Between 2002 and 2019, life expectancy at birth for both males and females in Italy constantly increased. It decreased in 2020 and reached **** years for males and **** years for females. After the COVID-19 pandemic, expecting living years settled to **** for men and **** for women.

The total life expectancy at birth in the United States saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 78.39 years. However, 2023 marked the second consecutive increase of the life expectancy at birth. These figures refer to the expected lifespan of the average newborn in a given country or region, providing that mortality patterns at the time of birth remain constant thereafter.Find more statistics on other topics about the United States with key insights such as crude birth rate, life expectancy of women at birth, and life expectancy of men at birth.

In 2022, the average life expectancy of women in Japan was approximately **** years, whereas the life expectancy of men reached around **** years. The average life expectancy of both men and women in Japan indicated a ******** for two consecutive years. Aging workforce Japan has one of the highest proportions of senior citizens worldwide, with almost ** percent of the country’s population aged 65 years and older. The growing average life expectancy and declining fertility rates led to this demographic shift. To secure the nation's workforce despite the aging population, the Japanese government amended the Act on Stabilization of Employment of Elderly Persons in 2021 and requested Japanese enterprises to raise the retirement age to 70 for employees who wish to continue working after turning 60 or 65. Causes of death The leading causes of death in Japan are *****************************************************************. Lung cancer is the most mortal cancer site among Japanese men and women, but its mortality risk has declined from the 1990s onward. This development can be partially attributed to the downward trend in tobacco consumption. Since peaking in the 1970s, tobacco consumption in Japan has steadily declined, noticeable from the continuous decrease in the cigarette industry’s annual sales volume growth. Apart from a growing awareness regarding health risks, this downward movement can be explained by a tightening of prefectural no-smoking policies in the streets, many restaurants, and public places in general.

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

BackgroundSocioeconomic disparities in life expectancy are well-documented in various contexts, including Chile. However, there is a lack of research examining trends in life expectancy inequalities and lifespan variation over time. Addressing these gaps can provide crucial insights into the dynamics of health inequalities.MethodsThis study utilizes data from census records, population surveys, and death certificates to compare the life expectancy and the lifespan variation at age 26 of individuals according to their rank in the distribution of years of education within their own birth cohort. The analysis spans three periods (1991, 2002, and 2017) and focuses on two educational groups: individuals in the first (lowest) quintile and tenth (highest) decile of educational attainment. Changes in life expectancy are disaggregated by major causes of death to elucidate their contributions to overall trends.ResultsConsistent with existing literature, our findings confirm that individuals with lower education levels experience lower life expectancy and higher lifespan variation compared to their more educated counterparts. Notably, by 2017, life expectancy for individuals in the lowest quintile of education has caught up with that of the top decile in 1991, albeit with contrasting trends between genders. Among women, the gap has reduced, while it has increased for males. Moreover, lifespan variation decreased (increased) over time for individuals in the tenth decile (first quintile). The leading causes of death that explain the increase in life expectancy in women and men in the tenth decile as well as women in the first quintile are cardiovascular, cancer, respiratory and digestive diseases. In the case of males in the first quintile, few gains have been made in life expectancy resulting from cancer and a negative contribution is associated with digestive conditions.ConclusionsThis study underscores persistent socioeconomic disparities in life expectancy in Chile, emphasizing the importance of ongoing monitoring of health inequalities across different demographic segments. The gender-specific and educational gradient trends highlight areas for targeted interventions aimed at reducing health disparities and improving overall population health outcomes. Further research is warranted to delve into specific causes of death driving life expectancy differentials and to inform evidence-based policy interventions.

In 2024, life expectancy at 65 years was 21.2 years, compared to 20.9 in 2023. A 65-year-old man in Italy had an average life expectancy of another 19.8 years, while a 65-year-old woman could live 22.6 years more. Lately, the average life expectancy at birth in the north-eastern regions of Italy registered the highest figures.

