The role of the multiple Index of deprivation in predicting mental health outcomes after the COVID-19 pandemic in adolescents: a cross-sectional study.

BACKGROUND: The number of children and young people (CYP) diagnosed with mental health problems has increased over the past decade. The COVID-19 pandemic also has accelerated this increase, raising significant concerns about adolescent emotional wellbeing. Research suggests that adolescents who live in more deprived areas are more likely to experience poor emotional wellbeing. Children in the northwest of England are among those with the poorest outcomes in the UK. We aimed to investigate the association between deprivation and mental health outcomes from 2019 to 2022. The aim was to support local authorities with targeted provision of public health services as well as predicting service need for 2022 onwards. METHODS: In this cross-sectional study, we analysed routinely collected Schools Health Needs Assessment (SHNA) data. The School Health Needs Assessment dataset contained 32 676 responses from Year 6 (ages 10-11 years) and Year 9 (ages 13-14 years) who completed the annual survey in 2019-22. The questionnaire was offered to all mainstream schools, delivered by the public health school nursing service. Index of multiple deprivation (IMD) data were provided for household postcodes. Data were analysed using IBM SPSS. Factor analysis created a composite emotional wellbeing scale (EWS) and estimates generated by school year (Years 6 and 9) and three academic years (2019-22). We calculated correlations between IMD and EWS overall and within school and academic year samples. FINDINGS: The final total sample across the three consecutive survey years and the two school years was 32 659. The sample consisted of 15 932 (49%) female students and 5066 (16%) students who registered at school as from an ethnic minority. Of the total sample, 9209 (28%) lived in a postcode in the most deprived IMD quintile in England. There was an overall decrease in EWS from Year 6 to Year 9 and from 2019 to 2022. The Year 6 students in 2022 reported mean levels of EWS equivalent to Year 9 students in 2019 indicating a shift toward poorer mental health in younger children. The correlational analyses showed no significant associations between IMD and EWS scores within the school or academic year cohorts. A follow-up analysis of children in receipt or not in receipt of free school meals also showed no significant association with EWS scores. INTERPRETATION: Findings showed that the emotional wellbeing of children and young people in the northwest of England has deteriorated since 2019, with greatest changes observed in the younger cohort of children in Year 6. This was not explained by postcode-based indices of multiple deprivation. Although it is recognised that deprivation is both a cause and a result of poor mental health, policy decisions on service provision for children and young people should not be based solely on IMD or receipt of free school meals. A rapid response is required to address the decline in emotional wellbeing currently observed in younger children of the northwest of England. FUNDING: None.

Community transmission of monkeypox in the United Kingdom, April to May 2022.

Between 7 and 25 May, 86 monkeypox cases were confirmed in the United Kingdom (UK). Only one case is known to have travelled to a monkeypox virus (MPXV) endemic country. Seventy-nine cases with information were male and 66 reported being gay, bisexual, or other men who have sex with men. This is the first reported sustained MPXV transmission in the UK, with human-to-human transmission through close contacts, including in sexual networks. Improving case ascertainment and onward-transmission preventive measures are ongoing.

Enhancing epidemiological surveillance of the emergence of the SARS-CoV-2 Omicron variant using spike gene target failure data, England, 15 November to 31 December 2021.

When SARS-CoV-2 Omicron emerged in 2021, S gene target failure enabled differentiation between Omicron and the dominant Delta variant. In England, where S gene target surveillance (SGTS) was already established, this led to rapid identification (within ca 3 days of sample collection) of possible Omicron cases, alongside real-time surveillance and modelling of Omicron growth. SGTS was key to public health action (including case identification and incident management), and we share applied insights on how and when to use SGTS.

Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data.

A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services.

Semiautonomous Vehicle Risk Analysis: A Telematics-Based Anomaly Detection Approach.

The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities. The evolving risk landscape will force a departure from traditional risk assessment approaches that rely on historical data to quantify insurable risks. This article investigates the risk structure of SAVs and employs a telematics-based anomaly detection model to assess split risk profiles. An unsupervised multivariate Gaussian (MVG) based anomaly detection method is used to identify abnormal driving patterns based on accelerometer and GPS sensors of manually driven vehicles. Parameters are inferred for vehicles equipped with semiautonomous capabilities and the resulting split risk profile is determined. The MVG approach allows for the quantification of vehicle risks by the relative frequency and severity of observed anomalies and a location-based risk analysis is performed for a more comprehensive assessment. This approach contributes to the challenge of quantifying SAV risks and the methods employed here can be applied to evolving data sources pertinent to SAVs. Utilizing the vast amounts of sensor-generated data will enable insurers to proactively reassess the collective performances of both the artificial driving agent and human driver.

Real-time face & eye tracking and blink detection using event cameras.

Event cameras contain emerging, neuromorphic vision sensors that capture local-light​ intensity changes at each pixel, generating a stream of asynchronous events. This way of acquiring visual information constitutes a departure from traditional frame-based cameras and offers several significant advantages - low energy consumption, high temporal resolution, high dynamic range and low latency. Driver monitoring systems (DMS) are in-cabin safety systems designed to sense and understand a drivers physical and cognitive state. Event cameras are particularly suited to DMS due to their inherent advantages. This paper proposes a novel method to simultaneously detect and track faces and eyes for driver monitoring. A unique, fully convolutional recurrent neural network architecture is presented. To train this network, a synthetic event-based dataset is simulated with accurate bounding box annotations, called Neuromorphic-HELEN. Additionally, a method to detect and analyse drivers' eye blinks is proposed, exploiting the high temporal resolution of event cameras. Behaviour of blinking provides greater insights into a driver level of fatigue or drowsiness. We show that blinks have a unique temporal signature that can be better captured by event cameras.