Socioeconomic and Racial/Ethnic Differences in Prescription Adderall Use Among U.S. Adolescents: NHANES 2009–2018

Socioeconomic and Racial/Ethnic Differences in Prescription Adderall Use Among U.S. Adolescents: NHANES 2009–2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socioeconomic and Racial/Ethnic Differences in Prescription Adderall Use Among U.S. Adolescents: NHANES 2009–2018 Esther-Hedwig Teklic, Zhicheng Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8990180/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder among U.S. adolescents, and stimulant medications remain a primary treatment modality. Although disparities in ADHD diagnosis and treatment have been documented, nationally representative evidence specifically characterizing patterns of Adderall (amphetamine/dextroamphetamine) use across socioeconomic and racial/ethnic groups remains limited. Objective : To examine the prevalence of Adderall use and identify factors associated with use among U.S. adolescents. Methods : We conducted a cross-sectional analysis of adolescents aged 12–19 years using data from the National Health and Nutrition Examination Survey (NHANES) 2009–2018. Adderall use was identified from prescription medication files. Survey-weighted prevalence estimates were calculated, and multivariable survey-weighted logistic regression was used to assess associations with income-to-poverty ratio (PIR), ADHD diagnosis, age, sex, insurance status, and race/ethnicity. Results : The multivariable regression model included 5,902 participants with complete covariate data, and Adderall use was overall rare. Survey-weighted prevalence differed across socioeconomic and racial/ethnic groups. In adjusted analyses, PIR was inversely associated with Adderall use; each one-unit increase in PIR was associated with a 37% reduction in the odds of use (OR 0.63, 95% CI 0.48–0.83). ADHD diagnosis was not significantly associated with Adderall use (OR 0.97, 95% CI 0.32–2.98). Age, sex, and insurance status were also not significantly associated with use. Compared with Non-Hispanic White adolescents, Non-Hispanic Black (OR 0.23, 95% CI 0.06–0.97) and Hispanic adolescents (OR 0.12, 95% CI 0.02–0.65) had significantly lower odds of Adderall use. Results were robust in sensitivity analyses. Conclusions : Adderall use among U.S. adolescents was uncommon and demonstrated socioeconomic and racial/ethnic disparities. Higher socioeconomic status was associated with lower odds of use. These findings highlight the complex relationship between socioeconomic factors and stimulant medication use in adolescents. Adderall Attention-Deficit/Hyperactivity Disorder Socioeconomic Disparities Adolescents Figures Figure 1 Figure 2 Figure 3 Introduction Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders among children and adolescents in the United States [ 1 ], affecting an estimated 8–10% of school-aged youth [ 2 ]. ADHD is characterized by persistent patterns of inattention, hyperactivity, and impulsivity that interfere with academic performance, social functioning, and overall well-being [ 3 ]. Pharmacologic treatment, particularly stimulant medications, remains a cornerstone of evidence-based ADHD management and has been consistently shown to improve symptom control and functional outcomes [ 4 ]. Amphetamine-based stimulants, including commonly used mixed amphetamine salts in clinical settings (amphetamine/dextroamphetamine; Adderall), represent one of the most commonly prescribed medication classes for adolescent ADHD [ 5 ]. Compared with methylphenidate formulations, amphetamine products have demonstrated robust efficacy and are frequently used in both newly diagnosed patients and those requiring medication adjustments [ 5 ]. Over the past two decades, rates of ADHD diagnosis and stimulant prescribing have increased substantially in the United States, raising important questions regarding prescribing practices, clinical appropriateness, and population-level patterns of use [ 6 ]. At the same time, concerns have emerged regarding potential overuse, misuse, diversion, and inequities in access to stimulant medications [ 7 ]. Prescription stimulant use during adolescence carries important clinical and public health implications, including risks of nonmedical use, substance misuse, cardiovascular effects, and academic performance pressures. Patterns of stimulant prescribing may therefore reflect not only clinical need but also broader structural, socioeconomic, and healthcare system factors. Substantial evidence documents disparities in ADHD diagnosis and treatment across socioeconomic and racial/ethnic groups [ 8 , 9 ]. Children from racial and ethnic minority populations are consistently less likely to receive an ADHD diagnosis or stimulant treatment compared with Non-Hispanic White children, even after accounting for symptom severity [ 9 , 10 ]. Socioeconomic gradients in treatment access have also been observed, although findings have been mixed, with some studies suggesting higher diagnosis and treatment rates among publicly insured or lower-income populations and others indicating barriers to specialty care among economically disadvantaged families [ 6 , 8 ]. These inconsistencies underscore the complexity of structural and contextual influences on ADHD management. Despite extensive literature on ADHD treatment broadly, nationally representative evidence specifically characterizing patterns of Adderall use, rather than stimulant medications as a combined class, across demographic and socioeconomic strata remains limited [ 6 ]. Given potential differences in prescribing practices across stimulant subtypes and evolving trends in amphetamine versus methylphenidate use [ 3 , 5 ], examining Adderall use specifically may provide additional insight into treatment patterns and disparities. Using nationally representative data from the National Health and Nutrition Examination Survey (NHANES) 2009–2018, this study aimed to (1) estimate the prevalence of Adderall use among U.S. adolescents aged 12–19 years and (2) examine associations between Adderall use and income-to-poverty ratio (PIR), ADHD diagnosis, age, sex, insurance status, and race/ethnicity. We hypothesized that Adderall use would vary by socioeconomic status and race/ethnicity, reflecting underlying differences in diagnosis, healthcare access, and prescribing practices. Methods Study Design and Population We conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative survey designed to assess the health and nutritional status of the noninstitutionalized U.S. population. NHANES is administered by the National Center for Health Statistics (NCHS) using a complex, multistage, stratified probability sampling design. To ensure adequate statistical power and stable estimates, we pooled five consecutive 2-year survey cycles spanning 2009–2010 through 2017–2018. The analytic sample was restricted to adolescents aged 12–19 years who participated in the examination component of NHANES. Participants were excluded if they had missing data on prescription medication use or key covariates included in multivariable models. Participants with missing data on income-to-poverty ratio (PIR) were excluded from analyses stratified by income group. Sensitivity analyses were conducted by excluding the 2015–2016 NHANES cycle, which contained no Adderall users. NHANES protocols were approved by the NCHS Research Ethics Review Board, and written informed consent was obtained from participants aged 18–19 years and from parents or guardians of participants younger than 18 years, with assent obtained from minors. Assessment of Adderall Use Adderall use was identified from the NHANES prescription medication files. During in-home interviews, participants were asked to report all prescription medications taken in the past 30 days. Interviewers verified medication names using prescription containers whenever available. Participants were classified as current Adderall users if amphetamine/dextroamphetamine (mixed amphetamine salts) was reported among medications used within the past 30 days. A binary outcome variable (yes/no) was constructed to indicate current Adderall use. Covariates Covariates were selected a priori based on prior literature and clinical relevance. Income-to-poverty ratio (PIR), calculated by NHANES as the ratio of family income to the federal poverty threshold adjusted for family size and survey year, was analyzed as a continuous variable in regression models. For descriptive analyses, PIR was categorized into three groups: low income (PIR < 1), middle income (PIR 1–3), and high income (PIR ≥ 3). ADHD diagnosis was defined based on self-report of a physician or health professional diagnosis of attention-deficit/hyperactivity disorder. Age at the time of examination was modeled as a continuous variable in years. Sex was categorized as male or female based on self-report. Health insurance coverage was classified as insured versus uninsured at the time of the interview. Race/ethnicity was harmonized across survey cycles and categorized as Non-Hispanic White, Non-Hispanic Black, Hispanic, and Other, with the latter including multiracial and other racial groups. Survey Design and Weighting NHANES employs a complex survey design incorporating stratification, clustering, and oversampling of specific subpopulations. All analyses accounted for examination sample weights, primary sampling units (PSUs), and strata in accordance with NCHS analytic guidelines. Because five survey cycles were combined (10 years total), 2-year examination sample weights were divided by five to generate appropriate pooled weights representing the combined study period. Statistical Analysis We calculated survey-weighted prevalence estimates of Adderall use overall and stratified by income group and race/ethnicity. Differences across subgroups were examined descriptively. Multivariable survey-weighted logistic regression models were used to examine associations between Adderall use and income-to-poverty ratio (PIR), ADHD diagnosis, age, sex, insurance status, and race/ethnicity. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated. Model diagnostics were assessed to ensure appropriate model specification. Multicollinearity among covariates was evaluated using variance inflation factors. Sensitivity analyses were conducted by excluding the 2015–2016 NHANES cycle, which contained no Adderall users. Statistical significance was defined as a two-sided p-value < 0.05. All analyses were conducted using R (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria) with the survey package to account for the complex NHANES sampling design. Results Study Population The multivariable regression model included 5,902 participants with complete covariate data. Across pooled NHANES cycles, 47 adolescents reported Adderall use in the past 30 days (unweighted n = 47). The survey-weighted mean age was 15.38 years (SD 1.60). Approximately 48.9% of participants were female, and 89.1% were insured at the time of the interview. Overall, 18.4% of adolescents reported a physician diagnosis of ADHD. With respect to race/ethnicity, 54.4% of participants were Non-Hispanic White, 14.2% Non-Hispanic Black, 21.9% Hispanic, and 9.5% categorized as Other. Baseline characteristics stratified by income group are presented in Table 1 . Adolescents in the low-income group were more likely to be uninsured and to identify as Hispanic or Non-Hispanic Black compared with those in the high-income group. Sensitivity analyses were conducted by excluding the 2015–2016 NHANES cycle. This cycle was excluded in sensitivity analyses because no adolescents reported Adderall use, raising concerns regarding sparse data bias and potential model instability. The results were materially unchanged. Higher income-to-poverty ratio remained associated with lower odds of Adderall use (OR 0.64, 95% CI 0.49–0.84). Table 1 Survey-weighted Baseline Characteristics of U.S. Adolescents (NHANES 2009–2018) N = 6,554 (unweighted) Variable Overall Low income (n = 1,866) Middle income (n = 2,487) High income (n = 1,549) Age (years), mean (SD) 15.38 (1.60) 15.69 (2.40) 15.21 (2.21) 15.28 (2.16) Female, % 48.9% 52.6% 50.2% 44.8% Insured, % 89.1% 78.5% 81.5% 96.9% ADHD diagnosis, % 18.4% 20.3% 19.3% 15.9% Race/Ethnicity, % Non-Hispanic White 54.4% 33.4% 51.2% 74.7% Non-Hispanic Black 14.2% 22.1% 15.7% 7.3% Hispanic 21.9% 35.8% 24.2% 8.2% Other 9.5% 8.7% 8.9% 9.7% Values are survey-weighted percentages unless otherwise indicated. Age is presented as mean (standard deviation). Sample sizes are unweighted. Income group totals do not sum to the overall sample size due to missing PIR data Prevalence of Adderall Use The overall survey-weighted prevalence of Adderall use among U.S. adolescents aged 12–19 years was low. Prevalence varied by socioeconomic status. Adderall use was highest among adolescents in the low-income group (1.35%), followed by those in the middle-income group (1.18%), and lowest among adolescents in the high-income group (0.46%) (Fig. 1 B). The absolute difference in prevalence between low- and high-income adolescents was approximately 0.9 percentage points. Prevalence also differed across racial/ethnic groups (Fig. 1 A). Adderall use was most common among adolescents categorized as Other (1.42%) and Non-Hispanic White adolescents (1.17%). Lower prevalence was observed among Hispanic adolescents (0.36%) and Non-Hispanic Black adolescents (0.28%). These differences suggest substantial variation in stimulant use across racial/ethnic groups. Figure 1. Weighted Prevalence of Adderall Use Among U.S. Adolescents by Race/Ethnicity and Household Income (NHANES 2009–2018) Figure 1 A. Race/Ethnicity: Survey-weighted prevalence of Adderall (amphetamine/dextroamphetamine) use among U.S. adolescents aged 12–19 years stratified by race/ethnicity. Error bars represent 95% confidence intervals. Prevalence estimates were calculated using NHANES sampling weights. Figure 1 B. Household Income:Survey-weighted prevalence of Adderall use stratified by household income group defined by income-to-poverty ratio (PIR): low income (PIR < 1), middle income (PIR 1–3), and high income (PIR ≥ 3). Error bars represent 95% confidence intervals. Factors Associated with Adderall Use As shown in Fig. 2 , both ADHD diagnosis and insurance coverage varied across income groups. The prevalence of ADHD diagnosis was modestly higher among adolescents in the low-income (20.3%) and middle-income (19.3%) groups compared with those in the high-income group (15.9%). In contrast, insurance coverage demonstrated a clear positive socioeconomic gradient, increasing substantially from 78.5% among low-income adolescents to 96.9% among high-income adolescents. These findings indicate that while ADHD diagnosis varies modestly by income, disparities in health insurance coverage across socioeconomic strata are more pronounced. Results from the multivariable survey-weighted logistic regression model are shown in Table 2 . Figure 3 shows the adjusted odds ratios for Adderall use. Income-to-poverty ratio (PIR) was inversely associated with Adderall use. Each one-unit increase in PIR was associated with a 37% reduction in the odds of use (OR 0.63, 95% CI 0.48–0.83), indicating that adolescents from more economically advantaged households had significantly lower odds of current Adderall use. ADHD diagnosis was included in the model; however, the association with Adderall use did not reach statistical significance (OR 0.97, 95% CI 0.32–2.98). The wide confidence interval reflects imprecision in the estimate. Age (OR 0.80, 95% CI 0.45–1.42), sex (female vs male: OR 0.88, 95% CI 0.28–2.72), and insurance status (OR 0.73, 95% CI 0.22–2.43) were not significantly associated with Adderall use. Compared with Non-Hispanic White adolescents, Non-Hispanic Black adolescents had significantly lower odds of Adderall use (OR 0.23, 95% CI 0.06–0.97), as did Hispanic adolescents (OR 0.12, 95% CI 0.02–0.65). Adolescents categorized as Other did not differ significantly from Non-Hispanic White adolescents (OR 0.38, 95% CI 0.09–1.58). Figure 2. ADHD Diagnosis and Health Insurance Coverage by Household Income Among U.S. Adolescents Aged 12–19 Years, NHANES 2009–2018 Figure 2 . Survey-weighted prevalence of self-reported physician-diagnosed attention-deficit/hyperactivity disorder (ADHD) and current health insurance coverage among U.S. adolescents aged 12–19 years, stratified by household income group defined by income-to-poverty ratio (PIR). Income groups were categorized as low income (PIR < 1), middle income (PIR 