Examining Autism Detection in Early Childhood (ADEC) in the Early Identification of Young Children with Autism Spectrum Disorders (ASD)

Autism Detection in Early Childhood (ADEC) in a Low-Income Spanish-Speaking Population in Guayaquil (Ecuador)

Autism Spectrum Disorder (ASD) must be present early in development, but may not fully manifest until social demands exceed capacities. In the absence of adequate biological or brain imaging markers to detect and diagnose autism, diagnosis relies on clinical judgment based on observation of symptoms. Many tools have been developed in English-speaking countries (questionnaires for parents, symptom checklists for professionals, observation systems, etc.). Screening in countries with other languages requires cultural and linguistic adaptation of these instruments. This paper presents the adaptation of the ADEC (Autism Detection in Early Childhood).

METHODS

The original version of the ADEC was translated and culturally and linguistically adapted to the characteristics of the population of Guayaquil (Ecuador).

PARTICIPANTS

A pilot study was conducted with a sample of 613 children aged 18-48 months.

RESULTS

Cronbach's alpha values (0.89) indicate high internal consistency. The correlation between the MCHAT-R/F follow-up interview and the ADEC (mean r = 0.93) indicates high construct validity. In terms of predictive validity, using the original cut-off points of the ADEC, they show excellent diagnostic ability. The sensitivity and specificity results (sensitivity 1.00; specificity 0.92; positive predictive value 0.83; negative predictive value 0.99) are even better than those obtained in a similar study in the Mexican population.

CONCLUSIONS

Considering that the MCHAT R/F is a parent-reported instrument, the Guayaquil Spanish version of the ADEC (ADEC-GU) seems to be a suitable instrument to be used in a complementary way as a second-level screening instrument for autism, before resorting to a full diagnostic process.

The relationship between pitch contours in infant-directed speech and early signs of autism in infancy

PURPOSE

Mother-infant interactions during the first year of life are crucial to healthy infant development. The infant-directed speech (IDS), and specifically pitch contours, used by mothers during interactions are associated with infant language and social development. However, little research has examined pitch contours towards infants with socio-communication and language differences, such as those displaying early signs of autism spectrum disorder (autism). This study aimed to explore the association of infant autism signs and pitch contours used by mothers with their 12-month-old infants.

METHOD

Mother-infant dyads (n = 109) were recruited from the University of Newcastle BabyLab. Parent-infant dyads completed a 15-min interaction, from which a total of 36,128 pitch contours were measured and correlated with infant autism signs. Infant autism signs were assessed via parent-report (First Year Inventory; Reznick et al., 2007). A subset of high-risk infants (admitted to a neonatal intensive care unit, n = 29) also received an observation-based assessment (Autism Detection in Early Childhood; Young & Nah, 2016).

RESULTS

Mothers used fewer sinusoidal contours when they rated their infant as displaying more autism signs (rs = - .30, p = .004) and more autism-related sensory regulation issues (rs = - .31, p = .001). Mothers used fewer flat contours if their infant displayed more researcher-rated autism signs (r2 = - .39, p = .04).

CONCLUSIONS

This study provides the early evidence that maternal pitch contours in IDS are related to early autism signs in infancy. If our findings are replicated in follow up studies where infants are followed to diagnosis, maternal IDS may be an important element of future early intervention protocols that focus on communication for infants with risk for autism.

Preliminary Validation and Feasibility of the Autism Detection in Early Childhood-Virtual (ADEC-V) for Autism Telehealth Evaluations in a Hospital Setting

This study provided preliminary validation of the Autism Detection in Early Childhood-Virtual (ADEC-V) for telehealth assessment of possible autism. Participants were 121 children (24.79% female) aged 18–47 months who completed telehealth evaluations at a large pediatric hospital in the Midwestern United States between October 2020 and February 2021. The ADEC-V showed good sensitivity (0.82) and specificity (0.78) and was significantly correlated with other ASD symptom measures (i.e., CARS-2, ADI-R). Internal consistency was acceptable (α = 0.77). These results need replication in a larger and broader sample including more children without ASD. This preliminary validation study identifies the ADEC-V as a promising measure for telehealth ASD assessments in young children.

Development of a brief version of the Autism Detection in Early Childhood

While autism spectrum disorder screening tools provide a useful resource for practitioners, the reality is they are underused. The justifications often provided include the time required for administration and the training involved. A brief tool with good psychometric properties that require minimal training is required. This study examined the development and the psychometric properties of a brief version of the Autism Detection in Early Childhood. The data showed the potential of the brief version of Autism Detection in Early Childhood for screening children age 12-36 months. Our dataset comprised 106 Diagnostic and Statistical Manual of Mental Disorders, 5th edition autism spectrum disorder, 86 non-typical development and 78 typical development participants age 12-36 months. Analyses comparing autism spectrum disorder and non-typical development groups supported the use of five critical items (i.e. response to name, social smiling, gaze switch, response to verbal command and use of gestures) to form the brief version Autism Detection in Early Childhood. The brief version of Autism Detection in Early Childhood's optimal cutoff score of 4 had sensitivity of 0.81, specificity of 0.78, positive predictive value of 0.81 and negative predictive value of 0.78. However, the results would need to be viewed as preliminary given the nature of the study sample and the findings might not be generalisable to samples with higher levels of cognitive functioning.