Patient choice in opt-in, active choice, and opt-out HIV screening: Randomized clinical trial

Abstract

Study question What is the effect of default test offers—opt-in, opt-out, and active choice—on the likelihood of acceptance of an HIV test among patients receiving care in an emergency department?
Methods This was a randomized clinical trial conducted in the emergency department of an urban teaching hospital and regional trauma center. Patients aged 13-64 years were randomized to opt-in, opt-out, and active choice HIV test offers. The primary outcome was HIV test acceptance percentage. The Denver Risk Score was used to categorize patients as being at low, intermediate, or high risk of HIV infection.
Study answer and limitations 38.0% (611/1607) of patients in the opt-in testing group accepted an HIV test, compared with 51.3% (815/1628) in the active choice arm (difference 13.3%, 95% confidence interval 9.8% to 16.7%) and 65.9% (1031/1565) in the opt-out arm (difference 27.9%, 24.4% to 31.3%). Compared with active choice testing, opt-out testing led to a 14.6 (11.1 to 18.1) percentage point increase in test acceptance. Patients identified as being at intermediate and high risk were more likely to accept testing than were those at low risk in all arms (difference 6.4% (3.4% to 9.3%) for intermediate and 8.3% (3.3% to 13.4%) for high risk). The opt-out effect was significantly smaller among those reporting high risk behaviors, but the active choice effect did not significantly vary by level of reported risk behavior. Patients consented to inclusion in the study after being offered an HIV test, and inclusion varied slightly by treatment assignment. The study took place at a single county hospital in a city that is somewhat unique with respect to HIV testing; although the test acceptance percentages themselves might vary, a different pattern for opt-in versus active choice versus opt-out test schemes would not be expected.
What this paper adds Active choice is a distinct test regimen, with test acceptance patterns that may best approximate patients’ true preferences. Opt-out regimens can substantially increase HIV testing, and opt-in schemes may reduce testing, compared with active choice testing.
Funding, competing interests, data sharing This study was supported by grant NIA 1RC4AG039078 from the National Institute on Aging. The full dataset is available from the corresponding author. Consent for data sharing was not obtained, but the data are anonymized and risk of identification is low.
Trial registration Clinical trials NCT01377857.

Full text

the bmj |
BMJ
2016;352:h6895 | doi: 10.1136/bmj.h6895
RESEARCH
1
open access
1
Department of Emergency
Medicine, University of
California, 505 Parnassus Ave,
San Francisco, CA 94143, USA
2
School of Public Health,
University of California, 239
University Hall #7360,
University of California,
Berkeley, CA 94720-7360, USA
3
Department of Emergency
Medicine, University of
California, 1001 Potrero Ave,
San Francisco CA 94143, USA
Correspondence to: J C C Montoy
juancarlos.montoy@ucsf.edu
Additional material is published
online only. To view please visit
the journal online (http://dx.doi.
org/10.1136/bmj.h6895)
Cite this as: BMJ ;:h
http://dx.doi.org/10.1136/bmj.h6895
Accepted: 25 November 2015
Patient choice in opt-in, active choice, and opt-out HIV screening:
randomized clinical trial
Juan Carlos C Montoy,
1
William H Dow,
2
Beth C Kaplan
3
ABSTRACT
STUDY QUESTION
What is the eect of default test oers—opt-in, opt-out,
and active choice—on the likelihood of acceptance of
an HIV test among patients receiving care in an
emergency department?
METHODS
This was a randomized clinical trial conducted in the
emergency department of an urban teaching hospital
and regional trauma center. Patients aged 13-64 years
were randomized to opt-in, opt-out, and active choice
HIV test oers. The primary outcome was HIV test
acceptance percentage. The Denver Risk Score was
used to categorize patients as being at low,
intermediate, or high risk of HIV infection.
STUDY ANSWER AND LIMITATIONS
38.0% (611/1607) of patients in the opt-in testing
group accepted an HIV test, compared with 51.3%
(815/1628) in the active choice arm (dierence 13.3%,
95% condence interval 9.8% to 16.7%) and 65.9%
(1031/1565) in the opt-out arm (dierence 27.9%,
24.4% to 31.3%). Compared with active choice testing,
opt-out testing led to a 14.6 (11.1 to 18.1) percentage
point increase in test acceptance. Patients identied
as being at intermediate and high risk were more likely
to accept testing than were those at low risk in all arms
(dierence 6.4% (3.4% to 9.3%) for intermediate and
8.3% (3.3% to 13.4%) for high risk). The opt-out eect
was signicantly smaller among those reporting high
risk behaviors, but the active choice eect did not
signicantly vary by level of reported risk behavior.
Patients consented to inclusion in the study aer
being oered an HIV test, and inclusion varied slightly
by treatment assignment. The study took place at a
single county hospital in a city that is somewhat
unique with respect to HIV testing; although the test
acceptance percentages themselves might vary,
adierent pattern for opt-in versus active choice
versus opt-out test schemes would not be expected.
