The 7th Human Mortality Database (HMD) Symposium will be dedicated to investigating the main drivers and obstacles to longevity progress in the pre- and post-COVID eras. Contributions from demographers, epidemiologists, public health experts and researchers from related fields conducting analyses based on the cause-of-death series newly integrated into the Human Mortality Database (HMD) are particularly encouraged. The Symposium seeks to address major topics and challenges of substantive research as well as methodological and data issues. In addition to contributing to the scientific program, participants will be invited to provide feedback and suggestions on the currently available content of the database and on the methods used, as well as to propose future developments for the HMD Project in general and the Human Cause-of-Death series in
particular.
Place and date of the event
The Symposium is scheduled to take place on 9-10 October 2025 at the French Institute for Demographic Studies (INED) in Aubervilliers (a suburb of Paris), France.
There are no participation fees, but participants are expected to cover their own travel, meals and accommodation expenses.
Contact
For any questions regarding the Symposium, please contact Magali Barbieri and/or Dmitri Jdanov.
About
The Human Mortality Database (HMD, ) is the leading international resource for mortality and population data. It is a collaborative project sponsored by the Max Planck Institute for Demographic Research (Germany), the Department of Demography at the University of California, Berkeley (USA), and the French Institute for Demographic Studies (INED). The project consists of two companion databases: The Human Mortality Database and the Human Life table Database (www.lifetable.de). The cause-of-death series is a newly added component of the HMD (HCD@HMD). The HCD@HMD provides cause-specific mortality indicators for 18 countries. Its originality resides in the fact that, in contrast to other existing data resources on causes of death, it publishes time series with causes classified according to constant (fixed) lists/classifications taking changes to the International Classification of Diseases into account. This approach makes the cause-specific data series highly consistent over time and across countries.