Learning Trajectories of Children With Special Health Care Needs Across the Severity Spectrum Sharon Goldfeld, PhD, FRACP; Meredith O’Connor, DEdPsych; Jon Quach, PhD; Joanne Tarasuik, PhD; Amanda Kvalsvig, MSc (Epidemiology), MRCP From the Royal Children’s Hospital (Dr Goldfeld, Dr O’Connor, Dr Quach, Dr Tarasuik, and Dr Kvalsvig), Murdoch Childrens Research Institute (Dr Goldfeld, Dr O’Connor, Dr Quach, and Ms Kvalsvig), University of Melbourne (Dr Goldfeld, Dr O’Connor), and Swinburne University of Technology (Dr Tarasuik), Melbourne, Victoria, Australia The authors declare that they have no conflict of interest. Address correspondence to Sharon Goldfeld, PhD, FRACP, Murdoch Childrens Research Institute, Royal Children’s Hospital, Parkville Victoria 3052, Australia (e-mail: [email protected]). Received for publication January 23, 2014; accepted September 4, 2014.

ABSTRACT OBJECTIVE: A significant proportion of school-aged children experience special health care needs (SCHN) and seek care from pediatricians with a wide range of condition types and severity levels. This study examines the learning pathways of children with established (already diagnosed at school entry) and emerging (teacher identified) SHCN from school entry through the elementary school years. METHODS: The Longitudinal Study of Australian Children (LSAC) is a nationally representative clustered crosssequential sample of 2 cohorts of Australian children which commenced in May 2004. Data were analyzed from the LSAC kindergarten cohort (n ¼ 4,983), as well as a subsample of 720 children for whom teachers also completed the Australian Early Development Index checklist, a measure of early childhood development at school entry that includes SHCN. RESULTS: Latent class analysis was utilized to establish 3 academic trajectories from 4–5 to 10–11 years: high (24.3%), average (49.8%), and low (23.6%). Descriptive statistics re-

vealed a trend for both children with established and emerging SHCN to fall into weaker performing learning pathways. Multinomial logistic regression focusing on those children with emerging SHCN confirmed this pattern of results, even after adjustment for covariates (relative risk 3.06, 95% confidence interval 1.03–9.10). Children who additionally had low socioeconomic standing were particularly at risk. CONCLUSIONS: Even children with less complex SCHN are at risk for academic failure. Early identification, together with integrated health and educational support, may promote stronger pathways of educational attainment for these children. Achieving these better outcomes will require the involvement of both educational and health practitioners.

KEYWORDS: academic achievement; chronic health condition; disability; school functioning; special health care needs

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emotional condition and. also require health and related services of a type or amount beyond that required by children generally.”4 Prevalence estimates from the United States range between 13% and 19% for 0- to17-year-olds.5 Australian data similarly suggest that around a fifth of children in their first year of schooling experience SCHN.6 This definition of SHCN is purposefully inclusive of children considered to be at increased risk for being diagnosed with a chronic condition (which we refer to as experiencing emerging SHCN), in recognition of the important potential benefits of early intervention.7 Australian estimates suggest that while 4% of children have established SHCN formally recognized within the educational system, many more—around 18%—experience emerging SHCN.6 Despite their number, these children tend to be underrepresented in research and policy discussions, and they continue to be at high risk of missing out on services.8

Even children with less complex special health care needs are at risk for poorer learning pathways over the early years of schooling. These differences appear early and may even widen by the end of elementary school.

PEDIATRICIANS AND FAMILY physicians working with children are increasingly confronted with the so-called millennial morbidities: chronic and sometimes intractable problems associated with complex biopsychosocial and environmental dimensions.1 This includes primarily organic chronic conditions such as obesity, as well as mental health and developmental difficulties such as attention-deficit/hyperactivity disorder (ADHD), behavior problems, autism spectrum disorders, and learning issues.1,2 Children can also differ widely in the severity of their condition and complexity of their needs.3 Often referred to as children with special health care needs (SHCN), these children “have or are at increased risk for a chronic physical, developmental, behavioral, or ACADEMIC PEDIATRICS Copyright ª 2015 by Academic Pediatric Association

IMPACT OF SHCN ON LEARNING AND SCHOOL ACHIEVEMENT Children with SHCN tend to begin school with weaker early academic skills than their peers,6 and disparate

