Evidence map›Paper›PMID 34489477›Full record

ArticleScientific data2021

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

Dmitri A Jdanov, Ainhoa Alustiza Galarza, Vladimir M Shkolnikov, Domantas Jasilionis, László Németh, David A Leon, Carl Boe, Magali Barbieri

Abstract readDataset
In one paragraph

Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Observational
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Dmitri A JdanovMax Planck Institute for Demographic Research, Rostock, Germany. Jdanov@demogr.mpg.de.ORCID 0000-0003-4633-2011
Ainhoa Alustiza GalarzaMax Planck Institute for Demographic Research, Rostock, Germany.
Vladimir M ShkolnikovMax Planck Institute for Demographic Research, Rostock, Germany.
Domantas JasilionisMax Planck Institute for Demographic Research, Rostock, Germany.
László NémethMax Planck Institute for Demographic Research, Rostock, Germany.ORCID 0000-0002-8845-2540
David A LeonResearch University Higher School of Economics, Moscow, Russia.ORCID 0000-0001-9747-1762
Carl BoeUniversity of California, Berkeley, USA.
Magali BarbieriUniversity of California, Berkeley, USA.

Funding

Program Development (Pilot)P30AG012839 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI Magali Barbieri · 1999 to 2026
$18.6M
Monitoring Inter-County Variations in Mortality in the United StatesR03AG058110 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI BARBIERI, MAGALI · 2018 to 2019
$147k
NIA NIH HHS P30 AG012839NIA NIH HHS R03 AG058110
6 · The paper itself

Abstract

The COVID-19 pandemic has revealed substantial coverage and quality gaps in existing international and national statistical monitoring systems. It is striking that obtaining timely, accurate, and comparable across countries data in order to adequately respond to unexpected epidemiological threats is very challenging. The most robust and reliable approach to quantify the mortality burden due to short-term risk factors is based on estimating weekly excess deaths. This approach is more reliable than monitoring deaths with COVID-19 diagnosis or calculating incidence or fatality rates affected by numerous problems such as testing coverage and comparability of diagnostic approaches. In response to the emerging data challenges, a new data resource on weekly mortality has been established. The Short-term Mortality Fluctuations (STMF, available at www.mortality.org ) data series is the first international database providing open-access harmonized, uniform, and fully documented data on weekly all-cause mortality. The STMF online vizualisation tool provides an opportunity to perform a quick assessment of the excess weekly mortality in one or several countries by means of an interactive graphical interface.

Indexed as

Databases, FactualMortalityPandemicsCOVID-19HumansRisk Factors

Identifiers

PMID34489477
PMCPMC8421508

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.