Cost-effectiveness of Methadone Maintenance Treatment Centers in Prevention of Human Immunodeficiency Virus Infection.
Bibliographic record
Abstract
BACKGROUND: Acquired immune deficiency syndrome (AIDS) is one of the greatest social health problems in many communities in the twenty-first century. Methadone maintenance treatment (MMT) could decrease HIV infection among injection drug users (IDU). The main aim of this paper was to determine the cost-effectiveness of the governmental MMT program to prevent human immunodeficiency virus (HIV) infection among IDU. METHODS: This analytical study was performed through a before-after assessment during a one-year period. Using census sampling, 251 IDU referred to the public MMT program of Kerman, Iran, were selected. The expenditures of MMT centers were calculated in the view of government (public sector). The cost-effectiveness was calculated using TreeAge software. FINDINGS: MMT centers averted 86 new cases of HIV infection. The total cost of centers was US$471 per client in the year. The share of IDU from current expenditures was 35% and from capital expenditures was 32%. Also, methadone per capita for each person who injected drug was US$514. Per capita expenditure of HIV drug treatment was estimated US$8535 per year. Incremental cost effectiveness ratio (ICER) was US$2856 per year, which means governmental MMT program is cost-effective according to the World Health Organization (WHO) criteria. CONCLUSION: MMT centers are cost-effective in preventing HIV infection and the access to this program should be facilitated for IDU.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".