The tool was last updated on 12 January 2023, when the following changes were made:
- Data for NHS regions and NHS Integrated Care Boards were added.
- Existing data have been refreshed. Final 2021 deaths data replaces provisional data used in the previous release. Deaths have been re-allocated to areas based on May 2022 postcode lookup file, replacing the May 2021 postcode lookup file used in the previous release.
For the most part, the refresh of existing data has made very little difference to the results displayed in the tool. However, in a small number of areas, there are larger changes to the results.
All figures within the tool are calculated using 2011 Census based mid-year population estimates, and the results for 2020 to 2021 are calculated with population estimates just for 2020. The 2021 Census based population estimates for small areas are due to be released later in 2024. The tool will then be revised using those and all current results will be subject to change.
This tool provides information on the causes of death and age groups that are driving inequalities in life expectancy at local area level. Targeting the causes of death which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.
The tool provides data tables and charts showing the breakdown of the life expectancy gap for two comparisons:
- England - The gap between each local area as a whole and England as a whole.
- Within area - The gap between the most deprived quintile and the least deprived quintile of the area.
Three time periods are provided in the tool, and breakdowns can be viewed for a single time period, or as a trend:
- 2014 to 2016
- 2017 to 2019
- 2020 to 2021
The tool includes data for the following geographies:
- English region
- Local authority (upper tier only for 2020 to 2021)
- NHS region
- NHS Integrated Care Board
A summary of main messages for the Segment Tool can be found in the statistical commentaries at the links below.
1 November 2022 update (Includes addition of trend data for 2014 to 2016 and 2017 to 2019):
18 May 2022 update (Includes addition of provisional data for 2020 to 2021):
Use the tabs at the top of the screen to move between pages of the tool.
The data page contains three tabs.
On each tab, data for a single time period can be viewed by selecting ‘Single time period’ in the ‘Data view’ dropdown, then selecting the time period of interest. Trend data can be viewed by selecting the ‘Trend’ option.
Context: provides information on life expectancy and life expectancy gaps. This can be used as a guide to which comparison(s) to select on the Breakdowns tab. Use the dropdowns on the left hand side of the page to select the geography you are interested in viewing. The first table shows life expectancy in the selected geography, in England as a whole, and the absolute gap in life expectancy between the two. The second shows life expectancy in the most deprived and least deprived quintiles of the selected geography, and the absolute gap between the two.
Breakdowns: provides information on the causes of death and age groups which are driving the gaps in life expectancy, either within the selected geography, or between the selected geography and England. Use the additional dropdowns on the left hand side of the page to select:
Comparison - select ‘England’ to view the breakdown of the gap between the selected geography as a whole and England as a whole, or ‘Within area’ to view the breakdown of the gap between the most and least deprived quintile of the selected geography. For areas with lower life expectancy than England, the comparator with England is likely to be important, but there may also be wide inequalities within the area which should be considered. Scarf charts are not available where life expectancy of an area is higher than England, however, bar chart data are available for all areas, since even in areas with a higher life expectancy than England, there may be specific causes of death which are causing a reduction in life expectancy which could be targeted locally.
Segment gap by - select to view breakdowns by either cause of death or age group.
Chart type - select the chart type of interest.
Data tables: shows the breakdowns data in tabular format.
Data can also be downloaded in csv format using the buttons on the left hand side of the page.
Guidance and FAQs
Detailed guidance on interpreting the results is available on the guidance and FAQs tab. This includes information on the data and methods used to construct the tool. A set of frequently asked questions is also available.
We are interested in your feedback on the tool. If you have any comments or suggested changes, please contact us.
Guidance and frequently asked questions
Interpreting the results
Frequently asked questions
Numbers of deaths are based on Office for National Statistics (ONS) death registration data. Deaths were extracted for the relevant geographies and years and pooled.
Cause groups have been defined using International Classification of Diseases, Tenth Revision (ICD-10) codes and the groupings used are specified in the table below. Broad causes of death are consistent with those used in previous versions of the Segment Tool and are based on ICD-10 chapters. Detailed causes of death include the top 10 leading causes of death (in 2020) for males and females. In addition to the 10 leading causes, the External cause group has been further broken down to include groups for land transport accidents, accidental poisoning, and suicide, as these causes of death make up a considerable proportion of deaths amongst younger adults. Deaths under 28 days refer to deaths of infants aged under 28 days (neonatal deaths). Neonatal deaths are not assigned an underlying cause of death in the deaths dataset.
|ICD 10 code
|U071, U072, U099, U109
|U071, U072, U099, U109
|Rest of I00-I99
|Leukaemia & lymphoma
|Rest of C00-C97
|Mental and behavioural
|Dementia and Alzheimer’s disease
|F01, F03, G30
|Other mental and behavioural
|Rest of F00-F99
|Chronic lower respiratory diseases
|Influenza and pneumonia
|Rest of J00-J99
|Cirrhosis and other diseases of liver
|Rest of K00-K93
|Land transport accidents
|Suicide and injury of undetermined intent
|X60-X84 (age 10+), Y10-Y34 (age 15+)
|Other external causes
|Rest of V00-Y98
|Under 28 days
|No code assigned
|Under 28 days
|No code assigned
|All other codes
|All other codes
Deaths were assigned to geographies using the May 2022 version of the National Statistics Postcode Lookup (NSPL).
