Methodological Evaluation of Public Health Surveillance Systems in Rwanda Using Panel Data to Measure Cost-Effectiveness
Bibliographic record
Abstract
Public health surveillance systems are crucial for monitoring diseases in Rwanda. However, their cost-effectiveness remains a subject of debate. Panel data analysis was employed to estimate the cost-effectiveness of surveillance systems over time. Robust standard errors were used for inference. The study found that a specific intervention model reduced healthcare costs by 15% (95% CI: -3%, 42%) in the first quarter compared to baseline year. The analysis highlights the importance of continuous evaluation and improvement of surveillance systems for cost-effectiveness. Investment in surveillance system upgrades should be prioritised to maximise health benefits and financial returns. Public Health Surveillance, Cost-Effectiveness Analysis, Panel Data, Rwanda 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 | 0.388 | 0.560 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".