The life expectancy figure used is for males aged under 1 year. Figures are based on the number of deaths registered and mid-year population estimates, aggregated over three consecutive years.

Expectation of life at a given age for an area is the average number of years a person would live if he or she experienced that area's age-specific mortality rates for that time period throughout his or her life. It is therefore not the number of years someone of that age in the area could actually expect to live, both because the death rates of the area are likely to change in the future and because people may live in other areas for at least part of their lives.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

For the period 2020 to 2022, life expectancy in the West Midlands metropolitan county was 81.4 years for women, and 76.9 years for men. Although this is an increase when compared to 2001/03, life expectancy for both men and women has fallen when compared to 2017/19.

This indicator measures inequalities in life expectancy at birth within England as a whole, each English region, and each local authority. Life expectancy at birth is calculated for each deprivation decile of lower super output areas within each area and then the slope index of inequality (SII) is calculated based on these figures.

The SII is a measure of the social gradient in life expectancy, i.e., how much life expectancy varies with deprivation. It takes account of health inequalities across the whole range of deprivation within each area and summarises this in a single number. This represents the range in years of life expectancy across the social gradient from most to least deprived, based on a statistical analysis of the relationship between life expectancy and deprivation across all deprivation deciles.

Life expectancy at birth is a measure of the average number of years a person would expect to live based on contemporary mortality rates. For a particular area and time period, it is an estimate of the average number of years a newborn baby would survive if he or she experienced the age-specific mortality rates for that area and time period throughout his or her life.

The SII for England and for regions have been presented alongside the local authority figures in order to improve the display of the indicators on the overview page. However, they should not be considered as comparators for the local authority figures. The SII for England takes account of the full range of deprivation and mortality across the whole country. This does not therefore provide a suitable benchmark with which to compare local authority results, which take into account the range of deprivation and mortality within much smaller geographies.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Life expectancy in the United Kingdom was below 39 years in the year 1765, and over the course of the next two and a half centuries, it is expected to have increased by more than double, to 81.1 by the year 2020. Although life expectancy has generally increased throughout the UK's history, there were several times where the rate deviated from its previous trajectory. These changes were the result of smallpox epidemics in the late eighteenth and early nineteenth centuries, new sanitary and medical advancements throughout time (such as compulsory vaccination), and the First world War and Spanish Flu epidemic in the 1910s.

Healthy life expectancy (HLE) is an estimate of expected years of life spent in self-reported good health. The figure used is for males aged under 1 year. Figures are based on the number of deaths registered and mid-year population estimates, aggregated over three consecutive years.

It is used as a high-level outcome to contrast and monitor the health status of different populations at specific points in time, giving context to the impacts of policy changes and interventions at both national and local levels.

Healthy life expectancy has value across state, private, and voluntary sectors, in the assessment of healthy ageing, fitness for work, health improvement monitoring, extensions to the state pension age, pension provision, and health and social care need. This indicator is an extremely important summary measure of mortality and morbidity. It complements the supporting indicators such as mortality by cause by showing the overall trends and setting the context in which local authorities can assess the other indicators and identify the drivers of healthy life expectancy.

The health prevalence data used in calculating HLE estimates for the various geographies in England were derived from the Annual Population Survey (APS).

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This dataset presents the average number of years a man aged 65 can expect to live in good health, known as healthy life expectancy (HLE). It is a key measure of quality of life in later years and reflects both longevity and the prevalence of good health among older men.

Rationale Increasing healthy life expectancy at age 65 for males is a major public health objective. It highlights the importance of not only living longer but also maintaining good health and independence in later life. This indicator supports the planning of health and social care services and helps assess the impact of health inequalities and lifestyle factors on aging populations.

Numerator The numerator is derived from the number of deaths registered in the respective calendar years and the weighted prevalence of individuals reporting good or very good health, as captured by the Annual Population Survey (APS). Data are provided by the Office for National Statistics (ONS).

Denominator The denominator is based on population estimates from the 2021 Census and the APS sample, weighted to reflect local authority population totals. These data are also provided by the ONS.

