I’ve been working on some bits of COVID-19 research within the LTC setting since the beginning of the pandemic. The first piece of work was the Scotland report which is available on the LTC-COVID-19 website here. Next was the UK report, in which we compared experiences across the UK during the first wave. I’ve also helped produce the LTC-COVID-19 international report on mortality in care homes. Since then, like many other countries, Scotland has experienced a second wave of COVID-19 cases.
Before I go on to describe the mortality figures in Scotland, I think it is important to acknowledge the individuals who sit behind each of the figures. That is, individuals who have lost their lives, staff who have cared for them and families who have lost people who they love to this dreadful virus.
In total, as of the 6th December, there have been 5,868 confirmed or suspected COVID-19 deaths in Scotland. Of those, 2,393 occurred within care homes. That’s around 41% of all COVID-19 deaths. The chart here shows weekly COVID-19 deaths in care homes and weekly deaths from other causes in care homes. What we can see is the differing magnitudes between the first and second waves. Clearly, the magnitude of the second wave is much smaller, but nonetheless there is a visible increase in deaths during this period.
It is important to mention that these deaths do not include those care home residents who died outwith care homes. The data available in Scotland show that throughout the pandemic, there were at least 2,686 deaths of care home residents. That takes the total percentage of COVID-19 deaths accounted for by care home residents to 46%.There is a noticeable difference in this figure between the first part of the pandemic (16th March 2020 to 28th June 2020) and the second part (29th June – 6th December). In the former, this figure was 50% and in the latter it was 35%. This change might reflect that via the implementation of new guidance and policies, we have been able to partly protect care home residents during the second wave. These include more comprehensive testing strategies, which routinely test staff and residents.
Of course, these figures are conditional on the accurate recording of cause of death, which is a relatively strong assumption. Another way to measure the impact of COVID-19 on mortality is to look at excess deaths, that is the number of deaths over and above what would normally be expected. We define excess deaths as the percentage change in deaths compared to the previous 5-year average. In the peak of the pandemic, during week 17, excess deaths in care homes were 178% higher compared to the previous 5-year average. Throughout the pandemic overall, deaths in care homes were 25% higher. It is worth pointing out that recent evidence from the Lothian region of Scotland has found that among those homes where there was no outbreak of COVID-19, there were few non-COVID-19 related excess deaths (Burton et al, 2020).
Overall, like many countries, the impact of COVID-19 in care homes has been devastating. Some of the difficulties faced by the sector from the beginning of the pandemic included a focus from government of protecting the NHS. This meant that guidance and policies for care homes on testing and PPE were slower to respond. At the same time, a large number of older patients were discharged from hospital and into care homes during the first wave of the pandemic. Having said that, recent evidence from Public Health Scotland has demonstrated that these discharges had no significant impact on the likelihood of a care home outbreak.
The pandemic has exposed the urgent need for better LTC data in Scotland, even on a very fundamental level such as who lives in Scotland’s care homes. The pandemic has spurred on huge efforts to improve administrative data collection within the social care setting, especially those in care homes.
Whilst the focus of COVID-19 within the LTC setting has been on care homes, little attention has been paid to those older people who receive care within their own homes. In Scotland, around 47,000 people aged 65+ receive free personal care within their own homes (compared to around 36,000 adult care home residents) (Bell et al, 2020). At present, we know that their care has been adversely affected by the pandemic (Care Inspectorate, September 2020), but we know virtually nothing about how the pandemic has affected their outcomes. This presents a huge gap in our understanding of how COVID-19 has affected those in receipt of domiciliary care services at home. Evidence from England suggests that the proportional increase in deaths in the home care setting exceeds that of the care home setting (Hodgson et al, 2020). Again, better administrative data on those receiving care at home might help to address this gap in Scotland.
However, administrative data can only go so far as to truly explaining the devastating impact COVID-19 has had on so many lives. In order to provide this understanding, for example the impact on unpaid carers, families, front-line workers and more, survey data is required.
