How long people live is a core marker of social progress, reflecting a range of social and economic factors from living conditions to timely access to appropriate health care. Throughout the 20th century, the UK saw significant increases in life expectancy. Of people born in 1905, only 62% lived to 60 compared with 89% of those born in 1955. For people born today, 96% can be expected to live to 60.

Life expectancy is a statistical measure of the average number of years that a person is expected to live informed by, among other things, mortality rates. Mortality rates continued to improve during the 2000s – the average fall was 26 deaths per 100,000 population. Since 2011 these improvements have all but stalled, slowing to an average annual fall of just under 2 deaths per 100,000 population in the last decade. For certain groups of the population mortality rates are deteriorating.

Given the social and political importance of these trends and their implications for life expectancy, the Health Foundation commissioned a research team from the London School of Economics and the Vienna Institute of Demography to carry out a comprehensive literature review and analysis of trends, and how they compared with what is happening in other countries.

What is different about mortality trends in the UK?

The slowing improvements in life expectancy are not unique to the UK – and are seen in other socioeconomically comparable countries across Europe. However, the UK already has a lower life expectancy than many comparable countries – particularly for women – making any reduction in improvement especially worrying.

As in other similar countries, mortality and life expectancy figures largely reflect what is happening with older age groups, where most deaths occur. It is therefore important to look beyond the headline figures at subgroups of the population to pick up atypical trends that merit attention. The research found that while there has been a change in the trend in mortality for all population groups, since 2011 certain sections of the UK population have fared particularly badly.

• People living in the most affluent areas have experienced slower improvements in life expectancy since 2011. For people living in the most deprived areas improvements in life expectancy have stalled for men and decreased for women. This is widening inequalities.
• Women have experienced a greater slowdown in mortality improvements than men and for women aged over 85 mortality rates actually increased between 2011 and 2016.
• Although the slowdown in mortality rate improvements has happened in all UK countries, Scotland continues to have notably higher mortality rates (in absolute terms) for both men and women than the other UK countries.
• When compared internationally, the UK has seen similar changes in mortality for the older population. However, relative to our closest comparators, outcomes for under 50s are worse. There has been no improvement since 2011 for the younger (under 50) population as a whole and mortality has actually increased for 45–49 year olds.

What factors are driving these UK trends?

Put simply, this analysis shows that there is no single driver of the current slowdown in mortality rate improvements. This means there will be no single solution.

As most deaths are among the 80-plus population, variations in mortality rates among the oldest can have a big effect on life expectancy estimates. This has led to inconclusive debate focusing on the impact of flu or austerity through constraints in social care or NHS budgets.

However, the longevity of today’s oldest generations largely reflects the experiences over their entire lives. While health care is particularly important in later life, it is a wider mix of factors – better incomes and living conditions, changing habits and medical advances – that has led to many of the improvements in life expectancy. These are large, long-term population-level effects. So, to understand what is happening to life expectancy now it is necessary to look at the past.

Advances in public health and health care in the last century drove big improvements in life expectancy: eradication of many infectious diseases in the 1950s and 1960s, reductions in smoking rates from the mid-1970s, advances in treatment of heart disease in the 1990s and, more recently, better at diagnosing and treating cancer. A consequence of having already achieved large gains in life expectancy is that it is becoming increasingly difficult to achieve further big improvements.

Future life expectancy will be shaped by the lifetime experiences of the population today. The strongest influences on people’s health are the social, economic, environmental and commercial conditions of people’s lives – the ‘wider determinants’ of health. The complex and interrelated nature of these determinants of health makes measuring the impact of any single one on mortality challenging.

Current trends that are likely to affect future life expectancy are the widening mortality rates associated with people’s socioeconomic status – particularly among young people – and wider indicators of poor health such as rising levels of childhood obesity, the latter being worse for children living in the most deprived areas.

