Transcript Presentation Slides - Center for Advancing Longitudinal Drug Abuse

Screening of drug use in primary care:
Implication for a collaborative
chronic care model
Betty Tai, Ph.D.
Director, Center for the Clinical Trials Network, NIDA
July 29th, 2015
CALDAL Summer Institute
NIDA
NATIONAL INSTITUTE
ON DRUG ABUSE
SUD Screening in Primary Care
• Opportunity for engagement in SUD treatment:
– Many people with SUDs who do not seek SUD
treatment are seen in Primary care, due to SUD related and other health problems.
• Opportunity for health care savings:
– SUDs are associated with increased health
problems and health care costs, early screening to
engage such patients in effective SUD prevention
and early intervention is likely to save health care
costs.
B. Tai 2013
Diagnostic Prevalence
Very frequent
Use
In Treatment ~ 2,300,000
~12% more
Qualify!
“Harmful Use”
~ 45,000,000
Qualify!
Very Rare
Use
B. Tai 2013
Little or No Use
McLellan 2013
Care of Substance Use Disorders
Very Frequent
Use
Chronic Care Model
Office-Based PC Treatment
Very Rare
Use
B. Tai 2013
Prevention & Early Intervention
McLellan 2013
Substance Use Impact on Healthcare
Alcohol and drug use - even at levels below “addiction”
- regularly lead to:
• misdiagnoses,
• poor adherence to prescribed care,
• interference with commonly prescribed medications,
• greater amounts of physician time,
• unnecessary medical testing,
• poor outcomes and
• increased costs
particularly in the management of chronic illness.
Vinson D, Ann Fam Med, 2004. Brown RL, J Amer Board Fam Prac, 2001. Humeniuk R, WHO, 2006. Manwell LB, J
Addict Dis, 1998. Longabaugh R. Alcohol Res Health, 1999. Healthiest Wisconsin 2010, WI DHFS, 2000. USPSTF,
Screening for Alcohol Misuse, 2004. National Quality Forum, National Voluntary Consensus Standards, 2006. Bernstein
J, Drug Alcohol Depend, 2005. Saunders B, Addiction, 1995. Stephens RS, J Consult Clin Psychol, 2000. Copeland J, J
Subst Abuse Treat 2001. Fleming MF, Med Care, 2000. Fleming MF, Alcohol Clin Exp Res, 2002. Gentilello LM, Ann
Surg, 1999. Estee S, Medicaid Cost Outcomes, Interim Report 4.61.1.2007.2, Washington State Department of Social
and Health Services. Yarnall KSH, Am J Public Health, 2003. Solberg LI, Am J Prev Med, 2008. National Committee on
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Prevention Priorities, http://www.prevent.org/content/view/43/71/.
Mclellan 2015
Trends in the US fatal medication error (FME) death rate by type of circumstance in which the
FME occurs (A) and for various comparison groups (B) (January 1, 1983-December 31, 2004)
Type 1: domestic + Alcohol/drugs
Type 2: domestic
Type 3: nondomestic + alcohol/drugs
Type 4: nondomestic
Phillips, D. P. et al. Arch Intern Med 2008;168:1561-1566.
BTai 2013
Copyright restrictions may apply.
USPSTF Recommendation on screening
of alcohol misuse in primary care
The USPSTF recommends that clinicians screen adults
aged 18 years or older for alcohol misuse and provide
persons engaged in risky or hazardous drinking with
brief behavioral counseling interventions to reduce
alcohol misuse
Evidence grade: B
B. Tai 2013
SBIRT for SUD
• SBIRT = Screening, Brief Intervention and Referral to
Treatment
• Allow health care professionals the opportunity to provide
proper services resulted from the screening of SUD
• Provide proper services such as prevention, early
intervention, brief treatment and/or referral to specialty
treatment based on the risk levels of SUD
• It is important to care for patients with other co-morbid
conditions
(http://beta.samhsa.gov/sites/default/files/sbirtwhitepaper_0.pdf).
B. Tai 2013
ASPIRE (Saitz et al., JAMA 2014)
“These results do not support widespread implementation
of illicit drug use and prescription drug misuse screening
and brief intervention.”
ROY-BYRNE (Roy-Byrne et al., JAMA 2014)
“This finding suggests a need for caution in promoting
widespread adoption of this intervention for drug use in
primary care.”
SMART-ED (Bogenschutz et al., JAMA Intern Med 2014)
