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. Author manuscript; available in PMC: 2024 Sep 1.
Published in final edited form as: Psychiatry Res. 2023 Aug 2;327:115393. doi: 10.1016/j.psychres.2023.115393

Sociodemographic Disparities in ADHD Diagnosis and Treatment Among U.S. Elementary Schoolchildren

Paul L Morgan a,b,1, Eric Hengyu Hu a,b
PMCID: PMC10662107  NIHMSID: NIHMS1943707  PMID: 37595343

Abstract

We examined whether some groups of U.S. elementary schoolchildren are less likely to be diagnosed and treated for ADHD in analyses of a population-based cohort (N=10,920). We predicted ADHD diagnosis using measures of race and ethnicity, age, socioeconomic status, birthweight, individually assessed academic, behavioral, and executive functioning, family language use, mental health, health insurance coverage, marital status, school composition, and geographic region. We predicted prescription medication use among those diagnosed with ADHD. We stratified additional analyses by biological sex. Black children (aOR, 0.60), girls (aOR, 0.55), and emergent bilinguals (aOR, 0.29) were less likely to have an ADHD diagnosis than observationally similar White children, boys, or those from English-speaking households. Black children’s under-diagnosis occurred among boys. Emergent bilingual children’s under-diagnosis occurred among both boys and girls. Girls (aOR, 0.52) and emergent bilinguals (aOR, 0.24) with ADHD were less likely to use prescription medication. Sociodemographic disparities in ADHD diagnosis and treatment occur among U.S. elementary schoolchildren. Measured confounds including independently assessed ADHD symptomatology and impairment do not explain the disparities. The findings empirically support cultural, linguistic, and biological sensitivity in the ADHD diagnostic and treatment procedures in use for the U.S. pediatric population.

Keywords: Attention-deficit/hyperactivity disorder, elementary school, longitudinal, racial and ethnic disparities, racial/ethnic minorities, language minorities, ECLS-K: 2011

1. Introduction

Attention-deficit/hyperactivity disorder (ADHD) is characterized by chronic symptoms of inattention and/or hyperactivity-impulsivity that interfere with social, academic, or occupational functioning (American Psychiatric Association, 2022). About 8% of U.S. children ages 4–11 have an ADHD diagnosis (Xu et al., 2018). ADHD prevalence is increasing in the U.S. pediatric population including among diverse racial and ethnic, economic, language, and geographic groups (Bozinovic et al., 2021; Fairman et al., 2020; Polanczyk et al., 2014).

Sociodemographic disparities in ADHD diagnosis and treatment have been repeatedly observed (Bax et al., 2019; Morgan et al., 2013; Mowlem et al., 2019). Although Black and Hispanic children are not less likely to display ADHD symptomatology (Coker et al., 2016; Miller et al., 2009; Morgan et al., 2014), they are less likely to be diagnosed including in analyses adjusted for household income, biological sex, and geographic region (Glasofer & Dingley, 2022; Morgan et al., 2013; Shi et al., 2021; Zhao et al., 2023). Girls are less likely to be diagnosed with ADHD (American Psychiatric Association, 2022; Hinshaw et al., 2022), in part due to referral bias (Sciutto et al., 2004; Young et al., 2020). Children from non-English-speaking households are less likely to be diagnosed and treated for ADHD in adjusted analyses (Sikov et al., 2022). The new DSM-5-TR guidance reports lower clinical identification rates for Black and Hispanic children as well as for girls (American Psychiatric Association, 2022).

Children who are racial, ethnic, or language minorities may be less likely to be diagnosed for ADHD due to differences in parental beliefs about ADHD, concerns about potential stigma, and limited access to culturally sensitive primary care or specialty providers including those proficient in the family’s preferred language (Coker et al., 2016; Gerdes et al., 2014; Miller et al., 2009; Vázquez et al., 2022; Zuckerman et al., 2014). Girls may be less likely to diagnosed with ADHD because their behavioral symptoms typically involve inattention instead of hyperactivity or impulsivity (Hinshaw et al., 2022). This may result in lower referral rates due to referral bias, in which the inattentive behaviors are viewed as less disruptive of home or classroom routines and so less indicative of an underlying disability (American Psychiatric Association, 2022; Bauermeister et al., 2007; Rhinehart et al., 2022; Sciutto et al., 2004). Children in the U.S. South may be especially likely to be diagnosed and treated for ADHD due to socioeconomic factors and the region’s large rural population (Bozinovic et al., 2021; Danielson et al., 2018; Fulton et al., 2009; Lopera et al., 2022; Zgodic et al., 2023).

Yet whether sociodemographic disparities in ADHD diagnosis and treatment are occurring among U.S. elementary schoolchildren is unclear and in dispute, resulting in ambiguity about whether the DSM-5-TR’s guidance is empirically supported (Glasofer & Dingley, 2022; Khoury et al., 2022). Two meta-analyses (Cénat et al., 2022, 2021) report that Black and White children display similar ADHD prevalence rates, and so “refutes the DSM-5’s statement that Black children and adolescents tend to be less likely to be diagnosed with ADHD compared to White children” (Cénat et al., 2022, p. 12). No significant differences in ADHD prevalence by ethnicity were observed (Cénat et al., 2022). To date, very few studies have approximated contrasts between observationally similar children by accounting for ADHD symptomatology and impairment, particularly in analyses of population-based cohorts (Glasofer & Dingley, 2022; Morgan et al., 2017, 2014). Accounting for clinical need is necessary to establish whether health disparities are occurring (Institute of Medicine, 2003). Other studies have analyzed relatively dated samples, used cross-sectional designs, or relied on insurance claims or medical records that exclude vulnerable populations unable to access healthcare (Polanczyk et al., 2014; Shi et al., 2021). Whether disparities in ADHD diagnosis and treatment in the U.S. pediatric population vary in analyses stratified by biological sex is currently unclear (Hartung & Lefler, 2019; Rhinehart et al., 2022), despite recent calls for such work (Hinshaw et al., 2022; Young et al., 2020). Studies reporting geographic variation in ADHD diagnosis and treatment have been unable to account for individual measures of clinical need (Bozinovic et al., 2021; Danielson et al., 2018; Fulton et al., 2009; Zgodic et al., 2023). Doing so would provide stronger evidence that geographic disparities are occurring (Institute of Medicine, 2003).

