A pilot randomized clinical trial of an intervention to reduce overdose risk behaviors among emergency department patients at risk for prescription opioid overdose

Drug

and

Alcohol

Dependence

163

(2016)

40–47

Contents

lists

available

at

ScienceDirect

Drug

and

Alcohol

Dependence

j

ourna

l

ho

me

pa

g

e:

www.e

lsevier.com/locate/druga

lcdep

Full

length

article

A

pilot

randomized

clinical

trial

of

an

intervention

to

reduce

overdose

risk

behaviors

among

emergency

department

patients

at

risk

for

prescription

opioid

overdose

Amy

S.B.

Bohnert

a

,b,c,d,∗

,

Erin

E.

Bonar

a

,

Rebecca

Cunningham

c

,d,e,f

,

Mark

K.

Greenwald

g

,

Laura

Thomas

a

,b

,

Stephen

Chermack

a

,b

,

Frederic

C.

Blow

a

,b

,

Maureen

Walton

a

,c

a

Department

of

Psychiatry,

University

of

Michigan

Medical

School,

4250

Plymouth

Rd.,

Ann

Arbor,

MI

48109,

USA

b

VA

Center

for

Clinical

Management

Research

(CCMR),

Department

of

Veterans

Affairs

Healthcare

System,

2800

Plymouth

Rd.,

Bldg.

16,

Ann

Arbor,

MI

48109,

USA

c

University

of

Michigan

Injury

Center,

University

of

Michigan

Medical

School,

2800

Plymouth

Rd.,

Bldg.

10,

Ann

Arbor,

MI

48109,

USA

d

Institute

for

Healthcare

Policy

and

Innovation,

University

of

Michigan,

2800

Plymouth

Rd.,

Bldg.

16,

Ann

Arbor,

MI

48109,

USA

e

Department

of

Emergency

Medicine,

University

of

Michigan

Medical

School,

1500

East

Medical

Center

Drive,

Ann

Arbor,

MI

48109,

USA

f

Department

of

Health

Behavior

and

Health

Education,

University

of

Michigan

School

of

Public

Health,

1415

Washington

Heights,

Ann

Arbor,

MI

48109,

USA

g

Department

of

Psychiatry

and

Behavioral

Neurosciences,

and

Department

of

Pharmacy

Practice,

3901Chrysler

Service

Drive,

Suite

2A,

Wayne

State

University,

Detroit,

MI

48201,

USA

a

r

t

i

c

l

e

i

n

f

o

Article

history:

Received

17

December

2015

Received

in

revised

form

14

March

2016

Accepted

17

March

2016

Available

online

26

March

2016

Keywords:

Overdose

Prescription

opioids

Pain

Behavioral

intervention

a

b

s

t

r

a

c

t

Background

and

aims:

Prescription

opioid

overdose

is

a

signiﬁcant

public

health

problem.

Interventions

to

prevent

overdose

risk

behaviors

among

high-risk

patients

are

lacking.

This

study

examined

the

impact

of

a

motivational

intervention

to

reduce

opioid

misuse

and

overdose

risk

behaviors.

Methods:

This

study

was

a

pilot

randomized

controlled

trial

set

in

a

single

emergency

department

(ED)

in

which,

204

adult,

English-speaking

patients

seeking

care

who

reported

prescription

opioid

misuse

during

the

prior

3

months

were

recruited.

Patients

were

randomized

to

either

the

intervention,

a

30-minute

motivational

interviewing-based

session

delivered

by

a

therapist

plus

educational

enhanced

usual

care

(EUC),

or

EUC

alone.

Participants

completed

self-reported

surveys

at

baseline

and

6

months

post-baseline

(87%

retention

rate)

to

measure

the

primary

outcomes

of

overdose

risk

behaviors

and

the

secondary

outcome

of

non-medical

opioid

use.

Findings:

Participants

in

the

intervention

condition

reported

signiﬁcantly

lower

levels

of

overdose

risk

behaviors

(incidence

rate

ratio

[IRR]

=

0.72,

95%

CI:

0.59–0.87;

40.5%

reduction

in

mean

vs.

14.7%)

and

lower

levels

of

non-medical

opioid

use

(IRR

=

0.81,

95%

CI:

0.70–0.92;

50.0%

reduction

in

mean

vs.

39.5%)

at

follow-up

compared

to

the

EUC

condition.

Conclusions:

This

study

represents

the

ﬁrst

clinical

trial

of

a

behavioral

intervention

to

reduce

overdose

risk.

Results

indicate

that

this

single

motivational

enhancement

session

reduced

prescription

opioid

overdose

risk

behaviors,

including

opioid

misuse,

among

adult

patients

in

the

ED.

Published

by

Elsevier

Ireland

Ltd.

1.

Introduction

Starting

in

the

1990s,

opioids

were

increasingly

prescribed

to

treat

pain

in

the

U.S.,

particularly

for

chronic

non-cancer

pain

(Paulozzi

et

al.,

2011).

An

unintended

consequence

of

these

∗

Corresponding

author

at:

University

of

Michigan

North

Campus

Research

Com-

plex

2800

Plymouth

Rd,

Bldg

16,

Room

227W,

Ann

Arbor,

MI

48109,

USA.

E-mail

address:

[email protected]

(A.S.B.

Bohnert).

efforts

to

reduce

pain-related

suffering

has

been

an

alarming

increase

in

opioid-related

addiction

and

overdoses

(Compton

and

Volkow,

2006;

Ofﬁce

of

National

Drug

Control

Policy,

2011).

Specif-

ically,

the

rate

of

prescription

opioid

overdose

deaths

in

the

U.S.

increased

293%

between

1999

and

2009

(Calcaterra

et

al.,

2013),

and

opioid-related

emergency

department

(ED)

visits

nearly

tripled

from

2004

to

2011

(Substance

Abuse

and

Mental

Health

Services

Administration

(SAMHSA),

2013).

Additionally,

the

non-medical

use

of

prescription

opioids

(NMUPO)

is

a

problem

associated

with

opioid

prescribing

that

has

an

important

role

in

overdose

risk,

with

http://dx.doi.org/10.1016/j.drugalcdep.2016.03.018

0376-8716/Published

by

Elsevier

Ireland

Ltd.

A.S.B.

Bohnert

et

al.

/

Drug

and

Alcohol

Dependence

163

(2016)

40–47

41

NMUPO

commonly

found

in

investigations

of

opioid

overdose

fatal-

ities

(Hall

et

al.,

2008).

