The Impact of Transfer Patients on the Local Cascade of HIV Care Continuum
Bibliographic record
Abstract
BACKGROUND: The Cascade of Care (COC) visualizes stages of HIV care progression within a population. It is predicated on a local population model and thus may not address the impact on the COC of HIV-experienced individuals diagnosed and cared for elsewhere who move into the area. METHODS: All individuals with a confirmed HIV+ test in Calgary, Canada, between January 1, 2006, and January 1, 2013 were included. Individuals were categorized as "local" if diagnosed within the area, or "transfer" if diagnosed elsewhere. Subgroups were separately placed within the COC and then aggregated. RESULTS: Of 1019 new cases, 47% were transfers. Transfer patients were more likely female (35% vs. 23%; P < 0.01), non-white (61% vs. 46%; P < 0.001), heterosexual (56% vs. 38%; P < 0.001), and have higher CD4 counts (400 vs. 282/mm) with undetectable viremia in 57% [63% on antiretroviral therapy (ART)] at baseline. Engagement was higher at every stage for transfer patients: 94% of transfer vs. 92% of local patients linked to HIV care, 90% vs. 76% (P < 0.001) were retained, 86% vs. 67% (P < 0.001) received ART, and at study's end, 75% vs. 58% (P < 0.001) had undetectable viremia. When patients were aggregated, linkage increased by 1%, retention by 6%, patient use of ART by 8%, and patients with viral suppression by 7%. CONCLUSIONS: The COC of local and transfer patients differs so significantly that both need to be considered separately in measuring COC, adding a previously under-recognized level of complexity. Use of aggregate COC without considering different levels of engagement could lead to imprecise information for public health initiatives and program metrics.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".