Across most indicators of health there is a clear relationship with socioeconomic circumstances. Life expectancy is a prominent example of this. While the difference in life expectancy between the richest and poorest people in the UK narrowed during the 2000s, it has widened since 2011. The growing gap in life expectancy between rich and poor is a consequence of improvements in life expectancy completely stalling for the poorest people in the population, while it continues to increase for the richest people, albeit at a slower rate than before 2011.

What action is needed to address the slowdown?

The current UK mortality trends are very concerning. The slowdown is widespread among comparable high-income countries, but has been more rapid and sizeable in the UK than elsewhere. Furthermore, younger adults are generally continuing to see improvements in comparable European countries, in the UK this is not the case.

As the drivers of these trends are multiple, highly complex and interrelated, halting the current slowdown or stalling in life-expectancy improvements – and reducing the wide socioeconomic and geographical inequalities in life expectancy – will require a whole-government approach to the wider determinants of health.

If UK national and local governments are serious about addressing the situation, particularly for the most affected and most vulnerable groups, there needs to be greater political and policy attention on trends in health, mortality and their implications for life expectancy.

This demands two areas of action.

An independent body with responsibility for:

• providing regular and consistent analysis of the factors that influence mortality in different population groups, and the complex interrelationships between these
• regular and consistent independent tracking of future opportunities and risks to people’s health in order to understand what is likely to influence mortality trends in the future
• public reporting of these trends on a regular basis, in an accessible and understandable format.

A whole-government approach to:

• policymaking and action that takes a long-term view and is informed by the understanding of the influences on current and future mortality
• actions at local and national level that are aligned and sufficiently resourced.

No government wants to see the life expectancy of its population fall on its watch. However, the risk of this happening is very real – particularly within specific subgroups of the population. This can only be prevented through coordinated, wide-ranging, long-term action, led from the centre of government.

This section provides definitions for commonly used measures of mortality and then goes on to describes the analyses carried out by Murphy et al.

Common measures of mortality

Measuring mortality presents a complex challenge. Not only is mortality itself affected by so many different interconnecting factors, but researchers have several different measures at their disposal to understand mortality. Each one can shed light on a different aspect of the overall picture of what is happening in the population.

The conclusion drawn can be influenced not only by the choice of measure but also how the change is expressed. For this reason, it is important to understand the meaning, implications and potential limitations of each measure, to be sure that the measures selected are appropriate to the question being asked.

When assessing the impact of mortality change, the figures can be looked at in one of two ways:

• absolute change – the difference in an indicator between two points in time
• relative change – the absolute change relative to the size of the initial value (change expressed as a percentage of the initial value).

This section sets out the indicators that are most often used when looking at life expectancy and related issues.

Counting deaths in a population

Number of deaths

This is a count of the number of deaths in a population (or population group) in a given period of time. It does not reflect the size or demographic composition of the population in any way. In the UK, the vast majority of deaths occur in older age, so the total number of deaths in a population will be strongly driven by these figures (Figure 4).

Crude death rate

This is a measure of the number of deaths per year in a given population relative to the size of the total population. It is usually expressed as deaths per 1,000 people. It adjusts only for overall size, and takes no account of the age structure of the population. This means that (as with the number of deaths, above) in the UK it is strongly driven by the older age groups, as this is the age at which most people die (Figure 4).

Figure 4: Distribution of deaths by age and sex: England and Wales, 2018

Source: ONS, Deaths registered in England and Wales, 2018.

Comparing mortality rates

As overall mortality in a population is strongly influenced by the age and sex structure of the population (Figure 4), these influences need to be understood and removed to allow appropriate comparisons between times or different populations or groups. This is done by calculating mortality rates. Two types of mortality rate are referred to in this report:

• Age-specific mortality rate

This is the total number of deaths per year, expressed as per 100,000 population within a given age or age group, most usually expressed for men and women separately. Age-specific rates provide understanding of the different mortality experiences of different age groups, and can be used to explore how this changes over time or between different populations.

• Age-standardised mortality rate

As the crude death rate is strongly influenced by the age distribution of a population, age-standardised mortality rates are commonly used to allow comparison between whole populations in different areas or time periods, taking account of differences in size and age distribution of those populations.

