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Risk factors associated with smoking initiation among Chinese adolescents: a matched case-control study

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Risk factors associated with smoking initiation among Chinese adolescents: a matched case-control study

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Content RISK FACTORS ASSOCIATED WITH SMOKING INITIATION AMONG CHINESE
ADOLESCENTS: A MATCHED CASE-CONTROL STUDY

by

Jie Yao

A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPILED BIOSTATISTICS & EPIDEMIOLOGY)

August 2008

Copyright 2008 Jie Yao

ii

DEDICATION

This thesis is dedicated to my parents, Xuejun Yao & Mo Chen and my wife Lei
Duan for their loving support.

iii

ACKNOWLEDGEMENTS

I would like to express my gratitude to Dr. Chih-Ping Chou for his invaluable
guidance and support during my graduate studies. I would also like to extend my
special thanks to my guidance committee, Dr. Stanley Paul Azen, Dr. Jennifer Unger
and Dr. Paula Palmer, for their guidance and support while I was working on my
Master’s thesis.

iv

TABLE OF CONTENTS

Dedication

ii
Acknowledgements

iii
List of Tables

v
List of Figures

vi
Abstract

vii
Introduction

1
Methods
Data Sources
Data Collection and Sample Selection
Measures
Data analysis

7
7
8
10
13
Results
Demographic Characteristics
Overview of predictors for cases and controls
Unadjusted Odds Ratios for potential risk factors of smoking
Multiple logistic regression of potential risk factors
Testing interaction

15
15
16
17
18
19
Discussion

26
References

32
Appendix 1

37
Appendix 2

38

v

LIST OF TABLES

Table 1. Characteristics of Wuhan Intervention Trial population at baseline
and one year follow-up, by program condition.
16
Table 2. Overview of predictors for cases and controls, by intervention
condition.
18
Table 3. Unadjusted Odds Ratios (ORs) and 95% Confidence Interval (CIs)
for potential risk factors of smoking.
23
Table 4. Multiple logistic regression of potential risk factors of smoking
behavior, unconditional vs conditional at one year follow-up
24
Table 5. Testing of Intervention effect on intervention related risk factors at
baseline
25

vi

LIST OF FIGURES

Fig.1 Intention to smoke OR by program condition over time 21

vii

ABSTRACT

A longitudinal school-based smoking prevention trail with a social normative
approach developed in the western culture was implemented with 7
th
grade students
in Wuhan, China. A nested matched case-control study was used to examine the
risk factors and their associations with adolescents’ smoking initiation, and whether
the intervention effect on follow-up smoking behaviors is dependent on different
levels of baseline risk factors. Data were obtained from 817 baseline non-smoking
7
th
grade students in Wuhan. Both conditional and unconditional logistic regression
analyses were performed to assess the associations of various risk factors with
smoking initiation. Non-smoking students who had high intention to smoke,
negative knowledge toward to smoking addiction and low smoking refusal
self-efficacy were more likely to become smokers at the follow-up survey. We did
not detect the effect of the intervention program in preventing smoking initiation.
Neither did we find the interaction effect of intervention program with individual’s
baseline risk factor profile. Future tobacco prevention interventions should focus
on reducing smoking intention, empowering non-smoking adolescents with the
self-efficacy and knowledge on the harm of nicotine to prevent smoking initiation.

1

INTRODUCTION

China ranks the highest in the world for both tobacco production and
consumption (Corrao, Guindon, Sharma, & Shokoohi, 2000). The consumption in
China is more than three times that of the USA and accounts for approximately
one-third (31%) of the world total tobacco consumption (Yang et al., 1999). A
population-based national survey conducted in 30 provinces of China in 1996
reported that approximately 63% of Chinese males and 3.8% of Chinese females
were current smokers (Yang et al., 1999); 72% of these current smokers have no
intention to quit (Yang et al., 2001). The number of smokers increased from 320
million in 1996 to 350 million in 2002 (Yang, Ma, Liu, & Zhou, 2005). China’s
national surveys on adolescent smoking conducted in 1984, 1996, and 2002 indicated
that the prevalence of smoking among adolescents has been increasing at a
substantial rate (Weng, Hong, & Chen, 1987; Yang et al., 1999; Yang et al., 2005).
Current figures show that Chinese adolescent smoking prevalence ranges from 28%
to 48% among boys and 3% to 18% among girls depending on measures of cigarette
consumption, age, and geographical area of the respondents (Hesketh, Ding, &
Tomkins, 2001; Johnson et al., 2006; Li, Fang, & Stanton, 1996; Sun & Ling, 1997;
Unger et al., 2001; Zhu, Liu, Shelton, Liu, & Giovino, 1996; Zhu et al., 1992). It is
estimated that about 200 million children living in China today will become regular

2

smokers, and that at least 50 million will die prematurely of smoking related illnesses
(Cheng, 1999).
There is a growing urgency for effective prevention and intervention for
adolescent smoking. When smoking is initiated at a young age, the risks of heavy
smoking (Escobedo, Marcus, Holtzman, & Giovino, 1993; Taioli, 1991 ) and
nicotine dependence increase (McNeill, 1986 ). Furthermore, among those with
early onset of cigarette smoking, cessation would be more difficult (Khuder, 1999;
Prokhorov, 2001 ). Thus, prevention of smoking initiation and effective
intervention at an early age may have significant public health benefits (Kumra &
Markoff, 2000).
Prevention of tobacco use among adolescents has been recognized as an
important public health priority in China (Yang et al., 1999). However, only a few
studies provided inconsistent evidence of effectiveness of school-based intervention
programs for smoking prevention in China. A pilot trial of a school-base
intervention program conducted in Beijing reported that the intervention program
Stay Away from Tobacco (SAFT) significantly reduced the prevalence and the level
of cigarette smoking among 381 middle and high school students (Chen, 2006). In
Zhejiang Province, another school-based intervention program did not find
significant changes in smoking behavior among 10,395 students in grades 1–7 in 23

