| Literature DB >> 35292000 |
Jin-Won Noh1, Min-Hee Kim2, Yejin Lee3, Young Dae Kwon4, Kyoung-Beom Kim5, Hae-Jeung Lee6, Ki-Bong Yoo7.
Abstract
BACKGROUND: The use of smokeless tobacco has increased worldwide among young people. This study aimed to investigate the association between smokeless tobacco use and cigarette smoking amount in adult smoker groups stratified by age.Entities:
Keywords: Propensity score matching; Smokeless tobacco; Smoking-cessation
Mesh:
Year: 2022 PMID: 35292000 PMCID: PMC8922879 DOI: 10.1186/s12889-022-12929-z
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
General characteristics of study population before and after propensity score matching
| Number of cigarettes used per day | 13.9 ± 0.1 | 15.0 ± 0.5 | 14.4 ± 0.2 | 15.0 ± 0.5 | 0.07 |
| Age (year) | 46.7 ± 0.2 | 37.5 ± 0.7 | 38.3 ± 0.3 | 37.5 ± 0.7 | 0.01 |
| Body mass index | 29.2 ± 0.1 | 28.5 ± 0.4 | 28.4 ± 0.3 | 28.5 ± 0.4 | -0.00 |
| Ln (household income) | 10.0 ± 0.0 | 10.0 ± 0.0 | 10.1 ± 0.0 | 10.0 ± 0.0 | -0.00 |
| Sex | |||||
| Men | 9,163 (84.3) | 520 (15.7) | 2,543 (71.0) | 513 (29.0) | 0.02 |
| Women | 9,885 (93.3) | 67 (6.7) | 357 (63.5) | 67 (36.5) | |
| Race | |||||
| Whites | 15,219 (88.3) | 525 (11.7) | 2,590 (69.1) | 520 (30.9) | 0.03 |
| African Americans | 2,763 (94.3) | 35 (5.7) | 165 (80.6) | 34 (19.4) | |
| Others | 1,066 (93.6) | 27 (6.4) | 145 (83.0) | 26 (17.0) | |
| Marital status | |||||
| Single | 6,597 (86.4) | 240 (13.6) | 1,276 (70.7) | 236 (29.3) | 0.08 |
| Widowed, divorced or separated | 6,409 (88.7) | 156 (11.3) | 770 (69.6) | 153 (30.4) | |
| Married | 6,042 (86.5) | 191 (13.5) | 854 (66.2) | 191 (33.8) | |
| Education level | |||||
| Under high school | 864 (86.9) | 25 (13.1) | 102 (66.9) | 25 (33.1) | 0.04 |
| High school | 9,768 (87.8) | 337 (12.2) | 1,656 (71.3) | 333 (28.7) | |
| Above high school | 8,416 (87.7) | 225 (12.3) | 1,142 (66.7) | 222 (33.3) | |
| Job status | |||||
| All others | 11,550 (91.2) | 220 (8.8) | 1,473 (74.8) | 217 (25.2) | 0.04 |
| Blue collar workers | 6,104 (82.0) | 316 (18) | 1,193 (60.8) | 312 (39.2) | |
| Unemployed | 1,394 (79.5) | 51 (20.5) | 234 (66.7) | 51 (33.3) | |
| Alcohol consumption | |||||
| Never drinkers | 1,682 (89.5) | 33 (10.5) | 126 (63.5) | 32 (36.5) | 0.06 |
| Former drinkers | 3,595 (88.4) | 63 (11.6) | 289 (65.8) | 62 (34.2) | |
| Current drinkers | 13,771 (87.4) | 491 (12.6) | 2,485 (70.6) | 486 (29.4) | |
| Physical activity | |||||
| Inactive | 8,906 (88.3) | 238 (11.7) | 1,167 (70.4) | 236 (29.6) | 0.01 |
| Insufficient | 4,617 (87.9) | 122 (12.1) | 604 (66.7) | 121 (33.3) | |
| Active | 5,525 (86.1) | 227 (13.9) | 1,129 (70.5) | 223 (29.5) | |
| Tried to quit smoking | |||||
| Yes | 8,410 (86.6) | 287 (13.4) | 1,417 (67.9) | 285 (32.1) | 0.01 |
| No | 10,638 (88.2) | 300 (11.8) | 1,483 (69.9) | 295 (30.1) | |
| Year | |||||
| 2013 | 4,548 (85.4) | 123 (14.6) | 596 (65.8) | 121 (34.2) | 0.03 |
| 2014 | 3,939 (87.2) | 131 (12.8) | 683 (70.5) | 131 (29.5) | |
| 2015 | 3,726 (84.5) | 114 (15.5) | 538 (65.9) | 111 (34.1) | |
| 2016 | 3,904 (88.8) | 133 (11.2) | 658 (71.6) | 131 (28.4) | |
| 2017 | 2,931 (89.8) | 86 (10.2) | 425 (71.8) | 86 (28.2) | |
Values: weighted mean ± standard error or n (weighted %)
Fig. 1Number of cigarettes used per day for each subgroup
Associations of smokeless tobacco use with number of cigarettes used per day
| Subgroups | PSM | ||
|---|---|---|---|
| (1) All subjects | 0.044 | 0.229 | (-0.028—0.115) |
| (2) Tried to quit smoking: yes | 0.025 | 0.655 | (-0.085—0.135) |
| (3) Tried to quit smoking: no | 0.039 | 0.477 | (-0.069 – 0.148) |
| (4) Age < 30 | 0.164 | 0.015 | (0.032 – 0.230) |
| (5) Age 30–44 | -0.044 | 0.441 | (-0.156—0.068) |
| (6) Age 45- | 0.027 | 0.733 | (-0.129 – 0.184) |
| (2) & (4) | 0.230 | 0.036 | (0.015—0.444) |
| (2) & (5) | -0.073 | 0.344 | (-0.225 – 0.079) |
| (2) & (6) | -0.094 | 0.335 | (-0.285 – 0.097) |
| (3) & (4) | 0.088 | 0.457 | (-0.145 – 0.321) |
| (3) & (5) | -0.004 | 0.948 | (-0.132 – 0.123) |
| (3) & (6) | 0.160 | 0.121 | (-0.042—0.362) |
PSM: results from propensity score matching with adjusting age, body mass index, ln (household income), sex, race, marital status, education level, job status, alcohol consumption, physical activity, tried to quit smoking, and year. Calculating propensity score and matching were conducted for each subgroup