The Prevalence of ADHD

Abstract

Objective: To describe the epidemiology of ADHD in communities using a DSM-IVTR case definition. Method: This community-based study used multiple informants to develop and apply a DSM -IVTR-based case definition of ADHD to screening and diagnostic interview data collected for children 5-13 years of age. Teachers screened 10,427 children (66.4%) in four school districts across two states (SC and OK). ADHD ratings by teachers and parent reports of diagnosis and medication treatment were used to stratify children into high and low risk for ADHD. Parents (n = 855) of high risk and gender frequency-matched low risk children completed structured diagnostic interviews. The case definition was applied to generate community prevalence estimates, weighted to reflect the complex sampling design. Results: ADHD prevalence was 8.7% in SC and 10.6% in OK. The prevalence of ADHD medication use was 10.1% (SC) and 7.4% (OK). Of those medicated, 39.5% (SC) and 28.3% (OK) met the case definition. Comparison children taking medication had higher mean symptom counts than other comparison children. Conclusions: Our ADHD estimates are at the upper end of those from previous studies. The identification of a large proportion of comparison children taking ADHD medication suggests that our estimates may be conservative; these children were not included as cases in the case definition, although some might be effectively treated.

Keywords

ADHD prevalence rate school sample community-based sample epidemiology

Monitoring the prevalence of attention-deficit/hyperactivity disorder (ADHD) is an important public health activity as public awareness has increased and treatment options have expanded across the globe. A metaregression analysis comprised of over 102 studies revealed a worldwide ADHD prevalence rate of 5.3% for studies published between 1978 and 2005 (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007). The pooled prevalence rate in North America was higher than those of Africa or the Middle East, but was comparable to those of Europe, South American, Oceania, and Asia.

Conclusions about the epidemiology of ADHD within the US are complicated by ongoing reports of limited access to care (Burns et al., 1995), conflicting evidence of over- and under-diagnosis, and clinical debates about the appropriateness of medication use (Angier, 1994; Bussing et al., 2005; Diller, 1996; McGinnis, 1997). Nevertheless, parent-reported rates of ADHD diagnosis have increased steadily since 1998 (Akinbami, Liu, Pastor, & Reuben, 2011; Centers for Disease Control and Prevention, 2010; Pastor & Reuben, 2008). A recent parent survey revealed that 9.5% of those 4-17 years of age (approximately 5.4 million American children) had an ADHD diagnosis by 2007, representing a 22% increase in four years (Centers for Disease Control and Prevention, 2010). Although parent surveys reveal important trends, they lack clinical validation and are predicated on access to health care.

Application of DSM-IVTR criteria for ADHD requires “some impairment from the symptoms be present in two or more settings (e.g., at school and home)” and clear evidence of significant impairment in social, academic, or occupational functioning (APA, 2000). Thus, the diagnosis requires a degree of impairment (“some” and “clinically significant”) and multiple (“two or more”) settings but does not direct application of these criteria. The lack of explicit in impairment criteria likely contributes to variable prevalence rates (Gathje, Lewandowski, & Gordon, 2008) and is compounded by the lack of clarity regarding the inclusion of multiple informants (American Academy of Pediatrics [AAP] Committee on Quality Improvement and Subcommittee on Attention-Deficit/Hyperactivity Disorder, 2000); AAP’s practice guidelines recommend using information from parents and teachers (AAP’s Subcommittee on Attention-Deficit/Hyperactivity Disorder Steering Committee on Quality Improvement and Management, 2011), although the correlation between parent and teacher ratings is often low (Lahey et al., 1987; Newcorn et al., 1994; Wolraich et al., 2004) with no clear recommendation for resolving these conflicts.

Many previous population-based estimates of ADHD have relied on a single reporter or existing clinical diagnoses (Barbaresi et al., 2002; Burd, Klug, Coumbe, & Kerbeshian, 2003; Mandell, Thompson, Weintraub, Destefano, & Blank, 2005)—methods that are insufficient to evaluate DSM-IVTR diagnostic criteria. Nationally representative cross-sectional surveys that are used to estimate ADHD prevalence have relied solely on parental report of the diagnosis (Pastor & Reuben, 2008; Visser, Lesesne, & Perou, 2007) or have otherwise used less than the full DSM-IVTR criteria (Hargreaves, Shumway, Tei-Wei, & Cuffel, 1998). Thus, very few studies have documented symptoms in multiple settings, using information from multiple sources, possibly resulting in inaccurate estimates of prevalence (Barkley, 2006). Although clinical studies allow for more rigorous application of diagnostic criteria, community-based epidemiological studies that include participants regardless of health care access promise greater generalizability of findings to the population (Eaton et al., 2008; Goldberg, 1995; Susser, Schwartz, Morabia, & Bromet, 2006).

Given the upward trend in parent-reported ADHD and the complicated nature of DSM-IVTR diagnostic requirements, community-based studies that apply strict diagnostic criteria (APA, 2000) are clearly needed. Such work has great potential to provide important information for resource management and efficient delivery of services in school and health care. The objectives of this study were to describe the epidemiology of ADHD, using a DSM-IVTR case definition that includes multiple informants and multiple settings, among community-drawn samples of elementary-age children.

