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Abstract

Background:

The Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) tool is a screening and brief assessment instrument to identify unhealthy tobacco, alcohol, drug use, and prescription medication use in primary care patients. This secondary analysis compares the TAPS tool to the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) for alcohol screening.

Methods:

Adult primary care patients (1124 female, 874 male) completed the TAPS tool followed by AUDIT-C. Performance of each instrument was evaluated against a reference standard measure, the modified World Mental Health Composite International Diagnostic Interview, to identify problem use and alcohol use disorder (AUD). Area under the curve (AUC) appraised discrimination, and sensitivity and specificity were calculated for Youden optimal score thresholds.

Results:

For identifying problem use: On the AUDIT-C, AUC was 0.90 (95% Confidence Interval: 0.86-0.92) for females and 0.91 (0.89-0.93) for males. Sensitivity and specificity for females were 0.89 (0.83-0.93) and 0.78 (0.75-0.80), respectively, and for males were 0.84 (0.79-0.88) and 0.82 (0.79-0.85). On the TAPS tool, AUC was 0.82 (0.79-0.86) for females and 0.81 (0.78-0.84) for males. Sensitivity and specificity for females were 0.78 (0.72-0.84) and 0.78 (0.75-0.81), respectively, and for males were 0.76 (0.71-0.81) and 0.76 (0.72-0.79). For AUD: On the AUDIT-C, AUC was 0.90 (0.88-0.93) for both females and males. Sensitivity and specificity for females were 0.83 (0.74-0.90) and 0.83 (0.80-0.85), respectively, while for males, they were 0.81 (0.74-0.87) and 0.84 (0.81-0.87). On the TAPS tool, AUC was 0.84 (0.80-0.89) for females and 0.82 (0.78-0.86) for males. Sensitivity and specificity for females were 0.73 (0.63-0.81) and 0.85 (0.83-0.88), respectively, while for males, they were 0.75 (0.68-0.81) and 0.84 (0.81-0.86).

Conclusion:

The AUDIT-C performed somewhat better than the TAPS tool for alcohol screening. However, the TAPS tool had an acceptable level of performance for alcohol screening and may be advantageous in practice settings seeking to identify alcohol and other substance use with a single instrument.

Keywords

unhealthy alcohol use substance use alcohol use disorder screening primary care AUDIT-C

Highlights

Screening for tobacco, alcohol, and drug use is recommended in adult primary care patients.

Commonly used alcohol screening tools such as Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) do not identify other substance use.

The Tobacco, Alcohol, Prescription Drug, and Other Substance (TAPS) tool is a brief multi-substance screening and assessment tool that identifies unhealthy alcohol use alongside tobacco and other drugs.

While the AUDIT-C outperformed the TAPS tool for alcohol screening, the TAPS tool still worked well and may be advantageous in practice settings seeking to identify alcohol and other substance use with a single instrument.

Introduction

Alcohol use and its consequences have major impacts on public health. Internationally, 5.3% of all deaths (3 million per year) are attributable to alcohol use.¹ In the United States, alcohol is ranked as the third leading cause of preventable death.² Screening for unhealthy alcohol use has been recommended by the US Preventive Services Task Force since 1996, a status that was maintained in the most recent update in 2018.^3,4 Despite the availability of brief and accurate screening tools, unhealthy alcohol use, which refers to a spectrum of alcohol use that includes risky use, problem use, and alcohol use disorder (AUD), continues to be under-identified in primary care.^5,6 Major barriers to screening in routine practice include time constraints in busy clinical settings, and competing tasks.^6-8 Therefore, screening methods should be short, feasible, and minimize the burden for primary care practitioners. The Tobacco, Alcohol, Prescription Medication, and Other substance Use (TAPS) tool is a screening and brief assessment instrument developed to address some of these barriers^9,10: It is short (on average 2.4 minutes in the interviewer format and 4.5 minutes in the self-administered format),¹¹ screens for all major substance classes in a single-screening instrument, and it can be self-administered by patients electronically (thus allowing its completion in advance of primary care visits).