“More work is needed to determine how drug use disorders
may be addressed effectively in the ED.”
DLiu 2015
# of Sites
ASPIRE
ROY-BYRNE
SMART-ED
1
7
6
Location of Sites
Boston
King County, WA
Boston, New York,
Miami,
Morgantown,
Cincinnati,
Albuquerque
# Enrolled
528
868
1,285
% Hispanic
10%
9%
24%
Enrollment Period
2.5 years
3.5 years
1.5 years
% Primary
Outcome
Availability
98%
88%
89%
Participant Characteristics (at Baseline)
ASPIRE
ROY-BYRNE
SMART-ED
Eligibility Criterion
ASSIST ≥ 4
Problem Drug Use
≥ 1x/Past 90 Days
DAST-10 ≥ 3; Drug
Use ≥ 1x/Past 30
Days
(Primary) Drug
Marijuana 63%
Cocaine 19%
Opioids 17%
Marijuana 76%
Cocaine 37%
Opioids 26%
Marijuana 44%
Cocaine 27%
Opioids 22%
Days of Use of Primary
Drug, Past 30 Days
14.4
13.8
16.2
Scores
ASSIST, mean = 16.8
(SD 9.6)
ASSIST ≥ 27: 18%
Other
Past 3 months:
Hospitalization 14%
ED visit 36%
Self help group 18%
Outpatient SUD/MH
treatment 23%
DAST-10 ≥ 6: 30%
Co-morbid mental
illness 56%
Homeless in past 90
days 30%
DAST-10, mean = 5.8
(SD 2.3)
ASSIST ≥ 27: 57%
Unemployed 42%
Income ≤ $15K 63%
*Assessment Reactivity?
ASPIRE
ROY-BYRNE
SMART-ED
Demographics
Tobacco use
ASSIST
TLFB
Past-month drinking
Short form CIDI
SIP-D
Readiness to change
HIV risk behaviors
PHQ-9
OASIS
EuroQoL
Health care utilization
Hair sample
Demographics
DAST-10
ASI
Thoughts about
Abstinence
HIV risk behaviors
Urine sample
Demographics
DAST-10
Heavy Smoking Index
AUDIT-C (3 questions)
Hair sample
[TLFB]
[ASSIST]
ASPIRE
“Observational studies found reductions in drug use after
SBI, but these are likely due to regression to the mean and
other exposures.”
“The negative results obtained in this study are unlikely to
be due to regression to the mean…because drug use did
not decrease.”
ROY-BYRNE “Both groups showed modest and similar
reductions in drug use frequency over the first 3 months with no
subsequent change, possibly suggesting a regression to the
mean in both groups.”
SMART-ED “Overall, drug use decreased over time in all
treatment groups, suggesting that the ED visit may mark a
turning point for many drug-using patients, regardless of what
specific treatment they receive.”
Conclusions?
Conclusions?
SMART-ED
“The findings of this study are relevant to
the population represented by the sample,
the types of intervention used in the trial, and
the outcomes that were examined.”
Conclusions?
ROY-BYRNE The study’s negative findings may reflect:
Participant factors
Heterogeneity of drug use
Severity of drug use problems
Marijuana legalization climate
Poverty
Co-morbid mental illness
Intervention factors
Single brief contact
Low compliance (47%) with booster
Delivery by physician extenders
Measurement factors
Assessment reactivity
Assessment of frequency of drug use, but not quantity
Conclusions?
Pragmatic Science:
The 2005-6 NIAAA
Clinicians Guide
Mark L. Willenbring, MD
Director, Div. of Treatment & Recovery Research
National Institute on Alcohol Abuse and
Alcoholism, National Institutes of Health
AUDIT-C
Public health perspective of
alcohol use and disorders
Use &
Problems
None
Moderate
Severe
Chronic
Rehabilitation
Modality
2º Prevention
1º Prevention
Disease
Management
Dimensional Diagnosis of
AUD: One Approach
None
Mild
(“At-risk”)
Never exceeds • Exceeds
daily limits
daily limits
• No current
sequelae
Moderate
(Harmful use)
• Exceeds
daily limits
• Current
sequelae
Severe
(Dependence)
• Daily or near
daily heavy
drinking
• Current
sequelae
• Withdrawal
Chronic
dependence
• Daily or near
daily heavy
drinking
• Current
sequelae
• Withdrawal
• Chronic or
relapsing
Extended Continuum
Heavy drinking
only
Harmful
drinking
Dependence
Chronic
Increased quantity, frequency & consequencesof alcohol use
Facilitated
self change
Brief
motivational
counseling
Disease
Specialized Management
Medical
remissionmanagement + oriented
pharmacotx
treatment
or CBI
USPSTF Recommendation on depression
screening in primary care
• The USPSTF recommends screening adults for depression
when staff-assisted depression care supports are in place to