In 2025, the average age of the population in Italy is estimated to be **** years. This figure constantly rose over the last decade. In 2010, the mean age was **** years, steadily growing in the following years. Recent studies indicate that the median age is projected to increase in the future as well. By 2050, it could reach **** years. Few births over the past years Italy has the highest share of the elderly population in Europe. In 2023, ** percent of the Italian inhabitants were aged 65 years and over. One of the main reasons for the population aging is the low number of births recorded in the past years. In fact, Italy counted about *** births every 100,000 inhabitants in 2023, the lowest figure recorded since 2002 at least. Longer lifespan In addition to a low birth rate, Italy is among the countries with the highest life expectancy worldwide. In 2024, life expectancy at birth for Italian women was **** years, whereas Italian men could expect to live up to **** years. A longer life expectancy combined with fewer births explain why the average age of Italian inhabitants has been rising recently.

ObjectivesThe world is witnessing a sharp increase in its elderly population, accelerated by longer life expectancy and lower birth rates, which in turn imposes enormous medical burden on society. Although numerous studies have predicted medical expenses based on region, gender, and chronological age (CA), any attempt has rarely been made to utilize biological age (BA)—an indicator of health and aging—to ascertain and predict factors related to medical expenses and medical care use. Thus, this study employs BA to predict factors that affect medical expenses and medical care use.Materials and methodsReferring to the health screening cohort database of the National Health Insurance Service (NHIS), this study targeted 276,723 adults who underwent health check-ups in 2009−2010 and kept track of the data on their medical expenses and medical care use up to 2019. The average follow-up period is 9.12 years. Twelve clinical indicators were used to measure BA, while the total annual medical expenses, total annual number of outpatient days, total annual number of days in hospital, and average annual increases in medical expenses were used as the variables for medical expenses and medical care use. For statistical analysis, this study employed Pearson correlation analysis and multiple regression analysis.ResultsRegression analysis of the differences between corrected biological age (cBA) and CA exhibited statistically significant increases (p

ObjectivesThe world is witnessing a sharp increase in its elderly population, accelerated by longer life expectancy and lower birth rates, which in turn imposes enormous medical burden on society. Although numerous studies have predicted medical expenses based on region, gender, and chronological age (CA), any attempt has rarely been made to utilize biological age (BA)—an indicator of health and aging—to ascertain and predict factors related to medical expenses and medical care use. Thus, this study employs BA to predict factors that affect medical expenses and medical care use.Materials and methodsReferring to the health screening cohort database of the National Health Insurance Service (NHIS), this study targeted 276,723 adults who underwent health check-ups in 2009−2010 and kept track of the data on their medical expenses and medical care use up to 2019. The average follow-up period is 9.12 years. Twelve clinical indicators were used to measure BA, while the total annual medical expenses, total annual number of outpatient days, total annual number of days in hospital, and average annual increases in medical expenses were used as the variables for medical expenses and medical care use. For statistical analysis, this study employed Pearson correlation analysis and multiple regression analysis.ResultsRegression analysis of the differences between corrected biological age (cBA) and CA exhibited statistically significant increases (p

In 2022 life expectancy for both males and females at birth fell when compared to 2021. Male life expectancy fell from 78.71 years to 78.57 years, and from 82.68 years to 82.57 years for women.

Male life expectancy at birth fell in all four countries of the United Kingdom in 2020-22 when compared with 2019/21. English men had a life expectancy of 78.83, compared with 76.52 in Scotland, 77.93 in Wales and 78.43 in Northern Ireland. In both England and Wales, life expectancy ticked up for the period 2021/23.

Over the last two observations, the life expectancy has significantly increased in all gender groups Comparing the two different gender groups for the year 2023, the 'life expectancy of women at birth' leads the ranking with 69.6 years. Contrastingly, 'life expectancy of men at birth' is ranked last, with 62.61 years. Their difference, compared to life expectancy of women at birth, lies at 6.99 years. Life expectancy at birth refers to the number of years that the average newborn can expect to live, providing that mortality patterns at the time of their birth do not change thereafter.Find further similar statistics for other countries or regions like Iran and Angola.

Prediction results of annual medical expenses and medical care use depending on the differences between cBA and CA.

