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Regional Life Expectancy in Western Europe

Source: R/regional.R
regional_life_expectancy.Rd

Life expectancy at birth by NUTS2 region for Western European countries, based on Eurostat data and the methodology from Bonnet et al. (2026).

Usage

regional_life_expectancy(
  country = NULL,
  year = NULL,
  sex = NULL,
  classification = NULL
)

Arguments

country

Character vector. Filter to specific countries using ISO 2-letter codes (e.g., "FR", "DE", "ES"). Default NULL returns all countries.

year

Integer or vector. Filter to specific years. Default NULL returns all years (1992-2023).

sex

Character. Filter by sex: "Male", "Female", or "Total". Default NULL returns all.

classification

Character. Filter by region classification: "vanguard", "average", or "laggard". Default NULL returns all.

Value

A tibble with columns:

region_code

NUTS2 region code (e.g., "FR10" for Île-de-France)

region_name

Human-readable region name

country_code

ISO 2-letter country code

year

Data year (1992-2023)

sex

Sex category: "Male", "Female", or "Total"

life_expectancy

Life expectancy at birth in years

microlives_vs_eu_avg

Daily microlives difference vs EU average

classification

"vanguard", "average", or "laggard" based on 2019 trends

source_url

DOI link to source publication

Details

Data Structure: Aggregated Population Statistics

Each row represents one region-year-sex combination, NOT individual survey responses. For example, a dataset with 450 regions × 28 years × 3 sex categories = 37,800 rows of aggregated statistics.

region_codeyearsexlife_expectancyMeaning
FR102019Male82.5Avg LE for all males in Île-de-France in 2019
FR102019Female87.1Avg LE for all females in Île-de-France in 2019
FR102019Total84.8Avg LE for entire population of Île-de-France in 2019

The underlying Eurostat data represents ~400 million people across Western Europe. Life expectancy is calculated from official death registrations and census population counts—not a sample survey.

Data Source

Primary data from Eurostat dataset demo_r_mlifexp. Regional classifications based on Bonnet et al. (2026) methodology identifying:

  • Vanguard regions: Top 20% life expectancy with sustained gains (≥1.5 months/year)

  • Laggard regions: Bottom 20% life expectancy or stagnant gains (<0.5 months/year)

  • Average regions: All others

Microlives Interpretation

The microlives_vs_eu_avg column converts life expectancy differences to daily microlives using the approximation: 1 year LE difference ≈ 1.2 microlives/day (assuming 40 years remaining life expectancy).

Example: A region with +2 years above EU average = +2.4 microlives/day, equivalent to the benefit of 20 minutes daily exercise.

Ecological Fallacy Warning

IMPORTANT: Regional life expectancy reflects population averages, NOT individual-level causation. High life expectancy in "vanguard" regions results from multiple factors including:

  • Healthcare system quality and access

  • Socioeconomic composition (income, education)

  • Selection effects (healthy/wealthy people moving to certain regions

  • Historical and cultural factors

Moving to a high-LE region does NOT guarantee increased personal longevity.

References

Bonnet F, et al. (2026). "Potential and challenges for sustainable progress in human longevity." Nature Communications 17, 996. doi:10.1038/s41467-026-68828-z

Eurostat (2024). Life expectancy by age, sex and NUTS 2 region (demo_r_mlifexp). https://ec.europa.eu/eurostat/databrowser/product/view/demo_r_mlifexp

See also

demographic_factors(), chronic_risks()

Other regional: laggard_regions(), regional_mortality_multiplier(), vanguard_regions()

