Background: To assess gender differences in the prevalence of self-reported mental disorders, psychological distress and psychotropic drug consumption, and to identify sociodemographic and health-related variables associated with these conditions in the male and female population (aged ≥ 18 years). Methods: A cross-sectional study was carried on 22,141 subjects aged 18 and over, using data from the Spanish National Health Interview Survey 2017. Results: We found an overall prevalence of mental disorders, psychological distress and psychotropic drug consumption of 13.8%, 18.3% and 13.9%, respectively. After multivariable adjustment, women showed significantly increased probabilities of 1.74-fold for mental disorders, 1.26-fold for psychological distress and 1.26-fold for psychotropic drug consumption compared to men. Variables such as gender, age, nationality, marital status, educational level, self-rated health, the presence of different chronic disorders, alcohol consumption and smoking habit were independently associated with mental disorders, psychological distress and psychotropic drug consumption. Several variables showed a differential effect on mental health status and psychotropic drug consumption according to gender. Conclusions: Women suffer from mental disorders, experience psychological distress and consume psychotropic drugs significantly more than men in Spain. Possible explanations for these results may be related to differences in emotional processing, willingness to report diseases and even intrinsic biological traits. Screening for mental health status and psychotropic drug consumption should be considered, particularly in Spanish women, younger adults and individuals who are not married, are obese, have poor self-rated health, suffer from chronic diseases or have a smoking habit.

(1) Background: We examined trends in incidence and outcomes in women with existing type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) and gestational diabetes mellitus (GDM) compared with a control group without diabetes. (2) Methods: This was an observational, retrospective epidemiological study using the National Hospital Discharge Database. (3) Results: There were 2,481,479 deliveries in Spain between 2009 and 2015 (5561 mothers with T1DM, 4391 with T2DM, and 130,980 with GDM). Incidence and maternal age of existing diabetes and GDM increased over time. Women with T2DM were more likely to have obstetric comorbidity (70.12%) than those with GDM (60.28%), T1DM (59.45%), and no diabetes (41.82%). Previous cesarean delivery, preeclampsia, smoking, hypertension, and obesity were the most prevalent risk factors in all types of diabetes. Women with T1DM had the highest rate of cesarean delivery (Risk Ratio (RR) 2.34; 95% Confidence Interval (CI) 2.26–2.43) and prolonged maternal length of stay. Labor induction was higher in T2DM (RR 1.99; 95% CI 1.89–2.10). Women with T1DM had more severe maternal morbidity (RR 1.97; 95% CI 1.70–2.29) and neonatal morbidity (preterm birth, RR 3.32; 95% CI 3.14–3.51, and fetal overgrowth, RR 8.05; 95% CI 7.41–8.75). (4) Conclusions: existing and GDM incidence has increased over time. We found differences in the prevalence of comorbidities, obstetric risk factors, and the rate of adverse obstetric outcomes among women with different types of diabetes. Pregnant women with diabetes have the highest risk of adverse pregnancy outcomes.

(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately.

The objectives of this study were to estimate coverage of influenza vaccination in Spain among adults suffering chronic conditions, to assess time trends from 2014 to 2017 and to identify vaccine uptake predictors. We used individualized data of persons ≥15 years interviewed in the 2017 Spanish National Health Survey. Vaccine uptake and the presence of the chronic conditions analyzed (diabetes; cancer; chronic respiratory disease; chronic heart disease and cerebrovascular disease) were self-reported. Independent variables included sex, age and nationality. In 2017 overall influenza vaccination uptake among subjects with high risk chronic conditions remained low (40.3%) and decreased significantly from 2014 (41.7%, adjusted OR 0.98 95%CI 0.84-0.98). The highest coverage was found among those with cerebrovascular disease (52.2%), diabetes (51.5%) and heart disease (51.4%) and the lowest figures for those suffering cancer (34.9%) and respiratory disease (35.1%). Coverage for cancer patients declined a 25% from 2014 to 2017. Older persons had higher coverages whereas females and immigrant population had lower uptakes. We conclude that influenza vaccination coverage among the high risk population in Spain for suffering chronic conditions remains at a low level and has decreased significantly from 2014 to 2017, this affect more intensely to females and immigrants.

(1) Background: The aims of this study were to examine trends in the incidence, clinical characteristics, and in-hospital outcomes of patients hospitalized with pulmonary embolism in Spain and to identify factors associated with in-hospital mortality (IHM). (2) Methods: We included all patients who were hospitalized for pulmonary embolism between 2001 and 2018. Data were collected from the Spanish National Hospital Discharge Database. (3) Results: We identified 241,821 hospitalizations for pulmonary embolism during the study period. The incidence of pulmonary embolism increased from 20.49 cases per 100,000 inhabitants in the period 2001–2002 to 35.9 cases in the period 2017–2018 (p < 0.001). After controlling for possible confounders, there was a significant increase in the incidence over the study period (adjusted incidence rate ratio 1.53, 95% Confidence Interval I 1.51–1.56). The median length of hospital stay was 11 days in the period 2001–2002, decreasing to seven days in the period 2017–2018 (p < 0.001). For the total time period, the crude IHM rate was 9.51%. After multivariable adjustment, IHM decreased significantly over time. The IHM was significantly higher in women, in patients suffering from more comorbidities, and in those with a massive pulmonary embolism. (4) Conclusions: Our results revealed an increase in the incidence of pulmonary embolism hospitalizations from 2001 to 2018 in Spain, with older patients being the most affected.

Background: The American Joint Committee on Cancer (AJCC), in its 8th edition, introduces modifications to the previous TNM classification, incorporating tumour depth of invasion (DOI). The aim of this research is to analyse the prognosis (in terms of disease-free survival and overall survival) of clinical early stage (I and II) squamous cell carcinomas of the oral tongue according to the DOI levels established by the AJCC in its latest TNM classification to assess changes to the T category and global staging system and to evaluate the association between DOI and other histological risk factors. Methods: A retrospective longitudinal observational study of a series of cases was designed. All patients were treated with upfront surgery at our institution between 2010 and 2019. The variables of interest were defined and classified into four groups: demographic, clinical, histological and evolutive control. Univariate and multivariate analyses were carried out and survival functions were calculated using the Kaplan–Meier method. Statistical significance was established for p values below 0.05. Results: Sixty-one patients were included. The average follow-up time was 47.42 months. Fifteen patients presented a loco-regional relapse (24.59%) and five developed distant disease (8.19%). Twelve patients died (19.67%). Statistically significant differences were observed, with respect to disease-free survival (p = 0.043), but not with respect to overall survival (p = 0.139). A total of 49.1% of the sample upstaged their T category and 29.5% underwent modifications of their global stage. The analysis of the relationship between DOI with other histological variables showed a significant association with the presence of pathological cervical nodes (p = 0.012), perineural invasion (p = 0.004) and tumour differentiation grade (p = 0.034). Multivariate analysis showed association between depth of invasion and perineural invasion. Conclusions: Depth of invasion is a histological risk factor in early clinical stages of oral tongue squamous cell carcinoma. Depth of invasion impacts negatively on patient prognosis, is capable per se of modifying the T category and the global tumour staging, and is associated with the presence of cervical metastatic disease, perineural invasion and tumoural differentiation grade.