Absenteeism and psychopathology may act as reciprocal risk factors for one another during childhood and adolescence. There was somewhat more evidence of this effect for psychopathology→absenteeism links than for absenteeism→psychopathology links (particularly with regard to conduct problems), for adolescents as compared to children, and in the Add Health dataset as compared to the two regionally representative datasets. Of note, no predicted absenteeism→psychopathology longitudinal paths were conventionally significant below the 5th to 6th grade time period. Relations among absenteeism and symptomatology varied from sample to sample at the model level, but there was at least some support in each dataset that a higher level of one of these factors in one year tended to presage the onset of increases in the other factor in the following year, over and above autoregressive associations and covariation with demographic variables.
Most previous studies of the linkage between school absenteeism and youth psychopathology have used convenience samples and relatively simple statistical models with few controls, no consideration of reciprocal influences over time, and limited capacity to investigate issues of timing and sequencing or the role of developmental level. Of the few studies to use a regionally representative dataset to examine this linkage, Egger et al. (2003)
found strong cross-sectional links between DSM-IV
disorders and absenteeism, but the direction of effects could not be specified since the two variables were assessed simultaneously. Up to this point in the field, it has been unclear whether this association is largely spurious and attributable to third variables. In contrast, the analytic approach taken here is less easily explained away as a spurious finding than most correlational approaches.
The developmental level of the youth appeared to influence the pattern of findings for both anxiety and depression as well as conduct problems. For example, the only conventionally significant effects obtained in this study in which time 1 absenteeism significantly predicted time 2 conduct problems involved youth in secondary school (high school students in the LIFT sample and middle school students in the Add Health sample) rather than elementary school. Adolescent onset conduct problems appear to have different origins when compared to the childhood onset variety, with some evidence pointing to influence of the peer environment and cultural factors in adolescent onset conduct problems as compared to more influence of intrapersonal and family factors regarding childhood onset conduct problems (McCabe, Hough, Wood, & Yeh, 2001
). Potentially, school absenteeism in secondary school can sometimes be a product of early adolescent peer and cultural influences, but may inadvertently become an early step along the path of accelerating conduct problems. An interactive combination of hormonal changes, decreased parental monitoring, increased autonomy, and increased influence of peers during this developmental period heighten the risk of deviant, sensation-seeking behaviors even among some youth who were previously behaviorally well-regulated (McCabe et al., 2001
; Moffitt, 1993; Silberg, Rutter, Tracy, Maes, & Eaves, 2007
). School absenteeism may reflect early experimentation with self-directed behaviors previously restricted by parents and school personnel as well as by a youth’s relative balance of impulse control and pleasure-seeking, which actually declines (in favor of pleasure-seeking) following the hormonal changes of puberty, likely contributing to the puberty-related increase in nonviolent conduct problems seen in adolescence (Rowe, Maughan, Worthman, Costello, & Angold, 2004
; Steinberg, Albert, Cauffman, Banich, Graham, & Woolard, 2008
). Such experimentation may ultimately be benign in nature for some, but for others may potentiate entry into more deviant behaviors during the unsupervised period of the school day that develop momentum and expand in range beyond mere truancy.
In considering that this effect was found for middle school but not high school students in the Add Health dataset, it is worth considering that early absenteeism is also a significant predictor of eventually dropping out of school (Lehr, Sinclair, & Christenson, 2004
). It may be that many of those who are most at risk for a truancy-mediated pathway into conduct problems drop out of school upon entering or during high school and thus are not well-represented in samples such as the Add Health dataset which select students who are currently enrolled in target high schools.
Reciprocal relations among absenteeism and anxiety and depression varied among the three samples, with evidence of cross-lagged effects at the secondary school level for both the Add Health (nationally representative) and LIFT (regionally representative) samples. In considering the significant findings from these samples, it is worthwhile considering that separation anxiety disorder, social phobia, specific phobia (e.g., of school), and depression each have in common an element of avoidance or withdrawal (e.g., as exhibited by symptoms related to avoiding specific activities or losing interest in previously pleasurable activities) that can include school as a threatening or unpleasurable stimulus. As with conduct problems, it is plausible that early in the course of developing one of these disorders, school absenteeism can play a gateway function by creating conditions that promote increased anxiety or depressive symptoms. Avoidance of feared stimuli tends to promote intensification of fear symptoms (Deacon & Maack, 2008
) as well as depressed mood (Moitra, Herbert, & Forman, 2008
) and social withdrawal tends to prolong or exacerbate episodes of depression (Palinkas & Browner, 1995
). For some youth, school absenteeism may therefore be an early symptom of an anxiety or depressive disorder that, through a chain reaction, elicits additional symptoms. As one example, a student who begins to avoid school due to fear of separation from parents may, through negative reinforcement, learn a basic coping strategy (i.e., avoidance of separation) that reduces the unpleasant sensation of fear, and therefore generalize this strategy to additional situations (e.g., playdates, staying in a room alone, sleeping by oneself) that begin to increase the pervasiveness of separation anxiety. As such, psychopathology would seem to be an important risk factor for absenteeism just as absenteeism might heighten symptoms of negative mood.
