Abstract
Introduction
Alcohol screening and brief intervention has been shown to reduce unhealthy alcohol use, although widespread adoption into primary care practice has been slow. Patients undergoing bariatric surgery are at an increased risk of unhealthy alcohol use. The authors compared a novel, web-based screening tool called ATTAIN to usual care for real-world effectiveness and accuracy among bariatric surgery registry patients.
Methods
The authors analyzed the results of a quality improvement project that tested ATTAIN among bariatric surgery registry patients. Participants were stratified into 3 groups by surgery status (preoperative vs postoperative) and prior screening for unhealthy alcohol use (screened vs not screened in the past year). Participants in these 3 groups were divided into intervention plus usual care (n = 2249) and control (n = 2130) groups, with intervention being an email to complete ATTAIN, and control being usual care (eg, office-based screening). Primary outcomes included screening and positivity rates for unhealthy drinking behavior between groups. Secondary outcomes included positivity rates via ATTAIN vs usual care for individuals who were screened by both modalities. Chi-square test was used for statistical analysis.
Results
The overall screening rates were 67.4% (intervention arm) and 38.6% (control). The ATTAIN response rate was 47% of those invited. The overall positive screen rate was 7.7% (intervention) and 2.6% (control); p < .001 for both. For dual screened intervention participants, the positive screen rate was 10% (ATTAIN) vs 2% (usual care) with p < .001.
Conclusion
ATTAIN is a promising method of increasing screening and detection rates for unhealthy drinking behavior.
Keywords: Alcohol, alcohol use disorder, screening, web-based, telehealth, preventive medicine, addiction medicine
Introduction
Background
Excessive consumption of alcohol is one of the leading causes of preventable death and health care costs in the United States. 1–4 Alcohol screening and brief intervention (ASBI) has been shown to reduce excessive consumption. In 2017, the National Committee for Quality Assurance added a quality measure to its Health Effectiveness Data and Information Set to address the need for improved ASBI across health care settings. 5 This measure requires health care systems to screen for unhealthy drinking behavior and follow those up with positive screens. While much work existed on ASBI in traditional office-based settings, there was a gap in research covering computer-based, electronic screening and brief interventions (e-SBIs).
By 2020 the Southern California Permanente Medical Group had developed an automated, computer-based screening and brief intervention process named AuTomaTed Alcohol misuse INterventions (ATTAIN) as a quality improvement (QI) project to improve Health Effectiveness Data and Information Set measures. 6 ATTAIN screened for unhealthy drinking behavior, alcohol use disorders (AUDs), and readiness to change alcohol consumption with minimal staff involvement. In an initial pilot study (N = 301), 46% of invited participants successfully completed ATTAIN screening, and the detection rates for unhealthy drinking behavior and AUD among them were 16% and 5%, respectively. ATTAIN was well accepted by participants and was estimated to be 1/10 of the cost of office-based screening. 7 Limitations of this study included the lack of a control arm and small sample size. This article describes the next step in the validation of ATTAIN, as part of an ongoing QI project.
ASBI and Bariatric Surgery
Unhealthy drinking behavior and AUDs are of interest to those who are preparing for or have had bariatric surgery. This population is at an increased risk of poor health outcomes resulting from alcohol use; for example, recent studies have shown that obesity and AUD share several common neuropsychological mechanisms and characteristics. 8 Also, post–bariatric surgery patients are known to be disproportionately represented in substance and alcohol use recovery program utilization. 9
This topic is the subject of a great deal of research, and although evidence is preliminary, there are several recurring findings in the literature. First, some predictive factors have been identified for those patients at highest risk of AUD after bariatric surgery. These include male sex, younger age, tobacco use, poor social support, worsening mental health, and getting divorced (vs remaining married). Risk has been shown to increase with time passed since surgery, with peak incidence after 2 years postsurgery. 9,10 Several studies showed that Roux-en-Y gastric bypass patients are at an increased risk of AUD compared to sleeve gastrectomy, 9,10 although one study failed to show a between-group difference with respect to surgical procedure. 11
Patients’ subjective experiences have also been measured and may provide additional insight. Study participants interviewed about alcohol use following bariatric surgery reported several contributory factors, including increased sensitivity to the effects of alcohol, ongoing mental health difficulties, and the need to replace food as a self-soothing coping mechanism for stress. 12,13 Moreover, one study found increases in alcohol sensitivity after (vs before) bariatric surgery in women using the Alcohol Sensitivity Questionnaire (ASQ), a well-validated measure of sensitivity to alcohol. 14
These findings have led to the development of the “cross-addiction hypothesis,” which suggests that an addiction-like process occurs in many obese patients with respect to their relationship to food, and that alcohol use may substitute for food post bariatric surgery. Recent discoveries regarding the hunger-signaling hormone, ghrelin, and its role in dopamine signaling in the dopamine-sensitive reward centers of the brain, such as the ventral tegmental area and nucleus accumbens, have led researchers to propose a neuropsychological mechanism for increased risk of AUD in patients who have undergone bariatric surgery. 9,15 While this process appears to be supported by animal models, some researchers refute the cross-addiction hypothesis nonetheless, on the grounds that preoperative (preop) binge-eating and self-control disorders are not well correlated with postoperative (postop) AUD as well as the fact that the model does not adequately explain why AUD risk increased 2 years after surgery. 9,15 Regardless of the etiology of the increase in risk associated with bariatric surgery, accurate information and regular screening regarding alcohol use is crucial for preop and postop bariatric surgery patients.
