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. Author manuscript; available in PMC: 2023 Mar 1.
Published in final edited form as: Alcohol Clin Exp Res. 2022 Mar;46(3):422–433. doi: 10.1111/acer.14781

Alcohol withdrawal in past-year drinkers with unhealthy alcohol use: Prevalence, characteristics, and correlates in a national epidemiologic survey

Ofir Livne 1, Richard Feinn 2, Justin Knox 3,4, Emily E Hartwell 5, Joel Gelernter 6, Deborah S Hasin 3, Henry R Kranzler 5
PMCID: PMC8928097  NIHMSID: NIHMS1775586  PMID: 35275407

Abstract

Background:

Despite its potential to produce serious adverse outcomes, DSM-5 alcohol withdrawal syndrome (AWS) has not been widely studied in the general population.

Methods:

We used cross-sectional data from 36,309 U.S. adults from the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions-III to examine the past-year prevalence of AWS and its correlates. We focused on an important clinical population–past-year drinkers with unhealthy alcohol use–i.e., those with a positive score on the Alcohol Use Disorders Identification Test–Consumption (AUDIT-C) questionnaire. We also examined the association of AWS with sociodemographic measures, psychiatric disorders, alcohol-related measures, and healthcare utilization.

Results:

Approximately one-third (n=12,634) of respondents reported unhealthy alcohol use. Of these, 14.3% met criteria for a DSM-5 AWS diagnosis. The mean (SE) number of withdrawal symptoms among the subsample with AWS was 2.83 (1.88), with the most common being nausea/vomiting and insomnia (19.8% and 11.6%, respectively). Among AUDIT-C+ respondents, the odds of AWS were significantly higher among males (adjusted odds ratio [aOR]=1.17 [95% CI, 1.02–1.33]), unmarried participants (aOR=1.55 [95% CI, 1.25–1.92]) and those at the lowest (vs. highest) income levels (aOR=1.62 [95% CI, 1.37–1.92]). Among AUDIT-C+ respondents, AWS was also associated with psychiatric disorders (with aORs that ranged from 2.08 [95% CI, 1.79–2.41]) for major depressive disorder to 3.14 (95% CI, 1.79–2.41) for borderline personality disorder). AUDIT-C+ respondents with AWS also had higher odds of past-year alcohol use disorder (aOR=11.2 [95% CI, 9.66–13.07]), other alcohol-related features (e.g., binge drinking), and healthcare utilization.

Conclusions:

Among individuals with unhealthy alcohol use, AWS is prevalent, highly comorbid, and disabling. Given the risk of AWS among unhealthy drinkers, a comparatively large segment of the general population, clinicians should seek to identify individuals with AWS and intervene with them to prevent serious adverse outcomes.

Keywords: Alcohol withdrawal, unhealthy alcohol use, alcohol use disorder, AUDIT-C

INTRODUCTION

Signs and symptoms of alcohol withdrawal syndrome (AWS) generally appear within 6–24 hours of discontinuing or substantially reducing alcohol consumption in individuals with prolonged periods of heavy drinking. The DSM-5 diagnosis of AWS requires the co-occurrence of two or more of eight withdrawal symptoms, resulting in significant distress or impairment in social, occupational, or other functioning and not attributable to other medical conditions, a mental disorder, or the influence of another substance (American Psychiatric Association 2013). Alcohol withdrawal is associated with numerous adverse health outcomes, including medical and neuropsychiatric complications (e.g., cognitive impairment), and poorer long-term drinking outcomes (Hasin et al. 2000, Loeber et al. 2009, Schuckit et al. 2003). Severe alcohol withdrawal symptoms are also associated with an elevated risk of morbidity and mortality and increased length of hospitalization stay and healthcare costs (De Wit et al. 2007, Campos et al. 2011, Genther et al. 2012, Mainerova et al. 2015, Long et al. 2017, Griessbach et al. 2019).

Population studies have yielded estimates of AWS prevalence that vary widely in different samples. These variations are attributable to 1) different questionnaires used to assess withdrawal; 2) whether respondents were in alcohol treatment or diagnosed with alcohol use disorders (AUD); and 3) inherent differences in sample population characteristics (e.g., veterans, individuals from community settings) (Proudfoot et al. 2006, Ruan et al. 2008, Keyes et al. 2011, Casey et al. 2012, Moore et al. 2017). An important shortcoming of the literature is the lack of population estimates of the prevalence and characteristics of AWS among individuals who engage in unhealthy alcohol use and not only those who have an AUD.