The age-standardised mortality rate is calculated as a weighted average of the age-specific mortality rates – in other words, the average is scaled to take away the effect of differing age and sex structures in different populations. This scaling is done to a fixed standard hypothetical population (such as the European Standard Population) and enables comparison of whether and how mortality would differ in two or more geographical areas or timepoints if they had the same age and sex make-up. Without this adjustment, one population could appear to have much higher overall mortality rates than another with comparable age-specific mortality rates at all ages, simply due to having a higher proportion of older adults.

Survival rates

This term describes the proportion of people in a group who are alive after a given period of time.

Life expectancy

Life expectancy is a statistical measure of the average time someone is expected to live, based on the mortality rate they subsequently experience. Life expectancy and age-standardised mortality rates are both summary measures, using the same data (age and sex-specific mortality rates). So, they are alternative ways of summarising mortality rates in a given period, and they mirror each other.

Life expectancy is used for setting the state pension age, and setting and assessing health policy, among other purposes. It is often presented at one of two ages:

• at birth – how long someone born in a given year and place is expected to live
• at age 65 – how long someone at this age in a given year and place might have left to live.

The UK’s life expectancy projections (both period and cohort) are currently produced by the Office for National Statistics. Life expectancy is calculated using a life table. This shows, for each age, the probability that a person will die before their next birthday. There are two types of life expectancy measure: period and cohort (see the box). They differ based only on changes in mortality over time, as explained below, and would only ever be identical if there were no changes to age-specific mortality rates over time, which is highly unlikely.

Period life expectancy

Period life expectancy represents the average number of years a person would be expected to live, based on the assumption that their likelihood of death at each age throughout life is the same as for the population at a given point in time. There is no account taken of possible future improvements in mortality at each age between cohorts.

So, period life expectancies are useful summary measures for the entire population of a given place and time. They provide an objective way of comparing trends in mortality over time, or between different populations and population subgroups. (This is how they are used in this report.)

But period measures are less useful for predicting lifespans of current or future cohorts. And, like age-standardised mortality rates, changes are influenced by deaths in older people (who are the majority of people who die). Period-based projections of life expectancy are based on assumptions about future changes in mortality and reflect the mortality rates of the entire population in each year.

Period life expectancy may also be distorted by ‘tempo effects’: a bias that may arise if period statistics (such as period life expectancy) are interpreted as a reflection of current mortality patterns, when mortality is changing during an observation period. The impact of tempo effects in the recent patterns in mortality is debated, but explored in detail in by Murphy et al.

Cohort life expectancy

In this measure, ‘cohort’ means a group of people born at the same point in time. Cohort life expectancy takes into account age-specific probabilities of death for the specific cohort calculated from observed mortality data, where available, for that cohort. It then combines it with mortality rate projections for the cohort in future years, using assumptions based on historic trends.

This can be used to estimate how much longer a person of a given age and sex, in a given place, would be expected to live. However, these measures tend to rely more heavily on assumptions about the future and predictions are unlikely to be correct.

Figure 5 illustrates historic estimates of period life expectancy, and how these have been revised as actual changes in mortality have been measured over time.

Figure 5: Successive projections of period life expectancy at birth, males: UK, 1966–2030

Source: ONS National Population Projections Accuracy Report underlying data, UK, 1966 to 2030; expectation of life, principal projection, United Kingdom, 2012-based, 2014-based and 2018-based.

Healthy life expectancy

Healthy life expectancy is an estimate of the average number of years someone would live in a state of ‘good’ general health, usually expressed at birth or at age 65. Healthy life expectancy adds a ‘quality of life’ dimension to estimates of life expectancy, dividing it into time spent in different states of health. Health status estimates are self-assessed, based on respondents’ answers to a survey question asking ‘How is your health in general?’.

This report focuses on life expectancy, but healthy life expectancy – and inequalities in this – are of critical importance as measures of population health. Inequalities in healthy life expectancy are wider than in life expectancy, meaning that with increasing levels of deprivation, people are living a greater proportion of shorter lives in poor health. This is the focus of other Health Foundation work.

