Measuring Quality Care for Adult ADHD Patients: How Much Does Gender and Gender Identity Matter?

Abstract

Objective:

Studies show adult ADHD presents differently in men and women, however few studies contrast ADHD in cisgender and gender diverse adults. We assessed care differences between these groups using previously identified quality measures (QMs).

Methods:

Using EHR data, we matched a group of male ADHD patients to a female group. We followed the same procedure with a cisgender group and one identified as gender diverse through a gender dysphoria diagnosis. QM achievement was measured using logistic regression models.

Results:

Most QMs exhibited increasing achievement over time for all groups. Variations in care quality between males and females persisted, with female patients achieving QMs more often. There were no appreciable differences between the cisgender and gender diverse groups.

Conclusion:

Though quality care for adult ADHD improved from 2010 to 2020, differences between male and female patients lingered. This effect was not observed in cisgender and gender diverse patients.

Keywords

adult ADHD quality measures secondary data analysis gender electronic health records

Dr. Biederman Tribute

This is one of three papers in this issue assessing the tracking of quality measures for ADHD in primary care. for ADHD in adults. Joe was one of the first to systematically study gender differences in ADHD via his family-genetic, longitudinal studies of boys and girls from both psychiatric and pediatric settings. Although it may be hard to believe, his first NIMH grant proposal to study girls with ADHD (about three decades ago) was rejected because one reviewer was adamant that the prevalence in girls was too low to warrant the study. Joe rose to that challenge, collected additional preliminary data and the grant was eventually funded and renewed sufficiently for him to follow the sample into adulthood. His work helped stimulate the field to do a better job of identifying and treating girls and women with ADHD. He would have been pleased to read the data in this paper showing few differences in quality care between men and women.

Introduction

ADHD is a common, neurologically based disorder with varied and multifactorial symptom presentation (S. V. Faraone et al., 2015). Two-thirds of cases persist into adulthood (S. V. Faraone et al., 2006). ADHD is also more common in males, with a prevalence of 3:1 in boys over girls (Cortese et al., 2016; Gaub & Carlson, 1997). However, in adulthood the sex difference attenuates as women more often self-refer to receive treatment due to internally recognized symptom severity (Holthe & Langvik, 2017). Previous studies additionally show the clinical picture of ADHD appears different in girls and women from boys and men, with the inattentive subtype and emotional dysregulation more frequently occurring in girls and women rather than hyperactivity and impulsivity (Fairman et al., 2020; Gómez-Benito et al., 2019; Staller & Faraone, 2006).

Due to the historically unrecognized prevalence in ADHD treatment for girls and women (Gutman et al., 2020; Holthe & Langvik, 2017), the study team questioned the quality of care received by a gender diverse population. A systematic review by Goetz and Adams suggests a higher prevalence rate for ADHD among transgender and gender diverse (TGD, transgender men and women and non-binary individuals) people than cisgender people, however the paucity of literature in this subject makes it challenging to measure (Goetz & Adams, 2022). A factor adding complexity to this topic is that gender identity disorder was changed in the DSM-5 after scholarly critique and advocacy against the pathologizing of gender diversity (Ault & Brzuzy, 2009), however it was replaced with gender dysphoria, which still receives criticism for unnecessarily medicalizing transgender and nonbinary individuals (Daley & Mulé, 2014). Because little is known about the quality of care for TGD people with ADHD, this study sought to characterize the quality of care for ADHD patients over a 10-year period and to assess if quality was impacted by patient gender categories (male and female) or gender dysphoria (diagnosis codes to identify TGD people).

Quality measures (QMs) in healthcare have long been implemented to quantify processes and procedures necessary to administer high-quality healthcare to patients (Nigam, 2012). They aim to streamline and reduce variations in care, improve care delivery, and identify elements of care that lead to more favorable patient outcomes (Nigam, 2012). Furthermore, they are instrumental in examining healthcare quality and determining reimbursement rates for services (Marjoua & Bozic, 2012). Our preceding study on adult ADHD analyzed 10 previously defined QMs of ADHD care (S. V. Faraone et al., 2019) applied to an Electronic Health Record (EHR) dataset of four million de-identified patient records from primary care and behavioral health clinics across the US (Callen et al., 2023). Through this study, we explore a matched cohort of male and female patients and a matched cohort of patients with and without gender dysphoria to reveal true disparities in the provisioning of quality healthcare for adult ADHD.

