The economic value of changing mortality risk in low- and middle-income countries: a systematic breakdown by cause of death
Bibliographic record
Abstract
BACKGROUND: We develop a framework for quantifying monetary values associated with changes in disease-specific mortality risk in low- and middle-income countries to help quantify trade-offs involved in investing in mortality reduction due to one disease versus another. METHODS: We monetized the changes in mortality risk for communicable and non-communicable diseases (CD and NCD, respectively) between 2017 and 2030 for low-income, lower-middle-income, and upper-middle-income countries (LICs, LMICs, and UMICs, respectively). We modeled three mortality trajectories ("base-case", "high-performance", and "low-performance") using Global Burden of Disease study forecasts and estimated disease-specific mortality risk changes relative to the base-case. We assigned monetary values to changes in mortality risk using value of a statistical life (VSL) methods and conducted multiple sensitivity analyses. RESULTS: In terms of NCDs, the absolute monetary value associated with changing mortality risk was highest for cardiovascular diseases in older age groups. For example, being on the low-performance trajectory relative to the base-case in 2030 was valued at $9100 (95% uncertainty range $6800; $11,400), $28,300 ($24,200; $32,400), and $30,300 ($27,200; $33,300) for females aged 70-74 years in LICs, LMICs, and UMICs, respectively. Changing the mortality rate from the base-case to the high-performance trajectory was associated with high monetary value for CDs as well, especially among younger age groups. Estimates were sensitive to assumptions made in calculating VSL. CONCLUSIONS: Our framework provides a priority setting paradigm to best allocate investments toward the health sector and enables intersectoral comparisons of returns on investments from health interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".