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.

In recent years, the challenge of drug resistance in solid tumors has emerged as a significant barrier to effective cancer treatment, often leading to treatment failure and poor patient outcomes. As traditional therapeutic approaches struggle to overcome this hurdle, there is a growing interest in leveraging innovative machine learning techniques to enhance our understanding of drug resistance mechanisms and improve therapeutic strategies. This article collection aims to gather cutting-edge research that explores the intersection of machine learning and oncology, focusing on how advanced algorithms can be applied to predict drug resistance, optimize treatment plans, and ultimately foster personalized medicine approaches in cancer care. By fostering interdisciplinary collaboration and sharing novel insights, this collection seeks to pave the way for transformative advancements in the fight against cancer.

This article collection explores integrating advanced machine learning methodologies—including ensemble learning, deep learning, reinforcement learning, explainable AI, and quantum computing—in understanding and overcoming drug resistance in solid tumors. We invite contributions highlighting novel algorithms, examples of innovative machine learning technique implementation, and interdisciplinary approaches, ultimately leading to improved therapeutic outcomes and enhanced patient care in oncology.

opics of Interest

The collection welcomes original research, reviews, commentary and methodology articles on the following topics:

• Ensemble Learning Techniques;

• Applications in predicting drug resistance profiles;

• Comparative studies of ensemble methods versus traditional approaches;

• Deep Learning in Oncology;

• Neural network architectures for tumor characterization;

• Image analysis and interpretation for drug response prediction;

• Reinforcement Learning;

• Adaptive treatment strategies using reinforcement learning;

• Simulation models for drug resistance evolution;

• Explainable AI;

• Methods for interpreting machine learning models in clinical settings;

• The role of explainability in improving treatment outcomes;

• Quantum Computing Applications;

• Quantum algorithms for optimizing drug discovery processes;

• Examples of quantum-enhanced machine learning techniques in oncology.

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.

This collection supports and amplifies research related to SDG 3: Good Health and Well-Being.