Method

To directly address the DSM-IVTR criteria for ADHD, this study used a two-phase design. In the first phase, school district-wide behavioral screenings were conducted by elementary school teachers within four school districts in South Carolina (SC) and Oklahoma (OK). In the second phase, parents (or caregivers) of children who met high- or low-screen criteria were invited to participate in direct, structured interview assessments of ADHD, co-occurring conditions, treatment history, demographics, and health-risk behaviors. A strict, DSM-IVTR-based case definition was developed and applied to generate weighted estimates of ADHD within each of the two sites. A detailed description of each phase is included below.

Phase I

Sampling population

Four school districts were targeted: one large district in SC (15 schools) and three districts in OK (20 schools). All elementary schools in three of the districts participated; in one large OK district, 8 of the 65 schools in the district were systematically selected to participate, and 7 schools participated. The communities included rural, suburban, and urban neighborhoods, and reflect a diverse student population.

The sampling universe included children in mainstream elementary classrooms (8,487 in SC; 7,212 in OK), targeting kindergarten through fifth grade. Screening included a small number of elementary schools that offered a sixth grade (OK only), and preK programs. Demographic characteristics are presented for the district and the screened populations in Table 1.

Table 1.

Characteristics of the Project to Learn About ADHD in Youth School Population and Screened Sample, by Site.

	Participating school population				Screened sample
	South Carolina (n = 8,694)^a		Oklahoma (n = 7,212)		South Carolina (n = 4,606)		Oklahoma (n = 5,851)
	n	%	n	%	n	%	n	%
Gender
Male	4,411	51	3,730	52	2,332	51	2,993	52
Female	4,283	49	3,482	48	2,272	49	2,780	48
Race/ethnicity
African American (NH)	4,573	53	969	11	2,172	51	611	10
American Indian (NH)	22	<1	1,166	16	9	< 1	708	12
Caucasian (NH)	3,226	37	3,369b	48^b	1,736	41	2,755	47
Hispanic	411	5	1,708	24	200	5	1,574	27
Others (NH)	462	5	^b	^b	102	2	126	2
Grade level
PreK	262	3	589	8	183	4	465	8
K	1,284	15	1,152	16	824	18	907	16
1st	1,424	16	1,188	16	667	15	959	16
2nd	1,359	16	1,094	15	745	16	881	15
3rd	1,397	16	1,064	15	746	16	802	14
4th	1,459	17	1,073	15	865	19	854	15
5th	1,509	17	1,052	15	566	12	953	16

Demographic characteristics were not available at the district level by special education classification. Therefore, the population total includes those children in self-contained classrooms.

Population estimates were only available for a combined Caucasian/Other classification.

Screening procedures

Participating teachers rated each classroom student on the presence of ADHD core symptoms (Vanderbilt ADHD Diagnostic Teacher Rating Scale [VADTRS]; Wolraich et al., 2003), general behavior and associated functioning (the Strength and Difficulties Questionnaire [SDQ]; Goodman, 2001), and two questions about previous ADHD diagnosis (“Has this child been diagnosed with ADHD or ADD?”) and current ADHD medication treatment (“Is the child on medication for ADHD or ADD?”). A screening form was sent home by teachers, asking parents the same two questions about diagnosis and treatment. Research staff received research numbers, but no identifying information; school personnel retained names and research numbers, but no assessment results.

Data from teachers and parents were used to divide the children into two strata from which eligible Phase II participants were drawn: those likely to have ADHD (high screen) and those not likely to have ADHD (low screen). Children were categorized as high screen if they (a) had six or more ADHD core symptoms in either or both ADHD dimensions on the Vanderbilt scale and intermediate impairment ratings on the SDQ or (b) received an ADHD diagnosis or were taking medication for ADHD, based on either the parent or teacher report. The remaining children were categorized as low screen.

Sampling procedures

Both sites used representative sampling procedures. An eligible subsample was selected from the two screening strata by taking all high screens and a sample of low screens, proportionally (in OK) or frequency (in SC) matched on gender. The school districts required a process in which school staff contacted eligible families and requested permission to release their information to study staff. Families expressing an interest in study participation (n = 1,167) were invited into the diagnostic phase (Phase II) in which parents (n = 844; 73.3% of those invited) completed the diagnostic interview. Site-specific participation rates are documented in Figure 1.

Figure 1.

Project to Learn about ADHD in Youth screening and case ascertainment flowchart.

Phase II

Case ascertainment

Following screening, consenting families were interviewed after a median 13 months in SC (interquartile range [IQR] = 8-16 months) and 10 months in OK (IQR = 6-12 months), using modules of the parent DISC-IV (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) parent report (generalized anxiety disorder, obsessive compulsive disorder, posttraumatic stress disorder, major depression/dysthymic disorder, mania/hypomania, ADHD, oppositional defiant disorder, conduct disorder), a health-risk behavior survey, and a demographic survey. In most cases, the participating caregiver was the child’s mother (94% SC and 86% OK). DISC-IV interviews completed in OK were primarily conducted via telephone with most other measures returned by mail, due to large catchment size; in-person interviews were conducted to accommodate hearing difficulties or Spanish translation. SC interviews were completed primarily in person; written measures were collected during the interview or returned by mail. Participants were given an incentive (gift cards or checks) for participation.

