BACKGROUND: A substantial challenge in addressing adolescent tobacco use is that smoking behaviors occur in complex environments that involve the school setting and larger community context. PURPOSE: This study provides an integrated description of factors from the school and community environment that affect youth smoking and explains variation in individual smoking behaviors both within and across schools/communities. METHODS: Data were collected from 82 randomly sampled secondary schools in five Canadian provinces (British Columbia, Manitoba, Ontario, Quebec, Newfoundland, and Labrador) during the 2003-2004 school year. Cross-sectional data were obtained from students; school administrators (school-based tobacco control policies and programs); and from observations in the community. In 2009, hierarchic logistic regression was used to model the role of individual, school, and community variables in predicting student smoking outcomes. RESULTS: Students who attended a school with a focus on tobacco prevention (OR=0.87, 95% CI=0.81, 0.94) and stronger policies prohibiting tobacco use (OR=0.92, 95% CI=0.88, 0.97) were less likely to smoke than students who attended a school without these characteristics. A student was more likely to smoke if a greater number of students smoked on the school periphery (OR=1.25, 95% CI=1.07, 1.47). Within the community, price per cigarette (OR=0.91, 95% CI=0.84, 0.99) and immigrants (OR=0.99, 95% CI=0.98, 0.99) were inversely related to students' smoking status. CONCLUSIONS: The results suggest that school and community characteristics account for variation in smoking levels across schools. Based on the current findings, the ideal school setting that supports low student smoking levels is located in a neighborhood where the cost of cigarettes is high, provides tobacco prevention education, and has a policy prohibiting smoking.
BACKGROUND: A substantial challenge in addressing adolescent tobacco use is that smoking behaviors occur in complex environments that involve the school setting and larger community context. PURPOSE: This study provides an integrated description of factors from the school and community environment that affect youth smoking and explains variation in individual smoking behaviors both within and across schools/communities. METHODS: Data were collected from 82 randomly sampled secondary schools in five Canadian provinces (British Columbia, Manitoba, Ontario, Quebec, Newfoundland, and Labrador) during the 2003-2004 school year. Cross-sectional data were obtained from students; school administrators (school-based tobacco control policies and programs); and from observations in the community. In 2009, hierarchic logistic regression was used to model the role of individual, school, and community variables in predicting student smoking outcomes. RESULTS: Students who attended a school with a focus on tobacco prevention (OR=0.87, 95% CI=0.81, 0.94) and stronger policies prohibiting tobacco use (OR=0.92, 95% CI=0.88, 0.97) were less likely to smoke than students who attended a school without these characteristics. A student was more likely to smoke if a greater number of students smoked on the school periphery (OR=1.25, 95% CI=1.07, 1.47). Within the community, price per cigarette (OR=0.91, 95% CI=0.84, 0.99) and immigrants (OR=0.99, 95% CI=0.98, 0.99) were inversely related to students' smoking status. CONCLUSIONS: The results suggest that school and community characteristics account for variation in smoking levels across schools. Based on the current findings, the ideal school setting that supports low student smoking levels is located in a neighborhood where the cost of cigarettes is high, provides tobacco prevention education, and has a policy prohibiting smoking.
Authors: Chris Lovato; Allison Watts; K Stephen Brown; Derrick Lee; Catherine Sabiston; Candace Nykiforuk; John Eyles; Steve Manske; H Sharon Campbell; Mary Thompson Journal: Am J Public Health Date: 2012-12-13 Impact factor: 9.308
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Authors: Bernard Fuemmeler; Chien-Ti Lee; Krista W Ranby; Trenette Clark; F Joseph McClernon; Chongming Yang; Scott H Kollins Journal: Drug Alcohol Depend Date: 2013-03-15 Impact factor: 4.492