Prevalence of Biochemically-Verified Substance Use in Healthy Adolescents Across the United States: Hair Toxicology Results in the ABCD Study

Weighted Prevalence of Biochemically-Verified Substance Use in Healthy Adolescents Across the United States | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Weighted Prevalence of Biochemically-Verified Substance Use in Healthy Adolescents Across the United States Natasha E. Wade , Yajuan Si , Susan F. Tapert , Janosch Linkersdörfer , Krista M. Lisdahl , Hailley R. Moore , Laila Tally , Biplabendu Das , Marilyn A. Huestis , Alexander L. Wallace , Ryan M. Sullivan , Veronica Szpak , Le Zhang , Laura Ziemer , Wesley K. Thompson doi: https://doi.org/10.1101/2025.09.19.25336190 Natasha E. Wade 1 Department of Psychiatry, University of California Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: nwade{at}ucsd.edu Yajuan Si 2 Survey Research Center, University of Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan F. Tapert 1 Department of Psychiatry, University of California Find this author on Google Scholar Find this author on PubMed Search for this author on this site Janosch Linkersdörfer 3 Center for Multimodal Imaging & Genetics, J. Craig Venter Institute Find this author on Google Scholar Find this author on PubMed Search for this author on this site Krista M. Lisdahl 4 Univeristy of Wisconsin Milwaukee Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hailley R. Moore 4 Univeristy of Wisconsin Milwaukee Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laila Tally 5 Florida International University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Biplabendu Das 3 Center for Multimodal Imaging & Genetics, J. Craig Venter Institute Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marilyn A. Huestis 6 Huestis & Smith Toxicology , LLC, Fredericksburg, VA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexander L. Wallace 1 Department of Psychiatry, University of California Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ryan M. Sullivan 1 Department of Psychiatry, University of California Find this author on Google Scholar Find this author on PubMed Search for this author on this site Veronica Szpak 1 Department of Psychiatry, University of California Find this author on Google Scholar Find this author on PubMed Search for this author on this site Le Zhang 3 Center for Multimodal Imaging & Genetics, J. Craig Venter Institute Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Ziemer 3 Center for Multimodal Imaging & Genetics, J. Craig Venter Institute Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wesley K. Thompson 7 Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background and Aims Adolescent substance use is a significant public health concern, though the scope of the problem is difficult to ascertain given reliance on self-reported substance use information. Recent, large cohort studies in adolescents and young adults suggest underreporting of substance use. Analyses here aim to determine concordance between self-reported substance use and biochemical verification through hair samples and to estimate U.S. prevalence of the three most reported substances used by adolescents. Design Observational cohort longitudinal study design. Liquid chromatography and gas chromatography tandem mass spectrometry (LC or GC/MS-MS) were used to test hair samples for biochemically verified substance use, with results compared to self-reported substance use. Multi-step weighting methods estimated national prevalence trends of cannabis, alcohol, and nicotine use over time, adjusting for discrepancies in sample representation due to recruitment demography, missed visits, and hair sample testing. Setting and Participants Data came from the U.S., nationwide Adolescent Brain Cognitive Development Study (n=11,868; age 9-10 at Baseline, age 15-16 at Wave 6). Participants were followed annually, with data here from 2016 to 2024. Measurements Hair samples objectively detected at least several days of substance use in a subsample of participants (n samples =11,865; n=6,133 unique participants). Participants self-reported past 3-month substance use. Sociodemographic, individual, and environmental-level factors were included in inverse propensity weights to estimate national prevalence rates of cannabis, alcohol, and nicotine use in teens. Findings Concordance between self-report and toxicological data improved with age (for cannabis, ages 11-12=<1%; ages 15-16=45%). Weighted estimates of biochemically verified substance use indicated 7.1% (95%CI: 6.0-8.3) of 15-16 year olds engaged in biochemically detected cannabis use, 0.2% (95%CI: 0.1-0.4) used alcohol, and 4.7% (95%CI: 3.7-6.0) used nicotine. Conclusions Youth reported substance use patterns demonstrated improved biochemical verification with age. Biochemical verification reflects substantive cannabis and nicotine use by ages 15-16, supporting combining toxicological and self-report data to improve identification of substance use in youth when possible. Introduction Recent scientific efforts across the globe enlist broad recruitment of large, diverse cohorts to investigate physical and mental health risk and resilience factors, across the US 1 – 3 , UK 4 – 6 , Europe 7 , the Netherlands 8 , and Switzerland 9 . In studies of youth and young adults, a common aim is to investigate predictors and consequences of substance use. As substance use represents sensitive information, self-report could be inaccurate in youth recruited into normative studies, especially those with parental involvement 9 – 11 . Biochemical verification through toxicological samples can inform self-reported substance use, particularly hair analysis, which provides up to 3 months of detection (a much longer window of detection with less invasive methods than urine or blood) 12 . Initial data from several large, epidemiologically based studies suggest that many adolescents and young adults may underreport substance use. In one Zurich study, hair testing of young adults reveals substance underreporting by 30-60% 9 , with follow-up analyses indicating similar rates of under-reporting by the cohort four years later 13 . In the Adolescent Brain Cognitive Development Study (ABCD), 1,390 samples collected at ages 9-13 out of 11,868 enrolled participants indicated that hair testing could double identification of youth using substances 10 . These data highlight the utility of hair analysis to improve substance use estimate