long-term course of opioid addiction long-term course of opioid addiction yih-ing hser, ph.d. ucla...
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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
4 CALDAR
4 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
2
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
3
Examples of CALDAR’sLong-term Follow-up Studies
1. The 33-year follow-up study of heroin addicts
2. A 12-year follow-up of a cocaine-dependent sample
3. A 5-year follow-up of participants in the Amity treatment program at a correction facility
4. Follow-up studies of methamphetamine patients
5. An 10-year follow-up of mothers and their children
6. START follow-up study
(Starting Treatment with Agonist Replacement Therapy—Randomization to Suboxone vs. Methadone)
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Longitudinal Research Design
In contrast to cross-sectional research design—data are collected on one or more variables for a single time periodLongitudinal 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
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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
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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
Longitudinal Drug Abuse Research
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Life Course Perspective on Drug use
1. 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
2. Persistence of drug use resembles chronic diseases: high relapse rates, non-compliance, require long-term care/management
3. Critical life events often lead to or explain changes
4. Social capital, situated choice are additional key concepts
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Longitudinal Approach to Study Drug Use over Time
Protective Factore.g. family support
Protective Factor(occurrence of positive life events)
e.g. got married, got employed
Risk Factorse.g. crime involvement
Estimated trajectory of Drug use
Age
Life-course Drug Use Career
Age
Trajectories of drug use are heterogeneous among individuals and can be classified as several distinctive
trajectory groups
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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
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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
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Life Course of Heroin Addiction
Death: 14%Negative urine on heroin: 29%
Incarcerated: 18%
28%25%12%
49%23%6%
Childhood/Adolescence
Young Adulthood Adulthood Middle-aged
Late-middle-aged & Older
CAPAdmission
Mean age = 25
Larger society/environment Drug itself Individual: social relationship (family, school, church),
education/employment, institutional interaction (CJS, treatment), health/mental health
Influencing Factors
Onset of HeroinMean age = 18
Follow-up at 1974/75
Mean age = 40
Follow-up at 1985/86
Mean age = 50
Follow-up at 1996/97
Mean age = 60
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13
Hypothetical Drug Use Trajectories
0
5
10
15
20
25
30
1 24 36 48 60 84 96 108 120 132 180
Months
Days
of use
Person 1 Person 2 Person 3
IncarceratedDrug txEmploymentMental health txCriminally active
The Natural History of Narcotics Addiction Among CAP Sample
(N=581)
0
20
40
60
80
100
56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96
Per
cen
t of
Sam
ple
Daily Narcotic Use
Methadone Maintenance
Abstinent
Occasional Narcotic Use
Incarcerated
Dead
Unknown
Years 1956 through 1996 14
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
15
Mean Number of Days Per MonthUsing Heroin, 33 Year Follow-up
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Years since first heroin use
Days
use
d
Decelerate Stably High Quitter
9%
32%
59%
16
Differences in Trajectory Groups: Demographics
59
29
3834
66
50
7612
0
20
40
60
80
Late Decelerated Stably High Use Early Quitter
%
White Hispanic African American
17**p < .01Note: (no difference in education or age)
Differences in Trajectory Groups: Mortality
50.3
38.1
25.0
0
20
40
60
80
Late Decelerated Stably High Use Early Quitter
Per
cen
t
18**p < .01
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”
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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
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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 21
Is stable long-term recovery possible?
22
Rates of Abstinence by Years Abstinent Prior to Last Interview
(N = 242)
15% 17%
75% 72%
0
20
40
60
80
0(n = 85)
1-5(n = 66)
6-15(n = 36)
15+(n = 34)
Years Abstinent Prior to 1985/86
Per
cen
t A
bst
inen
t (1
985/
86 t
o p
rese
nt)
23
24
More than 5 Years of Abstinence:Predicting lower depression
1.61.5
1.3 1.3 1.31.2
0
0.4
0.8
1.2
1.6
2
Me
an
Sc
ore
Depression * Anxiety
No abstinence (N=121) 1-<5 Years (n=31) 5+ Years (n=69)
* p < .05SCL58 Scale (1- 4) at the 33-year follow-up: higher scores indicate greater symptom severity.