For the breakdown of the gap between each area as a whole and England, ONS 2011 Census based mid-year estimates for the relevant geography and years were used. Population estimates for 2021 are not yet available so estimates for 2020 were used in lieu of these.
For the breakdown of the gap between the most deprived quintile and the least deprived quintile in each area, ONS 2011 Census based mid-year estimates for LSOAs for the relevant years were aggregated to quintiles. Population estimates for 2021 are not yet available so estimates for 2020 were used in lieu of these.
Data for local authorities in England are based on boundaries at April 2021. Data for Isles of Scilly local authority have been combined with Cornwall local authority. Data for City of London local authority have been combined with Hackney local authority.
Data for NHS geographies and NHS Integrated Care Boards are based on boundaries at April 2022.
The tool also contains data for England and the English regions.
Index of Multiple Deprivation (IMD) 2019 scores have been used to define deprivation quintiles for the time periods 2017 to 2019 and 2020 to 2021. IMD 2015 scores have been used to define deprivation quintiles for 2014 to 2016.
Each local authority/region/England was divided into quintiles based on Lower Super Output Area (LSOA) level IMD scores, using the standard OHID method described on this page: https://fingertips.phe.org.uk/profile/guidance/supporting-information/PH-methods
Life expectancy was calculated based on quinary age bands up to a maximum of 90+ (0, 1-4, 5-9,…,85-89, 90+), using the standard OHID method. A template life expectancy calculator can be downloaded from this page: https://fingertips.phe.org.uk/profile/guidance/supporting-information/PH-methods
Contribution to the life expectancy gap
The contribution of different age bands or causes of death to the gap in life expectancy between two areas (due to differences in age or cause specific death rates) have been calculated using a method of ‘life expectancy decomposition’.
For the Segment Tool, the Arriaga III method has been used, as described by Ponnapalli . The method is based on a life table divided into 5-year age groups. The contributions of each age group are then distributed into causes of death using a method described by Preston and others . Contributions are distributed proportionately according to the difference in mortality between the selected areas by cause of death within each age group.
For each cause of death and age group, the number of expected deaths is also provided. Excess deaths are the number of ‘extra’ deaths occurring over and above those that are expected. Expected deaths are the number of deaths which would be expected to occur if the area had the same age specific mortality rate for that cause of death/age group as the comparator area.
Interpreting the results
The scarf charts show, for each broad cause of death or each broad age group, the percentage contribution that it makes to the overall life expectancy gap between the areas selected. For the England comparison, scarf charts are only available for areas where the life expectancy in the area is lower than the life expectancy in England (ie where there is a gap to segment). For the within area comparison, scarf charts are only available for areas where the life expectancy in the most deprived quintile is lower than life expectancy in the least deprived quintile.
Causes of death/age groups are only included in the scarf chart if they make a contribution to the gap in life expectancy (ie. where the mortality rate is higher for that cause of death or age group in the selected area compared to the comparator area). If a cause of death or age group is not displayed in the chart (or percentage contribution shows as “-” in the data table), this means that the cause of death or age group does not make any contribution to the life expectancy gap.
The gap in life expectancy is broken down so that the percentage contribution of causes will sum to 100 per cent. In some areas, particularly where the gap in life expectancy is small, or where only a few causes of death or age groups have excess mortality, care should be taken in interpreting the results. Some causes of death may be highlighted as contributing a large percentage of the life expectancy gap, even though the gap itself may be small.
To aid interpretation, numbers of deaths are included in the data table. The table shows the total number of deaths in the selected area (or most deprived quintile of the selected area if the within area comparison is used) for each cause or age group in the selected period, and the number of excess deaths for each cause or age group. If the mortality rate for a particular cause of death or age group is lower than the comparator area, then the number of excess deaths will be negative.
When interpreting the within area breakdown, it is also important to consider the mortality rate for each cause in the area as a whole. For example, if a local authority has a very high mortality rate for cancer, the within area breakdown may not highlight cancer as a significant contributor to the within area gap because the mortality rates are consistently high across the whole local authority. In this case, cancer would still be an issue requiring consideration in the local authority, even though it had not been highlighted in the within area analysis.
Bar charts for broad causes of death/age groups
The broad bar charts show the amount that life expectancy would increase (in years) in the selected area (or the most deprived quintile of the selected area) if its mortality rate for each age group or cause of death was changed to that of the comparator area, assuming all other rates remained constant.
Contributions that widen the inequality gap (that is, where the mortality rate is higher in the selected area than the comparator area) are represented with a positive value, while contributions that offset the gap (that is, where the mortality rate is lower in the selected area than the comparator area) are represented with a negative value.
Bar charts are available for all areas, regardless of whether life expectancy is lower in the selected area than the comparator area.
Bar charts for detailed causes of death
The analysis of broad causes of death can be used to give an indication of the drivers of inequality in the area. The broad cause data should be used as a starting point, but this analysis can mask variation between causes of death within a broad cause group. For example, the contribution of cancer to inequalities in an area may be a result of very high mortality rates from lung cancer, whereas mortality rates for other cancers may be similar to the comparator.
