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Keywords:

  • Stratum-Specific Likelihood Ratio;
  • Alcohol Use Disorder Identification Test;
  • Alcohol;
  • Injury;
  • Screening

Abstract

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Background

The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) is a brief alcohol screening test and a candidate for inclusion in recommended screening and brief intervention protocols for acute injury patients. The objective of the current study was to examine the performance of the AUDIT-C to risk stratify injury patients with regard to their probability of having an alcohol use disorder.

Methods

Participants (n = 1,004) were from a multisite Australian acute injury study. Stratum-specific likelihood ratio (SSLR) analysis was used to examine the performance of previously recommended AUDIT-C risk zones based on a dichotomous cut-point (0 to 3, 4 to 12) and risk zones derived from SSLR analysis to estimate the probability of a current alcohol use disorder.

Results

Almost a quarter (23%) of patients met criteria for a current alcohol use disorder. SSLR analysis identified multiple AUDIT-C risk zones (0 to 3, 4 to 5, 6, 7 to 8, 9 to 12) with a wide range of posttest probabilities of alcohol use disorder, from 5 to 68%. The area under receiver operating characteristic curve (AUROC) score was 0.82 for the derived AUDIT-C zones and 0.70 for the recommended AUDIT-C zones. A comparison between AUROCs revealed that overall the derived zones performed significantly better than the recommended zones in being able to discriminate between patients with and without alcohol use disorder.

Conclusions

The findings of SSLR analysis can be used to improve estimates of the probability of alcohol use disorder in acute injury patients based on AUDIT-C scores. In turn, this information can inform clinical interventions and the development of screening and intervention protocols in a range of settings.

Excessive alcohol use is associated with severe acute injury and is a significant risk factor for injury and trauma recidivism (Rivara et al., 1993). In addition, alcohol use disorders and other psychiatric disorders contribute to the significant mental health burden following acute injury (Bryant et al., 2010). Service delivery models that incorporate screening, assessment, tailored treatment, and ongoing monitoring of mental health needs are required to address the complex needs of acute injury survivors (O'Donnell et al., 2008). Acute medical settings are well placed to implement alcohol screening and brief intervention (SBI) for at-risk and problem drinkers to further reduce the burden of both alcohol use and injury. Importantly, brief interventions have been shown to be an effective and cost-effective intervention to reduce alcohol consumption and injury among acute injury patients (Gentilello et al., 1999, 2005). The available evidence has led to significant changes to service delivery policy. Significantly, the American College of Surgeons Committee on Trauma has recommended that all trauma centers should incorporate SBI for alcohol as part of routine trauma care (American College of Surgeons, 2006).

The availability of a valid alcohol screening test is essential to implement SBI among acute injury patients. Ideally, a test will be brief and acceptable to both staff and patients and will accurately identify individuals at risk of problem drinking who require brief or more intensive intervention. The Alcohol Use Disorders Identification Test (AUDIT) (Saunders et al., 1993) has been recommended for use as part of an SBI protocol for use with acute injury patients (U.S. Department of Health and Human Services, 2007). However, the time taken to administer the 10-item AUDIT is a potential barrier to incorporating the full screen into routine screening procedures. As a result, studies have assessed the performance of the AUDIT-Consumption (AUDIT-C), which is a brief alcohol screening test comprising the first 3 consumption items of the AUDIT (Bradley et al., 2007; Bush et al., 1998). The AUDIT-C has been used in quality improvement initiatives in large healthcare systems (Rose et al., 2008; United States Department of Veterans Affairs, 2012).

Most studies to date that have examined the performance of the AUDIT-C to screen for problem drinking have relied upon the areas under receiver operating characteristic curves (AUROCs) to estimate the optimal cut off score based on a comparison of the sensitivity and specificity at each possible score threshold (or cut-point) on the test. Although it can prove difficult to determine the optimal cutoff score (e.g., sensitivity is sometimes more important than specificity, or vice versa, depending on the cost of a false-positive versus a false-negative screen), a total score of 4 or more for males and 3 or more for females on the AUDIT-C has been recommended as an indicator of an alcohol use disorder and/or hazardous alcohol use (Bradley et al., 2007). An alternative approach to a single cutoff score is to calculate stratum-specific likelihood ratios (SSLRs) that provide a summary measure of sensitivity and specificity for strata, or risk zones, of a screening measure. An advantage of likelihood ratios is that they are based on a ratio of sensitivity and specificity so they should be relatively stable across settings with varying prevalence of the target disorder. In addition, for acute injury (and other) patients, multilevel risk zones of the AUDIT-C based on SSLR analysis (rather than the use of a dichotomous cut-point) retains more information provided by a patient's score and thus provides a more accurate estimate of a patient's probability of an alcohol use disorder. A practical advantage of this approach is that it can help practitioners to determine the probability that any given patient has an alcohol use disorder based on their result on the AUDIT-C (Attia, 2003; Peirce and Cornell, 1993).

