Template for creating life expectancy and health expectancies estimates.

This series has been discontinued.

Life expectancy at birth and age 65 by sex and ward, London borough, region, 1999/03 - 2010/14.

The population data used is revised 2002-2010 ONS mid year estimates (MYE) - revised post 2011 Census. Revised population estimates by single year of age for wards can also be found on the ONS website for 2002-2010, 2011, 2012, and 2013. These figures are consistent with the published revised mid-2002 to mid-2010 local authority estimates.

Rolling 5-year combined life expectancies are used for wards to reduce the effects of the variability in number of deaths in each year. The same method is applied to higher geographies to enable meaningful comparisons. However, 3-year combined expectancies are published separately on the Datastore for geographical areas that are local authority and above.

If the GLA publish revised 2002-2010 population data for wards then these life expectancy figures will also be revised to reflect them.

The ONS vital statistics mortality data breaks deaths into 10 year age bands. 5 year age band deaths were modelled using this data.

Vital Statistics: Population and Health Reference Tables are available on the ONS website http://www.ons.gov.uk/ons/rel/vsob1/vital-statistics--population-and-health-reference-tables/index.html">here.

The tool for calculating life expectancy is available from Public Health England.
The highest age band in the calculator is currently 85+. If the tool is updated with a higher upper age band (ie 90+), this data will be revised to reflect this change.

Healthy life expectancy and disability-free life expectancy (1999-2003) at birth have been calculated for wards in England and Wales. These can be found on the ONS website.

This data is also presented in the GLA ward profiles.

Modelled good health prevalence estimates from Annual Population Survey data between 2011 to 2013 and 2021 to 2023 used to calculate healthy life expectancy.

Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Methods Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance.

Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071.

Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/.

Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067.

Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322.

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013.

Please see README.txt for further information

Potential years of life lost (PYLL) due to alcohol-related conditions, all ages, directly age-standardised per 100,000 population (standardised to the ESP).

Rationale Alcohol consumption is a contributing factor to hospital admissions and deaths from a diverse range of conditions. Alcohol misuse is estimated to cost the NHS about £3.5 billion per year and society as a whole £21 billion annually. The Government has said that everyone has a role to play in reducing the harmful use of alcohol - this indicator is one of the key contributions by the Government (and the Department of Health and Social Care) to promote measurable, evidence-based prevention activities at a local level, and supports the national ambitions to reduce harm set out in the Government's Alcohol Strategy. This ambition is part of the monitoring arrangements for the Responsibility Deal Alcohol Network. Alcohol-related deaths can be reduced through local interventions to reduce alcohol misuse and harm.

Potential years of life lost (PYLL) is a measure of the potential number of years lost when a person dies prematurely. The basic concept of PYLL is that deaths at younger ages are weighted more heavily than those at older ages. The advantage in doing this is that deaths at younger ages may be seen as less important if cause-specific death rates were just used on their own in highlighting the burden of disease and injury, since conditions such as cancer and heart disease usually occur at older ages and have relatively high mortality rates.

To enable comparisons between areas and over time, PYLL rates are age-standardised to represent the PYLL if each area had the same population structure as the 2013 European Standard Population (ESP). PYLL rates are presented as years of life lost per 100,000 population.

Definition of numerator The number of age-specific alcohol-related deaths multiplied by the national life expectancy for each age group and summed to give the total potential years of life lost due to alcohol-related conditions.

Definition of denominator ONS Mid-Year Population Estimates aggregated into quinary age bands.

Caveats There is the potential for the underlying cause of death to be incorrectly attributed on the death certificate and the cause of death misclassified. Alcohol-attributable fractions were not available for children. Conditions where low levels of alcohol consumption are protective (have a negative alcohol-attributable fraction) are not included in the calculation of the indicator.

The national life expectancies for England have been used for all sub-national geographies to illustrate the disparities in the burden caused by alcohol between local areas and the national average.

The confidence intervals do not take into account the uncertainty involved in the calculation of the AAFs – that is, the proportion of deaths that are caused by alcohol and the alcohol consumption prevalence that are included in the AAF formula are only an estimate and so include uncertainty. The confidence intervals published here are based only on the observed number of deaths and do not account for this uncertainty in the calculation of attributable fraction - as such the intervals may be too narrow.