The TAPS tool has 2 parts: TAPS-1 (4 questions) screens for any unhealthy substance use (tobacco, alcohol, illicit drugs, and nonmedical use of prescription medications) in the past 12 months; TAPS-2 (2-4 questions for each substance class) provides a substance-specific risk-level assessment of each screened positive substance. The TAPS-2 evaluates use in the past 3 months of tobacco, alcohol, illicit drugs (including cannabis, cocaine or methamphetamine, heroin), and other drugs (including hallucinogens, ecstasy/MDMA) and nonmedical use of psychoactive prescription medications (sedatives, opioids, and stimulants) via a yes/no format. When the answer is “yes” to any use, the individual receives 2 to 3 follow-up items specific to that substance class, and responses are summed within each substance class to generate a substance-specific risk score (0-3 for tobacco and other drugs, and 0-4 for alcohol). For example, if a patient reported on TAPS-1 using tobacco and alcohol (over the threshold of 4+ drinks/day for men, 5+ drinks/day for women) in the past-year, but did not used other drugs, they would receive only TAPS-2 questions specific to tobacco and alcohol, and risk scores would be calculated for each of these substances.

Previously, a validation study conducted in 2000 adult primary care patients found that the TAPS tool performed well for detecting problem use of alcohol and AUD in both its electronic self-administered and interviewer-administered formats.⁹

However, before recommending the broad adoption of the TAPS tool for primary care alcohol screening, it is important to understand how it compares to existing alcohol screening tools, such as the widely used Alcohol Use Disorders Identification Test-Consumption (AUDIT-C).^12-16 The AUDIT-C consists of 3 items and takes approximately 1 to 2 minutes to administer, but it screens for alcohol only. We undertook a secondary analysis of data collected in the TAPS tool validation study to compare the performance of the multi-substance TAPS tool screener to the AUDIT-C for detecting problem use of alcohol and AUD in primary care adult patients. There has been no prior rigorous comparison between the TAPS tool and AUDIT-C for alcohol screening, and we hypothesized that they would perform similarly for identifying problem use and AUD.

Methods

This study is a planned secondary analysis of data collected prospectively in the TAPS tool validation study. Parent study procedures were previously reported,^9,17 and are briefly summarized here.

Participants and Recruitment

The TAPS tool validation study enrolled 2000 patients in 5 diverse US primary care sites between August 2014 and April 2015. Adult (18 years or older) patients of the participating clinics were eligible. Individuals were excluded if they could not understand spoken English or were physically unable to use the tablet computer. Research assistants (RAs) consecutively approached patients in the waiting room to invite them to participate and obtained verbal informed consent.

Study Procedures and Measures

All participants completed the TAPS tool in an interviewer-administered (interviewer-TAPS) and electronic self-administered (myTAPS) format, administered on an iPad.¹¹ Individuals were randomized either to receive the myTAPS first, followed by the interviewer-TAPS, or to receive the interviewer-TAPS first, followed by the myTAPS. After completing both versions of the TAPS tool, participants received additional assessments for alcohol and other substance use that included (1) AUDIT-C and (2) modified World Health Organization World Mental Health Composite International Diagnostic Interview (CIDI) Substance Use Section (http://www.hcp.med.harvard.edu/wmhcidi/index.php), here referred to as the CIDI.^17,18 These assessments were administered verbally by a RA who recorded participant responses. Participant demographic characteristics, including sex (male or female), were self-reported.

We used the CIDI as the reference standard measure to evaluate the performance of each instrument for identifying problem use and AUD. As in prior studies, problem use was defined as past-year use with endorsement of 1 or more DSM-5 criteria (CIDI score of 1 or greater), while AUD was defined as past-year use with 2 or more DSM-5 criteria (CIDI score of 2 or greater).^9,19,20

Analysis

Models

We examined the performance of the AUDIT-C and the TAPS tool for identifying problem use of alcohol and then for identifying AUD. These were obtained separately for female and male participants because prior studies have found that sex-specific thresholds may improve the accuracy of alcohol screening.^12,21

