Evidence map›Paper›PMID 34990827›Full record

ArticleAnnals of epidemiology2022

What is the prevalence of drug use in the general population? Simulating underreported and unknown use for more accurate national estimates.

Natalie S Levy, Joseph J Palamar, Stephen J Mooney, Charles M Cleland, Katherine M Keyes

Abstract read
In one paragraph

Article in Annals of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Productivity Losses From Substance Use Disorder in the U.S. in 2023.American journal of preventive medicine · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Natalie S LevyDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York City, NY. Electronic address: nsl2110@columbia.edu.
Joseph J PalamarDepartment of Population Health, New York University Grossman School of Medicine, New York City, NY; Center for Drug Use and HIV/HCV Research, New York University School of Global Public Health, New York City, NY.
Stephen J MooneyDepartment of Epidemiology, University of Washington School of Public Health, New York City, NY.
Charles M ClelandDepartment of Population Health, New York University Grossman School of Medicine, New York City, NY; Center for Drug Use and HIV/HCV Research, New York University School of Global Public Health, New York City, NY.
Katherine M KeyesDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York City, NY.

Funding

Drug use among nightclub and dance festival attendees in New York CityR01DA044207 · NIDA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI PALAMAR, JOSEPH J · 2018 to 2021
$1.5M
The Automatic Context Measurement Tool: bringing environmental data to non-specialistsR00LM012868 · NLM · UNIVERSITY OF WASHINGTON · PI MOONEY, STEPHEN JOHN · 2019 to 2021
$687k
NIDA NIH HHS R01 DA044207NLM NIH HHS R00 LM012868
6 · The paper itself

Abstract

purposeTo 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.

methodsWe 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.

resultsSimulated 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%).

conclusionsNational 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.

Indexed as

CocaineSubstance-Related DisordersAdultHealth SurveysHumansPrevalenceSelf ReportUnited StatesCocaineAlgorithmsCannabisCocainePrevalenceQuantitative bias analysisSelf-reportSurveys

Identifiers

PMID34990827
PMCPMC9216169

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.