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Objectives To assess the benefits of remdesivir in hospitalized COVID-19 patients receiving combined immunomodulatory therapy (CIT) with dexamethasone and tocilizumab. Methods This was a cohort study of microbiologically confirmed COVID-19 hospitalized patients. The primary outcome was all-cause 28 day mortality. Secondary outcomes were need for invasive mechanical ventilation (IMV) and IMV/death. Subgroup analyses according to SARS-CoV-2 cycle threshold (Ct) values and inflammation biomarkers were performed. Multivariable marginal structural Cox proportional hazards regression models were used to analyse the association between remdesivir therapy and the risk of outcomes of interest. Results Of 1368 hospitalized patients treated with corticosteroids, 1014 (74%) also received tocilizumab, 866 (63%) remdesivir and 767 (56%) tocilizumab + remdesivir. The 28 day mortality was 9% in the overall cohort, with an adjusted HR (aHR) of 0.32 (95% CI = 0.17–0.59) for patients receiving CIT. In the latter group, the 28 day mortality was 6.5%, with an aHR of 1.11 (95% CI = 0.57–2.16) for remdesivir use and there were no differences in secondary outcomes. The risk of primary and secondary outcomes with remdesivir differed by Ct and C-reactive protein (CRP) levels in patients receiving CIT: for 28 day mortality, the aHR was 0.48 (95% CI = 0.21–1.11) for Ct Conclusions The benefits of remdesivir administered with dexamethasone and tocilizumab in hospitalized COVID-19 patients differ depending on Ct and CRP. Remdesivir decreases the risk of mortality and need for IMV in patients with high viral loads and low-grade systemic inflammation.

Population confinements have been one of the most widely adopted non-pharmaceutical interventions (NPIs) implemented by governments across the globe to help contain the spread of the SARS-CoV-2 virus. While confinement measures have been proven to be effective to reduce the number of infections, they entail significant economic and social costs. Thus, different policy makers and social groups have exhibited varying levels of acceptance of this type of measures. In this context, understanding the factors that determine the willingness of individuals to be confined during a pandemic is of paramount importance, particularly, to policy and decision-makers. In this paper, we study the factors that influence the unwillingness to be confined during the COVID-19 pandemic by the means of a large-scale, online population survey deployed in Spain. We perform two types of analyses (logistic regression and automatic pattern discovery) and consider socio-demographic, economic and psychological factors, together with the 14-day cumulative incidence per 100,000 inhabitants. Our analysis of 109,515 answers to the survey covers data spanning over a 5-month time period to shed light on the impact of the passage of time. We find evidence of pandemic fatigue as the percentage of those who report an unwillingness to be in confinement increases over time; we identify significant gender differences, with women being generally less likely than men to be able to sustain long-term confinement of at least 6 months; we uncover that the psychological impact was the most important factor to determine the willingness to be in confinement at the beginning of the pandemic, to be replaced by the economic impact as the most important variable towards the end of our period of study. Our results highlight the need to design gender and age specific public policies, to implement psychological and economic support programs and to address the evident pandemic fatigue as the success of potential future confinements will depend on the population’s willingness to comply with them.

Population confinements have been one of the most widely adopted non-pharmaceutical interventions (NPIs) implemented by governments across the globe to help contain the spread of the SARS-CoV-2 virus. While confinement measures have been proven to be effective to reduce the number of infections, they entail significant economic and social costs. Thus, different policy makers and social groups have exhibited varying levels of acceptance of this type of measures. In this context, understanding the factors that determine the willingness of individuals to be confined during a pandemic is of paramount importance, particularly, to policy and decision-makers. In this paper, we study the factors that influence the unwillingness to be confined during the COVID-19 pandemic by means of a large-scale, online population survey deployed in Spain. We apply both quantitative (logistic regression) and qualitative (automatic pattern discovery) methods and consider socio-demographic, economic and psychological factors, together with the 14-day cumulative incidence per 100,000 inhabitants. Our analysis of 109,515 answers to the survey covers data spanning over a 5-month time period to shed light on the impact of the passage of time. We find evidence of pandemic fatigue as the percentage of those who report an unwillingness to be in confinement increases over time; we identify significant gender differences, with women being generally less likely than men to be able to sustain long-term confinement of at least 6 months; we uncover that the psychological impact was the most important factor to determine the willingness to be in confinement at the beginning of the pandemic, to be replaced by the economic impact as the most important variable towards the end of our period of study. Our results highlight the need to design gender and age specific public policies, to implement psychological and economic support programs and to address the evident pandemic fatigue as the success of potential future confinements will depend on the population’s willingness to comply with them.

Detecting social vulnerability is a complex process that involves many personal and environmental factors to take into account. In this paper, a new methodology is proposed to detect which individuals are more likely to live under vulnerable situations merging all those personal and environmental factors in one metric. This methodology assigns each person who asks The Red Cross for help different normalized key performance indicators (KPI) regarding several areas as personal, social, health, environmental, economic, labour and familiar. All these fields are combined to represent the person in two axes: the internal or personal vulnerability axis (composed by personal, health and economical indexes) and external axis (composed by environmental, labour, social and familiar indexes). Based on these axes and through unsupervised machine learning techniques, this methodology assigns each person a vulnerability group which may be related with a series of actions to cover their needs and act upon their situation. This way, the proposed methodology allows us to go from a high dimensionality to a reduced problem that considerably simplifies the study. This process permits The Red Cross act quicker in those high-vulnerable or more-likely-vulnerable situations, improving the assistance process and helping the estimation of needed resources.

This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The aim of the present study was to propose a model that optimizes the communication design of educational toys&rsquo, packaging. The data extracted from the experiments was studied using new analytical models, based on machine learning techniques, to predict which area of packaging is observed in the first instance and which areas are never the focus of attention of potential customers. The results suggest that the most important elements are the graphic details of the packaging and the methodology fully analyzes and segments these areas, according to social circumstance and which consumer type is observing the packaging.