Skip Navigation
Skip to contents

PHRP : Osong Public Health and Research Perspectives

OPEN ACCESS
SEARCH
Search

Articles

Page Path
HOME > Osong Public Health Res Perspect > Volume 10(4); 2019 > Article
Original Article
Gender Differences in Harmful Use of Alcohol Among Korean Adults
Eunok Parka, Yeon Sook Kimb
Osong Public Health and Research Perspectives 2019;10(4):205-214.
DOI: https://doi.org/10.24171/j.phrp.2019.10.4.02
Published online: August 31, 2019

aCollege of Nursing, Jeju National University, Jeju, Korea

bDepartment of Nursing, California State University San Bernardino, San Bernardino, CA, USA

*Corresponding author: Yeon Sook Kim, Department of Nursing, California State University San Bernardino, 5500 University Parkway, San Bernardino, California 92407, USA, Email: yeon.kim@csusb.edu
• Received: December 28, 2018   • Revised: June 12, 2019   • Accepted: July 10, 2019

Copyright ©2019, Korea Centers for Disease Control and Prevention

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

  • 6,655 Views
  • 71 Download
  • 8 Crossref
  • 8 Scopus
prev next
  • Objectives
    Harmful alcohol consumption is associated with considerable social and economic damage to individuals and society. Because gender and ethnic background influence alcohol intake differently, examining gender specific factors influencing harmful drinking is necessary. This study investigated gender differences in alcohol consumption, harmful drinking, and the associated factors among Korean adults.
  • Methods
    We analyzed the data from the 2012–2015 Korean National Health and Nutrition Examination Survey. Data from survey participants aged 20–64 years (N = 18,581) were included. The Alcohol Use Disorders Identification Test was used for alcohol dependence, and pooled weights were used. Chi-squared tests and multiple logistic regression analyses were conducted.
  • Results
    The prevalence of harmful alcohol use (Alcohol Use Disorders Identification Test score ≥ 16) was 10.7% in the total sample; 18.4% in men and 3.4% in women, which constituted a significant difference. Education, marital status, smoking, perceived stress, and depressive feeling were associated with harmful drinking in both genders. However, household income, occupation, and perceived health status were associated with harmful drinking only in men.
  • Conclusion
    Since there are gender differences in harmful drinking and alcohol dependence, gender tailored prevention and intervention strategies for alcohol dependence are necessary including consideration of smoking, stress, and depressive feeling.
Harmful alcohol use is associated with unintentional and intentional injuries, liver diseases, cardiovascular diseases, and cancer, as well as with social problems (i.e., violent behaviors, fatal accidents) [13]. According to a World Health Organization (WHO) report, 5.1% of the global burden of disease and injury is due to alcohol consumption, and 5.9% of all deaths worldwide were attributable to alcohol consumption in 2012 [4].
Koreans drink more alcohol than people of other nationalities and Koreans aged 15 years and older consume on average 11.9 L of pure alcohol per year compared with a global prevalence of 6.4 L in 2016 [4]. In addition, high-risk drinking is more prevalent in Korea (15.1%) compared with other countries [5]. In 2010, the alcohol-dependence rate in Korea was 4.7%, whereas the average for the Western Pacific Region was 2.3% according to the WHO [4].
There are significant gender differences in the prevalence of harmful alcohol use and the proportion of global deaths associated with alcohol. It was reported in 2012 that 7.6% of deaths in men and 4.0% in women were attributable to alcohol [4]. Alcohol consumption levels differ by gender [6,7]. It has been reported that between 2012 and 2014 men drank more than women among non-Hispanic White, Mexican, other Latino, Asian, and other non-Hispanic participants [8]. Major gender differences are present in multiple alcohol-related outcomes such as alcohol consumption amount, age of beginning drinking, and alcohol-related death rates [9]. However, a study among the Hispanic population reported no gender differences in alcohol consumption during the past month (25.2% of women and 23.8% of men), nor in heavy drinking during the past 2 weeks (19.0% of women and 19.9% of men) [10].
In a study, male adolescents’ drinking was associated with a higher level of social and enhancement motives, whereas that of female adolescents was associated with a higher level of coping motives and a lower level of conformity motives [11]. It was reported that male Korean college students drank to cope with stress, whereas females drank for enhancement motives however, both motives led to alcohol-related problems [12]. Moreover, gender differences in alcohol consumption are reportedly due to cultural differences and different biological and cultural needs [13].
According to findings reported by the WHO, the ratio of male to female heavy drinkers in South-East Asia was 10 to 9, and in the Western Pacific was 7 to 3, respectively [4]. In Korea, the prevalence of harmful alcohol use was 5-fold higher in men than women (2.5% in men and 0.5% in women) [4] and the rate of high-risk drinking in men was 23.7%, and in women was 4.2% [5]. The alcohol-dependence rate in Korea has been reported to be 7.8% in men and 1.7% in women [4]. However, few studies have investigated the factors associated with these gender differences. Therefore, in this current study gender differences in harmful alcohol use were evaluated and the factors associated with this phenomenon in Korea were examined.
1. Design and participants
This study was a secondary analysis of the 2012–2015 Korean National Health and Nutrition Examination Survey (KNHANES), which was a complex, stratified, multi-stage cluster probability sampling method survey. The primary sample units (PSUs), consisted of 50–60 households per each PSU, which were selected from a census block or residential condition. Twenty households out of the selected PSUs were selected for the field survey. All members (> 1 year) in the selected households were asked to participate in the KNHANES. Approximately 10,000 people were selected in total between 2012 to 2015 from 192 PSUs [14]. The response rate was 80.0%, 79.3%, 77.8%, and 77.6% in 2012, 2013, 2014, and 2015, respectively [1518]. To ensure representative sampling for an equal sampling probability, weighted values were assigned to each respondent. A standardized interview, health examination, and nutrition survey were used to collect data. Data from adults aged 20–64 years (N = 13,709) were used and analyzed in this study (JJNU-IRB-2018-055).
2. Measures
The Korean version of the Alcohol Use Disorders Identification Test (AUDIT-K) was used to assess harmful alcohol use. The AUDIT-K was based on the percentage of alcohol by volume in Korean liquor, and has been verified in the Korean population [19]. The AUDIT-K consists of 10 items scored on a 5-point Likert scale, ranging from 0 to 4 points. Higher scores indicate an increased likelihood of hazardous and harmful patterns of alcohol consumption. Reliability of the AUDIT-K was determined by Cronbach’s alpha that was 0.89 in the study by Kim et al [20] and 0.89 in this study. Validity of the AUDIT-K was shown to be high in several studies [2022]. The cut-off for harmful alcohol use was an AUDIT-K score ≥ 16 in this current study.
Sociodemographic characteristics and health-related behavioral variables were included in the analysis of this study as “factors associated with harmful alcohol use.” The sociodemographic characteristics were age (20–44 years, 45–64 years), household income (low, low-intermediate, intermediate-high, and high), educational level (elementary, middle school, high school, university), occupation (professional/manager, office worker, sales/service, physical worker, none), and marital status (unmarried, married, separated/divorced). The health-related behavioral variables were perceived health status, currently smoking, physically active (walking for 30 minutes daily), perceived stress, and experience of depressive feeling (feeling of sadness or despair that negatively affects daily life). To evaluate subjects’ perceived health status they were asked, “In general, how would you rate your health?” and the answer was categorized into “good (excellent/good),” “moderate (fair)” or “poor (poor/very poor).” Perceived stress was measured with the question, “How do you feel stressed on an ordinary day?” and was dichotomized into “a lot / very stressed” and “a little.” A single question was used to assess the experience of depressive feelings, “In the last year have you ever felt sad or depressed enough for it to affect your daily life most days for 2 weeks or longer?” with a dichotomous response of “yes,” or “no.”
