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 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%).
CONCLUSION: 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.
What is the prevalence of drug use in the general population? Simulating underreported and unknown use for more accurate national estimates
Annals of Epidemiology, 68, 45-53. doi: 10.1016/j.annepidem.2021.12.013. PMCID: PMC9216169.