Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-17T21:17:02.217Z Has data issue: false hasContentIssue false

Assessing risk for nonmedical opioid use among patients with cancer: Stability of the CAGE-AID questionnaire across clinical care settings

Published online by Cambridge University Press:  13 July 2023

Tonya Edwards
Affiliation:
Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer, Houston, TX, USA
Joseph Arthur*
Affiliation:
Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer, Houston, TX, USA
Manju Joy
Affiliation:
Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer, Houston, TX, USA
Zhanni Lu
Affiliation:
Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer, Houston, TX, USA
Seyedeh Dibaj
Affiliation:
Department of Biostatistics, The University of Texas MD Anderson Cancer, Houston, TX, USA
Eduardo Bruera
Affiliation:
Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer, Houston, TX, USA
Donna Zhukovsky
Affiliation:
Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer, Houston, TX, USA
*
Corresponding author: Joseph Arthur; Email: jaarthur@mdanderson.org
Rights & Permissions [Opens in a new window]

Abstract

Objectives

The Cut down, Annoyed, Guilty, and Eye opener- Adapted to Include Drugs (CAGE-AID) questionnaire (CA) is a validated screening tool used to assess risk for nonmedical opioid use (NMOU) in patients receiving opioids for cancer pain. Data on consistencies and variations in responses to the CA between different clinical settings are lacking. We evaluated the frequency and consistency in scoring of the CA among patients seen between the first inpatient consult (T1) and the first outpatient follow-up (T2) visits.

Methods

A retrospective chart review of 333 consecutive patients seen at both T1 and T2 within 3 months between August 2016 and March 2017 was reviewed.

Results

Median age was 58 years (range, 18–87 years); 53% were female. CA was completed for 88% of patients at T1 and 94% at T2. Of these, 10% and 13% were CAGE-AID positive, respectively. CA score changed from negative to positive in 4% and from positive to negative in 1% of patients between T1 and T2. Kappa coefficient for agreement of CA between T1 and T2 was 0.74 (95% CI: 0.62–0.86, p = 0.02).

Significant of results

Completion rate and consistency of patient responses to the CA were high irrespective of clinical setting. Of these patients, 10% and 13% were CA positive which is suggestive of high risk for NMOU. Further studies are needed to evaluate ways to ensure more consistency in the completion of the CA and enhance its utilization in routine clinical practice.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press.

Introduction

Patients with a history of excessive alcohol use are more likely to actively engage in smoking and illicit drug use, and are at risk for nonmedical opioid use (NMOU) (Dev et al. Reference Dev, Parsons and Palla2011), a condition which occurs in 20% of patients receiving opioids for cancer pain (Arthur et al. Reference Arthur, Tang and Lu2021; Carmichael et al. Reference Carmichael, Morgan and Del Fabbro2016, Yennurajalingam Reference Yennurajalingam, Arthur and Reddy2021). Several screening tools are used in clinical practice to detect alcohol-related issues including the Alcohol Use Disorders Identification Test, Michigan Alcohol Screening Test, Adolescent Drinking Index, and the Cut down, Annoyed, Guilty, and Eye opener (CAGE) questionnaire (Reid et al. Reference Reid, Fiellin and O’Connor1999). Of these, the CAGE questionnaire is the most widely used because of its concise nature which makes it more desirable among clinicians working in busy clinical settings (Allen et al. Reference Allen, Maisto and Connors1995). In a previous study, patients who screened positive for the CAGE questionnaire had worsening symptoms such as pain, sleep, dyspnea, well-being, and total symptom distress, as determined by the Edmonton Symptom Assessment System (ESAS) (Parsons et al. Reference Parsons, Delgado-Guay and El Osta2008). A variation of the CAGE questionnaire, the CAGE-Adapted to Include Drugs (CAGE-AID) questionnaire which substitutes “drink” with “drink or drugs,” has also been evaluated for the detection of substance misuse (Brown and Rounds Reference Brown and Rounds1995; Childers et al. Reference Childers, King and Arnold2014; Dev et al. Reference Dev, Parsons and Palla2011; Kwon et al. Reference Kwon, Tanco and Park2015). It is a well-validated patient self-administered screening tool with a high internal consistency reliability coefficient (r = 0.92) (Leonardson et al. Reference Leonardson, A. Koplin and Ness2005).

