Evidence map›Paper›PMID 40010642›Full record

ArticleJournal of substance use and addiction treatment2025

Variations in documentation of alcohol use disorder (AUD) diagnoses across race, ethnicity, and sex in a health system that assesses AUD symptoms as part of routine primary care.

Robert L Ellis, Kevin A Hallgren, Emily C Williams, Joseph E Glass, Isaac C Rhew, Malia Oliver, Katharine A Bradley

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of substance use and addiction treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. 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

7 authors.

Robert L EllisDepartment of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA 98195, USA; University of California, Davis, Center for Healthcare Policy and Research, 4900 Broadway Suite 1430, Sacramento, CA 95820, USA; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA; Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. Electronic address: robellis@ucdavis.edu.
Kevin A HallgrenDepartment of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA 98195, USA; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA; Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. Electronic address: khallgre@uw.edu.
Emily C WilliamsDepartment of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA 98195, USA; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA; Health Services Research & Development (HSR&D) Center for Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA 98101, USA. Electronic address: emwilli@uw.edu.
Joseph E GlassKaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA. Electronic address: joseph.e.glass@kp.org.
Isaac C RhewDepartment of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. Electronic address: rhew@uw.edu.
Malia OliverKaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA. Electronic address: malia.m.oliver@kp.org.
Katharine A BradleyDepartment of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA 98195, USA; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA; Department of Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA. Electronic address: katharine.a.bradley@kp.org.

Funding

Understanding practical alcohol measures in primary care to prepare for measurement-based care: Scaled EHR measures of alcohol use and DSM-5 AUD symptomsR33AA028073 · NIAAA · UNIVERSITY OF WASHINGTON · PI HALLGREN, KEVIN A. · 2021 to 2023
$1.3M
NIAAA NIH HHS R33 AA028073
6 · The paper itself

Abstract

introductionPrior studies have highlighted potential inequities in provider-documented alcohol use disorder (AUD) across race, ethnicity, and sex. Whether subgroup differences in AUD reflect true variation or diagnostic disparities is unknown. This study aims to describe variations in the prevalence of provider-documented AUD across race, ethnicity, and sex: 1) after adjustment for alcohol consumption, and 2) after additional adjustment for patient-reported AUD symptoms.

methodsIn Kaiser Permanente Washington, patients with high-risk drinking (AUDIT-C score 7-12; 2.4 % of screened patients) complete a validated Alcohol Symptom Checklist of DSM-5 AUD symptoms with results documented in electronic health records. This study included Asian, Black, Latine, and White patients in primary care settings (03/2015-02/2022) who indicated high-risk drinking and thus completed an Alcohol Symptom Checklist. The prevalence of AUD was estimated for women and men across race or ethnic groups using marginally standardized generalized linear models. Models were first unadjusted, then adjusted for consumption (AUDIT-C scores 7-12), and then consumption plus AUD symptom counts (0-11).

resultsAmong 14,442 patients with high-risk drinking (6.0 % Asian, 5.8 % Black, 7.8 % Latine, 80.4 % White; 32.1 % women), provider-documented AUD increased with alcohol consumption and the number of AUD symptoms. The prevalence of AUD across 8 subgroups defined by race, ethnicity, and sex varied in analyses adjusted for alcohol consumption alone (range 11.6 % [95 % CI: 9.3-14.4] to 20.2 % [18.9-21.5]). However, after adjustment for both alcohol consumption and AUD symptoms, the prevalence of AUD ranged from 11.2 % [95 % CI: 7.9-15.6] to 15.0 % [95 % CI: 13.9-16.3] in women, and from 11.0 % [95 % CI: 8.7-13.8] to 15.1 % [95 % CI: 14.3-16.0] in men. AUD did not appear to vary across race or ethnicity.

conclusionsIn this study of primary care patients with high-risk drinking in a regional healthcare system that routinely assesses AUD symptoms, variations in provider-documented AUD diagnosis across race, ethnicity, and sex were observed after adjusting for alcohol consumption but were diminished after adjusting for AUD symptoms. This may suggest that among patients with similar alcohol consumption and AUD symptoms, intersectional variations in AUD diagnosis may be less apparent. Assessing AUD severity with Alcohol Symptom Checklists may help support equitable clinical AUD diagnosing.

Indexed as

AlcoholismHispanic or LatinoPrimary Health CareRacial GroupsAdultAlcohol-Related DisordersAsianBlack or African AmericanElectronic Health RecordsFemaleHumansMaleMiddle AgedPrevalenceSex FactorsWashingtonAlcohol consumptionAlcohol symptomsAUD diagnosisEthnicityRaceSex

Identifiers

PMID40010642
PMCPMC13082686

What OpenQuestion holds

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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.