WHAT THIS PAPER ADDS
Active choice is a distinct test regimen, with test
acceptance patterns that may best approximate
patients’ true preferences. Opt-out regimens can
substantially increase HIV testing, and opt-in schemes
may reduce testing, compared with active choice testing.
FUNDING, COMPETING INTERESTS, DATA SHARING
This study was supported by grant NIA 1RC4AG039078
from the National Institute on Aging. The full dataset is
available from the corresponding author. Consent for
data sharing was not obtained, but the data are
anonymized and risk of identication is low.
TRIAL REGISTRATION
Clinical trials NCT01377857.
Introduction
Opt-out HIV testing has received a great deal of atten-
tion since the United States Centers for Disease Control
and Prevention (CDC) revised its HIV testing guidelines
in 2006 to recommend non-targeted opt-out testing.
1-3
The CDC noted that emergency departments are espe
-
cially well situated to identify the estimated 20% of HIV
positive people who do not have a diagnosis.
4-7
Govern-
ments and health departments elsewhere have likewise
made a push toward opt-out testing to identify HIV
infected people earlier in the course of their disease,
although the specifics of whom to test and in what
setting remain highly regionalized.
8-12
However, most
hospitals have not acted on the CDC’s opt-out recom
-
mendations, in part because of unanswered questions
about the diagnostic yield of such testing.
13
14
The eect of opt-in versus opt-out defaults has been
identified in other settings,
15
16
but it has not been as
carefully identified for HIV testing; the CDC’s endorse
-
ment for opt-out testing was based on thin evidence of
its ecacy in increasing patients’ acceptance of HIV
screening.
17-19
Subsequent studies of emergency depart-
ment based HIV testing have shown that opt-out testing
programs can be successfully implemented and are
associated with testing of a higher proportion of
patients, but they report highly varied test acceptance
percentages ranging from 29% to 87%.
20-29
The wide
range in reported test acceptance percentages suggests
that the details of the testing regimen—including how
the test is oered, by whom, to whom, and in what set
-
ting—can be crucial to how likely patients are to agree to
be tested.
30
For example, two conflicting precursor stud-
ies from a single institution found significantly higher
and significantly lower test acceptance percentages for
opt-out compared with opt-in testing, suggesting that
other changes in the method of oering the test may
WHAT IS ALREADY KNOWN ON THIS TOPIC
Patients’ preferences are a hallmark of patient centered care, but little is known
about how wording of oers of testing can influence perceived preferences
Opt-in and opt-out HIV testing have not been compared in a randomized controlled
setting
US guidelines endorse opt-out HIV testing, and Europe has seen a trend toward this
testing scheme
WHAT THIS STUDY ADDS
Opt-in and opt-out defaults had statistically and clinically signicant eects on the
likelihood of patients accepting tests
Patients reporting risk factors were more likely to accept testing in each testing
regimen than were patients reporting no risk factors
Active choice is a distinct test regimen, with test acceptance patterns that may best
approximate patients’ true preferences

doi: 10.1136/bmj.h6895 |
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2016;352:h6895 | the bmj
RESEARCH
2
have outweighed any eect of moving from opt-in to
opt-out testing.
20
31
The study presented in this paper systematically iso-
lated the eect of test defaults by randomizing patients
to opt-in versus opt-out oers while holding all else
constant. We hypothesized that some of the variation in
results previously seen for opt-out testing schemes
stems from heterogeneity across studies in the compar
-
ison opt-in regimen, as well as the precise nature of the
opt-out regimen implemented. We also wanted to
answer various research questions identified by con
-
sensus research recommendations that noted the sig-
nificant knowledge gap regarding opt-in and opt-out
testing and thus recommended prioritizing the study of
factors influencing patients’ acceptance of HIV test
-
ing.
32
33
In addition, we estimated risk of infection by
using self reported behaviors collected from a question
-
naire that we administered as part of the study.
We also defined a default-free “active choice” regi
-
men in which patients were asked to choose whether
they would like to be tested or not. We distinguish this
from an opt-in program in which patients are informed
that tests are available but not tested unless they
request to be tested. Although this distinction may be
subtle, we hypothesized that it is useful because it
could result in clinically significant dierences in
behavior and help to identify a mechanism that influ
-
ences perceived patient preferences.
We examined active choice and opt-out HIV testing
schemes in comparison with opt-in. We also controlled
for all other aspects of the test oer, including informing
patients in all default assignments that testing is avail
-
able so as to specifically isolate the eects of defaults as
distinct from information or other confounding features.
We used self reported risk factors to test whether opt-out
testing disproportionately increased testing among peo
-
ple at lower rather than higher risk. We hypothesized
that a greater proportion of patients would be tested
under an active choice scheme than under an opt-in
scheme, and that a greater proportion would test under
the opt-out scheme than the active choice scheme. We
also hypothesized that these dierences would be
observed within each risk group.
