Pretreatment and during treatment risk factors for dropout among patients with substance use disorders
Abstract
Objective The aim of this study was to use pretreatment and treatment factors to predict dropout from residential substance use disorder program and to examine how the treatment environment modifies the risk for dropout. Method This study assessed 3649 male patients at entry to residential substance use disorder treatment and obtained information about their perceptions of the treatment environment. Results Baseline factors that predicted dropout included younger age, greater cognitive dysfunction, more drug use, and lower severity of alcohol dependence. Patients in treatment environments appraised as low in support or high in control also were more likely to drop out. Further, patients at high risk of dropout were especially likely to dropout when treated in a highly controlling treatment environment. Conclusion Better screening of risk factors for dropout and efforts to create a less controlling treatment environment may result in increased retention in substance use disorder treatment.
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Frequency of substance use is a proxy for severity, which has been shown to predict treatment attrition and other poor outcomes.
“For example, frequency of substance use, is a proxy for severity, which has been shown to predict treatment attrition and other poor outcomes”
From Development and initial evaluation of the Brief Addiction Monitor (BAM) (2013) · cites this paper as McKellar, Kelly, Harris, & Moos, 2006
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The brief symptom inventory: an introductory report.
Derogatis, L. R., & Melisaratos, N. (1983) · Psychological Medicine · doi:10.1017/s0033291700048017 · Cited by 6,650 · see citing works
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012 · cited 1× · see citing works
“case-wise deletion, mean imputation, etc.) have been shown to introduce serious bias to analyses ( Schafer, 1997; Schafer & Graham, 2002).”
BackgroundGreen, C. A., Polen, M. R., Dickinson, D. M., Lynch, F. L., & Bennett, M. D. (2002) · Journal of Substance Abuse Treatment · doi:10.1016/s0740-5472(02)00278-7 · Cited by 201 · cited 1× · see citing works
“Studies report that the presence of an additional axis I disorder increases retention (Siqueland et al., 1998), decreases retention ( Green, Polen, Dickinson, Lynch, & Bennett, 2002; Kokkevi, Stefanis, Anastasopoulou, & Kostogianni, 1998), or is unrelated to retention (Miller, Ninonuevo, Hoffmann, & Astrachan, 1999).”
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Drop-out from addiction treatment: A systematic review of risk factors
Brorson, H. H., Ajo Arnevik, E., Rand-Hendriksen, K., & Duckert, F. (2013) · Clinical Psychology Review · doi:10.1016/j.cpr.2013.07.007 · Cited by 654 · see citing works
Mennis, J., & Stahler, G. J. (2016) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2015.12.007 · Cited by 198 · see citing works
Development and initial evaluation of the Brief Addiction Monitor (BAM)
Cacciola, J. S., Alterman, A. I., DePhilippis, D., Drapkin, M. L., Valadez, C., Fala, N. C., Oslin, D., & McKay, J. R. (2013) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2012.07.013 · Cited by 203 · see citing works
Emerging adult age status predicts poor buprenorphine treatment retention
Schuman-Olivier, Z., Weiss, R. D., Hoeppner, B. B., Borodovsky, J., & Albanese, M. J. (2014) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2014.04.006 · Cited by 127 · see citing works
Cavicchioli, M., Movalli, M., Vassena, G., Ramella, P., Prudenziati, F., & Maffei, C. (2019) · Addictive Behaviors · doi:10.1016/j.addbeh.2019.106035 · Cited by 94 · see citing works
Witkiewitz, K., Falk, D. E., Kranzler, H. R., Litten, R. Z., Hallgren, K. A., O'Malley, S. S., Anton, R. F., & Workgroup, I. C. W. T. A. C. T. I. (. (2014) · Alcoholism: Clinical and Experimental Research · doi:10.1111/acer.12543 · Cited by 60 · see citing works
A Naturalistic Evaluation of a Group Intervention for Parents of Youth with Substance Use Disorders
Ameral, V., Yule, A., McKowen, J., Bergman, B. G., Nargiso, J., & Kelly, J. F. (2020) · Alcoholism Treatment Quarterly · doi:10.1080/07347324.2019.1633978 · Cited by 6 · see citing works
Self-citation
- Authors
- McKellar, J., Kelly, J., Harris, A., & Moos, R.
- Affiliations
- VA Palo Alto Health Care System (US)
- Corresponding author
- McKellar, John
- Venue
- Addictive Behaviors
- Publisher
- Elsevier BV
- Published
- 2006-03
- Volume
- 31
- Issue
- 3
- Pages
- 450-460
- ISSN
- 0306-4603 (ISSN-L)
- OpenAlex
- W2133576457
- Cited by
- 125
- References
- 3
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 7 · 2023: 3 · 2022: 4 · 2021: 15 · 2020: 8 · 2019: 7 · 2018: 7 · 2017: 6 · 2016: 7 · 2015: 7 · 2014: 6 · 2013: 8 · 2012: 4
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Addictive Behaviors (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs
- Health Services Research and Development
- Quality Enhancement Research Initiative
- Topics
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Opioid Use Disorder Treatment
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Keywords
- substance use disorders, treatment discontinuation, residential treatment, therapeutic environment, perceived control, perceived support, risk factors, cognitive impairment, alcohol dependence severity, treatment retention, male patients
- MeSH terms
- Adult, Humans, Male, Middle Aged, Patient Dropouts, Predictive Value of Tests, Surveys and Questionnaires, Risk Factors, Social Environment, Logistic Models, Patient Satisfaction, Substance-Related Disorders
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