This dataset shows the life expectancy at regional level for 2011.

Life expectancy in the EU, which is a reflection of well-being, is among the highest in the world. Of the 50 countries in the world with the highest life expectancy in 2012, 21 were EU Member States, 18 of which had a higher life expectancy than the US. Differences between regions in the EU are marked. Life expectancy at birth is less than 74 in many partsof Bulgaria as well as in Latvia and Lithuania, while overall across the EU it is over 80 years in two out of every three regions. In 17 regions in Spain, France and Italy, it is 83 years or more.

EU-28 = 80.3 . BE, IT, UK: 2010. Source: Eurostat

Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

50-e0diffT.csv

Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019.

51-arriagaT.csv

Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age.

52-sexdiff.csv

Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes.

53-e0diffcodT.csv

Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.

55-vaxe0.csv

Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages <60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE.

54-tab_arriaga.csv

Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.

Air pollution globalization, as a combined effect of atmospheric transport and international trade, can lead to notable transboundary health impacts. Life expectancy reduction attribution analysis of transboundary pollution can reveal the effect of pollution globalization on the lives of individuals. This study coupled five state-of-the-art models to link the regional per capita life expectancy reduction to cross-boundary pollution transport attributed to consumption in other regions. Our results revealed that pollution due to consumption in other regions contributed to a global population-weighted PM2.5 concentration of 9 μg/m3 in 2017, thereby causing 1.03 million premature deaths and reducing the global average life expectancy by 0.23 year (≈84 days). Trade-induced transboundary pollution relocation led to a significant reduction in life expectancy worldwide (from 5 to 155 days per person), and even in the least polluted regions, such as North America, Western Europe, and Russia, a 12–61-day life expectancy reduction could be attributed to consumption in other regions. Our results reveal the individual risks originating from air pollution globalization. To protect human life, all regions and residents worldwide should jointly act together to reduce atmospheric pollution and its globalization as soon as possible.

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. 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 HMD international_updated Jan 2019.xlsx Comprises 20 worksheets, of which 14 contain data. These data are arranged by country and by year. Missing data codes: "" The tab 'contents and sources' provides descriptions of the data source and contents of each sheet. HMD Scotland time trend analysis.xlsx Comprises 5 worksheets, including a combination of data and charts. The sheet 'contents' describes the data source and contents of other sheets. The variables include year, life expectancy, and various measures of change in life expectancy Missing data codes: "" Segmented regression chart.xlsx Comprises 2 worksheets, 'Data' and 'Chart'. Variables within the 'data' worksheet include: Year 4 quarter rolling period ending Female observed mortality rate Female predicted by one-break model Female predicted by two-break model Male observed mortality rate Male predicted by one-break model Male predicted by two-break model Chart breakpoint indicator Missing data codes: (blank space) Summary findings from segmented regression.xlsx Excel workbook containing table 1 of paper 'summary of results of segmented regression by population group and model/test'

In 2024, the average life expectancy for those born in more developed countries was 76 years for men and 82 years for women. On the other hand, the respective numbers for men and women born in the least developed countries were 64 and 69 years. Improved health care has lead to higher life expectancy Life expectancy is the measure of how long a person is expected to live. Life expectancy varies worldwide and involves many factors such as diet, gender, and environment. As medical care has improved over the years, life expectancy has increased worldwide. Introduction to health care such as vaccines has significantly improved the lives of millions of people worldwide. The average worldwide life expectancy at birth has steadily increased since 2007, but dropped during the COVID-19 pandemic in 2020 and 2021. Life expectancy worldwide More developed countries tend to have higher life expectancies, for a multitude of reasons. Health care infrastructure and quality of life tend to be higher in more developed countries, as is access to clean water and food. Africa was the continent that had the lowest life expectancy for both men and women in 2023, while Oceania had the highest for men and Europe and Oceania had the highest for women.

The indicator Healthy Life Years (HLY) at age 65 measures the number of years that a person at age 65 is still expected to live in a healthy condition. HLY is a health expectancy indicator which combines information on mortality and morbidity. The data required are the age-specific prevalence (proportions) of the population in healthy and unhealthy conditions and age-specific mortality information. A healthy condition is defined by the absence of limitations in functioning/disability. The indicator is calculated separately for males and females. The indicator is also called disability-free life expectancy (DFLE). Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

Correlations between conversation style variables and laughter for both Latina and White-European mothers.