The clinical importance of this information is illustrated by the increasing emphasis in recent years on screening for unhealthy alcohol use in clinical settings (Higgins-Biddle and Babor 2018, Toner et al. 2019) and the growing number of reports showing that such screening measures predict the development of AUD and other adverse alcohol-related outcomes (Glass et al. 2010, Chavez et al. 2012, Berger et al. 2013, Rubinsky et al. 2013, US Preventive Services Task Force 2018). AWS screening and management guidelines used by clinicians (e.g., American Society of Addiction Medicine [Lindsay et al. 2020]) rely on evidence-based monitoring and treatment strategies and on results from past studies that have reported associations between AWS and sociodemographic and clinical covariates. Nevertheless, these studies often were not based on nationally representative data and did not control for potential confounders. Further, population-level data on the associations between AWS and several other clinically relevant measures (e.g., alcohol-related medical disorders, psychiatric disorders, healthcare utilization, and family history of problem drinking) are not available. Such information can assist in identifying unhealthy drinkers at risk of developing AWS and in facilitating preventive and intervention measures in this population.

Thus, in the present study, we examined the prevalence of alcohol withdrawal symptoms and DSM-5 AWS in past-year unhealthy drinkers, i.e., those with an elevated Alcohol Use Disorders Identification Test–Consumption (AUDIT-C) score, a measure that is widely used in primary care settings to identify hazardous drinking (Bush et al. 1998, Rubinsky et al. 2013). We also examined whether AWS is associated with sociodemographic and clinical correlates, including medical and psychiatric disorders, healthcare utilization, quality-of-life measures, and alcohol-related covariates. Such information can help assess the public health burden of AWS and assist in identifying at-risk populations for targeted preventive and intervention efforts.

MATERIALS AND METHODS

Participants

We analyzed data from the National Epidemiologic Survey of Alcohol and Related Conditions-III (NESARC-III), a U.S. population survey of 36,309 respondents sponsored by the National Institute on Alcohol Abuse and Alcoholism and conducted between April 2012 and June 2013 (Grant et al. 2015a). The study targeted the U.S. non-institutionalized civilian population aged ≥18 years, including residents of selected group quarters. A complex multi-stage sample design was used to select respondents (Grant et al. 2015). The analysis of NESARC-III data reported here did not require Institutional Review Board review as the data are publicly available.

We report here the features of the 12,634 respondents with unhealthy alcohol use, evidenced by a positive score on the AUDIT-C, a 3-item instrument that measures alcohol consumption over the past year (Saunders et al. 1993). The AUDIT-C is a valid, reliable screening instrument used to identify individuals with unhealthy alcohol use (Dawson et al. 2005a and 2005b, Bradley et al. 2007, Bush et al. 1998).

The first AUDIT-C item queries the frequency of drinking, the second queries the typical number of standard drinks consumed on a drinking day, and the third queries the frequency of drinking 6 or more drinks on an occasion. Each item is scored 0–4, yielding a total AUDIT-C score of 0–12. AUDIT-C scores of ≥4 for men and ≥3 for women are considered positive for unhealthy alcohol use (US Preventive Services Task Force et al. 2018). For men, a score of 0–3, 4–5, 6–7, and 8–12 points indicate low, moderate, high, and severe risk for harm, respectively. For women, a score of 0–2, 3–5, 6–7, and 8–12 points indicate low, moderate, high, and severe risk for harm, respectively.

At a cut-off of ≥4, the AUDIT-C has good sensitivity and specificity in detecting unhealthy alcohol use. However, achieving good sensitivity for women requires a cutoff that is 1 point lower than for men (US Preventive Services Task Force et al. 2018). A dichotomous (yes/no) variable was constructed to indicate whether past-year drinkers were positive for unhealthy drinking based on AUDIT-C scores.

The Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5).

This structured, computer-assisted diagnostic interview was designed to be administered by lay interviewers and has documented reliability and validity (Ruan et al. 2008, Grant et al. 2015b, Hasin et al. 2015a and 2015b). In addition to measuring the quantity and frequency of alcohol and drug use, the AUDADIS-5 elicits sociodemographic characteristics, measures DSM-5 substance use disorders and psychiatric disorders and provides indices of quality of life.

Alcohol Withdrawal Diagnosis

In DSM-5, the alcohol withdrawal syndrome (AWS) is both one of the 11 criteria for the diagnosis of AUD and a distinct diagnosis. We constructed a variable based on the DSM-5 AWS, which requires the cessation of or reduction in alcohol use that has been heavy and prolonged. The presence of 2 or more of 8 withdrawal symptoms that develop within several hours to a few days after cessation of alcohol use reflects the requirement that withdrawal symptoms emerge upon the cessation of drinking. This criterion was elicited in AUDADIS-5 using the following statement: “The next few questions are about the bad aftereffects of drinking that people may have when the effects of alcohol are wearing off. This includes the morning after drinking or in the first few days after stopping or cutting down”. The requirement for evidence of heavy, prolonged alcohol consumption was satisfied by including only past-year drinkers with positive AUDIT-C scores (i.e., unhealthy alcohol use).

