Clinical Course of Alcohol Use in Veterans Following an AUDIT-C Positive Screen

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1 MILITARY MEDICINE, 179, 11:1198, 2014 Clinical Course of Alcohol Use in Veterans Following an AUDIT-C Positive Screen Jennifer S. Funderburk, PhD* ; Stephen A. Maisto, PhD* ; Michael J. Wade, MS*; Aileen Kenneson, PhD*; Clare E. Campbell, BS* ABSTRACT There is little known regarding the typical trajectory of alcohol use following a positive screen for hazardous alcohol use. This information would help primary care providers as they attempt to determine the best use of patient visits that might include brief alcohol interventions versus other competing medical demands. This longitudinal observational study included 98 Veterans who screened positive on the Alcohol Use Disorders Identification Test- Consumption (>3) and were asked to report on their alcohol use every 3 months for 1 year. Using latent class growth modeling, we identified the best fitting latent class structure for each outcome of high-risk and heavy drinking, respectively. There was a class of participants with increased probability of having a high-risk week or episode of heavy drinking as well as a group of participants who appeared to maintain their current drinking pattern. Although the latent class growth modeling suggested that none of the groups of participants reduced the likelihood of occurrence of heavy drinking days, two groups did significantly reduce the probability of having a hazardous alcohol use week. These results suggest that there are specific classes of patients who are less likely to change their alcohol use following a positive screen, especially those patients who report engaging in heavy drinking. INTRODUCTION Primary care providers are advised to screen adults for hazardous alcohol use within primary care. 1 This recommendation for regular screening is based on the prevalence of hazardous alcohol use 2 ; associations between hazardous drinking and morbidity, mortality, and quality of life 3 8 ; as well as the availability of several empirically validated screening measures 9 and evidence-based brief alcohol interventions Hazardous alcohol use is typically defined in primary care as (1) drinking more than 14 standard alcoholic drinks in 1-week period for males under 65 years old, or more than 7 alcoholic drinks in 1-week period for males 65 and older or females or (2) more than 4 drinks in a day for males under age 65, or more than 3 drinks in a day for males 65 years or older or females. 13 Individuals identified as hazardous drinkers may often reduce their alcohol consumption to low-risk drinking, and even sustain it, without formal intervention Thus, it is important to understand the typical trajectories of alcohol use for patients following a positive screen in primary care, so we direct resources appropriately. Such knowledge *Center for Integrated Healthcare, Syracuse VA Medical Center, 800 Irving Avenue (116C), Syracuse, NY Department of Psychology, Syracuse University, 443 Huntington Hall, Syracuse NY Department of Psychiatry, University of Rochester, Rochester, NY The preliminary data were presented in poster format at the Society of Behavioral Medicine annual conference by Labbe A, Funderburk JS, Maisto SA, titled The utility of AUDIT-C cut scores in VA primary care settings in Washington, DC, April The views expressed in this article are those of the authors and do not reflect the official policy of the Veterans Affairs department, Department of Defense, or other Departments of the U.S. Government. doi: /MILMED-D would provide an empirical basis for directing provider attention, clinical services, and future research. This is especially significant as providers are trying to juggle various competing demands and often weighing what priority level should be given to a brief alcohol intervention amidst other medical problems and negative health behaviors that also deserve attention. Previous research has examined typical trajectories of alcohol use within the community, but no study was conducted within the context of primary care. As primary care patients are