1–3), and high income (PIR ≥ 3). Estimates were calculated using NHANES examination sample weights to account for the complex survey design. Table 2 Adjusted Odds Ratios for Adderall Use Among U.S. Adolescents (NHANES 2009–2018) Variable Adjusted OR 95% CI Income-to-poverty ratio (PIR) 0.63 0.48–0.83 ADHD diagnosis 0.97 0.32–2.98 Age 0.80 0.45–1.42 Female (vs Male) 0.88 0.28–2.72 Insured (vs Uninsured) 0.73 0.22–2.43 Race/Ethnicity (ref: Non-Hispanic White) Non-Hispanic Black 0.23 0.06–0.97 Hispanic 0.12 0.02–0.65 Other 0.38 0.09–1.58 Figure 3. Adjusted Odds Ratios for Adderall Use Among U.S. Adolescents Aged 12–19 Years, NHANES 2009–2018 Figure 3 . Multivariable survey-weighted logistic regression estimates of associations between demographic and socioeconomic characteristics and current Adderall (amphetamine/dextroamphetamine) use among U.S. adolescents. Odds ratios (ORs) and 95% confidence intervals (CIs) are displayed on a logarithmic scale. The vertical dashed line indicates the null value (OR = 1.0). The model adjusted for income-to-poverty ratio (continuous), ADHD diagnosis, age (continuous), sex, insurance status, and race/ethnicity. Race/ethnicity was modeled with Non-Hispanic White adolescents as the reference group. Discussion This study examined patterns of Adderall (amphetamine/dextroamphetamine) use among U.S. adolescents using nationally representative NHANES data from 2009–2018. Overall, Adderall use was uncommon in this population, consistent with prior nationally representative estimates of prescription stimulant use [ 6 , 11 ]. However, meaningful socioeconomic and racial/ethnic differences were observed, suggesting that prescription stimulant use during adolescence is shaped by factors extending beyond clinical need alone [ 8 , 12 ]. We found that adolescents from lower socioeconomic backgrounds exhibited higher prevalence of Adderall use, and income-to-poverty ratio (PIR) was inversely associated with use in adjusted analyses. Several mechanisms may contribute to this pattern. First, socioeconomic differences in ADHD diagnosis rates may partially explain variation in medication use, although prior literature has shown inconsistent associations between income and diagnosis [ 13 , 12 ]. Second, prescribing practices may differ across healthcare settings that disproportionately serve publicly insured or lower-income populations [ 14 ]. Third, parental health-seeking behavior, school-based referrals, and differential access to behavioral therapies may influence reliance on pharmacologic treatment [ 15 , 16 ]. Importantly, higher medication prevalence among lower-income adolescents should not be interpreted as evidence of inappropriate prescribing; rather, it may reflect complex interactions between access to care, diagnostic pathways, and treatment decision-making [ 13 , 14 ]. Racial and ethnic disparities were also evident. Non-Hispanic Black and Hispanic adolescents demonstrated lower prevalence and significantly lower adjusted odds of Adderall use compared with Non-Hispanic White adolescents. These findings are consistent with a substantial body of literature documenting disparities in ADHD diagnosis and stimulant treatment [ 8 , 12 , 16 ]. Multiple explanations have been proposed, including underdiagnosis of ADHD among minority youth, differences in symptom recognition by caregivers or educators, structural barriers to specialty mental health care, cultural perceptions of behavioral diagnoses, and provider-level prescribing patterns [ 12 , 8 ]. It is also possible that differential access to healthcare continuity and follow-up influences long-term medication management [ 14 ]. The persistence of these disparities in a nationally representative sample underscores the need to better understand how structural and systemic factors shape adolescent mental health treatment [ 8 ]. Contrary to expectations, ADHD diagnosis was not significantly associated with Adderall use. This likely reflects limited statistical precision due to the small number of users rather than a true absence of association, and the corresponding confidence interval was wide. This finding should be interpreted cautiously. The low prevalence of Adderall use resulted in a relatively small number of medication users, which likely limited statistical power and produced imprecise estimates. Prior studies consistently demonstrate strong associations between ADHD diagnosis and stimulant use [ 16 , 15 ], suggesting that the null association observed here more likely reflects limited precision rather than a true absence of relationship. Additionally, NHANES captures self-reported physician diagnosis but does not measure ADHD severity, symptom persistence, treatment indication, or medication adherence, all of which are critical determinants of pharmacologic treatment [ 3 ]. Age, sex, and insurance status were not significantly associated with Adderall use. Although sex differences in ADHD diagnosis and stimulant prescribing have been widely reported in younger children, findings among adolescents are less consistent [ 16 , 15 ]. The absence of statistically significant associations in this study may reflect evolving prescribing patterns, heterogeneity in treatment trajectories across adolescence, or insufficient power due to the low outcome frequency [ 6 ]. Similarly, while insurance coverage varied substantially by income group, insurance status itself was not independently associated with Adderall use after adjustment, suggesting that insurance may operate indirectly through other pathways such as access to diagnostic evaluation or provider networks [ 14 ]. These findings have several potential clinical and public health implications. First, observed disparities may indicate inequities in diagnosis, treatment access, or prescribing practices, rather than differences in underlying ADHD prevalence [ 12 , 8 ]. Second, socioeconomic gradients in medication use highlight the importance of examining how structural determinants influence treatment decisions [ 13 ]. Third, given ongoing concerns regarding stimulant misuse, diversion, and nonmedical use, understanding population-level prescribing patterns remains critical [ 17 ]. However, this study assessed prescription medication use rather than misuse, and interpretations should remain within that context. Several limitations should be considered. Adderall use was based on self-reported prescription medication data and may be subject to recall bias or misclassification [ 11 ]. The low prevalence of Adderall use resulted in small numbers of users, limiting statistical precision and contributing to wide confidence intervals. NHANES is cross-sectional, precluding causal inference and temporal assessment of diagnosis and treatment. The survey also lacks detailed clinical information, including ADHD severity, treatment indication, dosage, duration of use, and adherence [ 16 ]. Finally, residual confounding by unmeasured factors such as healthcare utilization patterns, provider specialty, or comorbid psychiatric conditions cannot be excluded. Despite these limitations, this study has important strengths. NHANES provides nationally representative data with rigorous sampling methodology, allowing generalization to the noninstitutionalized U.S. adolescent population. The use of survey-weighted analyses appropriately accounted for the complex sampling design. Additionally, focusing specifically on Adderall, rather than stimulant medications as a combined class, offers insight into potential variation across stimulant subtypes [ 5 , 6 ]. Future research should aim to address the limitations inherent in cross-sectional survey data. Studies incorporating longitudinal designs, electronic health records, or claims data may better characterize treatment trajectories, medication persistence, and switching patterns [ 6 ]. Further investigation into mechanisms underlying socioeconomic and racial/ethnic disparities—including diagnostic processes, prescribing practices, and access to behavioral interventions—is warranted [ 8 , 12 ]. Given evolving stimulant prescribing trends and increasing attention to adolescent mental health, continued surveillance of prescription stimulant use remains essential [ 17 ]. Declarations Ethics approval and consent to participate The present study used publicly available, de-identified data from the National Health and Nutrition Examination Survey (NHANES). NHANES protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and written informed consent was obtained from all participants or their legal guardians prior to participation. Because this study involved secondary analysis of publicly available, de-identified data, additional institutional review board approval was not required. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are publicly available from the National Health and Nutrition Examination Survey (NHANES) website at: https://www.cdc.gov/nchs/nhanes/ Competing interests The authors declare that they have no competing interests . Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions E.-H.T. conceived and designed the study, performed the statistical analyses, and drafted the main manuscript text. Z.Z. supervised the study design, contributed to interpretation of the data, and critically revised the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements The authors thank the participants and staff of the National Health and Nutrition Examination Survey for their contributions to data collection and dissemination. References Feldman HM, Reiff MI. 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Racial and ethnic disparities in ADHD diagnosis from kindergarten to eighth grade. Pediatrics. 2013;132(1):85–93. Froehlich TE, Lanphear BP, Epstein JN, et al. Prevalence, recognition, and treatment of ADHD in a national sample of U.S. children. Arch Pediatr Adolesc Med. 2007;161(9):857–864. Garfield RL, Zuvekas SH, Lave JR, Donohue JM. The impact of national health reform on adults with severe mental disorders. Am J Psychiatry. 2012;169(5):486–494. Danielson ML, Bitsko RH, Ghandour RM, et al. Prevalence of parent-reported ADHD diagnosis and associated treatment among U.S. children and adolescents. J Clin Child Adolesc Psychol. 2018;47(2):199–212. Visser SN, Danielson ML, Bitsko RH, et al. Trends in the parent-report of ADHD diagnosis and treatment. J Am Acad Child Adolesc Psychiatry. 2014;53(1):34–46. Compton WM, Volkow ND. Abuse of prescription drugs and the risk of addiction. N Engl J Med. 2021;374(2):125–133. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8990180","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602137932,"identity":"3c26d1d3-1ba1-4d4d-9785-3db15affeaef","order_by":0,"name":"Esther-Hedwig Teklic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIie3PsQqDMBCA4RMhU0pWhUJf4UrAxaKvciC0aydnQXDqA/g4J4KT9RmEvoDQpUMLjZ26GbdC80835OMuAC7Xb+bDiCAV9GYWNkAYQgjbsFhHAA7I0pKoXdkinROpmyvDlLfLJKxERoSZjHggrx4sCHZSM5mrIu7R31QWJO3U/UN0YcjLhqCQPs0EwRDPhgTdUeP8l8Ac1lyG0zJRZXsLpmeSqrrfj488XiZf+xh4zft5X7ESuFwu19/0BtiHOR8uwqYBAAAAAElFTkSuQmCC","orcid":"","institution":"Johns Hopkins University","correspondingAuthor":true,"prefix":"","firstName":"Esther-Hedwig","middleName":"","lastName":"Teklic","suffix":""},{"id":602137933,"identity":"95f13098-a25b-4c51-aa01-8f45f196e84f","order_by":1,"name":"Zhicheng Zhou","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhicheng","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-02-27 16:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8990180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8990180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104405081,"identity":"2bfc42db-ac38-44b5-8395-88e778f70719","added_by":"auto","created_at":"2026-03-11 12:21:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted Prevalence of Adderall Use Among U.S. Adolescents by Race/Ethnicity and Household Income (NHANES 2009–2018)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1A. Race/Ethnicity: Survey-weighted prevalence of Adderall (amphetamine/dextroamphetamine) use among U.S. adolescents aged 12–19 years stratified by race/ethnicity. Error bars represent 95% confidence intervals. Prevalence estimates were calculated using NHANES sampling weights. Figure 1B. Household Income:Survey-weighted prevalence of Adderall use stratified by household income group defined by income-to-poverty ratio (PIR): low income (PIR \u0026lt; 1), middle income (PIR 1–3), and high income (PIR ≥ 3). Error bars represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8990180/v1/1ec63b802b3f1a454d14f6bf.png"},{"id":104233251,"identity":"18c7519c-e09a-4471-b3f7-2d6f17417639","added_by":"auto","created_at":"2026-03-09 12:46:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eADHD Diagnosis and Health Insurance Coverage by Household Income Among U.S. Adolescents Aged 12–19 Years, NHANES 2009–2018\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2. Survey-weighted prevalence of self-reported physician-diagnosed attention-deficit/hyperactivity disorder (ADHD) and current health insurance coverage among U.S. adolescents aged 12–19 years, stratified by household income group defined by income-to-poverty ratio (PIR). Income groups were categorized as low income (PIR \u0026lt; 1), middle income (PIR 1–3), and high income (PIR ≥ 3). Estimates were calculated using NHANES examination sample weights to account for the complex survey design.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8990180/v1/8e070178f181c80503660318.png"},{"id":104233249,"identity":"1b084b9c-b629-4aa0-a67f-42a8d3ba2836","added_by":"auto","created_at":"2026-03-09 12:46:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted Odds Ratios for Adderall Use Among U.S. Adolescents Aged 12–19 Years, NHANES 2009–2018\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3. Multivariable survey-weighted logistic regression estimates of associations between demographic and socioeconomic characteristics and current Adderall (amphetamine/dextroamphetamine) use among U.S. adolescents. Odds ratios (ORs) and 95% confidence intervals (CIs) are displayed on a logarithmic scale. The vertical dashed line indicates the null value (OR = 1.0). The model adjusted for income-to-poverty ratio (continuous), ADHD diagnosis, age (continuous), sex, insurance status, and race/ethnicity. Race/ethnicity was modeled with Non-Hispanic White adolescents as the reference group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8990180/v1/2e69e03d933411b6cb5e5bda.png"},{"id":108006821,"identity":"118017c9-e764-43ef-9121-026336539498","added_by":"auto","created_at":"2026-04-28 12:57:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":378146,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8990180/v1/77b91cd1-ee10-4e2f-9251-44a001bedd1f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioeconomic and Racial/Ethnic Differences in Prescription Adderall Use Among U.S. Adolescents: NHANES 2009–2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders among children and adolescents in the United States [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], affecting an estimated 8\u0026ndash;10% of school-aged youth [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ADHD is characterized by persistent patterns of inattention, hyperactivity, and impulsivity that interfere with academic performance, social functioning, and overall well-being [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Pharmacologic treatment, particularly stimulant medications, remains a cornerstone of evidence-based ADHD management and has been consistently shown to improve symptom control and functional outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmphetamine-based stimulants, including commonly used mixed amphetamine salts in clinical settings (amphetamine/dextroamphetamine; Adderall), represent one of the most commonly prescribed medication classes for adolescent ADHD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Compared with methylphenidate formulations, amphetamine products have demonstrated robust efficacy and are frequently used in both newly diagnosed patients and those requiring medication adjustments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Over the past two decades, rates of ADHD diagnosis and stimulant prescribing have increased substantially in the United States, raising important questions regarding prescribing practices, clinical appropriateness, and population-level patterns of use [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At the same time, concerns have emerged regarding potential overuse, misuse, diversion, and inequities in access to stimulant medications [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Prescription stimulant use during adolescence carries important clinical and public health implications, including risks of nonmedical use, substance misuse, cardiovascular effects, and academic performance pressures. Patterns of stimulant prescribing may therefore reflect not only clinical need but also broader structural, socioeconomic, and healthcare system factors.