1. Participant characteristics and status of nicotine products use
Table 1 shows characteristics of participants. The final sample was composed of 9361 individuals (attrition rate = 6.5%), providing a response rate of 93.5%. In the full sample of 9631 participants, 58.3% (n = 5461) were male and 41.7% (n = 3900) were female.
Table 1
Characteristics of participants
Participant characteristics
|
n
|
%
|
Age (Mean ± SD)
|
22.4 ± 3.6
|
|
Gender
|
|
|
Male
|
5461
|
58.3
|
Female
|
3900
|
41.7
|
Ethnicity
|
|
|
Han Chinese
|
9010
|
96.3
|
Ethnic minorities
|
351
|
3.7
|
Type of University
|
|
|
Vocational schools
|
1781
|
19
|
Non-prestigious Chinese universities
|
4941
|
52.8
|
Prestigious Chinese universities
|
2639
|
28.2
|
School
|
|
|
Sun Yat-sen University
|
2702
|
28.9
|
Guangzhou University of Chinese Medicine
|
2075
|
22.2
|
Guangzhou City Polytechnic
|
1382
|
14.8
|
Guangzhou Institute of Science and Technology
|
923
|
9.9
|
Guangzhou Huashang University
|
951
|
10.2
|
Jinan University
|
843
|
9
|
Southern Medical University
|
485
|
5.2
|
Education Level
|
|
|
Vocational school students
|
1874
|
20
|
Undergraduates
|
6242
|
66.7
|
Master students
|
1007
|
10.8
|
PhD students
|
238
|
2.5
|
Specialization
|
|
|
Philosophy
|
291
|
6.3
|
Economics
|
1478
|
15.8
|
Law
|
896
|
9.6
|
Education
|
788
|
8.4
|
Literature
|
725
|
7.7
|
History
|
204
|
2.2
|
Science
|
1087
|
11.6
|
Engineering
|
993
|
10.6
|
Agronomy
|
117
|
1.2
|
Medicine
|
1370
|
14.6
|
Management
|
557
|
6
|
Art
|
216
|
2.3
|
Others
|
339
|
3.6
|
Nicotine products use
|
|
|
Use nicotine products
|
2786
|
29.8
|
Not use nicotine products
|
6575
|
70.2
|
Types of nicotine products used
|
|
|
Tobacco cigarettes only users
|
975
|
35.0
|
E-cigarettes only users
|
464
|
16.7
|
Dual users
|
1347
|
48.3
|
The average age of the 9361 university students was 22.4years (SD = 3.6). There were 29.8%(n = 2786) of the participants had used nicotine products. There were 1,874 vocational school students (20.0%), 6,242 undergraduates (66.7%), 1007 master students (10.8%), and 238 PhD students (2.5%). There were 1,874 students (20.0%) from vocational schools, 3,942 students (42.1%) from non-prestigious Chinese universities, and 3,545 students (37.9%) from prestigious Chinese universities. Participants who had used nicotine products were far less likely than those who had not used any nicotine products (70.2% vs. 29.8%) Among nicotine products users, 16.7% (n = 464) were e-cigarettes only users, 35.0% (n = 975) were tobacco cigarettes only users, 48.3% (n = 1347) were dual users. Among the dual users, 51.2% (n = 690) participants were developed from tobacco cigarettes users, 34.4% (n = 464) participants were developed from e-cigarettes users, 14.4% (n = 193) said they did not have an exact order (Fig. 1).
2. Factors associated with using nicotine products
Table 2 shows factors associated with using nicotine products. Among nicotine products users, females were more likely to choose e-cigarettes compared to males (43.3 vs. 25.5%, P < 0.0001).
