Adolescent school absenteeism, depressive symptoms, and internalizing/externalizing problems: a network and simulated intervention study based on CFPS

AI Summary

Loneliness emerged as the central symptom and strongest predictor of network worsening in simulated interventions.
Being easily distracted and depressed mood were identified as candidate targets for easing interventions, sensitivity varying by dichotomization cut-point.
Results are exploratory; cross-sectional design, merged absenteeism reasons, and zero-inflated distribution limit causal inference and generalisation.

BMC Psychol. 2026 May 23. doi: 10.1186/s40359-026-04834-6. Online ahead of print.

ABSTRACT

BACKGROUND: School absenteeism is closely associated with adolescent mental health problems, yet little is known about the covariation patterns between the short-term, mild attendance fluctuations commonly observed among community adolescents and their psychological distress. This study aimed to combine network analysis with simulated intervention methods to explore the conditional dependence network among absenteeism, depression, and internalizing/externalizing problems, and to preliminarily identify potential intervention targets.

METHODS: Data were drawn from the 2022 wave of the China Family Panel Studies (CFPS), including 2,734 community adolescents. The sample was first randomly split into an exploratory sample (N = 1,367) and a validation sample (N = 1,367). Exploratory and confirmatory factor analyses were conducted to examine the reliability and validity of the CES-D-8 and the internalizing/externalizing scales. After controlling for covariates, the EBICglasso method was used to estimate the network structure in the effective sample (N = 1,894), and expected influence (EI) and bridge expected influence (bEI) were calculated. To evaluate the sensitivity of results to dichotomization cut-points, Ising models were constructed using both lenient and strict cut-points. The NodeIdentifyR algorithm was then applied to perform simulated interventions (node thresholds shifted by ± 2 standard deviations), with the network’s overall expected total score serving as the outcome measure.

RESULTS: After removing seven low-loading items, the measurement model showed good overall fit (CFI = 0.979, RMSEA = 0.031). Network analysis identified CESD5 (“I felt lonely”), CESD7 (“I felt sad”), and CESD2 (“I felt that everything I did was an effort”) as central nodes. Bridge nodes included CESD2 (“I felt that everything I did was an effort”), CESD3 (“My sleep was restless”), and N (absenteeism frequency), with 84% of participants reporting zero absences. In worsening simulated interventions, results were highly consistent across both cut-points: CESD5 (loneliness) produced the largest increase in the network’s overall expected total score. In easing simulated interventions, the node with the largest decrease differed by cut-point: Q6 (“I got distracted easily”) under the lenient cut-point, and CESD1 (“I felt depressed”) under the strict cut-point.

CONCLUSIONS: This study revealed a covariation network among short-term absenteeism fluctuations, depression, and internalizing/externalizing problems in community adolescents. Loneliness was not only a central node in the network but also a strong predictor in worsening interventions, whereas being easily distracted and depressed mood emerged as candidate directions for easing interventions that warrant further investigation. Given the cross-sectional design, the merging of different reasons for absenteeism into a single indicator, and the zero-inflated skewed distribution of the data, all conclusions should be regarded as exploratory hypotheses pending validation through future longitudinal studies or experimental designs.

PMID:42177559 | DOI:10.1186/s40359-026-04834-6

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