Having described the most commonly used measures of mortality, we go on to look at the new analyses of mortality undertaken in the research by Murphy et al.

New analyses of mortality trends in the UK

Murphy et al. carried out a comprehensive review of previous research into the recent trends in mortality in the UK, covering what had previously been published on the trends and potential drivers of these trends.

This review identified areas where further research would build understanding of the trends and potential drivers, and new analyses were carried out. These analyses primarily used data from the Human Mortality Database – an open-access resource providing detailed and consistent population and mortality data for 40 countries or areas. This enables long-term and international comparisons of mortality. For full detail of the research see the full research report.,

Analyses included the following:

Exploration of mortality trends in the UK

To comprehensively describe the trends in UK mortality rates and life expectancy, the report looks at these in the context of a longer-term picture, as well as in more detail in recent periods.

• Long-term changes in age-standardised mortality rates and period life expectancy in the UK overall and constituent countries, by sex, from 1950 to 2016. In order to understand current trends, this is a relevant period to examine, beginning after the rapid transition in leading causes and ages of death that occurred in the early part of the 20th century (Figure 2), and to exclude the first and second world wars.
• Changes in age-standardised mortality rates and period life expectancy over the 21st century, by sex, from 2000 to 2016. These analyses focused on the 21st century, 2000–2016, with the aim of better understanding the change in trend during this period. Further analyses looked at 2006–2016 to examine the decade around the change in trend and explore the differential impact of the slowdown in different age/sex groups. (This is the period used in most analyses to date.)

International comparisons of mortality trends

To provide additional insight, the report explores differences and similarities in mortality trends between the UK and other countries with similar socioeconomic structures and mortality levels.

For cross-national European comparisons, the authors mainly used the set of high-income countries largely located in western Europe that were considered to provide the most appropriate comparators for the UK. These countries are the European countries defined as ‘developed economies’ according to the MCSI Index.

They included the USA due to considerable interest in recent mortality trends there, and Australia and Japan as examples of high-income countries in other parts of the globe. The HMD provides mortality data separately for England and Wales, Scotland and Northern Ireland, treated in a consistent way with the other countries included. The following analyses were carried out:

• Comparison of life expectancy at birth in the UK and selected comparable countries, by sex, from 2000–2016 The ranking and change in ranking over the period were looked at, plus the UK position for men and women relative to the EU average.
• Changes in age-standardised mortality rates in selected comparable countries, by sex, from 2000–2016 Remaining with this period of particular interest during which the changes have occurred in mortality trends, these analyses were to identify similarities and differences between countries, and the time point at which trends changed.
• Changes in age-standardised mortality rates by age for the UK and closest comparable countries (Netherlands and France), 2000–2016 To further understand similarities and differences by age group, annual percentage changes in age-standardised mortality rates for age groups were plotted.

Further analyses were then carried out to explore some of the possible causal factors identified in the literature review – particularly focusing on the UK, the Netherlands and France. These included austerity-related and other measures, including:

• gross domestic product (GDP), government spending and spending on health care as a percentage of GDP
• spending on health care per capita
• self-assessed health
• obesity prevalence
• age-standardised mortality rates for CVD and non-CVD causes.

Other contributing factors explored included the positive contribution of the ‘golden cohort’ – born between 1925–1935 – to mortality trends, and also the possible role of tempo effects (see definition above).

Following the analyses summarised in the previous section, Murphy et al. critically assessed putative factors contributing to the recent trends, based on the research reviewed plus further new analyses (described in full by Murphy et al. in 2019).

The trends point to multiple, complex drivers

The recent Public Health England review of mortality trends in England, carried out in parallel with the early stages of this research, concluded that:

‘the main findings suggest that the overall slowdown in improvement is due to factors operating across a wide range of age groups, geographies and causes of death… It is not possible to attribute the recent slowdown in improvement to any single cause and it is likely that a number of factors, operating simultaneously, need to be addressed.’

