Intersectional forces of urban inequality and the global HIV pandemic: a retrospective analysis
Bibliographic record
Abstract
To determine how the intersection of increased urban growth and poverty has impacted HIV incidence and prevalence, given growing HIV inequalities globally. Retrospective analysis using combined data from five publicly available, population-level datasets to determine city- and within-urban countrywide estimates of 95-95-95 treatment targets, prevalence and incidence rates from 2015 to 2019. For city-level estimates, we analysed combined data from: Fast-Track City (FTC), SINAN from Brazil and UNAIDS Naomi-Spectrum. Countrywide estimates of HIV prevalence in the urban slum versus non-slum since 2012 were compiled from Population-Based HIV Impact Assessment (PHIA) surveys in 12 countries and Demographic Health Surveys (DHS) in 28 countries. HIV prevalence is generally higher among the urban slum, compared to their non-slum counterparts, thus resulting in national HIV estimates masking nuances in HIV inequalities between the urban slum and non-slum. Specifically, national and city-level HIV estimates mask inequalities within and between cities, with secondary cities often having higher HIV prevalence and incidence rates than capital cities and large urban areas. The urban divide between slum and non-slum populations is a contributor to HIV inequality, often with poorer outcomes in smaller cities than their larger counterparts. Interventions tailored to cities, and particularly those considering local nuances in subpopulations (eg, different genders, ages, roles), are necessary to reduce HIV inequality. Focused HIV programming accounting for structural drivers of inequalities between urban slum and non-slum populations such as inequalities in wealth, education, employment and housing are crucial to closing gaps driving HIV inequalities globally.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".