Each withdrawal symptom was coded dichotomously, and a summary variable was constructed and coded positive if ≥2 of 8 symptoms were endorsed. The DSM-5 AUD alcohol withdrawal criterion requires either the presence of the characteristic AWS or the use of alcohol (or a closely related substance) to relieve or avoid withdrawal symptoms. Thus, respondents were queried both about whether they experienced alcohol withdrawal symptoms and whether they took a drink or used “any drug or medicine other than aspirin, Advil, or Tylenol to get over or to keep from having any of the bad aftereffects of drinking”. A final AWS variable, coded positive if either criterion was met, was used in all study analyses.

Alcohol Consumption and Alcohol Use Disorder

The AUDADIS-5 queries past-year alcohol consumption by asking how frequently in the last year different beverage types were typically consumed. A dichotomous variable was used to indicate whether participants reported any past-year drinking. Past-year DSM-5 AUD diagnoses required at least two of the 11 criteria in the 12 months preceding the interview (Grant et al. 2015a). To examine associations between AWS and AUD, we used a modified past-year 10-item AUD variable that excluded the withdrawal criterion. Based on this modified variable and the number of AUD criteria, we constructed a 4-level AUD severity variable: no AUD (0–1 criteria), mild (2–3 criteria), moderate (4–5 criteria), severe (≥6 criteria).

Other Measures

Alcohol use-related measures

A dichotomous variable was used to indicate the presence of past-year binge drinking (defined as ≥5 alcoholic drinks for males or ≥4 alcoholic drinks for females on the same occasion on at least 1 day in the past month).

Family history

Participants were coded positive for a family history of problem drinking if any biological parent or sibling was reported by the respondent to have a history of problems with alcohol; these measures have been shown to be reliable (Grant et al. 1995, Hasin et al. 1997).

Sociodemographic variables

Variables included sex, race/ethnicity, age, educational level, annual household income, marital status, urbanicity, and U.S. region of residence.

Psychiatric disorders

Psychiatric disorders included any past-year mood disorder (major depressive disorder, dysthymia, bipolar 1, or bipolar 2), any past-year anxiety disorder (generalized anxiety disorder, social phobia, agoraphobia, specific phobias, or panic disorder), and any personality disorder (borderline, schizotypal, or antisocial). Test-retest reliability was fair to moderate for depressive (k=0.39–0.40) and anxiety (k=0.43–0.51) disorders, with generally good-to-excellent reliability for corresponding dimensional measures (intraclass correlation coefficients [ICC], 0.59–0.79) (Grant et al. 2015b). The test-retest reliability of the personality disorders was moderate (κ=0.46–0.54), and higher for corresponding dimensional measures (0.60–0.70) (Grant et al. 2015b).

Medical disorders

These included past-year diagnoses of gastrointestinal disease, cardiovascular disease, metabolic disease, and cancer, confirmed by a physician. A dichotomous variable was used to indicate the presence of any past-year medical diagnosis.

Substance use disorders

Past-year DSM-5 substance use disorders (SUD) included tobacco use disorder (TUD) and other drug use disorders (DUD), including cannabis, cocaine, hallucinogens, opioids, sedatives, inhalants/solvents, heroin, club drugs, stimulants, and ‘other drugs.’ Reliability and procedural validity of SUD diagnoses are good to excellent (Grant et al. 2015b, Hasin et al. 2015a).

Healthcare utilization

A dichotomous variable indicated past-year alcohol use-related emergency room visits. Another dichotomous variable was constructed to indicate any past-year alcohol use-related healthcare utilization (use of social service agencies, detoxification clinics, inpatient treatment settings, outpatient treatment settings, rehabilitation programs, halfway house or therapeutic community, crisis center, employee assistance programs; healthcare professional or agencies; nonprofessional peer support [attendance at Alcoholics Anonymous; visits with clergy]).

Health-related quality of life

We used the mental and physical components of the 12-item Short-Form Health Survey (SF-12v2) (Ware et al. 1996, Gandek et al. 1998), both reliable and valid measures of current impairment that are widely used in population surveys (Gandek et al. 1998). Each component has a mean of 50, standard deviation of 10, and range of 0–100, with lower scores indicating greater impairment.