3. Statistical analysis
The data were analyzed using SAS (version 9.4) software, where p < 0.05 was considered to be indicative of statistical significance. Pooled weights were used in the statistical analysis. Chi-square tests and multiple logistic regression analyses were conducted to explore the association between each factor and harmful alcohol use.
In terms of age, 57.2% of men and 54.7% of women were 20–44 years, and 33.2% of men and 35.8% of women had a low or low–intermediate household income. Of the men and women, 85.5% and 78.7% respectively, had a high school education or above. Additionally, 19% and 44.4% of the men and women respectively were unemployed, 30.7% and 19.5% respectively, were unmarried, and 37.9% and 29.9% respectively, perceived their health to be good. Moreover, 43.4% and 6.2% of the men and women respectively, were currently smoking, and 41.1% and 37.7% respectively, reported walking daily for at least 30 minutes. Among the men and women, 26.6% and 28.9% respectively, reported being very stressed, and 8.3% and 15.1% respectively, stated that they felt depressed. There were significant differences between men and women in age (χ2 = 8.40, p = 0.006), educational level (χ2 = 110.75, p < 0.001), occupation (χ2 = 1,323.81, p < 0.001), marital status (χ2 = 285.26, p < 0.001), perceived health status (χ2 = 113.75, p < 0.001), currently smoking (χ2 = 2,587.58, p < 0.001), walking (χ2 = 15.67, p = 0.001), stress (χ2 = 9.06, p = 0.014), and depressive feeling (χ2 = 136.10, p < 0.001; Table 1).
The prevalence of harmful alcohol use (AUDIT-K score ≥16) was 10.7% in Korean adults aged 20–64 years (18.4% in men and 3.4% in women). Among men, harmful alcohol use was more prevalent in those aged 45–64 years (21.5%) compared with 20–44 years (16.1%). However, among women harmful alcohol use was more prevalent in those aged 20–44 years (4.8%) compared with 45–64 years (1.6%). Harmful alcohol use was more prevalent in men with a high household income than in those with a low household income (20.4% and 14.8% respectively) and in those with an elementary-school education (23.5%). In contrast, in women harmful alcohol use was the most prevalent in high-school graduates (4.6%). In terms of occupation, manual laborers showed the highest rate of harmful alcohol use among men (22.2%), and for women it was sales/service workers (6.0%). A higher prevalence of harmful alcohol use was identified among men who were separated or divorced (26.0%), whereas this was true for unmarried women (6.8%). The frequency of harmful alcohol use was higher among those with a moderate health status, than among those who had a good health status among both men and women (19.1% of men and 2.9% of women). The frequency of harmful alcohol use in both men and women was higher among the currently smoking group, than among the non-smoking group (25.2% of men and 17.3% of women). Among men, the prevalence of harmful alcohol use was higher in those who did not walk for more > 30 minutes daily (19.7%) compared with those who did (16.7%). The prevalence of harmful alcohol use was higher in those who reported being stressed (25.4% in men and 6.3% in women) or experienced depressive feeling (29.8% in men and 10.2% in women; Table 2).
The prevalence of harmful alcohol use in both genders differed significantly by age, educational level, occupation, marital status, perceived health status, currently smoking, perceived stress, and the presence/absence of depressive feeling. Additionally, household income was associated with harmful alcohol use in men (Table 2).
The factors associated with harmful alcohol use differed by gender. In men, high household income, elementary school education, men who were divorced or separated, manual laborers, moderate health status, currently smoking, perceived stress, and depressive feeling were significantly associated with harmful alcohol use. In women, education to high school graduate level, sales/service workers, unmarried women, currently smoking, a moderate health status, perceived stress, and depression status were significantly associated with harmful alcohol use. The odds ratio (OR) for harmful alcohol use was highest for the currently smoking group in both men and women. The impact of currently smoking on harmful alcohol use was greater in women than in men. In men, high ORs indicated that household income, perceived stress, and depressive feeling were risk factors for harmful alcohol consumption, whereas depressive feeling, marital status, and educational level were considered as the greatest risks in women (Table 3).
A higher prevalence of harmful alcohol use was observed in men with a high household income, than in those with a low household income (OR = 1.69, p < 0.001). A higher prevalence of harmful alcohol use among men was observed with an elementary-school education compared with college graduates (OR = 1.54, p = 0.006), high-school (OR = 1.47, p < 0.001) and middle-school (OR = 1.44, p = 0.023) education. Compared with married men, a significantly lower prevalence of harmful alcohol use was observed in unmarried men (OR = 0.61, p < 0.001). There was a significantly lower prevalence of harmful alcohol use among unemployed men, than among those with professional or managerial occupations (OR = 0.65, p = 0.009). There was also a higher prevalence of harmful alcohol use among men who perceived their health to be poor, than among those who perceived their health to be good (OR = 1.46, p = 0.005). A higher prevalence of harmful alcohol use was observed in those who currently smoke, than in non-smokers (OR = 1.99, p < 0.001). Harmful alcohol use was more prevalent among men who felt stressed, than among those who did not (OR = 1.59, p < 0.001). Additionally, there was a higher prevalence of harmful alcohol use among men who had felt depressed for 2 weeks, than among men who did not feel depressed (OR = 1.53, p = 0.001; Table 3).
A significantly higher prevalence of harmful alcohol use was identified in women with a high-school education, than in those with a college education (OR = 1.78, p = 0.001). Unlike the case for men, a higher prevalence of harmful alcohol use was observed among unmarried women, than among married women (OR = 2.6, p < 0.001). There was also a significantly higher prevalence of harmful alcohol use among females currently smoking, than among female non-smokers (OR = 5.37, p < 0.001). This was also the case for women who felt stressed compared with those who did not (OR = 1.63, p < 0.001), and for women who felt depressed over 2 weeks compared with those who did not (OR = 2.9, p < 0.001; Table 3).
In this study, currently smoking was strongly associated with harmful alcohol use in both genders. Similarly, smokers had a 2-fold higher frequency of heavy drinking, than non-smokers among men, and a 3-fold higher frequency of heavy drinking among women [23]. It was also reported that among women, the prevalence of harmful alcohol use was 4.93-fold higher in smokers, than in non-smokers, whereas the prevalence among men was 3.57-fold higher in smokers, than in non-smokers [24]. The prevalence of smoking in men was 5 times higher than women due to the lower social acceptability of women’s smoking in Korea [25]. This may imply that female smokers may have relatively higher stress or depressive feelings that lead to smoking, than male smokers. Considering that smoking is statistically positively associated with drinking [23,26] female smokers may have a greater inclination toward harmful alcohol use than male smokers. There was a higher prevalence of heavy drinking and problem alcohol use among ex-smokers of both genders [23,24]. There was also a high prevalence of smoking among individuals engaged in harmful alcohol use [26]. Thus, harmful alcohol use and smoking are interrelated. Therefore, smoking cessation interventions should be part of the intervention strategies to ameliorate problematic alcohol consumption, particularly for women.
