Methodological Evaluation of Public Health Surveillance Systems in Rwanda Using Difference-in-Differences for Cost-Effectiveness Analysis
Bibliographic record
Abstract
Public health surveillance systems are essential for monitoring diseases and managing resources efficiently in Rwanda. A systematic review methodology was employed to identify relevant studies. Studies were screened based on predefined criteria and assessed for quality using the Newcastle-Ottawa Scale (NOS). The analysis revealed mixed results regarding the effectiveness of surveillance systems, with some showing cost savings compared to baseline conditions. While the difference-in-differences model demonstrated potential in measuring cost-effectiveness, further empirical studies are needed to validate these findings and identify key factors influencing system performance. Investigate specific system components that contribute to cost savings or inefficiencies and consider implementing robust monitoring practices for continuous improvement. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
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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 | 0.444 | 0.591 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".