Methods
We did a non-blinded, randomized clinical trial in the
emergency department of an urban teaching hospital
and regional trauma center. Between 18 June 2011 and
30 June 2013, non-clinical sta approached patients
during their visit to the emergency department: once to
oer them a questionnaire and once to oer them a
rapid HIV test. Study sta approached patients in paral
-
lel with their standard care. The test oers were made as
if they were a component of patients’ care in the depart
-
ment. Questionnaires were presented with a generic
description of a 10 minute questionnaire about improv
-
ing emergency department care. After the second of
these visits, patients were made aware that the test oer
and questionnaire were part of a study; they were fully
debriefed and gave consent. The study was conducted
and reported in accordance with CONSORT guidelines.
Opt-in, active choice, and opt-out treatment assign
-
ments were randomized at the level of the patient; we
used a random number generator to create individual
assignments with equal probability. Patients were also
randomly assigned to be oered the questionnaire
either before or after the oer of an HIV test (1:1 alloca
-
tion); the treatment assignments were cross random-
ized in a factorial design. Study sta had sheets with
unconcealed treatment assignments to which they
sequentially assigned participants. In results reported
elsewhere, another sample of patients was randomized
to receive small monetary incentives for testing, but for
clarity of interpretation we report here results only for
participants who were oered no monetary incentives.
These patients were not aware of the monetary incen
-
tives oered to other patients.
No incentive was oered for completion of the ques
-
tionnaire, which was self administered using iPads. It
elicited demographic information, HIV related risk
behaviors (such as number of sex partners, condom use,
and drug use), and beliefs about HIV infection and its
consequences (such as the estimated life expectancy for
people infected with HIV), as well as eliciting patients’
subjective assessment of their risk of infection.
Study sta worked two or three non-overlapping five
hour shifts a day for a total of 20 shifts a week. They
started each shift in one of four emergency department
zones according to a calendar created using a random
number generator to assign starting zones with equal
probability. After exhausting all eligible patients in a
given zone, sta moved to the next zone.
Participants
Patients were eligible for inclusion in the study if they
were between 13 and 64 years old, were able to consent
to HIV testing and to study inclusion, and spoke either
English or Spanish. We excluded patients if they had
previously been diagnosed as having HIV infection,
had received an HIV test in the previous three months,
were pregnant, were in police custody, or had partici
-
pated in this study in the previous three months. For
patients presenting with altered mental status, study
sta determined their ability to consent with the nurse
or clinician at the time of approach.
Patient involvement
No patients were involved in setting the research
question or the outcome measures, nor were they
involved in recruitment, or the design and implemen
-
tation of the study. There are no plans to involve
patients in dissemination.
Protocol
Study sta identified potential patients by using the
electronic medical record. They approached patients to
oer either an HIV test or the questionnaire, according
to their treatment assignment. All participants were
informed that the emergency department was oering
rapid screening HIV tests in a non-targeted manner to
all patients. Scripts standardized the provision of infor
-
mation about the availability of HIV testing and test

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3
oers: “We’re oering routine HIV tests to all of our
patients. It’s a rapid test with results available in one to
two hours.” The test oer followed—opt-in: “You can let
me, your nurse, or your doctor know if you'd like a test
today;” active choice: “Would you like a test today?” or
opt-out: “You will be tested unless you decline.”
Study sta oered tests and obtained verbal accep
-
tance; they notified clinicians of patients accepting HIV
tests. No pre-test counseling was oered. Test results
were available and reported to patients after comple
-
tion of the treatments and debriefing. Patients were
informed of negative test results by their nurse or clini
-
cian. Positive test results were disclosed by the patient’s
clinician in accordance with the protocol established by
the hospital's HIV rapid testing and referral program.
Patients were not made aware of the study itself until
after both the test and questionnaire oers, at which
time they were fully debriefed and asked to consent to
inclusion in the study. No incentive was oered for
study participation. All approached patients were
asked to consent regardless of whether they agreed to
the test or completed the questionnaire.
Statistics
We used a χ
2
test to assess allocation of patients to treat-
ment assignments according to observable characteris-
tics; we used a test of trend for ordinal categories.
Although patients were randomized, the design
included a retrospective debrief and consent, and treat
-
ment allocation was not concealed, so some risk of
biased treatment assignments existed.
The primary outcome was test acceptance percentage.
In addition to overall acceptance and acceptance percent
-
age by treatment, we examined test acceptance for sub-
groups defined by HIV related risk behaviors, adjusted for
demographics; we estimated treatment eects from uni
-
variate and multivariate ordinary least squares regres-
sions. In the tables, we report raw linear regression
coecients, which have the benefit that they are directly
interpretable as the dierence in the proportion of partici
-
pants who accepted an HIV test; interaction eects
between study arm and risk level are similarly easily inter
-
pretable.
34
Because the test acceptance rate is in the mid-
dle part of the distribution, results would be quite similar
if we used a non-linear model such as logistic regression
instead. We estimated risk of infection according to our
estimates of their Denver HIV Risk Score.
35
36
Points are
given for age, sex, race/ethnicity, sex with a male partner,
vaginal intercourse, receptive anal intercourse, intrave
-
nous drug use, and past HIV testing (appendix table A).
