Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort of Spanish HCWs and (2) identify the most important variables in terms of contribution to the model’s predictive accuracy.

This is a prospective, multicentre cohort study of Spanish HCWs active during the COVID-19 pandemic. A total of 8,996 HCWs participated in the web-based baseline survey (May–July 2020) and 4,809 in the 4-month follow-up survey. A total of 219 predictor variables were derived from the baseline survey. The outcome variable was any STB at the 4-month follow-up. Variable selection was done using an L1 regularized linear Support Vector Classifier (SVC). A random forest model with 5-fold cross-validation was developed, in which the Synthetic Minority Oversampling Technique (SMOTE) and undersampling of the majority class balancing techniques were tested. The model was evaluated by the area under the Receiver Operating Characteristic (AUROC) curve and the area under the precision–recall curve. Shapley’s additive explanatory values (SHAP values) were used to evaluate the overall contribution of each variable to the prediction of future STBs. Results were obtained separately by gender.

The prevalence of STBs in HCWs at the 4-month follow-up was 7.9% (women = 7.8%, men = 8.2%). Thirty-four variables were selected by the L1 regularized linear SVC. The best results were obtained without data balancing techniques: AUROC = 0.87 (0.86 for women and 0.87 for men) and area under the precision–recall curve = 0.50 (0.55 for women and 0.45 for men). Based on SHAP values, the most important baseline predictors for any STB at the 4-month follow-up were the presence of passive suicidal ideation, the number of days in the past 30 days with passive or active suicidal ideation, the number of days in the past 30 days with binge eating episodes, the number of panic attacks (women only) and the frequency of intrusive thoughts (men only).

Machine learning-based prediction models for STBs in HCWs during the COVID-19 pandemic trained on web-based survey data present high discrimination and classification capacity. Future clinical implementations of this model could enable the early detection of HCWs at the highest risk for developing adverse mental health outcomes.

NCT04556565

The RedETS horizon scanning (HS) program in Spain is focused on identifying non-pharmaceutical emerging health technologies. HS is organized in three steps: (i) identification using different sources (PubMed, the biomedical press, and others); (ii) screening performed by the HS Working Group and clinicians; and (iii) prioritization using the PriTec tool. This study aimed to evaluate the accuracy of RedETS HS in identifying disruptive emerging technologies for our health system.

Data from brief files and full reports related to the identified emerging technologies were collected. Full health technology assessment (HTA) reports were also reviewed. The period of analysis was from 2016 to 2023. The information collected included the name, type, category, and indication of the emerging technology and the source of identification. An ad hoc Excel spreadsheet was designed to collect the information. The analysis consisted of a description of the variables and an assessment of concordance between the emerging technologies identified and those with full HTA reports.

There were 338 emerging technologies identified. These technologies were mainly therapeutic (52.1%) or diagnostic (25.7%). In addition, about 45 percent were medical devices and 15.7 percent were in vitro diagnostic tests; imaging comprised 7.4 percent. Most of the emerging technologies were identified through the biomedical press (22.2%), PubMed (23.6%) and industry (20.3%). In a preliminary analysis of these main sources, 31 percent of the technologies identified by HS had full HTA reports, with all of these being identified three years before the HTA.

HS systems might help identify the most relevant technologies for healthcare systems, enabling them to be more ready to incorporate the new technologies. Therefore, HS must be able to detect emerging technologies that will have an impact on the health system. Periodic evaluation of the accuracy of HS programs will improve their impact in the HTA process.

The participation of people with mental health conditions or patient representatives is of relevance in the health technology assessment (HTA) field, although there are some challenges that need to be considered. This study was designed to gain the perspectives of patient representatives with experience participating in HTA projects, with the aim of delving into the main issues and proposing ideas for the future.

A structured interview, based on five open-ended questions, was formulated and distributed via email. Five patient representatives from the Saúde Mental FEAFES Galicia (the Federation of Associations of Family Members and Persons with Mental Illness of Galicia, Spain) who had actively engaged in HTA projects over the past five years participated in the study. The collected data were analyzed using thematic analysis.