Abstract copyright UK Data Service and data collection copyright owner.

The English Longitudinal Study of Ageing (ELSA) study is a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. The main objectives of ELSA are to:

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• investigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

Further information may be found on the the ELSA project website or the Natcen Social Research: ELSA web pages.

Health conditions research with ELSA - June 2021

The ELSA Data team have found some issues with historical data measuring health conditions. If you are intending to do any analysis looking at the following health conditions, then please contact elsadata@natcen.ac.uk for advice on how you should approach your analysis. The affected conditions are: eye conditions (glaucoma; diabetic eye disease; macular degeneration; cataract), CVD conditions (high blood pressure; angina; heart attack; Congestive Heart Failure; heart murmur; abnormal heart rhythm; diabetes; stroke; high cholesterol; other heart trouble) and chronic health conditions (chronic lung disease; asthma; arthritis; osteoporosis; cancer; Parkinson's Disease; emotional, nervous or psychiatric problems; Alzheimer's Disease; dementia; malignant blood disorder; multiple sclerosis or motor neurone disease).

Secure Access Data:Secure Access versions of ELSA have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access' section below).
Secure Access versions of ELSA include:

• Primary Data from Wave 8 onwards (SN 8444) includes all the variables in the SL primary dataset (SN 8346) as well as day of birth, combined SIC 2003 code (5 digit), combined SOC 2000 code (4 digit), NS-SEC long version including and excluding unclassifiable and non-workers.
• Pension Age Data from Wave 8 onwards (SN 8445) includes all the variables in the SL pension age data (SN 8375) as well as year reached pension age variable.
• Detailed geographical identifier files for each wave, grouped by identifier held under SN 8423 (Index of Multiple Deprivation Score), SN 8424 (Local Authority District Pre-2009 Boundaries), SN 8438 (Local Authority District Post-2009 Boundaries), SN 8425 (Census 2001 Lower Layer Super Output Areas), SN 8434 (Census 2011 Lower Layer Super Output Areas), SN 8426(Census 2001 Middle Layer Super Output Areas), SN 8435 (Census 2011 Middle Layer Super Output Areas), SN 8427 (Population Density for Postcode Sectors), SN 8428 (Census 2001 Rural-Urban Indicators), SN 8436 (Census 2011 Rural-Urban Indicators).

Where boundary changes have occurred, the geographic identifier has been split into two separate studies to reduce the risk of disclosure. Users are also only allowed one version of each identifier:

• either SN 8424 (Local Authority District Pre-2009 Boundaries) or SN 8438 (Local Authority District Post-2009 Boundaries)
• either SN 8425 (Census 2001 Lower Layer Super Output Areas) or SN 8434 (Census 2011 Lower Layer Super Output Areas)
• either SN 8426 (Census 2001 Middle Layer Super Output Areas) or SN 8435 (Census 2011 Middle Layer Super Output Areas)
• either SN 8428 (Census 2001 Rural-Urban Indicators) or SN 8436 (Census 2011 Rural-Urban Indicators)

English Longitudinal Study of Ageing: Waves 1-10, 2002-2023: Index of Multiple Deprivation Score: Secure Access
This dataset contains an Index of Multiple Deprivation Score variable for each Wave of ELSA to date, and a unique individual serial number variable is also included for matching to the main data files. These data have more restrictive access conditions than those available under the standard End User Licence or Special Licence (see 'Access' section).

Latest edition information
For the second edition (October 2024), data for waves 9 and 10 have been added to the study and data for waves 1 to 8 have been updated.

Main Topics:

This dataset contains an Index of Multiple Deprivation Score variable for each Wave of ELSA to date, as well as a unique individual serial...

Abstract copyright UK Data Service and data collection copyright owner.

The English Longitudinal Study of Ageing (ELSA) study is a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. The main objectives of ELSA are to:

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• investigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

Further information may be found on the the ELSA project website or the Natcen Social Research: ELSA web pages.

