current status and on-going developments · the human mortality database: current status and...
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The Human Mortality Database:Current status and
on-going developments
Magali Barbieri Dmitri JdanovUniv. California, Berkeley Max Planck Institute for
INED, Paris Demographic Research, Rostock
Acknowledgement: this presentation is based on the work conducted by manymembers of the HMD team over the years, at both the University of California,
Berkeley, and the Max Planck Institute for Demographic Research (MPIDR), Rostock.
House of Finance Days, Université Paris-Dauphine, HMD users meeting, March 8, 2019
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Background
• Goal of the HMD:To provide detailed high-quality mortality and population data free of charge to all persons interested in the history of human longevity
• 50,000+ registered users :Academics (5,000 publications)StudentsActuariesPolicy analystsJournalistsOther corporations (pension funds, banks, etc…)
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What is in the HMD?• Detailed historical data and supporting
documentation for 40 national populations:
– Death counts and estimated population exposures (person-years lived) at the finest detail possible
– Original estimates of age-specific death rates and life tables in various formats (age x time)
• Computed using various forms of input data:
– Death counts from national statistical offices
– Census counts
– Birth counts
– Official population estimates3
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Additional information in
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HMD funders and sponsors
Grants and donations:
Support provided by the U.S. National Institute on Aging (grants R01-AG011552 and R01-AG040245), the U.K. Institute and
Faculty of Actuaries, the Canadian Institute of Actuaries, the Dutch Royal Actuarial Association, AXA, Hannover-Re, Milliman-
France, RGA, SCOR and the Society of Actuaries.
Disclaimer: The author is solely responsible for the content of this presentation, which does not necessarily represent the official views of the
National Institutes of Health and other sponsors.
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Who is responsible for the HMD?
Two teams of researchers:
• Max Plank Institute for Demographic Research (in Rostock, Germany)led by Vladimir Shkolnikov, Director
• UC Berkeley (Dept of Demography)led by Magali Barbieri, Associate Director(previously John Wimoth, Founding Director)
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John R. WilmothFounding Director,
UCB in 2000, now UN
Vladimir M. Shkolnikov Director, MPIDR
Magali BarbieriAssociate Director,
Head of the UCB Team, UCB&INED
Dmitry Jdanov Head of the MPIDR
Team, MPIDR
Domantas Jasilionis
Sebastian Kluesener
Pavel Grigoriev
EvgenyAndreev
Eva KibeleSigrid Gellers
Rembrandt Scholz
Max Planck Team(members present and some former)
Berkeley Team(members present and some former)
Carl Boe
Dana Glei
Tim Riffe
Celeste Winant MonicaAlexanderLisa Yang
Gabriel Borges
Vladimir Canudas-Romo
Kirill Andreev
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HMD Project Staff (March 2019)
• Directors (1 + 1 = 2)
• Country specialists (4 + 5 = 9)
• Administrative assistants (2)
• Others providing technical support (4 + 1 = 5)
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Guiding principles• Comparability
– Over time (from 1751 to 2017)– Across countries (40 mostly high-income)
• Accessibility– free and easy access to data and metadata
• Flexibility – Data files in multiple formats
• Reproducibility– Access to all initial (input) data– Full documentation– HMD scripts are freely available
• Quality control– Standardized rigorous data quality checks for regular updates – Intensive data check procedures and research work for new countries– Work with external experts
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Core activities:HMD country updates (1)
• Update of all HMD countries on rotating basis
• Priority countries:– The US
– The UK and components
– Germany
– Japan
– France
– Russia
– Sweden
– Canada
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Core activities:HMD country updates (2)
• Steps involved in country updates:1. Collect data (births, deaths, and populations from NSOs
– publicly available or customized tables)2. Format as standard input files3. Prepare cocktail script (from a palette of standard HMD
computer routines)4. Run script and adjust if needs be5. Check internal and external consistency of output
(automatic diagnostic charts and other standard verifications) => exchange with in-country experts
6. Update all documentation files (internal and public)7. Submit to HMD Directors for verification8. Publish on HMD website=> 1 to 3 weeks per country(sometimes more if particular issues arise)
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Core activities:Investigate new countries
• Recently added countries:– Greece– Croatia– South Korea
• Other countries investigated– Costa Rica– Moldova
• In progress / plans– Serbia– Romania– EU28– Hong Kong
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Core activities:Improve the HMD methods
• Motivation: increase accuracy of mortalityestimates
• Current Methods Protocol = Version 6 (Dec. 2017 => all countries updated to this new version in 2018)
• Version 5: work in progress
– New inter-censal method
– Old age mortality
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Why HMD?