often coming to a primary care office because of acute or chronic medical conditions demonstrating at least a modest interest in improving their health by seeking medical advice, a community sample s trajectories of alcohol use may not be generalizable as they may be more likely to be healthy and/or not as interested in medical advice. Furthermore, earlier studies did not examine alcohol consumption outcomes (e.g., hazardous alcohol use weeks) consistent with recommendations for primary care, nor characteristics of patients (e.g., presence of certain medical diagnoses) that may increase the priority given to addressing hazardous alcohol use within an appointment. For instance, one study examined problem drinkers as defined by a combination of current alcohol use, consequences, and presence of dependence symptoms and found that the largest group consisted of individuals who did not change their alcohol use across a 5-year period. 19 These individuals were more likely to have received fewer suggestions to change their alcohol use. Another study also examined the trajectory of alcohol consumption among a sample of hazardous drinkers, as defined by the total score on the Alcohol Use Disorders Identification Test (AUDIT), 22 and showed an overall decline in alcohol consumption as measured by the percentage of heavy drinking days over a 2-year period, particularly 1198

2 among those with lower levels of severity. 20 They found that individuals who were younger as well as those with a higher level of guilt proneness demonstrated greater reductions in alcohol consumption. Although these studies provide an initial understanding for trajectories of alcohol consumption across time, the inability to apply the findings to the setting of primary care and determine any clinical impact reduces their significance. Therefore, the purpose of this study was to build upon earlier research by examining the trajectory of alcohol use among a sample of Veteran primary care patients. The Veterans Health Administration (VA) is an ideal location for such a study because it is the largest health care system in the United States and has successfully implemented regular screening for hazardous alcohol use using the AUDIT- Consumption (AUDIT-C). 14,23 The VA has achieved a 93% alcohol screening rate. 24 Because of our interests in being consistent with primary care teams experience, we used the definition of hazardous drinking defined by NIAAA rather than other standardized self-report measures (e.g., AUDIT) to determine those Veterans reporting hazardous drinking following a positive AUDIT-C screen. We also used that definition to guide our choice of clinical outcomes: the percentage of heavy drinking days (i.e., binge drinking) and high-risk weeks. 13 Then, we explored patient characteristics that are readily available to primary care providers via the electronic medical record or clinical practice in an effort to differentiate the different clusters and thereby help guide clinical practice and future research. METHODS Participants A total of 235 Veterans were referred to this study by the VA s Behavioral Telehealth Center, a care management program that helps primary care providers assess mental health symptomatology after a patient screens positive for various behavioral health concerns, 25 including the AUDIT-C. The AUDIT-C is a screening questionnaire regarding alcohol use in the previous year and primary care teams are instructed to follow-up with further assessment to verify the presence of hazardous drinking. At the end of the Behavioral Telehealth Center phone call, any Veteran at least 18 years old who screened positive for hazardous alcohol consumption on the AUDIT-C (score > 3), 26 and lacked any gross neurological impairment according to the Blessed Orientation-Memory- Concentration Test (score 8), 27 was referred to the research study. Twelve Veterans were never able to be contacted and another 120 Veterans declined participation. A total of 103 (98% male, 95.9% White) Veterans were recruited to participate in this study. Procedures After obtaining informed consent, participants completed an in-person