A disproportionate number of individuals who sustain traumatic brain injuries (TBIs) in the state sector are children and adolescents (Thurman, 2016). Although TBI is a global problem, and reported as a leading cause of mortality and morbidity among youth in high-income countries (HICs) the burden thereof is reportedly more potent in resource-poor countries (Hyder et al., 2007; Harris et al., 2008; Alexander et al., 2009; Kumar and Mahapatra, 2009; Bener et al., 2010; Figaji, 2017). This disproportionate effect on poorer economies is attributed to the quality of the environment and lack of resources. There are several factors often associated with resource-poor countries, such as poverty, lack of access to education, differences in infrastructure, and social problems, like alcoholism and higher road traffic accidents rates, that create greater risk for TBI (Levin, 2004; Hyder et al., 2007; Alexander et al., 2009). Further, home and work environments in indigent areas are often less secure, with residents more exposed to potential hazards. Prevention endeavors and access to rehabilitation may also be less available in such environments (World Health Organization, 2011). Other adverse factors that can impact on the incidence and/or consequences of TBI include the inconsistent scope of care for TBI survivors and health care facilities that are ill-prepared to cope with the degree of injury and care required for a public health problem of this magnitude (Hyder et al., 2007; Jerome et al., 2017). Hence, the burden of trauma, and in particular TBI, as the leading cause of death and neurological disability in trauma patients, is far greater in resource-poor countries than in the developed world (De Silva et al., 2009; Figaji, 2017).

The formidable economic sequelae associated with TBI results not only from the expenses associated with direct healthcare, but also from indirect costs linked to loss of the potential future productivity of that individual (Jaffe et al., 1993; Ragnarsson, 2002; Flanagan et al., 2008). The loss of potential future productivity is especially important in the case of children, clearly because most of their life will be spent in the shadow of the TBI. Also, there are associated costs, such as a loss of productivity for extended periods of time for caregivers. TBI can therefore be economically challenging and exhausting at the societal, individual and familial levels (Jaffe et al., 1993; Tilford et al., 2005; Gontkovsky et al., 2006). These factors are compounded in resource-poor environments.

Published literature on the epidemiology of pediatric TBIs (pTBIs) in developing world countries is generally limited. Researchers highlight the dearth of much-needed research of this nature generally and in SA specifically (Bruns and Hauser, 2003; De Silva et al., 2009; Haaring et al., 2011; Naidoo, 2013). Although it is suggested that the incidence of pTBI in SA must be high, exact rates are not available because systematic research on the topic is lacking (Levin, 2004; Penn et al., 2009; Corrigan et al., 2010; Naidoo, 2013; Tabish and Syed, 2015). There is little quantification of the neurological disability of survivors and the impact this has on families and state sector services.

Although SA is considered a developing country with upper to middle income levels (The World Bank, 2016), its Gini index, which represents inequality in the spread of income, is among the highest in the world. This unequal socio-economic climate, which stems from the country's apartheid history, is also clear in the school system where inequality (in terms of financial and resource provision) is rife (Du Plessis, 2001; Engelbrecht, 2006; Soudien and Baxen, 2006; Donohue and Bornman, 2014). The apartheid-based Bantu Education Act of 1953 engendered such unequal education (Donohue and Bornman, 2014; Letgotlo, 2014). Even after SA's movement to a democracy in 1994, the scars of fragmentation from past segregation and discrimination practices remain, which has long resulted in the deprivation of adequate education for large numbers of SA people (Du Plessis, 2001; Engelbrecht, 2006).

Even though some progress has been made with the SA Schools Act (SASA) of 1996 to democratize and make uniform the school system post-democracy, and despite the knowledge that all learners (as per the Bill of Rights) have the right to equity and quality education, SA's schooling system is in a precarious state (Letgotlo, 2014, South African Council for Educators SACE, 2016). A recent news publication (The Economist, 2017) described some of the inequality (schools “with cricket pitched as smooth as croquet lawns” vs. others built from mud) and consequent dire outcomes (worrying percentages of learners who do not finish¹ school and not being able to read or work out basic division sums after 5–6 years of schooling) associated with some of the country's schools.

There are also reported problems with schools for learners with special education needs (LSEN²) in the country. Donohue and Bornman (2014) report that as many as 70% of LSEN who should be in school given their age, are not; most of those who are in school attend LSEN, rather than mainstream schools. This finding contrasts with the inclusive³ education policy that the Department of Education (DOE) in SA have been aiming to implement, in line with the global trend of inclusive education and the Education for All initiative [United Nations Educational, Scientific, and Cultural Organization (UNESCO, 1990)]. SA's unique complex sociopolitical and economic background distinguishes it from other countries following this trend, however.

