This slide deck includes a short briefing which summarises key findings from the full analysis ‘Health inequality:’ Closing the life expectancy gap over time?’ for both Camden and Islington.

Contains all of the data and code to reproduce the tables and figures contained in: Harper S, MacLehose RA, Kaufman JS. Trends in the Black-White Life-Expectancy Gap among US States, 1990-2009. Health Affairs 2014;33(8):1375-82.

IntroductionAdult male and female mortality declines in Japan have been slower than in most high-income countries since the early 1990s. This study compares Japan’s recent life expectancy trends with the more favourable trends in Australia, measures the contribution of age groups and causes of death to differences in these trends, and places the findings in the context of the countries’ risk factor transitions.MethodsThe study utilises data on deaths by age, sex and cause in Australia and Japan from 1950–2016 from the Global Burden of Disease Study. A decomposition method measures the contributions of various ages and causes to the male and female life expectancy gap and changes over four distinct phases during this period. Mortality differences by cohort are also assessed.FindingsJapan’s two-year male life expectancy advantage over Australia in the 1980s closed in the following 20 years. The trend was driven by ages 45–64 and then 65–79 years, and the cohort born in the late 1940s. Over half of Australia’s gains were from declines in ischaemic heart disease (IHD) mortality, with lung cancer, chronic respiratory disease and self-harm also contributing substantially. Since 2011 the trend has reversed again, and in 2016 Japan had a slightly higher male life expectancy. The advantage in Japanese female life expectancy widened over the period to 2.3 years in 2016. The 2016 gap was mostly from differential mortality at ages 65 years and over from IHD, chronic respiratory disease and cancers.ConclusionsThe considerable gains in Australian male life expectancy from declining non-communicable disease mortality are attributable to a range of risk factors, including declining smoking prevalence due to strong public health interventions. A recent reversal in life expectancy trends could continue because Japan has greater scope for further falls in smoking and far lower levels of obesity. Japan’s substantial female life expectancy advantage however could diminish in future because it is primarily due to lower mortality at old ages.

Additional file 1: Table A1. Age- and cause-specific contributions to gap in life expectancy between Iran and the neighbour countries in males. Table A2. Age- and cause-specific contributions to gap in life expectancy between Iran and the neighbour countries in females. Table A3. Age- and cause-specific contributions to gap in lifespan inequality between Iran and the neighbour countries in males. Table A4. Age- and cause-specific contributions to gap in lifespan inequality between Iran and the neighbour countries in females. Table A5. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in males based on the life table from the institute for health metrics and evaluation. Table A6. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in females based on the life table from the institute for health metrics and evaluation. Table A7. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in males based on the life table from the United Nations. Table A8. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in females based on the life table from the United Nations. Table A9. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in males based on the life table from the world health organization. Table A10. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in females based on the life table from the world health organization.

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The chart reveals the education gap in life expectancy at 30 years old among Italian men and women. The graph shows that people with higher education enjoyed a longer life expectancy compared to individuals with a lower education. The gap appeared to be very significant. Women with a higher education could expect to live nearly three years longer than women with a lower education. For men, the gap was even larger (4.5 years).

This statistic shows the average life expectancy in Europe for those born in 2024, by gender and region. The average life expectancy in Western Europe was 79 years for males and 84 years for females in 2024. Additional information on European life expectancy The difference in life expectancy seen between men and women across all European regions is in line with the global trends of women outliving men, on average. The average life expectancy at birth worldwide by income group shows that the gender life expectancy gap is not only a consistent trend across countries, but also income groups. Moreover, the higher life expectancy for those in high income groups may help to explain the lower average life expectancy for those born in Eastern Europe where average incomes are generally lower than other European regions. Although income and length of life are not directly correlated, higher income individuals are generally able to afford access to superior nutrition and healthcare as well as having leisure time for exercise. That said, current trends in the increases in life expectancy worldwide by country between 1970 and 2017 suggest economic growth will lead to larger increases in life expectancy. Those increases are less likely to occur to such a degree in the more developed regions of Europe where Italy, Spain, France, Switzerland, Iceland and Austria all rank in the top 20 countries with the highest life expectancy.

This slide set is an analysis intended to inform Camden and Islington Public Health team and Clinical Commissioning Groups about causes of death contributing to changes in the life expectancy gap over time. Specifically, this analysis shows how the gap in life expectancy between the most and least deprived areas have changed over time in Camden and Islington, and explores what causes of death are contributing to these changes

In each region of the world, men spend greater proportions of their lives in good health than women. On average, women spend ** percent of their life expectancy at birth in good health, while men spend ** percent of their life expectancy at birth in good health. Out of each region, North Africa and Western Asia has the largest gender gap at ***** percent. Sub-Saharan Africa, Latin America and the Caribbean, and North America and Europe follow with a gap of *** percent. Australia and New Zealand have the smallest gap, at *** percent.

Additional file 1. Table A1. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Bahrain. Table A2. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Egypt. Table A3. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Iran. Table A4. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Jordan. Table A5. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Kuwait. Table A6. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Libya. Table A7. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Morocco. Table A8. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Oman. Table A9. Age- and cause-specific contributions to gender gap in life expectancy/life disparity in Qatar.