So, while the overall slowdown of improvement in mortality rates in the population points to a global driver or drivers, the fact that the trend is not consistent across population subgroups (with women, deprived groups and younger adults being most affected) suggests that these drivers do not act equally on all groups, and also – given the leading causes of death are very different in older and younger adults – that there are additional specific drivers at play. Thus, it is most likely that both common and specific drivers are acting in a complex interrelationship. This complexity needs to be taken into account in seeking to better understand the drivers of recent patterns in mortality.

It is important to assess the plausibility of the contribution of different possible drivers alongside each other, as done by both the Public Health England review and this research (discussion of different theories posited by Murphy et al. is included in Table 1, based on findings of their review of the literature (including the Public Health England review) and further new analyses described in full by Murphy et al. Understanding the relative contributions and complex interactions between factors is, however, far more difficult, and requires further attention.

Consider the complex system of determinants of mortality

A number of previous reports have attempted to investigate the role of austerity in recent mortality trends, given the aligned timing and plausibility of this as a contributor. However, these have largely focused on health and social care cuts and consequent falls in performance, with wider aspects of austerity not being paid much attention. The commonality of mortality trends in different countries despite very differing experiences of austerity (in terms of which budgets have been cut and when, and how severe the cuts and resulting impact have been) would suggest that a single definition of ‘austerity’ cannot fully explain recent patterns. Furthermore, some of the countries that have experienced the greatest austerity have not seen slowdowns as great as those that have had little or no austerity policies.

Further analysis to assess whether there is any association between the timing of the slowdown (as discussed above, this varied between countries) and the implementation of austerity policies would be informative, as would examination of the detail of the specific policies in different countries, including which services were prioritised for protection from cuts.

There has also been some examination of other potential drivers, including migration, the ageing population, data and statistical artefacts, and ‘harvesting’ effects of deaths being shifted between periods (either brought forward or shifted back, as even if a death is postponed, people will die eventually). However, none of these alone can explain trends.

It should not be surprising that no single plausible driver of the recent mortality trends has been identified. Health and mortality emerge from a complex system of interrelated influences. As set out in the recent Public Health England review of mortality trends, understanding what is happening to mortality in the UK will therefore require a developed understanding of the multiple positive and negative influences on health, the interrelationships between these, and the pathways by which putative drivers may interact and effect changes.

For example, austerity and flu have largely been considered individually, however their impact is not likely to be completely independent. They arise for very different reasons and have very different implications, but plausibly interact in their pathways of action. Theoretically, cuts to health and social care service funding and high prevalence of flu in older adults could jointly contribute to pressure on services, and the ability of services to respond to any increase in demand, potentially jointly resulting in unmet need for health and social care among frail older adults and increases in mortality. Previous analyses attempting to estimate the impact of each separately may have sometimes therefore conflated the effects. An additional theoretical example is that trends in CVD mortality and cohort effects may be more appropriately understood as not being entirely distinct from each other, as different cohorts will have very different profiles of exposure to risk factors. Attempts to understand interactions between these and other drivers are needed.

Murphy et al. concluded that a framework is needed to consider the complexity and interrelationships between different drivers and their pathways and impacts, and that this needs to take account of short, medium and longer-term drivers, and the differing time to impact that these have. For instance, flu has undoubtedly contributed to annual fluctuations, such as the spike in 2015, but other intertwined and slower-acting factors are acting concurrently at different levels in different groups to result in the observed slowing or stalling of mortality improvements.

Furthermore, there are complex cohort effects, and the timing of events and exposures over people’s lives may be an important aspect to consider. Individuals in their 80s–90s in 2016 belong to the ‘golden cohort’, born 1925–1935. This cohort experienced a higher rate of mortality improvements than those born before or after them. The reasons for this are not fully understood, although the most commonly cited theory is changing smoking patterns between cohorts. Other hypotheses include: better diet and environmental conditions during and after World War II, including better foetal and early life environment; improvements in food preparation and packaging in the 1920s–1930s; a competitive advantage from being born in a low fertility period; and benefits from the introduction of the welfare state in the late 1940s, or from medical advances. Given that the reasons for the high levels of mortality improvement seen in this cohort are not understood, it is not known whether their relative advantage will continue into the oldest ages, or what will happen when following cohorts come to dominate mortality trends due to reaching the age at which most deaths occur, and thus take a more dominant role in driving trends, with the declining numbers of the ‘golden cohort’ having less influence on overall trends.