Statistical Analysis

The analytic sample comprised the 12,634 individuals with unhealthy alcohol use (i.e., those who were AUDIT-C+). We calculated weighted frequencies of the AWS diagnosis and each of the eight withdrawal symptoms in this sample. To identify unhealthy drinkers at risk of developing AWS, odds ratios derived from multiple logistic regression reflect associations between the response variable (i.e., past-year AWS) and sociodemographic characteristics and clinical correlates (exposure variables). In further analyses that assessed the public health burden of AWS among respondents with unhealthy alcohol use, AWS was modeled as an exposure variable and clinical correlates (medical diagnoses) and alcohol use-related variables (AUD, binge drinking, and healthcare utilization) were modeled as response variables. All analyses were run in three models: 1) uncontrolled, 2) controlled for sociodemographic characteristics, and 3) controlled for sociodemographic characteristics and psychiatric disorders. Relationships of past-year AWS and SF-12v2 quality-of-life scales were determined using multiple linear regression. In analyses that examined associations between AWS and medical diagnoses, quality of life scales, AUD, binge drinking, and treatment utilization, AWS was modeled as the exposure variable. To test whether associations were confounded by withdrawal from other substances, we created a dichotomous “withdrawal overlap” variable for each substance, indicating overlap with alcohol withdrawal symptoms (Livne et al. 2019). Logistic regression analyses examined associations between each withdrawal overlap variable with AWS, controlling for sociodemographic characteristics and all other substance withdrawal overlap variables (Supplemental Table 3). Because withdrawal from tobacco, cannabis, and opioids were significantly associated with AWS, in a fourth model we controlled for sociodemographics and withdrawal from these substances. Techniques that correct for multiple comparisons, such as Bonferroni correction, were not used as previous studies indicate that alongside reducing the occurrence type I error, they increase the occurrence of type II error, thus reducing the likelihood of observing true effects (Althouse, 2016; Feise, 2002; Perneger, 1998). Analyses were conducted in SUDAAN 11.0 (Research Triangle Institute 2012), which accounts for the complex sample design.

RESULTS

Of NESARC-III respondents, 71% (n=25,778) reported past-year drinking, of whom 48.9% (n=12,634) endorsed unhealthy alcohol use (positive AUDIT-C score), and 18.5% (n=4,986) met the diagnostic criteria for DSM-5 AUD. Supplemental Table 1 summarizes the sociodemographic and clinical covariates of unhealthy alcohol use among past-year drinkers.

Prevalence and Number of Alcohol Withdrawal Symptoms

Table 1 shows past-year prevalence of the DSM-5 AWS diagnosis (i.e., criterion 11 of the DSM-5 AUD diagnostic criteria) and withdrawal symptoms among past-year drinkers with unhealthy alcohol use. Supplemental Table 2 shows the prevalence of each DSM-5 AUD criterion in past-year drinkers with unhealthy alcohol use. A total of 1,948 respondents—14.3% of past-year drinkers with unhealthy alcohol use—met criteria for the DSM-5 AWS. The mean number (standard error) of withdrawal symptoms among unhealthy alcohol users with AWS was 2.83 (1.88). The most common alcohol withdrawal symptoms were nausea/vomiting (19.82%), insomnia (11.59%), and psychomotor agitation (11.0%). The most severe symptoms were also the least commonly endorsed: hallucinations (1.6%) and seizures (0.44%).

Table 1.

Prevalence of DSM-5 alcohol withdrawal and specific withdrawal symptoms reported by NESARC-III past-year drinkers with positive AUDIT-C scores for unhealthy alcohol use.

Unhealthy Alcohol Use (Positive AUDIT-C Score)
(n=12,634)
N (%) SE
DSM-5 alcohol withdrawal * 1,948 (14.3) 0.35
Mean number of withdrawal symptoms † 2.83‡ 1.88§
Alcohol withdrawal symptoms
 Insomnia 1,468 (11.59) 0.38
 Autonomic symptoms 1,396 (10.12) 0.35
 Increased hand tremors 790 (5.11) 0.26
 Nausea and/or vomiting 2,623 (19.82) 0.50
 Psychomotor agitation 1,511 (11.00) 0.37
 Anxiety 1,029 (7.47) 0.31
 Seizures 60 (0.44) 0.07
 Hallucinations 273 (1.60) 0.12
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*

Using criterion 11 of the DSM-5 AUD diagnostic criteria

†

Among respondents endorsing the DSM-5 AUD criterion 11 (withdrawal)

‡

Mean

§

Standard deviation

Characteristics of Respondents with Alcohol Withdrawal

Individuals with past-year unhealthy alcohol use who endorsed AWS were primarily male (57.3%), white (66.6%), aged 18–29 years (45.3%), college educated (61.0%), never married (45.7%), and with a household income of <$20,000 (31.5%).

Association With Sociodemographic and Clinical Correlates

Among AUDIT-C+ respondents, and across all models, males, compared to females (adjusted odds ratio [aOR], 1.17 [95% CI, 1.02–1.33]), unmarried compared to married (aOR, 1.55 [95% CI, 1.25–1.92]), respondents of the lowest income level compared to those of the highest income level (aOR, 1.62 [95% CI, 1.37–1.92]), and those living in urban areas compared to rural areas (aOR, 1.33 [95% CI, 1.07–1.64]) had higher odds of past-year AWS. Among AUDIT-C+ respondents, those belonging to older age categories (45–64 years and ≥65 years) had lower odds of AWS compared to those aged 18–29 (Table 2). Past-year mood disorders (except for bipolar 2 disorder), personality disorders, and SUD were also associated with AWS (Table 3). Across all models, AUDIT-C+ respondents reporting AWS scored lower on the SF-12 mental component than those without AWS (adjusted β, −4.75 [95% CI, −5.44, −4.07]; Table 3). The modified 10-item AUD variable was strongly associated with AWS, with a monotonic increase in strengths of association as the severity of AUD increased. Past-month binge drinking, family history of problem drinking, and alcohol-related healthcare utilization were also associated with AWS (Table 4).