Inconsistent responses to the CAGE-AID questionnaire (CA) may reflect inaccurate patient self-report (Brown and Rounds Reference Brown and Rounds1995; Couwenbergh et al. Reference Couwenbergh, Van Der Gaag and Koeter2009; Katz and Fanciullo Reference Katz and Fanciullo2002) or irregular clinician administration of the questionnaire and has the potential to negatively impact effective patient care. The CA is theoretically considered a stable tool, but potential discrepancies in patient responses to the CA questionnaires in different clinical settings and visits have not been investigated. The main objective of this study was to evaluate the completion and positivity rate of the CA at the inpatient and outpatient supportive care center (SCC) visits among the same patient cohort. We also assessed changes in scoring of the questionnaire across the 2 clinical settings.

Method

Study participants, procedure, and data collection

Electronic records of 333 consecutive patients who were seen in the hospital and at the outpatient SCC between August 1, 2016 and March 31, 2017, were reviewed. Eligible patients were ≥18 years with a diagnosis of cancer who received an inpatient supportive care consultation, followed by an outpatient follow-up visit within 3 months of the initial consultation. The following demographic and clinical characteristics were collected at both visits: age, gender, race, and cancer diagnosis, ESAS, and the CA. Both CA and ESAS questionnaire are routinely administered during routine clinical care at the SCC. This study protocol was approved by the institutional review board at the University of Texas MD Anderson Cancer.

Instruments

The CA is a well-validated screening tool for excessive alcohol use and may suggest the possibility of an increased risk of maladaptive behavior when there is an exaggerated and inappropriate request for opioid medication. It comprises the following 4 question items: “have you ever felt that you should cut down on your drinking or drug use,” “have you ever been annoyed by people criticizing your drinking or drug use,” “have you ever felt bad or guilty about your drinking or drug use,” and “have you ever had a drink or used drugs first thing in the morning to get rid of a hangover (eye-opener).” Studies have shown that the CAGE-AID has a sensitivity of 88–91% and a specificity of 55–97% for the detection of current substance use disorder (Couwenbergh et al. Reference Couwenbergh, Van Der Gaag and Koeter2009; Dyson et al. Reference Dyson, Appleby and Altman1998). For our study, CAGE-AID positivity was defined as a total score ≥2 out of 4. A positive score raises concern for potential opioid misuse and chemical coping, which is an inappropriate and/or excessive use of opioids to cope with the various stressful events associated with the diagnosis and management of cancer (Childers et al. Reference Childers, King and Arnold2014; Dev et al. Reference Dev, Parsons and Palla2011; Kwon et al. Reference Kwon, Tanco and Park2015).

The ESAS is a validated tool used to screen for the presence and intensity of symptoms in patients with advanced cancer. It comprises a numerical rating scale from 0 to 10 with 0 being the absence of symptoms and 10 being the worst symptom intensity imaginable. It assesses symptoms of pain, fatigue, nausea, depression, anxiety, drowsiness, appetite, and sensation of well-being, financial distress, and spiritual pain (Bruera et al. Reference Bruera, Kuehn and Miller1991; Chang et al. Reference Chang, Hwang and Feuerman2000; Philip et al. Reference Philip, Smith and Craft1998).

Statistical analysis

Descriptive statistics such as frequencies and percentages for categorical data and medians and ranges for continuous variables were provided to summarize the baseline demographic and clinical characteristics. CAGE-AID scores between the inpatient consult and outpatient follow-up visits were evaluated by McNemar’s test. Estimate of Kappa coefficient along with its 95% CI was provided to further investigate the agreement between the 2 visit scores. The rate of completion was calculated for individual CAGE-AID items at both visits. Association of demographics and symptom characteristics with CAGE-AID status at follow-up was assessed by Wilcoxon rank sum test for continuous and Chi square/Fisher’s exact test for categorical variables.

Results

Table 1 provides information on the baseline demographic and clinical characteristics of eligible patients during the first inpatient consult (T1) visit. A total of 333 consecutive patients who also had an outpatient follow-up visit within 3 months were reviewed. Median age was 58 years (range, 18–87 years). Majority of the patients were female (53%), White (62%), and had gastrointestinal cancer (23%). Table 2 shows the completion rates and positive scores of CAGE-AID items at the T1 and first outpatient follow-up (T2) visits. Overall CAGE-AID status was documented for 294 (88%) and 315 (94%) patients at T1 and T2 visits, respectively. Of these, 10% and 13% were CAGE-AID positive, respectively. “Cut down” was the most frequent positive CAGE-AID item both in the inpatient (10%) and outpatient (14%) settings.