Receiver Operating Characteristic Analysis to Assess Classification Performance

The performance of the AUDIT-C was compared with that of the interviewer-TAPS and myTAPS using the area under the curve (AUC) of the receiver operating characteristic (ROC). Analyses were conducted using the rocgold and roccomp command for correlated data in the STATA 14 software (StataCorp. 2015. Stata Statistical Software: Release 14.2, StataCorp LP, College Station, TX, USA) and in the R statistical software (v4.2.2; R Core Team, 2022), RStudio (v2022.7.2.576; RStudio Team, 2022), and using the “EpiR” package (v2.0.76; Stevenson & Sergeant, 2024).^22-24 Tests of the null hypothesis that the AUC did not differ from 0.5 and tests of the equality of multiple AUCs utilized a chi-squared statistic. Bonferroni-adjusted significance levels were used to avoid the problem of multiplicity.

Youden Optimal Threshold Scores in the Study Sample for the AUDIT-C and the TAPS Tool

We used the Youden criteria to define the threshold value for the classification of individuals.²⁵ The Youden Index is often used as a summary measure of the ROC curve. It measures the effectiveness of a diagnostic screening test and allows the selection of an optimal threshold value or cutoff point for the test of interest.²⁶ Usually, this point occurs at the threshold value for which specificity and sensitivity are equal. However, with only a finite number of candidate thresholds, as for an instrument like the TAPS tool, there may be none for which there is exact equality. In our analysis, where sensitivity and specificity were similar for 2 threshold values the Youden threshold was chosen based on clinical rationale. For classifying problem use of alcohol, we chose the threshold with higher sensitivity while maintaining good specificity (over 0.70) because, in primary care, it is preferred to not miss cases of problem use for which further clinical assessment and counseling may be needed (ie, review of medication interactions, screening for comorbid conditions, and brief interventions).^12,27 For identifying individuals with AUD, we chose the threshold with higher specificity while maintaining good sensitivity (over 0.70) to decrease the risk of false-positive results. This choice was made because incorrectly identifying a patient as likely having an AUD can lead to significant consequences (eg, stigma, labeling with a chronic condition) and lead to the unnecessary use of clinical resources (eg, behavioral health assessment, specialty care referral).

Comparison of the TAPS Tool with AUDIT-C using the Youden Threshold

To more directly compare the TAPS tool to AUDIT-C for problem use of alcohol, we then used a second approach, which identified the theoretical thresholds (Point A in Figures 1 –4) at which the interviewer-TAPS and myTAPS achieve the approximately equivalent sensitivity as the Youden threshold of the AUDIT-C, and used the AUC curves to identify the specificity of the TAPS tool at these thresholds. For AUD, we followed a similar procedure to compare the TAPS tool with the AUDIT-C for AUD, identifying the theoretical thresholds at which interviewer-TAPS and myTAPS achieve the same specificity as the Youden threshold of the AUDIT-C, and using the AUC curves to identify the sensitivity of the TAPS tool at these thresholds. Third, we compared the Youden threshold of the AUDIT-C with the best existing threshold of the interviewer-TAPS and of the myTAPS (Point B in Figures 1 –4) to reach an approximately equivalent level of sensitivity as the AUDIT-C for problem use and to reach an approximately equivalent level of specificity as the AUDIT-C for AUD.

Figure 1.

Problem use in females: the receiver operating characteristic (ROC) and the area under the curve (AUC) for AUDIT-C, self-administered (myTAPS) and interviewed-administered TAPS (interviewer-TAPS). AUC, area under the curve; AUDIT-C, Alcohol Use Disorders Identification Test-Consumption; TAPS, Tobacco, Alcohol, Prescription Medication, and other substance.

Missing Data

Logistic regression models were used to obtain an estimate of the probability of problem use and of AUD for the interviewer-TAPS, myTAPS, and AUDIT-C. AUDIT-C data were missing for 97 participants, and multiple imputation was used to explore whether imputing the missing values was needed. Imputing missing values led to no difference in results (see Supplemental Material), and so the analysis is presented without imputation. Sex information was missing for 2 participants, and interviewer-TAPS information was missing for 1 participant, so these cases were dropped from the analysis.

Results

Participant Characteristics

Among the 2000 participants, more than half were female (56.2%, n = 1124), and the mean age was 46.0 years (SD = 14.7). A majority was black/African American (55.6%, n = 1112), and 11.7% (n = 233) were Hispanic. A total of 19.2% (n = 383) reported having less than a high school education. The prevalence of any past-year alcohol use (identified by CIDI) was 62.0% (n = 1239).

