Factors Associated with Current E-Cigarettes Use of Female Workers: Using the 2023 Community Health Survey

Article information

Korean J Health Promot. 2026;.kjhp.2026.00318
Publication date (electronic) : 2026 September 21
doi : https://doi.org/10.15384/kjhp.2026.00318
School of Nursing, Kunsan National University, Gunsan, Korea
Corresponding author: Eungyeong KIM, PhD, RN School of Nursing, Kunsan National University, 558 Daehak-ro, Gunsan 54150 Korea Tel: +82-63-469-1994 Fax: +82-63-469-7429 E-mail: egkim@kunsan.ac.kr
Received 2026 July 22; Revised 2026 September 11; Accepted 2026 September 17.

Abstract

Background

The use of e-cigarettes has emerged as a growing public health concern, particularly among females. This study aimed to identify factors associated with e-cigarette use among female workers in Korea.

Methods

A secondary data analysis was conducted using representative data from the 2023 Community Health Survey. A final complete-case sample of 70,511 female workers was included in the analysis. Sociodemographic characteristics, health-related behaviors, and psychological factors associated with current e-cigarette use were identified using complex-samples binary logistic regression analysis.

Results

Compared with females aged ≥40 years, the odds of current e-cigarette use were higher among females aged 19–29 years (odds ratio [OR]=5.58, 95% confidence interval [CI]=4.42–7.03) and 30–39 years (OR=4.28, 95% CI=3.46–5.30). Single females had higher odds of e-cigarette use than married females (OR=2.07, 95% CI=1.73–2.48). Higher odds were also observed among females reporting stress (OR=1.42, 95% CI=1.12–1.78), depressive symptoms (OR=2.26, 95% CI=1.88–2.71), breakfast consumption <3 times/wk (OR=2.18, 95% CI=1.85–2.56), and current alcohol use (OR=2.29, 95% CI=1.94–2.71). Females who were aware of smoke-free areas had lower odds of current e-cigarette use than those who were unaware (OR=0.58, 95% CI=0.49–0.65).

Conclusions

Among Korean female workers, e-cigarette use was significantly associated with younger age, single marital status, psychological distress, and alcohol use. Workplace health promotion and targeted interventions addressing these risk factors may help reduce e-cigarette use among female workers.

INTRODUCTION

Tobacco smoking remains a leading cause of preventable morbidity and mortality worldwide. It is a major risk factor for cardiovascular and cerebrovascular diseases, chronic obstructive pulmonary disease, lower respiratory infections, tuberculosis, and multiple cancers [1]. Despite continuing tobacco-control efforts, tobacco use remains an important public-health concern in Korea. According to the 2022 Korea National Health and Nutrition Examination Survey, the current smoking rate among Korean adults was 17.7%, with a marked sex disparity (males, 30.0%; females, 5.0%) [2]. Alternative nicotine products have also become increasingly visible. National data reported that liquid-type e-cigarette use among adults increased from 1.1% in 2013 to 4.5% in 2023, whereas heated tobacco products (HTPs) use changed from 6.2% in 2019 to 6.1% in 2023, indicating a slight decrease or relative stability rather than an increase [3]. E-cigarette aerosols and emissions contain toxicants associated with airway inflammation and cardiovascular risks [4,5], and dual use with conventional cigarettes further complicates cessation [6,7].

Despite growing attention to novel tobacco products, evidence regarding e-cigarette use specifically among employed female workers remains limited. Female workers represent a distinct demographic whose health behaviors are uniquely shaped by workplace environments, occupational stress, and socio-behavioral dynamics [8-12]. Furthermore, female smoking in Korea is frequently underreported due to social stigma, masking the true burden of nicotine reliance in this population [2]. A recent study using the same 2023 Community Health Survey (CHS) examined e-cigarette use among females of childbearing age, identifying employment as a significant risk factor [13]. However, that study encompassed a broad population including non-female workers, limiting specific insights into the working population [13]. By focusing exclusively on economically active females, the present study builds upon these findings to isolate population-specific determinants within the female workforce. Therefore, this study aimed to identify the sociodemographic, psychological, and health-related behavioral factors associated with current e-cigarette use among Korean female workers using representative data from the 2023 CHS.

