Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48504
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dc.contributor.authorPinilla Domínguez, Jaimeen_US
dc.contributor.authorGonzález Lopez-Valcarcel, Beatrizen_US
dc.contributor.authorBarber Pérez, Patricia Lucíaen_US
dc.contributor.authorSantana Jiménez, Yolandaen_US
dc.contributor.otherSantana, Yolanda-
dc.contributor.otherPinilla, Jaime-
dc.contributor.otherBarber, Patricia-
dc.contributor.otherValcarcel, Bea-
dc.contributor.otherPinilla Dominguez, Jaime-
dc.date.accessioned2018-11-23T22:23:30Z-
dc.date.available2018-11-23T22:23:30Z-
dc.date.issued2002en_US
dc.identifier.issn0143-005Xen_US
dc.identifier.urihttp://hdl.handle.net/10553/48504-
dc.description.abstractStudy objective: To understand the context for tobacco smoking in young adolescents, estimating the effects of individual, family, social, and school related factors. Design: Cross sectional analysis performed by multilevel logistic regression with pupils at the first level and schools at the second level. The data came from a stratified sample of students surveyed on their own, their families' and their friends' smoking habits, their schools, and their awareness of cigarette prices and advertising. Setting: The study was performed in the island of Gran Canaria, Spain. Participants: 1877 students from 30 secondary schools in spring of 2000 (model's effective sample sizes 1697 and 1738). Main results: 14.2% of the young teenagers surveyed use tobacco, almost half of them (6.3% of the total surveyed) on a daily basis. According to the ordered logistic regression model, to have a smoker as the best friend increases significantly the probability of smoking (odds ratio: 6.96, 95% confidence intervals (CI) (4.93 to 9.84), and the same stands for one smoker living at home compared with a smoking free home (odds ratio: 2.03, 95% CI 1.22 to 3.36). Girls smoke more (odds ratio: 1.85, 95% CI 1.33 to 2.59). Experience with alcohol, and lack of interest in studies are also significant factors affecting smoking. Multilevel models of logistic regression showed that factors related to the school affect the smoking behaviour of young teenagers. More specifically, whether a school complies with antismoking rules or not is the main factor to predict smoking prevalence in schools. The remainder of the differences can be attributed to individual and family characteristics, tobacco consumption by parents or other close relatives, and peer group. Conclusions: A great deal of the individual differences in smoking are explained by factors at the school level, therefore the context is very relevant in this case. The most relevant predictors for smoking in young adolescents include some factors related to the schools they attend. One variable stood out in accounting for the school to school differences: how well they enforced the no smoking rule. Therefore we can prevent or delay tobacco smoking in adolescents not only by publicising health risks, but also by better enforcing no smoking rules in schools.en_US
dc.languageengen_US
dc.publisher0143-005X
dc.relation.ispartofJournal of Epidemiology and Community Healthen_US
dc.sourceJournal of Epidemiology and Community Health[ISSN 0143-005X],v. 56, p. 227-232en_US
dc.subject531207 Sanidaden_US
dc.subject.otherTabaquismoen_US
dc.subject.otherAdolescentesen_US
dc.titleSmoking in young adolescents: an approach with multilevel discrete choice modelsen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1136/jech.56.3.227
dc.identifier.scopus0036177635-
dc.identifier.isi000174000900017-
dcterms.isPartOfJournal Of Epidemiology And Community Health
dcterms.sourceJournal Of Epidemiology And Community Health[ISSN 0143-005X],v. 56 (3), p. 227-232
dc.contributor.authorscopusid7005595836
dc.contributor.authorscopusid7005595836-
dc.contributor.authorscopusid6507677112-
dc.contributor.authorscopusid7102119002-
dc.contributor.authorscopusid6507763074-
dc.description.lastpage232-
dc.description.firstpage227-
dc.relation.volume56-
dc.investigacionCiencias Sociales y Jurídicasen_US
dc.type2Artículoen_US
dc.identifier.wosWOS:000174000900017-
dc.contributor.daisngid1717005-
dc.contributor.daisngid7190036-
dc.contributor.daisngid3537283-
dc.contributor.daisngid7557827-
dc.identifier.investigatorRIDK-5794-2014-
dc.identifier.investigatorRIDF-8132-2016-
dc.identifier.investigatorRIDB-4788-2017-
dc.identifier.investigatorRIDA-9891-2010-
dc.identifier.investigatorRIDNo ID-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Pinilla, J
dc.contributor.wosstandardWOS:Gonzalez, B
dc.contributor.wosstandardWOS:Barber, P
dc.contributor.wosstandardWOS:Santana, Y
dc.date.coverdateMarzo 2002
dc.identifier.ulpgces
dc.description.jcr2,127
dc.description.jcrqQ1
dc.description.scieSCIE
dc.description.ssciSSCI
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptEconomía de la salud y políticas públicas-
crisitem.author.deptMétodos Cuantitativos en Economía y Gestión-
crisitem.author.deptEconomía de la salud y políticas públicas-
crisitem.author.deptMétodos Cuantitativos en Economía y Gestión-
crisitem.author.deptEconomía de la salud y políticas públicas-
crisitem.author.deptMétodos Cuantitativos en Economía y Gestión-
crisitem.author.deptFinanzas Cuantitativas y Computacionales-
crisitem.author.deptMétodos Cuantitativos en Economía y Gestión-
crisitem.author.orcid0000-0002-7126-4236-
crisitem.author.orcid0000-0002-5571-3257-
crisitem.author.orcid0000-0001-8904-8358-
crisitem.author.orcid0000-0002-4505-2678-
crisitem.author.parentorgMétodos Cuantitativos en Economía y Gestión-
crisitem.author.parentorgMétodos Cuantitativos en Economía y Gestión-
crisitem.author.parentorgMétodos Cuantitativos en Economía y Gestión-
crisitem.author.parentorgMétodos Cuantitativos en Economía y Gestión-
crisitem.author.fullNamePinilla Domínguez, Jaime-
crisitem.author.fullNameGonzález Lopez-Valcarcel, Beatriz-
crisitem.author.fullNameBarber Pérez, Patricia Lucía-
crisitem.author.fullNameSantana Jiménez, Yolanda-
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