Diagnosis and Treatment of ADHD in the United States: Update by Gender and Race

Abstract

Objective: The aim of this article is to update ADHD diagnosis/treatment trends by age, gender, and race. Method: National Ambulatory Medical Care Survey data were obtained for 2008-2009 to 2012-2013. Physician office visits including ADHD diagnosis and pharmacotherapy were measured per 1,000 population and per 1,000 office visits overall, and by demographic group. Logistic regression models controlled for demographics, psychiatric comorbidities, insurance type, and time period. Interactions of time, demographics, comorbidities, and insurance type were tested. Results: Diagnoses of ADHD increased by 36% in adults and 18% in youth, and diagnosis + drug by 29% in female and 10% in male youths. ADHD diagnosis was 77% less likely among Black than White adults but 24% more likely among Black than White youths in 2012-2013. Conduct disorder (CD) in youths multiplied odds of diagnosis + drug by 3.31; interaction of Black race × CD by 3.78. Conclusion: Upward trends in ADHD diagnosis and treatment have continued but vary markedly by group. Studies of undertreatment/overtreatment are needed.

Keywords

ADHD gender epidemiology race prescription prevalence

Introduction

Over a period of about two decades beginning in 1990, substantial upward trends in the rates of diagnosis and treatment for ADHD were observed in nationally representative samples of U.S. physician office visits obtained through the National Ambulatory Medical Care Survey (NAMCS; Olfson, Blanco, Wang, & Greenhill, 2013; Robison, Sclar, & Skaer, 2005; Robison, Skaer, & Sclar, 2004; Robison, Skaer, Sclar, & Galin, 2002; Sclar, Robison, Bowen, et al., 2012; Sclar, Robison, Castillo, et al., 2012). Since the most recent time periods studied, 2006-2009 for adults (Olfson et al., 2013) and 2007-2008 for children and youth (Sclar, Robison, Bowen, et al., 2012), several important developments have taken place. These include the approval of several new ADHD drugs and formulations (CenterWatch, n.d.); the formation of the American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders–5 Task Force in 2007, culminating in the publication of Diagnostic and Statistical Manual of Mental Disorders (DSM-5; 5th ed.; APA, 2013, n.d.); and updated guidelines, from the American Academy of Pediatrics (AAP) in 2011 (Wolraich et al., 2011) and from the American Academy of Family Practice in 2012 (adults) and 2014 (children and teens; Felt, Biermann, Christiner, Kochhar, & Harrison, 2014; Post & Kurlansik, 2012).

These changes may have increased the number of cases diagnosed and treated for ADHD in several ways. First, the age criteria for diagnosis broadened, both in the DSM-5 (symptoms no later than age 12 years vs. age 6 years in the Diagnostic and Statistical Manual of Mental Disorders [4th ed.; DSM-IV]; APA, 1994) and in the AAP guidelines (age 4-18 years in 2011 vs. 6-12 years in 2001; APA, 2013; Wolraich et al., 2011). Second, the DSM-5 guidelines require adults and adolescents to display only five ADHD symptoms, rather than the six required for a diagnosis in children (APA, 2013). Finally, a description of ADHD symptomatology in those aged 17 years or older was added to the DSM-5 for clarity (APA, 2013).

Given this expansion of diagnostic criteria and available pharmacotherapies for ADHD, the present study was conducted to provide an update and reassessment of three previously observed demographic trends:

Large increases in ADHD diagnosis and treatment among adults. From 1995-1996 to 2007-2008, the number of office visits at which an ADHD diagnosis was made increased from 3.1 to 14.5 per 1,000 U.S. adults (aged 20 years or older; Sclar, Robison, Castillo, et al., 2012). For the same time span and group, office visits including both ADHD diagnosis and medication increased from 1.9 to 11.4 per 1,000 (Sclar, Robison, Castillo, et al., 2012). Similarly, the percentage of visits in which stimulants were prescribed to adults (aged 18 years or older) diagnosed with ADHD increased from 52% in 1994-1997 to 67% in 2006-2009 (Olfson et al., 2013).