METHODS

Material

The data for this study were drawn from the 2023 CHS. Established in 2008, the CHS aims to provide comparable regional health statistics and support the planning and evaluation of local public health policies. Conducted annually across all 253 administrative districts in Korea (including cities, counties, and districts), the survey samples approximately 900 individuals per district using a probability proportional to size method to select survey areas. Within each area, households are selected through systematic sampling, and all adults aged 19 years and older in the selected households are surveyed. Data collection took place between May 16 and July 31, 2023, via one-on-one computer-assisted personal interviews conducted by trained interviewers using laptops with the survey program. A total of 231,752 adults participated in the 2023 CHS. Among them, 125,998 were females. In this study, “female workers” were operationally defined as females who responded “yes” to the CHS item assessing economic activity status. Based on this criterion, an initial analytical sample of 70,552 females was identified. Item-specific missing values were observed for marital status (n=11), educational attainment (n=28), and depressive symptoms (n=3). Because some participants had missing data across multiple variables, a total of 41 unique cases were excluded from the multivariable analysis. Missing values were not imputed, and a complete-case analysis approach was applied. Consequently, the final complex-sample logistic regression analysis included 70,511 participants.

Measures

E-cigarette use

In this study, current e-cigarette use was defined as current use of HTPs, including daily or occasional use, and/or use of nicotine-containing liquid-type e-cigarettes on at least 1 day during the past 30 days. Participants who reported either type of use were classified as current users (coded 1), whereas those who reported neither type were classified as non-users (coded 0). The CHS includes separate items for current e-cigarette use, current HTPs use, and use of nicotine-containing liquid-type e-cigarettes during the past month. In the present study, the current HTP-use item and the nicotine-containing liquid-type e-cigarette item were combined to create a single binary outcome variable. Accordingly, the term “current e-cigarette use” in this study refers to the combined current use of HTPs and/or nicotine-containing liquid-type e-cigarettes, rather than to liquid-type e-cigarette use alone. Product-specific use and dual use were not analyzed separately.

Demographic characteristics

Demographic characteristics included age, marital status, and education level. Age was categorized into three groups: 19–29, 30–39, and 40 years or older. Marital status was classified as married or single, and education level was divided into two groups: high school or lower and more than high school.

Health-related characteristics

Health-related characteristics included subjective health status, stress, depressive symptoms, breakfast consumption, current drinking, and awareness of smoke-free areas. These variables were selected based on previous research examining mental-health comorbidity and e-cigarette use [10], health-related behaviors [11], and dietary and smoking-related behaviors [13,14]. The variables were intended to represent complementary domains of perceived health, psychological well-being, daily routine, substance-use behavior, and awareness of tobacco-control policies. Subjective health status was assessed with the question, “How do you perceive your overall health?” Responses were grouped as good (very good, good), moderate (average), and poor (poor, very poor). Stress and depression were dichotomized as “yes” or “no.” Stress was measured using the responses “I feel very much,” “I feel a lot,” “I feel a little,” and “I hardly feel.” For the present analysis, “I feel very much” and “I feel a lot” were combined and coded as “yes,” whereas “I feel a little” and “I hardly feel” were combined and coded as “no.” Depression was assessed with the question, “Have you ever felt sadness or hopelessness that interfered with your daily life for two or more consecutive weeks in the past 12 months?” Breakfast consumption was assessed using the question, “On how many days during the past week did you eat breakfast?” Consistent with the categorization used in our previous study [13], responses were categorized as <3 days per week and ≥3 days per week. This cut-off was retained to ensure methodological consistency and comparability across studies. However, the ≥3 days-per-week category represents an operational definition used in the present study and should not be interpreted as a universally validated definition of regular breakfast consumption. Therefore, the term “breakfast consumption ≥3 days per week” was used throughout the manuscript instead of “regular breakfast consumption.” Current drinking was defined as consuming alcohol at least once per month over the past year. Awareness of smoke-free areas was assessed using the question, “Do you know that this area is designated as a smoke-free area?” The response options were “I know that it is designated and know the specific area” and “I know that it is designated, but do not know the specific area.” Participants who selected either of these responses were classified as being aware of smoke-free areas. Participants who answered that they did not know that the area was designated as smoke-free were classified as unaware. In this item, “this area” refers to the area specified in the 2023 CHS questionnaire.

Data analysis

Data were analyzed using IBM SPSS Statistics version 21.0 (IBM Corp.). All analyses accounted for the complex sampling design of the Korea CHS. The individual sampling weight, stratification variable, and cluster variable (primary sampling unit, represented by the survey-point identification number) provided by the survey were incorporated to obtain population-representative estimates and design-based standard errors. Differences in current e-cigarette use according to participants’ characteristics were examined using complex-sample cross-tabulation analysis. Statistical significance was assessed using the second-order Rao–Scott adjusted chi-square test. Weighted percentages and unweighted frequencies were calculated.

