East of England Population Health Research Hub

Research Bites

The latest East of England PHResH Research Bites:

Bioethical and human rights considerations for triaging in the COVID-19 pandemic

COVID-19 | University of Essex | 3 min read


Four nations survey finds big increase in people affected by loneliness due to coronavirus during lockdown weeks, especially those aged 18-24

COVID-19 | University of Cambridge | 2 min read


Collaboration between Cambridge University, NHS and PHE trials cutting edge tool for individual hospitals to predict demand during Covid-19

COVID-19 | University of Cambridge | 2 min read


Covid-19 response likely to increase income inequalities in society warn researchers

COVID-19 | University of Cambridge | 2 min read


Online educational tool developed to demystify modelling predictions around Covid-19 control measures

COVID-19 | University of Cambridge | 2 min read


Research Bites

Bioethical and human rights considerations for triaging in the COVID-19 pandemic

Background

Triage processes are based primarily on clinical considerations to maximise the number of lives saved. However, the COVID-19 pandemic may lead to situations where healthcare professionals are faced with severe shortages as well as large numbers of patients with similar diagnoses and prognoses. In these situations, prioritisation becomes difficult and clinical considerations may not be enough to aide in the triage process. The Essex Autonomy Project and the Ethics of Powerlessness project at the University of Essex conducted a survey of existing research and guidance and produced a joint technical report to highlight some of the bioethical and human rights issues that are particularly relevant for policy makers when formulating prioritisation criteria for triage guidelines and decisions during the COVID-19 pandemic. 

Keep in mind

This paper does not provide a comprehensive overview of the ethical issues involved in making triage decisions; it is limited to those that the authors considered most relevant. The authors themselves also caution that there are no easy or uncontroversial approaches to prioritisation in triage, and that it is crucial to have clear, transparent criteria in place.

Key Bioethical and Human Rights Considerations

Utilitarian (maximising benefits) and equitable (fair distribution of benefits) ethics need to be combined and care needs to be taken to avoid discrimination when designing triage criteria.

Specific Clinical Criteria:

  • Triage based solely on clinical factors such as probability of survival with or without treatment may prioritise younger people and discriminate against those with underlying health conditions.
  • Age limits for critical care admission discriminate against the elderly and place too much weight on age in the triage process, as age already factors into clinical criteria assessing overall health.
  • While NICE originally recommended prioritising patients with low frailty scores, this has been retracted as it would likely result in the discrimination of those with disabilities and those who require care support. Frailty should only be considered as part of a holistic assessment.
  • Extubating a patient to allow intubation of an incoming patient may be justified if the patient being extubated is in a lower access category according to the Ventilator Allocation Guidelines.

Non-Clinical Criteria:

  • Randomisation may be more just than first come first served principles if cases are clinically equal as it does not discriminate against those who are rural, disadvantaged, or less mobile.
  • Number of life-years to be saved is an attractive criterion, however prognoses can be uncertain and may be based on underlying disabilities; care needs to be taken to avoid discrimination.
  • Prioritising healthcare and other key workers may be justified to maintain life-saving and essential services and discourage absenteeism among those who face high risk of infection at work. However, this approach can be subjective and may not directly support the goal of maximising the number of lives saved.

Procedural Issues

  • Blinded triage systems can reduce the psychological burden on the triage team and improve the efficiency and consistency with which triage principles are applied. However, it could result in the loss of more detailed clinical information and increase administrative burdens.

The full report is available here: https://www.researchgate.net/publication/340548071_Triage_in_the_COVID-19_Pandemic_Bioethical_and_Human_Rights_Considerations

Hub Partner: University of Essex


Four nations survey finds big increase in people affected by loneliness due to coronavirus during lockdown weeks, especially those aged 18-24

The Research
A recent survey of 2221 people led by charity the Mental Health Foundation has found a quarter of respondents felt lonely as a result of coronavirus. The online survey was conducted on 2-3 April and asked people how they had felt in the previous two weeks — which covered the period of lockdown in the UK. This is a big increase since the same question was asked 17-18 March: when nearly 10% of respondents said they had felt lonely.

Key Points:

  • The survey found a quarter of respondents felt lonely as a result of coronavirus.
  • The group that said they were most lonely were aged 18-24  years old – 44% were affected.
  • The second most affected group were aged 25-34.
  • About one in six of those aged over 55 felt lonely.
  • Women were affected more than men both times the survey has been conducted.
  • We don’t know yet from this research why people felt lonely — for example, from the lockdown situation itself, or uncertainty over coronavirus, their personal circumstances or other reasons.

Things to Note
The survey was weighted to ensure the respondents represented the wider UK population age structure. However, the respondents are different for each survey although they come from the same representative pool. Because it was conducted online that may affect who is able to respond. Resondents came from across the UK but their socio economic demographic information is not included in the information available. 0000

Background
The survey is led by the Mental Health Foundation, a charity that promotes mental health issues and is a collaboration between four universities from the 4 nations: University of Cambridge, University of Strathclyde, Swansea University and Queen’s University Belfast. The project will last 4 to 6 months and repeat the cross-sectional survey using the same method to provide a tracker of responses.

