📊 update life expectancy dependencies #3675
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data-diff: ❌ Found differences= Dataset garden/demography/2024-12-02/survivor_percentiles
= Table survivor_percentiles
~ Dim country
- - Removed values: 18381 / 143682 (12.79%)
year sex percentile country
1861 female 30 England and Wales (Civilians)
1932 male 50 England and Wales (Civilians)
1896 total 80 France (Civilians)
1938 male 60 France (Civilians)
2001 female 70 New Zealand (Maori)
~ Dim year
- - Removed values: 18381 / 143682 (12.79%)
country sex percentile year
England and Wales (Civilians) female 30 1861
England and Wales (Civilians) male 50 1932
France (Civilians) total 80 1896
France (Civilians) male 60 1938
New Zealand (Maori) female 70 2001
~ Dim sex
- - Removed values: 18381 / 143682 (12.79%)
country year percentile sex
England and Wales (Civilians) 1861 30 female
England and Wales (Civilians) 1932 50 male
France (Civilians) 1896 80 total
France (Civilians) 1938 60 male
New Zealand (Maori) 2001 70 female
~ Dim percentile
- - Removed values: 18381 / 143682 (12.79%)
country year sex percentile
England and Wales (Civilians) 1861 female 30
England and Wales (Civilians) 1932 male 50
France (Civilians) 1896 total 80
France (Civilians) 1938 male 60
New Zealand (Maori) 2001 female 70
~ Column age (changed data)
- - Removed values: 18381 / 143682 (12.79%)
country year sex percentile age
England and Wales (Civilians) 1861 female 30 27.110001
England and Wales (Civilians) 1932 male 50 67.959999
France (Civilians) 1896 total 80 76.239998
France (Civilians) 1938 male 60 68.800003
New Zealand (Maori) 2001 female 70 82.540001
+ Dataset garden/demography/2024-12-03/broken_limits_le
+ + Table broken_limits_le
+ + Column life_expectancy
+ + Column country_with_max_le
+ Dataset garden/demography/2024-12-03/gini_le
+ + Table gini_le
+ + Column life_expectancy_gini
+ Dataset garden/demography/2024-12-03/life_expectancy
+ + Table life_expectancy
+ + Column life_expectancy
+ + Column life_expectancy_0
+ + Table life_expectancy_only_proj
+ + Column life_expectancy_only_proj
+ + Column life_expectancy_0_only_proj
+ + Table life_expectancy_with_proj
+ + Column life_expectancy_with_proj
+ + Column life_expectancy_0_with_proj
+ Dataset garden/demography/2024-12-03/life_tables
+ + Table diff_ratios
+ + Column life_expectancy_fm_diff
+ + Column life_expectancy_fm_ratio
+ + Column central_death_rate_mf_ratio
+ + Table life_tables
+ + Column central_death_rate
+ + Column probability_of_death
+ + Column average_survival_length
+ + Column number_survivors
+ + Column number_deaths
+ + Column number_person_years_lived
+ + Column number_person_years_remaining
+ + Column life_expectancy
+ + Column probability_of_survival
+ + Column survivorship_ratio
+ Dataset garden/demography/2024-12-03/phi_gender_le
+ + Table phi_gender_le
+ + Column phi
= Dataset garden/hmd/2024-12-01/hmd
= Table life_tables
~ Dim country
- - Removed values: 304437 / 2136227 (14.25%)
year sex age type country
1867 total 100-104 cohort England and Wales (Civilians)
1839 male 87 period France (Civilians)
1904 female 79 period France (Civilians)
1972 female 62 period New Zealand (Maori)
1976 total 26 period New Zealand (Maori)
~ Dim year
- - Removed values: 304437 / 2136227 (14.25%)
country sex age type year
England and Wales (Civilians) total 100-104 cohort 1867
France (Civilians) male 87 period 1839
France (Civilians) female 79 period 1904
New Zealand (Maori) female 62 period 1972
New Zealand (Maori) total 26 period 1976
~ Dim sex
- - Removed values: 304437 / 2136227 (14.25%)
country year age type sex
England and Wales (Civilians) 1867 100-104 cohort total
France (Civilians) 1839 87 period male
France (Civilians) 1904 79 period female
New Zealand (Maori) 1972 62 period female
New Zealand (Maori) 1976 26 period total
~ Dim age
- - Removed values: 304437 / 2136227 (14.25%)
country year sex type age
England and Wales (Civilians) 1867 total cohort 100-104
