| Literature DB >> 35125991 |
Dan Wu1, Guihua Jiao2, Huan Hu1, Lu Zhang1, Lixin Huang1, Shuhan Jiang3.
Abstract
INTRODUCTION: The purpose of this study was to investigate the patterns and factors associated with cigarette sharing and gifting, and to explore whether smoking can be predicted by these social practices.Entities:
Keywords: behavioral outcome; beliefs; cigarette gifting; cigarette sharing; social participation
Year: 2022 PMID: 35125991 PMCID: PMC8796849 DOI: 10.18332/tid/144054
Source DB: PubMed Journal: Tob Induc Dis ISSN: 1617-9625 Impact factor: 2.600
The distribution, n (%), of cigarette sharing and gifting across demographic characteristics, China 2020 (N=1512)
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| 518 (34.3) | 841 (55.6) | 361 (23.9) | 280 (18.5) | |
| p=0.099 | p=0.031 | p=0.634 | p=0.158 | ||
| <45 | 336 (22.2) | 107 (31.8) | 171 (50.9) | 74 (22.0) | 56 (16.7) |
| 45–49 | 593 (39.2) | 192 (32.4) | 323 (54.5) | 147 (24.8) | 102 (17.2) |
| ≥50 | 583 (38.6) | 219 (37.6) | 347 (59.5) | 140 (24.0) | 122 (20.9) |
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| p<0.001 | p<0.001 | p<0.001 | p<0.001 | |
| Male | 1248 (82.5) | 509 (40.8) | 818 (65.5) | 331 (26.5) | 266 (21.3) |
| Female | 264 (17.5) | 9 (3.4) | 23 (8.7) | 30 (11.4) | 14 (5.3) |
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| p=0.340 | p=0.004 | p=0.076 | p=0.130 | |
| Han | 1502 (99.3) | 516 (34.4) | 849 (55.9) | 361 (24.0) | 280 (18.6) |
| Minority | 10 (0.7) | 2 (20.0) | 1 (10.0) | 0 (0) | 0 (0) |
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| p=0.004 | p<0.001 | p=0.001 | p=0.001 | |
| Married | 1334 (88.2) | 474 (35.5) | 774 (58.0) | 337 (25.3) | 263 (19.7) |
| Others | 178 (11.8) | 44 (24.7) | 67 (37.6) | 24 (13.5) | 17 (9.6) |
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| p=0.288 | p<0.001 | p=0.591 | p=0.406 | |
| Rural area | 715 (47.3) | 257 (35.9) | 431 (60.3) | 173 (24.2) | 142 (19.9) |
| Micropolis | 437 (28.9) | 149 (34.1) | 234 (53.5) | 109 (24.9) | 78 (17.8) |
| Large- and medium-sized cities | 360 (23.8) | 112 (31.1) | 176 (48.9) | 79 (21.9) | 60 (16.7) |
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| p=0.319 | p=0.004 | p=0.695 | p=0.200 | |
| Rural area | 1296 (85.7) | 447 (34.5) | 733 (56.6) | 305 (23.5) | 240 (18.5) |
| Micropolis | 147 (9.7) | 53 (36.1) | 83 (56.5) | 37 (25.2) | 32 (21.8) |
| Large- and medium-sized cities | 69 (4.6) | 18 (26.1) | 25 (36.2) | 19 (27.5) | 8 (11.6) |
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| p=0.081 | p=0.018 | p=0.069 | p=0.300 | |
| Elementary school or less | 282 (18.7) | 102 (36.2) | 163 (57.8) | 66 (23.4) | 48 (17.0) |
| Junior high school | 595 (39.4) | 213 (35.8) | 344 (57.8) | 161 (27.1) | 124 (20.8) |
| High school | 353 (23.3) | 125 (35.4) | 201 (56.9) | 80 (22.7) | 62 (17.6) |
| Junior college, college or higher | 282 (18.7) | 78 (27.7) | 133 (47.2) | 54 (19.1) | 46 (16.3) |
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| p=0.015 | p=0.006 | p=0.194 | p=0.053 | |
| Manager/owner | 31 (2.1) | 14 (45.2) | 18 (58.1) | 11 (35.5) | 9 (29.0) |
| White-collar | 244 (16.1) | 72 (29.5) | 128 (52.5) | 50 (20.5) | 42 (17.2) |
| Blue-collar | 670 (44.3) | 249 (37.2) | 402 (60.0) | 162 (24.2) | 133 (19.9) |
| Service class | 229 (15.1) | 86 (37.6) | 131 (57.2) | 63 (27.5) | 49 (21.4) |
