Sex differences in houselessness, injection drug use, and mental health conditions among people newly diagnosed with HIV in Manitoba, Canada from 2018 to 2021: a retrospective cohort study
Bibliographic record
Abstract
Background: Manitoba saw the highest number of new HIV diagnoses in the province's history in 2021 and is the only Canadian province not meeting any of the previous UNAIDS 90-90-90 targets. Our goal was to describe sex differences and syndemic conditions within an incident HIV cohort in Manitoba, and the HIV treatment initiation and undetectable viral load outcomes. Methods: : CD4 counts, viral load, HIV treatment, hospitalizations, and number of visits to HIV care. Main exposures evaluated: methamphetamine use, injection drug use, houselessness, and mental health conditions. Outcomes: started antiretroviral treatment and achieved an undetectable viral load. A descriptive statistical analysis was used. Findings: There were 404 new HIV diagnoses in Manitoba from 2018 to 2021; 44.8% were female, 55.2% male; 76.% self-identified as Indigenous, 13.4% white/European, 4.7% African/black; 86.6% cis-gender; 60.9% heterosexual, 13.4% gay, bisexual and men who have sex with men, and 1.7% lesbian. Injection drug use was reported by 71.8% and 43.5% of females and males respectively. Methamphetamine was the most frequently injected drug (62.4%). Amongst females, 81.8% experienced at least one of the following: houselessness (43.1%), mental health comorbidities (46.4%), and injection drug use (71.8%). Only 64.9% of all individuals had an undetectable viral load (61.1% females and 67.9% males), 56.5% among people experiencing houselessness, 59% among young people (≤29 years), and 60.1% among people who inject drugs. Interpretation: People newly diagnosed with HIV in Manitoba are disproportionately experiencing houselessness, mental illness, and injection drug use (mostly methamphetamine). This pattern is more pronounced for female individuals. These findings highlight the need for syndemic and gender-specific approaches, simultaneously addressing social and health conditions, to treat HIV. Funding: This work was supported by the Canadian Institutes of Health Research, The Manitoba Medical Service Foundation, The James Farley Memorial Fund and the Canada Research Chairs Program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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".