Evidence map›Paper›PMID 32483160›Full record

ArticleScientific reports2020

Determining correlates of the average number of cigarette smoking among college students using count regression models.

Parami Sharareh, Tapak Leili, Moghimbeigi Abbas, Poorolajal Jalal, Ghaleiha Ali

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Parami ShararehDepartment of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Tapak LeiliDepartment of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. l.tapak@umsha.ac.ir.
Moghimbeigi AbbasDepartment of Biostatistics, School of Public health, Alborz University of Medical Sciences, Alborz, Iran.
Poorolajal JalalDepartment of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Ghaleiha AliDepartment of Psychiatry, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

College students, as a large part of young adults, are a vulnerable group to several risky behaviors including smoking and drug abuse. This study aimed to utilize and to compare count regression models to identify correlates of cigarette smoking among college students. This was a cross-sectional study conducted on students of Hamadan University of Medical Sciences. The Poisson, negative binomial, generalized Poisson, exponentiated-exponential geometric regression models and their zero-inflated counterparts were fitted and compared using the Vuong test (α = 0.05). A number of 1258 students participated in this study. The majority of students were female (60.8%) and their average age was 23 years. Most of the students were non-smokers (84.6%). Negative binomial regression was selected as the most appropriate model for analyzing the data (comparable fit and simpler interpretation). The significant correlates of the number of cigarettes smoked per day included gender (male: incident-rate-ratio (IRR = 9.21), birth order (Forth: IRR = 1.99), experiencing a break-up (IRR = 2.11), extramarital sex (heterosexual (IRR = 2.59), homosexual (IRR = 3.13) vs. none), and drug abuse (IRR = 5.99). Our findings revealed that several high-risk behaviors were associated with the intensity of smoking, suggesting that these behaviors should be considered in smoking cessation intervention programs for college students.

Indexed as

Cigarette SmokingModels, StatisticalAdolescentAdultCross-Sectional StudiesFemaleHumansMaleRisk-TakingStudentsUniversitiesYoung Adult

Identifiers

PMID32483160
PMCPMC7264191

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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.