The focus on screening for alcohol use disorders in the current study is consistent with current practice guidelines (National Institute on Alcohol Abuse and Alcoholism, 2007), and alcohol use disorders are a priority target in acutely injured patients using evidence-based brief interventions (Zatzick et al., 2004). To date, only 2 published studies (Johnson et al., 2013; Rubinsky et al., 2010) have used SSLR analysis to examine the performance of the AUDIT-C, and in both studies the target disorder was alcohol dependence (rather than any alcohol use disorder) in a primary care sample. The objective of the current study was to use SSLR analysis to examine the performance of previously recommended AUDIT-C risk zones (based on a single dichotomous cut-point 0 to 3, 4 to 12) and derived AUDIT-C risk zones (based on SSLR analysis) to risk stratify injury patients at the time of their hospital admission with regard to their probability of having an alcohol use disorder.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Participants

Participants were from the Australian Injury Vulnerability Study (Bryant et al., 2010). Weekday admissions to 4 level I trauma centers in Australia were recruited into the study between April 2004 and February 2006. Inclusion criteria were as follows: a traumatic injury that required a hospital admission of at least 24 hours; aged between 16 and 70 years of age; and reasonable comprehension of English to complete the assessment. Exclusion criteria were moderate or severe traumatic brain injury, and currently suicidal or psychotic. For the present analyses, participants were included if they had complete age, gender, and AUDIT and alcohol diagnostic data at intake.

Measures

The AUDIT is a widely used and validated alcohol screening test that consists of 10 questions (Saunders et al., 1993). The AUDIT-C screening test consists of the first 3 consumption items of the AUDIT and has been validated and implemented in primary care settings (Bradley et al., 2007; Rose et al., 2008).

The Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998) is a brief structured diagnostic interview to assess for a range of DSM-IV (American Psychiatric Association, 1994) psychiatric disorders.

For the current study, the AUDIT-C was the screening measure of interest. Similar to Bush and colleagues (1998), we defined current alcohol use disorder as lifetime alcohol abuse or dependence with at least 1 alcohol-related symptom in the past year. The MINI was used to assess for lifetime alcohol use disorder. The presence of alcohol-related symptoms was assessed using AUDIT items 4 to 10 that screen for alcohol-related consequences.

We were guided by the Australian Guidelines to Reduce Health Risks Associated with Alcohol (National Health Medical Research Council, 2009) to estimate hazardous alcohol use. The guidelines recommend the following for adults: (i) drink no more than 2 standard drinks on any day to reduce the risk of harm from alcohol-related disease or injury, and (ii) drink no more than 4 standard drinks on a single occasion to reduce the risk of alcohol-related injury (National Health Medical Research Council, 2009). For the current study, at-risk drinking was operationalized as a score of 1 or more on AUDIT item 2 (i.e., 3 or more standard alcohol drinks on a typical day when drinking) and/or item 3 (6 or more drinks on 1 occasion at least occasionally). Heavy drinking was operationalized as a score of 2 or more on AUDIT item 2 (5 or more standard alcohol drinks on a typical day when drinking) and/or item 3 (6 or more drinks on 1 occasion at least monthly).

Procedure

Hospital research and ethics committees approved the study and all participants provided written informed consent prior to participating in the research study. As part of standardized assessments for the study, patients were assessed by researchers at intake and at 3 and 12 months, but for this current study, we only used data collected at the intake assessment, which was during the acute hospitalization following injury. Assessment results were not shared with trauma center staff, and the AUDIT and other self-report screening questionnaires were completed by patients following the MINI diagnostic interview.

