National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically?

Quantifying the impact of lockdowns on COVID-19 mortality risks is an important priority in the public health fight against the virus, but almost all of the existing research has only conducted macro country-wide assessments or limited multi-country comparisons. In contrast, the extent of within-country variation in the impacts of a nation-wide lockdown is yet to be thoroughly investigated, which is the gap in the knowledge base that this paper fills. Our study focuses on England, which was subject to 3 national lockdowns between March 2020 and March 2021. We model weekly COVID-19 mortality counts for the 312 Local Authority Districts in mainland England, and our aim is to understand the impact that lockdowns had at both a national and a regional level. Specifically, we aim to quantify how long after the implementation of a lockdown do mortality risks reduce at a national level, the extent to which these impacts vary regionally within a country, and which parts of England exhibit similar impacts. As the spatially aggregated weekly COVID-19 mortality counts are small in size we estimate the spatio-temporal trends in mortality risks with a Poisson log-linear smoothing model that borrows strength in the estimation between neighbouring data points. Inference is based in a Bayesian paradigm, using Markov chain Monte Carlo simulation. Our main findings are that mortality risks typically begin to reduce between 3 and 4 weeks after lockdown, and that there appears to be an urban-rural divide in lockdown impacts.

Quantifying the impact of air pollution on Covid-19 hospitalisation and death rates in Scotland.

Better understanding the risk factors that exacerbate Covid-19 symptoms and lead to worse health outcomes is vitally important in the public health fight against the virus. One such risk factor that is currently under investigation is air pollution concentrations, with some studies finding statistically significant effects while other studies have found no consistent associations. The aim of this paper is to add to this global evidence base on the potential association between air pollution concentrations and Covid-19 hospitalisations and deaths, by presenting the first study on this topic at the small-area scale in Scotland, United Kingdom. Our study is one of the most comprehensive to date in terms of its temporal coverage, as it includes all hospitalisations and deaths in Scotland between 1st March 2020 and 31st July 2021. We quantify the effects of air pollution on Covid-19 outcomes using a small-area spatial ecological study design, with inference using Bayesian hierarchical models that allow for the residual spatial correlation present in the data. A key advantage of our study is its extensive sensitivity analyses, which examines the robustness of the results to our modelling assumptions. We find clear evidence that PM2.5 concentrations are associated with hospital admissions, with a 1 µgm-3 increase in concentrations being associated with between a 7.4% and a 9.3% increase in hospitalisations. In addition, we find some evidence that PM2.5 concentrations are associated with deaths, with a 1 µgm-3 increase in concentrations being associated with between a 2.9% and a 10.3% increase in deaths.

Working together better for mental health in children and young people during a pandemic: experiences from North Central London during the first wave of COVID-19.

Direct risk from infection from COVID-19 for children and young people (CYP) is low, but impact on services, education and mental health (so-called collateral damage) appears to have been more significant. In North Central London (NCL) during the first wave of the pandemic, in response to the needs and demands for adults with COVID-19, general paediatric wards in acute hospitals and some paediatric emergency departments were closed. Paediatric mental health services in NCL mental health services were reconfigured. Here we describe process and lessons learnt from a collaboration between physical and mental health services to provide care for CYP presenting in mental health crisis. Two new 'hubs' were created to coordinate crisis presentations in the region and to link community mental health teams with emergency departments. All CYP requiring a paediatric admission in the first wave were diverted to Great Ormond Street Hospital, a specialist children's hospital in NCL, and a new ward for CYP mental health crisis admissions was created. This brought together a multidisciplinary team of mental health and physical health professionals. The most common reason for admission to the ward was following a suicide attempt (n=17, 43%). Patients were of higher acute mental health complexity than usually admitted to the hospital, with some CYP needing an extended period of assessment. In this review, we describe the challenges and key lessons learnt for the development of this new ward setting that involved such factors as leadership, training and also new governance processes. We also report some personal perspectives from the professionals involved. Our review provides perspective and experience that can inform how CYP with mental health admissions can be managed in paediatric medical settings.

Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity.

Modelling the small-area spatio-temporal dynamics of the Covid-19 pandemic is of major public health importance, because it allows health agencies to better understand how and why the virus spreads. However, in Scotland during the first wave of the pandemic testing capacity was severely limited, meaning that large numbers of infected people were not formally diagnosed as having the virus. As a result, data on confirmed cases are unlikely to represent the true infection rates, and due to the small numbers of positive tests these data are not available at the small-area level for confidentiality reasons. Therefore to estimate the small-area dynamics in Covid-19 incidence this paper analyses the spatio-temporal trends in telehealth data relating to Covid-19, because during the first wave of the pandemic the public were advised to call the national telehealth provider NHS 24 if they experienced symptoms of the virus. Specifically, we propose a multivariate spatio-temporal correlation model for modelling the proportions of calls classified as either relating to Covid-19 directly or having related symptoms, and provide software for fitting the model in a Bayesian setting using Markov chain Monte Carlo simulation. The model was developed in partnership with the national health agency Public Health Scotland, and here we use it to analyse the spatio-temporal dynamics of the first wave of the Covid-19 pandemic in Scotland between March and July 2020, specifically focusing on the spatial variation in the peak and the end of the first wave.