Table 2.

Associations (odds ratios and 95% confidence intervals) between 12-month DSM-5 alcohol withdrawal* and sociodemographic characteristics in individuals in NESARC-III who reported past-year unhealthy alcohol use (n=12,364)

Model 1:
Unadjusted		 Model 2:
Adjusted for sociodemographics† 		Model 3:
Adjusted for sociodemographics and psychiatric disorders‡ 		Model 4:
Adjusted for sociodemographics and withdrawal from other substances§
Total N (%) NA NA NA NA
Sex
 Female 898 (42.7) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Male 1,050 (57.3) 1.17 (1.03, 1.33) 1.17 (1.02, 1.33) 1.28 (1.12, 1.47) 1.14 (1.01, 1.29)
Race/Ethnicity
 White 1059 (66.6) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Black 387 (11.5) 1.26 (1.05, 1.51) 0.89 (0.74, 1.08) 0.93 (0.77, 1.13) 0.94 (0.78, 1.13)
 American Indian/ Alaska Native 43 (2.5) 1.68 (1.14, 2.48) 1.27 (0.85, 1.90) 1.09 (0.68, 1.74) 1.18 (0.80.74)
 Asian/Native Hawaiian/Pacific Islander 54 (3.5) 1.04 (0.74, 1.46) 0.72 (0.51, 1.03) 0.77 (0.53, 1.10) 0.73 (0.50, 1.07)
 Hispanic 405 (15.9) 1.16 (1.01, 1.34) 0.78 (0.65, 0.93) 0.84 (0.70, 1.00) 0.86 (0.72, 1.03)
Age (years)
 18–29 811 (45.3) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 30–44 653 (31.6) 0.63 (0.53, 0.75) 0.81 (0.68, 0.97) 0.79 (0.66, 0.95) 0.83 (0.69, 1.00)
 45–64 462 (22.2) 0.40 (0.33, 0.47) 0.51 (0.42, 0.62) 0.54 (0.44, 0.66) 0.54 (0.45, 0.65)
 ≥65 22 (0.9) 0.04 (0.02, 0.07) 0.05 (0.03, 0.10) 0.07 (0.04, 0.12) 0.07 (0.04, 0.12)
Educational level
 Some high school or less 266 (12.0) 1.33 (1.12, 1.58) 1.21 (1.00, 1.46) 1.11 (0.91, 1.34) 1.06 (0.87, 1.29)
 High school graduate (or GED) 536 (27.0) 1.22 (1.06, 1.40) 1.15 (0.99, 1.34) 1.04 (0.89, 1.21) 1.05 (0.90, 1.22)
 Some college or higher 1,146 (61.0) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
Household income
 $0–19,999 695 (31.5) 2.28 (1.88, 2.76) 1.55 (1.25, 1.92) 1.29 (1.04, 1.61) 1.30 (1.05, 1.61)
 $20,000–34,999 425 (19.2) 1.61 (1.32, 1.96) 1.27 (1.03, 1.57) 1.17 (0.95, 1.44) 1.19 (0.96, 1.48)
 $35,000–69,999 485 (24.8) 1.26 (1.04, 1.54) 1.09 (0.90, 1.33) 1.06 (0.86, 1.29) 1.03 (0.85, 1.26)
 ≥$70,000 343 (24.5) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
Marital status
 Married/Living with someone as if married 613 (37.8) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Widowed/ Divorced/ Separated 409 (16.5) 1.41 (1.21, 1.64) 1.54 (1.31, 1.81) 1.39 (1.17, 1.65) 1.44 (1.23, 1.69)
 Never married 926 (45.7) 2.63 (2.26, 3.06) 1.62 (1.37, 1.92) 1.58 (1.33, 1.86) 1.56 (1.31, 1.86)
Urbanicity
 Urban 1,729 (86.3) 1.50 (1.22, 1.85) 1.33 (1.07, 1.64) 1.40 (1.12, 1.75) 1.37 (1.10, 1.69)
 Rural 219 (13.7) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
Region
 Northeast 278 (17.8) 0.76 (0.61, 0.94) 0.79 (0.64, 0.99) 0.78 (0.62, 0.98) 0.77 (0.61, 0.95)
 Midwest 437 (23.2) 0.79 (0.64, 0.99) 0.80 (0.64, 1.00) 0.82 (0.65, 1.03) 0.79 (0.63, 0.98)
 South 660 (31.5) 0.76 (0.64, 0.90) 0.74 (0.62, 0.88) 0.76 (0.63, 0.90) 0.72 (0.61, 0.86)
 West 573 (27.5) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
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Table 3.