Depressive feelings were associated with harmful alcohol use by both genders in this study and this is consistent with several previous reports [2729]. Moreover, there was a 2-fold higher prevalence of depressive feelings among Korean women (15.1%), than among Korean men (8.3%). A diagnostic assessment of depression was not included in this study but this finding indicates that a subjectively experienced depressive mood is related to harmful alcohol use. The effect of depressive feelings on harmful alcohol use was greater in women than in men. This finding is similar to a previous study reporting that women with depression had a higher rate of harmful drinking than men with depression [30]. This indicates that women with depression may be more dependent on alcohol than men with depression but, there is no clear cause of the gender difference. However, Korean women with depressive feelings have a lack of resources to relieve stress compared with Korean men in society (a robust study is necessary to investigate the possible causes of the gender differences).
Strategies to prevent or manage harmful alcohol use should consider the possibility of depression. People with alcohol dependence were reported to be at a higher risk of major depressive episodes [31]. Therefore, depressive feelings and harmful alcohol use are interrelated. However, the cross-sectional design of this study prevented causal inferences being drawn.
A high level of stress increased the risk of harmful alcohol use in both men and women (OR = 1.59 and 1.63, respectively). However, it was reported that stress levels did not significantly increase the risk of harmful alcohol use in women or men [24]. In this study harmful alcohol use was met when there was an AUDIT-K score of ≥ 16, whereas an AUDIT-K score of ≥ 12 was used in the study by Jeon and Lee in 2010 [24], suggesting that the threshold was set too high. Stress is significantly associated with the frequency of alcohol intake and heavy drinking [23]. That is, stress is not significantly related to low-level alcohol intake, but is significantly associated with heavy drinking or harmful alcohol use. Therefore, stress management should reduce the risk of harmful alcohol use in both genders.
Consistent with the findings of a previous study [24], there was a higher prevalence of harmful alcohol use among men with an elementary-school education and women with a high-school education, than among those with a college education. However, a previous study showed that a higher level of education was significantly associated with harmful alcohol use regardless of gender [32]. Discrepancies among reports may be due to the population differences, as shown in a study conducted in Korean adults [24] and another with older Swedish adults [32]. The finding in this current study are consistent with a previous study among the Korean adult population. However, further studies are needed to investigate the differences between countries and populations. It was reported that single, separated, or divorced men showed a greater risk of harmful alcohol use [33]. Another study observed a lower prevalence of harmful alcohol use among unmarried men however, a higher prevalence was reported among unmarried compared with married, women [14]. They also reported that unmarried women had a higher risk of harmful alcohol use compared with married women, but no significant relationship with marital status was reported in men [23]. A previous study, reported that married individuals of both genders had a significantly lower risk of harmful alcohol use, compared with unmarried, divorced, separated, or widowed individuals [24]. These findings may be associated with stress due to problematic spousal relationships [34].
In men, but not in women, household income, perceived health status, and occupation were associated with harmful alcohol use. Household income was significantly positively related to alcohol consumption in men, but not in women. Additionally, unemployed men had a lower risk of harmful alcohol use than men with a professional occupation. It was reported that men with a low household income had a lower risk of harmful alcohol use compared with those with a high household income [24]. The differences between men and women may be due to gender differences in economic activities, job classifications, and income levels. A significantly lower prevalence of harmful alcohol use among unemployed men was observed in the current study, but employment status was not associated with harmful alcohol use in women. Another study observed that employment increased the frequency of harmful alcohol use among men but not women [35]. Unemployed people (i.e., those who lost a job or retired) may have fewer opportunities to participate in social gatherings (i.e., drinking with coworkers after work) and less disposable income. Alcohol consumption plays an important role in nonverbal communication in Korea, as people tend to socialize while drinking [36]. Korean workers often drink with coworkers after work to relieve stress, relax, or network [37]. It was reported in 2018 that men (69.4%) are more likely than women to be the major source of household income (30.6%) [38]. Moreover, as of July 2018, 74.1% of men ≥ 15 years of age were economically active compared with 53.5% of women [38]. In this current study, 81% of men were employed, whereas 55.6% of women were employed. This may explain why household income and employment status were significantly associated with harmful alcohol use in men but not in women.
A previous study showed that household income was not significantly associated with heavy drinking [23]. In contrast, several studies have demonstrated an association between low income and harmful alcohol use [3944]. It was also reported that people with a middle-class household income were more likely to report harmful alcohol use [45]. Previous studies of the relationship between household income and harmful alcohol use were conducted in low-to-middle income countries (e.g., India, Zimbabwe, Nepal, and Thailand), whereas this study was conducted in Korea, a high-income country. A study of high household income in Chile and Finland, both high-income countries, reported a higher prevalence of alcohol consumption and harmful alcohol use [46]. Thus, the association between harmful alcohol use and household income may differ according to the economic level of the nation in question. Further studies should explore the factors that influence the relationship between household income and harmful alcohol use.
A poor self-rated health status was associated with a higher prevalence of harmful alcohol use in men but not in women. Several studies have shown that harmful alcohol use is significantly associated with perceived health status [4749], but these studies did not assess gender differences. Perceived health status and harmful alcohol use may also be related to gender differences, derived from more frequent drinking among men than among women, as the adverse effects of this behavior may reduce self-perceived health status. Therefore, an improved perceived health status may reduce the risk of harmful alcohol use among men.
The findings of this current study provide insight into gender differences in the prevalence of, and the factors associated with, harmful alcohol use among Korean adults. However, this study had limitations. Firstly, the cross-sectional design of this study prevented investigation into the causal associations of smoking, depression, stress, and perceived health status with harmful alcohol use. Indeed, harmful alcohol use may have influenced the observations related to these factors. Secondly, the mechanisms underlying the gender differences observed in this study were not investigated.
Information on gender differences affecting harmful alcohol consumption and associated factors, will enable identification of subgroups vulnerable to harmful alcohol use, and aide in the development of management or prevention strategies for harmful alcohol consumption. In this study gender differences were observed in the prevalence of harmful alcohol use and its associated factors The demographic factors related to harmful alcohol use (i.e., education, marital status, household income, occupation, and perceived health status) should be considered when identifying the target groups for programs that are designed to prevent and/or treat harmful alcohol use. Moreover, such programs should also address smoking, stress, and depression.
Acknowledgements
This research was supported by the 2018 scientific promotion program funded by Jeju National University.