Greater rates of increase in ADHD diagnosis and treatment among female than male children and teens (aged 5 to 18 years). From 1991-1992 to 2007-2008, rates of ADHD diagnosis increased 3.7-fold for boys (from 39.5 to 144.6 per 1,000 population) and 5.6-fold for girls (from 12.3 to 68.5 per 1,000 population; Sclar, Robison, Bowen, et al., 2012). In the same time period, rates of ADHD diagnosis coupled with drug treatment increased 4.4-fold for boys and 6.5-fold for girls (Sclar, Robison, Bowen, et al., 2012).

Lower rates of diagnosis and treatment for non-White than White populations. Several studies conducted in the 1990s and early 2000s found that non-White youths were less likely than White youths to receive health care services for mental disorders, including ADHD (Coker et al., 2016; Kataoka, Zhang, & Wells, 2002; Zimmerman, 2005). Similarly in 2006-2009, 0.68% of office visits made by White adults, compared with 0.36% for non-White adults, included the prescribing of a stimulant medication (Olfson et al., 2013).

Method

Data Source

Study data were drawn from the NAMCS, a cross-sectional, nationally representative survey of U.S. physician office visits that is conducted by the National Center for Health Statistics (NCHS) and has been used extensively in studies of ADHD treatment trends (Olfson et al., 2013; Robison et al., 2005; Robison et al., 2004; Robison et al., 2002; Sclar, Robison, Bowen, et al., 2012; Sclar, Robison, Castillo, et al., 2012). Survey data are collected annually by the U.S. Bureau of the Census using a three-stage probability sampling procedure that has been described in detail elsewhere (U.S. Centers for Disease Control and Prevention, [NCHS], 2010a, 2010b). Briefly, the NAMCS unit of analysis is an individual office visit. Sampling is conducted first by primary sampling units (PSUs), which are geographic areas comprising counties, county groups, or geographic equivalents; then within PSU, by physician name based on master lists maintained by the American Medical Association and the American Osteopathic Association, stratified by physician specialty. Each selected physician is randomly assigned to 1 of the 52 weeks of data collection within the survey year, and within each reporting week, a systematic random sample of office visits is made. Visits represent office-based patient care, and contacts made outside the office (e.g., by telephone, in-home) or for administrative purposes only (e.g., medication refill) are excluded.

For each recorded office visit, the NAMCS data file includes a sampling weight that adjusts for the multistage (i.e., clustered) sampling design and for survey nonresponse. Application of the weights to the data yields nationally representative information for all community-based physician office visits in the United States. In accordance with NAMCS recommendations for use of the data and with the methodology of previous studies using this data set, visits were grouped into multiyear increments for the present study analyses to increase the reliability of the weighted estimates (Olfson et al., 2013; Robison et al., 2005; Robison et al., 2004; Robison et al., 2002; Sclar, Robison, Bowen, et al., 2012; Sclar, Robison, Castillo, et al., 2012). Three time periods were studied: 2008-2009, 2010-2011, and 2012-2013.

Data Elements

Counts of visits in which a diagnosis of ADHD was made were based on International Classification of Diseases–Ninth Revision (ICD-9) codes of 314.0x to 314.9x in any of the three diagnosis fields available in the NAMCS data set (Olfson et al., 2013). Identification of the prescribing of ADHD medication was made using the NAMCS drug identifiers, available for up to eight drugs continued or newly prescribed during the office visit, for generic and brand formulations of stimulant and nonstimulant medications to treat ADHD: amphetamines and amphetamine salt combinations, atomoxetine, dextroamphetamine, dexmethylphenidate, extended-release guanfacine or clonidine, lisdexamfetamine, and methylphenidate. Estimates measured whether an ADHD diagnosis had been made and whether it was accompanied by a prescription for ADHD medication (diagnosis + drug). Although sometimes used to treat ADHD in adults, especially those with comorbid psychiatric disorders, antidepressants were not counted as ADHD pharmacotherapy because they are not considered first-line treatment for ADHD (Post & Kurlansik, 2012).

