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

Abstract

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

Predicting medical usage rate at mass gathering events in Belgium: development and validation of a nonlinear multivariable regression model

“…This is in line with results of a recent study that used multivariable regression trees for prediction of PPR at a wide variety of more than 200 MGs in Australia [ 24 ]. In a recent systematic review [ 10 ], we found that MG category was significantly associated with PPR in 5 out of 6 studies that included MG category as a predictor variable for PPR [ 2 , 5 , 20 , 24 – 26 ].…”

“…In our systematic review [ 10 ], we found that temperature (including heat index, a combined measure of air temperature and relative humidity) was positively associated with PPR in 5 out of 7 papers that evaluated ambient temperature at outdoor events [ 2 , 5 , 22 , 24 – 27 ]. This relationship is confirmed by our current analysis.…”

“…Based on the conclusions from our systematic review on prediction modelling studies for MUR in MGs [ 10 ] and on the availability of data, we identified 11 candidate predictor variables for PPR: MG category, age of patients, time, number of days, number of attendees, camping, alcohol, indoor/outdoor, bounded/unbounded, ambient temperature, and humidity; and 3 additional candidate predictor variables for TTHR: distance and time to the nearest hospital, and predicted PPR. See Additional file 2 for more details on the predictor variables.…”

“…Our previous systematic review [ 10 ] concluded that classic linear or Poisson regression models poorly predicted MUR. Moreover, we also found poor predictive power when applying a preliminary multivariable Poisson regression model to our dataset.…”

Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis

“…The initial certainty level of the included prediction modelling studies was set at 'high' because the association between the predictors and outcomes was considered irrespective of any causal connection. 39 The overall certainty level was, however, downgraded to 'moderate' and then 'low' because of inconsistent results (high heterogeneity) and the large proportion of high overall risk of bias amongst studies. The final overall certainty of 'low' implies that our confidence in the effect estimates is limited and further research is very likely to change the effect estimate.…”