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BACKGROUND: Although ambient temperature has been linked with injury incidence, there have been few nationwide studies to quantify the temperature-related risk and burden of cause-specific injury hospitalizations. Additionally, the impact of human-induced climate change to injury burden remains unknown. OBJECTIVES: Our objectives are to examine the associations between ambient temperature and injury hospitalizations from various causes and to quantify the contribution of human-induced warming to the heat-related burden. METHODS: We collected injury hospitalization data from a nationwide hospital-based registry in China during 2000-2019. Using a time-stratified case-crossover design, we investigated the associations between daily mean temperature ([degrees]C) and cause-specific injury hospitalizations. We also quantified the burden of heat-related injuries under the scenarios with and without anthropogenic forcing, using the Detection and Attribution Model Intercomparison Project to assess the contribution of human-induced warming. RESULTS: Our study included a total of 988,087 patients with hospitalization records for injuries. Overall, compared to the temperature at minimum risk of hospitalization (--12.1[degrees]C), the relative risk of hospitalization at extreme hot temperature (30.8[degrees]C, 97.5th percentile) was 1.18 [95% confidence interval (CI): 1.14, 1.22], with an approximately linear association between temperature and hospitalization. Vulnerability to heat-related injuries was more pronounced among males, young ( DISCUSSION: This nationwide study presents new evidence of significant associations between temperature and cause-specific injury hospitalizations in China and highlights the increasing contribution of human-induced warming to the injury burden. https://doi.org/10.1289/EHP14057, Introduction External causes of injury, including transportation-related injuries, self-harm, falls, and other injuries, are significant causes of death, particularly among young and middle-aged individuals. (1-3) According to the global burden [...]

Background: The most effective method for improving the prognosis of lung cancer is the application of low-dose computed tomography (LDCT) for pulmonary nodule screening in populations at high risk. Timely diagnosis and treatment of early-stage lung cancer can contribute to higher long-term survival rates. However, it remains difficult to differentiate malignant from benign pulmonary nodules measuring 8–15 mm, and avoid overtreatment on the one hand and delayed diagnosis on the other hand. In this consensus paper, we aimed to clarify the definition of “high-risk indeterminate pulmonary nodules (IPNs)” and discuss appropriate evaluation and management to facilitate timely diagnosis of lung cancer to improve lung cancer outcome. Direction for future research was discussed. Methods: A multi-disciplinary panel of doctors and IT experts from Asia, Canada and the U.S. were invited to participate. Published evidence and consensus guidelines were used to develop this consensus was clarified. Their evaluation and management were discussed. Findings: The experts believed that the prevalence of pulmonary nodules was very high, and it that was difficult to diagnose early-stage lung cancer due to the small size of the nodules, often leading to delayed diagnosis or overtreatment. To address this issue and to improve long-term outcome, the panel considered important to revise the classification of high-risk IPNs, (1) as pulmonary nodules that cannot be clearly diagnosed with non-surgical biopsy procedures, but is highly suspicious for early-stage lung cancer. The panel also recommends the most responsible should arrange imaging evaluations and follow-ups, taking new technologies into account. Artificial intelligence (AI) assessment based on the Medical Internet of Things (MIoT) can be combined with expert opinion to form a human–computer multidisciplinary team (MDT) that can fully implement the three core procedures of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing. This will help to upgrade the non-standard diagnosis and treatment, the so-called “handicraft workshop model”, to a modern assembly-line model that meets international standards. The MIoT technology, which has the potential to realize “simplification of complex problems, digitalization of simple problems, programming of digital problems, and systematization of programming problems”, can promote the homogeneous evaluation of pulmonary nodules by enhancing both the sensitivity and the specificity of detecting early-stage lung cancer, in order to avoid delayed diagnosis and overtreatment. Conclusion: To optimize the evaluation of early-stage lung cancer, and to avoid delayed diagnosis and overtreatment, it is necessary to propose and promote the concept of “high-risk IPNs”. The application of current technologies, AI, and a human–computer MDT, will facilitate improvement in nodule evaluation, transforming the current diagnosis and treatment model, which is akin to production in handicraft workshops, into a modern assembly-line model that meets international standards, and will ultimately result in better prognosis.