Calculations

Prevalence estimates were made using two different denominators, both of which have been used in previous research using the NAMCS (Olfson et al., 2013; Robison et al., 2005; Robison et al., 2004; Robison et al., 2002; Sclar, Robison, Bowen, et al., 2012; Sclar, Robison, Castillo, et al., 2012). First, overall and by sex, separately for adults (aged 20 years or older) and youth (aged 19 years or younger), estimates were calculated as annual number of visits per U.S. population, with annual averages calculated as the total number of visits for each 2-year period divided by 2 (Sclar, Robison, Bowen, et al., 2012; Sclar, Robison, Castillo, et al., 2012). Use of this method allowed for comparison of the present study’s results with those of previously published work using the same method, to produce updated trend estimates. Population estimates for each of the three time periods were obtained from U.S. Bureau of the Census figures for 2008, 2010, and 2012, respectively (“Age and sex composition”) (U.S. Department of Commerce, U.S. Census Bureau, n.d.). Second, estimates of the percentage of visits resulting in an ADHD diagnosis were calculated overall and by factors associated with ADHD diagnosis and treatment in previous work: sex, age group, race, and insurance type (Olfson et al., 2013). Sensitivity analyses compared trends determined using each denominator.

Two changes from previous calculations made for samples of youth are notable. First is that, although children younger than 5 years were excluded from some previous samples, they were included in the present study sample, and their rates of drug therapy were assessed separately from those of older children. This change was made because recent guidelines recommend behavioral therapy as first-line treatment in this group (Felt et al., 2014; Wolraich et al., 2011). Second, teens aged 19 years were included in the youth cohort because of the discrepancy in definitions of an adult across different previous studies; specifically, those 19 years of age have been excluded from some studies of youth and were defined as adults in one previous study (Olfson et al., 2013; Sclar, Robison, Castillo, et al., 2012).

Finally, because demographic differences in ADHD diagnosis and treatment may be confounded by relevant clinical characteristics, binary logistic regression models of diagnosis and of diagnosis + drug were estimated separately for youth and adults. Predictors included the aforementioned demographic characteristics, time period, and several mental disorders commonly associated with ADHD such as anxiety disorders (ICD-9 codes: 293.84, 300.0x, 300.2x, 300.3x, 300.7x, and 300.8x), mood disorders (293.83, 296.xx, 300.4x, 301.1x, and 311), substance addiction/abuse (291.xx, 292.xx, 303.xx, 304.xx, and 305.xx), conduct disorders (312.xx), and psychoses (293.81, 293.82, 295.xx, 297.xx, 298.xx, and 301.2x) (Felt et al., 2014; Olfson et al., 2013; Wolraich et al., 2011).

For each outcome and group, modeling was performed using backward stepwise regression (minimum F probability for entry = .05, maximum F probability for removal = .10) in three stages: (a) Test first-order (single factor) terms for age, sex, time period, race, insurance type, and the mental disorders. (b) To the resulting model, add and test terms representing the interaction of time period with each demographic group term. (3) To the resulting model, add and test terms representing interactions of race and sex with comorbid disorders and with insurance type. All analyses were performed at alpha of .05 using SPSS Version 23.0 (IBM SPSS, Armonk, New York).

Results

The data set comprised 254,326 individual office visit records, representing approximate annual averages of 194 million visits for youth up to age 19 years and 780 million visits for adults aged 20 years or older from 2008 through 2013. A diagnosis of ADHD, with or without prescribed medication, was made at about 4.0 million adult and 7.7 million youth visits in 2008-2009; these numbers grew to 5.7 million and 9.1 million, respectively, in 2012-2013 (Table 1).

Table 1.

Number of ADHD Diagnosis and Pharmacotherapy Visits Per 1,000 Population by Sex and Age Group.

	ADHD diagnosis				ADHD diagnosis + drug
	2008-2009	2010-2011	2012-2013	Change T1 to T3 (%)	2008-2009	2010-2011	2012-2013	Change T1 to T3 (%)
Adults aged ≥20 years
Total unweighted number of visits	311	369	867	—	245	284	628	—
Total weighted number of visits^a	4,019,154	4,886,182	5,723,614	—	3,264,301	3,835,189	4,117,705	—
Rates per 1,000 population
All adults	18.5	22.1	25.3	36.4	15.1	17.4	18.2	20.8
Female adults	19.2	19.4	23.7	23.2	15.6	14.8	17.6	12.5
Male adults	17.8	25.0	27.0	51.6	14.4	20.1	18.8	30.4
Youth aged ≤19 years
Total unweighted number of visits	542	582	1,432	—	428	454	1,108	—
Total weighted number of visits^a	7,657,410	8,286,634	9,085,860	—	6,115,333	6,484,622	7,079,461	—
Rates per 1,000 population
All youth	93.1	99.6	110.3	18.5	74.3	77.9	86.0	15.6
Female youth	56.3	56.9	68.5	21.7	45.5	41.4	58.5	28.8
Male youth	128.3	140.6	150.3	17.1	102.0	113.0	112.2	10.0

Note. T = time period (T1 = 2008-2009; T3 = 2012-2013).

