From the Old World to the New World: an ecologic study of population susceptibility to HIV infection
Bibliographic record
Abstract
It remains unclear why the global distribution of human immunodeficiency virus (HIV), between and within continents, is so heterogeneous. This ecologic study of 34 populations of the Americas explored the hypothesis that populations differ in their intrinsic, biological susceptibility to HIV which, together with exposure, might determine the ultimate 'mature' prevalence. If true, national HIV prevalence in populations of the Americas should be predictable from each country's ethnic mosaic, inter-racial admixture and HIV prevalence in regions of Africa, Europe and Asia from where their ancestors migrated. For each country, the adult population (15-49 years) was multiplied by the proportion corresponding to each ethnic group by HIV prevalence in the country/region of origin of each group, yielding the predicted prevalences, which were then compared with observed prevalences documented by UNAIDS for 2001. Predicted and observed HIV prevalences were highly correlated (r = 0.70, P < 0.001). In North America, predicted prevalences were within 0.5% of the observed values, except for African-Americans and African-Canadians. In Central and South America, differences between predicted and observed prevalences were <1.0% except in Honduras and Guyana. Some Caribbean countries had a predicted prevalence identical to the observed one, but there were outliers. Overall, predicted prevalence was 0.93% and observed prevalence 0.64%; two-thirds of this difference was attributed to Brazil. Although it was not possible to adjust to the confounding effects of sexual behaviour and cofactors of transmission (such as sexually transmitted infections) because of the lack of nationally representative data for each and every country, a number of arguments reviewed in the paper suggest that confounders cannot explain all this association and that differential susceptibility might be an important determinant of steady-state HIV prevalence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".