Abstract
Purpose:
To investigate the association of environmental factors, rehabilitation services during therapy and socioeconomic status (SES – insurance type), with neurocognitive outcomes at the end of therapy for survivors of childhood acute lymphoblastic leukemia (ALL).
Methods:
Survivors (n = 236) treated on the St. Jude Total Therapy Study 16 completed end of therapy testing with performance measures (IQ, attention, processing speed, fine motor skills, academics) and caregiver ratings (attention, executive function, adaptive skills). Environmental factors were abstracted from the medical record.
Results:
Distribution of sex (47.3% female, p=0.399), treatment arm (45.5% low risk, 54.5% standard/high risk p=0.929), insurance type (47.7% private, 52.3% public/none, p=0.117), and mean age at diagnosis (7.7 vs. 6.8 years, p=0.143) were similar for groups with (n=110; 46.6%) and without (n=126; 53.6%) rehabilitation services during therapy. Compared to those without rehabilitation, the rehabilitation group (n=110; 46.4%) had more caregiver reported problems with attention (Z =−0.28 vs. 0.43, p=0.022), executive function (Z =−0.50 vs. −0.08, p=0.003), and adaptive skills (Z = −0.41 vs. - 0.13, p=0.031). Among the rehabilitation group, there was no difference in outcomes by insurance status. Among those without rehabilitation, those with public insurance had worse neurocognitive outcomes than those with private insurance in IQ (Z =−0.04 vs. −0.45, p=0.0115), processing speed (Z=−0.10 vs. −0.75, p=0.0030), reading (Z=0.18 vs. −0.59, p<0.0001), and math (Z=−0.04 vs. −0.50, p=0.0021).
Conclusion:
Participation in rehabilitation services during early intensive therapy is associated with end of therapy caregiver-reported neurocognitive outcomes in daily life.
Keywords: acute lymphoblastic leukemia, physical therapy, socioeconomic status, cognition, rehabilitation
Acute lymphoblastic leukemia (ALL) makes up about 29% of childhood cancer cases and 3 out of 4 cases with childhood leukemia [1]. The incidence rate of ALL peaks between about 2 to 5 years of age [2, 3]. Treatment advances over the past several decades have positively impacted the survival rate for childhood ALL, with 5-year survival rates greater than 90% for children treated in high-income countries [4]. Therapy modifications such as the replacement of cranial radiation therapy with intensified intrathecal and systemic chemotherapy for CNS prophylaxis have resulted in relatively preserved global cognitive outcomes [2, 5]. However, neurocognitive risk is still a factor for children treated on chemotherapy only protocols, with the most impacted domains including attention, executive function, learning and memory, and processing speed [6]. Documented clinical risk factors for neurocognitive problems in survivors of childhood ALL include greater intensity of CNS-directed therapy [5–8], younger age at diagnosis, and increased time since treatment [3, 6, 9–11]. Demographic variables such as female sex [6, 12, 13] and lower social economic status [6, 7, 11, 14] may also impact neurocognitive vulnerability. Neurocognitive problems emerge during therapy, persist into survivorship, and negatively impact functional and quality of life outcomes [6, 15–16].
Since ALL diagnosis peaks between ages 2 to 5 years, typical early childhood development is often disrupted due to this being a critical period for brain development. There are multiple pathways by which younger age at diagnosis may confer neurocognitive vulnerability. During early childhood, white matter pathways are rapidly developing and therefore disruption of the CNS during this time has been associated with leukoencephalopathy [17, 18]. These white matter pathways are a crucial aspect of neurodevelopment and allow for rapid and efficient information processing. Neuroimaging studies have demonstrated worse white matter integrity, particularly in the frontal lobes, which has been associated with poorer executive functioning in survivors of ALL [19, 20]. Additionally, exploration into the brain networks using diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have revealed alterations in childhood ALL survivor’s global efficiency, due to distinct alterations in white matter [21, 22].
A second pathway is the loss of early experiences promoting acquisition of new skills and socioemotional development. Indeed, a growing body of literature in the broader childhood cancer population demonstrates that young children treated for cancer are at risk for delays in development [23–25]. The interruption during critical and sensitive developmental periods within early childhood because of intensive two and a half year ALL treatment has the potential to pose a significant threat to typical neurodevelopment for children with ALL. CNS directed therapy at very young ages (during periods of rapid neurodevelopment) may significantly alter brain development moving forward, further altering their developmental trajectory.
Rehabilitation services have the potential to support continued motor development and physical and metabolic fitness in children with ALL. The positive impact of rehabilitation services on children with ALL has been well established in the existing literature [26–28]. ALL impacts the neuromuscular mechanisms involved in gross and fine motor skills [29, 30]. Unfortunately, the engagement in rehabilitation services during treatment for ALL is often challenging due to treatment-related factors (e.g., illness and fatigue). There is a lack of existing studies examining the impact of missed intervention services on neurocognitive late effects in childhood cancer survivors.
Our study aimed to better understand the role of environmental factors, including engagement in rehabilitation services, during early intensive therapy, and sociodemographic variables, on neurocognitive outcomes at the end of therapy (i.e., consolidation). We leveraged neurocognitive data obtained in the context of a clinical trial of frontline therapy for newly diagnosed childhood ALL, the St. Jude Total Therapy Study 16 (Total 16) [31]. We hypothesized engagement in rehabilitation services early in intensive therapy would impose significant positive impact on neurocognitive outcomes, specifically in the domains of attention and executive functioning.
METHODS
Study population
The study population was defined as patients enrolled on Total 16 between 2010 and 2014. Of the 315 patients treated during this time period, 267 were eligible for protocol-directed neurocognitive testing on Total 16. Reasons for ineligibility included Down syndrome (n = 5), primary language other than English (n = 11), and off study (n = 17). An additional 15 participants withdrew consent before the end of therapy time point (Figure 1).
Figure 1.
Flowchart of Participants
Ethics Statement and Informed Consent
This study was approved by the Institutional Review Board at St. Jude Children’s Research Hospital (IRB Approval number 19–0015, IRB approval date 5-28-2019). Informed consent was obtained from participants in accordance with institutional IRB policies.