Looking at causes of death will not by itself lead to a solution

There has been some examination of single causes of death, in an attempt to understand the trends. As discussed above, examination of single causes of death as contributors to trends is not straightforward, especially at older ages where most deaths occur. There have been ‘real’ as well as technical (including coding) effects, and increases in coding of any single cause would have a knock-on impact on what is not being coded instead. Determination and coding of primary and secondary causes of death is also complex, particularly at older ages when multimorbidity is common.

There have been important, real changes at younger ages in drug-related deaths, accidental poisoning and suicide, but again differentiating between these in establishing cause of death is not always clear.

A problem with looking at single causes is that it provides limited understanding of why changes are happening – particularly given the context of rising multimorbidity – and therefore what can be changed to improve circumstances. The leading causes of death have common risk factors, including smoking, alcohol, obesity, air pollution, poor diet and insufficient physical activity. These risk factors are intimately linked to social and economic circumstances.

For example, cause of death trends from cancers, CVD and respiratory diseases are all in part a consequence of smoking patterns, so examining all three causes separately will provide less insight, especially for policy options, than examining smoking. Furthermore, these risk factors have common drivers and are all socioeconomically clustered, with segments of the population exposed to multiple risks at hazardous levels.

The importance of looking at the ‘causes of the causes’

These common drivers – known as the ‘wider determinants’ of health – are the social, economic, commercial and environmental conditions in which people live. The literature review identified a notable absence of consideration of the wider determinants of health in previous attempts to understand the current mortality trends. However, these circumstances shape people’s exposure to risk factors. For example, understanding the reasons why people smoke or have unhealthy diets can point to important targets for policy action beyond traditional health promotion and prevention services that would, over time, be expected to have wide-ranging impacts on health and – critically, given the social patterning of health and mortality and wide (and widening) gaps in life expectancy and healthy life expectancy – health inequalities. The wider determinants will also be relevant to understanding the concerning trends in younger adults, with both drug-related deaths and suicide being issues of inequality, which are strongly socioeconomically patterned.

Table 1: Discussion of some theories posited as contributing to recent mortality trends (based on the literature review by Murphy, Luy and Torrisi including the Public Health England review, and further analysis by Murphy, Luy and Torrisi)

