Objective:
To examine psychological distress in healthcare workers (HCWs) during the COVID-19 pandemic in April–May 2020.
Methods:
A cross-sectional survey examining demographic, employment and ...mental health characteristics of HCWs in a large metropolitan hospital in Australia.
Results:
HCWs showed significant symptoms of moderate-severe level depression (21%), anxiety (20%) and posttraumatic stress disorder (PTSD; 29%), associated with burnout, prior psychiatric history, profession and resilience.
Conclusion:
Despite low levels of COVID contact, moderate to high levels of psychological distress were reported. Continued monitoring and support for HCWs’ mental well-being is warranted as the COVID-19 pandemic develops.
High-efficacy therapies in multiple sclerosis are traditionally used after unsuccessful treatment with first-line disease modifying therapies. We hypothesised that early commencement of high-efficacy ...therapy would be associated with reduced long-term disability. We therefore aimed to compare long-term disability outcomes between patients who started high-efficacy therapies within 2 years of disease onset with those who started 4–6 years after disease onset.
In this retrospective international observational study, we obtained data from the MSBase registry and the Swedish MS registry, which prospectively collect patient data that are specific to multiple sclerosis as part of routine clinical care. We identified adult patients (aged ≥18 years) with relapsing-remitting multiple sclerosis, with at least 6 years of follow-up since disease onset, and who started the high-efficacy therapy (rituximab, ocrelizumab, mitoxantrone, alemtuzumab, or natalizumab) either 0–2 years (early) or 4–6 years (late) after clinical disease onset. We matched patients in the early and late groups using propensity scores calculated on the basis of their baseline clinical and demographic data. The primary outcome was disability, measured with the Expanded Disability Status Score (EDSS; an ordinal scale of 0–10, with higher scores indicating increased disability), at 6–10 years after disease onset, assessed with a linear mixed-effects model.
We identified 6149 patients in the MSBase registry who had been given high-efficacy therapy, with data collected between Jan 1, 1975, and April 13, 2017, and 2626 patients in the Swedish MS Registry, with data collected between Dec 10, 1997, and Sept 16, 2019. Of whom, 308 in the MSBase registry and 236 in the Swedish MS registry were eligible for inclusion. 277 (51%) of 544 patients commenced therapy early and 267 (49%) commenced therapy late. For the primary analysis, we matched 213 patients in the early treatment group with 253 in the late treatment group. At baseline, the mean EDSS score was 2·2 (SD 1·2) in the early group and 2·1 (SD 1·2) in the late group. Median follow-up time for matched patients was 7·8 years (IQR 6·7–8·9). In the sixth year after disease onset, the mean EDSS score was 2·2 (SD 1·6) in the early group compared with 2·9 (SD 1·8) in the late group (p<0·0001). This difference persisted throughout each year of follow-up until the tenth year after disease onset (mean EDSS score 2·3 SD 1·8 vs 3·5 SD 2·1; p<0·0001), with a difference between groups of −0·98 (95% CI −1·51 to −0·45; p<0·0001, adjusted for proportion of time on any disease-modifying therapy) across the 6–10 year follow-up period.
High-efficacy therapy commenced within 2 years of disease onset is associated with less disability after 6–10 years than when commenced later in the disease course. This finding can inform decisions regarding optimal sequence and timing of multiple sclerosis therapy.
National Health and Medical Research Council Australia and MS Society UK.
Data from cohorts, registries, randomised trials, electronic medical records and administrative claims databases have increasingly been used to inform the use of therapies for neurological diseases. ...While novel sophisticated methods are enabling us to use existing data to guide treatment decisions, the complexity of statistical methodology is making appraisal of clinical evidence increasingly demanding. In this narrative review, we provide a brief overview of the most commonly used methods for evaluation of treatment effectiveness in neurology. This primer discusses complementarity of randomised and non-randomised study designs, sources of observational data, different forms of bias and the appropriate mitigation strategies, statistical significance, Bayesian approaches and provides an overview of multivariable regression models, propensity score-based models, causal inference, mediation analysis and Mendelian randomisation.
Alzheimer's disease (AD) is characterised by two cardinal pathologies, namely the extracellular accumulation amyloid-related aggregates, and the intracellular formation of tau-related neurofibrillary ...tangles (NFTs). While both pathologies disrupt cognitive function, a large body of evidence suggests that tau-pathology has a stronger relationship with the clinical manifestation of the disease compared to amyloid. Given the ordinal nature of histopathological staging systems, however, it is possible that the effect of amyloid pathology has been underestimated in clinicopathological studies.
We investigated this possibility using data from the National Alzheimer's Coordinating Center (NACC) database, which contains data from patients in the United States of America. Bayesian ordinal models were used to directly investigate the relative contribution of Braak NFT, diffuse plaque, and neuritic plaque staging to the severity of antemortem clinical impairment.
Data from 144 participants were included in the final analysis. Bayesian ordinal models revealed that Braak NFT stage was the only predictor of global cognitive status, clinical dementia stage, functional abilities, and neuropsychiatric symptoms. When compared directly, Braak NFT stage was a stronger predictor than diffuse or neuritic plaques across these domains.