Treatment
Participants were treated with risk-adapted therapy on the SJCRH Total 16 study. Treatment details and primary outcomes have been previously reported [31]. Briefly, patients were classified as having low-risk, standard-risk (intermediate-risk), or high-risk leukemia based on presenting characteristics and early response to treatment as determined by minimal residual disease assay. Remission Induction consisted of prednisone, vincristine, daunorubicin, and PEG-asparaginase, followed by cyclophosphamide, cytarabine, and mercaptopurine. All patients received antimetabolite-based Continuation therapy, including two cycles of Reinduction therapy, as well as four courses of high-dose methotrexate and mercaptopurine. The total duration of Continuation therapy was 120 weeks for both males and females. No patients received prophylactic cranial radiation therapy. CNS-directed therapy (triple intrathecal therapy - cytarabine, hydrocortisone, and methotrexate) was administered during all phases of therapy at an age-appropriate dose.
Demographic and clinical variables
Patient sex, race, ethnicity, treatment risk arm, and age at diagnosis were abstracted from the clinical trial and medical record. Data for insurance type (private, public) were abstracted from the medical record; consistent with other studies [32, 33] and were used as a proxy for socioeconomic status (SES).
Participation in rehabilitation services was examined from the time of Remission Induction to end of Consolidation (i.e., week 120; early intensive therapy). Rehabilitation services included any physical or occupational therapy session beyond an initial evaluation. Rehabilitation services was then coded either (yes/no) based on their medical record. For the purposes of this study, we did not examine number or intensity of rehabilitation sessions.
Neurocognitive outcomes
We report on data from protocol-directed serial neurocognitive monitoring at the end of Total 16 therapy (Continuation Week 120). Psychological examiners administered study measures under the supervision of a licensed clinical psychologist. Measures included standardized performance-based and caregiver rating measures with demonstrated reliability and validity (Supplemental Table 1). Patients completed age-appropriate measures of estimated global intelligence [34], sustained attention [35–37] working memory [38–40], processing speed [40], executive function [40] verbal learning and memory [41, 42], fine motor speed, and academics (reading, spelling, math) [40]. Caregivers completed standardized ratings of the patient’s attention [37], executive function [43] and adaptive skills in daily life [44].
Analyses
Descriptive statistics were used to characterize participants on demographic and clinical characteristics. Frequency comparisons (chi-square and one-way ANOVA) were used to compare groups with and without rehabilitation services. Descriptive statistics were used to characterize neurocognitive outcomes for the overall group and for groups with or without rehabilitation services. One-sample t-tests were used to compare neurocognitive outcomes in the overall group to age normative expectations. One-way ANOVA was used to compare neurocognitive outcomes by groups with and without rehabilitation services.
Univariate analyses by sex, age at diagnosis, insurance type, and treatment risk arm were completed separately for groups with and without rehabilitation services. All results are reported as Z scores, with a mean of 0 and a standard deviation of 1. All tests of statistical significance were 2-sided, with p-values considered significant at p < 0.05. Analyses were completed using PASW (Version 18.0) [45].
RESULTS
Participant Characteristics
Of the 267 eligible patients on Total 16, 236 patients completed end of therapy testing (Table 1). Most patients were White (80.4%) and non-Hispanic (93.2%). 53.2% (n = 125) had private insurance, 40.3% (n = 95) were female, and 54.2% (n = 128) were treated with standard-to-high risk therapy. On average, the group was 7.27 years old at diagnosis (SD = 4.85) and 9.91 years old at assessment (SD = 4.86).
Table 1.
Demographic and clinical characteristics for the overall group and groups with and without rehabilitation services
| Neurocognitive Testing | Rehabilitation Services | |||||||
|---|---|---|---|---|---|---|---|---|
| Yes (n = 236) | Yes (n = 110) | No (n = 126) | ||||||
| n | % | n | % | n | % | p 2-sided | ||
| Sex | Female | 95 | 40.3 | 52 | 47.3 | 43 | 34.1 | 0.3995 |
| Male | 141 | 59.7 | 58 | 52.7 | 83 | 65.9 | ||
| Race | White | 189 | 80.4 | 88 | 80.0 | 101 | 80.8 | 0.8668 |
| Black | 35 | 14.9 | 16 | 14.5 | 19 | 15.2 | ||
| Multiracial | 10 | 4.3 | 5 | 4.5 | 5 | 4.0 | ||
| Asian | 1 | 0.4 | 1 | 0.9 | 0 | 0.0 | ||
| Ethnicity | Hispanic | 16 | 6.8 | 8 | 7.3 | 8 | 6.4 | 0.7910 |
| Non-Hispanic | 219 | 93.2 | 102 | 92.7 | 117 | 93.6 | ||
| Risk arm | Low | 108 | 45.8 | 50 | 45.5 | 58 | 46.0 | 0.9293 |
| Standard/High | 128 | 54.2 | 60 | 54.5 | 68 | 54.0 | ||
| Insurance type | Private | 125 | 53.2 | 52 | 47.7 | 73 | 57.9 | 0.1170 |
| Public/None | 110 | 46.8 | 57 | 52.3 | 53 | 42.1 | ||
| Mean | SD | Mean | SD | Mean | SD | p 2-sided | ||
| Age at diagnosis | 7.27 | 4.85 | 7.77 | 4.67 | 6.84 | 4.97 | 0.1434 | |
| Age at assessment | 9.91 | 4.86 | 10.39 | 4.68 | 9.49 | 4.99 | 0.1539 | |
SD = standard deviation. Chi-square for race based on 3 categories. Race missing for one patient. p-value from chi-square or one-way ANOVA comparing groups based on rehabilitation services (yes/no)
Of the 236 participants completing neurocognitive testing, 110 received rehabilitation services during early intensive therapy (46.6%). There were no significant differences between groups participating or not participating in rehabilitation services on distribution of patient sex (p = 0.3995), race (p = 0.8668), ethnicity (p = 0.7910), risk arm (p = 0.9293), or insurance type (p = 0.1170). There were no significant differences in mean age at diagnosis (p = 0.1434) or mean age at assessment (p = 0.1539).
Neurocognitive outcomes for the overall group
Overall group means were in the Average range based on the normative sample for all neurocognitive outcomes (Table 2); however, mean scores were statistically significantly below age expectations on measures of global intelligence (Estimated IQ, p = 0.0007), attention (Omissions, p = 0.0001; Hit Reaction Time, p = 0.0470; Variability, p < 0.0001; Detectability, p = 0.0028), working memory (Digit Span, p < 0.0001), processing speed (Visual Matching, p < 0.0001), executive function (Retrieval Fluency, p = 0.0001; Global Executive, p = 0.0001), memory and learning (Learning Slope, p = 0.0328), fine-motor (Purdue Pegs, p < 0.0001), academics (Letter-Word Identification, p = 0.0118; Applied Problems, p = 0.0006) and adaptive functioning (General Adaptive Composite, p = 0.0002).
Table 2.