| Irregular employment | 338 (22.4) | 97 (37.0) | 162 (47.9) | 75 (22.2) | 47 (13.9) |
| p=0.015 | p=0.015 | p=0.134 | p=0.207 | ||
| <20000 | 494 (32.7) | 176 (35.6) | 276 (55.9) | 125 (25.3) | 84 (17.0) |
| 20000–49999 | 479 (31.7) | 178 (37.2) | 289 (60.3) | 121 (25.3) | 103 (21.5) |
| 50000–79999 | 208 (13.8) | 53 (25.5) | 97 (46.6) | 43 (20.7) | 34 (16.3) |
| 80000–99999 | 122 (8.1) | 48 (39.3) | 70 (57.4) | 34 (27.9) | 26 (21.3) |
| ≥100000 | 209 (13.8) | 63 (30.1) | 109 (52.2) | 38 (18.2) | 33 (15.8) |
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| p<0.001 | p<0.001 | p<0.001 | p<0.001 | |
| Guangdong | 982 (64.9) | 258 (26.3) | 489 (49.8) | 175 (17.5) | 155 (15.8) |
| Shaanxi | 530 (35.1) | 260 (49.1) | 352 (66.4) | 189 (35.7) | 125 (23.6) |
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| p<0.001 | p<0.001 | p<0.001 | p<0.001 | |
| Daily smoker | 501 (33.1) | 372 (74.3) | 418 (83.4) | 199 (39.7) | 180 (35.9) |
| Occasional smoker | 133 (8.8) | 85 (63.9) | 106 (79.7) | 54 (40.6) | 45 (33.8) |
| Non-smoker | 878 (58.1) | 61 (6.9) | 317 (36.1) | 108 (12.3) | 55 (6.3) |
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| p=0.875 | P=0.075 | p=0.042 | p=0.003 | |
| High score | 696 (46.0) | 237 (34.1) | 370 (53.2) | 183 (26.3) | 151 (21.7) |
| Low score | 816 (54.0) | 281 (34.4) | 471 (57.7) | 178 (21.8) | 129 (15.8) |
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| p<0.001 | p<0.001 | p<0.001 | p<0.001 | |
| High score | 508 (33.6) | 240 (47.2) | 341 (67.1) | 175 (34.4) | 151 (21.7) |
| Low score | 1004 (66.4) | 278 (27.7) | 500 (49.8) | 186 (18.5) | 129 (15.8) |
The cutoff value is the mean score. The average item score was higher than the mean score, indicating a high score for social participation and cigarette gifting beliefs. RMB: 1000 Chinese Renminbi about US$160.
p<0.05;
p<0.01.
Beliefs about cigarette gifting among the total sample across different smoking status groups, China 2020 (N=1512)
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| 1. The more expensive the cigarette, the more suitable to give away | 373 (24.7) | 703 (46.5) | 436 (28.8) | 3.19 (0.98) | 2.92 (0.98) | 5.28 | <0.001 |
| 2. Gifting cigarettes helps me maintain relationships | 306 (20.2) | 622 (41.1) | 584 (38.6) | 3.45 (0.91) | 2.97 (1.01) | 9.58 | <0.001 |
| 3. Gifting cigarettes helps me solve practical problems | 454 (30.0) | 696 (46.0) | 362 (23.9) | 3.16 (0.99) | 2.69 (0.98) | 9.18 | <0.001 |
| 4. When gifting cigarettes to others, I don’t think about the health hazards of the cigarettes | 452 (29.9) | 678 (44.8) | 382 (25.3) | 3.15 (0.99) | 2.69 (1.07) | 8.91 | <0.001 |
| 5. I wouldn’t buy cigarettes and gift them if they have a clear picture of a tobacco warning on the package | 392 (25.9) | 727 (48.1) | 393 (26.0) | 2.92 (0.96) | 3.08 (0.99) | -3.26 | 0.001 |
| 6. If the price of cigarettes goes up, I’ll still buy and use them as gifts | 384 (25.4) | 734 (48.5) | 394 (26.1) | 3.21 (0.92) | 2.77 (0.95) | 8.98 | <0.001 |
| 7. The price-limit policy limits my choice of cigarettes as expensive gifts | 416 (27.5) | 785 (51.9) | 311 (20.6) | 2.96 (0.95) | 2.79 (0.92) | 3.52 | <0.001 |
| 8. If there is a channel in the market to buy cigarettes that are more expensive than 1000 RMB a carton, I am very willing to buy them as gifts | 877 (58.0) | 510 (33.7) | 125 (8.3) | 2.30 (1.04) | 2.17 (1.01) | 2.47 | 0.014 |
p<0.05;
p<0.01.
