Evidence map›Paper›PMID 33621806›Full record

ArticleDrug and alcohol dependence2021

Using ICD-10-CM codes to detect illicit substance use: A comparison with retrospective self-report.

Christopher L Rowe, Glenn-Milo Santos, Wiley Kornbluh, Sumeet Bhardwaj, Mark Faul, Phillip O Coffin

Abstract read
In one paragraph

Article in Drug and alcohol dependence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

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  8. Racial, ethnic, and sex disparities in buprenorphine treatment from emergency departments by discharge diagnosis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025
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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

6 authors.

Christopher L RoweSan Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, California, 94102, USA; University of California, Berkeley, 2121 Berkeley Way, 5th Floor, Berkeley, California, 94702, USA. Electronic address: chris.rowe@sfdph.org.
Glenn-Milo SantosSan Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, California, 94102, USA; University of California, San Francisco, 500 Parnassus Avenue, San Francisco, California, 94143, USA.
Wiley KornbluhSan Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, California, 94102, USA.
Sumeet BhardwajSan Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, California, 94102, USA; Western University, 800 Commissioners Road East, London, Ontario, N61 5W9, Canada.
Mark FaulCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, Georgia, 30329, USA.
Phillip O CoffinSan Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, California, 94102, USA; University of California, San Francisco, 500 Parnassus Avenue, San Francisco, California, 94143, USA.

Funding

Midcareer K24 Award for Mentoring and Patient-Oriented ResearchK24DA042720 · NIDA · SAN FRANCISCO DEPARTMENT OF PUBLIC HEALTH · PI PHILLIP O COFFIN · 2016 to 2026
$1.6M
Substance use outcomes of opioid dose reduction and discontinuationU01CE002793 · CE · PUBLIC HEALTH FOUNDATION ENTERPRISES · PI COFFIN, PHILLIP O · 2016 to 2017
$799k
ACL HHS U01CE002793Intramural CDC HHS CC999999NCIPC CDC HHS U01 CE002793NIDA NIH HHS K24 DA042720
6 · The paper itself

Abstract

backgroundUnderstanding whether International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes can be used to accurately detect substance use can inform their use in future surveillance and research efforts.

methodsUsing 2015-2018 data from a retrospective cohort study of 602 safety-net patients prescribed opioids for chronic non-cancer pain, we calculated the sensitivity and specificity of using ICD-10-CM codes to detect illicit substance use compared to retrospective self-report by substance (methamphetamine, cocaine, opioids [heroin or non-prescribed opioid analgesics]), self-reported use frequency, and type of healthcare encounter.

resultsSensitivity of ICD-10-CM codes for detecting self-reported substance use was highest for methamphetamine (49.5 % [95 % confidence interval: 39.6-59.5 %]), followed by cocaine (44.4 % [35.8-53.2 %]) and opioids (36.3 % [28.8-44.2 %]); higher for participants who reported more frequent methamphetamine (intermittent use: 27.7 % [14.6-42.6 %]; ≥weekly use: 67.2 % [53.7-79.0 %]) and opioid use (intermittent use: 21.4 % [13.2-31.7 %]; ≥weekly use: 52.6 % [40.8-64.2 %]); highest for outpatient visits (methamphetamine: 43.8 % [34.1-53.8 %]; cocaine: 36.8 % [28.6-45.6 %]; opioids: 33.1 % [25.9-41.0 %]) and lowest for emergency department visits (methamphetamine: 8.6 % [4.0-15.6 %]; cocaine: 5.3 % [2.1-10.5 %]; opioids: 6.3 % [3.0-11.2 %]). Specificity was highest for methamphetamine (96.4 % [94.3-97.8 %]), followed by cocaine (94.0 % [91.5-96.0 %]) and opioids (85.0 % [81.3-88.2 %]).

conclusionsICD-10-CM codes had high specificity and low sensitivity for detecting self-reported substance use but were substantially more sensitive in detecting frequent use. ICD-10-CM codes to detect substance use, particularly those from emergency department visits, should be used with caution, but may be useful as a lower-bound population measure of substance use or for capturing frequent use among certain patient populations.

Indexed as

Illicit DrugsInternational Classification of DiseasesAdultAnalgesics, OpioidChronic PainCocaineEmergency Service, HospitalFemaleHeroinHumansMaleMethamphetamineMiddle AgedOpioid-Related DisordersRetrospective StudiesSelf ReportAnalgesics, OpioidCocaineHeroinIllicit DrugsMethamphetamineCocaineDiagnostic codesElectronic health recordsHeroinMethamphetaminePrescription opioidsSubstance useSurveillance

Identifiers

PMID33621806
PMCPMC11008535

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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.