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

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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.
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the bmj |
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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
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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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oers: “Were 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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(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
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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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Appendix

Supplementary resource (1)

... For example, while older persons are more likely to participate in the initial survey, they are less likely to agree to the linkage request after responding. Another study (Montoy et al., 2016) asking patients to consent to further HIV tests found that acceptance rates were 38% in the opt-in option, 51% in the choice option, and 66% in the opt-out option. The positive opt-out effect was, however, smaller for those with high-risk behaviours. ...
... -the greater their understanding of the survey content and their trust in the confidentiality of the data linkage request (Jäckle et al., 2021;Sakshaug et al., 2012) -the higher their trust in the survey organizers (Das & Couper, 2014), in institutions (Bacher, 2023), and in science (Hutchings et al., 2021) -when they find the survey interesting and not too long, or if the survey topic concerns them (Hülle, 2024 for linkage and panel consent, Montoy et al., 2016;Sakshaug et al., 2012;Sala et al., 2012) -the higher their inattentiveness and satisficing (e.g., acquiescence) (Sakshaug et al., 2012). ...
... For our study, we hypothesize that consenters in the three designs have similar distributions of socio-demographic variables. Yet, in line with Montoy et al. (2016), we expect fewer people who are more concerned by the survey topic, and therefore typically overrepresented in political surveys (i.e., higher educated, politically interested people, left voters, those with positive feelings about the survey, etc.), in the consenting group under the opt-out design, relative to the choice and in particular the opt-in design. ...
Article
Full-text available
Some surveys ask respondents for consent to be recontacted for follow-up surveys after the initial part of the survey has been completed. Based on an experiment, we compare three options for asking for this panel consent: choice (yes/no), opt-in, and opt-out. We analyse panel consent rates and compare consenters with non-consenters against a comprehensive set of socio-demographic characteristics, political attitudes, and survey-related variables in a probability-based web survey. In a second step, we analyse consenters' actual participation in the first follow-up wave. The opt-out option yields higher panel consent rates than the other two options. Based on socio-demographic variables, panel consenters and non-consenters are most similar to each other in the choice design, and most different in the opt-out design. Based on typically biased variables, such as political interest or how the survey was perceived, the opt-out design performs better than the opt-in design in terms of consent, followed by the choice design. When it comes to actually participating in the first follow-up wave, the three panel consent options work in a similar way to giving consent. Overall, these findings speak in favour of the opt-out design, followed by the opt-in design.
... No definitive correct answer exists regarding attitudes about IC acquisition or confidentiality obligations. While it is important that only the HIV test be independent and IC be obtained to better respect the autonomy of the examinee, the fact that the test be presented by the provider as part of a routine check-up is also important to eliminate barriers from other tests to expand opportunities for examinees [34,35]. However, people in Japan cannot be said to have a sufficient understanding of HIV infection, and the Ministry of Health, Labour, and Welfare of Japan encourages individuals to obtain individual IC for HIV tests [36]. ...
Article
Full-text available
Background Although Japan has successfully mitigated HIV infections, several issues related to the disease remain to be addressed. As the people living with HIV are aging, their medical care needs are expected to become more diversified and regionalized. Those residing beyond the boundaries of specialized hospitals will rely on general physicians for medical services. Hence, general physicians must have a non-discriminatory medical attitude toward people living with HIV and give more ethical consideration than for other diseases, such as privacy protection. Therefore, a nationwide survey was conducted to clarify the attitudes of general physicians, who do not specialize in HIV treatment, toward HIV and people living with HIV. Methods An online questionnaire-based quantitative survey (February 14–16, 2022) yielded 212 valid responses. Questions covered proactivity in HIV care, attitudes toward ethical issues, and awareness of HIV in the context of stigmas. Although the sample size was small due to limited feasibility, similar populations were obtained in terms of distribution of mean age, gender, and type of practice, compared to official physician statistics. Results Approximately 20% of respondents answered that refusing medical care due to HIV infection is acceptable. Younger physicians tended to be more negative toward HIV treatment, and, regardless of age, the negative attitude is correlated with aversion toward HIV infection itself. Conclusions The findings aligned with concerning situations in Japan highlighted by other studies. They also suggested that more careful attitudes may be needed regarding the protection of the privacy of people living with HIV. However, research has also suggested that some physicians could become more positive by providing specialist support for the treatment and prevention of HIV infection. Large-scale and ongoing surveys are imperative to continuously implement effective and reliable interventions that could change the attitudes of general physicians toward people living with HIV.