Associations (odds ratios and 95% confidence intervals) between past-year DSM-5 alcohol withdrawal* and psychiatric disorders, medical disorders, and substance use disorders in NESARC-III past-year drinkers with unhealthy alcohol use (n=12,364)

Model 1:
Unadjusted		 Model 2:
Adjusted for sociodemographics† 		Model 3:
Adjusted for sociodemographics and psychiatric disorders‡ 		Model 4:
Adjusted for sociodemographics and withdrawal from other substances§
Mood disorders N (%)
 Major depressive disorder 4,226 (21.1) 2.44 (2.12, 2.81) 2.08 (1.79, 2.41) 1.49 (1.26, 1.77) 1.76 (1.51, 2.04)
 Bipolar 1 124 (5.9) 3.97 (2.97, 5.32) 3.12 (2.31, 4.21) 1.64 (1.15, 2.34) 2.20 (1.60, 3.02)
 Bipolar 2 13 (0.6) 2.04 (0.86, 4.84) 1.72 (0.68, 4.35) 1.29 (0.49, 3.36) 1.28 (0.43, 3.79)
 Dysthymia 197 (9.5) 3.30 (2.57, 4.24) 2.95 (2.29, 3.80) 1.42 (1.05, 1.92) 2.35 (1.81, 3.03)
Anxiety disorders
 Panic disorder 163 (8.4) 2.87 (2.19, 3.75) 2.50 (1.86, 3.38) 1.33 (0.95, 1.86) 1.89 (1.39, 2.59)
 Agoraphobia 89 (4.2) 3.08 (2.24, 4.25) 2.56(1.82, 3.58) 1.08 (0.70, 1.65) 1.95 (1.36, 2.79)
 Social phobia 115 (5.9) 2.24 (1.72, 2.92) 2.01 (1.49, 2.71) 0.99 (0.70, 1.40) 1.64 (1.22, 2.19)
 Specific phobia 190 (8.4) 1.46 (1.19,1.80) 1.39 (1.12, 1.72) 0.90 (0.74, 1.11) 1.18 (0.96, 1.46)
 Generalized anxiety disorder 219 (10.6) 2.27 (1.87, 2.76) 2.18 (1.76, 2.69) 1.08 (0.84, 1.39) 1.74 (1.39, 2.17)
Personality disorders
 Schizotypal 288 (13.3) 3.56 (2.97, 4.25) 2.87 (2.37, 3.47) 1.23 (0.97, 1.57) 2.15 (1.74 2.65)
 Borderline 602 (29.5) 3.69 (3.19, 4.26) 3.14 (2.68, 3.67) 2.14 (1.72, 2.65) 2.56 (2.17, 3.02)
 Antisocial 202 (9.2) 3.38 (2.72, 4.19) 2.56 (2.02, 3.24) 1.48 (1.13, 1.95) 1.98 (1.51, 2.60)
Medical diagnoses ‖
 Any 12-month medical diagnosis 570 (28.2) 0.78 (0.68, 0.89) 1.35 (1.13, 1.60) 1.12 (0.92, 1.35) 1.27 (1.06, 1.52)
 12-month gastrointestinal disease 86 (4.0) 1.76 (1.32, 2.36) 1.74 (1.26, 2.40) 1.18 (0.83, 1.68) 1.45 (0.99, 2.11)
 12-month cardiovascular disease 399 (18.4) 0.76 (0.65, 0.89) 1.30 (1.10, 1.54) 1.10 (0.91, 1.33) 1.23 (1.04, 1.45)
 12-month metabolic disease 282 (13.8) 0.69 (0.58, 0.81) 1.24 (1.02, 1.50) 1.11 (0.91, 1.36) 1.21 (0.99, 1.48)
 12-month cancer 42 (2.3) 0.67 (0.46, 0.97) 1.51 (0.99, 2.31) 1.27 (0.83, 1.94) 1.50 (0.97, 2.31)
Substance use disorders
 Tobacco use disorder 942 (48.2) 2.65 (2.33, 3.02) 2.20 (1.92, 2.53) 1.84 (1.58, 2.13) 1.56 (1.31, 1.86)
 Other drug use disorder 466 (23.9) 6.58 (5.56, 7.78) 4.62 (3.85, 5.55) 3.69 (3.05, 4.46) 3.66 (2.93, 4.56)
Quality of Life ‖ ¶
 SF-12 mental component 44.70 (9.64) −5.57 (−6.27, −4.87) −4.75 (−5.44, −4.07) −2.64 (−3.25, −2.04) −3.91 (−4.32, −3.20)
 SF-12 physical component 50.94 (9.72) 0.1 (−0.36, 0.57) −0.68 (−1.11, −0.25) −0.08 (−0.53, 0.38) −0.16 (−0.59, 0.26)
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Table 4.