Logistic regression results of sociodemographic factors associated with cigarette sharing and gifting among all participants, China 2020 (N=1512)
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| Male | 4.93 (2.36–10.32) | 10.49 (6.60–16.68) | ||
| Female | 1 | 1 | ||
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| Han | 11.55 (1.14–116.68) | |||
| Minority | 1 | |||
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| Married | 1.55 (1.02–2.37) | 2.41 (1.49–3.90) | 2.14 (1.23–3.71) | |
| Others | 1 | 1 | 1 | |
| <20000 | 1.13 (0.70–1.84) | |||
| 20000–49999 | 1.09 (0.67–1.76) | |||
| 50000–79999 | 0.69 (0.39–1.22) | |||
| 80000–99999 | 1.99 (1.02–3.92) | |||
| ≥100000 | 1 | |||
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| Shaanxi | 2.18 (1.59–2.99) | 1.48 (1.13–1.93) | 2.13 (1.63–2.77) | |
| Guangdong | 1 | 1 | 1 | |
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| Daily smoker | 29.05 (20.57–41.03) | 5.26 (3.94–7.02) | 3.94 (2.98–5.20) | 7.73 (5.52–10.81) |
| Occasional smoker | 16.69 (10.52–26.50) | 4.68 (2.89–7.57) | 3.69 (2.43–5.60) | 6.98 (4.41–11.03) |
| Non-smoker | 1 | 1 | 1 | 1 |
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| High score | 1.32 (1.02–1.71) | 1.59 (1.19–2.11) | ||
| Low score | 1 | 1 | ||
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| High score | 1.41 (1.03–1.93) | 1.53 (1.17–2.01) | 1.60 (1.23–2.09) | 1.68 (1.26–2.24) |
| Low score | 1 | 1 | 1 | 1 |
The statistically significant variables from univariate analysis in Table 1 were included in each model, correspondingly. AOR: adjusted odds ratio.
The cutoff value is the mean score. The average item score was higher than the mean score, indicating a high score for social participation and cigarette gifting beliefs. RMB: 1000 Chinese Renminbi about US$160.
p<0.05;
p<0.01.
Logistic regression analysis for predicting smoking status by behaviors and beliefs towards cigarette sharing and gifting among all participants, China 2020 (N=1512)
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| Yes | 25.95 (18.81–35.80) | 19.17 (13.31–27.61) | |||
| No | 1 | 1 | |||
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| Yes | 4.98 (3.82–6.49) | 1.50 (1.08–2.09) | |||
| No | 1 | 1 | |||
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| Yes | 3.64 (2.74–4.84) | 0.78 (0.52–1.19) | |||
| No | 1 | 1 | |||
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| Yes | 6.70 (4.76–9.44) | 2.58 (1.66–4.00) | |||
| No | 1 | 1 | |||
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| High score | 1.76 (1.30–2.38) | 1.84 (1.43–2.38) | 1.85 (1.44–2.38) | 1.83 (1.41–2.36) | 1.63 (1.20–2.22) |
| Low score | 1 | 1 | 1 | 1 | 1 |
AOR: adjusted odds ratio; the statistically significant sociodemographic characteristics for smoking such as gender, education level and region were adjusted.
Four categories of cigarette exchange behaviors were entered into Model 1 through Model 4, respectively.
Four categories of cigarette exchange behaviors were entered into the full model, simultaneously.
p<0.05;
p<0.01.