This dimension was measured by three items: reported chronic disease, injury or disability that restricts daily activities (no/yes); a summary index of weekly perceived stress symptoms namely, stomachaches, tension or nervousness, irritability or outbursts of anger, trouble falling asleep or waking at night, headache, trembling of hands, feeling tired or weak, and feeling dizzy, categorised as having none, one symptom/week, 2–3/week and 4–8/week; and, self-rated health categorised as very good, average/good or poor.
5.3.2.2 Health-promoting behaviour
This dimension included frequency of tooth brushing (several times a day, once a day, 1–5 times/week or less), and efficiency of physical activity. Efficiency of physical activity was measured by combining information from two variables: frequency of physical activity in leisure time and intensity of exercise (shortness of breath/sweating). This combination used the following categories: does not exercise, exercises with low/occasional efficiency, active efficient exerciser, or very active efficient exerciser.
5.3.2.3 Social support
The social support dimension was indicated by four variables: having a nuclear family (living with both parents or not); ease of talking about troubling issues (easy, difficult, very difficult) to the following persons: father, mother or friends. Those who did not have a father (5%), mother (1%) or friends (0.5%) were set to “very difficult.” In Study II, these variables were dichotomised and the category “very difficult” was combined with “difficult”.
72
Table 5. Reserve capacity characteristics of the adolescent boys and girls in the study (N=41,833)
Reserve capacity Boy (n=19,509) Girl (n=22,324)
No. % No. %
Perceived health
Chronic disease
No 17,791 91.19 20,134 90.19
Yes 1,718 8,81 2,190 9.81
Perceived stress symptoms
None 9897 50.73 7144 32.00 1/week 4181 21.43 5129 22.98 2-3/week 3937 20.18 6442 28.86 4-8/week 1494 7.66 3609 16.17 Self-rated health Very good 7465 38.26 6233 27.92 Average or good 11637 59.65 15568 69.74 Poor 328 1.68 458 2.05 Missing 79 0.40 65 0.29 Health-promoting behaviour Physical activity
Very active efficient exerciser 5114 26.21 3930 17.60
Active efficient exerciser 6017 30.84 6623 29.67
Occasional/low efficient exerciser 4645 23.81 7224 32.36
Does not exercise 3671 18.82 4503 20.17
Missing 62 0.32 44 0.20
Regular toothbrushing
Several times/day 3982 20.41 10831 48.52
About once/day 9737 49.91 9689 43.40
About 1-5 times/week or less 5689 29.16 1754 7.86
Missing 101 0.52 50 0.22 Social support Nuclear family Yes 15366 78.76 17040 76.33 No 4022 20.62 5173 23.17 Missing 121 0.62 111 0.5
Talking about issues to father
Easy 10421 53.42 8157 36.54
Difficult 6010 30.81 8470 37.94
Very difficult/No father 2571 13.18 5314 23.80
Missing 507 2.60 383 1.72
Talking about issues to mother
Easy 13705 70.25 16235 72.72
73
Figure 5. Measurement of reserve capacity as a latent variable in Study III
Note: The latent variable is enclosed in a circle with arrows indicating measurement from the actual variables (in boxes) collected in the study. Double-headed arrows under the boxes show covariances within variables in the same dimension.
5.3.3 Pubertal timing
To obtain an indicator of pubertal timing based on survey data, boys were asked about their age at first ejaculation while girls were asked about their age at menarche. Classification of pubertal timing as early, average and late, followed those groupings previously used by Koivusilta and Rimpelä (2004). In boys, the categories were chosen to be at age 12 or earlier (early), at 13 or 14 (average), at 15 or later or did not occur by the time of enquiry (late). In girls, the categories were at age 11 or earlier (early), at 12 or 13 (average), at 14 or later or did not occur by the time of enquiry (late). Close to 4000 (9.46%) adolescents aged 12 years at the time of surveys were excluded to minimise information bias since we cannot distinguish among them who
Very difficult/No father 1037 5.32 1175 5.26
Missing 338 1.73 171 0.77
Talking about issues to friends
Easy 14764 75.68 20078 89.94
Difficult 3558 18.24 1772 7.94
Very difficult/No friends 762 3.91 288 1.29
74
had average or late pubertal timing. Figure 6 presents the distribution of the adolescents according to pubertal timing categories.
