Objectiv Objectiv es: es: TOBACCO TOBACCO NETWORKS IN A VOCATIONAL NETWORKS IN A VOCATIONAL EDUCATION EDUCATION SCHOOL SCHOOL IN THAILAND. IN THAILAND. Introduction Introduction : : Methods: Methods: Conclusions: Conclusions: To study a social network of smokers and non- smokers amongst first year students in a vocational education school in Thailand (ages 15-18 years). While these are preliminary and illustrative results, we believe that understanding the role of social networks in influencing smoking behavior among students in vocational education schools is potentially valuable for guiding and planning interventions to reduce smoking amongst students. Future extensions of the work will include formal statistical analysis of the data accounting for potentially important confounding factors including smoking behavior of family members and friends outside the school. D. Pudthum 1 , S. Pitayarangsarit 2, 3 , A. Meeyai 1, 2 1 Department of Epidemiology, Faculty of Public Health , 2 Tobacco Control Research and Knowledge Management Center, Mahidol University, Bangkok, 3 International Health Policy Program, Nonthaburi, Thailand Social networks can have a substantial influence on many aspects of human behavior. For example, smoking behaviour of peer group members is likely to affect smoking initiation among young adults. Smoking behaviour is therefore, to some extent, communicable and this can have profound effects on system dynamics. In Thailand,15-18 year olds have high initiation rates of smoking. Within this age group, students in vocational education schools have the highest rates of smoking. We used a cross-sectional survey to collect a set of social network data of all first year students (223 students: 189 male, 34 female) from a vocational education school in Thailand. All students were asked to nominate their five best friends within the school. For each of the 223 students the following data were also collected: age, sex, date of enrollment at school, current smoking behavior, previous smoking behavior, date they first started to smoke. Data were also collected on potentially important confounding factors including smoking behavior of family members and friends outside the school, and current residence. The information was used to construct a school- wide network, where individuals represented nodes, and links between nodes were represented by nominations. Nodes were classified either as current smokers, and non-current smokers (never smokers and former smokers). Network analysis was performed using the free open source software program, R. Results: Results: Most of the student were aged between 16 and18 years, about half of them were smokers. Only one sixth of all 223 students were female and about 25% of females were smokers. 50% of males were smokers. There were eight tracks of education in this vocational education school: auto-mechanic, bilateral mechanics, electronics, welding, electrical power, computing accountancy, and music science. There was evidence of clustering of smokers in the social network (Figure1). Table 1: Current smoking by age. Figure 1: Illustrative social network of smoking (red circles) and non-smoking (green circles) students. Arrows emanating from each node represent nominated friendships. Table 2: Current smoking by sex. Figure 2: Illustrative social network with accounting students and music science students highlighted. Account students Music Science students Among eight tracks of education in this vocational school there are two tracks (accountancy and music science) for which all members have social relationship only within the group (Figure 2).

Objectives: TOBACCO NETWORKS IN A VOCATIONAL EDUCATION SCHOOL IN THAILAND. Introduction : Methods: Conclusions: To study a social network of smokers and

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Page 1: Objectives: TOBACCO NETWORKS IN A VOCATIONAL EDUCATION SCHOOL IN THAILAND. Introduction : Methods: Conclusions: To study a social network of smokers and

Objectives:Objectives:

TOBACCOTOBACCO NETWORKS IN A VOCATIONALNETWORKS IN A VOCATIONAL EDUCATION EDUCATION SCHOOLSCHOOL IN THAILAND.IN THAILAND.

IntroductionIntroduction::

Methods:Methods:

Conclusions:Conclusions:

To study a social network of smokers and non-smokers amongst first year students in a vocational education school in Thailand (ages 15-18 years).

While these are preliminary and illustrative results, we believe that understanding the role of social networks in influencing smoking behavior among students in vocational education schools is potentially valuable for guiding and planning interventions to reduce smoking amongst students.

Future extensions of the work will include formal statistical analysis of the data accounting for potentially important confounding factors including smoking behavior of family members and friends outside the school.

D. Pudthum1, S. Pitayarangsarit 2, 3, A. Meeyai 1, 2

1Department of Epidemiology, Faculty of Public Health , 2Tobacco Control Research and Knowledge Management Center, Mahidol University, Bangkok, 3International Health Policy Program, Nonthaburi, Thailand

Social networks can have a substantial influence on many aspects of human behavior. For example, smoking behaviour of peer group members is likely to affect smoking initiation among young adults. Smoking behaviour is therefore, to some extent, communicable and this can have profound effects on system dynamics.

In Thailand,15-18 year olds have high initiation rates of smoking. Within this age group, students in vocational education schools have the highest rates of smoking.

We used a cross-sectional survey to collect a set of social network data of all first year students (223 students: 189 male, 34 female) from a vocational education school in Thailand. All students were asked to nominate their five best friends within the school. For each of the 223 students the following data were also collected: age, sex, date of enrollment at school, current smoking behavior, previous smoking behavior, date they first started to smoke. Data were also collected on potentially important confounding factors including smoking behavior of family members and friends outside the school, and current residence.

The information was used to construct a school-wide network, where individuals represented nodes, and links between nodes were represented by nominations. Nodes were classified either as current smokers, and non-current smokers (never smokers and former smokers). Network analysis was performed using the free open source software program, R.

Results:Results:

Most of the student were aged between 16 and18 years, about half of them were smokers. Only one sixth of all 223 students were female and about 25% of females were smokers. 50% of males were smokers.

There were eight tracks of education in this vocational education school: auto-mechanic, bilateral mechanics, electronics, welding, electrical power, computing accountancy, and music science. There was evidence of clustering of smokers in the social network (Figure1).

Table 1: Current smoking by age.

Figure 1: Illustrative social network of smoking (red circles) and non-smoking (green circles) students. Arrows emanating from each node represent nominated friendships.

Table 2: Current smoking by sex.

Figure 2: Illustrative social network with accounting students and music science students highlighted.

Account students

Music Science students

Among eight tracks of education in this vocational school there are two tracks (accountancy and music science) for which all members have social relationship only within the group (Figure 2).