Evidence map›Paper›PMID 39071964›Full record

ArticleData in brief2024

Compilation of open access time-series datasets for studying temperature-mortality association.

Aurelio Tobias, Chris Fook Sheng Ng, Yoonhee Kim, Masahiro Hashizume, Lina Madaniyazi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Aurelio TobiasInstitute of Environmental Assessment and Water Research (IDAEA), Spanish Research Council (CSIC), Barcelona, Spain.
Chris Fook Sheng NgDepartment of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yoonhee KimDepartment of Global Environmental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Masahiro HashizumeDepartment of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Lina MadaniyaziSchool of Tropical Medicine and Global Health (TMGH), Nagasaki University, Nagasaki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, we present a comprehensive compilation of open access daily time-series datasets tailored to assess the temperature-mortality association. The data consists of daily mortality counts and average ambient temperature at various levels of geographic aggregation, including data from four cities, ten regions, and two counties, which have been utilised in previously published studies. These datasets are applicable for time-series regression analysis to estimate location-specific temperature-mortality associations. Additionally, the availability of data from multiple geographical locations enabled the exploration of geographical differences by pooling associations using meta-analysis. This compilation aims to serve as a valuable resource for researchers, educators, and students, facilitating their application of time-series regression modelling for research endeavours and training activities.

Indexed as

Climate changeEnvironmental epidemiologyEnvironmental healthMulti-location studiesTime-series regression

Identifiers

PMID39071964
PMCPMC11282929

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.