The data used to produce the figures shown in this blog come from: National Records of Scotland (NRS) deaths involving coronavirus, Scottish Government Coronavirus deaths reported to the Care Inspectorate Scotland and an ad-hoc release by NRS on deaths of care home residents up until the 3rd June 2020.
A couple of weeks ago, we posted a blog from Emma Wilson, an undergraduate economics student here in Edinburgh, who spent some time working with Edinburgh Health Economics on a voluntary internship during the summer. This week we hear from a second student, Georgina, who also worked on a project with EHE. Georgina shares a bit about what she worked on and reflects on how she found the experience overall.
Project lead reflections
This summer, after running our first undergraduate Health Economics module, a couple of enthusiastic economics students approached us to ask if there were any opportunities to get some work experience with Edinburgh Health Economics. Whilst we don’t run any formal internship schemes, we thought we could definitely think of some useful projects for the students to get involved with. In this post, one of those students talks to us about the work that she did during her voluntary internship and offers some reflections on how she found the experience overall.
Project lead reflections
Our Research Fellow, Elizabeth Lemmon, has been contributing to several reports on the impact of COVID-19 within the long term care (LTC) system in Scotland and around the world, with colleagues from the University of Stirling, Edinburgh Napier University, London School of Economics and the Bruyère Research Institute. These reports are being produced for the Long Term Care Policy Research Network’s newly established LTC responses to COVID-19 platform, which aims to:
The Scotland report – available here – focuses mainly on the impact of COVID-19 within Scottish Care Homes, where the impact has been particularly devastating. At the most recent release of this report (3rd June), around 47% of all COVID-19 deaths occurred in care homes. Furthermore, as shown in the figure below, more than 62% of all Scottish care homes had reported at least one outbreak of COVID-19.
The International report – available here – also focuses on care homes and care home residents, but aims to draw comparisons between countries around the world. This is incredibly difficult due to the different geographical, demographic and political contexts, which is further complicated by significant differences in the recording of data. The report highlights the key difficulties associated with mortality recording and the need to use data on all care home residents and not only those occurring within the care home.
Both reports are live documents that are being updated as new data become available. Elizabeth is also involved in a new report which is currently underway on the UK situation. This report hopes to draw useful comparisons between the four UK nations.
Can you tell us a little about your role in ECTU?
My role involves a variety of tasks – however, primarily my role is the statistical reporting of trials run from within ECTU. I typically have up to eight active trials throughout the year. My role varies on these – I am Trial Statistician for approximately half of them, and the ‘reporting’ statistician for the other half. When I have my reporting statistician hat on, I’m responsible for the statistical programming and generating the analysis and results.
How trials have you worked on that have involved using administrative data?
Since I joined ECTU in 2014, I have worked on three trials using administrative data. Two of them used solely routine healthcare data and the third one is running currently, based on a blend of routine data plus data captured within the trial.
Is the use of administrative data in trials becoming more common over time?
The use of administrative data in the trials setting is definitely becoming more common since clinical trials are known to be expensive and time-consuming. The use of administrative healthcare data is viewed as a more efficient means of understanding the health of the population using readily available data. However, there is a trade-off in terms of the quality of the data being captured.
What was the High-STEACS trial?
High- Sensitivity Troponin in the Evaluation of patients with suspected Acute Coronary Syndrome (High-STEACS) was a step wedge, cluster- randomised control trial. In plain English this means…
It’s a relatively recent study design that’s increasingly being used to evaluate service delivery type interventions. The design involves crossover of clusters (usually hospitals or other healthcare settings) from control (standard care) to an alternative intervention until all the clusters are exposed to the intervention. This differs to traditional parallel studies where only half of the clusters will receive the intervention and the other half will receive the control. This diagram helps to demonstrate the difference in designs:
The population of interest were patients presenting in hospital with heart attack symptoms. The trial sought to test a new high-sensitivity cardiac troponin assay against the standard care contemporary assay. Specifically, to test if the new assay could detect heart attacks earlier and with a more accurate diagnosis.