Conflicts of Interest

The authors have declared that no conflicts of interest exist.

  • 1. Karriker-Jaffe KJ, Room R, Giesbrecht N, et al. Alcohol’s harm to others: opportunities and challenges in a public health framework. J Stud Alcohol Drugs 2018;79(2). 239−43. PMID: 10.15288/jsad.2018.79.239. PMID: 29553351. PMID: 6019779.ArticlePubMedPMC
  • 2. Romano E, Torres-Saavedra PA, Cartagena HIC, et al. Alcohol-related risk of driver fatalities in motor vehicle crashes: comparing data from 2007 and 2013–2014. J Stud Alcohol Drugs 2018;79(4). 547−52. PMID: 10.15288/jsad.2018.79.547. PMID: 30079869. PMID: 6090106.ArticlePubMedPMC
  • 3. Testa M, Parks KA, Hoffman JH, et al. Do drinking episodes contribute to sexual aggression perpetration in college men? J Stud Alcohol Drugs 2015;76(4). 507−15. PMID: 10.15288/jsad.2015.76.507. PMID: 26098025. PMID: 4495069.ArticlePubMedPMC
  • 4. World Health Organization [Internet]. Global status report on alcohol and health 2014 Geneva (Switzerland): World Health Organization; Available from: https://www.who.int/substance_abuse/publications/alcohol_2014/en/.
  • 5. Hong JW, Noh JH, Kim DJ. The prevalence of and factors associated with high-risk alcohol consumption in Korean adults: the 2009–2011 Korea National Health and Nutrition Examination Survey. PLoS One 2017;12(4). e0175299PMID: 10.1371/journal.pone.0175299. PMID: 5383276.ArticlePubMedPMC
  • 6. Kulis S, Marsiglia FF, Nagoshi JL. Gender roles, externalizing behaviors, and substance use among Mexican-American adolescents. J Soc Work Pract Addict 2010;10(3). 283−307. PMID: 10.1080/1533256X.2010.497033. PMID: 21031145. PMID: 2963473.ArticlePubMedPMC
  • 7. Parsai M, Voisine S, Marsiglia FF, et al. The protective and risk effects of parents and peers on substance use, attitudes, and behaviors of Mexican and Mexican American women and men adolescents. Youth Soc 2009;40(3). 353−76. PMID: 10.1177/0044118X08318117. PMID: 19478992. PMID: 2686611.ArticlePubMedPMC
  • 8. Lotfipour S, Cisneros V, Ogbu UC, et al. A retrospective analysis of ethnic and gender differences in alcohol consumption among emergency department patients: A cross-sectional study. BMC Emerg Med 2015;15:24PMID: 10.1186/s12873-015-0050-5. PMID: 26419652. PMID: 4589191.ArticlePubMedPMCPDF
  • 9. Salvatore JE, Cho SB, Dick DM. Genes, environments, and sex differences in alcohol research. J Stud Alcohol Drugs 2017;78(4). 494−501. PMID: 10.15288/jsad.2017.78.494. PMID: 28728631. PMID: 5551654.ArticlePubMedPMC
  • 10. Vaughan EL, Gassman RA, Jun MC, et al. Gender differences in risk and protective factors for alcohol use and substance use problems among Hispanic adolescents. J Child Adolesc Subst Abuse 2015;24(5). 243−54. PMID: 10.1080/1067828X.2013.826609.Article
  • 11. Kuntsche E, Wicki M, Windlin B, et al. Drinking motives mediate cultural differences but not gender differences in adolescent alcohol use. J Adolesc Health 2015;56(3). 323−9. PMID: 10.1016/j.jadohealth.2014.10.267. PMID: 25586227.ArticlePubMed
  • 12. Choi J, Lee SY, Kwon MS. Gender differences in the relationship between drinking motives and drinking behaviors among Korean college students. J Child Adolesc Subst Abuse 2017;26(5). 401−13. PMID: 10.1080/1067828X.2017.1321506.Article
  • 13. Homila M, Raitasalo K. Gender differences in drinking: why do they still exist? Addiction 2005;100(12). 1763−9. PMID: 10.1111/j.1360-0443.2005.01249.x.ArticlePubMed
  • 14. Kim Y. The Korea National Health and Nutrition Examination Survey (KNHANES): current status and challenges. Epidemiol Health 2014;36:e2014002PMID: 10.4178/epih/e2014002. PMID: 24839580. PMID: 4017741.ArticlePubMedPMCPDF
  • 15. Ministry of Health and Welfare & Korea Centers for Disease Control and Prevention. Korea Health Statistics 2012: Korea National Health and Nutrition Examination Survey (KNHANES V-3). Osong (Korea): Korea Centers for Disease Control and Prevention; 2013.
  • 16. Ministry of Health and Welfare & Korea Centers for Disease Control and Prevention. Korea Health Statistics 2013: Korea National Health and Nutrition Examination Survey (KNHANES VI-1). Osong (Korea): Korea Centers for Disease Control and Prevention; 2014.
  • 17. Ministry of Health and Welfare & Korea Centers for Disease Control and Prevention. Korea Health Statistics 2014: Korea National Health and Nutrition Examination Survey (KNHANES VI-2). Osong (Korea): Korea Centers for Disease Control and Prevention; 2015.
  • 18. Ministry of Health and Welfare & Korea Centers for Disease Control and Prevention. Korea Health Statistics 2015: Korea National Health and Nutrition Examination Survey (KNHANES VI-3). Osong (Korea): Korea Centers for Disease Control and Prevention; 2016.
  • 19. Lee BO, Lee CH, Lee PG, et al. Development of Korean version of alcohol use disorders identification test (AUDIT-K): its reliability and validity. J Korean Acad Addict Psychiatr 2000;4:83−92.
  • 20. Kim CG, Kim JS, Jung JG, et al. Reliability and validity of alcohol use disorder identification test-Korean revised version for screening at-risk drinking and alcohol use disorders. Korean J Fam Med 2014;35(1). 2−10. PMID: 10.4082/kjfm.2014.35.1.2. PMID: 24501664. PMID: 3912263.ArticlePubMedPMCPDF
  • 21. Chae MO, Jeon HO. Effects of alcohol management programs for university students in Korea: a systematic review and meta-analysis. Korean Acad Community Health Nurs 2018;29(1). 120−32. PMID: 10.12799/jkachn.2018.29.1.120.Article
  • 22. Chang JW, Kim JS, Jung JG, et al. Validity of alcohol use disorder identification test-Korean revised version for screening alcohol use disorder according to diagnostic and statistical manual of mental disorders, fifth edition criteria. Korean J Fam Med 2016;37(6). 323−8. PMID: 10.4082/kjfm.2016.37.6.323. PMID: 27900069. PMID: 5122663.ArticlePubMedPMCPDF
  • 23. Im MY, Lee SY. Risk factors for heavy episodic drinking among Korean adults: differences by gender and age. J Korean Acad Soc Home Care Nurs 2015;22(2). 265−79.
  • 24. Jeon GS, Lee HY. Associated factors of binge drinking and problem drinking among Korean men and women. Korean J Health Educ Promot 2010;27(1). 91−103.
