The cost of inaction: a global tool to inform nutrition policy and investment decisions on global nutrition targets
Bibliographic record
Abstract
At present, the world is off-track to meet the World Health Assembly global nutrition targets for 2025. Reducing the prevalence of stunting and low birthweight (LBW) in children, and anaemia in women, and increasing breastfeeding rates are among the prioritized global nutrition targets for all countries. Governments and development partners need evidence-based data to understand the true costs and consequences of policy decisions and investments. Yet there is an evidence gap on the health, human capital, and economic costs of inaction on preventing undernutrition for most countries. The Cost of Inaction tool and expanded Cost of Not Breastfeeding tool provide country-specific data to help address the gaps. Every year undernutrition leads to 1.3 million cases of preventable child and maternal deaths globally. In children, stunting results in the largest economic burden yearly at US$548 billion (0.7% of global gross national income [GNI]), followed by US$507 billion for suboptimal breastfeeding (0.6% of GNI), US$344 billion (0.3% of GNI) for LBW and US$161 billion (0.2% of GNI) for anaemia in children. Anaemia in women of reproductive age (WRA) costs US$113 billion (0.1% of GNI) globally in current income losses. Accounting for overlap in stunting, suboptimal breastfeeding and LBW, the analysis estimates that preventable undernutrition cumulatively costs the world at least US$761 billion per year, or US$2.1 billion per day. The variation in the regional and country-level estimates reflects the contextual drivers of undernutrition. In the lead-up to the renewed World Health Assembly targets and Sustainable Development Goals for 2030, the data generated from these tools are powerful information for advocates, governments and development partners to inform policy decisions and investments into high-impact low-cost nutrition interventions. The costs of inaction on undernutrition continue to be substantial, and serious coordinated action on the global nutrition targets is needed to yield the significant positive human capital and economic benefits from investing in nutrition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".