Impact of extreme weather events on food security among older people: a systematic review
Bibliographic record
Abstract
BACKGROUND: Climate change has intensified the frequency and severity of extreme weather events, disproportionately affecting vulnerable populations, including older people for which the literature is still limited. This systematic review investigated the impact of extreme weather events on malnutrition and food security among individuals aged 60 and older. METHODS: A systematic search of PubMed/MEDLINE, Scopus, and Web of Science was conducted without restrictions (October 2024), and following PRISMA guidelines. Observational studies examining older adults exposed to extreme weather events (e.g., droughts, floods, heatwaves, hurricanes) and their effects on malnutrition or food security were included. The Newcastle-Ottawa Scale assessed study quality. Protocol was registered in PROSPERO (ID: CRD42024596910). RESULTS: From 1,709 articles, six observational studies involving 265,000 participants (aged 60 years and over) were included. These studies spanned multiple geographies, with a concentration in the United States. Findings revealed a dual impact: while some studies reported protective factors, such as social support and economic stability, others highlighted increased malnutrition risk due to disrupted food supply, economic hardship, and inadequate adaptive responses. Heterogeneity in study designs, exposure definitions, and outcome measures limited comparability. CONCLUSION: Extreme weather events significantly impact malnutrition and food security among older adults, with outcomes influenced by socio-economic and geographical factors. Further longitudinal studies are needed to clarify causal pathways and inform targeted public health interventions to enhance resilience in aging populations.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".