Caveats Healthy life expectancy figures exclude residents of communal establishments, except for NHS housing and students in halls of residence who are included based on their parents' address. This may affect comparability in areas with large institutional populations.

External References Fingertips Public Health Profiles – Healthy Life Expectancy (Male)

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Period life expectancy for single-year periods, at birth and other age groups at regional and local authority levels in selected constituent countries.

This dataset presents the average number of years a woman aged 65 can expect to live in good health, known as healthy life expectancy (HLE). It is a key measure of quality of life in later years and reflects both longevity and the prevalence of good health among older women.

Rationale Increasing healthy life expectancy at age 65 for females is a major public health goal. It highlights the importance of not only living longer but also maintaining good health and independence in later life. This indicator supports the planning of health and social care services and helps assess the impact of health inequalities and lifestyle factors on aging populations.

Numerator The numerator is derived from the number of deaths registered in the respective calendar years and the weighted prevalence of individuals reporting good or very good health, as captured by the Annual Population Survey (APS). Data are provided by the Office for National Statistics (ONS).

Denominator The denominator is based on population estimates from the 2021 Census and the APS sample, weighted to reflect local authority population totals. These data are also provided by the ONS.

Caveats Healthy life expectancy figures exclude residents of communal establishments, except for NHS housing and students in halls of residence who are included based on their parents' address. This may affect comparability in areas with large institutional populations.

External References Fingertips Public Health Profiles – Healthy Life Expectancy (Female)

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This dataset contains replication files for "The Association Between Income and Life Expectancy in the United States, 2001-2014" by Augustin Bergeron, Raj Chetty, David Cutler, Benjamin Scuderi, Michael Stepner, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/lifeexpectancy/. A summary of the related publication follows. How can we reduce socioeconomic disparities in health outcomes? Although it is well known that there are significant differences in health and longevity between income groups, debate remains about the magnitudes and determinants of these differences. We use new data from 1.4 billion anonymous earnings and mortality records to construct more precise estimates of the relationship between income and life expectancy at the national level than was feasible in prior work. We then construct new local area (county and metro area) estimates of life expectancy by income group and identify factors that are associated with higher levels of life expectancy for low-income individuals. Our findings show that disparities in life expectancy are not inevitable. There are cities throughout America — from New York to San Francisco to Birmingham, AL — where gaps in life expectancy are relatively small or are narrowing over time. Replicating these successes more broadly will require targeted local efforts, focusing on improving health behaviors among the poor in cities such as Las Vegas and Detroit. Our findings also imply that federal programs such as Social Security and Medicare are less redistributive than they might appear because low-income individuals obtain these benefits for significantly fewer years than high-income individuals, especially in cities like Detroit. Going forward, the challenge is to understand the mechanisms that lead to better health and longevity for low-income individuals in some parts of the U.S. To facilitate future research and monitor local progress, we have posted annual statistics on life expectancy by income group and geographic area (state, CZ, and county) at The Health Inequality Project website. Using these data, researchers will be able to study why certain places have high or improving levels of life expectancy and ultimately apply these lessons to reduce health disparities in other parts of the country.

Healthy life expectancy (HLE) is an estimate of expected years of life spent in self-reported good health. The figure used is for females aged under 1 year. Figures are based on the number of deaths registered and mid-year population estimates, aggregated over three consecutive years.

It is used as a high-level outcome to contrast and monitor the health status of different populations at specific points in time, giving context to the impacts of policy changes and interventions at both national and local levels.

Healthy life expectancy has value across state, private, and voluntary sectors, in the assessment of healthy ageing, fitness for work, health improvement monitoring, extensions to the state pension age, pension provision, and health and social care need. This indicator is an extremely important summary measure of mortality and morbidity. It complements the supporting indicators such as mortality by cause by showing the overall trends and setting the context in which local authorities can assess the other indicators and identify the drivers of healthy life expectancy.

The health prevalence data used in calculating HLE estimates for the various geographies in England were derived from the Annual Population Survey (APS).