\u003c/p\u003e \u003cp\u003eSubstantial evidence documents disparities in ADHD diagnosis and treatment across socioeconomic and racial/ethnic groups [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Children from racial and ethnic minority populations are consistently less likely to receive an ADHD diagnosis or stimulant treatment compared with Non-Hispanic White children, even after accounting for symptom severity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Socioeconomic gradients in treatment access have also been observed, although findings have been mixed, with some studies suggesting higher diagnosis and treatment rates among publicly insured or lower-income populations and others indicating barriers to specialty care among economically disadvantaged families [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These inconsistencies underscore the complexity of structural and contextual influences on ADHD management.\u003c/p\u003e \u003cp\u003eDespite extensive literature on ADHD treatment broadly, nationally representative evidence specifically characterizing patterns of Adderall use, rather than stimulant medications as a combined class, across demographic and socioeconomic strata remains limited [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Given potential differences in prescribing practices across stimulant subtypes and evolving trends in amphetamine versus methylphenidate use [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], examining Adderall use specifically may provide additional insight into treatment patterns and disparities.\u003c/p\u003e \u003cp\u003eUsing nationally representative data from the National Health and Nutrition Examination Survey (NHANES) 2009\u0026ndash;2018, this study aimed to (1) estimate the prevalence of Adderall use among U.S. adolescents aged 12\u0026ndash;19 years and (2) examine associations between Adderall use and income-to-poverty ratio (PIR), ADHD diagnosis, age, sex, insurance status, and race/ethnicity. We hypothesized that Adderall use would vary by socioeconomic status and race/ethnicity, reflecting underlying differences in diagnosis, healthcare access, and prescribing practices.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative survey designed to assess the health and nutritional status of the noninstitutionalized U.S. population. NHANES is administered by the National Center for Health Statistics (NCHS) using a complex, multistage, stratified probability sampling design. To ensure adequate statistical power and stable estimates, we pooled five consecutive 2-year survey cycles spanning 2009\u0026ndash;2010 through 2017\u0026ndash;2018. The analytic sample was restricted to adolescents aged 12\u0026ndash;19 years who participated in the examination component of NHANES. Participants were excluded if they had missing data on prescription medication use or key covariates included in multivariable models. Participants with missing data on income-to-poverty ratio (PIR) were excluded from analyses stratified by income group. Sensitivity analyses were conducted by excluding the 2015\u0026ndash;2016 NHANES cycle, which contained no Adderall users.\u003c/p\u003e \u003cp\u003eNHANES protocols were approved by the NCHS Research Ethics Review Board, and written informed consent was obtained from participants aged 18\u0026ndash;19 years and from parents or guardians of participants younger than 18 years, with assent obtained from minors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of Adderall Use\u003c/h3\u003e\n\u003cp\u003eAdderall use was identified from the NHANES prescription medication files. During in-home interviews, participants were asked to report all prescription medications taken in the past 30 days. Interviewers verified medication names using prescription containers whenever available. Participants were classified as current Adderall users if amphetamine/dextroamphetamine (mixed amphetamine salts) was reported among medications used within the past 30 days. A binary outcome variable (yes/no) was constructed to indicate current Adderall use.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates were selected a priori based on prior literature and clinical relevance. Income-to-poverty ratio (PIR), calculated by NHANES as the ratio of family income to the federal poverty threshold adjusted for family size and survey year, was analyzed as a continuous variable in regression models. For descriptive analyses, PIR was categorized into three groups: low income (PIR\u0026thinsp;\u0026lt;\u0026thinsp;1), middle income (PIR 1\u0026ndash;3), and high income (PIR\u0026thinsp;\u0026ge;\u0026thinsp;3). ADHD diagnosis was defined based on self-report of a physician or health professional diagnosis of attention-deficit/hyperactivity disorder. Age at the time of examination was modeled as a continuous variable in years. Sex was categorized as male or female based on self-report. Health insurance coverage was classified as insured versus uninsured at the time of the interview. Race/ethnicity was harmonized across survey cycles and categorized as Non-Hispanic White, Non-Hispanic Black, Hispanic, and Other, with the latter including multiracial and other racial groups.\u003c/p\u003e\n\u003ch3\u003eSurvey Design and Weighting\u003c/h3\u003e\n\u003cp\u003eNHANES employs a complex survey design incorporating stratification, clustering, and oversampling of specific subpopulations. All analyses accounted for examination sample weights, primary sampling units (PSUs), and strata in accordance with NCHS analytic guidelines. Because five survey cycles were combined (10 years total), 2-year examination sample weights were divided by five to generate appropriate pooled weights representing the combined study period.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe calculated survey-weighted prevalence estimates of Adderall use overall and stratified by income group and race/ethnicity. Differences across subgroups were examined descriptively. Multivariable survey-weighted logistic regression models were used to examine associations between Adderall use and income-to-poverty ratio (PIR), ADHD diagnosis, age, sex, insurance status, and race/ethnicity. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated. Model diagnostics were assessed to ensure appropriate model specification. Multicollinearity among covariates was evaluated using variance inflation factors. Sensitivity analyses were conducted by excluding the 2015\u0026ndash;2016 NHANES cycle, which contained no Adderall users. Statistical significance was defined as a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria) with the survey package to account for the complex NHANES sampling design.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Population\u003c/h2\u003e\n \u003cp\u003eThe multivariable regression model included 5,902 participants with complete covariate data. Across pooled NHANES cycles, 47 adolescents reported Adderall use in the past 30 days (unweighted n\u0026thinsp;=\u0026thinsp;47). The survey-weighted mean age was 15.38 years (SD 1.60). Approximately 48.9% of participants were female, and 89.1% were insured at the time of the interview. Overall, 18.4% of adolescents reported a physician diagnosis of ADHD. With respect to race/ethnicity, 54.4% of participants were Non-Hispanic White, 14.2% Non-Hispanic Black, 21.9% Hispanic, and 9.5% categorized as Other. Baseline characteristics stratified by income group are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Adolescents in the low-income group were more likely to be uninsured and to identify as Hispanic or Non-Hispanic Black compared with those in the high-income group.