accuracy, which is key as the primary ABCD study aims focus on identifying the sequelae of substance use 14 . Further, hair testing of cannabis, nicotine, and alcohol generally indicates more than occasional use 15 – 17 , and therefore is more likely to be linked to a neurotoxic effect 18 . Due to the costs of hair toxicology, the ABCD study initially prioritized selecting samples for analyses from participants who reported risk characteristics for early substance use 11 , with a small portion of typical risk youth randomly selected for hair analyses 10 ; more recent funding expanded testing all available samples at select waves. Prioritization of participant characteristics and attrition in longitudinal studies can limit the generalizability of toxicology-based estimates. Probability sampling is the gold standard for representative prevalence estimation. Cohort studies, a crucial type of nonprobability sample, require adjustment of sample representation to match the target population 19 , 20 . Propensity weighting aims to statistically reduce biases (e.g., selection, retention) in order to increase accuracy of prevalence estimates. Weighting adjusts the influence of individual responses and is often recommended to better represent population norms when implemented using an appropriate external benchmark dataset 21 ; accordingly, ABCD population weights are included in annual data releases 21 . While the ABCD sample was recruited through epidemiologically guided processes, it is not directly representative of the U.S., so population weights (computed in conjunction with the American Community Survey [ACS]) can help mitigate selection bias 22 – 24 . Further, attrition or fluctuating participation in longitudinal cohorts is expected, which may further impact generalizability. ABCD’s retention rate is 95% 25 , yet not all participants complete each wave of data collection, so accounting for attrition and correcting potential biases through weighting are crucial to generalizability. Here we aim to better understand the general scope of substance use in youth ages 9-16 through biochemical verification and propensity weighting to achieve rigorous prevalence estimates. In following a young, community-based cohort over time and using hair testing, we are better able to evaluate the extent to which generally healthy youth are using substances at more than experimental or low-level use. We first report concordance between hair testing results and self-reported substance use patterns in the three most commonly reported substances used by adolescents (i.e., alcohol, nicotine, and cannabis). We further assess the prevalence of positive test results after adjusting sample representation with the ABCD cohort through inverse propensity weighting, providing an estimate of the biochemically verified population prevalence of these three most used substances in youth ages 9-16 across the U.S. Methods Data were drawn from ABCD Data Release 6.0 ( https://doi.org/10.82525/jy7n-g441 ), including all annual visits from Baseline (ages 9-10) through half of Wave 6 follow-ups (ages 15-16) in 11,868 youth. Hair testing was conducted on a subset of participants (n samples =11,865 from 6,133 unique participants; 52% of the cohort; see Figure 1 ). ABCD participants were enrolled from their local catchment centers through epidemiologically guided school recruitment from 2016-2019 at 21 sites across the United States, with four sites mainly recruiting participants from twin registries 26 . Study sites were selected, in part, to ensure recruitment of a cohort reflective of the demography of the U.S. population and covering all four census zones, with over 20% of youth ages 9-10 living within catchment regions 26 . Since one of ABCD’s primary aims is to study how substance use influences development 14 , the cohort was recruited at an age when they were expected to be substance naïve and continues to follow them as some initiate and escalate substance use 27 . Inclusion criteria at Baseline included living within the catchment area; being 9-10 years old and fluent in English; having no history of substance use disorder, severe mental illness, moderate-to-severe intellectual or developmental disability, or history of major neurological disorder including severe traumatic brain injury; birthweight >2 lbs; and no magnetic resonance imaging contraindication. Participants needed an English or Spanish speaking parent/guardian to consent and participate. Eligible participants attend data collection sessions at their local site annually and complete brief mid-year interviews remotely. Download figure Open in new tab Figure 1. Visualization of Study Timeline, Ages, and Number of Hairs Samples Per Wave The present analyses include all participants with data from any visit (n=11,868). To estimate the biochemically verified three-month prevalence of substance use across the ABCD follow-up waves, we compute weighted hair testing results, where we combine (1) the ABCD population weights at Baseline 21 with two sets of newly created weights to sequentially adjust for (2) whether a participant completed or missed a given wave and (3) whether hair samples were successfully assayed by drug test for that wave. We apply the final combined weight to positive hair results for cannabis, alcohol, and nicotine by wave of data collection, estimating the population prevalence of these three most used substances in youth ages 9-16 across the U.S. Measures Hair Sample Collection and Toxicological Analysis ABCD research assistants collected ∼100mg of hair closest to the root around the crown of the head from all participants who had hair styles that would not be negatively impacted by collection and were willing 11 , incorporating culturally-informed techniques 28 . Hair samples were securely stored in envelopes inside plastic sleeves and placed in locked cabinets in temperature-controlled rooms until selected for shipping, consistent with instructions from the testing laboratories. Upon receipt by the laboratory, hair was measured and trimmed to 3.9 cm, if necessary, and washed to mitigate against environmental exposure contamination, as recommended by the Society of Hair Testing 12 . The hair sample then underwent screening by immunoassays for most analytes and tested directly on mass spectrometry for cannabinoids and ethyl glucuronide (EtG). Presumptive positives were confirmed via liquid chromatography tandem mass spectrometry (LC-MS/MS) or