ASBI and the Effect of the COVID-19 Pandemic
There has been speculation that the COVID-19 pandemic has led to an increase in unhealthy drinking behavior. 16,17 A 25% increase in alcohol-related deaths was observed during the early COVID-19 pandemic in 2020. In fact, there were more alcohol-related deaths than COVID-related deaths in 2020. 18 Although the extent of the impact remains uncertain, there has been some evidence that different populations/demographics were affected with varied severities and with effects varying over time. 19–21 Few population-wide trends have been identified.
Objectives
For the present study, the primary objective was to describe the results from using the ATTAIN tool plus usual care (vs usual care alone) for ASBI in bariatric surgery registry patients. Secondary objectives were to analyze the associations of other variables with ASBI rates, including the potential impact of the COVID-19 pandemic on this population.
Methods
This is a data-only study reporting results from a QI project database at Kaiser Permanente Southern California. The study was approved by the IRB at Kaiser Permanente Southern California. The QI project (ATTAIN Bariatric Pilot) used a prospective, quasi-experimental (ie, non-randomized) design, with an intervention and a control arm.
The study included all participants in the active Kaiser Permanente Southern California bariatric registry as of April 1, 2020 who had an active account on the Kaiser Permanente Southern California electronic medical record patient portal (N = 4530). Registry members were adults (ages ≥ 18) either in a preop preparation program or within 1 year of their surgery date.
The trial was performed in 3 separate phases, each with its own intervention and control groups with distinct characteristics. Groups were sorted based on their bariatric surgery status (preop vs postop) and whether or not they had been screened in the previous 12 months at any time for unhealthy alcohol use (see Table 1). Group 1 consisted of preop patients who had previously screened negative for unhealthy alcohol use; 800 participants were selected for the intervention, and the remaining 657 were assigned to be controls (n = 1457). Sixty-five were excluded. The intervention for the first group began June 23, 2020. Group 2 consisted of both preop and postop patients who had not been screened for unhealthy drinking in the previous year; 800 participants were selected for the intervention, and the remaining 357 were assigned to be controls (n = 1157). Fifty-five were excluded. The intervention for the first group began July 20, 2020. Group 3 consisted of postop patients who had previously screened negative for unhealthy alcohol use; 800 participants were selected for the intervention, and the remaining 1116 were assigned to be controls (n = 1916). Sixty-five were excluded. The intervention for the first group began August 24, 2020.
Table 1:
Group definitions and attrition. Intervention = ATTAIN + Usual Care; Control = Usual Care alone. ‘previously screened (*)’ refers to office-based screening for unhealthy drinking behavior having been completed in the year prior to this study with resulting negative screens (patients did not meet criteria for unhealthy drinking behavior). ‘no previous screen’ refers to a lack of screening having been done for unhealthy drinking behavior in the year prior to this study. Exclusions in the intervention group were for registry members who did not have an active account on the KPSC electronic medical record patient portal
| Group | Characteristics | Total, N (Before Exclusions) | n (Intervention) | n (Control) |
|---|---|---|---|---|
| 1 | Pre-operative, previously screened (*) | 1457 | 737(63 excluded out of 800) | 657 |
| 2 | Preop (2 a) and postop (2b), no previous screen | 1157 | 745(55 excluded out of 800) | 357 |
| 3 | Post-operative, previously screened (*) | 1916 | 767(33 excluded out of 800) | 1116 |
| Total | 4530 | 2249 (151 excluded out of 2400) | 2130 |
All participants had either screened negative in the previous 12 months or had not been screened at all. Those with a positive screen were not included in the initial 4530 participants.