\u003c/p\u003e\n \u003cp\u003eSensitivity analyses were conducted by excluding the 2015\u0026ndash;2016 NHANES cycle. This cycle was excluded in sensitivity analyses because no adolescents reported Adderall use, raising concerns regarding sparse data bias and potential model instability. The results were materially unchanged. Higher income-to-poverty ratio remained associated with lower odds of Adderall use (OR 0.64, 95% CI 0.49\u0026ndash;0.84).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSurvey-weighted Baseline Characteristics of U.S. Adolescents (NHANES 2009\u0026ndash;2018) N\u0026thinsp;=\u0026thinsp;6,554 (unweighted)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow income (n\u0026thinsp;=\u0026thinsp;1,866)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMiddle income (n\u0026thinsp;=\u0026thinsp;2,487)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh income (n\u0026thinsp;=\u0026thinsp;1,549)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years), mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.38 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.69 (2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.21 (2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.28 (2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e48.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e52.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e50.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e44.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsured, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e89.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e78.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e81.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e96.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eADHD diagnosis, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e54.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e74.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e35.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eValues are survey-weighted percentages unless otherwise indicated. Age is presented as mean (standard deviation). Sample sizes are unweighted. Income group totals do not sum to the overall sample size due to missing PIR data\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003ePrevalence of Adderall Use\u003c/h2\u003e\n \u003cp\u003eThe overall survey-weighted prevalence of Adderall use among U.S. adolescents aged 12\u0026ndash;19 years was low.\u003c/p\u003e\n \u003cp\u003ePrevalence varied by socioeconomic status. Adderall use was highest among adolescents in the low-income group (1.35%), followed by those in the middle-income group (1.18%), and lowest among adolescents in the high-income group (0.46%) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). The absolute difference in prevalence between low- and high-income adolescents was approximately 0.9 percentage points. Prevalence also differed across racial/ethnic groups (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Adderall use was most common among adolescents categorized as Other (1.42%) and Non-Hispanic White adolescents (1.17%). Lower prevalence was observed among Hispanic adolescents (0.36%) and Non-Hispanic Black adolescents (0.28%). These differences suggest substantial variation in stimulant use across racial/ethnic groups.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 1. Weighted Prevalence of Adderall Use Among U.S. Adolescents by Race/Ethnicity and Household Income (NHANES 2009\u0026ndash;2018)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA. Race/Ethnicity: Survey-weighted prevalence of Adderall (amphetamine/dextroamphetamine) use among U.S. adolescents aged 12\u0026ndash;19 years stratified by race/ethnicity. Error bars represent 95% confidence intervals. Prevalence estimates were calculated using NHANES sampling weights. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB. Household Income:Survey-weighted prevalence of Adderall use stratified by household income group defined by income-to-poverty ratio (PIR): low income (PIR\u0026thinsp;\u0026lt;\u0026thinsp;1), middle income (PIR 1\u0026ndash;3), and high income (PIR\u0026thinsp;\u0026ge;\u0026thinsp;3). Error bars represent 95% confidence intervals.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eFactors Associated with Adderall Use\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, both ADHD diagnosis and insurance coverage varied across income groups. The prevalence of ADHD diagnosis was modestly higher among adolescents in the low-income (20.3%) and middle-income (19.3%) groups compared with those in the high-income group (15.9%). In contrast, insurance coverage demonstrated a clear positive socioeconomic gradient, increasing substantially from 78.5% among low-income adolescents to 96.9% among high-income adolescents. These findings indicate that while ADHD diagnosis varies modestly by income, disparities in health insurance coverage across socioeconomic strata are more pronounced.\u003c/p\u003e\n \u003cp\u003eResults from the multivariable survey-weighted logistic regression model are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the adjusted odds ratios for Adderall use. Income-to-poverty ratio (PIR) was inversely associated with Adderall use. Each one-unit increase in PIR was associated with a 37% reduction in the odds of use (OR 0.63, 95% CI 0.48\u0026ndash;0.83), indicating that adolescents from more economically advantaged households had significantly lower odds of current Adderall use. ADHD diagnosis was included in the model; however, the association with Adderall use did not reach statistical significance (OR 0.97, 95% CI 0.32\u0026ndash;2.98). The wide confidence interval reflects imprecision in the estimate. Age (OR 0.80, 95% CI 0.45\u0026ndash;1.42), sex (female vs male: OR 0.88, 95% CI 0.28\u0026ndash;2.72), and insurance status (OR 0.73, 95% CI 0.22\u0026ndash;2.43) were not significantly associated with Adderall use. Compared with Non-Hispanic White adolescents, Non-Hispanic Black adolescents had significantly lower odds of Adderall use (OR 0.23, 95% CI 0.06\u0026ndash;0.97), as did Hispanic adolescents (OR 0.12, 95% CI 0.02\u0026ndash;0.65). Adolescents categorized as Other did not differ significantly from Non-Hispanic White adolescents (OR 0.38, 95% CI 0.09\u0026ndash;1.58).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 2. ADHD Diagnosis and Health Insurance Coverage by Household Income Among U.S. Adolescents Aged 12\u0026ndash;19 Years, NHANES 2009\u0026ndash;2018\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Survey-weighted prevalence of self-reported physician-diagnosed attention-deficit/hyperactivity disorder (ADHD) and current health insurance coverage among U.S. adolescents aged 12\u0026ndash;19 years, stratified by household income group defined by income-to-poverty ratio (PIR). Income groups were categorized as low income (PIR\u0026thinsp;\u0026lt;\u0026thinsp;1), middle income (PIR 1\u0026ndash;3), and high income (PIR\u0026thinsp;\u0026ge;\u0026thinsp;3). Estimates were calculated using NHANES examination sample weights to account for the complex survey design.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted Odds Ratios for Adderall Use Among U.S. Adolescents (NHANES 2009\u0026ndash;2018)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted OR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome-to-poverty ratio (PIR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u0026ndash;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADHD diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026ndash;2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u0026ndash;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale (vs Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u0026ndash;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsured (vs Uninsured)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u0026ndash;2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity (ref: Non-Hispanic White)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u0026ndash;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u0026ndash;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026ndash;1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 3. Adjusted Odds Ratios for Adderall Use Among U.S. Adolescents Aged 12\u0026ndash;19 Years, NHANES 2009\u0026ndash;2018\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Multivariable survey-weighted logistic regression estimates of associations between demographic and socioeconomic characteristics and current Adderall (amphetamine/dextroamphetamine) use among U.S. adolescents. Odds ratios (ORs) and 95% confidence intervals (CIs) are displayed on a logarithmic scale. The vertical dashed line indicates the null value (OR\u0026thinsp;=\u0026thinsp;1.0). The model adjusted for income-to-poverty ratio (continuous), ADHD diagnosis, age (continuous), sex, insurance status, and race/ethnicity. Race/ethnicity was modeled with Non-Hispanic White adolescents as the reference group.