Contribution of Population Factors to Estimation of Human Immunodeficiency Virus Prevalence Trends: A Cohort Study in Rural Uganda, 1989-2007
Bibliographic record
Abstract
Because the incidence of human immunodeficiency virus (HIV) infection is difficult to measure directly, prevalence trends often serve to track epidemiologic changes. Adult HIV prevalence in open population cohort studies, however, reflects changes in incidence, population factors (migration, deaths, and aging), and survey coverage. Data from an open cohort in rural Uganda enabled estimation of the contribution of these factors to prevalence trends from 1989 to 2007. New infections within this cohort represented on average 44% of new prevalent cases per year. Other factors affecting changes in prevalence included migration and death. Migrants and mobile people (those who leave and return to the study area) are in a higher-risk group and thus can affect prevalence trends. Incidence of HIV infection among mobile people was 2-4 times greater than among stable residents. The importance of mortality is shown by the rise in prevalence from 6.8% in 2005 to 7.4% in 2007, which was accompanied by a fall in mortality among HIV-infected participants (8.7% of HIV-infected in 2005, 5.2% in 2006, and 4.3% in 2007). Assessing HIV epidemic trends through prevalence requires consideration of population factors. Measuring HIV incidence directly remains the most accurate measure of trends with which to monitor the effect of intervention activities and should complement strategies such as national prevalence surveys.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".