Healthcare Inequalities in Autism Services: Evidence from the United States, Africa, and High-Income Countries Using Machine Learning

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.