Non-fatal

overdose

is

more

common

than

fatal

overdose,

with

an

estimated

23

non-fatal

overdoses

for

every

fatality

(Centers

for

Disease

Control

and

Prevention

(CDC),

2014).

Non-fatal

pre-

scription

opioid

overdose

is

associated

with

substantial

morbidity,

such

as

pulmonary

impairment

and

neurological

damage

from

prolonged

hypoxia

(Warner-Smith

et

al.,

2001).

People

who

have

experienced

a

non-fatal

overdose

are

at

heightened

risk

for

future

overdose

(Cofﬁn

et

al.,

2007).

In

addition

to

NMUPO,

a

number

of

speciﬁc

behaviors

have

been

found

to

increase

risk,

such

as

com-

bining

different

substances,

injecting,

using

alone,

and

consuming

more

than

usual

amounts

of

a

given

substance

(Cofﬁn

et

al.,

2007;

Cone

et

al.,

2004;

Gutierrez-Cebollada

et

al.,

1994;

Park

et

al.,

2015;

Paulozzi

et

al.,

2012;

Strang

et

al.,

2008).

Reducing

these

behaviors

is

an

important

target

of

overdose

prevention

interventions.

The

ED

is

a

critical

setting

to

address

the

public

health

problem

of

prescription

opioid

overdose.

In

2010,

an

estimated

51%

of

ED

visits

by

adults

nationally

were

for

a

painful

condition,

and

31%

of

all

ED

visits

resulted

in

an

opioid

being

prescribed.

ED

physicians

write

a

substantial

proportion

of

opioid

prescriptions,

particularly

to

those

under

age

40

(Cantrill

et

al.,

2012).

ED

patients

are

also

more

likely

to

be

engaged

in

risky

substance

use

than

the

general

community

(

Cherpitel,

2003;

Cunningham

et

al.,

2003).

Thus,

clini-

cal

encounters

in

the

ED

could

provide

an

opportunity

to

intervene

with

patients

who

are

at

increased

risk

for

opioid

overdose.

To

date,

there

have

been

no

published

ED-based

trials

to

address

overdose

risk.

To

address

this

gap,

we

developed

a

30-minute,

therapist-

delivered

and

tailored

intervention

for

the

ED

setting.

The

intervention

was

primarily

informed

by

motivational

interviewing

(MI)

strategies

(Miller

and

Rollnick,

2013;

Miller

and

Rose,

2009)

due

to

the

demonstrated

utility

of

MI

in

promoting

changes

in

health-related

behaviors,

including

substance

use

(Miller

and

Roll-

nick,

2013).

We

conducted

a

pilot

randomized

controlled

trial

that

compared

the

intervention

to

enhanced

usual

care

(EUC)

only.

The

present

study

tests

the

hypothesis

that

the

motivational

interven-

tion

results

in

reduced

self-reported

overdose

risk

behaviors

during

the

six

months

following

randomization

compared

to

EUC

alone.

2.

Material

and

methods

2.1.

Setting

Recruitment

occurred

in

the

ED

at

the

University

of

Michigan

Medical

Center

(UMMC)

between

April,

2013

and

March,

2014.

Standard

care

for

opioid

safety

in

this

ED

is

that

all

patients

leaving

with

opioids

or

a

prescription

receive

instructions

to

not

operate

machinery

and

avoid

alcohol

use.

The

study

received

approval

from

the

institutional

review

board

at

the

University

of

Michigan.

2.2.

Recruitment

and

participants

Fig.

1

displays

the

ﬂow

of

participants

through

the

study.

Research

staff

approached

patients

aged

18–60

while

waiting

for

care

in

private

rooms

and

recruited

them

to

participate

in

a

brief

(∼5

min)

computerized

screening

survey,

before

which

partici-

pants

provided

informed

consent.

Pen-and-paper

surveys

were

used

in

170

screenings

due

to

unavailability

of

computers.

This

age

range

was

selected

because

the

vast

majority

of

fatal

overdoses

in

the

U.S.

occur

within

this

group

(Bohnert

et

al.,

2010).

Participants

were

compensated

with

a

token

gift

valuing

$1.00

(e.g.,

decks

of

cards,

puzzle

books).

Exclusion

criteria

for

screening

were:

(1)

pre-

senting

in

the

ED

for

suicidality

or

sexual

assault

(based

on

the

medical

record),

(2)

inability

to

speak

or

read

English,

(3)

active

psychosis

(4)

unstable

medically

(i.e.,

level

one

trauma),

(5)

altered

mental

status

or

cognition

suggesting

an

inability

to

give

consent,

and,

(6)

inability

to

provide

any

contact

information

for

follow-up.

The

primary

eligibility

criterion

for

the

trial

determined

by

screening

survey

was

self-reported

NMUPO

in

the

prior

three

months

on

any

of

8

items

from

the

Current

Opioid

Misuse

Measure

(COMM;

Butler

et

al.,

2007).

Individuals

with

a

prior

non-fatal

over-

dose

(due

to

any

substance

and

broadly

deﬁned;

see

Section

2.7.4)

were

oversampled.

Eligible

participants

were

consented

for

the

trial

and

completed

a

computerized

baseline

survey,

for

which

they

were

compensated

$20.

Computerized

randomization

was

strati-

ﬁed

by

overdose

history

and

thus

was

unknown

to

both

participant

and

research

staff

until

the

completion

of

all

assessments

(clinical

staff

were

continuously

blind

to

assignment).

In

total,

205

individu-

als

were

eligible

and

randomized;

one

randomized

participant

was

excluded

from

analysis

due

to

dying

(for

reasons

unrelated

to

the

study)

during

the

follow-up

period,

resulting

in

a

sample

size

of

204.

In

some

cases

patients

had

completed

their

ED

visit

before

they

had

received

all

study

protocols.

Research

staff

successfully

completed

protocols

with

seven

participants

in

the

community.

2.3.

Trial

design

This

study

used

a

two-group

parallel

trial

design

with

1:1

allo-

cation.

The

study

was

initially

designed

to

include

only

individuals

with

a

prior

overdose

in

addition

to

past

three

month

NMUPO.

Due

to

challenges

in

recruiting

with

this

strategy,

we

modiﬁed

the

criteria

to

allow

individuals

with

NMUPO,

regardless

of

prior

over-

dose,

to

participate.

Sample

size

for

this

pilot

trial

was

selected

to

provide

sufﬁcient

information

on

study

protocols,

intervention

acceptability,

clinical

meaningfulness,

and

effect

sizes

for

a

future

trial.

2.4.

Intervention

and

control

conditions

The

two

conditions

of

this

study

were

the

motivational

inter-

vention

plus

enhanced

usual

care

(EUC)

and

EUC

only.