Participants were living in urban and suburban zones, and recruited from 5 primary care clinics in Baltimore, MD; New York, NY; Richmond, VA; and Kannapolis, NC.

Youden Threshold scores in the study sample for the AUDIT-C and the TAPS Tool

The Youden thresholds for the identification of problem use were as follows: for the AUDIT-C, scores of ≥2 in females and ≥3 in males; for the TAPS tool (both myTAPS and interviewer-TAPS versions), score of ≥1 in both sexes. For the identification of AUD, the Youden thresholds for AUDIT-C were a score of ≥3 in females and ≥4 in males; for the TAPS tool (myTAPS and interviewer-TAPS versions), a score of ≥2 in both sexes (Tables S2 and S3 in supplemental material).

Identification of Problem Use of Alcohol

The prevalence of problem alcohol use (CIDI 1+) was 18% (n = 202) in females and 31% (n = 271) for males.

Problem Use in Females

For detecting problem alcohol use in females, the null hypothesis of equal AUCs of the ROCs of the AUDIT-C, myTAPS, and interviewer-TAPS was rejected at P < .0001. The AUDIT-C had the highest AUC (0.90 [95% CI 0.86-0.92]), and the AUC was lower for the myTAPS (0.82 [95% CI 0.79-0.86]) and the interviewer-TAPS (0.82 [95% CI 0.78-0.85]), chi² = 24.98, P ≤ .0001 (Figure 1).

For detecting problem use using the Youden threshold for AUDIT-C (score ≥2), sensitivity was 0.89 (95% CI 0.83-0.93), and specificity was 0.78 (95% CI 0.75-0.80) (Table S2, supplemental material). The best existing thresholds (score ≥1) for the TAPS tool resulted in sensitivity of 0.78 (95% CI 0.72-0.84), and specificity of 0.78 (95% CI 0.75-0.81) for myTAPS, and sensitivity of 0.76 (95% CI 0.70-0.82) and specificity of 0.78 (95% CI 0.76-0.81) for the interviewer-TAPS.

Problem Use in Males

For detecting problem alcohol use in males (Figure 2), the null hypothesis of equal AUCs of the ROCs of the AUDIT-C, myTAPS, and interviewer-TAPS was also rejected at P < .0001. The AUDIT-C had the highest AUC (0.91 [95% CI 0.89-0.93]) and was significantly higher than the AUC for the myTAPS (0.81 [95% CI 0.78-0.84]) and the interviewer-TAPS (0.80 [95% CI 0.76-0.83]), chi² = 69.32, P ≤ .0001.

Figure 2.

Problem use in males: the receiver operating characteristic (ROC) and the area under the curve (AUC) for AUDIT-C, self-administered (myTAPS) and interviewed-administered TAPS (interviewer-TAPS). AUC, area under the curve; AUDIT-C, Alcohol Use Disorders Identification Test-Consumption; TAPS, Tobacco, Alcohol, Prescription Medication, and Other Substance.

For detecting problem use, the Youden threshold for AUDIT-C (score ≥3) resulted in sensitivity of 0.84 (95% CI 0.79-0.88) and specificity of 0.82 (95% CI 0.79-0.85) (Table S2, supplemental material). The best existing thresholds for the TAPS tool resulted in sensitivity of 0.76 (95% CI 0.71-0.81) and specificity of 0.76 (95% CI 0.72-0.79) for myTAPS, and sensitivity of 0.73 (95% CI 0.67-0.78) and specificity of 0.79 (95% CI 0.75-0.82) for the interviewer-TAPS (Table S2, supplemental material).

Identification of AUD

The prevalence of AUD (CIDI 2+) was 9% (n = 100) among females and 20% (n = 177) among males in the sample.

AUD in Females

For detecting AUD, the null hypothesis of equal AUCs of the ROCs of the AUDIT-C, myTAPS, and interviewer-TAPS was rejected at P < .005. The AUC for the AUDIT-C was highest (0.90 [95% CI 0.88-0.93]), and was significantly higher than the AUC for the myTAPS (0.84 [95% CI 0.80-0.89]) and the interviewer-TAPS (0.85 [95% CI 0.81-0.89]), chi² = 6.57, P < .05.