The MINI alcohol abuse and dependence modules were administered during hospitalization to assess lifetime history of alcohol abuse and/or dependence. The AUDIT was administered during hospitalization to assess alcohol use in the 12 months prior to the injury event.

Statistical Analyses

For patients with complete data and those with incomplete data, assessment of group differences on nominally measured variables was undertaken by cross-tabulating the data and performing chi-squared tests of independence. Group differences on continuous variables were assessed using independent sample t-tests. These analyses were undertaken using IBM SPSS Statistics for Windows, Version 19.0 (Armonk, NY).

Receiver operating characteristic (ROC) analysis was used to determine the utility of recommended and derived strata of the AUDIT-C screening test to identify patients with a current alcohol use disorder. ROC curves plot sensitivity versus (1 − specificity). Curves toward the upper left-hand corner of an ROC graph represent stronger screening tests. The AUROCs are useful for choosing which screening test offers the optimal combination of sensitivity and specificity overall. An AUROC score of 0.50 indicates no better than chance discrimination of 2 groups; 1.00 indicates perfect ability to discriminate groups. An AUROC score higher than 0.80 is generally considered excellent. AUROCs were compared using the method described by DeLong and colleagues (1988). ROC analyses were undertaken using STATA, Stata Statistical Software, Release 10.0 (College Station, TX).

SSLR analysis was used to identify and examine strata (i.e., a range of scores) on the AUDIT-C screening test to identify patients based on their likelihood of having an alcohol use disorder. SSLRs provide a summary measure of sensitivity and specificity for any given stratum. The SSLR of a given stratum corresponds to the change in sensitivity divided by the change in specificity over the defined range of scores. For the current analyses, SSLRs were calculated by dividing the proportion of patients with an alcohol use disorder (as defined in this study by lifetime alcohol use disorder plus at least 1 alcohol-related symptom in the past 12 months) who have a screening score in a given stratum, by the proportion of patients without an alcohol use disorder who have a screening score in the same stratum. The SSLR of an individual's stratum was then used to determine the posttest probability that an individual had an alcohol use disorder as follows. First, the pretest probability (or prevalence) of alcohol use disorder among patients in the study sample was converted to pretest odds. Second, the posttest odds were calculated by multiplying the pretest odds by the SSLR of a given stratum. Third, the posttest probability of alcohol use disorder for that stratum was calculated from the posttest odds. The equations used to undertake these calculations were as follows:

  • display math

Consistent with the recommendations of Peirce and Cornell (1993), the following rules were adopted to derive the optimal number of strata based on the following rules. First, SSLRs with 95% confidence intervals were calculated for 12 risk zones based on each possible score on the AUDIT-C screening test. Second, any stratum that did not include at least 1 patient with or without alcohol use disorder (i.e., score was 0) was combined with an adjacent stratum. Third, when strata were not monotonically related (i.e., when the SSLRs did not increase in magnitude across strata), or when the SSLRs were close to each other and the 95% confidence interval of an SSLR included an adjacent SSLR, then the strata were collapsed into each other. SSLRs were calculated using a spreadsheet developed by Peirce and Cornell (1993).

Following calculation of derived AUDIT-C strata, the percentage of at-risk drinking and heavy drinking within each derived and recommended stratum was calculated.

Results

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Participants

Of the 1,166 participants in the injury study, 1,004 participants had complete data. Compared with patients with incomplete data, those with complete data were more likely to be older, mean (SD) = 38.3 (13.7) versus 34.7 (12.4); p = 0.003, and to have a less severe Injury Severity Score, mean (SD) = 10.9 (8.0) versus 12.3 (8.6); p = 0.04. There were no between-group differences on gender (p = 0.09).

The demographic and clinical characteristics at hospital admission of all patients with complete data (n = 1,004) are presented in Table 1. Key features of the sample were that the mean age of patients was 38 years old, almost three-quarters (73%) of the patients were male, the cause of injury for two-thirds (65%) of patients was a motor vehicle accident, and almost one-quarter (23%) met criteria for a current alcohol use disorder.

Table 1. Demographic and Clinical Characteristics of Injury Patients at Hospital Admission (n = 1,004)
VariableMeanSD
Age38.313.7
Injury severity scorea10.98.0
Days in hospitalb12.513.1
Total AUDIT6.66.3
AUDIT-C4.53.0
  N %
  1. SD, standard deviation.

