Large-Scale Analysis Reveals Racial Disparities in the Prevalence of ADHD and Conduct Disorders

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Williams This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4177866/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The primary purpose of this study is to highlight trends in the prevalence of Attention Deficit/Hyperactivity Disorders (ADHD) and Conduct Disorders (CD) between non-Hispanic White and non-Hispanic Black populations and identify potential diagnostic disparities between these groups. De-identified electronic health record data on the TriNetX platform of patients diagnosed with ADHD, CD, or both between January 2013 and May 2023 from 50 healthcare organizations in the US were used to investigate racial and sex disparities in the prevalence of ADHD and CD diagnoses. With a cohort of 849,281 ADHD patients and 157,597 CD patients, non-Hispanic Whites were ~26% more likely to receive ADHD diagnosis and ~61% less likely to be diagnosed with CD than non-Hispanic Blacks. The mean age of diagnosis of ADHD was over 8 years higher for White patients than for Black patients, with a disproportionately higher number of White patients diagnosed in adulthood, compared to a comparatively negligible number of Blacks diagnosed with ADHD in the same age group. Additionally, Black females were the cohort least likely to be diagnosed with ADHD, while White females were the cohort least likely to be diagnosed with CD. Race disparities exist between Black and White populations, and sex disparities exist within each population. More information is needed to determine contributors to these differences, although implicit biases and systemic racism may be key contributing factors. Presenting evidence and increasing awareness of culturally relevant diagnoses can reduce unconscious bias and move toward more informed and objective psychiatric evaluations. Health sciences/Diseases/Psychiatric disorders/Adhd Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Figure 2 Figure 3 Introduction Attention-Deficit/Hyperactivity Disorder (ADHD) and Conduct Disorder (CD) are behavioral conditions that affect approximately 10% and 3% of children in the United States, respectively. 1,2 However, the subjective nature of symptom assessment and the apparent overlap in behavioral manifestations between these disorders pose a significant risk for misdiagnosis. 3,4 This issue is further complicated by the cultural context in which symptoms are evaluated, leading to potentially inappropriate categorization of behaviors. 5-7 Both ADHD and CD have high societal and economic costs. ADHD persists into adulthood in about one-half to two-thirds of cases. 8-11 Moreover, despite being characterized as a neurodevelopmental disorder in childhood, there is a recent recognition of adult ADHD cases. 11,12 Current studies investigate whether ADHD in adults stems from missed childhood symptoms or comorbid mental health disorders rather than a distinct ‘late onset’ presentation. 10-13 The long-term burden of ADHD lies in its comorbidity with other psychiatric disorders and increased rates of accidents, occupational failures, criminality, and addiction. 8 As for CD, about 50% of individuals have a remission of symptoms in adulthood, while the rest frequently grow up to have a high risk of substance abuse, display criminal behaviors, or develop personality disorders. 2,14 There is growing evidence that minority populations are less likely to be diagnosed with ADHD and less likely to take medication for ADHD compared to non-Hispanic White populations. 5,6,15 Additionally, Black and Hispanic children are more likely to be diagnosed with CD than non-Hispanic White children. 5,6 We hypothesize that non-Hispanic Black populations exhibit a lower likelihood of ADHD diagnosis, and this difference in diagnosis rates between Black and White populations may be linked to an overrepresentation of ODD and CD diagnosis in Black communities. There is also consensus that males are two to four times more likely to be diagnosed with neurodevelopmental disorders than females. 5,16 Although this sex discrepancy may be related to the interplay between biological and societal factors, there is concern over diagnostic bias contributing to the underdiagnosis of females with neurodevelopmental disorders. 16 In this study, we utilized large-scale data from de-identified electronic health records of patients diagnosed with ADHD and/or CD to identify the discrepancies in ADHD and CD diagnosis between non-Hispanic White and non-Hispanic Black populations. We analyzed the distribution of age of diagnosis and sex differences for each disorder. Methods Data Collection This retrospective study was conducted using the TriNetX Research Network, which provides access to approximately 117 million anonymized patient electronic medical records from nearly 80 healthcare organizations across 4 countries. We first acquired data from patients diagnosed with ADHD (ICD-10 code F90), totaling 1,659,318 records. The second cohort acquired were patients with a CD diagnosis (ICD-10 code F91), with 422,625 patient records. These data were collected on June 5th, 2023. Our primary focus was on patient records within the US, consisting of approximately 96 million patients from 57 healthcare organizations. Study design With the high prevalence and increased knowledge of ADHD worldwide, the American Psychiatric Association’s (APA) 2013 update to the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM–5) 17 included revised criteria for the ADHD diagnosis. 18 Plotting the annual incidence of ADHD and CD diagnoses in our cohort data from 2000 to 2022 ( Figure S1 ) showed a steep increase in cases in recent years, particularly following 2013. Therefore, only patients with an initial diagnosis after 2013 were counted in each cohort. Using patient demographics, all patient records from outside the US or patients whose location was marked as “Unknown” were excluded. The focus was narrowed down to two groups of interest, a non-Hispanic White group, and a non-Hispanic Black/African American group. Each of these groups was further divided by sex into males and females. ICD-10 codes were used to stratify each cohort by presentation, with some individuals falling under multiple presentations. The ADHD cohorts were divided into ADHD, predominantly inattentive type (F90.0), ADHD, predominantly hyperactive type (F90.1), ADHD, combined type (F90.2), Other ADHD (F90.8), and Unspecified ADHD (F90.9). In this study, Other and Unspecified ADHD were combined under the label Unspecified ADHD. The CD cohorts were divided into Oppositional Defiant Disorder (ODD, F91.3), Childhood-onset CD (F91.1), Adolescent-onset CD (F91.2), Other CD (F91.8), and Unspecified CD (F91.9). In this study, Other and Unspecified CD were combined under the label Unspecified CD. Each patient's age of diagnosis (AoD) was determined by subtracting the year of the first recorded incidence of the disorder from the year of birth. An AoD of 0 indicates that the birth year was missing from the patient's record. Analysis and Statistics Fisher’s exact tests were used to determine the significance of the differences in the prevalence of each disorder and each disorder’s presentations across race and sex. The reference population numbers for non-Hispanic White, non-Hispanic Black, and the total male and female populations were derived using queries on the TriNetX platform. Student t-tests were used to determine the significance of the difference in the mean of the AoD between White and Black patients diagnosed with either ADHD or CD. Linear regression was applied to examine the relationship between the ADHD AoD of Black and White patients and the CD AoD of Black and White patients. The coefficient of determination or correlation (R 2 ) was calculated to measure the strength of the linear relationships. Chi-squared tests were used to determine the degree of association between race and sex in the prevalence of ADHD or CD diagnoses in a population by creating two-way contingency tables combining the race and sex distribution of each disorder. All data processing, statistical analyses, and figure generation were conducted in R. 19-22 Ethics Declarations TriNetX, LLC, complies with the Health Insurance Portability and Accountability Act (HIPAA). The data reviewed is a secondary analysis of existing data, does not involve intervention or interaction with human subjects, and is de-identified per the de-identification standard defined in Section 164.514(a) of the HIPAA Privacy Rule. The process by which the data is de-identified is attested to through a formal determination by a qualified expert as defined in Section 164.514(b)(1) of the HIPAA Privacy Rule. This formal determination by a qualified expert was refreshed in December 2020. This study only uses data collected from the TriNetX Research Network which contains data provided by healthcare organizations that allow the use of their data for scientific research and publications and warrant that they have all necessary rights, consents, approvals, and authority to provide the data to TriNetX under a Business Associate Agreement, so long as their name remains anonymous as a data source and their data are utilized for research purposes. This retrospective study is therefore exempt from informed consent by the University at Buffalo’s Institutional Review Board. The methods used in this study reveal no identifying information of either the subjects or the healthcare organizations. Results Amongst US patients on the TriNetX platform, 32,489,776 were non-Hispanic Whites, including 17,111,169 (52.7%) females. The non-Hispanic Black population