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How HMD is used by actuaries
Three major applications
1. Standard for relational models (to link client pools to national population)
2. Analyses of variability in risks over time and across populations
3. Mortality improvement models (mainly for model development and experimentation)
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An example: life expectancy in MoldovaAll correct figures are highlighted in yellow
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Numerator-denominator bias: an example of Moldova
Source: Penina, Jdanov, Grigoriev (2015)
* Since 1998 official population counts do not include Transnistriaregion
The problem: systematic bias (deaths and births refer to the de facto population, (.e. occurred within the country, while population estimates also include long-term emigrants - Moldavian citizens living abroad) leads to an under-estimation of mortality and fertility
The solution: population estimates were corrected using data on border crossing and additional data collected at the census 2004
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Censuses and assessment of the population denominator
Data challenges
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Bulgaria: correction of population data
The standard HMD inter-censal method is not applicable to the period 1985-1992 because of an irregular pattern of out-migration. In 1985-8, international migration was very restricted in Bulgaria. After the collapse of communism in 1989 - mass emigration (mostly of the Turkish minority) over the next several years.
HMD Solution: official population estimates were used for 1985-8, but new population estimates were calculated for the latter period. The year 1988 was treated as a “pseudo-census point” as the beginning of the inter-censal interval.
1985
(census year)
2001
census year
1984
2000
1992
(census year)
1991
3500000
3700000
3900000
4100000
4300000
4500000
4700000
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
MALES
FEMALES
3500000
3700000
3900000
4100000
4300000
4500000
4700000
1980 1985 1990 1995 2000
Females
Males
Trends in the total number of males and females. Bulgaria, 1961-2003. Official population estimates (left) and HMD data (right). Source: Jasilionis D., Jdanov D.A. Human Mortality Database: Background and Documentation for Bulgaria
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The HMD inter-censal estimates for Germany
1) Using additional migration data and cubic spline interpolation for migration trends across cohorts we removed the population changes due to the earlier “cleaning” by the statistical offices.
2) We distributed the accumulated error (not the net migration!) uniformly over the adjustment period of 24 years (30 years for East German lands):
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Changeable population definitions across time
Data challenges
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Changes in the definition of population: Poland
Figure: Official and adjusted (Tymicki et al. , 2015) estimates of population of Poland
14,000,000
15,000,000
16,000,000
17,000,000
18,000,000
19,000,000
20,000,0001
96
01
96
21
96
41
96
61
96
81
97
01
97
21
97
41
97
61
97
81
98
01
98
21
98
41
98
61
98
81
99
01
99
21
99
41
99
61
99
82
00
02
00
22
00
42
00
62
00
82
01
02
01
22
01
4
Pre- and post-censal population estimates according to the 2002
Post-censal population estimates calculated according to the 1988
census
Post-censal population estimates calculated according
to the 1970 census
Post-censal population estimates calculated according to the 1960
census
FEMALES
MALES Post-censal population estimates according to the
2011 census
Unfofficial inter-censal estimates
based on the 2011 census
In the 2000s, Poland faced a massive out-migration that followed the EU enlargement of 2004. It was expected that the population counts will be corrected downward after the next population census of 2011. But Statistics Poland has unexpectedly decided to change the official definition of the population status from the permanently resident (acting in 2010 and earlier) to the usually resident (from 2011 onward). Statistics Poland did not re-estimate age-specific population counts back to previous census. Due to irregular migration pattern the standard HMD inter-censalmethod for reconstruction of annual population estimates is not applicable.
