Authors : Irene Serwaa Agyapong; Sampson Agyapong Atuahene

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/y655hcww

Scribd : https://tinyurl.com/4rzheb99

DOI : https://doi.org/10.38124/ijisrt/26jul104

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This article presents a refined empirical framework for studying global disparities in autism spectrum disorder (ASD) diagnosis and service access across the United States, selected African countries, and high-income comparator countries. Unlike earlier drafts that treated the subject as a generic macroeconomic panel problem, the present manuscript is aligned with the accompanying data workbook, which identifies concrete sources for country-level ASD prevalence and burden estimates, U.S. Autism and Developmental Disabilities Monitoring (ADDM) Network surveillance measures, World Bank development indicators, WHO policy benchmarks, and optional clinical imaging data from ABIDE. The paper is written as a reproducible data-driven study rather than a claim of completed causal estimation: where the workbook provides templates rather than populated country-year values, the text distinguishes actual data sources from proposed estimands. The conceptual argument is that observed autism prevalence is not only a neurodevelopmental measure; it is also shaped by diagnostic infrastructure, clinical workforce capacity, school-based screening, insurance coverage, social awareness, stigma, income, and digital readiness. The proposed analytical design combines descriptive inequality indices, multilevel regression, Oaxaca–Blinder decomposition, random forest, gradient boosting, and SHAP-based explainability to identify the predictors most associated with cross-national differences in autism identification. The central contribution is a transparent, human-centered methodology for comparing autism diagnosis systems without overstating causal claims. The article emphasizes that lower reported prevalence in many African countries should not be interpreted as lower underlying need, because underdiagnosis, late identification, and limited surveillance capacity remain central measurement challenges. Policy implications focus on early screening, workforce development, culturally valid tools, telehealth, data infrastructure, and ethically governed AI systems for low-resource settings.

Keywords : Autism Spectrum Disorder; Diagnosis Disparities; Africa; United States; Machine Learning; SHAP; Health Inequality; Early Identification; Global Mental Health.

References :