Demographic characteristics of the interviewed sample are presented in Table 2. Parents were notified of probable diagnoses and given referral information when requested, following review of the DISC reports by a clinician. Triage protocols were in place for timely response to identified risks of harm to self or others or reported abuse. All study procedures were reviewed and approved by the Institutional Review Boards of the Centers for Disease Control and Prevention, and respective universities.

Table 2.

Characteristics of the Project to Learn About ADHD in Youth Interview Participants, by Site.

	Interviewed sample
	South Carolina (n = 481)			Oklahoma (n = 374)
	n	Weighted %	95% CI	n	Weighted %	95% CI
Gender
Male	323	49.1	[46.1, 52.1]	264	53.2	[47.8, 58.5]
Female	158	50.9	[48.0, 53.9]	110	46.8	[41.5, 52.2]
Ethnicity/race
African American (NH)	184	50.6	[44.3, 56.9]	41	8.5	[4.7, 14.9]
American Indian (NH)	0	0.0	—	40	8.7	[5.4, 13.6]
Caucasian (NH)	269	44.2	[38.1, 50.5]	225	50.5	[41.5, 59.6]
Hispanic	21	3.4	[1.8, 6.2]	44	26.1	[17.3, 37.2]
Others (NH)	7	1.8	[0.4, 7.6]	24	6.3	[3.0, 12.7]
Grade level
PreK	21	7.1	[4.2, 11.7]	26	7.1	[3.3, 14.4]
K	69	19.1	[13.8, 25.8]	45	16.1	[9.9, 25.0]
1st	68	12.3	[9.2, 16.4]	68	19.7	[13.4, 28.0]
2nd	89	15.0	[11.4, 19.4]	49	9.4	[5.6, 15.4]
3rd	96	20.3	[15.6, 26.1]	47	10.4	[6.7, 15.9]
4th	87	17.5	[13.4, 22.5]	64	16.6	[8.7, 29.5]
5th	51	8.7	[6.1, 12.2]	75	20.7	[13.1, 31.3]
Age
5-7 years old	140	32.5	[26.5, 39.2]	102	30.0	[21.4, 40.3]
8-10 years old	235	45.9	[39.7, 52.3]	146	42.1	[31.8, 53.2]
11-13 years old	106	21.6	[17.1, 26.8]	126	27.9	[19.9, 37.5]
Insurance
Private insurance	335	76.4	[70.8, 81.2]	117	35.8	[28.9, 43.4]
Medicaid	100	18.3	[14.2, 23.2]	235	53.0	[42.3, 63.4]
No insurance	17	3.2	[1.8, 5.6]	30	16.6	[8.7, 29.4]
Caregiver education
<High school	10	1.5	[0.8, 3.1]	83	31.4	[22.8, 41.7]
High school grade	54	10.9	[7.5, 15.6]	85	21.6	[13.9, 32.1]
Some college	209	38.7	[32.9, 44.8]	176	35.4	[27.0, 44.8]
College graduate+	192	48.9	[42.5, 55.4]	30	11.6	[6.7,19.3]
Annual income
<US$20,000	53	8.8	[6.2, 12.2]	149	41.4	[31.6, 51.9]
US$20,000-US$45,000	120	25.7	[20.7, 31.5]	174	41.0	[31.5, 51.2]
US$45,000-US$70,000	107	27.0	[21.3, 33.7]	29	8.6	[5.1, 13.9]
>US$70,000	170	38.5	[32.3, 45.1]	22	9.1	[4.9, 16.1]

Note: CI = confidence interval.

ADHD Study Case Definition

The DSM-IVTR case definition was based on ADHD symptom counts (at least six of nine symptoms in either or both of the inattentive or hyperactive/impulsive subscales), impairment ratings (at least one DISC-IV impairment domain rated severe, or two rated moderate), and a report of age of onset from the diagnostic interview of parents (Figure 2). Case criteria were met through endorsement of the following criteria:

Figure 2.

Project to Learn about ADHD in youth case definition diagram.

High-screen children (six or more symptoms plus some impairment, as reported by teachers)

Six or more symptoms plus positive impairment rating and age of onset before 7 years, as reported by parents during the interview phase (Figure 2, case inclusion Group h).

High-screen children (report of ADHD diagnosis or current ADHD medication by the teacher or parent)

Six or more symptoms plus positive impairment rating and age of onset before 7 years, as reported by parents during the interview phase, and four or five screening symptoms, as reported by teachers (Figure 2, case inclusion Group g).

Low-screen children (fewer than six symptoms and/or no impairment due to reported symptoms, as reported by teachers; no report of ADHD diagnosis or current ADHD medication by the teacher or parent)

Six or more symptom plus positive impairment rating and age of onset before 7 years, as reported by parents during the interview phase; and four or five symptoms and some SDQ impairment, as reported by teachers (Figure 2, case inclusion Group f).