gas chromatography tandem mass spectrometry (GC-MS/MS). Collection and testing details are in the Supplement. A full drug panel was assessed; here we report on the most commonly self-reported substances used by adolescents: (1) cannabinoids (THCCOOH, the definitive indicator of personal ingestion 29 ); (2) alcohol (EtG); and (3) nicotine (cotinine). The two testing laboratories employed by ABCD used different cutoffs to determine positive results across drug classes, so to harmonize results across labs, positive results were identified in several ways: for general concordance rates, each lab’s determination of a positive was used; for a more conservative estimate when applying prevalence weights (“high threshold”), quantitative results with the higher cutoff between the two labs were used; for a more sensitive estimate when applying prevalence weights (“low threshold”; presented within the Supplement), samples above the limit of detection (LOD) at each laboratory were identified. While the high threshold partially reduces sensitivity as described in prior reports 10 , it allows for more direct comparison across labs. LODs and cutoffs are detailed in the Supplement. To select which hair samples to test, the ABCD Study originally relied prior known risk factors for substance use were relied upon (i.e., self-reporting substance use, family history of substance use disorder, peer use, curiosity about substance use, toxicological screening indicating substance use, externalizing symptoms, parent report of youth drinking, and older age) 11 , with an additional ∼5% of typical risk youth randomly selected for hair analyses 10 . However, in 2024, additional funding allowed for the testing of all hair samples available at Year 2, 4, and 6, as well as a larger proportion of Year 5 samples. Self-Reported Substance Use Trained research staff annually administered the Timeline Followback (TLFB) interview 30 , 31 assessing use pattern, frequency, and quantity of all major substance categories in standard units including cannabis (flower, concentrates, edibles, tinctures, synthetics), alcohol, nicotine (tobacco products, electronic nicotine delivery systems or vapes, and nicotine replacement products), and other drug use. Participants are guided through a calendar, with each month between study sessions displayed on an app designed to log substance use. Participants then report their use on each given day, and are encouraged to use memory cues (e.g., significant dates like holidays or birthdays; logs from their social media accounts). While this provides means of summing lifetime use (with calendar logs from annual visit to annual visit) reported in standardized units of use, 27 , 32 past-3-month substance use can also be calculated (e.g., through the ABCDscores package 33 ) by limiting the calculation to only the three months leading up to the study visit. Here, we used the total sum of days of reported use of each substance from their annual visit going back three months. Predictors We fit logistic regression models to predict wave-specific participation and hair testing selection and use the inverse propensities as weighting adjustment factors. Predictors included in logistic regressions for creating the weights were selected based on substantive knowledge and their correlations to ensure no potential confounding factors were missed. Time-varying measures included age, parent- and youth-reported youth substance use and attitudes, youth mental health, and visit type (in-person, hybrid, or remote). Several time-invariant predictors collected at Baseline were also included: sex at birth, race, ethnicity, household income, highest level of parental education, family history of mental health disorders, the Childhood Opportunity Index, and state laws regarding recreational and medicinal cannabis 34 (see the Supplement for a detailed list). Statistical Analyses Concordance Between Self-Report and Hair Toxicology Descriptive statistics were computed for participants’ self-report and hair toxicology results, including correlations between self-reported use days and drug analyte concentration. Hair testing is not expected to detect single use episodes of alcohol, nicotine, or cannabis, 15 – 17 but typically requires at least several uses or more. Therefore, thresholds for number of days of use to expect a positive hair toxicology test were determined based on prior literature. For cannabis, a threshold of self-reporting at least two use days a month on average was used (i.e., ≥6 use days in past three months; consistent with prior findings for positive cannabinoid hair testing using detailed self-report and a full battery of toxicological testing in young adults 35 ). For alcohol, other data in young adults suggests a couple drinks per week are required; however, authors of that study also note to use the most sensitive methods possible when using a community cohort and that hair can be used to detect lack of abstinence 36 , and so ≥2 days/month were used as a threshold for alcohol. Finally, for nicotine, a study of young adults indicate ≥1 day per month of nicotine use can result in detectable levels of nicotine 15 , with ≥1 days/month used in the present analyses. Self-reported and hair testing rates of cannabis, alcohol, and nicotine use were compared, and analyses of the most reported substance within participants with tested hair samples (cannabis) assessed concordance of self-report and biochemical verification over time. Concordance rates were unweighted. Multi-step Weighting Propensity weighting is useful as a tool to reduce selection and other biases from a dataset to generate a more representative prevalence estimate. To do this, the created weight increases the influence on estimated prevalence of those who have a lower probability of being included in a study design, and reduces the influence of those who have a higher probability of being included. Inverse propensity weighting was used to approximate the prevalence of positive hair drug tests, were biases due to cohort selection, attrition, and hair collection not present; through statistical adjustment, this process aims to reflect generalizability to the U.S. adolescent population. To obtain unbiased estimates of substance use prevalence across time, we adjusted for sample representation to reduce selection bias through population weighting: Baseline weights, created by Heeringa & Berglund 21 ; attrition/missing an annual visit (Wave