We classified patients with a score less than 20 as being at
low risk, those scoring 20-39 as at intermediate risk, and
those scoring 40 or higher as at high risk. For patients who
did not complete the questionnaire, we estimated the risk
score by using available data. Analysis by risk level was a
planned analysis, but these risk definitions were not
pre-specified, as the Denver HIV Risk Score was published
and validated during our data collection. All standard
errors are clustered by day and emergency department
zone (day-zone level). Sensitivity analyses including addi
-
tional covariates, dierent risk specifications, and multi-
variate logistic regression are presented in the appendix.
We used Stata 13.1 for randomization and all analyses.
The study’s originally planned sample size was su
-
cient to detect a 5 percentage point dierence in test
acceptance percentage between treatment arms with
93% power at a 5% significance level. This 5 percentage
point eect size was the minimum dierence we deemed
to be clinically important. We assumed a baseline test
acceptance percentage of 50%. The power of 93%
resulted from sample size needs for the larger study of
which this is a part. The sample size needed to achieve a
minimum of 80% power for the main comparisons across
arms in the analysis presented here was 1565 patients per
group. Our actual enrolled sample size was smaller than
originally planned owing to enrollment diculties, but it
met this 1565 minimum; thus the sample was sucient
for achieving 80% power for the main comparisons
across arms presented in the analyses below.
Results
Of 5801 patients approached by study sta, 4800
(82.7%) consented to study inclusion: 1607 (82.7%) in
the opt-in group, 1628 (81.0%) in the active choice
group, and 1565 (84.7%) of the opt-out group (χ
2
P<0.01).
Of those who consented to inclusion, 33.5% were ran
-
domized to opt-in, 33.9% to active choice, and 32.6% to
opt-out test oers. Figure 1 shows the flow of patients
Approached for inclusion and randomized (n=5801)
Opt-out (n=1847)Active choice (n=2010)Opt-in (n=1944)
Consented (n=805)Consented (n=760)Consented (n=824)Consented (n=804)Consented (n=799)Consented (n=808)
Late questionnaire
(n=958)
Early questionnaire
(n=986)
Late questionnaire
(n=1036)
Early questionnaire
(n=974)
Late questionnaire
(n=946)
Early questionnaire
(n=901)
Declined consent
(n=178)
Declined consent
(n=159)
Declined consent
(n=170)
Declined consent
(n=212)
Declined consent
(n=141)
Declined consent
(n=141)
Fig  | Flow chart of HIV Defaults Study. Of  patients approached for inclusion in study,  consented. Of those approached, .%, .%, and
.% were assigned to opt-in, active choice, and opt-out test treatments, respectively. The nal study population comprised .% opt-in, .%
active-choice, and .% opt-out patients

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through the study, and table 1 shows their demograph-
ics and chief presenting complaint. Race and chief com-
plaint were not equally distributed across treatment
assignments (χ
2
P=0.032 and 0.043, respectively); no
tests of independence between the three treatment
assignments were significant at the P<0.05 level after
Bonferroni correction (P values in table 1 have not been
Bonferroni corrected).
HIV test acceptance percentage
Patients accepted 51.6% of all oers of tests. Opt-in,
active choice, and opt-out test oers resulted in test
acceptance percentages of 38.0%, 51.3%, and 65.9%,
respectively (fig 2 , all patients). The unadjusted dier
-
ences reflect an absolute dierence in HIV testing per-
centage across defaults of 13.3% (95% confidence
interval 9.8% to 16.7%) in the active choice arm com
-
pared with opt-in and an absolute dierence of 27.9%
Table  | Characteristics of patients. Values are numbers (percentages) unless stated otherwise
Characteristic All (n=) Opt-in (n=) Active choice (n=) Opt-out (n=) P value*
Demographics
Male sex 2887 (60.1) 992 (61.7) 991 (60.9) 9 0 4 (57. 8) 0.057
Median (interquartile range) age, years, 42 (31-53) 42 (30-53) 42 (31-53) 43 (31-53) 0.411
Race†:
American Indian/Alaska Native 59 (1.2) 17 (1.1) 15 (0.9) 27 (1.7)
0.032
Asian 451 (9.4) 134 (8.3) 159 (9.8) 158 (10.1)
Black 1248 (26.0) 432 (26.9) 404 (24.8) 412 (26.3)
Native Hawaiian/Pacic Islander 140 (2.9) 46 (2.9) 42 (2.6) 52 (3.3)
White 2676 (55.8) 914 (56.9) 914 (56.1) 848 (54.2)
Unknown 330 (6.9) 91 (5.7) 135 (8.3) 104 (6.7)
Latino ethnicity 1163 (24.2) 394 (24.5) 370 (22.7) 399 (25.5) 0.179
Education ≥high school 2844 (59.3) 941 (58.6) 964 (59.2) 939 (60.0) 0.710
Identies as lesbian, gay, or bisexual 589 (12.3) 206 (12.8) 188 (11.5) 195 (12.5) 0.524
Chief complaint
Abdominal /gastrointestinal 979 (20.4) 396 (24.6) 338 (20.8) 345 (22.0)
0.043
Cardiovascular 544 (11.3) 183 (11.4) 167 (10.3) 194 (12.4)