Participants underscored the pivotal role of involving persons with mental health conditions and their representatives in HTA and emphasized the importance of knowing their perspectives, preferences, and values. Challenges included the complexity of HTA reports and processes, in terms of length and the technical language used, and socioeconomic barriers. Proposed solutions included material adaptation, streamlined processes, and institutional and professional support. Future recommendations emphasized improving awareness, disseminating the HTA field, fostering active participation, and emphasizing the relevance of participating in the HTA process. Of particular significance was the encouragement of training and capacity building among patients with mental health conditions.

This study reveals the need to strengthen and facilitate the participation of individuals with mental health conditions in HTA processes, and emphasizes the importance of knowing their perspectives, preferences, and values. Recommendations include training and capacity building, simplifying materials and processes, and providing adequate support.

This study aims to outline Clostridioides difficile infection (CDI) trends and outcomes in Mexican healthcare facilities during the COVID-19 pandemic.

Observational study of case series.

Sixteen public hospitals and private academic healthcare institutions across eight states in Mexico from January 2016 to December 2022.

CDI patients.

Demographic, clinical, and laboratory data of CDI patients were obtained from clinical records. Cases were classified as community or healthcare-associated infections, with incidence rates calculated as cases per 10,000 patient days. Risk factors for 30-day all-cause mortality were analyzed by multivariate logistic regression.

We identified 2,356 CDI cases: 2,118 (90%) were healthcare-associated, and 232 (10%) were community-associated. Common comorbidities included hypertension, diabetes, and cancer. Previous high use of proton-pump inhibitors, steroids, and antibiotics was observed. Recurrent infection occurred in 112 (5%) patients, and 30-day mortality in 371 (16%). Risk factors associated with death were a high Charlson score, prior use of steroids, concomitant use of antibiotics, leukopenia, leukocytosis, elevated serum creatine, hypoalbuminemia, septic shock or abdominal sepsis, and SARS-CoV-2 coinfection. The healthcare-associated CDI incidence remained stable at 4.78 cases per 10,000 patient days during the pre-and pandemic periods. However, the incidence was higher in public hospitals.

Our study underscores the need for routine epidemiology surveillance and standardized CDI classification protocols in Mexican institutions. Though CDI rates in our country align with those in some European countries, disparities between public and private healthcare sectors emphasize the importance of targeted interventions.

There is a lack of standardised psychometric data in electronic health record (EHR)-based research. Proxy measures of symptom severity based on patients' clinical records may be useful surrogates in mental health EHR research.

This study aimed to validate proxy tools for the short versions of the Positive and Negative Syndrome Scale (PANSS-6), Young Mania Rating Scale (YMRS-6) and Montgomery–Åsberg Depression Rating Scale (MADRS-6).

A cross-sectional, multicentre study was conducted in a sample of 116 patients with first-episode psychosis from 12 public hospitals in Spain. Concordance between PANSS-6, YMRS-6 and MADRS-6 scores and their respective proxies was evaluated based on information from EHR clinical notes, using a variety of statistical procedures, including multivariate tests to adjust for potential confounders. Bootstrapping techniques were used for internal validation, and an independent cohort from the Treatment and Early Intervention in Psychosis Program (TIPP-Lausanne, Switzerland) for external validation.

The proxy versions correlated strongly with their respective standardised scales (partial correlations ranged from 0.75 to 0.84) and had good accuracy and discriminatory power in distinguishing between patients in and not in remission (percentage of patients correctly classified ranged from 83.9 to 91.4% and bootstrapped optimism-corrected area under the receiver operating characteristic curve ranged from 0.76 to 0.89), with high interrater reliability (intraclass correlation coefficient of 0.81). The findings remained robust in the external validation data-set.

The proxy instruments proposed for assessing psychotic and affective symptoms by reviewing EHR provide a feasible and reliable alternative to traditional structured psychometric procedures, and a promising methodology for real-world practice settings.

Although several studies have documented the impact of the COVID-19 pandemic on mental health, the long-term effects remain unclear.

To examine longitudinal changes in mental health before and during the consecutive COVID-19 waves in a well-established probability sample.