... Opt-out infectious diseases testing both increases overall frequency of testing and decreases racial disparities in testing. (Feld et al. 2023;Levano et al. 2023;Maner et al. 2022;Montoy et al. 2016). Unlike opt-in testing, which requires explicit agreement to perform testing, opt-out testing informs individuals that they will be tested unless they decline. ...
Article
Full-text available
Background Eliminating infectious diseases epidemics requires resources for testing, prevention, and treatment in jails. The 2022 Centers for Diseases Control and Prevention guidelines recommend offering hepatitis C virus (HCV), HIV, and STI testing at jail intake. Currently, the impact of offering testing at intake in jails has only been analyzed in the context of multi-modal strategies to increase testing. There is a lack of real-world data about the impact of offering testing at jail intake as a strategy to increase testing. In May 2022, Plymouth County Correctional Facility in Massachusetts added questions to their intake form offering HIV, HCV, syphilis, gonorrhea, and chlamydia testing. The goal of this project was to assess frequency of testing before and after the addition of infectious diseases testing questions to the intake form. Case presentation Data about infectious diseases testing completion per month were compared between February-April 2022 and May 2022-June 2023. The transition from rapid to venipuncture HIV testing was also compared between September 2021-June 2023. Data was assessed in monthly intervals. The median number of urine tests decreased from 39 to 28, and the median number of blood tests decreased from 21 to 15 after testing was offered during intake. Conclusion There were no significant trends in the run chart after the intervention. Although offering testing at intake is one important part of healthcare in jails, intake testing should be supported with other systems including access to phlebotomy, facilitated movement from the housing areas to the medical unit, and stigma reduction tools.
... html), and was approved by the Institutional Review Board of Kagawa University, Faculty of Medicine (Serial number: 2024-014). Opt-out methods were adopted to guarantee patients or their relatives the opportunity to refuse enrollment in the current study [9]. ...
Article
Full-text available
Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide, with a rising incidence. The most common therapeutic choice for HCC is transarterial chemoembolization (TACE). While the standard protocol of TACE adopts cisplatin, the application of cisplatin needs hydration before and after the procedure to alleviate adverse effects on kidney function. Miriplatin, a lipophilic platinum complex, enables the omission of periprocedural hydration compared to cisplatin‐based TACE. This study aimed to compare the survival benefit between miriplatin‐based TACE and cisplatin‐based TACE. Briefly, a retrospective cohort study in a single hospital was designed. Patients with HCC complicated by vascular invasion or distant metastasis were excluded. Background variability was adjusted using a propensity score matching; then, overall survival rates were compared using the Gehan‐Breslow‐Wilcoxon test. As a result, cisplatin and miriplatin were administered to 166 and 120 patients in TACE procedures. After adjusting baseline characteristics using a propensity score including age, sex, tumor burden, functional hepatic reserve, baseline year, and HbA1c, a pair of 99‐patient cohorts was generated. Overall survivals did not differ significantly, despite poorer serum creatinine at baseline (0.89 vs. 0.74 mg/dL, p < 0.0001) and fewer patients being prepared for TACE through prehydration (18 patients vs. 38 ones, p = 0.0025) in the miriplatin group than in the cisplatin group. The median survival time was 1490 days for the miriplatin group and 1,830 days for the cisplatin group (p = 0.4022; ratio = 0.814; 95% confidence interval 0.546–1.215). In conclusion, miriplatin will benefit patients with HCC who cannot tolerate perioperative hydration.
... The screening program is designed as opt-out, meaning that nurses were instructed to tell patients that everyone gets screened unless they decline, and then offer them an opportunity to decline, as compared to opt-in screening, in which patients are asked if they would like to be screened. Opt-out strategies may be more effective at increasing participation in screening, in part due to reduction in stigma around testing (Montoy et al., 2016). Although the program is designed as opt-out, LAs had more variability in what they understood this to mean, and as a result, how they reported presenting screening to patients, more often inadvertently using opt-in language. ...
Article
Full-text available
Emergency department (ED) HIV screening is a key component of the strategy to end the HIV epidemic, reaching populations with limited access to care for screening and early diagnosis. Many screening programs rely primarily on participation from ED nurses; however, little is known about the factors affecting nurse participation in screening. Guided by the Consolidated Framework for Implementation Research, 20 semi-structured interviews were conducted with ED nurses to explore perceptions of HIV screening, barriers and facilitators to participation, and implementation insights. Nurses were categorized as either high adopters or low adopters based on the number of HIV tests ordered 3 months prior to interviews. The Stanford Lightning Report Method, a rapid qualitative analysis approach, was used to analyze field notes. All participants generally agreed that the ED was an appropriate location for screening and that frequent, multimodal education about screening was needed. Integration of screening into standard workflows, education about the public health impact of screening, and the use of peer champions and mentors were identified as important strategies to increase participation. By incorporating these findings into implementation strategies, EDs may be able to increase nurse participation in screening, addressing important health equity issues in HIV diagnosis.