France (Civilians) 1839 male period 87
France (Civilians) 1904 female period 79
New Zealand (Maori) 1972 female period 62
New Zealand (Maori) 1976 total period 26
~ Dim type
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type
England and Wales (Civilians) 1867 total 100-104 cohort
France (Civilians) 1839 male 87 period
France (Civilians) 1904 female 79 period
New Zealand (Maori) 1972 female 62 period
New Zealand (Maori) 1976 total 26 period
~ Column average_survival_length (changed data)
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type average_survival_length
England and Wales (Civilians) 1867 total 100-104 cohort 1.61
France (Civilians) 1839 male 87 period 0.5
France (Civilians) 1904 female 79 period 0.5
New Zealand (Maori) 1972 female 62 period 0.5
New Zealand (Maori) 1976 total 26 period 0.5
~ Column central_death_rate (changed data)
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type central_death_rate
England and Wales (Civilians) 1867 total 100-104 cohort 533.47998
France (Civilians) 1839 male 87 period 221.800003
France (Civilians) 1904 female 79 period 131.759995
New Zealand (Maori) 1972 female 62 period 38.389999
New Zealand (Maori) 1976 total 26 period 1.29
~ Column life_expectancy (changed data)
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type life_expectancy
England and Wales (Civilians) 1867 total 100-104 cohort 1.86
France (Civilians) 1839 male 87 period 4.06
France (Civilians) 1904 female 79 period 5.12
New Zealand (Maori) 1972 female 62 period 13.19
New Zealand (Maori) 1976 total 26 period 41.73
~ Column number_deaths (changed data)
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type number_deaths
England and Wales (Civilians) 1867 total 100-104 cohort 67
France (Civilians) 1839 male 87 period 459
France (Civilians) 1904 female 79 period 1997
New Zealand (Maori) 1972 female 62 period 2504
New Zealand (Maori) 1976 total 26 period 123
~ Column number_person_years_lived (changed data)
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type number_person_years_lived
England and Wales (Civilians) 1867 total 100-104 cohort 126
France (Civilians) 1839 male 87 period 2071
France (Civilians) 1904 female 79 period 15157
New Zealand (Maori) 1972 female 62 period 65233
New Zealand (Maori) 1976 total 26 period 95711
~ Column number_person_years_remaining (changed data)
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type number_person_years_remaining
England and Wales (Civilians) 1867 total 100-104 cohort 132
France (Civilians) 1839 male 87 period 9342
France (Civilians) 1904 female 79 period 82652
New Zealand (Maori) 1972 female 62 period 876954
New Zealand (Maori) 1976 total 26 period 3996308
~ Column number_survivors (changed data)
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type number_survivors
England and Wales (Civilians) 1867 total 100-104 cohort 71
France (Civilians) 1839 male 87 period 2301
France (Civilians) 1904 female 79 period 16156
New Zealand (Maori) 1972 female 62 period 66486
New Zealand (Maori) 1976 total 26 period 95773
~ Column probability_of_death (changed data)
- - Removed values: 304437 / 2136227 (14.25%)
country year sex age type probability_of_death
England and Wales (Civilians) 1867 total 100-104 cohort 95.020004
France (Civilians) 1839 male 87 period 19.966
France (Civilians) 1904 female 79 period 12.362
New Zealand (Maori) 1972 female 62 period 3.767
New Zealand (Maori) 1976 total 26 period 0.129
= Table exposures
~ Dim country
- - Removed values: 428064 / 3271455 (13.08%)
year sex age type country
1867 female 30 cohort England and Wales (Civilians)
1899 male 41 cohort France (Civilians)
2018 total 39 period France (Civilians)
2020 total 62 period France (Civilians)
1987 female 75 period New Zealand (Maori)
~ Dim year
- - Removed values: 428064 / 3271455 (13.08%)
country sex age type year
England and Wales (Civilians) female 30 cohort 1867
France (Civilians) male 41 cohort 1899
