Integrating climate services into health systems for nutrition security: a scoping review
Bibliographic record
Abstract
Abstract Climate information services (CISs) are science-based tools used to inform decision-making in climate-sensitive sectors, such as agriculture, water resources, energy, disaster risk reduction and health. These CISs rely on high-quality climate and weather data in order to predict and prepare for specific extreme weather or climate events such as droughts and floods. Within the health sector, most CISs have been developed to prevent and treat specific infectious diseases or food insecurity; however less is known on how CISs have been used for nutrition programming. We conducted a scoping review of available evidence, on the use of CISs to implement direct and indirect nutrition interventions in health-care and other sectors ahead of extreme weather or climate events. We searched PubMed, Web of Science and Scopus, and grey literature sources for primary studies (observational, intervention, and program evaluations) conducted in low- and middle-income countries from 1 January 2000 to 1 April 2024. We included 48 studies, representing 67 country-level programs. The majority of programs were found in the African region ( n = 38), followed by South-East Asian Region ( n = 10), Region of the Americas ( n = 9), Western Pacific Region ( n = 8), Eastern Mediterranean Region ( n = 1), and the European Region ( n = 1). Most CISs were developed in response to vector-borne diseases (17 countries), droughts (10 countries), floods (9 countries) or multi-hazards (11 countries). The types of nutrition programs deployed were largely outside of the health sector using social protection schemes or vector control including poverty alleviation ( n = 49 programs), water, sanitation and hygiene ( n = 24 programs), disease prevention ( n = 23 programs) or emergency nutrition ( n = 19 programs). Few studies evaluated impacts of CISs on the nutritional status of women, children and adolescents affected by climate events. There is urgency and opportunity for better integration of weather and climate information into health systems decision-making and workforce preparedness at local levels to improve both short- and long-term nutrition 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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".