assessment, followed by telephone assessments at 1, 3, 6, 9, and 12 months after the initial visit. A chart review was conducted after the final assessment to identify mental health or substance use treatment as well as the presence of medical and psychiatric diagnoses. All procedures were approved by the Syracuse VA Medical Center Institutional Review Board. Measures Initial inclusion required a positive screen on the AUDIT-C. 23 Within the VA, the three-item AUDIT-C is administered yearly to assess the quantity and frequency of drinking. Specifically, Veterans are asked to respond using a Likert scale to three items: (1) how often they have a drink containing alcohol, (2) how many drinks containing alcohol they have in a typical day when drinking, and (3) how often they have six or more drinks on one occasion. Among male Veterans, a score greater than 3 on the AUDIT-C is considered a positive screen for at-risk drinking, with a sensitivity of 0.86 and specificity of The AUDIT 22 is a 10-item self-report questionnaire, which includes the 3-items of the AUDIT-C as well as an additional 7 other items assessing problems associated with drinking within the past year. Participants are asked to rate each item on a 5-point Likert scale. The AUDIT has been extensively validated as a screen for detecting various levels of hazardous alcohol use, with evidence of favorable internal consistency and test retest reliability. 28,29 Atotalscoreof 8 or more is considered to indicate high-risk drinking and scores above 19 are considered indicative of dependence. 30 The AUDIT was administered at baseline, 6 months, and 12 months. Alcohol consumption was assessed via the Timeline Follow-back (TLFB), 31 which is a widely used retrospective self-report measure of daily alcohol use. The TLFB has been extensively studied and found to be valid and reliable measure of alcohol consumption The TLFB was used to document the number of standard drinks consumed per day throughout the assessment period, starting with the 90 days before baseline. After the baseline assessment, the TLFB was administered every 3 months throughout the 12-month follow-up period. Outcome variables derived from the TLFB were the number of high-risk weeks per assessment period and the number of heavy drinking days per time period as defined by NIAAA guidelines. 13 Negative drinking consequences were measured at every assessment via the 15-item Short Inventory of Problems (SIP), 36 a validated measure adapted from the Drinker Inventory of Consequences. 37 The SIP represents five different domains of negative drinking consequences: physical health, social (including financial), intrapersonal well-being, impulsive behavior, and interpersonal consequences. 38 Readiness to change was measured at every assessment session using the Readiness Ruler, 39 which asks the participant to rate his/her current willingness to change their pattern of alcohol use on a scale from 1 (not ready to change) to 1199

3 10 (trying to change). Depression was assessed at every time point via the 9-item Patient Health Questionnaire (PHQ-9), 40,41 which is a reliable and valid self-report measure of depressed mood for the past 2 weeks. The substance use portion of the Addiction Severity Index (ASI) 42 assessed the participant s illicit or nonprescribed drug use at every assessment. A demographic questionnaire assessed age, gender, race, ethnicity, education, and military service. Participants were also asked about any mental health or substance use treatment (inpatient or outpatient) at baseline. Data Preparation/Analysis For each medical/psychiatric condition identified in the chart review, we searched the following web sites for treatment guidelines related to alcohol consumption: the Mayo Clinic, BMJ Best Practices, AHRQ, emedicine, and VA/DoD Guidelines. We reviewed each participant s current problem list during chart review and assigned each participant to one of three categories based on the medical/psychiatric condition with the most restrictive alcohol-related recommendation: (1) abstinence recommended, (2) moderation recommended, or (3) no alcohol restrictions (see