assure accurate diagnosis, effective treatment, and followup.(Grade B)
• The USPSTF recommends against routinely screening adults
for depression when staff-assisted depression care supports are
not in place. There may be considerations that support
screening for depression in an individual patient. (Grade C)
BTai 2015
B Tai 2015
Extended Continuum
Minimal
Mild
Moderate
Moderately severe severe
PHQ – 9 Patient Depression Questionnaire
20-27
10 - 14
1- 4
B Tai 2015
5 -9
15 - 19
Depression Screening
Background:
In primary care settings, prevalence estimates of major depressive disorder
range from 5% to 13% in all adults, with lower estimates in those older than 55
years (6% to 9%). In 2002, the U.S. Preventive Services Task Force (USPSTF)
recommended screening adults for depression in clinical practices that have
systems to ensure accurate diagnosis, effective treatment, and follow-up.
Conclusion:
Depression screening programs without substantial staff-assisted depression
care supports are unlikely to improve depression outcomes. Close monitoring
of all adult patients who initiate antidepressant treatment, particularly those
younger than 30 years, is important both for safety and to ensure optimal
treatment.
O’Connor, et.al Ann Intern Med. 2009;151:793-803.
Participant Characteristics (at Baseline)
ASPIRE
ROY-BYRNE
SMART-ED
Eligibility Criterion
ASSIST ≥ 4
Problem Drug Use
≥ 1x/Past 90 Days
DAST-10 ≥ 3; Drug
Use ≥ 1x/Past 30
Days
(Primary) Drug
Marijuana 63%
Cocaine 19%
Opioids 17%
Marijuana 76%
Cocaine 37%
Opioids 26%
Marijuana 44%
Cocaine 27%
Opioids 22%
Days of Use of Primary
Drug, Past 30 Days
14.4
13.8
16.2
Scores
ASSIST, mean = 16.8
(SD 9.6)
ASSIST ≥ 27: 18%
Other
Past 3 months:
Hospitalization 14%
ED visit 36%
Self help group 18%
Outpatient SUD/MH
treatment 23%
DAST-10 ≥ 6: 30%
Co-morbid mental
illness 56%
Homeless in past 90
days 30%
DAST-10, mean = 5.8
(SD 2.3)
ASSIST ≥ 27: 57%
Unemployed 42%
Income ≤ $15K 63%
Brief Intervention for Medical Inpatients with
Unhealthy Alcohol Use: A Randomized, Controlled
Trial
Richard Saitz, MD, MPH; Tibor P. Palfai, PhD; Debbie M. Cheng, ScD; Nicholas J. Horton, ScD;
Naomi Freedner, MPH; Kim Dukes, PhD; Kevin L. Kraemer, MD, MSc; Mark S. Roberts, MD,
MPP; Rosanne T. Guerriero, MPH; and Jeffrey H. Samet, MD, MA, MPH
Conclusions:
“Brief intervention is insufficient for linking medical
inpatients with treatment for alcohol dependence and for
changing alcohol consumption. Medical inpatients with
unhealthy alcohol use require more extensive, tailored
alcohol interventions.”
Ann Intern Med. 2007;146:167-176.
The Fundamental Question
What works for which patient at
what time with what benefits?
Risks and costs?
Alcohol Screening Model?
or
Depression Screening Model?
The Success Story?!
Screen and Treat
Opioid Dependent in ED
Gail D’Onofrio,MD, MS. et.al JAMA. 2015;313(16):1636-1644
• Emergency Department at Yale
• Identify Opioid Dependent patients (329)
• Randomized into 3 arms
– Received a referral (104)
– Received a brief intervention (111)
– Received a brief intervention and Buprenorphine
(114)
Emergency Department–Initiated Buprenorphine/Naloxone
Treatment for Opioid Dependence: A Randomized Clinical Trial
Gail D’Onofrio,MD, MS; Patrick G. O’Connor,MD, MPH; Michael V. Pantalon, PhD; Marek C. Chawarski,
PhD; Susan H. Busch, PhD; Patricia H. Owens, MS; Steven L. Bernstein, MD; David A. Fiellin, MD
CONCLUSIONS AND RELEVANCE:
“Among opioid-dependent patients, ED-initiated buprenorphine
treatment vs brief intervention and referral significantly increased
engagement in addiction treatment, reduced self-reported illicit opioid
use, and decreased use of inpatient addiction treatment services but did
not significantly decrease the rates of urine samples that tested positive
for opioids or of HIV risk. These findings require replication in other
centers before widespread adoption.”
JAMA. 2015;313(16):1636-1644
CTN 0060
Computer-facilitated
intervention for Adolescent
Marijuana use Prevention
“CAMP”
R Schwartz 2015
Study Overview

