Laboratory

Demographic Data

At a Glance Projects Publications Team

Project

Estimating Mortality and Human Losses during Health Crises

Conducted by Dmitri A. Jdanov; Domantas Jasilionis, Vladimir M. Shkolnikov, László Németh, Isabella Marinetti, Marina Kolobova, Marília R. Nepomuceno; in Collaboration with David Leon (The London School of Hygiene & Tropical Medicine, United Kingdom), Nazrul Islam (University of Southampton, United Kingdom)

Detailed Description

Epidemics and natural and human-made disasters create major challenges for population statistics. First, in situations where official registration systems are not able to cover all vital events or deliberately exclude some subpopulations, estimating total mortality leads to incorrect estimates. Unadjusted mortality estimates based on official data can be further biased due to the failure to account for substantial unregistered population movements. Second, the impact of short-term risk factors on longevity trends cannot be assessed based solely on direct losses using cause-specific data. Estimating the total impact of such events requires the use of special methods and data. This is especially important for tracing pandemic outbreaks because effective public health responses require timely and reliable monitoring of the situation. 

Evaluating the human costs of various disasters has always been an important task for demography and epidemiology. However, since the emergence of new health threats, such as global pandemics and recent military conflicts, new studies and methodological innovations are needed to provide better and more complete evidence. Thus, the main project aims are: (i) to develop methods for the reconstruction of mortality surfaces disrupted by short-term events; and (ii) to develop methodological approaches for the precise estimation of excess mortality, while also taking into account cause-of-death components.

The project builds on the research experience based on the prior project Methods for Estimation of War-Related Mortality, which proposed a methodological approach that requires only minimal available input data to model war-related mortality in several countries within the HMD project. The current project extends this approach by using a wider set of direct and indirect methods to estimate mortality surfaces for countries that sustain disaster-related mortality in cases of incomplete population statistics.

The excess mortality approach is used as the methodological basis to provide internationally comparable estimates of disaster-related mortality. This approach is considered the gold standard, and can be applied to weekly, monthly, or annual data.

An important component of the project is data. High-frequency mortality data (monthly, weekly, and daily statistics) have been used in demography and epidemiology for a long time. The importance of these data was rediscovered during the COVID-19 pandemic, which initiated a new data revolution in demographic statistics. In order to trace the course of the pandemic, a huge amount of detailed weekly data were released and made publicly available. In May 2020, as part of the project, we launched the Short-Term Mortality Fluctuations (STMF@HMD) data series, the first scientific resource for monitoring weekly mortality. These data have been widely used to estimate excess mortality attributable to the pandemic, which is recognized as the most objective monitoring tool for measuring the course of and the total losses due to the pandemic or any other public health disaster. This unique international dataset, which is currently the part of the Human Mortality Database, is still regularly updated and provides estimates of excess mortality that can capture the impacts of other public health threats, including those caused by climate change (e.g., heat waves or cold temperatures). The STMF@HMD is one of the most cited sources for weekly mortality estimates.

The new scientific evidence and policy recommendations obtained in the framework of this project contribute to the assessment and development of national and international policies and strategies aimed at reducing the mortality burden of epidemics, heat waves, and other unexpected disasters.

Within the framework of the project, the dissertation titled "Effects of Short-Term Events on Subnational Mortality Trends in Europe" has been prepared and submitted by Isabella Marinetti.

Publications

Jdanov, D. A.; Jasilionis, D.; Tarkiainen, L.; Martikainen, P.:

Inequality in life expectancy losses by education in Finland during COVID-19 pandemic

International Journal for Equity in Health 25, 1–12. (2026)    

Shkolnikov, V. M.; Timonin, S. A.; Jdanov, D. A.; Medina-Jaudes, N.; Islam, N.; Leon, D. A.:

Widening East-West inequality in life expectancy in Europe during the COVID-19 pandemic: an international comparative study

PLOS One 21:2, e0344003–e0344003. (2026)    

Islam, N.; López, F. J. G.; Jdanov, D. A.; Royo-Bordonada, M. Á.; Khunti, K.; Lewington, S.; Lacey, B.; White, M.; Morris, E. J.; Zunzunegui, M. V.:

Unequal impact of COVID-19 on excess deaths, life expectancy, and premature mortality in Spanish regions (2020-2021)

Gaceta Sanitaria 38:102424, 1–14. (2024)    

Shkolnikov, V. M.; Jdanov, D. A.; Majeed, A.; Islam, N.:

Making sense of national and international disparities in excess mortality from the COVID-19 pandemic [Editorial]

BMJ Global Health 9:4, e015737–e015737. (2024)    

Islam, N.; Jdanov, D. A.:

Age and sex adjustments are critical when comparing death rates [Editorial]

BMJ 381, p845–p845. (2023)    

Shkolnikov, V. M.; Klimkin, I.; McKee, M.; Jdanov, D. A.; Alustiza Galarza, A.; Németh, L.; Timonin, S. A.; Nepomuceno, M. R.; Andreev, E. M.; Leon, D. A.:

What should be the baseline when calculating excess mortality? New approaches suggest that we have underestimated the impact of the COVID-19 pandemic and previous winter peaks

SSM-Population Health 18:101118, 1–13. (2022)    

Timonin, S. A.; Klimkin, I.; Shkolnikov, V. M.; Andreev, E. M.; McKee, M.; Leon, D. A.:

Excess mortality in Russia and its regions compared to high income countries: an analysis of monthly series of 2020

SSM-Population Health 17:101006, 1–14. (2022)    

Islam, N.; Jdanov, D. A.; Shkolnikov, V. M.; Khunti, K.; Kawachi, I.; White, M.; Lewington, S.; Lacey, B.:

Effects of covid-19 pandemic on life expectancy and premature mortality in 2020: time series analysis in 37 countries

BMJ 375:e066768, 1–14. (2021)    

Islam, N.; Shkolnikov, V. M.; Acosta, R. J.; Klimkin, I.; Kawachi, I.; Irizarry, R. A.; Alicandro, G.; Khunti, K.; Yates, T.; Jdanov, D. A.; White, M.; Lewington, S.; Lacey, B.:

Excess deaths associated with covid-19 pandemic in 2020: age and sex disaggregated time series analysis in 29 high income countries

BMJ 373:n1137, 1–14. (2021)    

Jdanov, D. A.; Alustiza Galarza, A.; Shkolnikov, V. M.; Jasilionis, D.; Németh, L.; Leon, D. A.; Boe, C.; Barbieri, M.:

The short-term mortality fluctuation data series, monitoring mortality shocks across time and space

Scientific Data 8:235, 1–8. (2021)    

Klimkin, I.; Shkolnikov, V. M.; Jdanov, D. A.:

Calculation of week-specific age-standardized death rates from STMF data on mortality by broad age intervals

MPIDR Working Paper WP-2021-004. (2021)    

Németh, L.; Jdanov, D. A.; Shkolnikov, V. M.:

An open-sourced, web-based application to analyze weekly excess mortality based on the Short-term Mortality Fluctuations data series

PLOS One 16:2, e0246663–e0246663. (2021)    

Leon, D. A.; Jarvis, C. I.; Johnson, A. M.; Smeeth, L.; Shkolnikov, V. M.:

What can trends in hospital deaths from COVID-19 tell us about the progress and peak of the pandemic? An analysis of death counts from England announced up to 25 April 2020

medRxiv preprints. unpublished. (2020)    

Trias Llimós, S.; Alustiza Galarza, A.; Prats, C.; Tobías, A.; Riffe, T.:

The need for detailed COVID-19 data in Spain [Correspondence]

The Lancet Public Health 5:11, e576–e576. (2020)