France (Civilians) total 39 period 2018
France (Civilians) total 62 period 2020
New Zealand (Maori) female 75 period 1987
~ Dim sex
- - Removed values: 428064 / 3271455 (13.08%)
country year age type sex
England and Wales (Civilians) 1867 30 cohort female
France (Civilians) 1899 41 cohort male
France (Civilians) 2018 39 period total
France (Civilians) 2020 62 period total
New Zealand (Maori) 1987 75 period female
~ Dim age
- - Removed values: 428064 / 3271455 (13.08%)
country year sex type age
England and Wales (Civilians) 1867 female cohort 30
France (Civilians) 1899 male cohort 41
France (Civilians) 2018 total period 39
France (Civilians) 2020 total period 62
New Zealand (Maori) 1987 female period 75
~ Dim type
- - Removed values: 428064 / 3271455 (13.08%)
country year sex age type
England and Wales (Civilians) 1867 female 30 cohort
France (Civilians) 1899 male 41 cohort
France (Civilians) 2018 total 39 period
France (Civilians) 2020 total 62 period
New Zealand (Maori) 1987 female 75 period
~ Column exposure (changed data)
- - Removed values: 428064 / 3271455 (13.08%)
country year sex age type exposure
England and Wales (Civilians) 1867 female 30 cohort 251530.25
France (Civilians) 1899 male 41 cohort 272527.78125
France (Civilians) 2018 total 39 period 793355.375
France (Civilians) 2020 total 62 period 797636.0625
New Zealand (Maori) 1987 female 75 period 184.179993
= Table births
~ Dim country
- - Removed values: 1398 / 14763 (9.47%)
year sex country
1934 female England and Wales (Civilians)
1943 total England and Wales (Civilians)
2003 female England and Wales (Civilians)
1915 female France (Civilians)
2005 male New Zealand (Non-Maori)
~ Dim year
- - Removed values: 1398 / 14763 (9.47%)
country sex year
England and Wales (Civilians) female 1934
England and Wales (Civilians) total 1943
England and Wales (Civilians) female 2003
France (Civilians) female 1915
New Zealand (Non-Maori) male 2005
~ Dim sex
- - Removed values: 1398 / 14763 (9.47%)
country year sex
England and Wales (Civilians) 1934 female
England and Wales (Civilians) 1943 total
England and Wales (Civilians) 2003 female
France (Civilians) 1915 female
New Zealand (Non-Maori) 2005 male
~ Column birth_rate (changed data)
- - Removed values: 1398 / 14763 (9.47%)
country year sex birth_rate
England and Wales (Civilians) 1934 female 6.959682
England and Wales (Civilians) 1943 total 9.072053
England and Wales (Civilians) 2003 female 5.656828
France (Civilians) 1915 female 5.628813
New Zealand (Non-Maori) 2005 male 6.098083
~ Column births (changed data)
- - Removed values: 1398 / 14763 (9.47%)
country year sex births
England and Wales (Civilians) 1934 female 290768
England and Wales (Civilians) 1943 total 684334
England and Wales (Civilians) 2003 female 303041
France (Civilians) 1915 female 190141
New Zealand (Non-Maori) 2005 male 20756
= Table diff_ratios
~ Dim country
- - Removed values: 101212 / 708943 (14.28%)
year age type country
1871 76 period England and Wales (Civilians)
1906 18 period England and Wales (Civilians)
1857 93 period France (Civilians)
1920 17 cohort France (Civilians)
1924 70-74 period France (Civilians)
~ Dim year
- - Removed values: 101212 / 708943 (14.28%)
country age type year
England and Wales (Civilians) 76 period 1871
England and Wales (Civilians) 18 period 1906
France (Civilians) 93 period 1857
France (Civilians) 17 cohort 1920
France (Civilians) 70-74 period 1924
~ Dim age
- - Removed values: 101212 / 708943 (14.28%)
country year type age
England and Wales (Civilians) 1871 period 76
England and Wales (Civilians) 1906 period 18
France (Civilians) 1857 period 93
France (Civilians) 1920 cohort 17
France (Civilians) 1924 period 70-74
~ Dim type
- - Removed values: 101212 / 708943 (14.28%)
country year age type
England and Wales (Civilians) 1871 76 period
England and Wales (Civilians) 1906 18 period
France (Civilians) 1857 93 period
France (Civilians) 1920 17 cohort