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academic outcomes continue to be evident in the later elementary years.9 In the long term, these children have lower rates of high school completion, postsecondary education, and decreased earnings and labor market participation.10 Many different factors are likely to contribute to these poorer outcomes, such as school absenteeism, decreased participation at school, and restricted involvement in social activities with peers.11 However, research to date has tended to focus on children at the severe end of the disability spectrum, with the academic pathways of children with emerging SHCN (those with undiagnosed or “gray area” difficulties) neglected in both empirical investigations and policy discussion.12 In many countries, including Australia, funding systems are still aligned to limited diagnostic categories, and children with emerging SHCN can miss out on access to support if their difficulties do not fit neatly into often rigid criteria for eligibility based on severe impairment.8,11 Yet the potential benefits of early intervention13 constitutes a strong argument for also understanding and addressing the support needs of these children. Compounding the risk for underachievement and disengagement at school is the impact of socioeconomic disadvantage. Children with SHCN are overrepresented in more disadvantaged settings in countries such as the United Kingdom,14 the United States,15 Canada,16 and Australia,6 and disadvantage in turn is associated with poorer academic pathways throughout the school years.17,18 These effects are further amplified in the context of SHCN,19 making the potential for double jeopardy a real risk. CURRENT STUDY The impact of SHCN on early (and therefore more mutable) pathways of learning and school functioning has been underexplored,20 particularly in relation to children with emerging SHCN.11 In this study, we capitalized on the unique opportunity provided by the Longitudinal Study of Australian Children (LSAC),21 combined with teacher report from the Australian Early Development Index (AEDI), a population measure of early childhood development,22 to examine the learning and academic pathways of children with SHCN across the elementary school period. We hypothesized that children with both established and emerging SHCN would be overrepresented in trajectories characterized by weaker academic skill development. Socioeconomic disadvantage was expected to account for some but not all of this relationship.

METHODS DATA SOURCES Growing Up in Australia: the Longitudinal Study of Australian Children (LSAC) is a nationally representative clustered cross-sequential sample of 2 cohorts of Australian children—the birth cohort (B cohort) of 5,107 infants and the kindergarten cohort (K cohort) of 4,983 4-yearolds—which commenced in May 2004.21 A cluster design and stratification of postal codes were used to ensure a geographically representative sample of the Australian

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population for each age cohort, with the exception of children living in remote areas.23 The families of 18,800 children received letters of invitation to take part in the LSAC, and the final overall response rate for both cohorts was 54%.24 To date, 4 main waves of data have been collected, approximately every 2 years. Approval for this methodology was gained from the Australian Institute of Family Studies human ethics review board. Families were visited by an interviewer at the main waves of data collection who conducted a parent interview, completed direct-child assessments and observational measures, and left behind a self-completed questionnaire to return via postal mail. Teacher report was collected during each of the main waves via a mail-out survey. Information was also gained using data linkage to a number of administrative datasets, including the National Assessment Program—Literacy and Numeracy assessments (NAPLAN), which is an Australia-wide assessment conducted in schools with all children in school years 3, 5, 7 and 9.25 Here we focus on a subsample of 720 children from the K cohort (4- and 5-year-olds) who also had data on SHCN collected through the Australian Early Development Index (AEDI).22 The AEDI is an Australian adaptation of the Canadian Early Development Instrument (EDI); it is a relative population measure of young children’s development completed by teachers.22 This subsample consisted of all the children in LSAC who resided in the states of Queensland, Victoria, or Western Australia. With the parents’ consent obtained, the child’s teacher was sent a battery of questionnaires that included the AEDI checklist. Preliminary analyses found this subset to be representative of the full LSAC K cohort. MEASURES SPECIAL HEALTH CARE NEEDS Three questions from the AEDI checklist were used to determine SHCN status. For all children, teachers were asked: 1) whether the child required “special assistance due to chronic medical, physical, or intellectually disabling conditions (eg, autism, cerebral palsy, Down syndrome),” with instructions to base their answer on an established medical diagnosis; 2) whether any of 9 physical and psychosocial impairments in their view impacted the student’s ability to do school work in a regular classroom (Table 1); and 3) whether they thought that the child needed further assessment and/or was currently being assessed. All children who were reported by teachers to have previously diagnosed SHCN according to item (1) were categorized as having established SHCN. Children were categorized as experiencing emerging SHCN if teachers responded yes to either or both questions (2) and (3), indicating that the child needed further assessment and/or had area/s of impairment affecting their learning. Finally, all other children were categorized as belonging to the standard population.6 This assessment of SHCN status has been shown to correlate with other developmental indicators as expected.6