accounted for 8,702,848 individuals, 4,654,839 (53.5%) females. In the White group, 708,004 individuals (2.18%) were diagnosed with ADHD. Out of those, 313,138 (44.2%) were females. Additionally, 110,160 (0.34%) were diagnosed with ODD or CD, and 36,300 (33%) were females. In the Black group, 141,277 (1.62%) were diagnosed with ADHD, including 51,323 (36.3%) females and 47,437 (0.55%) were diagnosed with ODD or CD, including 17,047 (35.9%) females. The incidences of each diagnosis and its associated presentations in each race and sex cohort were normalized by their population, then by the White cohort for racial analysis and the White male cohort for the sex analysis ( Figures S2 & S3; Table S1 ). In Figure 1a, apart from ADHD hyperactive type (ADHD-HT), ADHD diagnoses are significantly less prevalent in the Black population than in the White population. In the total ADHD cohort, a diagnosis in the Black population is 26% (OR, 0.74; 95% CI, 0.73-0.74; P<0.0001) less prevalent than in Whites. ADHD inattentive-type (ADHD-IT) is 55% (OR, 0.45; 95% CI, 0.45-0.46; P<0.0001) less prevalent in the Black population, while ADHD-HT is 0.03% (OR, 1.03; 95% CI, 1.01-1.05; P = 0.007) more prevalent in the Black population, although this result is not clinically significant. For ADHD combined-type (ADHD-CT) and unspecified ADHD, a diagnosis is, respectively, 13% (OR, 0.87; 95% CI, 0.86-0.88; P<0.0001) and 17% (OR, 0.83; 95% CI, 0.82-0.84; P<0.0001) less prevalent in the Black population than in the White population. In Figure 1b, CD diagnoses are more prevalent in the Black population than in the White population. In the total CD cohort, a diagnosis in the Black population is 61% (OR, 1.61; 95% CI, 1.59-1.63; P<0.0001) more prevalent than in the White population, while ODD is 35% (OR, 1.35; 95% CI, 1.33-1.37; P<0.0001) more prevalent. Childhood-onset CD and adolescent-onset CD are, respectively, 128% (OR, 2.28; 95% CI, 2.21-2.37; P<0.0001) and 73% (OR, 1.73; 95% CI, 1.56-1.92; P<0.0001) more prevalent in the Black population while unspecified CD was 78% (OR, 1.78; 95% CI, 1.76-1.81; P<0.0001) more prevalent than in the White population. Figure 1c shows the odds ratio and 95% confidence interval for the differences in the incidence for each disorder cohort and its presentations with the exact values listed in Table 1 . Table 1: Fisher's Exact Test comparing the prevalence of ADHD and its presentations and CD and its presentations between the Black and White populations. Number of Black Patients Number of White Patients Odds Ratio 95 % Confidence Interval p-value Total ADHD 141,277 708,004 0.74 0.73-0.74 <0.0001 Predominantly Inattentive 24,047 197,246 0.45 0.45-0.46 <0.0001 Predominantly Hyperactive 9,416 34,073 1.03 1.01-1.05 0.007 Combined ADHD 53,895 230,083 0.87 0.86-0.88 <0.0001 Unspecified ADHD 107,660 482,871 0.83 0.82-0.84 <0.0001 Total CD 47,437 110,160 1.61 1.59-1.63 <0.0001 ODD 17,826 49,248 1.35 1.33-1.37 <0.0001 Childhood-onset CD 4,698 7,673 2.28 2.21-2.37 <0.0001 Adolescent-onset CD 530 1,140 1.73 1.56-1.92 <0.0001 Unspecified CD 34,590 72,490 1.78 1.76-1.81 <0.0001 The AoD for each cohort was calculated, and we discovered that the average ADHD AoD of White patients is 23.9, which is significantly higher than the average ADHD AoD of Black patients at 15.7 (95% CI, 8.1-8.24; P < 0.0001), attributed to the relative increase in the diagnosis of Whites between the ages of 18 and 40 years old ( Figure 2a; Table S2 ). Linear regression analysis resulted in a correlation coefficient of 0.86 between the AoD of ADHD in Black versus White patients ( Figure S4A ). However, a linear regression performed on a subset of patients 18 years old or younger yielded a higher correlation coefficient of 0.97 ( Figure S4B ). These data suggest that the AoD between Blacks and Whites with ADHD are essentially the same in childhood, yet there is a substantial increase in the diagnosis of ADHD in White adults compared to Blacks. The AoD distribution of Black and White CD patients highlights more parallel trends between the two groups ( Figure 2b ). Linear regression analysis shows a high correlation between AoD in CD cases in Black and White patients, with a coefficient of 0.97 ( Figure S4C ). Figure 3a and Table 2 show that Black females are the most underrepresented group across sex and race, being 59% (OR, 0.41; 95% CI, 0.41-0.42; P < 0.0001) less prevalent than White males. White females diagnosed with ADHD are 31% (OR, 0.69; 95% CI, 0.69-0.70; P < 0.0001) less prevalent, while Black males are 12% (OR, 0.88; 95% CI, 0.87-0.89; P < 0.0001) less prevalent. With ADHD-IT diagnosis in the Black male population is 47% (OR, 0.53; 95% CI, 0.52-0.54; P < 0.0001) less prevalent than the White male population, while diagnosis in the Black female population is 63% (OR, 0.37; 95% CI, 0.36-0.37; P < 0.0001) less prevalent. With ADHD-IT prevalence of diagnosis in Black males are 16% (OR, 1.16; 95% CI, 1.12-1.19; P < 0.0001) higher than in White males, while in Black females and White females, the prevalence of diagnosis is, respectively, 61% (OR, 0.39; 95% CI, 0.37-0.40; P < 0.0001) and 55% (OR, 0.45; 95% CI, 0.44-0.46; P < 0.0001) less. The prevalence of ADHD-CT diagnosis in Black females and White females are, respectively, 63% (OR, 0.37; 95% CI, 0.36-0.37; P < 0.0001) and 48% (OR, 0.52; 95% CI, 0.51-0.52; P < 0.0001) less than in White males. Unspecified ADHD diagnosis is 54% (OR, 0.46; 95% CI, 0.45-0.47; P < 0.0001) less in Black females, and 31% (OR, 0.69; 95% CI, 0.69-0.70; P < 0.0001) less in White females compared to White males. Table 2: Fisher's Exact Test comparing the prevalence of ADHD and its presentations in each of the Black Female, Black Male and White Female populations to their prevalence in the White Male population. Cohort Number of Patients Odds Ratio 95% Confidence Interval p-value Total ADHD Black Female 51,323 0.41 0.41 - 0.42 <0.0001 Black Male 89,935 0.88 0.87 - 0.89 <0.0001 White Female 313,138 0.69 0.69 - 0.70 <0.0001 White Male 394,607 NA Predominantly Inattentive ADHD Black Female 10,790 0.37 0.36 - 0.37 <0.0001 Black Male 12,953 0.53 0.52 - 0.54 <0.0001 White Female 100,539 0.93 0.92 - 0.94 <0.0001 White Male 94,857 NA Predominantly Hyperactive ADHD Black Female 2,680 0.39 0.37 - 0.40 <0.0001 Black Male 6,680 1.16 1.12 - 1.19 <0.0001 White Female 11,558 0.45 0.44 - 0.46 <0.0001 White Male 22,345 NA Combined ADHD Black Female 16,424 0.37 0.36 - 0.37 <0.0001 Black Male 36,983 0.99 0.98 - 1.01 0.62 White Female 84,691 0.52 0.51 - 0.52 <0.0001 White Male 143,452 NA Unspecified ADHD Black Female 38,154 0.46 0.45 - 0.47 <0.0001 Black Male 66,593 0.97 0.96 - 0.98 <0.0001 White Female 211,128 0.69 0.69 - 0.70 <0.0001 White Male 264,589 NA Figure 3b and Table 3 shows that compared to the White male population, total Black male CD diagnosis is 59% (OR, 1.59; 95% CI, 1.57-1.62; P < 0.0001) higher, while in Black females it is 25% (OR, 0.75; 95% CI, 0.73-0.76; P < 0.0001) less and in White females it is 57% (OR, 0.43; 95% CI, 0.42-0.44; P < 0.0001) less. ODD diagnosis in Black males is 29% (OR, 1.29; 95% CI, 1.26-1.32; P < 0.0001) more prevalent than in White males, but 39% (OR, 0.61; 95% CI, 0.59-0.63; P < 0.0001) less prevalent in Black females and 59% (OR, 0.41; 95% CI, 0.40-0.41; P < 0.0001) less prevalent in White females. Childhood-onset CD is 119% (OR, 2.19; 95% CI, 2.10-2.29; P < 0.0001) more prevalent in Black males and 64% (OR, 0.36; 95% CI, 0.34-0.38; P < 0.0001) less overall in White females than in White males. As for adolescent-onset CD, Black males have a 65% (OR, 1.65; 95% CI, 1.44-1.89; P < 0.0001) higher diagnosis prevalence, and White females have a 53% (OR, 0.47; 95% CI, 0.42-0.54; P < 0.0001) lower diagnosis prevalence than White males. Unspecified CD is 78% (OR, 1.78; 95% CI, 1.75-1.81; P < 0.0001) more prevalent in Black males, 19% (OR, 0.81; 95% CI, 0.79-0.82; P < 0.0001) less prevalent in Black females and 57% (OR, 0.43; 95% CI, 0.42-0.44; P < 0.0001) less prevalent in White females, all compared to prevalence in White males. The odds ratio and 95% confidence interval for the prevalence differences in ADHD, CD, and their presentations of each race and sex cohort are plotted in Figure S5 comparing A. Black male, B. White female, and C . Black female, all to White male. Pearson’s Chi-squared test results show a significant association between the categories of race and sex in each disorder (Table S3) . Table 3: Fisher's Exact Test comparing the prevalence of CD and its presentations in each of the Black Female, Black Male and White Female populations to their prevalence in the White Male population. Cohort Number of Patients Odds Ratio 95% Confidence Interval p-value Total CD Black Female 17,047 0.75 0.73 - 0.76 <0.0001 Black Male 30,381 1.59 1.57 - 1.62 <0.0001 White Female 36,300 0.43 0.42 - 0.44 <0.0001 White Male 73,837 NA ODD Black Female 6,256 0.61 0.59 - 0.63 <0.0001 Black Male 11,017 1.29 1.26 - 1.32 <0.0001 White Female 15,233 0.41 0.40 - 0.41 <0.0001 White Male 32,997 NA Childhood-onset CD Black Female 1,567 0.94 0.89 - 1.00 0.07 Black Male 3,037 2.19 2.10 - 2.29 <0.0001 White Female 2,203 0.36 0.34 - 0.38 <0.0001 White Male 5,350 NA Adolescent-onset CD Black Female 200 0.91 0.77 - 1.06 0.26 Black Male 304 1.65 1.44 - 1.89 <0.0001 White Female 383 0.47 0.42 - 0.54 <0.0001 White Male 712 NA Unspecified CD Black Female 11,915 0.81 0.79 - 0.82 <0.0001 Black Male 21,987 1.78 1.75 - 1.81 <0.0001 White Female 23,528 0.43 0.42 - 0.44 <0.0001 White Male 47,730 NA Discussion ADHD is a complex neurodevelopmental disorder of varying presentations with social and cultural considerations. We hypothesized that Blacks are likely to have a lesser prevalence of an ADHD diagnosis and a higher prevalence of an ODD or CD diagnosis when compared to Whites. In this study, countrywide large-scale analysis supports our hypothesis, revealing that ADHD diagnoses are overrepresented in White patients compared to Blacks. Apart from ADHD-HT, all other presentations of ADHD have significantly less prevalence in Blacks than Whites. The most notable difference was found to be in the diagnosis of ADHD-IT. ADHD-IT is generally the most under-recognized and undertreated presentation of ADHD, with affected individuals having a low likelihood of receiving clinical and behavioral services. 