Methods

Dataset Overview and Creation

The data acquired for this study came from DARTNet Institute’s Practice Performance Registry (PPR), which is standardized to the Observational Medical Outcomes Partnership Common Data Model v6 (OMOP CDM v6) (Pai et al., 2020; Ratigan et al., 2021). Included in the data were four million de-identified patient records containing demographics, diagnosis codes (e.g., ICD-9, ICD-10, and SNOMED), procedure codes (e.g., CPT), measurements, observations, and medications. This dataset contained all historical patient data beginning in January 2005 and ending in December 2020. DARTNet Institute masked dates of service ±30 days and maintained the spacing between visits.

To prepare the dataset to include the final QMs, three of the authors (EC, TC, and JA) worked with an external consultant and DARTNet Institute to clarify variables in the data and refine the QM definitions. After this process those three authors finalized the definitions and generated a list of codes needed to create the files to represent them in the EHR data. Individual QMs were expressed using 498 constructed value sets with nearly 700,000 unique codes, which had been tested repeatedly for quality prior to implementation. The value sets were run against the provided PPR data and combined with the initial dataset to create the QM definitions needed for analysis. Concluding file preparation, each patient was assigned a home clinic, which was the last primary care clinic the patient had been seen at in the dataset. The initial analysis revealed that the first QM’s requisite data could not be extracted from the EHR due to the ADHD screening tool being tied in with a depression screening tool within the CPT coding structure. The final analyses include nine QMs. Final QM definitions can be found in our preceding study and Supplemental Materials (Callen et al., 2023). SAS 9.4 (Cary, NC) was used in the creation of the datasets.

Matching

The EHR data from the PPR did not distinguish between sex and gender identity, with only the category “gender” being provided with sex options. Due to this, we conducted an analysis using only the gender data (males and females) and a separate analysis using diagnostic codes to identify patients with gender dysphoria (with dysphoria and no dysphoria; used as a proxy for transgender and non-binary individuals). While not ideal for the TGD analysis, the study team used this procedure due to the lack of access to clinical notes, which limited other methods of identification. Codes used to identify gender dysphoria can be found in Supplemental Materials. In the gender analysis we matched patients 1:1 (1 male to 1 female), while the gender identity analysis matched patients 1:10 (1 with gender dysphoria to 10 without). Patients were matched based on race, ethnicity, age, practice type, and organization ownership in each analysis. This was done with genetic matching, without replacement and with exact matching turned on, using propensity scores calculated with Mahalanobis distance with the nearest neighbor. A two-sided alpha of 0.05 was used throughout. R 4.2.0 (“Vigorous Calisthenics,” R Core Team 2022) was used for the genetic matching with the MatchIt package (v042.8.1, Ho, 2011).

Statistical Analysis

The reported analyses were completed using the matched data to assess potential differences in care patterns between male and female patients and patients with or without gender dysphoria. Descriptive statistics were calculated for the patients in the matched dataset (counts and percentages). A series of binary logistic regression models were used to determine the role in gender/gender dysphoria had in the groups meeting or not meeting the QM. We completed this series for each QM on the matched male/female sample and the matched gender dysphoria/no gender dysphoria sample. Interaction terms were included in the regression models for gender when significant (year*gender). No interaction terms were included in the regression models for gender dysphoria because of the small sample size and sparsity of data. All models covered the years 2010 through 2020 and complete diagnostics are in Supplemental Materials. The remaining analyses were completed using SPSS 27 (Armonk, NY).

Results

Demographics

We analyzed 57,490 matched patient records from the EHR with an adult ADHD diagnosis (male: 28,745; female: 28,745). Patients were 71.7% white, 67.8% non-Hispanic or Latinx, and 76.7% were born during or after the 1970s and 23.3% were born during or before the 1960s (Table 1). Females achieved more QMs than males (X²(1) = 110.161; p < .001) in the analysis period from 2010 to 2020 (Figure 1A and B).

Table 1.

Matched Demographics.

