ArticleJournal of public health policy2026
Data for famine and extreme emergencies: challenges and opportunities.
Article in Journal of public health policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Heading toward a famine: modeling seasonality of malnutrition in Yemen, 2018-2023.Journal of public health policy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-quality data are a pre-requisite for the development of models-whether of famine or food and nutrition-related emergencies. We highlighted challenges, related to data in famines or near-famine emergencies, including data availability, quality, timeliness, and sharing; the misuse of data could be a real threat for applying new advanced data analytic tools, such as artificial intelligence and machine learning. We also explored promising opportunities, such as democratization of data related to famine. With growing demands for real-time assessment and modeling of famine and extreme emergencies, it is increasingly important to understand how predictive models and emerging data-integration technologies are affected by biased, incomplete, inconsistent, or outdated data. As we push for addressing data constraints, we call for ethical data democratization to minimize the risks of data misuse, weaponization, and politization and to enable famine modeling for collective efforts to enhance early warning, real-time analysis, and response.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.