Mental Health from Childhood to Early Adolescence and its impact on School Attendance Problems: A Latent Transition Analysis

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The aim of this paper was to examine how the developmental trajectory of emotional and behavioural difficulties from childhood to early adolescence might impact the frequency of chronic absenteeism and truancy at 13 years. Using a sample (N = 8570) from the Longitudinal Growing Up In Ireland Study (GUI’98), the research used Latent Class Analysis (LCA) and Latent Transition Analysis (LTA) to examine combinations of mental health symptoms at 9 and 13 years and their developmental impact on SAPs. The Strengths and Difficulty Questionnaire (SDQ) was used to measure a range of emotional and behavioural difficulties at both time points, yielding four mental health classes. Children who remained in High Risk classes, had higher odds of chronic absenteeism and/or truancy. Movement between classes significantly altered the odds of truancy, but not chronic absenteeism, highlighting the importance of differentiating between SAPs and early intervention. A secondary aim was to investigate how family, school and socio-demographic risk factors impacted those trajectories. Family factors were significantly linked to transitions into the co-morbid class, indicating that family risk factors can negatively impact the trajectory of emotional and behavioural difficulties between childhood and adolescence. This paper contributes to current knowledge on the complexities of mental health difficulties in primary school children and their impact on SAPs in early adolescence. Mental health School Attendance Problems Latent Transition Analysis Figures Figure 1 Figure 2 Figure 3 Introduction School Attendance Problems (SAPs) are a complex phenomenon associated with a myriad of risk factors and outcomes [ 1 – 3 ]. Using the typology model, SAPs can be distinguished between school withdrawal, school exclusion, school avoidance (SA) and truancy [ 1 ]. Under this approach, school withdrawal refers to parent led absenteeism [ 4 ], school exclusion to school led absenteeism, school avoidance (SA) to a child’s difficulty in attending school, despite parents’ efforts and characterised by strong negative emotions, with truancy characterised as absence without parents’ knowledge relating to a negative attitude toward school [ 2 , 5 ]. This paper focuses on the types of SAPs that relate to SA and truancy. There has been a substantial increase in SAPs post covid [ 6 ]. In the academic year 2021/22, 40.3% of Irish primary and 26.8% of post primary students were chronically absent from school compared to an average of 11% and 15% respectively between 2014 and 2018 [ 7 ]. In Ireland, school absences are classified as explained and unexplained in accordance with the Education Welfare Act, 2000 [ 8 ]. However, whilst data is recorded, little information is available on the occurrence of SAPs and the factors that influence particular student groups [ 9 , 10 ]. As SAPs can lead to a range of academic, social and emotional challenges [ 11 – 13 ] that increase as student’s get older [ 14 , 15 ], insight into the developmental trajectory of school absenteeism is needed. Research has also highlighted strong associations between mental health symptoms and SAPs [ 16 ], with up to 20% of young people worldwide experiencing mental health difficulties [ 17 ]. Although mental health symptoms are generally conceptualised into categories of emotional and behavioural difficulties [ 16 , 18 , 19 ], research indicates that there is also a co-occurrence between the two dimensions [ 11 , 20 ] with several studies evidencing the association between SAPs and these symptoms [ 21 – 24 ]. A systematic review, for example, found a strong association between SA and emotional disorders whilst a latent class analysis exploring truancy identified a number of behavioural difficulties [ 25 , 26 ]. Other studies have highlighted an association between co-occurring SA and truancy and increased rates of emotional and behavioural disorders [ 11 ]. The association is thought to be bidirectional, with mental health difficulties leading to SAPs and SAPs to mental health difficulties [ 27 ]. In recent years, a number of cross sectional studies have explored associations between mental health and SAPs. (24, 28, 29). Sharpe et al., (2023), for example, using a sample of 9 year olds assessed how rates of student absenteeism varied across classes of mental health and found that primary school children at risk of mental health difficulties had significantly higher odds of absenteeism compared to those at low-risk. They also observed significantly greater odds of family, school and demographic risk factors for the high risk symptomology classes [ 24 ]. To our knowledge, however, few longitudinal studies have examined mental health and its impact on school absenteeism. To address this gap, the authors, using the same sample as Sharpe et al., (2023), applied LTA [ 30 ] to examine the extent to which the 9 year olds remained or transitioned between classes and how the outcomes chronic absenteeism and have truanted at 13 years could be predicted by those transitions. To this end the paper asked: i) Using the GUI (’98 cohort) datasets, can mental health classes identified at 9 years, be identified again at age 13 years ii) What is the probability of remaining or transitioning from one class to another across time (9–13 years)? iii) How are transitions affected by school, family and demographic risk factors? iv) How does remaining or transitioning between classes impact parents report of c hronic absenteeism (> 20 days) and child’s report of have truanted at 13 years. Methods Participants Data was collected from the first two waves of the GUI child cohort ’98, a longitudinal study examining developmental factors for children growing up in Ireland. The analyses was completed using data from GUI Researcher Microdata Files. In the first wave (2007-2008), 9-year-olds were first selected through the random sampling of 910 Irish national schools from which children were randomly selected [31]. The respondents (N=8568) were evenly distributed between females (49.8 %) and males (50.2 %). The second wave was completed in 2011/2012 age 13 (n = 7,423) [39]. Informed consent was obtained from all participants with ethical approval received from the Health Research Board’s Research Ethics Committee in Ireland [39]. Measures Mental Health Indicators for latent classes were taken from the parents report of the Strength and Difficulty Questionnaire (SDQ) [32], a measure used to identify moderate and high-risk symptoms in children and adolescence. The current study used the twenty items from the four subscales representing adjustment difficulties: Emotional Problems, Conduct Problems, Hyperactivity–Inattention and Peer Problems. Primary carers were asked to rate whether the statements pertaining to their child were ‘not true’, ‘certainly true’, ‘somewhat true’. Responses were dichotomized into 0 = ‘not true’ and 1 = ‘somewhat and certainly true’. Individual responses were used to create classes with combinational differences of individual SDQ items. Outcomes Chronic absenteeism , based on parental reports during the last 12 months was dichotomised into 20 days, with >20 days representing chronic absenteeism as per the Education Welfare Act, 2000 [8]. Have Truanted was collected using the child’s report of whether they ever skipped classes or mitched in the last 12 months, combining responses ‘now and again’, ‘quite often’ and ‘all the time’ to equal Have Truanted . Risk Factors Based on previous research [26], the following risk factors were selected. Data was collected from the children, parents and teachers as part of the GUI data collection. Socio-demographics Gender , 0 = male, 1 = female. Socio-economic , parents report of ‘making ends meet’. ‘Not difficult’ = 0, ‘Great Difficulty’ and ‘Difficulty’ = 1. School Bullying , child’s report of being bullied in the last year. No = 0, Yes = 1. Relationship with teacher , child’s report of liking/disliking their teacher. ‘Sometimes ‘and ‘Always’ = 0, Never = 1 (Dichotomised to eliminate ambiguity on the scale response of ‘sometimes and always’). Attitude to school, child’s report of liking school, with ‘Sometimes’ and Always’ = 0 and Never = 1. (Dichotomised to eliminate ambiguity on the scale response of ‘sometimes and always’.) Family Parent conflict , primary carer report of conflict between parents. No = 0 and Yes = 1. Parent Depression, Centre for Epidemiological Studies in Depression 8-item scale (CES-D). Cronbach’s alpha was .87 [33]. Scores of seven or above indicate presence of depressive symptoms, scores below seven indicate ‘not depressed’. Problems at home affecting school participation based on teacher response to – ‘Do any of the following limit the kind or amount of activity the study child can do at school?’ – home environment/problems at home. No= 0, Yes = 1. Statistical Analysis LCA and LTA with binary indicators were computed in Mplus 8.7. LCA was applied to categorise individuals with similar patterns of behaviour into latent classes, LTA was used to explore changes in those classes over time. This technique allowed for the combining of cross-sectional measurements of the categorical latent variables and longitudinal descriptions of change in the classes between time points. Analyses were conducted using maximum likelihood estimation and MLR algorithms [34]. The number of missing cases with unknown or missing values for the LCA’s at wave 1 and 2 was 13 and 1. Missing cases with unknown or missing values for the LTA was 13, missing on one or more covariate was 1453. The sample from wave 1 reduced from 8,568 participants to 7,423. To account for missing data the maximum-likelihood based estimation default in Mplus was applied. Model data was used in the estimation of the parameters under the assumption that missing data was at random (MAR). Further, since the data was obtained at the school level, this source of non-independence was taken into account through the use of the Huber-White ‘sandwich’ estimator [35, 36]. The following approach was applied (i) measurement models explored at both time points, (ii) measurement invariance examined, (iii) structural and configural LTA model specifications observed with the final LTA model specified. Creating Latent Classes Mental health symptoms were examined by dichotomizing items of the SDQ with 0 = not true and 1 = somewhat and certainly true, with 5 items reversed. Dichotomisation was necessary to reduce model complexity, an approach used in a number of LCA studies using SDQ scores [24, 37, 38]. To determine the number of classes, the best model fit was selected using statistical and theoretical criteria. The estimation process started with a 2 class model increasing to 5 with the lowest indicator of Bayesian Information Criteria (BIC) and the Akaike Information Criteria (AIC) observed. Both the entropy and the Lo-Mendell Rubin likelihood ratio test (LMR-LRT) were used to examine class separation and significance. The entropy 0.714 of a four class solution at wave 1 (age 9) suggested acceptable accuracy of assignment into the correct class [39]. Given the level of accuracy within a number of the classes significant (p < 0.05) values on the LMR-LRT also indicated that the 4 class model was the significantly best fit, . Finally the theoretical interpretability of classes was considered, providing both statistical and theoretical validity. The same procedure was completed for wave 2 (Table 1), revealing a class with primarily emotional symptoms, a class with behavioural symptoms, a class with mixed emotional and behavioural symptoms and a low risk class at 9 and 13 years. Latent Transitional Analysis Configural and structural similarities were examined to explore longitudinal invariance between classes over