The Gendered Neurodiversity Gap: A Data Analysis Exploring the Relationships Between Autism, ADHD, and Gender

Sarah Yip | August 1, 2025

Abstract

This study examines correlations between DSM-5 autism spectrum disorder (ASD) levels and key clinical symptoms, including verbal ability, social deficits, repetitive behaviors, and sensory hypo-reactivity. Findings indicate a consistent alignment between ASD level and symptom severity, with repetitive behaviors and sensory hypo-reactivity showing the strongest associations.

Verbal ability varied significantly among individuals with level 1 or 2 ASD but showed a strong correlation with level 3 ASD. Social deficits followed a predictable gradient across ASD levels, with notable distinctions between levels 1 and 2. Gender-based analysis revealed diagnostic delays in females and a greater prevalence of internalized symptoms, consistent with existing literature. Although females exhibited repetitive behaviors more frequently, such behaviors more often prompted diagnosis in males. Sensory hypo-reactivity and social deficits appeared across genders without significant statistical differences.

Comorbidity analysis showed attention-deficit hyperactivity disorder (ADHD) and learning disorders as the most common co-occurring conditions. While co-occurring ASD and ADHD did not reveal major gender differences in symptom profiles, participants with ASD and learning disorders showed reduced verbal ability and tended toward lower ASD severity.

These results emphasize the value of symptom-based profiling in ASD assessment and highlight the need for greater diagnostic sensitivity, particularly for females and individuals with co-occurring conditions.

Introduction

Autism Spectrum Disorder

In the United States, “a recent study reported that 1 in 36 children” live with autism spectrum disorder (ASD). ASD refers to a complex set of neurodevelopmental disorders “characterized by deficits in three main areas of functioning, including social interaction, communication skills, and pervasive or repetitive behaviors,” as described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (Salehi, M. et al., 2025). In the psychiatric industry, the DSM-5 provides practitioners with diagnostic codes and identifying pathologies, with a large majority of practitioners using the DSM-5 at least once a month as a requirement by their employers and third-party payers and to help in the psychiatric diagnostic process (Raskin et al., 2022).

 

Importance of Assessing Autism

Research highlights “the value of early intervention in improving outcomes for children with ASD,” stressing “the importance of starting interventions early enough to capitalize on the ample neural plasticity of infants and preschool-aged children” (Kauley, N. et al., 2024). Although “evidence suggests that parents might be capable of identifying concerns related to ASD in their children even before the child reaches 12 months of age,” many children receive a diagnosis later possibly due to “a shortage of experts, lengthy evaluation processes, high costs of care, and reluctance among healthcare providers to make referrals,” ultimately resulting “in delays and extended wait times for evaluations” (Salehi, M. et al., 2025).

 

Autism and Gender

Historically, people thought of autism as a “predominantly male disorder,” but recent research “increased recognition that women and girls with autism spectrum disorders” face multiple barriers by the “clinical criteria and processes required to receive a diagnosis” (Lockwood Estrin, G. et al., 2021). Autistic females often face underdiagnosis, misdiagnosis, and late diagnosis, potentially due to “more nuanced behavioral profiles” (Harrop, C. et al., 2024). Some of the key barriers to diagnosis include differences in behavior, social and communication abilities, language, relationships, additional diagnoses, and restricted and repetitive behaviors and interests. (Lockwood Estrin, G. et al., 2021).

 

Autism and ADHD Comorbidity

In addition to ASD, research shows that one of the most common comorbidities include Attention-Deficit Hyperactivity Disorder (ADHD). ADHD involves “severe deficits in attention, hyperactivity, and impulsivity.” Both ASD and ADHD top the list of most common disorders among school-aged children, and despite their differences in diagnostic criteria, they also often co-occur. Furthermore, children with co-occurring ASD and ADHD often receive their diagnoses “an average of one to three years later than peers with ASD alone,” resulting in missed early intervention windows (Justus, S. A. et al., 2025).

 

Statistical Analysis in Autism Research

To improve the rates of early intervention for people with ASD, researchers must identify gaps between diagnosis, comorbidities, and specific demographics to develop a more comprehensive diagnostic process. To ensure the reliability of research, researchers can use statistical analysis to test hypotheses, assess results, and improve decision-making. This study utilizes descriptive statistics, cross-tabulation analysis, and more to explore the relationships between autism spectrum disorder, ADHD, other comorbidities, and demographics like age and gender.