Factors associated with current e-cigarette use were examined using complex-sample binary logistic regression analysis. Design-based standard errors were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Variables significant in the univariable analyses were considered for inclusion in the multivariable model. Education level was also included a priori as a theoretically relevant socioeconomic covariate, regardless of its statistical significance in the univariable analysis, based on previous Korean evidence [14].

Ethical considerations

CHSs were conducted with the approval of the Medical Research Ethics Review Committee of the Korea Disease Control and Prevention Agency. This study was a secondary analysis of publicly available anonymized data and was granted an Institutional Review Board (IRB) exemption (Kunsan National University; IRB approval No. 1040117-202607-HR-047-01). Consistent with the user-signed original data usage agreement, we requested and received raw data from which all personal identification numbers and personal information were deleted. Therefore, participant anonymity was strictly guaranteed as all individual data were handled using designated identification numbers only.

RESULTS

Among the 70,552 female workers, 1,108 participants were classified as current e-cigarette users and 69,444 as non-users. The weighted prevalence of current e-cigarette use was 2.5% (Table 1). Table 2 presents the differences in current e-cigarette use according to participants’ characteristics. Current e-cigarette use was significantly associated with age and marital status, but not with educational attainment. Younger participants were more likely to use e-cigarettes: among current e-cigarette users, 46.7% were aged 19–29 years, 28.1% were aged 30–39 years, and 26.2% were aged 40 years or older (P<0.001). Single females accounted for 69.3% of current e-cigarette users, compared with 30.7% who were married (P<0.001).

Current e-cigarette use of participants (N=70,552)

Comparison of current e-cigarette use by characteristics of participants (N=70,552)

Among the health-related characteristics, subjective health status was significantly associated with current e-cigarette use (P=0.008). Among current e-cigarette users, 40.6% rated their health as good, 49.5% as moderate, and 9.8% as poor. Stress was reported by 91.4% of current e-cigarette users, compared with 84.4% of non-users (P<0.001). Depressive symptoms were reported by 19.9% of current e-cigarette users and 8.0% of non-users (P<0.001). Breakfast consumption was also significantly associated with current e-cigarette use. Among current e-cigarette users, 70.2% reported eating breakfast fewer than <3 times/wk, whereas 29.8% reported eating breakfast three or more times per week (P<0.001). Current drinking was reported by 76.6% of current e-cigarette users, compared with 50.2% of non-users (P<0.001). Awareness of smoke-free areas was lower among current e-cigarette users than among non-users: 49.5% of users were aware of smoke-free areas, compared with 60.7% of non-users (P<0.001).

Table 3 presents the results of the complex-sample logistic regression analysis examining factors associated with current e-cigarette use among female workers. The final model was statistically significant (Wald F=101.680, P<0.001) and yielded a Nagelkerke pseudo-R2 of 0.172. After adjustment for the covariates, marital status, educational attainment, subjective health status, stress, depressive symptoms, breakfast consumption, current drinking, awareness of smoke-free areas, and age group were significantly associated with current e-cigarette use. Age was significantly associated with e-cigarette use. Compared with females aged 40 years or older, females aged 19–29 years (OR=5.58, 95% CI=4.42–7.03) and those aged 30–39 years (OR=4.28, 95% CI=3.46–5.30) had higher odds of e-cigarette use (overall P<0.001). Compared with married females, single females had higher odds of current e-cigarette use (OR=2.07, 95% CI = 1.73–2.48, P<0.001). Females with a high school education or less had higher odds of e-cigarette use than those with a college education or higher (OR=2.62, 95% CI=2.20–3.12, P<0.001). Compared with females who reported good subjective health, those reporting poor health (OR=1.87, 95% CI=1.44–2.45) or moderate health (OR=1.57, 95% CI=1.35–1.82) had higher odds of e-cigarette use; the overall effect of subjective health status was statistically significant (P<0.001). Females who experienced stress had higher odds of e-cigarette use than those who did not (OR=1.42, 95% CI=1.12–1.78, P=0.003). Participants who reported depressive symptoms also had higher odds of e-cigarette use (OR=2.26, 95% CI=1.88–2.71, P<0.001). Females who ate breakfast two times or fewer per week had higher odds of e-cigarette use than those who ate breakfast three or more times per week (OR=2.18, 95% CI=1.85–2.56, P<0.001). Current drinkers had higher odds of e-cigarette use than non-drinkers (OR=2.29, 95% CI=1.94–2.71, P<0.001). Compared with females who were unaware of smoke-free areas, those who were aware had lower odds of current e-cigarette use (OR=0.58, 95% CI=0.49–0.65, P<0.001). Overall, current e-cigarette use was associated with younger age, single marital status, lower educational attainment, poorer subjective health, stress, depressive symptoms, infrequent breakfast consumption, and current alcohol use.