We investigated whether disparities in ADHD diagnosis and treatment are occurring among U.S. elementary schoolchildren including by race or ethnicity, language use, biological sex, and geographic region. We expected that Black and Hispanic children would be about as likely to be diagnosed and treated for ADHD as White children in unadjusted analyses (Cénat et al., 2022, 2021), but less likely to be diagnosed and treated for ADHD in analyses adjusted for potential confounds including individually assessed symptomatology and impairment (Coker et al., 2009; Gallegos et al., 2021; Glasofer & Dingley, 2022; Morgan et al., 2014; Mowlem et al., 2019; Shi et al., 2021). We expected to observe additional sociodemographic disparities attributable to language use, biological sex, and geographic region (Bax et al., 2019; Center for Disease Control and Prevention, 2022; Danielson et al., 2022; Fairman et al., 2020; Sikov et al., 2022). We conducted additional analyses stratified by biological sex to investigate for intersectional disparities (Bax et al., 2019; Hinshaw et al., 2022; Young et al., 2020).

2. Method

2.1. Database and analytical sample

We analyzed data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011 (ECLS-K: 2011), a population-based cohort of U.S. elementary schoolchildren who entered kindergarten in 2010 to 2011. The ECLS-K: 2011 is maintained by the National Center for Educational Statistics (Tourangeau et al., 2019). Data were collected from children, parents, and teachers during the fall and the spring of kindergarten, first grade, and second grade as well as during the springs of third, fourth, and fifth grade. We used the child base weight w12p0 as well as multiple imputation (MI) to adjust for non-response bias. The online supplementary materials describe the study’s missing data procedures (see eMethods).

Table 1 displays the proportions of missingness as well as weighted Ms or proportions of the study’s variables before and after the MI procedure. Our weighted samples included: (1) a full sample (Table 2, N=10,920), (2) subsamples of only boys and girls (Table 3, n=5,620 and n=5,300, respectively), (3) a subsample of children ever diagnosed with ADHD across first to fifth grade (Table 4, n=880), and (4) subsamples of only boys and girls ever diagnosed with ADHD across first to fifth grade (Table 5, n=640 and n=240, respectively). We rounded the sample sizes to the nearest 10 to comply with participant confidentiality requirements.

Table 1.

Weighted Ms or Proportions of Criterion and Explanatory Variables Before and After Multiple Imputation (N=10,920)a

Before MI After MI
Criterion Variables (1st to 5th grade) Proportion Missing proportion Proportion
Ever diagnosed ADHD (n = 1,270) 0.08 0.11 0.08
Ever used prescription medication for ADHD (n = 990) 0.08 0.21 0.08
Explantory Variables b
Child’s race/ethnicity (reference = White)
 Black 0.12 < 0.01 0.12
 Hispanic 0.22 0.22
 Other race 0.09 0.09
Child’s gender (reference = boys)
 Girls 0.48 < 0.01 0.48
Child’s primary home language (reference = English)
 Primary home language is not English, K-spring 0.13 0.12 0.13
Child’s birth weight (reference = equal or higher than 5 pounds)
 Child’s birth weight less than 5 pounds, K-spring 0.05 0.00 0.05
Child’s health insurance status (reference = covered)
 Health insurance not covered, K-spring 0.05 0.28 0.05
Child’s parents marital status (reference = currently married)
 Parents currently not married, K-spring 0.29 0.26 0.29
School type (reference = private school)
 Public school 0.89 0.02 0.89
School region (reference = Northeast)
 School region, Midwest 0.24 0.02 0.24
 School region, South 0.38 0.38
 School region, West 0.24 0.24
School location (reference = non-rural)
 School location, rural 0.23 0.04 0.23
M (SD) M
SES, K-spring 0.00 (0.80) 0.12 0.00
Child’s age at assessment, K-spring 6.12 (0.37) 0.05 6.12
Number of siblings, K-spring 1.52 (1.11) 0.26 1.52
Parental depressed level, K-spring 0.26 (0.59) 0.31 0.26
Biological mother’s age at child’s birth, K-fall 24.45 (5.83) 0.29 24.42
Reading achievement, K-spring 69.67 (14.64) 0.05 69.60
Math achievement, K-spring 50.64 (13.51) 0.06 50.55
Cognitive flexibility, K-spring 15.29 (2.74) 0.06 15.28
Working memory, K-spring 451.66 (29.90) 0.06 451.49
Inhibitory control, K-spring 5.10 (1.28) 0.12 5.10
Externalizing problem behavior, K-spring 1.62 (0.63) 0.13 1.63
Internalizing problem behavior, K-spring 1.50 (0.49) 0.13 1.50
Percent non-White students in School, K-spring 42.95 (32.88) 0.02 42.98
Percent students approved for free lunch program, K-spring 41.20 (31.35) 0.02 41.25
Percent students approved for reduced lunch program, K-spring 7.48 (9.01) 0.02 7.49
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Abbreviation: MI, multiple imputation; M, mean; SD, standard deviation; SES, socioeconomic status.

a

Analytic sample size after weight (w12p0) applied. Number was rounded to nearest 10 based on NCES restricted-use data requirement.

b

Reference groups were as follows: White, non-Hispanic student; Boy; Student’s primary language is English; Child’s birth weight equal or higher than 5 pounds; Student has health insurance coverage in the spring of kindergarten; Student’s parents were currently married in the spring of kindergarten; Private school; School located in the Northeast and non-rural aera.