 

Methodology

This study employed a quantitative, exploratory, cross-sectional design to analyze patterns in a dataset of children diagnosed with autism, allowing for the examination of associations between diagnostic levels, behavioral traits, communication abilities, and comorbid conditions. The analysis followed a workflow involving data cleaning, transformation, visualization, and interpretation using Python and its ecosystem.

 

Data Cleaning and Preparation

The dataset originated from a structured questionnaire called “Dataset for Autism Diagnosis Based on DSM-5,” collected through parent or caregiver responses, located in Nigeria. Participants included children diagnosed with autism spectrum disorder under DSM-5 criteria. Each entry contained demographic, developmental, behavioral, and clinical data (Ibrahim, U. & Sani Adamu, A., 2023).


Initial data handling used the Pandas library, which enabled efficient loading, cleaning, and transformation of the dataset. Column names underwent normalization by removing whitespace, converting to lowercase, and replacing characters for consistent access. Standardization of text data used str.strip(), str.lower(), and str.title(). Unstructured fields (like open-ended behavior descriptions) used keyword search with str.contains() to quantify behavior mentions. Missing values in key variables—such as diagnosis level, verbal ability, and social communication scores—prompted filtering to ensure analytical consistency. The dropna() function from Pandas handled these exclusions.


Categorical variables, such as "DSM-5 diagnosis level," received manual ordering using pd.Categorical() to preserve the logical progression from lowest to highest severity, for example: “Level 1" to "Level 3,” allowing coherent axis sorting across visualizations.

 

Exploratory Data Analysis and Visualization

The analysis relied on visual techniques to uncover relationships between key variables. Data visualization primarily utilized matplotlib.pyplot, with support from Pandas plotting capabilities. All charts used color schemes that emphasized aesthetic consistency and clarity.


For categorical comparisons like diagnosis level vs. verbal ability, bar plots presented normalized values as percentages. These visualizations followed construction through pd.crosstab(), often combined with normalize='index' or normalize=True to produce row-wise or total percentage distributions. Each bar chart included percentage labels placed above each column using annotations generated with ax.text() in matplotlib. Pie charts, used for overall distribution metrics, applied the plt.pie() function with a fixed starting angle to maintain visual consistency.


Where open-text behavioral descriptions appeared, such as parent-reported unique traits prompting diagnosis, keyword detection techniques helped conduct thematic analysis and quantify common behaviors. The str.contains() method enabled Boolean filtering across these descriptions, specifically searching for terms like "hyperactivity", "inattention", and "stimming".

 

Statistical Methods

While the research focused primarily on visual and descriptive analysis, several statistical techniques guided interpretation. Cross-tabulations functioned as contingency tables, providing the foundation for comparative group analysis. Frequencies and relative frequencies across diagnostic categories, verbal levels, and social scores clarified how support needs varied across populations. Percentages were computed either within each group (row-wise normalization) or across the whole dataset (global normalization), depending on the research question.


Histograms supported distribution analysis for continuous variables and frequency distributions across continuous or binned values, such as age at diagnosis. The numpy.histogram() function handled binning, while matplotlib enabled percentage-based rendering with uniform bin width. Data labels enhanced interpretability by showing the exact proportion of cases per bin.

 

Software and Tools

All analysis occurred within the Python programming environment, specifically using Pandas for data wrangling and tabulation, matplotlib.pyplot for visualization, and NumPy for numerical calculations and histogram binning. All plots followed clean, minimalist design conventions. Y-axes used percentage scales when comparisons involved proportionate group sizes, and X-axis labels were ordered logically, for example: yes to sometimes to no, or mild to severe.

 

Demographics of the Dataset

Figure 3.1. Gender Distribution of Autism Diagnosis Dataset Participants

Gender Distribution of Autism Diagnosis Dataset Participants
Figure 3.2. ASD Diagnosis Percentage of Dataset

Note. In this dataset, 92.9% of participants received a formal diagnosis of autism spectrum disorder, while 7.1% did not, but received an informal diagnosis of ASD.

ASD Diagnosis Percentage of Dataset
Figure 3.3. Distribution of Primary Autism Spectrum Diagnoses in this Dataset

Note.Out of 92.9% of participants with a formal diagnosis of autism spectrum disorder, 83.7% received an autism spectrum disorder diagnosis with a level, 3.1% received an Asperger’s diagnosis, 1.0% received a PDD-NOS diagnosis, and 7.1% received an informal diagnosis of ASD.

