The Continuous Mortality Investigation (CMI) provides authoritative and independent mortality and sickness rate tables for actuaries advising UK life insurers and pension funds. It operates as a not-for-profit private company that is wholly owned by the Institute and Faculty of Actuaries (IFoA). The CMI Mortality Projections Model (the “CMI Model”) is widely used by actuaries in the financial management of UK pension schemes and life insurance companies.
This note accompanies the release of the latest version of the CMI Mortality Projections Model, CMI_2024. It describes the CMI Model and contains background information on recent mortality experience. We include a section on “mortality terminology” at the end of this FAQ to explain the terms used.
The CMI is funded by subscriptions from commercial users, and most CMI outputs are only available to Subscribers, and to academics and researchers for non-commercial use. This note is publicly available and its use is subject to the Disclaimer and Copyright Notice in the back page.
We strongly encourage actuaries using CMI_2024 to refer to the more detailed results in Working Paper 201 and its associated outputs, which are restricted to authorised users. Details of how to access the CMI’s research can be found on the CMI section of the IFoA’s website.
Key findings
All-age standardised mortality rates in England & Wales in 2024 were the lowest on record. However, mortality rates for younger adults were above the 2014-2023 average. The CMI is providing frequent updates on mortality through its regular mortality monitor.
Mortality in 2020 and early-2021 was volatile, making it difficult to assess the longer-term impact of the coronavirus pandemic on mortality. The CMI took the view that while mortality experience in 2020 and 2021 would affect actuarial calculations, it was likely to be an outlier and not indicative of the future path that mortality rates will follow. For this reason, the standard versions of CMI_2020 to CMI_2023 placed no weight on the data for these years, and reduced weight on data for 2022 and 2023, when projecting mortality rates.
CMI_2024 takes a different approach to these years (and 2024), giving full weight to data for all years and introduces a new “overlay” term to explicitly model the increase in mortality caused by the pandemic. The overlay starts in 2020 and decays exponentially, halving in each successive year in the Core version.
CMI_2024 also introduces “multiple period terms” to better reflect different trends in mortality at different ages. Having multiple period terms allows modelled mortality in recent years to increase at younger ages but fall at older ages, reflecting recent experience.
The standard version of CMI_2024 produces cohort life expectancies at age 65 that are about three months higher for males and about two weeks higher for females than in CMI_2023.
FAQ sections
- The latest version of the CMI Model, CMI_2024
- Historical mortality in England & Wales
- How the CMI Model works
- Mortality terminology
1. The latest version of the CMI Model, CMI_2024
What happened to mortality in 2020 to 2024?
Standardised all-age mortality rates in England & Wales compared to the 2015-2019 average were around 10% higher in 2020, 4% higher in 2021, 0.6% higher in 2022, 0.1% lower in 2023 and 4% lower in 2024.
However, trends in mortality have varied by age. Mortality in 2024 was 6% lower than the 2015-2019 average for ages 75-100 but 5% higher than the 2015-2019 average for ages 20-44.
How has the CMI Model changed compared to CMI_2023?
In normal circumstances, we update the CMI Model each year to use more recent mortality data. We have updated the dataset for CMI_2024 to use data for calendar years 1984-2024, rather than 1983-2023 for CMI_2023. The dataset is largely based on data published by the Office for National Statistics (ONS) but we make our own estimate for population data in 2024 and for the distribution of the population at older ages in all years.
CMI_2023 reflected the pandemic by giving reduced weight or no weight to data for years since 2020. Instead, CMI_2024 gives full weight to data for all years and reflects the pandemic through an explicit “overlay” term. The overlay starts in 2020 and decays exponentially, halving in each successive year in the Core version.
CMI_2024 also introduces “multiple period terms” to better reflect different trends in mortality at different ages. Having multiple period terms allows modelled mortality in recent years to increase at younger ages but fall at older ages, reflecting recent experience.
What does CMI_2024 mean for cohort life expectancy?
