Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown

Caring for People with Rare Diseases: A Systematic Review of the Challenges of, and Strategies for Dealing with, COVID-19

The COVID-19 pandemic took a toll on everyone's lives, and patients with rare diseases (RDs) had to pay an even higher price. In this systematic review, we explored the impact of the COVID-19 pandemic on individuals with RDs from a psychological perspective. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we retrieved articles from the Google Scholar, Scopus, and PubMed databases focusing on 'COVID-19,' 'psychology,' and 'rare diseases.' Seventeen primary articles were identified (mainly from continental Europe). The results revealed the psychological effects of the pandemic on rare disease patients, including increased anxiety, stress, and depressive moods. This review also highlighted the increased vulnerability and reduced quality of life of rare disease patients during the pandemic, as well as the importance of telecare and psychological support as critical interventions for improving their well-being. There is an urgent need for multidisciplinary research and stronger healthcare systems to meet the unique challenges of rare disease patients, who represent 3.5-5.9% of the global population.

Rationale for the shielding policy for clinically vulnerable people in the UK during the COVID-19 pandemic: a qualitative study

INTRODUCTION

Shielding aimed to protect those predicted to be at highest risk from COVID-19 and was uniquely implemented in the UK during the first year of the pandemic from March 2020. As the first stage in the EVITE Immunity evaluation (Effects of shielding for vulnerable people during COVID-19 pandemic on health outcomes, costs and immunity, including those with cancer:quasi-experimental evaluation), we generated a logic model to describe the programme theory underlying the shielding intervention.

DESIGN AND PARTICIPANTS

We reviewed published documentation on shielding to develop an initial draft of the logic model. We then discussed this draft during interviews with 13 key stakeholders involved in putting shielding into effect in Wales and England. Interviews were recorded, transcribed and analysed thematically to inform a final draft of the logic model.

RESULTS

The shielding intervention was a complex one, introduced at pace by multiple agencies working together. We identified three core components: agreement on clinical criteria; development of the list of people appropriate for shielding; and communication of shielding advice. In addition, there was a support programme, available as required to shielding people, including food parcels, financial support and social support. The predicted mechanism of change was that people would isolate themselves and so avoid infection, with the primary intended outcome being reduction in mortality in the shielding group. Unintended impacts included negative impact on mental and physical health and well-being. Details of the intervention varied slightly across the home nations of the UK and were subject to minor revisions during the time the intervention was in place.

CONCLUSIONS

Shielding was a largely untested strategy, aiming to mitigate risk by placing a responsibility on individuals to protect themselves. The model of its rationale, components and outcomes (intended and unintended) will inform evaluation of the impact of shielding and help us to understand its effect and limitations.

Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark

The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.

Ethics of selective restriction of liberty in a pandemic

Liberty-restricting measures have been implemented for centuries to limit the spread of infectious diseases. This article considers if and when it may be ethically acceptable to impose selective liberty-restricting measures in order to reduce the negative impacts of a pandemic by preventing particularly vulnerable groups of the community from contracting the disease. We argue that the commonly accepted explanation-that liberty restrictions may be justified to prevent harm to others when this is the least restrictive option-fails to adequately accommodate the complexity of the issue or the difficult choices that must be made, as illustrated by the COVID-19 pandemic. We introduce a dualist consequentialist approach, weighing utility at both a population and individual level, which may provide a better framework for considering the justification for liberty restrictions. While liberty-restricting measures may be justified on the basis of significant benefits to the population and small costs for overall utility to individuals, the question of whether it is acceptable to discriminate should be considered separately. This is because the consequentialist approach does not adequately account for the value of equality. This value may be protected through the application of an additional proportionality test. An algorithm for making decisions is proposed. Ultimately whether selective liberty-restricting measures are imposed will depend on a range of factors, including how widespread infection is in the community, the level of risk and harm a society is willing to accept, and the efficacy and cost of other mitigation options.

Comparison of COVID-19 outcomes among shielded and non-shielded populations

Many western countries used shielding (extended self-isolation) of people presumed to be at high-risk from COVID-19 to protect them and reduce healthcare demand. To investigate the effectiveness of this strategy, we linked family practitioner, prescribing, laboratory, hospital and death records and compared COVID-19 outcomes among shielded and non-shielded individuals in the West of Scotland. Of the 1.3 million population, 27,747 (2.03%) were advised to shield, and 353,085 (26.85%) were classified a priori as moderate risk. COVID-19 testing was more common in the shielded (7.01%) and moderate risk (2.03%) groups, than low risk (0.73%). Referent to low-risk, the shielded group had higher confirmed infections (RR 8.45, 95% 7.44-9.59), case-fatality (RR 5.62, 95% CI 4.47-7.07) and population mortality (RR 57.56, 95% 44.06-75.19). The moderate-risk had intermediate confirmed infections (RR 4.11, 95% CI 3.82-4.42) and population mortality (RR 25.41, 95% CI 20.36-31.71) but, due to their higher prevalence, made the largest contribution to deaths (PAF 75.30%). Age ≥ 70 years accounted for 49.55% of deaths. In conclusion, in spite of the shielding strategy, high risk individuals were at increased risk of death. Furthermore, to be effective as a population strategy, shielding criteria would have needed to be widely expanded to include other criteria, such as the elderly.

Informing the public health response to COVID-19: a systematic review of risk factors for disease, severity, and mortality

BACKGROUND

Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics.

METHODS

Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised.

RESULTS

One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity.

CONCLUSIONS

The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected.

REGISTRATION

This review was registered on PROSPERO as CRD42020177714 .

Individual and community-level risk for COVID-19 mortality in the United States

Reducing COVID-19 burden for populations will require equitable and effective risk-based allocations of scarce preventive resources, including vaccinations¹. To aid in this effort, we developed a general population risk calculator for COVID-19 mortality based on various sociodemographic factors and pre-existing conditions for the US population, combining information from the UK-based OpenSAFELY study with mortality rates by age and ethnicity across US states. We tailored the tool to produce absolute risk estimates in future time frames by incorporating information on pandemic dynamics at the community level. We applied the model to data on risk factor distribution from a variety of sources to project risk for the general adult population across 477 US cities and for the Medicare population aged 65 years and older across 3,113 US counties, respectively. Validation analyses using 54,444 deaths from 7 June to 1 October 2020 show that the model is well calibrated for the US population. Projections show that the model can identify relatively small fractions of the population (for example 4.3%) that might experience a disproportionately large number of deaths (for example 48.7%), but there is wide variation in risk across communities. We provide a web-based risk calculator and interactive maps for viewing community-level risks.

Will the Higher-Income Country Blueprint for COVID-19 Work in Low- and Lower Middle-Income Countries?

Strategies to radically suppress incidence of COVID-19, as used in higher-income countries, may be unrealistic and counterproductive in most low- and lower middle-income countries. Instead, strategies should be tailored to the setting, balancing expected benefits, potential harms, and feasibility.