Logistic regression of factors influencing e-cigarette use (N=70,511)

DISCUSSION

The present study investigated factors associated with current e-cigarette use among Korean female workers using data from the 2023 CHS. Younger age, marital status, educational attainment, subjective health status, psychological distress, breakfast frequency, current alcohol use, and awareness of smoke-free areas were significantly associated with e-cigarette use. Our findings both align with and meaningfully extend recent research on tobacco use among Korean females [13]. Whereas the previous study focused on females of childbearing age, the present study specifically examined economically active females and identified population-specific sociodemographic, psychological, and health-related behavioral correlates of e-cigarette use among female workers.

Younger age and single marital status showed strong positive associations with e-cigarette use, consistent with prior evidence highlighting young adults’ exposure to social media marketing and social support dynamics [15-20]. Notably, an important methodological finding emerged regarding educational attainment. While bivariate analysis indicated a higher proportion of e-cigarette users among college graduates, multivariate analysis revealed that lower educational attainment (high school or less) was independently associated with substantially higher odds of e-cigarette use. This reversal reflects a suppression effect caused by age and socio-behavioral covariates. In Korea, higher educational attainment is concentrated among younger demographics who also exhibit higher vaping rates. Once age and lifestyle factors were controlled for, lower educational attainment emerged as a true socioeconomic risk factor, aligning with literature on health literacy and resource inequalities [14].

Health status and psychological distress—including self-rated health, stress, and depression—were consistently associated with elevated odds of vaping [21,22]. Female workers facing occupational strain and gendered workplace pressures may utilize e-cigarettes as a coping mechanism. Similarly, health-risk behaviors such as breakfast skipping and current alcohol use were linked to e-cigarette use, underscoring behavioral clustering [22-24]. Conversely, awareness of smoke-free policies served as a protective factor, supporting the rigorous enforcement of workplace tobacco-free policies [1,25].

Study limitations include its cross-sectional design, reliance on self-reports susceptible to social desirability bias [2], and the lack of unmeasured workplace characteristics (e.g., job strain, working hours, shift work). Nevertheless, these findings highlight the need for comprehensive, gender-responsive, and workplace-based interventions targeting vulnerable female workers.

Notes

AUTHOR CONTRIBUTIONS

Dr. Eungyeong KIM had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Author reviewed this manuscript and agreed to individual contributions.

Conceptualization: EK. Writing–original draft: EK. Writing–review & editing: EK.

CONFLICTS OF INTEREST

No existing or potential conflict of interest relevant to this article was reported.

FUNDING

None.

DATA AVAILABILITY

The data presented in this study are available upon reasonable request from the corresponding author.

