Evidence map›Paper›PMID 35688981›Full record

ArticleEnvironmental science and pollution research international2022

Can smoking prevalence explain COVID-19 indicators (cases, mortality, and recovery)? A comparative study in OECD countries.

Yuval Arbel, Chaim Fialkoff, Amichai Kerner, Miryam Kerner

Abstract read
In one paragraph

Article in Environmental science and pollution research international, 2022. 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

4 authors.

Yuval ArbelSir Harry Solomon School of Economics and Management, Western Galilee College, 2412101, Acre, Israel. YuvalAr@wgalil.ac.il.ORCID http://orcid.org/0000-0003-4365-6280
Chaim FialkoffInstitute of Urban and Regional Studies, Hebrew University of Jerusalem, Mt. Scopus, 9190501, Jerusalem, Israel.
Amichai KernerSchool of Real Estate, Netanya Academic College, 1 University Street, 4223587, Netanya, Israel.
Miryam KernerThe Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There are many risk factors associated with the spread of the COVID-19 pandemic, including low wind speed, fossil fuel energy production, air pollution, and smoking. Several studies argue that smoking is not a risk factor for COVID-19 morbidity among males or any other sub-group. The study aims to analyze the following research questions: (1) can smoking prevalence explain COVID-19 indicators (cases, mortality, and recovery)? Are these relationships monotonically increasing or decreasing? In an attempt to test the counter-intuitive possibility of a non-linear relationship, the proposed empirical model relaxes the assumption of monotonic change by applying the quadratic design and testing which one of the two competing models (quadratic or linear) better fits the data. Findings suggest more complex relationships between corona indices and prevalence of smoking than previously thought. These patterns might be explained by several conditions such as the attenuation of hypercytokinemia for mild levels of smoking prevalence compared with non-smokers, elevated social distancing of smokers in countries with lower smoking prevalence, and unidentified factors that should be examined in future research.

Indexed as

COVID-19HumansMaleOrganisation for Economic Co-Operation and DevelopmentPandemicsPrevalenceSmokingACE2 receptorCOVID-19HypercytokinemiaOECDSmoking Prevalence

Identifiers

PMID35688981
PMCPMC9187335

What OpenQuestion holds

Textmetadata
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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.