Characterizing trends in HIV infection among men who have sex with men in Australia by birth cohorts: results from a modified back‐projection method
Bibliographic record
Abstract
BACKGROUND: We set out to estimate historical trends in HIV incidence in Australian men who have sex with men with respect to age at infection and birth cohort. METHODS: A modified back-projection technique is applied to data from the HIV/AIDS Surveillance System in Australia, including "newly diagnosed HIV infections", "newly acquired HIV infections" and "AIDS diagnoses", to estimate trends in HIV incidence over both calendar time and age at infection. RESULTS: Our results demonstrate that since 2000, there has been an increase in new HIV infections in Australian men who have sex with men across all age groups. The estimated mean age at infection increased from ~35 years in 2000 to ~37 years in 2007. When the epidemic peaked in the mid 1980s, the majority of the infections (56%) occurred among men aged 30 years and younger; 30% occurred in ages 31 to 40 years; and only ~14% of them were attributed to the group who were older than 40 years of age. In 2007, the proportion of infections occurring in persons 40 years or older doubled to 31% compared to the mid 1980s, while the proportion of infections attributed to the group younger than 30 years of age decreased to 36%. CONCLUSION: The distribution of HIV incidence for birth cohorts by infection year suggests that the HIV epidemic continues to affect older homosexual men as much as, if not more than, younger men. The results are useful for evaluating the impact of the epidemic across successive birth cohorts and study trends among the age groups most at risk.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".