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Table 1. Areas of Impairment Identified by Teachers* Total

Established SHCN

Emerging SHCN

Area of Impairment

n

%

n

%

n

%

Speech impairment Behavioral problems Home environment Emotional problems Learning disability Visual impairment Physical disability Hearing impairment Other enduring problem

54 30 28 27 15 13 11 10 21

44.63 24.79 23.14 22.31 12.40 10.74 9.09 8.26 17.36

17 7 3 4 12 5 8 3 6

73.91 30.43 13.04 17.39 52.17 21.74 34.78 13.04 39.13

37 23 25 23 3 8 3 7 15

37.76 23.47 25.51 23.47 3.06 8.16 3.06 7.14 15.31

SHCN indicates special health care needs. *Percentages may sum to more than 100% because areas of impairment were not mutually exclusive. Proportions are of the 23 children with established SHCN and 98 with emerging SHCN for whom it was indicated that there was some area of impairment.

ACADEMIC DEVELOPMENT Academic skill development was measured over 4 waves through a combination of direct assessments and teacherrated scales focusing on literacy and numeracy skills. Details of these measures of academic achievement are summarized in Table 2. COGNITIVE SKILLS At the second wave of LSAC data collection (age 6 to 7 years), children were assessed on the Matrix reasoning task, an indicator of nonverbal cognitive skills from the Wechsler Intelligence Scale for Children, 4th edition (WISC-IV). For each item, children were presented with an incomplete diagram and asked to select 1 of 5 options that would correctly complete the set. Scores more than 1 standard deviation below the mean were categorized as below average nonverbal cognitive skills. DEMOGRAPHIC COVARIATES Family socioeconomic position was examined as a derived measure combining the parents’ educational attainments, income, and occupational prestige,26 and the child’s gender was provided via parent report.

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ANALYSIS Groups of children with similar patterns of academic achievement over time (academic trajectories) were firstly identified using the full LSAC K cohort (n ¼ 4,983). To achieve this, MPlus was used to conduct a latent class analysis (LCA), which identifies groups of participants with similar patterns of outcomes over time (in this case, with similar trajectories of academic learning during the elementary school years). Measures of academic achievement at 4 to 5, 6 to 7, 8 to 9, and 10 to 11 years (Table 2) were included in the LCA analysis. The number of groups or classes extracted from the LCA was determined on the basis of the minimum Bayesian information criterion and significant Lo-Mendell-Rubin likelihood ratio test, which estimates the number of groups that best represents the data, and whether extracting additional groups would significantly improve model fit. Estimation was by maximum likelihood with robust standard errors taking account of missing data by inferring on the basis of available measures, using survey methods weighting.27,28 The academic trajectories identified in the LCA were cross-tabulated with SHCN status (established SHCN, emerging SHCN, or standard population) to explore distribution patterns at the descriptive level. Multinomial logistic regression using Stata version 13.0 was then used to estimate the probability of being in each academic trajectory group for children with emerging SHCN, accounting for covariates. This analysis was conducted with the AEDI subsample (n ¼ 720), as these children had data on both their academic trajectory group and SHCN status. The rate of missing data was very low for the predictor variables (on average 0.1%), and cases with missing data were thus excluded from the analyses, as they were unlikely to affect the results.

RESULTS SAMPLE CHARACTERISTICS The K cohort subsample with AEDI data included 720 children (n ¼ 351, 48.75% boys and n ¼ 369, 51.25% girls). A substantial proportion of children in this sample were classified as having SHCN, including 3.89%

Table 2. Measures of Academic Achievement Across Ages Measure 36

Who Am I? (WAI)

LSAC teacher questionnaire

Academic Rating Scale (ARS)37

National Assessment Program—Literacy and Numeracy assessments (NAPLAN)25

Age 4–5 y

Informant

Subscale

Direct assessment Literacy Numeracy Teacher Literacy Numeracy

Example

Write words. Copy shapes and write numbers. 4–5 y Is this child able to read simple sentences? Can the child classify objects by shape or color? 6–7, 8–9, 10–11 y Teacher Literacy Composes a story with a clear beginning, middle, and end. Numeracy Can model, read, write, and compare fractions. 8–9 y Direct assessment Literacy Kate (“see,” “saw,” “sawed,” or “seen”) 2 kittens playing in the grass Numeracy Dana started at 10 and made this number pattern: 10, 11, 13, 16, 20, 25, —. What is the next number in the pattern?