time [40]. The longitudinal weight assigned for wave I was used in the model. LTA was conducted to identify changes in latent statuses over time based on responses to indicators at T2 (wave 2), conditional on membership to a latent status at T1 (wave 1) [30]. The LTA was constrained to be equal across both time points. The four class solution indicated that measurement invariances hold. Risk factors (covariates) from Time 1 were included to determine if they were predictors of the latent transition probabilities. After their inclusion the effect of latent transition patterns from T1 on outcomes at T2 were examined using Mplus 8.7 (fig.1). Results Identification of Latent classes The accelerated flattening of the Log Likelihood and BIC values in the four class model, the decrease in entropy the non-significant CI for the LMT-IRT test for class 5 and the conceptual composition of the four class model suggested an appropriate solution to identifying meaningful classes of mental health symptoms, resulting in a four class model that had configural and structural similarity for both waves (Table 1). Table 1 Model Fit Wave 1 C LL Replicated Parameters AIC BIC SSABIC LMR-LRT (p) Entropy 2 -90182.722 YES 41 180447.445 180736.665 180606.374 (p <0.0000) .745 3 -88503.928 YES 62 177131.857 177569.214 177372.190 (p<0.0000) .736 4 -87566.067 YES 83 175298.633 175883.628 175619.869 (p<0.0000) .717 5 -87250.496 YES 104 174708.993 175442.625 175113.132 (p<0.0827) .692 Wave 2 C LL Replicated Parameters AIC BIC SSABIC LMR-LRT (p) Entropy 2 -74265.966 YES 41 148613.931 148897.886 148767.596 (p <0.0000) .767 3 -72680.471 YES 62 145484.942 145914.336 145717.313 (p<0.0000) .763 4 -71780.622 YES 83 143727.244 144302.079 144038.323 (p<0.0000) .748 5 -71527.533 YES 104 143263.066 143988.341 143652.851 (p<0.6654) .757 C = classes; LL = log-likelihood; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; LMR-LRT = Lo– Mendell–Rubin Likelihood Ratio Test; p = p value. Classes: Class 1 - High Risk of Emotional and Behavioural Difficulties (HEBD) , had moderate to high probabilities of behavioural difficulties, namely hyperactivity, inattentiveness, disobedience, tantrums, lying and emotional difficulties, somatic illnesses, worry, nervousness, fearfulness, unhappiness, solitary playing, bullying and better adult than peer relationships. Class 2- High Risk of Emotional Difficulties (HED), had moderate to high probabilities of somatic illnesses, worry, fear, nervousness, solitary play and moderate probabilities of tantrums. Class 3- High Risk of Behavioural Difficulties (HBD), had moderate to high probabilities of hyperactivity, inattentiveness, disobedience and tantrums with low probabilities of emotional difficulties. Class 4 - Low Risk of Emotional and Behavioural Difficulties (LEBD) , presented with low probabilities of emotional and behavioural difficulties. (Fig.2). See Table S1a/b/c (supplementary materials) for item responses. Latent Transition Analysis LTA was conducted next to examine rates of transitions across classes from age 9 to 13 years (Fig.3). The LEBD class had the highest stability rate with a small proportion transitioning to the high risk classes. The HEBD class had the lowest stability rate, with transitions mostly to the HED and HBD class with a small proportion transitioning to the LEBD Class. The HED class and HBD class had moderate to high rates of stability with transitions occurring mostly to the LEBD class. (Table 2). Table 2 Probability and Transition Matrix without covariates Latent Classes Wave 1 (C1) HEBD (13yrs) HED (13yrs) HBD (13yrs) LEBD (13yrs) Wave 2 (C2) HEBD (9yrs) .173 0.557 0.140 0.283 0.018 .132 HED (9yrs) .233 0.046 0.595 0.060 0.298 .187 HBD (9yrs) .313 0.072 0.039 0.678 0.211 .293 LEBD (9yrs) .281 0.011 0.044 0.060 0.886 .388 Bold values indicate probability of remaining in the same class, wave 1 (9 years), wave 2 (age 13 years). Probability and Transition Matrix with covariates Latent Classes Time 1 (C1) HEBD (13yrs) HED (13yrs) HBD (13yrs) LEBD (13yrs) Time 2 (C2) EBD (9yrs) .173 0.517 0.165 0.293 0.025 .130 ED (9yrs) .246 0.044 0.569 0.081 0.305 .194 BD (9yrs) .297 0.068 0.044 0.669 0.211 .293 LEBD (9yrs) .283 0.010 0.046 0.066 0.879 .388 Bold values indicate probability of remaining in the same class, wave 1 (9 years), wave 2 (age 13 years). Risk Factors on Transition Probabilities Bullying, dislike of teacher, dislike of school , parent conflict and socio-economic status did not affect movement between classes whilst parental depression and problems at home that affect school participation at 9 years significantly affected transitions. Children in the HED and HBD classes with reports of parental depression had higher odds of transitioning to the HEBD class at 13 years compared to those who remained, OR = 2.568 (1.224, 5.390), OR =1.835 (1.089, 5.094), Children who transitioned from the HEBD class to the HED, HBD or LEBD classes at 13 years had significantly lower odds of having problems at home affecting school participation compared to those who remained, OR = 0.155 (0.031, 0.775), OR = 0.525 (0.291, 0.946), OR = 0.281 (0.094, 0.842) respectively. Conversely, children who transitioned from the HED and LEBD classes to the HEBD class had significantly higher odds of having problems at home affecting school participation OR= 6.435 [1.290, 32.101], OR = 3.560 [1.188, 10.670]. These results indicate that problems at home that affect school participation and parental depression can negatively impact developmental trajectories of emotional and behavioural difficulties between 9 years and 13 years. Females had significantly higher odds of transitioning from the HEBD and HBD class to the HED class at 13 years OR = 2.004 (1.295, 3.101), OR = 2.045 (1.402, 2.983), indicating that females are more likely than males to transition away from behavioural difficulties. There was also higher odds of females transitioning from the HED class to the LEBD class OR = 1.533 [1.142, 2.058]. (See supplementary Table S2 for transition probabilities). Stability on Outcomes Children who remained in The HEBD and HED classes compared to those who remained in the LEBD class were three times more likely to be chronically absent at 13 years, OR = 2.670 (1.231, 5.788), OR = 2.844 (1.400, 5.775). Children who remained in the HEBD and BD classes were 5 times more likel y to report have truanted at 13 years compared to those remaining in the LEBD class OR = 5.598 ( 3.121, 10.043), OR = 4.600 ( 2.565, 8.24) respectively. Children remaining in the HED class did not have significant odds of having truanted and children remaining in the HBD Class did not have greater odds of chronic absenteeism These results indicate greater odds for chronic absenteeism for children experiencing stability of emotional difficulties and greater odds of truancy for children experiencing stability of behavioural difficulties. Children experiencing stability of co-morbid symptoms had greater odds of chronic absenteeism and truancy (Table 5). Table 3 OR for Chronic Absenteeism and Have Truanted for stability Stability OR Chronic Absenteeism 95% CI Est. S.E Lower Upper OR Have Truanted 95% CI Est. S.E Lower Upper Stability in HEBD compared to stability in HED class stability in HBD class stability in LEBD class 0.939(.36) (0.441, 1.998) 1.580(.67) (0.684, 3.653) 2.670 (1.05) (1.231, 5.788) 4.223(1.8) ( 1.778, 10.028) 1.217(.35) (0.685, 2.163) 5.598(1.6) (3.121, 10.043) Stability in HED compared to Stability in HBD class Stability in LEBD class 1.683(0.5) (0.845, 3.354) 2.844 (1.02) (1.400, 5.775) 0.288(0.124) ( 0.109, 0.816) 1.326 (0.5) (0.547, 3.214) Stability in HBD compared to Stability in LEBD class 1.682(0.6) (0.836, 3.412) 4.600 (0.05) ( 2.565, 8.24) Transitions on Outcomes Odds Ratio was also calculated to compare the odds of chronic absenteeism and/or have truanted for children who leave one group compared to those who remain. No significant OR for chronic absenteeism was found for any group leaving one class compared to those who remained suggesting that mental health at 9 years had a greater effect on chronic absenteeism at 13 years than the class status transitioned to (Table 6). Transitions did affect the outcome Have Truanted for children moving between groups. Children who transitioned from the HBD to the HED class had significantly lower odds of reporting have truanted, OR = 0.233 (0.082, 0.662). Children who remained in the HEBD group were 6 times more like to report having truanted compared to those who transitioned to the HED and 10 times more likely than those who moved to the LEBD class OR = 6.893 (2.620, 18.136), OR = 10.177 (4.639, 22,327). Children who remained in the HED class were significantly less likely to truant compared to those who moved to the HBD class, OR = 0.233 (0.082,0.662). The results indicate that children with emotional difficulties at 9 years were more likely to be absent with their parents knowledge at 13 years whilst children with behavioural difficulties at 9 years or who later develop behavioural difficulties were at risk of truancy. The children in the HEBD had significantly high odds of chronic absenteeism and have truanted indicating that children with co-morbid symptoms have a greater risk of SAPs. Table 4 OR for Chronic Absenteeism and Have Truanted for Class Transitions. Transitions OR Chronic Absenteeism 95% CI Est. S.E Lower Upper OR Have Truanted 95% CI Est. S.E Lower Upper Remaining in the HEBD class compared to transitioning from HEBD to ED class Remaining in the HEBD class compared to transitioning from HEBD to BD class Remaining in the HEBD class compared to transitioning from HEBD to LEBD class Transitioning from the HED to HEBD class compared to remaining in the HED class Remaining in the HED class compared to transitioning from the HED to HBD class Remaining in the HED class compared to transitioning from the HED to LEBD class Transitioning from the HBD to HEBD class compared to remaining in the HBD to HBD Transitioning from the HBD to HED class compared to remaining in the HBD class Remaining in the BD class compared to transitioning from the BD to LEBD Transitioning from the LEBD to HED class compared to remaining in the LEBD class Transitioning from the LEBD to HBD class compared to remaining in the LEBD 0.901 (0.77) (0.166, 4.886) 0.825(0.49) (0.256, 2.660) 1.857(1.41) (0.418, 8.256) 0.901(0.767) (0166, 4,866) 0.915(0.515) (0.304, 2.755) 2.061(0.88) (0.892, 4.762) 0.825(0.49) (0.256, 2.660) 0.915(0.51) (0.304, 2,755) 2.522(1.01) (0.935, 5.424) 2.016(0.8) (0.892, 4.762) 2.522(1.01) (0.935, 5.424) 6.893 (3.4) (2.620, 18.136) 1.603(0.47) (0.897, 2.865) 10.177(4.80) (4.639, 22,327) 0.613 (0.24) (0,283, 1.326) 0.233 (0.12) (0.082,0.662) 1.476 (0.82) (0.495,4,407) 0.759(0.25) (0.392, 1.472) 0.233(0.12) (0.082,0.662) 6.349(2.3) (3.081,13.085) 1.476(.82) (0.495, 4.407) 6.349(2.3) (3.081,13.085) Bold values indicate significant OR’s for Chronic Absenteeism /Truancy Discussion The aim of the paper was to examine how the developmental trajectory of patterns of mental health symptoms from childhood to adolescence impact the frequency of chronic absenteeism and have truanted at 13 years. A secondary aim was to investigate how family, school and socio-demographic risk factors impacted those trajectories. To achieve this LCA and LTA were computed to identify classes of mental health symptoms. Analysis yielded four classes; a class with mixed emotional and behavioural symptoms, a class with patterns of emotional symptoms, a class with patterns of behavioural symptoms and a low risk class. Mental Health Transitions The researchers examined the extent to which 9 year olds transitioned or remained in one mental health class before establishing if those remaining or transitioning were at greater risk of chronic absenteeism and/or having truanted at 13 years. Following the inclusion of risk factors, 27% of the sample transitioned with 73% remaining. Consistent with previous