Source: U.S. Department of Education, National Center for Education Statistics (NCES), Early Childhood Longitudinal Study, Kindergarten Class of 2011(ECLS-K), Kindergarten Through Fifth Grade Full Sample Restricted-Use Data File.

Table 2.

Logistic Regression Predicting Child ever Diagnosed as ADHD across First to Fifth Grade among Full Sample (N=10920)a

Model 1 Model 2
OR 95%CIb OR 95%CI
Blackc 0.97 0.77 1.23 0.60** 0.44 0.80
Hispanic 0.62*** 0.50 0.77 0.90 0.69 1.19
Other race 0.61** 0.46 0.81 0.78 0.57 1.06
Girls 0.55*** 0.45 0.67
SES 1.10 0.97 1.24
Child’s age at assessment, K-spring 1.22*** 1.12 1.33
Primary home language is not English 0.29*** 0.19 0.44
Number of siblings, K-spring 0.98 0.90 1.07
Child’s birth weight less than 5 pounds 0.98 0.69 1.37
Parental depressed level, K-spring 1.11** 1.03 1.20
Health insurance not covered, K-spring 0.85 0.58 1.25
Parents currently not married, K-spring 0.96 0.78 1.19
Biological mother’s age at child’s birth, K-fall 0.99 0.89 1.10
Reading achievement, K-spring 0.99 0.86 1.13
Math achievement, K-spring 0.78** 0.67 0.90
Cognitive flexibility, K-spring 0.99 0.91 1.08
Working memory, K-spring 0.93 0.83 1.03
Inhibitory control, K-spring 0.55*** 0.48 0.63
Externalizing problem behavior, K-spring 1.22*** 1.09 1.36
Internalizing problem behavior, K-spring 1.09* 1.00 1.19
Percent non-White students in School, K-spring 0.95 0.81 1.11
Percent students approved for free lunch program, K-spring 1.02 0.89 1.17
Percent students approved for reduced lunch program, K-spring 0.98 0.86 1.11
Public school 1.21 0.84 1.73
School region, Midwest 1.20 0.91 1.57
School region, South 1.35* 1.05 1.73
School region, West 0.65** 0.48 0.87
School location, rural 1.06 0.85 1.32
Constant 0.11*** 0.10 0.12 0.08*** 0.05 0.12
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Abbreviation: SES, socioeconomic status; OR, odds ratio; CI, confidence interval.

a

Analytic sample size after weight (w12p0) applied. Number was rounded to nearest 10 based on NCES restricted-use data requirement.

b

Standard error adjusted for clustering by using school ID (kindergarten spring).

c

Reference groups were as follows: White, non-Hispanic student; Boy; Student’s primary language is English; Child’s birth weight equal or higher than 5 pounds; Student has health insurance coverage in the spring of kindergarten; Student’s parents were currently married in the spring of kindergarten; Private school; School located in the Northeast and non-rural aera.

*

p <0.05,

**

p < 0.01,

***

p < 0.001

Source: U.S. Department of Education, National Center for Education Statistics (NCES), Early Childhood Longitudinal Study, Kindergarten Class of 2011(ECLS-K), Kindergarten Through Fifth Grade Full Sample Restricted-Use Data File.

Table 3.

Logistic Regression Predicting Child ever Diagnosed as ADHD across First to Fifth Grade Stratified by Biological Sex

Within Boys (n = 5,620)a Within Girls (n = 5,300)
Model 1 Model 2 Model 1 Model 2
OR 95%CIb OR 95%CI OR 95%CI OR 95%CI
Blackc 0.82 0.62 1.09 0.45*** 0.32 0.64 1.36 0.91 2.05 0.95 0.57 1.59
Hispanic 0.67** 0.52 0.86 0.91 0.66 1.27 0.48** 0.31 0.74 0.79 0.46 1.35
Other race 0.63** 0.45 0.87 0.75 0.52 1.08 0.63 0.37 1.09 0.85 0.47 1.52
SES 1.01 0.88 1.17 1.34* 1.06 1.68
Child’s age at assessment, K-spring 1.18** 1.07 1.30 1.39*** 1.16 1.65
Primary home language is not English, K-spring 0.27*** 0.17 0.43 0.33** 0.15 0.73
Number of siblings, K-spring 1.00 0.90 1.11 0.96 0.81 1.14
Child’s birth weight less than 5 pounds, K-spring 1.24 0.84 1.84 0.51 0.24 1.09
Parental depressed level, K-spring 1.13** 1.03 1.24 1.10 0.94 1.27
Health insurance not covered, K-spring 0.80 0.49 1.31 0.89 0.42 1.88
Parents currently not married, K-spring 0.93 0.74 1.17 1.09 0.73 1.63
Biological mother’s age at child’s birth, K-fall 0.99 0.88 1.13 0.98 0.81 1.20
Reading achievement, K-spring 1.05 0.90 1.22 0.82 0.61 1.11
Math achievement, K-spring 0.80** 0.68 0.94 0.69* 0.51 0.93
Cognitive flexibility, K-spring 0.99 0.89 1.10 1.01 0.85 1.21
Working memory, K-spring 0.95 0.84 1.07 0.87 0.70 1.09
Inhibitory control, K-spring 0.57*** 0.49 0.67 0.48*** 0.37 0.61
Externalizing problem behavior, K-spring 1.22** 1.07 1.40 1.17 0.96 1.43
Internalizing problem behavior, K-spring 1.16** 1.06 1.28 0.92 0.77 1.10
Percent non-White students in School 1.09 0.91 1.31 0.71** 0.55 0.91
Percent students approved for free lunch program 0.97 0.81 1.15 1.11 0.90 1.36
Percent students approved for reduced lunch program 0.97 0.86 1.11 1.02 0.84 1.24
Public school 1.41 0.93 2.15 0.89 0.52 1.53
School region, Midwest 1.15 0.83 1.60 1.40 0.84 2.35
School region, South 1.00 0.74 1.34 2.92*** 1.78 4.79
School region, West 0.54*** 0.39 0.77 1.00 0.58 1.74
School location, rural 1.16 0.91 1.50 0.82 0.55 1.23
Constant 0.16*** 0.14 0.17 0.09*** 0.05 0.14 0.06*** 0.04 0.07 0.03*** 0.01 0.06
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Abbreviation: SES, socioeconomic status; OR, odds ratio; CI, confidence interval.

