Costs and consequences of large-scale vector control for malaria
Bibliographic record
Abstract
BACKGROUND: Five large insecticide-treated net (ITN) programmes and two indoor residual spraying (IRS) programmes were compared using a standardized costing methodology. METHODS: Costs were measured locally or derived from existing studies and focused on the provider perspective, but included the direct costs of net purchases by users, and are reported in 2005 USD. Effectiveness was estimated by combining programme outputs with standard impact indicators. FINDINGS: Conventional ITNs: The cost per treated net-year of protection ranged from USD 1.21 in Eritrea to USD 6.05 in Senegal. The cost per child death averted ranged from USD 438 to USD 2,199 when targeting to children was successful.Long-lasting insecticidal nets (LLIN) of five years duration: The cost per treated-net year of protection ranged from USD 1.38 in Eritrea to USD 1.90 in Togo. The cost per child death averted ranged from USD 502 to USD 692.IRS: The costs per person-year of protection for all ages were USD 3.27 in KwaZulu Natal and USD 3.90 in Mozambique. If only children under five years of age were included in the denominator the cost per person-year of protection was higher: USD 23.96 and USD 21.63. As a result, the cost per child death averted was higher than for ITNs: USD 3,933-4,357. CONCLUSION: Both ITNs and IRS are highly cost-effective vector control strategies. Integrated ITN free distribution campaigns appeared to be the most efficient way to rapidly increase ITN coverage. Other approaches were as or more cost-effective, and appeared better suited to "keep-up" coverage levels. ITNs are more cost-effective than IRS for highly endemic settings, especially if high ITN coverage can be achieved with some demographic targeting.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".