Amollo (2008) reports on a briefing by the SA DOE and their aims to reserve LSEN schools for those with severe disabilities and that as far as possible, have mainstream schools accommodate those with less severe disabilities, in a move from an exclusive toward a more inclusive educational model (Du Plessis, 2001). While a DOE policy document “White Paper 6” (Department of Education, 2001) was meant to frame and direct this transformation (Pillay and Di Terlizzi, 2009) toward inclusive education, the implementation thereof has been challenging and consequently poor (Engelbrecht, 2006; Donohue and Bornman, 2014).

In trying to understand the barriers to the implementation of inclusive education in the country, both societal and contextual factors need to be considered (Engelbrecht, 2006). Donohue and Bornman (2014) describe school-related and culture- and psychosocial-related barriers. In terms of the former, the authors conjecture that because most of the country's teaching complement are of the “older generation”, that some might not have embraced or repositioned their thinking (perhaps as a function of previous training) to align with the new inclusive education strategy. They do however note that low resources remain the main obstruction, even among those who are aligned with the new inclusive policy. Regarding culture- and psychosocial-related barriers, in some communities, individuals with disabilities may be devalued and prejudiced such that schooling may not be viewed as major priority for them, as their potential is questioned. On the other hand, parents may want to protect their children with special needs from ill treatment and stigma, and may therefore choose to keep them home.

Other authors question the preparedness of the country to promote inclusive education. Pillay and Di Terlizzi (2009) note that more resources and infrastructure (facilities) are needed in mainstream schools first. From their case study, they report that despite the move toward inclusive education, LSEN schools, though few, may still be better equipped to accommodate learners with special needs than mainstream schools. According to principals of mainstream schools, some of the main challenges to inclusive quality education, is educators coping with learners with special needs who are included in mainstream schooling, managing behaviorally challenged learners and behaviors and emotions of children who fail to progress, perceived lack of support (including parental support) and training for teachers and management. Large class sizes and considerable workloads for educators were some additional barriers reported for educators (Materechera, 2014).

SA DOE audits reported on by Amollo (2008) also revealed a weak infrastructure and a host of similar and other problems in terms of some LSEN schools locally including classroom overcrowding, ill-prepared educators, and lack of necessary assistive devices. Issues with learner progression and skill recognition, nutrition, transport, and discrimination were also listed. There were also challenges in terms of the lack of LSEN schools in rural areas and closure of several LSEN schools (Amollo, 2008; Materechera, 2014). Further, principals of LSEN schools perceive lack of funding, adequately trained staff and specialists, parental support, resources, and support services, as well as stigmatization of special needs school learners by mainstream school learners and acceptance by peers, diversity in disabilities and closure of special needs schools to be the main barriers to inclusive education (Materechera, 2014).

When one considers the experience of pTBI survivors in SA, the commonly reported academic and behavioral post-TBI challenges may be exacerbated by these contextual limitations of the burden of disease and barriers to accessing appropriate education.

The sample included 27 children with severe TBI who were of school-going age at the time of their injury. These children were identified from a database of 137 children who had been admitted to Red Cross War Memorial Children's Hospital (RXH) for severe TBI (post-resuscitation Glasgow Coma Scale (GCS)⁴ score of ≤ 8) over a 5-year period (2006–2011) and who underwent intracranial monitoring (Schrieff et al., 2013).

Participants had to be at least 1 year post-injury, because the recovery trajectory reportedly then tends to stabilize (Jaffe et al., 1995; Taylor, 2010). We excluded children who had sustained open TBIs, given the differing pathophysiology and outcomes (compared to closed TBI; Anderson et al., 2001), and those who were not attending school at the time of injury and assessment, as this limited the academic data available for those children. Figure 1 shows the reasons for exclusion of participants, resulting in a final sample of 27 children.

We contacted caregivers of potential participants telephonically or approached them at their follow up Neurosurgery outpatient clinic visit if contact details were unavailable in the medical folders⁵. We collected the data between 2012 and 2013. Three caregivers completed the measures at their homes due to time or transport constraints; the remainder at RXH. First language isiXhosa-speaking research assistants assisted with interpreting when necessary.

We had the original English versions of our measures translated into two other common local languages, Afrikaans and isiXhosa, to facilitate administration. These were linguistically validated through forward and back translations and authentication by Stellenbosch University's Language Services (Cape Town, SA).