Neurocognitive outcomes for the overall group and the groups with and without rehabilitation services
| Overall Group | Rehab-Yes | Rehab-No | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Mean | SD | p 1 | Mean | SD | Mean | SD | p 2 | ||
| Global Intelligence | Estimated IQ | −0.21 | 0.93 | 0.0007 | −0.20 | 0.97 | −0.21 | 0.91 | 0.9440 |
| Attention | Omissions** | 0.65 | 2.29 | 0.0001 | 0.49 | 1.35 | 0.80 | 2.90 | 0.3460 |
| Hit Reaction Time** | −0.16 | 1.17 | 0.0470 | −0.22 | 1.09 | −0.12 | 1.24 | 0.5419 | |
| Variability** | 0.57 | 1.09 | <0.0001 | 0.62 | 1.07 | 0.53 | 1.12 | 0.5752 | |
| Detectability** | 0.22 | 1.04 | 0.0028 | 0.20 | 1.09 | 0.24 | 1.00 | 0.7747 | |
| Beta** | 0.06 | 0.95 | 0.3689 | 0.11 | 0.98 | 0.01 | 0.92 | 0.4695 | |
| Attention Problems*, ** | 0.11 | 1.03 | 0.1139 | 0.28 | 1.04 | −0.04 | 1.00 | 0.0222 | |
| Working Memory | Digit Span | −0.50 | 0.94 | <0.0001 | −0.52 | 0.98 | −0.48 | 0.91 | 0.7957 |
| Auditory Working Memory | 0.11 | 0.87 | 0.0774 | 0.13 | 0.80 | 0.09 | 0.93 | 0.7761 | |
| Processing Speed | Visual Matching | −0.35 | 1.14 | <0.0001 | −0.31 | 1.08 | −0.39 | 1.20 | 0.6304 |
| Decision Speed | 0.05 | 1.16 | 0.5629 | 0.07 | 1.24 | 0.02 | 1.08 | 0.7550 | |
| Executive Function | Retrieval Fluency | −0.23 | 0.89 | 0.0001 | −0.21 | 0.92 | −0.25 | 0.86 | 0.6826 |
| Global Executive*, ** | 0.33 | 1.20 | 0.0001 | 0.59 | 1.17 | 0.10 | 1.18 | 0.0029 | |
| Learning & Memory | List A Total | 0.02 | 0.90 | 0.7307 | 0.15 | 0.81 | −0.10 | 0.96 | 0.0573 |
| Short Delay Free Recall | −0.01 | 0.98 | 0.9416 | 0.07 | 0.89 | −0.07 | 1.06 | 0.3068 | |
| Long Delay Free Recall | −0.08 | 1.05 | 0.3058 | 0.07 | 0.96 | −0.22 | 1.12 | 0.0598 | |
| Learn Slope | −0.15 | 0.99 | 0.0328 | −0.15 | 0.91 | −0.16 | 1.06 | 0.9751 | |
| Discriminability | 0.10 | 1.18 | 0.2352 | 0.23 | 1.05 | −0.02 | 1.28 | 0.1567 | |
| Fine Motor | Purdue Pegs | −0.63 | 1.11 | <0.0001 | −0.62 | 1.11 | −0.64 | 1.11 | 0.9269 |
| Academics | Letter Word Identification | −0.18 | 1.02 | 0.0118 | −0.19 | 1.04 | −0.18 | 1.00 | 0.9538 |
| Applied Problems | −0.21 | 0.86 | 0.0006 | −0.15 | 0.93 | −0.26 | 0.79 | 0.3879 | |
| Adaptive Skills | Global Adaptive* | −0.30 | 1.11 | 0.0002 | −0.47 | 1.16 | −0.14 | 1.03 | 0.0310 |
Caregiver ratings.
Higher scores = more problems or worse performance.
SD = standard deviation. All results are reported as Z scores, with a mean of 0 and a standard deviation of 1. 1: 2-sided p-value from one-sample t-test comparing overall group mean to normative expectations. 2: 2-sided p-value from one way ANOVA comparisons in groups with and without rehabilitation services.
Neurocognitive outcomes for rehabilitation groups
There were no significant differences between groups with and without rehabilitation services on performance-based measures of global intelligence (Estimated IQ, p = 0.9440), attention (Omissions, p = 0.3460; Hit Reaction Time, p = 0.5419; Variability, p = 0.5752; Detectability, p = 0.7747), working memory (Digit Span, p = 0.7957), processing speed (Visual Matching, p = 0.6304), executive function (Retrieval Fluency, p = 0.6826), memory and learning (Learning Slope, p = 0.9751), fine-motor (Purdue Pegs, p = 0.9269), and academics (Letter-Word Identification, p = 0.9538; Applied Problems, p = 0.3879).
Compared to those without rehabilitation services, caregivers rated those with rehabilitation services as having significantly more problems with attention (Attention Problems, Mean[SD], 0.28[1.04] vs. −0.04[1.00], p = 0.0222) and global executive function (Global Executive, 0.59[1.17] vs. 0.10[0.96], p = 0.0029), and significantly lower adaptive skills (Global Adaptive, −0.47[1.16] vs. −0.14[1.03], p = 0.0310).
Univariate analyses of groups with and without rehabilitation services
Tables 3 and 4 show results from univariate analyses examining associations of patient sex, age at diagnosis, insurance type, and treatment risk arm with neurocognitive outcomes for groups with and without rehabilitation services. Among the rehabilitation services group, patient sex, age at diagnosis, and treatment risk arm were significant predictors of neurocognitive outcomes. Compared to females, males had significantly lower performance on measures of processing speed (Visual Matching, −0.08[1.06] vs. −0.53[1.06], p = 0.0292), and elevated caregiver ratings of executive dysfunction (Global Executive, 0.29[0.95] vs. 0.85[1.28], p = 0.0156). Compared to patients who were older at diagnosis, younger patients had performed significantly worse on measures of executive function (Retrieval Fluency, −0.40[0.97] vs. 0.01[0.82], p = 0.0228) and working memory (Digit Span, −0.95[0.94] vs. −0.24[0.91], p = 0.0009). Compared to those in the standard-to-high risk arm, patients treated with low risk therapy performed significantly worse on a measure of working memory (Digit Span, −0.89[0.89] vs. −0.28[0.97], p = 0.0047).
Table 3.