research the most prevalent and stable group was the LEBD class [ 41 , 42 ], followed by The HBD, HED and HEBD classes . Stability rates for the single symptomology classes were consistent with previous studies that reported some developmental continuity of behavioural and/or emotional symptoms between childhood and adolescence [ 42 , 43 , 44 ]. However, transitions indicated some improvement for both classes with 21% and 30% respectively moving to the LEBD class. Although the HEBD class had the lowest stability rate, transitions for this class occurred mainly between the high risk classes , with just 2.5% transitioning to the LEBD class . These patterns support previous research, indicating that children with co-morbid symptoms are most likely to remain in one of the symptomatic groups during early adolescence, highlighting a persistence of mental health symptoms for that group [ 42 ]. These results highlight the importance of intervening early to disrupt the emergence of emotional and behavioural problems in adolescence. Findings also indicated that whilst bullying, dislike of teacher, dislike of school, parental conflict and socio-economics did not influence movement between classes, parental depression and problems at home that affect school participation did. Children who moved to the EBD class , had significantly higher odds of having a parent with parental depression and/or problems at home affecting participation at 9 years, supporting the notion that youths with co-morbid symptoms are more likely to experience disrupted family relationships [ 43 ]. Females had significantly higher odds of moving away from classes with behavioural symptoms , supporting research that highlights gender differences in the prevalence of internalizing problems during adolescence [ 20 , 44 ]. Outcomes The odds of chronic absenteeism and have truanted were first compared between the stable high risk classes and LEBD class . Children continuing to exhibit emotional symptoms had the highest odds of chronic absenteeism but did not have significant odds of reporting having truanted . Conversely children continuing to exhibit behavioural difficulties had significantly higher odds of reporting have truanted but did not have significant odds of chronic absenteeism . As literature has historically associated emotional symptoms with SA [ 25 , 1 ] and behavioural symptoms with truancy [ 26 ], these results support research that highlights the importance of differentiating between types of SAPs , indicating that children with SA are more likely to be absent with their parents knowledge and less likely to truant [ 1 , 26 , 46 ]. These findings also provide insight into how the trajectory of mental health symptoms from childhood to adolescence can affect SA and truancy, highlighting the importance of identifying the developmental patterns associated with the continuous dimensions of SAPs [ 47 ]. Children remaining in the HEBD class , had high odds of both chronic absenteeism and having truanted suggesting that youth with co-morbid symptoms are at risk of both types of SAPs [ 11 ]. Consistent with previous research children with good mental health had lower odds of absenteeism. The researchers next compared the odds of chronic absenteeism and truancy for children who left a class compared to those who remained. Surprisingly, children who moved from the high risk classes to the LEBD class did not have significantly reduced odds of chronic absenteeism compared to those who remained. This is a key finding, suggesting that mental health symptoms experienced at 9 years have a greater influence on chronic absenteeism at 13 years than the mental health profile transitioned in to. These results indicate that mental health symptoms experienced in childhood rather than the stability of those symptoms alone may impact a young person’s ability to attend school at 13 years. As previous literature has highlighted that absenteeism and emotional difficulties in early education can lead to poor academic outcomes, social difficulties, behavioural challenges and parental adaptive responses, it might be possible that these factors develop into entrenched behaviour patterns that influence chronic absenteeism at 13 years [ 45 ]. As early childhood is a crucial time for the development of emotional symptoms [ 20 , 25 ] early school based interventions are advised to reduce the risk of SA. Having truanted , on the other hand, was impacted by transitions, highlighting the importance of identifying different mental health patterns associated with different categories of SAPs . Movement between classes indicated that developing behavioural symptoms increased the odds of truancy in adolescence. Equally children with behavioural difficulties at 9 years who moved to the HED or LEBD class were less likely to truant than those children who continued to have behavioural difficulties. These findings highlight that childhood symptoms of emotional difficulties can have long term effects on SA with behavioural difficulties increasing the risk of truancy. Children presenting with co-morbid symptoms are at increased risk of SA and truancy requiring intensive interventions during the transition to early adolescence. Both parental depression and problems at home that affect participation at school influenced movement into the HEBD class . These findings expand on previous research highlighting the adverse effects of family risk factors on mental health in primary school students and absenteeism in adolescence. Based on these findings family based supports and policy efforts designed to prevent SAPs emerging in primary school children could reduce the number of children experiencing SAPs at post primary [ 24 , 10 ]. As stability in high risk groups was also predictive of chronic absenteeism and/or truancy at 13 years, noting persistent mental health symptoms could be a useful early indicator tool to reduce the number of adolescence experiencing SAPs. There are several limitations acknowledged. First, dichotomizing the SDQ variables may have resulted in loss of information. Although the prevalence of emotional and behavioural symptoms based on the SDQ may be reflected differently in studies using cut offs, the size of the high risk classes were consistent with LTA studies that examined emotional and behavioural profiles in children [ 37 , 38 ]. The authors dichotomised the outcome have truanted based on the child’s report of whether they had skipped classes or mitched in the last 12 months. As combining the responses likely reduced the severity of the outcome, it was expected that probabilities and odds would be greater for truancy then chronic absenteeism. Although a large sample size was utilised, the sample size for > 20 days absent was small, representing 2% of the sample at both waves. At the time of data collection Irish data recorded an average of 11% absences for > 20 days in primary schools and 16% for post primary [58], suggesting that the data for chronic absenteeism in the sample is underestimated. Although previous research highlights that parents under report their child’s absence [59] parents reports of days absent was used due to the large amount of missing data for teachers reports. Although risk factor problems at home affecting school participation was a teachers' subjective response, previous research using the same sample found this risk factor was significantly associated with HEBD at 9 years. As a result the risk factor was included in the analysis. The confidence intervals were high for the some of the OR’s measuring the effect of transitions on outcomes. Following inspection of the probabilities and estimates for the effect of transitions on the outcomes (supplementary files Tables 3 and 4 ) the authors were satisfied with the reliability of the data and included the OR’s with wide confidence intervals. Conclusion Findings indicated that the mental health status of 9 year olds significantly impacts chronic absenteeism at age 13. The study highlights the need for continued efforts at developing and implementing early intervention in early primary school children. The complex nature of SAPs requires an interdisciplinary response that supports students showing early signs of SA and/or truancy. The authors recommend the implementation of school based mental health and wellbeing interventions that incorporate a ‘whole child’ and whole school approach to school attendance [ 6 ]. Findings also highlighted the importance of family factors, indicating early multi-faceted interventions are needed to support families of children who are absent from school. As schools can play a key role in facilitating positive school and family engagement, efforts to increase school awareness about the benefit of tiered interventions, family support and well-being are advised. In this paper, it was not possible to determine the underlying mechanisms through which the mental health status of 9 year olds influenced chronic absenteeism at 13 years. Further research could provide additional insight into developed behaviours associated with mental health difficulties at 9 years that might later influence chronic absenteeism at 13 years. Declarations Author Contribution All authors contributed to the study conception and design. Data analysis was conducted by Jane Sharpe. Brendan Bunting reviewed outputs released from the Central Statistics Office (CSO). The first draft of the manuscript was written by Jane Sharpe and all authors commented on versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement The data used in this study were obtained from the Growing Up in Ireland (GUI) study. It is managed by DCEDIY in association with the Central Statistics Office (CSO). Results in this report are based on analyses of data from Research Microdata Files provided by (CSO). References Heyne, D., Gren-Landell, M., Melvin, G. A., & Gentle-Genitty, C. (2019). Differentiation Between School Attendance Problems: Why and How? Cognitive and Behavioral Practice , 26 (1), 8–34. https://doi.org/10.1016/j.cbpra.2018.03.006 Kearney, C. A., & Graczyk, P. A. (2020). 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Government Publications. Muthén, L. K., & Muthén, B. (1998-2017). Mplus User’s Guide. Los Angeles, CA: Muthén & Muthén. Huber, P. J. (1967). The behaviour of Maximum Likelihood Estiamtes Under Nonstandard Conditions. In Proceedings of the Fifth Berkeley Symposium on Mathemtical Statisitcs and Probability. Berkeley, CA: University of California Press, vol. 1, 221-233. White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica , 48 (4), 817. https://doi.org/10.2307/1912934 Morales, A., Melero, S., Tomczyk, S., Espada, J. P., & Orgilés, M. (2021). Subtyping of Strengths and Difficulties in a Spanish Children Sample: A Latent Class Analysis. Journal of Affective Disorders , 280 , 272–278. https://doi.org/10.1016/j.jad.2020.11.047 Petersen, K. S., Humphrey, N., & Qualter, P. (2020). Latent Class Analysis of Mental Health in Middle Childhood: Evidence for the Dual-Factor Model. School Mental Health , 12 (4), 786–800. https://doi.org/10.1007/s12310-020-09384-9 Fonseca, J. R. S., & Cardoso, M. G. M. S. (2007). Mixture-model cluster analysis using information theoretical criteria. Intelligent Data Analysis , 11 (2), 155–173. https://doi.org/10.3233/ida-2007-11204 Morin, A. J. S., & Litalien, D. (2017). Webnote: Longitudinal Tests of Profile Similarity and Latent Transition Analyses. Montreal, QC: Substantive Methodological Synergy Research Laboratory. Moore, S., Dowdy, E., Nylund‐Gibson, K., & Furlong, M. J. (2019). A latent transition analysis of the longitudinal stability of dual-factor mental health in adolescence. Journal of School Psychology , 73 , 56–73. https://doi.org/10.1016/j.jsp.2019.03.003 Petersen, K. S., Humphrey, N., & Qualter, P. (2021). Dual-Factor Mental Health from Childhood to Early Adolescence and Associated Factors: A Latent Transition Analysis. Journal