Table 1. Baseline demographic and clinical characteristics of study participants (N = 333)

ESAS = Edmonton Symptom Assessment System, CAGE-AID = Cut down, Annoyed, Guilty, and Eye opener-Adapted to Include Drugs questionnaire, MEDD = Oral Morphine equivalent daily dose in milligrams per day.

a Self-pay, veterans, and other.

Table 2. Completion rates and positive scores of CAGE-AID items at the inpatient consult and first outpatient follow-up visits (N = 333)

CAGE-AID = Cut down, Annoyed, Guilty, and Eye opener-Adapted to Include Drugs questionnaire.

a A positive CAGE-AID score is ≥2/4

Factors associated with CAGE-AID positivity were also evaluated. Hispanics (23%) were more likely to be CAGE-AID positive as compared with Caucasians (15%) and African Americans (11%), p = 0.02. Table 3 illustrates the level of agreement of completed CAGE-AID scores between the 2 time periods. The Kappa coefficient for agreement was 0.74 (95% CI: 0.62–0.86, p = 0.02). CAGE-AID status changed from negative to positive in 4% and from positive to negative in 1% of patients between the 2 time periods.

Table 3. Level of agreement of completed CAGE-AID scores between inpatient consult and outpatient follow-up visits (N = 283)

CAGE-AID, Cut down, Annoyed, Guilty, and Eye opener-Adapted to Include Drugs questionnaire.

McNemar’s test p-value = 0.02; Kappa coefficient = 0.74 (95% CI: 0.62–0.86).

a A positive score is ≥2/4 in men or≥1/4 in women.

Discussion

In this pilot study, most patients had CA information, but the questionnaire was not universally completed for all patients seen in the inpatient and the outpatient settings. CA is a vital screening tool that helps identify patients who are at high risk for NMOU (Lau et al. Reference Lau, Mazzotta and Fazelzad2021). It may assist prescribers to arrive at the right diagnosis, develop appropriate treatment plans, and enhance their therapeutic decision-making process for patients receiving opioids. While a positive CAGE-AID score does not necessarily mean a patient is engaging in NMOU behaviors, it heightens provider vigilance and allows them to access further assistance from an interdisciplinary team if needed (Arthur et al. Reference Arthur, Edwards and Reddy2018). Deficiencies in CAGE-AID completion can impair clinicians’ ability to identify at-risk patients and may further affect subsequent opioid therapy. In this era of the opioid crisis, best practices require clinicians to adopt safe and rational opioid prescribing practices. Utilization of validated risk assessment tools such as CA is a key component of such measures. One reason that could potentially decrease its utilization is inadequate time in an already busy clinical setting (Pereira et al. Reference Pereira, Chasen and Molloy2016). Efforts should be made to improve adherence and increase universal utilization of this tool during opioid therapy. These may include ongoing educational sessions, email reminders, documentation checklists, reminder cards, and prompts in electronic health records (Innis and Berta Reference Innis and Berta2016). Since the questionnaire is self-administered by patients themselves, electronic health records can be designed to make the questionnaire accessible for patients to complete before their visit with the clinician.

Of these patients, 10% and 13% were CAGE-AID positive which is an indicator of elevated risk for NMOU. In a similar study by Parsons et al., 17% of patients with advanced cancer seen at an ambulatory SCC were CAGE positive. The patients had more symptom expression and were taking opioids more frequently than patients who were CAGE negative (Parsons et al. Reference Parsons, Delgado-Guay and El Osta2008). The reason why the CAGE-AID positivity rate in this current study was relatively lower than studies that used the CAGE questionnaire (Arthur et al. Reference Arthur, Tang and Lu2021; Bruera et al. Reference Bruera, Moyano and Seifert1995; Dev et al. Reference Dev, Kim and Reddy2019, Reference Dev, Parsons and Palla2011; Hui et al. Reference Hui, Abdelghani and Chen2020; Parsons et al. Reference Parsons, Delgado-Guay and El Osta2008; Yennurajalingam et al. Reference Yennurajalingam, Edwards and Arthur2018) is unclear, but it is possible that the inclusion of “drugs” in the CA version could have deterred some participants from accurately reporting on their substance use. Future research is needed to compare the epidemiological performance of CAGE with CA.

