ABSTRACT
INTRODUCTION: Cancer patients are more susceptible to infections and infection can be more severe than in patients without cancer diagnosis. We conducted this retrospective study in patients admitted for SARS-CoV-2 infection in order to find differences in inflammatory markers and mortality in cancer patients compared to others. METHODS: We reviewed the electronic records of patients admitted for SARS-CoV-2 infection confirmed by PCR from March to September 2020. Data on socio-demographics, comorbidities, inflammatory makers and cancer-related features were analysed. RESULTS: 2,772 patients were admitted for SARS-CoV-2, to the Hospital Universitario Ramón y Cajal in Madrid during this period. Of these, 2597 (91%) had no history of neoplastic disease, 164 (5.9%) patients had a prior history of cancer but were not undergoing oncological treatment at the time of infection, and 81 (2.9%) were in active treatment. Mortality in patients without a history of cancer was 19.5%, 28.6% for patients with a prior history of cancer and 34% in patients with active cancer treatment. Patients in active oncology treatment with the highest mortality rate, were those diagnosed with lung cancer (OR 5.6 95% CI 2.2-14.1). In the multivariate study active oncological treatment (OR 2.259 95% IC 1.35-3.77) and chemotherapy treatment (OR 3.624 95% IC 1.17-11.17), were statistically significant factors for the risk of death for the whole group and for the group with active oncological treatment, respectively. CONCLUSION: Cancer patients on active systemic treatment have an increased risk of mortality after SARS-CoV-2 infection, especially with lung cancer or chemotherapy treatment.
ABSTRACT
There is a widely accepted belief in new technologies that the digital divide in using a service will disappear as the service reaches an advanced level of maturity. The work presented here shows that this idea is debatable. Data from Spain, a country where daily internet users are 75.9 percent of the population, prove that the gender gap still exists. The paper explores if this gap can be entirely explained by the socioeconomic differences between men and women. We build a micro panel model and incorporate a set of socioeconomic variables (age, education, income, employment status, digital skills, and resident population) that allow us to isolate the effects of gender on the decision to become a daily Internet user. The results conclude that the Internet gap is a phenomenon with a specific gender component. Other things being equal a woman negatively affects the probability of using the Internet. Applying a similar model to 15 Internet services, we obtain that gender is always significant to explain the likelihood of being a user of each service. However, in some services (7 out of 15), the effect is favorable to women, and for other services (8), the gender effect favors men. The work concludes by analyzing the impact of the first wave of the Covid-19 pandemic on the use of Internet services, paying particular attention to its possible implications for the gender gap.