Measuring Population Health in the Era of Climate Change

Climate change is increasingly recognized as a major challenge for population health worldwide. Rising global temperatures, more frequent and severe extreme weather events, and changing seasonal patterns affect mortality, morbidity, and broader population health dynamics across countries and regions. These impacts are not distributed equally across populations, with older adults, socioeconomically disadvantaged populations, people from culturally and linguistically diverse backgrounds, Indigenous populations, and other vulnerable groups often facing disproportionate risks.

A growing body of research has documented the adverse health impacts of climate-related exposures, including heat and cold, floods, droughts, and wildfires. Over recent decades, substantial progress has been made in improving methodologies for assessing climate-health associations, increasing the availability and granularity of environmental, geospatial, and health data, and developing cross-national initiatives aimed at strengthening the evidence base. Nevertheless, important challenges remain in measuring and comparing climate-related health impacts across population subgroups, geographical contexts, and time periods.

This Special Collection seeks contributions that advance the measurement and understanding of climate-related impacts on population health. We welcome ecological and population-level studies, methodological innovations in estimating the burden associated with non-optimal temperatures and extreme weather events, and improved approaches for measuring excess mortality and mortality displacement. We also encourage contributions examining seasonality and temporal dynamics in population health, climate-related impacts on non-communicable diseases and mental health conditions, and the interplay between climatic, behavioural, and socioeconomic determinants of health outcomes. Contributions focusing on low- and middle-income countries are particularly welcomed.

Relevant contributions include, but are not limited to:

• Methodological innovations in measuring and modelling climate-related impacts on non-communicable, mental, and infectious diseases
• Advances in measuring excess mortality and short-term mortality displacement (harvesting) associated with extreme weather events
• Determinants of seasonal variation in mortality and morbidity across climatic zones and population groups
• Small-area estimation and spatial modelling of local climate impacts on population health
• Measuring climate-related health outcomes in data-constrained settings
• Comparative and cross-national studies of the socioeconomic, behavioural, and demographic determinants of climate-related health outcomes
• Population vulnerability, adaptation, and resilience to climate-related health risks
• Healthy ageing in a changing climate
• Forecasting and scenario-based approaches to climate-related population health burdens

This Collection welcomes original research articles, methodological contributions, interdisciplinary studies, comparative analyses, and systematic reviews that align with the scope of Population Health Metrics.

All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.

Computational approaches are becoming indispensable in the field of translational pharmacology and systems medicine. In particular, the integration of computational modelling and network medicine is reshaping our understanding of drug toxicity, safety profiling, and clinical pharmacovigilance. With the rapid accumulation of multi-omics, electronic health records, and real-world pharmacovigilance data, there is an urgent need for advanced computational frameworks to identify hidden drug–target–phenotype associations and elucidate mechanisms underlying adverse drug reactions (ADRs) and toxicities. Drug toxicology and pharmacovigilance are key components of translational medicine, bridging molecular mechanisms with patient outcomes. However, predicting and preventing drug-induced toxicities remain challenging due to the complexity of biological systems and individual heterogeneity. Network-based and machine learning methods, such as graph neural networks, causal inference, and mechanistic modelling, are providing new opportunities to reveal system-level toxicity pathways, optimize drug safety assessment, and enhance early signal detection in clinical practice.

This collection aims to gather latest research and comprehensive reviews that explore computational, network-based, and AI-driven strategies in drug toxicology and pharmacovigilance. We seek contributions that demonstrate how integrative modelling, network pharmacology, and multi-scale data analysis can advance the prediction, understanding, and management of drug-related adverse effects, ultimately contributing to safer and more effective therapeutics.

Potential topics include, but are not limited to:

- Computational modelling for drug toxicity prediction

- Network medicine approaches to drug safety and toxicity mechanisms

-Machine learning and AI in pharmacovigilance signal detection

-Integrative modelling of drug–target–pathway interactions

-Systems pharmacology and multi-omics network analysis in toxicology

-Network-based biomarker discovery for adverse drug reactions

-Data-driven prediction of idiosyncratic and immune-related toxicities

-Translational applications of network and computational models in clinical drug safety assessment

All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.