a

Analytic sample size after weight (w12p0) applied. Number was rounded to nearest 10 based on NCES restricted-use data requirement.

b

Standard error adjusted for clustering by using school ID (kindergarten spring).

c

Reference groups were as following: White, non-Hispanic student; Student’s primary language is English; Child’s birth weight equal or higher than 5 pounds; Student has health insurance coverage in the spring of kindergarten; Student’s parents were currently married in the spring of kindergarten; Private school; School located in the Northeast and non-rural aera.

*

p <0.05,

**

p < 0.01,

***

p < 0.001

Source: U.S. Department of Education, National Center for Education Statistics (NCES), Early Childhood Longitudinal Study, Kindergarten Class of 2011(ECLS-K), Kindergarten Through Fifth Grade Full Sample Restricted-Use Data File.

Table 4.

Logistic Regression Predicting Child ever used Prescription Medication for ADHD across First to Fifth Grade among Child ever Diagnosed as ADHD (n=880)a

Model 1 Model 2
OR 95%CIb OR 95%CI
Blackc 0.98 0.54 1.77 0.95 0.47 1.91
Hispanic 0.77 0.46 1.28 1.40 0.73 2.70
Other race 0.57 0.30 1.09 0.72 0.34 1.52
Girls 0.63* 0.42 0.93 0.52** 0.34 0.80
SES 1.02 0.79 1.31
Child’s age at assessment, K-spring 1.08 0.89 1.32
Primary home language is not English, K-spring 0.24** 0.10 0.59
Number of siblings, K-spring 0.84 0.70 1.02
Child’s birth weight less than 5 pounds, K-spring 1.24 0.57 2.69
Parental depressed level, K-spring 0.83 0.68 1.02
Health insurance not covered, K-spring 0.40* 0.16 0.99
Parents currently not married, K-spring 1.04 0.63 1.71
Biological mother’s age at child’s birth, K-fall 0.95 0.74 1.20
Reading achievement, K-spring 1.20 0.93 1.56
Math achievement, K-spring 0.84 0.61 1.14
Cognitive flexibility, K-spring 1.20 0.97 1.48
Working memory, K-spring 0.71** 0.55 0.92
Inhibitory control, K-spring 0.80 0.61 1.04
Externalizing problem behavior, K-spring 0.97 0.75 1.24
Internalizing problem behavior, K-spring 0.88 0.74 1.05
Percent non-White students in School 0.79 0.58 1.09
Percent students approved for free lunch program 1.16 0.85 1.59
Percent students approved for reduced lunch program 0.87 0.75 1.02
Public school 0.90 0.46 1.75
School region, Midwest 3.14*** 1.76 5.61
School region, South 3.31*** 1.96 5.60
School region, West 1.31 0.73 2.36
School location, rural 1.14 0.69 1.86
Constant 5.39*** 4.14 7.01 2.46* 1.10 5.47
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Abbreviation: SES, socioeconomic status; OR, odds ratio; CI, confidence interval.

a

Analytic sample size after weight (w12p0) applied. Number was rounded to nearest 10 based on NCES restricted-use data requirement.

b

Standard error adjusted for clustering by using school ID (kindergarten spring).

c

Reference group were as follows: White, non-Hispanic student; Boy; Student’s primary language is English; Child’s birth weight equal or higher than 5 pounds; Student has health insurance coverage in the spring of kindergarten; Student’s parents were currently married in the spring of kindergarten; Private school; School located in the Northeast and non-rural aera.

*

p <0.05,

**

p < 0.01,

***

p < 0.001

Source: U.S. Department of Education, National Center for Education Statistics (NCES), Early Childhood Longitudinal Study, Kindergarten Class of 2011(ECLS-K), Kindergarten Through Fifth Grade Full Sample Restricted-Use Data File.

Table 5:

Logistic Regression Predicting Child ever used Prescription Medication for ADHD across First to Fifth Grade among Child ever Diagnosed as ADHD Stratified by Biological Sex