of Youth and Adolescence , 51 (6), 1118–1133. https://doi.org/10.1007/s10964-021-01550-9 McElroy, E., Shevlin, M., & Murphy, J. (2017). Internalizing and externalizing disorders in childhood and adolescence: A latent transition analysis using ALSPAC data. Comprehensive Psychiatry , 75 , 75–84. https://doi.org/10.1016/j.comppsych.2017.03.003 Briggs-Gowan, M. J., Carter, A. S., Bosson-Heenan, J., Guyer, A. E., & Horwitz, S. M. (2006). Are Infant-Toddler Social-Emotional and behavioral problems transient? Journal of the American Academy of Child and Adolescent Psychiatry , 45 (7), 849–858. https://doi.org/10.1097/01.chi.0000220849.48650.59 Connell, A. M., Bullock, B. M., Dishion, T. J., Shaw, D. S., Wilson, M. N., & Gardner, F. (2008). Family intervention effects on co-occurring early childhood behavioral and emotional problems: A Latent Transition Analysis approach. Journal of Abnormal Child Psychology , 36 (8), 1211–1225. https://doi.org/10.1007/s10802-008-9244-6 Berg, I. (1997). School refusal and truancy. Archives of Disease in Childhood, 76 (2), 90-91. Leduc, K., Tougas, A. M., Robert, V., & Boulanger, C. (2022). School Refusal in Youth: A Systematic Review of Ecological Factors. Child Psychiatry Human Development 24 , 1–19. https://doi.org/10.1007/s10578-022-01469-7 Millar, D. (2011). Analysis of School Attendance Data in Primary and Post-Primary Schools, 2006/7 and 200.7/8. Report to the National Educational Welfare Board. Dublin: Educational Research Centre Rogers, T., & Feller, A. (2018). Reducing student absences at scale by targeting parents’ misbeliefs. Nature Human Behaviour , 2 (5), 335–342. https://doi.org/10.1038/s41562-018-0328-1 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5255452","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366403344,"identity":"c6717fa5-ad9e-4b98-a2c2-ba5179db4831","order_by":0,"name":"Jane Sharpe","email":"data:image/png;base64,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","orcid":"","institution":"University of Galway","correspondingAuthor":true,"prefix":"","firstName":"Jane","middleName":"","lastName":"Sharpe","suffix":""},{"id":366403345,"identity":"2a7e429e-d787-4ad9-8645-25c8f9f89241","order_by":1,"name":"Caroline Heary","email":"","orcid":"","institution":"University of Galway","correspondingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Heary","suffix":""},{"id":366403346,"identity":"4e12394d-5b94-4263-ba3e-30a846875764","order_by":2,"name":"Brendan Bunting","email":"","orcid":"","institution":"University of Ulster","correspondingAuthor":false,"prefix":"","firstName":"Brendan","middleName":"","lastName":"Bunting","suffix":""}],"badges":[],"createdAt":"2024-10-13 13:23:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5255452/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5255452/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67292432,"identity":"4f3697ef-3673-48b6-aa18-2c91401e8965","added_by":"auto","created_at":"2024-10-23 10:29:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual model illustrating the latent transition model and associations with covariates and outcomes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5255452/v1/a2415a42314f637fe2e2b0aa.png"},{"id":67293576,"identity":"66b93d20-7b57-4824-9678-bd885fa68600","added_by":"auto","created_at":"2024-10-23 10:37:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36644,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of estimated probability for endorsing emotional and behavioural symptoms for the four class model W1 and W2\u003c/p\u003e\n\u003cp\u003eClass 1 (HEBD,15.3%) Class 2 (HED,20.9%) Class 3 (HBD,31.1%) Class 4 (LEBD,32.7%). Probabilities correspond to the dichotomized items (0 = not true; 1 = somewhat and certainly true), higher probability indicates higher chance of difficulties in the respective subscale.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5255452/v1/ed00ea9db45de38fb7a849d3.png"},{"id":67292435,"identity":"27a2821a-6f16-40f5-8ea3-dfaf23415dc3","added_by":"auto","created_at":"2024-10-23 10:29:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43474,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of average responses for each mental health status in the final latent transition model.\u003c/p\u003e\n\u003cp\u003eProbabilities correspond to the dichotomized items (0 = not true; 1 = somewhat and certainly true), higher probability indicates higher chances of difficulties in the respective subscale. Class 1(HEBD,17.2%,13.2%) Class 2(HED,23.2%, 18.7%) Class 3(HBD,31.3%,29.2%) Class 4(LEBD,28.1%,38.7%).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5255452/v1/19d1e6a12af2a40e49aaccdc.png"},{"id":87178696,"identity":"8ab2e3a3-3a30-463a-924c-a9cabc4b81cf","added_by":"auto","created_at":"2025-07-21 09:23:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1125992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5255452/v1/04d90a33-2519-4a09-940b-fa9f7dcacf5f.pdf"},{"id":67292433,"identity":"c3d01ada-0efc-439c-a8b6-9b246ea4b5cf","added_by":"auto","created_at":"2024-10-23 10:29:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":33921,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-5255452/v1/5280e2568df7e8683421ea83.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mental Health from Childhood to Early Adolescence and its impact on School Attendance Problems: A Latent Transition Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSchool Attendance Problems (SAPs) are a complex phenomenon associated with a myriad of risk factors and outcomes [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Using the typology model, SAPs can be distinguished between school withdrawal, school exclusion, school avoidance (SA) and truancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Under this approach, school withdrawal refers to parent led absenteeism [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], school exclusion to school led absenteeism, school avoidance (SA) to a child\u0026rsquo;s difficulty in attending school, despite parents\u0026rsquo; efforts and characterised by strong negative emotions, with truancy characterised as absence without parents\u0026rsquo; knowledge relating to a negative attitude toward school [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This paper focuses on the types of SAPs that relate to SA and truancy.\u003c/p\u003e \u003cp\u003eThere has been a substantial increase in SAPs post covid [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the academic year 2021/22, 40.3% of Irish primary and 26.8% of post primary students were chronically absent from school compared to an average of 11% and 15% respectively between 2014 and 2018 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In Ireland, school absences are classified as explained and unexplained in accordance with the Education Welfare Act, 2000 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, whilst data is recorded, little information is available on the occurrence of SAPs and the factors that influence particular student groups [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As SAPs can lead to a range of academic, social and emotional challenges [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] that increase as student\u0026rsquo;s get older [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], insight into the developmental trajectory of school absenteeism is needed.\u003c/p\u003e \u003cp\u003eResearch has also highlighted strong associations between mental health symptoms and SAPs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with up to 20% of young people worldwide experiencing mental health difficulties [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although mental health symptoms are generally conceptualised into categories of emotional and behavioural difficulties [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], research indicates that there is also a co-occurrence between the two dimensions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] with several studies evidencing the association between SAPs and these symptoms [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A systematic review, for example, found a strong association between SA and emotional disorders whilst a latent class analysis exploring truancy identified a number of behavioural difficulties [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Other studies have highlighted an association between co-occurring SA and truancy and increased rates of emotional and behavioural disorders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The association is thought to be bidirectional, with mental health difficulties leading to SAPs and SAPs to mental health difficulties [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, a number of cross sectional studies have explored associations between mental health and SAPs. (24, 28, 29). Sharpe et al., (2023), for example, using a sample of 9 year olds assessed how rates of student absenteeism varied across classes of mental health and found that primary school children at risk of mental health difficulties had significantly higher odds of absenteeism compared to those at low-risk. They also observed significantly greater odds of family, school and demographic risk factors for the high risk symptomology classes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To our knowledge, however, few longitudinal studies have examined mental health and its impact on school absenteeism. To address this gap, the authors, using the same sample as Sharpe et al., (2023), applied LTA [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to examine the extent to which the 9 year olds remained or transitioned between classes and how the outcomes \u003cem\u003echronic absenteeism\u003c/em\u003e and \u003cem\u003ehave truanted\u003c/em\u003e at 13 years could be predicted by those transitions.\u003c/p\u003e \u003cp\u003eTo this end the paper asked: i) Using the GUI (\u0026rsquo;98 cohort) datasets, can mental health classes identified at 9 years, be identified again at age 13 years ii) What is the probability of remaining or transitioning from one class to another across time (9\u0026ndash;13 years)? iii) How are transitions affected by school, family and demographic risk factors? iv) How does remaining or transitioning between classes impact parents report of c\u003cem\u003ehronic absenteeism\u003c/em\u003e (\u0026gt;\u0026thinsp;20 days) and child\u0026rsquo;s report of \u003cem\u003ehave truanted\u003c/em\u003e at 13 years.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was collected from the first two waves of the GUI child cohort ’98, a longitudinal study examining developmental factors for children growing up in Ireland. The analyses was completed using data from GUI Researcher Microdata Files. In the first wave (2007-2008), 9-year-olds were first selected through the random sampling of 910 Irish national schools from which children were randomly selected [31]. The respondents (N=8568) were evenly distributed between females (49.8 %) and males (50.2 %). The second wave was completed in 2011/2012 age 13 (n = 7,423) [39]. Informed consent was obtained from all participants with ethical approval received from the Health Research Board’s Research Ethics Committee in Ireland [39].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMental Health Indicators for latent classes were taken from the parents report of the Strength and Difficulty Questionnaire (SDQ) [32], a measure used to identify moderate and high-risk symptoms in children and adolescence. The current study used the twenty items from the four subscales representing adjustment difficulties: Emotional Problems, Conduct Problems, Hyperactivity–Inattention and Peer Problems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrimary carers were asked to rate whether the statements pertaining to their child were ‘not true’, ‘certainly true’, ‘somewhat true’. Responses were dichotomized into\u0026nbsp;0 = ‘not true’ and 1 = ‘somewhat and certainly true’. Individual responses were used to create classes with\u0026nbsp;combinational differences of individual SDQ items.