Associations (odds ratios and 95% confidence intervals) between past-year DSM-5 alcohol withdrawal* and past-year alcohol use variables, family history of drinking, healthcare utilization in NESARC-III past-year drinkers with unhealthy alcohol use (n=12,364)

Model 1:
Unadjusted		 Model 2:
Adjusted for sociodemographics*		 Model 3:
Adjusted for sociodemographics
and psychiatric disorders† 		Model 4:
Adjusted for sociodemographics and withdrawal from other substances‡
Alcohol Use Disorder § N (%)
 None 140 (8.0) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Any 1,808 (92.0) 13.4 (11.64, 15.45) 11.2 (9.66, 13.07) 10.1 (8.59,11.77) 10.4 (8.94, 12.21)
 Mild 435 (22.4) 6.0 (4.98, 7.22) 5.2 (4.31,6.35) 5.0 (4.15,6.12) 5.1 (4.23, 6.25)
 Moderate 442 (23.6) 15.9 (13.07, 19.39) 13.5 (10.91,16.67) 12.6 (10.11, 15.60) 12.4 (9.95, 15.35)
 Severe 931 (45.9) 54.8 (45.42, 66.22) 45.8 (36.84,56.97) 39.04 (30.90, 49.34) 40.8 (32.42, 51.44)
12-month binge drinking §
 No 438 (21.7) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 1,510 (78.3) 4.14 (3.61, 4.73) 3.29 (2.84, 3.80) 3.25 (2.80, 3.78) 3.14 (2.71, 3.64)
Family history of drinking
 No 772 (41.8) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 1,176 (58.2) 1.89 (1.66, 2.15) 1.95 (1.71, 2.23) 1.64 (1.43, 1.89) 1.77 (1.55, 2.014)
Healthcare utilization §
 Any 12-month healthcare utilization 268 (12.8) 8.14 (6.28, 10.54) 6.58 (5.06, 8.55) 5.17 (3.96, 6.74) 5.55 (4.23, 7.28)
 12-month emergency room visits 61 (3.1) 21.64 (11.70, 40.01) 17.29 (8.94, 33.44) 12.53 (6.45, 24.35) 14.27 (7.02, 29.00)
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DISCUSSION

In a nationally representative sample of U.S. adults, the prevalence of AWS among individuals with past-year unhealthy alcohol use was 14.3%. AWS was associated with several sociodemographic and clinical correlates, including binge drinking, AUD, psychiatric disorders, family history of problem drinking, and healthcare utilization. These findings underscore the clinical importance of identifying and offering intervention to individuals suffering from AWS.

This is the first study to report the prevalence and characteristics of AWS in a sample of individuals with unhealthy alcohol use from the general population. The estimated prevalence of AWS in our analysis of the NESARC-III sample is similar to previous estimates based on the AUDADIS (Keyes et al. 2011, Casey et al. 2012). Further, our findings that nausea and vomiting, insomnia, and psychomotor agitation were the most frequently reported withdrawal symptoms are consistent with findings from previous NESARC studies (Martin et al. 2018), which also used the AUDADIS. The low mean number of withdrawal symptoms (<3) among past-year individuals with unhealthy alcohol use raises questions as to whether weighting all alcohol withdrawal symptoms equally is a valid approach. Studies that incorporate item response theory models could shed light on the extent to which each withdrawal symptom contributes to the AWS diagnosis. Our finding of increased odds of AWS among males is consistent with conclusions from prior studies (Desmukh et al. 2003, Schuckit et al. 2003, Martin et al. 2018, Sanvisens et al. 2021). This contrasts with the conclusions of a review that neither age nor gender are significant correlates of alcohol withdrawal (Goodson et al. 2014), though the studies reviewed there focused on specific severe withdrawal symptoms (e.g., delirium tremens), rather than the withdrawal syndrome per se. Nationally representative longitudinal studies are needed to define more clearly the sociodemographic correlates of AWS.

With few exceptions, AWS was associated with all psychiatric disorders and SUDs. These findings are generally consistent with findings from previous studies and add to the literature insofar as they reflect the first application of DSM-5 diagnostic criteria to measure both psychiatric disorders and SUD and are the product of models that controlled for a wide array of confounders. Results from our detailed analysis of psychiatric correlates of AWS strengthen those from a comprehensive analysis in the VA Healthcare System (Moore et al. 2017), which showed that alcohol withdrawal is associated with homelessness, an array of comorbid disorders, and psychotropic medication use. Together, these findings underscore the need for clinicians to evaluate patients with AWS for co-occurring disorders, which can affect the response to withdrawal treatment and rehabilitative efforts in detoxified patients. The significantly lower scores on the SF-12 mental component among respondents with AW, compared to those without AW, points to the clinical importance of this entity.

