Cited by 168Insurance, Mortality, Demography, Risk ManagementHistorical Economic and Social StudiesHealthcare Systems and Public Health

Data resource profile: the Human Mortality Database (HMD)

Open access
Barbieri, Magali, Wilmoth, John R, Shkolnikov, Vladimir M, Glei, Dana, Jasilionis, Domantas, Jdanov, Dmitri, Boe, Carl, Riffe, Timothy +2 more·2015·International Journal of Epidemiology

Abstract

Over the past 100 years, human longevity has increased more than during all preceding human history combined. This remarkable change was caused by reductions in mortality; first generally at younger ages, then later progressing to older ages. The precise causes as well as the social and economic impact of this change are still unclear. What are the major patterns, driving forces and future prospects of this process? To what extent are these changes determined by period factors such as advances in medicine and economic development or by cohort effects such as early life experiences and accumulation of behavioural risks over the life course? How much further can human longevity extend? These are central questions for scholars and policy makers. The Human Mortality Database (HMD), as a collection of detailed, consistent and high quality human mortality data, is an important resource for addressing these fundamental questions. Although there are other international databases of mortality indicators, some of which cover many more countries (eg those compiled by the United Nations Population Division, the World Health Organization, Eurostat and the Institute for Health Metrics and Evaluation), they all have limitations mostly related to quality and detail of the data. For example, most lack information on advanced ages, are restricted to short time periods or focus on periods but not cohorts; these disadvantages are addressed by the HMD. The HMD strives to include all populations for which death registration and census data are virtually complete. As a result, countries and areas in the HMD are relatively wealthy and for the most part highly industrialized. At present the database contains detailed population and mortality data for 37 developed countries and 46 populations (including sub-national groups), with series starting as early as 1751 in Sweden and covering more than a 100 years for 16 populations. The selection criteria for inclusion of a country in the HMD rely mainly on the quality of its data. In order to be included in the HMD, a country’s death registration system must be nearly complete (close to 99%). For such countries, original statistics are then collected extending as far back in time as there are age-classified census data and annual death counts in a sufficiently detailed format (at a minimum, there should be 5-year age groups with a separate category for infants and an open interval at age 80 or higher). Note that for historical data (especially for years preceding 1870–80), the quality is often lower, especially at ages 80 and above. In such situations, the HMD has well-defined and transparent methods that are applied in a consistent manner, accompanied by prominent warnings that alert users to potential problems and issues. Figure 1 shows the years for which data series are available for each population in the collection. HMD period and cohort mortality series by population and year (as of January 2015). Cohort life tables are indexed by the year of birth. Cohort life tables are unavailable in Belgium because of missing input data for the period 1914–18. Based on an earlier design by Adrien Remund (2015) 1 . For some data series, additional details are provided on specified sub-populations: (i) for populations that have suffered substantial war losses and where available data allow computation of population and mortality indicators for the total (including the military) and civilian populations separately, such as is the case for England and Wales and for France; and (ii) whenever original data series are available for important population sub-groups as collected by the official statistical agency for a country. These include separate series for West and East Germany; for England and Wales, Scotland and Northern Ireland in the United Kingdom; and for the Maori and non-Maori of New Zealand. Figure 2 shows life expectancy trends in all HMD countries and averages for selected groups of countries since 1970 for men and women. The figure highlights the progress in most of the populations along with a broadening inter-country variation, in particular the growing disparity between the countries of the former Soviet Union and other advanced countries. Trends in life expectancy at birth by sex from 1970 to 2010 for HMD populations and population groups. Individual populations: 36 selected HMD national or sub-national populations. Established market economies (EME): Australia, Austria, Belgium, Canada, Denmark, England and Wales, Finland, France, Iceland, Ireland, Italy, Japan, Luxembourg, The Netherlands, New Zealand, Northern Ireland, Norway, Portugal, Scotland, Spain, Sweden, Switzerland, USA, West Germany. Central and Eastern Europe (CEE): Bulgaria, Czech Republic, East Germany, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia. Former Soviet Union (FSU): Belarus, Russia, Ukraine. The HMD contains uniform death rates and complete (by single year of age up to 110+) and abridged (ages 0, 1–4,

Integrity signals

Registry

Crossref · 10.1093/ije/dyv105

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Barbieri, M., Wilmoth, J. R., Shkolnikov, V. M., Glei, D., Jasilionis, D., Jdanov, D., Boe, C., Riffe, T., Grigoriev, P., & Winant, C.
Affiliations
  • United Nations Department of Economic and Social Affairs (US)
  • Georgetown University (US)
  • Max Planck Institute for Demographic Research (DE)
  • New Economic School (RU)
  • University of California, Berkeley (US)
Venue
International Journal of Epidemiology
Published
2015-06-23
Volume
44
Issue
5
Pages
1549-1556
ISSN
  • 0300-5771 (ISSN-L)
  • 1464-3685
OpenAlex
W2218415617
Cited by
168
References
14
Citations by year
2026: 18 · 2025: 18 · 2024: 21 · 2023: 11 · 2022: 21 · 2021: 23 · 2020: 10 · 2019: 15 · 2018: 13 · 2017: 4 · 2016: 2 · 2015: 2 · 2014: 1 · 2013: 1
FWCI
17.77
Type
journal article
Language
en
Open access
Locations
  • International Journal of Epidemiology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Archined (submittedVersion, other-oa)
  • Vytautas Magnus University (submittedVersion)
Funders
  • Institute for Health Metrics and Evaluation
  • National Institute on Aging (R01AG011552, R01AG040245, P30AG012839)
Topics
  • Social Sciences › Demography › Insurance, Mortality, Demography, Risk Management
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Historical Economic and Social Studies
  • Health Sciences › Medicine › Epidemiology › Healthcare Systems and Public Health
Keywords
Human Mortality Database, mortality statistics, human longevity, mortality trends, period and cohort effects, death registration, historical mortality data, age-specific mortality, life expectancy, developed countries, old age, data citation, data licensing
MeSH terms
Humans, Life Expectancy, Mortality, Databases, Factual, Databases as Topic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review