Harnessing registry data to identify socio-demographic and socio-economic gaps in HIV care in the Netherlands
Bibliographic record
Abstract
To ensure progress towards zero new HIV infections, more detailed information is needed about why certain individuals might not successfully transition through the steps of the HIV care continuum. We used data from 21,788 individuals with HIV who were enrolled in the ATHENA cohort before 31 December 2023, and combined these with registry data from Statistics Netherlands. This allowed modeling socio-demographic, -economic, and health-related determinants of not achieving two milestones of the HIV care continuum, i.e., suppressed viral load and engagement in care. Across all subgroups of men who have sex with men (MSM), cisgender heterosexual men, and women, living in poverty was associated with having detectable viral loads and disengagement from care, and younger age with only detectable viral loads. In MSM, having only primary education, a second-generation migration background, and living in a single-parent, institutionalized, or other household was also associated with having a detectable viral load. The HIV care continuum in the Netherlands is heavily influenced by socio-economic, rather than health-related, determinants. Efforts to optimize HIV care through specialized interventions should consider individual economic vulnerability. Our findings also illustrate the value of using registry data to identify gaps in care. Understanding why individuals drop out of the HIV continuum of care is important for improving disease control programs. Here, the authors use linked population-level data to identify socioeconomic factors associated with unsuppressed viral load and disengagement from HIV care in the Netherlands.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".