supplementary table). Latent class growth modeling 43 was used to identify distinct patterns of change over 12 months of observation. Patients were classified into homogeneous subgroups sharing a common intercept and slope describing the rate of change in the probability of heavy drinking as well as percent of weeks with high-risk drinking. A total of 22% of the participants had incomplete data (i.e., missing 1 2 assessment time points), but we were able to include them in the latent class growth modeling. The number of alcohol drinking trajectories, or latent classes, was determined by comparing the Bayesian Information Criterion (BIC) statistic, a measure of model fit, among successive models. The lower the BIC, the better the model fit. Other factors that were used in determining the most parsimonious were internal reliability, class size, and clinical interpretation. Once a model was chosen for each outcome, we used posterior probabilities (PP) to assign each participant to a class. To assess the accuracy of assignment for each participant to a class compared to random chance (RC), we used the odds of correct classification (OCC), 44 which had a modified formula of: (PP/(1-PP))/(RC/(1-RC)). An OCC less than 5 was excluded from subsequent analyses as it indicated that the participant was not a good representative of the class. RESULTS Of the 103 participants, there were 24 participants who scored positive on the AUDIT-C, but who did not report any hazardous drinking 13 in the past 60 days during the baseline assessment. In addition, we decided to exclude the TABLE I. Descriptives for LCGA Sample and Excluded Participants Sample Used in LCGA (n = 76) Excluded From LCGA (n = 27) Sex (Male) 76 (100%) 25 (92.6%) Age (Years) Median = 74.0 Median = 64.0 Mean = 69.9 Mean = 66.4 SD = 13.3 SD = Race White 72 (94.7%) 25 (92.6%) Black or African American 3 (3.95%) 2 (7.41%) Other 1 (1.32%) 0 (0%) Education High School 18 (23.7%) 5 (18.5%) Some College (+) 58 (76.3%) 22 (81.5%) Marital Status (Married) 52 (68.4%) 18 (66.7%) Employment Status Employed 20 (26.3%) 10 (37.0%) Retired 49 (64.5%) 15 (55.6%) Other 7 (9.21%) 2 (7.41%) Combat 31 (41.9%) 15 (55.6%) Arthritis 20 (26.3%) 8 (29.6%) Asthma 4 (5.26%) 1 (3.70%) CAD 23 (30.3%) 5 (18.5%) Chronic Pain 26 (34.2%) 7 (25.9%) Diabetes 12 (15.8%) 5 (18.5%) Hypertension 54 (71.1%) 18 (66.7%) Migraine 3 (3.95%) 2 (7.41%) Alcohol Restriction Category Abstinence Recommended 3 (3.95%) 3 (11.1%) Moderation Recommended 70 (92.1%) 22 (81.5%) No Recommendations 3 (3.95%) 2 (7.41%) Readiness Ruler Median = 1.00 Median = 1.00 Mean = 2.82 Mean = 2.70 SD = 2.81 SD = 2.71 AUDIT Score Median = 5.00 Median = 4.00 Mean = 6.43 Mean = 5.07 SD = 3.95 SD = 4.22 SIP Score Median = 0.00 Median = 0.00 Mean = 2.56 Mean = 1.07 SD = 4.50 SD = 4.82 Missing = 1 ASI Score Median = Median = Mean = Mean = SD = SD = Missing = 2 PHQ-9 Score Median = 1.50 Median = 2.00 Mean = 2.50 Mena = 2.93 SD = 3.21 SD = 4.10 Attempted to Change Alcohol 16 (21.1%) 6 (22.2%) Use in Past Year Advised by Health Care Professional to Reduce or Quit Alcohol Use in Past Year 13 (17.1%) 5 (83.3%) LCGA, Latent Class Growth Analysis; CAD, Coronary artery disease; AUDIT, Alcohol Use Disorders Identification Test; SIP, Short Inventory of Problems; ASI, Addiction Severity Index; PHQ9, Patient Health Questionnaire for depression; AA, Alcoholics Anonymous. 1200

4 two females from the analysis because the standards for hazardous alcohol use differ by gender, and we did not have enough females to fully examine this difference. We excluded one participant who did not provide any data past the baseline assessment. Therefore, the excluded group included 25 White participants and two Black or African- American participants, which is not significantly different from thefinalsample(p > 0.05). However, the excluded group was significantly younger (mean = 66.4 years) than was the final sample (mean = 69.9 years; p < 0.05). See Table I for the final sample s descriptive statistics. High-Risk Week s The 5-class model yielded a minimal change in the BIC statistic compared to the 4-class model (Table II). When examining the differences