3

primary schools in the intervention group, compared with 9,987 students in the same
grades from 21 control schools (CDC, 1993).
To develop and implement cost-effective smoking prevention programs for
adolescents in China, it is necessary and important to identify the most significant
determinants that lead to adolescent smoking. Intention to smoke is often
correlated with smoking, and is therefore considered to be a valid and reliable
predictor of adolescent behavior (Stanton, Barnett, & Silva, 2005). According to
the Theory of Reasoned Action (TRA), developed by Fishbein and Ajzen (Ajzen &
Fishbein, 1980; Fishbein, 1975), intention to perform a behavior is a key construct of
the predictive models. Several longitudinal studies of adolescent smoking have
examined the association specifically for those who had not smoked and have found
an association between intention to smoke and subsequent initiation of smoking
(Chassin, Presson, Sherman, Corty, & Olshavsky, 1984; Choi, Gilpin, Farkas, &
Pierce, 2001; Maddahian, Newcomb, & Bentler, 1988; McNeill et al., 1988).
Another important risk factor for smoking is low self-efficacy to refuse
cigarette offers. High self-efficacy can enhance an adolescent’s capability to resist
influences from parents or peer. Some studies conducted in the United States have
found that low refusal self-efficacy reduces the ability to say no to an offer of a
cigarette, which is positively associated with adolescent cigarette smoking (Ellickson
& Hays, 1990; Epstein, Griffin, & Botvin, 2000; Lawrance & Rubinson, 1986).

4

More positive attitude toward smoking and smokers tended to be related with
an increased likelihood of smoking. The associations have been established in
western nations and in China as well (Zhang, Wang, Zhao, & Vartiainen, 2000; Zhu
et al., 1996). However, attitude may not be as important as other factors. For
example, Stanton and Silva (Stanton & Silva, 1991) did not find an association for
attitude after controlling for friends’ smoking behaviors.
The impact of parental smoking has been studied in a wide range of contexts
and in a large number of studies with a variety of outcomes. Studies examining the
effect of paternal and maternal smoking separately have reported inconsistent results.
Tuakli (Tuakli, Smith, & Heaton, 1990) found paternal and maternal smoking were
both significantly associated with adolescents’ smoking behavior. But some studies
observed increased risk either associated with either paternal or maternal smoking
only (Hops, Tildesley, & Lichtenstein, 1990; Hover & Gaffney, 1988). In another
study by Jensen et al, no significant effect of parental smoking was found (Jensen &
Overgaard, 1993). A positive association between parental smoking and adolescent
smoking has been reported in Chinese population (Chen, 2006; de Vries, 2007;
Shakib et al., 2005).
Primary influence of parents declines as children reach adolescence, and peer
influence becomes more important in western countries (Hoffman, Sussman, Unger,
& Valente, 2006; Kobus, 2003). However, peer influences on adolescent smoking

5

have been less studied in China. According to the limited findings on peer smoking
in China, peer smoking has been found as a strong risk factor for adolescent smoking
in urban areas (Chen, 2006; Grenard et al., 2006).
In both western countries and China, smoking status has been found to be highly
related to academic performance (Hops et al., 1990; Hover & Gaffney, 1988; Li et
al., 1996; Zhu et al., 1996). Students who do well in school and have high
academic aspirations are less likely to smoke than those who do not possess these
characteristics. The protective effect of academic performance, or aspirations, on
adolescent smoking may reflect beliefs necessary for academic success (Grenard et
al., 2006; Yang et al., 1999).
Gender differences in smoking prevalence among adolescents in China are
larger than those among western teenagers. In some western countries smoking
rates among girls were equal or higher than boys (Oakley, Brannen, & Dodd, 1992;
Stanton, Oei, & Silva, 1994). In contrast, a significantly higher prevalence of
smoking among boys (28% to 43%) than girls (3% to 18%) was observed in China
(Li et al., 1996; Unger et al., 2001; Zhu et al., 1996).
The purpose of the present study is to examine baseline risk factors and their
associations with adolescents’ smoking initiation, and whether the intervention effect
on follow-up smoking behaviors is dependent on (or modified by) different levels of
baseline risk factors. Using a nested matched case-control study design, we aim to

6

evaluate the hypothesis that the intervention program may reduce the progression to
ever smokers among students who intended to smoke at baseline but not among
students who expressed no intension to smoke at the baseline survey. To evaluate
these hypotheses, we used the data from a longitudinal school-based Wuhan
Smoking Prevention Trial (WSPT) which was implemented with 7
th
grade students
in Wuhan, China. The primary purpose of the WSPT is to prevent never smokers at
baseline from progression to ever smokers or recent smokers after one year
follow-up. The investigators found no evidence for program effect on smoking
between intervention group and reference group among baseline never smokers
(Chou, 2006). Moreover, the association between baseline risk factors and
initiation of smoking and how these baseline risk factors may modify the effect of
the intervention program on smoking initiation has not been explored in that study.

