Best Investments in Chronic, Noncommunicable Disease Prevention and Control in Low- and Lower–Middle-Income Countries
Bibliographic record
Abstract
Abstract The world remains off-track for the sustainable development goal (SDG) target 3.4, which calls for a one-third reduction in noncommunicable diseases (NCDs) mortality by 2030. This paper presents benefit–cost analyses of various NCD interventions in low-income (LICs) and lower–middle-income (LMCs) countries. We looked at 30 interventions recommended by the Disease Control Priorities Project, including six intersectoral policies (e.g., taxes) and 24 clinical services. We used a previously published model to estimate intervention costs and benefits through 2030, discounted at 8%. We focused on interventions with benefit–cost ratios (BCRs) > 15 and their contribution toward achieving the SDG target. We found that intersectoral policies often provided great value for money, with BCRs ranging from 40 ( trans -fat bans) to 100 (tobacco excise taxes). However, seven clinical interventions (e.g., basic treatment of cardiovascular disease or breast cancer) also had BCRs > 15. The overall population impact of clinical interventions over the 2023–2030 period would be much higher than that of the intersectoral policies, which can take many years to reach their peak effects. Fully implementing the best-investment interventions would accelerate progress toward SDG 3.4 everywhere, but only one in 10 countries would achieve the target. This strategy would require an additional US$ 2.4 billion annually across all LICs and LMCs. We conclude that there are several cost-beneficial opportunities to tackle NCDs in LICs and LMCs. In countries with very limited resources, the best-investment interventions could begin to address the major NCD risk factors and build greater health system capacity, with benefits continuing to accrue beyond 2030.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".