in these models, there was no additional helpful information collected when examining how the trajectories changed when examining the 5-class model; therefore, the 4-class model was the best fit. Participants in classes 2 and 4 demonstrated substantial reductions in their probability of reporting a high-risk drinking week across the 1-year period (Fig. 1). At baseline, class 4 had a higher probability of experiencing a high-risk week than class 2. Classes 1 and 3 showed no change in alcohol consumption and consistently demonstrated a high probability of engaging in alcohol use that is defined as high risk across the year period (Fig. 1). Table III displays participants descriptive statistics for each of the four classes. Across all four classes, only two individuals participated in formal substance treatment. Only a small percentage of patients (5% 20%) reported that their health care providers advised them to change their alcohol use at baseline, even though all of them screened positive on the AUDIT-C. In addition, a majority of the participants were identified as having medical conditions for which the recommendations were either to abstain or drink in moderation. More than 50% of the sample was assigned to one of the two classes (i.e., classes 1 and 3) that maintained a high probability of experiencing at least one high-risk week across each assessment period during the year. Individuals assigned to class 2, which was characterized by a reduction in the probability of having a high-risk week, consisted of those participants who were younger and more likely employed, and who reported a higher level of readiness to change at baseline. TABLE II. Bayesian Information Criteria Statistics for Determining Best-Fit Linear Class Growth s 1-Class 2-Class 3-Class 4-Class 5-Class 6-Class Heavy Drinking High-Risk Week FIGURE 1. Four-class model for probability of a high-risk week. Heavy-Drinking s The 4-class model was the best fit to the data (Table II). All of the classes appeared to maintain their baseline probability of having a heavy drinking day (Fig. 2). More than 70% of the sample was assigned to one of the three classes with a low to nonexistent probability of having a heavy drinking day during the year of assessment, with class 3 consistently maintaining approximately 20% chance of heavy drinking. Class 4 had a high probability of engaging in heavy drinking across the 1-year period. Table IV is a summary of the descriptive statistics for each class of those participants who were not dropped from the heavy drinking model because of a low OCC. Class 4 participants appeared at baseline to be younger and employed, and they reported the highest level of readiness to change, a higher level of problems associated with drinking, more depressive symptoms, and the highest number who remember having been advised by a health care professional to reduce their alcohol use. They also were the individuals who had the highest percentage of high-risk weeks at baseline, suggesting they tended to meet criteria for both definitions of hazardous alcohol use. Class 1 consisted of older participants who had a lower probability of heavy drinking, but a high probability of engaging in alcohol consumption throughout the week to meet the high-risk week definition. DISCUSSION In an effort to help guide clinical practice, the results from this prospective study provide an initial glimpse into the descriptions of different 1-year trajectories of alcohol consumption following a positive alcohol screen in a primary care setting. There are several strengths to this study, including the use of primary care patients following a positive screen in primary care, the prospective design, the use 1201