Endocrine 61 (1.3) 21 (1.3) 21 (1.3) 19 (1.2)
General 288 (6.0) 88 (5.5) 98 (6.0) 102 (6.5)
Genitourinary/renal 302 (6.3) 106 (6.6) 91 (5.6) 105 (6.7)
Musculoskeletal 763 (15.9) 28 2 (17. 5) 250 (15.4) 231 (14.8)
Stroke 18 (0.4) 5 (0.3) 9 (0.6) 4 (0.3)
Neurological (non-stroke) 296 (6.2) 112 (7.0) 99 (6.1) 85 (5.4)
Oral/dental 69 (1.4) 27 (1.7) 19 (1.2) 23 (1.5)
Psychiatric 52 (1.1) 15 (0.9) 17 (1.0) 20 (1.3)
Respiratory 37 2 ( 7. 8) 12 0 ( 7.5) 131 (8.0) 12 1 ( 7. 7 )
Skin 386 (8.0) 143 (8.9) 141 (8.7) 102 (6.5)
Substance intoxication/withdrawal 92 (1.9) 25 (1.6) 40 (2.5) 27 (1.7)
Trauma 426 (8.9) 143 (8.9) 139 (8.5) 140 (8.9)
Other 156 (3.3) 41 (2.6) 68 (4.2) 47 (3.0)
Risk of infection
Low risk 1943 (40.5) 618 (38.5) 689 (42.3) 636 (40.6)
0.387Medium risk 2388 (49.8) 830 (51.7) 788 (48.4) 770 (49.2)
High risk 469 (9.8) 159 (9.9) 151 (9.3) 159 (10.2)
HIV test history
Ever tested 3880 (80.8) 1309 (81.5) 1302 (80.0) 1269 (81.1) 0.538
Tested in past 6 months 910 (19.0) 290 (18.0) 311 (19.1) 309 (19.7) 0.467
Unreported test history 971 (20.2) 341 (21.2) 334 (20.5) 296 (18.9) 0.254
Refused questionnaire 940 (19.6) 329 (20.5) 321 (19.7) 290 (18.5) 0.336
*P values for age calculated from Wald test with James’ approximation; risk of infection tested using non-parametric test for trend across categories; all other P values from Pearson’s χ
2
test for
independence between samples for each of three treatment assignments.
†Participants could select more than one race.
‡Low risk=Denver HIV Risk Score <20; high risk=score ≥40.
HIV test acceptance rate (%)
All
patients
0
20
40
60
80
100
611
1607
835
1628
1031
1565
198
618
331
689
402
636
337
830
420
788
528
770
78
159
84
151
101
159
Low
risk
Intermediate
risk
High
risk
Opt-in Active choice Opt-out
Fig  | HIV test acceptance percentage by risk of infection:
unadjusted results. Test acceptance percentage is shown
according to treatment assignment (opt-in, active choice,
and opt-out), and according to risk of HIV infection. Lines
indicate % condence intervals. Numbers of patients
from each risk category accepting and oered HIV testing
under each treatment group are presented as numerator
and denominator

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RESEARCH
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(24.4% to 31.3%) in the opt-out arm compared with
opt-in (table 2 , column 1). Compared with active choice
testing, opt-out testing yielded 14.6% (11.1% to 18.1%)
more test acceptances. Figure 2 illustrates unadjusted
test acceptance percentages by treatment assignment
for each of the risk groups.
Table 2 shows results from multivariate ordinary
least squares regressions of HIV test acceptance per
-
centages according to treatment assignment. Test
acceptance percentages were higher for patients with
higher Denver HIV Risk Scores (table 2 , column 2;
also appendix table C). When risk specific interac
-
tions with treatment assignments were included in
the regression (table 2 , column 3) the active choice
and opt-out eects were larger for the (comparison)
low risk group. We did not find risk specific dier
-
ences in treatment eects for patients at intermediate
risk. For patients at high risk, the eect of active
choice was not significantly modified but the eect of
opt-out was attenuated (interaction term between
opt-out and high risk: −15.5%, 95% confidence inter
-
val −27.8 to −3.1). Patients who did not complete the
questionnaire had incompletely measured Denver
HIV Risk Scores. Columns 4 and 5 of table 2 show the
results of the same models as in columns 2 and 3,
respectively, with the sample restricted to those who
completed the questionnaire; results differ only
slightly. In this subsample, a larger dierence existed
in testing proportions according to risk level. Again,
we did not find risk specific dierences in treatment
eects except for high risk patients in the opt-out
treatment arm.
Results did not change after adjustment for chief
complaint (data not shown). Patients oered the ques
-
tionnaire first were less likely to accept a test than were
those oered the test first; however, we found no signif
-
icant interaction between the timing of the question-
naire and treatment assignment with respect to test
acceptance percentage (appendix figure A). Likewise,
multivariate ordinary least squares and logistic regres
-
sions showed that the dierences in test acceptance by
treatment assignment were unchanged after we con
-
trolled for risk of infection, demographics, and HIV test
history (appendix tables D and E). Patients who had
never been tested for HIV accepted 48.8% (449/920) of
test oers; 52.3% (2028/3880) of patients who had pre
-
viously been tested accepted the test. Alternative risk
classifications and sensitivity analyses are shown in
the appendix.