How were patients enrolled into the trial and how does this differ from a standard trial?
Step wedge trials usually randomise at a cluster (hospital) level, rather than randomising patients individually, so this was the main difference to a standard trial. So patients were enrolled rather than randomised into the trial. Standard trials require patient consent before randomisation, but in this context, individual patient consent was not needed due to the randomisation being performed at hospital level. Appropriate approvals for consent were sought through the hospitals.
If patients presenting with heart attack symptoms at any of the hospitals were eligible for the trial (based on our pre-specified inclusion/exclusion criteria), then we had permission (at hospital level) to include them in the study and use their securely anonymised data.
How many patients were enrolled into the trial?
Approximately 48,000 patients were enrolled from 10 hospital sites in NHS Lothian (3 sites) and NHS Greater Glasgow and Clyde (7 sites), over a period of just under three years.
Which administrative data sets were used?
We used a total of 12 distinct data sources which were a combination of general administrative datasets and datasets more specific to our area of research from locally held electronic health care records. Prescribing data was obtained from the Prescribing Information System, also ECG data, plus general patient demographics. Trial-specific outcome data was obtained from the Scottish Morbidity Record (SMR01) and also from the register of deaths (National Records of Scotland).
All data were captured separately for each Health Board – there is currently no amalgamated data source which holds all data. Health Boards are the owners of their own data.
The main linking mechanism for these 12 data sources was the patient CHI (Community Health Index) number. To ensure patient anonymity, CHI numbers were securely encrypted prior to use.
How did you get approval for these data sets? How long did this approvals process take?
Approvals were required at a number of levels. We required ethics approval, approval to use patient data without consent and Health and Social Care approval (through the Privacy Approvals Committee, predecessor to the Public Benefit Privacy Panel). There were also health board specific approvals required for local data to be released. In addition, we required data supplier approval. Finally, approval was needed for the data to be hosted on the Safe Haven platform.
This process was long! This was ongoing throughout the duration of the trial. Although the data was being captured automatically via routine records, the final dataset wasn’t confirmed until relatively late on in the process due to complexities of mapping locally held healthcare records. One of the advantages of the national datasets is that they are the same across all health boards.
Where were the data sets stored?
Datasets from NHS Lothian and NHS GG&C were supplied separately in their own Safe Havens. The combined dataset was hosted on the NHS Lothian Safe haven space on the National Safe Haven analysis platform .
How did the linkage of the data sets happen?
The data sources from both health boards were combined and hosted on the National Safe Haven analysis platform. This wasn’t a straightforward process. Although we’d anticipated capturing exactly the same patient data across both health boards, the reality was quite different.
Data were captured in different formats with different variable names and different definitions. So there was an unexpected element of data cleaning required before the data could effectively be merged into one large analysis dataset.
The final linkage was done using the securely encrypted CHI number for each patient.
What do you see as the major benefits of using administrative data in this setting?
Use of administrative data in this context is a more efficient process – less resource spent on the administrative aspects of trial enrolment e.g. capturing demographic details such as age, sex, postcode or medical history.
Using administrative data also gave us the opportunity to research a large representative patient population in comparison to the setting of an RCT where a strict pre-specified population, not necessarily representative of the target population, are studied.
Overall, what were the major challenges of the study?
From the data side of things, ensuring the correct data was extracted was difficult. The diagram above is very over-simplified view of what happened! The reality of picking up the required variables from two separate health boards which capture data very differently was difficult.
Another challenging aspect was ensuring that a patient wasn’t enrolled more than once in the study. Patients can present in any hospital with heart attack symptoms more than once, so we needed to ensure they weren’t included in the study each time they came to hospital. This required a de-duplication algorithm using encrypted and de-identified patient data.
However, I think the biggest challenge was for those in the team tasked with obtaining the correct approvals. It was underestimated how complex this would be. While approval for the national datasets was straightforward and the eDRIS team were very helpful, processes for locally held data at the time of trial set up were not established. Legislation around patient data confidentiality was continually changing, so we were faced with keeping abreast of new legislation as time progressed. The safe haven networks are now more established and hopefully, the processes are more straight forward.