Examples

# All data
regional_life_expectancy()
#> # A tibble: 17,221 × 9
#>    region_code region_name country_code  year sex    life_expectancy
#>    <chr>       <chr>       <chr>        <int> <chr>            <dbl>
#>  1 AT11        Burgenland  AT            1992 Female            79  
#>  2 AT11        Burgenland  AT            1992 Male              71.8
#>  3 AT11        Burgenland  AT            1992 Total             75.5
#>  4 AT11        Burgenland  AT            1993 Female            79.6
#>  5 AT11        Burgenland  AT            1993 Male              72.7
#>  6 AT11        Burgenland  AT            1993 Total             76.2
#>  7 AT11        Burgenland  AT            1994 Female            80  
#>  8 AT11        Burgenland  AT            1994 Male              72.5
#>  9 AT11        Burgenland  AT            1994 Total             76.3
#> 10 AT11        Burgenland  AT            1996 Female            79.7
#> # ℹ 17,211 more rows
#> # ℹ 3 more variables: microlives_vs_eu_avg <dbl>, classification <chr>,
#> #   source_url <chr>

# French regions in 2019
regional_life_expectancy(country = "FR", year = 2019)
#> # A tibble: 78 × 9
#>    region_code region_name           country_code  year sex    life_expectancy
#>    <chr>       <chr>                 <chr>        <int> <chr>            <dbl>
#>  1 FR10        Ile de France         FR            2019 Female            87.1
#>  2 FR10        Ile de France         FR            2019 Male              81.8
#>  3 FR10        Ile de France         FR            2019 Total             84.6
#>  4 FRB0        Centre — Val de Loire FR            2019 Female            85.7
#>  5 FRB0        Centre — Val de Loire FR            2019 Male              79.6
#>  6 FRB0        Centre — Val de Loire FR            2019 Total             82.7
#>  7 FRC1        Bourgogne             FR            2019 Female            85.6
#>  8 FRC1        Bourgogne             FR            2019 Male              79.3
#>  9 FRC1        Bourgogne             FR            2019 Total             82.4
#> 10 FRC2        Franche-Comté         FR            2019 Female            85.9
#> # ℹ 68 more rows
#> # ℹ 3 more variables: microlives_vs_eu_avg <dbl>, classification <chr>,
#> #   source_url <chr>

# Compare vanguard vs laggard regions
regional_life_expectancy(year = 2019, sex = "Total") |>
  dplyr::group_by(classification) |>
  dplyr::summarise(mean_le = mean(life_expectancy))
#> # A tibble: 3 × 2
#>   classification mean_le
#>   <chr>            <dbl>
#> 1 average           82.8
#> 2 laggard           81.7
#> 3 vanguard          84.3

# Top 10 regions by life expectancy (2019, Total)
regional_life_expectancy(year = 2019, sex = "Total") |>
  dplyr::slice_max(life_expectancy, n = 10)
#> # A tibble: 10 × 9
#>    region_code region_name              country_code  year sex   life_expectancy
#>    <chr>       <chr>                    <chr>        <int> <chr>           <dbl>
#>  1 ES30        Comunidad de Madrid      ES            2019 Total            85.8
#>  2 CH07        Ticino                   CH            2019 Total            85  
#>  3 ES22        Comunidad Foral de Nava… ES            2019 Total            85  
#>  4 ITH2        Provincia Autonoma di T… IT            2019 Total            84.9
#>  5 ES41        Castilla y León          ES            2019 Total            84.7
#>  6 CH01        Région lémanique         CH            2019 Total            84.6
#>  7 FR10        Ile de France            FR            2019 Total            84.6
#>  8 ES21        País Vasco               ES            2019 Total            84.5
#>  9 ITH1        Provincia Autonoma di B… IT            2019 Total            84.5
#> 10 ITI2        Umbria                   IT            2019 Total            84.5
#> # ℹ 3 more variables: microlives_vs_eu_avg <dbl>, classification <chr>,
#> #   source_url <chr>

# Microlives advantage of Catalonia vs EU average
regional_life_expectancy(country = "ES", year = 2019, sex = "Total") |>
  dplyr::filter(grepl("Catalonia", region_name))
#> # A tibble: 0 × 9
#> # ℹ 9 variables: region_code <chr>, region_name <chr>, country_code <chr>,
#> #   year <int>, sex <chr>, life_expectancy <dbl>, microlives_vs_eu_avg <dbl>,
#> #   classification <chr>, source_url <chr>