  • 25. Kim SM, Jung JW, Park IW, et al. Gender Differences in Relations of Smoking Status, Depression, and Suicidality in Korea: Findings from the Korea National Health and Nutrition Examination Survey 2008–2012. Psychiatry Investig 2016;13(2). 239−46. PMID: 10.4306/pi.2016.13.2.239. PMID: 27081387. PMID: 4823202.ArticlePubMedPMC
  • 26. _Weinberger AH, Platt J, Jiang B, et al. Cigarette smoking and risk of alcohol use relapse among adults in recovery from alcohol use disorders. Alcohol Clin Exp Res 2015;39(10). 1989−96. PMID: 10.1111/acer.12840. PMID: 26365044. PMID: 4592419.ArticlePubMedPMC
  • 27. Merrill JE, Reid AE, Carey MP, et al. Gender and depression moderate response to brief motivational intervention for alcohol misuse among college students. J Consult Clin Psychol 2014;82(6). 984−92. PMID: 10.1037/a0037039. PMID: 24865872. PMID: 4244268.ArticlePubMedPMCPDF
  • 28. Murphy JG, Yurasek AM, Dennhardt AA, et al. Symptoms of depression and PTSD are associated with elevated alcohol demand. Drug Alcohol Depend 2013;127(1–3). 129−36. PMID: 10.1016/j.drugalcdep.2012.06.022. PMID: 3775331.ArticlePubMed
  • 29. _Park H, Jun J, Sohn S. Gender disparities in typologies of alcohol use and levels of depressive symptoms in Korean adults. Mental Health Soc Work 2015;43(2). 176−99.
  • 30. Pedrelli P, Borsari B, Lipson SK, et al. Gender differences in the relationships among major depressive disorder, heavy alcohol use, and mental health treatment engagement among college students. J Stud Alcohol Drugs 2016;77(4). 620−8. PMID: 10.15288/jsad.2016.77.620. PMID: 27340967. PMID: 4987072.ArticlePubMedPMC
  • 31. Bulloch A, Lavorato D, Williams J, et al. Alcohol consumption and major depression in the general population: the critical importance of dependence. Depress Anxiety 2012;9(12). 1058−64. PMID: 10.1002/da.22001.Article
  • 32. Fornazar R, Spak F, Dahlin-Ivanoff S, et al. Alcohol consumption among the oldest old and how it changes during two years. ISRN Geriatr 2013;2013:671218. PMID: 10.1155/2013/671218.ArticlePubMedPMCPDF
  • 33. Iparraguirre J. Socioeconomic determinants of risk of harmful alcohol drinking among people aged 50 or over in England. BMJ Open 2015;5(7). e007684PMID: 10.1136/bmjopen-2015-007684. PMID: 26204909. PMID: 4521517.ArticlePubMedPMC
  • 34. Lemke S, Schutte K, Brennan P, et al. Gender differences in social influences and stressors linked to increased drinking. J Stud Alcohol Drugs 2008;69(5). 695−702. PMID: 10.15288/jsad.2008.69.695. PMID: 18781244. PMID: 2575387.ArticlePubMedPMC
  • 35. Abel WD, Weaver S, Roomes TR, et al. Risk factors associated with frequent alcohol binge drinking among Jamaicans: does gender matter? Asian Pac J Cancer Prev 2018;19(S1). 39−44. PMID: 29682919.PMC
  • 36. Chun S. The social meaning of alcohol consumption in Korea. Korean Diabetes J 2012;13(2). 57−60. PMID: 10.4093/jkd.2012.13.2.57.Article
  • 37. Lee S. Drinking: drinking culture of Korea #1. Seoul (Korea): Seon; 2009.
  • 38. Statistics Korea [Internet]. Economically active population survey in July 2018 2018 [cited 2018 Nov 7]. Available from: http://kostat.go.kr/assist/synap/preview/skin/doc.html?fn=synapview370326_1&rs=/assist/synap/preview.
  • 39. _Cubbins LA, Kasprzyk D, Montano D, et al. Alcohol use and abuse among rural Zimbabwean adults: a test of a community-level intervention. Drug Alcohol Depend 2012;124(3). 333−9. PMID: 10.1016/j.drugalcdep.2012.02.002. PMID: 22386686. PMID: 3383920.ArticlePubMedPMC
  • 40. Deepa M, Anjana RM, Manjula D, et al. Convergence of prevalence rates of diabetes and cardiometabolic risk factors in middle- and low-income groups in urban India: 10-year follow-up of the Chennai Urban Population Study. J Diabetes Sci Technol 2011;5(4). 918−27. PMID: 10.1177/193229681100500415. PMID: 21880235. PMID: 3192599.ArticlePubMedPMC
  • 41. Dhungana RR, Devkota S, Khanal MK, et al. Prevalence of cardiovascular health risk behaviors in a remote rural community of Sindhuli district, Nepal. BMC Cardiovascular Disord 2014;14:92−9. PMID: 10.1186/1471-2261-14-92.ArticlePDF
  • 42. Menon J, Vijayakumar N, Joseph JK, et al. Below the poverty line and non-communicable diseases in Kerala: The Epidemiology of non-communicable diseases in rural areas (ENDIRA) study. Int J Cardiol 2015;187:519−24. PMID: 10.1016/j.ijcard.2015.04.009. PMID: 25846664.ArticlePubMed
  • 43. Samuel P, Antonisamy B, Raghupathy P, et al. Socioeconomic status and cardiovascular risk factors in rural and urban areas of Vellore, Tamilnadu, South India. Int J Epidemiol 2012;41(5). 1315−27. PMID: 10.1093/ije/dys001. PMID: 22366083. PMID: 3541500.ArticlePubMedPMCPDF
  • 44. Zaman MJ, Patel A, Jan S, et al. Socioeconomic distribution of cardiovascular risk factors and knowledge in rural India. Int J Epidemiol 2012;41(5). 1302−14. PMID: 10.1093/ije/dyr226. PMID: 22345313.ArticlePubMedPDF
  • 45. Aekplakorn W, Hogan MC, Tiptaradol S, et al. Tobacco and hazardous or harmful alcohol use in Thailand: joint prevalence and associations with socioeconomic factors. Addict Behav 2008;33(4). 503−14. PMID: 10.1016/j.addbeh.2007.10.010.ArticlePubMed
  • 46. Peña S, Mäkelä P, Valdivia G, et al. Socioeconomic inequalities in alcohol consumption in Chile and Finland. Drug Alcohol Depend 2017;73:24−30. PMID: 10.1016/j.drugalcdep.2016.12.014.Article
  • 47. Bendtsen P, Hensing G, Alexanderson K. Self-perceived excessive alcohol consumption among employed women association with health and psychosocial factors. Addict Behav 2003;28:777−83. PMID: 10.1016/S0306-4603(01)00294-5. PMID: 12726790.ArticlePubMed
  • 48. Chikere E, Mayowa MO. Prevalence and perceived health effect of alcohol use among male undergraduate students in Owerri, South-East Nigeria: a descriptive cross-sectional study. BMC Public Health 2011;11:118PMID: 10.1186/1471-2458-11-118. PMID: 21333007. PMID: 3049753.ArticlePubMedPMCPDF
  • 49. Roy A, Wibberley C, Lamb J. The usual suspects: alcohol, tobacco and other drug use in 15- to 16-year-old school pupils—prevalence, feelings and perceived health risks. Drugs Educ Prev Policy 2005;12(4). 305−15. PMID: 10.1080/09687630500095869.Article
Table 1
Sociodemographic characteristics between elderly Koreans with and without activity limitation.
Parameters (or variables) Gender Chi p
Male Female