Is there anything you would do differently next time?
I think the data validation aspect of the trial is crucial. Ideally we would have had more time spent on this in order to ensure the data was as correct as possible. Involving the clinical team much sooner in this process would have helped – they have a really important role to play in terms of ensuring the data picked up makes sense from a clinical perspective.
For High-STEACS, the access to the data was highly restricted and did not include the clinical team. Many of the data discrepancies were only picked up at the final review stage once data and results had been released out of the Safe Haven area.
Working within the Safe Haven environment creates time lags on both sides of the process – data being imported into the Safe Haven and also results exported out at the end take time. We hadn’t considered this time lag when working to tight timelines.
Do you know if anyone is using the learning from this trial for future trials of this kind?
The High-STEACS trial was directly followed by the HiSTORIC trial, addressing similar research questions and using many of the same data sources. So we have been through the loop again which has made for a more streamlined process.
Other trials within ECTU are also making use of the learnings from High-STEACS, particularly from the governance and approvals side of things.
Thanks for sharing this with us Catriona! It is great to see that administrative data are being utilised alongside clinical trials in Scotland. It is also interesting to hear that despite being part of a trials unit like ECTU, the High-STEACS team still faced many of the same challenges that researchers and eCRUSADers have experienced when using administrative data for research. In particular, we can relate to the issues of permissions, timing and working within the Safe Haven environment. Overall, it seems that the timing issues were due to the use of the locally held data rather than using the national data.
Author: Joanne Mair
Hello, my name is Jo and I am undertaking a part time PhD within the Usher Institute, my supervisors are Dr Nazir Lone, Dr Peter Hall and Professor Kev Dhaliwal. I currently work for the Centre for Inflammation Research as a Clinical Project Manager within Professor Kev Dhaliwal’s team and have done so for the last 6 years. To date my further education consists of an undergraduate degree in Pharmacology, a MSc in Bioinformatics and a MSc in Public Health Research.
I have worked for the University of Edinburgh for the last 13 years in various roles within research. These include working in a clinical research facility, a research and knowledge exchange office and in research governance. I also have experience working within the NHS in clinical trials. Prior to that I worked in various ad hoc roles and travelled/worked in South America, New Zealand and South Africa (some photos below!). I have been a member of the NHS Research Ethics Service for nearly ten years, being a member of SESREC 2, then the chair and I now sit on Scotland A.
The group I work within focus their research on respiratory disease and the ways in which diagnosis of respiratory disease can be improved. Whilst working within this group I have been able to get fully immersed in translational research. Day to day working can involve anything from writing protocols and regulatory applications to being in the laboratory building medical devices, being in the clinics and wards assisting the clinical staff with study participants, negotiating commercial contracts for third party outsourcing, dealing with finances and creating structures and processes for forging our way through unknown territory and getting novel compounds and devices into man.
One area the group is researching is looking at fast, bedside, point of care diagnosis of pneumonia and identifying the gram status of the infection in ventilated patients. The group have developed a novel diagnostic technique consisting of an imaging system, compounds that allow diagnosis of lung infection and a delivery device to deliver the compounds into the distal lung. Work is currently underway to pilot the novel technique leading to a second stage clinical study within Edinburgh and three other UK sites.
Whilst working within my current research group within the media of novel developments to improve health, I became more aware of the difficulties in pushing research through to a stage where we can get it into the NHS. Gathering evidence of the impact a novel diagnostic tool could have on the NHS and the lives of patients is a time consuming and arduous process. Whilst most people appreciate the necessity of ensuring a diagnostic test is safe and does what it intends to do, perhaps measuring the potential impact on the patients and the NHS could be done in more than one way? Increasing evidence and developments support this way of thinking. The availability of observational data is increasing all the time and the skill set to put it to use expanding. As such I finally felt I had a focus I could put towards a PhD! With amazing help and input from Dr Nazir Lone, great support from Professor Kev Dhaliwal (and team, especially Dr Anne Moore) and invaluable time with Dr Peter Hall I developed the outline of a workable PhD, applied for and obtained a staff scholarship.