The rate of CAGE-AID completion was lower in the inpatient setting as compared with that in the outpatient setting. The reason for this observed discrepancy is unclear, but this might be reflective of the nature of the inpatient setting where contact of the clinical team with the patient is usually episodic, with a predominant focus on the management of acute issues. Decreased reporting and documentation of the CA is therefore more likely to occur in the inpatient setting as compared with the outpatient setting. The prevalence of NMOU in the inpatient setting is presumably comparable to the outpatient oncologic settings since it is the same cancer patient population who are eventually hospitalized for management of acute issues. Moreover, patients are more likely to display NMOU behaviors in the inpatient setting due to access to parenteral opioids which may increase the peak serum concentration in the brain more rapidly, leading to heightened reward (Arthur et al. Reference Arthur, Reddy and Smith2019). It is therefore critical that inpatient clinical teams utilize risk mitigation strategies to the same degree as in the outpatient settings (Chou Reference Chou2009; Dowell et al. Reference Dowell, Haegerich and Chou2016).

The level of agreement in CAGE-AID scoring was significantly high in the 2 different clinical settings and time periods, but the scoring changed unexpectedly from positive to negative for a minority of patients within the 3-month period. Theoretically, the CA information should remain stable over time since the anchor for timeline is based on alcohol or illicit drug use in their entire life. A prior history of problematic alcohol use as determined by CA therefore carries a lifetime risk for NMOU. The observed discrepancy may be due to errors in patient reporting. Notably, CAGE-AID is based on patient self-report which limits their efficacy (Cook et al. Reference Cook, Bernstein and Arrington1995; Fishbain et al. Reference Fishbain, Cutler and Rosomoff1999). Information from this questionnaire should therefore be combined with other tools such as urine drug testing, pill counts, and prescription monitoring programs when assessing or monitoring for NMOU behaviors. The observed variation could also suggest errors in clinician documentation rather than a real change in the patient’s alcohol behavior. This will require further provider education and awareness to rectify such potential errors. To our knowledge, this is the first study to test the stability and variation in CAGE-AID scoring between 2 clinical settings within the same patient cohort.

The study found that Hispanics were more likely to be CAGE-AID positive compared with other racial groups. Future studies may be necessary to further investigate this finding since NMOU is not known to be relatively higher in the Hispanic population as compared to others. Epidemiological data indicate that Native Americans have the highest prevalence (12.1%) of heavy drinking, followed by Caucasians (8.3%) before Hispanics (6.1%). The prevalence of alcohol use in adults is highest for Whites (59.8%), similar for Native Americans (47.8%), Hispanics (46.3%), and Blacks (43.8%), and lowest for Asian Americans (38.0%) (Vaeth et al. Reference Vaeth, Wang-Schweig and Caetano2017). Risk for NMOU is not well understood among the Hispanic population. One study suggested that regular opioid use might be less common in the Hispanic population compared with other demographic groups (Kelly et al. Reference Kelly, Cook and Kaufman2008). The Hispanic community represents a vital growing segment of the American population with increasing access to health care and opioid analgesics. Hence, a better understanding of their level of risk for NMOU based on the CAGE-AID risk assessment tool will allow clinicians to proactively address any potential opioid-related complications in this population.

One limitation of our study was its retrospective design. Additionally, the study was conducted in a single tertiary cancer center, and the results may not be generally applicable to patients in other clinical settings. Future studies may need to be conducted prospectively at multiple centers to ensure its generalizability and improve its methodological rigor. Another potential limitation is that responses were based on patient self-report. It is possible that patients inaccurately reported on the severity of their alcohol intake. Regrettably, this is an inherent limitation of many NMOU research.

Conclusion

In this study, 10% to 13% of patients with advanced cancer were CAGE-AID positive which is an indicator of elevated NMOU risk. The rate of completion and consistency of the CA was high, but it was not universally completed for all patients seen in both the inpatient and the outpatient settings. Further studies are needed to evaluate ways to ensure more consistency in the completion of the questionnaire and enhance its utilization among patients receiving opioids for cancer pain in routine clinical practice.

Acknowledgments

We would like to thank Ms. Leela Kuriakose for assisting with the acquisition of data for this study.

Author contributions

Tonya Edwards and Joseph Arthur equally contributed to the manuscript.

Competing interests

None.