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Figure. Academic trajectory groups identified using LCA.

(n ¼ 28) with established SHCN and 16.67% (n ¼ 120) with emerging SHCN. The remainder of the sample (n ¼ 572, 79.44%) formed the standard population. Children with SHCN in this sample experienced a diverse range of difficulties, including a large proportion with psychosocial problems such as speech, behavioral, or learning issues (Table 1). IDENTIFICATION OF ACADEMIC TRAJECTORIES Groups of children with similar patterns of academic learning over time were identified using LCA, with measures of academic achievement at 4 to 5, 6 to 7, 8 to 9, and 10 to 11 years included in the analysis. Four trajectory groups were identified, and almost all children in the AEDI subsample (99.6%, n ¼ 717) were successfully assigned to one of these. Characteristics of each trajectory group were then explored by examining their average academic performance at each time point, expressed as standardized scores (z scores). This is illustrated in the Figure, where mean scores of 0 can be interpreted as average, while mean scores above 1 or below 1 can be interpreted as 1 standard deviation above or below the mean, respectively. The first group included 174 children (24.27%) who were steadily above average in their academic performance over the 4 time points (hereafter called the high trajectory). The second group comprised 357 children (49.79%) who scored close to the mean across each time point; this group was labeled the average trajectory. A further group of 169 children (23.57%) was identified that could be characterized as having a low academic trajectory. Finally, a small group of 17 children (2.37%) consistently performed at a very low level. Because of the small numbers, the latter 2 groups were combined into a single low-trajectory group comprising 186 children (25.94%) in the following analyses. SPECIAL HEALTH CARE NEEDS AND ACADEMIC TRAJECTORIES Descriptive statistics revealed that children with established and emerging needs were both overrepresented in the low and average trajectories, and underrepresented in the high-performing trajectory (Table 3). Indeed, only a single child (3.9%) with established SHCN was observed in the high trajectory, compared to 27.5% of children from the standard population.

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Multinomial logistic regression further estimated the association between SHCN and academic trajectory. Children with established SHCN were excluded from this analysis as a result of their small numbers. Two models were projected. Model 1 shows the association between emerging SHCN and academic trajectory accounting for gender and socioeconomic status. Model 2 additionally includes cognitive ability and type of impairment, in order to discern whether the association between emerging SHCN and membership in the low trajectory could be due to the nature of the SHCN itself (ie, learning problems predicting learning problems). The results are interpreted as the risk of being in a given trajectory associated with each predictor compared to being in the high trajectory. In model 1, children with emerging SHCN showed an increased risk of being in the low rather than high academic trajectory, even when accounting for demographic factors. Socioeconomic disadvantage posed a substantial risk for being in both the low and average trajectories, while gender was not associated with academic trajectory membership. Model 2 further revealed that, as expected, belowaverage cognitive skills put children at considerably greater risk of being in the low-trajectory group; importantly, the effect of emerging SHCN remained robust even when accounting for this. ROLE OF SOCIOECONOMIC DISADVANTAGE To further determine the impact of SES, we explored the breakdown of academic trajectories across SHCN and SES categories. Table 4 illustrates that the proportion of children in the low trajectory was much higher if children with emerging SHCN were from a disadvantaged (n ¼ 21, 51.22%) rather than advantaged (n ¼ 28, 35.44%) family background. The negative impact of disadvantage is again demonstrated by the lack of any (0%) poorer children with emerging SHCN in the high academic trajectory compared to around a fifth (20.25%) of children with emerging needs from wealthier families.

DISCUSSION A significant proportion of children in this sample experienced SHCN at school entry, including 4% with formally identified special needs and 17% with emerging SHCN, consistent with Australian population estimates.6 These children were overrepresented in poorly performing academic pathways as they moved through the elementary school years. Importantly, the risk of being on a poorer academic trajectory was clearly apparent for children with emerging needs, despite these children often being ineligible for funding support in the Australian education system.11 As observed in previous studies,29 the academic trajectories identified in our study showed a high level of rankorder stability, with some slight fanning of the trajectories by 10 to 11 years. This may reflect modest effects of cumulative advantage, whereby children with strong early academic skills are able to take advantage of environmental and educational opportunities. In contrast, children who