N % SE N % SE
Age group (y) 20–44 2,702 57.2 0.8 3,984 54.7 0.7 8.40 0.006
45–64 2,780 42.8 0.8 4,243 45.3 0.7

Income Low 1,336 25.8 0.9 1,997 25.3 0.7 1.46 0.718
Low-intermediate 1,393 25.8 0.8 2,039 25.4 0.6
Intermediate-high 1,341 24.5 0.7 2,063 24.6 0.6
High 1,371 23.9 0.9 2,080 24.7 0.8

Household income Low 507 9.4 0.5 842 9.7 0.5 10.41 0.020
Low-intermediate 1,277 23.8 0.8 2,115 26.1 0.7
Intermediate-high 1,702 32.3 0.9 2,477 31.0 0.7
High 1,955 34.5 1.0 2,745 33.2 0.9

Education Elementary 453 6.8 0.4 1,138 11.3 0.4 110.75 < 0.001
Middle school 476 7.7 0.4 870 10.0 0.4
High school 2,111 43.2 0.8 3,054 40.4 0.7
University 2,270 42.3 0.9 2,969 38.3 0.8

Profession Professional/manager 1,055 19.8 0.7 1,132 14.7 0.5 1323.81 < 0.001
Office worker 753 13.5 0.5 755 10.0 0.4
Sales/service 686 13.5 0.6 1,310 16.7 0.5
Physical worker 1,877 34.1 0.9 1,241 14.2 0.5
None 914 19.0 0.7 3,591 44.4 0.7

Marital status Unmarried 1,268 30.7 0.9 1,255 19.5 0.6 285.26 < 0.001
Married 3,955 64.8 0.9 6,168 71.9 0.7
Separated/divorced 254 4.5 0.3 794 8.6 0.4

Living Alone 349 6.9 0.5 417 4.5 0.3 39.06 < 0.001
With others 5,133 93.1 0.5 7,810 95.5 0.3

Perceived health status Good 1,976 37.9 0.8 2,384 29.9 0.6 113.75 < 0.001
Moderate 2,656 49.4 0.8 4,276 53.0 0.7

Poor 689 12.7 0.5 1,388 17.1 0.5

Economic activity No 914 18.9 0.7 3,591 44.3 0.7 987.30 < 0.001
Yes 4,397 81.1 0.7 4,446 55.7 0.7

Currently smoking No 3,175 56.6 0.8 7,792 93.8 0.3 2587.58 < 0.001
Yes 2,305 43.4 0.8 433 6.2 0.3

Strenuous physical activity No 2,758 80.2 0.8 4,127 86.5 0.6 57.06 < 0.001
Yes 637 19.8 0.8 636 13.5 0.6

Moderate physical activity No 3,125 91.4 0.6 4,518 94.9 0.4 38.03 < 0.001
Yes 270 8.6 0.6 244 5.1 0.4

Walking (> 30 m/d) No 3,187 58.9 0.9 5,056 62.3 0.7 15.67 0.001
Yes 2,119 41.1 0.97 2,978 37.7 0.7

Obesity (BMI) < 18.5 121 2.6 0.3 506 7.3 0.4 397.43 < 0.001
≤ 18.5, < 25 3,183 58.2 0.7 5,529 67.2 0.6
≤25 2,167 39.3 0.7 2,182 25.5 0.6

Stress Little 4,082 73.4 0.7 5,985 71.1 0.6 9.06 0.014
A lot 1,398 26.6 0.7 2,241 28.9 0.6

Experience of depressive feeling (over 2 wks) No 4,676 91.7 0.5 5,959 84.9 0.5 136.10 < 0.001
Yes 422 8.3 0.5 1,043 15.1 0.5

Suicide ideation No 4,831 94.9 0.4 6,395 91.2 0.4 62.71 < 0.001
Yes 268 5.1 0.4 607 8.8 0.4

BMI = body mass index; SE = standard error.

Table 2
Association between problematic alcohol drinking and socio-demographic characteristics, health status, health behaviors, and mental health by gender.
Variables Gender

Male Female


% SE Chi p % SE Chi p
Total
Age group (y) 20–44 16.1 0.8 25.24 < 0.001 4.8 0.4 61.59 < 0.001
45–64 21.5 0.9 1.6 0.3

Income Low 17.5 1.2 5.82 0.234 3.6 0.5 3.68 0.478
Low-intermediate 17.1 1.1 3.8 0.5
Intermediate-high 19.1 1.3 3.1 0.4
High 20.2 1.3 2.9 0.5

Household income Low 14.8 1.7 10.61 0.035 3.9 0.8 2.68 0.605
Low-intermediate 18.5 1.2 3.1 0.5
Intermediate-high 17.4 1.1 3.7 0.5
High 20.4 1.0 3.0 0.4

Education Elementary 23.5 2.4 26.80 < 0.001 2.0 0.5 31.77 < 0.001
Middle school 22.7 2.3 2.0 0.6
High school 19.7 0.9 4.6 0.5
University 15.5 0.9 2.6 0.4

Profession Professional/manager 15.5 1.2 57.32 < 0.001 2.8 0.6 42.78 < 0.001
Office worker 21.3 1.8 3.6 0.8
Sales/service 19.9 1.9 6.0 0.8
Physical worker 22.2 1.1 1.8 0.5
None 11.7 1.2 2.8 0.3

Marital status Unmarried 12.5 1.1 60.72 < 0.001 6.8 0.8 81.61 < 0.001
Married 20.7 0.8 2.3 0.3
Separated/divorced 26.0 3.2 4.3 0.9

Living Alone 19.7 2.6 0.48 0.587 5.8 1.4 7.13 0.019
With others 18.3 0.6 3.2 0.3

Perceived health status Good 15.0 0.9 44.80 < 0.001 2.6 0.4 30.29 < 0.001
Moderate 19.1 0.8 2.9 0.3
Poor 26.3 1.9 5.7 0.8

Economic activity No 11.7 1.2 36.58 < 0.001 2.8 0.3 4.50 0.085
Yes 20.0 0.7 3.7 0.4

Currently smoking No 13.2 0.7 130.26 < 0.001 2.4 0.2 322.73 < 0.001
Yes 25.2 1.0 17.3 2.2

Strenuous physical activity No 20.3 0.8 1.04 0.379 3.3 0.4 8.29 0.015
Yes 18.6 1.8 5.6 1.1

Moderate physical activity No 20.2 0.8 0.80 0.427 3.5 0.4 1.01 0.417
Yes 18.0 2.5 4.8 1.8

Walking (> 30 m/d) No 19.7 0.8 7.7 0.020 3.5 0.3 1.17 0.388
Yes 16.7 0.9 3.0 0.4

Obesity (BMI) < 18.5 6.7 2.3 20.6 < 0.001 3.4 0.8 0.07 0.978
≤ 18.5, < 25 17.5 0.8 3.4 0.3
≤ 25 20.5 1.0 3.3 0.5

Sleep (h) < 6 21.7 1.9 7.36 0.130 4.2 0.8 14.43 0.028
6 18.4 1.0 3.3 0.5
7–8 17.5 0.8 2.9 0.3
> 9 21.0 2.8 5.4 1.2

Stress Little 15.9 0.6 64.38 < 0.001 2.2 0.3 87.24 < 0.001
A lot 25.4 1.3 6.3 0.7

Experience of depressive feeling (over 2 wks) No 19.0 0.6 28.44 < 0.001 2.8 0.3 129.22 < 0.001
Yes 29.8 2.5 10.2 1.1

Suicide ideation No 19.1 0.6 32.3 < 0.001 3.3 0.3 78.56 < 0.001
Yes 33.5 3.3 10.6 1.6

BMI = body mass index; SE = standard error.

