Opportunities and Alternative Approaches for Improving Language Outcomes in Young Children

In this issue of Pediatrics, Wong et al1 used a speech auditory brain stem response (ABR) method, administered between 7 and 24 months of age, to predict those infants and toddlers who would later score more than 1 SD below the mean on the language subscale of the Bayley Scales of Infant Development. The ABR is a functional assessment of early neural responses, generated from electroencephalogram recordings acquired during the repeated presentations of auditory stimuli. The speech ABR substitutes speech stimuli for clicks or tones used in conventional ABR. In this study, machine learning techniques generated a set of neural features used for prediction. As the authors point out, the significance of the study is that if this early screening would allow accurate identification of children at high risk for language delay, it could lead to the provision of targeted early intervention, ideally mitigating the long-term consequences of language disorders in a cost-effective manner. This commentary has 2 aims: (1) to discuss limitations in the study’s methods that should be addressed in the further development of the speech ABR technique as a universal screening tool and (2) to offer an alternative approach to improving early language skills in infants and toddlers that can be applied in both high- and low-risk infant populations, thereby reducing the need for screening.Wong et al1 found that a set of features on the speech ABR test substantially improved prediction of later poor language outcomes compared with use of pediatric variables alone, including child sex, birth weight, and gestational age. In this manuscript, they did not label the features or provide a theoretical account of why the specific set might be useful in prediction. Nonetheless, the data are impressive.It is important to evaluate variables in this study to determine whether their selection impacted the effectiveness of the speech ABR as a potential screening tool. (1) Age of screening. The study conducted the speech ABR on children aged 1 to 24 months. Brain stem responses to auditory stimuli mature between birth and 24 months of age. The authors did not discuss whether the features had to be adjusted for child age or whether prediction accuracy changed across the age range tested. Determining an optimal age for screening is an important next step. (2) Age of outcomes assessment. The standardized language assessment was collected between 7 and 32 months of age. Prediction from infant to preschool language skills is generally limited, except in children with extreme delays.2 Even scores at 24 months of age are not excellent predictors of later language skills; approximately half of the children identified as delayed at age 2 have caught up to peers by age 3.3 Again, while Wong et al1 demonstrated impressive gains in predictive validity over traditional factors at 7 to 32 months of age, a rigorous evaluation requires outcomes testing at age 3 years or older. (3) Speech stimuli. Wong et al1 used 3 tones embedded in the same syllable (/ga/) as the speech stimuli. In Cantonese, varying the tone of a syllable can change word meaning. In English and many other languages, tone differentiates the speaker’s feelings (eg, angry vs pleased) or sentence meaning (eg, statement vs question) but not word meaning. The authors predict these stimuli will work regardless of the child’s language environment, but the stimuli must be tested in infants exposed to languages other than Cantonese. (4) Impact of socioeconomic status (SES). It is surprising that adding SES to the predictive models after neural factors were included did not improve model performance. Socioeconomic status is the most powerful predictor of language outcomes in many studies.4 Multiple methods are available for estimating SES, including income-to-needs ratios and levels of parent education. This study used an income variable. Replication efforts should ensure a wide range of SES in the assessed sample and should also include measures of parental education to assess the contribution of both income- and education-based measures of SES in relation to neural features in predicting outcomes. (5) Linkage to the universal newborn hearing screening (UNHS). An attractive feature about use of the speech ABR for early language screening is that the automated version of the ABR is currently used in the newborn period as part of UNHS. As the authors recognize, the addition of the speech ABR to current protocols could determine in a single session whether children can hear and their risk of language delay. Testing in this study began at age 7 months. Conducting this screening beyond the newborn period would require additional health care visits or additional procedures during scheduled infant health supervision visits, increasing expense and reducing the cost-effectiveness of this approach.A limitation of the use of the speech ABR for predicting language outcomes was the relatively low positive predictive value. This result means that many children who screened positive did not go on to display language delays. False-positive results risk worrying families or treating children unnecessarily. Additional research is necessary to understand what additional factors would increase the positive predictive value and other psychometric properties of the speech ABR.Importantly, the rationale behind early screening for language delay assumes that the provision for early intervention services would be extremely expensive, and therefore, only those children with the greatest need should receive such services. This rationale misses a key point about the impact of early intervention for language skills: All children benefit from early intervention services. Intervention methods have been shown to improve language outcomes in infants and toddlers similarly for children at high and low risk for language delays, including children with a medical complexity such as preterm birth.5,6The key ingredient for improving language outcomes is improving the child’s “language nutrition.”7 Language nutrition can be defined as the combination of environmental factors that is associated with favorable or healthy language outcomes. Specifically, good language nutrition includes a high quantity of adult speech directed to the child, a wide variety of sentence types, many sentences aligned with the child’s interests and attention, and provision of language input in warm, loving, and responsive caregiver-child interactions. Decades of research, including randomized clinical trials, have shown that improving language nutrition improves child language outcomes in the toddler-preschool period.8 The data are supportive of the same approach for children born preterm5,6 and children with intellectual disability and autism.5Thus, while early screening has value, a viable alternative to extensive screening is to provide high-quality coaching about language nutrition universally, to parents and caregivers of all newborns and infants. The method for disseminating the information need not be expensive and could include public service announcements, enhanced training of primary care clinicians and early care and education providers who connect routinely with parents, and direct individual or group parent education and training in community settings or online.9 It is critical that such programs operate in communities, such as low-resourced, low-SES neighborhoods, that may otherwise not receive these important messages. The benefit of universal design for this language intervention is that it holds the promise of raising language levels in all children regardless of early screening results.