ReviewBMC public health2025
"SCALE-up" - a new framework to assess the effectiveness of climate change adaptation interventions for human health and health systems.
Review in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Climate change and health system preparedness: A scoping review of adaptation plans in Central America and the Caribbean.Lancet regional health. Americas · 2026Review
- From the biomedical model to the climate-sensitive model: A necessary adjustment for research ethics committees.Dialogues in health · 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
5 authors.
Funding
Abstract
Climate change is a grave threat to human health and wellbeing. Adaptation is one mechanism (the other is mitigation) by which we can intervene to increase adaptive capacity and preparedness to protect people. Adaptation interventions (evidence-based adjustment of programs/practices that lead to improved response and resilience to climate change) are being conducted around the world. However, existing conceptual frameworks to assess the effectiveness of these interventions, especially with respect to improving health outcomes and systems are not readily applied in areas where these are needed. This is applicable to both interventions intended to improve health as well as those without a health-focus but which may have health co-benefits. To address this gap, we conducted a multi-vocal review comprised of a scoping review and key informant interviews, which informed the development of an initial assessment framework. We included 21 academic articles and 12 reports (from the grey literature) for data collation and synthesis. Of the 21 articles analyzed, only seven presented primary evidence of health improvement outcomes, such as reduction in neo-natal care unit admissions was partially attributed to moving the maternity ward to the cooler, lower floor of the hospital. From the 10 interviewees, we learnt that most existing tools to assess the effectiveness of adaptation are for country or regional (several countries sharing borders within a large section of a continent) scales (e.g., Notre Dame Global Adaptation Initiative Index) and none focused specifically on health / health co-benefits. From these learnings together with a guiding concept, we crafted the first iteration of an assessment framework, SCALE-up, comprising six steps that prompt a researcher to consider the effectiveness of their adaptation intervention at a project-scale, including from a health benefit perspective. We apply the framework in four scenarios: hot days-heat; floods; droughts; and vector-borne diseases, to illustrate how the framework may help guide the researcher to think about effectiveness from project proposal stage. The next steps are to implement and pilot the framework in the four proposed scenarios and refine the framework.
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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.