... In such situations, the research may be done only after consideration and approval of a research ethics committee. ' We provided opt-out methods for the participants by publishing a summary of this study on our university website 26,27 . Informed consent was waived by the Research Ethics Committee of the Faculty of Medicine at the University of Tokyo in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan. ...
Article
Full-text available
Purpose The relationship between the height of the V wave in the central venous pressure (CVP) waveform and the severity of tricuspid regurgitation (TR) is well known. Their diagnostic ability is unconfirmed. This study explored CVP waveform variations with TR. Methods All patients who underwent preoperative echocardiography and CVP waveform measurements before surgery at our institution were included. Indices were created to capture each feature of the CVP waveform. The median value for each case was obtained and statistically analyzed according to the severity of TR. A deep learning technique, Transformer, was used to handle the complex features of CVP waveforms. Results This study included 436 cases. The values for C wave – Y descent, X descent – Y descent, and V wave – Y descent differed significantly in the Jonckheere–Terpstra test (p = 0.0018, 0.027, and 0.077, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) for X descent – Y descent in two groups, none to moderate TR and severe TR, was 0.83 (95% confidence interval (CI) [0.68, 0.98]). For Transformer, the accuracy of the validation dataset was 0.97. Conclusions The shape of the CVP waveform varied with the severity of TR in a large dataset.
Article
"Opt-out" Emergency Department (ED) blood-borne-virus screening enables early diagnosis, improving outcomes. Whereas some EDs encourage verbal reminders at blood draw, others emphasise "implied consent". Associations between these approaches and screening equity have not been explored. This retrospective cohort evaluation quantified demographic disparities in screening in two EDs following "reminder model" screening rollout. Staff attitudes were explored, identifying screening barriers. ED attendees from July-October 2022 were identified electronically. Associations between age, sex, self-identified ethnicity, attendance time and admission status on screening were analysed using odds ratios (ORs). Twenty ED staff underwent semi-structured interviews. There were 33,388 eligible ED attendances (54.8% female; median age 53y). 58.9% of attendees received screening. In unadjusted analysis, the screening rate was higher in men (OR 1.05; 95%CI 1.00-1.10) and in non-admitted attendees. People of Black, Asian or Other ethnic backgrounds had lower rates compared to White ethnicity. Attendees between 5pm-11pm had lower rates and 11pm-9am higher rates compared to 9am-5pm. All associations persisted in multivariable models. Interviews revealed low confidence in follow-up discussion in attendees who opted out and a high workload precluding screening. Demographic disparities were seen in this "reminder model" context. Simplifying processes and emphasising implied consent may improve equitable screening.
Article
Full-text available
This paper outlines the implementation of opt-out HIV and Hepatitis C (HCV) screening at a syringe services program (SSP) in Florida, highlighting its effectiveness in reducing the transmission of these infectious diseases. Historically, many SSPs have utilized opt-in testing models, which require participants to actively choose testing and often result in low participation rates. Recognizing the need for a more effective approach and to comply with Florida’s regulatory requirements under the Infectious Disease Elimination Act, we transitioned to an opt-out testing model at our SSP. This model integrates routine, anonymous, and voluntary testing into standard care, normalizing the process and reducing stigma associated with infectious disease screening. Initially, our policy tied testing to access to specific services, including syringe exchange, to meet compliance with Florida Department of Health mandates. However, after feedback from participants, staff, and community members, we revised our approach to allow all participants to access all services, regardless of their decision to participate in testing. Importantly, this policy change did not decrease testing rates, with only 6 out of 226 new enrollments (3%) opting out since the implementation of opt-out screening. By fostering a trusting, non-coercive environment and normalizing screening as part of routine care, we achieved high rates of participation while maintaining participant autonomy. Since transitioning to an opt-out model, we have conducted nearly 3,000 HIV and HCV tests, with seropositivity rates of 3.8% and 54%, respectively. These efforts have facilitated early detection, rapid linkage to care, and reduced transmission within the community. Our findings underscore the importance of comprehensive, repeat testing in high-risk populations and demonstrate the potential for opt-out models to serve as a scalable framework for SSPs nationwide. This approach not only fulfills regulatory and public health objectives but also strengthens the role of SSPs as critical interventions in combating HIV and HCV transmission.