We used a parent information questionnaire and asset index (Myer et al., 2008) to obtain demographic and socioeconomic background information of the participants. It captures demographic information such as parental/guardian employment and education, home language, and annual household income. The asset index groups asset ownership into three categories: 0–5 (low), 6–12 (medium), and 13–17 (high) and reflects the material and financial resources of the household, for example, appliances (e.g., microwave oven, refrigerator, television), a flushing toilet, running water and car, as well as whether the responder makes use of bank accounts and credit cards.

We used a questionnaire from the RXH pediatric neuropsychology clinic to obtain information on the developmental history (including pregnancy and birth, development, and family composition) of the participants.

We used the informant (parent) measures of the Child Behavior Checklist (CBCL; Achenbach and Rescorla, 2001) and the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000) to assess behavioral and emotional functioning, and executive functioning, respectively. These are recognized ecologically valid tools for assessing these outcomes (Schwartz et al., 2003; Gioia and Isquith, 2004).

The CBCL is a 112-item questionnaire used to assess behavioral and emotional functioning of children aged 6–18 years (Achenbach and Rescorla, 2001). Besides demographic questions, there are items that assess competency of activities, social functioning and school. Scores are also produced for eight syndrome scales: Anxious/Depressed, Withdrawn/Depressed, Somatic Complaints, Social Problems, Attention Problems, Rule-breaking Behavior and Aggressive Behavior, and six Diagnostic and Statistical Manual (DSM)-oriented scales: Affective Problems, Anxiety Problems, Somatic Problems, Attention Deficit/Hyperactivity Problems, Oppositional Defiant Problems, and Conduct Problems. On the syndrome and DSM-Oriented scales, age-standardized T-scores above 70 suggest clinically significant behavioral problems, and those from 67 to 70 are borderline clinical. Scores are also produced for two broad syndrome groups: Internalizing Problems and Externalizing Problems that together with the remaining syndrome scales produce a Total Problems score. Age-standardized T-scores above 63 suggest clinically significant behavioral problems and those from 60 to 63, are borderline clinical. The CBCL has both reported validity and reliability (Achenbach and Rescorla, 2001). There are some published research studies using the CBCL with South African samples (e.g., Shields et al., 2008; Palin et al., 2009; Schrieff-Elson et al., 2015).

The BRIEF is an 86-item standardized rating scale assessing everyday executive function behaviors within the home environment for children aged 5–18 years (Gioia et al., 2000). Two index scores [Behavior Regulation Index and Metacognition Index] and an overall composite score of executive function, the Global Executive Composite (GEC), are produced. Scores for eight clinical subscales, that assess interrelated executive function domains, are also produced. Inhibit, Shift, and Emotional Control are subscales of the BRI, while Initiate, Working Memory, Plan/Organize, Organization of Materials, and Monitor are subscales of the MI. T-scores of ≥63 are clinically relevant. The BRIEF shows high levels of internal consistency and stability, as well as test-retest reliability (Malloy and Grace, 2005; Chapman et al., 2010). It has been used cross-culturally in published studies, for example, in the Han Chinese and Dutch populations (Qian et al., 2010; Huizinga and Smidts, 2011) and in a published SA study (Schrieff-Elson et al., 2015).

We sourced academic information regarding education type and grade repetition for the entire sample from relevant questions on the CBCL. For four participants, specific details of the academic data (i.e., whether the child was making use of remedial services or not) were unclear; we therefore consulted the caregiver directly and/or consulted the participants' case files for these details.

We used SPSS 22.0 to compute the analyses.

Table 4 compares the academic information obtained for the sample and presents the ratios of grade repetition, and type of education pre- and post-injury. When considering the enrollment in remedial and special needs education services from pre- to post-injury, there was a three-fold increase from before (n = 6; 22.22%) to after (n = 18; 66.67%) the TBI. This increase reveals that for 44.44% of the sample (n = 12), the use of remedial services or special needs education was required only after the TBI. Remedial help was usually in the form of extra lessons or placement in a remedial-oriented class at the mainstream school. For six of these 12 participants, school personnel were in the process of applying to a school that catered for their specific learning or physical needs and were awaiting the outcome of this application, or applications were already approved and they were awaiting transfer. In these cases, children continued to attend mainstream schools in the interim, where they may or may not have received remedial help during that time. Four (14.81%) of children in special needs schools at the time of the study had been placed in those schools when they resumed schooling post-injury, or were placed a later stage before commencement of the study. More than half of the sample (n = 16; 59.26%) had repeated a grade following their injury, which represents a 275% increase from pre- to post-TBI.