5 TABLE III. Descriptives for High-Risk Week Latent Classes Class Final N N Dropped Because of Low OCC Age (Years) Median = 74.0 Median = 62.0 Median = 76.0 Median = 75.0 Mean = 70.3 Mean = 63.9 Mean = 74.1 Mean = 73.9 SD = 13.8 SD = 17.2 SD = 8.22 SD = 9.43 Race White 20 (100%) 20 (95.2%) 21 (95.5%) 10 (90.9%) Black or African American 0 (0%) 1 (4.76%) 1 (4.55%) 0 (0%) Other 0 (0%) 0 (0%) 0 (0%) 1 (9.09%) Education High School 2 (10.0%) 7 (33.3%) 4 (18.2%) 4 (36.4%) Some College (+) 8 (80.0%) 14 (66.7%) 18 (71.8%) 7 (65.6%) Marital Status (Married) 12 (60.0%) 13 (61.9%) 18 (81.8%) 7 (63.6%) Employment Status Employed 7(35.0%) 6 (28.6%) 4 (18.2%) 3 (27.3%) Retired 13 (65.0%) 10 (47.6%) 17 (77.3%) 8 (72.7%) Other 0 (0%) 5 (23.8%) 1 (4.55%) 0 (0%) Combat 9 (47.4%) 10 (47.6%) 7 (33.3%) 3 (27.3%) Arthritis 9 (45.0%) 2 (9.52%) 3 (13.6%) 5 (45.5%) Asthma 1 (5.00%) 2 (9.52%) 0 (0%) 1 (9.09%) CAD 6 (30.0%) 6 (28.6%) 7 (31.8%) 4 (36.6%) Chronic Pain 5 (25.0%) 11 (52.4%) 4 (18.2%) 6 (54.6%) Diabetes 3 (15.0%) 4 (19.1%) 4 (18.2%) 1 (9.09%) Hypertension 14 (70.0%) 13 (61.9%) 19 (86.4%) 7 (63.6%) Migraine 0 (0%) 2 (9.52%) 1 (4.55%) 0 (0%) Alcohol Restriction Category Abstinence Recommended 1 (5.00%) 1 (4.76%) 1 (4.55%) 0 (0%) Moderation Recommended 19 (95.0%) 19 (90.5%) 20 (90.9%) 11 (100%) No Recommendations 0 (0%) 1 (4.76%) 1 (4.55%) 0 (0%) Readiness Ruler Median = 1.00 Median = 4.00 Median = 1.00 Median = 1.00 Mean = 2.50 Mean = 3.81 Mean = 2.55 Mean = 1.45 SD = 2.80 SD = 3.09 SD = 2.63 SD = AUDIT Score Median = 4.5 Median = 5.00 Median = 5.00 Median = 4.00 Mean = 6.5 Mean = 6.62 Mean = 6.45 Mean = 5.09 SD = 4.15 SD = 3.89 SD = 4.60 SD = 1.64 SIP Score Median = 0 Median = 0 Median = 0 Median = 0 Mean = 2.60 Mean = 2.85 Mean = 2.82 Mean = 0.82 SD = 4.35 SD = 4.74 SD = 5.45 SD = 1.47 Missing = 1 ASI Score Median = Median = Median = Median = Mean = Mean = Mean = Mean = SD = SD = SD = SD = Missing = 1 Missing = 1 PHQ-9 Score Median = 2.00 Median = 2.00 Median = 0 Median = 1.00 Mean = 2.40 Mean = 3.52 Mean = 2.18 Mean = 1.45 SD = 2.84 SD = 3.53 SD = 3.75 SD = 1.69 Attempted to Change Alcohol Use in Past Year 3 (15.0%) 5 (23.8%) 5 (22.7%) 1 (9.09%) Advised by Health Care Professional to Reduce or Quit Alcohol 2 (10.0%) 5 (23.8%) 5 (22.7%) 1 (9.09%) Use in Past Year Chart Review Indicated Formal Treatment for SUD in Past 3 Years 1 (5.00%) 1 (4.76%) 0 (0%) 0 (0%) Number of Positive AUDIT-C Screens in Past 3 Years Mean = 1.89 Mean = 1.59 Mean = 1.86 Mean = 2.00 Median = 2 Median = 0.73 Median = 2 Median = 2 SD = 0.88 SD = 1 SD = 0.77 SD = 0.89 Percent of Weeks With High-Risk Drinking at Baseline Mean = 86.9% Mean = 41.4% Mean = 100% Mean = 58.7% Median = 100% Median = 15.4% Median = 100% Median = 69.2% SD = 23.3% SD = 41.6% SD = 0% SD = 36.9% Percent of Days With Heavy Drinking at Baseline Mean = 8.3% Mean = 5.5% Mean = 11.9% Mean = 6.9% Median = 2.8% Median = 1.1% Median = 0% Median = 0% SD = 12.1% SD = 10.3% SD = 28.9% SD = 13.0% OCC, Odds of correct classification; CAD, Coronary artery disease; AUDIT, Alcohol Use Disorders Identification Test; SIP, Short Inventory of Problems; ASI, Addiction Severity Index; PHQ9, Patient Health Questionnaire for depression; SUD, Substance use disorder; AUDIT-C, Alcohol Use Disorders Identification Test-Consumption. 1202

6 FIGURE 2. Four-class model for probability of a heavy drinking day. of a definition of hazardous alcohol use and clinical outcomes that are recommended for primary care settings, and the use of patient characteristics that are clinically relevant and available to primary care providers. Consistent with previous research, these data highlight that certain groups of patients will reduce their alcohol consumption without formal intervention. However, this finding primarily applied to patients engaging in hazardous alcohol use as defined by number of drinks across a week period, not to patients endorsing heavy drinking (i.e., binge drinking) on one occasion. In fact, none of the groups of patients showed any reduction in heavy drinking. This suggests that patients who endorse heavy drinking may require a greater amount of consideration from providers as they attempt to balance other competing priorities in visits for time. Additional research also needs to investigate the factors that contribute to change in weekly alcohol consumption, but not to heavy alcohol consumption, and whether there are aspects of the screening/intervention process that are directly related to that finding. A large number of patients within this sample qualified as high-risk drinkers due only to their weekly