  2. a

    n = 959 due to missing data.

  3. b

    n = 998 due to missing data.

  4. c

    n = 999 due to missing data.

  5. d

    Current alcohol use disorder was defined as lifetime alcohol abuse or dependence with at least 1 alcohol-related symptom in the past year.

Male gender73172.8
Cause of injuryc
Motor vehicle accident65565.2
Fall16516.4
Assault616.1
Work-related accident505.0
Other injuries686.8
Lifetime alcohol use disorder34134.0
Current alcohol use disorderd22822.7

SSLR and ROC Analyses

Table 2 shows the number of patients with and without an alcohol use disorder, and the percentage of patients with at-risk and heavy drinking, as well as the AUROCs, SSLRs, and posttest odds and probabilities of an alcohol use disorder, for each of the recommended and derived AUDIT-C zones.

Table 2. AUROC, SSLR, and Posttest Odds and Probability of Alcohol Use Disorder for Derived and Recommended AUDIT-C Zones Among Injury Patients (n = 1,004)
Screening test risk zones N %Alcohol use disorder% At-risk drinking within zone% Heavy drinking within zoneAUROC (95% CI)SSLR (95% CI)Posttest oddsPosttest probability
YesNo
  1. AUDIT-C, Alcohol Use Disorders Identification Test-Consumption; AUROC, area under receiver operating characteristic curve; SSLR, stratum-specific likelihood ratio; CI, confidence interval; NA, not applicable.

  2. a

    Comparison with recommended AUDIT-C zones, p < 0.001.

Recommended AUDIT-C zones
0 to 339839.62037870.110.10.70 (0.67 to 0.73)0.18 (0.12 to 0.27)0.055.1
4 to 1260660.420839899.376.11.78 (1.64 to 1.93)0.5334.7
Derived AUDIT-C zones
0 to 339839.62037870.110.10.82 (0.78 to 0.85)a0.18 (0.12 to 0.27)0.055.1
4 to 524924.83421598.851.40.54 (0.39 to 0.75)0.1613.9
610210.2327010086.31.56 (1.06 to 2.29)0.4731.8
7 to 814714.6697899.393.23.01 (2.26 to 4.01)0.9047.3
9 to 1210810.873351001007.10 (4.89 to 10.30)2.1268.0

Based on a pretest prevalence rate (probability) of alcohol use disorder of 23%, SSLR analysis identified multiple derived AUDIT-C risk zones (0 to 3, 4 to 5, 6, 7 to 8, 9 to 12) with posttest probabilities of alcohol use disorder ranging from 5.0 to 67.6%. Seventy percent of patients with scores in the lowest recommended and derived AUDIT-C risk zone (0 to 3) and almost all patients with scores in higher zones met criteria for at-risk drinking. As would be expected, substantially fewer patients met criteria for heavy drinking: 10.1% in the lowest recommended and derived AUDIT-C zone (0 to 3), 76.1% in the higher recommended zone (4 to 12), and 51.4, 86.3, 93.2 and 100% in the 4 highest derived zones (4 to 5, 6, 7 to 8, 9 to 12), respectively.

The AUROC score was 0.70 for the recommended AUDIT-C zones based on a single cut-point and 0.82 for the derived AUDIT-C zones based on SSLR analysis. A comparison between AUROCs revealed that overall the derived zones performed significantly better than the recommended zones (p < 0.001) in being able to discriminate between patients with and without alcohol use disorder.

Discussion

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

This study examined the potential benefits of SSLRs to estimate the probability of an alcohol use disorder among acute injury patients using AUDIT-C scores and risk zones. The focus on screening for alcohol use disorders in the current study is consistent with the U.S. National Institute on Alcohol Abuse and Alcoholism (NIAAA) guide that focuses on addressing 2 types of problem drinking: hazardous drinking and an alcohol use disorder, with the latter causing significant distress or impairment, which may require more intensive management and treatment (National Institute on Alcohol Abuse and Alcoholism, 2007).

In this study, AUROC analyses showed that the derived AUDIT-C risk zones performed better than the recommended zones, with the greater number of derived zones increasing the power of the AUDIT-C to discriminate injury patients with and without alcohol use disorder compared with the recommended 2 zones based on a single cut-point.