	Gender matched		Gender dysphoria matched
	Male (n = 28,745)	Female (n = 28,745)	No gender dysphoria (n = 570)	Gender dysphoria (n = 57)
Gender
Male	28,745	0	340	34
Female	0	28,745	230	23
Gender identity
Gender dysphoria	34	23	0	57
No gender dysphoria	28,711	28,725	570	0
Race
White	20,619	20,619	370	37
People of color	1,458	1,458	60	6
Unknown	6,668	6,668	140	14
Ethnicity
Hispanic or Latinx	19,495	19,495	390	39
Non-Hispanic or Latinx	938	938	30	3
Unknown	8,312	8,312	150	15
Decade of birth
1930s	99	99	0	0
1940s	674	674	0	0
1950s	2,112	2,112	0	0
1960s	3,802	3,802	50	5
1970s	5,741	5,741	40	4
1980s	9,498	9,498	170	17
1990s	6,778	6,778	300	30
2000s	41	41	10	1
Practice type
Primary care	20,085	20,085	420	42
Behavioral health	815	815	50	5
Unknown	7,845	7,845	100	10
Organization ownership
Clinician owned	8,513	8,513	220	22
Hospital owned	9,567	9,567	210	21
Unknown	10,665	10,665	140	14

Figure 1.

Male and female matched patients overall QM achievement: (A) male matched patients overall QM achievement and (B) female matched patients overall QM achievement.

For the gender dysphoria analyses, we extracted 627 patients with an adult ADHD diagnosis (without gender dysphoria: 570; with gender dysphoria: 57). Demographics for this dataset were similar with patients being 59.6% male; 64.9% white, and 68.4% non-Hispanic or Latinx; however, they skewed younger with 84.2% being born in the 1980s or after and 15.8% being born in the 1960s or 1970s (Table 1). There were no significant differences in QM achievement between the gender dysphoria groups over the analysis period (X²(1) = 0.001; p = .973) (Figure 2A and B).

Figure 2.

Matched patients with and with no gender dysphoria overall QM achievement: (A) matched patients with no gender dysphoria overall QM achievement and (B) matched patients with gender dysphoria overall QM achievement.

Percent Patients Treated for ADHD Having Documented DSM-5 Diagnosis of ADHD

Male and female patients showed steady rising improvement of this QM through the analysis period, and by 2020, nearly all patients were achieving treatment after receiving an ADHD diagnosis (Figure 3A). Increasing year of visit date was significantly associated with better QM achievement (1.3 times greater each year, W(1) = 7,009.8, p < .001). However, female patients were 1.2 times more likely to meet the QM over their male counterparts (W(1) = 97.6, p < .001) during the 11-year timespan. Achievement of the patients with and without gender dysphoria also trended upwards over time (1.4 times greater each year, W(1) = 65.3, p < .001), though no significant differences between the matched groups were shown (without gender dysphoria 1.6 times greater; W(1) = 1.8, p = .18, Figure 4A).

Figure 3.

Percentage of matched patients by gender for individual QM achievement by year (orange line for female and blue line for male): (A) percent patients treated for ADHD having documented DSM-5 diagnosis of ADHD, (B) percent patients with ADHD with review of other psychiatric disorders, (C) percent patients with ADHD with documentation of impairment, (D) percent patients receiving ADHD medications for whom treatment alternatives, benefits, and risks have been discussed, (E) percent patients with ADHD assessed for vitals prior to medication treatment, (F) percent patients with ADHD for whom warnings and contraindications for medication were reviewed, (G) percent patients with ADHD where validated measure of symptom change used to assess treatment efficacy at least annually, (H) percent patients stabilized on an ADHD medication seen at least once per year, and (I) percent patients prescribed medication for ADHD seen within 1 month of initial prescription.

Figure 4.

Percentage of matched patients by gender dysphoria for individual QM achievement by 2 years moving average (orange line for gender dysphoria and blue line for no gender dysphoria): (A) percent patients treated for ADHD having documented DSM-5 diagnosis of ADHD, (B) percent patients with ADHD with review of other psychiatric disorders, (C) percent patients with ADHD with documentation of impairment, (D) percent patients receiving ADHD medications for whom treatment alternatives, benefits, and risks have been discussed, (E) percent patients with ADHD assessed for vitals prior to medication treatment, (F) percent patients with ADHD for whom warnings and contraindications for medication were reviewed, (G) percent patients with ADHD where validated measure of symptom change used to assess treatment efficacy at least annually, (H) percent patients stabilized on an ADHD medication seen at least once per year, and (I) percent patients prescribed medication for ADHD seen within 1 month of initial prescription.

