Scaling up malaria intervention “packages” in Senegal: using cost effectiveness data for improving allocative efficiency and programmatic decision-making
Bibliographic record
Abstract
BACKGROUND: Senegal's National Malaria Control Programme (NMCP) implements control interventions in the form of targeted packages: (1) scale-up for impact (SUFI), which includes bed nets, intermittent preventive treatment in pregnancy, rapid diagnostic tests, and artemisinin combination therapy; (2) SUFI + reactive case investigation (focal test and treat); (3) SUFI + indoor residual spraying (IRS); (4) SUFI + seasonal malaria chemoprophylaxis (SMC); and, (5) SUFI + SMC + IRS. This study estimates the cost effectiveness of each of these packages to provide the NMCP with data for improving allocative efficiency and programmatic decision-making. METHODS: This study is a retrospective analysis for the period 2013-2014 covering all 76 Senegal districts. The yearly implementation cost for each intervention was estimated and the information was aggregated into a package cost for all covered districts. The change in the burden of malaria associated with each package was estimated using the number of disability adjusted life-years (DALYs) averted. The cost effectiveness (cost per DALY averted) was then calculated for each package. RESULTS: The cost per DALY averted ranged from $76 to $1591 across packages. Using World Health Organization standards, 4 of the 5 packages were "very cost effective" (less than Senegal's GDP per capita). Relative to the 2 other packages implemented in malaria control districts, the SUFI + SMC package was the most cost-effective package at $76 per DALY averted. SMC seems to make IRS more cost effective: $582 per DALY averted for SUFI + IRS compared with $272 for the SUFI + IRS + SMC package. The SUFI + focal test and treat, implemented in malaria elimination districts, had a cost per DALY averted of $1591 and was only "cost-effective" (less than three times Senegal's per capita GDP). CONCLUSION: Senegal's choice of deploying malaria interventions by packages seems to be effectively targeting high burden areas with a wide range of interventions. However, not all districts showed the same level of performance, indicating that efficiency gains are still possible.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".