Data resource profile: the Human Mortality Database (HMD)
The Human Mortality Database (HMD, www.mortality.org) is the world’s leading data resource on mortality in developed countries. The HMD is a collaborative project of the Department of Demography at the University of California, Berkeley (UCB) and the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany. The main purposes of the HMD are to document the longevity revolution of the modern era and to facilitate research into its causes and consequences by providing high-quality data to researchers, students, journalists, policy analysts, and others interested in the history of human longevity. As of 2019, this unique open-access collection provides detailed, high-quality mortality and population data for 40 countries. The database is still growing.
Insurance companies and pension funds are affected by many different kinds of risks. In life insurance there are two main risks: the demographic risk and the investment risk. The demographic risk can be dividing into insurance risk and longevity risk. The first risk associated with the random deviation of the number of deaths from its expected value, the second deriving from the improvement in mortality rates. Numbers of actuarial stochastic models have been developed to analyse the mortality changes. This work focuses on Lee-Carter, Cairns-Blake-Dowd models and their extended versions with the inclusion of the cohort effect. We construct 6 stochastic actuarial models on Russian data at the first time. For modelling we use age-specific mortality rates and the probability of dying between 1959 and 2014 for the population aged 20 to 88 years from the Human Mortality Database. We consider age range from 20 to 88. Using the "StMoMo" package in the R software environment, code was written for modelling and predicting mortality with the help of actuarial stochastic models. For comparison of models, information criteria (Bayesian information criterion and Akaike criterion) were used, as well as sensitivity to changing the time range.
The COVID-19 pandemic stimulated the interest of scientists, decision makers and the general public in short-term mortality fluctuations caused by epidemics and other natural or man-made disasters. To address this interest and provide a basis for further research, in May 2020, the Short-term Mortality Fluctuations data series was launched as a new section of the Human Mortality Database. At present, this unique data resource provides weekly mortality death counts and rates by age and sex for 38 countries and regions. The main objective of this paper is to detail the web-based application for visualizing and analyzing the excess mortality based on the Short-term Mortality Fluctuation data series. The application yields a visual representation of the database that enhances the understanding of the underlying data. Besides, it enables the users to explore data on weekly mortality and excess mortality across years and countries. The contribution of this paper is twofold. First, to describe a visualization tool that aims to facilitate research on short-term mortality fluctuations. Second, to provide a comprehensive open-source software solution for demographic data to encourage data holders to promote their datasets in a visual framework.