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Background/Objectives: The external environment constantly influences human health through many factors, including air quality, access to green spaces, exposure to pollutants, and climate change. Contamination poses a substantial threat to human well-being; conversely, environmental factors also positively impact health. The purpose of this study is to provide a comprehensive review of the complex relationship between various environmental factors and human health. While individual studies have explored specific aspects, a broader integrative understanding is lacking. Methods: Through databases (PubMed, Cochrane, Copernicus), 4888 papers were identified, with 166 selected for detailed analysis. Results: We summarized recent research, identifying multiple associations between environmental factors such as air pollution, climate change, solar radiation, and meteorological conditions and their impact on various health outcomes, including respiratory, cardiovascular, metabolic and gastrointestinal, renal and urogenital, neurological and psychological health, infectious and skin diseases, and major cancers. We use chord diagrams to illustrate these links. We also show the interaction between different environmental factors. Findings begin with exploring the direct impact of environmental factors on human health; then, the interplay and combined effects of environmental factors, elucidating their (often indirect) interaction and collective contribution to human health; and finally, the implications of climate change on human health. Conclusions: Researchers and policymakers need to consider that individuals are exposed to multiple pollutants simultaneously, the "multipollutant exposure phenomenon". It is important to study and regulate environmental factors by considering the combined impact of various pollutants rather than looking at each pollutant separately. We emphasize actionable recommendations and solutions. [ABSTRACT FROM AUTHOR]

This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia, which is crucial for diabetes management. We analyzed the impact of missing samples at different times before meals using two datasets: virtual patient data and real patient data. The study uses six commonly used machine learning models under varying conditions of missing samples, including custom and random patterns reflective of device failures and arbitrary data loss, with different levels of data removal before mealtimes. Additionally, the study explored different interpolation techniques to counter the effects of missing data samples. The research shows that missing samples generally reduce the model performance, but random forest is more robust to missing samples. The study concludes that the adverse effects of missing samples can be mitigated by leveraging complementary and informative non-point features. Consequently, our research highlights the importance of strategically handling missing data, selecting appropriate machine learning models, and considering feature types to enhance the performance of postprandial hypoglycemia predictions, thereby improving diabetes management. [ABSTRACT FROM AUTHOR]