France (Civilians) 1924 70-74 period
~ Column central_death_rate_mf_ratio (changed data)
- - Removed values: 101212 / 708943 (14.28%)
country year age type central_death_rate_mf_ratio
England and Wales (Civilians) 1871 76 period 1.076823
England and Wales (Civilians) 1906 18 period 1.187919
France (Civilians) 1857 93 period 1.005175
France (Civilians) 1920 17 cohort 1.087301
France (Civilians) 1924 70-74 period 1.282162
~ Column life_expectancy_fm_diff (changed data)
- - Removed values: 101212 / 708943 (14.28%)
country year age type life_expectancy_fm_diff
England and Wales (Civilians) 1871 76 period 0.47
England and Wales (Civilians) 1906 18 period 2.859997
France (Civilians) 1857 93 period 0.03
France (Civilians) 1920 17 cohort 8.510002
France (Civilians) 1924 70-74 period 1.19
~ Column life_expectancy_fm_ratio (changed metadata, changed data)
- - {}
+ + title: Life expectancy ratio (f/m)
+ + description_short: The ratio of the << type >> life expectancy (females to males) at age << age >>.
+ + description_key:
+ + - Higher values indicate longer life expectancy among females than males.
+ + - |-
+ + <%- if type == "period" -%>
+ + Period life expectancy is a metric that summarizes death rates across all age groups in one particular year.
+ + <%- else -%>
+ + Cohort life expectancy is the average lifespan of a group of people, usually a birth cohort – people born in the same year.
+ + <%- endif -%>
+ + - |-
+ + <%- if type == "period" -%>
+ + <%- if age == '0' -%>
+ + For a given year, it represents the average lifespan for a hypothetical group of people, if they experienced the same age-specific death rates throughout their whole lives as the age-specific death rates seen in that particular year.
+ + <%- else -%>
+ + For a given year, it represents the remaining average lifespan for a hypothetical group of people, if they experienced the same age-specific death rates throughout the rest of their lives as the age-specific death rates seen in that particular year.
+ + <%- endif -%>
+ + <%- else -%>
+ + <%- if age == '0' -%>
+ + It is calculated by tracking individuals from that cohort throughout their lives until death, and calculating their average lifespan.
+ + <%- else -%>
+ + It is calculated by tracking individuals from that cohort throughout the rest of their lives until death, and calculating their average remaining lifespan.
+ + <%- endif -%>
+ + <%- endif -%>
+ + origins:
+ + - producer: Human Mortality Database
+ + title: Human Mortality Database
+ + description: |-
+ + The Human Mortality Database (HMD) contains original calculations of all-cause death rates and life tables for national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources.
+ +
+ +
+ + # Scope and basic principles
+ +
+ + The database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included here are relatively wealthy and for the most part highly industrialized.
+ +
+ + The main goal of the Human Mortality Database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. As much as possible, the authors of the database have followed four guiding principles: comparability, flexibility, accessibility, reproducibility.
+ +
+ +
+ + # Computing death rates and life tables
+ +
+ + Their process for computing mortality rates and life tables can be described in terms of six steps, corresponding to six data types that are available from the HMD. Here is an overview of the process:
+ +
+ + 1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
+ + 2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
+ + 3. Population size. Annual estimates of population size on January 1st are either obtained from another source or are derived from census data plus birth and death counts.
+ + 4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
+ + 5. Death rates. Death rates are always a ratio of the death count for a given age-time interval divided by an estimate of the exposure-to-risk in the same interval.
