Overweight and perception of overweight as predictors of smokeless tobacco use and of cigarette smoking in a cohort of Swedish adolescents

Carìa, Maria Paola; Bellocco, Rino; Zambon, Antonella; Horton, Nicholas J.; Galanti, Maria Rosaria

doi:10.1111/j.1360-0443.2009.02506.x

Cited by 28 publications

(30 citation statements)

References 49 publications

(62 reference statements)

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“…Applications of imputation by chained equations have now appeared in quite diverse fields: addiction (Schnoll et al 2006;MacLeod et al 2008;Adamczyk and Palmer 2008;Caria et al 2009;Morgenstern et al 2009), arthritis and rheumatology (Wolfe et al 2006;Rahman et al 2008;van den Hout et al 2009), atherosclerosis (Tiemeier et al 2004;van Oijen et al 2007;McClelland et al 2008), cardiovascular system (Ambler et al 2005;van Buuren et al 2006a;Chase et al 2008;Byrne et al 2009;Klein et al 2009), cancer (Clark et al 2001Royston et al 2004;Barosi et al 2007;Fernandes et al 2008;Sharma et al 2008;McCaul et al 2008;Huo et al 2008;Gerestein et al 2009), epidemiology (Cummings et al 2006;Hindorff et al 2008;Mueller et al 2008;Ton et al 2009), endocrinology (Rouxel et al 2004;Prompers et al 2008), infectious diseases (Cottrell et al 2005;Walker et al 2006;Cottrell et al 2007;Kekitiinwa et al 2008;Nash et al 2008;Sabin et al 2008;Thein et al 2008;Garabed et al 2008;Michel et al 2009), genetics …”

Section: Applications Of Chained Equationsmentioning

confidence: 99%

mice: Multivariate Imputation by Chained Equations inR

Buuren¹,

2011

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The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice 2.9, which extends the functionality of mice 1.0 in several ways. In mice 2.9, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice 2.9 adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice 2.9 can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

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Section: Applications Of Chained Equationsmentioning

confidence: 99%

mice: Multivariate Imputation by Chained Equations inR

Buuren¹,

2011

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“…An acceptable concordance between measured and self-reported height and weight has previously been reported for this cohort in grade 8, in which measurement by nurse as well as self-assessment took place [21]. The adolescents were instructed to measure height and weight in a standardized way (standing, in light clothes, and without shoes).…”

Section: Assessment Of Height and Weightmentioning

confidence: 95%

Low Physical Activity Is a Predictor of Thinness and Low Self-rated Health: Gender Differences in a Swedish Cohort

Elinder

Sundblom

Rosendahl

2011

Journal of Adolescent Health

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“…Total concordance between the classification of overweight based on self-reported weight and height at the age 14 and the weight and height measured by the nurse at the same age was 94.7%; kappa statistic for inter-rater agreement was 0.76. The concordance was higher for girls (96.8%, kappa = 0.80), than for boys (92.6%, kappa = 0.73) [12].…”

Section: Data Collectionmentioning

confidence: 99%

“…In an earlier study on the same cohort, missing information on co-variates was addressed comprehensively according to recommended procedures [25]. However, in sensitivity analyses, it was clear that results were stable and very close to those obtained with complete case analysis [12]; thus we used the latter approach in our analyses.…”

Section: Disclosure Statementmentioning

confidence: 99%

Predictors of Erroneous Perception of Being Overweight among Adolescents

et al. 2011

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Objective: Adolescents’ perception of overweight often disagrees with objective measurements; if factors explaining this discrepancy are potentially modifiable is unclear. Methods: We analyzed cross-sectional data from a cohort of 2,001 Swedish adolescents (1,026 females, 975 males) at the age of 15 years in 2003. Erroneous perception of overweight was defined as the perception of being overweight in relation to normal or lower than normal BMI (kg/m²). BMI measured by the school nurse at 11 years, school-based education on nutrition/physical activity, smoking, alcohol consumption, physical activity, and healthy/unhealthy eating were examined as potential predictors/correlates of overweight misperception in multivariate logistic regression models. Results: At the age of 15 years, 32% of girls and 10% of boys misclassified themselves as overweight. Overweight at 11 years predicted misperception of overweight 4 years later (adjusted odds ratio (OR) = 2.3, 95% confidence interval (95% CI) = 1.4–3.6 in boys; OR = 1.9, 95% CI = 1.3–2.8 in girls). Among girls, smoking was associated with a higher probability of misperception (adjusted OR = 1.6, 95% CI = 1.2–2.2). Among boys, physical activity (>6 h/ week) was associated with a lower probability of misperception (OR = 0.4, 95% CI = 0.2–0.7). No association was observed with school-based education on nutrition and physical activity. Conclusion: Preventing overweight in pre-adolescence may be a way to avoid later unjustified weight concerns in adolescence.

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Overweight and perception of overweight as predictors of smokeless tobacco use and of cigarette smoking in a cohort of Swedish adolescents

Abstract: In Sweden, adolescent girls with actual or perceived overweight are at increased risk to start smoking, while indications that this increased risk applies to smokeless tobacco (snus) are limited to girls of low socio-economic status.

Cited by 28 publications

References 49 publications

mice: Multivariate Imputation by Chained Equations inR

mice: Multivariate Imputation by Chained Equations inR

Low Physical Activity Is a Predictor of Thinness and Low Self-rated Health: Gender Differences in a Swedish Cohort

Predictors of Erroneous Perception of Being Overweight among Adolescents

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