Cost utility analysis of Remdesivir and Dexamethasone treatment for hospitalised COVID-19 patients - a hypothetical study

Background: Sars-Cov-2 is a novel corona virus associated with significant morbidity and mortality. Remdesivir and Dexamethasone are two treatments that have shown to be effective against the Sars-Cov-2 associated disease. However, a cost-effectiveness analysis of the two treatments is still lacking. Objective: The cost-utility of Remdesivir, Dexamethasone and a simultaneous use of the two drugs with respect to standard of care for treatment Covid-19 hospitalized patients is evaluated, together with the effect of Remdesivir compared to the base model but based on alernative assumptions. Methods: A decision tree for an hypothetical cohort of Covid-19 hospitalized patients, from an health care perspective and a one year horizon is specified. Efficacy data are retrieved from a literature review of clinical trials, whilst costs and utility are obtained from other published studies. Results: Remdesivir, if health care costs are related to the days of hospitalization, is a cost saving strategy. Dexamethasone is cost effective with an ICER of $5208/QALY, and the concurrent use of Remdesivir and Dexamethasone is the most favorable strategy for higher level of willingness to pay thresholds. Moreover, if Remdesivir has a positive effect on mortality the utility is three times higher respect to base case. Whereas, if health care costs are not related to the length of patient hospitalization Remdesivir has an ICER respect to standard of care of$384412.8/QALY gained, which is not cost effective. We also find that Dexaamethasone is cost effective respect to standard care if we compute the cost for live saved with an ICER of $313.79 for life saved. The uncertainty of the model parameters is also tested through both a one-way deterministic sensitivity analysis and a probabilistic sensitivity analysis. Conclusion: We find that the use of Remdesivir and/or Dexamethasone is effective from an economic standpoint

Practical implementation of the Kelly criterion: optimal growth rate, number of trades, and rebalancing frequency for equity portfolios

We develop a general framework to apply the Kelly criterion to the stock market data, and consequently, to portfolio optimization. Under few conditions, using Monte Carlo simulations with different scenarios we prove that the Kelly criterion beats any other approach in many aspects. In particular, it maximizes the expected growth rate and the median of the terminal wealth. We also show that, under a normal distribution of returns, the Kelly criterion has the best performance in the long run. Next, we optimize a portfolio with the Kelly criterion with no leverage and no short selling conditions and show that this portfolio lays in the mean-variance efficient frontier and has higher expected return and higher variance, although it is less diversified, respect to the tangent portfolio optimized under the Markowitz approach. Finally, we implement a dynamic strategy applied on the European stock market data and compare the results between the tangent and the optimal Kelly portfolios. In a dynamic setting, the rolling Kelly portfolio outperforms competitors particularly in the case of rebalanced portfolios optimized with a 2-years window width

Application of the eye tracking technology in medicine: a bibliometric analysis

Eye tracking provides a quantitative measure of eye movements during different activities. We report the results from a bibliometric analysis to investigate trends in eye tracking research applied to the study of different medical conditions. We conducted a search on the Web of Science Core Collection (WoS) database and analyzed the dataset of 2456 retrieved articles using VOSviewer and the Bibliometrix R package. The most represented area was psychiatry (503, 20.5%) followed by neuroscience (465, 18.9%) and psychology developmental (337, 13.7%). The annual scientific production growth was 11.14% and showed exponential growth with three main peaks in 2011, 2015 and 2017. Extensive collaboration networks were identified between the three countries with the highest scientific production, the USA (35.3%), the UK (9.5%) and Germany (7.3%). Based on term co-occurrence maps and analyses of sources of articles, we identified autism spectrum disorders as the most investigated condition and conducted specific analyses on 638 articles related to this topic which showed an annual scientific production growth of 16.52%. The majority of studies focused on autism used eye tracking to investigate gaze patterns with regards to stimuli related to social interaction. Our analysis highlights the widespread and increasing use of eye tracking in the study of different neurological and psychiatric conditions

Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy’s country reputation and stock market performance

During the recent Coronavirus disease 2019 (COVID-19) outbreak, the microblogging service Twitter has been widely used to share opinions and reactions to events. Italy was one of the frst European countries to be severely afected by the outbreak and to establish lockdown and stay-at-home orders, potentially leading to country reputation damage. We resort to sentiment analysis to investigate changes in opinions about Italy reported on Twitter before and after the COVID-19 outbreak. Using diferent lexicons-based methods, we fnd a breakpoint corresponding to the date of the frst established case of COVID-19 in Italy that causes a relevant change in sentiment scores used as a proxy of the country’s reputation. Next, we demonstrate that sentiment scores about Italy are associated with the values of the FTSE-MIB index, the Italian Stock Exchange main index, as they serve as early detection signals of changes in the values of FTSE-MIB. Lastly, we evaluate whether diferent machine learning classifers were able to determine the polarity of tweets posted before and after the outbreak with a diferent level of accuracy

Threshold-based Naïve Bayes classifier

The Threshold-based Naive Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original Naive Bayes classifier. Tb-NB extracts the sentiment from a Natural Language text corpus and allows the user not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. It is based on the estimation of a single threshold value that concurs to define a decision rule that classifies a text into a positive (negative) opinion based on its content. One of the main advantage deriving from Tb-NB is the possibility to utilize its results as the input of post-hoc analysis aimed at observing how the quality associated to the different dimensions of a product or a service or, in a mirrored fashion, the different dimensions of customer satisfaction evolve in time or change with respect to different locations. The effectiveness of Tb-NB is evaluated analyzing data concerning the tourism industry and, specifically, hotel guests' reviews from all hotels located in the Sardinian region and available on Booking.com. Moreover, Tb-NB is compared with other popular classifiers used in sentiment analysis in terms of model accuracy, resistance to noise and computational efficiency

Validity and reliability of the Structured Clinical Interview for Depersonalization-Derealization Spectrum (SCI-DER).

This study evaluates the validity and reliability of a new instrument developed to assess symptoms of depresonalization: the Structured Clinical Interview for the Depersonalization-Derealization Spectrum (SCI-DER). The instrument is based on a spectrum model that emphasizes soft-signs, sub-threshold syndromes as well as clinical and subsyndromal manifestations. Items of the interview include, in addition to DSM-IV criteria for depersonalization, a number of features derived from clinical experience and from a review of phenomenological descriptions. Study participants included 258 consecutive patients with mood and anxiety disorders, 16.7% bipolar I disorder, 18.6% bipolar II disorder, 32.9% major depression, 22.1% panic disorder, 4.7% obsessive compulsive disorder, and 1.5% generalized anxiety disorder; 2.7% patients were also diagnosed with depersonalization disorder. A comparison group of 42 unselected controls was enrolled at the same site. The SCI-DER showed excellent reliability and good concurrent validity with the Dissociative Experiences Scale. It significantly discriminated subjects with any diagnosis of mood and anxiety disorders from controls and subjects with depersonalization disorder from controls. The hypothesized structure of the instrument was confirmed empirically

Defense Mechanisms, Gender, and Adaptiveness in Emerging Personality Disorders in Adolescent Outpatients

The present study focused on demographic and personality differences in the use of 30 defense mechanisms in adolescents with personality psychopathology and explored the hierarchical organization of personality traits based on the adaptiveness of defensive functioning. A total of 102 self-referred adolescent outpatients were interviewed and assessed on defense mechanisms and personality traits using the Defense Mechanisms Rating Scales and the Shedler-Westen Assessment Procedure 200 for Adolescents, respectively. Age and gender differences were found throughout the hierarchy. Pearson's correlations revealed a hierarchical organization of emerging personality disorders (PDs) in adolescence. More adaptive defenses were clearly associated with healthier personality style, whereas more pathological personality styles such as those with borderline traits were characterized by more rigid and maladaptive defenses. Dissociation was also associated with maladaptive personality types. Identifying the defenses associated with emerging personality disorders may inform the unconscious function of defense mechanisms in specific PDs. The systematic assessment of defense mechanisms might also help therapists to monitor changes during treatment

