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. 2024 Oct 8;7(10):e2438128. doi: 10.1001/jamanetworkopen.2024.38128

Incidence of Hospitalizations Involving Alcohol Withdrawal Syndrome in a Primary Care Population

Tessa L Steel 1,2,✉, Theresa E Matson 3, Kevin A Hallgren 4, Malia Oliver 3, Helen E Jack 5, Douglas Berger 5,6, Katharine A Bradley 3,5
PMCID: PMC11581492  PMID: 39378033

Key Points

Question

How often do primary care patients in an integrated health system experience hospitalizations involving alcohol withdrawal syndrome (AWS)?

Findings

In this cohort study of more than 500 000 adult primary care patients, AWS was common during hospitalizations, especially in men aged 30 to 49 years (9%-11% of hospitalizations) and individuals with high-risk alcohol screening (23%-44% of hospitalizations). Alcohol withdrawal syndrome was generally as common or more common than complications of other chronic conditions (eg, diabetes, hypertension) during hospitalizations in patients younger than 60 years.

Meaning

Alcohol withdrawal syndrome is common during hospitalizations, highlighting opportunities for health system interventions to improve prevention and treatment of alcohol use disorder and its consequences.

This cohort study evaluates the incidence of hospitalizations involving alcohol withdrawal syndrome among adult primary care patients.

Abstract

Importance

Alcohol withdrawal syndrome (AWS) is an important cause and complication of hospitalizations. Although common and preventable, the incidence of AWS during hospitalizations is poorly described.

Objective

To evaluate the incidence and proportional incidence of hospitalizations involving AWS in an adult primary care population overall and across patient characteristics.

Design, Setting, and Participants

This retrospective cohort study used electronic health records and insurance claims from Kaiser Permanente Washington (KPWA) between July 1, 2018, and June 30, 2022. The study included adults with 1 or more primary care visits during this period or the year prior, where primary care included annual standardized alcohol screening using the Alcohol Use Disorders Identification Test–Consumption (AUDIT-C).

Exposures

Age, sex, race, Hispanic ethnicity, AUDIT-C scores, and comorbid diagnoses.

Main Outcome and Measures

Hospitalizations involving AWS were defined by diagnosis codes documented during hospitalizations (incidence numerator). Time enrolled in KPWA determined person-enrolled-years (incidence denominator). Proportional incidence was calculated as the incidence of hospitalizations involving AWS divided by the incidence of all-cause hospitalizations. Proportional incidence was also estimated for hospitalizations involving other common chronic conditions (chronic obstructive pulmonary disease, diabetes, heart failure, and hypertension), which were also defined using hospital diagnosis codes.

Results

Among 544 825 adults engaged in primary care (mean [SD] age, 47.0 [17.9] years; 310 069 [56.9%] female; 3656 [0.7%] American Indian or Alaska Native, 55 206 [10.1%] Asian, 25 406 [4.7%] Black, 5204 [1.0%] Native Hawaiian or Other Pacific Islander, 365 780 [67.1%] White, 19 791 [3.6%] multiracial, 15 963 [2.9%] other races, and 53 819 [9.9%] unknown race; 33 987 [6.2%] Hispanic, 414 269 [76.0%] not Hispanic, and 96 569 [17.7%] unknown ethnicity), incidence of hospitalizations involving AWS was 169 (95% CI, 159-179) per 100 000 person-enrolled-years overall but as high as 15 347 (95% CI, 13 502-17 331) in patients with other alcohol-attributable diagnoses. The proportional incidence of hospitalizations involving AWS was 2.3% overall, with variation by age, sex, and AUDIT-C scores (eg, 9%-11% in male patients aged 30-49 years and 23%-44% in patients with high-risk AUDIT-C scores of 7-12 points). In most cases, among adults younger than 60 years, proportional incidence of hospitalizations involving AWS matched or surpassed that of other common chronic conditions (chronic obstructive pulmonary disease, diabetes, heart failure, and hypertension).

Conclusions and Relevance

In this cohort study of a large primary care population served by an integrated health system, AWS hospitalizations were common, especially in male patients, younger age groups, and individuals with high-risk alcohol use. During hospitalizations, the burden of AWS was similar to or exceeded complications of other chronic diseases that receive greater medical attention.

Introduction

Alcohol withdrawal syndrome (AWS) is a highly morbid,1,2 potentially fatal3 cause and complication of hospitalizations. Alcohol withdrawal syndrome is indicative of physical dependence on alcohol, a diagnostic criterion for alcohol use disorder (AUD). Alcohol use disorder is associated with a host of health risks beyond AWS, including organ failure, severe infections, suicide, and death.1,4,5,6,7,8 Complications of AUD are increasing, frequently lead to hospitalization,5,6,9 and have been linked to declining life expectancy in the US.10,11,12,13,14 Although common and preventable, little is known about the incidence of hospitalizations involving AWS, particularly in primary care populations served by health care systems that could support proactive identification and management of AUD and its complications.15

Few studies have evaluated the epidemiology of inpatient AWS. A brief report from a multihospital system in Delaware found that rates of inpatient AWS increased by 34% during the COVID-19 pandemic.16 Other studies describing prevalence or incidence of inpatient AWS have focused on single hospitals17,18,19,20 or hospitalizations within the Veterans Health Administration (VHA).21,22 In 2013, the estimated prevalence of inpatient AWS was 5% to 6% in a national sample of VHA patients that was 96% male with a mean age of 68 years. These prior studies have limited generalizability.

For several common chronic conditions (chronic obstructive pulmonary disease [COPD], diabetes, heart failure, and hypertension), prevention of disease-related hospitalizations is both a treatment goal and measure of (poor) quality care that can be monitored by health care systems.15,23,24,25 Similar to hospitalizations for acute complications of diabetes or heart failure, inpatient AWS indicates opportunities for improvement of another common chronic disease—AUD. Despite increased focus on identification and treatment of AUD in primary care settings,26 however, less attention has been given to prevention of hospitalizations (and rehospitalizations) involving AWS as a treatment outcome or quality measure.

This study primarily evaluated the incidence of hospitalizations involving AWS across age, sex, race, ethnicity, patient-reported alcohol screening scores, and comorbid diagnoses in a large primary care sample served by an integrated health care system. Secondarily, the incidence of hospitalizations involving AWS was compared with the incidence of hospitalizations involving other common chronic conditions conventionally used to monitor the quality of primary care: COPD, diabetes, heart failure, and hypertension.27

Methods

Study Design and Setting

This was a retrospective cohort study of patients seen in primary care in Kaiser Permanente Washington (KPWA), which is an integrated health system that included 30 primary care clinics across Washington State at the time of this study. Kaiser Permanente Washington serves diverse individuals, with distributions of age, sex, race, and Hispanic ethnicity that are similar to the population of Washington.28 As a payer and provider of health care services, KPWA has outpatient electronic health record (EHR) data and both inpatient and outpatient insurance claim data. Between 2015 and 2018, KPWA implemented standardized protocols for annual alcohol screening in primary care using the 3-question Alcohol Use Disorders Identification Test–Consumption (AUDIT-C).29,30 Annual AUDIT-C screening rates among primary care patients have since exceeded 80%.30,31 The KPWA Health Research Institute’s Institutional Review Board approved the study with a waiver of consent and Health Insurance Portability and Accountability Act authorization due to minimal risk to human participants and impracticability of conducting the research without a waiver. This report follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for observational studies.