An online survey was completed by the participants of the COVID-19 add-on study at four time points: pre-COVID-19 period (2014–2015, n = 1823), first COVID-19 wave (April to May 2020, n = 788), second COVID-19 wave (August to October 2020, n = 532) and third COVID-19 wave (March to April 2021, n = 383). Data were collected via a set of validated instruments, and analysed with latent growth models.

During the pandemic, we observed a significant increase in stress levels (standardised β = 0.473, P < 0.001) and depressive symptoms (standardised β = 1.284, P < 0.001). The rate of increase in depressive symptoms (std. covariance = 0.784, P = 0.014), but not in stress levels (std. covariance = 0.057, P = 0.743), was associated with the pre-pandemic mental health status of the participants. Further analysis showed that secondary stressors played a predominant role in the increase in mental health difficulties. The main secondary stressors were loneliness, negative emotionality associated with the perception of COVID-19 disease, lack of resilience, female gender and younger age.

The surge in stress levels and depressive symptoms persisted across all three consecutive COVID-19 waves. This persistence is attributable to the effects of secondary stressors, and particularly to the status of mental health before the COVID-19 pandemic. Our findings reveal mechanisms underlying the surge in mental health difficulties during the COVID-19 waves, with direct implications for strategies promoting mental health during pandemics.

The coronavirus disease 2019 (COVID-19) has serious physiological and psychological consequences. The long-term (>12 weeks post-infection) impact of COVID-19 on mental health, specifically in older adults, is unclear. We longitudinally assessed the association of COVID-19 with depression symptomatology in community-dwelling older adults with metabolic syndrome within the framework of the PREDIMED-Plus cohort.

Participants (n = 5486) aged 55–75 years were included in this longitudinal cohort. COVID-19 status (positive/negative) determined by tests (e.g. polymerase chain reaction severe acute respiratory syndrome coronavirus 2, IgG) was confirmed via event adjudication (410 cases). Pre- and post-COVID-19 depressive symptomatology was ascertained from annual assessments conducted using a validated 21-item Spanish Beck Depression Inventory-II (BDI-II). Multivariable linear and logistic regression models assessed the association between COVID-19 and depression symptomatology.

COVID-19 in older adults was associated with higher post-COVID-19 BDI-II scores measured at a median (interquartile range) of 29 (15–40) weeks post-infection [fully adjusted β = 0.65 points, 95% confidence interval (CI) 0.15–1.15; p = 0.011]. This association was particularly prominent in women (β = 1.38 points, 95% CI 0.44–2.33, p = 0.004). COVID-19 was associated with 62% increased odds of elevated depression risk (BDI-II ≥ 14) post-COVID-19 when adjusted for confounders (odds ratio; 95% CI 1.13–2.30, p = 0.008).

COVID-19 was associated with long-term depression risk in older adults with overweight/obesity and metabolic syndrome, particularly in women. Thus, long-term evaluations of the impact of COVID-19 on mental health and preventive public health initiatives are warranted in older adults.

To study the respiratory patterns and the hemodynamic variations related to postural changes in inpatients with coronavirus disease (COVID-19).

This report is a prospective study in a cohort of inpatients admitted with COVID-19. We recruited 10 patients admitted to the hospital with moderate or severe COVID-19 who showed improvement in oxygen saturation with prone positioning. We performed cardiorespiratory polygraphy and hemodynamic evaluations by thoracic electrical bioimpedance.

We observed a median minimum oxygen saturation of 85.00% (IQR: 7.00) in the supine position versus 91.00% (IQR: 8.00) (P = 0.173) in the prone position. The airflow restriction in the supine position was 2.70% (IQR: 6.55) versus 1.55% (IQR: 2.80) (P = 0.383) in the prone position. A total of 36.4% of patients were classified as having a normo-hemodynamic state in the supine position, whereas 54.5% were classified in this group in the prone position (P = 0.668). A decrease in vascular resistance was observed in the prone position (18.2% of vasoconstriction) compared to the supine position (36.4% of vasoconstriction) (P = 0.871).

This brief report describes the effects of prone positioning on respiratory and hemodynamic variables in 10 patients with moderate or severe COVID-19.