Univariate analyses of the association of risk factors with neurocognitive outcomes in the group with rehabilitation services
| Sex | Age at Diagnosis (years) | Treatment Risk Arm | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| M | SD | p | M | SD | P | M | SD | p | M | SD | p | ||||||
| Global Intelligence | Estimated IQ | female | −0.11 | 0.89 | 0.3207 | ≤ 7 | −0.29 | 1.12 | 0.3404 | private | −0.04 | 0.97 | 0.1337 | low | −0.28 | 1.10 | 0.4696 |
| male | −0.29 | 1.03 | > 7 | −0.11 | 0.76 | public/none | −0.32 | 0.93 | std/high | −0.14 | 0.84 | ||||||
| Attention | Omissions ** | female | 0.50 | 1.38 | 0.1731 | ≤ 7 | 0.69 | 1.42 | 0.1531 | private | 0.31 | 1.31 | 0.2041 | low | 0.61 | 1.26 | 0.4548 |
| male | 0.49 | 1.33 | > 7 | 0.30 | 1.26 | public/none | 0.66 | 1.38 | std/high | 0.40 | 1.41 | ||||||
| female | 0.13 | 0.96 | 0.9706 | ≤ 7 | 0.29 | 1.09 | 0.9809 | private | 0.08 | 1.06 | 0.0628 | low | 0.28 | 1.10 | 0.9933 | ||
| male | 0.42 | 1.09 | > 7 | 0.28 | 0.99 | public/none | 0.47 | 1.00 | std/high | 0.28 | 0.99 | ||||||
| Working Memory | Digit Span | female | −0.37 | 0.85 | 0.1975 | ≤ 7 | −0.95 | 0.94 | 0.0009 | private | −0.44 | 0.90 | 0.5711 | low | −0.89 | 0.89 | 0.0047 |
| male | −0.65 | 1.07 | > 7 | −0.24 | 0.91 | public/none | −0.56 | 1.04 | std/high | −0.28 | 0.97 | ||||||
| Processing Speed | Visual Matching | female | −0.08 | 1.06 | 0.0292 | ≤ 7 | −0.13 | 1.10 | 0.0800 | private | −0.10 | 1.16 | 0.0762 | low | −0.21 | 1.03 | 0.3685 |
| male | −0.53 | 1.06 | > 7 | −0.50 | 1.04 | public/none | −0.48 | 1.00 | std/high | −0.40 | 1.12 | ||||||
| Executive Function | Retrieval Fluency | female | −0.01 | 0.79 | 0.3580 | ≤ 7 | −0.40 | 0.97 | 0.0228 | private | −0.15 | 0.90 | 0.6610 | low | −0.29 | 0.94 | 0.4186 |
| male | −0.39 | 1.00 | > 7 | 0.01 | 0.82 | public/none | −0.23 | 0.92 | std/high | −0.14 | 0.90 | ||||||
| female | 0.29 | 0.95 | 0.0156 | ≤ 7 | 0.61 | 1.25 | 0.8286 | private | 0.51 | 1.19 | 0.5941 | low | 0.58 | 1.22 | 0.9329 | ||
| male | 0.85 | 1.28 | > 7 | 0.56 | 1.08 | public/none | 0.64 | 1.15 | std/high | 0.60 | 1.14 | ||||||
| Learning & Memory | Learn Slope | female | −0.13 | 0.91 | 0.8611 | ≤ 7 | −0.11 | 0.96 | 0.7135 | private | −0.17 | 0.93 | 0.9830 | low | −0.12 | 0.92 | 0.7899 |
| male | −0.17 | 0.92 | > 7 | −0.18 | 0.88 | public/none | −0.17 | 0.89 | std/high | −0.17 | 0.91 | ||||||
| Fine Motor Speed | Purdue Pegs | female | −0.55 | 1.05 | 0.4961 | ≤ 7 | −0.48 | 1.11 | 0.1646 | private | −0.57 | 1.03 | 0.6680 | low | −0.40 | 1.05 | 0.0718 |
| male | −0.69 | 1.18 | > 7 | −0.78 | 1.12 | public/none | −0.66 | 1.21 | std/high | −0.80 | 1.15 | ||||||
| Academics | Letter Word Identification | female | −0.16 | 0.94 | 0.8390 | ≤ 7 | −0.07 | 1.20 | 0.2989 | private | 0.01 | 0.99 | 0.1429 | low | −0.11 | 1.10 | 0.5114 |
| male | −0.21 | 1.15 | > 7 | −0.30 | 0.86 | public/none | −0.30 | 1.04 | std/high | −0.25 | 1.00 | ||||||
| female | −0.08 | 0.91 | 0.4540 | ≤ 7 | −0.04 | 0.98 | 0.2534 | private | 0.00 | 1.04 | 0.2054 | low | −0.02 | 0.96 | 0.2166 | ||
| male | −0.22 | 0.96 | > 7 | −0.26 | 0.88 | public/none | −0.25 | 0.82 | std/high | −0.26 | 0.91 | ||||||
| Adaptive Skills | Global Adaptive* | female | −0.28 | 1.15 | 0.1373 | ≤ 7 | −0.57 | 1.15 | 0.3564 | private | −0.51 | 1.15 | 0.7680 | low | −0.45 | 1.06 | 0.8599 |
| male | −0.63 | 1.15 | > 7 | −0.35 | 1.18 | public/none | −0.44 | 1.18 | std/high | −0.49 | 1.25 | ||||||
Caregiver ratings.
Higher scores = worse performance or more problems.
SD = standard deviation. All results are reported as Z scores, with a mean of 0 and a standard deviation of 1. Bolded font = significant at p<.05. 2-sided p-values from one way ANOVA comparing outcomes risk factors (sex, age at diagnosis, insurance type, treatment risk arm.)
Table 4.
Univariate analyses of the association of risk factors with neurocognitive outcomes in patients without rehabilitation services.