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChronic absenteeism\u003c/em\u003e, based on parental reports during the last 12 months was\u0026nbsp;dichotomised into \u0026lt;20 days and \u0026gt;20 days, with \u0026nbsp;\u0026gt;20 days representing \u003cem\u003echronic absenteeism\u003c/em\u003e as per the Education Welfare Act, 2000 [8].\u0026nbsp;\u0026nbsp;\u003cem\u003eHave Truanted\u003c/em\u003e was collected using the child’s report of whether they ever skipped classes or mitched in the last 12 months, combining responses ‘now and again’, ‘quite often’ and ‘all the time’ to equal\u0026nbsp;\u003cem\u003eHave Truanted\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Factors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on previous research [26], the following risk factors were selected. Data was collected\u0026nbsp;from the children, parents and teachers as part of the GUI data collection. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSocio-demographics\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGender\u003c/em\u003e,\u0026nbsp;0 = male, 1 = female. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSocio-economic\u003c/em\u003e, parents report of ‘making ends meet’. ‘Not difficult’ = 0, ‘Great Difficulty’ and ‘Difficulty’ = 1.\u003c/p\u003e\n\u003cp\u003eSchool\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBullying\u003c/em\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003echild’s report of being bullied in the last year. \u0026nbsp;No = 0, Yes = 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelationship with teacher\u003c/em\u003e, child’s report of liking/disliking their teacher. \u0026nbsp; ‘Sometimes ‘and ‘Always’ = 0, Never = 1 (Dichotomised\u0026nbsp;to eliminate ambiguity on the scale response of ‘sometimes and always’).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAttitude to school,\u0026nbsp;\u003c/em\u003echild’s report of liking school, with ‘Sometimes’ and Always’ = 0 and Never = 1. (Dichotomised\u0026nbsp;to eliminate ambiguity on the scale response of ‘sometimes and always’.)\u003c/p\u003e\n\u003cp\u003eFamily\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParent conflict\u003c/em\u003e, primary carer report of conflict between parents. No = 0 and Yes = 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParent Depression,\u0026nbsp;\u003c/em\u003eCentre for Epidemiological Studies in Depression 8-item scale (CES-D). Cronbach’s alpha was .87 [33]. Scores of seven or above indicate presence of depressive symptoms, scores below seven indicate ‘not depressed’.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProblems at home affecting school participation\u003c/em\u003e based on teacher response to – ‘Do any of the following limit the kind or amount of activity the study child can do at school?’ – home environment/problems at home.\u0026nbsp;\u0026nbsp;No= 0, Yes = 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLCA and LTA with binary indicators were computed in Mplus 8.7. \u0026nbsp;LCA was applied to\u0026nbsp;categorise individuals with similar patterns of behaviour into latent classes,\u0026nbsp;LTA was used to explore changes in those classes over time. This technique allowed for the combining of cross-sectional measurements of the categorical latent variables and longitudinal descriptions of change in the classes between time points.\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted using maximum likelihood estimation and MLR algorithms [34]. The number of missing cases with unknown or missing values for the LCA’s at wave 1 and 2 was 13 and 1. Missing cases with unknown or missing values for the LTA was 13, missing on one or more covariate was 1453. \u0026nbsp;The sample from wave 1 reduced from 8,568 participants to 7,423. To account for missing data the maximum-likelihood based estimation default in Mplus was applied. Model data was used in the estimation of the parameters under the assumption that missing data was at random (MAR).\u0026nbsp;Further, since the data was obtained at the school level, this source of non-independence was taken into account through the use of the Huber-White ‘sandwich’ estimator [35, 36].\u003c/p\u003e\n\u003cp\u003eThe following approach was applied\u0026nbsp;(i) measurement models explored at both time points, (ii) measurement invariance examined, (iii) structural and configural LTA model specifications observed with the final LTA model specified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreating Latent Classes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMental health symptoms were examined by dichotomizing items of the SDQ with 0 = not true and 1 = somewhat and certainly true, with 5 items reversed. \u0026nbsp;Dichotomisation was necessary to reduce model complexity, an approach used in a number of LCA studies using SDQ scores [24, 37, 38].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine the number of classes, the best model fit was selected using statistical and theoretical criteria. The estimation process started with a 2 class model increasing to 5 with the lowest indicator of Bayesian Information Criteria (BIC) and the Akaike Information Criteria (AIC) observed. Both the entropy and the Lo-Mendell Rubin likelihood ratio test (LMR-LRT) were used to examine class separation and significance. \u0026nbsp; The entropy 0.714 of a four class solution at wave 1 (age 9) suggested acceptable accuracy of assignment into the correct class [39]. Given the level of accuracy within a number of the classes significant (p \u0026lt; 0.05) values on the LMR-LRT also indicated that the 4 class model was the significantly best fit, . Finally the theoretical interpretability of classes was considered, providing both statistical and theoretical validity. \u0026nbsp; The same procedure was completed for wave 2 (Table 1), revealing a class with primarily emotional symptoms, a class with behavioural symptoms, a class with mixed emotional and behavioural symptoms and a low risk class at 9 and 13 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLatent Transitional Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConfigural and structural similarities were examined to explore longitudinal invariance between classes over time [40]. \u0026nbsp; The longitudinal weight assigned for wave I was used in the model. LTA was conducted to identify changes in latent statuses over time based on responses to indicators at T2 (wave 2), conditional on membership to a latent status at T1 (wave 1) [30]. \u0026nbsp;The LTA was constrained to be equal across both time points. \u0026nbsp;The four class solution indicated that measurement invariances hold. Risk factors (covariates) from Time 1 were included to determine if they were predictors of the latent transition probabilities. \u0026nbsp; After their inclusion the effect of latent transition patterns from T1 on outcomes at T2 were examined using Mplus 8.7 (fig.1).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of Latent classes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe accelerated flattening of the Log Likelihood and BIC values in the four class model, the decrease in entropy the non-significant CI for the LMT-IRT test for class 5 and the conceptual composition of the four class model suggested an appropriate solution to identifying meaningful classes of mental health symptoms, resulting in a four class model that had configural and structural similarity for both waves (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1 Model Fit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWave 1\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003eReplicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003eSSABIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003eLMR-LRT \u003cem\u003e(p)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-90182.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e180447.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e180736.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e180606.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(p \u0026lt;0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e-88503.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e177131.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e177569.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e177372.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e(p\u0026lt;0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003e.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-87566.067\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e175298.633\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e175883.628\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e175619.869\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(p\u0026lt;0.0000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.717\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e-87250.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e174708.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e175442.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e175113.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e(p\u0026lt;0.0827)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003e.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWave 2\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003eReplicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003eSSABIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003eLMR-LRT \u003cem\u003e(p)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-74265.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e148613.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e148897.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e148767.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(p \u0026lt;0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e-72680.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e145484.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e145914.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e145717.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e(p\u0026lt;0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003e.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-71780.622\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e143727.244\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e144302.079\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e144038.323\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(p\u0026lt;0.0000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.748\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7686%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e-71527.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0496%;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e143263.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e143988.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e143652.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.562%;\"\u003e\n \u003cp\u003e(p\u0026lt;0.6654)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.42149%;\"\u003e\n \u003cp\u003e.757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eC = classes; LL = log-likelihood; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; LMR-LRT = Lo\u0026ndash; Mendell\u0026ndash;Rubin Likelihood Ratio Test; p = p value.\u003c/p\u003e\n\u003cp\u003eClasses: \u0026nbsp;Class 1 - \u003cem\u003eHigh Risk of\u003c/em\u003e \u003cem\u003eEmotional and Behavioural Difficulties (HEBD)\u003c/em\u003e, had moderate to high probabilities of behavioural difficulties, namely hyperactivity, inattentiveness, disobedience, tantrums, lying and emotional difficulties, somatic illnesses, worry, nervousness, fearfulness, unhappiness, solitary playing, bullying and better adult than peer relationships.