Within Boys (n=640)a Within Girls (n=240)
Model 1 Model 2 Model 1 Model 2
OR 95%CIb OR 95%CI OR 95%CI OR 95%CI
Blackc 1.47 0.68 3.19 1.24 0.50 3.05 0.65 0.26 1.59 0.82 0.23 2.86
Hispanic 0.66 0.38 1.16 1.01 0.48 2.16 1.32 0.48 3.58 6.35** 1.67 24.22
Other race 0.51 0.23 1.14 0.64 0.25 1.62 0.70 0.22 2.22 0.74 0.20 2.71
SES 1.04 0.77 1.41 0.93 0.54 1.59
Child’s age at assessment, K-spring 1.05 0.81 1.36 1.21 0.84 1.75
Primary home language is not English, K-spring 0.19** 0.06 0.54 0.17 0.02 1.34
Number of siblings, K-spring 0.92 0.71 1.18 0.65* 0.45 0.94
Child’s birth weight less than 5 pounds, K-spring 1.02 0.42 2.50 1.72 0.31 9.46
Parental depressed level, K-spring 0.91 0.71 1.18 0.62* 0.39 0.98
Health insurance not covered, K-spring 0.29* 0.10 0.86 0.83 0.17 4.08
Parents currently not married, K-spring 0.81 0.44 1.49 1.93 0.70 5.32
Biological mother’s age at child’s birth, K-fall 0.92 0.69 1.22 0.93 0.57 1.51
Reading achievement, K-spring 1.08 0.82 1.42 1.67 0.86 3.24
Math achievement, K-spring 0.85 0.61 1.19 0.83 0.40 1.73
Cognitive flexibility, K-spring 1.18 0.90 1.56 1.20 0.82 1.75
Working memory, K-spring 0.70* 0.53 0.94 0.63 0.34 1.16
Inhibitory control, K-spring 0.70* 0.50 0.98 1.09 0.65 1.83
Externalizing problem behaviors, K-spring 0.89 0.64 1.22 1.20 0.69 2.07
Internalizing problem behaviors, K-spring 0.89 0.73 1.09 0.76 0.52 1.12
Percent non-White students in School 0.89 0.59 1.33 0.58 0.31 1.08
Percent students approved for free lunch program 1.25 0.85 1.84 1.03 0.56 1.89
Percent students approved for reduced lunch program 0.83** 0.72 0.95 0.97 0.71 1.33
Public school 0.85 0.36 1.99 1.27 0.35 4.64
School region, Midwest 3.60** 1.73 7.52 2.45 0.69 8.68
School region, South 3.07*** 1.65 5.73 4.05* 1.30 12.59
School region, West 1.42 0.69 2.91 0.83 0.22 3.09
School location, rural 1.07 0.58 1.97 1.03 0.41 2.58
Constant 5.38*** 4.05 7.15 3.15* 1.14 8.71 3.38*** 2.13 5.36 0.65 0.12 3.50
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Abbreviation: SES, socioeconomic status; OR, odds ratio; CI, confidence interval.

a

Analytic sample size after weight (w12p0) applied. Number was rounded to nearest 10 based on NCES restricted-use data requirement.

b

Standard error adjusted for clustering by using school ID (kindergarten spring).

c

Reference groups were as follows: White, non-Hispanic student; Student’s primary language is English; Child’s birth weight equal or higher than 5 pounds; Student has health insurance coverage in the spring of kindergarten; Student’s parents were currently married in the spring of kindergarten; Private school; School located in the Northeast and non-rural aera.

*

p <0.05,

**

p < 0.01,

***

p < 0.001

Source: U.S. Department of Education, National Center for Education Statistics (NCES), Early Childhood Longitudinal Study, Kindergarten Class of 2011(ECLS-K), Kindergarten Through Fifth Grade Full Sample Restricted-Use Data File.

2.2. Criterion variables, 1st-5th grade

Parents reported their children’s ADHD diagnosis and prescription medication use in the spring of each wave from first to fifth grade. We used a dummy variable to indicate whether parents ever reported their child was diagnosed with ADHD or attention-deficit disorder (ADD) from a professional at any time during first, second, third, fourth, or fifth grade. Parents reporting that their children had been diagnosed with ADHD were asked about their children’s prescription medication use. Parents are reliable reporters of a provider’s ADHD diagnosis (Cree et al., 2023).

2.3. Explanatory variables, kindergarten

Explanatory variables were measured during the spring of kindergarten, and so plausibly exogenous to children’s ADHD diagnosis and treatment. Parents identified their children’s race and ethnicity separately during the fall and spring of kindergarten. Parents were asked to indicate which of five race categories (i.e., White, Black or African American, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native) that their child belonged. Parents were allowed to indicate more than one racial category. Based on these responses, a series of five dichotomous race variables were created that indicated separately whether the child belonged to each of the five specified race groups. In addition, one additional dichotomous variable was created to indicate whether parents respondent that their child belonged to more than one racial category. Parents also were asked whether their child was Hispanic. Using the six race dichotomous variables and the Hispanic ethnicity variable, the race/ethnicity composite variables for the child was created for the ECLS-K:2011. The categories for these variables are White, not Hispanic; Black or African American, not Hispanic; Hispanic, race specified; Hispanic, no race specified; Asian, not Hispanic; Native Hawaiian or other Pacific Islander, not Hispanic; American Indian or Alaska Native, not Hispanic; and more than one race specified, not Hispanic. We used this composite race/ethnicity variable to recode children’s race or ethnicity as White, non-Hispanic, Black, non-Hispanic, Hispanic, no race specified, and other non-Hispanic race (i.e., Asian, non-Hispanic, Native Hawaiian or other Pacific Islander, non-Hispanic, American Indian or Alaska Native, non-Hispanic, and two or more races, non-Hispanic). We used White, non-Hispanic as the reference group. Parents reported their children’s biological sex. Children’s primary home language was obtained from the parental questionnaires completed during the fall and spring of kindergarten. We used English regularly spoken or English and another language was equally used in the home as the reference group. Parents were surveyed about each parent’s or guardian’s occupation and educational level as well as the family’s household income. Children’s age in months during the spring of kindergarten wave assessment was obtained from the date children completed the direct assessments and their date of birth. Parents reported their child’s birth weight in pounds. We used birth weight equal or higher than 5 pounds as the reference group.

Parents rated the frequency of depressive feelings by responding to the following question: “How often during the past week have you felt depressed.” Responses ranged from 1 to 4, corresponding to “never,” “some of the time,” “a moderate amount of the time,” and “most of the time.” We recoded the scale to 0–3 and standardized the variable. Parents reported the number of siblings (including those who were biological, step-, adopted, or in foster care) living in the household. This and additional information about the family’s health insurance coverage was collected during the spring of kindergarten. Families with health insurance coverage were the reference group. Parents indicated their marital status and the biological mother’s age at the children’s birth. The reference group was married at the spring of kindergarten.