Potential driver	Scope	Impact
Population and environmental factors
Cohort effects	National	Cohort effects have the potential to have a large impact on mortality trends. However, multiple factors may be involved and sufficient data to identify cohort effects in short time periods are lacking to date. The ‘golden cohort’ born between 1925–1934 has received some attention. This cohort has experienced particularly high levels of health and mortality improvement over time compared with prior and later generations, possibly in part due to dramatic declines in smoking. Some argue the slowdown is in part due to the fact that this cohort is now in their 80s and 90s and make a declining contribution to overall mortality rates, and/or their relative advantage has disappeared due to increasing prevalence of comorbidity. They are also the cohort potentially most affected in recent years with high levels of virulent influenza.
Population ageing	National	Impacts on numbers of deaths, but no direct influence on age-standardised mortality rates or life expectancy. Compositional changes of the population affect unstandardised overall values, if improvement at older ages is lower than at younger ones, and ageing within older age brackets (eg 85+) may impact. However population ageing is a slow-acting process unlikely to cause the sharp rises in mortality observed in recent years. The Public Health England review found demographic changes to have an insignificant impact. There may however be interaction with other contributing factors, including austerity.
Weather trends	National	As discussed in detail in the Public Health England review, there is no evidence for a secular shift, and individual year effects are small. Excess mortality (especially among deprived older people) linked to temperature, cold homes and fuel poverty has received less attention recently.
Social and political factors
Austerity	National	The slowdown in mortality improvements and widening inequalities in mortality coincided with implementation of wide-ranging austerity policies. A number of commentators cited cuts to health and social care funding as drivers, including falling NHS performance, and cuts in spending on health and social care. ‘Austerity’ is a broad term that also includes numerous components including standards of living and changes in benefit systems, which have received less attention in terms of impact on mortality. Other studies do suggest wider impacts of austerity, including negative effects on homelessness, mental health and self-reported general health; but without a link to mortality. Looking at associations at area level has not found clear associations, mainly focusing on population averages and temporal associations. Austerity policies are implemented very differently, and will affect individuals very differently, and this detail would be important. It is also impossible to disentangle effects of interrelated changes. Countries that have experienced very different extents of austerity, or differing austerity policies in terms of what is prioritised for protection have seen similar trends, including across UK countries, although looking at the detail of these differences may help explain differences between the UK and other countries, and austerity plausibly has a large impact in explaining between-country differences. There is a need to much better understand the complexity (rather than just considering ‘austerity’ or just ‘health and social care’), and the pathways of impact, in determining whether findings are causal impacts, or whether confounding or coincidence explain them. There are various plausible pathways from austerity to mortality.
Migration	National	There are complex relationships that are challenging to assess, due to data limitations. Information on mortality by country of birth is not generally available, hindering assessment. Recent migrants tend to be in low mortality groups though, so have very limited possibility of influencing overall values, and the Public Health England review examined this and concluded that the impact was small. Possible effects of the ‘healthy migrant effect’ (migrants tend to be healthier than the average of population they originate from) or ‘salmon bias’ (relating to UK-born returning emigrants, generally at older ages as a result of declining health) have not been considered. The healthy migrant effect is known to decline with time in the host country, and disadvantage may be observed in second generation rather than first generation migrants. A better understanding of migrant effects is needed, considering the changing position of the UK within the EU, and the broader question of the total contribution of migrants to UK mortality trends would need to also consider the role of overseas workers in maintaining the health and social care system.
Disease factors
Specific role of CVD	International	Slowing of CVD mortality improvement appears to contribute significantly to overall slowing or stalling of mortality improvements, as quantified in the Public Health England review. However, alone it might be expected to have a more gradual effect in slowing mortality. Explanations for the sharp change in trend observed, related to CVD, have not been advanced. In addition, problems exist with recent cause-specific data including due to changes in the coding of dementia.
Influenza outbreaks	International	There is substantial evidence supporting a large role of influenza in high mortality winters, but the magnitude of annual impact has not been precisely established. Peaks for mortality rates coincided with bad flu years (for the A(H3N2) strain in particular, which is the strain most associated with mortality in older people).
Increase in Alzheimer’s disease and dementias	National	This has had a small effect on overall trends in mortality. Increases in dementia coding are largely due to increased diagnosis and changing coding practices (although the ONS has provided figures that make it possible to adjust for coding changes), together with population ageing in unstandardised measures. Increased overall prevalence would tend to increase annual fluctuations in deaths. Large reported increases affect the interpretation of CVD effects due to impact on coding of other causes as leading cause of death.
Data and analytical factors
Choice of reference period	International	The relative and absolute magnitude of mortality stalling depends substantially on the choice of reference period, eg 2006–2011 vs 2011–2016 vs 2006–2016. The interpretation of results is particularly sensitive to the anomalous patterns observed in the first decade of this century.
Data artefacts	National	Factors considered include the selection of time series used to calculate trends, and the way population size and structure are adjusted for including the population used for standardisation. Revisions to older age groups following the 2011 census are not considered but continue to be monitored for impact. Similarities of trends across countries and data sources means this is unlikely to be an explanator. Both this research and the Public Health England review concluded data artefacts did not contribute significantly to observed mortality trends.

A full reference list of primary sources reviewed by Murphy et al. is provided in the research paper.

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