+ + 6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.
+ +
+ +
+ + # Corrections to the data
+ +
+ + The data presented here have been corrected for gross errors (e.g., a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected). However, the authors have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events).
+ +
+ + Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, the authors did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.
+ +
+ +
+ + # Age misreporting
+ +
+ + Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, there is evidence of both age heaping (overreporting ages ending in "0" or "5") and age exaggeration in these data.
+ +
+ + In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases, it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement.
+ +
+ + Age exaggeration, on the other hand, is a more insidious problem. The authors' approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, the authors derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.
+ +
+ +
+ + # Uniform set of procedures
+ +
+ + A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that the authors have not introduced biases by the authors' own manipulations. The desire of the authors for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). The authors' general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, they compute death rates and life tables in a variety of age-time configurations.
+ +
+ + It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, the authors' uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what they considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because the authors have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.
+ +
+ + Although the authors adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to an individual researcher, who takes responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks the authors' data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!
+ + citation_full: |-
+ + HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
+ +
+ + See also the methods protocol:
+ + Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). [Available online](https://www.mortality.org/File/GetDocument/Public/Docs/MethodsProtocolV6.pdf) (needs log in to mortality.org).
+ + attribution_short: HMD
+ + url_main: https://www.mortality.org/Data/ZippedDataFiles
+ + date_accessed: '2024-11-27'
+ + date_published: '2024-11-13'
+ + license:
+ + name: CC BY 4.0
+ + url: https://www.mortality.org/Data/UserAgreement
+ + unit: ''
+ + short_unit: ''
+ + display:
+ + numDecimalPlaces: 1
+ + name: Life expectancy (female-to-male ratio) at << 'birth' if (age == '0') else age >>, << type >>
+ + processing_level: major
+ + presentation:
+ + title_public: Life expectancy at << age if age != '0' else 'birth'>>
+ + title_variant: female-to-male ratio, << type >> tables
+ + attribution_short: HMD
+ + topic_tags:
+ + - Life Expectancy
+ + - Gender Ratio
- - Removed values: 101212 / 708943 (14.28%)
country year age type life_expectancy_fm_ratio
England and Wales (Civilians) 1871 76 period NaN
England and Wales (Civilians) 1906 18 period NaN
France (Civilians) 1857 93 period NaN
France (Civilians) 1920 17 cohort NaN
France (Civilians) 1924 70-74 period NaN
~ Changed values: 706787 / 708943 (99.70%)
country year age type life_expectancy_fm_ratio - life_expectancy_fm_ratio +
Denmark 1896 79 period NaN 1.103512
Luxembourg 2015 15-19 period NaN 1.06461
Sweden 1776 25 cohort NaN 1.094012
Switzerland 1948 103 period NaN 1.059603
Ukraine 1975 55-59 period NaN 1.250394
~ Column life_expectancy_mf_ratio (changed metadata, changed data)
+ + {}
- - title: Life expectancy ratio (f/m)
- - description_short: The ratio of the << type >> life expectancy (males to females) at age << age >>.
- - description_key:
- - - Higher values indicate longer life expectancy among females than males.
- - - |-
- - <%- if type == "period" -%>
- - Period life expectancy is a metric that summarizes death rates across all age groups in one particular year.
- - <%- else -%>
- - Cohort life expectancy is the average lifespan of a group of people, usually a birth cohort – people born in the same year.
- - <%- endif -%>
- - - |-
- - <%- if type == "period" -%>
- - <%- if age == '0' -%>
- - For a given year, it represents the average lifespan for a hypothetical group of people, if they experienced the same age-specific death rates throughout their whole lives as the age-specific death rates seen in that particular year.
- - <%- else -%>
- - For a given year, it represents the remaining average lifespan for a hypothetical group of people, if they experienced the same age-specific death rates throughout the rest of their lives as the age-specific death rates seen in that particular year.
- - <%- endif -%>
- - <%- else -%>
- - <%- if age == '0' -%>
- - It is calculated by tracking individuals from that cohort throughout their lives until death, and calculating their average lifespan.