Chart A illustrates the progression of cohort life expectancy for members of defined benefit pension schemes since the first version of the CMI Model, CMI_2009. CMI_2023 produces similar cohort life expectancies to CMI_2022. At age 65, cohort life expectancy is around 5 weeks lower for males and 2 weeks lower for females than in CMI_2022.
Cohort life expectancy is a subjective measure that depends on assumed future changes in mortality. Although Chart A shows that cohort life expectancy has typically been falling in recent years, this does not necessarily mean that mortality rates were increasing throughout the period – instead it typically reflects lower projected rates of future mortality improvement.
Chart A: Cohort life expectancies as at 1 January 2025 at age 65 from CMI_2024 and earlier versions
How are these cohort life expectancy figures calculated?
In order to calculate cohort life expectancies, the CMI Model requires its users to make assumptions about current mortality rates and the long-term rate of mortality improvements:
- We have used an illustrative long-term rate of 1.5% a year, as this is a commonly-used value, but the CMI does not comment on its appropriateness or make a recommendation.
- We have used the latest pension scheme mortality tables, S4PMA and S4PFA. These lead to higher life expectancy than mortality tables for the general population
2. Historical mortality in England & Wales
How has mortality changed in the past?
Chart B shows standardised mortality rates (SMRs, see the “mortality terminology” section below for further information) in the general population of England & Wales from 1984 to 2024, corresponding to the data used to calibrate CMI_2024. Standardised mortality rates fell markedly between 1984 and 2019, although the fall in mortality rates was modest between 2011 and 2019. Mortality was notably higher in 2020 and 2021 than in the years immediately before the pandemic.
Chart B: Standardised mortality rates since 1983
Chart C shows mortality improvements, based on SMRs, for recent five-year periods (e.g. the point for 2024 is the average annual improvement between 2019 and 2024). Mortality improvements for individual years can be very volatile because of short-term effects such as flu and cold winters, as well as the coronavirus pandemic, so we use five-year averages to reduce the impact of these short-term variations and show the trend more clearly. Average mortality improvements were above 2% a year for most of 2002-2011 but fell to around 0.5% a year for periods ending in 2015-2019. The exceptional nature of the coronavirus pandemic led to five-year average improvements being negative for 2015-2020, 2016-2021 and 2017-2022, and close to zero for 2018-2023 and 2019-2024.
Chart C: Standardised mortality improvements (five year averages) since 2000
How do mortality improvements vary between different groups of people?
Analysis of data published by the Office for National Statistics (ONS) shows that people aged 65-89 living in less-deprived areas of England & Wales:
- have higher life expectancy than those in more-deprived areas; and
- between 2001 and 2021 have experienced mortality improvements about 1.5% a year higher on average (i.e. life expectancy of individuals living in less-deprived areas has been increasing at a higher rate than for people in more-deprived areas).
Pension scheme members and insurance company policyholders tend to be from less deprived areas on average, so may be expected to experience higher mortality improvements than the general population.
3. How the CMI Mortality Projections Model works
Why does the CMI produce the CMI Model?
Actuaries need to make assumptions about mortality rates to help manage pension schemes and insurance companies. They typically do this in two stages – first assessing current mortality rates, and then considering how these rates may change over time. The CMI Model relates only to the second stage.
Many UK pension schemes and insurance companies use the CMI Model either to set their assumptions of how mortality rates will change in future or as a “common language” to communicate assumptions that have been set by another method.
How does the CMI Model work?
The CMI Model enables actuaries to project mortality improvements. They can then apply these improvements to any mortality table to get future mortality rates.
The CMI Model makes its projections by blending between short-term and long-term improvements:
- The short-term improvements are based on historical mortality data. The CMI Model smooths the data, to reduce the effect of short-term volatility, and estimates current mortality improvements by age and gender. The CMI encourages users of the CMI Model to consider adjusting the initial improvements to reflect the portfolio that they are applying it to.
- The long-term improvements are set by assumption, rather than being based on data. As the CMI does not make an assumption for long-term mortality improvements, users need to make their own assessment of the long term. The CMI Model is therefore a framework for mortality assumptions, rather than giving a single answer, as different users will have different opinions of long-term improvements, and use different methods to form their views.
What mortality data does the CMI Model use?