Distribution of Primary Autism Spectrum Diagnoses in this Dataset

 

Ethical Considerations

The dataset did not contain personal identifiers, and all data had undergone anonymization prior to use. The analytical focus remained on aggregate patterns, not individual-level data, which allowed the study to proceed without ethical conflicts related to privacy or consent.

 

Results

Through systematic analysis using Python’s data science libraries—Pandas for data structuring, Matplotlib for visualization, and NumPy for numerical operations—the results illustrate how diagnostic severity aligns with verbal development, social deficits, comorbid traits, and demographic context.

 

Distribution of ASD Diagnosis Levels, Maternal Age, and Symptoms

Figure 4.1. Distribution of DSM-5 Autism Diagnosis Levels

Distribution of DSM-5 Autism Diagnosis Levels
Figure 4.2. Mother’s Age at Childbirth and Level of Autism Diagnosis

Mother’s Age at Childbirth and Level of Autism Diagnosis
Figure 4.3. Verbal Abilities and Autism Diagnosis Levels

Verbal Abilities and Autism Diagnosis Levels
Figure 4.4. Social Deficit Levels Across DSM-5 Autism Diagnosis Levels

Social Deficit Levels Across DSM-5 Autism Diagnosis Levels
Figure 4.5. Repetitive Behaviors, Echolalia, and Stimming by DSM-5 Autism Diagnosis Level

Repetitive Behaviors, Echolalia, and Stimming by DSM-5 Autism Diagnosis Level
Figure 4.6. Sensory Hypo-reactivity by DSM-5 Autism Diagnosis Level

Sensory Hypo-reactivity by DSM-5 Autism Diagnosis Level

Gender, Age, and Autism Symptoms

Figure 4.7. Female and Male, Distribution of Ages at Autism Diagnosis

Female and Male, Distribution of Ages at Autism Diagnosis
Figure 4.8. Female and Male, Common Behaviors Prompting Autism Diagnosis

Female and Male, Common Behaviors Prompting Autism Diagnosis
Figure 4.9. Autism Spectrum Disorder: Social Deficit Levels by Gender

Autism Spectrum Disorder: Social Deficit Levels by Gender
Figure 4.10. Autism Spectrum and Repetitive Behaviors, Echolalia, or Stimming by Gender

Autism Spectrum and Repetitive Behaviors, Echolalia, or Stimming by Gender
Figure 4.11. Autism Spectrum Disorder: Sensory Hypo-reactivity Severity by Gender

Autism Spectrum Disorder: Sensory Hypo-reactivity Severity by Gender
Figure 4.12. Autism Spectrum Disorder: Top Comorbid Diagnoses by Gender

Autism Spectrum Disorder: Top Comorbid Diagnoses by Gender

Gender and Autism Symptoms of Comorbid ASD and ADHD

Figure 4.13. Gender Distribution Among Participants with Both Autism and ADHD

Gender Distribution Among Participants with Both Autism and ADHD
Figure 4.14. Social Deficit Levels Among Participants with Both Autism and ADHD

Social Deficit Levels Among Participants with Both Autism and ADHD
Figure 4.15. Repetitive Behaviors Among Participants with Both Autism and ADHD

Repetitive Behaviors Among Participants with Both Autism and ADHD
Figure 4.16. Sensory Hypo-reactivity Levels Among Participants with Both Autism and ADHD

Sensory Hypo-reactivity Levels Among Participants with Both Autism and ADHD

Autism Symptoms and Learning Disorders

Figure 4.17. DSM-5 Diagnosis Levels Among Participants with Both Autism and a Learning Disorder

DSM-5 Diagnosis Levels Among Participants with Both Autism and a Learning Disorder
Figure 4.18. Verbal Abilities Among Participants with Both Autism and Learning Disorder

Verbal Abilities Among Participants with Both Autism and Learning Disorder
Figure 4.19. Social Deficit Levels Among Participants with Both Autism and a Learning Disorder

Social Deficit Levels Among Participants with Both Autism and a Learning Disorder
Discussion

Results on ASD Diagnosis Levels and Autism Symptoms

The analysis shows a consistent alignment between DSM-5 ASD levels (1: requiring support, 2: requiring substantial support, 3: requiring very substantial support) and symptom severity in verbal communication, social deficits, repetitive behaviors, and sensory hypo-reactivity.