23 Our results indicate that this problem may be compounded in the Black population, with disproportionately lower diagnoses of Black patients with ADHD-IT potentially contributing to significant disparities in access to clinical and behavioral services and utilization. Untreated ADHD-IT in adolescents and adults can be expected to impact an individual’s academic pursuits and career due to symptoms such as forgetfulness and difficulty maintaining attention for tasks, chores, or workplace responsibilities. 23 In Black children, ADHD symptoms can be disproportionately misconstrued as willful or defiant behaviors, contributing to a greater likelihood of a diagnosis of ODD or CD and a corresponding lack or mismatch of appropriate interventions or inappropriate use of disciplinary strategies. 3,6,24 A study using data from the 2011-2012 National Survey of Children’s Health found that White children were more likely to be diagnosed with ADHD alone, while Black children were more likely to be diagnosed with ADHD with an ODD or CD comorbidity. 25 Our findings add to the characterization of this disparity, demonstrating a significantly higher prevalence of ODD and CD diagnoses in all presentations between Blacks and Whites. The cause of the gap in childhood-onset CD is unclear. Clinicians have demonstrated a reluctance to assign an early CD diagnosis for aggressive behavior in childhood, possibly expecting children to mature out of these patterns developmentally or, at times, giving an ODD diagnosis instead to avoid the stigma associated with a CD diagnosis. 2 However, our results indicate that this cautionary approach may not be afforded to Black children as often. Along with ADHD symptoms that persist from childhood into adulthood, there is a growing trend of adults presenting with inattention, disorganization, and impulsivity not recognized in childhood. 26-28 There is skepticism about the diagnosis of ADHD in adulthood, as it is not fully understood and may be driven by individuals seeking stimulant medication with the symptoms more likely explained by other psychiatric or substance use disorders. 12,13,29 Moreover, adult ADHD is not easy to identify. With the difficulty of obtaining a neuropsychiatric evaluation outside of childhood, especially for underprivileged populations, this job usually falls to primary care physicians (PCP) with little training in diagnosing complex psychiatric disorders. 26,27 The high prevalence of unspecified types of ADHD and CD in our cohorts indicates that PCPs might diagnose neurodevelopmental disorders without a closer examination of the diagnostic criteria necessary to identify the disorder presentation or possible comorbid conditions. 26 While most ADHD diagnoses take place before the age of 18 in both populations, our analysis reveals that adult ADHD is diagnosed much more prominently in White adults compared to Blacks. The Black population has a steady decline in ADHD diagnoses after adolescence. However, in White patients, there is a disproportionately higher number of patients diagnosed between 18 and 40 than in Black patients. Receiving an ADHD diagnosis as an adult can be more complex, potentially requiring access to specialized or extended evaluations and necessitating greater expenditure of significant social capital. This disparity between the two racial groups could result from unequal access to general and psychiatric healthcare services in adulthood. Furthermore, implicit biases stemming from societal or cultural differences could potentially lead to the misinterpretation of service-seeking behaviors as stimulant-seeking actions, thereby contributing to the underdiagnosis of Black adults who may have ADHD. 7,28 Females are less likely to be diagnosed with neurodevelopmental disorders. 5,16,30 Studies have shown that despite males displaying a higher prevalence of ADHD and CDs, females suffer from more severe symptoms, significant lifetime psychiatric comorbidities, and functional impairments. 5,16,30 Apart from diagnostic bias, a potential contributing factor to sex disparities in these disorders is variation in symptom presentation. Females tend to exhibit more inattentive behavior and less hyperactivity, making them perceived as less disruptive than males, and their ADHD might go unnoticed or undiagnosed. 31,32 As for CD, while males are inclined to display signs of proactive physical aggression, females are more likely to show relational, reactive aggression in bullying and manipulative behavior with less callous-unemotional traits. 30 Beyond developmental gender differences, this could also suggest that females must exhibit more pronounced symptoms before being referred for or diagnosed with these disorders. Our analysis shows that Black females are the least prevalent group diagnosed with all presentations of ADHD. Apart from predominantly inattentive ADHD, White females are the second most underrepresented group in ADHD diagnoses. White females also appear to be the group least diagnosed with CDs. Prior studies have revealed that conscious or unconscious bias can impact medical decisions and diagnoses. 6 These studies indicate clinicians are more responsive to non-Hispanic White patients seeking an ADHD diagnosis and treatment, whereas Black students receive fewer referrals from schoolteachers and administrators. 5,6 Moreover, obtaining a CD diagnosis will likely negatively impact a caregiver’s ability to detect inattentive or hyperactive behavior, limiting their access to psychiatric evaluations, medication, and therapy. 6 It could also lead to harsher disciplinary measures and exclusionary practices in school that could further compound mental and behavioral challenges. 5,6 One concern related to overdiagnosis and overtreatment of neurodevelopmental disorders like ADHD is the potential harm stemming from diverting resources from other populations who may be underdiagnosed or undertreated. 33 Furthermore, the perception of ADHD as an overdiagnosed disorder in any racial group is likely. 34 This study has some limitations. First, our lack of control subjects hindered our ability to perform more detailed analyses. Second, we did not have access to robust information about our patient cohorts' socioeconomic status and insurance status, which is often a factor in a patient’s ability to receive clinical care, particularly mental healthcare and treatment. 5 Third, with our reliance on ICD-10 codes, there were no symptom details to determine diagnostic accuracy. Fourth, we had very few quantitative measurements that could have been used to control confounding variables. In conclusion, our analysis found race and sex disparities in ADHD and CD diagnosis in the US. The non-Hispanic Black population is less likely to be diagnosed with ADHD but more likely to be diagnosed with ODD or CD than non-Hispanic Whites. White patients get diagnosed with ADHD in adulthood more often than Black patients. Black females are the cohort least likely to be diagnosed with ADHD, and White females are the cohort least likely to be diagnosed with CD. Future work that integrates patients' socioeconomic status and insurance status will give a deeper understanding of racial disparities. Detailed clinical symptom presentations, with quantifiable assessments compared to control cohorts, could be used to analyze further and measure accurate rates of over- and under-diagnosis in suspected populations. The disproportionally high rates of ODD and CD diagnoses carried by Black patients may indicate unconscious/implicit bias by healthcare practitioners and a corresponding tendency to miss underlying conditions that could better explain disruptive behaviors. Presenting evidence and increasing awareness of such disparities has effectively reduced unconscious bias and sustained the movement toward more culturally informed and objective psychiatric evaluations. 6 Declarations Author Contributions: N.S. analyzed the data. N.S. and J.W. wrote the manuscript. S.S. provided medical background and interpretation, and reviewed and edited the manuscript. J.W. designed the study. Data Availability: All data used in the present study is available through TriNetX at https://trinetx.com/ Competing Interests: The authors declare no competing interests. References Posner, J., Polanczyk, G. V. & Sonuga-Barke, E. Attention-deficit hyperactivity disorder. The Lancet (British edition) 395 , 450-462 (2020). https://doi.org:10.1016/S0140-6736(19)33004-1 Fairchild, G. et al. Conduct disorder. Nature Reviews Disease Primers 5 , 43 (2019). https://doi.org:10.1038/s41572-019-0095-y Azeredo, A., Moreira, D. & Barbosa, F. 