Figure 2 presents a cross-tabulation of pre- and post-injury academic information. In the sample, 15 (55.55%) children who had not repeated a grade pre-injury, went on to repeat one or more grades post-injury. Of the four participants (14.80%) that repeated a grade pre-injury, only one (3.70%) also repeated a grade post-injury. This participant attended a special needs school pre- and post-injury and had premorbid cerebral palsy. The three participants (11.10%) who repeated a grade pre-, but not post-injury, attended mainstream schools both pre- and post-injury. Only one of these participants required the use of remedial services post-injury. Of those that attended mainstream school pre-injury (n = 26; 96.30%), four (14.81%) were then placed in special needs school post-injury.

Table 7 shows the correlations between academic and behavioral outcomes. There were strong significant positive correlations between repeated grade post-injury and the CBCL Total Problems and CBCL Externalizing Problem scales and the three BRIEF indices. These relationships suggest that grade repetition post-injury was associated with poorer reported scores on these behavioral measures.

There were medium to strong positive correlations between post-injury school type and the BRIEF indices, suggesting that poorer executive function behaviors were associated with increased need for remedial and special needs education.

As expected, were the significant positive correlations between outcome scores on the CBCL and the BRIEF, which were all in the expected direction.

Two thirds (18/27) of the sample were, at the time of assessment, receiving some form of remedial or special needs education or were in the process of being placed in remedial or special needs education post TBI. Thus, there were more children requiring some form of specialized education following their TBI than those who were integrated back into mainstream schooling. This result is consistent with literature on pTBI survivors being reintegrated into the schooling system and the associated increased need for specialized educational services, particularly with more severe TBI (Donders, 1994; Kinsella et al., 1997; Ewing-Cobbs et al., 1998; Savage et al., 2005; Jantz and Coulter, 2007; Arnett et al., 2013; Prasad et al., 2017). Taylor et al. (2003) found that 62% of the children with severe TBI in their sample were in programs that catered for special education needs, even several years after their injuries. Placement in these programs occurred soon after injury. Kinsella et al. (1997) previously reported similar high rates, where 70% of children with severe TBI required special needs intervention or attended school part-time after their injury; while Donders (1994) reported a 40% increase in the number of children requiring special needs education and Hawley (2004) a two-fold increase in the number children in her TBI (mixed severity) sample. In the current study, there was a three-fold increase (from n = 6 to n = 18) in the number of children using some form of special education services, from pre- to post-TBI.

Post-injury, 22% (6/27) of learners had applied for placement in a LSEN school, or had already been offered placement, but were awaiting transfer to that school. There are several reasons that could account for the delay in placement at an LSEN school. It may reflect a delayed process of identification of needs, for example, when deficits are not immediately evident and but are only evident at a later stage when cognitive demands on the child increase. The delay in placement may also be due to the unavailability of a place at a suitable school, especially when resources are limited (Taylor et al., 2003; Mayfield and Homack, 2005; Ciccia and Threats, 2015). In SA, the delay in placement in LSEN schools is often due to it being a lengthy bureaucratic process. Furthermore, the number of special needs schools as well as their capacity for learners is limited with more than 10,000 learners on a waiting list for placement (Amollo, 2008; Donohue and Bornman, 2014). This lengthy waiting list is particularly problematic when one considers that once learners reach 16 years of age, they are considered too old to be placed in LSEN schools.

More than half (59%) of the sample had to repeat at least one grade after returning to school post-injury. This result is consistent with the outcomes for the “children group” (5–10 years) in the Ewing-Cobbs et al. (1998) study with 55% having repeated a grade within two years following their injury. There was a four-fold increase in the number of children who had to repeat one or more grades from before to after the TBI.

The reported reasons for these adverse academic outcomes in the literature are varied. The cognitive deficits associated with TBI, including difficulties in attention, memory, executive function, and essential skills such reading and writing, can impact on academic performance (Donders, 1994; Ewing-Cobbs et al., 1998; Arroyos-Jurado et al., 2000; Hawley, 2004; Taylor, 2010; Max, 2014). Other factors such as behavioral impairments and absence from school may also play a role (Ewing-Cobbs et al., 1998; Babikian and Asarnow, 2009). In some cases, the child missed out on weeks or months of school during their post-TBI recovery period, especially when the TBI occurred during the school term. Frequently, the child also missed periods of school to attend doctors' appointments even after the initial hospital stay.

