HIV treatment outcomes among newcomers living with HIV in Manitoba, Canada
Bibliographic record
Abstract
Background: Despite the overrepresentation of immigrants and refugees (newcomers) in the HIV epidemic in Canada, research on their HIV treatment outcomes is limited. This study addressed this knowledge gap by describing treatment outcomes of newcomers in comparison with Canadian-born persons living with HIV in Manitoba. Methods: Clinical data from 1986 to 2017 were obtained from a cohort of people living with HIV and receiving care from the Manitoba HIV Program. Retrospective cohort analysis of secondary data was completed using univariate and multivariate statistics to compare differences in socio-demographic and clinical characteristics and treatment outcomes among newcomers, Canadian-born Indigenous persons, and Canadian-born non-Indigenous persons on entry into HIV care. Results: By end of 2017, 86 newcomers, 259 Canadian-born Indigenous persons, and 356 Canadian-born non-Indigenous persons were enrolled in the cohort. Newcomers were more likely than Canadian-born Indigenous and non- Indigenous cohort participants to be younger and female and have self-reported HIV risk exposure as heterosexual contact. Average CD4 counts at entry into care did not differ significantly between groups. A higher proportion of newcomers was also diagnosed with tuberculosis within 6 months of entry into care (21%), compared with 6% and 0.6% of Canadian-born Indigenous non-Indigenous persons, respectively. Newcomers and Canadian-born non-Indigenous persons had achieved viral load suppression (< 200 copies/mL) at a similar proportion (93%), compared with 82% of Canadian-born Indigenous participants ( p < 0.05). Conclusions: The distinct demographic and clinical characteristics of newcomers living with HIV requires a focused approach to facilitate earlier diagnosis, engagement, and support in care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 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) | 0.002 | 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".