Bridging Disciplines: A Review of Uncertainty Treatment and Representation of Vulnerable Groups in Planetary Health Projections Based on Earth System Models

Variability and extreme conditions within the Earth system are major drivers of adverse population-level health outcomes. To better understand how these risks may evolve under future climate change, outputs from Earth System Models (ESMs) are increasingly integrated into research within the field of Planetary Health. This study builds on a systematic literature review that assessed the current state of ESM usage in planetary health research and extends it by examining two cross-cutting aspects that emerged as particularly underexplored.

The first aspect concerns how errors are handled between the two disciplines and which complications arise. Since in the majority of the studies reviewed the results were presented without a formal error analysis, we sought to identify the challenges of error propagation from the ESM over to the health model component and how uncertainties may be accounted for in different ways across studies. The results of this analysis show that the methodologies employed across different scientific disciplines vary in their treatment, quantification, and presentation of uncertainties. However, a proper error analysis is crucial for the credibility of scientific work, especially when communicating the results to a broad, including an non-academic, audience. Since climate change and its projected risks are already a highly politicized issue, particular care is required to not generate false assumptions.

As a second focus, the study investigates whether and how vulnerable groups are accounted for  in climate-related health projections. Because of growing evidence that climate-sensitive parameters such as heat stress or apparent temperature affect different genders or ages in distinct ways, we examined the extent to which these aspects are considered in the reviewed literature and whether ESM-derived climatic outputs are used as inputs for health models representing diverse population groups. Inspecting the set of research papers showed that only a few even mentioned words like “gender/sex” or even “vulnerability” of different groups. The word “women” was not found at all. Health risk projections that do not account for gender and other population subgroups, such as children, women and older adults, may systematically over- or underestimate climate change-related risks.

Overall this study highlights the need for good and close collaboration and communication between scientific disciplines to guarantee reliable and unambiguous publications.