Longitudinal analysis of HIV outcomes for persons living with HIV in non-urban areas in southern Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Many challenges remain in successfully engaging people with HIV (PWH) into lifelong HIV care. Living in non-urban or rural areas has been associated with worse outcomes. Uncertainties remain regarding how to provide optimal HIV care in non-urban areas. METHODS: Using a retrospective descriptive analysis framework, we compared multiple measurable HIV care metrics over time on the basis of urban versus non-urban residency, under a centralized HIV care model. We examined rates of HIV diagnosis, access to and retention in HIV care, and longitudinal outcomes for all newly diagnosed PWH between January 1, 2008, and January 1, 2020, categorized by their home location at the time of HIV diagnosis in southern Alberta. RESULTS: Of 719 newly diagnosed PWH, 619 (86%) lived in urban areas and 100 (14%) lived in non-urban areas. At HIV diagnosis, the groups had no significant differences in initial CD4 count or clinical characteristics ( p = 0.73). Non-urban PWH, however, had slightly longer times to accessing HIV care and initiating antiretroviral therapy (ART) ( p < 0.01). Non-urban PWH showed trends toward slightly lower retention in care and lower sustained ART use, with higher rates of unsuppressed viral loads at 12, 24, and 36 months after diagnosis ( p < 0.01). However, by 2020 both cohorts had suppression rates above 90%. CONCLUSIONS: Sustained retention in care was more challenging for non-urban PWH; however, adherence to ART and viral suppression rates were more than 90%. Although encouraging, challenges remain in identifying and reducing unique barriers for optimal care of PWH living in non-urban areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".