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Attention deficit hyperactivity disorder in African American children: What can be concluded from the past ten years? Clinical Psychology Review 29 , 77-86 (2009). https://doi.org:https://doi.org/10.1016/j.cpr.2008.10.001 Faraone, S. V. et al. Attention-deficit/hyperactivity disorder. Nature Reviews Disease Primers 1 , 15020 (2015). https://doi.org:10.1038/nrdp.2015.20 Gallo, E. F. & Posner, J. Moving towards causality in attention-deficit hyperactivity disorder: overview of neural and genetic mechanisms. Lancet Psychiatry 3 , 555-567 (2016). https://doi.org:10.1016/S2215-0366(16)00096-1 Simon, V., Czobor, P., Bálint, S., Mészáros, A. & Bitter, I. Prevalence and correlates of adult attention-deficit hyperactivity disorder: meta-analysis. British Journal of Psychiatry 194 , 204-211 (2009). https://doi.org:10.1192/bjp.bp.107.048827 Asherson, P. & Agnew-Blais, J. Annual Research Review: Does late-onset attention-deficit/hyperactivity disorder exist? 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Current Opinion in Neurology 32 , 622-626 (2019). https://doi.org:10.1097/wco.0000000000000714 Diagnostic and statistical manual of mental disorders : DSM-5 . 5th edn, (American Psychiatric Association, 2013). Steinau, S. Diagnostic Criteria in Attention Deficit Hyperactivity Disorder - Changes in DSM 5. Frontiers in psychiatry 4 , 49-49 (2013). https://doi.org:10.3389/fpsyt.2013.00049 R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2023). ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016). ggpattern: 'ggplot2' Pattern Geoms (2022). hrbrthemes: Additional Themes, Theme Components and Utilities for 'ggplot2' (2020). de la Peña, I. C., Pan, M. C., Thai, C. G. & Alisso, T. Attention-Deficit/Hyperactivity Disorder Predominantly Inattentive Subtype/Presentation: Research Progress and Translational Studies. Brain Sciences 10 , 292 (2020). Wexler, D., Salgado, R., Gornik, A., Peterson, R. & Pritchard, A. What's race got to do with it? Informant rating discrepancies in neuropsychological evaluations for children with ADHD. Clinical Neuropsychology 36 , 264-286 (2022). https://doi.org:10.1080/13854046.2021.1944671 Visser, S. N., Deubler, E. L., Bitsko, R. H., Holbrook, J. R. & Danielson, M. L. Demographic Differences Among a National Sample of US Youth With Behavioral Disorders. Clinical Pediatrics, Philadelphia 55 , 1358-1362 (2016). https://doi.org:10.1177/0009922815623229 Huang, H., Huang, H., Spottswood, M. & Ghaemi, N. Approach to Evaluating and Managing Adult Attention-Deficit/Hyperactivity Disorder in Primary Care. Harvard Review of Psychiatry 28 , 100-106 (2020). https://doi.org:10.1097/hrp.0000000000000248 Faraone, S. V., Spencer, T. J., Montano, C. B. & Biederman, J. Attention-Deficit/Hyperactivity Disorder in Adults: A Survey of Current Practice in Psychiatry and Primary Care. Archives of Internal Medicine 164 , 1221-1226 (2004). https://doi.org:10.1001/archinte.164.11.1221 Chung, W. et al. Trends in the Prevalence and Incidence of Attention-Deficit/Hyperactivity Disorder Among Adults and Children of Different Racial and Ethnic Groups. JAMA Network Open 2 , e1914344 (2019). https://doi.org:10.1001/jamanetworkopen.2019.14344 Lovett, B. J. & Harrison, A. G. Assessing adult ADHD: New research and perspectives. Journal of Clinical and Experimental Neuropsychology 43 , 333-339 (2021). https://doi.org:10.1080/13803395.2021.1950640 Konrad, K. et al. Sex differences in psychiatric comorbidity and clinical presentation in youths with conduct disorder. Journal of Child Psychology and Psychiatry 63 , 218-228 (2022). https://doi.org:10.1111/jcpp.13428 Mowlem, F. D. et al. Sex differences in predicting ADHD clinical diagnosis and pharmacological treatment. European Child & Adolescent Psychiatry 28 , 481-489 (2019). https://doi.org:10.1007/s00787-018-1211-3 Rucklidge, J. J. Gender differences in attention-deficit/hyperactivity disorder. Psychiatric Clinics of North America 33 , 357-373 (2010). https://doi.org:10.1016/j.psc.2010.01.006 Kazda, L. et al. Overdiagnosis of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents: A Systematic Scoping Review. JAMA Network Open 4 , e215335 (2021). https://doi.org:10.1001/jamanetworkopen.2021.5335 Sciutto, M. J. & Eisenberg, M. Evaluating the evidence for and against the overdiagnosis of ADHD. Journal of Attention Disorders 11 , 106-113 (2007). https://doi.org:10.1177/1087054707300094 Additional Declarations No competing interests reported. Supplementary Files SupplementalData.pdf Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviews received at journal 29 Apr, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviewers invited by journal 18 Apr, 2024 Editor assigned by journal 15 Apr, 2024 Editor invited by journal 05 Apr, 2024 Submission checks completed at journal 05 Apr, 2024 First submitted to journal 27 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4177866","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288978537,"identity":"4f0613fb-6e99-4488-be57-8651061416e1","order_by":0,"name":"Noha Shalaby","email":"","orcid":"","institution":"State University at Buffalo","correspondingAuthor":false,"prefix":"","firstName":"Noha","middleName":"","lastName":"Shalaby","suffix":""},{"id":288978538,"identity":"7c20fdd9-02cf-4a6c-9ef4-08c7a8489186","order_by":1,"name":"Sourav Sengupta","email":"","orcid":"","institution":"State University at Buffalo","correspondingAuthor":false,"prefix":"","firstName":"Sourav","middleName":"","lastName":"Sengupta","suffix":""},{"id":288978539,"identity":"656c9eb9-0715-483b-ba92-855672f51146","order_by":2,"name":"Jamal B. Williams","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACxhkMDAceFEgw8EswMHyoYLAACRoQ1pJgIMEgOQPIPsMgQVgLWE0CUI3BDWK1MM/uMQTaYiFvfLvHsOFAjYQcA3vzNgm8DptzxgDkMMNtd84AtRyTMGbgOVaGX8uM3A0gLYzbbuSYP/7AJpHYIJFjRpQW+80zcoC2/ANqkX9DnJbEDRJALQfbQLbwENAy5/wHkJbkGXeOFTYc7JMwZuNJK7bAp8Vwdlvyhw8Vdbb9s5s3Nhz4ZiPHz3544w28WhrQRdjwKQcBeUIKRsEoGAWjYBQwAAAH9k/aCY6ZGwAAAABJRU5ErkJggg==","orcid":"","institution":"State University at Buffalo","correspondingAuthor":true,"prefix":"","firstName":"Jamal","middleName":"B.","lastName":"Williams","suffix":""}],"badges":[],"createdAt":"2024-03-27 18:18:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4177866/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4177866/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-75954-5","type":"published","date":"2024-10-24T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54394902,"identity":"0dc7258c-96ec-4921-8295-36dc1a9074df","added_by":"auto","created_at":"2024-04-09 21:14:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":411396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of ADHD and CD Between Blacks and Whites.\u003c/strong\u003e Normalized incidence of \u003cstrong\u003ea\u003c/strong\u003e. ADHD and its presentations, and \u003cstrong\u003eb.\u003c/strong\u003e CD and its presentations in Black and White populations. \u003cstrong\u003ec.\u003c/strong\u003e Odds Ratio and 95% confidence interval of the incidence of ADHD and CD and their presentations in the Black population compared to the White population. ADHD; Black N = 141,277, White N = 708,004; CD; Black N = 47,437, White N = 110,160.\u003c/p\u003e","description":"","filename":"Figures11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4177866/v1/c21cd4e82eae84dc5adfae4e.jpg"},{"id":54394910,"identity":"9a82394a-d23e-4cce-b1f4-1de2377dcc3c","added_by":"auto","created_at":"2024-04-09 21:14:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":266014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge of Diagnosis Distributions. a.\u003c/strong\u003e Density plot of the AoD distribution of ADHD in Black and White patients, \u003cstrong\u003eb.\u003c/strong\u003e Density plot of the AoD distribution of CD in Black and White patients.\u003c/p\u003e","description":"","filename":"Figures12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4177866/v1/e2321aaa85d82608546469d5.jpg"},{"id":54394907,"identity":"b5fc5810-b279-4b70-b105-10e1ded86fcd","added_by":"auto","created_at":"2024-04-09 21:14:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":590833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSex Differences in ADHD and CD Prevalence Between Blacks and Whites.\u003c/strong\u003e Normalized incidence of \u003cstrong\u003ea.\u003c/strong\u003eADHD and its presentations and \u003cstrong\u003eb.\u003c/strong\u003e CD and its presentations in males and females in Black and White populations. ADHD; Black Female N = 51,323, Black Male N = 89,935, White Female N = 313,138, White Male N = 394,607; CD; Black Female N = 17,047, Black Male N = 30,381, White Female N = 36,300, White Male N = 73,837.\u003c/p\u003e","description":"","filename":"Figures13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4177866/v1/7de101e084bc1e30b8acee8a.jpg"},{"id":67682435,"identity":"65d7b7bf-9654-4fe6-ad9d-14344f4dd33b","added_by":"auto","created_at":"2024-10-28 16:13:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1852714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4177866/v1/a6d0d7e2-23b4-4a54-b49a-d966c0660b1e.pdf"},{"id":54394913,"identity":"a00eda9a-1343-4cad-9f23-b92672426946","added_by":"auto","created_at":"2024-04-09 21:14:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1060039,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4177866/v1/e99f2722598f3f8ba925dcd7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Large-Scale Analysis Reveals Racial Disparities in the Prevalence of ADHD and Conduct Disorders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAttention-Deficit/Hyperactivity Disorder (ADHD) and Conduct Disorder (CD) are behavioral conditions that affect approximately 10% and 3% of children in the United States, respectively.\u003csup\u003e1,2\u003c/sup\u003e However, the subjective nature of symptom assessment and the apparent overlap in behavioral manifestations between these disorders pose a significant risk for misdiagnosis.\u003csup\u003e3,4\u003c/sup\u003e This issue is further complicated by the cultural context in which symptoms are evaluated, leading to potentially inappropriate categorization of behaviors.