 

In the sample, 62.1% of participants came from mothers under 35, while 17.3% came from mothers 35 or older. Although previous studies link advanced maternal age with higher autism risk (Myat et al., 2025), this study suggests a weak correlation between maternal age and ASD level, possibly due to skewed maternal age distribution.

 

Verbal ability strongly correlated with ASD level 3; 68% of level 3 individuals did not speak. Levels 1 and 2 showed wide variation, indicating a weaker correlation. These patterns may reflect the ASD level spread in the dataset.

 

Social deficits aligned with ASD levels. Among level 1 participants, 90.7% showed mild or moderate deficits. Level 2 participants showed 87.2% moderate or severe, while level 3 showed 80% in those same categories. A large gap exists between levels 1 and 2, with less distinction between levels 2 and 3.

 

Repetitive behaviors occurred across all levels. Every level 3 participant exhibited them. Among level 2 participants, 78.9% showed repetitive behaviors, compared to 67.7% of level 1 participants. These results suggest stronger ties between repetitive behaviors and ASD diagnosis than between ASD and other symptoms.

 

Not to confuse with hyper-reactivity, sensory hypo-reactivity refers to an atypical under-reaction to the sensory environment, such as “not addressing a loud sound.” Sensory hypo-reactivity can “interfere with self-care skills” and “academic progress.” The “prevalence of sensory symptoms in people with autism spectrum disorder (ASD) ranges from 69% to 93% in children and adults” (Savarese, G. et al., 2025). Most participants in the current study experienced sensory hypo-reactivity. Among level 3, 100% showed moderate to severe hypo-reactivity. Levels 2 and 1 showed 69.2% and 56.4% respectively. These patterns align with DSM-5 criteria and current literature.

 

The most common behaviors prompting an autism spectrum disorder diagnosis include lack of communication, hyperactivity, repetitive behavior, and lack of social interaction. However, this varied among different genders.

 

Gender and Autism Results

Girls and women often receive delayed or missed psychiatric diagnoses due to more internalized symptoms (Craddock, 2024). By age 4, 87.8% of males and 64.3% of females in the study had an ASD diagnosis. If we consider a diagnosis as late starting by age 12 or later, 3.6% of females and 1.5% of males received late diagnoses, aligning with research on diagnostic delays in females. These findings highlight the need for increased awareness and understanding of autism in females to ensure timely and accurate diagnoses.

 

The current study reveals that the most common behaviors that prompted parents, guardians, and caregivers to seek assessment for an ASD diagnosis slightly differed between females and males. Nearly half (46.4%) of females exhibited either a lack of communication or hyperactivity, compared to 31.8% of males. In males, 30.4% exhibited repetitive behavior or lack of social interaction, compared to 21.4% of females. These trends may reflect gendered perceptions rather than true behavioral differences.

 

Past research suggests that “females with ASD may be more socially oriented than males and are better at masking their autism symptoms” (Zack, D. et al., 2025). In the current study, those with a mild social deficit level included 35.7% of females and 22.1% of males, and those with a moderate to severe social deficit included 64.3% of females and 77.9% of males. Though not statistically significant, these results support prior research suggesting greater social masking in females.

 

Repetitive behaviors occurred in 86.3% of females and 72.3% of males. Although this study showed that repetitive behavior prompted an ASD diagnosis about 60% more often in males than females, more females exhibited repetitive behavior in general, suggesting gender-based recognition bias despite higher prevalence in females.

 

Moderate to severe sensory hypo-reactivity occurred in 75% of females and 67.1% of males. Gender differences did not reach statistical significance.

 

Autism Comorbidity Results

ADHD and learning disorders appeared as the most common comorbidities, with over half of participants with ADHD and over a quarter with a learning disorder, with little difference in prevalence between females and males. Anxiety and learning disorders show stronger links to moderate-severe social deficits. Females were over twice as likely than males to have an anxiety disorder, while males were over twice as likely than females to have epilepsy.

 

Autism and ADHD Comorbidity Results

Among participants with both autism spectrum disorder and ADHD, 71.7% of females and 74.3% of males showed moderate to severe social deficit levels, 85.7% of females and 75.7% of males exhibited repetitive behaviors, and 64.3% of females and 68.6% of males experienced moderate to severe sensory hypo-reactivity.