For more information visit: https://www.cam.ac.uk/research/news/almost-a-quarter-of-adults-living-under-lockdown-in-the-uk-have-experienced-loneliness

Hub Partner: University of Cambridge


Collaboration between Cambridge University, NHS and PHE trials cutting edge tool for individual hospitals to predict demand during Covid-19

Research Aim
Professor Mihaela van der Schaar and her team from the University of Cambridge is collaborating with PHE and NHS Digital to trial their system Cambridge Adjutarium in acute trusts. The system’s goal is to help individual clinicians and hospitals predict demand for their resources during Covid-19, and therefore utilise limited resources such as ventilators and ICU beds efficiently. The system is designed to be user friendly and easy to use.

The Technology
The technology this system uses is called “machine learning” – a part of artificial intelligence. It’s a process that uses large amounts of sample data, in this case from patients, to build mathematical models which can “learn” to make predictions without following a preset algorithm. The idea is that this process can see trends within unpredictable real-world situations, which are influenced by countless changing and interconnecting factors. It is however, a new field of research and so applying it to real life situations to determine outcomes is much less tried and tested.

Next Steps
To date Professor van der Schaar and her team have used the technology successfully to look at outcomes for other chronic diseases, but since Covid-19 its added potential became clear. The system will now be trialed at a number of acute trusts including Addenbrookes to assist the capacity planning systems. If successful the next stage would be to make the technology nationally available.

For more information visit: https://www.linkedin.com/pulse/partnering-nhs-digital-public-health-england-mihaela-van-der-schaar/

Hub Partner: University of Cambridge


Covid-19 response likely to increase income inequalities in society warn researchers

The Research
A survey in the US and UK conducted on 24 and 25 March has found that workers expected to earn about a third less in the next four months compared to usual. They also expected to work fewer hours or lose their jobs by the summer. Those who were under 30 and those on lower incomes were most likely to be affected already, or to feel they soon would be.

The findings led researchers to conclude that this could lead to an increasing income gap between rich and poor, young and old, those with secure employment compared to those on insecure contracts. They conclude this could increase inequalities in society.

Key Points:

  • 57% of workers engaged in less paid work over the past week than usually.
  • 8% of workers in employment a month ago had already lost their job which they attributed to Covid-19, and those in work 33% expected to lose their job in next 4 months.
  • Workers expected, on average to earn 35% less in the next 4 months and about half felt they would have trouble paying bills.
  • The young (less than 30) and those on low incomes (below £20,000 annually) were disproportionately affected most.
  • Those who are young and earn lower incomes tend to have jobs with most tasks that cannot be completed at home.

The Survey
About 8000 people took part in the survey in both UK and the US. The UK survey was conducted two days after the lockdown in the UK but before the Chancellor announced the full series of economic measures to assist those who were self employed.

The respondents in the UK were from a geographically representative selection, 53% female, and an average age of 38.6 years. About 40% had a university degree and the average annual income of £28,000. The survey was a joint venture by the University of Cambridge, Oxford and Zurich.

Things to Note
There was no information on their socio economic status or demographic beyond this information. It was an online survey which may affect who can participate, and respondents were paid a ‘modest incentive’ for completing the survey.

For further information visit: https://www.cam.ac.uk/research/news/younger-workers-hit-harder-by-coronavirus-economic-shock-in-uk-and-us

Hub Partner: University of Cambridge


Online educational tool developed to demystify modelling predictions around Covid-19 control measures

The Research
The ‘lowighcovid’ tool has been developed as an educational aid to demystify the Covid-19 modelling prediction process. It allows users to look at the control measures in various countries and understand each one’s effect on Covid-19 spread. In addition to the interactive tool there are explanatory videos and explanations.  The aim is to illustrate some of what models can do, and that different control measures will influence the spread and outcomes of the virus.

The Need
Since the onset of Covid-19 there have been many control measures put in place by various countries and states. Comparing the potential impact of them has been generated by complex computer forecasting models.  Understanding the work of the models and the measures they are comparing has brought complex evidence into the spotlight, without much general public understanding about this process.

The ‘lowhighcovid’ Tool
It was developed by a team at the University of Cambridge in order to allow users to understand and interact with the most commonly used model using a real time data feed. They can explore the various control measures and their effect on different countries infection rates, hospitalisations and death.

Things to Note
It’s important to note that all models involve different assumptions and use different methods. The tool here uses a modified SIR model. This type of model is more established than some others. A SIR model will give one prediction based on a set of parameters, while other more complex models give a range of predictions.

For more information visit: https://www.cam.ac.uk/research/news/interactive-tool-shows-the-science-behind-covid-19-control-measures

Hub Partner: University of Cambridge