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus. Conclusion We recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility.

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Communicable Diseases Pattern in Religious Mass Gatherings: A Systematic Review

JOURNAL FOR CLINICAL AND DIAGNOSTIC RESEARCH ◽

10.7860/jcdr/2019/37916.12539 ◽

2019 ◽

Author(s):

Arezou Karampourian ◽

Davoud Khorasani-Zavareh ◽

Zohreh Ghomian

Keyword(s):

Systematic Review ◽

Communicable Diseases ◽

Mass Gatherings

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Effective Contact Tracing for COVID-19: A Systematic Review

10.1101/2020.07.23.20160234 ◽

2020 ◽

Cited By ~ 1

Author(s):

Carl-Etienne Juneau ◽

Anne-Sara Briand ◽

Tomas Pueyo ◽

Pablo Collazzo ◽

Louise Potvin

Keyword(s):

Systematic Review ◽

Contact Tracing ◽

Number Of Factors ◽

Effective Contact ◽

Health Practitioners ◽

Modelling Studies ◽

Cautious Interpretation ◽

Public Health Practitioners ◽

Or Modelling

Background: Contact tracing is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. This systematic review aimed to examine contact tracing effectiveness in the context of COVID-19. Methods: Following PRISMA guidelines, MEDLINE, Embase, Global Health, and All EBM Reviews were searched using a range of terms related to contact tracing for COVID-19. Articles were included if they reported on the ability of contact tracing to slow or stop the spread of COVID-19 or on characteristics of effective tracing efforts. Two investigators screened all studies. Results: A total of 32 articles were found. All were observational or modelling studies, so the quality of the evidence was low. Observational studies (n=14) all reported that contact tracing (alone or in combination with other interventions) was associated with better control of COVID-19. Results of modelling studies (n=18) depended on their assumptions. Under assumptions of prompt and thorough tracing with no further transmission, they found that contact tracing could stop an outbreak (e.g. by reducing the reproduction number from 2.2 to 0.57) or that it could reduce infections (e.g. by 24%-71% with a mobile tracing app). Under assumptions of slower, less efficient tracing, modelling studies suggested that tracing could slow, but not stop COVID-19. Conclusions: Observational and modelling studies suggest that contact tracing is associated with better control of COVID-19. Its effectiveness likely depends on a number of factors, including how many and how fast contacts are traced and quarantined, and how effective quarantines are at preventing further transmission. A cautious interpretation suggests that to stop the spread of COVID-19, public health practitioners have 2-3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts, and that once isolated, cases and contacts should infect zero new cases. Less efficient tracing may slow, but not stop, the spread of COVID-19. Inefficient tracing (with delays of 4-5+ days or less than 60% of contacts quarantined with no further transmission) may not contribute meaningfully to control of COVID-19.

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Implications of data-driven analyses for personalized therapy in psychosis: a systematic review of cluster- and trajectory-based modelling studies

10.1101/599498 ◽

2019 ◽

Cited By ~ 2

Author(s):

Tesfa Dejenie Habtewold ◽

Lyan H. Rodijk ◽

Edith J. Liemburg ◽

Grigory Sidorenkov ◽

H. Marike Boezen ◽

...

Keyword(s):

Systematic Review ◽

Longitudinal Studies ◽

Cognitive Deficits ◽

Negative Symptoms ◽

Clinical Symptoms ◽

Data Driven ◽

Schizophrenia Spectrum Disorders ◽

Cross Sectional ◽

Modelling Studies ◽

Positive And Negative Symptoms

AbstractIntroductionTo tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its (sub)clinical symptoms though there is no systematic review.AimsTo summarize the evidence from cluster- and trajectory-based studies of positive, negative and cognitive symptoms in patients with schizophrenia spectrum disorders, their siblings and healthy people. Additionally, we aimed to highlight knowledge gaps and point out future directions to optimize the translatability of cluster- and trajectory-based studies.MethodsA systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE, and Web of Science electronic databases. Both cross-sectional and longitudinal studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data.ResultsOf 2,285 studies retrieved, 50 studies (17 longitudinal and 33 cross-sectional) conducted in 30 countries were selected for review. Longitudinal studies discovered two to five trajectories of positive and negative symptoms in patient, and four to five trajectories of cognitive deficits in patient and sibling. In cross-sectional studies, three clusters of positive and negative symptoms in patient, four clusters of positive and negative schizotypy in sibling, and three to five clusters of cognitive deficits in patient and sibling were identified. These studies also reported multidimensional predictors of clusters and trajectories.ConclusionsOur findings indicate that (sub)clinical symptoms of schizophrenia are more heterogeneous than currently recognized. Identified clusters and trajectories can be used as a basis for personalized psychiatry.

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