In adjusted models, none of the associations between AWS and medical disorders were significant, consistent with the lack of association between AWS and the SF-12 physical component. Although some past studies reported associations between medical illness and severe AWS, those studies focused mostly on a few acute medical disorders (Smith 1995, Trevisan et al. 1998, Nordback et al. 2005), rather than on a wide array of medical disorders, including chronic illnesses. A rise in the prevalence of AWS in hospitalized patients during the COVID-19 pandemic (Sharma et al. 2021), along with some evidence, albeit conflicting, that alcohol consumption increased during this period (Grossman et al. 2020, Pollard et al. 2020), are consistent with a relationship between AWS and medical illness. Longitudinal studies that account for the role of the AWS in the development of medical disorders are needed to inform clinicians and researchers about patients’ risks of developing specific illnesses and to support preventive and early intervention efforts.

An association between AWS and AUD is expected given that alcohol withdrawal is one of the 11 AUD criteria in DSM-5. Our report of the increasing magnitude of the association as AUD severity increases is of clinical importance as it highlights that diagnosis and treatment of AWS are a crucial step in preventing the worsening of an existing AUD. Findings that past-year binge drinking was associated with higher odds of AWS may be due to high rates of binge drinking among individuals with moderate-to-severe AUD. Studies are needed to disentangle the relationships among binge drinking, AUD, and AWS, given the likelihood shown here of a direct association between binge drinking and the AWS. Associations between AWS and a family history of alcohol-related problems are consistent with findings from genome-wide association studies of both genetic variants (Smith et al. 2018) and epigenetic changes (Koller et al. 2019).

Our findings that AWS is associated with more frequent ER visits and greater utilization of other healthcare resources strengthen the evidence from previous studies (Clark et al. 2013) and add to the literature by showing that the associations were not driven by other mental disorders. This issue, which has not been adequately addressed in the past, points to the burdensome effects of alcohol-related disorders on healthcare systems.

Study limitations are noted. First, due to the cross-sectional design of NESARC-III, temporality cannot be established; prospective studies are necessary to establish causality between AWS and clinical correlates. Second, DSM-5 states that alcohol withdrawal symptoms should not be attributed to intoxication or withdrawal from another substance. While it was challenging to assess the direct contribution of each substance to reported withdrawal symptoms, especially among polysubstance users, we addressed the possibility of confounding by withdrawal from other substances analytically. Controlling for withdrawal from substances that significantly overlapped with alcohol withdrawal symptoms had little effect on most of our findings, suggesting that withdrawal from other substances did not drive our results. Third, recall bias is possible, as NESARC-III was based on self-reports, although our use of past-year rather than lifetime timeframes to measure alcohol use outcomes may mitigate this concern. Finally, studies that used AUDADIS as an interview tool report higher rates of AWS than epidemiological studies that used other assessments, partly due to the conflation with hangover symptoms (Boness et al. 2016). Thus, the prevalence of AWS may be overestimated in the study sample. Further, conflation with hangover symptoms may have, to some degree, driven associations between AWS and sex (Verster et al. 2018), and certain alcohol use-related covariates. Studies that more accurately differentiate between withdrawal and hangover symptoms are warranted. For example, the use of Item Response Theory could account for overestimation attributable to specific AWS criteria, as previously suggested (Martin et al., 2018).

Conclusion

Alcohol withdrawal is a common, highly comorbid, and potentially disabling condition among individuals with unhealthy alcohol consumption. Given that individuals with unhealthy alcohol use can be readily identified using effective screening instruments, such as the AUDIT-C, and considering that AWS severity can be gauged with well-validated assessments such as the Clinical Institute Withdrawal Assessment for Alcohol-revised scale (CIWA-Ar; Sullivan et al. 1989), clinicians should consider the serial use of the AUDIT-C and the CIWA-Ar; this could help to identify AWS in routine clinical settings. Because the CIWA-Ar is a more extensive assessment than may be suitable for screening, efforts are warranted to identify key withdrawal symptoms on which to focus screening efforts. Early identification of AWS could increase the likelihood of managing it successfully, thereby reducing the morbidity and mortality associated with a prevalent public health problem (Griessbach et al. 2019).

Supplementary Material

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Acknowledgments:

This study was supported by NIAAA grant AA026364; the Veterans Integrated Service Network 4 Mental Illness Research, Education and Clinical Center; and NIH grant T32 DA031099. This study was also supported by NIAAA grant R01AA025309 and by the New York State Psychiatric Institute. Dr. Knox’s effort on this project was funded by NIAAA grant K01AA028199.

Conflict of Interest Summary

Dr. Kranzler is a scientific advisory board member for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals, and Enthion Pharmaceuticals; a consultant for Sobrera Pharmaceuticals; and the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes. Drs. Kranzler and Gelernter are named as inventors on PCT patent application #15/878,640 entitled: “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. Dr. Kranzler and Dr. Hasin are members of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative (ACTIVE Group), which over the past three years was supported by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi, and Otsuka. Dr. Hasin reports a current contract with the Opioid Postmarketing Consortium (OPC) to contribute supportive services to Food and Drug Administration (FDA) post marketing requirement (PMR) study 3033-1 on the prevalence and incidence of misuse, abuse, and addiction to prescription opioid analgesics.

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