Children who were medicated at the time of the DISC-IV were excluded as cases if they did not otherwise meet the preceding case criteria.

Analysis

Selection of participants was by a multistage sampling stratified design by district. Therefore, sampling weights were calculated to account for the differential sampling design (screening status and gender) as well as adjustments for nonresponse. When applied, the weights produce estimates that more closely reflect the demographic characteristics of the sampled district populations. Consideration of the complex design features in the analytic phase results in asymptotically unbiased estimates and standard errors. This sampling design was developed and executed within each of the school districts separately, and district-level estimates therefore best represent the population of children in that district.

SAS version 9.2 was used for data management; analyses were conducted using SAS-callable SUDAAN 10.0. Confidence intervals (CIs) are presented alongside weighted point estimates as an indicator of precision; a relative standard error greater than 30% was used as an indicator of statistical instability. Inferential tests of association for weighted analyses were based on Fellegi-adjusted Wald F statistics (Fellegi, 1980). More details on the design can be found in the appendix.

Results

Site participation rates can be found in Figure 1. The SC screened sample included 4,606 children (54.3% of the district’s population). The OK screened sample included 5,851 children (81% of the three districts’ population). Children were not screened for one of three reasons: (a) The teachers did not complete the screener, (b) the children were not included in the screening universe (i.e., self-contained special education classrooms), and (c) the parents indicated that they did not want their child screened. Of those meeting eligibility requirements, 28.7% in SC (633) and 32.1% in OK (537) returned a letter sent by their school, indicating interest in participating in the study. Ultimately, of those volunteering for the study, 76% of SC and 70% of OK were interviewed.

Overall, 8.7% (95% CI = [7.2, 10.5]) of the children in SC (99 cases) and 10.6% (95% CI = [7.5, 14.9]) of the children in OK (117 cases) met the case definition. In OK, the weighted prevalence was 15.3% (95% CI = [9.4, 23.8]) for the suburban/rural district (54 cases), 9.0% (95% CI = [4.2, 18.5]) for the urban district (29 cases), and 9.1% (95% CI = [6.3, 13.0]) for the rural district (34 cases). The OK district estimates were not significantly different, nor were they explained by variations in within-stratum teacher screening scores. Combining the SC and OK samples, the district indicator was not statistically significant, nor were any of the district pairwise comparisons (all multiple-comparison adjusted p values > .17). Of the 258 high screens for SC, 36.8% met the case definition, whereas of the 223 low screens, only 1.8% met the case definition.

Among children meeting the case definition, the percentage of parent-reported ADHD medication use in the last month in SC was 46.7%, compared with 19.3% for OK. Overall, the percentage of children taking ADHD medication was 10.1% in SC and 7.4% in OK; 4.0% in SC and 2.1% in OK met the case definition despite medication, and 6.1% in SC and 5.4% in OK were on medication and did not meet the case definition.

The two sites exhibited comparable differences in prevalence across most sociodemographic factors (Table 3). Thus, the data were pooled across sites for the purpose of making demographic comparisons. Pooled prevalence estimates were higher among males (13.6% vs. 5.3%), F(1, 835) = 20.8, p < .01, children covered under Medicaid (13.7% vs. 6.5%), F(1, 808) = 13.1, p < .01, and families earning less than US$45,000 per year (12.1% vs. 5.1%), F(1, 804) = 16.4, p < .01. Small race/ethnic group sample sizes limited race/ethnicity comparisons within the OK sample and race/ethnic group comparisons across sites. A comparison of prevalence among the largest racial groups in SC (Caucasian and African American) was not statistically significant, nor was the cross-site difference in prevalence among Caucasians. At both sites, the mean symptom counts for medicated comparison children were significantly higher than for unmedicated comparison children (pooled M = 5.6 vs. 2.2), F(1, 619) = 40.6, p < .01.

Table 3.

Project to Learn About ADHD in Youth Case Prevalence by Site and Demographic Factors.

	South Carolina		Oklahoma
Prevalence rates	Weighted %	95% CI	Weighted %	95% CI
Gender
Male	13.0	[10.4, 16.1]	14.1	[9.5, 20.4]
Female	4.6	[3.2, 6.8]	6.8^a	[3.3, 13.3]
Ethnicity/race
African American (NH)	8.9	[6.4, 12.3]	12.0^a	[5.7, 23.8]
Caucasian (NH)	7.9	[6.1, 10.2]	9.4	[7.3, 11.9]
Age
5-10 years old	8.5	[6.9, 10.5]	8.8	[5.9, 12.9]
11-13 years old	9.5	[5.6, 15.7]	15.4^a	[7.7, 28.4]
Insurance
Any insurance	8.2	[6.7, 10.0]	11.8	[8.1, 16.9]
Private insurance	8.0	[6.3, 10.1]	6.3	[4.6, 8.8]
Medicaid	13.5	[9.2, 19.5]	14.6	[9.0, 22.9]
No insurance	17.8^a	[6.8, 39.2]	3.8^a	[1.5, 9.1]
Caregiver education
HS degree or less	15.2	[9.3, 23.9]	10.8^a	[5.6, 19.6]
Some college or technical training or more	7.6	[6.0, 9.6]	10.5	[8.2, 13.4]
Annual income
<US$20,000	14.9	[8.7, 24.3]	9.1	[6.1, 13.2]
US$20,000-US$45,000	12.3	[8.2, 17.9]	14.8	[8.1, 25.3]
>US$45,000	5.5	[3.9, 7.6]	4.8	[2.7, 8.4]

Note: CI = confidence interval.