7

METHODS

Data Sources
Adolescents included in this study were obtained from a large longitudinal
school-based Wuhan Smoking Prevention Trial (WSPT) that initiated in1998.
Wuhan is the capital of the Hubei Province and has a population of 7 million people
and a transient or floating population of approximately 1 million additional people.
Two middle schools with similar school size, teacher/student ratio, and academic
rating in the same district were selected from each of the 7 urban districts in Wuhan.
One school from each matched pair was randomly assigned to the intervention group
and the other school was assigned to the reference group. Four 7th grade
classrooms from each school were randomly selected to participate in the evaluation
of the WSPT. The WSPT lessons were delivered in classrooms by health educators
from the Wuhan Center for Disease Control and Prevention. Prior to implementing
the program, each health educator was trained by health educators and researchers
from the United States. Students in the experimental group received a 45-minute
curriculum session weekly for 13 consecutive weeks. Because the curriculum
required substantial time, the experimental schools replaced some of their regular
classes with the intervention sessions. The control schools received their usual
academic instruction. Participating students completed a 200-item paper-and-pencil
questionnaire that assessed smoking and related psychosocial factors at baseline and

8

at the 1-year follow-up. Baseline data collection started in December 1998. The
first follow-up survey was conducted approximately 1 year later. Details of this
study can be found in elsewhere (Chou, 2006; Unger et al., 2003; Unger et al., 2001).

Data Collection
Written consent was obtained from all respondents and their parents. The
informed consent and data collection procedures were reviewed and approved by
both the University of Southern California and Chinese institutional review board of
Wuhan CDC. Students completed a 200 items self-administered, paper-and-pencil
questionnaire in their classrooms. The survey proctors (native Chinese data
collectors trained in the United States) explained to the students that their responses
would remain confidential. Students were allowed to decline participation at any
time. Of the students invited to participate, the response rate is 97% (2661 out of
2774 surveyed 7
th
grade students) at baseline.

Sample Selection
Among a total of 2454 7
th
students who were surveyed both at the baseline
and at the one-year follow-up, 1683 (68.58%) students were never smokers at
baseline. Out of these, 288 (17.11%) baseline never smoking students became ever
smokers at one year follow-up and were considered as the cases in our nested

9

case-control design. Controls were randomly selected from the rest of 1395
students who remained nonsmoker at the one year follow-up. In order to minimize
sampling bias, cases and controls were matched on gender, school and class.
Of the 288 cases, 251 had two control subjects for each case, 27 had one
control subject for each case, and 10 cases couldn’t find any non-duplicated matched
controls. A total of 817 7
th
grade students (288 cases and 529 controls) were
included for analysis. The process of matching was done by using SAS 9.1.3
software (SAS Institute Inc.). The program consisting of DATA step, PROC SQL,
and MACRO programming is presented in Appendix 1.

Measures
The questionnaire was designed to assess demographics (e.g. age gender),
tobacco and tobacco-related behaviors, beliefs, and exposures. An English version
of the questionnaire was developed by a team of researchers from Wuhan and the
United States. The questionnaire was then translated into Chinese and
back-translated into English to ensure accuracy of translation. Bilingual research
staff compared the two translations and reconciled any discrepancies in meaning.
The Chinese version of the questionnaire was pilot-tested with a small group of
adolescents in Wuhan to identify any potential wording ambiguities. Measures

10

used in this study were classified into three categories: dependent variables, potential
predictors, and covariates.
Dependent variable:
Change in smoking status is the main dichotomous outcome variable. Here,
we defined a case as a student who was non-smoker at the baseline but became an
ever smoker at the one year follow up, and a control as a student who was
non-smoker at baseline and remained as non-smoker at follow up. Ever, or lifetime,
smoking status was assessed with the question “Have you ever tried smoking, even a
few puffs?” (0 = no, 1 = yes).

Potential risk and protective factors:
Peer smoking and friend smoking. For peer smoking, students were asked
about their estimate of how many of their peers smoked: ‘‘In your opinion, how
many out of 100 people your age smoke at lease once a month ?’’ Students
responded on an 11-point scale: (0 = none, 1 = about 10, 2 = about 20, 3 = about 30,
4 = about 40, 5 = about 50, 6 = about 60, 7 = about 70, 8 = about 80, 9 = about 90, 10
= about 100). For friend smoking, two questions were asked, one for male friends
and one for female friends. The questions asked, “Among your best friends who
are boys (girls), how many smoke?” Responses were rated on a 13-point scale
ranging from “0” to “12 or more.” Respondents who indicated that any of their

11

male friends or female friends smoked were coded as having friends who smoked.
A dichotomous variable representing the presence of any friends who were smokers
(0 = none, 1 = from a few to all).

Parental smoking. Students were asked: “Does your dad smoke?” and “Does your
mom smoke?” (0 = no, 1 = yes, 2= I don’t know). Students who indicated that any
of their father or mother smoked were coded as having parents smoked.

Knowledge toward smoking addiction. The students answered one question about
tobacco use: ‘‘Do you think people can get addicted to smoking just as they can to
other drugs?’’ Students responded on a four-point scale ranging from ‘‘definitely
not’’ to ‘‘definitely yes’’. We recoded this scale into a dichotomous variable (0 =
maybe/definitely not, 1 = maybe/definitely yes). Students who responded
“definitely yes” and “maybe yes” may indicate more concern about the addictive
nature of tobacco.