The CMI Model is based on data for the general population of England & Wales, published by the ONS, but we make a number of estimates and adjustments:
- We adjust the population size at ages 85 and above, and at younger ages where we consider the implied mortality rates to be implausible.
- We make our own estimates of the population for the final year of data, as the official ONS figures are not available when we update the CMI Model.
Why does the CMI Model use ONS data for England & Wales?
We use population data, rather than datasets collected by the CMI, in order to have a large and consistent dataset over a long period of time.
We use data for England & Wales, rather than the UK, because the relevant data becomes available sooner for England & Wales, so we can keep the CMI Model more up-to-date. We regularly look at results using data for the whole UK and, to date, have found that they are little different from results that only rely on data for England & Wales. We generally expect this to be the case as even though significant differences in mortality between the nations are sometimes observed in some years, the bulk of the UK’s population live in England & Wales.
Why is the CMI Model updated?
The CMI Model was introduced in 2009 and has been regularly updated since then:
- We update it annually to take account of mortality data for the most recent calendar year.
- We make occasional changes to the way the CMI Model works to reflect evolving thinking on the most appropriate methodologies. For example:
- In CMI_2016 we revised the approach to estimate initial mortality improvements from historical data.
- In CMI_2018 we introduced the initial addition to mortality improvements, to help users to tailor the CMI Model to their portfolios, and reduced the period smoothing parameter.
- In CMI_2020 we introduced the ability to place little or no weight on mortality data for exceptional years.
- In CMI_2024 we introduced a fitted overlay to model the pandemic and introduced multiple period terms to better model different trends at different ages.
4. Mortality terminology
What is a mortality rate?
A mortality rate is the proportion of people that are expected to die in the following year. For example, if 100 of every 1,000 85-year old males die within a year, then the mortality rate for males at age 85 is 10%.
Mortality rates vary by factors including age, gender and socio-economic status; in particular, mortality rates tend to be higher at older ages.
What is a mortality table?
A mortality table is a set of mortality rates for each age, for a particular group of people at a point in time. Mortality tables are usually based on historical data over a number of years, to smooth out short-term volatility. For example, the CMI’s “S4” mortality tables are for pension scheme members in 2014 to 2019.
What is a standardised mortality rate?
Because mortality rates are higher at older ages, changes in the age distribution of a population can lead to apparent changes in average mortality rates, even if mortality rates at each age do not change. A standardised mortality rate (SMR) can be thought of an average mortality rate for a group of lives, assuming that it has a standard age profile. This enables us to make a consistent comparison of mortality rates over time and between groups. We use the age profile of the 2013 European Standard Population for ages 20 to 100.
What is a mortality improvement?
A mortality improvement measures the reduction in mortality rates from one year to the next. Actuaries usually refer to “improvements” rather than “changes” because mortality rates have tended to fall over the long term. A positive improvement represents a fall in mortality rates. A negative improvement represents a rise in mortality rates.
For example, if:
- Last year, 100 of every 1,000 85-year old males died within a year (a mortality rate of 10%).
- This year, only 98 of every 1,000 85-year old males die within a year (a mortality rate of 9.8%).
Then this year’s mortality improvement for 85-year old males is +2%, as there are 2% fewer deaths than in the previous year.
What are period and cohort life expectancies?
Life expectancy is a measure of how much longer, on average, a person in a particular group (by age, gender etc) is expected to live. For example, if females aged 65 have a life expectancy of 25 years, then they are expected to live to 90 on average. Individuals within the group could have much shorter or longer lives.
There are two distinct types of life expectancy, which measure different things:
- Period life expectancy takes no account of future mortality improvements, only historical data. This means that it is an objective measure for comparing mortality between populations and over time, but it isn’t a realistic assessment of how long people may actually live.
- Cohort life expectancy does take account of expected future changes in mortality. This makes it useful to actuaries who need to make realistic assumptions about what may happen in the future. Consequently, the CMI tends to focus on cohort life expectancy. However because future changes in mortality are unknown, cohort life expectancy is subjective, and different actuaries will have different views of cohort life expectancy
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