Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org. This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description_LE_ Sex_Race_Ethnicity.pdf

Effect of suicide rates on life expectancy dataset

Abstract In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy. The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Analysis of ‘Life Expectancy (WHO)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kumarajarshi/life-expectancy-who on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Although there have been lot of studies undertaken in the past on factors affecting life expectancy considering demographic variables, income composition and mortality rates. It was found that affect of immunization and human development index was not taken into account in the past. Also, some of the past research was done considering multiple linear regression based on data set of one year for all the countries. Hence, this gives motivation to resolve both the factors stated previously by formulating a regression model based on mixed effects model and multiple linear regression while considering data from a period of 2000 to 2015 for all the countries. Important immunization like Hepatitis B, Polio and Diphtheria will also be considered. In a nutshell, this study will focus on immunization factors, mortality factors, economic factors, social factors and other health related factors as well. Since the observations this dataset are based on different countries, it will be easier for a country to determine the predicting factor which is contributing to lower value of life expectancy. This will help in suggesting a country which area should be given importance in order to efficiently improve the life expectancy of its population.

Content

The project relies on accuracy of data. The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The data-sets are made available to public for the purpose of health data analysis. The data-set related to life expectancy, health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. Among all categories of health-related factors only those critical factors were chosen which are more representative. It has been observed that in the past 15 years , there has been a huge development in health sector resulting in improvement of human mortality rates especially in the developing nations in comparison to the past 30 years. Therefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. The individual data files have been merged together into a single data-set. On initial visual inspection of the data showed some missing values. As the data-sets were from WHO, we found no evident errors. Missing data was handled in R software by using Missmap command. The result indicated that most of the missing data was for population, Hepatitis B and GDP. The missing data were from less known countries like Vanuatu, Tonga, Togo, Cabo Verde etc. Finding all data for these countries was difficult and hence, it was decided that we exclude these countries from the final model data-set. The final merged file(final dataset) consists of 22 Columns and 2938 rows which meant 20 predicting variables. All predicting variables was then divided into several broad categories:​Immunization related factors, Mortality factors, Economical factors and Social factors.

Acknowledgements

The data was collected from WHO and United Nations website with the help of Deeksha Russell and Duan Wang.

Inspiration

The data-set aims to answer the following key questions: 1. Does various predicting factors which has been chosen initially really affect the Life expectancy? What are the predicting variables actually affecting the life expectancy? 2. Should a country having a lower life expectancy value(<65) increase its healthcare expenditure in order to improve its average lifespan? 3. How does Infant and Adult mortality rates affect life expectancy? 4. Does Life Expectancy has positive or negative correlation with eating habits, lifestyle, exercise, smoking, drinking alcohol etc. 5. What is the impact of schooling on the lifespan of humans? 6. Does Life Expectancy have positive or negative relationship with drinking alcohol? 7. Do densely populated countries tend to have lower life expectancy? 8. What is the impact of Immunization coverage on life Expectancy?

--- Original source retains full ownership of the source dataset ---

Life expectancy at birth is defined as how long, on average, a newborn can expect to live, if current death rates do not change. This dataset can help you gain insights regarding the life expectancy and mortality rate.

description: This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P: EPI OEPHI MATERIALS REFERENCES Life Expectancy Dataset description_LE_ Sex_Race_Ethnicity.pdf; abstract: This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P: EPI OEPHI MATERIALS REFERENCES Life Expectancy Dataset description_LE_ Sex_Race_Ethnicity.pdf

This dataset gives the average life expectancy and corresponding confidence intervals for each Chicago community area for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/qjr3-bm53/files/AAu4x8SCRz_bnQb8SVUyAXdd913TMObSYj6V40cR6p8?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description - LE by community area.pdf

Analysis of ‘Life Expectancy vs GDP, 1950-2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/luxoloshilofunde/life-expectancy-vs-gdp-19502018 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Life expectancy at birth is defined as the average number of years that a newborn could expect to live if he or she were to pass through life subject to the age-specific mortality rates of a given period. The years are from 1950 to 2018.

Content

For regional- and global-level data pre-1950, data from a study by Riley was used, which draws from over 700 sources to estimate life expectancy at birth from 1800 to 2001.

Riley estimated life expectancy before 1800, which he calls "the pre-health transition period". "Health transitions began in different countries in different periods, as early as the 1770s in Denmark and as late as the 1970s in some countries of sub-Saharan Africa". As such, for the sake of consistency, we have assigned the period before the health transition to the year 1770. "The life expectancy values employed are averages of estimates for the period before the beginning of the transitions for countries within that region. ... This period has presumably the weakest basis, the largest margin of error, and the simplest method of deriving an estimate."