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The life expectancy in Finland increased almost constantly over the period from 2014 to 2024. As of 2024, women in Finland had a life expectancy of 84.76 years. The life expectancy of men was over five years less.

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

1) Data Introduction • The Life Expectancy (WHO) Dataset is a WHO-based national health dataset that tabulates life expectancy, vaccination, mortality, economy, and society in 193 countries around the world from 2000 to 2015.

2) Data Utilization (1) Life Expectancy (WHO) Dataset has characteristics that: • Each row contains more than 20 health, economic, and social variables and target variables (life expectancy), including country, year, life expectancy, vaccination rates (e.g., hepatitis B, polio, diphtheria), infant and adult mortality, GDP, population, education level, drinking and smoking. • Although some missing values exist in the data, they are well structured for analysis of health levels and influencing factors by country, including data from various countries and time series. (2) Life Expectancy (WHO) Dataset can be used to: • Analysis of factors affecting life expectancy: The effects of various factors such as vaccination, mortality, economic and social variables on life expectancy can be assessed using statistical methods such as regression analysis. • Health Policy and International Comparative Study: Using national and annual health indicators, it can be used for international health research, such as evaluating the effectiveness of health policies, analyzing health gaps, and establishing strategies to support low-income countries.

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.

In 1970, women born in the U.S. could expect to live for 1.3 years more than women in the Soviet Union, and men in the U.S. could expect to live for 2.7 years longer than their Soviet counterparts. U.S. figures would steadily increase over the following decade, whereas the economic decline of the Soviet Union would see life expectancy fall by two years for men and 0.8 years for women. In 1980, the difference in life expectancy from birth between the two countries was 7.5 years for men, and 4.8 years for women. This difference has largely been attributed to an increase in alcohol and substance abuse and accidental deaths among males in the Soviet Union, as well as more accurate reporting methods in the Soviet Union (suggesting that early figures may no be fully representational). Although Soviet life expectancy did increase in the 1980s, the gap between life expectancy there and in the U.S. remained significantly larger than in 1970, and this trend continued well into the 1990s and early-2000s as the post-Soviet states adjusted to the socio-economic impact of the Union's dissolution.

BackgroundCombination antiretroviral therapy (ART) has significantly increased survival among HIV-positive adults in the United States (U.S.) and Canada, but gains in life expectancy for this region have not been well characterized. We aim to estimate temporal changes in life expectancy among HIV-positive adults on ART from 2000–2007 in the U.S. and Canada.MethodsParticipants were from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), aged ≥20 years and on ART. Mortality rates were calculated using participants' person-time from January 1, 2000 or ART initiation until death, loss to follow-up, or administrative censoring December 31, 2007. Life expectancy at age 20, defined as the average number of additional years that a person of a specific age will live, provided the current age-specific mortality rates remain constant, was estimated using abridged life tables.ResultsThe crude mortality rate was 19.8/1,000 person-years, among 22,937 individuals contributing 82,022 person-years and 1,622 deaths. Life expectancy increased from 36.1 [standard error (SE) 0.5] to 51.4 [SE 0.5] years from 2000–2002 to 2006–2007. Men and women had comparable life expectancies in all periods except the last (2006–2007). Life expectancy was lower for individuals with a history of injection drug use, non-whites, and in patients with baseline CD4 counts <350 cells/mm3.ConclusionsA 20-year-old HIV-positive adult on ART in the U.S. or Canada is expected to live into their early 70 s, a life expectancy approaching that of the general population. Differences by sex, race, HIV transmission risk group, and CD4 count remain.

Age-specific contributions to changes in the gender gap in life expectancy between 2005 and 2010 (Note: positive values indicate widening the gender gap in life expectancy attributable to specific age groups and negative values indicate narrowing the gender gap).

Additional file 1: Table A1. Age- and cause-specific contributions to sex gap in life expectancy in Iran. Table A2. Age- and cause-specific contributions to sex gap in life disparity in Iran.

On average, women live almost 6 years more than men in France. In 2024, female life expectancy at birth in France reached **** years compared to ** years for males. In 2023, life expectancy in France, regardless of gender, was ***** years. Thus, France is one of the countries in the world with the highest life expectancy. Women outlive men According to the source, there are differences in life expectancy between men and women in France. In 2004, female life expectancy in France was ****, compared to ** years for males. Since then, life expectancy for both genders has been evolving similarly. When life expectancy decreased slightly in 2015, it affected both men and women. Similarly, when life expectancy increased. But one aspect remained the same: male life expectancy remains lower than female life expectancy. This difference has been seen not only in France. In Europe, females are expected to live longer than men in every region. While women in France have a longer life expectancy, they are also expected to have a higher number of healthy life years. In 2013, a study from Eurostat showed that French women had several expected healthy years of ****, compared to ** years for men. An aging population Like other Western countries, France has an aging population. French citizens aged 65 years and older are now more than the French aged from 0 to 14 years old. The median age of the population in the country has been increasing since the nineties, while the share of seniors reached almost ** percent of the population in 2013.