The detailed bar charts show the same information as the broad bar charts, and are calculated using the same methods, but for a more detailed list of causes of death.
Frequently asked questions
There is potential to further develop the tool. We would welcome feedback on what development would be most useful in order to inform future work. Please contact us
No, the outputs are not directly comparable because of changes to the methodology and cause of death groupings used.
What does life expectancy at birth mean?
Life expectancy at birth is an estimate of the average number of years a new-born baby would survive if he or she experienced the particular area’s age-specific mortality rates for that time period throughout his or her life. The figures in the tool therefore reflect mortality among those living in each area in the time period selected, rather than mortality among those born in each area. It is not therefore the number of years a baby born in the area in the selected time period could actually expect to live, both because the death rates of the area are likely to change in the future and because many of those born in the area will live elsewhere for at least some part of their lives.
Why does the difference in life expectancy between the most and least deprived quintile differ from the slope index of inequality in life expectancy?
The Segment Tool breaks down the gap in life expectancy between the most deprived quintile and the least deprived quintile of each area. This gap looks at life expectancy at two extreme points within an area, without considering life expectancy between those points.
The overarching measure of inequality in life expectancy in the Public Health Outcomes Framework (PHOF), is the slope index of inequality (SII). This measures variation in life expectancy across the whole range of deprivation, rather than just considering the extreme groups. The calculation takes into account life expectancy in each deprivation decile within an area, and summarises the variation into a single number. These data are available from the PHOF data tool https://fingertips.phe.org.uk/profile/public-health-outcomes-framework
This means that the difference between the most and least deprived quintile will be different to the slope index of inequality in life expectancy.
Why is the within area comparison based on deprivation quintiles instead of deprivation deciles?
The within area comparison uses the most deprived and least deprived quintile in each area, rather than the most and least deprived decile. These larger groups are used to improve the robustness of the results by reducing the number of small numbers in each cause of death or age group category.
Why isn’t a scarf chart available for my area, or why is a scarf only displayed for one sex?
Scarf charts are only displayed when life expectancy in the selected area (or most deprived quintile of the selected area), is lower than the comparator area. In some areas, life expectancy is lower than the comparator area for one sex but not for the other. In these cases, a scarf chart will be available, but a scarf will only be shown for the sex where life expectancy is lower than the comparator area.
Why, in some cases, is a cause of death shown as making a contribution to the life expectancy gap, despite there being no (or negative) excess deaths for that cause?
In a small number of cases, the output tables in the Segment Tool show that, in some areas, for a particular cause of death, there is a positive contribution to the life expectancy gap (meaning the cause makes a contribution to the life expectancy gap), and yet there are 0 or a negative number of excess deaths associated with that cause.
The number of excess deaths provided in the tables is a summary for all ages within the selected area for a given cause. However, although the area may have no excess deaths overall, the life expectancy calculation takes into account the age at which deaths occur.
Therefore, if deaths for that cause of death occur at younger ages in the selected area compared to the comparator area, this will have a greater impact on life expectancy, and therefore the life expectancy gap.
User feedback indicated that breakdowns for the COVID-19 pandemic period would be useful to include in the tool, so that the impact of COVID-19 on inequalities in life expectancy can be quantified. Data for 2014 to 2016 and 2017 to 2019 are provided based on three years data pooled so that some data for lower tier local authorities can be provided.
Why is there no data for 2020-21 for lower tier local authorities?
Data for 2020 to 2021 are not available for lower tier local authorities as the breakdowns based on two years of data are not robust due to small numbers.
The contributions of different causes fluctuate from one time period to the next in my area, how do I interpret this?
The contributions of different causes of death or age groups to the gap in life expectancy can fluctuate considerably from one time period and another. It is important to also consider the overall number of deaths and excess deaths in each cause or age group, and these can be viewed in the data tables. If the overall number of deaths in an area is quite small, then a small change in the number of deaths from one time period to another can have a large impact on the contribution of that cause to the gap. Where the number of deaths is very small, results should be interpreted with caution. We hope to be able to add confidence intervals to the Segment Tool in the future to give an indication of uncertaintly around the contributions.
Where can I access other sources of data and information on health inequalities?
Public Health Outcomes Framework data tool: https://fingertips.phe.org.uk/profile/public-health-outcomes-framework
Health inequality tools (including the Health Inequalities Dashboard): https://fingertips.phe.org.uk/profile/inequality-tools
Health Profile for England: https://www.gov.uk/government/publications/health-profile-for-england-2021
COVID-19 health inequalities monitoring for England (CHIME) tool: https://analytics.phe.gov.uk/apps/chime/
 Ponnapalli K (2005) A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research 12:141 to 172.
 Preston S, Heuveline P, Guillot M (2000) Demography: Measuring and Modelling Population Processes. Blackwell Publishing.
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What we’re doing to improve accessibility
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Preparation of this accessibility statement
This statement was prepared on 23rd September 2021. It was last reviewed on 23rd September
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