The end of AIDS: Possibility or pipe dream? A tale of transitions
Bibliographic record
Abstract
Globally, in the last 20 years health has improved. In this generally optimistic setting HIV and AIDS accounts for the fastest growing burden of disease. The data show the bulk of this is experienced in Southern Africa. In this region, HIV and AIDS (and tuberculosis [TB]) peaks among young adults. Women carry the greater proportion of infections and provided most of the care. South Africa has the dubious distinction of having the largest number of people living with HIV in the world, 6.4 million. HIV began spreading from about 1990 and today the prevalence among antenatal clinic attendees is 29.5%. A similar situation exists in other nations of the region. It is an expensive disease, requiring more resources than are available, and it is slipping off the global agenda, both in terms of attention and international funding. Those halcyon days of the decade from 2000 to 2010 are over. This paper explores the concept of three transition points: economic, epidemiological and programmatic. The first two have been developed and written about by others. We add a third transition point, namely programmatic, argue this is an important concept, and show how it can become a powerful tool in the response to the epidemic. The economic transition point assesses HIV incidence and mortality of people infected with HIV. Until the number of newly infected people falls below the number of deaths of people living with HIV, the demand for treatment and costs will increase. This is a concern for the health sector, finance ministry and all working in the field of HIV. Once an economic transition occurs the treatment future is predictable and the number of people living with HIV and AIDS decreases. This paper plots two more lines. These are the number of new people from the HIV infected pool initiated on treatment and the number of people from the HIV infected pool requiring treatment. This introduces new transition points on the graph. The first when the number of people initiated on treatment exceeds the number of people needing treatment. The second when the number initiated on treatment exceeds the new infections. That is the theory. When we applied South African data from the ASSA2008 model, we were able to plot transition points marking progress in the national response. We argue these concepts can and should be applied to any country or HIV epidemic.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.017 | 0.038 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".