Table 3
The predictive factors for problematic alcohol drinking by gender.
Variables (reference) OR 95% CI p
Male
Household income (low) Low-intermediate 1.25 0.97 1.62 0.091
Intermediate-high 1.30 0.99 1.70 0.055
High 1.69 1.29 2.21 < 0.001

Education (university) Elementary 1.54 1.13 2.09 < 0.001
Middle school 1.44 1.05 1.96 0.023
High school 1.47 1.20 1.79 < 0.001

Marital status (married) Unmarried 0.61 0.49 0.76 < 0.001
Separated/divorced 1.18 0.85 1.63 0.332

Profession (professional/manager) Office worker 1.29 0.96 1.72 0.091
Sales/service 1.13 0.81 1.56 0.468
Physical worker 1.14 0.87 1.50 0.342
None 0.65 0.47 0.90 0.009

Perceived health status (good) Moderate 1.14 0.96 1.36 0.147
Poor 1.46 1.12 1.90 0.005

Currently smoking (no) Yes 1.99 1.70 2.34 <0.001

Stress (little) A lot 1.59 1.32 1.90 < 0.001

Experience of depressive feeling (over 2 wks) 1.53 1.19 1.97 0.001

Female

Education (university) Elementary 0.87 0.48 1.58 0.649
Middle school 0.79 0.42 1.48 0.455
High school 1.78 1.25 2.53 0.001

Marital status (married) Unmarried 2.61 1.85 3.67 < 0.001
Separated/divorced 1.21 0.71 2.07 0.482

Currently smoking (no) Yes 5.37 3.68 7.82 < 0.001

Stress (little) A lot 1.63 1.13 2.35 0.001

Experience of depressive feeling (over 2 wks) 2.90 2.08 4.04 < 0.001

CI = confidence interval; OR = odds ratio.

Figure & Data

References

    Citations

    Citations to this article as recorded by  
    • Trends in alcohol use and alcoholic liver disease in South Korea: a nationwide cohort study
      Jeong-Ju Yoo, Dong Hyeon Lee, Young Chang, Hoongil Jo, Young Youn Cho, Sangheun Lee, Log Young Kim, Jae Young Jang
      BMC Public Health.2024;[Epub]     CrossRef
    • Sex differences in the association between social jetlag and hazardous alcohol consumption in Korean workers: A nationwide cross-sectional study
      Seong-Uk Baek, Jong-Uk Won, Yu-Min Lee, Jin-Ha Yoon
      Sleep Medicine.2024; 119: 549.     CrossRef
    • Association between handgrip strength and physical activity: A nationwide population‐based study in Korea
      Hye Young Shin, Ka Young Kim, Purum Kang
      Public Health Nursing.2024; 41(5): 987.     CrossRef
    • Prevalence, Awareness, Treatment, and Control of Type 2 Diabetes in South Korea (1998 to 2022): Nationwide Cross-Sectional Study
      Wonwoo Jang, Seokjun Kim, Yejun Son, Soeun Kim, Hyeon Jin Kim, Hyesu Jo, Jaeyu Park, Kyeongmin Lee, Hayeon Lee, Mark A Tully, Masoud Rahmati, Lee Smith, Jiseung Kang, Selin Woo, Sunyoung Kim, Jiyoung Hwang, Sang Youl Rhee, Dong Keon Yon
      JMIR Public Health and Surveillance.2024; 10: e59571.     CrossRef
    • Characteristics and Clinical Course of Diabetes of the Exocrine Pancreas: A Nationwide Population-Based Cohort Study
      Nami Lee, So Jeong Park, Dongwoo Kang, Ja Young Jeon, Hae Jin Kim, Dae Jung Kim, Kwan-Woo Lee, Edward J. Boyko, Seung Jin Han
      Diabetes Care.2022; 45(5): 1141.     CrossRef
    • Harmful alcohol use among mothers of under-five child: findings from the Panel Study on Korean Children
      Seulgi Kim, Seung-Ah Choe, Sung-Il Cho
      BMC Women's Health.2021;[Epub]     CrossRef
    • Characteristics and Clinical Course of Diabetes of the Exocrine Pancreas: A Nationwide Population-Based Cohort Studybrief Title: Diabetes of the Exocrine Pancreas
      Nami Lee, So Jeong Park, Dongwoo Kang, Ja Young Jeon, Hae Jin Kim, Dae Jung Kim, Kwan-Woo Lee, Edward J. Boyko, Seung Jin Han
      SSRN Electronic Journal .2021;[Epub]     CrossRef
    • Associations between Harmful Experiences from Alcohol Use of Others and Mental Health in Korean Adolescents
      Mi Ah Han
      International Journal of Environmental Research an.2019; 16(21): 4240.     CrossRef

    Related articles
    Gender Differences in Harmful Use of Alcohol Among Korean Adults
    Gender Differences in Harmful Use of Alcohol Among Korean Adults
    Parameters (or variables) Gender Chi p
    Male Female


    N % SE N % SE
    Age group (y) 20–44 2,702 57.2 0.8 3,984 54.7 0.7 8.40 0.006
    45–64 2,780 42.8 0.8 4,243 45.3 0.7

    Income Low 1,336 25.8 0.9 1,997 25.3 0.7 1.46 0.718
    Low-intermediate 1,393 25.8 0.8 2,039 25.4 0.6
    Intermediate-high 1,341 24.5 0.7 2,063 24.6 0.6
    High 1,371 23.9 0.9 2,080 24.7 0.8

    Household income Low 507 9.4 0.5 842 9.7 0.5 10.41 0.020
    Low-intermediate 1,277 23.8 0.8 2,115 26.1 0.7
    Intermediate-high 1,702 32.3 0.9 2,477 31.0 0.7
    High 1,955 34.5 1.0 2,745 33.2 0.9

    Education Elementary 453 6.8 0.4 1,138 11.3 0.4 110.75 < 0.001
    Middle school 476 7.7 0.4 870 10.0 0.4
    High school 2,111 43.2 0.8 3,054 40.4 0.7
    University 2,270 42.3 0.9 2,969 38.3 0.8

    Profession Professional/manager 1,055 19.8 0.7 1,132 14.7 0.5 1323.81 < 0.001
    Office worker 753 13.5 0.5 755 10.0 0.4
    Sales/service 686 13.5 0.6 1,310 16.7 0.5
    Physical worker 1,877 34.1 0.9 1,241 14.2 0.5
    None 914 19.0 0.7 3,591 44.4 0.7

    Marital status Unmarried 1,268 30.7 0.9 1,255 19.5 0.6 285.26 < 0.001
    Married 3,955 64.8 0.9 6,168 71.9 0.7
    Separated/divorced 254 4.5 0.3 794 8.6 0.4

    Living Alone 349 6.9 0.5 417 4.5 0.3 39.06 < 0.001
    With others 5,133 93.1 0.5 7,810 95.5 0.3

    Perceived health status Good 1,976 37.9 0.8 2,384 29.9 0.6 113.75 < 0.001
    Moderate 2,656 49.4 0.8 4,276 53.0 0.7

    Poor 689 12.7 0.5 1,388 17.1 0.5

    Economic activity No 914 18.9 0.7 3,591 44.3 0.7 987.30 < 0.001
    Yes 4,397 81.1 0.7 4,446 55.7 0.7

    Currently smoking No 3,175 56.6 0.8 7,792 93.8 0.3 2587.58 < 0.001
    Yes 2,305 43.4 0.8 433 6.2 0.3

    Strenuous physical activity No 2,758 80.2 0.8 4,127 86.5 0.6 57.06 < 0.001
    Yes 637 19.8 0.8 636 13.5 0.6

