a. The health behavior variables for clustering behavior patterns
Behavior patterns (clusters), theoutcomes of this study, were obtained by clustering health behavior variables. The following self-reported health behaviors were investigated: (1) substance use; (2) moderate-to-vigorous physical activity (MVPA); (3) Screen time
including TV and video viewing and video/computer game use; (4) other low-intensity activities that might co-vary with the above behaviors. Details on measurement are described below.
The Add Health substance use (i.e., cigarette smoking, tobacco chewing, alcoholic drinking, and drug use)survey items were adopted from the Young Risk Behavior
Surveillance System (YRBSS)—a validated national survey conducted among adolescents for health-risk behaviors (Eaton, Kann et al. 2006). Frequencies of cigarette smoking, tobacco chewing, and drinking alcoholic beverages (for convenience, ‘drinking alcoholic beverages’ is referred to ‘drinking’ in the following texts) were recorded as the number of days during the preceding 30 days of the survey on which the interviewed adolescents smoked cigarettes, chewed tobacco or drank. Drug use behavior was indicated by marijuana use, and its frequency was recorded as the number of times during the preceding 30 days of the survey on which the subjects used marijuana. Other drug use behaviors (i.e., cocaine, illicit drugs, and inhalant use) were assessed in the Add Health, but were not included in the analysis, because they were not common in adolescents (i.e., the prevalence was less than 10%) and they were highly correlated to marijuana use.The prevalence of tobacco chewing
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in males was more than 10%; however, it was less than 10% in females (approximately 6%), so it was excluded in the analysis of females.
Measures of physical activity and low-intensity activities were reported in the Add Health survey questionnaires, similar to those previously validated in other epidemiological studies (Heath, Pratt et al. 1994; Pate, Heath et al. 1996; Andersen, Crespo et al. 1998). Questionnaire items elicited information in the following form: “During the past week, how many times did you play an active sport, such as baseball, softball, basketball, soccer, swimming, or football?”
In this study, physical activity was defined as the number of bouts per week of
moderate to vigorous physical activity (MVPA; 5 to 8 metabolic equivalents [METs], with one MET equivalent to the energy used by the body at rest (3.5 mL O2/kg body per
minute)(Ainsworth, Haskell et al. 2000). Activities assessed in the Add Health and in this study were active sports (e.g., baseball, softball, basketball, soccer, swimming, or football); Wheel-based activity (e.g., rollerblading, roller-skating, skate-boarding, or bicycling); and exercise (e.g., jogging, walking, karate, jumping rope, gymnastics, or dancing).
Low-intensity activities (2 to 3 METs) included hobbies (e.g., collecting baseball cards, playing a musical instrument, reading, or doing arts and crafts), house work (e.g., cleaning, cooking, laundry, yard work, or caring for a pet), and hanging-out with friends. They were also measured as bouts/week.
Screen time defined as hours of television and video viewing per week is a widely
accepted practical indicator for sedentary lifestyles in the literature (Crespo, Smit et al. 2001; Eisenmann, Bartee et al. 2002; Eaton, Kann et al. 2006). In the Add Health, it was measured by two behaviors reported in hours per week: (1) TV and video viewing, and (2)
video/computer game use.
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Respondent race/ethnicity was self-defined by the subjects, and the final racial/ethnic groups in this study were White, Black, Hispanic, and Asian.
c. Joint distribution of students’ racial/ethnic composition and mean family income (the exposure at the school level)
To investigate how social economic and racial/ethnic composition of students at schools affected adolescent behavior patterns, a categorical indicator was created to indicate the joint distribution of racial/ethnic composition and mean family income for the students from the same schools. Categories of this indicator were: (a) “Percentage of Whites < 25% and school-wide mean family income level < $40K”; (b) “Percentage of Whites < 25% and school-wide mean family income level ≥ $40K”; (c) “Percentage of Whites ranging from 25% to 75% and school-wide mean family income level < $40K”; (d) “Percentage of Whites ranging from 25% to 75% and school-wide mean family income level ≥ $40K”; (e)
“Percentage of Whites >75% and school-wide mean family income level < $40K”; (f) “Percentage of Whites >75% and school-wide mean family income level ≥ $40K”. This indicator is referred to ‘schools’ race–SES’.
d. Measurement of covariates
Other social demographic variables as the controlled covariates in the regression models were those that potentially confounded (identified through DAGs) the relationship between the behavior patterns and the respondent race/ethnicity or the schools’ social economic environments. These covariates were provided in different questionnaires of the Add Health survey:
1) In-home self-reported questionnaires of adolescents provided information for age (continuous, calculated by interview date and birth date); religion (classified as
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Baptist, Christian Church, Catholic, others, and non-religion); generation of immigration (first generation, second generation, and third and more generation); adolescent working status (Yes, vs. No); presence of father in household (Yes vs. No); presence of mother in household (Yes vs. No).
2) In-school self-reported questionnaires of adolescents provided information for whether participating in sport clubs (Yes, vs. No).
3) Parental interviews had information of family income (continuous, reported by the head of household, where missing [n=2901] was imputed); parental
education( reported by parents, presented as highest level of either parents, and categorized as less than high school, high school and GED, some college, and college and higher ).
4) School characters that confounded the relationship between the adolescent behavior patterns and the schools’ social economic environments in the models were obtained from the questionnaires of school administrators. They were school session (Yes vs. no); school size (big, middle, and small); school urbanicity (urban, suburban, and rural); geographic regions (west, middle west, south, and northeast). School-wide parental education backgrounds (indicated by the percentage of parents with different education levels) and student religion background (indicated by the percentage of students with different religions) were generated by collapsing the individual values across the schools.
3. Cluster Analysis to Obtain Behavior Patterns (the Outcomes in the Modeling