For country-level data pre-1950, Clio Infra's dataset was used, compiled by Zijdeman and Ribeira da Silva (2015).

For country-, regional- and global-level data post-1950, data published by the United Nations Population Division was used, since they are updated every year. This is possible because Riley writes that "for 1950-2001, I have drawn life expectancy estimates chiefly from various sources provided by the United Nations, the World Bank’s World Development Indicators, and the Human Mortality Database".

For the Americas from 1950-2015, the population-weighted average of Northern America and Latin America and the Caribbean was taken, using UN Population Division estimates of population size.

Acknowledgements

Life expectancy:

Data publisher's source: https://www.lifetable.de/RileyBib.pdf Data published by: James C. Riley (2005) – Estimates of Regional and Global Life Expectancy, 1800–2001. Issue Population and Development Review. Population and Development Review. Volume 31, Issue 3, pages 537–543, September 2005., Zijdeman, Richard; Ribeira da Silva, Filipa, 2015, "Life Expectancy at Birth (Total)", http://hdl.handle.net/10622/LKYT53, IISH Dataverse, V1, and UN Population Division (2019) Link: https://datasets.socialhistory.org/dataset.xhtml?persistentId=hdl:10622/LKYT53, http://onlinelibrary.wiley.com/doi/10.1111/j.1728-4457.2005.00083.x/epdf, https://population.un.org/wpp/Download/Standard/Population/ Dataset: https://ourworldindata.org/life-expectancy

GDP per capita:

Data publisher's source: The Maddison Project Database is based on the work of many researchers that have produced estimates of economic growth for individual countries. Data published by: Bolt, Jutta and Jan Luiten van Zanden (2020), “Maddison style estimates of the evolution of the world economy. A new 2020 update”. Link: https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020 Dataset: https://ourworldindata.org/life-expectancy

Inspiration

The life expectancy vs GDP per capita analysis.

--- Original source retains full ownership of the source dataset ---

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

Life expectancy is an estimate of how long a person would live, on average.

Life expectancy is affected by many factors such as: • Socioeconomic status, including employment, income, education and economic wellbeing. • The quality of the health system and the ability of people to access it; health behaviors such as tobacco and excessive alcohol consumption, poor nutrition and lack of exercise. • Social factors; genetic factors; and environmental factors including overcrowded housing, lack of clean drinking water and adequate sanitation, etc.

With the help of the above-mentioned factors, I tried to analyse t the data and come up with measurable solutions to improve the Life Expectancy.

This data describes the average life expectancy at birth for various nations from 1543-2021 . Data Variable description: The average number of years that a newborn could expect to live, if he or she were to pass through life exposed to the sex- and age-specific death rates prevailing at the time of his or her birth, for a specific year, in a given country, territory, or geographic area. (Definition from the WHO) Data Variable time span: 1543 – 2021 Data published by : United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022, Online Edition; Zijdeman et al. (2015) (via clio-infra.eu); Riley, J. C. (2005). Estimates of Regional and Global Life Expectancy, 1800-2001. Population and Development Review, 31(3), 537–543. http://www.jstor.org/stable/3401478 Link https://population.un.org/wpp/Download/ ; https://clioinfra.eu/Indicators/LifeExpectancyatBirthTotal.html ; https://doi.org/10.1111/j.1728-4457.2005.00083.x;https://ourworldindata.org/health-meta License: Copyright © 2022 by United Nations, made available under a Creative Commons license CC BY 3.0 IGO: http://creativecommons.org/licenses/by/3.0/igo/

This table contains 2394 series, with data for years 1991 -1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 3;Income adequacy quintile 2 ...), Age (14 items: At 25 years; At 30 years; At 35 years; At 40 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Probability of survival; Low 95% confidence interval; life expectancy; High 95% confidence interval; life expectancy ...).

Life expectancy is a summary measure of the all-cause mortality rates in an area in a given period. It shows 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. Figures reflect mortality among those living in an area in the given time period, not the life expectancy of newborn children. That is because both the mortality rates of the area are likely to change in the future, and because many of those born in the area will live elsewhere for at least some part of their lives. Life expectancy is a summary measure of a population's health. It may be influenced by premature mortalities and health inequalities. Data source: Office for Health Improvement and Disparities (ODHI), indicator 90366.

