Red Flags in Global Autism Data: A Forensic Analysis of Prevalence Patterns and Official Aid Dependencies

Jun QiuAlishba Hania^*

• Nanchang Institute of Technology, Nanchang, China

The literature extensively examines the global incidence rate of autism, emphasizing the need to scrutinize reported figures for potential anomalies, particularly addressing overdiagnosis concerns. Our forensic analysis employing Benford's Law and Mean Absolute Deviation indicates significant statistical irregularities and potential overdiagnosis, especially post-DSM-5 implementation, suggesting diagnostic criteria changes drive upward trends. The segmented analysis reveals this relationship intensified in low-income countries post-DSM-5 while remaining non-significant in high-income nations. Based on 206 countries over 1990-2019, our findings suggest official aid received causes upward trends in autism cases for both genders. Sub-sample analysis indicates positive effects are pronounced in countries with low income, health expenditures, mental health services, government effectiveness, and weak democracies. Results remain robust through instrumental variable and lagged analyses addressing endogeneity concerns. While Benford's Law suggests overdiagnosis patterns, both genuine increases and diagnostic inflation produce similar empirical results, preventing definitive conclusions. Nevertheless, these statistical red flags warrant future research and governmental vigilance when monitoring dramatic prevalence increases. This research addresses a critical literature gap, encouraging scholarly inquiry into reported autism prevalence complexities.