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Prompt confirmation of human immunodeficiency virus (HIV) is critical. We examined 10 years of discordant results without reflex HIV RNA. Of patients with acute HIV infection, 43.9% (95% confidence interval, 36.2%–52.0%) had confirmation delays >30 days or were never confirmed, indicating a need for reflex RNA to facilitate diagnosis.
Article
The purpose of this document is to update and correct Figure 4 from "Optimal Expectations and Limited Medical Testing: Evidence from Huntington Disease" (Oster, Shoulson, and Dorsey 2013). This figure documents how perceptions about the risk of HD evolve with symptoms. It compares these perceptions with the "actual risk" of HD based on a Bayesian updating calculation described in the paper. The construction of Figure 4 is correctly described in the text of the paper and the data on perceptions are documented correctly. However, the construction of the "actual risk" series is not accurate. There are two central issues. First, there were data limitations at the time of publication which have since been relaxed and the better data now available changes the picture. Second, there was an error in the construction of Figure 4 which should have been recognized at the time. We detail the issues here and include the corrected figure. The original figure showed evidence of overoptimism at all levels of motor score. The corrected figure shows that for low symptom levels individuals are correct about their risk level, whereas those with more advanced symptoms are overly optimistic. Overall, the levels of overoptimism are lower than documented originally. We will briefly discuss the implications for the theory at the end of this document.
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Within 1 year of the initial report in 1981 of a deadly new disease that occurred predominantly in previously healthy persons and was manifested by Pneumocystis carinii pneumonia and Kaposi's sarcoma, the disease had a name: acquired immune deficiency syndrome (AIDS). Within 2 years, the causative agent had been identified: human immunodeficiency virus (HIV). On the 30th anniversary of the epidemic, to characterize trends in HIV infection and AIDS in the United States during 1981-2008, CDC analyzed data from the National HIV Surveillance System. This report summarizes the results of that analysis, which indicated that, in the first 14 years, sharp increases were reported in the number of new AIDS diagnoses and deaths among persons aged≥13 years, reaching highs of 75,457 in 1992 and 50,628 in 1995, respectively. With introduction of highly active antiretroviral therapy, AIDS diagnoses and deaths declined substantially from 1995 to 1998 and remained stable from 1999 to 2008 at an average of 38,279 AIDS diagnoses and 17,489 deaths per year, respectively. Despite the decline in AIDS cases and deaths, at the end of 2008 an estimated 1,178,350 persons were living with HIV, including 236,400 (20.1%) whose infection was undiagnosed. These findings underscore the importance of the National HIV/AIDS Strategy focus on reducing HIV risk behaviors, increasing opportunities for routine testing, and enhancing use of care (1).
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Routine screening is recommended for HIV detection. HIV risk estimation remains important. Our goal was to validate the Denver HIV Risk Score (DHRS) using a national cohort from the CDC. Patients ≥13 years of age were included, 4,830,941 HIV tests were performed, and 0.6% newly-diagnosed infections were identified. Of all visits, 9% were very low risk (HIV prevalence = 0.20%); 27% low risk (HIV prevalence = 0.17%); 41% moderate risk (HIV prevalence = 0.39%); 17% high risk (HIV prevalence = 1.19%); and 6% very high risk (HIV prevalence = 3.57%). The DHRS accurately categorized patients into different HIV risk groups.