Analysis of ‘🍷 Alcohol vs Life Expectancy’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/alcohol-vs-life-expectancye on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Credit: This dataset was created by Jonathan Ortiz! All credits for the original go to the original author!

About this dataset

Intro

Findings

There is a surprising relationship between alcohol consumption and life expectancy. In fact, the data suggest that life expectancy and alcohol consumption are positively correlated - 1.2 additional years for every 1 liter of alcohol consumed annually. This is, of course, a spurious finding, because the correlation of this relationship is very low - 0.28. This indicates that other factors in those countries where alcohol consumption is comparatively high or low are contributing to differences in life expectancy, and further analysis is warranted.

https://data.world/api/databeats/dataset/alcohol-vs-life-expectancy/file/raw/LifeExpectancy_v_AlcoholConsumption_Plot.jpg" alt="LifeExpectancy_v_AlcoholConsumption_Plot.jpg">

Methods

1. Addressing Missing Values

The original drinks.csv file in the UNCC/DSBA-6100 dataset was missing values for The Bahamas, Denmark, and Macedonia for the wine, spirits, and beer attributes, respectively. Drinks_solution.csv shows these values filled in, for which I used the Mean of the rest of the data column.

Other methods were considered and ruled out:

• Deleting the Bahamas, Denmark, and Macedonia instances altogether - This is a possible route, but the data file itself is just under 200 rows, and there is only one observation for each country. Because the dataset is relatively small by number of instances, removal should be avoided in order to give the model more data to use.
• Imputing missing values with k-Nearest Neighbors - Another possible route, knn impute can yield higher accuracy in certain cases when the dataset is fairly large. However, this particular dataset only contains 3 attributes, all of which seem unrelated to each other. If we had more columns with more data like availability, annual sales, preferences, etc. of the different drinks, it would be possible to predict these values with knn, but this approach should be avoided given the data we have.
• Filling missing values with a MODE - By visualizing the data, it is easy to see that each column is fairly skewed, with many countries reporting 0 in one or more of the servings columns. Using the MODE would fill these missing entries with 0 for all three (beer_servings, spirit_servings, and wine_servings), and upon reviewing the Bahamas, Denmark, and Macedonia more closely, it is apparent that 0 would be a poor choice for the missing values, as all three countries clearly consume alcohol.
• Filling missing values with MEDIAN - Due to the skewness mentioned just above in the MODE section, using a MEDIAN of the whole column would also be a poor choice, as the MEDIAN is pulled down by several countries reporting 0 or 1. A MEDIAN of only the observations reporting 1 or more servings--or another cutoff--could be used, however, and this would be acceptable.

Filling missing values with MEAN - In the case of the drinks dataset, this is the best approach. The MEAN averages for the columns happen to be very close to the actual data from where we sourced this exercise. In addition, the MEAN will not skew the data, which the prior approaches would do.

2. Calculating New Attributes

The original drinks.csv dataset also had an empty data column: total_litres_of_pure_alcohol. This column needed to be calculated in order to do a simple 2D plot and trendline. It would have been possible to instead run a multi-variable regression on the data and therefore skip this step, but this adds an extra layer of complication to understanding the analysis - not to mention the point of the exercise is to go through an example of calculating new attributes (or "feature engineering") using domain knowledge.

The graphic found at the Wikipedia / Standard Drink page shows the following breakdown:

• Beer - 12 fl oz per serving - 5% average ABV
• Wing - 5 fl oz - 12% ABV
• Spirits - 1.5 fl oz - 40% ABV

The conversion factor from fl oz to L is 1 fl oz : 0.0295735 L

Therefore, the following formula was used to compute the empty column:
total_litres_of_pure_alcohol
=
(beer_servings * 12 fl oz per serving * 0.05 ABV + spirit_servings * 1.5 fl oz * 0.4 ABV + wine_servings * 5 fl oz * 0.12 ABV) * 0.0295735 liters per fl oz

3. Joining To External Data

The lifeexpectancy.csv datafile in the https://data.world/uncc-dsba/dsba-6100-fall-2016 dataset contains life expectancy data for each country. The following query will join this data to the cleaned drinks.csv data file:

# Life Expectancy vs Alcohol Consumption

Life Expectancy vs. Alcohol Consumption with countryTable

PREFIX drinks: <http://data.world/databeats/alcohol-vs-life-expectancy/drinks_solution.csv/drinks_solution#>
PREFIX life: <http://data.world/uncc-dsba/dsba-6100-fall-2016/lifeexpectancy.csv/lifeexpectancy#>
PREFIX countries: <http://data.world/databeats/alcohol-vs-life-expectancy/countryTable.csv/countryTable#>

