Unveiling Female ADHD: Early Mental Health Challenges and Diagnosis Delays in University Students

In 2022, between 6.5 to 7.1 million children and adolescents in the United States received a diagnosis of attention-deficit hyperactivity disorder, or ADHD.[1] As a neurodevelopmental disorder, ADHD affects the ability to sustain attention, regulate motor and impulse behavior, and can lead to behavioral disturbances. Like the name, the core symptoms of ADHD include inattention, hyperactivity, and impulsivity.[2] “ADHD typically develops in childhood” and often leads to “poor long-term outcomes if untreated.”[3]

 

Compared to peers without ADHD, the likelihood increases for individuals with ADHD to “experience poor health outcomes like obesity, chronic illness, and accidental injury.”[1] Outside of health, individuals with ADHD can also experience “poor educational and professional attainment, social dysfunction, and lower economic status.”[3] While “ADHD symptoms may become less apparent as people get old,” adults with ADHD can still experience “disorganization, impulsive-decision-making, internal restlessness, wandering attention, and procrastination.”[2]

 

With the “functional dysregulation” that “can persist across” lifespans, “ADHD should be viewed as a chronic condition for many individuals.”[4] “Even in cases where people previously diagnosed with ADHD no longer meet the diagnostic criteria for the condition,” research findings show “brain structural differences in ADHD that persist into adulthood.” However, adult ADHD lacks research with “few clinical (including human case reports) and preclinical (animal)”[2] existing data. ADHD research also tends to underrepresent the female gender.[5] To contribute to ADHD research, this report aims to conduct data analysis using the University of Cape Town’s dataset of first-year students with ADHD.[6] The dataset received data from a majority of female first-year undergraduate students compared to male students and other genders.

 

In this report, the data analysis seeks to answer several questions:

 

• What are common comorbid mental health conditions for individuals with ADHD?
• What is the average age of onset that individuals with ADHD experienced a comorbid mental health condition?
• What is the average age of formal diagnosis of a comorbid mental health condition for individuals with ADHD?

 

To answer the research questions, the data analysis used several statistical methods:

 

• Data cleaning.
• Descriptive statistics.
• Text categorization.
• Data visualization.
• Categorical data analysis.
• Summary statistics.
• Density estimation.
• Data grouping and aggregation.

 

These methods focus on exploratory data analysis (EDA). The data analysis will use Python programming language on the free hosted Jupyter Notebook service named Google Colaboratory (Colab).

 

In Colab, there will be several steps. First, preprocess the dataset by loading and cleaning the data. Second, count occurrences of different responses in categorical data and write custom functions to categorize free-text responses into predefined categories based on keywords. Third, perform calculations like mean, standard deviation, and percentages. Fourth, group and reshape data for better visualization. Lastly, visualize the data and discuss the findings.

 

Fig. 1.

A pie chart showing that most students with ADHD experienced mental health difficulties before university.

Fig. 2.

Despite majority of students with ADHD experiencing mental health difficul-ties before university, less than half are formally diagnosed with a mental illness as shown in this pie chart.

Fig. 3.

For students with ADHD who are formally diagnosed with any mental illness, this pie chart shows that an overwhelming majority received their diagnoses before high school.

Fig. 4.

In this histogram, the age when mental health difficulties started by or before age 18, with nearly a third by age 12, over half by age 14, and an overwhelming majority by age 16.

Fig. 5.

For students with ADHD, the age of their first formal diagnosis of mental illness occurred later than their onset of mental health difficulties. Less than a third of students received a formal diagnosis by age 14 and the overwhelming majority received a formal diagnosis by age 16 and later.

Fig. 6.

Common mental health conditions among students with ADHD include depression and anxiety, which affect over two-thirds of students in this dataset.

Fig. 7.

About 1 in 10 students with ADHD experience severe mental health symp-toms like sleep difficulties, self-harm, trauma, eating disorders, and body dysmor-phia.

Fig. 8.

In this dataset, the sex distribution of ADHD first-year undergraduate stu-dents was an overwhelming majority of the female sex and a minority of the male sex and others.

Fig. 9.

In this dataset, the age distribution of ADHD first-year undergraduate students was an overwhelming majority of students who are age 18, with about a quarter age 19, and few who were ages 20, 21, and 22.

In this report, exploratory data analysis revealed several aspects of the relationships between mental health conditions and a diagnosis of ADHD in university students. Most students with ADHD experience mental health difficulties before university and over a third even before reaching their teenage years. Among the students with ADHD and mental health difficulties, the most common mental health conditions include depression and anxiety.

 

5.1 Formal Diagnosis of Mental Health Conditions

To receive a diagnosis for mental health conditions, people can use both formal and informal assessments. In both formal and informal assessments, they share similar overall goals but differ in the use, manner, and type of information collected. Formal assessments use “standardized, norm-referenced tests, whereas in informal assessment, information is based on careful observation of behaviors by the examiner.”[7] Despite most students with ADHD experiencing mental health symptoms and difficulties before university, less than half of the students in this dataset received a formal or informal diagnosis of any mental health conditions by age 18. This suggests that possibly nearly a quarter of people over age 18 with both ADHD and mental health conditions like depression and anxiety lack a formal diagnosis for their mental health conditions.

 

5.2 Gap Between Onset of Symptoms to Formal Diagnosis

In the United States, it can take 11 years on average for someone to receive a condition diagnosis.[8] In this analysis, over half of students with ADHD who are currently formally diagnosed with mental health conditions experienced mental health difficulties by age 14, with over a third by age 12, but less than a third received a formal diagnosis for any mental health conditions at age 14. However, over half of the formally diagnosed students received a formal diagnosis by age 16. This suggests that for many students with ADHD, getting a formal diagnosis for mental health conditions can occur a few to several years later than the onset of symptoms and difficulties.

 

5.3 Common Mental Health Conditions of People with ADHD

In this analysis, about two-thirds of students with ADHD experienced depression and anxiety, with 97.5% of those students receiving a formal diagnosis by age 18, and 2.5% afterward. In comparison, 22.8% of adults aged 18 and older in the United States live with a mental illness [8]. This suggests that people diagnosed with ADHD are possibly two to three times more likely to experience mental health difficulties than the general population.

 

This report shows the prevalence and impact of comorbid mental health conditions among university students with ADHD, especially students who identify as female, revealing that a significant portion of these individuals experience symptoms years before receiving a formal diagnosis. The most common mental health conditions of students with ADHD are depression and anxiety, with most diagnoses occurring during the teenage years.

 

The findings also suggest that individuals with ADHD experience mental health difficulties at a higher likelihood compared to the general population. In the future, the expansion of this project can include conducting data analysis on an ADHD dataset with a larger sample size. All in all, these findings emphasize the need for earlier detection, intervention, and comprehensive support for individuals with ADHD, especially women and girls, to mitigate long-term adverse outcomes.