Evidence map›Paper›PMID 33888744›Full record

ArticleScientific reports2021

Ambient temperature and subsequent COVID-19 mortality in the OECD countries and individual United States.

Costas A Christophi, Mercedes Sotos-Prieto, Fan-Yun Lan, Mario Delgado-Velandia, Vasilis Efthymiou, Gabriel C Gaviola, Alexandros Hadjivasilis, Yu-Tien Hsu, Aikaterini Kyprianou, Irene Lidoriki and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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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

13 authors.

Costas A Christophi *Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, 30 Archbishop Kyprianou Str., 3036, Lemesos, Cyprus.
Mercedes Sotos-Prieto *Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Fan-Yun LanDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Mario Delgado-VelandiaDepartment of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), and CIBERESP (CIBER of Epidemiology and Public Health), Madrid, Spain.
Vasilis EfthymiouDepartment of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
Gabriel C GaviolaDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Alexandros HadjivasilisCyprus International Institute for Environmental and Public Health, Cyprus University of Technology, 30 Archbishop Kyprianou Str., 3036, Lemesos, Cyprus.
Yu-Tien HsuDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Aikaterini KyprianouCyprus International Institute for Environmental and Public Health, Cyprus University of Technology, 30 Archbishop Kyprianou Str., 3036, Lemesos, Cyprus.
Irene LidorikiFirst Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece.
Chih-Fu WeiDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Fernando Rodriguez-ArtalejoDepartment of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), and CIBERESP (CIBER of Epidemiology and Public Health), Madrid, Spain.
Stefanos N KalesDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. skales@hsph.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidemiological studies have yielded conflicting results regarding climate and incident SARS-CoV-2 infection, and seasonality of infection rates is debated. Moreover, few studies have focused on COVD-19 deaths. We studied the association of average ambient temperature with subsequent COVID-19 mortality in the OECD countries and the individual United States (US), while accounting for other important meteorological and non-meteorological co-variates. The exposure of interest was average temperature and other weather conditions, measured at 25 days prior and 25 days after the first reported COVID-19 death was collected in the OECD countries and US states. The outcome of interest was cumulative COVID-19 mortality, assessed for each region at 25, 30, 35, and 40 days after the first reported death. Analyses were performed with negative binomial regression and adjusted for other weather conditions, particulate matter, sociodemographic factors, smoking, obesity, ICU beds, and social distancing. A 1 °C increase in ambient temperature was associated with 6% lower COVID-19 mortality at 30 days following the first reported death (multivariate-adjusted mortality rate ratio: 0.94, 95% CI 0.90, 0.99, p = 0.016). The results were robust for COVID-19 mortality at 25, 35 and 40 days after the first death, as well as other sensitivity analyses. The results provide consistent evidence across various models of an inverse association between higher average temperatures and subsequent COVID-19 mortality rates after accounting for other meteorological variables and predictors of SARS-CoV-2 infection or death. This suggests potentially decreased viral transmission in warmer regions and during the summer season.

Indexed as

Hot TemperatureAir PollutantsClimateComorbidityCOVID-19Global HealthHumansModels, StatisticalOrganisation for Economic Co-Operation and DevelopmentParticulate MatterSeasonsUnited StatesAir PollutantsParticulate Matter

Identifiers

PMID33888744
PMCPMC8062561

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Registered trials

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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.