25
More than 5 Years of Abstinence:Predicting better emotional well-being
74.078.0 83.0
53.0
65.0
59.0
0
20
40
60
80
100
Me
an
Sc
ore
Emotion* Health
No abstinence (N=121) 1-<5 Years (n=31) 5+ Years (n=69)
p < .05 SF36 Scale (0-100) at the 33-year Follow-up: higher scores indicate better a status
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19.121.5
22.8
8.39.7
12.4
0
10
20
30
Me
an
Sc
ore
Self-esteem ** Life satisfication **
No abstinence (N=121) 1-<5 Years (n=31) 5+ Years (n=69)
* p<.05; **p < .01Self-Esteem (0-30) and Life Satisfaction (0-18) Scales at the 33-year : Higher scores indicate a better status
More than 5 Years of Abstinence: Higher self-esteem and life satisfaction
27
64
45
9
35 32
14 18 16
6
5661
43
78
65
48
0
20
40
60
80
100
per
cen
t o
f su
bje
cts
Heroin ** Coca/Meth*
Other illictdrugs *
Alcohol Tobacco **
No abstinence (N=121) 1-<5 Years (n=31) 5+ Years (n=69)* p<.05; **p < .01
Alcohol, Tobacco and Illicit Drug Use
at the 33-year Follow-up
28
18
39
58
0
20
40
60
80
100
pe
rce
nt
of
su
bje
cts
Employed **
No abstinence (N=121) 1-<5 Years (n=31) 5+ Years (n=69)
* p<.05; **p < .01
Employment at the 33-year Follow-up
Summary of Findings Five years appear to be a good benchmark
4 Less future use4 Less CJS involvement4 Better emotional and social functioning
Timing may be critical4 Health is not much better4 Alcohol and tobacco still problematic
Need to 4 Understand the underlying mechanisms 4 Promote recovery in early stages of
addiction
CTN START: Background1267 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
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START: Starting Treatment with Agonist Replacement TherapySuboxone (Buprenorphine+naloxone) vs. Methadone
Medications for Opioid Addiction
Methadone: agonist Morphine Tincture of opium Naltrexone:antagonist Depo-naltrexone Buprenorphine: partial agonist
Subutex, Suboxone, Probuphine Clonidine: non-opioid Lofexidine
OOH O
N
OHCH3 CH2
CH2 CH N
CH3CH3
CH3
O
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)
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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
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Data CollectionBaseline (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
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Research Questions
Mortality Treatment retention Long-term use
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Description of Sample at Baseline: Demographics
Mean age 37Female 32%Ethnicity
White 72%Black 8%Hispanics 12%
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Description of Sample at Baseline: Opioid and Other
DrugsUrine
Positive (%) Use
Disorder (%)
Amphetamine 9 11Cannabis 24 20
Cocaine 37 33
Opiates 97 100
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Description of Sample at Baseline: smoking and alcohol
use
Current smoker 89%Alcohol use disorder 23%
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Description of Sample at Baseline: psychiatric history
Schizophrenia 2.5%Major depressive disorder 28%Bipolar 12%Anxiety or panic disorder 30%
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Description of Sample at Baseline: Quality of health1
Percentile 2
Physical 49 (9)Mental health 39 (13)
1. SF-362. Relative to the U.S. population with similar age & gender
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Baseline differences between the two treatment conditions
No differences inAge, gender, ethnicity, injection, sites, alcohol, amphetamine, cannabis, sedativePhysical and mental health quality
Exceptions: cocaine & smoking (higher in the methadone group)
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START Treatment
Treatment condition Suboxone58%
Methadone42%
Days in treatment1 (within 168 days)
99 (70) 138 (54)
Treatment completion2 46% 74%Average dose, mg 14 (8) 68 (35)
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
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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
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100
120
140
160
168
Surv
ival
Days in treatment during 24 weeks
Buprenorphine (n=738) Methadone (n=529)
Figure 1. Survival Curves for Buprenorphine Versus Methadone
1
11.8%
5.8%8.7%
23.3% 35.6%
17.1%
0102030405060708090
100
0 1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Week in Treatment
Suboxone Dose (n=739)
Suboxone with Positive Urine (n=739)
Methadone Dose (n=528)
Methadone with Positive Urine (n=528)
Figure 3. Average Weekly Dose and Positive Opiate over Weeks in Treatment (n=1,267)
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To improve retention, clinicians need to
1. use higher medication doses, particularly for BUP,
2. address continued use of opiates and other drugs, and
3. identify additional factors/strategies influencing BUP retention, particularly during the first 30 days of treatment.
48
The Future? Changing profiles of opioid addiction Evidence-based intervention, practice, &
principles Long-term care or management Service structure
4 Integration within treatment systems4 Integration across systems4 Affordable Care Act
Technology
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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
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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