Social Determinants and Prevention Strategies in the HIV Epidemic: The National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) Database Analysis
Bibliographic record
Abstract
BACKGROUND: The human immunodeficiency virus (HIV) epidemic remains a significant public health challenge, with social determinants and prevention strategies playing a crucial role in disease outcomes. While advancements in treatment and prevention have led to improvements in viral suppression and healthcare access, disparities in healthcare remain, particularly among vulnerable populations. AIM: This study analyzes HIV epidemiological trends, healthcare access, and social determinants influencing the HIV epidemic in the United States from 2018 to 2022, using data from the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) database. METHOD: A retrospective analysis of national surveillance data was conducted to assess trends in HIV-related mortality, viral suppression, incidence, prevalence, and healthcare access. Key indicators such as knowledge of HIV status, PrEP coverage, linkage to care, HIV stigma, and unstable housing were evaluated. Data were analyzed for temporal trends, with a focus on the impact of the COVID-19 pandemic on HIV outcomes. RESULTS: The findings indicate a reduction in new HIV infections and an increase in HIV prevalence, suggesting improvements in diagnosis and treatment. Although AIDS-related and HIV-related deaths spiked during the COVID-19 pandemic, viral suppression rates steadily improved. Healthcare access remained stable, with increased PrEP coverage and linkage to care. However, persistent barriers such as HIV stigma and unstable housing continued to affect health outcomes. CONCLUSION: The study highlights significant progress in HIV prevention and care but underscores the need for targeted interventions addressing social determinants. Continued investment in equitable healthcare access, stigma reduction, and housing stability is essential for sustaining control of the HIV epidemic.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".