Evidence map›Paper›PMID 39215378›Full record

ArticleAddiction science & clinical practice2024

Prevalence of alcohol use disorders documented in electronic health records in primary care across intersections of race or ethnicity, sex, and socioeconomic status.

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

Abstract read
In one paragraph

Article in Addiction science & clinical practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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. rle2126@uw.edu.ORCID 0000-0002-1606-4661
Kevin A HallgrenDepartment of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, 98195, USA.
Emily C WilliamsDepartment of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, 98195, USA.
Joseph E GlassKaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA.
Isaac C RhewDepartment of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA.
Malia OliverKaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA.
Katharine A BradleyDepartment of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, 98195, USA.

Funding

Scientific/Technical CoreP2CHD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI SARA R. CURRAN · 2017 to 2026
$6.3M
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 AA028073NIAAA NIH HHS R33AA028073NICHD NIH HHS P2C HD042828
6 · The paper itself

Abstract

backgroundDiagnosis of alcohol use disorder (AUD) in primary care is critical for increasing access to alcohol treatment. However, AUD is underdiagnosed and may be inequitably diagnosed due to societal structures that determine access to resources (e.g., structural racism that limits opportunities for some groups and influences interpersonal interactions in and beyond health care). This study described patterns of provider-documented AUD in primary care across intersections of race, ethnicity, sex, and community-level socioeconomic status (SES).

methodsThis cross-sectional study used EHR data from a regional healthcare system with 35 primary care clinics that included adult patients who completed alcohol screenings between 3/1/2015 and 9/30/2020. The prevalence of provider-documented AUD in primary care based on International Classification of Diseases-9 (ICD-9) and ICD-10 diagnoses was compared across intersections of race, ethnicity, sex, and community-level SES.

resultsAmong 439,375 patients, 6.6% were Latine, 11.0% Asian, 5.4% Black, 1.3% Native Hawaiian/Pacific Islander (NH/PI), 1.5% American Indian/Alaska Native (AI/AN), and 74.2% White, and 58.3% women. The overall prevalence of provider-documented AUD was 1.0% and varied across intersecting identities. Among women, the prevalence was highest for AI/AN women with middle SES, 1.5% (95% CI 1.0-2.3), and lowest for Asian women with middle SES, 0.1% (95% CI 0.1-0.2). Among men, the prevalence was highest for AI/AN men with high and middle SES, 2.0% (95% CI 1.1-3.4) and 2.0% (95% CI 1.2-3.2), respectively, and lowest for Asian men with high SES, 0.5% (95% CI 0.3-0.7). Black and Latine patients tended to have a lower prevalence of AUD than White patients, across all intersections of sex and SES except for Black women with high SES. There were no consistent patterns of the prevalence of AUD diagnosis that emerged across SES.

conclusionThe prevalence of provider-documented AUD in primary care was highest in AI/AN men and women and lowest in Asian men and women. Findings of lower prevalence of provider-documented AUD in Black and Hispanic than White patients across most intersections of sex and SES differed from prior studies. Findings may suggest that differences in access to resources, which vary in effects across these identity characteristics and lived experiences, influence the diagnosis of AUD in clinical care.

Indexed as

AlcoholismElectronic Health RecordsEthnicityPrimary Health CareSocial ClassAdolescentAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedPrevalenceRacial GroupsSex FactorsAlcoholPrimary careRaceSESSex

Identifiers

PMID39215378
PMCPMC11365182

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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.