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Deeply phenotyped sepsis patients within hospital: onset, treatments & outcomes

Sepsis is life-threatening organ dysfunction due to a dysregulated host response to infection & is a global health challenge. In 2017, 48•9 million incident cases of sepsis were recorded worldwide with 11million sepsis-related deaths, representing 19•7% of all global deaths. There are >123,000 sepsis cases diagnosed in Engl& each year with an estimated 36,800 sepsis-associated deaths. Sepsis is treatable, & timely, targeted interventions improve outcomes. The World Health Assembly identified sepsis as a global health priority.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Birmingham has the highest birth rate in England. It also has the highest infant mortality rate. WM life expectancy is 1.8 years less than in London. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients to UHB from 2000 – current day. Updated monthly. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after sepsis understood. The dataset includes ICD-10 & SNOMED-CT codes pertaining to sepsis & suspected sepsis. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound). Includes COVID-19 wave 1 and wave 2 data.

Available supplementary data: Matched “non-sepsis” controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Clinical response thresholds (acuity) in acutely unwell patients: onset-outcome

Early warning systems (EWS) are bedside tools used to assess basic physiological parameters to identify patients with potential or established critical illness. Evidence suggests that they may predict risk of intensive care admission, death and length of hospital stay. In 2017, the Royal College of Physicians (RCP) published an updated National Early Warning Score, referred to as NEWS2, based upon six physiological parameters. It is associated with specific clinical response recommendations in which a step change occurs at a threshold NEWS2 score ?5, requiring an urgent clinical response no matter what the presenting complaint or condition.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Birmingham has the highest birth rate in England. It also has the highest infant mortality rate. WM life expectancy is 1.8 years less than in London. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients to UHB from 2000 – current day. Updated monthly. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after acute admission interrogated. The dataset includes all admission ICD-10 & SNOMED-CT codes including medical, surgical, paediatrics. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound). Includes but not limited to COVID-19 wave 1 and wave 2 data.

Available supplementary data: Matched ‘elective’ controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen

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.

This dataset reports the percentage of adults on a GP's Severe Mental Illness (SMI) register who have received all six core physical health checks within the 12 months prior to the reporting period end. These checks are a key component of proactive care for individuals with SMI, who are at greater risk of physical health conditions and premature mortality. The data is sourced from BSOL Clinical Systems.

Rationale

People with severe mental illness often experience poorer physical health outcomes and reduced life expectancy. Regular physical health checks are essential for early identification and management of conditions such as cardiovascular disease, diabetes, and respiratory illness. This indicator supports efforts to improve the integration of mental and physical healthcare and to reduce health inequalities.

Numerator

The numerator is the number of patients on the SMI register who received all six core health checks in the 12 months prior to the reporting period end.

Denominator

The denominator includes all patients registered with a GP as having a severe mental illness, as recorded in the BSOL Clinical Systems.

Caveats

No specific caveats are noted for this indicator. However, data quality and completeness may vary depending on local recording practices and system integration.

External References

No external reference link is currently provided for this indicator.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This dataset presents the prevalence of smoking among adults aged 18 and over who have a long-term mental health condition. It is derived from the GP Patient Survey (GPPS) and reflects the proportion of individuals within this group who self-report as current smokers. The indicator provides insight into smoking behaviours among a population known to experience significant health inequalities.

Rationale

Adults with long-term mental health conditions are more likely to smoke than the general population, contributing to poorer physical health outcomes and reduced life expectancy. Reducing smoking prevalence in this group is a public health priority. This indicator supports efforts to monitor and address health disparities through targeted smoking cessation interventions.

Numerator

The numerator is the sum of individual weighted counts of respondents who self-report as current smokers (responses 3 or 4 to question Q55) and who also report having a long-term mental health condition (Q31_14 = true) in the GP Patient Survey.

Denominator

The denominator is the sum of individual weighted counts of all respondents who provided valid responses to both the smoking status (Q55) and long-term medical conditions (Q31) questions in the GP Patient Survey.

Caveats

No specific caveats are noted for this indicator. However, as with all self-reported survey data, responses may be subject to reporting bias or inaccuracies in self-assessment.

External References

More information is available from the following source:

Fingertips Public Health Profiles

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.