Leaving no one behind? An equity analysis of the HIV care cascade among a cohort of people living with HIV in Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: Manitoba is a central Canadian province with annual rates of new HIV infections consistently higher than the Canadian average. National surveillance statistics and data from the provincial HIV care program suggest that epidemiological heterogeneity exists across Manitoba. New HIV cases are disproportionately reported among females, Indigenous-identifying individuals, and those with a history of injection drug use. Given the heterogeneity in acquisition, it is of interest to understand whether this translates into inequalities in HIV care across Manitoba. METHODS: A sample of 703 participants from a clinical cohort of people living with HIV in Manitoba, with data current to the end of 2017, was used to conduct cross-sectional, disaggregated analyses of the HIV care cascade to identify heterogeneity in service coverage and clinical outcomes among different groups receiving HIV care in Manitoba. Equiplots are used to identify and visualize inequalities across the cascade. Exploratory multivariable logistic regression models quantify associations between equity variables (age, sex, geography, ethnicity, immigration status, exposure category) and progression along the cascade. Adjusted odds ratios (AOR) and 95% confidence intervals (95%CI) are reported. RESULTS: Equity analyses highlight inequalities in engagement in and coverage of HIV-related health services among cohort participants. Equiplots illustrate that the proportion of participants in each cascade step is greater for those who are older, white, non-immigrants, and report no history of injection drug use. Compared to those living in Winnipeg, participants in eastern Manitoba have greater odds of achieving virologic suppression (AOR[95%CI] = 3.8[1.3-11.2]). The odds of Indigenous participants being virologically suppressed is half that of white participants (AOR[95%CI] = 0.5[0.3-0.7]), whereas African/Caribbean/Black participants are significantly less likely than white participants to be in care and retained in care (AOR[95%CI] = 0.3[0.2-0.7] and 0.4[0.2-0.9], respectively). CONCLUSIONS: Inequalities exist across the cascade for different groups of Manitobans living with HIV; equiplots are an innovative method for visualizing these inequalities. Alongside future research aiming to understand why inequalities exist across the cascade in Manitoba, our equity analyses can generate hypotheses and provide evidence to inform patient-centred care plans that meet the needs of diverse client subgroups and advocate for policy changes that facilitate more equitable HIV care across the province.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".