Participation weights); and whether a hair sample was tested (Hair Test weights). Specifically, starting from the ABCD Study Baseline weights, we (1) fit logistic regression models to predict whether individuals participate in the follow-up wave and use the inverse propensity of participation as the weighting adjustment factor for attrition; (2) fit logistic regression models to predict whether hair samples were successfully tested by drug class, including as covariates selection criteria for testing and other relevant factors (e.g., sex, study site), and use the inverse propensity of being tested as the weighting adjustment factor of testing; and (3) multiplied the three weights, corresponding to Baseline selection, attrition, and hair testing, trimmed weights as necessary, and used these products as the final weights. Note that the final weight for cases with hair tested at Baseline is a product of the Baseline and Hair Test weights. All ABCD variables with potential relevance for selection for visit completion, hair testing, and substance use risk were considered for inclusion in models for Wave Participation and Hair Test weights. Each weight was then included to create the subsequent weights; the Baseline weights were included as a predictor in the logistic regression for computing Wave Participation weights, and both Baseline and Wave Participation weights were included as predictors in the logistic regression for computing Hair Test weights. The weighting algorithms are described in more detail below, with specific variables included in the Supplement. Baseline Weights The procedure for computing the ABCD Study Baseline weights has been described previously 21 , with details in the Supplement. Their validity depends on whether the Baseline weights can substantially balance Baseline sample enrollment on factors related to prevalence of pertinent study outcomes. Baseline sociodemographic and recruitment information was used to generate propensity weights within the ABCD cohort to reflect national representation for 9-10 year-olds 21 . These weights, available in the ABCD data release and generated leveraging a high-quality external source (i.e., the ACS) that benchmarks the US population, can reduce the effect of selection bias and improve generalizability of ABCD analyses. Here, we assume that the Baseline weights have substantially accounted for selection factors related to both Baseline sample enrollment and this study’s outcomes. Wave Participation Weights Participation rate at each wave is: Wave 1 = 94.5%; Wave 2 = 92.5%; Wave 3 = 82.1%; Wave 4 = 82.1%; Wave 5 = 74.9%; Wave 6 = 42.6% (not all participants’ data are included in Release 6.0 from Waves 5 and 6). We created wave-specific weights using Baseline variables in logistic regressions to predict whether a participant participated in a given wave. Variables included were potential barriers to study visit participation (e.g., youth spending a lot of time in multiple homes; see Supplement). Wave-specific weights for each participant were computed by taking the inverse of the probability of wave participation estimated from the logistic regression. Hair Test Weights by Substance We assigned a value of 1 to a successfully assayed hair sample for the examined substance and 0 to those participating at this wave but without hair testing and fit a logistic regression to predict the testing propensity of the substance at each wave. Samples sent in for testing that had insufficient quantity to test were also given a 0 outcome. Predictors included those used to determine which hair samples to prioritize for testing 11 and other variables that may influence substance use, created separately for each substance, given different rates of successful testing and prediction characteristics. Wave- and substance-specific hair testing weights for each participant were computed by taking the inverse of the probability of successful assaying estimated from the logistic regression. Hair Test weights were trimmed at the 95 th percentile to prevent the influence of extreme weights Calculating Substance Use Prevalence Estimates For prevalence estimates of the entire cohort over time for any positive hair results, data were restricted to participants who had hair testing results by substance (number of hair samples: n cannabis = 11,607; n alcohol = 11,373; n nicotine = 11,720). We used the survey package in R 37 to account for the final weights and family clustering effects in the prevalence estimation of any positive hair results by substance-specific hair testing results (cannabis [THCCOOH], alcohol, and nicotine) by wave. Confidence intervals of weighted estimates were obtained using the logit (Wald-type) method. Sensitivity analyses assessed trimming effects, where results including only the trimmed Hair Test weights were presented, given minimal difference between cannabis models (see Tables S4, S5 ). Boxplots of final weights by wave are provided in Figure S1 . Finally, in the Supplement, we estimated prevalence of all cannabis positives (i.e., THCCOOH, CBD, THC, Δ8-THC) in Table S6 and prevalence of all THCCOOH positives but not accounting for Baseline weights ( Table S7 ; i.e., adjusting to the cohort instead of the population), illustrating the crucial roles of weighting. Missing Predictor Values Missing predictor values were included as another level for each variable included in the prediction models. Continuous predictors were transformed into categorical variables (e.g., Externalizing T-score ≥65 38 ), except for age and weights. Where the missingness category perfect predicted whether or not hair samples were tested, multiple imputation via the mice package were employed 39 : cannabis state-level policies (n missing =10; 0.01%), number of peers who drink alcohol (n missing =986; 1.4%) and get drunk (n missing =987; 1.4%), and using drugs for non-medical purposes (n missing =901; 1.3%), Externalizing T-score (n missing =902; 1.3%), parental education (n missing =14; 0.1%), and household income (n missing =1,017; 8.6%). Results Assayed Hair Sample Characteristics 11,865 hair samples were assayed from 6,133 participants across all 21 sites, with 1 to 6 samples per participant (1 sample=2,588; 2=1,925; 3=1,103; 4=466; 5 or 6=50); 66.8% of samples were assayed by US Drug Testing Laboratory (USDTL) and 33.2% by Psychemedics. Most assayed samples were from Waves 2 (n=3,394) and 4 (n=2,900; see Table S8 for details). Insufficient hair quantity was