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Underreporting of past-year cannabis use on a national survey by people who smoke blunts.

Austin LeBenjamin H HanJoseph J Palamar
Published in: Substance abuse (2021)
Background: Accurate prevalence estimates are critical to epidemiological research but discordant responses on self-report surveys can lead to over- or underestimation of drug use. We sought to examine the extent and nature of underreported cannabis use (among those later reporting blunt use) from a national drug survey in the US. Methods: We used data from the 2015-2019 National Survey on Drug Use and Health (N = 281,650), a nationally representative probability sample of non-institutionalized populations in the US. We compared self-reported prevalence of past-year cannabis use and blunt use and delineated correlates of underreporting cannabis use, defined as reporting blunt use but not overall cannabis use. Results: An estimated 4.8% (95% CI: 4.4-5.2) of people reported blunt use but not cannabis use. Although corrected prevalence, cannabis use recoded as use only increased from 15.2% (95% CI: 15.0-15.4) to 15.5% (95% CI: 15.3-15.7), individuals who are aged ≥50 (aOR = 1.81, 95% CI: 1.06-3.08), female (aOR = 1.35, 95% CI: 1.12-1.62), Non-Hispanic Black (aOR = 1.43, 95% CI: 1.16-1.76), or report lower English proficiency (aOR = 3.32, 95% CI: 1.40-7.83) are at increased odds for providing such a discordant response. Individuals with a college degree (aOR = 0.57, 95% CI: 0.39-0.84) and those reporting past-year use of tobacco (aOR = 0.75, 95% CI: 0.62-0.91), alcohol (aOR = 0.42, 95% CI: 0.33-0.54), cocaine (aOR = 0.50, 95% CI: 0.34-0.73), or LSD (aOR = 0.52, 95% CI: 0.31-0.87) were at lower odds of providing a discordant response. Conclusion: Although changes in prevalence are small when correcting for discordant responses, results provide insight into subgroups that may be more likely to underreport use on surveys.
Keyphrases
  • risk factors
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  • emergency department
  • mass spectrometry
  • climate change
  • high resolution
  • electronic health record
  • deep learning
  • human health
  • drug induced