Annual averages, calculated as the total number of visits for each 2-year period divided by 2.

Expressed as visits per 1,000 population, diagnoses of adult ADHD increased over the 6-year study period by 36%, and diagnoses coupled with pharmacotherapy (diagnosis + drug) increased by 21% (Table 1). These rates continued trends observed in previous studies using the same metrics (Figure 1). Rates of increase for youth similarly trended upward, albeit more slowly than for adults, at 18% and 16%, respectively (Table 1).

Figure 1.

ADHD diagnosis and pharmacotherapy per 1,000 population: U.S. office visits for adults aged ≥20 years.

Growth trends by gender differed markedly for the two age groups (Table 1). Among adults, rates of increase in diagnosis and diagnosis + drug for males were more than double those for females, respectively (52% vs. 23% for diagnosis, 30% vs. 13% for diagnosis + drug). In contrast, rates of diagnosis increased somewhat more for female than male youths (22% vs. 17%, respectively), and the rate of increase in diagnosis + drug for female youths was nearly triple than that for males (29% vs. 10%, respectively). Increases overall and by sex displayed similar trends whether measured per 1,000 population or per 1,000 office visits. However, the magnitudes of upward trend were generally somewhat greater in the latter analysis (Table 2).

Table 2.

Rates of ADHD Diagnosis and Pharmacotherapy Per 1,000 Office Visits by Demographic Group.

	ADHD diagnosis				ADHD diagnosis + drug
	2008-2009	2010-2011	2012-2013	Change T1 to T3 (%)	2008-2009	2010-2011	2012-2013	Change T1 to T3 (%)
Adults aged ≥20 years
All	4.99	6.24	7.62	52.6	4.06	4.90	5.48	35.2
Female	4.32	4.67	6.16	42.6	3.52	3.57	4.58	30.2
Male	6.08	8.62	9.81	61.3	4.93	6.91	6.84	38.8
Age group (years)
20-29	17.86	29.06	23.59	32.1	15.37	22.80	17.67	15.0
30-39	9.36	8.12	15.89	69.8	7.29	6.54	11.51	58.0
40-49	8.37	6.25	10.62	26.9	6.43	4.93	7.14	11.1
50-59	2.78	4.57	6.45	131.9	2.32	3.50	4.97	114.5
60-64	1.65	2.90	2.87	73.8	1.31	2.50	1.85	41.4
65 or older	0.20	0.44	0.87	347.8	0.17	0.26	0.49	187.4
Race
White	5.70	6.93	8.40	47.4	4.61	5.48	6.02	30.6
Black	0.81	2.16	1.94	140.0	0.68	1.29	1.36	100.4
Other	1.45	3.60	4.90	238.3	1.45	2.96	4.03	178.3
Payment source
Private pay	5.44	7.32	10.25	88.4	4.56	5.85	7.53	65.1
Medicare	0.86	0.99	1.63	89.9	0.59	0.72	1.00	69.6
Medicaid	10.43	6.96	4.52	−56.7	8.38	5.02	2.62	−68.7
Other	13.02	18.15	16.28	25.0	9.74	13.97	11.69	20.0
Youth Aged ≤19 Years
All	39.90	38.61	52.05	30.4	31.87	30.21	40.55	27.3
Female	24.07	22.33	31.93	32.7	19.43	16.24	27.28	40.4
Male	55.16	53.81	71.79	30.1	43.86	43.26	53.59	22.2
Age group (years)
Younger than 5	1.85	0.92	1.42	−23.7	0.75	0.52	0.37	−51.2
5-9	64.60	69.91	78.64	21.7	51.67	55.23	61.39	18.8
10-14	97.22	80.03	113.00	16.2	79.41	66.45	92.85	16.9
15-19	43.33	43.58	66.30	53.0	34.45	30.16	48.51	40.8
Race
White	42.05	39.03	51.72	23.0	33.83	30.43	40.70	20.3
Black	38.11	46.85	64.34	68.8	31.22	37.49	48.55	55.5
Other	14.17	18.80	33.78	138.4	6.27	14.79	23.80	279.6
Payment source
Private pay	33.07	33.17	47.90	44.8	26.75	25.59	35.95	34.4
Medicare	39.33	33.50	45.72	16.3	24.98	22.00	25.24	1.0
Medicaid	44.17	49.22	48.33	9.4	35.52	40.14	39.53	11.3
Other	98.17	53.84	127.94	30.3	72.93	38.49	102.64	40.7

