Mental health and cannabis use among Canadian youth: Integrated findings from cross-sectional and longitudinal analyses

BACKGROUND AND PURPOSE

Using data from two methodologically independent youth research studies in Canada, the Health Behaviour in School-aged Children (HBSC) study and the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behaviour (COMPASS) study, the objective of this study was to compare associations between youth's mental health and cannabis use across samples. Using similar indicators in both studies, our goal was to affirm the potential for nationally representative cross-sectional analyses (HBSC) to replicate findings found in a longitudinal non-representative data source (COMPASS), enhancing opportunity for causal inferences.

METHODS

Data were collected from grade 9 and 10 Canadian students participating in the 2017/18 HBSC (n=8462) and 2017/18 to 2018/19 waves of COMPASS (n=32,023). Using multivariable Poisson regression models, the strength and statistical significance of the effects of mental health indicators on cannabis use outcomes were estimated within both studies and compared for consistency. Using a 2-year linked sample of students participating in COMPASS, models examining the impact of mental health indicators on cannabis use initiation and maintenance over time were similarly fit using Poisson regression to estimate relative risk.

RESULTS

Similar associations between mental health problems and cannabis use were observed in both data sources. The direction, magnitude, and precision of the estimates for restless sleep, loneliness, poor wellbeing, and cannabis use were highly comparable across both studies. Worse mental health was consistently associated with current and lifetime cannabis use among youth.

DISCUSSION

Cross-sectional and longitudinal findings from two large methodologically diverse studies in Canada demonstrate a replicable association between indicators of mental health and youth cannabis use. Similarities were identified and two generalizations may be concluded: 1) potentially causal etiological relationships inferred from HBSC data were supported in longitudinal findings based on COMPASS, and 2) longitudinal COMPASS data aligns with nationally representative data from HBSC.

Cross-sectional versus longitudinal designs for function estimation, with an application to cerebral cortex development

Motivated by studies of the development of the human cerebral cortex, we consider the estimation of a mean growth trajectory and the relative merits of cross-sectional and longitudinal data for that task. We define a class of relative efficiencies that compare function estimates in terms of aggregate variance of a parametric function estimate. These generalize the classical design effect for estimating a scalar with cross-sectional versus longitudinal data, and are shown to be bounded above by it in certain cases. Turning to nonparametric function estimation, we find that longitudinal fits may tend to have higher aggregate variance than cross-sectional ones, but that this may occur because the former have higher effective degrees of freedom reflecting greater sensitivity to subtle features of the estimand. These ideas are illustrated with cortical thickness data from a longitudinal neuroimaging study.

D-optimal cohort designs for linear mixed-effects models

The D-optimality criterion is used to construct optimal designs for different numbers of independent cohorts, which constitute a number of repeated measurements per subject over time. A cost function for longitudinal data is proposed, and the optimality criterion is optimized taking into account the cost of the study. First, an optimal number of design points for a given number of cohorts and cost was identified. Then, an optimal number of cohorts is identified by comparing the relative efficiencies (REs). A numerical study shows that for models describing the trend of a continuous outcome over time by polynomials, the most efficient number of repeated measurements is equal to the sum of the total number of cohorts and the degree of the polynomial in the model. REs of a purely longitudinal cohort design with only one cohort, and mixed longitudinal and cross-sectional cohort designs with more cohorts are compared. The results show that a purely longitudinal cohort design with only one cohort of subjects measured at the optimal time points is the most efficient design. The findings in this paper show that one can obtain a highly efficient design for parameter estimation with only a few repeated measurements. The results of this study will reduce the cost of data collection and ease the logistical burdens in cohort studies.

Depression in adult women: age changes and cohort effects

OBJECTIVES

We sought to separate age and cohort associations with depression, assessed 3 times within a 10-year period in 701 women born between 1928 and 1958.

METHODS

We used regression analysis to examine age differences in women with depression in 2 birth cohorts, pre-1945 and post-1944, who were assessed at comparable ages. Multilevel modeling was used to estimate changes with age in successive birth year cohorts.

RESULTS

An age by cohort interaction indicated more depression among younger than older women in the post-1944 cohort but a flat age profile in the pre-1945 cohort. Longitudinal analyses indicated declines in depression with age in more recent cohorts but increases in earlier ones.

CONCLUSIONS

Increases in depression in younger women in successive cohorts may be offset by decreases in middle age.

Growth in stature in fragile X families: a mixed longitudinal study

The effect of fragile X on growth in stature was estimated in individuals aged 5-20 years from 50 fragile X families. The multivariate normal model for pedigree analysis was applied to the mixed longitudinal data, which varied with regard to intervals between the measurements and their number in individual subjects, totalling 349 measurement data points from fragile X families, and 292 data points from unrelated normal subjects. The results of genetic and regression analysis showed that, in fragile X boys and girls, total pubertal height gain is impaired, whereas the rate of growth during the preadolescent period is increased, compared with the growth rate of nonfragile X subjects. Moreover, the growth parameters in fragile X males were found to be correlated with the size of CGG trinucleotide expansion. The hypothesis of premature activation of the hypothalamo-pituitary gonadal axis is postulated as the cause of growth impairment in fragile X boys and girls, which should be verified by data on the timing of pubertal stages, hormone levels, and bone maturation.

Intentionally incomplete longitudinal designs: I. Methodology and comparison of some full span designs

Longitudinal designs are important in medical research and in many other disciplines. Complete longitudinal studies, in which each subject is evaluated at each measurement occasion, are often very expensive and motivate a search for more efficient designs. Recently developed statistical methods foster the use of intentionally incomplete longitudinal designs that have the potential to be more efficient than complete designs. Mixed models provide appropriate data analysis tools. Fixed effect hypotheses can be tested via a recently developed test statistic, FH. An accurate approximation of the statistic's small sample non-central distribution makes power computations feasible. After reviewing some longitudinal design terminology and mixed model notation, this paper summarizes the computation of FH and approximate power from its non-central distribution. These methods are applied to obtain a large number of intentionally incomplete full-span designs that are more powerful and/or less costly alternatives to a complete design. The source of the greater efficiency of incomplete designs and potential fragility of incomplete designs to randomly missing data are discussed.