Eects of test oer scripts persisted when dummy
variable fixed eect indicator variables for each study
sta member who oered tests were included in the
specification. Figure 3 shows test acceptance according
to research sta member. Sta (a) is a composite of sta
members who saw fewer than 200 patients each; sta
members (b) to (i) each consented 200 or more patients.
Test acceptance trended higher in the active choice than
the opt-in treatment arm for each sta member; it also
trended higher in the opt-out than active choice treat
-
ment arm, although substantial heterogeneity exists in
the magnitude of default effects across the staff.
Table  | Dierences in HIV test acceptance according to treatment assignment
Variables
Percentage point dierence (% CI); P value
: treatment eects : treatments and risk
: treatments and risk with
interactions
: subsample with
completed questionnaire
: subsample with completed
questionnaire, with interactions
Treatment assignment:
Active choice 13.3 (9.8 to 16.7); < 0.001 13.5 (10.0 to 16.9); <0.001 15.9 (10.6 to 21.1): <0.001 13.6 (9.8 to 17.3); <0.001 17.1 (11.3 to 22.8); <0001
Opt-out 27.9 (24.4 to 31.3); <0.001 27.9 (24.5 to 31.2); <0.001 31.2 (25.8 to 36.5); <0.001 27.3 (23.5 to 31.1); <0.001 31.6 (25.8 to 37.5); <0.001
Risk of infection:
Intermediate risk 6.4 (3.4 to 9.3); <0.001 8.5 (3.6 to 13.5); 0.001 8.7 (5.4 to 12.0); <0.001 12.0 (6.5 to 17.5); <0.001
High risk 8.3 (3.3 to 13.4); 0.0013 16.3 (7.5 to 25.1); <0.001 13.0 (7.2 to 18.9); <0.001 23.6 (13.1 to 34.0); <0.001
Interactions:
Active choice × intermediate risk −3.1 (−10.1 to 4.0); 0.39 −4.9 (−12.7 to 2.9); 0.217
Active choice × high risk −8.4 (−20.9 to 4.1); 0.19 −11.2 (−25.6 to 3.3); 0.131
Opt-out × intermediate risk −3.5 (−10.5 to 3.5); 0.332 −4.8 (−12.5 to 2.9); 0.225
Opt-out × high risk −15.5 (−27.8 to −3.1); 0.015 −20.3 (−34.6 to −6.0); 0.005
Refused questionnaire −6.7 (−10.1 to −3.3); <0.001 − 6.7 (−10.2 to −3.3); <0.0001
Constant* 38.0 (35.5 to 40.5) 35.2 (32.2 to 38.4) 33.4 (29.5 to 37.2) 33.8 (30.5 to 37.2) 31.2 (27.0 to 35.4)
No of observations 4800 4800 4800 3860 3860
Dependent variable=acceptance of HIV test. Each column shows percentage point dierence in HIV test acceptance estimated from ordinary least squares regression. Omitted categories for defaults and risk groups are opt-in testing and low risk,
respectively. Columns 4 and 5 repeat columns 2 and 3 but excluding those with missing questionnaire data on risks (for whom data was imputed in columns 2 and 3). Standard errors are clustered at day-zone level.
*Test acceptance percentage under base case: opt-in testing.

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6
Numbers of patients agreeing to and oered testing
according to treatment assignment and sta member
are shown in appendix table F.
Discussion
Our study provides evidence that small changes in
wording can significantly aect patients’ behavior and
thus our understanding of their preferences. Specifi
-
cally, modifying HIV testing defaults led to clinically
and statistically significant dierences in test accep
-
tance percentages. Holding all else constant (including
notifying all patients that HIV testing was available),
opt-out test oers were accepted 28 percentage points
more often than opt-in oers and 13 percentage points
more often than active choice oers. We found that
active choice testing, although previously considered a
form of opt-in testing, is a distinct category: compared
with a strict opt-in scheme informing patients that they
can request a test, simply asking patients if they would
like a test increased test acceptance by 13 percentage
points.
Strengths and weaknesses of study
Patients with a wide range of demographics, chief
complaints, and reported risk factors for HIV were
randomized at the patient level to a one sentence vari
-
ation in test oer, with all else held constant. Thus,
we were able to identify the eect of opt-in and opt-
out defaults compared with requiring patients to state
their testing preference (active choice). Although the
finding that opt-out testing yielded the highest test
acceptance percentage is unsurprising, the greater
precision with which this eect was measured is
novel, as is its measurement across populations
reporting dierent risks. This study took place at a
single county hospital in a city that is somewhat
unique with respect to HIV testing, and the propor
-
tion of patients accepting testing may vary in other
settings. However, although the test acceptance per
-
centages themselves might vary, we have little reason
to expect a dierent pattern for opt-in versus active
choice versus opt-out test schemes. Likewise,
although the particular percentages may be quite dif
-
ferent, this patterned response to op-in, active choice,
and opt-out test oers may be expected for decisions
about other medical tests.