For each 1,000 office visits made by adults in 2012-2013, 7.62 included a diagnosis of ADHD and 5.48 included a diagnosis + drug (Table 2). Among youth, rates were about 7 times those of adults: 52.05 and 40.55 per 1,000 office visits for diagnosis and diagnosis + drug, respectively. For both adults and youth, absolute rates on both metrics were consistently lower for females than males. However, as in the analysis per 1,000 population, rates of growth were generally higher for male than female adults, especially for diagnosis, and higher for female than male youth, especially for diagnosis + drug.

For older and non-White adults, rates of diagnosis and diagnosis + drug were notably low, despite a sharply increased trend over time (Table 2). Compared with visits made by White adults, those made by Black adults were 86% less likely to include an ADHD diagnosis and 85% less likely to include a diagnosis + drug in 2008-2009. Growth trends in these metrics over time were much greater for Black than for White adults; however, a large racial difference persisted in 2012-2013, when Blacks were 77% less likely to receive a diagnosis or diagnosis + drug.

For youths, trends by race differed considerably from those observed for adults (Table 2). In 2008-2009, visits made by Black youths were somewhat less likely than those of White youths to include an ADHD diagnosis or drug. However, over the 6-year study period, rates of growth in both metrics for Blacks were approximately 3 times those of Whites. Thus, in 2012-2013, visits made by Black youths were 24% more likely to include an ADHD diagnosis and 19% more likely to include a diagnosis + drug than were visits made by White youths. Youths of other race, predominantly Asian/Pacific Islander, were persistently less likely than White youths to receive an ADHD diagnosis or drug, despite growth rates far surpassing those for youths of other races.

In logistic regression analyses, the factors most strongly and consistently predictive of ADHD diagnosis or diagnosis + drug were psychiatric comorbidities, particularly mood and anxiety disorders (Table 3). Conduct disorder was not significantly associated with diagnosis or treatment for adults, but among youths, multiplied the odds of diagnosis by a factor of 4.53 (95% confidence interval [CI] = [3.07, 6.68]) and diagnosis + drug by a factor of 3.31 (95% CI = [2.2, 4.98]).

Table 3.

Logistic Regressions of ADHD Diagnosis and Treatment on Demographic and Clinical Predictors.

