The diagonal approach: A theoretic framework for the economic evaluation of vertical and horizontal interventions in healthcare
Bibliographic record
Abstract
The diagonal approach is a health system funding concept wherein vertical approaches targeting specific diseases are combined with horizontal approaches intended to strengthen health systems broadly. This taxonomy can also be used to classify health system interventions as either vertical or horizontal. Previous studies have used mathematical programming to evaluate horizontal interventions, but these models have not allowed concurrent evaluation of different types of horizontal interventions or captured spillovers and intertemporal effects. This paper aims to develop a theoretic framework for the diagonal approach. The framework is articulated through integer programming, maximizing health benefits given constraints by identifying the optimal set of both vertical and horizontal interventions to fund. The theoretic framework for the diagonal approach is developed by synthesizing and expanding three prior works. The decision problem is synthesised to allow concurrent evaluation of three different types of horizontal interventions, those: (i) improving health system efficiency, (ii) improving capacity, and (iii) investing in new platforms. Linear programs are converted to integer form, relaxing previous assumptions related to constant returns to scale and divisibility of interventions. The framework is expanded to evaluate multiple budget constraints and options for new platforms. A new form for the value function is used to estimate the benefits of intervention combinations, capturing spillovers between vertical and horizontal interventions and dynamic returns to scale. The decision problem is specified inferotemporally, explicitly capturing the impact of the time horizon on the optimal choice set. Dynamic examples are provided to demonstrate the advantages of the diagonal approach over prior frameworks. This framework extends existing works, enabling simultaneous comparison of various combinations of both vertical and horizontal interventions, capturing spillovers and intertemporal effects. The diagonal approach framework defines decision problems flexibly and realistically, forming the basis for future applied work. Implementation would improve resource allocation and patient health outcomes.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".