Vertical dominance: Cost-effectiveness, randomisation, and the bias against horizontal interventions in global health
Bibliographic record
Abstract
Interventions in global health are frequently divided into two categories: vertical, which address one disease, and horizontal, which tackle multiple health problems through the building of health infrastructure. When identifying interventions to fund, global health practitioners place great weight on cost-effectiveness, which is determined through cost-effectiveness analyses. These analyses frequently draw on data from randomised controlled trials (RCTs), as they are considered the gold standard for determining causality. I argue that the use of RCT data in cost-effectiveness analyses leads to a bias in favour of vertical interventions and against horizontal interventions. This is because it is significantly easier to randomise vertical interventions compared to horizontal ones, so analyses that draw on RCTs will preferentially report on vertical initiatives. This bias contributes to a trend of underfunding horizontal interventions in global health. I argue that this trend is problematic, as horizontal interventions have the potential to be highly cost-effective. Finally, I argue that global health practitioners should find effective ways of evaluating horizontal interventions to ensure their benefits are captured.
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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 | 0.332 | 0.588 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".