In each group, the first 800 participants listed in the registry were designated to the intervention arm, with the remaining participants serving as controls. Allocation was not randomized but was based on the order of participants in the registry. The registry listed preop patients arbitrarily by medical record number and listed postop patients by date of surgery (with most recent date first). The most recent surgery date was March 30, 2020 and the earliest was July 17, 2019. Participants were excluded if they did not have an active patient portal account and therefore could not be invited to participate.
ATTAIN email invitations were sent manually by 16 family medicine residents who were part of the QI project. Residents each managed 50 participants per month, including review of patient responses, data entry of their responses, and follow-up of positive screens. This total was felt to be the maximum number of participants each resident could effectively manage, in addition to their clinical duties, allowing a total of 2400 participants in the intervention arm. The total excluded out of the 2400 was 151 (6%), leaving a total of 2249. The remaining 2130 served as a control arm with no exclusions; as these participants were not invited to use ATTAIN, no portal account was necessary.
Participants in the intervention arms were invited to complete the ATTAIN computer-based alcohol screening in addition to any usual care they may receive. The ATTAIN process is detailed in prior studies.6,7 Participants screening positive for unhealthy drinking behavior in the intervention arm were sent an educational follow-up email and were encouraged to view an educational video on the spectrum of alcohol use accessible in their patient portal: “A ReThink of the Way we Drink” (part of the ATTAIN brief intervention component). Data on the number of views of the video were collected as part of the outcomes measurement.
Intervention participants who had a change in their alcohol screening status from negative (in the year prior to the intervention) to positive (during the intervention period) were administered an additional questionnaire to better understand the possible impact of the COVID-19 pandemic on their drinking. The control arm received usual care only.
Data were collected on gender, age, the percentage who were postop, the percentage screened (during usual care) for unhealthy drinking behavior in the 12 months prior to the start of the intervention (including this rate among preop and postop), and the time between the surgery and the start of the intervention for postop participants (in months). Data queries by Kaiser Permanente Southern California analysts were used to acquire data regarding usual screening documentation and views of the educational video.
Covid-19 effect on alcohol use
Because the unique stresses of the COVID-19 pandemic were identified as a potential confounder, the authors administered a novel questionnaire to all intervention participants who had a change in their alcohol screening result from negative (in the year prior to the intervention) to positive (during the intervention period) to better understand the possible impact of the pandemic on drinking.
Baseline characteristics and outcomes
The authors compared groups based on gender, age, % postop, % screened (during usual care) for unhealthy drinking behavior in the 12 months prior to the start of the intervention (including this rate among preop and postop), and time between the surgery and the start of the intervention for postop participants (in months).
Primary outcomes were the screening rates and the unhealthy drinking behavior rates (for the 12 months from the start of the intervention) in the intervention vs the control groups.
Secondary outcomes included differences in the screening rates and the unhealthy drinking behavior rates based on gender, preop vs postop status, and time since surgery for those postop. Secondary outcomes also included presenting data on the number of views of the video and on the COVID-19 effect on alcohol questionnaire.
Results
For each intervention group of 800, those who did not have an active patient portal were excluded (63 from group 1; 55 from group 2; and 33 from group 3). The total excluded out of the 2400 was 151 (6%), leaving a total of 2249. The total in the control group was 2130 (Table 1).
Baseline characteristics
The female percentages of the intervention and control groups were 77% and 80%. The average age of the intervention and control groups was 44 and 43. The percentages screened in the year prior to the intervention between the intervention and control groups were 67% and 83%. The average times since surgery for the intervention and the control groups were 8 and 6.1 months (Table 2).