To test the effect of providing computerized screening, risk
assessment, and substance use education followed by pediatrician brief
advice (cSBA) on cannabis use among 12-17 year olds presenting for
well-visits.
Primary Prevention Cohort (N = 3,160)

Report no cannabis use in the 12 months prior to baseline

Will cSBA group have a lower percentage of marijuana use over
a 12 month follow-up period compared to TAU?
Secondary Prevention Cohort (N < 350)

Feasibility study

Report some cannabis use in the 12 months prior to baseline

Will cSBA group have greater reduction in days of cannabis use
at the 3 month follow-up period compared to TAU?
Study Intervention



The intervention
is being adapted from Knight’s cSBA
Quasi experimental study found cSBA reduced alcohol
initiation and increased alcohol abstinence rates (Harris
et al., 2012)
Tablet computer component




Substance use screen
Risk level feedback
Science and stories about substance
Pediatrician component

Brief advice and counseling
Study Schema
Obtain Verbal Assent
Assess Eligibility
Obtain Written Assent
Assess Cannabis Use Status
P
R
I
M
A
R
Y
No Past 12-month Use
Any Past 12-month Use
Randomize
TAU
Randomize
cSBA
TAU
P
R
E
V
E
N
T
I
O
N
3 month follow-up
cSBA
S
E
C
O
N
D
A
R
Y
P
R
E
V
E
N
T
I
O
N
6 month follow-up
9 month follow-up
12 month follow-up
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Organizational Chart: Implementation Phase
Executive Committee
Robert Schwartz, Lead Investigator (LI) and Chair
John Knight, Co-LI
Sharon Levy, Co-LI
Li-Tzy Wu, Co-LI
Sion Harris, Co-Investigator
Jen McCormack, Project Director
John Rush, Advisor
Geetha Subramaniam, Clinical Protocol Coordinator (CPC)
Udi Ghitza, Co-CPC
DSMB
Study Management Team
Consultants
Ardis Olson
Robert Schwartz, LI
Jen McCormack, Project Director
Geetha Subramaniam, CPC
Udi Ghitza, Co-CPC
John Rush, Advisor
Emmes Team
Supporting Organizations
CCC / DSC
CTN Nodes
The Emmes Corporation
Central IRB
Chesapeake IRB
Clinical Sites
TBD
Centralized Follow-up
Lighthouse Institute
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Future Endeavors for the CTN

Engage general medical providers and patients
as research partners

Develop evidence base with chronic care model
for SUD starting in general medical settings with
proper linkage