Table 2
Factors connected with the use of nicotine products
Variable
|
Nicotine products users
|
Non-nicotine products users
|
χ2
|
p-value
|
Tobacco cigarettes only users
|
E-cigarettes only users
|
Dual users
|
Gender
|
|
|
|
|
1251.6
|
< 0.0001
|
Male
|
888 (16.3)
|
359 (6.6)
|
1142 (20.9)
|
3072 (56.2)
|
|
|
Female
|
87 (2.2)
|
105 (2.7)
|
205 (5.3)
|
3503 (89.8)
|
|
|
School type
|
|
|
|
|
232.64
|
< 0.0001
|
Prestigious Chinese universities
|
312 (8.8)
|
104 (2.9)
|
339 (9.6)
|
2790 (78.7)
|
|
|
Non-prestigious Chinese universities
|
445 (11.3)
|
261 (6.6)
|
730 (18.5)
|
2506 (63.6)
|
|
|
Vocational schools
|
218 (11.6)
|
99 (5.3)
|
278 (14.8)
|
1279 (68.2)
|
|
|
Education level
|
|
|
|
|
243.48
|
< 0.0001
|
Vocational school students
|
218 (1 1.6)
|
99 (5.3)
|
278 (14.8)
|
1279 (68.2)
|
|
|
Undergraduates
|
690 (11.1)
|
353 (5.7)
|
1002 (16.1)
|
4197 (67.2)
|
|
|
Master students
|
42 (4.2)
|
9 (0.9)
|
47 (4.7)
|
909 (90.3)
|
|
|
PhD students
|
25 (10.5)
|
3 (1.3)
|
20 (8.4)
|
190 (79.8)
|
|
|
Specialization
|
|
|
|
|
221.01
|
< 0.001
|
Non-medical
|
900 (11.3)
|
440 (5.5)
|
1270 (15.9)
|
5381 (67.3)
|
|
|
Medical
|
75 (5.5)
|
24 (1.7)
|
77 (5.6)
|
1194 (87.2)
|
|
|
Lifestyles
|
|
|
|
|
1151.9
|
< 0.001
|
Drinking
|
166 (17.0)
|
79 (17.0)
|
198 (14.7)
|
193 (2.9)
|
|
|
Playing games
|
233 (23.9)
|
159 (34.3)
|
381 (28.3)
|
1239 (18.8)
|
|
|
Staying up late
|
219 (22.5)
|
107 (23.1)
|
342 (25.4)
|
2681 (40.8)
|
|
|
All of the above
|
277 (28.4)
|
78 (16.8)
|
361 (26.8)
|
998 (15.2)
|
|
|
None of the above
|
80 (8.2)
|
41 (8.8)
|
65 (4.8)
|
1464 (22.3)
|
|
|
Specialization
|
|
|
218.72
|
< 0.0001
|
Non-medical
|
5381(67.3)
|
2610 (32.7)
|
|
|
Medical
|
1194(87.2)
|
176 (12.8)
|
|
|
School type
|
|
|
208.84
|
< 0.0001
|
Prestigious Chinese universities
|
755(21.3)
|
2790(78.7)
|
|
|
Non-prestigious Chinese universities
|
1436(36.4)
|
2506(63.6)
|
|
|
Vocational schools
|
595(31.8)
|
1279(68.2)
|
|
|
Specialization
|
|
|
692.9
|
< 0.0001
|
Medicine
|
176(12.8)
|
1194 (87.2)
|
|
|
Management
|
94(16.9)
|
463 (83.1)
|
|
|
Art
|
45(20.8)
|
171 (79.2)
|
|
|
Engineering
|
216(21.8)
|
777 (78.2)
|
|
|
Science
|
239(22.0)
|
848 (78.0)
|
|
|
Literature
|
201(27.7)
|
524 (72.3)
|
|
|
Education
|
318(40.4)
|
470 (59.6)
|
|
|
Economics
|
613(41.5)
|
865 (58.5)
|
|
|
Agronomy
|
51(43.6)
|
66 (56.4)
|
|
|
Philosophy
|
259(43.8)
|
332 (56.2)
|
|
|
History
|
94(46.1)
|
110 (53.9)
|
|
|
Law
|
423(47.2)
|
473 (52.8)
|
|
|
Others
|
57(16.8)
|
282 (83.2)
|
|
|
In general, medical students have a higher level of knowledges about health and it is important to understand their perceptions of e-cigarettes as they need to communicate and interact with patients during their training and later in their careers. Therefore, we divided the specialization into non-medical specialization and medical specialization, using medicine as a criterion.