Standard

care

was

not

altered.

2.4.1.

Motivational

intervention.

Speciﬁc

intervention

content

was

based

on

MI

(Miller

and

Rollnick,

2013),

an

evidence-based

strat-

egy

for

reducing

risky

behaviors

by

enhancing

self-efﬁcacy

and

motivation

(Hettema

et

al.,

2005;

Resnicow

and

Rollnick,

2011;

Zahradnik

et

al.,

2009).

MI

is

delivered

from

a

non-judgmental,

empathic,

and

encouraging

stance,

with

a

focus

on

supporting

the

participant’s

autonomy

to

decide

if,

when,

and

how

to

make

changes.

It

employs

a

relational

foundation

in

which

clinicians

approach

patients

with

acceptance,

collaboration,

evocation,

and

compassion.

Technical

skills

(open

questions,

afﬁrmations,

strate-

gic

reﬂection,

summarizing)

are

used

to

respond

to

statements

that

favor

change

(i.e.,

change

talk)

and

statements

that

defend

the

sta-

tus

quo

(sustain

talk),

in

order

to

move

participants

through

four

processes:

engaging,

evoking,

focusing,

and

planning

for

change.

Educational

components

of

MI

interventions

use

an

“elicit-provide-

elicit”

strategy.

In

this

approach,

the

counselor

asks

questions

to

determine

the

need

for

new

information

(e.g.,

“What

are

some

things

you

think

increase

risk

for

overdose?”),

provides

new

infor-

mation

in

a

neutral

manner

after

afﬁrming

the

patient’s

knowledge,

and

then

asks

the

participant’s

perspective

on

this

new

informa-

tion

(e.g.,

“What

does

this

mean

to

you?”)

(Resnicow

and

Rollnick,

2011).

The

Supplementary

Material

lists

the

speciﬁc

intervention

components.

The

intervention

included

content

on

peer

outreach,

which

emphasized

ways

to

discuss

overdose

risk

reduction

with

others

at

risk

for

overdose.

This

peer

outreach

focus

has

been

used

in

HIV

risk

behavior

interventions,

and

studies

indicate

that

participants

42

A.S.B.

Bohnert

et

al.

/

Drug

and

Alcohol

Dependence

163

(2016)

40–47

Approached

2741

(61

.4%)

Completed Screen

2250

(82

.1%)

Eligible

245 (10.9%)

Refused Consent

445 (16.2%)

Not Eligible

2005 (89.1%)

Completed Baseline

206

*

(84.1%)

Refused

32 (13.1%)

Exclud

ed

7 (2.9%)

NMPOU

51 (25

%)

Intervenon

25 (49.0%)

EUC only

26 (51.0%)

Intervenon

77 (50.3%)

EUC only

76 (49.7%)

NMPOU + OD

15

3 (75

%)

6

-

Month Foll

ow

-

up

completed

73 (94.8%)

6

-

Month Foll

ow

-

up

completed

24 (92.3%)

6

-

Month Follow

-

up

completed

23 (92.0%)

6

-

Month Follow

-

up

completed

58 (76.3%)

Incomplete Scree

n

46 (1.7%)

Fig.

1.

Study

Participation

Flowchart.

*One

participant

declined

further

participation

after

completing

the

baseline

survey

and

was

not

randomized.

One

participant

died

of

causes

unrelated

to

the

study

prior

to

becoming

due

for

the

follow-up

assessment.

Thus,

ﬁnal

trial

n

=

204.

NMPOU:

Non-medical

prescription

opioid

use,

OD:

a

history

of

overdose

(see

Section

2.7.4

for

deﬁnition),

EUC:

enhanced

usual

care.

are

more

likely

to

change

their

own

drug

risk

behavior

in

outreach

interventions

compared

to

when

the

intervention

focuses

on

the

individual

participant’s

behavior

only

(Booth

et

al.,

2011).

Addi-

tionally,

public

health

interventions

have

trained

participants

to

respond

when

witnessing

an

overdose,

including

administration

of

naloxone

(Albert

et

al.,

2011;

Walley

et

al.,

2013).

The

intervention

included

content

on

response

to

a

witnessed

overdose

and

included

information

on

naloxone

distribution

locations,

but

naloxone

was

not

distributed

in

this

study.

Two

Master’s-level

therapists

with

prior

training

and

expe-

rience

in

MI

delivered

the

intervention.

The

therapists

received

approximately

1.5

additional

days

of

general

MI

training

and

2

days

of

training

in

delivering

the

speciﬁc

intervention

content

using

MI.

The

sessions

were

aided

by

a

computer

guide

to

enhance

ﬁdelity

and

to

provide

therapists

in

a

busy

clinical

setting

with

decision

support.

The

guide

provided

visual

aids

when

appropri-

ate

and

prompts

for

the

therapist

to

elicit

responses

to

open-ended

questions.

Therapists

received

weekly

supervision

from

licensed

clinicians

to

review

intervention

audio-recordings.

A

clinical

super-

visor

rated

adherence

on

a

randomly

selected

10%

sample

of

sessions

using

the

recordings.

Using

a

scale

of

0

(not

covered)

to

7

(mastery)

for

10

components,

the

average

adherence

ratings

were

between

4.8

and

5.5,

with

4

representing

“Solid”

adherence;

no

ratings

were

below

4.

2.4.2.

Enhanced

usual

care

(EUC).

Both

conditions

received

EUC,

in

which

therapists

provided

two

brochures:

(1)

an

overdose

pre-

vention

and

response

brochure,

and

(2)

a

resource

brochure.

The

overdose

prevention

and

response

brochure

included

a

deﬁnition

of

overdose,

signs

and

symptoms,

risk

factors,

and

bystander

response

to

overdose.

The

resource

brochure

contained

information

on

drug,

mental

health

and

alcohol

treatment,

mutual

help

groups,

local

resources

for

obtaining

naloxone,

free

health

clinics

in

the

area,

and

a

suicide

prevention

hotline.

The

therapists

brieﬂy

reviewed

the

brochure

sections

using

a

didactic

style

and

without

any

tailoring

to

speciﬁc

concerns

or

risk

factors.

2.5.

Follow-up

assessments

occurred

six

months

after

recruitment

and

participants

were

compensated

$30.

The

majority

of

partici-

pants

completed

the

follow-up

online;

in

36

cases,

follow-up

was

completed

in

person

via

pen-and-paper

based

on

participant

pref-

erence.

Follow-up

staff

members

were

blind

to

randomization.