References

Allen, JP, Maisto, SA and Connors, GJ (1995) Self-report screening tests for alcohol problems in primary care. Archives of Internal Medicine 155(16), 17261730. doi:10.1001/archinte.1995.00430160044005CrossRefGoogle ScholarPubMed
Arthur, J, Edwards, T, Reddy, S, et al. (2018) Outcomes of a specialized interdisciplinary approach for patients with cancer with aberrant opioid-related behavior. The Oncologist 23(2), 263270.CrossRefGoogle ScholarPubMed
Arthur, JA, Reddy, A, Smith, U, et al. (2019) Practices and perceptions regarding intravenous opioid infusion and cancer pain management. Cancer 125(21), 38823889. doi:10.1002/cncr.32380CrossRefGoogle ScholarPubMed
Arthur, JA, Tang, M, Lu, Z, et al. (2021) Random urine drug testing among patients receiving opioid therapy for cancer pain. Cancer 127(6), 968975. doi:10.1002/cncr.33326CrossRefGoogle ScholarPubMed
Brown, RL and Rounds, LA (1995) Conjoint screening questionnaires for alcohol and other drug abuse: Criterion validity in a primary care practice. Wisconsin Medical Journal 94(3), 135140.Google Scholar
Bruera, E, Kuehn, N, Miller, MJ, et al. (1991) The Edmonton Symptom Assessment System (ESAS): A simple method for the assessment of palliative care patients. Journal of Palliative Care 7(2), 69. doi:10.1177/082585979100700202CrossRefGoogle ScholarPubMed
Bruera, E, Moyano, J, Seifert, L, et al. (1995) The frequency of alcoholism among patients with pain due to terminal cancer. Journal of Pain and Symptom Management 10(8), 599603. doi:10.1016/0885-3924(95)00084-4CrossRefGoogle ScholarPubMed
Carmichael, AN, Morgan, L and Del Fabbro, E (2016) Identifying and assessing the risk of opioid abuse in patients with cancer: An integrative review. Substance Abuse and Rehabilitation 7, 7179.Google ScholarPubMed
Chang, VT, Hwang, SS and Feuerman, M (2000) Validation of the Edmonton Symptom Assessment Scale. Cancer 88(9), 21642171. doi:10.1002/(SICI)1097-0142(20000501)88:9<2164::AID-CNCR24>3.0.CO;2-53.0.CO;2-5>CrossRefGoogle ScholarPubMed
Childers, JW, King, LA and Arnold, RM (2014) Chronic pain and risk factors for opioid misuse in a palliative care clinic. The American Journal of Hospice & Palliative Care 32(6), 654659.CrossRefGoogle Scholar
Chou, R (2009) 2009 Clinical Guidelines from the American Pain Society and the American Academy of Pain Medicine on the use of chronic opioid therapy in chronic noncancer pain: What are the key messages for clinical practice? Polskie Archiwum Medycyny Wewnetrznej 119(7-8), 469477.Google Scholar
Cook, RF, Bernstein, AD, Arrington, TL, et al. (1995) Methods for assessing drug use prevalence in the workplace: A comparison of self-report, urinalysis, and hair analysis. International Journal of the Addictions 30(4), 403426. doi:10.3109/10826089509048734CrossRefGoogle ScholarPubMed
Couwenbergh, C, Van Der Gaag, RJ, Koeter, M, et al. (2009) Screening for substance abuse among adolescents validity of the CAGE-AID in youth mental health care. Substance Use & Misuse 44(6), 823834. doi:10.1080/10826080802484264CrossRefGoogle ScholarPubMed
Dev, R, Kim, YJ, Reddy, A, et al. (2019) Association between tobacco use, pain expression, and coping strategies among patients with advanced cancer. Cancer 125(1), 153160. doi:10.1002/cncr.31783CrossRefGoogle ScholarPubMed
Dev, R, Parsons, HA, Palla, S, et al. (2011) Undocumented alcoholism and its correlation with tobacco and illegal drug use in advanced cancer patients. Cancer 117(19), 45514556. doi:10.1002/cncr.26082CrossRefGoogle ScholarPubMed
Dowell, D, Haegerich, TM and Chou, R (2016) CDC guideline for prescribing opioids for chronic pain - United States, 2016. Mmwr Recommendations and Reports 65(1), 149. doi:10.15585/mmwr.rr6501e1CrossRefGoogle Scholar
Dyson, V, Appleby, L, Altman, E, et al. (1998) Efficiency and validity of commonly used substance abuse screening instruments in public psychiatric patients. Journal of Addictive Diseases 17(2), 5776. doi:10.1300/J069v17n02_05CrossRefGoogle ScholarPubMed
