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Long-Term Course of Opioid Addiction

Long-Term Course of Opioid Addiction. Yih-Ing Hser, Ph.D. UCLA Integrated Substance Abuse Programs Addiction Seminar (Psychiatry 434). Supported by the National Institute on Drug Abuse (P30 DA016383). Overview. Background CALDAR This topic

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Long-Term Course of Opioid Addiction

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  1. Long-Term Course of Opioid Addiction Yih-Ing Hser, Ph.D. UCLA Integrated Substance Abuse Programs Addiction Seminar (Psychiatry 434) Supported by the National Institute on Drug Abuse (P30 DA016383)

  2. Overview • Background • CALDAR • This topic • Overview of morality and opioid abstinence in long-term follow-up studies • The 33-year follow-up study • The START follow-up study

  3. Center for Advancing Longitudinal Drug Abuse Research (CALDAR) Increase knowledge of patterns of drug addiction & their interplay with treatment and other service systems Enhance scientific collaboration through integration analysis, training, consultation, dissemination

  4. Examples of CALDAR’sLong-term Follow-up Studies • The 33-year follow-up study of heroin addicts • A 12-year follow-up of a cocaine-dependent sample • A 5-year follow-up of participants in the Amity treatment program at a correction facility • Follow-up studies of methamphetamine patients • An 10-year follow-up of mothers and their children • START follow-up study (Starting Treatment with Agonist Replacement Therapy—Randomization to Suboxone vs. Methadone)

  5. Longitudinal Research Design • In contrast to cross-sectional research design—data are collected on one or more variables for a single time period • Longitudinal research design—data are collected on one or more variables for two or more time periods • Longitudinal research design allows measurement of change, and possibly explanation of change

  6. Goals of Longitudinal Analyses • Assess changes over time: • How does it change over time? • What is the time trend? • How does the time trend differ by group? • Group differences at end of study (group differences at end of study) minus (group differences at baseline) • Investigate factors related to the different patterns of changes • Time trends as functions of covariates

  7. Longitudinal Drug Abuse Research • Persistence of drug use: Drug addiction is a chronic condition • High relapse rates over long periods of time • Non-compliance, require long-term care management • Frequent encounters with social and health service systems

  8. Life Course Perspective on Drug use • Life course theory recognizes the importance of time, timing, and temporal processes in the study of human behavior and experience over the life span, characterized by trajectories, transitions, and turning points • Persistence of drug use resembles chronic diseases: high relapse rates, non-compliance, require long-term care/management • Critical life events often lead to or explain changes • Social capital, situated choice are additional key concepts

  9. Longitudinal Approach to Study Drug Use over Time

  10. Global Burden of Disease • Approximately 16.5 millions people worldwide are users of heroin or opium (UN World Drug Report 2013) • In the US, approximately 467,000 individuals with heroin use disorder; 2,056,000 with prescription pain relievers in 2012 (NSDUH) • Opioid dependence is the biggest contributor to overdose deaths • Opioid dependence is the biggest contributor to global burden of disease attributable to illicit drug use and dependence

  11. A 33-year Follow-up of Heroin-Dependent Sample • A cohort of 581 male heroin addicts admitted to the California Civil Addict Program (CAP) in 1962-64 has been followed-up and interviewed over more than 30 years • The CAP was the only major publicly-funded drug treatment program available in California in the 1960s • The CAP provided a combination of inpatient and outpatient drug treatment to narcotics-dependent criminal offenders committed under court order

  12. Life Course of Heroin Addiction CAP Admission Follow-up at 1974/75 Follow-up at 1985/86 Follow-up at 1996/97 Mean age = 25 Mean age = 40 Mean age = 50 Mean age = 60 Onset of Heroin Mean age = 18 Death: 14% Negative urine on heroin: 29% Incarcerated: 18% 49% 23% 6% 28% 25% 12% Young Adulthood Late-middle- aged & Older Childhood/ Adolescence Adulthood Middle-aged Influencing Factors

  13. Hypothetical Drug Use Trajectories Incarcerated Drug tx Employment Mental health tx Criminally active

  14. The Natural History of Narcotics Addiction Among CAP Sample (N=581) Daily Narcotic Use Methadone Maintenance Occasional Narcotic Use Abstinent Dead Incarcerated Unknown Years 1956 through 1996

  15. Identify Groups with Distinctive Heroin Use Trajectories • Growth Mixture Modeling • First half of the observation (16 years) since heroin initiation • Two-part model (skewness) • Linear and quadratic terms • Three Distinctive Groups • Standard statistical criteria: BIC, entropy

  16. 59% 32% 9% Mean Number of Days Per MonthUsing Heroin, 33 Year Follow-up

  17. Differences in Trajectory Groups: Demographics **p < .01 Note: (no difference in education or age)

  18. Differences in Trajectory Groups: Mortality **p < .01

  19. Consistent with other studies showing: • Some users did stop using • Many continued to use at high levels, over a long period of time • At any given time, 40-60% “relapsed”

  20. What New? • Distinctive patterns of drug use trajectory • Individual’s baseline not necessarily determines future • Important to identify why the different patterns of trajectory • Escalating • Decreasing • High vs. low vs. no use

  21. What Have we Learned? • Cyclical patterns of abstinence and use of different levels, protracting over a long time • Long-term observation is necessary to explicate addiction patterns and trajectories. Otherwise, we may miss the critical points or differences as well as opportunities for intervening • If addiction is a chronic disease and cumulative treatment effect exists, then long-term care makes sense for these individuals 21

