Big data science in environmental health research

Environmental diseases refer to illnesses caused by exposure to environmental risk factors, such as toxins, chemicals, infectious agents, and air pollution. Modern human complex diseases, such as cardiovascular disease, metabolic syndrome, neurodegenerative diseases, and cancer, are considered environmental diseases, as the development of these diseases is driven by the interactions between environmental factors and genetics.[1] Modern lifestyles, including diets, shift work schedules, and the use of drugs, represent major environmental risk factors that significantly impact public health. Investigating the correlative and causative relationships between environmental factors and diseases is currently a hot research topic. Recent advances in data science and environmental health research utilize large-scale databases to define the complex interplay between environmental and genetic factors involved in the development of environmental, complex diseases.[2] Big data science in environmental health research emphasizes the use of data science techniques to process and analyze large, complex datasets, particularly those related to environmental factors such as air quality, climate change, and other environmental influences on public health. In particular, machine learning-powered prediction and computational modeling tools have been increasingly used in environmental research to process large datasets and elucidate complex relationships between risk factors and system stability.[3] One significant advantage of machine learning approaches is that they can surpass hypothesis-based models to discover effects and patterns that might be missed by hypothesis-based approaches. Big data science has emerged as a transformative tool in environmental health research as information technologies – such as those utilized in environmental monitoring systems, satellite imaging, healthcare records, and social media – develop. The big data science approach allows for identifying health impacts at the population level, gaining personalized health insights, enabling predictive models and early intervention, and addressing inequities in environmental health. However, some challenges in big data science approaches remain.[4] In particular, data integration requires sophisticated data harmonization techniques. Ensuring data quality, completeness, and accuracy is critical to avoid misleading conclusions. Additionally, data interpretation and defining causative effects based on big data analyses are still challenging. However, as technologies develop and modeling systems improve, these difficulties are expected to be resolved. Big data science is a game-changer for environmental health research, opening up new opportunities for a deeper and more comprehensive understanding of the interplay between environmental factors and health and disease. Although challenges remain, big data science will play a unique role in advancing environmental health science and improving public health outcomes globally. Financial support and sponsorship This work is partially supported by the National Institutes of Health grant ES017829 (to K.Z.) and P30 pilot grant (P30 ES020957) from Wayne State University Institute of Environmental Health Sciences. Conflicts of interest Dr. Kezhong Zhang is an Editor-in-Chief of Environmental Disease. The article was subject to the journal’s standard procedures, with peer review handled independently of this Editor and their research groups.