Percent Patients With ADHD With Review of Other Psychiatric Disorders

There is a consistent rise with this QM with gender until 2014, where both groups start to plateau in rates of achievement (Figure 3B). Though advancing years show improvement in male and female patients (1.2 times greater each year, W(1) = 2,963.4, p < .001), females again have higher odds of meeting the QM (1.3 times greater, W(1) = 227.3, p < .001) than males. In the group of patients with and without gender dysphoria there is also a considerable uptick of QM achievement by 2014 and attainment remains high through the remainder of the 11-year period (1.2 times greater each year, W(1) = 18.8, p < .001, Figure 4B). Analysis did not reveal substantial differences between these groups (with gender dysphoria 1.3 times greater; W(1) = 0.5, p = .47).

Percent Patients With ADHD With Documentation of Impairment

For both the gender and gender identity datasets there is a stable upward trend in the achievement of this QM from 2010 to 2020 (Figures 3C and 4C). By the end of the analysis period at least 94% the gender matched patients have documentation of impairment in the EHR (1.4 times greater each year, W(1) = 22,701.0, p < .001), and at least 92% of the gender identity matched patients also show coded documentation (1.4 times greater each year, W(1) = 183.6, p < .001). Female patients continue to be more likely to achieve the QM (1.1 times greater, W(1) = 26.4, p < .001). The model with the interaction of year and gender also showed this (1.02 times greater, W(1) = 1.2, p < .001). The group of gender identity matched patients had similar rates of achievement between them (without gender dysphoria 1.3 times greater; W(1) = 2.3, p = .13).

Percent Patients Receiving ADHD Medications for Whom Treatment Alternatives, Benefits, and Risks Have Been Discussed

The gender matched data shows an increasing tendency toward meeting this QM until 2015, where unsteady plateau takes hold until 2020 (Figure 3D). Still, there is a significant association of increasing year of visit with coded discussion of treatment alternatives, benefits, and risks (1.1 times greater each year, W(1) = 194.7, p < .001). The model shows female patients are 1.1 times more likely to achieve the QM (W(1) = 6.5, p = .01) than males. The gender identity matched data is inconsistent over time (1.03 times greater each year; W(1) = 0.4, p = .53, Figure 4D) and does not indicate a significant association of realizing the QM, and the model shows comparable odds of achievement between those groups of patients (without gender dysphoria: 1.3 times greater; W(1) = 0.2, p = .62).

Percent Patients With ADHD Assessed for Vitals Prior to Medication Treatment

Generally, achievement for this QM is low in the gender dataset. The data show no year with greater than 50% of male or female patients attaining the QM, the high point being in 2015 (Figure 3E) with 40% males and 44% females meeting it. Though there is no appreciable gain over time (1.0 times greater each year; W(1) = 0.02, p = .89), females persist in having higher odds of achieving the QM (1.1 times greater, W(1) = 6.5, p = .01) over males. In the dataset of patients with and without gender dysphoria there is an increase in achievement from 2010 through 2016, however from 2017 until 2020 there is a precipitous decline in both groups (Figure 4E). Due to the drop off, the model shows a significant effect for increasing visit date over the 11-year period (1.2 times lesser each year, W(1) = 9.0, p = .003) for both groups. The patients with no gender dysphoria were 1.6 times more likely to meet the QM than patients with gender dysphoria, though it is not significant (W(1) = 0.6, p = .43).

Percent Patients With ADHD for Whom Warnings and Contraindications for Medication Were Reviewed

This QM exhibits more fluctuations over the analysis period for the gender matched patients, however the trend begins to stabilize by 2015 (Figure 3F). There is no significant effect for time (1.04 times greater each year; W(1) = 3.0, p = .08) and no effect between male and female patients (males were 1.03 times more likely; W(1) = 0.1, p = .77). For the patients with and without gender dysphoria, there is a wide gap between the groups in achievement (Figure 4F). However, the EHR data for this QM was quite thin, having a total of five patients with no dysphoria and one patient with gender dysphoria at most. For this reason, the model was unnecessary.