Given the already fragile state of the education system in SA and worrying statistics like 70% of children with special educational needs who should be in school, are not attending school, and given that many pTBI survivors (especially after severe TBI) have post-TBI special needs, one cannot help but be concerned about the future educational retention of these children. The tentative nature and lack of efficacy of the inclusive educational model also creates uncertainty around where SA pTBI survivors may be and are best placed.

The significant correlations between repeated grade post-injury and the Externalizing Problems scale (and Total Problems, which is likely a function of this) on the CBCL suggest that pTBI survivors who repeated a grade post-injury may display a greater the degree of externalizing behavior problems as assessed on the CBCL, or conversely, that those with externalizing problems, specifically showing more rule-breaking or aggressive behavior, are more likely to repeat grades after their injury. These findings are consistent with literature describing the impact of behavioral impairments on school performance and educational outcome (Keenan and Bratton, 2006; Babikian and Asarnow, 2009; Arnett et al., 2013). Nelson et al. (2004) found that among students who had emotional or behavioral disorders, those who exhibited externalizing as compared to internalizing behavioral problems were more likely to have deficits in academic achievement. These authors conjecture that externalizing behaviors (such as inattentiveness and disruptive behaviors) may have more of a pervasive influence than internalizing behavior problems with regards to interfering with the learning process and consequently coping and progressing academically. This is especially the case when the behaviors involve poor behavior regulation and self-monitoring secondary to executive dysfunction (Mayfield and Homack, 2005).

The positive significant associations between post-injury repeated grade and school type, and the outcome scores (GEC, BRI, MI) on the BRIEF, suggest that pTBI survivors with increased executive dysfunction, including problems with regulation of behavior and abilities related to problem solving, are more likely to be in need of special educational services or to have failed grades. The relationships between the academic outcome variables and the BRIEF outcomes are consistent with literature documenting the role of executive functions in the learning process and in academic achievement, as well as post-TBI executive function outcomes generally (Anderson et al., 2002; St Clair-Thompson and Gathercole, 2006; Babikian et al., 2015). In their sample of children and adolescents with Attention-Deficit Hyperactivity Disorder (ADHD), St Clair-Thompson and Gathercole (2006) found that there was increased risk of repeating grades, learning disabilities and poor academic achievement amongst those with deficits in executive functioning.

The significant positive relationships between the CBCL and BRIEF were expected due to the construct validity of these measures, and because the Internalizing Problems and Externalizing Problems scales, and the BRI and MI form part of the overall outcome scores (Total Problems and GEC, respectively) obtained on these measures.

In summary, the results show elevated problems with behavioral and executive functioning, and academic concerns in the sample. More than half of the participants experienced clinically significant behavioral problems and working memory appeared to be the most problematic subdomain of executive function. Two thirds of the children were receiving some form of, or in the process of being placed in, LSEN schools; and the increase in the number of children using some form of special education services from pre- to post-injury was three-fold. Furthermore, more than half of the sample repeated at least one grade after returning to school following their injury. The results in this study suggest that children with increased externalizing behavioral are more likely to repeat a grade post-injury; and that children with executive dysfunction post-TBI may be more likely to require some form of special educational services and more likely to repeat a grade post-injury (although the converse may also be true in both cases given the correlational nature of the study).

Ideally, the study design should include a model of change in academic grade performance and would incorporate a change in performance over three time periods: the period before injury, the initial period upon return to school, and the time of assessment. A change in performance could then be determined as a percentage recovery from the initial decline in academic performance and the total decline in academic performance. This model of change was however not achievable due to challenges in obtaining complete academic history (pre-injury scholastic and behavioral assessments, and term and year-end results) for most the sample. These limitations are not uncommon for a developing world setting and are important to address because it limits good data collection.

Due to the small sample size, the results of this study should be viewed with caution. The exclusion of children who were not attending school at the time of the study became a limiting factor in the size of the sample. Furthermore, the small sample had a wide age range of participants (6–12 years).

Further, this study made use of parent self-report measures, which may be limited by response sets, social desirability bias, and sometimes unreliable recall from memory of past behaviors. The measures used have not been validated in the SA context—as is the case generally with neuropsychological measures (Schrieff-Elson et al., 2017). Nevertheless, informant measures were used in this study due to their psychometric properties and that they are commonly used assessment tools. Moreover, they are more ecologically valid that standard pencil and paper measures, despite the lack of norms (Anderson et al., 2002; Gioia and Isquith, 2004). Further studies should look to supplement informant measures, for example, by including structured interviews with caregivers and teachers, as well as direct observations of behavior.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor declared a shared affiliation, though no other collaboration, with the authors.