Relative standard error (100% × SE/prevalence) > 30%.

Discussion

This study provides ADHD prevalence estimates in communities (school districts) based on a multistage screening and a DSM-IVTR diagnostic interview process, including multiple reporters (parents and teachers). The percentage of children meeting the case definition for ADHD was 8.7% in SC and 10.6% in OK. Our overall prevalence rates were higher than previous community-based estimates and were at the middle to upper end of the range found in a 2001 systematic review (Brown et al., 2001). The creation of subtypes in DSM-IVTR could be a contributing factor; however, parent-reported rates have increased steadily since 1998 (Akinbami et al., 2011), suggesting that other factors may be influencing the epidemiology of ADHD, including changes in the cultural acceptance of ADHD and health care provider characteristics (Eiraldi, Mazzuca, Clarke, & Power, 2006; Fulton et al., 2009). Other hypothesized contributing factors include changes in access to care, increased awareness of the condition, changing sociodemographic patterns within the United States, and changes to state-based policy and screening program efforts (Berry, Bloom, Foley, & Palfrey, 2010).

Our prevalence estimates may be conservative because we did not include self-contained special education classrooms in our sampling design. Furthermore, 5.7% of sample children were taking ADHD medication but did not meet our study’s case definition. Some of the medicated children were likely cases who were adequately managed with medication. These children were not included in the case definition because those adequately managed and those inappropriately treated were indistinguishable. Notably, medicated comparison children had roughly double the number of core symptoms than unmedicated comparison children; however, their symptom counts were less than half those of cases.

The SC and OK sites differed demographically, with the OK samples having lower incomes, less parental education, a higher rate of Medicaid coverage, and a lower rate of private insurance, all suggesting a lower socioeconomic level among the OK sample. These demographic differences could contribute to the higher prevalence of ADHD found in OK and higher medication use in SC.

Of the cases, 54.6% in SC and 80.7% in OK had not taken ADHD medication in the past month. This finding supports previous national reports of the geographic variability of ADHD medication treatment (Centers for Disease Control and Prevention, 2005). Froehlich et al. (2007) reported a similar rate of ADHD medication; 68% of those who met ADHD criteria had not taken ADHD medication in the previous year. Taken together, these data suggest that the majority of children who meet ADHD criteria, independent of diagnostic status, are not taking ADHD medication.

Study Strengths and Limitations

School-based samples can be used to efficiently collect behavioral information about children in communities, and these data are more likely to be generalizable than data from clinical samples. Teachers are valuable respondents, because they observe students for up to 6 hours per day; however, they may not be aware of students’ diagnoses and treatment. Parents usually are aware of diagnoses and treatment, and observe their children in varying circumstances, but they can be difficult to access in a defined community-sampling frame, such as a school district. Therefore, combining teacher and parent information may yield the strongest psychometric approach for identifying cases in a community. However, the estimates in this study may not be generalizable to other geographic areas or to children not enrolled in mainstream public school classrooms.

Importantly for this study, the case definition relied on parent and teacher reports and did not incorporate clinical information or judgment. In addition, the interview phase yielded approximately 30% of all of those who might have been interviewed (all eligible), due to school district regulations on how personally identifiable information could be released. Lower response rates among eligible participants increases the risk of response bias; however, there was no evidence of systematic biases within our samples. In addition, nonresponse adjustments were made for lower recruitment of African American students in SC and African American and Hispanic students in OK, yet the weighting methods somewhat underestimated race/ethnic group size in OK. Unmeasured bias is also possible, including bias introduced by greater participation of parents of children with ADHD.

Conclusion

Estimates from this community-based study corroborate the high prevalence (7.8%-9.5%) of ADHD found in recent parent-reported surveys (Centers for Disease Control and Prevention, 2005, 2010). Although typical gender ratios were found, there were no differences between Caucasian and African American children. Many children meeting case criteria had not been previously identified and were not receiving medication treatment, suggesting that the condition remains underdiagnosed for some children. In addition, many diagnosed children were not receiving treatment that adequately reduced their core symptoms and impairments. The impact of untreated ADHD is serious, with school failure, dropout, and poor functioning in adulthood common for these children (Ingram, Hechtman, & Morgenstern, 1999). This information lends itself to the development of community or school-based programs that address the needs of this large, school-age population. In addition, clinicians should use rigorous assessment procedures such as those recommended by the practice parameters of the American Academy of Child and Adolescent Psychiatry and the AAP (AAP’s Subcommittee on Attention-Deficit/Hyperactivity Disorder Steering Committee on Quality Improvement and Management, 2011; Pliszka & AACAP Work Group on Quality Issues, 2007). These parameters call for thorough assessment, input from parents and teachers, and the use of standardized behavior rating scales to diagnose children with ADHD.