available in 1,046 (8.8%) of samples to confirm testing of at least one drug analyte following a presumptive positive (see Supplement). For cannabinoid testing, 11,607 were successfully assayed; for alcohol, n samples =11,373; for nicotine, n samples =11,720 (see Supplement ) . Hair results across drug classes are available in Table S9 . Sociodemographics of the tested samples and ABCD cohort, relative to ACS at the same age as Baseline, are presented in Table 1 (breakdown by Wave in Table S10 ). View this table: View inline View popup Table 1. Sample Characteristics of Tested Hair Relative to the ABCD Cohort and ACS Benchmark Distribution Self-Report and Hair Toxicology Comparison Pearson correlations assessed correlations within drug class between drug analyte concentration and self-reported use days within those with hair samples that were positive for that drug class. Within those positive for cannabinoids, THCCOOH concentration correlated with past-90-day self-reported cannabis use days (r=.22, p<.001). Similarly, within those with positive hair samples, cotinine concentration correlated with self-reported nicotine use days (r=.17, p=.004), while EtG concentration did not correlate with self-reported alcohol use days (p=.72). For cannabis products (see Figure 2a ), 333 (2.8%, unweighted) reported use at least 2 times per month in the past 3 months. Products and amount of use varied, with some participants reporting up to 91 days of smoked cannabis (including vaped flower, smoked flower, concentrate use, and vaped oil), up to 90 days of edible use, and up to 33 days of cannabidiol use. Download figure Open in new tab Figure 2. Confusion matrix displaying concordance between hair results and self-report, by drug class. Notes: All concordance rates are unweighted, with raw data presented. + Hair indicates: positive hair sample based on each labs cutoff for confirmation; - Hair : negative hair sample; + Self : self-reported substance use of 6 use days in the past 3 months for cannabis and alcohol, and 3 use days in the past 3 months for nicotine; - Self : less than the required use days self-reported. Percentages reflect the 11,865 samples tested across all waves. Concordance between hair results and self-report for cannabinoids ranged was 93.9% overall, demonstrating some under-reporting of cannabis use. For alcohol use (see Figure 2b ), 83 (0.7%) participants self-reported 6+ alcohol use days in the past 3 months. Concordance between hair results and self-reported alcohol use was 98.1%; <10 participants were positive on hair testing and self-reported ≥6 alcohol use days in the past 3 months. For use of nicotine or tobacco products (see Figure 2c ), 382 (3.2%, unweighted) self-reported 3+ use days in the past 3 months. Participants reported up to daily nicotine use via cigarettes and e-cigarette/vaping products. Concordance between hair results and self-reported nicotine or tobacco use was 95.7%. Concordance rates by substance and wave are included in Supplemental Figure S2 . Cannabis was most frequently confirmed on hair testing and most frequently reported amongst those with hair samples tested. At Baseline no participants self-reported ≥2 cannabis use days per month in the past 3 months and had hair positive for cannabinoids (see Table 2 ); by Wave 6, 45% (unweighted) of participants who were positive for cannabinoids self-reported ≥2x/month cannabis use days. Overall concordance (matching hair results and self-report) varies by year, with lower overall concordance in the years with the highest number of hair cannabinoid positive results. Similar data for alcohol and nicotine are presented in Tables S11 and S12, respectively. View this table: View inline View popup Download powerpoint Table 2. Concordance between Hair Cannabinoids and Self-Report by Wave (n samples =11,865) Weighted Hair Toxicology Estimates Applying statistical weights (inverse propensity weights) to hair testing results provides a generalizable estimate of substance use prevalence. As can be seen in Figure 3 (full data in Table S13 ), there is an increasing prevalence rate with age in “High Threshold” (more strict threshold for determining positive results) weighting (“Low Threshold” results are displayed in Figure S2 ). Weighting largely attenuates estimates, particularly in later waves when participants are older. Variation in confidence intervals likely reflects sample size differences (smaller confidence intervals are observed with larger numbers of tested hair samples) and methodological testing differences, such as the methods used within and between testing labs and the cutoff used to establish positives. Download figure Open in new tab Figure 3. Weighted Prevalence Estimates by Substance and Wave Notes: Shaded regions represent 95% confidence interval. High threshold refers to a higher cutoff to determine a positive hair test and a more conservative estimate. Relative to large, important epidemiological studies, weighted prevalence varies by substance and by self-reported use frequency. Specifically, the wave of ABCD data that most closely corresponds to 8 th grade (Wave 4), 6.4% had confirmed cannabis use, 0.2% with alcohol use, and 1.4% with nicotine use, whereas the Monitoring the Future Study 40 found 8 th graders reported a past-month rate of 4.3% for cannabis, 1.5% for being drunk, and 4.9% for nicotine. Rates of hair positive for alcohol more closely fit with data indicating that 0.5% of 12-17 year-olds report binge drinking on 5 or more days in the past month in the National Survey on Drug Use and Health 41 . Similarly, in the National Youth Tobacco Survey, around 1.5% of middle schoolers report using e-cigarette products ≥6 days/month 42 . Discussion Based on current raw and weighted prevalence estimates of biochemically verified substance use, substantive substance use is present in the ABCD cohort, and for cannabis perhaps indicating more frequent use than is captured within large, epidemiological studies that do not have biochemical confirmation. Consistent with prior reports 9 , 10 , our data suggest some youth under-report substance use. However, concordance improves with age: while at Wave 2 (ages ∼11-12), <1% of participants were positive for hair cannabinoids and also self-reported cannabis use at a level which could be detectable on hair testing, by Wave 6 (ages ∼15-16) that rate increased to nearly half (45%) of participants. As expected, after applying statistical weights to reduce