The Youden AUDIT-C threshold for the sample (score ≥3) had a sensitivity of 0.83 (95% CI 0.74-0.90) and specificity of 0.83 (95% CI 0.80-0.85). The best existing thresholds for the TAPS tool resulted in sensitivity of 0.73 (95% CI 0.63-0.81) and specificity of 0.85 (95% CI 0.83-0.88) for myTAPS, and in sensitivity of 0.73 (95% CI 0.63-0.81) and specificity of 0.86 (95% CI 0.83-0.88) for the interviewer-TAPS (Table S3, supplemental material).

AUD in Males

For detecting AUD, the null hypothesis of equal AUCs of the ROCs of the AUDIT-C, myTAPS, and interviewer-TAPS was rejected at P < .0001. The AUC for the AUDIT-C (0.90 [95% CI 0.88-0.93]) was higher than the AUC for the myTAPS (0.82 [95% CI 0.78-0.86]) and the interviewer-TAPS (0.81 [95% CI 0.77-0.84]), chi² = 32.71, P ≤ .0001.

The Youden AUDIT-C threshold for our sample (score ≥4) had sensitivity of 0.81 (95% CI 0.74-0.87) and specificity of 0.84 (95% CI 0.81-0.87) (Figure 4). The best existing thresholds for the TAPS tool resulted in a sensitivity of 0.75 (95% CI 0.68-0.81) and a specificity of 0.84 (95% CI 0.81-0.86) for myTAPS and in sensitivity of 0.68 (95% CI 0.61-0.75) and specificity of 0.85, (95% CI 0.82-0.87) for the interviewer-TAPS (Table S3).

Figure 3.

Alcohol use disorder in females: the receiver operating characteristic (ROC) and the area under the curve (AUC) for AUDIT-C, self-administered (myTAPS), and interviewed-administered TAPS (interviewer-TAPS). AUC, area under the curve; AUDIT-C, Alcohol Use Disorders Identification Test-Consumption; TAPS, Tobacco, Alcohol, Prescription Medication, and Other Substance.

Figure 4.

Alcohol use disorder in males: the receiver operating characteristic (ROC) and the area under the curve (AUC) for AUDIT-C, self-administered (myTAPS), and interviewed-administered TAPS (interviewer-TAPS). AUC, area under the curve; AUDIT-C, Alcohol Use Disorders Identification Test-Consumption; TAPS, Tobacco, Alcohol, Prescription Medication, and Other Substance.

Discussion

In this large and diverse sample of adult primary care patients, we found that the AUDIT-C and the TAPS tool both performed well, using empirically derived thresholds, for identifying problem use of alcohol and AUD. The AUDIT-C performed somewhat better than the TAPS tool for alcohol-specific screening. However, now that the USPSTF recommends screening for both alcohol and drug use in adult primary care patients,^28-30 some practice settings may prefer the efficiency of screening for alcohol and drugs together with a single instrument. This could be achieved by the TAPS tool, which screens for unhealthy use of 6 different drug classes, while maintaining adequate sensitivity and specificity for alcohol.⁹ Another option would be to administer AUDIT-C along with other short screening tools for other drugs, such as the Substance Use Brief Screen, Single-Item Screening Question for drugs,^31,32 or a brief cannabis screener.³³ However, this approach requires the use of separate instruments, each with their own scoring systems, thereby making screening more challenging to implement.