Because

relatively

few

participants

were

lost

to

follow-up

(n

=

26)

or

had

missing

data

on

any

outcome

variable

(n

=

24),

we

com-

bined

these

groups

to

examine

the

potential

impact

of

loss

of

data

on

analyses.

Exclusion

from

outcome

analyses

was

not

associated

with

baseline

level

of

any

outcome,

age,

gender,

or

prior

overdose

history.

A.S.B.

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et

al.

/

Drug

and

Alcohol

Dependence

163

(2016)

40–47

43

2.6.

Measures

2.6.1.

Primary

outcomes.

All

items

used

to

assess

outcomes

are

listed

in

Table

1.

2.6.1.1.

Overdose

risk

behavior.

To

our

knowledge,

no

measure

of

overdose

risk

behaviors

had

been

published

at

the

time

recruitment

began

in

2013.

In

collaboration

with

other

study

teams

conduct-

ing

concurrent

trials

on

overdose

prevention

(PIs:

Phillip

Cofﬁn

and

Caleb

Banta-Green),

we

developed

a

measure

of

overdose

risk

behaviors

based

on

established

associations

with

overdose

(Cofﬁn

et

al.,

2007;

Cone

et

al.,

2004;

Gutierrez-Cebollada

et

al.,

1994;

Park

et

al.,

2015;

Paulozzi

et

al.,

2012;

Strang

et

al.,

2008).

Frequency

of

each

behavior

was

assessed

for

the

prior

six

months

at

both

baseline

and

follow-up.

Item

responses

were

summed,

with

higher

levels

indicating

a

greater

frequency

and

number

of

overdose

risk

behaviors.

The

Cronbach’s

alpha

for

these

items

was

0.82

and

0.79

at

baseline

and

follow-up,

respectively.

At

baseline,

the

total

over-

dose

risk

behavior

score

was

associated

with

the

number

of

prior

overdoses

(b

from

a

linear

regression

=

0.10,

p

<

0.001).

2.6.1.2.

Behavioral

intentions.

Based

on

structure

of

readiness

rulers

used

in

MI

(Resnicow

and

Rollnick,

2011),

we

developed

three

items

to

assess

intentions

to

reduce

overdose

risk.

There

was

a

strong

“ceiling”

effect,

and

items

were

reverse

coded

to

enable

use

of

appropriate

modeling

strategies

(see

Section

2.8).

Consequently,

higher

scores

indicate

lower

intention

to

avoid

overdose

risk.

2.6.1.3.

Overdose

knowledge.

Two

aspects

of

overdose

knowledge

were

assessed:

(1)

symptoms

and

(2)

risk

factors.

Items

to

assess

knowledge

of

overdose

symptoms

were

adapted

from

an

existing

checklist

measure

(Strang

et

al.,

2008).

The

measure

was

changed

by

dropping

an

intentionally

incorrect

item

of

“ﬁtting”

because

the

prior

study

was

speciﬁc

to

heroin

and

seizures

could

be

caused

by

non-opioid

overdoses.

The

total

number

of

symptoms

correctly

identiﬁed

was

summed

and

was

roughly

normally

distributed

and

standardized

for

analysis.

A

similar

checklist

assessment

of

knowl-

edge

of

risk

factors

for

overdose

was

also

developed

based

on

risk

factors

described

by

Warner-Smith

et

al.

(2001).

Due

to

the

strong

“ceiling”

effect

of

many

participants

identifying

a

high

number

of

risk

factors

correctly,

this

sum

was

also

reverse

coded

to

enable

use

of

appropriate

modeling

strategies,

and

thus

higher

scores

indi-

cated

more

incorrect

answers.

2.6.2.

Secondary

outcome.

2.6.2.1.

Non-Medical

prescription

opioid

use.

Selected

items

from

the

Current

Opioid

Misuse

Measure

(COMM;

Butler

et

al.,

2007)]

assessed

NMUPO.

The

original

COMM

has

good

test-retest

reliabil-

ity

among

patients

receiving

opioids

for

pain

(Butler

et

al.,

2007).

Given

that

assessment

in

the

ED

setting

must

be

brief

to

be

fea-

sible,

we

elected

to

use

a

shortened

version

of

the

COMM

in

this

study.

Our

prior

work

with

individuals

with

substance

use

disorders

(

Ashraﬁoun

et

al.,

2015)

indicated

that

the

COMM

items

can

assess

NMUPO

in

those

without

pain

as

well.

We

selected

eight

items

(see

Table

1)

in

the

domain

of

opioid

misuse

speciﬁcally,

rather

than

negative

psychological

states

generally,

based

on

our

prior

factor

analyses

(Ashraﬁoun

et

al.,

2015).

At

baseline,

these

items

were

assessed

for

the

past

three

months

to

assess

proximal

behavior

to

the

ED

visit

to

determine

eligibility,

and

at

follow-up

they

were

assessed

to

cover

the

entire

six

months.

The

Cronbach’s

alpha

for

the

COMM

items

was

0.89

at

both

baseline

and

follow-up

in

this

sample.

2.6.3.

Other

measures.

A

number

of

other

measures

were

collected

at

baseline

to

characterize

the

sample.

A

modiﬁed

version

of

the

Alcohol,

Smoking,

and

Substance

Involvement

Screening

Test

(ASSIST;

WHO

ASSIST

Working

Group,

2002)

was

used

to

mea-

sure

use

of

speciﬁc

substances

and

severity

of

opioid

use

problems.

Participants

also

self-reported

demographic

characteristics,

pain

treatment

history,

substance

use

treatment

and

counseling

history,

and

prior

ED

visits.

Prior

overdose

history

was

assessed

with

this

query:

“The

following

questions

are

about

experiences

with

tak-

ing

too

much

drugs

or

medications/pills,

and/or

drinking

too

much

alcohol.

This

is

sometimes

called

‘poisoning,’

‘passing

out,’

‘nodding

out,’

‘blacking

out,’

or

an

‘overdose’

or

‘OD.’

How

many

times

in

your

life

has

this

kind

of

situation

happened

to

you?”

This

was

intended

to

maximize

sensitivity

given

that

many

patients

do

not

identify

with

the

term

“overdose.”

2.7.

Statistical

methods

All

continuous

and

categorical

measures

were

evaluated

for

their

distributional

characteristics.

Analyses

to

examine

differences

between

intervention

and

EUC

participants

used

t-tests,

Fisher’s

exact

tests,

and

␹

2

tests

as

appropriate.

Poisson

regression

mod-

eling

was

used

for

outcomes

that

followed

a

Poisson

distribution,

which

was

true

for

all

outcomes

except

overdose

symptom

knowl-

edge,

which

was

normally

distributed.