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Table 3. Relationship Between Predictor Variables and Academic Trajectories† Academic Trajectory Group Low Variable

Average

Average Versus High Academic Trajectory

High

n

%

n

%

n

%

Model 1

Model 2

17 49 120

65.38 40.83 21.02

8 55 294

30.77 45.83 51.49

1 16 157

3.85 13.33 27.50

.‡ 3.10 (1.55–6.22)** Ref

.‡ 3.06 (1.03–9.10)* Ref

.‡ 1.69 (.96–2.98) Ref

.‡ 1.08 (0.42–2.81) Ref

97 89

27.79 24.18

172 185

49.28 50.27

80 94

22.92 25.54

0.91 (0.58–1.44) Ref

0.83 (0.51–1.38) Ref

.96 (.64–1.43) Ref

1.00 (0.68–1.49) Ref

26 95 65

15.12 25.27 38.69

88 179 89

51.16 47.61 52.98

58 102 14

33.72 27.13 8.33

Ref 2.15 (1.26–3.66)** 9.29 (4.49–19.24)**

141 45

21.89 61.64

334 23

51.86 31.51

169 5

26.24 6.85

Ref 10.66 (3.83–29.66)**

Ref 2.78 (0.98–7.87)

29 19 11 14 13

55.77 63.33 39.29 51.85 35.71

20 9 15 9 21

38.46 30.00 53.57 33.33 50.00

3 2 2 4 6

5.77 6.67 7.14 14.81 14.29

1.32 (0.26–6.80) 4.02 (0.80–20.14) 0.88 (0.10–7.38) 0.54 (0.10–2.78) 0.42 (0.09–0.86)

1.77 (0.46–6.71) 2.05 (0.34–12.47) 3.18 (0.38–26.33) 0.38 (0.07–2.08) 1.57 (0.45–5.54)

Ref 2.40 (1.34–4.31)* 16.58 (7.15–38.44)**

Model 1

Ref 1.15 (.77–1.74) 3.87 (2.05–7.31)**

Model 2

Ref 1.20 (0.78–1.83) 5.49 (2.62–11.51)**

SHCN indicates special health care needs; SES, socioeconomic status. * P < .05, ** P < .01. †Percentages reflect proportion within predictor group (n ¼ 690 in model 1 and n ¼ 646 in model 2). ‡Not included in the multivariate model due to low numbers. “Other” impairment type includes visual impairment, physical disability, hearing impairment, and other enduring problem.

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SHCN group Established SHCN Emerging SHCN Standard population Gender Male Female SES Top 25% Middle 50% Bottom 25% Cognitive skills Average or above Below average Impairment type Speech impairment Behavioral problems Home environment Emotional problems Other

Low Versus High Academic Trajectory

181

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Table 4. Trajectory Group Membership According to SHCN Status and Family SES* Academic Trajectory Group Low Population

SES

Standard Top 75% SES population Lowest 25% SES Emerging Top 75% SES SHCN Lowest 25% SES

n

%

Average n

%

High n

%

82 18.14 227 50.22 143 31.64 38 32.20 28 35.44

66 55.93 35 44.30

21 51.22

20 48.78

14 11.86 16 20.25 0

0.00

SHCN indicates special health care needs; SES, socioeconomic status. *Percentages reflect proportion by row.

lack foundational skills may face learning opportunities that are incongruent with their needs, such that these children fall increasingly behind.30 Although children with SHCN were overrepresented in the lower-performing academic trajectories, it is worth noting that there was significant heterogeneity in their outcomes. This suggests the capacity for positive interventions to support children in reaching their academic potential, and the need to tailor these interventions to individual needs. Crucial future work is now needed to identify modifiable risk and protective factors, especially those at the health/education nexus, that are capable of ameliorating the potential impact of SHCN on school functioning. Exploring the subgroup of children who had emerging SHCN but were also performing at the highest academic level may provide helpful insights into such potential buffers. Understanding these potential intervention pathways would also inform the role of health professionals in supporting children with SHCN. The role of medical practitioners in supporting children’s education is often limited to independently promoting health or managing symptoms so that children are not impeded by illness at school.31 However, professionals such as pediatricians who work at the forefront of the health/education interface have the potential to broaden their role considerably by acting as bridges between the school and health worlds. As an example, the medical home model currently includes a health practitioner (such as a pediatrician) as an important part of a multidisciplinary care team including nonhealth practitioners such as teachers, psychologists, and speech pathologists.32 Any one of these practitioners may take on a leadership role within the multidisciplinary team, depending on the needs of the child at that time. Yet even when coordination of children’s health care emanates from their educational home, consideration of how existing services can be utilized to better meet the needs of these children should be at the forefront of community pediatrics. Complicating intervention responses is the stark negative impact of socioeconomic disadvantage on the academic pathways of children with SHCN in this sample. This accords with previous research illustrating the adverse effect of disadvantage on learning outcomes for all children18 and for those with SHCN.19 This reorientation of