| Sex | Age at Diagnosis (years) | Treatment Risk Arm | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| M | SD | p | M | SD | p | M | SD | p | M | SD | p | ||||||
| Global Intelligence | Estimated IQ | female | 0.08 | 0.85 | 0.0113 | ≤ 7 | −0.23 | 1.00 | 0.8063 | private | −0.04 | 0.86 | 0.0115 | low | −0.21 | 1.03 | 0.9491 |
| male | −0.36 | 0.90 | > 7 | −0.19 | 0.74 | public/none | −0.45 | 0.92 | std/high | −0.22 | 0.79 | ||||||
| Attention | Omissions ** | female | 0.85 | 3.92 | 0.8817 | ≤ 7 | 0.55 | 1.18 | 0.2869 | private | 0.80 | 3.30 | 0.9990 | low | 0.32 | 1.04 | 0.1252 |
| male | 0.77 | 2.24 | > 7 | 1.16 | 4.33 | public/none | 0.80 | 2.34 | std/high | 1.19 | 3.78 | ||||||
| female | −0.10 | 0.97 | 0.6626 | ≤ 7 | 0.04 | 1.02 | 0.2035 | private | −0.01 | 1.00 | 0.6794 | low | 0.03 | 0.91 | 0.4654 | ||
| male | −0.01 | 1.02 | > 7 | −0.22 | 0.95 | public/none | −0.09 | 1.01 | std/high | −0.11 | 1.08 | ||||||
| Working Memory | Digit Span | female | −0.25 | 0.80 | 0.1336 | ≤ 7 | −0.66 | 0.86 | 0.1247 | private | −0.42 | 0.83 | 0.5185 | low | −0.37 | 0.86 | 0.3646 |
| male | −0.58 | 0.94 | > 7 | −0.35 | 0.94 | public/none | −0.55 | 1.01 | std/high | −0.56 | 0.95 | ||||||
| Processing Speed | Visual Matching | female | 0.04 | 0.97 | 0.0073 | ≤ 7 | −0.26 | 1.19 | 0.1486 | private | −0.10 | 1.15 | 0.0030 | low | −0.24 | 1.15 | 0.2139 |
| male | −0.59 | 1.25 | > 7 | −0.59 | 1.20 | public/none | −0.75 | 1.18 | std/high | −0.51 | 1.24 | ||||||
| Executive Function | Retrieval Fluency | female | −0.18 | 0.81 | 0.5156 | ≤ 7 | −0.29 | 0.85 | 0.5977 | private | −0.16 | 0.87 | 0.1854 | low | −0.31 | 0.85 | 0.5226 |
| male | −0.29 | 0.89 | > 7 | −0.20 | 0.90 | public/none | −0.38 | 0.85 | std/high | −0.21 | 0.88 | ||||||
| female | −0.15 | 1.05 | 0.1273 | ≤ 7 | 0.15 | 1.17 | 0.6066 | private | 0.05 | 1.12 | 0.6193 | low | 0.03 | 1.11 | 0.5737 | ||
| male | 0.22 | 1.23 | > 7 | −0.01 | 1.21 | public/none | 0.17 | 1.28 | std/high | 0.16 | 1.26 | ||||||
| Learning & Memory | Learn Slope | female | 0.10 | 1.09 | 0.0769 | ≤ 7 | −0.11 | 1.07 | 0.6339 | private | −0.18 | 1.10 | 0.7869 | low | −0.08 | 1.03 | 0.5301 |
| male | −0.29 | 1.02 | > 7 | −0.21 | 1.05 | public/none | −0.13 | 1.02 | std/high | −0.21 | 1.09 | ||||||
| Fine Motor Speed | Purdue Pegs | female | −0.35 | 0.93 | 0.0431 | ≤ 7 | −0.61 | 1.17 | 0.7504 | private | −0.56 | 1.06 | 0.3675 | low | −0.73 | 1.15 | 0.4285 |
| male | −0.78 | 1.16 | > 7 | −0.68 | 1.01 | public/none | −0.74 | 1.16 | std/high | −0.57 | 1.07 | ||||||
| Academics | Letter Word Identification | female | 0.12 | 0.84 | 0.0288 | ≤ 7 | −0.17 | 1.09 | 0.9502 | private | 0.18 | 0.90 | 0.0000 | low | 0.07 | 1.07 | 0.0199 |
| male | −0.33 | 1.05 | > 7 | −0.18 | 0.87 | public/none | −0.59 | 0.96 | std/high | −0.38 | 0.90 | ||||||
| female | −0.12 | 0.86 | 0.1841 | ≤ 7 | −0.16 | 0.83 | 0.1133 | private | −0.04 | 0.71 | 0.0021 | low | −0.08 | 0.85 | 0.0350 | ||
| male | −0.33 | 0.76 | > 7 | −0.40 | 0.73 | public/none | −0.50 | 0.82 | std/high | −0.40 | 0.72 | ||||||
| Adaptive Skills | Global Adaptive, * | female | 0.20 | 0.98 | 0.0130 | ≤ 7 | −0.23 | 1.05 | 0.1974 | private | −0.05 | 1.06 | 0.3094 | low | −0.17 | 1.06 | 0.7291 |
| male | −0.32 | 1.02 | > 7 | 0.05 | 0.99 | public/none | −0.26 | 0.99 | std/high | −0.10 | 1.01 | ||||||
Caregiver ratings.
Higher scores = worse performance or more problems.
SD = standard deviation. All results are reported as Z scores, with a mean of 0 and a standard deviation of 1. Bolded font = significant at p<.05. 2-sided p-values from one way ANOVA comparing outcomes risk factors (sex, age at diagnosis, insurance type, treatment risk arm.)
Among patients without rehabilitation services, patient sex, treatment risk arm, and insurance type were significant predictors of neurocognitive outcomes. Compared to females, males had significantly worse performance on measures of intellectual functioning (Estimated IQ, 0.08[0.85] vs. −0.36[0.90], p = 0.0113), processing speed (Visual Matching, 0.04[0.97] vs. −0.59[1.25], p = 0.0073), fine-motor coordination (Purdue Pegs, −0.35[0.93] vs. −0.78[1.16], p = 0.0431), and reading (Letter-Word Identification, 0.12[0.84] vs. −0.33[1.05], p = 0.0288), and significantly lower independence with adaptive skills (General Adaptive, 0.20[0.98] vs. −0.32[1.02], p = 0.0130). Compared to those with private insurance, patients with public insurance performed significantly worse on measures of intellectual functioning (Estimated IQ, −0.04[0.86] vs. −0.45[0.92], p = 0.0115), processing speed (Visual Matching, −0.10[1.15] vs. −0.75[1.18], p = 0.0030), reading (Letter-Word Identification, 0.18[0.90] vs. −0.50[0.96], p < 0.0001), and math (Applied Problems, −0.04[0.71] vs. −0.50[0.82], p = 0.0021). Compared to low risk patients, standard-to-high risk patients performed significantly worse in reading (Letter-Word Identification, 0.07[1.07] vs. −0.38[0.90], p = 0.0199) and math (Applied Problems, −0.08[0.85] vs. −0.40[0.72], p = 0.0350).