References

1. World Health Organization. WHO report on the global tobacco epidemic 2021: addressing new and emerging products [Internet]. World Health Organization; 2021 Jul 27 [cited 2026 Jul 21]. Available from: https://www.who.int/publications/i/item/9789240032095.
2. Park JE, Jeong WM, Choi YJ, Kim SY, Yeob KE, Park JH. Tobacco use in Korea: current epidemiology and public health issues. J Korean Med Sci 2024;39(45):e328. 10.3346/jkms.2024.39.e328. 39592131.
3. Korea Disease Control and Prevention Agency. 2023 Korea National Health and Nutrition Examination Survey results released [Internet]. Korea Disease Control and Prevention Agency; 2024 Dec 23 [updated 2025 Jan 20; cited 2026 Jul 21]. Available from: https://www.kdca.go.kr/eng/4289/subview.do;jsessionid=B5bqKc8ZVZGUm6AVwcMsBmNg27r7MOKm9WpWICwE.kdca_20?enc=Zm5jdDF8QEB8JTJGYmJzJTJGZW5nJTJGMTg5JTJGMjA2MzAzJTJGYXJ0Y2xWaWV3LmRvJTNG.
4. Glantz SA, Bareham DW. E-cigarettes: use, effects on smoking, risks, and policy implications. Annu Rev Public Health 2018;39:215–35. 10.1146/annurev-publhealth-040617-013757.
5. Chun LF, Moazed F, Calfee CS, Matthay MA, Gotts JE. Pulmonary toxicity of e-cigarettes. Am J Physiol Lung Cell Mol Physiol 2017;313(2):L193–206. 10.1152/ajplung.00071.2017. 28522559.
6. Jeong W, Song MJ, Shin JH, Kim JH. Association between electronic cigarette use and risk of obstructive sleep apnea among Korean adults: a cross-sectional nationwide population-based study. J Clin Med 2025;14(11):3616. 10.3390/jcm14113616. 40507378.
7. Kim CY, Paek YJ, Seo HG, Cheong YS, Lee CM, Park SM, et al. Dual use of electronic and conventional cigarettes is associated with higher cardiovascular risk factors in Korean men. Sci Rep 2020;10(1):5612. 10.1038/s41598-020-62545-3. 32221375.
8. Cho YS, Kim HR, Myong JP, Kim HW. Association between work conditions and smoking in South Korea. Saf Health Work 2013;4(4):197–200. 10.1016/j.shaw.2013.09.001. 24422175.
9. Boo S, Oh H. Females’s smoking: relationships among emotional labor, occupational stress, and health promotion. Workplace Health Saf 2019;67(7):361–70. 10.1177/2165079918823214.
10. Becker TD, Arnold MK, Ro V, Martin L, Rice TR. Systematic review of electronic cigarette use (vaping) and mental health comorbidity among adolescents and young adults. Nicotine Tob Res 2021;23(3):415–25. 10.1093/ntr/ntaa171. 32905589.
11. Jang BN, Kim HJ, Kim BR, Woo S, Lee WJ, Park EC. Effect of practicing health behaviors on unmet needs among patients with chronic diseases: a longitudinal study. Int J Environ Res Public Health 2021;18(15):7977. 10.3390/ijerph18157977.
12. Sørensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, et al. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health 2012;12:80. 10.1186/1471-2458-12-80. 22276600.
13. Kim E. Analysis of e-cigarettes related to health behaviors among females of childbearing age: focused on Korean Community Health Survey. J Korean Matern Child Health 2026;30(1):47–52. 10.21896/jkmch.2026.30.1.47.
14. Kim JE, Lee JA, Cho HJ. Gender-based socioeconomic inequality of electronic cigarettes and heated tobacco products in Korea. J Korean Soc Res Nicotine Tob 2024;15(3):96–106. 10.25055/jksrnt.2024.15.3.96.
15. Kava CM, Hannon PA, Harris JR. Use of cigarettes and e-cigarettes and dual use among adult employees in the US workplace. Prev Chronic Dis 2020;17:E16. 10.5888/pcd17.190217. 32078502.
16. Chivers LL, Hand DJ, Priest JS, Higgins ST. E-cigarette use among females of reproductive age: impulsivity, cigarette smoking status, and other risk factors. Prev Med 2016;92:126–34. 10.1016/j.ypmed.2016.07.029.
17. Pokhrel P, Fagan P, Herzog TA, Laestadius L, Buente W, Kawamoto CT, et al. Social media e-cigarette exposure and e-cigarette expectancies and use among young adults. Addict Behav 2018;78:51–8. 10.1016/j.addbeh.2017.10.017.
18. Kim J, Lee S, Chun J. An international systematic review of prevalence, risk, and protective factors associated with young people’s e-cigarette use. Int J Environ Res Public Health 2022;19(18):11570. 10.3390/ijerph191811570.
19. Shafie-Khorassani F, Piper ME, Jorenby DE, Baker TB, Benowitz NL, Hayes-Birchler T, et al. Associations of demographics, dependence, and biomarkers with transitions in tobacco product use in a cohort of cigarette users and dual users of cigarettes and e-cigarettes. Nicotine Tob Res 2023;25(3):462–9. 10.1093/ntr/ntac207. 36037523.
20. Krishnapillai A, Kee CC, Ariaratnam S, Jaffar A, Omar MA, Sanaudi RB, et al. Social support among older persons and its association with smoking: findings from the National Health and Morbidity Survey 2018. Healthcare (Basel) 2023;11(16):2249. 10.3390/healthcare11162249. 37628448.
21. Obisesan OH, Mirbolouk M, Osei AD, Orimoloye OA, Uddin SMI, Dzaye O, et al. Association between e-cigarette use and depression in the Behavioral Risk Factor Surveillance System, 2016-2017. JAMA Netw Open 2019;2(12):e1916800. 10.1001/jamanetworkopen.2019.16800. 31800073.
22. Hefner KR, Sollazzo A, Mullaney S, Coker KL, Sofuoglu M. E-cigarettes, alcohol use, and mental health: use and perceptions of e-cigarettes among college students, by alcohol use and mental health status. Addict Behav 2019;91:12–20. 10.1016/j.addbeh.2018.10.040.
23. Pengpid S, Peltzer K. Skipping breakfast and its association with health risk behaviour and mental health among university students in 28 countries. Diabetes Metab Syndr Obes 2020;13:2889–97.
24. Kim H, Kim V. Smoking rate of electronic cigarettes and its related factors within the last one month. J Converg Inf Technol 2021;11(2):153–62.
25. World Health Organization. Tobacco: e-cigarettes [Internet]. World Health Organization; 2024 Jan 19 [cited 2026 Jul 21]. Available from: https://www.who.int/news-room/questions-and-answers/item/tobacco-e-cigarettes.

