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Publication Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study(JMIR Publications, 2021-08-11) Rabiolo, Alessandro; Alladio, Eugenio; Morales, Esteban; McNaught, Andrew; Bandello, Francesco; Afifi, Abdelmonem; Marchese, Alessandro; Rabiolo, Alessandro; McNaught, Andrew; Medical and DentalBackground: Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. Objective: The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. Methods: An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. Results: The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). Conclusions: The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.Publication Recruitment and retention of participants in UK surgical trials: survey of key issues reported by trial staff(Oxford University Press, 2020-10-04) Crocker, Joanna; Farrar, Nicola; Cook, Jonathan; Treweek, Shaun; Woolfall, Kerry; Bostock, Jennifer; Locock, Louise; Rees, Sian; Olszowski, Sophie; bulbulia, richard; Bulbulia, Richard; Medical and DentalBackground: Recruitment and retention of participants in surgical trials is challenging. Knowledge of the most common and problematic issues will aid future trial design. This study aimed to identify trial staff perspectives on the main issues affecting participant recruitment and retention in UK surgical trials. Methods: An online survey of UK surgical trial staff was performed. Respondents were asked whether or not they had experienced a range of recruitment and retention issues, and, if yes, how relatively problematic these were (no, mild, moderate or serious problem). Results: The survey was completed by 155 respondents including 60 trial managers, 53 research nurses, 20 trial methodologists and 19 chief investigators. The three most common recruitment issues were: patients preferring one treatment over another (81·5 per cent of respondents); clinicians' time constraints (78·1 per cent); and clinicians preferring one treatment over another (76·8 per cent). Seven recruitment issues were rated moderate or serious problems by a majority of respondents, the most problematic being a lack of eligible patients (60·3 per cent). The three most common retention issues were: participants forgetting to return questionnaires (81·4 per cent); participants found to be ineligible for the trial (74·3 per cent); and long follow-up period (70·7 per cent). The most problematic retention issues, rated moderate or serious by the majority of respondents, were participants forgetting to return questionnaires (56·4 per cent) and insufficient research nurse time/funding (53·6 per cent). Conclusion: The survey identified a variety of common recruitment and retention issues, several of which were rated moderate or serious problems by the majority of participating UK surgical trial staff. Mitigation of these problems may help boost recruitment and retention in surgical trials.Publication To explore the experience of research nurses who obtain consent from adults in emergency settings to participate in clinical trials, either prospectively or post enrolment(Wiley, 2020-06-10) Brown, Pauline; Newham, Roger; Hewison, Alistair; Brown, Pauline; Nursing and Midwifery RegisteredAim: To explore the understanding and experiences of research nurses who obtain informed consent from adult patients participating in emergency care research. Design: Qualitative phenomenographic descriptive study. Methods: Ten research nurses from six hospitals in England were recruited. Data were collected using semi-structured face-to-face and telephone interviews between January 2019 and March 2019. Interviews were transcribed verbatim and analysed thematically, informed by phenomenography. COREQ was followed. Results: Three main themes were identified: (a) emergency research is different, (b) protecting the patient, and (c) experience and confidence with recruitment. It was found that obtaining patient consent in emergency care research was challenging and timing of the process was crucial. Nurses with more experience of emergency care were more confident in approaching patients and their families. There was variability in out-of-hours recruitment which was a consequence of the range of informed consent processes used and the different levels of engagement of clinical teams. Conclusion: There is a variety of organisational cultures, processes and procedures which affect the way consent is obtained in emergency care research. A team approach was evident in the hospitals where consent rates were high and was more successful than those reliant solely on the presence of a research nurse. Organisations were able to recruit successfully to emergency care research studies irrespective of size and configuration. Further investigation of their models of working and strategies for engagement is needed. Experienced research nurses made a positive difference to recruitment and were more likely to approach patients to obtain consent. Relevance to clinical practice: The understanding and experiences of recruitment to clinical trials in emergency care research by research nurses can help identify barriers to recruitment. This study provides useful insights for healthcare practitioners, clinical trials coordinators and sponsors about how best to develop protocols and policies to increase recruitment to emergency care research.