Article
Objectives: In this study, Increasing Viral Testing in the Emergency Department (InVITED), the authors investigated if a brief intervention about human immunodeficiency virus (HIV) and hepatitis C virus (HCV) risk-taking behaviors and drug use and misuse in addition to a self-administered risk assessment, compared to a self-administered risk assessment alone, increased uptake of combined screening for HIV and HCV, self-perception of HIV/HCV risk, and impacted beliefs and opinions on HIV/HCV screening. Methods: InVITED was a randomized, controlled trial conducted at two urban emergency departments (EDs) from February 2011 to March 2012. ED patients who self-reported drug use within the past 3 months were invited to enroll. Drug misuse severity and need for a brief or more intensive intervention was assessed using the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST). Participants were randomly assigned to one of two study arms: a self-administered HIV/HCV risk assessment alone (control arm) or the assessment plus a brief intervention about their drug misuse and screening for HIV/HCV (intervention arm). Beliefs on the value of combined HIV/HCV screening, self-perception of HIV/HCV risk, and opinions on HIV/HCV screening in the ED were measured in both study arms before the HIV/HCV risk assessment (pre), after the assessment in the control arm, and after the brief intervention in the intervention arm (post). Participants in both study arms were offered free combined rapid HIV/HCV screening. Uptake of screening was compared by study arm. Multivariable logistic regression models were used to evaluate factors related to uptake of screening. Results: Of the 395 participants in the study, the median age was 28 years (interquartile range [IQR] = 23 to 38 years), 44.8% were female, 82.3% had ever been tested for HIV, and 67.3% had ever been tested for HCV. Uptake of combined rapid HIV/HCV screening was nearly identical by study arm (64.5% vs. 65.2%; Δ = -0.7%; 95% confidence interval [CI] = -10.1% to 8.7%). Of the 256 screened, none had reactive HIV antibody tests, but seven (2.7%) had reactive HCV antibody tests. Multivariable logistic regression analysis results indicated that uptake of screening was not related to study arm assignment, total ASSIST drug scores, need for an intervention for drug misuse, or HIV/HCV sexual risk assessment scores. However, uptake of screening was greater among participants who indicated placing a higher value on combined rapid HIV/HCV screening for themselves and all ED patients and those with higher levels of perceived HIV/HCV risk. Uptake of combined rapid HIV/HCV screening was not related to changes in beliefs regarding the value of combined HIV/HCV screening or self-perceived HIV/HCV risk (post- vs. pre-risk assessment with or without a brief intervention). Opinions regarding the ED as a venue for combined rapid HIV/HCV screening were not related to uptake of screening. Conclusions: Uptake of combined rapid HIV/HCV screening is high and considered valuable among drug using and misusing ED patients with little concern about the ED as a screening venue. The brief intervention investigated in this study does not appear to change beliefs regarding screening, self-perceived risk, or uptake of screening for HIV/HCV in this population. Initial beliefs regarding the value of screening and self-perceived risk for these infections predict uptake of screening.
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The Centers for Disease Control and Prevention recommend routine HIV screening in clinical settings, including emergency departments (EDs), because earlier diagnosis enables treatment before symptoms develop and delivery of interventions to reduce continued transmission. However, patients frequently decline testing. This study delivered a 16-min video-based intervention to 160 patients who declined HIV tests in a high volume, urban ED. One third of participants (n = 53) accepted an HIV test post-intervention. Interviews with a subset of participants (n = 40) show that before the video, many were unaware HIV testing could be conducted without drawing blood, or that results could be delivered in 20 min.
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Early diagnosis of persons infected with human immunodeficiency virus (HIV) through diagnostic testing and screening is a critical priority for individual and public health. Emergency departments (EDs) have an important role in this effort. As EDs gain experience in HIV testing, it is increasingly apparent that implementing testing is conceptually and operationally complex. A wide variety of HIV testing practice and research models have emerged, each reflecting adaptations to site-specific factors and the needs of local populations. The diversity and complexity inherent in nascent ED HIV testing practice and research are associated with the risk that findings will not be described according to a common lexicon. This article presents a comprehensive set of terms and definitions that can be used to describe ED-based HIV testing programs, developed by consensus opinion from the inaugural meeting of the National ED HIV Testing Consortium. These definitions are designed to facilitate discussion, increase comparability of future reports, and potentially accelerate wider implementation of ED HIV testing.
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Universal HIV screening is recommended but challenging to implement. Selectively targeting those at risk is thought to miss cases, but prior studies are limited by narrow risk criteria, incomplete implementation, and absence of direct comparisons. We hypothesized that targeted HIV screening, when fully implemented and using maximally broad risk criteria, could detect nearly as many cases as universal screening with many fewer tests. This single-center, cluster-randomized trial compared universal and targeted patient selection for HIV screening in a lower prevalence urban emergency department. Patients were excluded for age (<18, >64), known HIV infection, or prior approach for HIV testing that day. Targeted screening was offered for any risk indicator identified from charts, staff referral, or self-disclosure. Universal screening was offered regardless of risk. Baseline seroprevalence was estimated from consecutive de-identified blood samples. There were 9,572 eligible visits during which the patient was approached. For universal screening, 40.8% (1,915/4,692) consented with six newly diagnosed (0.31%, CI95 0.13%-0.65%). For targeted screening, 37% (1,813/4,880) had no testing indication. Of the 3,067 remaining, 1,454 (47.4%) consented with 3 newly diagnosed (0.22%, CI95 0.06%-0.55%). Estimated seroprevalence was 0.36% (CI95 0.16%-0.70%). Targeted screening had a higher proportion consenting (47.4% v. 40.8%, p<0.002), but a lower proportion of ED encounters with testing (29.7% v. 40.7%, p<0.002). Targeted screening, even when fully implemented with maximally permissive selection, offered no important increase in positivity rate or decrease in tests performed. Universal screening diagnosed more cases, because more were tested, despite a modestly lower consent rate.