Fishbain, DA, Cutler, RB, Rosomoff, HL, et al. (1999) Validity of self-reported drug use in chronic pain patients. The Clinical Journal of Pain 15(3), 184191. doi:10.1097/00002508-199909000-00005CrossRefGoogle ScholarPubMed
Hui, D, Abdelghani, E, Chen, J, et al. (2020) Chronic non-malignant pain in patients with cancer seen at a timely outpatient palliative care clinic. Cancers (Basel) 12(1), . doi:10.3390/cancers12010214CrossRefGoogle Scholar
Innis, J and Berta, W (2016) Routines for change: How managers can use absorptive capacity to adopt and implement evidence-based practice. Journal of Nursing Management 24(6), 718724. doi:10.1111/jonm.12368CrossRefGoogle ScholarPubMed
Katz, N and Fanciullo, GJ (2002) Role of urine toxicology testing in the management of chronic opioid therapy. The Clinical Journal of Pain 18(4 Suppl), S7682. doi:10.1097/00002508-200207001-00009CrossRefGoogle ScholarPubMed
Kelly, JP, Cook, SF, Kaufman, DW, et al. (2008) Prevalence and characteristics of opioid use in the US adult population. Pain 138(3), 507513. doi:10.1016/j.pain.2008.01.027CrossRefGoogle ScholarPubMed
Kwon, JH, Tanco, K, Park, JC, et al. (2015) Frequency, predictors, and medical record documentation of chemical coping among advanced cancer patients. The Oncologist 20(6), 692697. doi:10.1634/theoncologist.2015-0012CrossRefGoogle ScholarPubMed
Lau, J, Mazzotta, P, Fazelzad, R, et al. (2021) Assessment tools for problematic opioid use in palliative care: A scoping review. Palliative Medicine 35(7), 12951322. doi:10.1177/02692163211015567CrossRefGoogle ScholarPubMed
Leonardson, GR, A. Koplin, B, Ness, FK, et al. (2005) Validity and reliability of the audit and CAGE-AID in Northern Plains American Indians. Psychological Reports 97(1), 161166. doi:10.2466/pr0.97.1.161-166CrossRefGoogle ScholarPubMed
Parsons, HA, Delgado-Guay, MO, El Osta, B, et al. (2008) Alcoholism screening in patients with advanced cancer: Impact on symptom burden and opioid use. Journal of Palliative Medicine 11(7), 964968. doi:10.1089/jpm.2008.0037CrossRefGoogle ScholarPubMed
Pereira, JL, Chasen, MR, Molloy, S, et al. (2016) Cancer care professionals’ attitudes toward systematic standardized symptom assessment and the edmonton symptom assessment system after large-scale population-based implementation in Ontario, Canada. Journal of Pain and Symptom Management 51(4), . doi:10.1016/j.jpainsymman.2015.11.023CrossRefGoogle ScholarPubMed
Philip, J, Smith, WB, Craft, P, et al. (1998) Concurrent validity of the modified Edmonton Symptom Assessment System with the Rotterdam Symptom Checklist and the Brief Pain Inventory. Supportive Care in Cancer 6(6), 539541. doi:10.1007/s005200050212CrossRefGoogle ScholarPubMed
Reid, MC, Fiellin, DA and O’Connor, PG (1999) Hazardous and harmful alcohol consumption in primary care. Archives of Internal Medicine 159(15), 16811689. doi:10.1001/archinte.159.15.1681CrossRefGoogle ScholarPubMed
Vaeth, PA, Wang-Schweig, M and Caetano, R (2017) Drinking, alcohol use disorder, and treatment access and utilization among U.S. Racial/Ethnic groups. Alcoholism, Clinical and Experimental Research 41(1), 619. doi:10.1111/acer.13285CrossRefGoogle ScholarPubMed
Yennurajalingam, S, Arthur, J, Reddy, S, et al. (2021) Frequency of and factors associated with nonmedical opioid use behavior among patients with cancer receiving opioids for cancer pain. JAMA Oncology 7(3), 404411. doi:10.1001/jamaoncol.2020.6789CrossRefGoogle ScholarPubMed
Yennurajalingam, S, Edwards, T, Arthur, JA, et al. (2018) Predicting the risk for aberrant opioid use behavior in patients receiving outpatient supportive care consultation at a comprehensive cancer center. Cancer 124(19), 39423949. doi:10.1002/cncr.31670CrossRefGoogle Scholar
Figure 0

Table 1. Baseline demographic and clinical characteristics of study participants (N = 333)

Figure 1

Table 2. Completion rates and positive scores of CAGE-AID items at the inpatient consult and first outpatient follow-up visits (N = 333)

Figure 2

Table 3. Level of agreement of completed CAGE-AID scores between inpatient consult and outpatient follow-up visits (N = 283)