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Население и экономика
Article . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Data Paper . 2023
Data sources: Pensoft
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Russian Short-Term Mortality Fluctuations Data Series

Authors: Aleksey E. Shchur; Sergey A. Timonin; Elena V. Churilova; Egor V. Sergeev; Vera V. Sokolova; Olga A. Rodina; Bulat A. Shamsutdinov; +2 Authors

Russian Short-Term Mortality Fluctuations Data Series

Abstract

The COVID-19 pandemic has revealed significant gaps in the coverage and quality of the existing international and national statistical surveillance systems. The most reliable approach to estimating mortality associated with short-term risk factors or factors fluctuating within calendar years is based on estimating weekly excess mortality. Although this approach is obvious, its application turned out to be problematic due to the lack of reliable data. In response to this challenge, a new Short-Term Mortality Fluctuations (STMF) data series was created in 2020. The Russian Short-Term Mortality Fluctuations Data Series (RuSTMF) is a further development of the STMF idea at the regional level. It contains weekly crude and standardized death rates for the Russian regions and Russia as a whole. The main source for calculating death rates is depersonalized individual data on the deceased provided by the Rosstat. This database provides for the analysis of short- and medium-term changes in mortality in males, females and total population in Russia as a whole and its regions, as well as the estimation of «excess» deaths during short-term sharp increases in mortality due to, for example, heat or cold waves, seasonal influenza epidemics, the Covid-19 pandemic or technogenic catastrophes.

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Keywords

demographic databases, HB1-3840, regions of Russia, Economic theory. Demography, excess mortality, short-term mortality fluctuations

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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