SELECT ?country ?alc ?years
WHERE {
  SERVICE <https://query.data.world/sparql/databeats/alcohol-vs-life-expectancy> {
    ?r1 drinks:total_litres_of_pure_alcohol ?alc .
    ?r1 drinks:country ?country .
    ?r2 countries:drinksCountry ?country .
    ?r2 countries:leCountry ?leCountry .
  }

  SERVICE <https://query.data.world/sparql/uncc-dsba/dsba-6100-fall-2016> {
    ?r3 life:CountryDisplay ?leCountry .
    ?r3 life:GhoCode ?gho_code .
    ?r3 life:Numeric ?years .
    ?r3 life:YearCode ?reporting_year .
    ?r3 life:SexDisplay ?sex .
  }
FILTER ( ?gho_code = "WHOSIS_000001" && ?reporting_year = 2013 && ?sex = "Both sexes" )
}
ORDER BY ?country

4. Plotting

The resulting joined data can then be saved to local disk and imported into any analysis tool like Excel, Numbers, R, etc. to make a simple scatterplot. A trendline and R^2 should be added to determine the relationship between Alcohol Consumption and Life Expectancy (if any).

https://data.world/api/databeats/dataset/alcohol-vs-life-expectancy/file/raw/LifeExpectancy_v_AlcoholConsumption_Plot.jpg" alt="LifeExpectancy_v_AlcoholConsumption_Plot.jpg">

This dataset was created by Jonathan Ortiz and contains around 200 samples along with Beer Servings, Spirit Servings, technical information and other features such as: - Total Litres Of Pure Alcohol - Wine Servings - and more.

How to use this dataset

• Analyze Beer Servings in relation to Spirit Servings
• Study the influence of Total Litres Of Pure Alcohol on Wine Servings
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Jonathan Ortiz

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Colombia CO: Life Expectancy at Birth: Total data was reported at 77.725 Year in 2023. This records an increase from the previous number of 76.508 Year for 2022. Colombia CO: Life Expectancy at Birth: Total data is updated yearly, averaging 68.768 Year from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 77.725 Year in 2023 and a record low of 56.609 Year in 1960. Colombia CO: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.;(1) United Nations Population Division. World Population Prospects: 2024 Revision; or derived from male and female life expectancy at birth from sources such as: (2) Statistical databases and publications from national statistical offices; (3) Eurostat: Demographic Statistics.;Weighted average;

Increase the average life expectancy of Aboriginal South Australians.

Lists the life expectancy at year of birth, by gender, year, and , municipality and municipal district. Life expectancy is the average number of years a hypothetical birth cohort of 10 years ending with the specified year would live if they were subjected to the current mortality conditions throughout the rest of their lives.

Life expectancy is the number of years a person would be expected to live, starting from birth (for life expectancy at birth) or at age 65 (for life expectancy at age 65), on the basis of the mortality statistics for a given observation period. Life expectancy is a widely used indicator of the health of a population. Life expectancy measures quantity rather than quality of life.

This dataset presents the footprint of the average number of years a person is expected to live at birth by sex, assuming that the current age-specific death rates are experienced throughout their life. The data spans the years of 2011-2016 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is based on the Australian Institute of Health and Welfare (AIHW) analysis of life expectancy estimates as provided by the Australian Bureau of Statistics (ABS). Life expectancies at birth were calculated with reference to state/territory and Australian life tables (where appropriate) for a three year period. The disaggregation used for reporting life expectancy at birth is PHN area. These values are provided by the ABS. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Life Expectancy and Potentially Avoidable Deaths 2014-2016 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Life expectancy for 2014-2016 are based on the average number of deaths over three years, 2014-2016, and the estimated resident population (ERP) as at 30 Jun 2015.

This table contains 2754 series, with data for years 2005/2007 - 2012/2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (153 items: Canada; Newfoundland and Labrador; Eastern Regional Integrated Health Authority, Newfoundland and Labrador; Central Regional Integrated Health Authority, Newfoundland and Labrador; ...);  Age group (2 items: At birth; At age 65);  Sex (3 items: Both sexes; Males; Females);  Characteristics (3 items: Life expectancy; Low 95% confidence interval, life expectancy; High 95% confidence interval, life expectancy).

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.