Sample

Adults (aged ≥18 years) insured by a KPWA health plan between July 1, 2018, and June 30, 2022, with at least 1 clinician visit in a primary care clinic during the same period or prior year were included. The sample was restricted to primary care patients to evaluate the association between AUDIT-C scores and hospitalizations involving AWS and based on guidelines recommending prevention and treatment of AUD in primary care settings.26,32,33,34 Individuals were excluded if their hospitalization records were not captured by KPWA claims data (ie, 7% of patients covered by managed Medicaid plans).

Measures

Primary Outcome: AWS Hospitalizations

For the main analyses, AWS hospitalizations were defined by primary or secondary AWS billing diagnosis codes (ie, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] codes) (eTable 1 in Supplement 1), documented any time during a hospitalization from July 1, 2018, to June 30, 2022. Only hospitalizations 24 hours or longer were included, and individuals could contribute more than 1 AWS hospitalization.35,36 We used primary or secondary diagnosis codes to define the primary outcome because AWS is often considered a secondary complication during hospitalizations focused primarily on other medical or surgical issues.20

Secondary Outcomes: Hospitalizations Involving ACSCs

To compare hospitalizations involving AWS with those involving other common chronic conditions, we used published ICD-10 codes to identify ambulatory care sensitive conditions (ACSCs): COPD, diabetes, heart failure, and hypertension (eTable 1 in Supplement 1).27 Because hospitalizations for ACSCs are conventionally defined using only primary hospital diagnosis codes,27 for secondary analyses comparing inpatient AWS and ACSCs, hospitalizations for AWS were likewise defined by primary diagnosis codes only. This approach was chosen to facilitate comparisons between AWS and ACSCs, acknowledging the likelihood that clinically significant AWS would be disproportionately underestimated; AWS coded as secondary likely reflects a true complication during hospitalization, whereas diabetes or heart failure (for example) coded as secondary may reflect a complication or merely ongoing management of a stable comorbidity.

Other Measures

Time enrolled in a KPWA health plan during the study was used to compute person-enrolled-years. Demographic characteristics collected from patients and documented in the EHR by the health system were used to approximate patients’ identities and lived experiences. These characteristics included age at hospital admission (18-29, 30-39, 40-49, 50-59, 60-69, and ≥70 years), sex listed on forms of identification used for health plan enrollment (female or male), self-identified race (American Indian or Alaska Native, Asian, Black, Native Hawaiian or Other Pacific Islander, White, multiracial [ie, multiple race selections], other racial groups [cannot be further specified], or unknown), and Hispanic or non-Hispanic ethnicity. The AUDIT-C scores (0-12 points) are the sum of scores for 3 questions about frequency and quantity of alcohol use (0-4 points each). Patients were categorized as no AUDIT-C completed, no alcohol use (0 points), or low (1-2 points for women and 1-3 points for men), unhealthy (3-6 points for women and 4-6 points for men), high-risk (7-8 points), or very high-risk (9-12 points) use.7,37,38 For patients with more than 1 AUDIT-C screen during the study period, the first screen was selected. Outpatient ICD-10 codes documented any time during the study period were used to identify common comorbid diagnoses associated with AUD (eTable 1 in Supplement 1).39,40,41 All-cause hospitalizations were defined in the same manner as hospitalizations involving AWS or ACSCs but without restriction to specific diagnoses (eTable 2 in Supplement 1).

Statistical Analysis

To describe and evaluate patterns of hospitalizations involving AWS, we estimated the incidence in the overall sample and across age categories. Incidence rates were calculated by dividing the total number of events (ie, eligible hospitalizations) by person-enrolled-years and multiplying by 100 000 to express events per 100 000 person-enrolled-years. Proportional incidence was calculated by dividing the incidence of AWS by the incidence of all-cause hospitalizations. The CIs were estimated using nonparametric bootstrapping with 10 000 resamples. We analyzed the incidence and proportional incidence of hospitalizations involving AWS across demographic and clinical characteristics. Furthermore, we stratified age-specific proportional incidence estimates by sex, race, ethnicity, and patient-reported levels of alcohol consumption (AUDIT-C categories) to understand patterns within intersections of these factors. Finally, we estimated incidence and proportional incidence of ACSCs for comparison to AWS estimates. As above, because ACSCs are conventionally measured using only primary diagnoses, hospitalizations involving AWS vs ACSCs were secondarily defined using only primary diagnosis codes. All analyses were conducted using R, version 4.2.3 (R Project for Statistical Computing).

Results

Characteristics of the KPWA sample (N = 544 825) are presented in Table 1. Of the included adults, 280 811 (51.5%) were enrolled in KPWA care for more than 3 years. Sample eligibility required at least 1 primary care visit; 453 589 patients (83.3%) had at least 2 primary care encounters with the health care system during the study period (median [IQR], 5 [2-11] encounters). The sample spanned all age groups, with the largest being aged 18 to 29 years (114 900 [21.1%]) and smallest being 70 years or older (63 402 [11.6%]); the mean (SD) age was 47.0 (17.9) years. A total of 310 069 patients (56.9%) were female, 234 754 (43.1%) male, and 2 (<0.01%) of unknown sex; 3656 (0.7%) identified as American Indian or Alaska Native, 55 206 (10.1%) Asian, 25 406 (4.7%) Black, 5204 (1.0%) Native Hawaiian or Other Pacific Islander, 365 780 (67.1%) White, 19 791 (3.6%) multiracial, 15 963 (2.9%) other races (cannot be further specified), and 53 819 (9.9%) unknown race; 33 987 (6.2%) were Hispanic, 414 269 (76.0%) non-Hispanic, and 96 569 (17.7%) of unknown ethnicity. Anxiety (157 370 [28.9%]) and mood (115 314 [21.2%]) disorders were the most common outpatient diagnoses evaluated, followed by chronic pulmonary conditions (75 240 [13.8%]), diabetes (57 621 [10.6%]), and cancer (36 338 [6.7%]). Only 15 333 patients (2.8%) had a documented AUD diagnosis, but 118 915 (21.8%) screened positive for unhealthy or greater alcohol use (AUDIT-C score >2 points for women and >3 points for men).

Table 1. Characteristics of the Study Patients .

Characteristic No. (%) of patients (N = 544 825)
Age group, y
18-29 114 900 (21.1)
30-39 100 175 (18.4)
40-49 85 056 (15.6)
50-59 92 925 (17.1)
60-69 88 367 (16.2)
≥70 63 402 (11.6)
Sex
Female 310 069 (56.9)
Male 234 754 (43.1)
Unknown 2 (<0.01)
Race
American Indian or Alaska Native 3656 (0.7)
Asian 55 206 (10.1)
Black 25 406 (4.7)
Native Hawaiian or Other Pacific Islander 5204 (1.0)
White 365 780 (67.1)
Multiracial 19 791 (3.6)
Othera 15 963 (2.9)
Unknown 53 819 (9.9)
Ethnicity
Hispanic 33 987 (6.2)
Not Hispanic 414 269 (76.0)
Unknown 96 569 (17.7)
Time enrolled in KPWA during the study period, y
>3 280 811 (51.5)
1-3 172 027 (31.6)
<1 91 987 (16.9)
Outpatient diagnoses
Alcohol-attributable diagnosesb 1749 (0.3)
Alcohol use disorder 15 333 (2.8)
Anxiety disorder 157 370 (28.9)
Cancer (other than nonmelanoma skin cancer) 36 338 (6.7)
Cerebrovascular disease 19 225 (3.5)
Chronic kidney disease 30 083 (5.5)
Chronic liver disease 28 066 (5.2)
Chronic pulmonary conditions 75 240 (13.8)
Dementia 10 436 (1.9)
Diabetes 57 621 (10.6)
Heart failure 19 049 (3.5)
Mood disorder (depression, bipolar) 115 314 (21.2)
Nicotine use disorder 35 530 (6.5)
Other substance use disorder 11 535 (2.1)
Peptic ulcer disease 3964 (0.7)
Posttraumatic stress disorder 16 210 (3.0)
Psychotic disorder 3357 (0.6)
Alcohol consumption (outpatient AUDIT-C screening scores, 0-12 points)
Very high-risk use (9-12 points) 3740 (0.7)
High-risk use (7-8 points) 8828 (1.6)
Unhealthy use (3-6 points female/4-6 points male) 118 915 (21.8)
Low use (1-2 points female/1-3 points male) 195 127 (35.8)
No use (0 points) 128 518 (23.6)
No AUDIT-C screen completed 89 697 (16.5)
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Abbreviations: AUDIT-C, Alcohol Use Disorders Identification Test–Consumption; KPWA, Kaiser Permanente Washington.