Percent Patients With ADHD Where Validated Measure of Symptom Change Used to Assess Treatment Efficacy at Least Annually

Overall, this QM had consistently high levels of attainment over the analysis period in the gender matched dataset, with neither males nor females dropping below 89% (Figure 3G), though female patients were more likely to meet the QM (1.1 times; W(1) = 3.4, p = .06). However, throughout the 11-year timeline the data reveal an overall decrease in achievement (1.3 times lesser each year, W(1) = 1,361.7, p < .001). There is a comparable trend in the gender identity matched dataset, though the data is more inconsistent from 2010 through 2015 (Figure 4G). The gender dysphoria model shows decreasing odds of meeting the QM over time (1.3 times lesser each year, W(1) = 10.0, p = .002) with no significant effect between the groups (without gender dysphoria 1.2 times greater; W(1) = 0.1, p = .71).

Percent Patients Stabilized on an ADHD Medication Seen at Least Once Per Year

In the gender matched data there is upward movement in achieving this QM until 2015, where male patients drop by 13.4% and female patients by 14.6%, but steady progression continues from then on through 2020 (Figure 3H). Despite the 2016 drop, the model presents a significant effect over time (1.1 times greater each year, W(1) = 107.6, p < .001) and, again, female patients demonstrate higher odds of attainment (1.1 times greater, W(1) = 24.6, p < .001). The progression is not as clear in the gender identity analysis, as the trendline oscillates for both patient groups (Figure 4H). The data do not show a significant effect for time (1.1 times greater each year; W(1) = 0.9, p = .34) or between groups (with gender dysphoria 1.3 times greater; W(1) = 1.3, p = .25).

Percent Patients Prescribed Medication for ADHD Seen Within 1 Month of Initial Prescription

Complementary to the previous QM, the gender dataset exhibits an upward movement toward achievement through 2015 before a decrease in 2016, though the decline is not as severe as the preceding QM. Male and female patients diverge somewhat in 2019 before the line closes back in by 2020 (Figure 3I). Throughout the analysis period the model shows a significant effect for time (1.2 times greater each year, W(1) = 540.9, p < .001) and that female patients are 1.1 times more likely to meet the QM (W(1) = 4.7, p = .03). For the patients with and without gender dysphoria, there is a sharp uptick in attainment in 2013 that remains stable until 2018, when there is a small decline (Figure 4I). Regardless of the sudden increase in achievement, the model does not exhibit a significant effect for time (1.1 times greater each year; W(1) = 2.2, p = .13) or between groups (with gender dysphoria 1.5 times greater; W(1) = 0.2, p = .62).

Discussion

The principal aim for this study was to investigate if patterns of care for adult ADHD patients are impacted by gender or gender dysphoria. We found that females meet quantifiable measures for high quality care more often than their male counterparts. This finding seems contradictory to previous studies showing that female ADHD patients experience greater barriers to receiving care (Holthe & Langvik, 2017; Young et al., 2020), however ADHD in women is frequently mistaken for anxiety or depression before receiving an accurate diagnosis (Gutman et al., 2020; Holthe & Langvik, 2017; Young et al., 2020). Female patients had a substantially higher likelihood of obtaining a review of other psychiatric disorders. There are several large clinical studies demonstrating other psychiatric disorders regularly co-occur with ADHD, but it is important for clinicians to understand the presence of other disorders do not rule out ADHD (Bernardi et al., 2012; S. Faraone et al., 2021; Solberg et al., 2018; Yao et al., 2019). Another explanation for this discrepancy is the propensity for women to utilize health care services, particularly preventative, more often and consistently than men (Pinkhasov et al., 2010; Vaidya et al., 2012). These QM analyses appears to reinforce this idea as we see female patients more frequently receiving treatment after diagnosis, showing evidence of clinician-documented impairments, being assessed for vitals prior to medication treatment, being assessed for a valid measure of symptom change at least annually, being assessed for stability on their medication at least annually, and being seen within the first month after starting their prescribed medication. Over the analysis period, the only QM that males achieved more often was the percentage of patients for whom warnings and contraindications for medication were reviewed. Though the difference was insubstantial, a possible reason for the variance could be that women more commonly show a preference for non-pharmacological interventions in the treatment of psychiatric disorders than men (McHugh et al., 2013).

