Addictive behaviors · 2017
Predictors of buprenorphine treatment success of opioid dependence in two Baltimore City grassroots recovery programs.
СтатьяМногоцентровое исследованиеПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Damian April Joy, Mendelson Tamar, Agus Deborah
Аннотация
INTRODUCTION: Despite evidence for the efficacy of buprenorphine treatment in primary care, few studies have identified factors associated with treatment success, nor have such factors been evaluated in community settings. Identifying correlates of treatment success can facilitate the development of treatment models tailored for distinct populations, including low-income communities of color. The current study examined client-level socio-demographic factors associated with treatment success in community-based buprenorphine programs serving vulnerable populations.
METHODS: Data were abstracted from client records for participants (N=445) who met DSM-IV criteria for opioid dependence and sought treatment at one of Behavioral Health Leadership Institute's two community-based recovery programs in Baltimore City from 2010 to 2015. Logistic regression estimated the odds ratios of treatment success (defined as retention in treatment for ≥90days) by sociodemographic predictors including age, race, gender, housing, legal issues and incarceration.
RESULTS: The odds of being retained in treatment ≥90days increased with age (5% increase with each year of age; p<0.001), adjusting for other sociodemographic factors. Clients who reported unstable housing had a 41% decreased odds of remaining in treatment for 90 or more days compared to clients who lived independently at intake. Treatment success did not significantly differ by several other client-level characteristics including gender, race, employment, legal issues and incarceration.
CONCLUSIONS: In vulnerable populations, the age factor appears sufficiently significant to justify creating models formulated for younger populations. The data also support attention to housing needs for people in treatment. Findings from this paper can inform future research and program development.
METHODS: Data were abstracted from client records for participants (N=445) who met DSM-IV criteria for opioid dependence and sought treatment at one of Behavioral Health Leadership Institute's two community-based recovery programs in Baltimore City from 2010 to 2015. Logistic regression estimated the odds ratios of treatment success (defined as retention in treatment for ≥90days) by sociodemographic predictors including age, race, gender, housing, legal issues and incarceration.
RESULTS: The odds of being retained in treatment ≥90days increased with age (5% increase with each year of age; p<0.001), adjusting for other sociodemographic factors. Clients who reported unstable housing had a 41% decreased odds of remaining in treatment for 90 or more days compared to clients who lived independently at intake. Treatment success did not significantly differ by several other client-level characteristics including gender, race, employment, legal issues and incarceration.
CONCLUSIONS: In vulnerable populations, the age factor appears sufficiently significant to justify creating models formulated for younger populations. The data also support attention to housing needs for people in treatment. Findings from this paper can inform future research and program development.