System-Wide Investments Enhance HIV, TB and Malaria Control in Malawi and Deliver Greater Health Impact
Bibliographic record
Abstract
Summary Global health initiatives have expanded access to treatment for infectious diseases - especially HIV, tuberculosis, and malaria (HTM) - in low- and middle-income countries. However, these “vertically”-funded programs often operate within fragile health systems, where workforce shortages and supply chain failures constrain their effectiveness and sustainability 1,2 . Meanwhile, evaluating the health impact and value-for- money of “horizontal” investments in systems, such as supply chain strengthening or boosting healthcare workforce, and their synergies with vertical programs (through “diagonal” investments combining both) - remains challenging because their benefits are mediated through improvements in many aspects of healthcare delivery and are therefore difficult to measure 3 . Using a dynamic microsimulation model of Malawi’s healthcare system, we show that a diagonal investment approach yields a four-fold greater health impact, measured in disability-adjusted life years (DALYs) averted, than the vertical approach. This approach not only improves health outcomes for non-HTM causes of DALYs but also amplifies the effect on DALYs caused by HTM. Additionally, diagonal investments offer greater value for money and a 24.94% higher return on investment (6.67 [5.81 - 6.85] compared with 5.34 [3.44 - 6.24]), even after accounting for their additional costs. Our findings demonstrate that HSS investments generate synergistic effects, amplifying the benefits of GHIs while also strengthening broader healthcare delivery. These results support a shift toward more integrated global health financing strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".