Leverage new technology and Big Health Data
to enhance research efficiency and to improve
SUD treatment outcomes
BTai 2014
Data Science:
CTN Health Care Data
Task Force
Harvey Finberg, IOM 2007
The learning health care system?(in 2014’s language)
Harvey Finberg, IOM 2007
BIG Health Care DATA:
new thinking, training, and methods and tools
• The need for the clinical research enterprise
to expand its approaches to generating new
clinical and population health knowledge.
• The approaches, which will involve
systematically collecting and harvesting big
data from many different sources, will
require new thinking, new training, and new
methods and tools.
Harlan Krumholz - 2014 NIH presentation
NIDA CTN Health Care Data
Science Task Force
• Stakeholders membership
• EHRs as clinical trial data source:
– Build validated SUD measures (CDEs) for EHRs
vendors
– Facilitate Primary care providers to routinely use the
SUD measures in the EHRs
– Create EHRs based SUD treatment data sources
– Use the data sources creatively to generate and support
evidence based SUD treatment
BTai 2015
By 2020…Digital Health Infrastructure
• All people in developed nations will have —
– An electronic health record
– Biological samples
– Digitized images
– Wearable sensors
• Healthcare will be personalized using an
individual’s images, samples and clinical data.
• The health of a community will be monitored
using aggregate records.
B Tai 2015
Harvey Finberg, IOM 2007
NIH Precision Medicine Initiative 2015
Innovation:
Wearable Sensors
NIH BD2K
Detecting Cocaine Use from
Wireless ECG worn in Field Study
Santosh Kumar
Director, MD2K Center of Excellence
Associate Professor, Computer Science
University of Memphis
MD2K is an NIH Big Data to Knowledge (BD2K) Center of Excellence. Visit www.md2k.org.
Collaborators & Contributors
Behavioral Science
•
•
•
•
Engineering
Dr. David Epstein, NIDA IRP
• Dr. Emre Ertin, Ohio State
Dr. Ashley Kennedy, NIDA IRP
• Dr. Yixin Chen, Wash. U.
Dr. Kenzie Preston, NIDA IRP
Dr. Annie Umbricht, Johns Hopkins
Students & Postdocs
•
•
•
Amin Ahsan Ali
Monowar Hossain
Md. Mahbubur Rahman
58
Sensors – AutoSense & Smartwatch
Chestband: ECG, GSR,
Resp., Accel., Temp.
Smartphone sensors: GPS,
accelerometers, self-report
Smartwatch sensors:
Accel., Gyro, Mag.
(AutoSense sensors designed and built by Dr. Emre Ertin at the Ohio State University)
Stress
Drug Use
Smoking
Risk factors – activity, movement, geoexposure, social influence
59
In-residence lab study
• At Johns Hopkins Medical School (n=3)
Johns Hopkins data collected at Dr. Annie Umbricht’s lab (NIDA RO1DA027065)
– Study weeks: 1, 3, and 5
• Monday: 1mg, 20 mg, 40 mg (45 min apart)
• Tu, We, Th: Sample-Choice session
– Wore AutoSense for 8 hours during study weeks
• Reported smoking, TV watching, video games, etc.
• At NIDA Intramural Research Program (n=6)
NIDA data collected at Dr. Kenzie Preston’s lab at NIDA IRP
– Wore AutoSense on 7 days (1, 3, 4, 5, 8, 9, 10)
– Cocaine (25mg) administered on 3 days (1, 5, 10)
60
Field Study at NIDA IRP
• Forty two illicit drug users
• Four weeks of wearing the sensors in field
– Self-report of stress, smoking, craving, drug use
– For drug use, marked how long ago
• < 5 min; 5-15 min; 15-30 min; or > 30 min ago
– Daily lab visit, and regular urine tests (3 per week)
Field data collected at Dr. Kenzie Preston’s lab at the Intramural Research Program
(IRP) of the National Institute on Drug Abuse (NIDA) (Sponsor: NIDA IRP)
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Harvey Finberg, IOM 2007
New CTN Model
Administrative data
RCTs gold
standard for
evidenced
medicine
NEW DATA
SOURCES
NEW STATISTICAL
TOOLS
TECHNIQUES
NEW TRIAL
METHODS
PRACTICE BASED
RESEARCH NETWORKS
A meta experimental approach to generate evidence
How can the several strategies be assessed separately and in concert?
BTai 2015