Table 2:
Baseline characteristics of participants, by group
| Group | Total, N (%) | Female, N (%) | Avg. Age | Had Surgery, N (%) | Screened in Prior Year (N) % | Time Since Surgery as of June 2020 |
|---|---|---|---|---|---|---|
| Intervention | 2249 (52%) | 1740 (77%) | 44 | 1187 (52%) | 1504 (67%) | 8 |
| Control | 2130 (48%) | 1704 (80%) | 43 | 1440 (67%) | 1773 (83%) | 6.1 |
| Intervention Group 1 | 737 | 565 (77%) | 45 | 0 | 100% | n/a |
| Control group 1 | 657 | 507 (77%) | 41 | 0 | 100% | n/a |
| Intervention Group 2 a | 325 | 240 (74%) | 42 | 0 | 0 | n/a |
| Intervention Group 2b | 420 | 326 (78%) | 43 | 100% | 0 | 5.9 |
| Control Group 2 a | 33 | 24 (76%) | 43 | 0 | 0 | n/a |
| Control Group 2b | 325 | 260 (80%) | 43 | 100% | 0 | 9.4 |
| Intervention Group 3 | 767 | 611 (80%) | 44 | 100% | 100% | 9.1 |
| Control Group 3 | 1116 | 913 (82%) | 44 | 100% | 100% | 5.1 |
Primary outcomes
Table 3 displays the results stratified by group, including the proportion of participants screened, those screening positive, and those wishing to receive additional help for unhealthy drinking behavior. In the intervention arm, the overall ATTAIN response rate was 1057 out of 2249, or 47% (number completing ATTAIN/number invited). Of these, 101 (9.6%) screened positive for unhealthy drinking behavior. Also in the intervention arm, 632 participants were screened via usual care. In total, 1516 (67.4%) of the intervention arm vs 822 (38.6%) of the control arm were screened during the study period (p < 0.001).
Table 3:
Primary outcomes, screening rate and positivity rate, by group.
| Group (N) | Screened Overall, N (%) | Screened Positive Overall, N (%) | Screened via ATTAIN, N (%) | Screened Positive via ATTAIN, N (%) | Screened via Usual Care, N (%) | Screened Positive via Usual Care, N (%) |
|---|---|---|---|---|---|---|
| Total | ||||||
| Intervention (2249) | 1516 (67%) | 117 (7.7%) | 1057 (47%) | 101 (10%) | 909 (40%) | 18 (2%) |
| Control (2130) | 822 (39%) | 22 (2.6%) | n/a | n/a | 815 (38%) | 22 (2.6%) |
| p value | < .001 | |||||
| Group 1 | ||||||
| Intervention (737) | 560 (76%) | 53 (10%) | 417 (57%) | 44 (11%) | 324 (44%) | 9 (3%) |
| Control (657) | 240 (37%) | 8 (3%) | n/a | n/a | 235 (36%) | 8 (3%) |
| Group 2 a (pre-op) | ||||||
| Intervention (325) | 250 (77%) | 31 (12%) | 200 (62%) | 30 (18%) | 138 (42%) | 2 (1.5%) |
| Control (33) | 12 (36%) | 1 (8%) | n/a | n/a | 12 (36%) | 1 (8%) |
| Group 2b (post-op) | ||||||
| Intervention (420) | 251 (60%) | 13(5.2%) | 159 (38%) | 10 (6.8%) | 159 (38%) | 3 (2%) |
| Control (324) | 119 (37%) | 4 (3.3%) | n/a | n/a | 118 (37%) | 4 (3.3%) |
| Group 3 | ||||||
| Intervention (767) | 455 (60%) | 20 (4.4%) | 281 (37%) | 17 (6.0%) | 288 (38%) | 4 (1.4%) |
| Control (1116) | 451 (40%) | 9 (2%) | n/a | n/a | 451 (40%) | 9 (2%) |
Overall, 117 (7.7%) participants screened in the intervention arm had a positive result during the study period, compared to 22 (2.6%) of the controls (p < 0.001). These findings are displayed graphically in Figure 1.
Figure 1:
Overall screening rates and screening result by study arm.
Participants who were dual screened during the study period, by both ATTAIN and usual care, were 450. Of these, 42 (10%) tested positive via ATTAIN and 8 (2%) tested positive via usual care. Of those participants, 40 screened positive via ATTAIN only, 6 via usual care only, and 2 via both screens.