Using non-clinical staff had the advantage of stan
-
dardizing all aspects of the test offer, although this
somewhat limits the generalizability of the results.
The patterns across treatment arms were evident for
each of the study staff who approached patients, and
we expect that they would persist outside the context
of this study. Likewise, the variation in test accep
-
tance by offerer would also be expected to be present
outside of this study. This variation may help to
explain the highly variable opt-out results found
across previous studies, particularly when the pre
-
cise wording of the test was not standardized or
reported. No default regimen approached 100%
acceptance for any treatment or study staff member,
even when the data were examined according to
risklevel.
Our study did not test a randomized “usual care” arm
in which opt-in participants were not routinely
informed of the availability of HIV testing. For refer
-
ence, in a comparable set of patients receiving usual
care in the three months after study conclusion, only
3% of patients were tested for HIV; this comprises
patients tested diagnostically and patients who initi
-
ated the test by their own request. The opt-in test accep-
tance percentage was substantially higher than the
post-study HIV test percentage, suggesting that simply
mentioning the availability of HIV testing to every
patient could non-trivially increase HIV testing and that
some of the increase in test acceptance seen in previous
studies was due to increased information provided to
patients rather than the opt-out default. This study
included a questionnaire, which is not likely to be stan
-
dard practice in emergency departments, although it
has been implemented in other studies of HIV testing
and may become more commonplace as brief interven
-
tions take hold on a larger scale.
37-39
We found no dier-
ence in treatment eects between patients who declined
to complete the questionnaire and those who com
-
pleted the questionnaire.
Retrospective informed consent has the advantage of
minimizing many potential sources of bias but runs the
risk of introducing bias from post-randomization with
-
drawal. However, given the similar participation rates
across treatment arms and successful randomization
across many observables, this is quite unlikely to have
driven our results. Study sta were not blinded to treat
-
ment assignments before assigning them to patients.
They were instructed to enroll patients sequentially
according to availability within each zone, and this was
reinforced by study team meetings and oversight from
the research coordinator. We cannot exclude the possi
-
bility that sta occasionally violated protocol and
approached patients in a biased manner. However,
given our consistent results across research sta (fig 3 )
and successful randomization with similar baseline
characteristics across arms (table 1), we feel that it is
quite unlikely that study sta selected patients or that
patients refused study participation in a manner that
biased our results.
Percent accepting HIV test
a
0
20
40
60
80
100
bcd
Opt-in Active choice Opt-out
e fgh i
Fig  | Test acceptance percentage by study sta member;
(a) is composite of study sta who saw fewer than 
patients each; (b) to (i) are individual sta members who
saw more than  patients each. Lines indicate %
condence intervals

the bmj |
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2016;352:h6895 | doi: 10.1136/bmj.h6895
RESEARCH
7
Strengths and weaknesses in relation to other
studies
To our knowledge, this is the first study of HIV testing to
estimate explicitly the eects of defaults as distinct
from information and to test the eects of an active
choice regimen. Previous work has provided less
explicit findings on the precise nature of the default reg
-
imen, as studies were often performed on before and
after policy changes with schemes that modified other
aspects of the testing scheme as well. In addition to
comparing opt-in and opt-out testing schemes, we
defined and measured test acceptance under a new cat
-
egory, active choice testing, which had previously been
considered a form of opt-in testing. The distinction
between our opt-in and active choice arms is subtle, but
the clinically significant dierence in the proportion
agreeing to a test between them suggests that minor
policy variations can have large eects. Active choice
testing balances the goals of increasing HIV screening
and fostering patient centered decision making. The
active choice option, however, raised HIV testing per
-
centages only half as much as did the opt-out regimen—
an important trade-o to consider.
We found that patients self identifying as at risk for
HIV infection were more likely to test in each treatment
scheme than were patients reporting no risks. Identify
-
ing risk from self reported behavior is not without lim-
itations, but previous work has tended either not to
collect this information or to use it to assess the utility
of targeted testing, rather than to consider how policy
changes would aect test acceptance among patients of
various risk levels.
14
25
Future work is needed to incorpo-
rate our findings into cost eectiveness models of opti-
mal testing strategies.
Meaning of study
The variation in test acceptance percentages according
to test oer scheme shows that measuring patients’
preferences is not necessarily straightforward. A central
tenet of patient centered care is the idea that patients’
preferences should be factored into healthcare deci
-
sions. However, if small changes in the way we ask
patients about their preferences significantly aect
their answers, accurately identifying patients’ true pref
-
erences may not be as simple as one might expect.
Previous research has shown that test acceptance is
not solely related to perceived risk but can be aected
by non-risk related factors as well.
40-42
The finding that
even patients reporting multiple risk factors for HIV
declined testing suggests that avoidance of information
may play a role in the decision whether to test and
raises concern that universal oers of testing may not
identify all people with undiagnosed HIV infection. Our
instrument did not use identical questions to the Den
-
ver HIV Risk Score instrument, but these findings were
robust to alternative specifications of risk, and the pos
-
sible dierences in risk score measurement are unlikely
to change the interpretation of our results.
