Influenza Hospitalisations in England during the 2022/23 Season: do different data sources drive divergence in modelled waves? A comparison of surveillance and administrative data

Accurate and representative data is vital for precisely reporting the impact of influenza in healthcare systems. Northern hemisphere winter 2022/23 experienced the most substantial influenza wave since the COVID-19 pandemic began in 2020. Simultaneously, new data streams become available within health services because of the pandemic. Comparing these data, surveillance and administrative, supports the accurate monitoring of population level disease trends. We analysed admissions rates per capita from four different collection mechanisms covering National Health Service hospital Trusts in England over the winter 2022/23 wave. We adjust for difference in reporting and extracted key epidemic characteristics including the maximum admission rate, peak timing, cumulative season admissions and growth rates by fitting generalised additive models at national and regional levels. By modelling the admission rates per capita across surveillance and administrative data systems we show that different data measuring the epidemic produce different estimates of key quantities. Nationally and in most regions the data correspond well for the maximum admission rate, date of peak and growth rate, however, in subnational analysis discrepancies in estimates arose, particularly for the cumulative admission rate. This research shows that the choice of data used to measure seasonal influenza epidemics can influence analysis substantially at sub-national levels. For the admission rate per capita there is comparability in the sentinel surveillance approach (which has other important functions), rapid situational reports, operational databases and time lagged administrative data giving assurance in their combined value. Utilising multiple sources of data aids understanding of the impact of seasonal influenza epidemics in the population

Real-time COVID-19 hospital admissions forecasting with leading indicators and ensemble methods in England

Hospitalisations from COVID-19 with Omicron sub-lineages have put a sustained pressure on the English healthcare system. Understanding the expected healthcare demand enables more effective and timely planning from public health. We collect syndromic surveillance sources, which include online search data, NHS 111 telephonic and online triages. Incorporating this data we explore generalised additive models, generalised linear mixed-models, penalised generalised linear models and model ensemble methods to forecast over a two-week forecast horizon at an NHS Trust level. Furthermore, we showcase how model combinations improve forecast scoring through a mean ensemble, weighted ensemble, and ensemble by regression. Validated over multiple Omicron waves, at different spatial scales, we show that leading indicators can improve performance of forecasting models, particularly at epidemic changepoints. Using a variety of scoring rules, we show that ensemble approaches outperformed all individual models, providing higher performance at a 21-day window than the corresponding individual models at 14-days. We introduce a modelling structure used by public health officials in England in 2022 to inform NHS healthcare strategy and policy decision making. This paper explores the significance of ensemble methods to improve forecasting performance and how novel syndromic surveillance can be practically applied in epidemic forecasting

Behavioural and molecular characterisation of the Dlg2 haploinsufficiency rat model of genetic risk for psychiatric disorder

Genetic studies implicate disruption to the DLG2 gene in copy number variants as increasing risk for schizophrenia, autism spectrum disorders and intellectual disability. To investigate psychiatric endophenotypes associated with DLG2 haploinsufficiency (and concomitant PSD-93 protein reduction) a novel clinically relevant Dlg2+/− rat was assessed for abnormalities in anxiety, sensorimotor gating, hedonic reactions, social behaviour, and locomotor response to the N-Methyl-D-aspartic acid receptor antagonist phencyclidine. Dlg gene and protein expression were also investigated to assess model validity. Reductions in PSD-93 messenger RNA and protein were observed in the absence of compensation by other related genes or proteins. Behaviourally Dlg2+/− rats show a potentiated locomotor response to phencyclidine, as is typical of psychotic disorder models, in the absence of deficits in the other behavioural phenotypes assessed here. This shows that the behavioural effects of Dlg2 haploinsufficiency may specifically relate to psychosis vulnerability but are subtle, and partially dissimilar to behavioural deficits previously reported in Dlg2+/− mouse models demonstrating issues surrounding the comparison of models with different aetiology and species. Intact performance on many of the behavioural domains assessed here, such as anxiety and reward processing, will remove these as confounds when continuing investigation into this model using more complex cognitive tasks