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClass 2- \u003cem\u003eHigh Risk of\u003c/em\u003e \u003cem\u003eEmotional Difficulties (HED),\u003c/em\u003e had moderate to high probabilities of somatic illnesses, worry, fear, nervousness, solitary play and moderate probabilities of tantrums.\u003c/p\u003e\n\u003cp\u003eClass 3- \u003cem\u003eHigh Risk of Behavioural Difficulties (HBD),\u0026nbsp;\u003c/em\u003ehad\u003cem\u003e\u0026nbsp;\u003c/em\u003emoderate to high probabilities of hyperactivity, inattentiveness, disobedience and tantrums with low probabilities of emotional difficulties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClass 4 - \u003cem\u003eLow Risk of Emotional and Behavioural Difficulties (LEBD)\u003c/em\u003e, presented with low probabilities of emotional and behavioural difficulties.\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Fig.2). See Table S1a/b/c (supplementary materials) for item responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLatent Transition Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLTA was conducted next to examine rates of transitions across classes from age 9 to 13 years (Fig.3). \u0026nbsp;The \u003cem\u003eLEBD class\u0026nbsp;\u003c/em\u003ehad the highest stability rate with a small proportion transitioning to the \u003cem\u003ehigh risk classes.\u0026nbsp;\u003c/em\u003eThe \u003cem\u003eHEBD class\u003c/em\u003e had the lowest stability rate, with transitions mostly to the HED and HBD class with a small proportion transitioning to the \u003cem\u003eLEBD Class.\u003c/em\u003e \u003cem\u003eThe HED class and HBD\u003c/em\u003e \u003cem\u003eclass\u003c/em\u003e had moderate to high rates of stability with transitions occurring mostly to the \u003cem\u003eLEBD class.\u0026nbsp;\u003c/em\u003e(Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eProbability and\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTransition Matrix without covariates\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eLatent Classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003eWave 1 (C1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eHEBD (13yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eHED (13yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eHBD (13yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eLEBD \u0026nbsp;(13yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eWave 2 (C2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eHEBD (9yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003e.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.557\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eHED (9yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003e.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.595\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eHBD (9yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003e.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.678\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eLEBD (9yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003e.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.886\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eBold values indicate probability of remaining in the same class, wave 1 (9 years), wave 2 (age 13 years).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eProbability and\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTransition Matrix with covariates\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eLatent Classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003eTime 1 (C1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eHEBD (13yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eHED (13yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eHBD (13yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eLEBD (13yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003eTime 2 (C2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eEBD (9yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003e.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.517\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eED (9yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003e.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.569\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eBD (9yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003e.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.669\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6406%;\"\u003e\n \u003cp\u003eLEBD (9yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.812%;\"\u003e\n \u003cp\u003e.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.879\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3095%;\"\u003e\n \u003cp\u003e.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eBold values indicate probability of remaining in the same class, wave 1 (9 years), wave 2 (age 13 years).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Factors on Transition Probabilities\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBullying, dislike of teacher, dislike of school\u003c/em\u003e, \u003cem\u003eparent conflict and socio-economic status\u003c/em\u003e did not affect movement between classes whilst \u003cem\u003eparental depression\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;problems at home that affect school participation\u003c/em\u003e at 9 years significantly affected transitions. Children in the \u003cem\u003eHED and HBD classes\u003c/em\u003e with reports of parental depression had higher odds of transitioning to the \u003cem\u003eHEBD\u003c/em\u003e class at 13 years compared to those who remained, OR = 2.568 (1.224, 5.390), OR =1.835 (1.089, 5.094),\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChildren who transitioned from the \u003cem\u003eHEBD\u003c/em\u003e class to the \u003cem\u003eHED, \u0026nbsp; HBD or \u0026nbsp;LEBD classes\u003c/em\u003e at 13 years had significantly lower odds of \u003cem\u003ehaving problems at home affecting school participation\u003c/em\u003e compared to those who remained, OR = 0.155 (0.031, 0.775), OR = 0.525 (0.291, 0.946), OR = 0.281 (0.094, 0.842) respectively. \u0026nbsp;Conversely, children who transitioned from the \u003cem\u003eHED and\u003c/em\u003e \u003cem\u003eLEBD\u003c/em\u003e \u003cem\u003eclasses\u003c/em\u003e to the \u003cem\u003eHEBD\u003c/em\u003e \u003cem\u003eclass\u003c/em\u003e had significantly higher odds of \u003cem\u003ehaving problems at home affecting school participation\u003c/em\u003e OR= 6.435 [1.290, 32.101], OR = 3.560 [1.188, 10.670]. These results indicate that \u003cem\u003eproblems at home that affect school participation\u0026nbsp;\u003c/em\u003eand \u003cem\u003eparental depression\u003c/em\u003e can negatively impact developmental trajectories of emotional and behavioural difficulties between 9 years and 13 years.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFemales\u003c/em\u003e had significantly higher odds of transitioning from the \u003cem\u003eHEBD and HBD\u003c/em\u003e class to the \u003cem\u003eHED\u003c/em\u003e class at 13 years OR = 2.004 (1.295, 3.101), OR = 2.045 (1.402, 2.983), indicating that females are more likely than males to transition away from behavioural difficulties. There was also higher odds of \u003cem\u003efemales\u0026nbsp;\u003c/em\u003etransitioning from the \u003cem\u003eHED\u003c/em\u003e class to the \u003cem\u003eLEBD class\u003c/em\u003e OR = 1.533 [1.142, 2.058]. \u0026nbsp;(See supplementary Table S2 for transition probabilities).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStability on Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChildren who remained in The\u003cem\u003e\u0026nbsp;HEBD\u0026nbsp;\u003c/em\u003eand \u003cem\u003eHED\u003c/em\u003e classes compared to those who remained in the \u003cem\u003eLEBD\u003c/em\u003e \u003cem\u003eclass\u003c/em\u003e were three times more likely to be \u003cem\u003echronically absent\u003c/em\u003e at 13 years, OR = 2.670 (1.231, 5.788), OR = 2.844 (1.400, 5.775).\u003cem\u003e\u0026nbsp;\u003c/em\u003eChildren who remained in the \u003cem\u003eHEBD and BD classes\u0026nbsp;\u003c/em\u003ewere 5 times more likel\u003cem\u003ey\u0026nbsp;\u003c/em\u003eto report \u003cem\u003ehave truanted\u003c/em\u003e at 13 years compared to those remaining in the \u003cem\u003eLEBD class\u0026nbsp;\u003c/em\u003eOR = 5.598\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e3.121, 10.043), OR = 4.600\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e2.565, 8.24) respectively. \u0026nbsp;Children remaining in the \u003cem\u003eHED\u003c/em\u003e \u003cem\u003eclass\u003c/em\u003e did not have significant odds of \u003cem\u003ehaving truanted\u003c/em\u003e and\u003cem\u003e\u0026nbsp;\u003c/em\u003echildren remaining in the \u003cem\u003eHBD Class\u003c/em\u003e did not have greater odds of \u003cem\u003echronic absenteeism\u003c/em\u003e These results indicate greater odds for \u003cem\u003echronic absenteeism\u003c/em\u003e for children experiencing stability of emotional difficulties and greater odds of truancy for children experiencing stability of behavioural difficulties. Children experiencing stability of co-morbid symptoms had greater odds of \u003cem\u003echronic absenteeism\u003c/em\u003e and \u003cem\u003etruancy\u0026nbsp;\u003c/em\u003e(Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eOR for Chronic Absenteeism and Have Truanted for stability\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.7278%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStability\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.7468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR Chronic Absenteeism\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst. \u0026nbsp; S.E \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lower Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5253%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR Have Truanted\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst. \u0026nbsp; S.E \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lower Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.7278%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStability in HEBD compared to\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003estability in HED class\u003c/p\u003e\n \u003cp\u003estability in HBD class\u003c/p\u003e\n \u003cp\u003estability in LEBD class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.7468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.939(.36) \u0026nbsp; (0.441, 1.998)\u003c/p\u003e\n \u003cp\u003e1.580(.67) \u0026nbsp; (0.684, 3.653)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.670\u003c/strong\u003e(1.05) (1.231, 5.788)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5253%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.223(1.8) (\u003c/strong\u003e1.778, 10.028)\u003c/p\u003e\n \u003cp\u003e1.217(.35) \u0026nbsp;(0.685, \u0026nbsp;2.163)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.598(1.6) \u0026nbsp;\u003c/strong\u003e(3.121, 10.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.7278%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStability in HED compared to\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStability in HBD class\u003c/p\u003e\n \u003cp\u003eStability in LEBD class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.7468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.683(0.5) \u0026nbsp; \u0026nbsp;(0.845, 3.354)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.844\u003c/strong\u003e(1.02) \u0026nbsp;(1.400, 5.775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5253%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.288(0.124) (\u003c/strong\u003e0.109, 0.816)\u003c/p\u003e\n \u003cp\u003e1.326 (0.5) \u0026nbsp; \u0026nbsp;(0.547, 3.214)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.7278%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStability in HBD compared to\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStability in LEBD class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.7468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.682(0.6) \u0026nbsp; \u0026nbsp;(0.836, 3.412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5253%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.600 (0.05) (\u003c/strong\u003e2.565, 8.