While the models demonstrated many significant differences between genders, the graphs for each QM in Figure 1 also show that differences were small and the trajectories for male and female patients were similar over the analysis period. Both groups improved their QM achievement with each year from 2010 to 2020. The only QM that decreased in achievement over time was the percentage of patients with ADHD where a validated measure of symptom change is used to assess treatment efficacy at least annually. It’s difficult to explain this decrease, as attainment started out at nearly 98% of all patients in the first 6 years of the study period and gradually decreased to both males and females meeting the QM 89% of the time by 2020. One speculation is that the Center for Medicare and Medicaid Services’ Meaningful Use regulations for EHR usage required health care systems to implement Stage 2 criteria by end of the fiscal year of 2014, which extended into calendar year 2015 (Anumula & Sanelli, 2012). This included more rigorous expectations for any health information exchange, such as more demanding requirements for e-prescribing, incorporating laboratory results, and an expectation that healthcare providers will transmit health care information electronically to support transitions in care across unaffiliated networks (Anumula & Sanelli, 2012). To already financially and administratively overburdened healthcare systems (Himmelstein et al., 2020; Kyle & Frakt, 2021; Reith, 2018), any additional tasks to remain in compliance with regulations could adversely affect the way services are coded (Hartman et al., 2012). Additionally, established patients that have been stabilized on medication for several years may not meet this QM within their clinic, as it the clinic may deem it unnecessary to adhere to more rigorous monitoring. However, the patients in the dataset are diverse in their coded diagnosis dates and treatment plans, therefore we would not expect to see a significant effect.

The analysis of matched patients with gender dysphoria and ADHD was less conclusive than the analysis of the gender matched dataset. In general, both groups of patients achieved more QMs as the years progressed over the analysis period akin to the gender analysis. We also found that the patients with no dysphoria had a higher likelihood of meeting the QMs, with the exceptions of the percentage of patients with ADHD with review of other psychiatric disorders, percentage of patients stabilized on an ADHD medication seen at least once per year, and percentage of patients prescribed medication for ADHD seen within 1 month of first prescription. This is unsurprising, as someone with coded gender dysphoria would have had to pursue care to receive that diagnosis and therefore would have necessitated other psychiatric screenings, likely utilizing more specialty care in addition to primary care (Strang et al., 2014). As the awareness of gender diversity increases, EHRs will need to have demographic categories for sex assigned at birth and gender identity. This will lead to more meaningful research on TGD populations in the future.

There are several limitations to our work. The study team had no access to written patient clinical notes (only codes were available) and therefore had no way to measure clinician behavior, so all results are a direct outcome of what was documented in the EHR. While EHR provides an extensive and rich source for analyzing patient data, the data can be inaccurate, incomplete, inconsistent, or in a format that is difficult to analyze. This could be due to non-reporting, administrative error, or software changes and updates to the EHR system. We did make several assumptions and used proxies in the dataset when data were not available, as in the case of the availability of ADHD screeners. Without access to clinical notes, we had to rely on codes indicating a diagnosis of gender dysphoria or gender identity disorder which limited sample size.

Conclusion

This work demonstrates that, regardless of gender, quality care for adult ADHD generally improved over the analysis period, though some measures of care are still inadequately realized. Notably, female patients were more likely to achieve nearly every QM compared with their male counterparts. This could indicate a greater personal investment in care after receiving a diagnosis by adult women, though further investigation is needed to qualify this finding. The analysis comparing patients with and without gender dysphoria was not as definitive, as the sample size was consequentially smaller. Further research is needed to quantify existing disparities in ADHD care for gender minority groups.

Supplemental Material

sj-docx-1-jad-10.1177_10870547231218449 – Supplemental material for Measuring Quality Care for Adult ADHD Patients: How Much Does Gender and Gender Identity Matter?

Supplemental material, sj-docx-1-jad-10.1177_10870547231218449 for Measuring Quality Care for Adult ADHD Patients: How Much Does Gender and Gender Identity Matter? by Tarin Clay, Elisabeth F. Callen, Jill Alai, David W. Goodman, Lenard A. Adler and Stephen V. Faraone in Journal of Attention Disorders

Footnotes

Acknowledgements

None

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Over the past 3 years, Ms. Clay received research support from NIH, Otsuka, Takeda, UnitedHealth Foundation, HRSA, SAMHSA, Eli Lilly and AAFP Foundation.

Over the past 3 years, Dr. Callen received research support from PCORI, Merck, NIH, AAFP Foundation, Otsuka, Takeda, UnitedHealth Foundation, HRSA, GlaxoSmithKline, SAMHSA, Eli Lilly, CDC, Helmsley Foundation, and VaxCare.