Smoking refusal self-efficacy was measured with question “Would you accept a
cigarette offered to you by your best friend?” The responses were “definitely yes”,
“maybe yes”, “maybe not”, and “definitely not”. We considered those who
responded “definitely yes” and “maybe yes” as having low refusal self-efficacy, and

12

the others who answered “maybe not” and “definitely not” as having high
self-efficacy (0 = maybe/definitely not, 1 = maybe/definitely yes).

Academic score and subjective academic performance. Academic score were
assessed by asking students to self-report their performance: “What was your
average score in all your classes last semester?” (1= Below 60, 2=60-69, 3=70-79,
4=80-89, 5=above 90). For subjective academic performance, the students were
asked to rate how well they thought they were performing at school compared with
other students: “How good do you think of your scores are, compared with your
classmates?” (1= very poor, 2= poor, 3=Average, 4=good, 5= excellent”) We
recoded the five-point scale into a new categorical variable which indicated
respondents’ low (1=very poor/poor), medium (2=average), and high (3=
good/excellent) subjective performance.

Perceived access to cigarettes was assessed by asking “If you want to have
cigarettes, do you think it is easy to get them?” Answers were rated on a four-point
scale (1= very hard, 2= fairly hard, 3=fairly easy, 4= very easy). This item was
recoded to a dichotomous variable with response “hard” vs. “easy”.

13

Intention to smoke in the next year was measured with question “Do you think you
will start smoking sometime during the next 12 months (one year)?” The 4-point
scale item (“1=No, definitely not, 2=Maybe no, 3=Maybe yes and 4= Definitely
yes”) was recoded to a dichotomous variable. Students who answered “No,
definitely not” to the question, were classified as not intending to smoke in the next
year, whereas those who answered “Maybe no, Maybe yes and Definitely yes” were
classified as susceptible.
Specific wording and coding for responses of the variables aforementioned
are summarized in Appendix 2.

Data analysis
All statistical analyses were performed using SAS 9.1.3. Chi-square test and
t-test were used to compare the proportion and mean, respectively, of age, gender, and
potential risk and protective factors between intervention and reference groups at
baseline and follow-up. The same procedure was repeated by case and control
status within intervention group and reference group separately. Univariate
conditional logistic regression was performed to evaluate the unadjusted odds ratios
(ORs) and 95% confidence intervals (95% CI) for each potential risk factor in
relation with the risk of becoming ever smokers. Adjusted ORs and 95% CIs were
also obtained using multiple conditional logistic regression with adjustment of risk

14

factors, as well as unconditional logistic regression with adjustment for matching
variables (gender and class). TPHREG procedure in SAS was used to perform
conditional logistic regression with matched case-control study data.
To further explore whether the intervention effect on follow-up smoking
behaviors is dependent on (or modified by) different levels of baseline risk factors
(e.g. knowledge toward smoking; intention to smoke; self-efficacy). We created an
interaction term with intervention group for each of the three risk factors. By
interaction term, we mean a cross-product between each of the risk factor variables
and intervention group. For example, the interaction term (multiplicative
interaction) for intend to smoke was represented by a new variable
INTEND*PROGRAM, where INTEND was the dichotomized variable for intention
to smoke, and was coded as 0 (no intend to smoke in future) and 1 (intend to smoke
in future), and PROGRAM represented the intervention group status (1=intervention
group; 0=reference group). P-values for each of the interaction term were obtained
in conditional logistic regression models. A statistically significant interaction term
suggests the difference of relationship between risk factor and risk of becoming ever
smokers across intervention and reference groups.

15

RESULTS
Demographics and risk and protective factors between the intervention group
and the reference group at baseline and follow-up are summarized in Table 1.
Among the 817 subjects who were included in the analysis, 369 were in the
intervention group and 448 were in the reference group. We found no significant
differences in gender distribution between the intervention group and the reference
group. Intervention group students were in average slightly older than students in
the reference group at baseline (mean age: 12.8 years for intervention, 12.4 years for
comparison; p<0.01). At baseline, 85% of the participants in both groups believed
in that people may get addicted to smoking, 4.42% of the intervention group and
2.58% of the reference group students indicated that they may try cigarette smoking
in one year (

p=0.17). At one year follow-up, the percentage of individuals who
believed people may get addicted to smoking significantly increased to 90% in the
intervention group; while the proportion remained the same at 85% for the reference
group (

p=0.04). More participants from both groups showed their intention to
cigarette smoking at one year follow-up (10.64% and 8.71% for intervention group
and reference group, respectively) than at the baseline. On average, the
intervention group students had a higher academic score than the students in the
reference group. We did not detect any other significant differences between
intervention group and the reference group for the potential smoking risk factors at
baseline and follow-up.

16
Table 1. Characteristics of Wuhan Intervention Trial population at baseline and one year follow-up, by program condition.
Baseline one year follow-up

Intervention
(N=369)
Reference
(N=448)
P-
value
a

Intervention
(N=369)
Reference
(N=448)
p-
value
a

Mean age (SD
c
) 12.80 (0.70) 12.40 (0.70) <0.01
b
13.70 (0.70) 13.40 (0.70) <0.01
b

Male (%) 51.27 55.14 0.16
Female (%) 48.73 44.86
Potential risk factor (%)
Mean Academic score 3.87 3.56 <0.01
b
3.34 3.08 <0.01
b

Subjective academic performance 2.34 2.23 0.03
b
2.19 2.07 0.02
b

Parental smoking 77.31 74.18 0.30 77.87 73.47 0.15
Good friend smoking 17.37 14.51 0.24 24.93 26.53 0.61
Perceived smoking prevalence out of
100 peers (mean and SD)
20 ( 23) 20 (23) 0.83
b
24 (23) 23 (23) 0.87
b