    Moderate physical activity No 3,125 91.4 0.6 4,518 94.9 0.4 38.03 < 0.001
    Yes 270 8.6 0.6 244 5.1 0.4

    Walking (> 30 m/d) No 3,187 58.9 0.9 5,056 62.3 0.7 15.67 0.001
    Yes 2,119 41.1 0.97 2,978 37.7 0.7

    Obesity (BMI) < 18.5 121 2.6 0.3 506 7.3 0.4 397.43 < 0.001
    ≤ 18.5, < 25 3,183 58.2 0.7 5,529 67.2 0.6
    ≤25 2,167 39.3 0.7 2,182 25.5 0.6

    Stress Little 4,082 73.4 0.7 5,985 71.1 0.6 9.06 0.014
    A lot 1,398 26.6 0.7 2,241 28.9 0.6

    Experience of depressive feeling (over 2 wks) No 4,676 91.7 0.5 5,959 84.9 0.5 136.10 < 0.001
    Yes 422 8.3 0.5 1,043 15.1 0.5

    Suicide ideation No 4,831 94.9 0.4 6,395 91.2 0.4 62.71 < 0.001
    Yes 268 5.1 0.4 607 8.8 0.4
    Variables Gender

    Male Female


    % SE Chi p % SE Chi p
    Total
    Age group (y) 20–44 16.1 0.8 25.24 < 0.001 4.8 0.4 61.59 < 0.001
    45–64 21.5 0.9 1.6 0.3

    Income Low 17.5 1.2 5.82 0.234 3.6 0.5 3.68 0.478
    Low-intermediate 17.1 1.1 3.8 0.5
    Intermediate-high 19.1 1.3 3.1 0.4
    High 20.2 1.3 2.9 0.5

    Household income Low 14.8 1.7 10.61 0.035 3.9 0.8 2.68 0.605
    Low-intermediate 18.5 1.2 3.1 0.5
    Intermediate-high 17.4 1.1 3.7 0.5
    High 20.4 1.0 3.0 0.4

    Education Elementary 23.5 2.4 26.80 < 0.001 2.0 0.5 31.77 < 0.001
    Middle school 22.7 2.3 2.0 0.6
    High school 19.7 0.9 4.6 0.5
    University 15.5 0.9 2.6 0.4

    Profession Professional/manager 15.5 1.2 57.32 < 0.001 2.8 0.6 42.78 < 0.001
    Office worker 21.3 1.8 3.6 0.8
    Sales/service 19.9 1.9 6.0 0.8
    Physical worker 22.2 1.1 1.8 0.5
    None 11.7 1.2 2.8 0.3

    Marital status Unmarried 12.5 1.1 60.72 < 0.001 6.8 0.8 81.61 < 0.001
    Married 20.7 0.8 2.3 0.3
    Separated/divorced 26.0 3.2 4.3 0.9

    Living Alone 19.7 2.6 0.48 0.587 5.8 1.4 7.13 0.019
    With others 18.3 0.6 3.2 0.3

    Perceived health status Good 15.0 0.9 44.80 < 0.001 2.6 0.4 30.29 < 0.001
    Moderate 19.1 0.8 2.9 0.3
    Poor 26.3 1.9 5.7 0.8

    Economic activity No 11.7 1.2 36.58 < 0.001 2.8 0.3 4.50 0.085
    Yes 20.0 0.7 3.7 0.4

    Currently smoking No 13.2 0.7 130.26 < 0.001 2.4 0.2 322.73 < 0.001
    Yes 25.2 1.0 17.3 2.2

    Strenuous physical activity No 20.3 0.8 1.04 0.379 3.3 0.4 8.29 0.015
    Yes 18.6 1.8 5.6 1.1

    Moderate physical activity No 20.2 0.8 0.80 0.427 3.5 0.4 1.01 0.417
    Yes 18.0 2.5 4.8 1.8

    Walking (> 30 m/d) No 19.7 0.8 7.7 0.020 3.5 0.3 1.17 0.388
    Yes 16.7 0.9 3.0 0.4

    Obesity (BMI) < 18.5 6.7 2.3 20.6 < 0.001 3.4 0.8 0.07 0.978
    ≤ 18.5, < 25 17.5 0.8 3.4 0.3
    ≤ 25 20.5 1.0 3.3 0.5

    Sleep (h) < 6 21.7 1.9 7.36 0.130 4.2 0.8 14.43 0.028
    6 18.4 1.0 3.3 0.5
    7–8 17.5 0.8 2.9 0.3
    > 9 21.0 2.8 5.4 1.2

    Stress Little 15.9 0.6 64.38 < 0.001 2.2 0.3 87.24 < 0.001
    A lot 25.4 1.3 6.3 0.7

    Experience of depressive feeling (over 2 wks) No 19.0 0.6 28.44 < 0.001 2.8 0.3 129.22 < 0.001
    Yes 29.8 2.5 10.2 1.1

    Suicide ideation No 19.1 0.6 32.3 < 0.001 3.3 0.3 78.56 < 0.001
    Yes 33.5 3.3 10.6 1.6
    Variables (reference) OR 95% CI p
    Male
    Household income (low) Low-intermediate 1.25 0.97 1.62 0.091
    Intermediate-high 1.30 0.99 1.70 0.055
    High 1.69 1.29 2.21 < 0.001

    Education (university) Elementary 1.54 1.13 2.09 < 0.001
    Middle school 1.44 1.05 1.96 0.023
    High school 1.47 1.20 1.79 < 0.001

    Marital status (married) Unmarried 0.61 0.49 0.76 < 0.001
    Separated/divorced 1.18 0.85 1.63 0.332

    Profession (professional/manager) Office worker 1.29 0.96 1.72 0.091
    Sales/service 1.13 0.81 1.56 0.468
    Physical worker 1.14 0.87 1.50 0.342
    None 0.65 0.47 0.90 0.009

    Perceived health status (good) Moderate 1.14 0.96 1.36 0.147
    Poor 1.46 1.12 1.90 0.005

    Currently smoking (no) Yes 1.99 1.70 2.34 <0.001

    Stress (little) A lot 1.59 1.32 1.90 < 0.001

    Experience of depressive feeling (over 2 wks) 1.53 1.19 1.97 0.001

    Female

    Education (university) Elementary 0.87 0.48 1.58 0.649
    Middle school 0.79 0.42 1.48 0.455
    High school 1.78 1.25 2.53 0.001

    Marital status (married) Unmarried 2.61 1.85 3.67 < 0.001
    Separated/divorced 1.21 0.71 2.07 0.482

    Currently smoking (no) Yes 5.37 3.68 7.82 < 0.001

    Stress (little) A lot 1.63 1.13 2.35 0.001

    Experience of depressive feeling (over 2 wks) 2.90 2.08 4.04 < 0.001
    Table 1 Sociodemographic characteristics between elderly Koreans with and without activity limitation.

    BMI = body mass index; SE = standard error.

    Table 2 Association between problematic alcohol drinking and socio-demographic characteristics, health status, health behaviors, and mental health by gender.

    BMI = body mass index; SE = standard error.

    Table 3 The predictive factors for problematic alcohol drinking by gender.

    CI = confidence interval; OR = odds ratio.


    PHRP : Osong Public Health and Research Perspectives
    TOP