Health conditions research with ELSA - June 2021

The ELSA Data team have found some issues with historical data measuring health conditions. If you are intending to do any analysis looking at the following health conditions, then please contact elsadata@natcen.ac.uk for advice on how you should approach your analysis. The affected conditions are: eye conditions (glaucoma; diabetic eye disease; macular degeneration; cataract), CVD conditions (high blood pressure; angina; heart attack; Congestive Heart Failure; heart murmur; abnormal heart rhythm; diabetes; stroke; high cholesterol; other heart trouble) and chronic health conditions (chronic lung disease; asthma; arthritis; osteoporosis; cancer; Parkinson's Disease; emotional, nervous or psychiatric problems; Alzheimer's Disease; dementia; malignant blood disorder; multiple sclerosis or motor neurone disease).

Secure Access Data:Secure Access versions of ELSA have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access' section below).
Secure Access versions of ELSA include:

• Primary Data from Wave 8 onwards (SN 8444) includes all the variables in the SL primary dataset (SN 8346) as well as day of birth, combined SIC 2003 code (5 digit), combined SOC 2000 code (4 digit), NS-SEC long version including and excluding unclassifiable and non-workers.
• Pension Age Data from Wave 8 onwards (SN 8445) includes all the variables in the SL pension age data (SN 8375) as well as year reached pension age variable.
• Detailed geographical identifier files for each wave, grouped by identifier held under SN 8423 (Index of Multiple Deprivation Score), SN 8424 (Local Authority District Pre-2009 Boundaries), SN 8438 (Local Authority District Post-2009 Boundaries), SN 8425 (Census 2001 Lower Layer Super Output Areas), SN 8434 (Census 2011 Lower Layer Super Output Areas), SN 8426(Census 2001 Middle Layer Super Output Areas), SN 8435 (Census 2011 Middle Layer Super Output Areas), SN 8427 (Population Density for Postcode Sectors), SN 8428 (Census 2001 Rural-Urban Indicators), SN 8436 (Census 2011 Rural-Urban Indicators).

Where boundary changes have occurred, the geographic identifier has been split into two separate studies to reduce the risk of disclosure. Users are also only allowed one version of each identifier:

• either SN 8424 (Local Authority District Pre-2009 Boundaries) or SN 8438 (Local Authority District Post-2009 Boundaries)
• either SN 8425 (Census 2001 Lower Layer Super Output Areas) or SN 8434 (Census 2011 Lower Layer Super Output Areas)
• either SN 8426 (Census 2001 Middle Layer Super Output Areas) or SN 8435 (Census 2011 Middle Layer Super Output Areas)
• either SN 8428 (Census 2001 Rural-Urban Indicators) or SN 8436 (Census 2011 Rural-Urban Indicators)

English Longitudinal Study of Ageing: Waves 1-10, 2002-2023: Census 2001 Lower Layer Super Output Areas: Secure Access
This dataset contains a Census 2001 Lower Layer Super Output Area variable for each Wave of ELSA to date, and a unique individual serial number variable is also included for matching to the main data files. These data have more restrictive access conditions than those available under the standard End User Licence or Special Licence (see 'Access' section).

Latest edition information
For the second edition (October 2024), data for waves 9 and 10 have been added to the study and data for waves 1 to 8 have been updated.

Main Topics:

This dataset contains a Census 2001 Lower Layer Super Output Area variable for each Wave of ELSA to date, as well as an unique...

Abstract copyright UK Data Service and data collection copyright owner.

The English Longitudinal Study of Ageing (ELSA) study is a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. The main objectives of ELSA are to:

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• nvestigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

Further information may be found on the the ELSA project website or the Natcen Social Research: ELSA web pages.

Health conditions research with ELSA - June 2021

The ELSA Data team have found some issues with historical data measuring health conditions. If you are intending to do any analysis looking at the following health conditions, then please contact the ELSA Data team at NatCen on elsadata@natcen.ac.uk for advice on how you should approach your analysis. The affected conditions are: eye conditions (glaucoma; diabetic eye disease; macular degeneration; cataract), CVD conditions (high blood pressure; angina; heart attack; Congestive Heart Failure; heart murmur; abnormal heart rhythm; diabetes; stroke; high cholesterol; other heart trouble) and chronic health conditions (chronic lung disease; asthma; arthritis; osteoporosis; cancer; Parkinson's Disease; emotional, nervous or psychiatric problems; Alzheimer's Disease; dementia; malignant blood disorder; multiple sclerosis or motor neurone disease).