DALIA dataset (Campobasso, Italy). DOI: 10.5281/zenodo.15395163

de Francesco M.C., Carranza M.L., Capotorti G., Del Vico E., D’Angeli C., Montaldi A., Paura B., Santoianni L.A., Varricchione M., Stanisci A. (2025).

In the framework of the National Biodiversity Future Centre (NBFC), specifically within the “Urban Biodiversity” working group (Spoke 5), we developed the DALIA relational database, which contains records of tree, shrub, and liana taxa recorded in the Functional Urban Area of Campobasso (Southern Italy). The DALIA database includes 170 species and subspecies (126 native and 44 alien) belonging to 46 taxonomic families (35 natives, 23 aliens). Each taxon, whether native or alien, was classified according with multiple ecological, functional, and biogeographic groups.

The database contains 6 tables described below:

Table “Taxonomy” including the scientific names according to WFO (WFO, 2024) and Flora d'Italia (Portal to the Flora of Italy, 2024) with the relative authors, and the common name in English and Italian languages (Portal to the Flora of Italy, 2024); the taxonomic family (Bartolucci et al. 2024), the classification in natives, archeophytes, neophytes and their status (invasive, casual, naturalized) (Galasso et al. 2024).

Table “Chorology” including the geographic distribution of the native species (Pignatti 1982) and the origin area of the archaeophyte and neophyte species (WFO, 2024).

Table “Traits” including life form and growth form categories according to the Raunkiær system (Pignatti et al. 2017; Raunkiær 1934); the plant growth habits, differentiating plants in tree, shrub, and liana (Diaz et al. 2022); the maximum height reached by the plants (Diaz et al. 2022); the leaf types based on leaf morphology, anatomy, and persistence (Chytry et al. 2024).

Table “Bloom & Dispersion” including flowering periods expressed as bloom months, total flowering length, and seasons (Pignatti et al. 2017); the generative diaspores, the classification of seeds, fruits, and any appendages serving a role in dispersal (fleshy, non-fleshy indehiscent, pappose, winged, unspecialized) (Sádlo et al. 2014); the dispersion modes (Lososová et al. 2023).

Table “Indicators” including the values for the EIVE’s - Ecological Indicator Values for Europe (Dengler et al. 2023); for the Disturbance Indices - Disturbance indicator values for European plants (Midolo et al. 2023); the GRIME values for the CSR strategies in plants (Pierce et al. 2016).

Table “Conservation status” including the possible diagnostic role for Habitat Directive (92/43/EEC) (Habitat Directive 92-43-CEE, 2024); for EUNIS habitats (Chytrý et al., 2020); the IUCN Status and Trend Population for Europe (IUCN, 2024).

The DALIA database reveals a high woody plant diversity for the FUA of Campobasso when compared with other similar studies (Roma-Marzio et al. 2016), with a high percentage of native species (Quaranta et al. 2025). This insight greatly differs from what has been recorded in large cities where aliens in the urban floras make up 40% of the total number of taxa (Pyšek 1998; Ricotta et al. 2009; Lososová et al. 2012).

DALIA is expected to act as a useful pilot tool for Nature-based Solutions (NBS) and environmental restoration actions in cities of Italian and Mediterranean inner territories. It also provides valuable ecological information that can be utilized in urban greenery projects, emphasizing the added value of avoidance of invasive and competitive alien plants while favoring native species found within the EU forest habitats of the nearby Natura 2000 areas (Capotorti et al. 2016; Resemini et al. 2025; EC2023).

References

Bartolucci F, Peruzzi L, Galasso G, Alessandrini A, Ardenghi NMG, Bacchetta G, Banfi E, Barberis G, Bernardo L, Bouvet D, Bovio M, Calvia G, Castello M, Cecchi L, Del Guacchio E, Domina G, Fascetti S, Gallo L, Gottschlich G, Guarino R, Gubellini L, Hofmann N, Iberite M, Jiménez-Mejías P, Longo D, Marchetti D, Martini F, Masina RR, Medagli P, Peccenini S, Prosser F, Roma-Marzio F, Rosati L, Santangelo A, Scoppola A, Selvaggi A, Selvi F, Soldano A, Stinca A, Wagensommer RP, Wilhalm T, Conti F (2024) A second update to the checklist of the vascular flora native to Italy. Plant Biosystems - An International Journal Dealing with All Aspects of Plant Biology 158(2): 219–296. https://doi.org/10.1080/11263504.2024.2320126