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined patterns of Adderall (amphetamine/dextroamphetamine) use among U.S. adolescents using nationally representative NHANES data from 2009\u0026ndash;2018. Overall, Adderall use was uncommon in this population, consistent with prior nationally representative estimates of prescription stimulant use [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, meaningful socioeconomic and racial/ethnic differences were observed, suggesting that prescription stimulant use during adolescence is shaped by factors extending beyond clinical need alone [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe found that adolescents from lower socioeconomic backgrounds exhibited higher prevalence of Adderall use, and income-to-poverty ratio (PIR) was inversely associated with use in adjusted analyses. Several mechanisms may contribute to this pattern. First, socioeconomic differences in ADHD diagnosis rates may partially explain variation in medication use, although prior literature has shown inconsistent associations between income and diagnosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Second, prescribing practices may differ across healthcare settings that disproportionately serve publicly insured or lower-income populations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Third, parental health-seeking behavior, school-based referrals, and differential access to behavioral therapies may influence reliance on pharmacologic treatment [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Importantly, higher medication prevalence among lower-income adolescents should not be interpreted as evidence of inappropriate prescribing; rather, it may reflect complex interactions between access to care, diagnostic pathways, and treatment decision-making [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRacial and ethnic disparities were also evident. Non-Hispanic Black and Hispanic adolescents demonstrated lower prevalence and significantly lower adjusted odds of Adderall use compared with Non-Hispanic White adolescents. These findings are consistent with a substantial body of literature documenting disparities in ADHD diagnosis and stimulant treatment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Multiple explanations have been proposed, including underdiagnosis of ADHD among minority youth, differences in symptom recognition by caregivers or educators, structural barriers to specialty mental health care, cultural perceptions of behavioral diagnoses, and provider-level prescribing patterns [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is also possible that differential access to healthcare continuity and follow-up influences long-term medication management [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The persistence of these disparities in a nationally representative sample underscores the need to better understand how structural and systemic factors shape adolescent mental health treatment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContrary to expectations, ADHD diagnosis was not significantly associated with Adderall use. This likely reflects limited statistical precision due to the small number of users rather than a true absence of association, and the corresponding confidence interval was wide. This finding should be interpreted cautiously. The low prevalence of Adderall use resulted in a relatively small number of medication users, which likely limited statistical power and produced imprecise estimates. Prior studies consistently demonstrate strong associations between ADHD diagnosis and stimulant use [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], suggesting that the null association observed here more likely reflects limited precision rather than a true absence of relationship. Additionally, NHANES captures self-reported physician diagnosis but does not measure ADHD severity, symptom persistence, treatment indication, or medication adherence, all of which are critical determinants of pharmacologic treatment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge, sex, and insurance status were not significantly associated with Adderall use. Although sex differences in ADHD diagnosis and stimulant prescribing have been widely reported in younger children, findings among adolescents are less consistent [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The absence of statistically significant associations in this study may reflect evolving prescribing patterns, heterogeneity in treatment trajectories across adolescence, or insufficient power due to the low outcome frequency [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, while insurance coverage varied substantially by income group, insurance status itself was not independently associated with Adderall use after adjustment, suggesting that insurance may operate indirectly through other pathways such as access to diagnostic evaluation or provider networks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings have several potential clinical and public health implications. First, observed disparities may indicate inequities in diagnosis, treatment access, or prescribing practices, rather than differences in underlying ADHD prevalence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Second, socioeconomic gradients in medication use highlight the importance of examining how structural determinants influence treatment decisions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Third, given ongoing concerns regarding stimulant misuse, diversion, and nonmedical use, understanding population-level prescribing patterns remains critical [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, this study assessed prescription medication use rather than misuse, and interpretations should remain within that context.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. Adderall use was based on self-reported prescription medication data and may be subject to recall bias or misclassification [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The low prevalence of Adderall use resulted in small numbers of users, limiting statistical precision and contributing to wide confidence intervals. NHANES is cross-sectional, precluding causal inference and temporal assessment of diagnosis and treatment. The survey also lacks detailed clinical information, including ADHD severity, treatment indication, dosage, duration of use, and adherence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Finally, residual confounding by unmeasured factors such as healthcare utilization patterns, provider specialty, or comorbid psychiatric conditions cannot be excluded.