a

Other races cannot be further specified.

b

Alcohol-attributable diagnoses include alcoholic psychosis, alcohol abuse, alcohol dependence syndrome, alcohol polyneuropathy, degeneration of the nervous system due to alcohol use, alcoholic myopathy, alcohol cardiomyopathy, alcoholic gastritis, alcoholic liver disease, alcohol-induced pancreatitis, and fetal alcohol syndrome (eTable 1 in Supplement 1).41

The incidence (reported per 100 000 person-enrolled years hereafter) of all-cause hospitalizations in the sample was 7306 (95% CI, 7239-7376) (eTable 2 in Supplement 1), with 108 455 all-cause hospitalizations during the study period. The overall incidence of hospitalizations involving AWS was 169 (95% CI, 159-179) (Table 2), with 1639 adults experiencing inpatient AWS, of whom 437 (26.7%) had more than 1 hospitalization involving AWS during the study period. The proportional incidence of hospitalizations involving AWS among all-cause hospitalizations was 2.3% (95% CI, 2.2%-2.4%).

Table 2. Incidence of Hospitalizations Involving AWS, Overall and by Patient Characteristics.

Characteristic IR (95% CI) for AWS hospitalizations per 100 000 person-enrolled years PI (95% CI) of AWS relative to all-cause hospitalizations, %a
Total 168.6 (158.5-179.2) 2.3 (2.2-2.4)
Age group, y
18-29 88.9 (73.6-105.7) 2.1 (1.7-2.5)
30-39 167.6 (143.4-193.2) 3.2 (2.7-3.6)
40-49 229.5 (197.1-263.8) 6.7 (5.8-7.7)
50-59 231.9 (203.7-262.5) 4.7 (4.1-5.2)
60-69 178.6 (155.1-203.5) 2.2 (1.9-2.5)
≥70 104.0 (84.3-125.4) 0.5 (0.4-0.6)
Sex
Female 116.6 (104.6-129.0) 1.5 (1.4-1.7)
Male 236.6 (218.3-254.9) 3.5 (3.2-3.7)
Race
American Indian or Alaska Native 218.4 (98.5-362.5) 2.4 (1.1-3.9)
Asian 22.4 (12.2-35.3) 0.5 (0.3-0.8)
Black 124.0 (88.2-164.8) 1.5 (1.1-2.0)
Native Hawaiian or Other Pacific Islander 14.7 (0.0-36.9) 0.2 (0.0-0.5)
White 196.9 (183.3-210.6) 2.5 (2.3-2.6)
Multiracial 116.8 (74.6-163.6) 1.9 (1.2-2.6)
Other racesb 81.5 (51.7-115.4) 1.5 (1.0-2.1)
Unknown 197.9 (159.8-239.4) 3.7 (3.0-4.4)
Ethnicity
Hispanic 151.1 (113.0-193.1) 2.4 (1.8-3.1)
Not Hispanic 166.2 (154.7-177.7) 2.1 (2.0-2.3)
Unknown 192.9 (163.5-225.4) 4.0 (3.4-4.7)
Outpatient diagnoses
Alcohol-attributable diagnosesc 15 346.7 (13 502.2-17 331.4) 39.3 (35.8-42.9)
Alcohol use disorder 5088.9 (4763.1-5419.8) 25.5 (24.1-26.9)
Anxiety disorder 398.3 (367.9-429.8) 4.1 (3.8-4.4)
Cancer (other than nonmelanoma skin cancer) 188.0 (146.3-235.6) 0.9 (0.7-1.1)
Cerebrovascular disease 421.4 (337.8-514.6) 1.3 (1.0-1.5)
Chronic kidney disease 263.2 (208.4-325.3) 0.9 (0.7-1.1)
Chronic liver disease 1226.9 (1102.8-1361.8) 6.2 (5.6-6.8)
Chronic pulmonary conditions 282.5 (249.1-317.0) 1.9 (1.7-2.2)
Dementia 536.4 (401.8-689.7) 1.2 (0.9-1.6)
Diabetes 228.9 (192.7-267.9) 1.3 (1.1-1.5)
Heart failure 525.3 (433.4-626.6) 1.1 (0.9-1.3)
Mood disorder (depression, bipolar) 508.5 (467.5-551.4) 4.1 (3.8-4.4)
Nicotine use disorder 1051.3 (953.5-1155.3) 7.2 (6.5-7.9)
Other substance use disorder 2385.1 (2101.4-2682.0) 9.1 (8.0-10.1)
Peptic ulcer disease 937.3 (692.0-1200.9) 2.5 (1.9-3.3)
Posttraumatic stress disorder 853.4 (702.7-1014.8) 6.9 (5.7-8.1)
Psychotic disorder 1256.2 (914.9-1639.3) 3.8 (2.8-4.9)
Alcohol consumption (outpatient AUDIT-C screening scores, 0-12 points)
Very high-risk alcohol use (outpatient AUDIT-C screening scores, 9-12 points) 5754.5 (5023.4-6543.8) 43.5 (39.5-47.6)
High-risk alcohol use (outpatient AUDIT-C screening scores, 7-8 points) 1241.7 (1036.6-1466.1) 22.5 (19.2-26.0)
Unhealthy alcohol use (outpatient AUDIT-C screening scores, 3-6 points for women and 4-6 points for men) 210.3 (187.1-235.1) 4.2 (3.7-4.6)
Low alcohol use (outpatient AUDIT-C screening scores, 1-2 points for women and 1-3 points for men) 59.0 (49.9-68.7) 0.9 (0.8-1.1)
No alcohol use (outpatient AUDIT-C screening scores, 0 points) 91.4 (75.7-108.8) 0.8 (0.7-1.0)
No AUDIT-C screen completed 152.8 (127.2-180.0) 2.3 (1.9-2.7)
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Abbreviations: AUDIT-C, Alcohol Use Disorders Identification Test-Consumption; AWS, alcohol withdrawal syndrome; IR, incidence rate; PI, proportional incidence.

a

Proportional incidence of AWS: incidence of AWS hospitalizations divided by incidence of all-cause hospitalizations; all-cause hospitalization data in eTable 2 in Supplement 1.

b

Other races cannot be further specified.

c

Alcohol-attributable diagnoses: alcoholic psychosis, alcohol abuse, alcohol dependence syndrome, alcohol polyneuropathy, degeneration of the nervous system due to alcohol use, alcoholic myopathy, alcohol cardiomyopathy, alcoholic gastritis, alcoholic liver disease, alcohol-induced pancreatitis, fetal alcohol syndrome (eTable1 in Supplement 1).41