Thus,

a

linear

regression

was

used

for

this

outcome.

Unlike

the

summary

scores

used

for

other

measures,

behavioral

intentions

were

analyzed

separately

based

on

prior

studies

(e.g.,

Bertholet

et

al.,

2012).

The

independent

vari-

able

of

interest

was

an

indicator

for

group

(intervention

coded

as

“1,”

EUC

as

“0”).

The

baseline

level

of

the

outcome

measures

was

included

as

a

covariate

in

each

model.

Statistical

signiﬁcance

was

set

at

a

two-sided

p

<

0.05

for

all

testing.

All

analyses

of

outcomes

used

an

intent-to-treat

framework

and

used

all

available

observa-

tions.

3.

Results

In

total,

2,250

ED

patients

completed

the

screening

survey

(see

Fig.

1).

Of

the

204

participants

in

the

trial,

178

(87%)

completed

the

six

month

assessment.

Six

participants

randomized

to

the

inter-

vention

did

not

receive

the

assigned

protocols;

of

those

6,

5

(83%)

completed

the

six

month

follow-up.

Participants

with

a

prior

his-

tory

of

overdose

who

were

randomized

to

EUC

only

were

more

likely

to

be

lost

to

follow-up

(24%)

compared

to

all

other

study

groups

(all

8%

or

less).

Table

2

reports

the

sample

characteristics.

In

terms

of

opiate

(heroin

or

opioid)

use,

the

sample

largely

reported

prescription

opi-

oid

use

only,

with

only

33

reporting

lifetime

heroin

use.

Roughly

half

of

the

sample

(48%)

had

levels

of

prescription

opioid

involve-

ment

considered

moderate

or

high

risk.

The

intervention

and

EUC

only

groups

signiﬁcantly

differed

in

the

proportion

that

reported

their

race

as

“other”

(10

vs.

2%)

and

the

proportion

that

reported

alcohol

during

the

prior

three

months

(53

vs.

69%).

Of

the

75%

who

had

experienced

at

least

one

overdose,

5%

reported

that

their

most

recent

overdose

was

a

suicide

attempt,

70%

reported

that

it

was

accidental,

12%

reported

that

they

“didn’t

want

to

die

but

did

not

care

about

the

risks

either”

and

13%

reported

being

unsure.

In

terms

of

between-group

differences

in

baseline

levels

of

the

outcomes,

intervention

participants

had

a

higher

frequency

of

over-

dose

risk

behaviors

at

baseline

compared

to

EUC

only,

with

a

mean

level

of

3.8

in

the

intervention

group

and

3.3

in

the

EUC

group,

(incidence

rate

ratio

[IRR]

=

1.16

in

a

Poisson

regression

model),

although

this

was

short

of

statistical

signiﬁcance

(p

=

0.05).

This

group

difference

was

attenuated

when

restricted

to

those

with

six

month

outcome

data

(IRR

=

1.10,

p

=

0.22).

Baseline

level

of

the

two

overdose

knowledge

summary

scores

and

COMM

score

did

not

dif-

fer

between

groups;

intervention

participants

had

lower

levels

of

44

A.S.B.

Bohnert

et

al.

/

Drug

and

Alcohol

Dependence

163

(2016)

40–47

Table

1

Outcome

measures.

Overdose

Risk

Behavior

Items

a

1.

How

often

have

you

used

opioid

pain

medications

when

nobody

else

was

around?

2.

How

often

have

you

used

opioid

pain

medications

in

a

place

where

you

don’t

usually

use

them?

3.

How

often

did

you

drink

alcohol

within

2

h

before

or

after

using

opioid

pain

medications?

4.

How

often

did

you

take

sedatives

(such

as

Xanax)

within

2

h

before

or

after

using

opioid

pain

medications?

5.

How

often

did

you

use

heroin

within

2

h

before

or

after

using

opioid

pain

medications?

6.

How

often

did

you

use

uppers

(such

as

crack,

cocaine,

crystal/meth)

within

2

h

before

or

after

using

opioid

pain

medications?

7.

How

often

have

you

increased

the

amount

of

opioid

pain

medications

you

used

to

more

than

you

usually

use?

8.

How

often

have

you

snorted

any

drugs?

9.

How

often

have

you

injected

any

drugs?

Behavioral

Intentions

b

1.

If

you

receive

an

opioid

prescription,

how

likely

it

is

that

you

would

use

prescription

opioids

as

prescribed

by

a

medical

professional?

2.

How

likely

is

it

that

you

will

reduce

or

avoid

using

alcohol,

drugs,

and/or

medications

(recreationally)?

3.

How

likely

is

it

that

you

will

avoid

combining

alcohol,

drugs,

and/or

medications?

Overdose

Knowledge

c

Risk

Factors:

For

each

item,

please

check

“Yes”

for

the

items

that

you

believe

can

lead

to

an

overdose

or

“No”

if

you

believe

it

cannot

cause

an

overdose.

(1)

Taking

more

alcohol,

drugs,

and/or

medications

than

usual;

(2)

Taking

less

alcohol,

drugs,

or

medications

than

usual*;

(3)

Having

an

illness;

(4)

Drug

impurities;

(5)

Drugs,

alcohol

and/or

medications

stronger

than

expected;

(6)

Injecting

drugs;

(7)

Using

drugs

at

a

young

age*;

(8)

Combining

drugs;

(9)

Combining

different

medications;

(10)

Drinking

alcohol

with

drugs

and/or

medications;

(11)

Combining

drugs

and

medications;

(12)

Low

tolerance;

(13)

Emotional

problems

or

life

difﬁculties;

(14)

Suicide

attempt.

Symptoms:

For

each

item

below,

please

check

“Yes”

for

the

items

that

you

believe

to

be

a

symptom

of

an

overdose

or

“No”

if

you

believe

it

is

not

a

symptom

of

overdose:

(1)

Shallow

breathing;

(2)

Turning

blue;

(3)

Bloodshot

eyes*;

(4)

Loss

of

consciousness;

(5)

Deep

snoring;

(6)

Pinpoint

pupils;

(7)

Blurred

vision.*

Current

Opioid

Misuse

Measure

Items

d

1.

How

often

have

you

had

to

go

to

someone

other

than

your

prescribing

physician

to

get

sufﬁcient

pain

relief

from

opioid

pain

medications?

(i.e.,

another

doctor,

the

Emergency

Room,

friends,

street

sources)

2.

How

often

have

you

taken

your

opioid

pain

medications

differently

from

how

they

are

prescribed?