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Mortality at advanced ages
Data challenges
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Growing problems at advanced ages
2.5
2.7
2.9
3.1
3.3
3.5
3.7
3.9
4.1
4.3
4.5
1980 1985 1990 1995 2000 2005 2010 2015
Life
exp
ect
ancy
at
age
90
Year
Males (Standard HMD)
Females (Standard HMD)
males (SR80)
females (SR80)
Russia: life expectancy at age 90
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Free & open access to all data
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Open Data
Availability and Access: the data must be available as a
whole and at no more than a reasonable reproduction cost,
preferably by downloading over the internet. The data must
also be available in a convenient and modifiable form.
Re-use and Redistribution: the data must be provided under
terms that permit re-use and redistribution including the
intermixing with other datasets.
Universal Participation: everyone must be able to use, re-use
and redistribute - there should be no discrimination against
fields of endeavour or against persons or groups. For example,
‘non-commercial’ restrictions that would prevent ‘commercial’
use, or restrictions of use for certain purposes (e.g. only in
education), are not allowed.
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Thorough documentation of data,
data sources, and
computational methods
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Data Sources:- Computed from microdata- Computed from tabulated data from government- Computed from tabulated data from report - Computed from tabulated population and death data- Final report- Others - Preliminary report- Unknown
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Challenge
How can high-quality data repositories compete with the “quick” data solutions covering the entire world and
easily available online?
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Ongoing work and future plans
• Adding cause-of-death data• Sub-national data• Less (statistically) developed countries
• Latin America• China• India
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Ongoing work and future plans:developing countries
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Coverage of death registration (December 2014)
Source: UN Population Division (http://unstats.un.org/unsd/demographic/CRVS/CR_coverage.htm)
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Data availability in the HMD (March, 2019)
HMD countries
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Principal data sources on mortality in China
CENSUSES OR SURVEYS BY NATIONAL BUREAU OF STATISTICS (NBS) Population censuses: 1982, 1990, 2000, and 2010.
- enumeration of people who died in a household one year or 18 months beforethe census or survey. Inter-censal 1% sample surveys: 1987, 1995, and 2005
Annual Population Change Surveys. Smaller surveys for inter-censal years.
HOUSEHOLD REGISTRY (“HUKOU”) BY MINISTRY OF PUBLIC SECURITY- each resident is legally required to register in the household registration system,
registration to be cancelled within a month after death. Serves as basis for census.
VITAL REGISTRATION / SURVEILLANCE SYSTEMS BY HEALTH MINISTRY Nationwide Vital Registration System: 8 % of the national population,
ca. 110 million people (2005 est.), mostly urban, Eastern China (Rao et al. 2005). Disease Surveillance Points (DSP): 161 surveillance points, ~10 mill. people. National Child and Maternal Mortality Surveillance Points:
336 counties / urban districts covering 140 mill. people, child and maternal mortality.
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China and Sweden (1950+), Male
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China and Sweden (1950+), Male
UnderestimatedInfant Mortality
Overestimated mortality for ages 1-15
No accidental mortality hump
Death underregistration at the oldest-old ages
Source: Human Mortality Database and China Census Data
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Life Expectancy for Chinese Males
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Life Expectancy for Chinese Females
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Infant mortality rates from NFHS III (2001-05) and SRS (2002-06) for 16 Indian states
Temporary life expectancy between exact ages 0 and 60 in India, 1970-75 to 2000-04.
Graphs from: Saikia, Jasilionis, Ram, Shkolnikov, 2011.
SRS - a nationwide system for collecting vital statisticsbased on a dual record system fora sample of villages/urban blocks.Key features: Set up in late 1960s, age-specific mortality
estimates /life tables from 1970-75 onwards;Covers major states by urban/rural
breakdown;Coverage: ~7.6 million pop. (2014).Problems: age heaping, over-estimation of old
age mortality….
Ongoing and future studies in cooperation with UshaRam (IIPS, Mumbai, India):- Data quality and coverage (NFHS, DLHS, SRS) focus on effects of age heaping.- Estimation of adult mortality by social statususing District Level Household Survey (DLHS) & National Family Health Survey (NFHS4).