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTransitions on Outcomes\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOdds Ratio was also calculated to compare the odds of \u003cem\u003echronic absenteeism\u003c/em\u003e and/or \u003cem\u003ehave\u003c/em\u003e \u003cem\u003etruanted\u003c/em\u003e for children who leave one group compared to those who remain. No significant OR for \u003cem\u003echronic absenteeism\u003c/em\u003e was found for any group leaving one class compared to those who remained suggesting that mental health at 9 years had a greater effect on \u003cem\u003echronic absenteeism\u003c/em\u003e at 13 years than the class status transitioned to \u0026nbsp;(Table 6). \u0026nbsp;Transitions did affect the outcome \u003cem\u003eHave Truanted\u0026nbsp;\u003c/em\u003efor children moving between groups. Children who transitioned from the \u003cem\u003eHBD\u0026nbsp;\u003c/em\u003eto the\u003cem\u003e\u0026nbsp;HED class\u003c/em\u003e had significantly lower odds of reporting \u003cem\u003ehave truanted,\u0026nbsp;\u003c/em\u003eOR =\u003cem\u003e\u0026nbsp;\u003c/em\u003e0.233 (0.082, 0.662). Children who remained in the \u003cem\u003eHEBD\u003c/em\u003e group were 6 times more like to report \u003cem\u003ehaving truanted\u003c/em\u003e compared to those who transitioned to the \u003cem\u003eHED\u003c/em\u003e and 10 times more likely than those who moved to the \u003cem\u003eLEBD class\u003c/em\u003e OR = 6.893 \u0026nbsp; (2.620, 18.136), OR = 10.177\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(4.639, 22,327). \u0026nbsp; Children who remained in the \u003cem\u003eHED class\u003c/em\u003e were significantly less likely to truant compared to those who moved to the \u003cem\u003eHBD class,\u003c/em\u003e OR = 0.233 (0.082,0.662).\u003c/p\u003e\n\u003cp\u003eThe results indicate that children with emotional difficulties at 9 years were more likely to be absent with their parents knowledge at 13 years whilst children with behavioural difficulties at 9 years or who later develop behavioural difficulties were at risk of truancy. \u0026nbsp; The children in the \u003cem\u003eHEBD\u003c/em\u003e had significantly high odds of \u003cem\u003echronic absenteeism\u0026nbsp;\u003c/em\u003eand \u003cem\u003ehave truanted\u003c/em\u003e indicating that children with co-morbid symptoms have a greater risk of SAPs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eOR for Chronic Absenteeism and Have Truanted for Class Transitions.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.7278%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransitions\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.7468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR Chronic Absenteeism\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst. \u0026nbsp; S.E \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lower Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5253%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR Have Truanted\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEst. \u0026nbsp; S.E \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lower Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.7278%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRemaining in the HEBD class compared to\u003c/p\u003e\n \u003cp\u003etransitioning from HEBD to ED class\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRemaining in the HEBD class compared to\u003c/p\u003e\n \u003cp\u003etransitioning from HEBD to BD class\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRemaining in the HEBD class compared to\u003c/p\u003e\n \u003cp\u003etransitioning from HEBD to LEBD class\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTransitioning from the HED to HEBD class\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ecompared to remaining in the HED class\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRemaining in the HED class compared to\u003c/p\u003e\n \u003cp\u003etransitioning from the HED to HBD class\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRemaining in the HED class compared to\u003c/p\u003e\n \u003cp\u003etransitioning from the HED to LEBD class\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTransitioning from the HBD to HEBD class\u003c/p\u003e\n \u003cp\u003ecompared to remaining in the HBD to HBD\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTransitioning from the HBD to HED class\u003c/p\u003e\n \u003cp\u003ecompared to remaining in the HBD class\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRemaining in the BD class compared to\u003c/p\u003e\n \u003cp\u003etransitioning from the BD to LEBD\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTransitioning from the LEBD to HED class\u003c/p\u003e\n \u003cp\u003ecompared to remaining in the LEBD class\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTransitioning from the LEBD to HBD class\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ecompared to remaining in the LEBD \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.7468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.901 (0.77) \u0026nbsp;(0.166, 4.886)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.825(0.49) \u0026nbsp;(0.256, 2.660)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.857(1.41) \u0026nbsp; (0.418, 8.256)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.901(0.767) \u0026nbsp; (0166, 4,866)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.915(0.515) \u0026nbsp;(0.304, 2.755)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.061(0.88) \u0026nbsp; (0.892, 4.762)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.825(0.49) \u0026nbsp; (0.256, 2.660)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.915(0.51) \u0026nbsp; (0.304, 2,755)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.522(1.01) \u0026nbsp; (0.935, 5.424)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.016(0.8) \u0026nbsp; \u0026nbsp;(0.892, 4.762)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.522(1.01) \u0026nbsp; (0.935, 5.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5253%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e6.893 (3.4)\u003c/strong\u003e\u0026nbsp; (2.620, 18.136)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.603(0.47) (0.897, 2.865)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e10.177(4.80)\u003c/strong\u003e (4.639, 22,327)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.613 (0.24)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(0,283, 1.326)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.233\u003c/strong\u003e (0.12) (0.082,0.662)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.476 (0.82)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(0.495,4,407)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.759(0.25) \u0026nbsp;(0.392, 1.472)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.233(0.12)\u003c/strong\u003e (0.082,0.662)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e6.349(2.3)\u0026nbsp;\u003c/strong\u003e(3.081,13.085)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.476(.82) \u0026nbsp; (0.495, 4.407)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e6.349(2.3)\u0026nbsp;\u003c/strong\u003e(3.081,13.085)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold values indicate significant OR\u0026rsquo;s for Chronic Absenteeism /Truancy\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of the paper was to examine how the developmental trajectory of patterns of mental health symptoms from childhood to adolescence impact the frequency of \u003cem\u003echronic absenteeism\u003c/em\u003e and \u003cem\u003ehave truanted\u003c/em\u003e at 13 years. A secondary aim was to investigate how family, school and socio-demographic risk factors impacted those trajectories. To achieve this LCA and LTA were computed to identify classes of mental health symptoms. Analysis yielded four classes; a class with mixed emotional and behavioural symptoms, a class with patterns of emotional symptoms, a class with patterns of behavioural symptoms and a low risk class.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMental Health Transitions\u003c/h2\u003e \u003cp\u003eThe researchers examined the extent to which 9 year olds transitioned or remained in one mental health class before establishing if those remaining or transitioning were at greater risk of \u003cem\u003echronic absenteeism\u003c/em\u003e and/or \u003cem\u003ehaving truanted\u003c/em\u003e at 13 years. Following the inclusion of risk factors, 27% of the sample transitioned with 73% remaining. Consistent with previous research the most prevalent and stable group was the \u003cem\u003eLEBD class\u003c/em\u003e [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], followed by The \u003cem\u003eHBD, HED and HEBD classes\u003c/em\u003e. Stability rates for the \u003cem\u003esingle symptomology classes\u003c/em\u003e were consistent with previous studies that reported some developmental continuity of behavioural and/or emotional symptoms between childhood and adolescence [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, transitions indicated some improvement for both classes with 21% and 30% respectively moving to the \u003cem\u003eLEBD class.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAlthough \u003cem\u003ethe HEBD class\u003c/em\u003e had the lowest stability rate, transitions for this class occurred mainly between the \u003cem\u003ehigh risk classes\u003c/em\u003e, with just 2.5% transitioning to the \u003cem\u003eLEBD class\u003c/em\u003e. These patterns support previous research, indicating that children with co-morbid symptoms are most likely to remain in one of the symptomatic groups during early adolescence, highlighting a persistence of mental health symptoms for that group [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These results highlight the importance of intervening early to disrupt the emergence of emotional and behavioural problems in adolescence.\u003c/p\u003e \u003cp\u003eFindings also indicated that whilst \u003cem\u003ebullying, dislike of teacher, dislike of school, parental conflict and socio-economics\u003c/em\u003e did not influence movement between classes, \u003cem\u003eparental depression and problems at home that affect school participation\u003c/em\u003e did. Children who moved to the \u003cem\u003eEBD class\u003c/em\u003e, had significantly higher odds of having a parent with \u003cem\u003eparental depression\u003c/em\u003e and/or \u003cem\u003eproblems at home affecting participation\u003c/em\u003e at 9 years, supporting the notion that youths with co-morbid symptoms are more likely to experience disrupted family relationships [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eFemales\u003c/em\u003e had significantly higher odds of moving away from classes with \u003cem\u003ebehavioural symptoms\u003c/em\u003e, supporting research that highlights gender differences in the prevalence of internalizing problems during adolescence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe odds of \u003cem\u003echronic absenteeism and have truanted\u003c/em\u003e were first compared between the stable \u003cem\u003ehigh risk classes and LEBD class\u003c/em\u003e. Children continuing to exhibit emotional symptoms had the highest odds of \u003cem\u003echronic absenteeism\u003c/em\u003e but did not have significant odds of reporting \u003cem\u003ehaving truanted\u003c/em\u003e. Conversely children continuing to exhibit behavioural difficulties had significantly higher odds of reporting \u003cem\u003ehave truanted\u003c/em\u003e but did not have significant odds of \u003cem\u003echronic absenteeism\u003c/em\u003e. As literature has historically associated emotional symptoms with SA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and behavioural symptoms with truancy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], these results support research that highlights the importance of differentiating between \u003cem\u003etypes of SAPs\u003c/em\u003e, indicating that children with SA are more likely to be absent with their parents knowledge and less likely to truant [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. These findings also provide insight into how the trajectory of mental health symptoms from childhood to adolescence can affect SA and truancy, highlighting the importance of identifying the developmental patterns associated with the continuous dimensions of SAPs [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Children remaining in the \u003cem\u003eHEBD class\u003c/em\u003e, had high odds of both \u003cem\u003echronic absenteeism\u003c/em\u003e and \u003cem\u003ehaving truanted\u003c/em\u003e suggesting that youth with co-morbid symptoms are at risk of both \u003cem\u003etypes of SAPs\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consistent with previous research children with good mental health had lower odds of absenteeism.