3.

How

much

of

your

time

was

spent

thinking

about

opioid

pain

medications

(having

enough,

taking

them,

dosing

schedule,

etc.)?

4.

How

often

have

you

needed

to

take

opioid

pain

medications

belonging

to

someone

else?

5.

How

often

have

you

been

worried

about

how

you’re

handling

your

opioid

pain

medications?

6.

How

often

have

you

had

to

take

more

of

your

opioid

pain

medication

than

prescribed?

7.

How

often

have

you

borrowed

opioid

pain

medication

from

someone

else?

8.

How

often

have

you

used

your

opioid

pain

medicine

for

symptoms

other

than

for

pain

(e.g.,

to

help

you

sleep,

improve

your

mood,

or

relieve

stress)?

a

Responses

options

were

“never

(0),”

“rarely

(1),”

“sometimes

(2),”

“often

(3),”

and

“very

often

(4),”

except

for

#6,

which

was

“never

(0),”

“once

(1),”

or

“more

than

once

(2).”

Sum

score

range:

0–32.

b

Response

options

were

“never

(0),”

“rarely

(1),”

“sometimes

(3),”

“often

(4),”

and

“very

often

(5);”

points

based

on

Butler

et

al.

(2007)

Sum

score

range:

0–40.

c

Response

options

were

on

a

scale

of

1

(“Not

Likely”)

to

10

“a

Very

Likely”).

d

*Denotes

intentionally

incorrect

item

that

was

reverse

coded

for

scoring.

Sum

score

range

0–14

for

risk

factors

and

0–7

for

symptoms.

intention

to

use

as

prescribed

at

baseline

(IRR

=

1.20,

p

<

0.05)

but

were

not

different

on

the

other

two

measures.

Table

3

reports

the

result

of

regression

models

examining

all

outcomes.

The

intervention

group

reported

greater

reduction

in

frequency

of

overdose

risk

behaviors

(Model

1;

IRR

=

0.72,

p

<

0.01).

The

percent

decrease

in

average

overdose

risk

behavior

frequency

was

40.5%

in

intervention

participants

and

14.7%

in

EUC

only

par-

ticipants

among

those

participants

with

data

at

both

timepoints.

Similarly,

the

intervention

group

reported

greater

reductions

in

NMUPO

(Model

5;

IRR

=

0.81,

p

<

0.01)

at

6

months

follow-up

com-

pared

to

EUC

only.

Percent

decrease

in

average

COMM

score

was

50.0%

in

intervention

participants

and

39.5%

in

EUC

partic-

ipants

among

those

participants

with

data

at

both

timepoints.

No

differences

by

group

were

observed

for

knowledge

and

inten-

tions

outcomes,

with

the

exception

that

intervention

participants

reported

greater

increases

in

intention

to

reduce

or

avoid

using

substances

(Model

2b;

p

<

0.01).

Adjusting

for

the

two

factors

associated

with

group

assignment

despite

randomization

(alcohol

use

and

other/missing

race)

did

not

change

statistical

inferences

for

the

models

for

overdose

risk

behav-

iors

and

COMM

scores

(<0.07

absolute

change

in

IRR).

Including

these

covariates

also

had

no

impact

on

inference

for

the

models

of

behavioral

intentions

or

overdose

symptom

knowledge.

For

the

model

of

overdose

risk

factor

knowledge,

the

effect

of

intervention

group

became

signiﬁcant

(IRR

=

1.28,

p

<

0.05)

after

adjusting

for

these

variables,

indicating

that

intervention

participants

had

more

incorrect

answers

at

follow-up

than

EUC

only

participants.

Because

we

oversampled

patients

with

a

prior

overdose,

we

conducted

sensitivity

analyses

by

re-estimating

the

models

in

that

group

alone;

effect

sizes

(IRRs)

were

relatively

similar

for

the

overdose

risk

behavior

(IRR

=

0.65)

and

COMM

outcomes

(IRR

=

0.74;

both

p’s

<

0.05)

but

the

effect

in

Model

2b

was

attenuated

(IRR

=

0.84,

p

=

0.07).

4.

Discussion

The

present

study

is

the

ﬁrst

trial

of

a

motivational

intervention

focused

on

reducing

opioid

overdose

risk

behaviors

among

those

with

a

prior

non-fatal

opioid

overdose

and/or

who

misuse

pre-

scription

opioids.

Analyses

indicated

that

the

intervention

reduced

self-reported

behavioral

outcomes

compared

to

a

control

condi-

tion.

These

ﬁndings

suggest

this

is

a

promising

strategy

for

reducing

overdose

morbidity

and

mortality.

Self-reported

behavioral

intentions

and

knowledge

about

over-

dose

risk

factors

and

symptoms

did

not

consistently

change

in

response

to

the

intervention.

One

possible

explanation

was

the

“ceiling”

effects

indicating

high

levels

of

knowledge

and

intention

to

avoid

overdose

overall.

Additionally,

the

mechanism

of

change

in

our

intervention

may

involve

factors

other

than

knowledge

and

intentions,

such

as

motivation

or

self-efﬁcacy.

Future

studies

exam-

ining

this

intervention

could

measure

other

potential

mechanisms

of

change.

In

the

meantime,

we

suggest

retaining

intervention

content

about

psychoeducation

for

overdose

prevention

because

other

settings/samples

may

not

have

the

same

baseline

knowledge.

Further,

we

found

that

reviewing

this

material

using

the

“elicit-

provide-elicit”

tool

provided

an

opportunity

for

collaboration

and

reinforcing

change

talk

for

avoiding

overdose,

which

are

thought

to

be

key

components

of

effective

MI

(Miller

and

Rollnick,

2013).

A.S.B.

Bohnert

et

al.

/

Drug

and

Alcohol

Dependence

163

(2016)

40–47

45

Table

2

Baseline

Sample

Characteristics.