bias and increase generalizability of substance use prevalence estimates, hair toxicology results indicate increased substance use with age, with estimated prevalence of cannabis use in 7.1%, alcohol use in 0.3%, and nicotine use in 4.7% of 15-16 year-olds. As hair testing for these substances typically requires several use episodes 15 – 17 , these data suggest more than just occasional use. Determining the consequences of adolescent substance use requires accurate categorization of substance use onset, and under-reporting of substance use is a significant concern. While studies recruiting specifically for substance use appear to show less underreporting (e.g., Wade et al. 35 ), community-based studies of youth (including ABCD) appear to have misreporting 9 – 11 , 13 , 43 . Results indicate that, when comparing self-report to unweighted hair testing results, concordance increases with adolescent age, perhaps due to increased comfort in disclosure with age, increased knowledge of one’s substance use with age, or increased trust with study staff 44 – 46 . As prior results in a Zurich cohort found consistent rates of underreporting at both ages 20 and 24 13 , it may be that age benefits concordance rates up until young adulthood, wherein accuracy in reporting may plateau. At the same time, this prior work did not identify THCCOOH, nor detect alcohol or nicotine, which may limit comparisons with the present findings. In studies with capacity (e.g., financial, staff, storage), then, use of both self-report and objective, biochemical verification through toxicological data are recommended to provide the most accurate estimate of adolescent substance use 47 , including continued monitoring within the ABCD cohort. Inverse propensity weights were used here to reduce biases in data collection from cohort recruitment, study session attendance, and hair testing selection, in order to estimate prevalence were these biases not present. Results here are helpful for establishing that non-randomized selection of biochemical verification for substance use is informative for research, though not directly generalizable. Without weighting, unweighted rates underestimate positives in early years (Waves 0-3) and may overestimate prevalence in later years (Waves 4-6). These overestimates in latter years, in particular, may reflect biases in the data (e.g., in who attends session in person, has a hairstyle which would not be disrupted by sample collection, and is willing to give a sample) which contribute to uncorrected prevalence rates which are not directly reflective of the population. Considering primary (e.g., Figure 3 ) and follow-up (Table S7 for assessing within-cohort generalizability) weighting models, unweighted hair data are not representative of the broader population or the full ABCD cohort, respectively. Mechanistic studies of specific substances or analytes (e.g., THCCOOH, Δ8-THC) can reliably use hair data to confirm use, while other analyses may choose to incorporate hair data as one data point within a range of measures to identify substance use. Weighted results for hair cannabinoids map well to other large epidemiological studies, while alcohol and nicotine in hair may be less readily detected. Notably, hair captures a larger window of use (∼3 months) than is captured in some questions from other large projects, and only detects more frequent use across substances which reduces identification of low level use 15 – 17 , 35 . Alcohol particularly requires substantial use for detection on hair testing 48 , 49 , explaining the low detection seen here. Therefore, results suggest broad consistency with epidemiological studies, but also that the low level of use in some self-reported and epidemiological studies (e.g., ≥1 use) may underestimate the severity and frequency of cannabis use, in particular, across adolescence. Some factors may increase noise within these estimates. For instance, the low number of samples tested on some years, use of different labs, or within lab method changes (e.g., using GC-MS/MS for a brief period for EtG) increase noise, demonstrated through increased confidence intervals in weighted estimates (e.g., at Wave 3). Yet the totality of the pattern indicate hair toxicology data are a robust means of enhancing identification of adolescent substance use. Applying weights in this way was previously shown to reveal differential impact on neurocognition and functional imaging 23 , and may be used similarly with hair data. Strengths of the present analyses are use of a benchmark dataset (ACS) for weighting 21 , broad consideration of factors potentially influencing hair testing results, focusing on metabolites (i.e., THCCOOH, EtG, and cotinine) to confirm personal ingestion of each substance, and thorough measurement of self-reported and biochemically verified substance use. Limitations include that hair samples cannot be collected from all participants, and methods are key for comfortable collection across participants 28 . Weighting results relied solely on hair drug testing results; incorporating self-report would increase prevalence estimates. Basic drugs are deposited in higher concentrations in hair than acidic or neutral drugs due to strong melanin binding, although cannabinoids do not 50 , 51 . Though there are concerns about cosmetic treatments to hair damaging detection of drug analytes, evidence suggests similar EtG, THC, and other drug analyte concentrations were found in cosmetically treated versus untreated hair 52 . Two different laboratories analyzed hair samples, with one lab changing EtG methods for a brief period of time. Considering different cutoffs, LOD, and level of quantitation (LOQ) can help inform interpretation of these results across laboratories. Other methodological differences across studies are noted. Some European studies collect smaller volumes of hair and test directly by tandem mass spectrometry rather than screening first, may not always test for metabolites (e.g., THC and CBN instead), and test to the lower level of quantitation 9 , 13 , 53 , 54 . Self-report questionnaires vary across all studies, including within each U.S.