Footnotes

Appendix

Acknowledgements

The authors wish to acknowledge the early contributions of those who participated in the collaborative meetings that led to the development of the screening protocol and design phase of the Project to Learn about ADHD in Youth. Contributors included: Dr. Edward Brann, Dr. Sandra Evans, Kurt Heisler, Dr. Gretchen LeFever, Dr. Catherine Lesesne, Dr. James Paulson, and Dr. Ruth Perou. We would like to further acknowledge Dr. Angelika Claussen for her assistance with manuscript formatting and the statistical and data management assistance from Dr. Cheryl Addy, Dr. Charity Moore, Gina Babka-Bryan, and Donna Wells.

Disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

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: The lead author has been a consultant to Shire, Lilly, Shinoghi and Nextwave pharmaceutical companies.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This manuscript was supported by the Centers for Disease Control and Prevention through cooperative agreements U50/CCU622315-02 and U84/CCU422516-02 and contracts 200-2006-18912 and 200-2006-18949.

Author Biographies

Mark L. Wolraich, MD, is the Chief of the Section of Developmental and Behavioral Pediatrics in the Department of Pediatrics at the University of Oklahoma Health Sciences Center (UOHSC). He is the CMRI Shaun Walters Professor of Pediatrics and the Edith Kinney-Gaylord Residential Professor. He is the UOHSC Principal Investigator for the Project to Learn about ADHD in Youth.

Robert E. McKeown,PhD, is Distinguished Professor Emeritus and Past Chair of the Department of Epidemiology and Biostatistics at the University of South Carolina. He is past president of the American College of Epidemiology and past chair of the Epidemiology Section of the American Public Health Association. His research focuses on psychiatric epidemiology and public health ethics. He is the University of South Carolina Principal Investigator for the Project to Learn about ADHD in Youth.

Susanna Visser, MS, is the Lead Epidemiologist for the Child Development Studies Team within the National Center on Birth Defects and Developmental Disabilities at the Centers for Disease Control and Prevention (CDC). Ms. Visser’s expertise includes the analysis and design of longitudinal and population-based epidemiological studies of neurobehavioral and mental health conditions, including ADHD and Tourette Syndrome. She is the CDC Principal Investigator for the Project to Learn about ADHD in Youth.

David Bard, PhD, is an Assistant Professor in the Department of Pediatrics at the University of Oklahoma Health Sciences Center. His research interests include medical decision-making, behavior genetics, clinical trials methodology, longitudinal analyses, and testing and measurement in the behavioral sciences. He is a Co-Investigator of the UOHSC Project to Learn about ADHD in Youth.

Steven P. Cuffe, M.D. is Professor and Chair of the Department of Psychiatry at the University of Florida College of Medicine-Jacksonville. He has spent 20 years researching the epidemiology, risk and protective factors of child and adolescent psychiatric disorders. He is the Co-Principal Investigator of the University of South Carolina Project to Learn about ADHD in Youth.

Barbara Neas, PhD is a David Ross Boyd Professor of Biostatistics in the Biostatistics and Epidemiology Department, School of Public Health, Oklahoma University Health Sciences Center. Her expertise is in the area of categorical data analysis of independent and mixed data obtained from longitudinal and survey designs. She was a statistician for the OUHSC Project to Learn about ADHD in Youth.

Lorie L. Geryk, MPH, is the Project Manager for the University of South Carolina Project to Learn about ADHD in Youth. She is currently completing her doctoral degree in Public Health at the University of South Carolina.

Melissa Doffing, MA, was the Project Manager for the University of Oklahoma Health Sciences Center Project to Learn about ADHD in Youth. Ms. Doffing has experience in managing multiple federal and state funded community-based research projects and contributed her expertise in database design and data management.

Matteo Bottai, ScD, is the Head of the Unit of Biostatistics, Institute of Environmental Health, Karolinska Institutet, Stockholm, Sweden. He received his Laurea degree in Mathematics summa cum laude from University of Pisa, Pisa, Italy, and his doctoral degree in Biostatistics from the Harvard School of Public Health, Boston, MA. His current research focuses on statistical methods for modeling and estimation of conditional percentiles and served as a statistician for the University of South Carolina Project to Learn about ADHD in Youth.

Ann J. Abramowitz, PhD, is a Professor of Practice in the Department of Psychology and an Associate Professor of Psychiatry and Behavioral Sciences at Emory University. She is the Director of Assessment in Emory’s doctoral program in Clinical Psychology and is Chair of the Professional Advisory Board of Children and Adults with ADHD (CHADD). She provides consultation to the CDC on ADHD and related areas.

Laoma Beck, MD, served as Project Manager during the final years of data collection for the Project to Learn about ADHD in Youth. She is the mother of two happy and healthy children.