Perceived access to cigarettes 30.41 32.35 0.57 49.44 50.58 0.74
Knowledge toward smoking addiction
(people can get addicted to smoking)
85.67 84.67 0.69 89.86 85.11 0.04
Smoking refusal self-efficacy 4.20 2.58 0.20 9.01 11.97 0.22
Intention to smoke 4.32 2.45 0.15 10.64 8.71 0.35
a. P-value ascertained from chi_square test, except where otherwise noted.
b. P-value ascertained from t-test.
c. SD =standard deviation

17
Based on the case-control study design, 288 students (132 in the intervention
group, 156 in the reference group) became ever smokers at one year follow-up; these
288 students were defined as ‘cases’, and the rest 529 participants were ‘controls’.
Table 2 provides an overview of predictors for cases and controls separately. More
cases intend to smoke than controls at both waves and both groups (For the
intervention group, p=0.04 at baseline and p<0.01 at follow-up; for the reference
group, p=0.02 at baseline and p<0.01 at follow-up). Compared to controls, cases
reported a higher proportion of good friends smoking (p=0.01 at baseline, p<0.01 at
follow-up for the intervention group; for the reference group, p=0.15 at baseline,
p<0.01 at follow-up) and lower smoking refusal self-efficacy (For the intervention
group, p=0.12 at baseline, p<0.01 at follow-up; for the reference group, p<0.01 at
baseline, p<0.01 at follow-up). We found no statistically significant differences in
parental smoking and perceived access to cigarettes between cases and controls.
Although there were no differences in knowledge toward smoking addiction (do you
think people can get addicted to smoking?) between cases and controls at baseline,
more controls were believed in that people could get addicted to cigarettes than cases
at one year follow-up (p<0.01 for both intervention group and reference group).

18

Table 2. Overview of predictors for cases and controls, by intervention condition.
Intervention Group
Baseline 1 year follow-up

Control
(N=286)
Case
(N=156)
P-value
a

Control
(N=286)
Case
(N=156)
p-value
a

Mean age (SD
c
) 12.59(0.60) 12.61(0.60) 0.81
b
13.55(0.70) 13.59(0.60) 0.72
b

Male% 51.27 51.27 - -
Female% 48.73 48.73
Potential risk factor (%)
Mean Academic score 3.70 3.46 0.04
b
3.31 3 0.02
b

Subjective academic
performance
2.28 2.05 0.01
b
2.18 1.9 0.01
b

Parental smoking 76.39 79.03 0.57 77.85 80.53 0.48
Good friend smoking 13.65 25.00 0.01 18.27 39.22 <0.01
Perceived smoking
prevalence out of 100 peers
18 23 0.08
b
23 26 0.23
b

Perceived access to cigarettes 29.66 32.48 0.55 46.34 58.69 0.02
Knowledge toward smoking 86.31 84.68 0.69 94.13 82.86 <0.01
Smoking refusal self-efficacy 3.04 6.45 0.12 3.46 19.13 <0.01
Intention to smoke 2.61 7.50 0.04 5.86 21.68 <0.01
a. P-value ascertained from chi_square test, except where otherwise noted.
b. P-value ascertained from T-test.
c. SD =standard deviation

19

Table 2，，，，Continued
Reference Group
Baseline 1 year follow-up

Control
(N=286)
Case
(N=156)
P-value
a

Control
(N=286)
Case
(N=156)
p-value
a

Mean age (SD
c
) 12.27(0.7) 12.46(0.7) 0.11
b
13.28(0.7) 13.42(0.7) 0.09
b

Male% 55.14 55.14
Female% 44.86 44.86
Potential risk factor (%)
Mean Academic score 4.12 3.76 0.01
b
3.61 3.15 0.01
b

Subjective academic
performance
2.35 2.07 0.01
b
2.19 2.01 0.03
b

Parental smoking 72.79 76.62 0.2 77.67 79.82 0.14
Good friend smoking 12.50 17.53 0.15 21.18 37.39 <0.01
Perceived smoking
prevalence out of 100 peers
18 22 0.06
b
20 30 0.01
b

Perceived access to cigarettes 29.70 36.99 0.11 46.8 58.12 0.04
Knowledge toward smoking 83.70 86.36 0.46 89.14 77.78 <0.01
Smoking refusal self-efficacy 0.59 6.49 <0.01 5.91 21.85 <0.01
Intention to smoke 1.23 4.86 0.02 2.88 18.94 <0.01
a. P-value ascertained from chi_square test, except where otherwise noted.
b. P-value ascertained from T-test.
c. SD =standard deviation

20

Table 3 presented results from the univariate conditional logistic regression
analyses detecting whether the potential risk factors are significantly associated with
initiation of cigarette smoking among adolescents. Smoking intention was strongly
associated with an increased risk of becoming an ever smoker (OR=3.19,
95%CI=1.06-9.63 for Intervention group, OR=4.67, 95%CI=1.21-18.05 for
Reference group at baseline; OR= 4.59, 95%CI=2.19-9.62 for Intervention group,
OR=15.44, 95%CI=4.66-51.19 for Reference group at one year follow-up). We
noticed that the OR increased from 4.67 in the baseline to 15.44 at one year
follow-up among the reference group, but only increased slightly among the
intervention group (Figure.1). Likewise, students who had lower cigarette refusal
self-efficacy were at greater risk of becoming an ever smoker. In intervention
group, those who had lower refusal self-efficacy were approximately 3-5.6 times
more likely to become ever smoker, and 5.99-7.37 times more likely to become ever
smoker in reference group. Students who reported having good friends smoking or
who perceived greater peer smoking were at a higher risk of becoming ever smokers
as well. Not surprisingly, students who reported having better academic
performance or subjective academic performances were at less chance of becoming
ever smoker. The association between knowledge toward smoking addiction and
smoking behavior was only statistically significant at follow-up. Parental smoking
was associated with an increased risk of ever smoking only at follow-up among

21

students in the reference group (OR=1.62, 95% CI=1.01-2.63). No other significant
relationships were found.

