Matthew Edwards and Steve Bale explain the CMI’s approach to the use of 2020/2021 data, given COVID-19’s effect on mortality during that period
This article sets out the CMI’s intentions regarding the use of 2020 and 2021 data, particularly pensioner, annuitant and life assurance mortality. How useful can mortality data from 2020 and 2021 be, given the abnormality of these years? The same question also faces actuaries working in life insurance and pensions.
The year 2020 was extraordinary (and we hope unique) for mortality because of the COVID-19 pandemic. In the UK there were approximately 73,000 excess deaths above that expected based on mortality in 2019 (the CMI’s excess deaths measure). We are now most of the way through 2021, and deaths from COVID-19 are still a significant number (70,000 to 5 November, based on ONS information on deaths with COVID-19 listed on the death certificate). However, the 2021 mortality experience is unusual owing to factors beyond COVID-19 deaths: in particular, increased other-cause (non-COVID-19) deaths arising from diagnoses and treatment that have been delayed due to lockdowns, and a reduction in deaths due to people having died from COVID-19 in 2020 who might otherwise have been expected to die in 2021 (the ‘forward displacement’ effect).
The CMI’s use of experience data
The experience investigations we carry out in the CMI fall broadly into two types:
- ‘Actual versus expected’ experience analyses, where we assess how the experience of a year or group of years compares with what would be expected based on the most appropriate tables. The CMI will carry on doing this type of analysis on 2020 and 2021 data. This will help subscribers to benchmark how their own experience compares with the market.
- Development of new mortality tables, from analysis of the probability of death at any age followed by smoothing across the age range (‘graduation’, primarily to remove noise). This work aims to derive mortality tables that are predictive of future experience. Clearly, deriving tables based partially on unadjusted 2020 and/or 2021 CMI data is unlikely to be predictive. However, we have not found a satisfactory way to adjust 2020 or 2021 CMI data for this purpose, as we will discuss. Therefore, as a general tenet, the CMI is not intending to develop new mortality tables using data from 2020 or 2021.
Possible approaches to adjusting the 2020 and 2021 experience
We have spent much time considering whether we can remove the pandemic’s effect from the 2020 and 2021 mortality data. We have considered two approaches – a ‘bottom up’ approach using data on deaths directly attributable to COVID-19, and a ‘top-down’ approach looking at ‘excess deaths’ (deaths above those expected, and hence likely attributable to the pandemic).
A bottom-up approach could work using (in the case of the UK) ONS data on deaths with COVID-19 listed on the death certificate, or UK Health Security Agency (formerly PHE) data on deaths within 28 days of a positive COVID-19 test. However, there are several areas of difficulty:
- Some COVID-19 deaths are likely to have been assigned as other causes of death
- We would need to calculate a COVID-19 mortality age curve from public domain data
- Insurance portfolios and pension funds typically exhibit different socioeconomic profiles from the general population, so we would need to allow for how COVID-19 affects these different ‘insured’ lives
- We would need to calculate an ‘amounts weighted’ equivalent of the above (without confounding with the socioeconomic effect).
Each of these steps involves substantial subjectivity and room for error, so the combination of these steps would likely lead to results that would be of little use.
A further concern with this approach is that, while it is almost plausible in dealing with 2020, it would be of no use in 2021 because the other elements making 2021 an abnormal year (for instance, forward displacement and delayed diagnoses) would not be allowed for. But we would want any adjustment approach to work well in both years (and perhaps even 2022).
Overall, therefore, we do not regard this approach as being a useful way to adjust 2020 and 2021 data.
A top-down approach would seek to define deaths caused by the pandemic as the difference between actual deaths and those that would otherwise have been expected, this difference being the ‘excess’. This has been a very useful approach for quantifying the pandemic’s overall mortality impact for the purpose of the CMI’s regular mortality monitoring.
However, it is not so well-suited to adjusting 2020 and 2021 data for the purpose of subsequent analyses of that adjusted data. The reason is that the ‘actual less expected’ method is sensitive to what we define ‘expected mortality’ to be. In simple terms, we would be quantifying ‘non-pandemic deaths’ as (actual deaths less excess deaths), where excess deaths are themselves defined as (actual deaths less expected deaths). In a circular fashion, we end up calculating non-pandemic deaths as expected deaths.
This means we are not bringing any information on actual 2020 or 2021 mortality into our analysis: we have simply brought in a prior expectation through the back door. For this reason, the top-down approach is of no use in adjusting data to arrive at an idea of what 2020 (or 2021) mortality has been ‘absent the pandemic’.
Perhaps more importantly, when will we start to understand the shape of post-pandemic mortality?
It may be that the first consecutive four-year period we are able to use for developing tables is the period 2022-2025, in which case the underlying work would not be done until 2027 at the earliest. However, work on ‘actual v expected’ in respect of the individual years (especially 2022 and 2023) will give a much earlier view on what post-pandemic mortality for insured portfolios and pension funds looks like.
Any question about post-pandemic mortality raises the question of what our plans are for the Mortality Projections Model. Here, we have similar concerns about the unrepresentative nature of 2020 and 2021. The Mortality Projections Committee has therefore adjusted the method in the model: for CMI_2020, ‘weights’ were introduced, with nil weight being the default option for 2020 (in other words, the model excludes the data for 2020 during its fitting process), and full weight placed on data for other years. The current intention is to take a similar approach for CMI_2021, placing nil weight on data for both 2020 and 2021.
We hope this has been useful in outlining the CMI’s direction of travel concerning the use of 2020 and 2021 data. The CMI is working in a very strange period of mortality experience, but we will do the most we can for our subscribers with the available data – just as we have done throughout the pandemic.
Matthew Edwards is chair of the CMI Executive.
Steve Bale is chair of the CMI COVID-19 Working Party.