Within my PhD I will be looking at the use of the observational data from ICU patients and how this can be used to model the potential impact of a novel diagnostic on patient outcomes and NHS (costs). I would ultimately like to compare this to the data being gathered as part of the clinical study to see how the observational data can add value to or replace some aspects of the clinical study. Initially, I am focusing on developing the care pathway map within ICU for patients with suspected pneumonia, gathering the necessary data (through extraction from NHS systems into a safe haven plus utilising other data sources/sets), assessing where the novel device could be most useful and comparison with the reference standard as developed through the clinical study. With regards to the health economic modelling I am working towards the construction, parameterisation and analysis of a health economic decision model based on the care pathway developed. The model will initially calculate expected costs and outcomes for the current care pathway. The new diagnostic test will then be incorporated into the model at key decision points, as indicated by the current evidence and recommendations from the specialist advisory group (for the clinical study). Divergence in the clinical pathway consequent on test results will be modelled based on the diagnostic properties and decision impact of the test.
I have entered into discussions about PhDs in the past and could never quite commit myself but I really feel this one ties in with my work and the group’s ethos whilst being interesting and worthwhile. I’m enjoying it so far, between this, work and living the fairly quiet life in East Lothian (Drem), if you can call having three young daughters (pictured with me below) a quiet life – it all keeps me rather busy!
Last week, Elizabeth was invited down to St Andrews House to present her PhD work to the Health and Social Care Analysis team. Specifically, this presentation covered her PhD research on the provision of long term care to older adults in Scotland, with a particular focus on the usefulness of Scotland’s administrative data (data that are collected routinely as part of service provision) in answering her PhD questions. In this post, Elizabeth gives you a quick overview of her presentation.
Author: Elizabeth Lemmon
You might be wondering why there is a photograph of me (top right), my mum (top left) and my grandmother (centre) at the top of this post. As well as showing the increasingly familiar image of a multi-generational family (my mum might prefer if there was another generation in there but I have told her she is going to have to wait a few years for that!), I like to use this photograph to tell the story of my PhD. So here goes….
Paper 1: Variations in domiciliary free personal care across Scottish local authorities
Data used: Social Care Survey (SCS) and other publically available, area level data sets
This paper looks at things from my Grandmother’s perspective as an older person who is receiving personal care services. In particular, it explores variation in the provision of Free Personal Care (FPC) across Scottish local authorities, in order to establish whether or not FPC provision matches the need of the population.
Paper 2: Utilisation of personal care services in Scotland: the influence of unpaid carers
Data used: Social Care Survey (SCS)
This paper looks at things from my mum’s perspective as an unpaid carer who is providing care to my Grandmother. In particular, this paper uses the SCS to try and understand how unpaid carers can influence older people’s use of personal care services.
Paper 3: The cost of unpaid care: a standard of living approach
Data used: Family Resource Survey (FRS)
My final paper looks at this from my perspective, as an onlooker to the caring situation going on between my mum and my Grandmother. This perspective asks, “who cares for the carer?”. The aim of this paper was to understand whether or not unpaid carers experience a reduction in their standard of living due to caring, if so then how much would they need to be compensated by in order for them to reach the same standard of living as a non-carer, and finally how would that level of compensation compare to the current Carers Allowance.
If you want to know what I actually did and found in each of these three papers, you can have a look at my thesis here.
My PhD and Administrative Data
I had planned to use a national, linked administrative health and social care data set for my PhD. In fact, I applied for this in April 2016. Unfortunately, I didn’t get access to it until April 2018 and by this point I had had to come up with a plan B and was running out of time/funding to be able to get to grips with the linked data.
As a result, I made do with publically available, geographical level data, survey data and administrative social care data.