Special Licence Data:

Special Licence Access versions of ELSA have more restrictive access conditions than versions available under the standard End User Licence (see 'Access' section below). Users are advised to obtain the latest edition of SN 5050 (the End User Licence version) before making an application for Special Licence data, to see whether that is suitable for their needs. A separate application must be made for each Special Licence study.

Special Licence Access versions of ELSA include:

• Primary data from Wave 8 onwards (SN 8346) includes all the variables in the EUL primary dataset (SN 5050) as well as year and month of birth, consolidated ethnicity and country of birth, marital status, and more detailed medical history variables.
• Wave 8 Pension Age Data (SN 8375) includes all the variables in the EUL pension age data (SN 5050) as well as year and age reached state pension age variables.
• Wave 8 Sexual Self-Completion Data (SN 8376) includes sensitive variables from the sexual self-completion questionnaire.
• Wave 3 (2007) Harmonized Life History (SN 8831) includes retrospective information on previous histories, specifically, detailed data on previous partnership, children, residential, health, and work histories.
• Detailed geographical identifier files for Waves 1-10 which are grouped by identifier held under SN 8429 (Local Authority District Pre-2009 Boundaries), SN 8439 (Local Authority District Post-2009 Boundaries), SN 8430 (Local Authority Type Pre-2009 Boundaries), SN 8441 (Local Authority Type Post-2009 Boundaries), SN 8431 (Quintile Index of Multiple Deprivation Score), SN 8432 (Quintile Population Density for Postcode Sectors), SN 8433 (Census 2001 Rural-Urban Indicators), SN 8437 (Census 2011 Rural-Urban Indicators).

Where boundary changes have occurred, the geographic identifier has been split into two separate studies to reduce the risk of disclosure. Users are also only allowed one version of each identifier:

• either SN 8429 (Local Authority District Pre-2009 Boundaries) or SN 8439 (Local Authority District Post-2009 Boundaries)
• either SN 8430 (Local Authority Type Pre-2009 Boundaries) or SN 8441(Local Authority Type Post-2009 Boundaries)
• either SN 8433 (Census 2001 Rural-Urban Indicators) or SN 8437 (Census 2011 Rural-Urban Indicators)

ELSA Wave 6 and Wave 8 Self-Completion Questionnaires included an open-ended question where respondents could add any other comments they may wish to note down. These responses have been transcribed and anonymised. Researchers can request access to these transcribed responses for research purposes by contacting the...

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• investigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

• Primary Data from Wave 8 onwards (SN 8444) includes all the variables in the SL primary dataset (SN 8346) as well as day of birth, combined SIC 2003 code (5 digit), combined SOC 2000 code (4 digit), NS-SEC long version including and excluding unclassifiable and non-workers.
• Pension Age Data from Wave 8 onwards (SN 8445) includes all the variables in the SL pension age data (SN 8375) as well as year reached pension age variable.
• Detailed geographical identifier files for each wave, grouped by identifier held under SN 8423 (Index of Multiple Deprivation Score), SN 8424 (Local Authority District Pre-2009 Boundaries), SN 8438 (Local Authority District Post-2009 Boundaries), SN 8425 (Census 2001 Lower Layer Super Output Areas), SN 8434 (Census 2011 Lower Layer Super Output Areas), SN 8426(Census 2001 Middle Layer Super Output Areas), SN 8435 (Census 2011 Middle Layer Super Output Areas), SN 8427 (Population Density for Postcode Sectors), SN 8428 (Census 2001 Rural-Urban Indicators), SN 8436 (Census 2011 Rural-Urban Indicators).