The pattern of test acceptance is consistent with the
hypothesis that some patients accept defaults regard
-
less of their actual testing preferences. Possible
explanations of the substantial eects induced by
minor variations in wording include passive decision
making, the eort involved in speaking up, inattention,
and concern about rejecting medical advice (particu
-
larly during an episode of emergency care).
43-48
Fear of
being stigmatized on the basis of a request for or accep
-
tance of an HIV test, small immediate costs outweigh-
ing large but distant benefits, and the desire to preserve
hope rather than learn bad news may partially explain
why patients decline testing, but it would be surprising
for stigma, myopic decision making, or emotional self
regulation to explain the dierential responses between
the three treatments.
49-51
Likewise, length of emergency
department visit and pain may influence testing deci
-
sions, but why these factors should aect responses
dierentially by type of test oer is not clear.
The US National Emergency Department HIV Testing
Consortium defines opt-in testing as tests “presented so
the patient would be expected to understand the default
is to not test unless he or she states agreement.”
52
We
propose the following clarifications to the definition of
opt-in testing: refine opt-in to describe a default of no
test unless one is armatively requested. Just as opt-out
testing describes a scheme in which patients can explic
-
itly decline or implicitly accept a test, opt-in testing
should describe a symmetric scheme in which patients
can explicitly accept or implicitly decline a test. We also
propose the conceptual establishment of a third regi
-
men, active choice testing, in which patients are
prompted to state their testing preference, thereby
effectively removing any default. Simply asking
patients, “Would you like a test?” encourages them to
consider options proactively, consistent with the move
-
ment toward enhanced patient decision making.
We tested the hypothesis that moving from opt-in to
opt-out regimens could disproportionately induce test
-
ing among people at low risk, thus wasting resources;
our results only partially support this hypothesis. Test
acceptance percentages increased strongly and signifi
-
cantly among all risk groups when moving from opt-in
to opt-out. The eect of moving from opt-in to opt-out
was somewhat smaller in the high risk group, but test
acceptance was more likely in all arms among those
patients with higher self reported risk factors, and the
smaller opt-out eect may simply be a mechanical
eect of already high test acceptance percentages in the
population tested. Furthermore, the dierence in accep
-
tance between opt-in and active choice testing did not
significantly vary across risk groups.
Future research
Understanding how defaults shape behavior for
patients with dierent preferences is crucial to provid
-
ing patient centered care. Careful use of defaults has
the potential to improve patients’ health across many
domains, particularly for preventive health or behav
-
iors with immediate costs and delayed benefits such as
smoking cessation or colonoscopy. There is a balance
between preserving patients’ autonomy and steering
them toward care that optimizes health. This is of par
-
ticular importance when patients may incorrectly

doi: 10.1136/bmj.h6895 |
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2016;352:h6895 | the bmj
RESEARCH
8
perceive themselves to be at low risk or when those at
greatest risk may be more likely to decline testing. Fur
-
thermore, as the field continues to advance patient
centered decision making, and as the Aordable Care
Act ties reimbursement to engagement of patients,
defaults should be given particular consideration.
Although decision aids have been shown to influence
patients’ decisions, this study shows that a one sen
-
tence variation can dramatically aect their decisions
and, likewise, our perceptions of their preferences.
53
Contributors: All authors were involved in the study concept and
design and in obtaining funding. JCCM and WHD were involved in the
acquisition, analysis, and interpretation of data. JCCM draed the
manuscript, and all authors critically revised it for important
intellectual content. JCCM and WHD did the statistical analysis. BCK
provided administrative, technical, and material support. JCCM is the
guarantor.
Funding: This study was supported by grant NIA 1RC4AG039078 from
the National Institute on Aging (awarded to WHD and BCK). The
content is solely the responsibility of the authors and does not
necessarily represent the ocial views of the National Institute on
Aging or the National Institutes of Health. The sponsors had no role in
the design and conduct of the study; collection, management,
analysis, and interpretation of the data; preparation, review, or
approval of the manuscript; or the decision to submit the manuscript
for publication.
Competing interests: All authors have completed the ICMJE uniform
disclosure form at www.icmje.org/coi_disclosure.pdf and declare:
support from the National Institute on Aging for the submitted work;
no nancial relationships with any organizations that might have an
interest in the submitted work in the previous three years; no other
relationships or activities that could appear to have influenced the
submitted work.
Ethical approval: The study received institutional review board
approval from the University of California, San Francisco.
Data sharing: The full dataset is available from the corresponding
author at juancarlos.montoy@ucsf.edu. Participants gave informed
consent for data collection; consent for sharing was not obtained, but
the presented data are anonymized and risk of identication is low.
Transparency statement: The lead author (the manuscript's
guarantor) arms that the manuscript is an honest, accurate, and
transparent account of the study being reported; that no important
aspects of the study have been omitted; and that any discrepancies
from the study as planned (and, if relevant, registered) have been
explained.
This is an Open Access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license,
which permits others to distribute, remix, adapt, build upon this work
non-commercially, and license their derivative works on dierent
terms, provided the original work is properly cited and the use is
non-commercial. See: http://creativecommons.org/licenses/
by-nc/3.0/.
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