- Examining validity of cause of death data.
Harmonized series of mortality estimates for India and its major states using Sample Registration System (SRS) and survey data
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General goal of the AXA project
• To construct HMD-like life table series for Hong Kong and for Mexico and assess their accuracies for monitoring actuarial longevity risks
• Collaboration between – the University of California- Berkeley team of the
HMD and
– the AXA group and local entities (AXA China Region and AXA Mexico)
– with technical support from the Mortality Branch of the United Nations Population Division
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Specific aims
1. Construct a time series of life tables at the national level for Hong Kong and for Mexico using the HMD approach and methods protocol
2. Establish a standard set of data quality indicators to evaluate the reliability of the life tables (building from the demographic literature)
3. Measure the impact of data quality issues on the assessment of variations in biometrical risks and future longevity trends
4. Propose adjustments to the series to improve accuracy, using indirect estimation techniques and/or statistical methods
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Motivations• Joint interest in assessing mortality trends and their accuracy in a
growing number of countries• For Academics:
– Pressure to include additional countries into the HMD while preserving the high quality of the database mortality series
– Opportunity provided by increased investments by international organizations and private sponsors to improve national demographic data collection systems=> need to monitor the international Millenium/Sustainable Development Goals
• For AXA:– Regulatory need to constantly improve data quality in countries of
operationLongevity and mortality risks are of paramount importance at Life level, not only to calculate the Solvency Capital Required, but also to define the Best Estimates. Both are based on historical data, and the more relevant the data, the more accurate the mortality and longevity risks monitoring.=> need to better assess variations in biometrical risks and future longevity trends in historical mortality series
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Result for Hong Kong promising
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MexicoThe perfect case study: complete but clearly
imperfect demographic information
50
1990
2016
.2.4
.6.8
1
log(2
0q60)
.001 .01 .1 .2 .4 .6log(5q0)
Females
19902016
.2.4
.6.8
1
log(2
0q60)
.001 .01 .1 .2 .4 .6log(5q0)
Males
HMD Countries MEX
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Ongoing work and future plans:CoD data
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• For all HMD countries with cause-of-death data following the International Classification of Diseases (ICD)
• Back to 1950 or earliest year available• Respectful of privacy issues
– No access to input data for some countries– Five-year age group
• Three set of data series consistent with all-cause series:– Cause-of-death fractions – death counts– age-specific death rates
• Shortlist of <100 exclusive cause-of-death categories (mostly compatible with EUROSTAT and NCHS)
• Emphasis on disruptions arising from revisions of the ICD52
Adding cause-of-death data
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Prepared COD series
• The United States (1959-2016)
• England and Wales (1950-2013)
• France (1958-2015)
• Canada (1950-2009)
• Sweden (1952-2012)
• Norway (1951-2012)
• Japan (1950-2013)
• The Czech Republic (1950-2013)
53
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COD project stalled
• Competition issue with the Human Cause-of-Death Database (HCD)
• Same research teams (MPIDR, INED, and UC Berkeley)
• Conceptual differences: same idea but withadjustments for changes in the International Classification of Diseases + HCD includes non-HMD countries (with indirect estimation methods)
• On-going discussions to combine both databasesand host them on the HMD website
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Ongoing work and future plans:sub-national data
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Sub-national databases à la HMD
• Canadian Human Mortality Database (Université de Montréal, Canada=> UC Berkeley – thanks to the CIA)
• Japan Mortality Database (Institute for Population and Social Security Research, Tokyo – Former PhD student at UC Berkeley)
• United States Mortality DataBase (USMDB, usa.mortality.org) at UC Berkeley (state seriespublished, county series in the work)
• Germany Mortality Database (MPIDR)
• Australia Mortality Database (new project)
• France Mortality Database (looking for funding)
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Monitoring mortality at sub-national level: data, methods, and evidence
Call for papers
A two-&-half-Day International workshop
To be held at ANU, Canberra, Australia, October 15-17, 2019.
Organized by: The Australian National University, the Human Mortality Database team.
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