Autism Spectrum Disorders, hereafter referred to as autism, emerge early and persist throughout life, contributing significantly to global years lived with disability. Typically, an autism diagnosis depends on clinical assessments by highly trained professionals. This high resource demand poses a challenge in resource-limited areas where skilled personnel are scarce and awareness of neurodevelopmental disorder symptoms is low. We have developed and tested a novel app, START, that can be administered by non-specialists to assess several domains of the autistic phenotype (social, sensory, motor functioning) through direct observation and parent report. N=131 children (2-7 years old; 48 autistic, 43 intellectually disabled, and 40 typically developing) from low-resource settings in the Delhi-NCR region, India were assessed using START in home settings by non-specialist health workers. We observed a consistent pattern of differences between typically and atypically developing children in all three domains assessed. The two groups of children with neurodevelopmental disorders manifested lower social preference, higher sensory sensitivity, and lower fine-motor accuracy compared to their typically developing counterparts. Parent-report further distinguished autistic from non-autistic children. Machine-learning analysis combining all START-derived measures demonstrated 78% classification accuracy for the three groups (ASD, ID, TD). Qualitative analysis of the interviews with health workers and families (N= 15) of the participants suggest high acceptability and feasibility of the app. These results provide proof of principle for START, and demonstrate the potential of a scalable, mobile tool for assessing neurodevelopmental disorders in low-resource settings.

Aims India does not yet have an estimate of prevalence of autism and autistic traits in the general population. Estimates drawn from studies in UK and USA suggest that India could have more than 2 million people with ASC; this prevalence estimate has never been verified. There is a critical need to measure the prevalence of autism and the distribution of autistic traits in a general population sample in India. Method Schools from all socio-economic sectors were selected from 3 boroughs. Teachers filled in the Social Communication Disorder Checklist (SCDC), a 10-item initial screening measure for autism (Skuse et al., 1995), in English or Hindi or Bengali. Parents filled in the SCDC, followed by the Social Communication Questionnaire (SCQ), a 40-item diagnostic tool (Rutter et al., 1999) – and the Autism Quotient – child version (AQ-C), a 50-item tool quantifying the distribution of autistic traits (Auyeung et al., 2009). Results SCDC: Teacher reports were obtained for 12764 children. 3.6% met the cut-off score of 9, with 3.8% meeting cutoff on English, 5.49% on Hindi and 1.29% on Bengali versions of the tool. Parent response rate for SCDC was 50.1%. 20.19% met cut off on SCDC parent report overall. 21.67% met cutoff on English, 10.24% on Hindi, and 20.11% on Bengali versions of the tool. Correlation between parent and teacher ratings were modest (r = 0.103, p < 0.001). SCQ: Of 2887 respondents 8.9% met the cut-off score of 15, overall. Specifically 9.64% met cut-off on English, 8.26% on Hindi, and 10.09% on Bengali versions of the tool. AQ-C: Of 2901 respondents 9.86% met the cut-off score of 76. Specifically, 9.48% met cutoff on English, 11.25% on Hindi and 8.67% on Bengali versions of the tool. Conclusions This is the first study providing data on the distribution of autistic symptoms in 12764 children in India. The results suggest over-reporting by parents in comparison to teachers, irrespective of language. This provides important directions for future epidemiological research into autism in India.