-based epidemiological study 40 – 42 and in European studies (e.g., using a checklist to mark drugs used then reporting general frequency) 9 . In conclusion, biochemical verification of substance use data from a diverse, large longitudinal study indicates substantive use of cannabis (7.1%) and nicotine (4.7%) by age 15-16. This is consistent with epidemiological self-report data, and provides novel insight into higher intake levels of use than previously measured in some self-report studies. Combining toxicological data with self-report provides clear indications of more accurate self-report as youth age. Results robustly support use of biochemical verification of substance use, when available, to ensure accurate determination of substance use consequences in youth. Data Availability Data is available to approved researchers via the NBDC portal https://www.nbdc-datahub.org/ Disclosure Authors have no conflicts of interest to disclose. Acknowledgements Authors would like to thank the families who participate in the ABCD Study and the staff who collect the data. N.E. Wade was supported by National Institute on Drug Abuse (DA050779, PI: Wade; DA054980, MPI: Doran/Jacobus). This work was also supported by T32 AA013525 (PI: Riley/Spadoni to Wallace, Sullivan, and Szpak), DA062011 (PI: Wallace), and DA064409 (PI: Sullivan). Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development™ (ABCD) Study , held in the NIH Brain Development Cohorts Data Sharing Platform . This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers: U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at Federal Partners – ABCD Study . ABCD Consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD Consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.82525/jy7n-g441 . Additional support for this work was made possible from NIEHS R01-ES032295 and R01-ES031074. Footnotes Clarify rationale, methods, expand on results, and further discuss implications References 1. ↵ Lyles CR , Lunn MR , Obedin-Maliver J , Bibbins-Domingo K . The new era of precision population health: insights for the All of Us Research Program and beyond . J Transl Med . 2018 ; 16 ( 1 ): 211 . OpenUrl PubMed 2. Jernigan TL , Brown SA , Coordinators AC . Introduction . Dev Cogn Neurosci . 2018 ; 32 : 1 – 3 . OpenUrl PubMed 3. ↵ Jordan CJ , Weiss SRB , Howlett KD , Freund MP . Introduction to the Special Issue on "Informing Longitudinal Studies on the Effects of Maternal Stress and Substance Use on Child Development: Planning for the HEALthy Brain and Child Development (HBCD) Study" . Advers Resil Sci . 2020 ; 1 ( 4 ): 217 – 221 . OpenUrl PubMed 4. ↵ Downey P , Peakman TC . Design and implementation of a high-throughput biological sample processing facility using modern manufacturing principles . Int J Epidemiol . 2008 ; 37 Suppl 1 : i46 – 50 . OpenUrl CrossRef PubMed 5. Sudlow C , Gallacher J , Allen N , et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age . PLoS Med . 2015 ; 12 ( 3 ): e1001779 . OpenUrl CrossRef PubMed 6. ↵ Golding J , Pembrey M , Jones R , Team . AS. ALSPAC--the Avon Longitudinal Study of Parents and Children. I. Study methodology . Paediatr Perinat Epidemiol . 2001 ; 15 ( 1 ): 74 – 87 . OpenUrl CrossRef PubMed Web of Science 7. ↵ Mascarell Maricic L , Walter H , Rosenthal A , et al. The IMAGEN study: a decade of imaging genetics in adolescents . Mol Psychiatry . 2020 ; 25 ( 11 ): 2648 – 2671 . OpenUrl CrossRef PubMed 8. ↵ Kooijman MN , Kruithof CJ , van Duijn CM , et al. The Generation R Study: design and cohort update 2017 . Eur J Epidemiol . 2016 ; 31 ( 12 ): 1243 – 1264 . OpenUrl CrossRef PubMed 9. ↵ Steinhoff A , Shanahan L , Bechtiger L , et al. When Substance Use Is Underreported: Comparing Self-Reports and Hair Toxicology in an Urban Cohort of Young Adults . J Am Acad Child Adolesc Psychiatry . 2023 ; 62 ( 7 ): 791 – 804 . OpenUrl CrossRef PubMed 10. ↵ Wade NE , Sullivan RM , Tapert SF , et al. Concordance between substance use self-report and hair analysis in community-based adolescents . Am J Drug Alcohol Abuse . 2023 ; 49 ( 1 ): 76 – 84 . OpenUrl CrossRef PubMed 11. ↵ Wade NE , Tapert SF , Lisdahl KM , Huestis MA , Haist F . Substance use onset in high-risk 9-13 year-olds in the ABCD study . Neurotoxicol Teratol . 2022 ; 91 : 107090 . OpenUrl PubMed 12. ↵ Cooper GA , Kronstrand R , Kintz P , Society of Hair T. Society of Hair Testing guidelines for drug testing in hair . Forensic Sci Int . 2012 ; 218 ( 1-3 ): 20 – 24 . OpenUrl CrossRef PubMed Web of Science 13. ↵ Janousch C , Eggenberger L , Steinhoff A , et al. Words versus Strands: Reliability and Stability of Concordance Rates of Self-Reported and Hair-Analyzed Substance Use of Young Adults over Time . Eur Addict Res . 2025 ; 31 ( 1 ): 60 – 74 . OpenUrl PubMed 14. ↵ Volkow ND , Koob GF , Croyle RT , et al. The conception of the ABCD study: From substance use to a broad NIH collaboration . Dev Cogn Neurosci . 2018 ; 32 : 4 – 7 . OpenUrl CrossRef PubMed 15. ↵ Doran N , Correa JB , Myers MG , Tully L . Associations Between Self-Reported and Biological Measures of Nicotine Consumption Among Young Adult Nondaily Cigarette Smokers . Am J Addict . 2020 ; 29 ( 6 ): 471 – 475 . OpenUrl PubMed 16. Bertol E , Vaiano F , Boscolo-Berto R , et al. Alcohol, caffeine, and nicotine consumption in adolescents: hair analysis versus self-report . Am J Drug Alcohol Abuse . 2017 ; 43 ( 3 ): 341 – 349 . OpenUrl CrossRef PubMed 17. ↵ Taylor M , Lees R , Henderson G , et al. Comparison of cannabinoids in hair with self-reported cannabis consumption in heavy, light and non-cannabis users . Drug Alcohol Rev . 2017 ; 36 ( 2 ): 220 – 226 . OpenUrl PubMed 18. ↵ Lisdahl KM , Gilbart ER , Wright NE , Shollenbarger S . Dare to delay? The impacts of adolescent alcohol and marijuana use onset on cognition, brain structure, and function . Front Psychiatry . 2013 ; 4 : 53 . 19. ↵ Wu C . Statistical inference with non-probability survey samples . Surv Methodol . 2022 ; 48 : 283 – 311 . OpenUrl 20. ↵ Elliott MR , Valliant R . Inference for Nonprobability Samples . Statistical Science . 2017 ; 32 ( 2 ). 21. ↵ Heeringa SG , Berglund PA . A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data . bioRxiv . 2020 . 22. ↵ Gard AM , Hyde LW , Heeringa SG , West BT , Mitchell C . Why weight? Analytic approaches for large-scale population neuroscience data . Dev Cogn Neurosci . 2023 ; 59 : 101196 . OpenUrl CrossRef PubMed 23. ↵ Lin Z , Molloy MF , Sripada C , Kang J , Y. S . Population-weighted Image-on-scalar Regression Analyses of Large Scale Neuroimaging Data . medRxiv [Preprint]. 2025 . 24. ↵ Si Y , Lee S , Heeringa SG . Population Weighting in Statistical Analysis . JAMA Intern Med . 2024 ; 184 ( 1 ): 98 – 99 . OpenUrl PubMed 25. ↵ Feldstein Ewing SW , Dash GF , Thompson WK , et al. Measuring retention within the adolescent brain cognitive development (ABCD)(SM) study . Dev Cogn Neurosci . 