Seismic Risk Analysis for a Site Along the Gorda Segment of the Cascadia Subduction Zone

A seismic risk evaluation was conducted on a site near Eureka, California. The site was subject to potential earthquake loading from a number of sources. These sources were: (1) Mendocino Fracture Zone, (2) Gorda Segment of the Cascadia Subduction Zone, (3) Little Salmon thrust fault under the site, (4) Mad River Fault Zone, and (5) Intra plate west - Gorda Plate. The geology of thrust faults in Northern California is examined along with that of the Mendocino Fracture Zone, and the southern section (Gorda Segment) of the Cascadia subduction zone. A trench log showing a splay of the Little Salmon Fault is presented. A seismic risk analysis of the site was performed using recurrence curves for the various seismic sources estimated from both trench studies and historic seismicity. Using this information the acceleration at the site due to the Maximum Credible Earthquake is estimated to be 0.85g. The corresponding acceleration due to the Maximum Probable Earthquake and assuming that the various fault zones act independently or co-seismically is estimated to be 0.5g

Reconstructing individual responses to direct questions: a new method for reconstructing malingered responses

Introduction: The false consensus effect consists of an overestimation of how common a subject opinion is among other people. This research demonstrates that individual endorsement of questions may be predicted by estimating peers’ responses to the same question. Moreover, we aim to demonstrate how this prediction can be used to reconstruct the individual’s response to a single item as well as the overall response to all of the items, making the technique suitable and effective for malingering detection. Method: We have validated the procedure of reconstructing individual responses from peers’ estimation in two separate studies, one addressing anxiety-related questions and the other to the Dark Triad. The questionnaires, adapted to our scopes, were submitted to the groups of participants for a total of 187 subjects across both studies. Machine learning models were used to estimate the results. Results: According to the results, individual responses to a single question requiring a “yes” or “no” response are predicted with 70–80% accuracy. The overall participant-predicted score on all questions (total test score) is predicted with a correlation of 0.7–0.77 with actual results. Discussion: The application of the false consensus effect format is a promising procedure for reconstructing truthful responses in forensic settings when the respondent is highly likely to alter his true (genuine) response and true responses to the tests are missing

Mindfulness, age and gender as protective factors against psychological distress during COVID-19 pandemic

Objective: Mindfulness disposition is associated with various psychological factors and prevents emotional distress in chronic diseases. In the present study, we analyzed the key role of mindfulness dispositions in protecting the individual against psychological distress consequent to COVID-19 social distancing and quarantining. Methods: An online survey was launched on March 13, 2020, with 6,412 responses by April 6, 2020. Socio-demographic information, exposure to the pandemic, and quarantining were assessed together with psychological distress and mindfulness disposition. Multivariate linear regression analysis was performed to study the influence of predictive factors on psychological distress and quality of life in Italian responders during the early days of lockdown. Pearson correlations were calculated to study the relationship between mindfulness and psychiatric symptoms. Results: Multivariate linear regression run on socio-demographics, COVID-19-related variables, and mindfulness disposition as moderators of overall psychological distress showed that mindfulness was the best predictor of psychological distress (β = −0.504; p < 0.0001). High negative correlations were found between mindfulness disposition and the overall Global Severity Index (r = −0.637; p < 0.0001), while moderate to high associations were found between mindfulness and all SCL-90 sub-scales. Discussion: Findings showed that high dispositional mindfulness enhances well-being and helps in dealing with stressful situations such as the COVID-19 pandemic. Mindfulness-based mental training could represent an effective intervention to stem post-traumatic psychopathological beginnings and prevent the onset of chronic mental disorders