Coronavirus conspiracy beliefs, mistrust, and compliance with government guidelines in England

BACKGROUND: An invisible threat has visibly altered the world. Governments and key institutions have had to implement decisive responses to the danger posed by the coronavirus pandemic. Imposed change will increase the likelihood that alternative explanations take hold. In a proportion of the general population there may be strong scepticism, fear of being misled, and false conspiracy theories. Our objectives were to estimate the prevalence of conspiracy thinking about the pandemic and test associations with reduced adherence to government guidelines. METHODS: A non-probability online survey with 2501 adults in England, quota sampled to match the population for age, gender, income, and region. RESULTS: Approximately 50% of this population showed little evidence of conspiracy thinking, 25% showed a degree of endorsement, 15% showed a consistent pattern of endorsement, and 10% had very high levels of endorsement. Higher levels of coronavirus conspiracy thinking were associated with less adherence to all government guidelines and less willingness to take diagnostic or antibody tests or to be vaccinated. Such ideas were also associated with paranoia, general vaccination conspiracy beliefs, climate change conspiracy belief, a conspiracy mentality, and distrust in institutions and professions. Holding coronavirus conspiracy beliefs was also associated with being more likely to share opinions. CONCLUSIONS: In England there is appreciable endorsement of conspiracy beliefs about coronavirus. Such ideas do not appear confined to the fringes. The conspiracy beliefs connect to other forms of mistrust and are associated with less compliance with government guidelines and greater unwillingness to take up future tests and treatment.

Predicting long length of stay in a paediatric intensive care unit using machine learning

Introduction Length of stay (LOS) prediction modelling in intensive care units is a valuable capacity planning tool as hospitals attempt to clear the backlog of surgical patients resulting from the COVID-19 pandemic. Recent work in adults has demonstrated the benefits of using machine learning over statistical methods for LOS prediction, however machine learning approaches have not been applied to paediatric populations. Objectives The study set out to develop machine learning models to predict long LOS in the paediatric intensive care unit at Great Ormond Street Hospital using electronic patient records. Methods Paediatric intensive care patients between 1st May 2019 and 30th April 2020 were extracted from electronic patient records. Random forest, XGBoost, and multilayer perceptron models were built to predict LOS greater than three or seven days. The dataset contained demographics, ventilation data, and summary statistics of physiological time-series data, taken from the first twelve hours of admission. Theperformance of the machine learning classifiers was compared to a baseline logistic regression model. Results There were 564 patients in the study population, of whom 307 had a LOS greater than three days and 105 had a LOS greater than seven days. Using the seven-day threshold, the optimal model was the random forest, which achieved an AUC of 0.785 and correctly classified 42.9% of long LOS patients. Using the three-day threshold, the optimal model was the multilayer perceptron, which achieved an AUC of 0.737 and correctly classified 85.7% of long LOS patients. The performance of the machine learning models was variable, and they did not unanimously outperform the baseline models. Conclusions The machine learning models performed poorly in predicting long LOS. Further work is required to assess the clinical utility and value of deep learning methods in an operational setting.