Global Measures of HIV Care Accessibility Across Urban, Suburban, and Rural Areas
Bibliographic record
Abstract
Despite advances in HIV treatment, disparities in healthcare access remain across low-income (LIC), middle-income (MIC), and high-income (HIC) countries, limiting access to clinical care, antiretroviral therapy, and viral load testing. This study developed the HIV Care Access Index (HIV-CAI) to measure and compare HIV care service access across countries and within geographic regions and examined associations with economic and health indicators. A cross-sectional survey was conducted between August 2021 and June 2023 across nine countries: Botswana, China, Colombia, Kenya, Nigeria, Puerto Rico, South Africa, Thailand, and the United States. Participants were adults living with HIV recruited from urban, suburban, and rural settings through HIV service organizations, sexual and gender minority centers, and resource-limited communities. The HIV-CAI was scored from 0 to 1 (worst to best access) across three domains: Access to HIV Clinical Care and Providers, Access to HIV Medication, and Access to Viral Load Testing. Among 1,598 participants, Botswana demonstrated the highest HIV care access (0.93), followed by Thailand (0.91) and Kenya (0.90), while Nigeria showed the lowest access (0.64). Access to HIV medication was relatively consistent across regions, whereas access to clinical care and providers was limited, particularly in rural areas. Countries with higher HIV burden, incidence, and prevalence were associated with better care access. The HIV-CAI reveals disparities in HIV care access across geographic areas. Future interventions should prioritize improving equitable access, particularly in rural areas and countries with lower overall access scores.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".