Incidence of AWS Hospitalizations

Rates of hospitalizations involving AWS varied by age, sex, race, and AUDIT-C scores (Table 2). Male patients experienced higher incidence of hospitalizations with AWS vs female patients (237 [95% CI, 204-263] vs 117 [95% CI, 105-129]). Individuals identifying as American Indian or Alaska Native or White had higher incidence of hospitalizations involving AWS (incidence rates [IRs], reported per 100 000 person-enrolled years hereafter, 218.4 [95% CI, 98.5-362.5] and 196.9 [95% CI, 183.3-210.6], respectively) compared with those identifying as Asian or Native Hawaiian or Other Pacific Islander (IRs, 22.4 [95% CI, 12.2-35.3] and 14.7 [95% CI, 0.0-36.9], respectively). The incidence of AWS hospitalizations among individuals identifying as Black, multiracial, or other races fell between the aforementioned groups (IRs, 124.0 [95% CI, 88.2-164.8], 116.8 [95% CI, 74.6-163.6], and 81.5 [95% CI, 51.7-115.4], respectively). There was not strong evidence to suggest variation by Hispanic ethnicity. High incidence of hospitalizations with AWS occurred in patients with other alcohol-attributable diagnoses (IR, 15 346.7 [95% CI, 13 502.2-17 331.4]) and/or diagnosed AUD (IR, 5088.9 [95% CI, 4763.1-5419.8]). Other outpatient diagnoses associated with high incidence of hospitalizations involving AWS included other substance use disorders, chronic liver disease, psychotic disorders, and nicotine use disorders. Although individuals reporting no or low alcohol use had relatively low incidence of hospitalizations involving AWS (IR, 91.4 [95% CI, 75.7-108.8] and 59.0 [95% CI, 49.9-68.7], respectively), the incidence was progressively higher as AUDIT-C scores increased from unhealthy (3-6 points for women and 4-6 points for men) (IR, 210.3 [95% CI, 187.1-235.1]) to high-risk (7-8 points) (IR, 1241.7 [95% CI, 1036.6-1466.1]) to very high-risk (9-12 points) (IR, 5754.5 [95% CI, 5023.4-6543.8]) alcohol use.

Proportional Incidence of AWS Among All Hospitalizations

The proportional incidence of hospitalizations involving AWS varied across age groups, by sex, and by race (Figure 1; eTables 3 and 4 in Supplement 1). Proportional incidence of hospitalizations involving AWS was highest at midlife (ages 40-49 years) and lowest with advanced age (ages ≥70 years). These findings held across racial groups. Male patients aged 18 to 39 years experienced more than 6 times the proportional incidence of hospitalizations involving AWS as comparable female patients and roughly double the proportional incidence in other age groups (Figure 1A; eTable 3 in Supplement 1). Peak proportional incidence of hospitalizations involving AWS also occurred earlier in male patients (ages 30-39 years in men vs 40-49 years in women). Notably, 9.4% (95% CI, 7.9%-11.1%) to 10.5% (8.8%-12.3%) of hospitalizations in male patients aged 30 to 49 years involved AWS. The proportional incidence of hospitalizations involving AWS was highest in patients with alcohol-attributable diagnoses (39.3%; 95% CI, 35.8%-42.9%), diagnosed AUD (25.5%; 95% CI, 24.1%-26.9%), and AUDIT-C scores in the high-risk (22.5%; 95% CI, 19.2%-26.0%) and very high-risk (43.5%; 95% CI, 39.5%-47.6%) categories, across all age groups but especially in patients aged 30 to 59 years (Table 2 and Figure 2; eTable 5 in Supplement 1).

Figure 1. Variation in the Proportional Incidence of Hospitalizations Involving Alcohol Withdrawal Syndrome (AWS) Across Age Groups by Sex and Race.

Figure 1.

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Proportional incidence is relative to all-cause hospitalizations. Alcohol withdrawal syndrome is defined by primary or secondary hospital diagnosis codes. Error bars indicate 95% CIs. Precise estimates and CIs are given in eTables 3 and 4 in Supplement 1.

Figure 2. Proportional Incidence of Hospitalizations Involving Alcohol Withdrawal Syndrome (AWS) Across Age Groups by Alcohol Use Disorders Identification Test–Consumption (AUDIT-C) Screening Scores.

Figure 2.

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Proportional incidence is relative to all-cause hospitalizations. Alcohol withdrawal syndrome is defined by primary or secondary hospital diagnosis codes. Error bars indicate 95% CIs. Precise estimates and CIs are given in eTable 5 in Supplement 1.

Comparing Proportional Incidence of AWS With Other Common Chronic Conditions

Overall, the proportional incidence of hospitalizations attributed to AWS vs ACSCs were similar (using primary diagnosis codes to define each outcome): 0.7% (95% CI, 0.6%-0.8%) for AWS, 0.7% (95% CI, 0.6%-0.8%) for COPD, 0.9% (95% CI, 0.8%-1.0%) for diabetes, 2.5% (95% CI, 2.3%-2.6%) for heart failure, and 0.3% (95% CI, 0.3%-0.3%) for hypertension (eTable 6 in Supplement 1); however, these estimates varied substantially by age (Figure 3). Alcohol withdrawal syndrome was as common or more common than most ACSCs during hospitalizations of patients younger than 60 years.

Figure 3. Proportional Incidence of Hospitalizations Due to Alcohol Withdrawal Syndrome (AWS), Chronic Obstructive Pulmonary Disease (COPD), Diabetes, Heart Failure, and Hypertension Across Age Groups.

Figure 3.

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Proportional incidence is relative to all-cause hospitalizations. Each outcome is defined using primary diagnosis codes only (consistent with the conventional approach used for measuring ambulatory care sensitive conditions27). Error bars indicate 95% CIs. Precise estimates and CIs are given in eTable 6 in Supplement 1.

Discussion

This study examined the incidence of hospitalizations involving AWS in a large, diverse primary care population in an integrated health care system. Approximately 2% of adult hospitalizations involved AWS, with substantial variation by age, sex, race, and outpatient alcohol screening scores. In male patients aged 30 to 49 years, approximately 9% to 11% of hospitalizations involved AWS. Among more than 12 000 individuals with high-risk or very high-risk alcohol screening scores, approximately 23% to 44% of hospitalizations involved AWS. In most cases, among adults younger than 60 years, rates of hospitalization for AWS matched or surpassed rates of hospitalization for complications of other common chronic conditions (ie, COPD, diabetes, heart failure, and hypertension).

This study’s findings markedly augment the scant existing literature describing the epidemiology of inpatient AWS. In prior studies of VHA hospitalizations, involving predominantly male veterans, the overall prevalence of inpatient AWS was approximately 6%, with highest risk occurring at ages 40 to 49 years21,22 rather than ages 30 to 39 years in male patients served by KPWA. Geography may help explain these differences: higher rates of heavy drinking and AUD have been reported in Western regions, where the current study is situated.21,42 These differences could also reflect alcohol-related risks and cohort effects specific to veterans and the VHA (eg, specialized mental health resources reaching younger age groups in the VHA system).