Capotorti, G, Del Vico, E, Anzellotti, I, Celesti-Grapow, L (2016). Combining the conservation of biodiversity with the provision of ecosystem services in urban green infrastructure planning: Critical features arising from a case study in the metropolitan area of Rome. Sustainability, 9(1), 10. https://doi.org/10.3390/su9010010

Ceralli D, D’Angeli C, Laureti L (2021) The “Carta della Natura” project: the case study of Molise region. Proceedings of the International Cartographic Association 4: 1–7. https://doi.org/10.5194/ica-proc-4-18-2021

Chytrý M, Řezníčková M, Novotný P, Holubová D, Preislerová Z, Attorre F, Biurrun I, Blažek P, Bonari G, Borovyk D, Čeplová N, Danihelka J, Davydov D, Dřevojan P, Fahs N, Guarino R, Güler B, Hennekens SM, Hrivnák R, Kalníková V, Kalusová V, Kebert T, Knollová I, Knotková K, Koljanin D, Kuzemko A, Loidi J, Lososová Z, Marcenò C, Midolo G, Milanović D, Mucina L, Novák P, von Raab-Straube E, Reczyńska K, Schaminée JHJ, Štěpánková P, Świerkosz K, Těšitel J, Těšitelová T, Tichý L, Vynokurov D, Willner S, Axmanová I (2024) FloraVeg.EU — An online database of European vegetation, habitats and flora. Applied Vegetation Science 27 (3): e12798. https://doi.org/10.1111/avsc.12798

Chytrý M, Tichý L, Hennekens SM, Knollová I, Janssen JAM, …, Schaminée JHJ (2020) EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science 23: 648–675. https://doi.org/10.1111/avsc.12519

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Díaz S, Kattge J, Cornelissen JHC et al. (2022) The global spectrum of plant form and function: enhanced species-level trait dataset. Scientific Data 9: 755. https://doi.org/10.1038/s41597-022-01774-9

Díaz S, Kattge J, Cornelissen JHC et al. (2022) The global spectrum of plant form and function: enhanced species-level trait dataset. Scientific Data 9: 755. https://doi.org/10.1038/s41597-022-01774-9

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This comparison statistic shows the difference in life expectancy of household appliances in 2011 and 2022 in the United States. The life expectancy of all household appliances has either stayed the same or declined in the last decade.

School expectancy corresponds to the expected years of education over a lifetime and has been calculated adding the single-year enrolment rates for all ages. This type of estimate will be accurate if current patterns of enrolment continue in the future. Estimates are based on headcount data. To illustrate the meaning of school expectancy, let us take an example: school expectancy for the age of 10 would be one year if all 10-year-old students (in the year of the data collection) were enrolled. If only 50 % of 10-year-olds were enrolled, school expectancy for the age of 10 would be half a year.

This is an extract of the decennial Public Use Microdata Sample (PUMS) released by the Bureau of the Census. Because the complete PUMS files contain several hundred thousand records, ICPSR has constructed this subset to allow for easier and less costly analysis. The collection of data at ten year increments allows the user to follow various age cohorts through the life-cycle. Data include information on the household and its occupants such as size and value of dwelling, utility costs, number of people in the household, and their relationship to the respondent. More detailed information was collected on the respondent, the head of household, and the spouse, if present. Variables include education, marital status, occupation and income. The stratified sample has unequal sampling rates across strata and requires the use of weights for analyses using more than one stratum. The epsem sample was selected in a second stage from the stratified sample and used compensating sampling rates within each stratum so that the overall probability of selection for each person is equal. The person level weight for use with the stratified sample and the household weight to be used with the epsem sample are included in the data file.Conducted by the United States Department of Commerce, Bureau of the Census. Stratified sample of adults contained in the Public Use Microdata Sample. Approximately 500 records were drawn from each of 28 sex/age/race strata. Additionally, an equal probability (epsem) sample was drawn from the stratified sample. Datasets: DS0: Study-Level Files DS1: United States Microdata Samples Extract File, 1940-1980: Demographics of Aging DS2: Frequencies, 1940-1980 For 1960-1980, all PUMS records for persons 18 and over. For 1940 and 1950, all sample line records.