In contrast with the Add Health and LIFT models, there was only one conventionally significant cross-lagged path in the JHU-PIRC sample (conduct problems→absenteeism at 2nd to 3rd grade). Notably, there were significant bivariate relations among the absenteeism and psychopathology variables over time in preliminary descriptive analyses for JHU-PIRC; the introduction of statistical controls eliminated the effect. One interpretation is that other sources of stress experienced by the urban youth studied in the JHU-PIRC sample might contribute to both absenteeism and psychopathology, overwhelming or at least masking any unique covariation among these two variables. Of course, it is important to note that applying statistical controls can be a fairly conservative analysis that assumes that the effects of an IV are only its unique effects, whereas it may be that some of the explained variance shared with the covariates also represents a direct effect of the IV on the DV and that the association between the covariates and the DV is to some degree noncausal or indirect. Nonetheless, while making this more conservative assumption eliminated any effects of absenteeism→psychopathology in the JHU-PIRC sample, there were findings consistent with the hypothetical model in the other samples that emerged even when statistical controls were applied.
It is important to recognize that the school environment itself probably plays an important role with regard to youth absenteeism and psychopathology. Structural school factors associated with urban neighborhoods such as poor maintenance and upkeep of school grounds are linked with increased absenteeism and in some cases have been found to predict to absenteeism more so than demographic factors, which are typically powerful predictors of absenteeism (e.g., Branham, 2004
). Absenteeism may serve as an avoidant coping mechanism for youth attending chaotic or unsafe schools. These same school characteristics, due to their stressful nature, are probable risk factors for increased anxiety and depression as well as conduct problems (Ma, Truong, & Sturm, 2007
). The literature on positive behavioral supports in schools shows that implementing violence prevention, conflict resolution, and related programs to reduce chaos and stress in schools can have a measureable impact on youth symptomatology (e.g., Dolan et al., 1993
; Eddy et al., 2000
; Lane, Wehby, Robertson, & Rogers, 2007
). It stands to reason that implementation of such programs on a schoolwide basis could impact the association between absenteeism and psychopathology in a number of ways—for example, by attenuating the association to some extent. In the modern era, with increasing use of positive behavioral supports in public schools, it would be useful to understand the impact of such supports on the longitudinal linkage between absenteeism and psychopathology. Another possible influence of the school environment on this linkage stems from the practice of suspending or expelling students for chronic absenteeism in some schools (e.g., Gottfredson, Gottfredson, Czeh, Cantor, Crosse, & Hantman, 2000
, p. 3–22). Seemingly, this practice might magnify the effects of absenteeism on the small group of students who are already developing patterns of school avoidance or psychopathology and are then required to miss additional time at school. Of note, the JHU-PIRC trial counted suspensions as excused absences. While this could have confounded the results by increasing the association between conduct problems and absenteeism, the JHU-PIRC trial in fact yielded minimal evidence of a link between these two variables, with only one significant finding out of eight tests of association suggesting a cross-lagged effect (with conduct problems serving as a risk factor for absenteeism in early elementary school), reducing concerns about interpretation of the results.
Other limitations of the study should be noted. The methodological differences between the three datasets add complexity to the interpretation of the overall pattern of findings. In the Add Health study, all measures were based on youth self-report, increasing the risk of method variance accounting for some of the findings. Second, studies of school absenteeism have noted that poor school record keeping and the difficulty in distinguishing true excused and unexcused absences generally renders it impossible to make this distinction effectively for data analytic purposes (McCluskey et al., 2004
). The conceptual model adopted in this paper, as described above, assumes that absences that occur for a variety of reasons may still have ill effects on youth psychological adjustment if they are too frequent. However, it is possible that absences could be effectively classified in ways that would show differential links with youth psychopathology (see, e.g., Kearney, 2003
). Additionally, while informative, the log-transformed models used for the Add Health dataset can only be interpreted in terms of direction of effect, not the magnitude of the effect. The reciprocal influences could be of a relatively small magnitude and this needs to be clarified in future research.
Implications for Practice and Conclusion
Identifying risk factors for psychopathology can inform preventive intervention development (Ialongo et al. 2004
; Kazdin, 1999
). If absenteeism acts as a risk factor for the onset or exacerbation of psychopathology, selective and indicated prevention models targeting absenteeism could be developed that might ultimately reduce the incidence of mental health disorders. Illustratively, research on other risk factors for childhood conduct problems (e.g., poor parenting practices) offered useful directions for intervention development (e.g., parent training). Risk factors that can be changed represent the best candidates for preventive intervention (Dishion & Patterson, 1999
). According to the conceptual model guiding this study, the occurrence of elevated absenteeism can trigger or exacerbate mental health problems; if prevented, such outcomes might be avoided. Notably, evidence-based treatments have been developed that significantly improve attendance rates, illustrating the malleability of the problem (Kearney & Hugelshofer, 2000
). Thus, absenteeism could be a useful target for preventive intervention if it indeed plays a contributing role in the development of psychological problems. It is of significance that public schools are natural allies in the prevention of absenteeism.
The present findings represent an important step towards examining reciprocal relations among absenteeism and youth psychopathology. These findings are consistent with the hypothesis that these two aspects of youth adjustment may at times exacerbate one another, leading over the course of time to more of each. Further delineation of the characteristics of youth exhibiting high levels of absenteeism who are most likely to go on to develop psychopathology would be useful in planning for the development of a school-based selective prevention model for this at-risk group of youth.