The use of volumetric modulated arc therapy (VMAT) on the breast has several dosimetric advantages but its impact on skin dose should be evaluated and compared to well-established treatment techniques using tangential fields. The aim of this work is to contrast the skin dose for VMAT and field-in-field (FIF) and to estimate the magnitude of the skin dose involved.

The skin dose was measured, without build-up, using thermoluminescent dosimeter (TLD) and optically stimulated luminescence dosimeter (OSLD) in breast radiotherapy by an in-house anthropomorphic phantom. Two different treatment techniques were used: FIF and VMAT, based on the planning strategy proposed by Nicolini et al. The dose levels were 4300 cGy, 4600 cGy and 5600 cGy in 20 fractions. In vivo dosimetry with TLD for VMAT was performed for different breast sizes in the same locations as phantom measurements.

The ipsilateral phantom breast skin dose using both treatment techniques was equivalent. TLD measured doses by the VMAT technique were up to 5% higher than OSLD, although they agree if we consider the geometry uncertainty of the TLD. In accordance with in vivo dosimetry, the mean dose of the ipsilateral breast skin was 62 ± 6% (51%, 75%) relative to the prescribed dose, regardless of the breast size for the volumes considered with this small population (n = 9) as shown by Mann–Whitney U-test (Z = 1·9, 95% confidence). The uncertainty expected in this region due to geometry (volume) changes is up to 9% higher for volumes from 225·9 cc to 968·8 cc. According to the treatment techniques and in vivo dosimetry, the contralateral breast skin dose was 1·0% in FIF and 2·5% in VMAT concerning the prescribed dose.

There is no difference in skin dosimetry between VMAT and FIF techniques on the ipsilateral breast. It provides useful support for the use of VMAT as a planning technique for breast irradiation. The work describes the importance of quantifying potential differences in skin dosimetry.

To examine the cross-sectional and longitudinal (2-year follow-up) associations between dietary diversity (DD) and depressive symptoms.

An energy-adjusted dietary diversity score (DDS) was assessed using a validated FFQ and was categorised into quartiles (Q). The variety in each food group was classified into four categories of diversity (C). Depressive symptoms were assessed with Beck Depression Inventory-II (Beck II) questionnaire and depression cases defined as physician-diagnosed or Beck II >= 18. Linear and logistic regression models were used.

Spanish older adults with metabolic syndrome (MetS).

A total of 6625 adults aged 55–75 years from the PREDIMED-Plus study with overweight or obesity and MetS.

Total DDS was inversely and statistically significantly associated with depression in the cross-sectional analysis conducted; OR Q4 v. Q1 = 0·76 (95 % CI (0·64, 0·90)). This was driven by high diversity compared to low diversity (C3 v. C1) of vegetables (OR = 0·75, 95 % CI (0·57, 0·93)), cereals (OR = 0·72 (95 % CI (0·56, 0·94)) and proteins (OR = 0·27, 95 % CI (0·11, 0·62)). In the longitudinal analysis, there was no significant association between the baseline DDS and changes in depressive symptoms after 2 years of follow-up, except for DD in vegetables C4 v. C1 = (β = 0·70, 95 % CI (0·05, 1·35)).

According to our results, DD is inversely associated with depressive symptoms, but eating more diverse does not seem to reduce the risk of future depression. Additional longitudinal studies (with longer follow-up) are needed to confirm these findings.

In recent years, the use of drones in health emergencies has increased. Among their main benefits are avoiding endangering rescuers, travelling long distances in a short time, or contacting victims in risky situations; but despite their multiple advantages, their use has not been fully demonstrated.

This study aims to identify the available evidence on the use of drones in emergency health care compared to traditional health care.

Systematic review of the literature was conducted. Search protocols were developed to locate studies that met the established selection criteria. Six experimental or quasi-experimental studies with high methodological quality published from the beginning of indexing until 2020 were included.

Drones covered a significantly larger area than other traditional tracking methods and were very useful for performing preliminary triage, determining needs, and knowing the scene prior to the arrival of rescuers. In addition, drones reduced the time required to locate the victim.

Drones are an element to be taken into account when attending health emergencies as they significantly improve the distance travelled to locate accident victims, have the possibility of performing triage prior to the arrival of the health care units, and improve the time and quality of the care provided.