A Key Comprehensive System for Biobehavioral Surveillance of Populations Disproportionately Affected by HIV (National HIV Behavioral Surveillance): Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The National HIV Behavioral Surveillance (NHBS) is a comprehensive system for biobehavioral surveillance conducted since 2003 in 3 populations disproportionately affected by HIV: gay, bisexual, and other men who have sex with men (MSM); people who inject drugs; and heterosexually active persons at increased risk for HIV infection (HET). This ongoing and systematic collection and analysis of data is needed to identify baseline prevalence of behavioral risk factors and prevention service use, as well as to measure progress toward meeting HIV prevention goals among key populations disproportionately affected by HIV. OBJECTIVE: This manuscript provides an overview of NHBS from 2003 to 2019. METHODS: NHBS is conducted in rotating, annual cycles; these 3 annual cycles are considered a round. Venue-based, time-space sampling is used for the MSM population. Respondent-driven sampling is used for people who inject drugs and HET populations. A standardized, anonymous questionnaire collects information on HIV-related behavioral risk factors, HIV testing, and use of prevention services. In each cycle, approximately 500 eligible persons from each participating area are interviewed and offered anonymous HIV testing. RESULTS: From 2003 to 2019, 168,600 persons were interviewed and 143,570 agreed to HIV testing across 17 to 25 cities in the United States. In the fifth round (2017 to 2019), over 10,000 (10,760-12,284) persons were interviewed each of the 3 population cycles in 23 cities. Of those, most (92%-99%) agreed to HIV testing. Several cities also conducted sexually transmitted infection or hepatitis C testing. CONCLUSIONS: NHBS is critical for monitoring the impact of the Ending the HIV Epidemic in the United States initiative. Data collected from NHBS are key to describe trends in key populations and tailor new prevention activities to ensure high prevention impact. NHBS data provide valuable information for monitoring and evaluating national HIV prevention goals and guiding national and local HIV prevention efforts. Furthermore, NHBS data can be used by public health officials and researchers to identify HIV prevention needs, allocate prevention resources, and develop and improve prevention programs directed to the populations of interest and their communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".