Discussion
Rehabilitation intervention during treatment for ALL and SES are associated with end of therapy neurocognitive outcomes. The research within the general pediatric population highlights the association between low SES and worse early brain development [46] and cognitive outcomes later in life [47]. Within the pediatric oncology population, lower SES has also been documented as a risk factor for worse neurocognitive outcomes in recent years [5, 7, 14, 48, 49]. Since SES is a significant risk factor, finding potential interventions to lessen the effect of low SES is critical. A few studies have documented early intervention services can have significant positive impact on child development [50] and even single intervention approaches can help reduce the negative effects of poverty [51]. In fact, early intervention within families with less resources showed even greater improvement with interventions than those with higher resources [50].
Our findings showed that compared to those without rehabilitation, children who required rehabilitation had worse neurocognitive outcomes at the end of therapy. However, we only observed differences by SES in the group without rehabilitation. Therefore, participation in rehabilitation may have mitigated the impact of lower SES. This highlights the role of early intervention as a potential mediator for neurocognitive risk conferred by SES, as well as the predictive value of SES on neurocognitive outcomes in childhood cancer survivors. Although using insurance type as a proxy for SES is well documented, it does have its limitations as it does not fully capture all potential aspects of SES and reduces the generalizability outside the United States. Future studies may consider using a more comprehensive measure of SES, such as the Area Deprivation Index (ADI) [60].
Patient sex was also a predictor of outcomes in groups with and without rehabilitation services, with female patients having better performance and caregiver outcomes. The literature on patient sex as a risk factor for neurocognitive late effects is mixed [5, 52] with some studies suggesting that sex differences may be more related to the specific neurocognitive skill being assessed rather than a global predictor [53]. The particular skills, in the existing research, impacted in females more than males are largely attention and executive function which are predominantly frontal lobe functions. From a neurodevelopmental perspective, the brain develops in a hierarchical way with the frontal lobes being the last to fully develop. Additionally, a more recent imaging study found that female survivors of ALL have functional differences in activation in particular brain regions (right parietal operculum, supramarginal gyrus and superior frontal medial gyrus) compared to males but did not find a significant difference between females and males on a performance-based task [54]. Ultimately, sex differences may be due to neuroanatomical differences putting females at greater risk for experiencing cognitive late effects, but these may not be observed on performance-based measures or present as challenges until adulthood.
Similarly, age was a significant predictor of outcomes in the group with rehabilitation services, with younger age at diagnosis being associated with worse outcomes on tasks of working memory and executive function. This finding is consistent with the existing literature that has documented younger age as a significant risk factor for greater cognitive effects [3, 9–11]. Although younger age was not a significant predictor of outcomes in the group without rehabilitation, this could be due to our neurocognitive data reflecting functioning at the end of Total 16 therapy (Continuation Week 120) versus during the survivorship period.
Compared to those not receiving rehabilitation services, those receiving services were rated as having elevated ratings of attention problems and executive function in daily life, as well as less independence with activities of daily life. We did not find differences between groups on performance outcomes. One possibility for the discrepancy in findings is rater bias. However, research has well-documented the discrepancy between caregiver rating forms and performance-based measures [55, 56, 57]. It is not uncommon to find discrepancies on performance outcomes and caregiver ratings, as rating measures provide information regarding daily behavior that is often challenging to elicit during structured clinical assessment.
For the group with rehabilitation, the results could still reflect a gain from their pre-rehabilitation functioning (e.g., delays prior to diagnoses, prematurity, very low birth weight). Future studies could aim to address this gap by collecting baseline data (pre-rehab) on cognitive and behavioral functioning to account for early delays and then collecting data on cognitive and behavioral functioning after treatment (post-rehab), in order to improve our understanding of the effects of engagement in rehabilitation on functioning.
Our study also utilized completed rehabilitation visits in either physical or occupation therapy to account for participation in rehabilitation during early intensive therapy. However, we did not examine number/frequency or intensity of these sessions. We also did not have an effective way to assess or determine the child’s level of engagement during these sessions. Knowing level of engagement during rehabilitation or the physical intensity of the session would provide greater detail on the child’s physical activity which can impact neurocognitive outcomes [58, 59]. Future studies could address this limitation through collecting more detailed information on participation during rehabilitation services as well as looking at number of rehabilitation sessions to account for a potential dose dependent relationship.
Individuals who are referred for rehabilitation services may have a greater level of deficits requiring rehabilitation during early intensive therapy. Therefore, our results may reflect continued difficulty rather than a true difference from their baseline functioning. However, it is reassuring that despite these caregiver’s reported difficulties, those with rehabilitation performed similarly compared to those without rehabilitation across neurocognitive domains. The lack of significant neurocognitive differences on performance measures between groups poses the possibility that engagement in rehabilitation services during early intensive therapy may serve as a buffer for neurocognitive functioning. Future studies can aim to address this by comparing pre-rehabilitation functioning to post-rehabilitation functioning to account for the potential effects of a lowered baseline.
Conclusion
Taken together, our study highlights the importance of early intervention services and need for increased access to rehabilitation services. SES impacts neurocognitive outcomes and impacts multiple aspects along the therapy journey with access to care being only one factor. Future studies may wish to focus on further delineating the frequency and intensity of rehabilitation services to determine if there is a dose/response relationship with neurocognitive outcomes. Continued focus on interventions that can further mitigate potential cognitive late effects in children undergoing any cancer therapies will be critical to maximize quality of life.
Supplementary Material
Funding:
This work was supported by the National Cancer Institute (P30 CA21765 and GM92666 to St. Jude Children’s Research Hospital) and the American Lebanese Syrian Associated Charities (ALSAC).
Footnotes
Conflicts of Interest: The authors have no conflicts of interest.
REFERENCES
- 1.American Cancer Society. Cancer Facts & Figures https://www.cancer.org/cancer/cancer-in-children/key-statistics.html#references. 2021.