Children’s reading and mathematics achievement was individually assessed using untimed and psychometrically well-designed measures during the spring of kindergarten. The reading and mathematics achievement scale scores had high reliabilities of .95 and .94, respectively (Tourangeau et al., 2019). Cognitive flexibility and working memory abilities were individually assessed using the Dimensional Change Card Sort (DCCS) and the Numbers Reversed subtest of the Woodcock-Johnson III Tests of Cognitive Abilities, respectively (Woodcock et al., 2001; Zelazo, 2006). Both tests have strong test-retest reliability (e.g., .90-.94 for DCCS; .69-.87 for Numbers Reversed task) (Beck et al., 2011; Vought & Dean, 2011). Teachers individually rated children’s inhibitory control using the Children’s Behavior Questionnaire (reliability of .87) (Putnam & Rothbart, 2006). Teachers rated how often children exhibited externalizing or internalizing problem behaviors using the Social Skills Rating System, which has strong psychometric properties (e.g., reliabilities of .89 and .78) (Tourangeau et al., 2019). Measured school characteristics included the percent of students receiving free school lunch and reduced-price school lunch, the percent of non-White students, public or private school, and the school’s location and region (using non-rural and the Northeast as the reference groups, respectively).

2.4. Analyses

We used logistic regression to predict children’s odds of being diagnosed with ADHD at any time between first and fifth grade. Model 1 included only race or ethnicity. Model 2 added additional explanatory variables measured during kindergarten. We first conducted these analyses for the full sample (Table 2). We then conducted the same analyses using samples stratified by biological sex (Table 3). Follow-up analyses based on the stratified samples allowed us to identify the extent to which any observed disparities attributable to race or ethnicity or language use were occurring among boys, girls, or both boys and girls. We then used logistic regression to predict the odds that children diagnosed with ADHD were using prescription medication (Table 4). We stratified the sample diagnosed with ADHD based on biological sex to examine sex-specific disparities in prescription medication use (Table 5). We used sampling weight and clustered standard errors to account for the ECLS-K: 2011’s complex survey design. We received Institutional Review Board approval and used Stata v.17.0 to analyze the data.

3. Results

Table 1 displays the full sample’s descriptive statistics before and after use of MI. Here we report unstandardized value for continuous variables. We then standardized these variables in the regression analyses. The full sample was diverse including in race, ethnicity, language use, and family SES. About 8% of children in the full sample were diagnosed with ADHD between first and fifth grade. Among children who are White, Black, Hispanic, or of other race or ethnicity, 10%, 10%, 6%, and 4% were diagnosed with ADHD (eTable 3). About 12% of boys and 5% of girls were diagnosed with ADHD (online supplement eTable 1). Among children with ADHD, 81% were using prescription medication (online supplement eTable 2). We also calculated and reported the prevalence rates for ADHD diagnosis and prescription medication use, respectively, within children’s race or ethnicity, primary home language, biological sex, as well as by race or ethnicity and biological sex, and biological sex by race or ethnicity (online supplemental eTable 3).

Table 2 displays the unadjusted and adjusted estimates of racial and ethnic disparities in ADHD diagnosis. Model 1’s results indicated that Hispanic children (odds ratio [OR], .62, 95% confidence interval [CI], 0.50–0.77) or those of another race or ethnicity (OR, .61, 95% CI, 0.46–0.81) were less likely to be diagnosed with ADHD. Model 2’s additional explanatory factors fully explained these initially observed disparities. Adjusted analyses indicted that Black children were less likely to be diagnosed than observationally similar White children (aOR, 0.60; 95% CI, 0.44–0.80). Children from households whose primary language was not English were less likely to be diagnosed with ADHD (aOR, 0.29; 95% CI, 0.19–0.44). Girls were significantly less likely to have been diagnosed with ADHD (aOR, 0.55; 95% CI, 0.45–0.67) in analyses adjusting for independently assessed academic, behavioral, and executive functioning. Older children, those whose parents experienced depressive feelings, and children displaying lower academic, behavioral, or executive functioning were more likely to be diagnosed with ADHD during elementary school. Children’s inhibitory control strongly predicted their likelihood for ADHD diagnosis (aOR, 0.55; 95% CI, 0.48–0.63). Children in the South and West were more and less likely, respectively, to be diagnosed with ADHD (South aOR, 1.35; 95% CI, 1.05–1.73; West aOR, 0.65; 95% CI, 0.48–0.87).

Table 3 displays the likelihood of ADHD diagnosis among subsamples of boys and girls. Black children’s lower likelihood of ADHD diagnosis occurred among boys (aOR, 0.45; 95% CI, 0.32–0.64) but not girls (OR, 0.95; 95% CI, 0.57–1.59). Consistent predictors of ADHD diagnosis among both boys and girls included age (boys aOR, 1.18; 95% CI, 1.07–1.30; girls aOR, 1.39; 95% CI, 1.16–1.65), primary use of a language other than English in the household (boys aOR, 0.27; 95% CI, 0.17–0.43; girls aOR, 0.33; 95% CI, 0.15–0.73), and children’s mathematics achievement (boys aOR, 0.80; 95% CI, 0.68–0.94; girls aOR, 0.69; 95% CI, 0.51–0.93) or inhibitory control (boys aOR, 0.57; 95% CI, 0.49–0.67; girls aOR, 0.48; 95% CI, 0.37–0.61). Boys but not girls engaging in more externalizing (aOR, 1.22; 95% CI, 1.07–1.40) and internalizing (aOR, 1.16; 95% CI, 1.06–1.28) problem behaviors were more likely to be diagnosed with ADHD. Girls but not boys in the South were more likely to be diagnosed with ADHD (aOR, 2.92, 95% CI, 1.78–4.79).