\u003c/p\u003e \u003cp\u003eThe researchers next compared the odds of \u003cem\u003echronic absenteeism\u003c/em\u003e and \u003cem\u003etruancy\u003c/em\u003e for children who left a class compared to those who remained. Surprisingly, children who moved from the \u003cem\u003ehigh risk classes\u003c/em\u003e to the \u003cem\u003eLEBD class\u003c/em\u003e did not have significantly reduced odds of chronic absenteeism compared to those who remained. This is a key finding, suggesting that mental health symptoms experienced at 9 years have a greater influence on \u003cem\u003echronic absenteeism\u003c/em\u003e at 13 years than the mental health profile transitioned in to. These results indicate that mental health symptoms experienced in childhood rather than the stability of those symptoms alone may impact a young person\u0026rsquo;s ability to attend school at 13 years. As previous literature has highlighted that absenteeism and emotional difficulties in early education can lead to poor academic outcomes, social difficulties, behavioural challenges and parental adaptive responses, it might be possible that these factors develop into entrenched behaviour patterns that influence chronic absenteeism at 13 years [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. As early childhood is a crucial time for the development of emotional symptoms [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] early school based interventions are advised to reduce the risk of SA.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHaving truanted\u003c/em\u003e, on the other hand, was impacted by transitions, highlighting the importance of identifying different mental health patterns associated with different categories of \u003cem\u003eSAPs\u003c/em\u003e. Movement between classes indicated that developing behavioural symptoms increased the odds of truancy in adolescence. Equally children with behavioural difficulties at 9 years who moved to the \u003cem\u003eHED or LEBD class\u003c/em\u003e were less likely to truant than those children who continued to have behavioural difficulties.\u003c/p\u003e \u003cp\u003eThese findings highlight that childhood symptoms of emotional difficulties can have long term effects on SA with behavioural difficulties increasing the risk of truancy. Children presenting with co-morbid symptoms are at increased risk of SA and truancy requiring intensive interventions during the transition to early adolescence.\u003c/p\u003e \u003cp\u003eBoth \u003cem\u003eparental depression\u003c/em\u003e and \u003cem\u003eproblems at home that affect participation at school\u003c/em\u003e influenced movement into the \u003cem\u003eHEBD class\u003c/em\u003e. These findings expand on previous research highlighting the adverse effects of family risk factors on mental health in primary school students and absenteeism in adolescence. Based on these findings family based supports and policy efforts designed to prevent SAPs emerging in primary school children could reduce the number of children experiencing SAPs at post primary [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As stability in \u003cem\u003ehigh risk groups\u003c/em\u003e was also predictive of \u003cem\u003echronic absenteeism and/or truancy\u003c/em\u003e at 13 years, noting persistent mental health symptoms could be a useful early indicator tool to reduce the number of adolescence experiencing SAPs.\u003c/p\u003e \u003cp\u003eThere are several limitations acknowledged. First, dichotomizing the SDQ variables may have resulted in loss of information. Although the prevalence of emotional and behavioural symptoms based on the SDQ may be reflected differently in studies using cut offs, the size of the high risk classes were consistent with LTA studies that examined emotional and behavioural profiles in children [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe authors dichotomised the outcome \u003cem\u003ehave truanted\u003c/em\u003e based on the child\u0026rsquo;s report of whether they had skipped classes or mitched in the last 12 months. As combining the responses likely reduced the severity of the outcome, it was expected that probabilities and odds would be greater for truancy then chronic absenteeism.\u003c/p\u003e \u003cp\u003eAlthough a large sample size was utilised, the sample size for \u0026gt;\u0026thinsp;20 days absent was small, representing 2% of the sample at both waves. At the time of data collection Irish data recorded an average of 11% absences for \u0026gt;\u0026thinsp;20 days in primary schools and 16% for post primary [58], suggesting that the data for chronic absenteeism in the sample is underestimated. Although previous research highlights that parents under report their child\u0026rsquo;s absence [59] parents reports of days absent was used due to the large amount of missing data for teachers reports.\u003c/p\u003e \u003cp\u003eAlthough risk factor \u003cem\u003eproblems at home affecting school participation\u003c/em\u003e was a teachers' subjective response, previous research using the same sample found this risk factor was significantly associated with \u003cem\u003eHEBD\u003c/em\u003e at 9 years. As a result the risk factor was included in the analysis.\u003c/p\u003e \u003cp\u003eThe confidence intervals were high for the some of the OR\u0026rsquo;s measuring the effect of transitions on outcomes. Following inspection of the probabilities and estimates for the effect of transitions on the outcomes (supplementary files Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) the authors were satisfied with the reliability of the data and included the OR\u0026rsquo;s with wide confidence intervals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFindings indicated that the mental health status of 9 year olds significantly impacts chronic absenteeism at age 13. The study highlights the need for continued efforts at developing and implementing early intervention in early primary school children.\u003c/p\u003e \u003cp\u003eThe complex nature of \u003cem\u003eSAPs\u003c/em\u003e requires an interdisciplinary response that supports students showing early signs of SA and/or truancy. The authors recommend the implementation of school based mental health and wellbeing interventions that incorporate a \u0026lsquo;whole child\u0026rsquo; and whole school approach to school attendance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFindings also highlighted the importance of family factors, indicating early multi-faceted interventions are needed to support families of children who are absent from school. As schools can play a key role in facilitating positive school and family engagement, efforts to increase school awareness about the benefit of tiered interventions, family support and well-being are advised.\u003c/p\u003e \u003cp\u003eIn this paper, it was not possible to determine the underlying mechanisms through which the mental health status of 9 year olds influenced chronic absenteeism at 13 years. Further research could provide additional insight into developed behaviours associated with mental health difficulties at 9 years that might later influence chronic absenteeism at 13 years.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Data analysis was conducted by Jane Sharpe. Brendan Bunting reviewed outputs released from the Central Statistics Office (CSO). The first draft of the manuscript was written by Jane Sharpe and all authors commented on versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe data used in this study were obtained from the Growing Up in Ireland (GUI) study. It is managed by DCEDIY in association with the Central Statistics Office (CSO). 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Reducing student absences at scale by targeting parents\u0026rsquo; misbeliefs. \u003cem\u003eNature Human Behaviour\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(5), 335\u0026ndash;342. https://doi.org/10.1038/s41562-018-0328-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mental health, School Attendance Problems, Latent Transition Analysis ","lastPublishedDoi":"10.21203/rs.3.rs-5255452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5255452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Despite concerted efforts among policy makers and practitioners, School Attendance Problems (SAPs) are increasing, with post covid figures indicating higher than ever rates of absenteeism. The aim of this paper was to examine how the developmental trajectory of emotional and behavioural difficulties from childhood to early adolescence might impact the frequency of chronic absenteeism and truancy at 13 years. Using a sample (N = 8570) from the Longitudinal Growing Up In Ireland Study (GUI’98), the research used Latent Class Analysis (LCA) and Latent Transition Analysis (LTA) to examine combinations of mental health symptoms at 9 and 13 years and their developmental impact on SAPs. The Strengths and Difficulty Questionnaire (SDQ) was used to measure a range of emotional and behavioural difficulties at both time points, yielding four mental health classes. Children who remained in High Risk classes, had higher odds of chronic absenteeism and/or truancy. Movement between classes significantly altered the odds of truancy, but not chronic absenteeism, highlighting the importance of differentiating between SAPs and early intervention. A secondary aim was to investigate how family, school and socio-demographic risk factors impacted those trajectories. Family factors were significantly linked to transitions into the co-morbid class, indicating that family risk factors can negatively impact the trajectory of emotional and behavioural difficulties between childhood and adolescence. This paper contributes to current knowledge on the complexities of mental health difficulties in primary school children and their impact on SAPs in early adolescence.","manuscriptTitle":"Mental Health from Childhood to Early Adolescence and its impact on School Attendance Problems: A Latent Transition Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 10:29:50","doi":"10.21203/rs.3.rs-5255452/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ede665a-3364-404c-b91e-263b2442ef7d","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T09:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-23 10:29:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5255452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5255452","identity":"rs-5255452","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}