'An overview of population level mortality data: availability, quality, emerging problems'
This Master Class will summarise the experience of the work on the Human Mortality Database Project. The Human Mortality Database (HMD, www.mortality.org) is the world´s leading data resource on mortality in developed countries. 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 41 countries. Each data series in the HMD has met rigorous quality requirements.
Unfortunately, for most of the world’s population, complete and accurate data on mortality are not available. Such data can only be produced by a well-organised system for the registration of vital events. This is a standard that the majority of developing nations have been unable to achieve. Death registration is still very fragmentary in most of the developing world, including in the countries with the largest populations (China, India, Indonesia) and those that are facing the greatest health challenges (in SubSaharan Africa). It is estimated that only 55% of all countries or territories in the world have at least 90% death registration coverage (UNSD 2019). But simply having a nearly complete vital registration system does not guarantee that the population statistics will be of sufficient quality. There are serious concerns about the quality of mortality data and population censuses in a substantial share of countries formally maintaining vital registration systems (see e.g. Palloni and Pinto-Aguirre 2011; Glei, Barbieri, and Santamaría-Ulloa 2019).
In the workshop, we will not touch problems related to some of the historical populations’ experience. We will focus on the newly emerging data quality problems. The understanding of problems hidden in the data is important in any demographic estimation, forecast or study. We will discuss several approaches which significantly increase the utility of the data even if data quality is problematic. We will also consider several examples of work with problematic population level mortality data for China and India. Finally, a short introduction to the HMD software will be given.
Dmitri Jdanov is Head of the Laboratory of Demographic Data at the Max Planck Institute for Demographic Research in Rostock, Germany. He leads the HMD project team and has research experience in developing methods of demographic analysis, including methods of decomposition, reconstruction of mortality surfaces, and data quality evaluation. Additionally, over the last eighteen years, he has worked developing mortality series for industrialised countries which can offer a better understanding of the past mortality trends and how they can be extrapolated in to future. In OctoberNovember 2019, Dmitri will be a Visiting Fellow with the ANU School of Demography in the Research School of Social Sciences.
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