Generalized and efficient skill assessment from IMU data with applications in gymnastics and medical training

Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework for skill assessment that generalizes across application domains and can be deployed for near-real-time applications. It is based on the notion of repeatability of activities defining skill. The analysis is based on two subsequent classification steps that analyze (1) movements or activities and (2) their qualities, that is, the actual skills of a human performing them. The first classifier is trained in either a supervised or unsupervised manner and provides confidence scores, which are then used for assessing skills. We evaluate the proposed method in two scenarios: gymnastics and surgical skill training of medical students. We demonstrate both the overall effectiveness and efficiency of the generalized assessment method, especially compared to previous work

The role of lumbar puncture in children with suspected central nervous system infection

BACKGROUND: The use of the lumbar puncture in the diagnosis of central nervous system infection in acutely ill children is controversial. Recommendations have been published but it is unclear whether they are being followed. METHODS: The medical case notes of 415 acute medical admissions in a children's hospital were examined to identify children with suspected central nervous system infection and suspected meningococcal septicaemia. We determined whether lumbar punctures were indicated or contraindicated, whether they had been performed, and whether the results contributed to the patients' management. RESULTS: Fifty-two children with suspected central nervous system infections, and 43 with suspected meningococcal septicaemia were identified. No lumbar punctures were performed in patients with contraindications, but only 25 (53%) of 47 children with suspected central nervous system infection and no contraindications received a lumbar puncture. Lumbar puncture findings contributed to the management in 18 (72%) of these patients, by identifying a causative organism or excluding bacterial meningitis. CONCLUSION: The recommendations for undertaking lumbar punctures in children with suspected central nervous system infection are not being followed because many children that should receive lumbar punctures are not getting them. When they are performed, lumbar puncture findings make a useful contribution to the patients' management

Perceived locus of causality and internalization: Examining reasons for acting in two domains.

Theories of internalization typically suggest that self-perceptions of the "causes" of(i.e., reasons for) behavior are differentiated along a continuum of autonomy that contains identifiable gradations. A model of perceived locus of causality (PLOC) is developed, using children's self-reported reasons for acting. In Project 1, external, introjected, identified, and intrinsic types of reasons for achievementrelated behaviors are shown to conform to a simplex-like (ordered correlation) structure in four samples. These reason categories are then related to existing measures of PLOC and to motivation. A second project examines 3 reason categories (external, introject, and identification) within the domain of prosoeial behavior. Relations with measures of empathy, moral judgment, and positive interpersonal relatedness are presented. Finally, the proposed model and conceptualization of PLOC are discussed with regard to intrapersonal versus interpersonal perception, internalization, causereason distinctions, and the significance of perceived autonomy in human behavior. A central issue for theories of motivation concerns the perceived locus relative to the person of variables that cause or give impetus to behavior, Heider (1958) introduced the concept of perceived locus of causality (PLOC) primarily in reference to interpersonal perception, and more specifically with regard to the phenomenal analysis of how one infers the motives and intentions of others. He distinguished between personal causation, the critical feature of which is intention, and impersonal causation, in which environments, independent of the person's intentions, produce a given effect. DeCharms (1968) elaborated and extended Heider's phenomenal analysis, particularly with regard to the explanation of behavior (as opposed to outcomes). DeCharms argued that there is a further distinction within personal causation or intentional behavior between an internal PLOC, in which the actor is perceived as an "origin" of his or her behavior, and an external PLOC, in which the actor is seen as a "pawn" to heteronomous forces. The distinction between internal and external PLOC has since been crucial for studies of intrinsic versus extrinsic motivation and of perceived autonomy more generally (Deci &amp

New insights into the impact of neuro-inflammation in rheumatoid arthritis.

