Influence of geographical origin and ethnicity on mortality in patients on antiretroviral therapy in Canada, Europe, and the United States.
Bibliographic record
Abstract
BACKGROUND: Our objective was to assess differences in all-cause mortality, as well as AIDS and non-AIDS death rates, among patients started on antiretroviral therapy (ART) according to their geographical origin and ethnicity/race in Europe, Canada, and the United States. METHODS: This was a collaboration of 19 cohort studies of human immunodeficiency virus-positive subjects who have initiated ART (ART Cohort Collaboration) between 1998 and 2009. Adjusted mortality hazard ratios (AHRs) were estimated using Cox regression. A competing risk framework was used to estimate adjusted subdistribution hazard ratios for AIDS and non-AIDS mortality. RESULTS: Of 46 648 European patients, 16.3% were from sub-Saharan Africa (SSA), 5.1% Caribbean and Latin America, 1.6% North Africa and Middle East, and 1.7% Asia/West; of 1371 patients from Canada, 14.9% were First Nations and 22.4% migrants, and of 7742 patients from North America, 55.5% were African American and 6.6% Hispanic. Migrants from SSA (AHR, 0.79; 95% confidence interval [CI], .68-.92) and Asia/West (AHR, 0.62; 95% CI, .41-.92) had lower mortality than Europeans; these differences appeared mainly attributable to lower non-AIDS mortality. Compared with white Canadians, mortality in Canadian First Nations people (AHR, 1.48; 95% CI, .96-2.29) was higher, both for AIDS and non-AIDS mortality rates. Among US patients, when compared with whites, African Americans had higher AIDS and non-AIDS mortality, and hazard ratios for all-cause mortality increased with time on ART. CONCLUSIONS: The lower mortality observed in migrants suggests "healthy migrant" effects, whereas the higher mortality in First Nations people and African Americans in North America suggests social inequality gaps.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".