PURPOSE: To outline a method for obtaining more accurate estimates of drug use in the United States (US) general population by correcting survey data for underreported and unknown drug use.
METHODS: We simulated a population (n = 100,000) reflecting the demographics of the US adult population per the 2018 American Community Survey. Within this population, we simulated the "true" and self-reported prevalence of past-month cannabis and cocaine use by using available estimates of underreporting. We applied our algorithm to samples of the simulated population to correct self-reported estimates and recover the "true" population prevalence, validating our approach. We applied this same method to 2018 National Survey on Drug Use and Health (NSDUH) data to produce a range of underreporting-corrected estimates.
RESULTS: Simulated self-report sensitivities varied by drug and sampling method (cannabis: 77.6%-78.5%, cocaine: 14.3%-22.1%). Across repeated samples, mean corrected prevalences (calculated by dividing self-reported prevalence by estimated sensitivity) closely approximated simulated "true" prevalences. Applying our algorithm substantially increased 2018 NSDUH estimates (self-report: cannabis = 10.5%, cocaine = 0.8%; corrected: cannabis = 15.6%-16.6%, cocaine = 2.7%-5.5%).
CONCLUSIONS: National drug use prevalence estimates can be corrected for underreporting using a simple method. However, valid application of this method requires accurate data on the extent and correlates of misclassification in the general US population.