A practical benefit of SSLR analysis is that it provides an empirical basis for a practitioner to estimate a given patient's risk for an alcohol use disorder based on their AUDIT-C score, which in turn enables more specific feedback to be given to that patient about their alcohol use. For example, based on the derived SSLRs found in this study, the following information could be communicated to a patient with a score of 9 or greater: “Approximately 70% of people with your score on these alcohol questions experience problems linked to their alcohol use. Would it be OK for us to briefly discuss your use of alcohol to see if there's anything I can do to help?” Depending on the availability of resources and the specifics of local protocols, the next steps could include further assessment, brief intervention and follow-up, and referral for specialist treatment.

An additional practical benefit of SSLR analyses is that it can assist with the development of empirically based screening and intervention protocols to guide practitioner practices. The current findings suggest that for any injury patient with an AUDIT-C score of 0 to 3, or 4 to 5, the probability of alcohol use disorder is relatively low (5 and 14%, respectively). For any patient with a higher AUDIT-C score, the probability is 32% for a score of 6, 47% for a score of 7 to 8, and 68% for a score of 9 to 12. Moreover, the majority of patients met criteria for at-risk drinking regardless of derived risk zone, and the large majority of patients in the 3 highest derived zones (scores 6, 7 to 8, 9 to 12) met criteria for heavy drinking. These findings are useful to inform the development of a protocol with decision rules for alcohol screening and intervention among injury patients (Wade et al., 2012). To illustrate, based on the current risk-stratification findings, one might propose the following steps of an SBI protocol. First, patients with an AUDIT-C score of 5 or less (n = 647 or 64.4% of patients in the current study) receive brief health promotion advice and information on the benefits of low-risk drinking. Second, patients with an AUDIT-C score of 6 to 8 (n = 249 or 24.8% of patients) receive a brief intervention on problem drinking with follow-up. Third, patients with a score of 9 to 12 (n = 108 or 10.8% of patients) receive a brief intervention and follow-up and are considered for specialist referral for a full diagnostic assessment and more intensive intervention. These 3 respective groups of patients include 23.7% (54/228), 44.3% (101/228), and 32.0% (73/228) of all patients with an alcohol use disorder, although the patients meeting criteria for an alcohol use disorder in the first group who only receive a health promotion intervention would likely have less severe alcohol problems than those patients in the other groups.

The current study has a number of limitations. First, the relatively small number of female patients meant that gender-specific risk zones and SSLRs could not be calculated. Among female patients, 5 strata derived from each of the AUDIT-C scores did not have at least 1 individual with or without alcohol use disorder, and these “0 scores” resulted in less than an optimal number of derived zones. Second, despite the rules proposed by Peirce and Cornell (1993), there is some subjective judgment required to select cutoff points when determining the optimal number of strata in screening tests. As a result, there will inevitably be some imprecision in the derived AUDIT-C risk zones. Third, hazardous alcohol use was, in part, estimated using AUDIT item 3 which refers to the frequency of having 6 or more drinks on a single occasion, whereas the Australian guidelines (National Health Medical Research Council, 2009) recommend against drinking 5 or more standard drinks on a single occasion to reduce the risk of injury. Despite this discrepancy, the AUDIT item closely approximates the guidelines recommendation, and there is no universally recognized definition of hazardous alcohol use. Fourth, the extent to which the zones derived from the SSLR analysis, and the posttest probabilities, generalize to other injury groups is not clear.

In summary, this study used SSLR analysis to examine the performance of recommended and derived AUDIT-C screening tests to estimate the probability of alcohol use disorder among injury patients. The results of the SSLR analysis provide an opportunity to assess the performance of the AUDIT-C based on multilevel risk zones rather than relying on a single dichotomous cut-point. The risk zones derived from the SSLR analysis can inform staff–patient communications and may prove useful in the development of more efficient and effective SBI protocols in a range of settings.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

This study was supported by a National Health and Medical Research Council Program grant (568970). The authors would like to thank patients and staff from the hospital trauma centers for their participation in the study, and an anonymous reviewer who provided expert feedback on the submitted manuscript. DW conducted the literature search, and contributed to the study design, data collection, analysis and interpretation, and writing. TV contributed to the data analysis and interpretation and critical revision. DF contributed to the data interpretation and critical revision. MO contributed to the study design, data collection and interpretation, and critical revision.

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  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References
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