Over the past 3 years, Ms. Alai received research support from PCORI, Abbott, Eli Lilly, and Otsuka.

Over the past 3 years, Dr. Goodman received honoraria, consulting income, potential income or travel expenses from Tris, Otsuka, Ironshore, Supernus, Sunovion, Noven, Shionogi, Ondosis, Medscape, HCPlive, Clinical Care Solutions, National Football League, WebMD, and the Neuroscience Education Institute.

Over the past 3 years Dr. Adler is a consultant for Takeda, Otsuka, Bracket/Signant, Major League Baseball, the National Football League, and the State University of New York; receives grants from Takeda and Otsuka; and royalty payments from New York University School of Medicine for scales and training material for adult attention-deficit/hyperactivity disorders.

Over the past 3 years, Mr. Shields received research support from PCORI, Merck, Humana, NIH, and AAFP Foundation.

Over the past 3 years, Dr. Faraone received income, potential income, travel expenses continuing education support and/or research support from Aardvark, Aardwolf, Akili, Arbor, Atentiv, Aveksham, Axsome, Corium, Genomind, Ironshore, Medice, Noven, Otsuka, Rhodes, Sandoz, Shire/Takeda, Sky Therapeutics, Supernus, Tris, and Vallon. With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. He also receives royalties from books published by Guilford Press: Straight Talk about Your Child’s Mental Health; Oxford University Press: Schizophrenia: The Facts; and Elsevier: ADHD: Non-Pharmacologic Interventions. In addition, he is the program director of . Dr. Faraone is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No 965381; NIMH grants U01AR076092-01A1, 1R21MH1264940, R01MH116037; 1R01NS128535 – 01; Oregon Health and Science University, Otsuka Pharmaceuticals, Noven Pharmaceuticals Incorporated, and Supernus Pharmaceutical Company.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Over the past 3 years, Dr. Faraone received income, potential income, travel expenses continuing education support and/or research support from Aardvark, Aardwolf, AIMH, Tris, Otsuka, Ironshore, Kanjo, Johnson & Johnson/Kenvue, KemPharm/Corium, Akili, Supernus, Atentiv, Noven, Sky Therapeutics, Axsome, Genomind, Shire/Takeda, Arbor, Medice, Ondosis, Rhodes, and Vallon. With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. He also receives royalties from books published by Guilford Press: Straight Talk about Your Child’s Mental Health; Oxford University Press: Schizophrenia: The Facts; and Elsevier: ADHD: Non-Pharmacologic Interventions. He is Program Director of www.ADHDEvidence.org and . Dr. Faraone is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965381; NIMH grants U01AR076092-01A1, 1R21MH1264940, R01MH116037; 1R01NS128535—01; Oregon Health and Science University, Otsuka Pharmaceuticals, Noven Pharmaceuticals Incorporated, and Supernus Pharmaceutical Company.

ORCID iDs

Lenard A. Adler

Stephen V. Faraone

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Tarin Clay, BA is a Statistical Research Analyst at DARTNet Institute where she supports the Senior Data Scientist in data management and analysis for a range of practice-based research studies, from small scale outcomes to big data projects.

Elisabeth F. Callen, PhD, PStat is the Senior Data Scientist at DARTNet Institute focusing on providing data management and analysis to a variety of practice-based patient-centered research studies. She also has expertise in data science, statistics, and biostatistics.

Jill Alai, MPH is a Research Project Manager at DARTNet Institute. She manages practice-based research activities and has experience in program evaluation, rural local health departments, and community-based participatory research.

David W. Goodman, MD, FAPA, is an Assistant Professor in Psychiatry and Behavioral Sciences at Johns Hopkins School of Medicine and an internationally recognized expert on ADHD in adults. He has been a PI for ADHD clinical trials, lectures to clinicians, and maintains a full-time clinical practice.

Lenard A. Adler, MD is the Director of the Adult ADHD Program in the Department of Psychiatry and is Professor in the Departments of Psychiatry and Child & Adolescent Psychiatry at the NYU Grossman School of Medicine. His research focuses on developing new treatments and rating instruments for adult ADHD.

Stephen V. Faraone, PhD, is a Distinguished Professor and Vice Chair for Research at the department of Psychiatry, Norton College of Medicine at SUNY Upstate Medical University. He is the president of the World Federation of ADHD and a leading expert in ADHD research.

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