\u003csup\u003e5-7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eBoth ADHD and CD have high societal and economic costs. ADHD persists into adulthood in about one-half to two-thirds of cases.\u003csup\u003e8-11\u003c/sup\u003e Moreover, despite being characterized as a neurodevelopmental disorder in childhood, there is a recent recognition of adult ADHD cases.\u003csup\u003e11,12\u003c/sup\u003e Current studies investigate whether ADHD in adults stems from missed childhood symptoms or comorbid mental health disorders rather than a distinct \u0026lsquo;late onset\u0026rsquo; presentation.\u003csup\u003e10-13\u003c/sup\u003e The long-term burden of ADHD lies in its comorbidity with other psychiatric disorders and increased rates of accidents, occupational failures, criminality, and addiction.\u003csup\u003e8\u003c/sup\u003e As for CD, about 50% of individuals have a remission of symptoms in adulthood, while the rest frequently grow up to have a high risk of substance abuse, display criminal behaviors, or develop personality disorders.\u003csup\u003e2,14\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;There is growing evidence that minority populations are less likely to be diagnosed with ADHD and less likely to take medication for ADHD compared to non-Hispanic White populations.\u003csup\u003e5,6,15\u003c/sup\u003e Additionally, Black and Hispanic children are more likely to be diagnosed with CD than non-Hispanic White children.\u003csup\u003e5,6\u003c/sup\u003e We hypothesize that non-Hispanic Black populations exhibit a lower likelihood of ADHD diagnosis, and this difference in diagnosis rates between Black and White populations may be linked to an overrepresentation of ODD and CD diagnosis in Black communities. There is also consensus that males are two to four times more likely to be diagnosed with neurodevelopmental disorders than females.\u003csup\u003e5,16\u003c/sup\u003e Although this sex discrepancy may be related to the interplay between biological and societal factors, there is concern over diagnostic bias contributing to the underdiagnosis of females with neurodevelopmental disorders.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In this study, we utilized large-scale data from de-identified electronic health records of patients diagnosed with ADHD and/or CD to identify the discrepancies in ADHD and CD diagnosis between non-Hispanic White and non-Hispanic Black populations. We analyzed the distribution of age of diagnosis and sex differences for each disorder.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eData Collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted using the TriNetX Research Network, which provides access to approximately 117 million anonymized patient electronic medical records from nearly 80 healthcare organizations across 4 countries. We first acquired data from patients diagnosed with ADHD (ICD-10 code F90), totaling 1,659,318 records. The second cohort acquired were patients with a CD diagnosis (ICD-10 code F91), with 422,625 patient records. These data were collected on June 5th, 2023. Our primary focus was on patient records within the US, consisting of approximately 96 million patients from 57 healthcare organizations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWith the high prevalence and increased knowledge of ADHD worldwide, the American Psychiatric Association\u0026rsquo;s (APA) 2013 update to the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM\u0026ndash;5)\u003csup\u003e17\u003c/sup\u003e included revised criteria for the ADHD diagnosis.\u003csup\u003e18\u003c/sup\u003e Plotting the annual incidence of ADHD and CD diagnoses in our cohort data from 2000 to 2022 (\u003cstrong\u003eFigure S1\u003c/strong\u003e) showed a steep increase in cases in recent years, particularly following 2013. Therefore, only patients with an initial diagnosis after 2013 were counted in each cohort. Using patient demographics, all patient records from outside the US or patients whose location was marked as \u0026ldquo;Unknown\u0026rdquo; were excluded. The focus was narrowed down to two groups of interest, a non-Hispanic White group, and a non-Hispanic Black/African American group. Each of these groups was further divided by sex into males and females.\u003c/p\u003e\n\u003cp\u003eICD-10 codes were used to stratify each cohort by presentation, with some individuals falling under multiple presentations. The ADHD cohorts were divided into ADHD, predominantly inattentive type (F90.0), ADHD, predominantly hyperactive type (F90.1), ADHD, combined type (F90.2), Other ADHD (F90.8), and Unspecified ADHD (F90.9). In this study, Other and Unspecified ADHD were combined under the label Unspecified ADHD. The CD cohorts were divided into Oppositional Defiant Disorder (ODD, F91.3), Childhood-onset CD (F91.1), Adolescent-onset CD (F91.2), Other CD (F91.8), and Unspecified CD (F91.9). In this study, Other and Unspecified CD were combined under the label Unspecified CD.\u003c/p\u003e\n\u003cp\u003eEach patient\u0026apos;s age of diagnosis (AoD) was determined by subtracting the year of the first recorded incidence of the disorder from the year of birth. An AoD of 0 indicates that the birth year was missing from the patient\u0026apos;s record.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis and Statistics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFisher\u0026rsquo;s exact tests were used to determine the significance of the differences in the prevalence of each disorder and each disorder\u0026rsquo;s presentations across race and sex. The reference population numbers for non-Hispanic White, non-Hispanic Black, and the total male and female populations were derived using queries on the TriNetX platform.\u003c/p\u003e\n\u003cp\u003eStudent t-tests were used to determine the significance of the difference in the mean of the AoD between White and Black patients diagnosed with either ADHD or CD.\u003c/p\u003e\n\u003cp\u003eLinear regression was applied to examine the relationship between the ADHD AoD of Black and White patients and the CD AoD of Black and White patients. The coefficient of determination or correlation (R\u003csup\u003e2\u003c/sup\u003e) was calculated to measure the strength of the linear relationships.\u003c/p\u003e\n\u003cp\u003eChi-squared tests were used to determine the degree of association between race and sex in the prevalence of ADHD or CD diagnoses in a population by creating two-way contingency tables combining the race and sex distribution of each disorder.\u003c/p\u003e\n\u003cp\u003eAll data processing, statistical analyses, and figure generation were conducted in R.\u003csup\u003e19-22\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics Declarations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTriNetX, LLC, complies with the Health Insurance Portability and Accountability Act (HIPAA). The data reviewed is a secondary analysis of existing data, does not involve intervention or interaction with human subjects, and is de-identified per the de-identification standard defined in Section 164.514(a) of the HIPAA Privacy Rule. The process by which the data is de-identified is attested to through a formal determination by a qualified expert as defined in Section 164.514(b)(1) of the HIPAA Privacy Rule. This formal determination by a qualified expert was refreshed in December 2020.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;This study only uses data collected from the \u003cem\u003eTriNetX Research\u003c/em\u003e Network which contains data provided by healthcare organizations that allow the use of their data for scientific research and publications and warrant that they have all necessary rights, consents, approvals, and authority to provide the data to TriNetX under a Business Associate Agreement, so long as their name remains anonymous as a data source and their data are utilized for research purposes. This retrospective study is therefore exempt from informed consent by the University at Buffalo\u0026rsquo;s Institutional Review Board. The methods used in this study reveal no identifying information of either the subjects or the healthcare organizations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmongst US patients on the TriNetX platform, 32,489,776 were non-Hispanic Whites, including 17,111,169 (52.7%) females. The non-Hispanic Black population accounted for 8,702,848 individuals, 4,654,839 (53.5%) females. In the White group, 708,004 individuals (2.18%) were diagnosed with ADHD. Out of those, 313,138 (44.2%) were females. Additionally, 110,160 (0.34%) were diagnosed with ODD or CD, and 36,300 (33%) were females. In the Black group, 141,277 (1.62%) were diagnosed with ADHD, including 51,323 (36.3%) females and 47,437 (0.55%) were diagnosed with ODD or CD, including 17,047 (35.9%) females. The incidences of each diagnosis and its associated presentations in each race and sex cohort were normalized by their population, then by the White cohort for racial analysis and the White male cohort for the sex analysis (\u003cstrong\u003eFigures S2 \u0026amp; S3; Table S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn \u003cstrong\u003eFigure 1a,\u003c/strong\u003e apart from ADHD hyperactive type (ADHD-HT), ADHD diagnoses are significantly less prevalent in the Black population than in the White population. In the total ADHD cohort, a diagnosis in the Black population is 26% (OR, 0.74; 95% CI, 0.73-0.74; P\u0026lt;0.0001) less prevalent than in Whites. ADHD inattentive-type (ADHD-IT) is 55% (OR, 0.45; 95% CI, 0.45-0.46; P\u0026lt;0.0001) less prevalent in the Black population, while ADHD-HT is 0.03% (OR, 1.03; 95% CI, 1.01-1.05; P = 0.007) more prevalent in the Black