From El Niño forecasts to proportionate public health action: a forecast-to-action framework for climate-related health risks

Abstract

El Niño creates an important opportunity for anticipatory public health action because its development can often be detected before many downstream hazards emerge. Yet predictability does not transfer uniformly from the large-scale El Niño Southern Oscillation (ENSO) signal to regional climate anomalies and ultimately to health outcomes. This Perspective proposes a locally adaptable forecast-to-action framework that distinguishes three levels of predictability: ENSO prediction, regional and local climate prediction, and health-risk prediction. We argue that an El Niño forecast should initiate a structured risk-assessment process rather than automatically trigger intervention. Decisions should integrate forecast probability and skill, expected hazard severity, local epidemiology and vulnerability, lead time, intervention effectiveness and reversibility, equity, and the consequences of acting versus not acting. Low-cost, no-regret measures can reasonably begin at lower levels of certainty, whereas costly or disruptive interventions require stronger evidence. Because empirical evidence for forecast-triggered interventions remains limited, prospective implementation and evaluation are essential. Linking climate information with local surveillance, One Health intelligence, resilient health systems, equity-sensitive decision-making, and continuous learning may convert forecast lead time into earlier, proportionate, and more defensible public health action.