  22. Is stable long-term recovery possible?

  23. Rates of Abstinence by Years Abstinent Prior to Last Interview(N = 242)

  24. More than 5 Years of Abstinence:Predicting lower depression SCL58 Scale (1- 4) at the 33-year follow-up: higher scores indicate greater symptom severity. 24 * p < .05

  25. More than 5 Years of Abstinence:Predicting better emotional well-being SF36 Scale (0-100) at the 33-year Follow-up: higher scores indicate better a status p < .05 25

  26. More than 5 Years of Abstinence: Higher self-esteem and life satisfaction Self-Esteem (0-30) and Life Satisfaction (0-18) Scales at the 33-year : Higher scores indicate a better status * p<.05; **p < .01 26

  27. Alcohol, Tobacco and Illicit Drug Use at the 33-year Follow-up 27 * p<.05; **p < .01

  28. Employment at the 33-year Follow-up * p<.05; **p < .01 28

  29. Summary of Findings • Five years appear to be a good benchmark • Less future use • Less CJS involvement • Better emotional and social functioning • Timing may be critical • Health is not much better • Alcohol and tobacco still problematic • Need to • Understand the underlying mechanisms • Promote recovery in early stages of addiction

  30. CTN START: Background • 1267 opioid dependent users • Randomly assigned to Suboxone vs. Methadone • Recruitment over the period of 2006 to 2009 • Mortality status (date of death) determined by 3/2012 START: Starting Treatment with Agonist Replacement Therapy Suboxone (Buprenorphine+naloxone) vs. Methadone

  31. Medications for Opioid Addiction Methadone: agonist Morphine Tincture of opium Naltrexone:antagonist Depo-naltrexone Buprenorphine: partial agonist Subutex, Suboxone, Probuphine Clonidine: non-opioid Lofexidine

  32. START: Study Sites 8 sites (across 5 states) • California • Bi-Valley Medical Clinic Inc., Sacramento (n=117/84; 201) • BAART, Turk St. Clinic, San Francisco (n=109/78; 187) • Matrix Institute, Los Angeles (n=78/50; 128) • Oregon • CODA-Research, Portland (n=136/89; 225) • Washington • Evergreen Treatment Services, Seattle (n=79/55; 134) • Connecticut • CT Counseling Centers, Waterbury (n=71/52; 123) • Hartford Dispensary, Hartford (n=101/71; 172) • Pennsylvania • NET Steps, Philadelphia (n=48/49; 97)

  33. START: Treatment & Randomization • 24 weeks (active phase), ending 36 weeks • 739 to Suboxone vs. 528 to Methadone 2006 71 vs. 72 1:1 2007 207 vs. 197 1:1 2008 254 vs. 139 2:1 2009 192 vs. 99 2:1

  34. Data Collection • Baseline (randomization) • Demographics, substance use/urine, physical and psychiatric history, quality of health • START treatment • Suboxone vs. methadone, days in treatment, dose • 3 waves of follow-up starting late 2011

  35. Research Questions • Mortality • Treatment retention • Long-term use

  36. Description of Sample at Baseline: Demographics Mean age 37 Female 32% Ethnicity White 72% Black 8% Hispanics 12%

  37. Description of Sample at Baseline: Opioidand Other Drugs

  38. Description of Sample at Baseline: smoking and alcohol use Current smoker 89% Alcohol use disorder 23%

  39. Description of Sample at Baseline: psychiatric history Schizophrenia 2.5% Major depressive disorder 28% Bipolar 12% Anxiety or panic disorder 30%

  40. Description of Sample at Baseline: Quality of health1 Percentile 2 Physical 49 (9) Mental health 39 (13) 1.SF-36 2. Relative to the U.S. population with similar age & gender

  41. Baseline differences between the two treatment conditions No differences in Age, gender, ethnicity, injection, sites, alcohol, amphetamine, cannabis, sedative Physical and mental health quality Exceptions: cocaine & smoking (higher in the methadone group)

  42. START Treatment 1. The difference between the two treatment groups was significant at p < .01 2. The difference between the two treatment groups was significant at p < .01

  43. Mortality • Mortality status (date of death) determined by 3/2012 • Web archives: date of death • CDC National Death Index: date and ICD-10 causes of death • CDC has a 2-year lag time in their data • Death certificates from local corner’s office

  44. 17.1% 23.3% 35.6% 8.7% 5.8% 11.8%

  45. Figure 3. Average Weekly Dose and Positive Opiate over Weeks in Treatment (n=1,267) 46

  46. To improve retention, clinicians need to • use higher medication doses, particularly for BUP, • address continued use of opiates and other drugs, and • identify additional factors/strategies influencing BUP retention, particularly during the first 30 days of treatment.

  47. The Future? Changing profiles of opioid addiction Evidence-based intervention, practice, & principles Long-term care or management Service structure Integration within treatment systems Integration across systems Affordable Care Act Technology 48

  48. Longitudinal Studies and Analyses • Two or more observations of the response variable taken at different times are made on the same individuals • Can be used to assess on-going/recurring behaviors & events • Adjust for correlated observations over time, and/or • Allow examination of both within- and between-subjects hypotheses, i. e. can separate • differences within individuals (e.g. aging/drug career progression), from • differences among people (cohort effects) • Allow complexity, depending on models chosen: covariates (both time-variant and time-invariant), missing data, clustering of observations, latent constructs, temporal structuring • More powerful for some hypotheses than cross-sectional designs

  49. Learn more about longitudinal research findings and modeling techniques?? See CALDAR website (www.caldar.org) for new findings, development, and workshops

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