health and education support systems to better meet the needs of children doubly disadvantaged in society presents a great challenge for services lacking the sorts of flexibility required to do so. In countries such as Australia, where support for children with SHCN is primarily diagnosis driven,8 moving to a broader classificatory framework centered on children’s functioning could be a positive first step in achieving this.11 STUDY STRENGTHS AND LIMITATIONS This study capitalized on high-quality longitudinal data spanning the entire elementary school period. This was a major strength of the study, as it allowed us to explore the developmental picture of how academic skills unfold over time. In addition, we were able to use a mixture of teacher report and direct assessments, adding to the robustness and ecological validity of the findings. However, a number of limitations to the current research also warrant consideration. In particular, the subsample with data on SHCN was relatively small, which limited analysis, particularly in relation to children with established SHCN. In addition, the teacher report of SHCN means that we may have missed some children with conditions that were well managed and had little impact on school life. We suggest that this is unlikely to have had an undue influence on our results, however, given that the total rate of SHCN in this sample accords with Australian population estimates.6 Data linkage will allow us in the future to examine the relationship between SHCN and academic outcomes at the population level; in the meantime, the current study provides initial insights into these longitudinal associations. IMPLICATIONS The heterogeneity observed in the academic pathways of children with SHCN in this sample reinforces the potential benefit of interventions that include both school- and health-based policy and practice responses.33 Interventions likely to be most effective include tailored individual health and educational responses that reflect the variable levels and types of support needs of these children and that involve a well-coordinated multidisciplinary approach that can flexibly respond to children’s difficulties across the severity spectrum.11 This would require a significant shift away from the current diagnosis-based funding provisions within the Australian educational system.11 Given that disparities in academic development are already evident at school entry, the opportunity to promote optimal educational pathways for children with SHCN lies in the provision of support before children start school and in the very early years of schooling, particularly for disadvantaged children.13 This is only possible with early identification of children’s difficulties, which are often only recognized once children have entered the school system.34 The high prevalence of children with SHCN suggests that the universal education platform provides the ideal opportunity for equitable responses with appropriate reach for more intensive interventions at a population level. For

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SPECIAL HEALTH CARE NEEDS AND LEARNING TRAJECTORIES

example, Finland begins compulsory schooling 1 year earlier for children with SHCN,35 in recognition of the benefits of intervening early. However, it is neither feasible nor fair that the complex needs of children with SHCN should be met through the education system alone. Addressing the risk of school failure for children with SHCN requires effective, coordinated support from educators and health professionals alike. This fundamental shift in considering children’s health and well-being across the health/education interface would be the first step in intervening to improve the academic pathways of children with SHCN.

CONCLUSIONS Children with SHCN, including those with emerging health and developmental concerns, are at increased risk for poorer learning pathways through the educational system. The current findings suggest that weaknesses evident at school entry tend to continue and even worsen over the elementary school years, particularly for disadvantaged children. Flexible, multidisciplinary, and timely intervention is needed to support children with SHCN such that they can achieve their full potential at school. More work to understand effect modifiers and identify potential targets for intervention should be of interest to educators and health professionals alike. ACKNOWLEDGMENTS There are a number of key groups to be acknowledged for their support of the Australian Early Development Index (AEDI), including the following: the Australian government, which funded the study; all schools, principals, and teachers across Australia who participated in the AEDI; and each of the state and territory AEDI coordinators and their coordinating committees, who helped to facilitate the AEDI data collection in their respective jurisdictions. We appreciate their time and commitment. Personnel support for this analysis was funded by the Australian government and was supported by the Victorian government’s Operational Infrastructure Support Program. The funding body had no role in relation to the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the article; and decision to submit the article for publication.

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Learning trajectories of children with special health care needs across the severity spectrum.

A significant proportion of school-aged children experience special health care needs (SCHN) and seek care from pediatricians with a wide range of con...
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