Secondary outcomes
The results, stratified by gender, are also shown in Table 3. Females had a higher ATTAIN response rate. Males were more likely to have an AUD.
The interventional video was viewed a total of 506 times, among the 92 participants invited to view it. The number and duration of views varied over time and per participant; average viewing time was 3 minutes, of the 10 minute video.
Of the 92 participants offered the novel COVID-19 questionnaire, 30 (33%) responded. Of respondents, 13% self-reported an increase in alcohol use since the pandemic began; 53% indicated no change; and 33.3% indicated a decrease in their drinking. Responses with respect to changes in drinking behavior, perceived stress, and overall mental health resulting from the pandemic were heterogeneous. The detailed items and results of the novel survey are displayed in Table 4.
Table 4:
Detailed items and responses of the novel COVID-19 questionnaire
| To what extent have COVID-19 related restrictions affected your quality of life? | % Response |
| Greatly worsened | 16.67% |
| Mildly worsened | 30.00% |
| No effect | 46.47% |
| Mildly improved | 3.33% |
| Greatly improved | 3.33% |
| How would you describe your current level of alcohol consumption, as compared to before the COVID-19 related restrictions? | |
| Increased | 13.33% |
| No change | 53.33% |
| Decreased | 33.33% |
| Prior to the COVID-19 restrictions, how often did you have a drink containing alcohol? | |
| I didn’t drink | 30.00% |
| Once per month | 26.67% |
| 2 to 4 times per month | 26.67% |
| 2 to 3 times per week | 6.67% |
| 4 or more times per week | 10.00% |
| Prior to the COVID-19 related restrictions, how many drinks did you have in a typical week? | |
| 7 or less (women and persons 65+); 14 or less (men under 65) | 3.33% |
| 8 or more (women and persons 65+); 15 or more (men under 65) | 96.67 |
| Prior to COVID-19 related restrictions, how often did you have: (all, women and men age 65+): 4 drinks or more in a 24 h period? (Men under 65): 5 drinks or more in a 24 h period? | |
| Never | 26.67% |
| Once or more | 73.33% |
| If your alcohol use increased after the start of COVID-19 related restrictions, why do you think this might be? Mark all that apply: | |
| Increased stress | 55.5% |
| Boredom | 33.3% |
| Spending more time at home | 11.1% |
| Lost my job | 44.4% |
| Feeling depressed | 44.4% |
| Feeling anxious | 33.3% |
| Difficulties with home life | 22.2% |
| Do you recall being informed that alcohol usage after bariatric surgery carries a higher risk of complications and addiction? | |
| Yes | 53.3% |
| No | 0% |
Discussion
The United States Preventive Services Task Force recommends screening for unhealthy alcohol use in all adults over 18 years of age. 22 Although evidence has shown ASBI to be effective in reducing unhealthy alcohol use, 23 widespread adoption of ASBI into primary care practice has been slow. 24 Several barriers to implementation have been identified, including lack of practitioner knowledge, lack of time, and lack of resources. 25 The use of web-based, e-SBI has been suggested as a means of overcoming these barriers and as a convenient, efficient method of reaching a large number of patients. 26 In addition to effectiveness, several studies attest to a high degree of acceptability of electronic interventions to patients, as well as a high degree of feasibility in program implementation, ease of use, and minimal disruption to workflow. 27–29
ATTAIN is a promising method of addressing these issues. The authors' primary results show ATTAIN to be highly effective in increasing screening rates for AUD. ATTAIN yielded a 72% higher screening rate in the intervention group, compared to the controls. Additionally, the intervention group had a nearly 3-fold higher rate of positive screening, suggesting a higher sensitivity for ATTAIN compared to traditional, in-office screening modalities.
The overall response rate was 39.3%, and although this resulted in a much higher screening rate compared to the control group, it is reasonable to believe that an even higher response rate could be achieved. Methods of doing so could include updates in the wording of the question, changes in the presentation or appearance of the tool, improvements in technical ease of access or use, or refinements of how it is advertised or promoted to the patient population.
As noted previously, a major confounder was the COVID-19 pandemic and its unpredictable effects on the baseline alcohol use in the population. As Table 4 demonstrates, there is a great deal of ambiguity regarding the effect COVID-19 had on the author' sample, making it difficult to determine if the new cases detected by ATTAIN were the result of increased sensitivity or of the stresses due to COVID-19. Moreover, nearly one-half of the authors' positive screens were from patients with no previous screen in the year prior, and it is therefore unknown if these are new cases.