alcohol consumption and showed a low probability of engaging in heavy drinking on any given day. At the same time, there was one group of patients (class 4 in the heavy drinking analysis) who had a higher probability of consuming alcohol above both the weekly and daily limits. These patients also reported the highest number of problems associated with drinking compared to the other classes, and recalled that health care professionals had advised them to change their drinking. Yet, no real changes in high-risk alcohol use were observed in the year following the initial assessment. Therefore, it appears based on the patient s report that providers recognize that these patients need an intervention; however, patients continued high-risk drinking suggests that more is needed. Additional research should examine how to engage these individuals in treatment and examine the quality of any interventions that are provided in primary care. We found that a majority of this sample had medical diagnoses that prompted a recommendation of moderate alcohol use. It is unclear what moderation exactly means as there is no clear definition; however, it is clear that heavy alcohol use complicates and increases certain risks associated with these specific medical diagnoses. Future research should examine the effects of hazardous alcohol use in such individuals who meet criteria for moderation and whether individuals with certain disorders should be targeted for more intense intervention because of an existing medical diagnosis. There was one class of patients (class 2) who demonstrated a substantial reduction in the probability of high-risk weeks, yet a majority of them indicated that they did not remember their health care provider advising them to reduce. Therefore, future research should examine what factors may have led to this change and if this subgroup of individuals can be easily identified as patients who have the capacity to and will reduce their alcohol consumption without a specific provider intervention, as this could reduce provider burden. This study has several limitations that need to be considered. First, the sample was small and based on Veteran primary care patients. Because this sample consisted of a larger number of older male primary care patients, which is typical of VA primary care, 45 it may not be representative of individuals at other clinics. Future research needs to examine a larger sample of primary care patients that is much more diverse in an effort to evaluate whether these findings are stable. Notable to this sample of older male Veterans was the large number of participants met criteria for hazardous alcohol use because of their weekly consumption of alcohol, and showed no change in their patterns of drinking habits are not changing in the course of the 1-year follow-up. Therefore, it is important to consider how primary care can help educate individuals that as we age, our drinking patterns also need to change. Another limitation is the lack of information on what was actually recommended or said to patients regarding their current alcohol use. Although we know that the electronic medical record provides support for VA primary care teams decision-making following a positive AUDIT-C, 46 future research should investigate how the trajectories change when the quality of the brief alcohol intervention varies. In sum, this study identified several groups of patients for whom providers can tailor discussions related to alcohol consumption reduction, based on their specific pattern of hazardous drinking. That is patients who meet the criteria for hazardous drinking based on heavy drinking are not likely to reduce drinking without further intervention and should be targeted for interventions. Thus, it is important to explore drinking patterns beyond a positive AUDIT screen. In addition, the data presented in this article provide researchers with a reference for what can be expected of primary care patients who are hazardous drinkers following treatment as usual. 1203