population, although this result is not clinically significant. For ADHD combined-type (ADHD-CT) and unspecified ADHD, a diagnosis is, respectively, 13% (OR, 0.87; 95% CI, 0.86-0.88; P\u0026lt;0.0001) and 17% (OR, 0.83; 95% CI, 0.82-0.84; P\u0026lt;0.0001) less prevalent in the Black population than in the White population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn \u003cstrong\u003eFigure 1b,\u003c/strong\u003e CD diagnoses are more prevalent in the Black population than in the White population. In the total CD cohort, a diagnosis in the Black population is 61% (OR, 1.61; 95% CI, 1.59-1.63; P\u0026lt;0.0001) more prevalent than in the White population, while ODD is 35% (OR, 1.35; 95% CI, 1.33-1.37; P\u0026lt;0.0001) more prevalent. Childhood-onset CD and adolescent-onset CD are, respectively, 128% (OR, 2.28; 95% CI, 2.21-2.37; P\u0026lt;0.0001) and 73% (OR, 1.73; 95% CI, 1.56-1.92; P\u0026lt;0.0001) more prevalent in the Black population while unspecified CD was 78% (OR, 1.78; 95% CI, 1.76-1.81; P\u0026lt;0.0001) more prevalent than in the White population. \u003cstrong\u003eFigure 1c\u003c/strong\u003e shows the odds ratio and 95% confidence interval for the differences in the incidence for each disorder cohort and its presentations with the exact values listed in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Fisher\u0026apos;s Exact Test comparing the prevalence of ADHD and its presentations and CD and its presentations between the Black and White populations.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of Black Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of White Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e95 % Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003eTotal ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e141,277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e708,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e0.73-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003ePredominantly Inattentive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e24,047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e197,246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e0.45-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003ePredominantly Hyperactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e9,416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e34,073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e1.01-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003eCombined ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e53,895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e230,083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e0.86-0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003eUnspecified ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e107,660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e482,871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e0.82-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003eTotal CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e47,437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e110,160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e1.59-1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003eODD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e17,826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e49,248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e1.33-1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003eChildhood-onset CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e4,698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e7,673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e2.21-2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003eAdolescent-onset CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e1,140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e1.56-1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.656500802568218%\" valign=\"top\"\u003e\n \u003cp\u003eUnspecified CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e34,590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e72,490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.53290529695024%\" valign=\"top\"\u003e\n \u003cp\u003e1.76-1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37239165329053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe AoD for each cohort was calculated, and we discovered that the average ADHD AoD of White patients is 23.9, which is significantly higher than the average ADHD AoD of Black patients at 15.7 (95% CI, 8.1-8.24; P \u0026lt; 0.0001), attributed to the relative increase in the diagnosis of Whites between the ages of 18 and 40 years old (\u003cstrong\u003eFigure 2a;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Table S2\u003c/strong\u003e). Linear regression analysis resulted in a correlation coefficient of 0.86 between the AoD of ADHD in Black versus White patients (\u003cstrong\u003eFigure S4A\u003c/strong\u003e). However, a linear regression performed on a subset of patients 18 years old or younger yielded a higher correlation coefficient of 0.97 (\u003cstrong\u003eFigure S4B\u003c/strong\u003e). These data suggest that the AoD between Blacks and Whites with ADHD are essentially the same in childhood, yet there is a substantial increase in the diagnosis of ADHD in White adults compared to Blacks.\u003c/p\u003e\n\u003cp\u003eThe AoD distribution of Black and White CD patients highlights more parallel trends between the two groups (\u003cstrong\u003eFigure 2b\u003c/strong\u003e). Linear regression analysis shows a high correlation between AoD in CD cases in Black and White patients, with a coefficient of 0.97 (\u003cstrong\u003eFigure S4C\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3a\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eTable 2\u003c/strong\u003e show that Black females are the most underrepresented group across sex and race, being 59% (OR, 0.41; 95% CI, 0.41-0.42; P \u0026lt; 0.0001) less prevalent than White males. White females diagnosed with ADHD are 31% (OR, 0.69; 95% CI, 0.69-0.70; P \u0026lt; 0.0001) less prevalent, while Black males are 12% (OR, 0.88; 95% CI, 0.87-0.89; P \u0026lt; 0.0001) less prevalent. With ADHD-IT diagnosis in the Black male population is 47% (OR, 0.53; 95% CI, 0.52-0.54; P \u0026lt; 0.0001) less prevalent than the White male population, while diagnosis in the Black female population is 63% (OR, 0.37; 95% CI, 0.36-0.37; P \u0026lt; 0.0001) less prevalent. With ADHD-IT prevalence of diagnosis in Black males are 16% (OR, 1.16; 95% CI, 1.12-1.19; P \u0026lt; 0.0001) higher than in White males, while in Black females and White females, the prevalence of diagnosis is, respectively, 61% (OR, 0.39; 95% CI, 0.37-0.40; P \u0026lt; 0.0001) and 55% (OR, 0.45; 95% CI, 0.44-0.46; P \u0026lt; 0.0001) less. The prevalence of ADHD-CT diagnosis in Black females and White females are, respectively, 63% (OR, 0.37; 95% CI, 0.36-0.37; P \u0026lt; 0.0001) and 48% (OR, 0.52; 95% CI, 0.51-0.52; P \u0026lt; 0.0001) less than in White males. Unspecified ADHD diagnosis is 54% (OR, 0.46; 95% CI, 0.45-0.47; P \u0026lt; 0.0001) less in Black females, and 31% (OR, 0.69; 95% CI, 0.69-0.70; P \u0026lt; 0.0001) less in White females compared to White males.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Fisher\u0026apos;s Exact Test comparing the prevalence of ADHD and its presentations in each of the Black Female, Black Male and White Female populations to their prevalence in the White Male population.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTotal ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e51,323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.41 - 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e89,935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.87 - 0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e313,138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.69 - 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e394,607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePredominantly Inattentive ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e10,790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.36 - 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e12,953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.52 - 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e100,539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.92 - 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e94,857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePredominantly Hyperactive ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e2,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.37 - 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e6,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e1.12 - 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e11,558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.44 - 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e22,345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eCombined ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e16,424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.36 - 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e36,983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.98 - 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e84,691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.51 - 0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e143,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eUnspecified ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e38,154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.45 - 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e66,593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.96 - 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e211,128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.69 - 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e264,589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3b\u003c/strong\u003e and \u003cstrong\u003eTable 3\u003c/strong\u003e shows that compared to the White male population, total Black male CD diagnosis is 59% (OR, 1.59; 95% CI, 1.57-1.62; P \u0026lt; 0.0001) higher, while in Black females it is 25% (OR, 0.75; 95% CI, 0.73-0.76; P \u0026lt; 0.0001) less and in White females it is 57% (OR, 0.43; 95% CI, 0.42-0.44; P \u0026lt; 0.0001) less. ODD diagnosis in Black males is 29% (OR, 1.29; 95% CI, 1.26-1.32; P \u0026lt; 0.0001) more prevalent than in White males, but 39% (OR, 0.61; 95% CI, 0.59-0.63; P \u0026lt; 0.0001) less prevalent in Black females and 59% (OR, 0.41; 95% CI, 0.40-0.41; P \u0026lt; 0.0001) less prevalent in White females. Childhood-onset CD is 119% (OR, 2.19; 95% CI, 2.10-2.29; P \u0026lt; 0.0001) more prevalent in Black males and 64% (OR, 0.36; 95% CI, 0.34-0.38; P \u0026lt; 0.0001) less overall in White females than in White males. As for adolescent-onset CD, Black males have a 65% (OR, 1.65; 95% CI, 1.44-1.89; P \u0026lt; 0.0001) higher diagnosis prevalence, and White females have a 53% (OR, 0.47; 95% CI, 0.42-0.54; P \u0026lt; 0.0001) lower diagnosis prevalence than White males. Unspecified CD is 78% (OR, 1.78; 95% CI, 1.75-1.81; P \u0026lt; 0.0001) more prevalent in Black males, 19% (OR, 0.81; 95% CI, 0.79-0.82; P \u0026lt; 0.0001) less prevalent in Black females and 57% (OR, 0.43; 95% CI, 0.42-0.44; P \u0026lt; 0.0001) less prevalent in White females, all compared to prevalence in White males.\u003c/p\u003e\n\u003cp\u003eThe odds ratio and 95% confidence interval for the prevalence differences in ADHD, CD, and their presentations of each race and sex cohort are plotted in \u003cstrong\u003eFigure S5\u003c/strong\u003e comparing \u003cstrong\u003eA.\u003c/strong\u003e Black male, \u003cstrong\u003eB.\u003c/strong\u003e White female, and \u003cstrong\u003eC\u003c/strong\u003e. Black female, all to White male. Pearson\u0026rsquo;s Chi-squared test results show a significant association between the categories of race and sex in each disorder \u003cstrong\u003e(Table S3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Fisher\u0026apos;s Exact Test comparing the prevalence of CD and its presentations in each of the Black Female, Black Male and White Female populations to their prevalence in the White Male population.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTotal CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e17,047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.73 - 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e30,381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e1.57 - 1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e36,300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.42 - 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e73,837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eODD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e6,256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.59 - 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e11,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e1.26 - 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e15,233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.40 - 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e32,997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eChildhood-onset CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e1,567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.89 - 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e3,037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e2.10 - 2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e2,203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.34 - 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e5,350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAdolescent-onset CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 - 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e1.44 - 1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.42 - 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eUnspecified CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.71794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e11,915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003e0.79 - 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.698717948717949%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e21,987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e1.75 - 1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e23,528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2465483234714%\" valign=\"top\"\u003e\n \u003cp\u003e0.42 - 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.398422090729783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.191321499013807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.14595660749507%\" valign=\"top\"\u003e\n \u003cp\u003e47,730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.662721893491124%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eADHD is a complex neurodevelopmental disorder of varying presentations with social and cultural considerations. We hypothesized that Blacks are likely to have a lesser prevalence of an ADHD diagnosis and a higher prevalence of an ODD or CD diagnosis when compared to Whites. In this study, countrywide large-scale analysis supports our hypothesis, revealing that ADHD diagnoses are overrepresented in White patients compared to Blacks. Apart from ADHD-HT, all other presentations of ADHD have significantly less prevalence in Blacks than Whites. The most notable difference was found to be in the diagnosis of ADHD-IT. ADHD-IT is generally the most under-recognized and undertreated presentation of ADHD, with affected individuals having a low likelihood of receiving clinical and behavioral services.\u003csup\u003e23\u003c/sup\u003e Our results indicate that this problem may be compounded in the Black population, with disproportionately lower diagnoses of Black patients with ADHD-IT potentially contributing to significant disparities in access to clinical and behavioral services and utilization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Untreated ADHD-IT in adolescents and adults can be expected to impact an individual\u0026rsquo;s academic pursuits and career due to symptoms such as forgetfulness and difficulty maintaining attention for tasks, chores, or workplace responsibilities.\u003csup\u003e23\u003c/sup\u003e In Black children, ADHD symptoms can be disproportionately misconstrued as willful or defiant behaviors, contributing to a greater likelihood of a diagnosis of ODD or CD and a corresponding lack or mismatch of appropriate interventions or inappropriate use of disciplinary strategies.\u003csup\u003e3,6,24\u003c/sup\u003e A study using data from the 2011-2012 National Survey of Children\u0026rsquo;s Health found that White children were more likely to be diagnosed with ADHD alone, while Black children were more likely to be diagnosed with ADHD with an ODD or CD comorbidity.\u003csup\u003e25\u003c/sup\u003e Our findings add to the characterization of this disparity, demonstrating a significantly higher prevalence of ODD and CD diagnoses in all presentations between Blacks and Whites. The cause of the gap in childhood-onset CD is unclear. Clinicians have demonstrated a reluctance to assign an early CD diagnosis for aggressive behavior in childhood, possibly expecting children to mature out of these patterns developmentally or, at times, giving an ODD diagnosis instead to avoid the stigma associated with a CD diagnosis.\u003csup\u003e2\u003c/sup\u003e However, our results indicate that this cautionary approach may not be afforded to Black children as often.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Along with ADHD symptoms that persist from childhood into adulthood, there is a growing trend of adults presenting with inattention, disorganization, and impulsivity not recognized in childhood.\u003csup\u003e26-28\u003c/sup\u003e There is skepticism about the diagnosis of ADHD in adulthood, as it is not fully understood and may be driven by individuals seeking stimulant medication with the symptoms more likely explained by other psychiatric or substance use disorders.\u003csup\u003e12,13,29\u003c/sup\u003e Moreover, adult ADHD is not easy to identify. With the difficulty of obtaining a neuropsychiatric evaluation outside of childhood, especially for underprivileged populations, this job usually falls to primary care physicians (PCP) with little training in diagnosing complex psychiatric disorders.\u003csup\u003e26,27\u003c/sup\u003e The high prevalence of unspecified types of ADHD and CD in our cohorts indicates that PCPs might diagnose neurodevelopmental disorders without a closer examination of the diagnostic criteria necessary to identify the disorder presentation or possible comorbid conditions.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWhile most ADHD diagnoses take place before the age of 18 in both populations, our analysis reveals that adult ADHD is diagnosed much more prominently in White adults compared to Blacks. The Black population has a steady decline in ADHD diagnoses after adolescence. However, in White patients, there is a disproportionately higher number\u0026nbsp;of patients diagnosed between 18 and 40 than in Black patients. Receiving an ADHD diagnosis as an adult can be more complex, potentially requiring access to specialized or extended evaluations and necessitating greater expenditure of significant social capital. This disparity between the two racial groups could result from unequal access to general and psychiatric healthcare services in adulthood. Furthermore, implicit biases stemming from societal or cultural differences could potentially lead to the misinterpretation of service-seeking behaviors as stimulant-seeking actions, thereby contributing to the underdiagnosis of Black adults who may have ADHD.