Are policy initiatives aligned to meet UNAIDS 90-90-90 targets impacting HIV testing and linkages to care? Evidence from a systematic review
Bibliographic record
Abstract
BACKGROUND: The Joint United Nations Programme on HIV/AIDS (UNAIDS) Fast-Track initiative seeks to eliminate AIDS as a health threat by 2030, with its focus on UNAIDS 90-90-90 targets. Effective policies and programs, if scaled nationally, have the potential to generate a greater impact on HIV control, yet a synthesis of successful HIV policies/programs aligned to the targets is currently unavailable. To fill this gap, we conducted a systematic review to evaluate successful HIV policies and programs to direct future interventions. METHODS: For the period 2007-2018, we searched 8 databases and classified eligible studies by country income level, UNAIDS targets, intervention type, and reported outcomes. Study outcomes were classified as per UNAIDS targets; proportionally: 90% target 1, 81% target 2, and 73% target 3. RESULTS: We retrieved 5201 citations and a final set of eight studies on policies. Break up by income: three (38%) from high income, one (12%) from middle income and four (50%) from low income. Break up by outcomes reported: 36% (4/11) focused on HIV testing, 46% (5/11) on antiretroviral therapy initiation, and 18% (2/11) on viral suppression. Across studies, UNAIDS targets were met in high-income countries, where policies and guidelines were adhered to, whereas in low and middle-income countries, non-adherence led to failure to reach the targets. Targets were also met when country infrastructure supported a targeted program and stakeholders were actively engaged. CONCLUSIONS: From the studies identified, we deduced a clear, positive correlation between implementation of policies and programs that resulted in an increase in patient awareness and an increase in partner notification with services that encouraged them, and together these resulted in increasing testing rates, and deployment of linkage/retention programs that improved retention in care. An analysis of these studies also suggests that policies, combined with the scale-up incentives, are needed to change the status quo. Incentives to improve the targets must exist; performance incentives at the health care worker level and country level incentives that could transform the nature of care. Given the complexity in reporting of targets, a one size fits all model is not a feasible option. However, the policies created a strong framework to shape future interventions.
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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 | 0.036 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".