• either SN 8424 (Local Authority District Pre-2009 Boundaries) or SN 8438 (Local Authority District Post-2009 Boundaries)
• either SN 8425 (Census 2001 Lower Layer Super Output Areas) or SN 8434 (Census 2011 Lower Layer Super Output Areas)
• either SN 8426 (Census 2001 Middle Layer Super Output Areas) or SN 8435 (Census 2011 Middle Layer Super Output Areas)
• either SN 8428 (Census 2001 Rural-Urban Indicators) or SN 8436 (Census 2011 Rural-Urban Indicators)

While the standard image of the nuclear family with two parents and 2.5 children has persisted in the American imagination, the number of births in the U.S. has steadily been decreasing since 1990, with about 3.6 million babies born in 2023. In 1990, this figure was 4.16 million. Birth and replacement rates A country’s birth rate is defined as the number of live births per 1,000 inhabitants, and it is this particularly important number that has been decreasing over the past few decades. The declining birth rate is not solely an American problem, with EU member states showing comparable rates to the U.S. Additionally, each country has what is called a “replacement rate.” The replacement rate is the rate of fertility needed to keep a population stable when compared with the death rate. In the U.S., the fertility rate needed to keep the population stable is around 2.1 children per woman, but this figure was at 1.67 in 2022. Falling birth rates Currently, there is much discussion as to what exactly is causing the birth rate to decrease in the United States. There seem to be several factors in play, including longer life expectancies, financial concerns (such as the economic crisis of 2008), and an increased focus on careers, all of which are causing people to wait longer to start a family. How international governments will handle falling populations remains to be seen, but what is clear is that the declining birth rate is a multifaceted problem without an easy solution.

The total fertility rate in China increased by 0.02 children per woman (+1.72 percent) in 2022. In total, the fertility rate amounted to 1.18 children per woman in 2022. This increase was preceded by a declining fertility rate.The total fertility rate is the average number of children that a woman of childbearing age (generally considered 15 to 44 years) can hypothetically expect to have throughout her reproductive years. As fertility rates are estimates (similar to life expectancy), they refer to a hypothetical woman or cohort, and estimates assume that current age-specific fertility trends would remain constant throughout this person's reproductive years.Find more statistics on other topics about China with key insights such as death rate, number of tuberculosis infections , and crude birth rate.

There were 667,479 deaths in the United Kingdom in 2021, compared with 689,629 in 2020. Between 2003 and 2011, the annual number of deaths in the UK fell from 612,085 to just over 552,232. Since 2011 however, the annual number of annual deaths in the United Kingdom has steadily grown, with the number recorded in 2020, the highest since 1918 when there were 715,246 deaths. Both of these spikes in the number of deaths can be attributed to infectious disease pandemics. The great influenza pandemic of 1918, which was at its height towards the end of World War One, and the COVID-19 pandemic, which caused a large number of deaths in 2020.  Impact of the COVID-19 pandemic The weekly death figures for England and Wales highlight the tragic toll of the COVID-19 pandemic. In two weeks in April of 2020, there were 22,351 and 21,997 deaths respectively, almost 12,000 excess deaths in each of those weeks. Although hospitals were the most common location of these deaths, a significant number of these deaths also took place in care homes, with 7,911 deaths taking place in care homes for the week ending April 24, 2020, far higher than usual. By the summer of 2020, the number of deaths in England and Wales reached more usual levels, before a second wave of excess deaths hit the country in early 2021. Although subsequent waves of COVID-19 cases resulted in far fewer deaths, the number of excess deaths remained elevated throughout 2022. Long-term life expectancy trends As of 2022 the life expectancy for men in the United Kingdom was 78.57, and almost 82.57 for women, compared with life expectancies of 75 for men and 80 for women in 2002. In historical terms, this is a major improvement in relation to the mid 18th century, when the overall life expectancy was just under 39 years. Between 2011 and 2017, improvements in life expectancy in the UK did start to decline, and have gone into reverse since 2018/20. Between 2020 and 2022 for example, life expectancy for men in the UK has fallen by over 37 weeks, and by almost 23 weeks for women, when compared with the previous year.