7 TABLE IV. Descriptives for Heavy Drinking Latent Classes Class Final N N Dropped Because of Low OCC Age (Years) Median = 79.0 Median = 65.0 Median = 66.0 Median = 61.0 Mean = 76.6 Mean = 62.5 Mean = 65.3 Mean = 57.0 SD = 8.97 SD = 15.0 SD = 12.6 SD = 8.72 Race White 36 (100%) 18 (94.7%) 15 (88.2%) 2 (66.7%) Black or African-American 0 (0%) 1 (5.26%) 1 (5.88%) 1 (33.3%) Other 0 (0%) 0 (0%) 1 (5.88%) 0 (0%) Education High School 7 (19.4%) 4 (21.1%) 6 (35.3%) 1 (33.3%) Some College (+) 29 (80.6%) 15 (78.9%) 11 (64.7%) 2 (66.7%) Marital Status (Married) 28 (77.8%) 12 (63.2%) 10 (58.8%) 2 (66.7%) Employment Status Employed 7 (19.4%) 6 (31.6%) 5 (29.4%) 2 (66.7%) Retired 28 (77.8%) 10 (52.6%) 10 (58.8%) 0 (0%) Other 1 (2.78%) 3 (15.8%) 2 (11.8%) 1 (33.3%) Combat 13 (37.1%) 8 (42.1%) 7 (43.8%) 3 (100%) Arthritis 10 (27.8%) 1 (5.26%) 8 (47.1%) 0 (0%) Asthma 1 (2.78%) 2 (10.5%) 1 (5.88%) 0 (0%) CAD 13 (36.1%) 7 (36.8%) 3 (17.7%) 0 (0%) Chronic Pain 12 (33.3%) 7 (36.8%) 5 (29.4%) 2 (66.7%) Diabetes 6 (16.7%) 3 (15.8%) 3 (17.7%) 0 (0%) Hypertension 26 (72.2%) 12 (63.2%) 13 (76.5%) 2 (66.7%) Migraine 1 (2.78%) 1 (5.26%) 1 (5.88%) 0 (0%) Alcohol Restriction Category Abstinence Recommended 1 (2.78%) 0 (0%) 1 (5.88%) 1 (33.3%) Moderation Recommended 35 (97.2%) 16 (84.2%) 16 (94.1%) 2 (66.7%) No Recommendations 0 (0%) 3 (15.8%) 0 (0%) 0 (0%) Readiness Ruler Median = 1.00 Median = 2.00 Median = 3.00 Median = 6.00 Mean = 1.81 Mean = 4.00 Mean = 3.29 Mean = 5.67 SD = 1.65 SD = 3.46 SD = 3.00 SD = 4.51 AUDIT Score Median = 4.00 Median = 5.00 Median = 8.00 Median = Mean = 4.75 Mean = 6.05 Mean = 8.88 Mean = 16.0 SD = 1.48 SD = 2.70 SD = 5.12 SD = 5.29 SIP Score Median = 0.00 Median = 1.00 Median = 3.00 Median = Mean = 1.06 Mean = 2.05 Mean = 4.29 Mean = 14.3 SD = 2.73 SD = 3.05 SD = 5.07 SD = 7.37 Missing = 1 ASI Score Median = Median = Median = Median = Mean = Mean = Mean = Mean = SD = SD = SD = SD = Missing = 1 Missing = 1 PHQ-9 Score Median = 1.00 Median = 2.00 Median = 2.00 Median = 8.00 Mean = 1.61 Mean = 2.89 Mean = 2.82 Mean = 9.00 SD = 1.96 SD = 3.63 SD = 3.50 SD = 4.58 Attempted to Change Alcohol Use in Past Year 2 (5.56%) 6 (31.6%) 7 (41.2%) 1 (33.3%) Advised by Health Care Professional to Reduce or Quit Alcohol 4 (11.1%) 5 (26.3%) 2 (11.8%) 2 (66.7%) Use in Past Year Chart Review Indicated Formal Treatment for SUD in Past Year % 0% 5.88% 0% Number of Positive AUDIT-C Screens in Past 3 Years Mean = 1.92 Mean = 1.58 Mean = 1.88 Mean = 1.67 Median = 2 Median = 1 Median = 2 Median = 2 SD = 0.81 SD = 0.77 SD = 0.89 SD = 0.58 Percent of Weeks With High-Risk Drinking at Baseline Mean = 74.4% Mean = 62.8% Mean = 75.1% Mean = 97.4% Median = 100% Median = 84.6% Median = 84.6% Median = 100% SD = 37.3% SD = 44.2% SD = 31.3% SD = 4.4% Percent of Days With Heavy Drinking at Baseline Mean = 2.0% Mean = 2.9% Mean = 15.8% Mean = 77.4% Median = 0% Median = 2.2% Median = 12.2% Median = 91.1% SD = 7.9% SD = 3.2% SD = 13.2% SD = 31.7% OCC, Odds of correct classification; CAD, Coronary artery disease; AUDIT, Alcohol Use Disorders Identification Test; SIP, Short Inventory of Problems; ASI, Addiction Severity Index; PHQ9, Patient Health Questionnaire for depression; SUD, Substance use disorder; AUDIT-C, Alcohol Use Disorders Identification Test-Consumption. 1204

8 ACKNOWLEDGMENTS This work was based on a pilot funding given by the VA Center for Integrated Healthcare. REFERENCES 1. U.S. Preventive Services Task Force: Screening and behavioral counseling interventions in primary care to reduce alcohol misuse: recommendation statement. Ann Intern Med 2004; 140(7): van Amsterdam J, van den Brink W: The high harm score of alcohol. Time for drug policy to be revisited? J Psychopharmacol 2013; 27: National Institute on Alcohol Abuse and Alcoholism: Tenth Special Report to the U.S. Congress on Alcohol and Health, NIH publication no , Available at 10report/intro.pdf; accessed January 28, Coughlin SS, Kang HK, Mahan CM: Alcohol use and selected health conditions of 1991 Gulf War veterans: survey results, Prev Chronic Dis 2011; 8(3): A Chavez LJ, Williams EC, Lapham G, Bradley KA: Associations between alcohol screening scores and alcohol-related risks among female Veterans Affairs patients. 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