Characteristic

Overalln

=

204

Interventionn

=

102

EUC

onlyn

=

102

p-value

a

Mean

(SD)

Mean

(SD)

Mean

(SD)

Age

36.8

(11.1)

37.5

(11.4)

36.1

(10.9)

0.38

n

(%)

n

Female

130

(64)

61

(60)

69

(68)

0.24

Race:

White

b

153

(75) 73

(72) 80

(78)

0.26

Black

40

(20)

24

(24)

16

(16)

0.16

Other/Missing

12

(6)

10

(10)

2

(2)

0.03

Education:

High

School

Degree

or

Less

51

(25)

27

(26)

24

(24)

0.80

Some

College

91

(45)

46

(45)

45

(44)

Competed

College

62

(30)

29

(28)

33

(32)

Employment

Status:

Disabled 75

(37) 40

(39) 35

(35) 0.31

Full-

or

Part-Time

Employment 93

(46) 42

(41)

51

(51)

Unemployed

31

(15)

19

(19)

12

(12)

Retired

4

(2)

1

(1)

3

(3)

Prior

Overdose

(any)

153

(75)

77

(75)

76

(75)

0.87

Number

of

past

year

ED

visits:

0

25

(12)

12

(12)

13

(13)

0.06

1–2

76

(37)

32

(31)

44

(43)

3–5

57

(28)

37

(36)

20

(20)

6+

46

(23)

21

(21)

25

(25)

Past

3

Month

Substance

Use

Any

Alcohol

Use

124

(61)

54

(53)

70

(69)

0.02

Use

Frequency:

Weekly

or

Greater

51

(25)

21

(21)

30

(29)

0.15

Any

Marijuana

Use

77

(38)

39

(38)

38

(37)

0.89

Use

Frequency:

Weekly

or

Greater

45

(22)

22

(22)

23

(23)

0.87

Any

Cocaine

Use

19

(9)

9

(9)

10

(10)

1.00

Use

Frequency:

Weekly

or

Greater

9

(4)

4

(4)

5

(5)

1.00

Any

Non-Medical

Sedative

Use 44

(22)

26

(26)

18

(18)

0.17

Use

Frequency:

Weekly

or

Greater

18

(9)

9

(9)

9

(9)

1.00

Chronic

Pain

Diagnosis,

Lifetime

c

115

(56)

57

(56)

58

(57)

0.89

Prescribed

Opioids

in

Prior

6

Months,

Self-Reported

0.13

None

64

(31)

32

(31)

32

(31)

For

Acute

Pain

Only 37

(18) 19

(19) 18

(18)

For

Chronic

Pain

Only

38

(19)

13

(13)

25

(25)

For

Acute

and

Chronic

Pain

65

(32)

38

(37)

27

(26)

Prescription

Opioid

Involvement,

ASSIST

0.85

Low

Risk

106

(52)

51

(50)

55

(54)

Moderate

Risk

80

(39)

42

(41)

38

(37)

High

Risk

18

(9)

9

(9)

9

(9)

Any

prior

substance

use

treatment

or

counseling 72

(35) 33

(32) 39

(38)

0.37

Any

prior

opiate

agonist

therapy

16

(8)

8

(8)

8

(8)

1.00

EUC:

enhanced

usual

care.

a

Fisher’s

exact

test

used

when

cell

sizes

<10,

otherwise

t-test

or

␹.

2

test.

b

Race

categories

are

not

mutually

exclusive.

c

Queried

as

“Have

you

ever

been

told

by

a

doctor

that

you

have

chronic

pain?”.

There

is

an

emerging

body

of

research

on

strategies

to

reduce

harms

associated

with

opioid

use.

These

include

prescription

drug

monitoring

programs,

overdose

education

and

naloxone

distribution

programs,

state

legislation,

development

of

clinical

guidelines

and

prescriber

education,

and

public

education

efforts

(

Haegerich

et

al.,

2014).

Studies

of

these

approaches

have

largely

been

observational,

with

very

few

trials

reported.

Additionally,

prior

studies

of

overdose

prevention

have

not

speciﬁcally

focused

on

motivating

individuals’

use

of

behavioral

harm

reduction

strate-

gies.

A

beneﬁt

of

the

intervention

approach

in

this

study

is

that

it

has

broad

application

for

individuals

with

elevated

risk

for

over-

dose.

A

focus

of

policy

statements

addressing

opioid

overdose

risk

has

been

the

implementation

of

naloxone

programs

(Health

and

Human

Services,

2015;

Ofﬁce

of

National

Drug

Control

Policy,

2011

).

These

programs

seek

to

distribute

an

opioid

overdose

anti-

dote

so

that

bystanders

could

administer

it

at

witnessed

overdoses

(

Cofﬁn

and

Sullivan,

2013;

Doe-Simkins

et

al.,

2009;

Walley

et

al.,

2013

).

In

contrast,

this

motivational

intervention

is

also

appropri-

ate

for

addressing

overdose

risk

among

those

who

use

opioids

in

a

private

setting

or

in

combination

with

drugs

that

are

not

treated

by

naloxone.

Future

work

could

examine

the

beneﬁt

of

combining

this

approach

with

naloxone

distribution.

The

ability

of

motivational

brief

interventions

(particularly

in

the

context

of

SBIRT)

to

change

drug

use

has

recently

come

into

question

after

null

ﬁndings

in

several

trials

(Hingson

and

Compton,

2014

).

A

potential

explanation

for

the

discrepancy

in

ﬁndings

is

that

the

primary

target

of

the

present

intervention

was

to

reduce

risks

associated

with

use,

rather

than

level

of

use,

and

a

harm

reduc-

tion

strategy

may

meet

less

resistance

from

patients

not

seeking

treatment.

Additionally,

there

is

concern

about

the

potential

for

dissemination

of

therapist-delivered

motivational

interventions,

given

costs

associated

with

staff

time

and

barriers

of

standardizing

ﬁdelity

(O’Donnell

et

al.,

2014).

It

is

possible

to

obtain

reim-

bursements

for

SBIRT,

and

our

intervention

used

a

computerized

workbook

that

served

as

a

clinical

decision

tool

for

therapist

and

prompting

session

content,

which

is

an

innovative

method

for

enhancing

ﬁdelity.

Several

EDs

across

the

country

have

a

health

behavior

specialist

present

to

deliver

such

interventions

(e.g.,

Bernstein

et

al.,

2009).

Nonetheless,

the

potential

for

dissemination

may

be

limited

in

many

ED

locations

by

the

lack

of

staff

time

avail-

able,

even

if

reimbursement

is

possible.

An

important

line

of

future

work

is

to

understand

MI

effectiveness

under

alternative

modes

of

delivery

that

reduce

burden

on

ED

staff,

such

as

computerized

interventions

(Murphy

et

al.,

2013)

or

interventions

that

occur

as

follow-up

after

the

ED

visit.

46

A.S.B.

Bohnert

et

al.

/

Drug

and

Alcohol

Dependence

163

(2016)

40–47

Table

3

Estimates

of

effect

of

the

intervention

on

outcomes

measured

six

months

later.

Primary

Outcomes

Model

1:

Overdose

Risk

Behaviors,

n

=

172

IRR

SE

95%

CI

Intervention

Group

vs.