	Youth, ADHD diagnosis		Adult, ADHD diagnosis		Youth, ADHD diagnosis + drug		Adult, ADHD diagnosis + drug
Number of cases^a	45,572		195,366		45,572		195,366
Model chi-square	3,960***		3,507***		3,093***		2,579***
Nagelkerke R²	.246		.210		.226		.196
C statistic	0.841		0.883		0.840		0.884
Predictors	Exp(B)	95% CI	Exp(B)	95% CI	Exp(B)	95% CI	Exp(B)	95% CI
2008-2011^b	Ref	—	Ref	—	Ref	—	Ref	—
2012-2013	1.158	[1.060, 1.265]	0.996	[0.726, 1.365]	1.120	[1.016, 1.236]	1.198	[1.059, 1.356]
Age <5	0.022	[0.016, 0.030]			0.014	[0.009, 0.022]
Age 5-14	Ref	—			Ref	—
Age 15-19	0.465	[0.416, 0.519]			0.430	[0.380, 0.487]
Age^c			0.937	[0.932, 0.943]			0.942	[0.937, 0.946]
Female	Ref	—	Ref	—	Ref	—	Ref	—
Male	2.884	[2.589, 3.213]	3.015	[2.570, 3.537]	2.872	[2.545, 3.241]	2.832	[2.366, 3.390]
White	Ref	—	Ref	—	Ref	—	Ref	—
Black	1.012	[0.821, 1.247]	0.267	[0.173, 0.413]	1.105	[0.953, 1.280]	0.138	[0.066, 0.288]
Other race	0.692	[0.560, 0.855]	0.678	[0.507, 0.907]	0.636	[0.499, 0.811]	0.738	[0.536, 1.018]
Private insurance	Ref	—	Ref	—	Ref	—	Ref	—
Medicare			0.571	[0.456, 0.716]			0.508	[0.387, 0.667]
Medicaid	1.581	[1.426, 1.753]	0.444	[0.336, 0.586]	1.707	[1.536, 1.897]	0.413	[0.297, 0.574]
Self-pay or other^d	1.855	[1.588, 2.165]			1.707	[1.434, 2.031]
Self-pay			2.265	[1.981, 2.590]			2.376	[2.044, 2.761]
Other			0.513	[0.375, 0.701]			0.488	[0.337, 0.705]
Anxiety disorder^e	3.429	[2.824, 4.163]	2.357	[2.030, 2.736]	2.840	[2.303, 3.502]	2.195	[1.846, 2.609]
Conduct disorder^e,f	4.528	[3.071, 6.677]			3.309	[2.199, 4.977]
Mood disorder^e	5.034	[4.286, 5.913]	3.944	[3.418, 4.550]	4.152	[3.490, 4.939]	3.508	[2.971, 4.142]
Black × Conduct	2.410	[0.878, 6.619]			3.780	[1.385, 10.319]
Black × Medicaid	1.302	[0.996, 1.702]
Female × Anxiety/Depression^g	1.657	[1.313, 2.091]	2.483	[2.015, 3.059]	1.904	[1.477, 2.455]	2.533	[1.996, 3.215]
Age × 2012-2013			1.008	[1.000, 1.015]
Male × Medicaid			2.087	[1.415, 3.079]			1.974	[1.234, 3.158]
Black × Anxiety/Depression^g			1.982	[1.109, 3.545]			2.855	[1.383, 5.893]
Black × 2012-2013							1.910	[0.923, 3.950]

Note. CI = confidence interval.

All case counts are unweighted. All models of youth (children and teens) excluded patients covered by Medicare because its coefficient was excluded in initial backward stepwise regression, and because these children are highly likely to be severely disabled. Patients with unknown insurance type (approximately 5% of visits for both youth and adults) were also excluded. The interaction of male sex with a conduct disorder was tested but excluded by backward stepwise regression from all models.

A term for 2010-2011 was tested but removed in backward stepwise regression from all models; therefore, the reference category represents 2008-2011.

Because age was linearly related to the likelihood of ADHD diagnosis and diagnosis + drug in adults, a single term representing age on a continuous scale was used.

Indicates that self-payment and other payment types were combined to increase power because coefficients indicated similar effects.

Additional disorders tested but removed from the first-order model were psychosis and substance abuse/addiction.

Term was removed from first-order model of adults; therefore, no interaction terms with this disorder were tested for adults.

Term represents interaction of demographic factor with either anxiety or mood disorder.

For both adults and children, the interaction of a mood or anxiety disorder with female sex increased the odds of ADHD diagnosis and treatment by factors ranging from approximately 1.7 to 2.5. Additional significant interactions for adults included male sex with Medicaid enrollment (odds ratio [OR] for diagnosis = 2.09, 95% CI = [1.42, 3.08]) and Black race with anxiety/depression (OR for diagnosis = 1.98, 95% CI = [1.11, 3.55]). Among youths, the interaction of Black race with a conduct disorder increased the odds of diagnosis + drug (OR = 3.78, 95% CI = [1.39, 10.32]). For ADHD diagnosis, this interaction effect trended in the positive direction but was not statistically significant.

Discussion

This study of a nationally representative sample of physician office visits produced two main findings. First, we found that the upward trend in ADHD diagnosis and treatment that began about 25 years ago has continued in recent years. In 2012-2013, the numbers of ADHD visits per 1,000 population were 25.3 for adults, 100.3 for youth overall, 68.5 for female youth, and 150.3 for male youth. For all these groups, the majority of ADHD office visits included the initiation or continuation of ADHD medication.

