Background The use of the internet and web-based platforms to obtain public health information and manage health-related issues has become widespread in this digital age. The practice is so pervasive that the first reaction to obtaining health information is to “Google it.” As SARS-CoV-2 broke out in Wuhan, China, in December 2019 and quickly spread worldwide, people flocked to the internet to learn about the novel coronavirus and the disease, COVID-19. Lagging responses by governments and public health agencies to prioritize the dissemination of information about the coronavirus outbreak through the internet and the World Wide Web and to build trust gave room for others to quickly populate social media, online blogs, news outlets, and websites with misinformation and conspiracy theories about the COVID-19 pandemic, resulting in people’s deviant behaviors toward public health safety measures. Objective The goals of this study were to determine what people learned about the COVID-19 pandemic through web searches, examine any association between what people learned about COVID-19 and behavior toward public health guidelines, and analyze the impact of misinformation and conspiracy theories about the COVID-19 pandemic on people’s behavior toward public health measures. Methods This infodemiology study used Google Trends’ worldwide search index, covering the first 6 months after the SARS-CoV-2 outbreak (January 1 to June 30, 2020) when the public scrambled for information about the pandemic. Data analysis employed statistical trends, correlation and regression, principal component analysis (PCA), and predictive models. Results The PCA identified two latent variables comprising past coronavirus epidemics (pastCoVepidemics: keywords that address previous epidemics) and the ongoing COVID-19 pandemic (presCoVpandemic: keywords that explain the ongoing pandemic). Both principal components were used significantly to learn about SARS-CoV-2 and COVID-19 and explained 88.78% of the variability. Three principal components fuelled misinformation about COVID-19: misinformation (keywords “biological weapon,” “virus hoax,” “common cold,” “COVID-19 hoax,” and “China virus”), conspiracy theory 1 (ConspTheory1; keyword “5G” or “@5G”), and conspiracy theory 2 (ConspTheory2; keyword “ingest bleach”). These principal components explained 84.85% of the variability. The principal components represent two measurements of public health safety guidelines—public health measures 1 (PubHealthMes1; keywords “social distancing,” “wash hands,” “isolation,” and “quarantine”) and public health measures 2 (PubHealthMes2; keyword “wear mask”)—which explained 84.7% of the variability. Based on the PCA results and the log-linear and predictive models, ConspTheory1 (keyword “@5G”) was identified as a predictor of people’s behavior toward public health measures (PubHealthMes2). Although correlations of misinformation (keywords “COVID-19,” “hoax,” “virus hoax,” “common cold,” and more) and ConspTheory2 (keyword “ingest bleach”) with PubHealthMes1 (keywords “social distancing,” “hand wash,” “isolation,” and more) were r=0.83 and r=–0.11, respectively, neither was statistically significant (P=.27 and P=.13, respectively). Conclusions Several studies focused on the impacts of social media and related platforms on the spreading of misinformation and conspiracy theories. This study provides the first empirical evidence to the mainly anecdotal discourse on the use of web searches to learn about SARS-CoV-2 and COVID-19.

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Your research problem and objectives Invasive mechanical ventilation is one of the leading life support machines in the intensive care unit (ICU). By identifying the predictors of ventilation time upon arrival, important information can be gathered to improve decisions regarding capacity planning. Purpose In this study, first day ventilated patients ventilation time was analyzed using survival analysis. The probabilistic behaviour of ventilation time duration was analyzed and the predictors of ventilation time duration were determined based on available first day covariates. Materials and methods A retrospective analysis of ICU ventilation time in Ontario was performed with data from ICU patients obtained from the Critical Care Information System (CCIS) in Ontario between July 2015 and December 2016. As part of the protocol for inclusion, a patient must have been connected to an invasive ventilator upon arrival to the ICU. Parametric survival methods were used to characterize ventilation time and to determine associated covariates. Parametric and non-parametric methods were used to determine predictors of ventilation duration for first-day ventilated patients. Results A total of 128,030 patients visited the ICUs between July 2015 and December 2016. 51,966 (40.59 pervcent) patients received invasive mechanical ventilation on arrival. Analysis of ventilation duration suggested that the log-normal distribution provided the best fit to ventilation time, whereas the log-logistic Accelerated Failure Time model best describes the association between the covariates and ventilation duration. ICU site, admission source, admission diagnosis, scheduled admission, scheduled surgery, referring physician, central venous line treatment, arterial line treatment, intracranial pressure monitor treatment, extra-corporeal membrane oxygen treatment, intraaortic balloon pump treatment, other interventions, age group, pre-ICU LOS, and MODS score were significant predictors of the ICU ventilation time. Conclusions The results show substantial variability in ICU ventilation duration for different ICUs, patients demographics, and underlying conditions, and highlight mechanical ventilation as an important driver of ICU costs. The predictive performance of the proposed model showed that both the model and the data can be used to predict an individual patients ventilation time and to provide insight into predictors.

A Step-Down Unit (SDU) provides an intermediate Level of Care for patients from an Intensive Care Unit (ICU) as their condition becomes less acute. SDU congestion, as well as upstream patient arrivals, forces ICU administrators to incur costs, either in the form of overstays or premature step-downs. Based on a proxy for patient acuity level called the `Nine Equivalents of Nursing Manpower Score (NEMS), patients were classified into two groups: high-acuity and low-acuity. Two patient flow policies were developed that select actions to optimize the system s net health service benefit: one allowing for premature step-down actions, and the other allowing for patient rejection actions when the system is congested. The results show that the policy with patient rejection has a net health service benefit that significantly exceeds that of the policy with the premature step-down option. Based on these results, it can be concluded that premature step-down contributes to congestion downstream. Counter-intuitively, premature step-down should therefore be discouraged and patient rejection actions should be further explored as viable options for congested ICUs.

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