Table 4 displays the likelihood of using prescription medication among children diagnosed with ADHD. Girls (aOR, 0.52; 95% CI, 0.10–0.59), those from households where a language other than English was primarily used (aOR, 0.24; 95% CI, 0.10–0.59), those without health insurance coverage (aOR, 0.40; 95% CI, 0.55–0.92), and children with better working memory abilities (aOR, 0.71; 95% CI, 0.55–0.92) were less likely to be using medication for ADHD. Children in the Midwest and South were more likely to be using medication (Midwest aOR, 3.14; 95% CI, 1.76–5.61; South aOR, 3.31, 95% CI, 1.96–5.60).

Table 5 displays the likelihood of using prescription medication among subsamples of boys and girls diagnosed with ADHD. Model 2’s results indicated that Hispanic girls (aOR, 6.35; 95% CI,1.67–24.22) but not boys (aOR, 1.01; 95% CI,0.48–2.16), were more likely to use medication. (We believe this finding should be interpreted cautiously given the very large CI.) Boys but not girls from households in which the primary home language was not English (boys aOR, 0.19; 95%, 0.06–0.54), those without health insurance coverage (boys aOR, 0.29; 95% CI, 0.10–0.86), those with better working memory abilities (boys aOR, 0.70; 95% CI, 0.53–0.94), those with better inhibitory control abilities (boys aOR, 0.70; 95% CI, 0.50–0.98) and those attending schools with higher percentage of students eligible for reduced lunch program (boys aOR, 0.83; 95% CI, 0.72–0.95) were less likely to use medication. Girls with more siblings (aOR, 0.65; 95% CI, 0.45–0.94) or whose parents had higher level of depression (aOR, 0.62; 95% CI, 0.39–0.98) were less likely to use medication. Both boys (aOR, 3.07; 95% CI, 1.65–5.73) and girls (aOR, 4.05; 95% CI, 1.30–12.59) living in the U.S. South were much more likely to use medication.

4. Discussion

Whether sociodemographic disparities in ADHD diagnosis and treatment are occurring among U.S. schoolchildren has been unclear and in dispute, resulting in ambiguity about the extent to which the new DSM-5-TR guidance is empirically supported (Cénat et al., 2021; Glasofer & Dingley, 2022; Khoury et al., 2022). Consistent with the DSM-5-TR’s guidance, our analyses of a population-based cohort of U.S. elementary schoolchildren indicated that Black children, particularly boys, are less likely to be diagnosed with ADHD than observationally similar White children. Girls and emergent bilinguals are also less likely to be diagnosed with ADHD. Girls, emergent bilinguals, and those lacking health insurance coverage are less likely to use prescription medication. Children attending schools in the U.S. South, particularly girls, are more likely to be diagnosed with ADHD. If diagnosed, both boys and girls living in the U.S. South are more likely to use prescription medication.

Measured confounds including independently assessed indicators of ADHD symptomatology and impairment do not explain these disparities. These indicators, which included multiple measures of children’s academic, behavioral, and executive functioning, consistently predicted ADHD diagnosis and treatment and so might inform universal screening instrument design. Our findings have public health implications. Children with ADHD are at increased risk of later experiencing poor health including substance abuse, self-harm, suicidal behavior, injuries, and mortality (Dalsgaard et al., 2015; Leffa et al., 2022; Ojala et al., 2022; Sundquist et al., 2015) These children are likely to benefit from timely ADHD diagnosis and treatment (Wolraich et al., 2019). Multiple evidence-based treatments exist for managing ADHD in the U.S. pediatric population including by elementary school (Baweja et al., 2021; Glasofer & Dingley, 2022; Winders Davis et al., 2021).

4.1. Strengths and limitations

We analyzed a population-based cohort of U.S. elementary schoolchildren followed from the beginning of kindergarten to the end of fifth grade whose academic, behavioral, and executive functioning was repeatedly assessed by independent observers. We accounted for missing data and used extensive statistical control for measured confounds to help limit omitted variable bias. Our measures of academic achievement, behavior, and executive functioning should have helped control for general intelligence, which is negatively correlated with ADHD symptomatology (Rommelse et al., 2017) but not measured in the ECLS-K: 2011. Our stratified analyses provide new evidence of intersectional disparities in ADHD diagnosis and treatment among U.S. elementary schoolchildren including that Black children’s under-diagnosis is occurring among boys. These stratified analyses also indicated that parental depression and children’s externalizing and internalizing problem behaviors increase the likelihood that boys but not girls are diagnosed with ADHD. In contrast, children’s inhibitory control increase the likelihood of ADHD diagnosis for both boys and girls.

Yet our study also has limitations. The ECLS-K: 2011’s data collection ended when children completed elementary school. We may have observed other types of disparities if data collection had continued into middle or high school (Dalsgaard et al., 2020). We used parent report of children’s ADHD diagnosis and medication use as well as race and ethnicity. Although parents are reliable and valid reporters of ADHD symptomology, diagnosis, and treatment, we were unable to independently verify the children’s diagnoses and medication use (Bied et al., 2017; Kuriyan et al., 2014; Shemmassian and Lee, 2016; Tahıllıoğlu et al., 2021). Our analyses estimated residual direct effects of sociodemographic characteristics by adjusting for a full range of covariates including independently assessed ADHD symptomatology and impairment (Coker et al., 2009; Gallegos et al., 2021). Our disparities estimates are likely conservative (Hunt et al., 2015; Lê Cook et al., 2010). We examined for disparities generally and so not across specific ADHD symptomatology or impairment thresholds. White children may be especially likely to be diagnosed and treated for ADHD among those displaying no or only mild impairments in academic, behavioral, or executive functioning (Morgan et al., 2022).