- - <%- else -%>
- - It is calculated by tracking individuals from that cohort throughout the rest of their lives until death, and calculating their average remaining lifespan.
- - <%- endif -%>
- - <%- endif -%>
- - origins:
- - - producer: Human Mortality Database
- - title: Human Mortality Database
- - description: |-
- - The Human Mortality Database (HMD) contains original calculations of all-cause death rates and life tables for national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources.
- -
- -
- - # Scope and basic principles
- -
- - The database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included here are relatively wealthy and for the most part highly industrialized.
- -
- - The main goal of the Human Mortality Database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. As much as possible, the authors of the database have followed four guiding principles: comparability, flexibility, accessibility, reproducibility.
- -
- -
- - # Computing death rates and life tables
- -
- - Their process for computing mortality rates and life tables can be described in terms of six steps, corresponding to six data types that are available from the HMD. Here is an overview of the process:
- -
- - 1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
- - 2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
- - 3. Population size. Annual estimates of population size on January 1st are either obtained from another source or are derived from census data plus birth and death counts.
- - 4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
- - 5. Death rates. Death rates are always a ratio of the death count for a given age-time interval divided by an estimate of the exposure-to-risk in the same interval.
- - 6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.
- -
- -
- - # Corrections to the data
- -
- - The data presented here have been corrected for gross errors (e.g., a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected). However, the authors have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events).
- -
- - Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, the authors did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.
- -
- -
- - # Age misreporting
- -
- - Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, there is evidence of both age heaping (overreporting ages ending in "0" or "5") and age exaggeration in these data.
- -
- - In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases, it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement.
- -
- - Age exaggeration, on the other hand, is a more insidious problem. The authors' approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, the authors derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.
- -
- -
- - # Uniform set of procedures
- -
- - A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that the authors have not introduced biases by the authors' own manipulations. The desire of the authors for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). The authors' general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, they compute death rates and life tables in a variety of age-time configurations.
- -
- - It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, the authors' uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what they considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because the authors have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.
- -
- - Although the authors adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to an individual researcher, who takes responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks the authors' data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!
- - citation_full: |-
- - HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
- -
- - See also the methods protocol:
- - Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). [Available online](https://www.mortality.org/File/GetDocument/Public/Docs/MethodsProtocolV6.pdf) (needs log in to mortality.org).
- - attribution_short: HMD
- - url_main: https://www.mortality.org/Data/ZippedDataFiles
- - date_accessed: '2024-11-27'
- - date_published: '2024-11-13'
- - license:
- - name: CC BY 4.0
- - url: https://www.mortality.org/Data/UserAgreement
- - unit: ''
- - short_unit: ''
- - display:
- - numDecimalPlaces: 1
- - name: Life expectancy (female-to-male ratio) at << 'birth' if (age == '0') else age >>, << type >>
- - processing_level: major
- - presentation:
- - title_public: Life expectancy at << age if age != '0' else 'birth'>>
- - title_variant: female-to-male ratio, << type >> tables
- - attribution_short: HMD
- - topic_tags:
- - - Life Expectancy
- - - Gender Ratio
- - Removed values: 101212 / 708943 (14.28%)
country year age type life_expectancy_mf_ratio
England and Wales (Civilians) 1871 76 period 0.927692
England and Wales (Civilians) 1906 18 period 0.939916
France (Civilians) 1857 93 period 0.988372
France (Civilians) 1920 17 cohort 0.863731
France (Civilians) 1924 70-74 period 0.874207
~ Changed values: 706806 / 708943 (99.70%)
country year age type life_expectancy_mf_ratio - life_expectancy_mf_ratio +
Czechia 2017 47 period 0.85834 NaN
Denmark 1912 67 cohort 0.768724 NaN
Italy 1965 62 period 0.842524 NaN
Spain 1949 31 period 0.894974 NaN
United Kingdom 1976 50-54 period 0.817639 NaN
= Table deaths
~ Dim country
- - Removed values: 222243 / 1739241 (12.78%)
year sex age country
1992 male 6 England and Wales (Civilians)
1942 total 79 France (Civilians)
1975 male 90 New Zealand (Maori)
1987 male 102 New Zealand (Maori)
1990 male 57 New Zealand (Maori)
~ Dim year
- - Removed values: 222243 / 1739241 (12.78%)
country sex age year
England and Wales (Civilians) male 6 1992
France (Civilians) total 79 1942
New Zealand (Maori) male 90 1975
New Zealand (Maori) male 102 1987
New Zealand (Maori) male 57 1990
~ Dim sex
- - Removed values: 222243 / 1739241 (12.78%)
country year age sex
England and Wales (Civilians) 1992 6 male
France (Civilians) 1942 79 total
New Zealand (Maori) 1975 90 male
New Zealand (Maori) 1987 102 male
New Zealand (Maori) 1990 57 male
~ Dim age
- - Removed values: 222243 / 1739241 (12.78%)
country year sex age
England and Wales (Civilians) 1992 male 6
France (Civilians) 1942 total 79
New Zealand (Maori) 1975 male 90
New Zealand (Maori) 1987 male 102
New Zealand (Maori) 1990 male 57
~ Column deaths (changed data)
- - Removed values: 222243 / 1739241 (12.78%)
country year sex age deaths
England and Wales (Civilians) 1992 male 6 60.0
France (Civilians) 1942 total 79 17490.279297
New Zealand (Maori) 1975 male 90 0.0
New Zealand (Maori) 1987 male 102 0.0
New Zealand (Maori) 1990 male 57 23.5
= Table population
~ Dim country
- - Removed values: 223839 / 1757595 (12.74%)
year sex age country
1856 female 25 England and Wales (Civilians)
1856 female 81 France (Civilians)
1866 total 82 France (Civilians)
1961 male 85 France (Civilians)
2021 male 32 France (Civilians)
~ Dim year
- - Removed values: 223839 / 1757595 (12.74%)
country sex age year
England and Wales (Civilians) female 25 1856
France (Civilians) female 81 1856
France (Civilians) total 82 1866
France (Civilians) male 85 1961
France (Civilians) male 32 2021
~ Dim sex
- - Removed values: 223839 / 1757595 (12.74%)
country year age sex
England and Wales (Civilians) 1856 25 female
France (Civilians) 1856 81 female
France (Civilians) 1866 82 total
France (Civilians) 1961 85 male
France (Civilians) 2021 32 male
~ Dim age
- - Removed values: 223839 / 1757595 (12.74%)
country year sex age
England and Wales (Civilians) 1856 female 25
France (Civilians) 1856 female 81
France (Civilians) 1866 total 82
France (Civilians) 1961 male 85
France (Civilians) 2021 male 32
~ Column population (changed data)
- - Removed values: 223839 / 1757595 (12.74%)
country year sex age population
England and Wales (Civilians) 1856 female 25 165890.8125
France (Civilians) 1856 female 81 24078.210938
France (Civilians) 1866 total 82 40966.199219
France (Civilians) 1961 male 85 23960.0
France (Civilians) 2021 male 32 392991.0
+ Dataset garden/un/2024-12-02/un_wpp_lt
+ + Table un_wpp_lt
+ + Column central_death_rate
+ + Column probability_of_death
+ + Column probability_of_survival
+ + Column number_survivors
+ + Column number_deaths
+ + Column number_person_years_lived
+ + Column survivorship_ratio
+ + Column number_person_years_remaining
+ + Column life_expectancy
+ + Column average_survival_length
Legend: +New ~Modified -Removed =Identical Details
Hint: Run this locally with etl diff REMOTE data/ --include yourdataset --verbose --snippetAutomatically updated datasets matching weekly_wildfires|excess_mortality|covid|fluid|flunet|country_profile|garden/ihme_gbd/2019/gbd_risk are not included Edited: 2024-12-03 16:23:19 UTC |
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Continue work from #3642.
Tracking: https://github.com/owid/owid-issues/issues/1693
un_wp_ltlife_tables(grapher)broken_limitsphi_gender_legini_le