Consistent with prior research in the VHA,22 patients with alcohol-related diagnoses (including those that are alcohol attributable, such as alcohol cardiomyopathy)43 and diagnosed AUD had a high burden of hospitalizations involving AWS (proportional incidence, 39.3% and 25.5%, respectively). In addition, the proportional incidence of hospitalizations involving AWS was elevated in individuals with high-risk and very high-risk AUDIT-C scores (7-8 and 9-12 points: 22.5% and 43.5%, respectively). This finding aligns with existing literature on associations between AUDIT-C alcohol screening scores and hospitalizations for all causes,7,44,45 alcohol-related diagnoses,7 and other diagnoses.46,47,48 This body of research shows that sharp increases in complications of alcohol use are associated with high-risk and very high-risk AUDIT-C scores (7-8 and 9-12 points, respectively),49,50,51 consistent with sharply increased rates of AWS identified in these groups in the current study.

In addition to characterizing age- and sex-based distributions of hospitalizations involving AWS, this study identified variation by self-identified race. Reasons for racial disparities involving complications of alcohol use are complex and tied to historical patterns of racism and persistent structural racism, which also vary by geographic location.52 The incidence of hospitalizations involving AWS was relatively higher among individuals identifying as American Indian or Alaska Native or White and relatively lower among individuals identifying as Asian or Native Hawaiian or Other Pacific Islander; this finding is consistent with prior literature suggesting higher and lower prevalence of AUD in these racial groups, respectively.52 Although these findings add to sparse existing data on race and AWS, small sample sizes after stratification by race and age limited precision of the estimates. Ultimately, this topic requires more nuanced research using larger, targeted samples; race-conscious frameworks; and measures that explicitly address racial discrimination at individual and environmental levels.53

Relative to disease burden,11,54 AUD has been neglected by the medical profession and health care systems: less than a quarter of patients with AUD receive evidenced-based treatments for their disease.55,56,57,58,59 The current study has several implications for clinicians, health system leaders, and policymakers seeking to address these gaps in care. We compared inpatient AWS with hospitalizations for other chronic diseases to assess the magnitude of the problem; this comparison may also suggest appropriate solutions. Hospitalizations for diabetes or heart failure, for example, represent severe manifestations of chronic illnesses that may or may not be previously diagnosed. Hospitalizations often prompt targeted interventions for affected patients, including specific preventive programs for those at highest risk for recurrent complications and health care system–wide efforts to improve disease management (eg, accurate diagnosis, appropriate pharmacotherapy, and behavioral interventions).15,60,61,62,63

Similar responses to AWS could improve outcomes of AUD. Outpatient clinicians can identify AUD using the AUDIT-C and an Alcohol Symptom Checklist34,37,64 and offer treatment to patients at risk for complications,30 including medications, counseling, residential programs, and peer support.33,65,66,67,68,69 Inpatient clinicians can anticipate AWS when patients with high-risk alcohol use are admitted to the hospital, consider prophylactic treatment,70,71 and use the hospitalization to engage, treat, and support patients in effective transitions to long-term AUD care8; these efforts are particularly important for reducing readmissions involving AWS, which are common.36,68 Preoperative abstinence programs are effective for improving surgical outcomes,72,73 but complementary strategies (eg, reduction in preoperative alcohol use and prophylactic treatment of AWS during unplanned admissions) should also be evaluated. Finally, researchers and health care system leaders could monitor AWS during hospitalizations as a marker of quality care for AUD. Efforts to prevent hospitalizations involving complications of other chronic conditions have led to defined ACSCs,27 which are monitored over time to evaluate the accessibility and quality of outpatient care.15,24,25 Hospitalizations involving AWS could be similarly used to assess and monitor the population burden of AUD in addition to other indicators of system performance (eg, rates of completed and positive alcohol screening, pharmacotherapy, and counseling).

Limitations

This study has some limitations. ICD-10 codes likely underestimate true AWS incidence because AWS is often not the primary focus of hospitalizations and thus is often undocumented or undetected.20,74,75 Identification of AWS using ICD-10 codes may also be biased by stereotypes (eg, unrecognized AWS associated with advanced age, female sex, and/or certain socioeconomic and racial groups).76 Unfortunately, no viable alternative methods for identifying AWS in large administrative datasets currently exist. The AUDIT-C scores used in this study could be biased by factors such as social desirability.77 Although the reliability of AUDIT-C scores is high in this study’s setting,78 patients can change or underreport alcohol use: 2% of hospitalizations of middle-aged adults in this sample who reported no or minimal alcohol use on the AUDIT-C nonetheless involved AWS. This finding could also reflect the timing of AUDIT-C screening, given that the first available AUDIT-C score was used during the 3-year study period. Additionally, this study focused on a single health care system serving an insured population in Washington State, which may limit generalizability given that AUD and AWS vary by geographic, social, economic, and cultural factors.21,52,79 Analogous studies in other health care systems are needed.

Conclusions

In this cohort study of a large primary care population in an integrated health care system, hospitalizations involving AWS were common, particularly in male patients, younger age groups, and individuals with high-risk alcohol screening and/or alcohol-related diagnoses. The incidence and proportional incidence of hospitalizations involving AWS varied considerably by age, sex, race, and outpatient alcohol screening scores. In adults younger than 60 years, the burden of hospitalizations attributed to AWS generally matched or surpassed hospitalizations for other chronic conditions that receive greater medical attention. The high incidence of hospitalizations involving AWS reinforces the need for health care systems to prioritize identification, treatment, and monitoring of AUD.

Supplement 1.

eTable 1. ICD-10 Codes used to define alcohol withdrawal syndrome (AWS), chronic ambulatory care sensitive conditions (ACSCs), and common outpatient diagnoses associated with alcohol use disorder (AUD)

eTable 2. Incidence of alcohol withdrawal syndrome (AWS) hospitalizations, overall and by patient characteristics, including all-cause hospitalization data

eTable 3. Proportional incidence of alcohol withdrawal syndrome (AWS) hospitalizations relative to all-cause hospitalizations across age groups by sex

eTable 4. Proportional incidence of alcohol withdrawal syndrome (AWS) hospitalizations relative to all-cause hospitalizations across age groups by race

eTable 5. Proportional incidence of alcohol withdrawal syndrome (AWS) hospitalizations relative to all-cause hospitalizations across age groups by Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) category

eTable 6. Proportional incidence of primary ICD-10 diagnosis codes only for alcohol withdrawal syndrome (AWS), heart failure, chronic obstructive pulmonary disease (COPD), diabetes, and hypertension hospitalizations relative to all-cause hospitalizations across age, race, and ethnicity categories

jamanetwopen-e2438128-s001.pdf (206.2KB, pdf)
Supplement 2.