For adults, these figures represent a more than eightfold increase in ADHD visits since 1995-1996, a trend that was, until this study, most recently measured at a rate of 14.5 per 1,000 in 2007-2008 (Sclar, Robison, Castillo, et al., 2012). Similarly, ADHD prevalence rates for youths have increased considerably in the past 25 years. Sclar et al. found growth rates for ADHD diagnosis averaging approximately 4.5 percentage points annually for youths aged 5 through 18 years (change from 26.2 per 1,000 in 1991-1992 to 107.4 per 1,000 in 2007-2008; Sclar, Robison, Bowen, et al., 2012). Prevalence rates for the present study cannot be compared directly with those of previous work because of expanded age criteria consistent with contemporary guidelines for the diagnosis and treatment of ADHD in youth (Felt et al., 2014; Wolraich et al., 2011). Nonetheless, findings of the present study suggest annual average growth rates of 2.9 percentage points for the same metric among youth aged up to 19 years.

Second, we found that demographic factors strongly predict both absolute rates and trends in ADHD diagnosis and treatment. The higher rate of increase for female compared with male youths has been noted previously and attributed to a change in DSM-IV that placed increasing focus on problems in attention, rather than hyperactivity, in diagnosing ADHD (Robison et al., 2002). In the present study, the rate of population-adjusted growth in ADHD diagnosis coupled with pharmacotherapy for females was nearly triple than that for males over the 6-year study period.

The question of racial disparity is more complex. Comparing those of Black with White race, we found large disparities in rates of diagnosis and treatment only for adults, not for children and teens. In 2012-2013, the percentages of visits that included ADHD diagnosis and diagnosis + drug were somewhat higher for Black than White youths. Although inconsistent with the findings of older studies, this finding is consistent with newer research (Coker et al., 2016; Collins & Clearly, 2016; Kataoka et al., 2002; Zimmerman, 2005; Zito, Safer, dosReis, & Riddle, 1998; Zito, Safer, dosReis, Magder, & Riddle, 1997). For example, in a cohort of students identified in fifth grade in 2004-2006, African American children were more likely than White children to have ADHD symptoms (12% vs. 7%, respectively) but less likely to have ever received a diagnosis of ADHD (9% vs. 16%, respectively) and to have received ADHD medication (9% vs. 14%, respectively; Coker et al., 2016). However, more recently and similar to the findings of the present study, an analysis of data from the National Survey of Children’s Health found that in 2011, parents reported a diagnosis of ADHD by “a doctor or other health care provider” for 14.0% of White youth and 12.8% of Black youth (aged 5-17 years). The same study found that the rate of ADHD diagnosis had increased by 46% for White youth and by 58% for Black youth from 2003 to 2011 (Collins & Clearly, 2016).

Results of the present study are broadly consistent with previous work that has documented enormous variation in reported prevalence and treatment of ADHD by small- and large-scale geographic region (Diller, 1999; Gellad et al., 2014; McDonald & Jalbert, 2013; Weinstein et al., 2014; Zito et al., 1997). In northern New England from 2007-2010, age- and gender-adjusted use of ADHD medication by children and teens varied across hospital service areas twofold or more, depending on type of insurance (Weinstein et al., 2014). Across U.S. communities, rates of geographic variation are even larger: 14-fold for children and sixfold for adults measured at the state level (McDonald & Jalbert, 2013).

Do these discrepancies represent overtreatment of high-prevalence or undertreatment of low-prevalence groups? Addressing that important question is out of scope of the present study, in part because numerous familial and cultural factors not measured in the NAMCS are known to be associated with ADHD diagnosis: population density for residence at the census block level; blood levels of lead; lower household income; state-level educational policies and medication-use laws; county-level variation in physician supply and age, population per-capita income and student-to-teacher ratios; family structure; and parental mental health (Baumgardner et al., 2010; Bokhari, Mayes, & Scheffler, 2005; Bruckner et al., 2012; Fulton, Scheffler, & Hinshaw, 2015; McDonald & Jalbert, 2013; Visser, Lesesne, & Perou, 2007). In addition, fully addressing this question would require detailed information about symptoms and experience with current and past pharmacotherapy (Coker et al., 2016; Jensen et al., 1999). These challenges, along with the controversy over the undertreatment/overtreatment question that inevitably arises because of geographic and demographic variation, have been exacerbated by changes in medication availability and diagnostic criteria over time (Coker et al., 2016; Diller, 2000; Jensen et al., 1999; Jensen, 2000; Perry, Gilbert, & Angkustsiri, 2000).