Table 4 compares the results of multiple conditional logistic regression model
and unconditional logistic regression model. In conditional logistic regression
model with multiple risk factors, three factors: intentions to smoke, knowledge
toward smoking and smoking refusal self-efficacy were found statistically
significantly associated with smoking behavior in the intervention group. However,
Fig.1 Intention to smoke OR by program condition over time
0
2
4
6
8
10
12
14
16
Baseline Follow-up
Odds Ratio of Intend to smoke
Program
Reference

22

in the reference group, only intentions to smoke and smoking refusal self-efficacy
were significant risk factors while knowledge toward smoking was no longer
significantly associated with becoming an ever smokers. The same significant risk
factors were found in the unconditional logistic regression model with adjustment of
gender and class. Both conditional logistic model and unconditional model results
showed that the intervention group students who had intentions to try cigarettes were
approximately 2.19-2.42 times more likely to becoming ever smokers comparing to
students who had no intention to smoke. Students in the reference group who had
intentions to smoke were 3.73-5.48 times more likely to become ever smokers. The
risk of becoming an ever smoker associated with intention to smoke was slight lower
among intervention group.

23
Table 3. Unadjusted Odds Ratios (ORs) and 95% Confidence Interval (CIs) for potential risk factors of smoking.
Baseline Follow-up
Smoking risk factors
Intervention
(N=369)
OR (95% CI)
Reference
(N=448)
OR (95% CI)

Intervention
(N=369)
OR (95% CI)
Reference
(N=448)
OR (95% CI)
Academic score 0.74 (0.59-0.94) 0.62 (0.47-0.83) 0.75 (0.58-0.97) 0.56 (0.43-0.74)
Subjective academic performance 0.62 (0.45-0.85) 0.66 (0.47-0.92) 0.68 (0.52-0.91) 0.64(0.47-0.88)
Parental smoking 1.34 (0.77-2.35) 1.36 (0.82-2.25) 1.22 (0.70-2.13) 1.62 (1.00-2.63)
Good friend smoking 2.10 (1.20-3.70) 1.60 (0.86-2.98) 3.17 (1.84-5.47) 2.25 (1.42-3.55)
Perceived peer smoking 1.08 (0.98-1.18) 1.11 (1.01-1.21) 1.09 (0.98-1.21) 1.21 (1.10-1.32)
Perceived access to cigarettes 1.19 (0.74-1.91) 1.44 (0.90-2.31) 1.59 (1.02-2.49) 1.51 (0.99-2.31)
Knowledge toward smoking 1.18 (0.60-2.32) 1.48 (0.81-2.72) 3.78 (1.70-8.43) 2.29 (1.32-3.98)
Smoking refusal self-efficacy 3.03 (0.88-10.43) 5.99 (2.28-14.91) 5.64 (2.52-12.62) 7.37 (3.39-16.06)
Intention to smoke 3.19 (1.06-9.63) 4.67 (1.21-18.05) 4.59 (2.19-9.62) 15.44 (4.66-51.19)
Note. Univariate conditional logistic regression model was used to test for each risk factor. ( Matched on gender and classroom)

24
Table 4. Multiple logistic regression of potential risk factors of smoking behavior, unconditional vs conditional at one year follow-up
Conditional Logistic Regression* Unconditional Logistic Regression**

Intervention
(N=369)
Reference
(N=448)

Intervention
(N=369)
Reference
(N=448)
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Academic score 1.00 (0.68-1.48) 0.57 (0.39-0.84) 1.17 (0.87-1.59) 0.72 (0.54-0.96)
Subjective academic performance 0.77 (0.47-1.26) 1.00 (0.64-1.55) 0.64 (0.41-1.00) 0.87 (0.59-1.27)
Parental smoking 0.92 (0.47-1.79) 1.62 (0.91-2.89) 0.98 (0.54-1.78) 1.29 (0.77-2.16)
Good friend smoking 1.75 (0.95-3.24) 1.42 (0.80-2.51) 2.00 (1.13-3.54) 1.25 (0.74-2.12)
Perceived peer smoking 1.07 (0.94-1.22) 1.11 (0.99-1.24) 1.01 (0.90-1.12) 1.10 (1.00-1.21)
Perceived access to cigarettes 1.24 (0.74-2.11) 1.29 (0.77-2.15) 1.12 (0.68-1.84) 1.14 (0.72-1.80)
Knowledge toward smoking 2.98 (1.19-7.47) 1.21 (0.59-2.48) 3.94 (1.77-8.76) 1.32 (0.70-2.50)
Smoking refusal self-efficacy 3.29 (1.24-8.77) 3.41 (1.37-8.46) 3.88 (1.51-9.98) 3.40 (1.55-7.47)
Intention to smoke 2.19 (1..01-5.51) 5.48 (1.36-22.06) 2.42(1.02-5.48) 3.73 (1.41-9.84)
Note: * Conditional logistic regression matched on gender and class.
** Unconditional logistic regression adjusted for gender and class.