Rheumatoid arthritis (RA) is considered to be, in many respects, an archetypal autoimmune disease that causes activation of pro-inflammatory pathways resulting in joint and systemic inflammation. RA remains a major clinical problem with the development of several new therapies targeted at cytokine inhibition in recent years. In RA, biologic therapies targeted at inhibition of tumor necrosis factor alpha (TNFα) have been shown to reduce joint inflammation, limit erosive change, reduce disability and improve quality of life. The cytokine TNFα has a central role in systemic RA inflammation and has also been shown to have pro-inflammatory effects in the brain. Emerging data suggests there is an important bidirectional communication between the brain and immune system in inflammatory conditions like RA. Recent work has shown how TNF inhibitor therapy in people with RA is protective for Alzheimer's disease. Functional MRI studies to measure brain activation in people with RA to stimulus by finger joint compression, have also shown that those who responded to TNF inhibition showed a significantly greater activation volume in thalamic, limbic, and associative areas of the brain than non-responders. Infections are the main risk of therapies with biologic drugs and infections have been shown to be related to disease flares in RA. Recent basic science data has also emerged suggesting that bacterial components including lipopolysaccharide induce pain by directly activating sensory neurons that modulate inflammation, a previously unsuspected role for the nervous system in host-pathogen interactions. In this review, we discuss the current evidence for neuro-inflammation as an important factor that impacts on disease persistence and pain in RA

The “Grey Zone” cold air outbreak global model intercomparison: A cross evaluation using large-eddy simulations

A stratocumulus-to-cumulus transition as observed in a cold air outbreak over the North Atlantic Ocean is compared in global climate and numerical weather prediction models and a large-eddy simulation model as part of the Working Group on Numerical Experimentation “Grey Zone” project. The focus of the project is to investigate to what degree current convection and boundary layer parameterizations behave in a scale-adaptive manner in situations where the model resolution approaches the scale of convection. Global model simulations were performed at a wide range of resolutions, with convective parameterizations turned on and off. The models successfully simulate the transition between the observed boundary layer structures, from a well-mixed stratocumulus to a deeper, partly decoupled cumulus boundary layer. There are indications that surface fluxes are generally underestimated. The amount of both cloud liquid water and cloud ice, and likely precipitation, are under-predicted, suggesting deficiencies in the strength of vertical mixing in shear-dominated boundary layers. But also regulation by precipitation and mixed-phase cloud microphysical processes play an important role in the case. With convection parameterizations switched on, the profiles of atmospheric liquid water and cloud ice are essentially resolution-insensitive. This, however, does not imply that convection parameterizations are scale-aware. Even at the highest resolutions considered here, simulations with convective parameterizations do not converge toward the results of convection-off experiments. Convection and boundary layer parameterizations strongly interact, suggesting the need for a unified treatment of convective and turbulent mixing when addressing scale-adaptivity

Changes in anti-viral effectiveness of interferon after dose reduction in chronic hepatitis c patients: a case control study

BACKGROUND: High dose interferon induction treatment of hepatitis C viral infection blocks viral production over 95%. Since dose reduction is often performed due to clinical considerations, the effect of dose reduction on hepatitis C virus kinetics was studied. METHODS: A new model that allowed longitudinal changes in the parameters of viral dynamics was used in a group of genotype-1 patients (N = 15) with dose reduction from 10 to 3 million units of interferon daily in combination with ribavirin, in comparison to a control group (N = 9) with no dose reduction. RESULTS: Dose reduction gave rise to a complex viral kinetic pattern, which could be only explained by a decrease in interferon effectiveness in blocking virion production. The benefit of the rapid initial viral decline following the high induction dose is lost after dose reduction. In addition, in some patients also the second phase viral decline slope, which is highly predictive of success of treatment, was impaired by the dose reduction resulting in smaller percentage of viral clearance in the dose reduction group. CONCLUSIONS: These findings, while explaining the failure of many induction schedules, suggest that for genotype-1 patients induction therapy should be continued till HCVRNA negativity in serum in order to increase the sustained response rate for chronic hepatitis C

A large-scale and PCR-referenced vocal audio dataset for COVID-19

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.Comment: 37 pages, 4 figure