EUC

only 0.72

0.07

0.59,

0.87

Baseline

Level

of

Overdose

Risk

Behaviors 1.07

0.01

1.06,

1.08

Model

2a:

Behavioral

Intention

to

Use

As

Prescribed,

n

=

170

IRR

SE

95%

CI

Intervention

Group

vs.

EUC

only

1.11

0.10

0.93,

1.33

Baseline

Level

of

Behavioral

Intention

1.12

0.02

1.09,

1.16

Model

2b:

Behavioral

Intention

to

Reduce

or

Avoid

Using,

n

=

169

IRR

SE

95%

CI

Intervention

Group

vs.

EUC

only

0.76

0.06

0.65,

0.90

Baseline

Level

of

Behavioral

Intention

1.07

0.02

1.04,

1.11

Model

2c:

Behavioral

Intention

to

Avoid

Combining,

n

=

169

IRR

SE

95%

CI

Intervention

Group

vs.

EUC

only

0.97

0.10

0.80,

1.19

Baseline

Level

of

Behavioral

Intention

1.07

0.02

1.03,

1.11

Model

3:

Overdose

Risk

Factor

Knowledge,

n

=

169

IRR

SE

95%

CI

Intervention

Group

vs.

EUC

only

1.19

0.12

0.97,

1.45

Baseline

Level

of

Overdose

Risk

Knowledge 1.05

0.03

1.01,

1.10

Model

4:

Overdose

Symptom

Knowledge,

n

=

172

B

SE

95%

CI

Intervention

Group

vs.

EUC

only

0.10

0.15

−0.20,

0.40

Baseline

Level

of

Overdose

Risk

Knowledge

0.22

0.08

0.08,

0.37

Secondary

Outcome

Model

5:

Non-Medical

Opioid

Use,

n

=

163

IRR

SE

95%

CI

Intervention

Group

vs.

EUC

only

0.81

0.06

0.70,

0.92

Baseline

Level

of

Non-Medical

Opioid

Use

1.04

0.003

1.03,

1.05

Note

for

interpretation:

Outcome

and

baseline

level

of

the

outcome

reverse

coded

in

models

2

and

3.

Bolded

indicates

p

<

0.05.

4.1.

Limitations

This

pilot

study

was

not

designed

to

be

a

deﬁnitive

trial

of

brief

MI

to

reduce

opioid

overdose

risk

and

had

a

relatively

small

sample

size.

The

study

included

only

six

months

of

follow-up

and

was

not

powered

to

detect

effects

on

non-fatal

overdose

and

repeat

ED

vis-

its,

which

have

the

potential

to

strengthen

the

economic

case

for

delivering

this

intervention

during

ED

visits.

Although

these

ﬁnd-

ings

are

promising,

further

research

is

needed

to

understand

the

potential

impact

on

key

health

outcomes

across

diverse

ED

settings.

We

did

not

collect

urine

drug

screens

or

other

biometrics

of

drug

use

for

this

study.

Standard

drug

panel

testing

would

not

have

been

useful

for

validating

any

of

the

primary

outcomes

of

the

study,

and

would

not

differentiate

medical

and

non-medical

use

of

opioids

for

validating

the

secondary

outcome.

Nonetheless,

biomarkers

have

a

role

as

a

“bogus

pipeline”

(Werch

et

al.,

1989)

to

promote

honest

self-reporting

of

substance

use,

and

it

is

possible

that

participants

in

the

intervention

condition

were

more

likely

to

underreport

NMUPO

and

concurrent

use

of

other

substances

at

follow-up.

How-

ever,

use

of

computerized

self-administered

assessments,

detailed

assurances

of

conﬁdentiality,

and

the

fact

that

follow-up

staff

mem-

bers

were

blinded

to

condition

assignment

increases

conﬁdence

in

veracity

of

self-report

(Beck

et

al.,

2014;

Harrison,

1997).

Addi-

tional

key

limitations

are

that

this

study

was

conducted

at

a

single

site

and

that

individuals

randomized

to

the

EUC

who

reported

a

prior

overdose

were

less

likely

to

complete

the

six

month

follow-

up

assessment

compared

to

other

participants;

thus,

replication

is

needed.

5.

Conclusions

This

study

provides

the

ﬁrst

examination

of

a

motivational

inter-

vention

for

reducing

prescription

opioid

overdose

risk.

This

study

established

the

feasibility

of

this

therapist-delivered

brief

interven-

tion

to

ED

patients

and

provided

evidence

of

intervention

effects

on

self-reported

overdose

risk

behavior.

Although

the

study

has

several

important

limitations,

the

ﬁndings

indicate

this

interven-

tion

is

a

promising

strategy

to

reduce

the

public

health

epidemic

of

opioid

overdose,

thus

warranting

further

study.

Contributors

Authors

Amy

Bohnert

(study

principal

investigator),

Maureen

Walton,

Rebecca

Cunningham,

Mark

Greenwald

and

Frederic

Blow

conceptualized

the

study

design

and

were

primarily

responsible

for

the

conduct

of

the

trial.

Authors

Stephen

Chermack

and

Erin

Bonar

provided

clinical

training

and

supervision

to

therapists

during

the

trial.

Author

Laura

Thomas

provided

project

management.

Amy

Bohnert

took

primary

responsibility

for

data

analysis

and

for

writ-

ing

and

revising

all

drafts.

All

other

authors

provided

substantive

feedback

on

all

drafts

and

approved

the

submitted

manuscript.

Conﬂicts

of

interest

No

conﬂict

declared.

Role

of

funding

Source:

This

project

was

supported

by

the

Centers

for

Disease

Control

and

Prevention

(CDC)

grant

R49CE002099.

The

funding

source

had

no

role

in

the

design

of

the

study

or

the

reporting

of

the

study.

ClinicalTrials.gov

identiﬁer

Safety

&

Prevention

Outcomes

Study

(SPOS);

NCT01894087

(Registered

July

2,

2013,

prior

to

the

end

of

data

collection).

A.S.B.

Bohnert

et

al.

/

Drug

and

Alcohol

Dependence

163

(2016)

40–47

47

Acknowledgements

The

authors

wish

to

thank

Anna

Eisenberg,

Mary

Jannausch,

Lynn

Massey,

Phillip

Nulph,

and

Lisa

Zbizek-Nulph

for

their

con-

tributions

to

this

study.

Appendix

A.

Supplementary

data

Supplementary

data

associated

with

this

article

can

be

found,

in

the

online

version,

at

http://dx.doi.org/10.1016/j.drugalcdep.2016.

03.018

.

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