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HIV Screening Take-up: Evaluating Incentives and Opt-out Strategies

HIV Screening Take-up: Evaluating Incentives and Opt-out Strategies

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT01377857
Enrollment
8572
Registered
2011-06-21
Start date
2011-05-31
Completion date
2013-12-31
Last updated
2015-05-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

HIV

Brief summary

Over twenty percent of HIV-positive persons in the United States are unaware of their infection, leading the Institute of Medicine to recently urge further work to compare the effectiveness of HIV screening strategies. This study will use a randomized trial to compare several variants of emergency-room-based HIV-testing policies in order to determine how HIV test acceptance rates can be increased. The testing policies will be designed using principles from behavioral economics, varying the choice architecture and offering small monetary incentives. This will be the first study to measure differences in take-up rates across a variety of promising but largely untested approaches within a unified randomized trial. Three defaults will be tested: traditional opt-in (test only those patients who request testing), opt-out (routinely testing unless patients decline), and active-choice testing (patients are required to state whether they want to be tested). The study will also be the first to test the effect of small monetary incentives ($1, $5, $10) on test take-up. An additional novel study contribution will be to test the hypothesis that compliance with large requests (accept an HIV test) increases after making a small request or pre-commitment - this foot in the door technique has not been previously studied in this setting. The factorial design will permit a direct comparison of all interventions, as well as interactions. The study will contribute a nuanced empirical understanding of how testing protocols from behavioral economics theory affect the effectiveness and efficiency of screening programs in an actual scaled- up setting (San Francisco General Hospital). This will assist in implementing and assessing recent CDC guidelines on HIV screening, while also more generally advancing scientific knowledge related to applying behavioral economics in comparative effectiveness research.

Interventions

$1, $5, or $10 incentive

BEHAVIORALQuestionnaire Timing

Timing of the questionnaire--either before or after testing is offered.

BEHAVIORALHIV Test Offering

HIV Test will be offered as opt-in, opt-out, or active choice.

Sponsors

National Institutes of Health (NIH)
CollaboratorNIH
National Institute on Aging (NIA)
CollaboratorNIH
University of California, San Francisco
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
13 Years to 64 Years
Healthy volunteers
Yes

Inclusion criteria

* Patients aged 13 - 64 years who are awake, alert, not intoxicated, and understand the premise of the test will be offered the test and questionnaire according to their treatment group.

Exclusion criteria

* Patients who have altered levels of consciousness, are critically ill (e.g., serious trauma), are known to have preexisting HIV diagnosis, or who have been tested for HIV in the past 3 months will be excluded from the study. * Pregnant patients will be excluded due to alternative guidelines for incorporating opt-out testing during prenatal care. * Any patients who are in police custody will also be excluded due to their lack of control over study participation decisions and ethical concerns over possible coercion.

Design outcomes

Primary

MeasureTime frame
Proportion of patients offered an HIV test who acceptMonthly

Secondary

MeasureTime frame
Proportion testing HIV positive of those testedMonthly
Proportion testing HIV positive among those offered a testMonthly

Countries

United States

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Mar 2, 2026