Data Sharing Statement

jamanetwopen-e2438128-s002.pdf (13.3KB, pdf)

References

  • 1.O’Brien JM Jr, Lu B, Ali NA, et al. Alcohol dependence is independently associated with sepsis, septic shock, and hospital mortality among adult intensive care unit patients. Crit Care Med. 2007;35(2):345-350. doi: 10.1097/01.CCM.0000254340.91644.B2 [DOI] [PubMed] [Google Scholar]
  • 2.Gupta NM, Lindenauer PK, Yu PC, et al. Association between alcohol use disorders and outcomes of patients hospitalized with community-acquired pneumonia. JAMA Netw Open. 2019;2(6):e195172. doi: 10.1001/jamanetworkopen.2019.5172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Monte R, Rabuñal R, Casariego E, López-Agreda H, Mateos A, Pértega S. Analysis of the factors determining survival of alcoholic withdrawal syndrome patients in a general hospital. Alcohol Alcohol. 2010;45(2):151-158. doi: 10.1093/alcalc/agp087 [DOI] [PubMed] [Google Scholar]
  • 4.Clark BJ, Keniston A, Douglas IS, et al. Healthcare utilization in medical intensive care unit survivors with alcohol withdrawal. Alcohol Clin Exp Res. 2013;37(9):1536-1543. doi: 10.1111/acer.12124 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Westerhausen D, Perkins AJ, Conley J, Khan BA, Farber M. Burden of substance abuse-related admissions to the medical ICU. Chest. 2020;157(1):61-66. doi: 10.1016/j.chest.2019.08.2180 [DOI] [PubMed] [Google Scholar]
  • 6.Suen LW, Makam AN, Snyder HR, et al. National prevalence of alcohol and other substance use disorders among emergency department visits and hospitalizations: NHAMCS 2014–2018. J Gen Intern Med. 2022;37(10):2420-2428. doi: 10.1007/s11606-021-07069-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Jack HE, Oliver MM, Berger DB, Bobb JF, Bradley KA, Hallgren KA. Association between clinical measures of unhealthy alcohol use and subsequent year hospital admissions in a primary care population. Drug Alcohol Depend. 2023;245:109821. doi: 10.1016/j.drugalcdep.2023.109821 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Steel TL, Bhatraju EP, Hills-Dunlap K. Critical care for patients with substance use disorders. Curr Opin Crit Care. 2023;29(5):484-492. doi: 10.1097/MCC.0000000000001080 [DOI] [PubMed] [Google Scholar]
  • 9.Smith MW, Stocks C, Santora PB. Hospital readmission rates and emergency department visits for mental health and substance abuse conditions. Community Ment Health J. 2015;51(2):190-197. doi: 10.1007/s10597-014-9784-x [DOI] [PubMed] [Google Scholar]
  • 10.Case A, Deaton A. Mortality and morbidity in the 21st century. Brookings Pap Econ Act. 2017;2017:397-476. doi: 10.1353/eca.2017.0005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Mokdad AH, Ballestros K, Echko M, et al. ; US Burden of Disease Collaborators . The state of US health, 1990-2016: burden of diseases, injuries, and risk factors among US states. JAMA. 2018;319(14):1444-1472. doi: 10.1001/jama.2018.0158 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Koob GF, Powell P, White A. Addiction as a coping response: hyperkatifeia, deaths of despair, and COVID-19. Am J Psychiatry. 2020;177(11):1031-1037. doi: 10.1176/appi.ajp.2020.20091375 [DOI] [PubMed] [Google Scholar]
  • 13.Esser MB, Leung G, Sherk A, et al. Estimated deaths attributable to excessive alcohol use among US adults aged 20 to 64 years, 2015 to 2019. JAMA Netw Open. 2022;5(11):e2239485. doi: 10.1001/jamanetworkopen.2022.39485 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.White AM, Castle IP, Powell PA, Hingson RW, Koob GF. Alcohol-related deaths during the COVID-19 pandemic. JAMA. 2022;327(17):1704-1706. doi: 10.1001/jama.2022.4308 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Petrosyan Y, Bai YQ, Koné Pefoyo AJ, et al. The relationship between diabetes care quality and diabetes-related hospitalizations and the modifying role of comorbidity. Can J Diabetes. 2017;41(1):17-25. doi: 10.1016/j.jcjd.2016.06.006 [DOI] [PubMed] [Google Scholar]
  • 16.Sharma RA, Subedi K, Gbadebo BM, Wilson B, Jurkovitz C, Horton T. Alcohol withdrawal rates in hospitalized patients during the COVID-19 pandemic. JAMA Netw Open. 2021;4(3):e210422. doi: 10.1001/jamanetworkopen.2021.0422 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Foy A, Kay J. The incidence of alcohol-related problems and the risk of alcohol withdrawal in a general hospital population. Drug Alcohol Rev. 1995;14(1):49-54. doi: 10.1080/09595239500185051 [DOI] [PubMed] [Google Scholar]
  • 18.Marik P, Mohedin B. Alcohol-related admissions to an inner city hospital intensive care unit. Alcohol Alcohol. 1996;31(4):393-396. doi: 10.1093/oxfordjournals.alcalc.a008168 [DOI] [PubMed] [Google Scholar]
  • 19.Spies CD, Neuner B, Neumann T, et al. Intercurrent complications in chronic alcoholic men admitted to the intensive care unit following trauma. Intensive Care Med. 1996;22(4):286-293. doi: 10.1007/BF01700448 [DOI] [PubMed] [Google Scholar]
  • 20.Maldonado JR, Sher Y, Ashouri JF, et al. The “Prediction of Alcohol Withdrawal Severity Scale” (PAWSS): systematic literature review and pilot study of a new scale for the prediction of complicated alcohol withdrawal syndrome. Alcohol. 2014;48(4):375-390. doi: 10.1016/j.alcohol.2014.01.004 [DOI] [PubMed] [Google Scholar]
  • 21.Steel TL, Malte CA, Bradley KA, Lokhandwala S, Hough CL, Hawkins EJ. Prevalence and variation of clinically recognized inpatient alcohol withdrawal syndrome in the Veterans Health Administration. J Addict Med. 2020;14(4):300-304. doi: 10.1097/ADM.0000000000000576 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Steel TL, Malte CA, Bradley KA, Hawkins EJ. Use of electronic health record data to estimate the probability of alcohol withdrawal syndrome in a national cohort of hospitalized veterans. J Addict Med. 2021;15(5):376-382. doi: 10.1097/ADM.0000000000000782 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Chartbook on care coordination: potentially avoidable hospitalizations. Agency for Healthcare Research and Quality . 2018. Accessed January 9, 2024. https://www.ahrq.gov/research/findings/nhqrdr/chartbooks/carecoordination/measure3.html
  • 24.Loyd C, Blue K, Turner L, et al. National norms for hospitalizations due to ambulatory care sensitive conditions among adults in the US. J Gen Intern Med. 2023;38(13):2953-2959. doi: 10.1007/s11606-023-08161-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Agarwal MA, Fonarow GC, Ziaeian B. National trends in heart failure hospitalizations and readmissions from 2010 to 2017. JAMA Cardiol. 2021;6(8):952-956. doi: 10.1001/jamacardio.2020.7472 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Curry SJ, Krist AH, Owens DK, et al. ; US Preventive Services Task Force . Screening and behavioral counseling interventions to reduce unhealthy alcohol use in adolescents and adults: US Preventive Services Task Force recommendation statement. JAMA. 2018;320(18):1899-1909. doi: 10.1001/jama.2018.16789 [DOI] [PubMed] [Google Scholar]
  • 27.Guide to prevention quality indicators: hospital admission for ambulatory care sensitive conditions. Department of Health and Human Services Agency for Healthcare Research and Quality . 2002. Accessed January 9, 2024. https://www.ahrq.gov/downloads/pub/ahrqqi/pqiguide.pdf