The rate of nicotine products use was significantly higher among non-medical specialization than medical specialization (32.7% vs. 12.8%, P < 0.0001), and the highest rate of nicotine products use was found among law specialization compared to medical specialization (47.2% vs. 12.8%, P < 0.0001), followed by history (46.1% vs. 12.8%) and philosophy (43.8% vs. 12.8%, P < 0.0001).However, there was no difference in the choice of tobacco cigarettes or e-cigarettes between non-medical and medical students.
The use of both e-cigarettes and tobacco cigarettes was lower in prestigious Chinese universities compared to other types of schools. Students in non-prestigious Chinese universities had the highest rate of nicotine products use and a correspondingly higher rate of e-cigarettes use.
Among the participants, undergraduates and vocational school students had the highest rate of nicotine products use (32.8% and 31.8%), followed by PhD students (20.2%), while master students had the lowest rate of nicotine products use at 9.7%, with a statistically significant difference (P < 0.001).
Among them, there was no difference in the distribution of nicotine products use among undergraduates and vocational school students, while the rate of tobacco cigarettes use among master students was significantly lower than other students (P < 0.001), and the rate of e-cigarettes use was also the lowest.
Lifestyles have significant impacts on the use of nicotine products. Compared to those with appropriate lifestyles, students who drank alcohol, played games, stayed up late, and did all of the above had an increased risk of tobacco cigarettes use, e-cigarettes use, and dual use. Multiple logistic regression analyses of tobacco cigarettes only users, e-cigarettes only users, and dual users indicated that the risk of tobacco cigarettes, e-cigarettes and dual use increased 8.13, 6.77 and 10.15 times respectively for those who drank alcohol compared to those who did not drink alcohol. The risk of tobacco cigarettes using, e-cigarettes using and dual using were 2.6, 3.21, and 4.73 times higher for gamers compared to non-gamers, respectively. The risk of tobacco cigarettes using, e-cigarettes using and dual using increased by 1.32, 1.97 and 5.58 times respectively for those who stayed up late compared to those who did not stay up late. All results are presented in Table 3.
Table 3
Multifactorial logistic regression of the reasons for using nicotine products
Variable
|
OR [95% CI]
|
Tobacco cigarettes only users
|
E-cigarettes only users
|
Dual users
|
None of the following
|
1.00
|
1.00
|
1.00
|
Drinking
|
8.13 [5.91,11.2]
|
6.77 [4.44,10.32]
|
10.15 [7.27,14.16]
|
Playing games
|
2.6 [1.98,3.42]
|
3.21 [2.23,4.60]
|
4.73 [3.56,6.29]
|
Staying up late
|
1.32 [1.00,1.74]
|
1.21 [0.83,1.76]
|
2.42 [1.82,3.22]
|
All of the above
|
3.7 [2.82,4.86]
|
1.97 [1.33,2.94]
|
5.58 [4.18,7.44]
|
Table 4 shows that 83.5% (n = 1125) of dual users chose nicotine products according to their emotion state, while 56.5% (n = 761) of dual users chose tobacco cigarettes when they are depressed and e-cigarettes when they are happy.
Table 4
Effects of emotion on product adjustment of dual users
Whether to choose nicotine products according to emotion
|
n
|
%
|
Yes, use tobacco cigarettes when you're down and e-cigarettes when you're happy
|
761
|
56.5
|
Yes, use e-cigarettes when you're down and tobacco cigarettes when you're happy
|
364
|
27
|
No, do not choose nicotine products according to emotion
|
222
|
16.5
|
3. Future choices of nicotine product users
Figure 2 indicates the ratio of products selection in the future among nicotine products users. For tobacco cigarettes only users, 41.8% (n = 308) would not choose e-cigarettes in the future, 30.9% (n = 301) use both tobacco cigarettes and e-cigarettes in the future, and 27.3% (n = 266) would give up tobacco cigarettes and use e-cigarettes. For e-cigarettes only users, 42.0% (n = 195) would give up e-cigarettes and only use tobacco cigarettes, 37.5% (n = 174) would use both tobacco cigarettes and e-cigarettes, and 20.5% (n = 95) would not use tobacco cigarettes in the future. For current dual users, 43.7% (n = 589) would give up tobacco cigarettes and only use e-cigarettes, 29.8% (n = 401) said they would use both tobacco cigarettes and e-cigarettes, and 26.5% (n = 357) said they would give up e-cigarettes and use only tobacco cigarettes.