HIV epidemic in Far-Western Nepal: effect of seasonal labor migration to India
Bibliographic record
Abstract
BACKGROUND: Because of limited work opportunities in Nepal and the open-border provision between Nepal and India, a seasonal labor migration of males from Far-Western Nepal to India is common. Unsafe sexual activities of these migrants in India, such as frequent visits to brothels, lead to a high HIV prevalence among them and to a potential transmission upon their return home to Nepal. The present study aims to evaluate the role of such seasonal labor-migration to India on HIV transmission in Far-Western Nepal and to assess prevention programs. METHODS: An HIV epidemic model was developed for a population in Far-Western Nepal. The model was fitted to the data to estimate the back and forth mobility rates of labor-migrants to India, the HIV prevalence among migrants and the HIV transmission rate in Far-Western Nepal. HIV prevalence, new infections, disease deaths and HIV infections recruited from India were calculated. Prevention programs targeting the general population and the migrants were evaluated. RESULTS: Without any intervention programs, Far-Western Nepal will have about 7,000 HIV infected individuals returning from India by 2015, and 12,000 labor-migrants living with HIV in India. An increase of condom use among the general population from 39% to 80% will reduce new HIV infections due to sexual activity in Far-Western Nepal from 239 to 77. However, such a program loses its effectiveness due to the recruitment of HIV infections via returning migrants from India. The reduction of prevalence among migrants from 2.2% to 1.1% can bring general prevalence down to 0.4% with only 3,500 recruitments of HIV infections from India. CONCLUSION: Recruitment of HIV infections from India via seasonal labor-migrants is the key factor contributing to the HIV epidemic in Far-Western Nepal. Prevention programs focused on the general population are ineffective. Our finding highlights the urgency of developing prevention programs which reduce the prevalence of HIV among migrants for a successful control of the HIV epidemic in Far-Western Nepal.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".