 

Although the ASD and ADHD group consisted of 28.6% females and 71.4% males, the social deficit levels, repetitive behaviors, and sensory hypo-reactivity showed minimal statistical significance between females and males. Gender differences did not show statistical significance, suggesting symptom similarity across genders in comorbid ASD and ADHD, whereas a diagnosis of only autism spectrum disorder sees more gendered variation in symptom profiles.

 

Autism and Learning Disorder Comorbidity Results

Among those with both ASD and a learning disorder, 25.9% were female and 32.8% male. Diagnosis levels included 39.1% level 1, 47.8% level 2, and 13% level 3. These numbers suggest lower ASD levels more often accompany learning disorders, though inaccessibility to traditional education for level 3 may skew results. Within this group, 71.4% spoke few words or none, indicating a strong correlation between limited verbal ability and ASD-learning disorder comorbidity. Moderate to severe social deficits appeared in 67.9% of this group, similar to the overall ASD population, suggesting no major difference linked to co-occurring learning disorders.

 

Implications

The current findings strengthen the practical utility of DSM-5 ASD-level classifications in clinical assessment and individualized support planning. Clear patterns between ASD levels and symptom severity—particularly in verbal ability, social deficits, repetitive behaviors, and sensory hypo-reactivity—demonstrate how symptom-based stratification supports accurate identification of support needs. These trends highlight the importance of comprehensive behavioral evaluations in early childhood and reinforce the need for diagnostic tools that capture symptom nuance rather than relying solely on age or stereotypical traits.

 

The analysis reveals multiple implications regarding ASD symptom patterns, maternal age, and gender differences. Symptom alignment across DSM-5 ASD levels shows consistency for social deficits and sensory hypo-reactivity, especially at level 3, while verbal ability and repetitive behaviors vary more widely at levels 1 and 2. While prior studies connect advanced maternal age with autism prevalence, this study found minimal linkage between maternal age and ASD severity. This discrepancy may stem from sample distribution, but it also raises questions about maternal age's role in outcome prediction. Gender discrepancies in diagnosis timing and symptom expression emphasize ongoing gaps in early identification for females, particularly due to differences in observed versus actual behaviors as well as gendered diagnostic bias. Higher rates of repetitive behaviors among females challenge traditional diagnostic biases that overlook subtle presentations. Comorbidity analysis shows strong overlap in symptom profiles among those with both ASD and ADHD, regardless of gender, while individuals with both ASD and learning disorders exhibit lower verbal communication, suggesting that communication challenges may increase with cognitive comorbidities. Together, these findings support the need for more tailored diagnostic tools that account for gender, communication profiles, and the interaction of co-occurring conditions.

 

Limitations

The study used a dataset with a sample size of 99 participants, which may not provide enough statistical power to detect a moderation effect. The study focused on exploratory data analysis and descriptive patterns, rather than inferential statistics. Future work may use a larger sample and incorporate inferential statistical methods to assess potential confounds. In addition, reliance on parent-reported data may introduce bias based on recall, interpretation, or access to diagnostic services. Still, the depth of open-ended responses and the detailed categorization of developmental characteristics provided a valuable foundation for exploratory analysis.

 

Conclusion

This study highlights clear patterns between DSM-5 ASD levels and core autism symptoms, with social deficits, verbal ability, repetitive behaviors, and sensory hypo-reactivity showing varying degrees of correlation. Repetitive behaviors and sensory hypo-reactivity appeared most consistently across diagnosis levels, while verbal ability varied more in individuals with level 1 or 2 ASD.

 

Gender differences emerged in diagnostic timing, symptom presentation, and comorbidity profiles. Females experienced more delayed diagnoses and exhibited higher rates of anxiety disorders, while males were more likely to receive earlier diagnoses and experience epilepsy. Despite these differences, the expression of symptoms in individuals with co-occurring ASD and ADHD showed minimal gender variance.

 

Participants with both ASD and learning disorders showed reduced verbal ability and lower ASD severity overall, though severe cases may have lacked access to assessment and education, skewing results.

 

Together, these findings underscore the complexity of autism spectrum disorder and the need for nuanced diagnostic approaches that account for gender, co-occurring conditions, and diverse symptom profiles.

 

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