Figure 6. Distribution of adolescent boys (n=17,531) and girls (n=20345) according to pubertal timing
5.3.4 School achievement
Using survey data, adolescents were categorised as having: highest, 2nd highest, 2nd lowest, or lowest academic achievement. All respondents were asked to assess whether their end-of-term school report was much better (highest), slightly better (2nd highest), average (2nd lowest), slightly poorer or much poorer than the class average (lowest). For 12-14-year-olds (all in comprehensive schools), this self- assessment was the sole basis of their school achievement. For 16-18-year-olds, in addition to self-assessment of their school performance, school status (academic upper secondary school/vocational school/not attending school) was also considered. Their achievement was classified as follows: highest (in academic upper secondary school with better performance); 2nd highest (in vocational school with better performance or academic upper secondary school with average performance); 2nd lowest (in vocational school with poor to average performance or high school
75
with poor performance); and lowest (not in school). Figure 7 shows the distribution of adolescents according to school achievement.
In Study II, the number of categories used for school achievement was reduced to three and renamed as high, average or low. For all age groups, those previously classified in the highest and 2nd highest categories, comprised the new “high” and “average” groups, respectively. The two lowest categories were combined and reclassified as having “low” achievement.
Figure 7. Distribution of adolescent boys (n=19,509) and girls (n=22,324) according to school achievement
5.4
Statistical analysis
5.4.1 Preliminary analysis
The relationships of the variables selected to comprise each dimension of reserve capacity were checked prior to multivariate analyses. Using Spearman’s correlation, coefficients obtained indicated moderate positive correlations of variables per dimension. Cross-tabulations were also performed to check the associations of
76
variables in each dimension and Pearson chi-square results showed that they were significantly associated with each other.
The characteristics of the respondents included in the study were presented as frequencies and percentages for categorical variables and means for continuous variables. In Study II, the characteristics of AHLS respondents with unknown data for grandparents were compared with those of respondents with known data to assess whether selection bias occurred. Further analyses were also made to examine the effect of including this group in our study (See Section 5.4.2.2).
5.4.2 Multivariate methods
5.4.2.1 Cox regression
In Study I, we fitted a Cox proportional hazards models, separately for boys and girls, to determine the effect of family SES, reserve capacity variables and school achievement on mortality. Graphical assessments of proportional hazards were made using log-log survival curves for each independent variable. An example is illustrated in Figure 8 presenting survival curves of adolescents according to parents’ education. Formally, adherence to the proportional hazards assumption was checked for each variable and globally, using a formal significance test based on the unscaled and scaled Schoenfeld residuals (UCLA: Statistical Consulting Group, 2015).
Figure 8. Example of survival curves of adolescents plotting survival probabilities (y- axis) versus categories of parental SES (x-axis)
77
First, a crude model, which considered family SES, each reserve capacity dimension and school achievement, was fitted to analyse each predictor’s unadjusted effect on mortality risk (Model 1). Then, to study whether the reserve capacity variables modified the relationship between SES and mortality, all statistically significant (p<0.05) reserve capacity variables together with SES were included in a backward selection procedure until none could be deleted from the model (Model 2). Finally, school achievement was added (Model 3). An interaction term between parental education and school achievement was also tested.
Results were presented as hazard ratios (HRs) with 95% confidence interval (CI) estimates. Model fit was assessed using likelihood ratio tests and Akaike Information Criteria (AIC) (Bozdogan, 1987). Postestimation tests were done (checking of residuals and other plots) to ensure that the final models had the best fit. Respondents with missing data (5%) in one or more main variables studied were dropped from analysis. All analyses were performed using STATA version 12.1.