P3.H161: Validation of the Arabic Language Autism Diagnostic Inventory (ALADIN).

Background: Nearly 80% of the world’s children live in low or low-middle income countries (LMIC) and have little to no access to autism services. Autism is the most common neurodevelopmental disorder. It affects 1-2% of the world’s children, is the leading cause of disability in children under five years of age and contributes significantly to the global burden of disease. Most information on the epidemiology, diagnosis and interventions for autism has originated in high-income countries (HIC) and are validated on high income and largely white populations. It is estimated that less than 10% of all research on autism comes from LMICs. This is problematic because it does not include relevant cultural perspectives or solutions applicable to an LMIC context. In particular, the lack of diagnostic instruments validated in LMIC and diverse populations is a major barrier for early diagnosis, intervention, and research. Studies have demonstrated that culture significantly impacts symptom expression and conceptualization of autism in diverse cultures. This may lead to differences in clinical presentation, help seeking behavior and outcomes.

Methods: A case-control study design was used to test the Arabic Language Autism Inventory (ALADIN) for sensitivity and specificity for the diagnosis of autism. The Arabic Language Autism Diagnostic Inventory was developed by the authors to create an inventory based on the DSM-5 adapted to the Arabic language and culture. Cultural and language adaptation was based on data obtained through qualitative research exploring presentations of autism in the local culture.

Results: Parents of 48 children with autism and 152 neurotypical children in Jordan completed ALADIN. Demographic information of the participants in each group is presented in table 1 and a received operating curve (ROC) is shown in Figure 1. A total score of 24 on the ALADIN had 98% sensitivity and 77% specificity for autism. ROC analysis indicated that the area under the curve (AUC) is 0.976, indicating very strong performance in identifying children with autism.

Conclusion: ALADIN is the first Arabic Language instrument created specifically for Arabic speaking populations and is informed by cultural and linguistic data of the target population. It has high diagnostic reliability for autism.
Culturally sensitive and informed instruments can improve the access to early diagnosis and interventions and enhance research with the goal of improving diagnosis, care, and outcomes in global populations.