Did the administrative data help? Some reflections…
Well yes, of course they did. I was able to do some pretty cool work in my PhD using the SCS and this wouldn’t have been possible without it. However, the PhD really taught me that the administrative data struggle is real! A few things I highlighted in the presentation to the Scottish Government were:
- Approvals process and linkage timing. Two years is simply too long in the lifetime of a PhD and I did not foresee that it would take this long.
- Administrative data aren’t designed for research- they typically lack important controls that we really want/need in econometric analysis. But if we want to answer policy relevant questions with administrative data, surely they should be designed with this in mind? See this recent blog post I did with my colleague from Napier for the Office of Statistical Regulation.
- There were lots of differences between local authorities in terms of data recording, missing information etc, which can cast doubt on the conclusions (of course I have carried out as many sensitivity and robustness checks to ensure this isn’t the case, but there is still doubt).
- There isn’t any information about the unpaid carers in the SCS. Again, this is important information that is lacking from the administrative data.
Sadly, I’m still enduring this administrative data struggle in my role here in Edinburgh Health Economics. In an attempt to do something about this, I have spent some time developing a new platform called Early Career Researchers Using Scottish Administrative Data (eCRUSADers). I’m hoping that this will reduce the struggle for any researchers who are new to the administrative data scene. You can find out more about eCRUSADers (or join us?!) on the website here.
Author: Katharina Diernberger
The PRC is a European Research Centre consisting of 17 international and 8 national collaborating centres. They plan and conduct international multicentre studies within palliative care, focusing on pain, cachexia and health care services. On the 30th January, the TVT (Two Versus Three Step) study, primarily run by the centre in Edinburgh, presented the initial clinical and health economics results at the PRC Seminar in Oslo ahead of the main results being published.
The TVT Study “An international, multicentre, open randomised parallel group trial comparing a two-step approach for cancer pain relief with the standard three-step approach of the WHO analgesic ladder in patients with cancer pain requiring step two analgesia” recruited patients in the UK, Mexico, Uganda and Israel and was comparing the 3-step approach for pain control currently recommended by the WHO* to a 2-step approach omitting weak opioids.
The primary outcome was time to achieving stable pain control, where stable pain control was defined as the first day of three consecutive days with average pain score ≤3 on a numeric rating scale of 0-10. The trial also looked at a potential increase in opioid-related side effects.
This was a very interesting trial from a health economic viewpoint, taking into account the different costing systems within the participating countries. Though the quality of life component (EQ-5D-5L) was captured in all countries, a full economic evaluation was only possible for the UK as the country specific value set was readily available.
The main results of the study can be summed up fairly quickly – omitting step 2 (weak opioids) showed no significant difference in terms of pain control, lead to a reduction in opioid related side effects, is cheaper and patients -reported better outcomes for quality of life. Further to this, more than half of the patients who started on a weak opioid had to switch to a strong opioid in order to achieve pain control. The link to the full publication will be added as soon as the results are published.
The introduction of the presentation highlighted the importance of health economics within palliative care (as a main tool for decision making within the health care sector) and tried to clarify misconceptions about health economics. It focussed on the challenges we are facing in terms of collecting costs, measure patients’ quality of life and putting a value on “life improvements” in this particular setting. This part of the presentation facilitated interesting discussion at the conference and some ideas for possible future collaborations.
After the seminar I stayed in Oslo for the weekend to catch up with friends and use the opportunity to visit the Henie Onstad Art Centre in Oslo which is currently hosting the “Picasso 347” (below), and “Claude Monet and Bærum” exhibitions as well as “Yayoi Kusamas’ – Hymn of Life” (at the top of this post). I can highly recommend a visit!
Step 1. Non-opioids (non-steroidal anti-inflammatory drugs [NSAIDS], acetylsalicylic acid [ASA, aspirin] and paracetamol [acetaminophen]) for mild pain.
Step 2. Add an opioid for mild to moderate pain (Codeine, Tramadol).
Step 3. Add opioid for moderate to severe pain (morphine) titrated to pain relief or alternatively to occurrence of dose-limiting adverse events (AE).