A point of interest is that the optimal threshold scores for the AUDIT-C were lower in our study than those that are most commonly recommended and applied. Namely, the thresholds in our study sample for identifying problem use of alcohol (≥2 for females and ≥3 for males) were 1 point lower than those usually recommended in the literature (≥3 for females and ≥4 for males).^{12,15,16,21,34-36} Our findings are similar to an early study of the AUDIT-C among females attending Veteran Affairs primary care clinics, which similarly showed that either a threshold of ≥2 or ≥3 for AUDIT-C was optimal to identify problem use in females.¹² It should be noted that consumption reported by patients to achieve an AUDIT-C score of >2 is quite low. For example, a female who reports having a drink containing alcohol 3 or 4 times per month, with no instances of having more than 2 drinks on any day, would receive a score of 2 and be classified as having problem use of alcohol on the AUDIT-C, even though her drinking fits within the guidelines for alcohol consumption in the United States.³⁷

Similarly, for identifying AUD the optimal thresholds identified in our sample were lower than the commonly recommended thresholds for AUDIT-C (empirically derived optimal thresholds were ≥3 for females or ≥4 for males, in contrast to the recommended threshold of ≥4 for females and ≥5 for males).^12,15,16,21 The lower thresholds obtained in our sample could be explained by the definition of AUD based on DSM-5, whereas in prior studies the definition was based on the DSM-4. In addition, some of the prior studies were limited to participants with DSM-4 alcohol dependence (meeting 3 or more criteria) to define the AUD threshold and did not include those with the less severe symptoms of DSM-4 alcohol abuse.^38,39 While our findings do appear to support a lower threshold for identifying AUD using the AUDIT-C, the benefits of greater sensitivity for identifying patients with AUD must be balanced against the need to avoid negative consequences relative to false-positive results, such as stigma and costs related to the assessment of and treatment for AUD in patients who do not actually require this level of intervention.^40,41 However, before recommending any change to the currently recommended AUDIT-C thresholds, these analyses should be repeated in other settings and populations.

Limitations

Our study has some limitations. First, the findings may not generalize to all populations and settings. Study sites were only in urban and suburban areas of the United States, and the AUDIT-C and the TAPS tool were administered only in English. The findings may also differ depending on the population and how screening is being used—for example, among individuals under criminal justice supervision or pregnant females, disclosing any alcohol use may have negative consequences, and sensitivity could be reduced as a result. Second, the optimal thresholds were derived in the same sample used for the validation analyses. Third, the screening instruments evaluated in our study as well as the instrument used as reference standard (CIDI) all rely on self-reported information, with no biochemical validation of alcohol use. However, self-reported measures of alcohol and other substance use have consistently shown good accuracy in the research context.^42-44 Fourth, the CIDI reference standard does not identify risky drinking that falls below the threshold of “problem use.” Patients with low levels of unhealthy alcohol use may be those who benefit the most from brief interventions in primary care settings,³ but we were unable to evaluate the performance of the TAPS tool for individuals who did not report at least 1 symptom of AUD based on the CIDI.

Conclusions

When primary care practices want to screen for alcohol alone, and not for drugs or tobacco, the AUDIT-C may be the preferred screening instrument, as it has the best performance for identifying problem use of alcohol and AUD. However, the TAPS tool may be advantageous if practitioners want to use a single instrument to identify problem use and use disorders of several substances, including tobacco and commonly misused classes of illicit and prescribed drugs, as well as alcohol. The TAPS tool achieves this goal while remaining brief (requiring on average just 2-4 minutes) and having the flexibility to allow for either interviewer- or electronic self-administration.¹¹ Notably, we also found that the AUDIT-C performed best for detecting problem use and AUD at lower thresholds than those that are currently recommended and widely used. This new finding should be confirmed by future studies in different samples, and using the DSM-5 criteria.

Supplemental Material

sj-docx-1-saj-10.1177_29767342251326678 – Supplemental material for Identifying Alcohol Use Disorder and Problem Use in Adult Primary Care Patients: Comparison of the Tobacco, Alcohol, Prescription Medication and Other Substance (TAPS) Tool With the Alcohol Use Disorders Identification Test Consumption Items (AUDIT-C)

Supplemental material, sj-docx-1-saj-10.1177_29767342251326678 for Identifying Alcohol Use Disorder and Problem Use in Adult Primary Care Patients: Comparison of the Tobacco, Alcohol, Prescription Medication and Other Substance (TAPS) Tool With the Alcohol Use Disorders Identification Test Consumption Items (AUDIT-C) by Angéline Adam, Eugene Laska, Robert P. Schwartz, Li-Tzy Wu, Geetha A. Subramaniam, Noa Appleton and Jennifer McNeely in Substance Use & Addiction Journal