2022 ; 54 : 101081 . OpenUrl PubMed 26. ↵ Garavan H , Bartsch H , Conway K , et al. Recruiting the ABCD sample: Design considerations and procedures . Dev Cogn Neurosci . 2018 ; 32 : 16 – 22 . OpenUrl CrossRef PubMed 27. ↵ Lisdahl KM , Sher KJ , Conway KP , et al. Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods . Dev Cogn Neurosci . 2018 ; 32 : 80 – 96 . OpenUrl PubMed 28. ↵ Wright KD , Ford JL , Perazzo J , et al. Collecting Hair Samples for Hair Cortisol Analysis in African Americans . J Vis Exp . 2018 ( 136 ). 29. ↵ Hill VA , Schaffer MI , Stowe GN . Carboxy-THC in Washed Hair: Still the Reliable Indicator of Marijuana Ingestion . J Anal Toxicol . 2016 ; 40 ( 5 ): 345 – 349 . OpenUrl CrossRef PubMed 30. ↵ Robinson SM , Sobell LC , Sobell MB , Leo GI . Reliability of the Timeline Followback for cocaine, cannabis, and cigarette use . Psychol Addict Behav . 2014 ; 28 ( 1 ): 154 – 162 . OpenUrl CrossRef PubMed 31. ↵ Sobell LC , Sobell MB . Timeline Follow-Back . Humana Press ; 1992 . 32. ↵ Lisdahl KM , Tapert S , Sher KJ , et al. Substance use patterns in 9-10 year olds: Baseline findings from the adolescent brain cognitive development (ABCD) study . Drug Alcohol Depend . 2021 ; 227 : 108946 . OpenUrl CrossRef PubMed 33. ↵ Zhang L , Celhay O , Das B , et al. ABCDscores: An R package for computing summary scores in the ABCD Study® . bioRxiv 2025 :2025.2009.2004.674066. 34. ↵ Fan CC , Marshall A , Smolker H , et al. Adolescent Brain Cognitive Development (ABCD) study Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data . Dev Cogn Neurosci . 2021 ; 52 : 101030 . OpenUrl CrossRef PubMed 35. ↵ Wade NE , Wallace AL , Baca R , et al. Cannabinoid Profiling Across Toxicology Samples in Adolescents and Young Adults by Route of Administration and in Relation to Depression Symptoms . Journal of Analytical Toxicology . 2025 : bkaf051 . 36. ↵ Pascali JP , Giorgetti A , Pelletti G , et al. Determination of ethyl glucuronide in hair and self-reported alcohol consumption in university students . Forensic Sci Med Pathol. 2024 ; 20 ( 3 ): 769 – 777 . OpenUrl PubMed 37. ↵ Lumley T. survey: analysis of complex survey samples . R package version 44 . 2024 . 38. ↵ Achenbach TM . The Achenbach System of Empirically Based Assessemnt (ASEBA) : Development, Findings, Theory, and Applications . 2009 , Burlington, VT . 39. ↵ van Buuren S , Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R . Journal of Statistical Software . 2011 ; 45 : 1 – 67 . OpenUrl 40. ↵ Miech RA , Johnston LD , Patrick ME , O’Malley PM. Monitoring the Future national survey results on drug use, 1975–2023 : Overview and detailed results for secondary school students . 2024 . Located at: Monitoring the Future Monograph Series , Ann Arbor, MI . 41. ↵ SAMHSA . Key substance use and mental health indicators in the United States: Results from the 2023 National Survey on Drug Use and Health (HHS Publication No. PEP24[ZJ07[ZJ021, NSDUH Series H[ZJ59) . 2024 . 42. ↵ Park-Lee E , Jamal A , Cowan H , et al. Notes from the Field: E-Cigarette and Nicotine Pouch Use Among Middle and High School Students — United States, 2024 . MMWR Morb Mortal Wkly Rep 2024 ; 73 : 774 – 778 . OpenUrl CrossRef PubMed 43. ↵ Folk JB , Hirschtritt ME , McCrary QD , Kalapatapu RK . Agreement between Youth Self-Report and Biospecimen-Confirmed Substance Use: A Systematic Review . Subst Use Misuse . 2022 ; 57 ( 4 ): 531 – 538 . OpenUrl PubMed 44. ↵ Mensch BS , Kandel DB . Underreporting of substance use in a national longitudinal youth cohort: Individual and interviewer effects . Public Opinion Quarterly . 1988 ; 52 : 100 – 124 . OpenUrl CrossRef Web of Science 45. Latkin CA , Edwards C , Davey-Rothwell MA , Tobin KE . The relationship between social desirability bias and self-reports of health, substance use, and social network factors among urban substance users in Baltimore, Maryland . Addict Behav . 2017 ; 73 : 133 – 136 . OpenUrl CrossRef PubMed 46. ↵ Johnson TP . Sources of Error in Substance Use Prevalence Surveys . Int Sch Res Notices . 2014 ; 2014 : 923290 . OpenUrl PubMed 47. ↵ Fortuna LR , Bagot KS. Editorial: Inconclusive (or "Elusive"): Concordance Between Substance Use Self-report and Toxicology for Diverse Youth Populations . J Am Acad Child Adolesc Psychiatry . 2023 ; 62 ( 7 ): 721 – 723 . OpenUrl PubMed 48. ↵ Biondi A , Freni F , Carelli C , Moretti M , Morini L . Ethyl glucuronide hair testing: A review . Forensic Sci Int . 2019 ; 300 : 106 – 119 . OpenUrl CrossRef PubMed 49. ↵ Boscolo-Berto R , Favretto D , Cecchetto G , et al. Sensitivity and specificity of EtG in hair as a marker of chronic excessive drinking: pooled analysis of raw data and meta-analysis of diagnostic accuracy studies . Ther Drug Monit . 2014 ; 36 ( 5 ): 560 – 575 . OpenUrl CrossRef PubMed 50. ↵ Huestis MA , Gustafson RA , Moolchan ET , et al. Cannabinoid concentrations in hair from documented cannabis users . Forensic Sci Int . 2007 ; 169 ( 2-3 ): 129 – 136 . OpenUrl CrossRef PubMed Web of Science 51. ↵ Mieczkowski T . Assessing the potential of a "color effect" for hair analysis of 11-nor-9-carboxy-delta(9)-tetrahydrocannabinol: analysis of a large sample of hair specimens . Life Sci . 2003 ; 74 ( 4 ): 463 – 469 . OpenUrl CrossRef PubMed 52. ↵ Agius R . Utility of coloured hair for the detection of drugs and alcohol . Drug Test Anal . 2014 ; 6 Suppl 1 : 110 – 119 . OpenUrl PubMed 53. ↵ Tassoni G , Cippitelli M , Mietti G , et al. Hair Analysis to Evaluate Polydrug Use . Healthcare (Basel) . 2021 ; 9 ( 8 ). 54. ↵ Tassoni G , Mirtella D , Zampi M , et al. Hair analysis in order to evaluate drug abuse in driver’s license regranting procedures . Forensic Sci Int . 2014 ; 244 : 16 – 19 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted April 09, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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Thompson medRxiv 2025.09.19.25336190; doi: https://doi.org/10.1101/2025.09.19.25336190 Share This Article: Copy Citation Tools Weighted Prevalence of Biochemically-Verified Substance Use in Healthy Adolescents Across the United States Natasha E. Wade , Yajuan Si , Susan F. Tapert , Janosch Linkersdörfer , Krista M. Lisdahl , Hailley R. Moore , Laila Tally , Biplabendu Das , Marilyn A. Huestis , Alexander L. Wallace , Ryan M. Sullivan , Veronica Szpak , Le Zhang , Laura Ziemer , Wesley K. 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