\u003csup\u003e7,28\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFemales are less likely to be diagnosed with neurodevelopmental disorders.\u003csup\u003e5,16,30\u003c/sup\u003e Studies have shown that despite males displaying a higher prevalence of ADHD and CDs, females suffer from more severe symptoms, significant lifetime psychiatric comorbidities, and functional impairments.\u003csup\u003e5,16,30\u003c/sup\u003e Apart from diagnostic bias, a potential contributing factor to sex disparities in these disorders is variation in symptom presentation. Females tend to exhibit more inattentive behavior and less hyperactivity, making them perceived as less disruptive than males, and their ADHD might go unnoticed or undiagnosed.\u003csup\u003e31,32\u003c/sup\u003e As\u0026nbsp;for CD, while males are inclined to display signs of proactive physical aggression, females are more likely to show relational, reactive aggression in bullying and manipulative behavior with less callous-unemotional traits.\u003csup\u003e30\u003c/sup\u003e Beyond developmental gender differences, this could also suggest that females must exhibit more pronounced symptoms before being referred for or diagnosed with these disorders.\u0026nbsp;Our analysis shows that Black\u0026nbsp;females are the least prevalent group diagnosed with all presentations of ADHD. Apart from predominantly inattentive ADHD, White females are the second most underrepresented group in ADHD diagnoses. White females also appear to be the group least diagnosed with CDs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior studies have revealed that conscious or unconscious bias can impact medical decisions and diagnoses.\u003csup\u003e6\u003c/sup\u003e These studies indicate clinicians are more responsive to non-Hispanic White patients seeking an ADHD diagnosis and treatment, whereas Black students receive fewer referrals from schoolteachers and administrators.\u003csup\u003e5,6\u003c/sup\u003e Moreover, obtaining a CD diagnosis will likely negatively impact a caregiver\u0026rsquo;s ability to detect inattentive or hyperactive\u0026nbsp;behavior, limiting their access to psychiatric evaluations, medication, and therapy.\u003csup\u003e6\u003c/sup\u003e It could also lead to harsher disciplinary measures and exclusionary practices in school that could further compound mental and behavioral challenges.\u003csup\u003e5,6\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOne concern related to overdiagnosis and overtreatment of neurodevelopmental disorders like ADHD is the potential harm stemming from\u0026nbsp;diverting resources from other populations who may be underdiagnosed or undertreated.\u003csup\u003e33\u003c/sup\u003e Furthermore, the perception of ADHD as an overdiagnosed disorder in any racial group is likely.\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. First, our lack of control subjects hindered our ability to perform more detailed analyses. Second, we did not have access to robust information about our patient cohorts\u0026apos; socioeconomic status and insurance status, which is often a factor in a patient\u0026rsquo;s ability to receive clinical care, particularly mental healthcare and treatment.\u003csup\u003e5\u003c/sup\u003e Third, with our reliance on ICD-10 codes, there were no symptom details to determine diagnostic accuracy. Fourth, we had very few quantitative measurements that could have been used to control confounding variables.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our analysis found race and sex disparities in ADHD and CD diagnosis in the US. The non-Hispanic Black population is less likely to be diagnosed with ADHD but more likely to be diagnosed with ODD or CD than non-Hispanic Whites. White patients get diagnosed with ADHD in adulthood more often than Black patients. Black females are the cohort least likely to be diagnosed with ADHD, and White females are the cohort least likely to be diagnosed with CD. Future work that integrates patients\u0026apos; socioeconomic status and insurance status will give a deeper understanding of racial disparities. Detailed clinical symptom presentations, with quantifiable assessments compared to control cohorts, could be used to analyze further and measure accurate rates of over- and under-diagnosis in suspected populations. The disproportionally high rates of ODD and CD diagnoses carried by Black patients may indicate unconscious/implicit bias by healthcare practitioners and a corresponding tendency to miss underlying conditions that could better explain disruptive behaviors. Presenting evidence and increasing awareness of such disparities has effectively reduced unconscious bias and sustained the movement toward more culturally informed and objective psychiatric evaluations.\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eN.S. analyzed the data. N.S. and J.W. wrote the manuscript. S.S. provided medical background and interpretation, and reviewed and edited the manuscript. J.W. designed the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e All data used in the present study is available through TriNetX at https://trinetx.com/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePosner, J., Polanczyk, G. V. \u0026amp; Sonuga-Barke, E. 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Evaluating the evidence for and against the overdiagnosis of ADHD. \u003cem\u003eJournal of Attention Disorders\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 106-113 (2007). https://doi.org:10.1177/1087054707300094\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4177866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4177866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The primary purpose of this study is to highlight trends in the prevalence of Attention Deficit/Hyperactivity Disorders (ADHD) and Conduct Disorders (CD) between non-Hispanic White and non-Hispanic Black populations and identify potential diagnostic disparities between these groups. De-identified electronic health record data on the TriNetX platform of patients diagnosed with ADHD, CD, or both between January 2013 and May 2023 from 50 healthcare organizations in the US were used to investigate racial and sex disparities in the prevalence of ADHD and CD diagnoses. With a cohort of 849,281 ADHD patients and 157,597 CD patients, non-Hispanic Whites were ~26% more likely to receive ADHD diagnosis and ~61% less likely to be diagnosed with CD than non-Hispanic Blacks. The mean age of diagnosis of ADHD was over 8 years higher for White patients than for Black patients, with a disproportionately higher number of White patients diagnosed in adulthood, compared to a comparatively negligible number of Blacks diagnosed with ADHD in the same age group. Additionally, Black females were the cohort least likely to be diagnosed with ADHD, while White females were the cohort least likely to be diagnosed with CD. Race disparities exist between Black and White populations, and sex disparities exist within each population. More information is needed to determine contributors to these differences, although implicit biases and systemic racism may be key contributing factors. Presenting evidence and increasing awareness of culturally relevant diagnoses can reduce unconscious bias and move toward more informed and objective psychiatric evaluations.","manuscriptTitle":"Large-Scale Analysis Reveals Racial Disparities in the Prevalence of ADHD and Conduct Disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 21:14:12","doi":"10.21203/rs.3.rs-4177866/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-25T06:37:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-21T16:05:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245986861486008807343570793921247171817","date":"2024-06-06T12:59:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-29T16:11:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"cf108e2e-ef73-4c8b-92e2-256785e63207","date":"2024-04-19T15:15:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"dd437f6f-5af0-4512-a70c-9ace4d53f703","date":"2024-04-19T06:29:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-19T01:40:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-15T13:32:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-05T05:25:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-05T05:21:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-27T18:16:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6aa7ad4d-6b01-4d14-8275-5860727c2b07","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30427423,"name":"Health sciences/Diseases/Psychiatric disorders/Adhd"},{"id":30427424,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2024-10-28T16:08:27+00:00","versionOfRecord":{"articleIdentity":"rs-4177866","link":"https://doi.org/10.1038/s41598-024-75954-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-24 15:57:53","publishedOnDateReadable":"October 24th, 2024"},"versionCreatedAt":"2024-04-09 21:14:12","video":"","vorDoi":"10.1038/s41598-024-75954-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-75954-5","workflowStages":[]},"version":"v1","identity":"rs-4177866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4177866","identity":"rs-4177866","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}