Footnotes

Acknowledgements

The authors thank John Rotrosen, Gaurav Sharma, Lauretta A. Cathers, Dace Svikis, Luke Sleiter, Saima Mili, Linnea Russel, Courtney Nordeck, Anjalee Sharma, Kevin E. O’Grady, Leah B. Bouk, Jan Gryczynski, Shannon G.Mitchell; Carol Cushing, Jaqueline King, Aimee Wahle, Phoebe Gauthier, Sarah Farkas, Patsy Novo, Laura McElherne, Kimberly Micki Roseman, Carla Kingsbury, Melissa Johnston, Eve Jelstrom, Patrice Yohannes, Jack Chally, Paul Van Veldhuisen, Anne Hoehn, Lauren Yesko, Alex Brobely, Robert Ali, Ziqiang Lin, and John Marsden for their participation in the TAPS tool validation study design and execution. The authors thank Joseph Studer for his statistical assistance.

Author Contributions

JM, LTW, GAS, RPS were involved in the conception and in the design of the study. The secondary analyses for this manuscript and the interpretation of the data were performed by AA, JM, EL. AA, JM, EL, LTW, GAS, and RPS drafted, revised, and approved the final version of the article. LTW and EL provided the statistical expertise.

Data Availability Statement

Deidentified data for the study (CTN-0059) is available on the NIDA Data Share Website: .

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: GA Subramaniam is an employee of the Center for Clinical Trials Networks (CCTN) and of the National Institute on Drug Abuse (NIDA). NIDA is the funding agency for the National Drug Abuse Treatment Clinical Trials Network; her participation in this paper is explained by her role as a project scientist under a cooperative agreement for this study. Dr McNeely declares intellectual property interests in the Substance Use Screening and Intervention Tool (SUSIT), which is unrelated to this work. Dr Schwartz has consulted for Verily Life Sciences, and he is PI of an unrelated NIDA-funded study that will receive free medication from Alkermes and Indivior. All other authors declare that they do not have a conflict of interest.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by National Institute on Drug Abuse cooperative agreements (grant numbers 5 U01 DA 012034; U10DA013727 and UG1DA040317; 3UG2DA013035; and UG1DA013034), Swiss Foundation for Alcohol Research (grand number 293), Prof. Dr Max Cloëtta Foundation & Unisciencia Foundation, and by Lausanne University Hospital.

Compliance,Ethical Standards,and Ethical Approval

This study was conducted in accordance with the Declaration of Helsinki. Each Institutional review board of the sites (Duke University Health System, Friends Research Institute, New York University School of Medicine, and Virginia Commonwealth University) participating in the study approved all study procedures (approval no. XYZ123). Participants gave verbal informed consent before starting interviews.

ORCID iD

Angéline Adam

Supplemental Material

Supplemental material for this article is available online at the SAJ website .

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Supplementary Material

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Identifying Alcohol Use Disorder and Problem Use in Adult Primary Care Patients: Comparison of the Tobacco,Alcohol,Prescription Medication and Other Substance (TAPS) Tool With the Alcohol Use Disorders Identification Test Consumption Items (AUDIT-C)

Abstract

Background:

Methods:

Results:

Conclusion:

Keywords

Highlights

Introduction

Methods

Participants and Recruitment

Study Procedures and Measures

Analysis

Models

Receiver Operating Characteristic Analysis to Assess Classification Performance

Youden Optimal Threshold Scores in the Study Sample for the AUDIT-C and the TAPS Tool

Comparison of the TAPS Tool with AUDIT-C using the Youden Threshold

Missing Data

Results

Participant Characteristics

Youden Threshold scores in the study sample for the AUDIT-C and the TAPS Tool

Identification of Problem Use of Alcohol

Problem Use in Females

Problem Use in Males

Identification of AUD

AUD in Females

AUD in Males

Discussion

Limitations

Conclusions

Supplemental Material

Footnotes

Acknowledgements

Author Contributions

Data Availability Statement

Declaration of Conflicting Interests

Funding

Compliance,Ethical Standards,and Ethical Approval

ORCID iD

Supplemental Material

References

Supplementary Material