BMC Global and Public Health is pleased to announce a call for papers for our upcoming collection entitled Environmental exposure, maternal health and child health, guest edited by Dr. Nuria Güil from the Barcelona Institute for Global Health, Spain, Dr. Pengfei Guo from University of South Carolina, USA and Dr. Wei Li from Yale University, USA. The aim of the collection is to capture global efforts and novel approaches in this multi-disciplinary area. Maternal and child health is a substantial domain of global public health, with outcomes that shape individual well-being and societal development. Pregnancy and early life are periods of heightened biological sensitivity, during which exposures exert lasting effects. Complications such as hypertensive disorders, gestational diabetes, and perinatal mental health challenges, as well as birth and child outcomes including preterm birth, growth restriction, and neurodevelopmental impairment, highlight the need for continued research and intervention to reduce risks and promote health equity.

Environmental exposures are increasingly recognized as critical and modifiable determinants of maternal and child health. Adverse exposures such as ambient air pollution, environmental tobacco or cannabis smoke, contaminated water, pesticides, endocrine disruptors, heavy metals, noise, light pollution, natural disasters, and other emerging pollutants contribute to reproductive and developmental risks, often acting in mixtures and interacting with social or neighborhood determinants. At the same time, positive environmental influences such as access to clean air and water, green space, safe housing, and supportive community environments, have been shown to promote healthier pregnancies, enhance child neurodevelopment, and reduce stress for families. Advancing knowledge of both harmful and protective pathways is essential for developing effective interventions, guiding policy, and addressing environmental challenges that threaten maternal and child health.

We are now inviting the submission of manuscripts of outstanding interest covering the breadth of multi-disciplinary studies, ranging from observational to interventional that are focused on the effect of environmental exposure on maternal and child health. We envision this work to inform future research, conceptual frameworks, intervention development, and policy advancement.

Sample foci of papers include:

• Epidemiological studies on environmental exposures (e.g., air pollutants, per- and polyfluoroalkyl substances, pesticides, heavy metals, plastics, greenspace, noise, climate-related hazards) and maternal, fetal, and child health outcomes
• Environmental tobacco or cannabis secondhand smoke exposure and postpartum physical and mental health on maternal and child health (e.g., postpartum depression and anxiety, maternal sleep and recovery, breastfeeding outcomes, infant respiratory illness and neurodevelopment)
• Population-level insights into contextual pathways and disparities (e.g., social determinants, neighborhood environment, climate vulnerability) that influence reproductive, perinatal, and developmental health outcomes
• Policy, intervention, and prevention strategies to reduce environmental risks and improve maternal and child health globally
• Interactions between environmental exposures and social determinants of health
• Novel data collection/assessment tools (e.g., mixture, exposome, or omics approaches)

This Collection supports and amplifies research related to SDG 3: Good Health and Well-being.

We encourage work from local, regional and global partnerships and collaboration among scientists from multi-disciplinary fields and using multiple methodologies. We ask that authors be attentive to the use of non-stigmatizing/preferred language in their manuscripts as outlined in relevant language guidelines for their respective fields.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

BMC Global and Public Health is calling for submissions to our Collection on how the integration of advanced data-driven methods in public health policy is rapidly transforming how we understand and respond to global health challenges. This Collection focuses on the application of innovative statistical modelling, machine learning, and geographic information systems (GIS) to inform health policy decisions. By integrating data from surveys and health records with information on social and environmental determinants, researchers can uncover actionable insights that enhance the effectiveness and efficiency of health interventions across diverse populations.

The relevance of these advanced methodologies is underscored by recent advancements in methods for data collection and analysis, which have enabled researchers to create more precise models for predicting disease outbreaks, evaluating health interventions, and addressing health disparities. As public health continues to confront complex issues such as pandemics and non-communicable diseases, the ability to leverage data-driven strategies becomes increasingly vital for crafting evidence-based policies that are responsive to real-world challenges.

Continued research in this area could lead to groundbreaking methodologies that enhance our understanding of health dynamics and improve public health responses. Future advancements may include the development of real-time data analytics for tracking disease spread, enhanced spatial epidemiological models that account for environmental variables, and improved collaboration between data scientists and public health professionals. By further integrating data science with public health, we can pave the way for more effective interventions and ultimately improve health outcomes globally.

We encourage submissions in the following areas, with a particular interest in studies relevant to low- and middle-income countries (LMICs); however, contributions from all settings are welcome, including:

• Applications of Machine Learning in Public Health
• Spatial Epidemiology and Disease Mapping
• Statistical Modeling for Health Policy Decisions
• GIS in Public Health Decision Making
• Evidence-Based Approaches to Infectious and Non-communicable Diseases

This Collection supports and amplifies research related to SDG 3: Good Health and Well-being.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.