- 2.Krull KR et al. Neurocognitive outcomes decades after treatment for childhood acute lymphoblastic leukemia: a report from the St Jude lifetime cohort study. J of Clin Oncol. 2013; 31: 4407. 10.1200/JCO.2012.48.2315 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lofstad GE et al. Neurocognitive outcomes and compensating possibilities in children and adolescents treated for acute lymphoblastic leukemia with chemotherapy only. Front Psychol. 2019; 10: 1027. 10.3389/fpsyg.2019.01027 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Pui C-H & Evans WEA 50-year journey to cure childhood acute lymphoblastic leukemia. Semin Hematol. 2013; 50: 185–96. 10.1053/j.seminhematol.2013.06.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Conklin HM et al. Cognitive outcomes following contemporary treatment without cranial irradiation for childhood acute lymphoblastic leukemia. J Natl Cancer Inst. 2012; 104: 1386–95. 10.1093/jnci/djs344 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.van der Plas E et al. Cognitive impairment in survivors of pediatric acute lymphoblastic leukemia treated with chemotherapy only. J Clin Oncol. 2021; 39(16): 1705–17. https://doi: 10.1200/jco.20.02322 [DOI] [PubMed] [Google Scholar]
- 7.Hardy KK et al. Neurocognitive functioning of children treated for high-risk B-acute lymphoblastic leukemia randomly assigned to different methotrexate and corticosteroid treatment strategies: a report from the children’s oncology group. J Clin Oncol. 2017; 35: 2700–7. https://doi: 10.1200/jco.2016.71.7587 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Liu W et al. Evolution of neurocognitive function in long-term survivors of childhood acute lymphoblastic leukemia treated with chemotherapy only. J Cancer Surviv. 2018;12(3): 398–406. https://doi: 10.1007/s11764-018-0679-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Jacola LM et al. Longitudinal assessment of neurocognitive outcomes in survivors of childhood acute lymphoblastic leukemia treated on a contemporary chemotherapy protocol. J of Clin Oncol. 2016; 34: 1239–47. 10.1200/jco.2015.64.3205 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sherief LM et al. A cross-sectional study of two chemotherapy protocols on long term neurocognitive functions in Egyptian children surviving acute lymphoblastic leukemia. Curr Pediatr Rev. 2018; 14(4): 253–60. 10.2174/1573396314666181031134919 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Sleurs C et al. Intellectual development of childhood ALL patients: a multicenter longitudinal study. Psychooncology 2017; 26(4): 508–14. 10.1002/pon.4186 [DOI] [PubMed] [Google Scholar]
- 12.Cheung YT et al. Impact of sleep, fatigue, and systemic inflammation on neurocognitive and behavioral outcomes in long‐term survivors of childhood acute lymphoblastic leukemia. Cancer. 2017; 123: 3410–9. 10.1002/cncr.30742 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.von der Weid N et al. Intellectual outcome in children and adolescents with acute lymphoblastic leukaemia treated with chemotherapy alone: age-and sex-related differences. Eur J Cancer. 2003; 39: 359–65. 10.1016/s0959-8049(02)00260-5 [DOI] [PubMed] [Google Scholar]
- 14.Jacola LM et al. Adaptive functioning and academic achievement in survivors of childhood acute lymphoblastic leukemia: A report from the Children’s Oncology Group. Pediatr Blood Cancer. 2021: 68(4): e28913. 10.1002/pbc.28913 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Jacola LM et al. Cognitive, behaviour, and academic functioning in adolescent and young adult survivors of childhood acute lymphoblastic leukaemia: a report from the Childhood Cancer Survivor Study. Lancet Psychiatry. 2016; 3(10): 965–72. 10.1016/S2215-0366(16)30283-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kunin-Batson A, Kadan-Lottick N & Neglia JP The contribution of neurocognitive functioning to quality of life after childhood acute lymphoblastic leukemia. Psychooncol. 2014; 23(6): 692–9. 10.1002/pon.3470 [DOI] [PubMed] [Google Scholar]
- 17.Bhojwani D et al. Methotrexate-induced neurotoxicity and leukoencephalopathy in childhood acute lymphoblastic leukemia. J Clin Oncol. 2014; 32(9): 949–59. 10.1200/jco.2013.53.0808 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Rijmenams I et al. Age- and intravenous methotrexate-associated leukoencephalopathy and its neurological impact in pediatric patients with lymphoblastic leukemia. Cancers (Basel). 2021; 13(8): 1939. 10.3390/cancers13081939 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Guo J, Han Y, Li Y & Reddick WE Reduced brain microstructural asymmetry in patients with childhood leukemia treated with chemotherapy compared with healthy controls. PLoS ONE. 2019; 14. 10.1371/journal.pone.0216554 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kesler SR et al. Brain network connectivity and executive function in long-term survivors of childhood acute lymphoblastic leukemia. Brain Connect. 2018; 8(6): 333–42. 10.1089/brain.2017.0574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Kesler SR, Gugel M, Huston-Warren E & Watson C Atypical structural connectome organization and cognitive impairment in young survivors of acute lymphoblastic leukemia. Brain Connect. 2016; 6(4): 273–82. 10.1089/brain.2015.0409 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zou L et al. Abnormal topological organization in white matter structural networks in survivors of acute lymphoblastic leukaemia with chemotherapy treatment. Oncotarget. 2017; 8:60568–60575. 10.18632/oncotarget.19104 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Harman JL et al. Parent-reported executive functioning in young children treated for cancer. Child Neuropsychol. 2019; 25(4): 548–60. 10.1080/09297049.2018.1503647 [DOI] [PubMed] [Google Scholar]
- 24.Kenney AE, Harman JL, Molnar AE et al. Early cognitive and adaptive functioning of clinically referred infants and toddlers with cancer. J Clin Psychol Med Settings. 2020; 27: 41–7. 10.1007/s10880-019-09619-1 [DOI] [PubMed] [Google Scholar]
- 25.Willard VW et al. Cognitive and psychosocial functioning of preschool-aged children with cancer. J Dev Behav Pediatr. 2017; 38(8): 638–45. 10.1097/DBP.0000000000000512 [DOI] [PubMed] [Google Scholar]
- 26.Coombs A, Schilperoort H & Sargent B The effect of exercise and motor interventions on physical activity and motor outcomes during and after medical intervention for children and adolescents with acute lymphoblastic leukemia: A systematic review. Crit Rev Oncol Hematol. 2020; 152: 103004. 10.1016/j.critrevonc.2020.103004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Tanner L, Sencer S & Hooke MC The stoplight program: a proactive physical therapy intervention for children with acute lymphoblastic leukemia. J Pediatr Oncol Nurs. 2017; 34(5): 347–57. 10.1177/1043454217698093 [DOI] [PubMed] [Google Scholar]