25
Interaction effect
We found no evidence for interaction between the intervention program and
baseline risk factors (intention to smoke, knowledge toward smoking and smoking
refusal self-efficacy) in relation to becoming an ever smoker at follow-up. As
shown in Table 5, the association between risk factors and becoming an ever smoker
did not significantly differ between the intervention conditions (all interaction terms
were non-significant with p>0.05). Overall, these results do not support our
hypothesis that effect of the intervention program may be dependent on individual’s
baseline risk factor profile.

Table 5. Testing of Interaction effects of intervention and related risk factors at
baseline

Overall
(N=817)
Intend to
smoke*Intervention
0.67
Knowledge toward
smoking*Intervention
0.21
Smoking refusal
self-efficacy*Intervention
0.15
Note: P-values reported for interaction terms were ascertained from Conditional logistic
regression model.

26

DISCUSSION

Understanding the risk factors that influence smoking initiation among
Chinese adolescents may be important for developing and implementing smoking
control intervention program. Using a nested case-control study design, this study
investigates the association of potential risk and protective factors at baseline and
chance of becoming ever smokers at one year follow-up among baseline
non-smoking adolescents in Wuhan, China. We found a strong association of
cigarette smoking behavior with adolescents’ psychosocial factors such as intention
to try cigarette, knowledge toward smoking addiction and smoking refusal
self-efficacy. These associations persisted even after controlling for potential
confounders (e.g. gender, classroom) and other risk factors (e.g. parental smoking,
good friend smoking, and academic score). Non-smoking students who had high
intention to smoke, negative knowledge toward to smoking addiction and low
smoking refusal self-efficacy at the baseline were more likely to report ever
smoking at the follow-up survey.
The results of the present study supported the theory of reasoned action
(TRA). The Theory of Reasoned Action (TRA), developed by Fishbein and Ajzen
(Ajzen & Fishbein, 1980; Fishbein, 1975), addresses the impacts of cognitive

27
components, such as attitudes, social norms, and intentions, on behaviors.
According to this theory, individuals' attitudes toward a certain behavior and norms
representing their perception of other people's view of such behavior will determine
their behavioral intentions, which may further lead to performance of the behavior.
The TRA have been applied successfully to investigate, explain, or predict behaviors
in various age groups, including adolescents. Collins and Ellickson (Collins &
Ellickson, 2004) conducted a prospective study that provided evidence consistent
with the theory that multiple influences should be considered when predicting
adolescent smoking. Hanson used TRA to identify beliefs related to smoking
behaviors among African American, Puerto Rican, and non-Hispanic White female
adolescents(Hanson, 1999). The applicability of TRA to predict smoking behaviors
in Chinese adolescents who live in a different cultural and economic environment
have also been evaluated by other studies (Grenard et al., 2006; Guo et al., 2007).
The current study provides further empirical evidence that the psychosocial factors
were important smoking predictors among non-smoking Chinese adolescent
population.
The data in the current study did not show a strong relationship between
parental smoking and smoking practice among Wuhan adolescents. It’s possibly
due to a high smoking prevalence of Chinese parents (more than 70%) that results in

28
a small variation in the influence that Chinese adolescents have received. The
similar weak influence of parental smoking on adolescent smoking was reported in a
recent study conducted in urban Guangzhou, China (de Vries, 2007), and in studies
conducted in Western countries (Hoffman et al., 2006). The present findings
indicated that good friend smoking may be an important risk factor for adolescent
smoking in China. The direction and the magnitude of the association between
adolescent smoking and good friend smoking observed in this study are compatible
with previous studies in China (Grenard et al., 2006; Li, Fang, & Stanton, 1999;
Yang et al., 2004; Zhu et al., 1996; Zhu et al., 1992).
In the present study, our study populations were nested in a longitudinal,
randomized experimental study. We used a nested case control method and concept
to examine the above risk factors and their associations with cigarette smoking
among the youth of Wuhan, China. The nested case-control study is a relatively new
observational design whereby a case-control approach is employed within an
established cohort to obtain estimates from a sample of the cohort that are similar to
estimates obtained from analysis of the entire cohort (Breslow, Lubin, Marek, &
Langholz, 1983). There are a number of advantages to the nested case control
design. First, it is less expensive and less time consuming compared with a full
cohort study since it has a smaller number of study subjects. Second, compared to

29
the conventional case-control study, the recall bias is not an issue since the exposure
data are more likely to have been collected prior to the disease or event occurrence.
Furthermore, unlike the traditional case-control design, it does not depend on the
assumption that the disease (or outcome) is rare, for the validity of estimates of
relative risk (Ernster, 1994; Lubin & Gail, 1984). Last, like the traditional
case-control design, matching can avoid bias due to potential confounders on the
study design stage.
Multiple conditional logistic regression models (CLR) were used to analyze
the matched data. A multiple unconditional logistic regression analysis which had
the same set up with CLR model except adjusted for the matching variables was used
to compare with the CLR model. The results from both models are very similar.
We were unable to directly estimate the effect of the intervention program of the
outcome variable by using the conditional logistic regression model. However, we
were able to estimate if the intervention modifies the effect of risk factors
(interaction with the program).
As reported previously (Chou, 2006), the study found no overall effect of the
intervention program on becoming ever smoker at follow-up among those who were
non-smokers at baseline. One plausible hypothesis is that the effect of the
intervention program implemented in this study population may be modified by the