1. American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). American Psychiatric Association.
2. Arora, N. K., Nair, M. K. C., Gulati, S., et al. (2018). Neurodevelopmental disorders in children aged 2–9 years: Population-based burden estimates across five regions in India. PLOS Medicine, 15(7), e1002615.
3. Baron-Cohen, S., Scott, F. J., Allison, C., et al. (2009). Prevalence of autism-spectrum conditions: UK school-based population study. British Journal of Psychiatry, 194(6), 500–509.
4. Bishop-Fitzpatrick, L., & Kind, A. J. H. (2017). A scoping review of health disparities in autism spectrum disorder. Journal of Autism and Developmental Disorders, 47, 3380–3391.
5. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
6. CDC. (2025). Data and statistics on autism spectrum disorder. Centers for Disease Control and Prevention.
7. Chakrabarti, S., & Fombonne, E. (2001). Pervasive developmental disorders in preschool children. JAMA, 285(24), 3093–3099.
8. Daniels, A. M., & Mandell, D. S. (2014). Explaining differences in age at autism spectrum disorder diagnosis: A critical review. Autism, 18(5), 583–597.
9. Dawson, G., Jones, E. J. H., Merkle, K., et al. (2012). Early behavioral intervention is associated with normalized brain activity in young children with autism. Journal of the American Academy of Child & Adolescent Psychiatry, 51(11), 1150–1159.
10. Durkin, M. S., Elsabbagh, M., Barbaro, J., et al. (2015). Autism screening and diagnosis in low resource settings: Challenges and opportunities. Autism Research, 8(5), 473–476.
11. Elsabbagh, M., Divan, G., Koh, Y.-J., et al. (2012). Global prevalence of autism and other pervasive developmental disorders. Autism Research, 5(3), 160–179.
12. Fombonne, E. (2009). Epidemiology of pervasive developmental disorders. Pediatric Research, 65, 591–598.
13. Fountain, C., King, M. D., & Bearman, P. S. (2011). Age of diagnosis for autism: Individual and community factors across 10 birth cohorts. Journal of Epidemiology and Community Health, 65(6), 503–510.
14. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
15. Global Burden of Disease 2021 Autism Spectrum Collaborators. (2025). Global, regional, and national burden of autism spectrum disorder, 1990–2021. The Lancet Psychiatry.
16. Goin-Kochel, R. P., Mackintosh, V. H., & Myers, B. J. (2006). How many doctors does it take to make an autism spectrum diagnosis? Autism, 10(5), 439–451.
17. Hahler, E.-M., & Elsabbagh, M. (2015). Autism: A global perspective. Current Developmental Disorders Reports, 2, 58–64.
18. Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2018). Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical, 17, 16–23.
19. Hyman, S. L., Levy, S. E., Myers, S. M., & Council on Children with Disabilities. (2020). Identification, evaluation, and management of children with autism spectrum disorder. Pediatrics, 145(1), e20193447.
20. Khan, N. Z., Gallo, L. A., Arghir, A., et al. (2012). Autism and the grand challenges in global mental health. Autism Research, 5(3), 156–159.
21. Klin, A., Shultz, S., & Jones, W. (2015). Social visual engagement in infants and toddlers with autism. Current Opinion in Psychiatry, 28(2), 88–94.
22. Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
23. Lord, C., Elsabbagh, M., Baird, G., & Veenstra-VanderWeele, J. (2018). Autism spectrum disorder. The Lancet, 392(10146), 508–520.
24. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
25. Mandell, D. S., Listerud, J., Levy, S. E., & Pinto-Martin, J. A. (2002). Race differences in age at diagnosis among Medicaid-eligible children with autism. Journal of the American Academy of Child & Adolescent Psychiatry, 41(12), 1447–1453.
26. Matson, J. L., & Kozlowski, A. M. (2011). The increasing prevalence of autism spectrum disorders. Research in Autism Spectrum Disorders, 5(1), 418–425.
27. Montiel-Nava, C., Chacín, J. A., & González-Ávila, Z. (2017). Age of diagnosis of autism spectrum disorder in Latino children: The case of Venezuelan children. Autism, 21(5), 573–580.
28. Our World in Data. (2025). Share of the population with neurodevelopmental disorders by type. Global Change Data Lab.
29. Raspa, M., Hebbeler, K., Bailey, D. B., & Scarborough, A. (2010). Evaluation of Part C early intervention systems. Topics in Early Childhood Special Education, 30(3), 128–142.
30. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
31. Risi, S., Lord, C., Gotham, K., et al. (2006). Combining information from multiple sources in the diagnosis of autism spectrum disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 45(9), 1094–1103.
32. Rudra, A., Belmonte, M. K., Soni, P. K., Banerjee, S., Mukerji, S., & Chakrabarti, B. (2017). Prevalence of autism spectrum disorder and autistic symptoms in a school-based cohort of children in Kolkata, India. Autism Research, 10(10), 1597–1605.
33. Shaw, K. A., Williams, S., Patrick, M. E., et al. (2025). Prevalence and early identification of autism spectrum disorder among children aged 4 and 8 years—Autism and Developmental Disabilities Monitoring Network, 16 sites, United States, 2022. MMWR Surveillance Summaries, 74(2), 1–22.
34. Siller, M., Reyes, N., Hotez, E., Hutman, T., & Sigman, M. (2014). Longitudinal change in the use of services in autism spectrum disorder. Journal of Autism and Developmental Disorders, 44, 1906–1916.
35. Szatmari, P. (2018). Risk and resilience in autism spectrum disorder. JAMA Psychiatry, 75(10), 1009–1010.
36. Thabtah, F. (2019). Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. Informatics for Health and Social Care, 44(3), 278–297.
37. Totsika, V., Hastings, R. P., Emerson, E., Lancaster, G. A., & Berridge, D. M. (2011). A population-based investigation of behavioural and emotional problems in children with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 52(1), 91–99.
38. Wall, D. P., Kosmicki, J., Deluca, T. F., Harstad, E., & Fusaro, V. A. (2012). Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational Psychiatry, 2, e100.
39. Wiggins, L. D., Baio, J., & Rice, C. (2006). Examination of the time between first evaluation and first autism spectrum diagnosis. Journal of Developmental and Behavioral Pediatrics, 27(2 Suppl), S79–S87.
40. World Bank. (2025). World Development Indicators. World Bank Group.
41. World Health Organization. (2025). Autism fact sheet. WHO.
42. Zwaigenbaum, L., Bauman, M. L., Choueiri, R., et al. (2015). Early identification and interventions for autism spectrum disorder. Pediatrics, 136(Suppl 1), S10–S40.