When Substance Use Is Underreported: Comparing Self-Reports and Hair Toxicology in an Urban Cohort of Young Adults

Abstract

Large-scale epidemiological research often uses self-reports to determine the prevalence of illicit substance use. Self-reports may suffer from inaccurate reporting but can be verified with objective measures. This study examined the following: the prevalence of illicit and non-medical substance use with self-reports and hair toxicology, the convergence of self-reported and objectively quantified substance use, and the correlates of under- and overreporting. The data came from a large urban cohort study of young adults (n= 1,002, mean age= 20.6 years, 50% female). The participants provided 3 cm of hair (covering the previous 3 months) and reported their illicit and non-medical substance use and their sociodemographic, psychological, and behavioral characteristics. Hair toxicology analyses targeted cannabinoids, ketamine, opiates/opioids, stimulants including 3,4-methylenedioxymethamphetamine, and relevant metabolites. Self-reports underestimated the prevalence of most substances by 30% to 60% compared to hair tests. The average detection ratio (hair test/self-report) was 1.50. Hair tests were typically more sensitive than self-reports. Underreporting was associated with a low level of that substance in hair. Self-reported delinquency and psychopathology were correlated with an increased likelihood of concordant positive self-reports and hair tests compared to underreporting. Overreporting was associated with infrequent self-reported use. Our study suggests that self-reports underestimate young adults' exposure to illicit substances and non-medical use of prescription drugs. Consequently, estimates of associations between substance use and risk factors or outcomes are likely biased. Combining self-reports with hair tests may be most beneficial in study samples with occasional substance use. Researchers can use specific factors (eg, detection ratios) to adjust prevalence estimates and correlations based on self-reports.