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different contextual treatments of movie smoking, clockwise from top left: Cruella de Vil, anuncomplicated villain

EDIATRICS Volume 130, Number 2, August 2012
Examples of different contextual treatments of movie smoking, clockwise from top left: Cruella de Vil, anuncomplicated villain in 101 Dalmatians (rated G; Walt Disney Productions, 1961); Gwyneth Paltrowsmoking in the context of a sexually provocative scene in Great Expectations (rated R; 20th Century FoxFilm Corporation, 1998); Ethan Hawke blowing smoke into a backlit wine glass to show what the planetTitan looks like in Gattica (rated PG-13; Columbia Pictures Corporation, 1997); and Brad Pitt smokingafter a brutal fight scene from Fight Club (rated R; Fox 2000 Pictures, 1999).
smoking. Indeed, a content analysisfound that MPAA ratings can reliablydistinguish levels of sex, violence, andprofanity but not tobacco use.4 Figure 1depicts several examples of moviesmoking by rating and a range of con-texts that might be seen with moviesmoking according to rating category:simple villainy (G [appropriate for gen-eral audiences]), visually stimulating (PG-13 [parents are strongly cautioned, con-tent may not be suitable for children aged,13 years]), and violence and sex (R).
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Examining how movie ratings affect themovie smoking–behavior associationcould have important implications onratings for movie smoking,5 especiallygiven that 60% of the movie smokingexposure (MSE) comes from youth-rated (almost entirely PG-13) movies.6In the United States, an R rating forsmoking would serve to effectively eli-minate smoking from movies marketedto youths, based on the current businessmodel for movie production, in whichthe rating is negotiated between pro-duction company and the director be-fore movie production.7 The implicationis that a production company intendingto include the youth market would haveto eliminate smoking in the production
process, as is currently done with sexand violence to obtain the PG-13 rating.However, the hypothetical benefits oflimiting MSE in youth-rated moviesdepends partly on how strongly thesmoking in them is linked with ado-lescent smoking. Importantly, limitingsmoking to R-rated movies would havelittle impact if the dose-response be-tween smoking in youth-rated moviesand adolescent smoking was small.
In addition, if only R-rated movie smokingwas linked with behavior, it would se-riously undermine the idea that it ismovie smoking specifically, as opposedto the sex, violence, profanity, and illicitdrug use that prompts smoking onset.Indeed, a recent essay speculated thatthe movie smoking–youth smoking re-lationship might not be causal becauseMSE is “inextricably entangled with ahost of other variables in movies…such as alcohol or recreational drugportrayal, violence, coarse language,and sexual content,”8 raising concernsabout specificity. The essay went fur-ther, suggesting that it may not be themovies at all that prompt adolescentsto smoke. Instead, adult movies mayattract risk-taking adolescents whocome to see the proscribed behaviors
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(ie, adolescents who end up smokingfor other reasons). In this scenario,R-rated MSE would be hypothesized tobe overwhelmingly strong in its abilityto predict youth smoking, because R-ratedMSE picks up the effect of seeing “adult”behaviors relegated to these moviesand identifies unmeasured risk factorsamong the adolescents that see them.
The current study examined smokingonset in a cohort of US adolescentsfollowed up for 4 waves over a 2-yearperiod. Exposure to smoking in moviesat study onset was divided into 3 cat-egories (G/PG, PG-13, and R) to assessthe prospective relationship betweeneach type of exposure and onset ofsmoking. Based on the idea that it isprimarily the movie smoking thatprompts adolescents to smoke (with theadult context being secondary), we hy-pothesized that R-rated movie smokingwould have only a slightly stronger as-sociation with adolescent smoking thanPG-13–rated movie smoking and thatPG-13–rated movie smoking would stillbe an important predictor of smoking,given that it accounts for a large shareof the exposure.
in the unweighted sample were compa-rable to those of the 2000 US Census.9
Missing data/attrition increased from 7adolescents at baseline to 2451 at 24months. Attrition analyses indicatedthat adolescents lost to follow-up weremore likely to be nonwhite; were fromfamilies with lower parental education/income and lived in rented versusparent-owned residences; had poorerschool performance; and scored higheron sensation-seeking scales. To mini-mize attrition bias, estimation wascarried out after multiple imputationusing the missing at random assump-tion (missing data are missing at ran-dom conditional on covariates includedin the model).10 The MICE procedure inR was used to stochastically imputemissing data.11 To improve the qualityof the imputations, baseline auxiliaryvariables that were predictive ofmissing data (but not necessarily theoutcomes) were also included in theimputation. All variables were treatedas numeric, and the predictive meanmatching procedure was used to cre-ate 15 imputed values for each missingscore. Convergence was assessed bychecking plots of the mean and vari-ance of the imputations for each vari-able across the 15 streams for signs ofproblems, such as trends or lack ofproper mixing. No problems were ap-parent. For descriptive statistics, weaveraged across the 15 imputations toobtain a single best estimate for eachmissing data point.


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