  • 28.Davis AC, Voelkel JL, Remmers CL, Adams JL, McGlynn EA. Comparing Kaiser Permanente members to the general population: implications for generalizability of research. Perm J. 2023;27(2):87-98. doi: 10.7812/TPP/22.172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Bradley KA, DeBenedetti AF, Volk RJ, Williams EC, Frank D, Kivlahan DR. AUDIT-C as a brief screen for alcohol misuse in primary care. Alcohol Clin Exp Res. 2007;31(7):1208-1217. doi: 10.1111/j.1530-0277.2007.00403.x [DOI] [PubMed] [Google Scholar]
  • 30.Lee AK, Bobb JF, Richards JE, et al. Integrating alcohol-related prevention and treatment into primary care: a cluster randomized implementation trial. JAMA Intern Med. 2023;183(4):319-328. doi: 10.1001/jamainternmed.2022.7083 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Sayre M, Lapham GT, Lee AK, et al. Routine assessment of symptoms of substance use disorders in primary care: prevalence and severity of reported symptoms. J Gen Intern Med. 2020;35(4):1111-1119. doi: 10.1007/s11606-020-05650-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.O’Connor EA, Perdue LA, Senger CA, et al. Screening and behavioral counseling interventions to reduce unhealthy alcohol use in adolescents and adults: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2018;320(18):1910-1928. doi: 10.1001/jama.2018.12086 [DOI] [PubMed] [Google Scholar]
  • 33.McPheeters M, O’Connor EA, Riley S, et al. Pharmacotherapy for alcohol use disorder: a systematic review and meta-analysis. JAMA. 2023;330(17):1653-1665. doi: 10.1001/jama.2023.19761 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Donroe JH, Edelman EJ. Alcohol use. Ann Intern Med. 2022;175(10):ITC145-ITC160. doi: 10.7326/AITC202210180 [DOI] [PubMed] [Google Scholar]
  • 35.Larson SA, Burton MC, Kashiwagi DT, Hugo ZP, Cha SS, Lapid MI. Multiple admissions for alcohol withdrawal. J Hosp Med. 2012;7(8):617-621. doi: 10.1002/jhm.1953 [DOI] [PubMed] [Google Scholar]
  • 36.Yedlapati SH, Stewart SH. Predictors of alcohol withdrawal readmissions. Alcohol Alcohol. 2018;53(4):448-452. doi: 10.1093/alcalc/agy024 [DOI] [PubMed] [Google Scholar]
  • 37.Hallgren KA, Matson TE, Oliver M, et al. Practical assessment of alcohol use disorder in routine primary care: performance of an alcohol symptom checklist. J Gen Intern Med. 2022;37(8):1885-1893. doi: 10.1007/s11606-021-07038-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Hallgren KA, Jack HE, Oliver M, et al. Changes in alcohol consumption reported on routine healthcare screenings are associated with changes in depression symptoms. Alcohol Clin Exp Res (Hoboken). 2023;47(6):1132-1142. doi: 10.1111/acer.15075 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Kwako LE, Patterson J, Salloum IM, Trim RS. Alcohol use disorder and co-occurring mental health conditions. Alcohol Res. 2019;40(1):arcr.v40.1.00. doi: 10.35946/arcr.v40.1.00 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Sterling SA, Palzes VA, Lu Y, et al. Associations between medical conditions and alcohol consumption levels in an adult primary care population. JAMA Netw Open. 2020;3(5):e204687. doi: 10.1001/jamanetworkopen.2020.4687 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Alcohol-related ICD codes. Centers for Disease Control and Prevention . 2021. Accessed January 9, 2024. https://www.cdc.gov/alcohol/ardi/alcohol-related-icd-codes.html
  • 42.Dixon MA, Chartier KG. Alcohol use patterns among urban and rural residents: demographic and social influences. Alcohol Res. 2016;38(1):69-77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.ARDI methods. Centers for Disease Control and Prevention . 2024. Accessed May 8, 2024. https://www.cdc.gov/alcohol/ardi/methods.html
  • 44.Bradley KA, Rubinsky AD, Lapham GT, et al. Predictive validity of clinical AUDIT-C alcohol screening scores and changes in scores for three objective alcohol-related outcomes in a Veterans Affairs population. Addiction. 2016;111(11):1975-1984. doi: 10.1111/add.13505 [DOI] [PubMed] [Google Scholar]
  • 45.Chavez LJ, Liu CF, Tefft N, et al. Unhealthy alcohol use in older adults: association with readmissions and emergency department use in the 30 days after hospital discharge. Drug Alcohol Depend. 2016;158:94-101. doi: 10.1016/j.drugalcdep.2015.11.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Chew RB, Bryson CL, Au DH, Maciejewski ML, Bradley KA. Are smoking and alcohol misuse associated with subsequent hospitalizations for ambulatory care sensitive conditions? J Behav Health Serv Res. 2011;38(1):3-15. doi: 10.1007/s11414-010-9215-x [DOI] [PubMed] [Google Scholar]
  • 47.Williams EC, Bryson CL, Sun H, et al. Association between alcohol screening results and hospitalizations for trauma in Veterans Affairs outpatients. Am J Drug Alcohol Abuse. 2012;38(1):73-80. doi: 10.3109/00952990.2011.600392 [DOI] [PubMed] [Google Scholar]
  • 48.Rubinsky AD, Sun H, Blough DK, et al. AUDIT-C alcohol screening results and postoperative inpatient health care use. J Am Coll Surg. 2012;214(3):296-305.e1. doi: 10.1016/j.jamcollsurg.2011.11.007 [DOI] [PubMed] [Google Scholar]
  • 49.Au DH, Kivlahan DR, Bryson CL, Blough D, Bradley KA. Alcohol screening scores and risk of hospitalizations for GI conditions in men. Alcohol Clin Exp Res. 2007;31(3):443-451. doi: 10.1111/j.1530-0277.2006.00325.x [DOI] [PubMed] [Google Scholar]
  • 50.Lembke A, Bradley KA, Henderson P, Moos R, Harris AHS. Alcohol screening scores and the risk of new-onset gastrointestinal illness or related hospitalization. J Gen Intern Med. 2011;26(7):777-782. doi: 10.1007/s11606-011-1688-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Clark BJ, Williams A, Feemster LMC, et al. ; NHLBI ARDS Network Investigators . Alcohol screening scores and 90-day outcomes in patients with acute lung injury. Crit Care Med. 2013;41(6):1518-1525. doi: 10.1097/CCM.0b013e318287f1bb [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Vaeth PAC, Wang-Schweig M, Caetano R. Drinking, alcohol use disorder, and treatment access and utilization among U.S. racial/ethnic groups. Alcohol Clin Exp Res. 2017;41(1):6-19. doi: 10.1111/acer.13285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Ford CL, Airhihenbuwa CO. Critical race theory, race equity, and public health: toward antiracism praxis. Am J Public Health. 2010;100 (suppl 1):S30-S35. doi: 10.2105/AJPH.2009.171058 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Griswold MG, Fullman N, Hawley C, et al. ; GBD 2016 Alcohol Collaborators . Alcohol use and burden for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2018;392(10152):1015-1035. doi: 10.1016/S0140-6736(18)31310-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Glass JE, Bohnert KM, Brown RL. Alcohol screening and intervention among United States adults who attend ambulatory healthcare. J Gen Intern Med. 2016;31(7):739-745. doi: 10.1007/s11606-016-3614-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Mintz CM, Hartz SM, Fisher SL, et al. A cascade of care for alcohol use disorder: using 2015-2019 National Survey on Drug Use and Health data to identify gaps in past 12-month care. Alcohol Clin Exp Res. 2021;45(6):1276-1286. doi: 10.1111/acer.14609 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Hu H, Mitra R, Han Y, et al. Prevalence and treatment for alcohol use disorders based on Kentucky Medicaid 2012-2019 datasets. J Alcohol Drug Depend. 2022;10(5):1000366. [PMC free article] [PubMed] [Google Scholar]