An incidental observation provides some insight into the comparative effectiveness of ATTAIN vs standard, in-office screening tools; a number of the authors' patients (N = 450) were screened using both ATTAIN and in-office modalities. Of these, 42 patients screened positive on ATTAIN, while 8 had positive results via usual care. A closer review of data reveals that largely these were not the same participants, as only 2 also had a positive result on both ATTAIN and usual care. Future research will focus on the comparative accuracy of ATTAIN vs traditional modalities.
ATTAIN provides an effective solution to the poor penetrance of regular ASBI into primary care practices and appears to be efficacious when applied to high-risk groups such as patients undergoing bariatric surgery. ATTAIN and e-SBIs may continue to yield further solutions to ensure best practices are achieved as health care models change in the wake of the COVID-19 pandemic and telehealth-based services become more normative.
Limitations
The QI project was not randomized, and although this may not have impacted the preop patients (who were listed by medical record number in ascending order), it may have impacted the postop results as these were listed by date of surgery. It would be expected that alcohol use may increase as the postop time increases, with a peak around 2 years from surgery.
Another limitation was that the interventions were done at 3 different times (June, July, and August of 2020). It is not known whether factors such as the state of national lockdown may have impacted drinking in different ways during these 3 months.
There was also a gap between the end of the registry period (April 2020) and the start dates of the interventions (June, July, and August of 2020). People who were labeled as preop in the registry may have had surgery done during this gap period and thus been labeled incorrectly as preop.
Another limitation is that there were differences in the baseline characteristics between the intervention and control groups. The control group had a higher rate of screening in the year prior to the intervention. This may have impacted future screening. Control group members were also on average 6 months out from surgery, whereas intervention group members were on average 8 months out from surgery. This is important because alcohol use may increase as time from surgery increases.
In screening electronically, patients are unable to ask clarifying questions prior to answering. Consequently, questions may have been answered incorrectly. Consideration should be given to identifying and clarifying the wording of any items found to be problematic for patients. However, it should be noted that ATTAIN uses the approved language recommended by the National Committee for Quality Assurance for this screening measure, and the authors believe it is more likely that questions may be asked more vaguely during office-based screening, leading to higher false negatives.
The ATTAIN survey was not translated into other languages at the time, limiting the patient population to English speaking. It will be important to translate the survey into other commonly spoken languages, such as Spanish, in order to reach a more diverse patient population. Cultural differences may also affect the acceptability of ATTAIN in different subpopulations, which may be an important consideration before widespread implementation can be achieved.
Lastly, this study was performed largely during the COVID-19 pandemic, when most visits were converted to telemedicine, which may have impacted the authors' estimate of “usual care” screening rates. However, this may serve to underscore the importance of electronic screening modalities to offset any resultant losses in usual care screening that may occur as telehealth visits become more normalized.
Conclusion
ATTAIN is a promising method of addressing the barriers to screening for AUD in primary care and at-risk patient groups. Further consideration can be given to translating the survey into multiple languages, adjusting the questions’ verbiage for increased accuracy, and making the tool more accessible. The higher positive rating on ATTAIN vs usual care may reflect increased accuracy of ATTAIN or may be suggestive of opportunities to improve the authors' tool, and further research will assist us in determining the significance of this finding. Proper utilization of electronic screening surveys can be a potential promising solution to bridging the gap in identification and treatment of AUD.
Acknowledgments
The authors would like to acknowledge Research Consulting Group, Loma Linda, CA, for verifying statistical analyses. The authors would like to acknowledge and honor patient contributions to this work, including for the use of data from the electronic health record.
Footnotes
Funding: None declared
Conflicts of Interest: None declared
Author Contributions: Robert W West, MD, MPH, contributed to study design, data analysis, and manuscript preparation. Shahriar B Davari, MD, contributed to study design and data analysis. Nadine M Hammoud, DO, MS, contributed to data collection, data analysis, and manuscript preparation. Mohamed H Ismail, MD, DrPH, contributed to study design, data collection, data analysis and manuscript preparation. All authors have given approval to the final manuscript.
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