Article information Continued

Table 1.

Current e-cigarette use of participants (N=70,552)

Current e-cigarettes use N (weighted %)
Yes 1,108 (2.5)
No 69,444 (97.5)

Table 2.

Comparison of current e-cigarette use by characteristics of participants (N=70,552)

Variables Current e-cigarettes use, N (weighted %) P-value
Yes No
Demographic characteristic
 Age (yr)
  19–29 438 (45.7)  6,330 (15.9) <0.001
  30–39 318 (28.1) 8,409 (17.5)
  ≥40 352 (26.2) 54,705 (66.6)
 Marital status
  Single 740 (69.3) 23,550 (36.7) <0.001
  Married 368 (30.7) 45,883 (63.3)
 Education
  High school or less 506 (41.1) 41,170 (43.5) 0.157
  College or higher 602 (58.9) 28,246 (56.5)
Health related characteristics
 Subjective health status
  Good 446 (40.6) 27,236 (45.8) 0.008
  Moderate 546 (49.5) 32,320 (45.6)
  Poor 116 (9.8) 9,888 (8.6)
 Stress
  Yes 1,007 (91.4) 56,019 (84.4) <0.001
  No 101 (8.6) 13,425 (15.6)
 Depression
  Yes 208 (19.9) 5,341 (8.0) <0.001
  No 900 (80.1) 64,100 (92.0)
 Breakfast consumption (times/wk)
  <3 766 (70.2) 20,772 (39.3) <0.001
  ≥3 342 (29.8) 48,672 (60.7)
 Current drinking
  Yes 833 (76.6)  29,514 (50.2) <0.001
  No 275 (23.4) 39,930 (49.8)
 Aware of smoke-free areas
  Yes 523 (49.5) 42,258 (60.7) <0.001
  No 585 (50.5) 27,186 (39.3)

Values are presented as unweighted frequencies and weighted column percentages. P-values were calculated using the second-order Rao–Scott adjusted chi-square test.

Table 3.

Logistic regression of factors influencing e-cigarette use (N=70,511)

Variables OR (95% CI) P-value
Demographic characteristic
 Age (yr), Ref. ≥40
  19–29 5.58 (4.42–7.03) <0.001
  30–39 4.28 (3.46–5.30) <0.001
 Marital status, Ref. Married
  Single 2.07 (1.73–2.48) <0.001
 Education, Ref. College or higher
  High school or less 2.62 (2.20–3.12) <0.001
Health related characteristics
 Subjective health status, Ref. Good
  Moderate 1.57 (1.35–1.82) <0.001
  Poor 1.87  (1.44–2.45) <0.001
 Stress, Ref. No
  Yes 1.42 (1.12–1.78) 0.003
 Depression, Ref. No
  Yes 2.26 (1.88–2.71) <0.001
 Breakfast consumption, Ref. ≥3
  <3 2.18 (1.85–2.56) <0.001
 Current drinking, Ref. No
  Yes 2.29 (1.94–2.71) <0.001
 Aware of smoke-free areas, Ref. No
  Yes 0.58 (0.49–0.65) <0.001

The final multivariable complex-sample logistic regression model included 70,511 participants. This represents the initial analytic population of 70,552 participants after exclusion of 41 unique cases with missing values in one or more covariates included in the model. Values are presented as OR (95% CI). The final complex-sample logistic regression model was statistically significant (Wald F=101.680, P<0.001) and yielded a Nagelkerke pseudo-R2 of 0.172. The model incorporated the sampling strata, clusters, and sampling weights.

CI, confidence interval; OR, odds ratio; Ref., reference.