Regardless of the reasons, it appears unlikely that the observed discrepancies in ADHD diagnosis and treatment are attributable solely to neurobiological factors (Diller, 1999). In making a diagnosis of ADHD, clinicians typically rely on the reports of patients and family members about behavioral criteria that are inherently subjective, such as distractibility and inefficiency (Gualtieri & Johnson, 2005). The present study’s finding that a conduct disorder multiplies the odds of ADHD pharmacotherapy several fold in youth, particularly in those who are Black, is consistent with the view that ADHD symptoms and diagnoses are partly attributable to cultural/environmental factors and norms (Baumgardner et al., 2010; Bokhari et al., 2005; Ford-Jones, 2015; Fulton et al., 2015; Gualtieri & Johnson, 2005; Sax & Kautz, 2003; Visser et al., 2007). Also supporting this view, rates of diagnosed ADHD are much lower in other developed nations than in the United States, for example, 0.5% among patients aged 6 to 17 years in the United Kingdom in 2009, and 1.4% of children in Denmark (Russell & Ford, 2014).

These potential cultural influences, coupled with the rapid rise in prevalence and concerns about misdiagnosis, have led to calls for more objective measures of ADHD, such as computer-assisted testing and neurological assessment (Arns, Conners, & Kraemer, 2013; Ford-Jones, 2015; Gualtieri & Johnson, 2005). Recently, the Neuropsychiatric Electroencephalograph (EEG)-Based Assessment Aid (NEBA) for ADHD has been created to bring objectivity to the diagnosis of ADHD and serve as a potential biomarker. Currently, NEBA may serve as a guidance tool, along with clinical impression, when determining a diagnosis of ADHD. NEBA uses a patient’s EEG to compare the ratio of theta to beta brain waves. Higher theta–beta ratios are associated with a diagnosis of ADHD (Arns et al., 2013). Although NEBA could advance the degree of objectivity in an ADHD diagnosis, it has yet to be widely utilized, and a recent American Academy of Neurology practice advisory recommended against its routine use (Gloss, Varma, Pringsheim, & Nuwer, 2016). Thus, the diagnosis of ADHD may remain inherently subjective in the coming years.

In that regard, the present study’s findings about ADHD prevalence contribute to ongoing dialogue in the United States about the processes used to identify, diagnose, and treat ADHD (Coker et al., 2016; Ford-Jones, 2015; Russell & Ford, 2014; Sax & Kautz, 2003; Visser et al., 2007). Future research should continue to assess rates of growth in ADHD diagnosis and treatment, both overall and by demographic groups.

Limitations

Several important limitations of the present study should be noted. First, the NAMCS is a sample of office visits to community-based physicians. It does not represent care delivered in inpatient or outpatient hospitals. Because the data are based on a sample of office visits, estimates of population-adjusted visit counts do not represent prevalence rates. Second, information about race is missing from about one third of NAMCS records and is, therefore, imputed by the NCHS based on a regression model accounting for U.S. Census data for the geographic region, type of center in which the visit took place, duration of the visit, physician specialty, and patient demographic and clinical characteristics (U.S. Centers for Disease Control and Prevention, [NCHS], 2011).

Conclusion

Growth in diagnosis of and pharmacotherapy for ADHD in the United States has persisted for more than two decades among adults and youths and is strongly associated with demographic characteristics. Future research should address whether disparities by sex, race, and insurance type represent undertreatment or overtreatment, as they are unlikely to be neurobiological in nature.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Kathleen A. Fairman, MA, is an adjunct assistant professor in the Department of Pharmacy Practice at Midwestern University College of Pharmacy-Glendale. She specializes in health economics and outcomes research, with a specific focus on managed care. She is the author of Health Care Research Done Right, which received an American Medical Writers Association Medical Book Award in 2013.

Alyssa M. Peckham, PharmD, BCPP, is an assistant professor in the Department of Pharmacy Practice at Midwestern University College of Pharmacy-Glendale. Her practice focuses on outpatient mental health, with an emphasis on schizophrenia, substance abuse, and depression.

David A. Sclar, BPharm., PhD, is a professor and chair of the Department of Pharmacy Practice at Midwestern University College of Pharmacy-Glendale. His research focuses on pharmacoeconomics, pharmacoepidemiology, and health policy, with specific emphasis on psychiatric pharmacotherapy.

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