  • 58.Kranzler HR. Overview of alcohol use disorder. Am J Psychiatry. 2023;180(8):565-572. doi: 10.1176/appi.ajp.20230488 [DOI] [PubMed] [Google Scholar]
  • 59.Bernstein EY, Baggett TP, Trivedi S, Herzig SJ, Anderson TS. Pharmacologic treatment initiation among Medicare beneficiaries hospitalized with alcohol use disorder. Ann Intern Med. 2023;176(8):1137-1139. doi: 10.7326/M23-0641 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Wu A. Heart failure. Ann Intern Med. 2018;168(11):ITC81-ITC96. doi: 10.7326/AITC201806050 [DOI] [PubMed] [Google Scholar]
  • 61.Goldgrab D, Balakumaran K, Kim MJ, Tabtabai SR. Updates in heart failure 30-day readmission prevention. Heart Fail Rev. 2019;24(2):177-187. doi: 10.1007/s10741-018-9754-4 [DOI] [PubMed] [Google Scholar]
  • 62.Guerci B, Roussel R, Levrat-Guillen F, et al. Important decrease in hospitalizations for acute diabetes events following FreeStyle libre system initiation in people with type 2 diabetes on basal insulin therapy in France. Diabetes Technol Ther. 2023;25(1):20-30. doi: 10.1089/dia.2022.0271 [DOI] [PubMed] [Google Scholar]
  • 63.Wang J, Imai K, Engelgau MM, Geiss LS, Wen C, Zhang P. Secular trends in diabetes-related preventable hospitalizations in the United States, 1998-2006. Diabetes Care. 2009;32(7):1213-1217. doi: 10.2337/dc08-2211 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Matson TE, Lee AK, Oliver M, Bradley KA, Hallgren KA. Equivalence of alcohol use disorder symptom assessments in routine clinical care when completed remotely via online patient portals versus in clinic via paper questionnaires: psychometric evaluation. J Med Internet Res. 2024;26:e52101. doi: 10.2196/52101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Bradley KA, Kivlahan DR. Bringing patient-centered care to patients with alcohol use disorders. JAMA. 2014;311(18):1861-1862. doi: 10.1001/jama.2014.3629 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Oslin DW, Lynch KG, Maisto SA, et al. A randomized clinical trial of alcohol care management delivered in Department of Veterans Affairs primary care clinics versus specialty addiction treatment. J Gen Intern Med. 2014;29(1):162-168. doi: 10.1007/s11606-013-2625-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Watkins KE, Ober AJ, Lamp K, et al. Collaborative care for opioid and alcohol use disorders in primary care: the SUMMIT randomized clinical trial. JAMA Intern Med. 2017;177(10):1480-1488. doi: 10.1001/jamainternmed.2017.3947 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Allaudeen N, Akwe J, Arundel C, et al. Medications for alcohol-use disorder and follow-up after hospitalization for alcohol withdrawal: a multicenter study. J Hosp Med. Published online July 19, 2024. doi: 10.1002/jhm.13458 [DOI] [PubMed] [Google Scholar]
  • 69.Perry C, Liberto J, Milliken C, et al. ; VA/DoD Guideline Development Group . The management of substance use disorders: synopsis of the 2021 U.S. Department of Veterans Affairs and U.S. Department of Defense Clinical Practice Guideline. Ann Intern Med. 2022;175(5):720-731. doi: 10.7326/M21-4011 [DOI] [PubMed] [Google Scholar]
  • 70.Wong J, Saver B, Scanlan JM, et al. The ASAM clinical practice guideline on alcohol withdrawal management. J Addict Med. 2020;14(3S suppl 1):1-72. doi: 10.1097/ADM.0000000000000668 doi: 10.1097/ADM.0000000000000668 [DOI] [PubMed] [Google Scholar]
  • 71.Seshadri A, Appelbaum R, Carmichael SP II, et al. Prevention of alcohol withdrawal syndrome in the surgical ICU: an American Association for the Surgery of Trauma Critical Care Committee clinical consensus document. Trauma Surg Acute Care Open. 2022;7(1):e001010. doi: 10.1136/tsaco-2022-001010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Tonnesen H, Rosenberg J, Nielsen HJ, et al. Effect of preoperative abstinence on poor postoperative outcome in alcohol misusers: randomised controlled trial. BMJ. 1999;318(7194):1311-1316. doi: 10.1136/bmj.318.7194.1311 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Fernandez AC, Chapman L, Ren TY, et al. Preoperative alcohol interventions for elective surgical patients: results from a randomized pilot trial. Surgery. 2022;172(6):1673-1681. doi: 10.1016/j.surg.2022.09.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Steel TL, Giovanni SP, Katsandres SC, et al. Should the CIWA-Ar be the standard monitoring strategy for alcohol withdrawal syndrome in the intensive care unit? Addict Sci Clin Pract. 2021;16(1):21. doi: 10.1186/s13722-021-00226-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Steel TL, Afshar M, Edwards S, et al. Research needs for inpatient management of severe alcohol withdrawal syndrome: an official American Thoracic Society research statement. Am J Respir Crit Care Med. 2021;204(7):e61-e87. doi: 10.1164/rccm.202108-1845ST [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Vickers-Smith R, Justice AC, Becker WC, et al. Racial and ethnic bias in the diagnosis of alcohol use disorder in veterans. Am J Psychiatry. 2023;180(6):426-436. doi: 10.1176/appi.ajp.21111097 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Graham A, Goss C, Xu S, Magid DJ, DiGuiseppi C. Effect of using different modes to administer the AUDIT-C on identification of hazardous drinking and acquiescence to trial participation among injured patients. Alcohol Alcohol. 2007;42(5):423-429. doi: 10.1093/alcalc/agl123 [DOI] [PubMed] [Google Scholar]
  • 78.Simon CB, McCabe CJ, Matson TE, Oliver M, Bradley KA, Hallgren KA. High test–retest reliability of the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire completed by primary care patients in routine care. Alcohol Clin Exp Res (Hoboken). 2024;48(2):302-308. doi: 10.1111/acer.15245 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Karlamangla A, Zhou K, Reuben D, Greendale G, Moore A. Longitudinal trajectories of heavy drinking in adults in the United States of America. Addiction. 2006;101(1):91-99. doi: 10.1111/j.1360-0443.2005.01299.x [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable 1. ICD-10 Codes used to define alcohol withdrawal syndrome (AWS), chronic ambulatory care sensitive conditions (ACSCs), and common outpatient diagnoses associated with alcohol use disorder (AUD)

eTable 2. Incidence of alcohol withdrawal syndrome (AWS) hospitalizations, overall and by patient characteristics, including all-cause hospitalization data

eTable 3. Proportional incidence of alcohol withdrawal syndrome (AWS) hospitalizations relative to all-cause hospitalizations across age groups by sex

eTable 4. Proportional incidence of alcohol withdrawal syndrome (AWS) hospitalizations relative to all-cause hospitalizations across age groups by race

eTable 5. Proportional incidence of alcohol withdrawal syndrome (AWS) hospitalizations relative to all-cause hospitalizations across age groups by Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) category

eTable 6. Proportional incidence of primary ICD-10 diagnosis codes only for alcohol withdrawal syndrome (AWS), heart failure, chronic obstructive pulmonary disease (COPD), diabetes, and hypertension hospitalizations relative to all-cause hospitalizations across age, race, and ethnicity categories

jamanetwopen-e2438128-s001.pdf (206.2KB, pdf)
Supplement 2.

Data Sharing Statement

jamanetwopen-e2438128-s002.pdf (13.3KB, pdf)

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