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study has important strengths. NHANES provides nationally representative data with rigorous sampling methodology, allowing generalization to the noninstitutionalized U.S. adolescent population. The use of survey-weighted analyses appropriately accounted for the complex sampling design. Additionally, focusing specifically on Adderall, rather than stimulant medications as a combined class, offers insight into potential variation across stimulant subtypes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFuture research should aim to address the limitations inherent in cross-sectional survey data. Studies incorporating longitudinal designs, electronic health records, or claims data may better characterize treatment trajectories, medication persistence, and switching patterns [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Further investigation into mechanisms underlying socioeconomic and racial/ethnic disparities\u0026mdash;including diagnostic processes, prescribing practices, and access to behavioral interventions\u0026mdash;is warranted [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given evolving stimulant prescribing trends and increasing attention to adolescent mental health, continued surveillance of prescription stimulant use remains essential [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study used publicly available, de-identified data from the National Health and Nutrition Examination Survey (NHANES). NHANES protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and written informed consent was obtained from all participants or their legal guardians prior to participation. Because this study involved secondary analysis of publicly available, de-identified data, additional institutional review board approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available from the National Health and Nutrition Examination Survey (NHANES) website at:\u003c/p\u003e\n\u003cp\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.-H.T. conceived and designed the study, performed the statistical analyses, and drafted the main manuscript text. Z.Z. supervised the study design, contributed to interpretation of the data, and critically revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participants and staff of the National Health and Nutrition Examination Survey for their contributions to data collection and dissemination.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFeldman HM, Reiff MI. Attention deficit\u0026ndash;hyperactivity disorder in children and adolescents. N Engl J Med. 2014;370(9):838\u0026ndash;846.\u003c/li\u003e\n \u003cli\u003eWehmeier PM, Schacht A, Barkley RA. Social and emotional impairment in children and adolescents with ADHD and the impact on quality of life. J Adolesc Health. 2010;46(3):209\u0026ndash;217.\u003c/li\u003e\n \u003cli\u003eCaye A, Swanson JM, Coghill D, Rohde LA. Treatment strategies for ADHD: An evidence-based guide to select optimal treatment. Mol Psychiatry. 2019;24(3):390\u0026ndash;408.\u003c/li\u003e\n \u003cli\u003eAlalalmeh SO, Hegazi OE, Shahwan M, et al. Amphetamines in child medicine: A review of ClinicalTrials.gov. Front Pharmacol. 2023.\u003c/li\u003e\n \u003cli\u003eChildress AC, Komolova M, Sallee FR. An update on the pharmacokinetic considerations in the treatment of ADHD with long-acting methylphenidate and amphetamine formulations. Expert Opin Drug Metab Toxicol. 2019;15(11):937\u0026ndash;974.\u003c/li\u003e\n \u003cli\u003eRaman SR, Man KKC, Bahmanyar S, et al. Trends in attention-deficit hyperactivity disorder medication use: A retrospective observational study using population-based databases. Lancet Psychiatry. 2018;5(10):824\u0026ndash;835.\u003c/li\u003e\n \u003cli\u003eBrantley S. The impact of social determinants of health on stimulant use disorders in the United States [dissertation]. University of Alabama; 2025.\u003c/li\u003e\n \u003cli\u003eCoker TR, Elliott MN, Toomey SL, et al. Racial and ethnic disparities in ADHD diagnosis and treatment. Pediatrics. 2016;138(3):e20160407.\u003c/li\u003e\n \u003cli\u003eRiccioni A, Radua J, Ashaye FO, Solmi M, Cortese S. Reporting and representation of race/ethnicity in randomized controlled trials of ADHD medications: A systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry. 2024;63(7):698\u0026ndash;707.\u003c/li\u003e\n \u003cli\u003eYang KG, Flores MW, Carson NJ, Cook BL. Racial and ethnic disparities in childhood ADHD treatment access and utilization. Psychiatr Serv. 2022;73(12):1338\u0026ndash;1345.\u003c/li\u003e\n \u003cli\u003eXu G, Strathearn L, Liu B, Yang B, Bao W. Twenty-year trends in diagnosed ADHD among U.S. children and adolescents. JAMA Netw Open. 2018;1(4):e181471.\u003c/li\u003e\n \u003cli\u003eMorgan PL, Staff J, Hillemeier MM, Farkas G, Maczuga S. Racial and ethnic disparities in ADHD diagnosis from kindergarten to eighth grade. Pediatrics. 2013;132(1):85\u0026ndash;93.\u003c/li\u003e\n \u003cli\u003eFroehlich TE, Lanphear BP, Epstein JN, et al. Prevalence, recognition, and treatment of ADHD in a national sample of U.S. children. Arch Pediatr Adolesc Med. 2007;161(9):857\u0026ndash;864.\u003c/li\u003e\n \u003cli\u003eGarfield RL, Zuvekas SH, Lave JR, Donohue JM. The impact of national health reform on adults with severe mental disorders. Am J Psychiatry. 2012;169(5):486\u0026ndash;494.\u003c/li\u003e\n \u003cli\u003eDanielson ML, Bitsko RH, Ghandour RM, et al. Prevalence of parent-reported ADHD diagnosis and associated treatment among U.S. children and adolescents. J Clin Child Adolesc Psychol. 2018;47(2):199\u0026ndash;212.\u003c/li\u003e\n \u003cli\u003eVisser SN, Danielson ML, Bitsko RH, et al. Trends in the parent-report of ADHD diagnosis and treatment. J Am Acad Child Adolesc Psychiatry. 2014;53(1):34\u0026ndash;46.\u003c/li\u003e\n \u003cli\u003eCompton WM, Volkow ND. Abuse of prescription drugs and the risk of addiction. N Engl J Med. 2021;374(2):125\u0026ndash;133.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adderall, Attention-Deficit/Hyperactivity Disorder, Socioeconomic Disparities, Adolescents","lastPublishedDoi":"10.21203/rs.3.rs-8990180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8990180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder among U.S. adolescents, and stimulant medications remain a primary treatment modality. Although disparities in ADHD diagnosis and treatment have been documented, nationally representative evidence specifically characterizing patterns of Adderall (amphetamine/dextroamphetamine) use across socioeconomic and racial/ethnic groups remains limited.\u003c/p\u003e \u003cp\u003e \u003cb\u003eObjective\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTo examine the prevalence of Adderall use and identify factors associated with use among U.S. adolescents.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eWe conducted a cross-sectional analysis of adolescents aged 12\u0026ndash;19 years using data from the National Health and Nutrition Examination Survey (NHANES) 2009\u0026ndash;2018. Adderall use was identified from prescription medication files. Survey-weighted prevalence estimates were calculated, and multivariable survey-weighted logistic regression was used to assess associations with income-to-poverty ratio (PIR), ADHD diagnosis, age, sex, insurance status, and race/ethnicity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe multivariable regression model included 5,902 participants with complete covariate data, and Adderall use was overall rare. Survey-weighted prevalence differed across socioeconomic and racial/ethnic groups. In adjusted analyses, PIR was inversely associated with Adderall use; each one-unit increase in PIR was associated with a 37% reduction in the odds of use (OR 0.63, 95% CI 0.48\u0026ndash;0.83). ADHD diagnosis was not significantly associated with Adderall use (OR 0.97, 95% CI 0.32\u0026ndash;2.98). Age, sex, and insurance status were also not significantly associated with use. Compared with Non-Hispanic White adolescents, Non-Hispanic Black (OR 0.23, 95% CI 0.06\u0026ndash;0.97) and Hispanic adolescents (OR 0.12, 95% CI 0.02\u0026ndash;0.65) had significantly lower odds of Adderall use. Results were robust in sensitivity analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eAdderall use among U.S. adolescents was uncommon and demonstrated socioeconomic and racial/ethnic disparities. Higher socioeconomic status was associated with lower odds of use. These findings highlight the complex relationship between socioeconomic factors and stimulant medication use in adolescents.\u003c/p\u003e","manuscriptTitle":"Socioeconomic and Racial/Ethnic Differences in Prescription Adderall Use Among U.S. Adolescents: NHANES 2009–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 12:46:46","doi":"10.21203/rs.3.rs-8990180/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa6c56a2-d366-46a6-a881-b6c8e1a04507","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T15:11:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 12:46:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8990180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8990180","identity":"rs-8990180","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}