30
baseline risk factors. We tested this hypothesis by examining the interactions
between intervention and there risk factors (intention to smoke, knowledge toward
smoking and smoking refusal self-efficacy) that showed significant association with
becoming ever smoker at follow-up, and found no evidence for any interaction.
The WSPT, however, demonstrated trends in reducing the odds of intention to smoke
in next 12 months among the intervention group students who never smoked at the
baseline comparing to the reference group students. This result suggests that the
program may be effective in reducing the risk of intentions to try cigarettes among
students who never smoking at baseline.
There are several potential limitations of the study worth considering. First,
our results are based on students’ self-reports of their smoking behavior and thus are
subject to self-reporting bias. Although adolescents’ self-reports of smoking
obtained under similar conditions have been shown to be quite accurate in the United
States (Bauman, 1982). It is not known whether this also is true in China.
Research is needed to evaluate the validity of adolescents’ self-reports of smoking
behavior in China. Second, this study was unable to control for socioeconomic
factors. It is possible that access to cigarettes may be risk factors for adolescent
smoking in China only under certain socioeconomic conditions (Unger et al., 2001).
The questionnaires used in this study were developed and validated in the United

31
States, and then they were carefully translated into Chinese. However, the SES
measures were not available in the original English questionnaires. Furthermore,
most large-scale classroom-based adolescent surveys do not include a measure of
SES because of confidentiality concerns and because SES is difficult to quantify and
measure from an adolescent’s point of view.
Our findings in this study can be used to inform the development of effective
health promotion programs to prevent initiation of smoking among non-smoking
adolescents in China. Because psychosocial factors were important predictors in the
initial decision to try smoking, future tobacco prevention interventions should focus
on the reducing smoking intention, empowering non-smoking adolescents with the
self-efficacy to avoid smoking and reminding them of the powerful addictive
properties of nicotine.

32
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37

Appendix 1. SAS program to select all potential matched controls

* Split subjects into two datasets one containing the study cases and
the other having the all potential controls.;
data study control;
set cohort3;
if caco=1 then output study;
else output control;
run;
*For each case we get all controls that match the case’s age, school and
class.;
PROC SQL;
CREATE table select_id
as select
one.ID as study_id,
two.ID as control_id,
one.SCH_ID as studySCH_id,
two.SCH_ID as controlSCH_id,
one.aage as study_aage,
two.aage as control_aage,
one.CLS_ID as studyCLS_ID,
two.CLS_ID as controlCLS_ID,
one.akgender as studyakgender,
two.akgender as controlakgender,
from study one, control two
where (one.akgender=two.akgender and one.SCH_ID=two.SCH_ID and
one.CLS_ID=two.CLS_ID);

38
Appendix 2. List of the variables included in the statistical models

Questions Coding for the analysis
Dependent variables
Change in Smoking
Status
Have you ever tried
smoking, even a few
puffs?
0 = no ( both answered no in
baseline and follow-up)
1 = yes ( answered no in baseline
but yes in follow-up)
Interested potential
risk factors

Good friend
smoking
Among your best friends
who are boys (girls), how
many smoke?
0 = none
1 = from a few to all
Perceived peer
smoking
In your opinion, how
many out of 100 people
your age smoke at lease
once a month?
0 = none ,1 = about 10 ,2 = about 20
3 = about 30 , 4 = about 40
5 = about 50 , 6 = about 60
7 = about 70 , 8 = about 80
9 = about 90, 10 = about 100
Parental smoking Does your dad or mom
smoke?
0 = no
1 = yes for mother or/and father
Intention to smoke Do you think you will
start smoking sometime
during the next 12 months
(one year?
0=No, definitely not,
1=Maybe no/Maybe yes/Definitely
yes
Knowledge toward
smoking
Do you think people can
get addicted to smoking
just as they can to other
drugs?
0 = maybe/definitely not
1 = maybe/definitely yes
Smoking refusal
self-efficacy
Would you accept a
cigarette offered to you by
your best friend?
0 = maybe/definitely not (high
refusal self-efficacy)
1 = maybe/definitely yes (low
refusal self-efficacy)
Academic score What was your average
score in all your classes
last semester?
1= Below 60, 2=60-69, 3=70-79,
4=80-89, 5=above 90
Subjective academic
performance
How good do you think of
your scores are, compared
with your classmates?
low (1=very poor/poor)
medium(2=average)
high (3= good/excellent)
Perceived access to
cigarettes
If you want to have
cigarettes, do you think it
is easy to get them?
0= very hard/fairly hard
1=fairly easy/ very easy

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Asset Metadata

Creator Yao, Jie (author)

Core Title Risk factors associated with smoking initiation among Chinese adolescents: a matched case-control study

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics

Degree Conferral Date 2008-08

Publication Date 08/04/2008

Defense Date 06/18/2008

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag a matched case-control study,Chinese,OAI-PMH Harvest,risk factors,smoking initiation

Place Name China (countries), Wuhan (city or populated place)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Azen, Stanley Paul (committee chair), Chou, Chih-Ping (committee chair), Unger, Jennifer B. (committee member)

Creator Email jiey@usc.edu,yaojay@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1531

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Identifier etd-Yao-2303 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-115259 (legacy record id),usctheses-m1531 (legacy record id)

Legacy Identifier etd-Yao-2303-0.pdf

Dmrecord 115259

Document Type Thesis

Rights Yao, Jie

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu