Evidence map›Paper›PMID 38090742›Full record

ArticleTobacco induced diseases2023

Tobacco consumption in Spain: Individual risk profiles.

Imanol L Nieto-Gonzalez, Carolina M Rodriguez-Donate, Ginés Guirao-Pérez

Abstract read
In one paragraph

Article in Tobacco induced diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Imanol L Nieto-GonzalezUniversidad de La Laguna, San Cristóbal de La Laguna, Spain.
Carolina M Rodriguez-DonateUniversidad de La Laguna, San Cristóbal de La Laguna, Spain.
Ginés Guirao-PérezUniversidad de La Laguna, San Cristóbal de La Laguna, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe aim of this study is to identify profiles of never smokers, ex-smokers, occasional smokers, and daily smokers, through their individual, lifestyle, and health characteristics. This analysis allows provides profiles of individuals with a greater or less propensity to use tobacco, which contributes to the design of effective prevention policies.

methodsEconometric models are used with data from the Spanish National Health Survey. Specifically, a multinomial logit model is estimated to evaluate the probabilities of tobacco use. Additionally, discrete changes, odds ratios, and predicted probabilities for prone individuals, are calculated.

resultsDifferences are found between the profiles of each alternative of tobacco use. The individual attributes with the most striking effect are being aged 26-45 years, which reduces the probability of being a non-smoker by 21 percentage points compared to the younger group, and regular exposure to secondhand smoke, which is 30% more likely to be a regular smoker. The characteristics that define an individual with higher probability of smoking daily are: belonging to a certain region of Spain, male, aged 26-45 years, born in Spain, unemployed, with primary studies, separated, no physical activity, consumes alcohol, and is exposed to smoke regularly, not chronically ill, and have very poor health.

conclusionsIdentification of the profiles most likely to choose each of the tobacco consumption alternatives can contribute to the design of more effective prevention strategies. The results confirm that the accumulation of bad habits results in a high risk of smoking. The quantification of the differences in the effects of each trait is an interesting contribution that is useful to orient the policies to specific segments of the population more prone to consume tobacco.

Indexed as

healthlifestylemultinomial logitpreventionsociodemographic factors

Identifiers

PMID38090742
PMCPMC10714415

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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.