Study population and data source
Data from the 12th Korea Youth Risk Behavior Web-based Survey (KYRBS) were used. KYRBS, a nationwide cross-sectional study and government-approved statistical survey, was performed by the South Korean Ministry of Education, Science and Technology; Ministry of Health and Welfare; and Korea Centers for Disease Control and Prevention using a stratified multistage cluster strategy. KYRBS is a self-report, anonymous, online survey performed on a nationally representative sample of Korean adolescents aged 12–18 years. It comprises 129 questions divided into 15 sections about health-related behaviors as well as mental and physical health. In 12th KYRBS, a total of 67,983 students from 800 middle and high schools were randomly selected, and 65,528 (boys = 33,803 and girls = 31,725) students (96.4% response rate) from 798 schools (99.8% response rate) responded to the survey. The participants were identified by numbers and were guaranteed anonymity by following method. All participants completed an online, self-report questionnaire in a school computer room after the survey had been fully explained. Participants were randomly assigned one computer per person, and teachers except the homeroom teacher were assigned as managers. In addition, the managing teachers instructed not to see the computer screen and not to respond to the questionnaire, to ensure the anonymity of responses. The Institutional Review Board of the Korea Centers for Disease Control and Prevention approved KYRBWS (Statistics Korea, approval No. 11758).
The general characteristics of the participants included age, sex, residential area (i.e., metropolitan cities, small and medium towns, and rural areas), family economic status (i.e., high, middle high, average, middle low, and low), paternal and maternal educational levels (i.e., college and higher, high school, middle school and below, and unknown), and academic achievement (i.e., high, middle high, average, middle low, and low).
Parental smoking status
The parental smoking status of the participants was identified using the following question: “Please check all household members who currently smoke.” and responses were chosen between the following options: (1) no one, (2) father, (3) mother, (4) siblings, (5) grandparents, (6) others, (7) do not know. We only included data showing more than one response to the following analysis. Considering the impact of parental smoking on adolescents, we recoded responses with a focus on parental smoking and classified into 4 categories: (1) nonsmoking(none of the parents smoke), (2) paternal smoking(the father is a smoker, but the mother is not a smoker), (3) maternal smoking (the mother is a smoker, but the father is not a smoker), and (4) parental smoking(both parents are smokers).
The second-hand smoke exposure at home over the last 7 days regardless of parental smoking status was measured. It was explored by the following question: In the last 7 days, how many days have you been with someone(such as a family member or guest) when they smokes in your home?” and the response were chosen from 0 to 7 days. To statistically adjust for the effects of secondhand smoke in the home, we divided the responses into two categories: "no secondhand smoke experience" and "have secondhand smoke experience"(if the respondent have experienced secondhand smoke for more than one day).
Current substance using status
Data of Substance use by the participants including alcohol drinking and smoking statuses were used. Their alcohol drinking status included lifetime alcohol drinking, current alcohol drinking, alcohol drinking days, and alcohol drinking volume. Lifetime alcohol drinking was measured with a dichotomous question: “Have you ever drank more than one glass of alcohol?”. Alcohol drinking days were measured through following question: "How many days have you drank more than one drink in the last 30 days?" and the responses were coded as days. Alcohol drinking volume was measured by the question, “In the last 30 days, what was the average amount for drinking?” and the response was coded as standard units. Current alcohol drinking item was created by recoding these items: data was recoded as "yes" in the case of reporting more than one day or one unit of drinking in the last 30 days and was recoded as "no" if neither is the case.
Similarly, their smoking status included lifetime smoking experience, current smoking, smoking days, and smoking volume lifetime smoking experience was explored by a dichotomous question: “Have you ever smoked a sip or two?”. Smoking days and volume were measured by following questions: “How many days have you smoked at least one cigarette in the last 30 days?” and “On average, how many cigarettes per day did you smoke in the last 30 days?”. Amount was coded as cigarettes per day. Current smoking item was created by recoding the data of these two questions. As similar as current alcohol drinking item, data was recoded as "yes" in the case of reporting more than one day or one cigarette smoking in the last 30 days and was recoded as "no" if neither is the case. Also, Lifetime drug use by the participants was also analyzed. The following dichotomous question was used: “Have you ever habitually or deliberately taken drugs or used inhalant (e.g. butane gas or adhesive)?”
Since the distribution was not revealed as normal distribution, amount and number of days of smoking and drinking variables were divided into several intervals and analyzed with multinomial logistic regression. First, the variables recoded as follow: Regarding alcohol consumption, the responses of days were recoded to 1) not drinking at all in the past month, 2) 1-9 days 3) 10-19 days 4) drinking more than 20 days, and responses of amount were recoded to 1) not at all in the past month, 2) 1-2 standard units(light drinking), 3) 3-4 standard units(moderate drinking), 4) 5 or more units(binge drinking). Regarding smoking, the responses of days were recoded to 1) not smoking at all in the past month, 2) 1-9 days 3) 10-19 days 4) smoking more than 20 days, and responses of amount were recoded to 1) not at all in the past month, 2) less than one cigarette per 1 day, 3) 1-9 cigarettes, 4) 10-19 cigarettes, 5) 20 or more cigarettes per 1 day.
Univariate analysis was conducted to estimate the proportion of smokers among mothers and fathers. One-way analysis of variance and Pearson’s chi-square test were performed to estimate the general characteristics (sex, age, education, urbanity, parental education level, family economic status) and second-hand smoke exposure of the 4 groups(i.e., parental nonsmoking, paternal smoking, maternal smoking, and both parental smoking). Binary variables such as lifetime alcohol drinking, current alcohol drinking, lifetime smoking experience, current smoking, and lifetime drug use were analyzed using binary logistic regression. Variables containing more than 3 categories, including the number of days spent drinking alcohol and smoking and the volumes of alcohol drinking and smoking, were analyzed using multinomial logistic regression. The model was statistically adjusted by age, urbanity, academic achievement, parental educational level and socioeconomic status (SES). Finally, to explore whether there was an effect of second-hand smoke exposure, analyses were adjusted for second-hand smoke exposure at home. The statistical analysis was performed not only to all youth but separately to gender because parents' smoking status had different effects depending on the offspring’s gender.