The impact of the COVID-19 pandemic on HIV treatment gap lengths and viremia among people living with HIV British Columbia, Canada, during the COVID-19 pandemic: Are we ready for the next pandemic?
Bibliographic record
Abstract
The SARS-CoV-2 (COVID-19) pandemic has impacted the care of people living with HIV (PLWH). This study aims to characterize the impact of the pandemic on the length of HIV treatment gap lengths and viral loads among people living with HIV (PLWH) in British Columbia (BC), Canada, with a focus on Downtown Eastside (DTES), which is one of the most impoverished neighbourhoods in Canada. We analyzed data from the HIV/AIDS Drug Treatment Program from January 2019 to February 2022. The study had three phases: Pre-COVID, Early-COVID, and Late-COVID. We compared results for individuals residing in DTES, those not residing in DTES, and those with no fixed address. Treatment gap lengths and viral loads were analyzed using a zero-inflated negative binomial model and a two-part model, respectively, adjusting for demographic factors. Among the 8982 individuals, 93% were non-DTES residents, 6% were DTES residents, and 1% had no fixed address during each phase. DTES residents were more likely to be female, with Indigenous Ancestry, and have a history of injection drug use. Initially, the mean number of viral load measurements decreased for all PLWH during the Early-COVID, then remained constant. Treatment gap lengths increased for all three groups during Early-COVID. However, by Late-COVID, those with no fixed address approached pre-COVID levels, while the other two groups did not reach Early-COVID levels. Viral loads improved across each phase from Pre- to Early- to Late-COVID among people residing and not residing in DTES, while those with no fixed address experienced consistently worsening levels. Despite pandemic disruptions, both DTES and non-DTES areas enhanced HIV control, whereas individuals with no fixed address encountered challenges. This study offers insights into healthcare system preparedness for delivering HIV care during future pandemics, emphasizing community-driven interventions with a particular consideration of housing stability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".