The association between child and adolescent depression and poor attendance at school: A systematic review and...

Depression in young people may lead to reduced school attendance through social withdrawal, loss of motivation, sleep disturbance and low energy. We systematically reviewed the evidence for an association between depression and poor school attendance.

Seven electronic databases were searched for quantitative studies with school-aged children and/or adolescents, reporting a measure of association between depression and school attendance. Articles were independently screened by two reviewers. Synthesis incorporated random-effects meta-analysis and narrative synthesis.

Searches identified 4930 articles. Nineteen studies from eight countries across North America, Europe, and Asia, were included. School attendance was grouped into: 1) absenteeism (i.e. total absences), 2) excused/medical absences, 3) unexcused absences/truancy, and 4) school refusal. Meta-analyses demonstrated small-to-moderate positive cross-sectional associations between depression and absenteeism (correlation coefficient r = 0.11, 95% confidence interval 0.07 to 0.15, p = 0.005, I² = 63%); and depression and unexcused absences/truancy (r = 0.15, 95% confidence interval 0.13 to 0.17, p < 0.001, I² = 4%; odds ratio = 3.74, 95% confidence interval 2.11 to 6.60, p < 0.001, I² = 65%). Few studies reported associations with school refusal or excused/medical absences, and few utilised longitudinal data, although results from two studies suggested an association between depression and subsequent absenteeism.

Study quality was poor overall, and methodological heterogeneity, despite creating a broad evidence-base, restricted meta-analysis to only small subsamples of studies.

Findings suggest associations between depression and poor school attendance, particularly absenteeism and unexcused absences/truancy. Clinicians and school staff should be alert to the possibility of depression in children and adolescents with poor attendance. Future research should utilise longitudinal data to confirm the direction of the association, investigate associations with excused absences, and test potential moderators of the relationship.