These findings confirm that tau-related pathology is more strongly related to clinical status than amyloid pathology. This suggests that conventional clinical markers of disease progression might be insensitive to amyloid-pathology, and hence might be inappropriate for use as outcome measures in therapeutic trials that directly target amyloid.
White matter development during childhood and adolescence is characterised by increasing white matter coherence and organisation. Commonly used scalar metrics, such as fractional anisotropy (FA), are ...sensitive to multiple mechanisms of white matter change and therefore unable to distinguish between mechanisms that change during development. We investigate the relationship between age and neurite density index (NDI) from neurite orientation dispersion and density imaging (NODDI), and the age-classification accuracy of NDI compared with FA, in a developmental cohort.
Diffusion-weighted imaging data from 72 children and adolescents between the ages of 4–19 was collected (M=10.42, SD=3.99, 36 male). We compared NODDI metrics against conventional DTI metrics (fractional anisotropy FA, mean diffusivity MD, axial diffusivity AD and radial diffusivity RD) in terms of their relationship to age. An ROC analysis was also performed to assess the ability of each metric to classify older and younger participants.
NDI exhibited a stronger relationship with age (median R2=.60) compared with MD (median R2=.39), FA (median R2=.27), AD (median R2=.14), and RD (median R2=.35) in a high proportion of white matter tracts. When participants were divided into an older and younger group, NDI achieved the best classification (median area under the curve AUC=.89), followed by MD (median AUC=.81), FA (median AUC=.80), RD (median AUC=.81), and AD (median AUC=.64).
Our results demonstrate the sensitivity of NDI to age-related differences in white matter microstructural organisation over development. Importantly, NDI is more sensitive to such developmental changes compared to commonly used DTI metrics. This knowledge provides justification for implementing NODDI metrics in developmental studies.
•We show evidence supporting the use of NODDI instead of FA in the developing brain.•We provide a direct comparison between Neurite density index (NDI) and DTI metrics.•NDI is better at classifying subject age group than FA.•NDI explains more variance in white matter with age than FA.•Over development NDI is more sensitive to age related white matter differences.
•An original model for the response to disease modifying therapies for patients affected by Multiple Sclerosis is proposed and validated against patients’ data.•Solutions to the equations correctly ...reproduce published experiments where different drugs against relapses are used.•The model explains why different treatments maintain a high degree of similarity, with analogous dynamical features.•Our analysis illustrates how certain drugs may be overall more successful in lowering the rate of relapses than others, as clinically verified.
Based on reported trends in relapse incidence among patients with relapsing-remitting multiple sclerosis, an original model for the response to disease modifying therapies is proposed. With a population approach and separate states for patients accounting for their risk of relapses, a system of nonlinear equations is formulated, similarly to established epidemiological models. Different parameters describe the effect of drugs and treatment switch in reducing the frequency of relapses. The model allows for a good fit to previously published data for experiments where different drugs are used. It also shows that different treatments maintain a high degree of similarity, with analogous dynamical features: a pre-treatment increment in relapse frequency leading to a distinct peak, a rapid drop after treatment switch and a plateau corresponding to a new base relapse activity, which seems dependant on the treatment chosen. A sensitivity analysis shows that the uncertainty in the initial proportions of different populations and the frequency of relapses can modify the overall dynamics of the response to treatment. Drugs are observed to induce effects that depend on patient sample’s intrinsic characteristics, producing two clearly distinct and independent dynamics of relapse response. This confirms the clinical observation that certain drugs may be overall more successful in lowering the rate of relapses more significantly than others, notwithstanding the fact that patients behave differently across experiments.
In interpreting attention-deficit/hyperactivity disorder (ADHD) symptoms, categorical and dimensional approaches are commonly used. Both employ binary symptom counts which give equal weighting, with ...little attention to the combinations and relative contributions of individual symptoms. Alternatively, symptoms can be viewed as an interacting network, revealing the complex relationship between symptoms. Using a novel network modelling approach, this study explores the relationships between the 18 symptoms in the Diagnostic Statistical Manual (DSM-5) criteria and whether network measures are useful in predicting outcomes. Participants were from a community cohort, the Children's Attention Project. DSM ADHD symptoms were recorded in a face-to-face structured parent interview for 146 medication naïve children with ADHD and 209 controls (aged 6-8 years). Analyses indicated that not all symptoms are equal. Frequencies of endorsement and configurations of symptoms varied, with certain symptoms playing a more important role within the ADHD symptom network. In total, 116,220 combinations of symptoms within a diagnosis of ADHD were identified, with 92% demonstrating a unique symptom configuration. Symptom association networks highlighted the relative importance of hyperactive/impulsive symptoms in the symptom network. In particular, the 'motoric'-type symptoms as well as interrupts as a marker of impulsivity in the hyperactive domain, as well as loses things and does not follow instructions in the inattentive domain, had high measures of centrality. Centrality-measure weighted symptom counts showed significant association with clinical but not cognitive outcomes, however the relationships were not significantly stronger than symptom count alone. The finding may help to explain heterogeneity in the ADHD phenotype.
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