A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

Neurocognitive functioning in patients with conversion disorder/functional neurological disorder

Neurocognitive symptoms are common in individuals with somatic symptom and related disorders (SSRD), but little is known about the specific impairments in neurocognitive domains in patients with conversion disorder (CD)/functional neurological disorder (FND). This study examines neurocognitive functioning in patients with CD/FND compared to patients with other SSRD. The sample consisted of 318 patients. Twenty-nine patients were diagnosed with CD/FND, mean age 42.4, standard deviation (SD) = 13.8 years, 79.3% women, and 289 patients had other SSRD (mean age 42.1, SD = 13.3, 60.2% women). Patients completed a neuropsychological test battery that addressed a broad range of neurocognitive domains, including information processing speed, attention and executive functioning. Patients with CD/FND had clinically significant neurocognitive deficits in all neurocognitive domains based on normative data comparison. Patients with CD/FND also performed significantly worse than patients with other SSRD on information processing speed (Digit Symbol Substitution Test (V = .115, p = .035), Stroop Color-Word Test (SCWT) card 1 (V = .190, p = .006), and SCWT card 2 (V = .244, p < .001). No CD/FND vs. other SSRD differences were observed in other neurocognitive domains. These findings indicate the patients with CD/FND perform worse on information processing speed tests compared to patients with other SSRD

Workplace Stress, Presenteeism, Absenteeism, and Resilience Amongst University Staff and Students in the COVID-19 Lockdown

Background: This study explored how the COVID-19 outbreak and arrangements such as remote working and furlough affect work or study stress levels and functioning in staff and students at the University of York, UK. Methods: An invitation to participate in an online survey was sent to all University of York staff and students in May-June 2020. We measured stress levels [VAS-scale, Perceived Stress Questionnaire (PSQ)], mental health [anxiety (GAD-7), depression (PHQ-9)], physical health (PHQ-15, chronic medical conditions checklist), presenteeism, and absenteeism levels (iPCQ). We explored demographic and other characteristics as factors which may contribute to resilience and vulnerability for the impact of COVID-19 on stress. Results: One thousand and fifty five staff and nine hundred and twenty five students completed the survey. Ninety-eight per cent of staff and seventy-eight per cent of students worked or studied remotely. 7% of staff and 10% of students reported sickness absence. 26% of staff and 40% of the students experienced presenteeism. 22-24% of staff reported clinical-level anxiety and depression scores, and 37.2 and 46.5% of students. Staff experienced high stress levels due to COVID-19 (66.2%, labeled vulnerable) and 33.8% experienced low stress levels (labeled resilient). Students were 71.7% resilient vs. 28.3% non-resilient. Predictors of vulnerability in staff were having children [OR = 2.23; CI (95) = 1.63-3.04] and social isolation [OR = 1.97; CI (95) = 1.39-2.79] and in students, being female [OR = 1.62; CI (95) = 1.14-2.28], having children [OR = 2.04; CI (95) = 1.11-3.72], and social isolation [OR = 1.78; CI (95) = 1.25-2.52]. Resilience was predicted by exercise in staff [OR = 0.83; CI (95) = 0.73-0.94] and in students [OR = 0.85; CI (95) = 0.75-0.97]. Discussion: University staff and students reported high psychological distress, presenteeism and absenteeism. However, 33.8% of staff and 71.7% of the students were resilient. Amongst others, female gender, having children, and having to self-isolate contributed to vulnerability. Exercise contributed to resilience. Conclusion: Resilience occurred much more often in students than in staff, although psychological distress was much higher in students. This suggests that predictors of resilience may differ from psychological distress per se. Hence, interventions to improve resilience should not only address psychological distress but may also address other factors