Diagnostic Disparities across Demographics: A Quantitative Analysis of Autism Spectrum Disorder Prevalence

Abstract

Since its inception as a diagnostic category only a few generations ago, most researchers of Autism Spectrum Disorder (ASD) have assumed that the condition is more prevalent in some communities than others. Within this thesis, I analyzed the distribution of autism prevalence across several demographic variables such as sex, ethnorace, socioeconomic status, age, and healthcare, using National Health Interview Survey data (2007-2018) and aggregated ASD prevalence data from the Centers for Disease Control and Prevention (2006-2018). Through data visualization and statistical analysis, I evaluated the extent of apparent ASD diagnostic gaps as well as posited possible solutions to closing these existing disparities. Because the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) resulted in major changes to what is known broadly as ‘autism’, the influence of the updated diagnostic manual was a major point of analysis throughout this study. Although the inclusion of the concept of “masking” or “camouflaging” Autistic traits, especially among Autistic females, was added to the diagnostic description of ASD in the DSM-V with the aim to increase the identification of Autistic females, ultimately, my study found that the period after the DSM-V’s publication (2014-2018) has experienced a more exacerbated gap in ASD prevalence between males and females.