Joseph R. Holbrook, PhD, is an Associate Service Fellow in the Child Development Studies Team within the Centers for Disease Control and Prevention. Prior to this position, he was a student, graduate assistant, and data manager for the University of South Carolina Project to Learn about ADHD in Youth (PLAY).

Melissa Danielson, MSPH, is a health scientist with the Child Development Studies Team within the National Center on Birth Defects and Developmental Disabilities at the Centers for Disease Control and Prevention. Her work includes epidemiological analyses related to ADHD and other mental, emotional and behavioral conditions among children, and the epidemiology of health outcomes among children with disabilities. She served as the data manager of the CDC Project to Learn about ADHD in Youth.

References

Akinbami

L. J.

Liu

Pastor

P. N.

Reuben

C. A.

(2011, August). Attention deficit hyperactivity disorder among children aged 5-17 years in the United States, 1998-2009. NCHS Data Brief, 1-8.

American Academy of Pediatrics Committee on Quality Improvement and Subcommittee on Attention-Deficit/Hyperactivity Disorder. (2000). Clinical practice guideline: Diagnosis and evaluation of the child with attention-deficit/hyperactivity disorder. Pediatrics, 105, 1158-1170.

American Academy of Pediatrics’ Subcommittee on Attention-Deficit/Hyperactivity Disorder Steering Committee on Quality Improvement and Management. (2011). ADHD: Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 128, 1007-1022.

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.

Angier

(1994, July 24). The debilitating malady called boyhood. New York Times, Section 4, pp. 1, 4.

Barbaresi

Katusic

S. K.

Colligan

R. C.

Pankratz

V. S.

Weaver

A. L.

Weber

K. J.

. . . Jacobsen

S. J.

(2002). How common is attention-deficit/hyperactivity disorder? Incidence in a population-based birth cohort in Rochester, Minn. Archives of Pediatrics & Adolescent Medicine, 156, 217-224.

Barkley

R. A.

(2006). Attention deficit hyperactivity disorder: A handbook for diagnosis and treatment. New York, NY: Guilford.

Bauermeister

J. J.

Canino

Bravo

Ramirez

Jensen

P. S.

Chavez

. . . García

(2003). Stimulant and psychosocial treatment of ADHD in Latino/Hispanic children. Journal of the American Academy of Child & Adolescent Psychiatry, 42, 851-855.

Berry

J. G.

Bloom

Foley

Palfrey

J. S.

(2010). Health inequity in children and youth with chronic health conditions. Pediatrics, 126(Suppl. 3), S111-S119.

10.

Bird

H. R.

Davies

Duarte

C. S.

Shen

Loeber

Canino

G. J.

(2006). A study of disruptive behavior disorders in Puerto Rican youth: II. Baseline prevalence, comorbidity, and correlates in two sites. Journal of the American Academy of Child & Adolescent Psychiatry, 45, 1042-1053.

11.

Brown

Freeman

W. S.

Perrin

J. M.

Stein

M. T.

Amler

R. W.

Feldman

H. M.

. . . Wolraich

M. L.

(2001). Prevalence and assessment of attention-deficit/hyperactivity disorder in primary care settings. Pediatrics, 107, e43.

12.

Burd

Klug

M. G.

Coumbe

M. J.

Kerbeshian

(2003). Children and adolescents with attention deficit-hyperactivity disorder: 1. Prevalence and cost of care. Journal of Child Neurology, 18, 555-561.

13.

Burns

B. J.

Costello

E. J.

Angold

Tweed

Stangl

Farmer

E. M.

Erkanli

(1995). Children’s mental health service use across service sectors. Health Affairs, 14, 147-159.

14.

Bussing

Zima

B. T.

Mason

Hou

Garvan

C. W.

Forness

(2005). Use and persistence of pharmacotherapy for elementary school students with attention-deficit/hyperactivity disorder. Journal of Child and Adolescent Psychopharmacology, 15, 78-87.

15.

Canino

Shrout

P. E.

Rubio-Stipec

Bird

H. R.

Bravo

Ramirez

. . . Martinez-Taboas

(2004). The DSM-IVTR rates of child and adolescent disorders in Puerto Rico: Prevalence, correlates, service use, and the effects of impairment. Archives of General Psychiatry, 61, 85-93.

16.

Centers for Disease Control and Prevention. (2005). Prevalence of diagnosis and medication treatment for ADHD—United States, 2003. Morbidity and Mortality Weekly Report, 54, 842-847.

17.

Centers for Disease Control and Prevention. (2010). Increasing prevalence of parent-reported attention-deficit/hyperactivity disorder among children—United States, 2003 and 2007. Morbidity and Mortality Weekly Report, 59, 1439-1443.

18.

Diller

L. H.

(1996). The run on Ritalin: Attention deficit disorder and stimulant treatment in the 1990s. Hastings Center Report, 26, 12-18.

19.

Eaton

W. W.

Martins

S. S.

Nestadt

Bienvenu

O. J.

Clarke

Alexandre

(2008). The burden of mental disorders. Epidemiologic Reviews, 30, 1-14.

20.