4.2. Clinical and research implications

We provide timely empirical evidence to support the DSM-5-TR’s guidance on sociodemographic disparities in ADHD diagnosis and treatment, particularly for Black children, girls, and emergent bilinguals. Our findings are also consistent with prior work suggesting that these socio-demographic groups may experience greater difficulties in obtaining care for ADHD due to economic, cultural, and language obstacles as well as referral bias (Bax et al., 2019; Coker et al., 2016; Gallegos et al., 2021; Morgan et al., 2013; Zuckerman et al., 2014). A clinical implication of our findings is the need for greater cultural, linguistic, and biological sensitivity in the ADHD diagnostic and treatment procedures currently in use for the diverse U.S. pediatric population. Use of standardized screening instruments, proactively soliciting developmental concerns, and ensuring access to translators may help to address these disparities (Zuckerman et al., 2014). Public health campaigns that disseminate evidence-based information about ADHD including to historically marginalized communities, address individual- and community-level obstacles to care, and establish developmental screening and surveillance programs in collaboration with community providers, and may also be of benefit (Gerdes et al., 2014; Kappi and Martel, 2022; Rothe, 2005; Zuckerman et al., 2014).

To our knowledge, the finding that Black children’s ADHD under-diagnosis is especially likely to occur among boys has not been previously reported. Prior work has not reported analyses stratified by biological sex (Coker et al., 2016; Morgan et al., 2014, 2013). Parents of Black boys may be distrustful of reports of hyperactive or impulsive classroom behaviors as indicative of ADHD instead of cultural differences in vervistic behaviors, particularly by teachers who themselves may be culturally or racially biased (Davison & Ford, 2001; Kang & Harvey, 2020; Miller et al., 2009; Moody, 2017; Olaniyan et al., 2007; Tyler et al., 2006; Villodas et al., 2019). Alternatively, it may also be that teachers are especially unlikely to consider Black boys as having ADHD, possibility due to lower behavioral expectations and cultural stereotypes (Cooc, 2017; Moody, 2016; Ramey, 2018). Prior work has found that White children are more likely than Black children to be diagnosed and treated for ADHD even among those displaying high levels of symptomatology (Coker et al., 2016). We also extend prior work by showing that children’s greater likelihood of being diagnosed with ADHD if they live in the U.S. South is especially likely to be experienced by girls. ADHD under-diagnosis is thought to be contributing to disparities in school discipline, adjudication, and incarceration in the U.S. that are disproportionately experienced by Black boys and other highly vulnerable populations (Moody, 2016; Ramey, 2020).

Our findings conflict with two recent meta-analyses reporting that ADHD prevalence rates between Black or Hispanic children and White children were not statistically significant, and an attending suggestion that the DSM-5’s guidance on lower clinical indication rates may be the result of a biased synthesis of existing studies (Cénat et al., 2022, 2021). Yet the meta-analyses reported pooled prevalence rates and did not adjust for between-group differences in ADHD symptomatology or impairment. The meta-analyses were unable to evaluate for disparities in ADHD diagnosis or treatment by approximating contrasts between children displaying similar clinical needs (Coker et al., 2016; Gallegos et al., 2021; Institute of Medicine, 2003; Morgan et al., 2013). Our analyses, which account for clinical need as well as additional potential confounds, yielded repeated evidence of disparities in ADHD diagnosis and treatment attributable to race, language use, and biological sex among observationally similar children. These new findings are consistent with prior studies adjusting for potential confounds including independently assessed ADHD symptomatology and impairment (Bax et al., 2019; Coker et al., 2016, 2009; Glasofer & Dingley, 2022; Morgan et al., 2014; Mowlem et al., 2019; Sikov et al., 2022). Our finding that ADHD under-diagnosis initially attributable to children’s status as Hispanic or of other race or ethnicity is instead explained by additional sociodemographic factors including non-English language use is also consistent with prior work (Coker et al., 2009; Morgan et al., 2014), and suggests that efforts to support access to care by linguistically diverse families may help lessen racial and ethnic disparities in ADHD diagnosis and treatment during early childhood.

Sociodemographic disparities in ADHD diagnosis and treatment are occurring among U.S. elementary schoolchildren. Our findings provide empirical support for the DSM-5-TR’s ADHD diagnostic and treatment guidance in diverse pediatric populations. Cultural, linguistic, and biological sensitivity in ADHD diagnosis and treatment for the U.S. pediatric population continues to be warranted.

Supplementary Material

supplement

Article summary:

We investigated whether and to what sociodemographic disparities in ADHD diagnosis and treatment are occurring among U.S. elementary schoolchildren in analyses adjusting for clinical need.

What’s known on this subject:

Whether sociodemographic disparities are occurring in ADHD diagnosis and treatment is unclear and in dispute. Methodological limitations may help explain the extant work’s conflicting findings. Most available studies are cross-sectional. Few have accounted for independently assessed ADHD symptomatology or impairment.

What this study adds:

Our analyses of a population-based cohort provide new evidence indicating that sociodemographic disparities are occurring among U.S. elementary school children in ADHD diagnosis and treatment. We extend prior work by clarifying that Black children’s observed under-diagnosis is occurring among boys.

Highlights:

  • Whether sociodemographic disparities occur in ADHD diagnosis and treatment is unclear

  • Methodological limitations may help explain the extant work’s conflicting findings

  • We predicted diagnosis and medication use in analyses adjusting for clinical need

  • Black children, girls, and emergent bilinguals were less likely to have an ADHD diagnosis

  • Black children’s under-diagnosis for ADHD occurred among boys

  • Among those with ADHD, girls and emergent bilinguals were less likely to use medication

Funding support:

The study’s analyses were supported by the U.S. Department of Education’s Institute of Education Sciences (grant #R324A200166) and infrastructure support by the Eunice Kennedy Shriver National Institute of Child Health and Human Development to Penn State’s Population Research Institute (grant #P2CHD041025).

Role of funder:

Neither IES nor NIH had a role in the design or conduct of the study.

Abbreviations:

ADHD

Attention-deficit/hyperactivity disorder

Footnotes

Conflict of interest disclosures: The investigators have no conflicts of interest to disclose.

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