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To examine symptoms of depression and generalised anxiety among nurses over 2 years during the pandemic and compare them to the general population.
The COVID-19 pandemic has led to a significant increase in mental stress among the population worldwide. Nursing staff have been identified as being under remarkable strain.
A multicentre prospective longitudinal study.
Symptoms of depression and generalised anxiety in 507 nurses were examined at four different time points (T1: April–July 2020, T2: November 2020–January 2021, T3: May–July 2021, T4: February–May 2022). Results were compared with values of the German general population, presence of gender-specific differences was analysed and frequencies of clinically relevant levels of depression and anxiety were determined.
Throughout the study (T1–T4), a significant increase in depressive and anxiety symptoms was observed. At all four measurement time points, nurses showed significantly higher prevalence for depression and anxiety compared to the German general population. No significant gender differences were found. Frequencies for probable depression and generalised anxiety disorder among nurses were: 21.6% and 18.5% (T1), 31.4% and 29.2% (T2), 29.5% and 26.2% (T3), 33.7% and 26.4% (T4).
During the pandemic, symptoms of depression and generalised anxiety among nurses increased significantly and remained elevated. Their symptom levels were permanently higher than in the general population. These findings strongly suggest that the circumstances of the pandemic severely affected nurses´ mental health.
The COVID-19 pandemic caused a great mental strain on caregivers. This study was able to demonstrate the significant increase in depression and anxiety among nurses during the pandemic. It highlights the urgent need for prevention, screening and support systems in hospitals.
Supportive programmes and preventive services should be developed, not least to prevent the growing shortage of nurses in the health care systems.
The study adhered to relevant EQUATOR guidelines. The STROBE checklist for cohort study was used as the reporting method.
Five hundred and seven nurses completed the questionnaire and provided data for analysis.
The study was registered with the German Clinical Trials Register (https://drks.de/search/en) under the following ID: DRKS00021268.
To explore whether large language models (LLMs) Generated Pre-trained Transformer (GPT)-3 and ChatGPT can write clinical letters and predict management plans for common orthopaedic scenarios.
Fifteen scenarios were generated and ChatGPT and GPT-3 prompted to write clinical letters and separately generate management plans for identical scenarios with plans removed.
Letters were assessed for readability using the Readable Tool. Accuracy of letters and management plans were assessed by three independent orthopaedic surgery clinicians.
Both models generated complete letters for all scenarios after single prompting. Readability was compared using Flesch-Kincade Grade Level (ChatGPT: 8.77 (SD 0.918); GPT-3: 8.47 (SD 0.982)), Flesch Readability Ease (ChatGPT: 58.2 (SD 4.00); GPT-3: 59.3 (SD 6.98)), Simple Measure of Gobbledygook (SMOG) Index (ChatGPT: 11.6 (SD 0.755); GPT-3: 11.4 (SD 1.01)), and reach (ChatGPT: 81.2%; GPT-3: 80.3%). ChatGPT produced more accurate letters (8.7/10 (SD 0.60) vs 7.3/10 (SD 1.41), p=0.024) and management plans (7.9/10 (SD 0.63) vs 6.8/10 (SD 1.06), p
This study shows that LLMs are effective for generation of clinical letters. With little prompting, they are readable and mostly accurate. However, they are not consistent, and include inappropriate omissions or insertions. Furthermore, management plans produced by LLMs are generic but often accurate. In the future, a healthcare specific language model trained on accurate and secure data could provide an excellent tool for increasing the efficiency of clinicians through summarisation of large volumes of data into a single clinical letter.
To investigate the association between older patients’ willingness to have one or more medications deprescribed and: (1) change in medications, (2) change in the appropriateness of medications and (3) implementation of prescribing recommendations generated by the electronic decision support system tested in the ‘Optimising PharmacoTherapy In the Multimorbid Elderly in Primary CAre’ (OPTICA) trial.
A longitudinal sub-study of the OPTICA trial, a cluster randomised controlled trial.
Swiss primary care settings.
Participants were aged ≥65 years, with ≥3 chronic conditions and ≥5 regular medications recruited from 43 general practitioner (GP) practices.
Patients’ willingness to have medications deprescribed was assessed using three questions from the ‘revised Patient Attitudes Towards Deprescribing’ (rPATD) questionnaire and its concerns about stopping score.
Medication-related outcomes were collected at 1 year follow-up. Aim 1 outcome: change in the number of long-term medications between baseline and 12 month follow-up. Aim 2 outcome: change in medication appropriateness (Medication Appropriateness Index). Aim 3 outcome: binary variable on whether any prescribing recommendation generated during the OPTICA medication review was implemented. We used multilevel linear regression analyses (aim 1 and aim 2) and multilevel logistic regression analyses (aim 3). Models were adjusted for sociodemographic variables and the clustering effect at GP level.
298 patients completed the rPATD, 45% were women and 78 years was the median age. A statistically significant association was found between the concerns about stopping score and the change in the number of medications over time (per 1-unit increase in the score the average number of medications use was 0.65 higher; 95% CI: 0.08 to 1.22). Other than that we did not find evidence for statistically significant associations between patients’ agreement with deprescribing and medication-related outcomes.
