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Arabic Language Autism Diagnostic Interview (ALADIN): A Validation Study.
Arwa K Nasir1, Amira T Masri2, Saja Shaheen2
1Department of Pediatrics, University of Nebraska Medical Center, Omaha, NE, USA. anasir@unmc.edu.
The Arabic Language Autism Diagnostic Inventory (ALADIN) shows high accuracy in diagnosing autism in young children. This culturally adapted tool is vital for improving early detection and intervention in Arabic-speaking populations.
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Area of Science:
- Neurodevelopmental disorders
- Global child health research
Background:
- Limited autism research exists in Low- and Middle-Income Countries (LMIC).
- Lack of culturally validated diagnostic tools hinders early autism diagnosis and intervention in these regions.
Purpose of the Study:
- To validate the Arabic Language Autism Diagnostic Inventory (ALADIN) for autism diagnosis.
- To assess the ALADIN's sensitivity and specificity in a Jordanian child population.
Main Methods:
- A case-control study design was employed.
- The ALADIN was administered to parents of 48 children with autism and 152 neurotypical children aged 2-5 years in Jordan.
- Receiver Operating Curve (ROC) analysis was used to evaluate diagnostic performance.
Main Results:
- An ALADIN total score of 24 demonstrated 77% sensitivity and 98% specificity for autism diagnosis.
- The Area Under the Curve (AUC) was 0.976, indicating strong diagnostic accuracy.
- The ALADIN is the first autism diagnostic instrument developed for Arab populations, incorporating cultural and linguistic data.
Conclusions:
- The ALADIN is a culturally adapted and validated tool for diagnosing autism in Arab children.
- High diagnostic specificity of the ALADIN can improve early diagnosis and intervention access.
- Culturally informed instruments are crucial for advancing autism research and improving global child outcomes.