Background: The current coronavirus disease (COVID-19) has a great impact worldwide. Healthcare workers play an essential role and are one of the most exposed groups. Information about the psychosocial impact on healthcare workers is limited.Methods: 3109 healthcare workers completed a national, internet-based, cross-sectional 45-item survey between 9 and 19 April 2020. The objective is to assess the psychological impact of the COVID-19 pandemic in Spanish healthcare workers. A Psychological Stress and Adaptation at work Score (PSAS) was defined combining four modified versions of validated psychological assessment tests (A) Healthcare Stressful Test, (B) Coping Strategies Inventory, (C) Font-Roja Questionnaire and (D) Trait Meta-Mood Scale.Results: The highest psychosocial impact was perceived in Respiratory Medicine, the mean (S.D.) PSAS was 48.3 (13.6) and Geriatrics 47.6 (16.4). Higher distress levels were found in the geographical areas with the highest incidence of COVID-19 (>245.5 cases per 100 000 people), PSAS 46.8 (15.2); p < 0.001. The least stress respondents were asymptomatic workers PSAS, 41.3 (15.4); p < 0.001, as well as those above 60 years old, PSAS, 37.6 (16); p < 0.001. Workers who needed psychological therapy and did not receive it, were more stressed PSAS 52.5 (13.6) than those who did not need it PSAS 39.7 (13.9); p < 0.001.Conclusions: The psychological impact in healthcare workers in Spain during COVID-19 emergency has been studied. The stress perceived is parallel to the number of cases per 100 000 people. Psychotherapy could have a major role to mitigate the experimented stress level.

Breast tumors can be classified into 5 intrinsic subtypes as measured by mRNA expression,¹ with most estrogen receptor (ER)–positive tumors classified as luminal A or luminal B subtype. In general, luminal A tumors have higher ER-expression and lower tumor cell proliferation than luminal B tumors.^2,3 Approximately 1 in 4 patients with ER-positive breast cancer will develop distant metastasis and eventually die from the disease,^4,5 and it is currently not possible to accurately predict a patient’s long-term risk or the benefit of endocrine therapy.

SUMMARY: Epidemiologists agree that there is a prevailing seasonality in the presentation of epidemic waves of respiratory syncytial virus (RSV) infections and influenza. The aim of this study is to quantify the potential relationship between the activity of RSV, with respect to the influenza virus, in order to use the RSV seasonal curve as a predictor of the evolution of an influenza virus epidemic wave. Two statistical tools, logistic regression and time series, are used for predicting the evolution of influenza. Both logistic models and time series of influenza consider RSV information from previous weeks. Data consist of influenza and confirmed RSV cases reported in Comunitat Valenciana (Spain) during the period from week 40 (2010) to week 8 (2014). Binomial logistic regression models used to predict the two states of influenza wave, basal or peak, result in a rate of correct classification higher than 92% with the validation set. When a finer three-states categorization is established, basal, increasing peak and decreasing peak, the multinomial logistic model performs well in 88% of cases of the validation set. The ARMAX model fits well for influenza waves and shows good performance for short-term forecasts up to 3 weeks. The seasonal evolution of influenza virus can be predicted a minimum of 4 weeks in advance using logistic models based on RSV. It would be necessary to study more inter-pandemic seasons to establish a stronger relationship between the epidemic waves of both viruses.