We did not find evidence for an association between most measures of patient agreement with deprescribing and medication-related outcomes over 1 year.
by Caterina Casalini, Yema D’Almeida, Moussa Ariziki Nassam, Essopha Kokoloko, Souley Wade, Jean Paul Tchupo, Messan Damarly, Justin Mandala, Michele Lanham, Natasha Mack, Chris Akolo, Vincent Polakinam Pitche, Hugues Guidigbi, Claver Anoumou Dagnra
BackgroundAccording to UNAIDS, Togo halved AIDS-related deaths among children ages 0–14 from 2010 to 2020. However, available data show low dolutegravir (DTG)-containing antiretroviral therapy (ART) coverage and low viral load suppression (VLS) among children living with HIV (CLHIV). We analyzed routine facility data before and after implementation of root-cause-based solutions for improving DTG coverage, viral load (VL) testing coverage, and VLS among CLHIV.
DescriptionWe analyzed routine data for CLHIV ≤14 years from October 2019 through September 2022. We assessed proportion of CLHIV on ART receiving DTG, VL testing coverage (CLHIV on ART with documented VL test result), and VLS (CLHIV with documented VL test result of Results
From baseline (October 2019–September 2020) to endline (October 2021–September 2022), increases were observed for DTG coverage (52% to 71%), VL testing coverage (48% to 90%), and VLS (64% to 82%). Age-disaggregated data showed positive trends.
ConclusionsRoot-cause-based solutions and granular data use increased DTG coverage, resulting in increased VL testing and VLS among CLHIV. These interventions should be scaled and become the national standard of care.
To assess the survival predictivity of baseline blood cell differential count (BCDC), discretised according to two different methods, in adults visiting an emergency room (ER) for illness or trauma over 1 year.
Retrospective cohort study of hospital records.
Tertiary care public hospital in northern Italy.
11 052 patients aged >18 years, consecutively admitted to the ER in 1 year, and for whom BCDC collection was indicated by ER medical staff at first presentation.
Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed haemoglobin, mean cell volume (MCV), red cell distribution width (RDW), platelet distribution width (PDW), platelet haematocrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils and platelets. Discretisation cut-offs were defined by benchmark and tailored methods. Benchmark cut-offs were stated based on laboratory reference values (Clinical and Laboratory Standards Institute). Tailored cut-offs for linear, sigmoid-shaped and U-shaped distributed variables were discretised by maximally selected rank statistics and by optimal-equal HR, respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges and in-hospital admission) were analysed using Cox multivariable regression. Receiver operating curves were drawn by summing the Cox-significant variables for each method.
Of 11 052 patients (median age 67 years, IQR 51–81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted to the hospital. After a 306-day median follow-up (IQR 208–417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73 years (HR=4.6, 95% CI=4.0 to 5.2), in-hospital admission (HR=2.2, 95% CI=1.9 to 2.4), ER admission during SARS-CoV2 surges (Wave I: HR=1.7, 95% CI=1.5 to 1.9; Wave II: HR=1.2, 95% CI=1.0 to 1.3). Gender, haemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophil counts were significant overall. Benchmark-BCDC model included basophils and platelet count (area under the ROC (AUROC) 0.74). Tailored-BCDC model included monocyte counts and PCT (AUROC 0.79).
Baseline discretised BCDC provides meaningful insight regarding ER patients’ survival.
Patients with heart failure experience multiple co-occurring symptoms that lower their quality of life and increase hospitalization and mortality rates. So far, no heart failure symptom cluster study recruited patients from community settings or focused on symptoms predicting most clinical outcomes. Considering physical and psychological symptoms together allows understanding how they burden patients in different combinations. Moreover, studies predicting symptom cluster membership using variables other than symptoms are lacking. We aimed to (a) cluster heart failure patients based on physical and psychological symptoms and (b) predict symptom cluster membership using sociodemographic/clinical variables.
Secondary analysis of MOTIVATE-HF trial, which recruited 510 heart failure patients from a hospital, an outpatient and a community setting in Italy.
Cluster analysis was performed based on the two scores of the Hospital Anxiety-Depression scale and two scores of the Heart-Failure Somatic Perception Scale predicting most clinical outcomes. ANOVA and chi-square test were used to compare patients' characteristics among clusters. For the predictive analysis, we split the data into a training set and a test set and trained three classification models on the former to predict patients' symptom cluster membership based on 11 clinical/sociodemographic variables. Permutation analysis investigated which variables best predicted cluster membership.
Four clusters were identified based on the intensity and combination of psychological and physical symptoms: mixed distress (high psychological, low physical symptoms), high distress, low distress and moderate distress. Clinical and sociodemographic differences were found among clusters. NYHA-class (New York Heart Association) and sleep quality were the most important variables in predicting symptom cluster membership.
These results can support the development of tailored symptom management intervention and the investigation of symptom clusters' effect on patient outcomes. The promising results of the predictive analysis suggest that such benefits may be obtained even when direct access to symptoms-related data is absent.
These results may be particularly useful to clinicians, patients and researchers because they highlight the importance of addressing clusters of symptoms, instead of individual symptoms, to facilitate symptom detection and management. Knowing which variables best predict symptom cluster membership can allow to obtain such benefits even when direct access to symptoms-data is absent.
Four clusters of heart failure patients characterized by different intensity and combination of psychological and physical symptoms were identified. NYHA class and sleep quality appeared important variables in predicting symptom cluster membership.
The authors have adhered to the EQUATOR guidelines STROBE to report observational cross-sectional studies.
Patients were included only for collecting their data.
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