- 28.Yildiz Kabak V, Ekinci Y, Atasavun Uysal S, Cetin M & Duger T Motor and basic cognitive functions in children with acute lymphoblastic leukemia undergoing induction or consolidation chemotherapy. Percept Mot Skills. 2021; 128(3): 1091–1106. 10.1177/00315125211002065 [DOI] [PubMed] [Google Scholar]
- 29.Hanna S, Elshennawy S, El-Ayadi M & Abdelazeim F Investigating fine motor deficits during maintenance therapy in children with acute lymphoblastic leukemia. Pediatr Blood Cancer. 2020; 67(7): e28385. 10.1002/pbc.28385 [DOI] [PubMed] [Google Scholar]
- 30.Marchese V, Rock K, York T, Creath R & Gray V Neuromuscular mechanisms that contribute to gross motor performance in survivors of childhood acute lymphoblastic leukemia. J Pediatr Rehabil Med. 2021; 14(3): 415–23. 10.3233/PRM-200784 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Jeha S et al. Improved CNS control of childhood acute lymphoblastic leukemia without cranial irradiation: St Jude Total Therapy Study 16. J Clin Oncol. 2019; 37(35): 3377–91. 10.1200/jco.19.01692 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Buck GM, Msall ME, Schisterman EF, Lyon NR & Rogers BT Extreme prematurity and school outcomes. Paediatr Perinat Epidemiol. 2000; 14(4): 324–31. 10.1046/j.1365-3016.2000.00276.x [DOI] [PubMed] [Google Scholar]
- 33.Marcin JP, Schembri MS, He J & Romano PS A population-based analysis of socioeconomic status and insurance status and their relationship with pediatric trauma hospitalization and mortality rates. Am J Public Health. 2003; 93: 461–6. 10.2105/ajph.93.3.461 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Roid GH Stanford-Binet Intelligence Scales–Fifth Edition. Itasca, IL: Riverside Publishing; 2003. [Google Scholar]
- 35.Conners CK Conners’ kiddie continuous performance test. Multi-Health Systems Incorporated; 2006. [Google Scholar]
- 36.Conners CK & staff MHS Conners’ Continuous Performance Test (CPT II): Version 5 for Windows technical guide and software manual. Multi-Health Systems Incorporated; 2004. [Google Scholar]
- 37.Kamphaus RW Behavior Assessment System for Children, (BASC‐2). The encyclopedia of clinical psychology 1–6. 2014. [Google Scholar]
- 38.Wechsler D WAIS-III Administration and scoring manual. The Psychological Association; 1997. [Google Scholar]
- 39.Wechsler D Wechsler intelligence scale for children—4th edition (WISC-IV®). The Psychological Corporation: San Antonio, TX; 2003. [Google Scholar]
- 40.Woodcock RW, McGrew KS & Mather N Woodcock-Johnson III NU Complete. Riverside Publishing; 2001. [Google Scholar]
- 41.Delis DC, Kramer JH, Kaplan E & Ober BA California Verbal Learning Test, 2nd edition. The Psychological Corporation: San Antonio, TX; 2000. [Google Scholar]
- 42.Fridlund AJ & Delis DC California verbal learning test–children’s version. San Antonio, TX: The Psychological Corporation: San Antonio, TX; 1994. [Google Scholar]
- 43.Gioia GA, Andrwes K & Isquith PK Behavior rating inventory of executive function-preschool version (BRIEF-P). Psychological Assessment Resources: Odessa, FL; 1996. [Google Scholar]
- 44.Harrison PL & Oakland T Adaptive behavior assessment system. Psychological Corporation: San Antonio, TX; 2000. [Google Scholar]
- 45.SPSS Inc. Released 2009. PASW Statistics for Windows, Version 18.0. Chicago: SPSS Inc. [Google Scholar]
- 46.Olson L, Chen B, & Fishman I (2021). Neural correlates of socioeconomic status in early childhood: a systematic review of the literature. Child Neuropsychol. 2021; 27(3): 390–423. 10.1080/09297049.2021.1879766 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Barch DM et al. Early childhood socioeconomic status and cognitive and adaptive outcomes at the transition to adulthood: the mediating role of gray matter development across five scan waves. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022;7(1):34–44. 10.1016/j.bpsc.2021.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Patel SK et al. Socioeconomic status as a possible moderator of neurocognitive outcomes in children with cancer. Psychooncology. 2016;25(1):115–8. 10.1002/pon.3797 [DOI] [PubMed] [Google Scholar]
- 49.Torres VA et al. The impact of socioeconomic status (SES) on cognitive outcomes following radiotherapy for pediatric brain tumors: a prospective, longitudinal trial. Neuro Oncol. 2021; 23(7): 1173–82. 10.1093/neuonc/noab018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Bann CM et al. Home-based early intervention and the influence of family resources on cognitive development. Pediatrics. 2016; 137(4): e20153766. 10.1542/peds.2015-3766 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Blair C, & Raver CC (2016). Poverty, stress, and brain development: New directions for prevention and intervention. Acad Pediatr. 2016; 16(3 Suppl): S30–6. 10.1016/j.acap.2016.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Jain N et al. Sex‐specific attention problems in long‐term survivors of pediatric acute lymphoblastic leukemia. Cancer. 2009; 115(18): 4238–45. 10.1002/cncr.24464 [DOI] [PubMed] [Google Scholar]
- 53.van der Plas E et al. Sex-specific associations between chemotherapy, chronic conditions, and neurocognitive impairment in acute lymphoblastic leukemia survivors: a report from the childhood cancer survivor study. J Natl Cancer Inst. 2021; 113(5): 588–596. 10.1093/jnci/djaa136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Gandy K et al. Sex-based differences in functional brain activity during working memory in survivors of pediatric acute lymphoblastic leukemia. JNCI Cancer Spectr. 2022; 6(2): 10.1093/jncics/pkac026 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Silver CH Sources of data about children’s executive functioning: Review and commentary. Child Neuropsychol. 2014;20(1):1–13. 10.1080/09297049.2012.727793. [DOI] [PubMed] [Google Scholar]
- 56.Krieger V, & Amador-Campos JA (2018). Assessment of executive function in ADHD adolescents: contribution of performance tests and rating scales. Child Neuropsychol, 2018; 24(8): 1063–1087. 10.1080/09297049.2017.1386781. [DOI] [PubMed] [Google Scholar]
- 57.Ten Eycke KD, & Dewey D Parent-report and performance-based measures of executive function assess different constructs. Child Neuropsychol. 2016;22(8): 889–906. 10.1080/09297049.2015.1065961 [DOI] [PubMed] [Google Scholar]
- 58.Sahnoune I et al. Exercise ameliorates neurocognitive impairments in a translational model of pediatric radiotherapy. Neuro Oncol. 2018; 20(5): 695–704. 10.1093/neuonc/nox197 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Zeng N et al. Effects of Physical Activity on Motor Skills and Cognitive Development in Early Childhood: A Systematic Review. Biomed Res Int. 2017; 2017: 2760716. 10.1155/2017/2760716 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Knighton AJ, Savitz L, Belnap T, Stephenson B, & VanDerslice J (2016). Introduction of an area deprivation index measuring patient socioeconomic status in an integrated health system: implications for population health. EGEMs, 4(3). [DOI] [PMC free article] [PubMed] [Google Scholar]
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