Eiraldi

R. B.

Mazzuca

L. B.

Clarke

A. T.

Power

T. J.

(2006). Service utilization among ethnic minority children with ADHD: A model of help-seeking behavior. Administration and Policy in Mental Health, 33, 607-622.

21.

Fellegi

I. P.

(1980). Approximate tests of independence and goodness of fit based on stratified multistage samples. Journal of American Statistical Association, 75, 261-268.

22.

Froehlich

T. E.

Lanphear

B. P.

Epstein

J. N.

Barbaresi

W. J.

Katusic

S. K.

Kahn

R. S.

(2007). Prevalence, recognition, and treatment of attention-deficit/hyperactivity disorder in a national sample of US children. Archives of Pediatric Adolescent Medicine, 161, 857-864.

23.

Fulton

B. D.

Scheffler

R. M.

Hinshaw

S. P.

Levine

Stone

Brown

T. T.

Modrek

(2009). National variation of ADHD diagnostic prevalence and medication use: Health care providers and education policies. Psychiatric Services, 60, 1075-1083.

24.

Gathje

R. A.

Lewandowski

L. J.

Gordon

(2008). The role of impairment in the diagnosis of ADHD. Journal of Attention Disorders, 11, 529-537.

25.

Goldberg

(1995). Epidemiology of mental disorders in primary care settings. Epidemiologic Reviews, 17, 182-190.

26.

Goodman

(2001). Psychometric properties of the Strengths and Difficulties Questionnaire (SDQ). Journal of the American Academy of Child & Adolescent Psychiatry, 40, 1337-1345.

27.

Hargreaves

W. A.

Shumway

Tei-Wei

Cuffel

(1998). Cost-outcome methods for mental health. California: Academic Press.

28.

Ingram

Hechtman

Morgenstern

(1999). Outcome issues in ADHD: Adolescent and adult long-term outcome. Mental Retardation and Developmental Disabilities Research Reviews, 5, 243-250.

29.

Lahey

McBurnett

Piacentini

Hartdagen

Walker

Frick

Hynd

(1987). Agreement of parent and teacher rating scales with comprehensive clinical assessments of attention deficit disorder with hyperactivity. Journal of Psychological Behavioral Assessment, 9, 429-439.

30.

Lavigne

J. V.

LeBailly

S. A.

Hopkins

Gouze

K. R.

Binns

H. J.

(2009). The Prevalence of ADHD, ODD, depression, and anxiety in a community sample of 4-year-olds. Journal of Clinical Child and Adolescent Psychology, 38, 315-328.

31.

Mandell

Thompson

W. W.

Weintraub

E. S.

Destefano

Blank

M. B.

(2005). Trends in diagnosis rates for autism and ADHD at hospital discharge in the context of other psychiatric diagnoses. Psychiatric Services, 56, 56-62.

32.

McGinnis

(1997, September 18). Attention deficit disaster. The Wall Street Journal, pp. A-14.

33.

Newcorn

J. H.

Halperin

J. M.

Schwartz

Pascualvaca

Wolf

Schmeidler

Sharma

(1994). Parent and teacher ratings of attention-deficit hyperactivity disorder symptoms: Implications for case identification. Journal of Developmental and Behavioral Pediatrics, 15, 86-91.

34.

Pastor

P. N.

Reuben

C. A.

(2008). Diagnosed attention deficit hyperactivity disorder and learning disability: United States, 2004-2006. Vital and Health Statistics, 10, 1-14.

35.

Pliszka

, & AACAP Work Group on Quality Issues. (2007). Practice parameter for the assessment and treatment of children and adolescents with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 46, 894-921.

36.

Polanczyk

de Lima

M. S.

Horta

B. L.

Biederman

Rohde

L. A.

(2007). The worldwide prevalence of ADHD: A systematic review and metaregression analysis. The American Journal of Psychiatry, 164(6), 942-948.

37.

Rubin

D. B.

(1976). Inference and missing data. Biometrika, 63, 581-592.

38.

Shaffer

Fisher

Lucas

Dulcan

Schwab-Stone

(2000). NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): Description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child & Adolescent Psychiatry, 39, 28-38.

39.

Susser

Schwartz

Morabia

Bromet

E. J.

(Eds.). (2006). Psychiatric epidemiology: Searching for the causes of mental disorders. New York, NY: Oxford University Press.

40.

Visser

S. N.

Lesesne

C. A.

Perou

(2007). National estimates and factors associated with medication treatment for childhood attention-deficit/hyperactivity disorder. Pediatrics, 119(Suppl. 1), S99-106.

41.

Wolraich

M. L.

Lambert

E. W.

Bickman

Simmons

Doffing

M. A.

Worley

K. A.

(2004). Assessing the impact of parent and teacher agreement on diagnosing ADHD. Journal of Developmental and Behavioral Pediatrics, 25, 41-47.

42.

Wolraich

M. L.

Lambert

E. W.

Doffing

M. A.

Bickman

Simmons

Worley

K. A.

(2003). Psychometric properties of the Vanderbilt ADHD Diagnostic Parent Rating Scale. Journal of Pediatric Psychology, 28, 559-568.