Global temperatures will continue to rise due to climate change, with high temperature periods expected to increase in intensity, frequency, and duration. Infectious diseases, including vector-borne diseases such as dengue fever and malaria, waterborne diseases such as cholera, and foodborne diseases such as salmonellosis are influenced by temperature and other climatic variables, thus contributing to higher disease burden and associated healthcare costs, particularly in socioeconomically disadvantaged regions. Targeted efforts and investments are therefore needed to support low and middle income countries to prepare for and respond to the increasing infectious disease threats posed by rising temperatures. This can be facilitated by the development and refinement of robust disease and entomological surveillance and early warning systems with integration of climatic information that promote enhanced understanding of the geographic distribution of disease risk. To enhance healthcare workforce capacity and capability to respond to these public health threats, medical curricula and continuing professional education programmes for healthcare providers must include evidence based components on the impacts of climate change on infectious diseases.

Background: Workplace heat exposure can cause a series of heat-related illnesses and injuries. Protecting workers especially those undertake work outdoors from the risk of heat strain is a great challenge for many workplaces in China under the context of climate change. The aim of this study is to investigate the perceptions and adaptation behaviors of heat exposure among construction workers and to provide evidence for the development of targeted heat adaptation strategies nationally and internationally. Methods: In 2020, we conducted a cross-sectional online questionnaire survey via WeChat Survey Star in China, using a purposive snowball sampling approach. A total of 326 construction workers submitted completed questionnaires. The perceptions of workplace heat exposure were measured using seven indicators: concerns over high temperature, perception of high temperature injury, attitudes towards both heat-related training and regulations, adjustment of working habits during heat, heat prevention measures in the workplace, and reduction of work efficiency. Bivariate and multivariate regression analyses were used to identify the factors significantly associated with workers’ heat perceptions and behavioral responses. Results: 33.3% of the respondents were moderately or very concerned about heat exposure in the workplace. Less than half of the workers (43.8%) were worried about heat-related injuries. Workers who have either experienced work-related injuries (OR = 1.30, 95% CI 1.03–1.62) or witnessed injuries to others during high temperatures (OR = 1.12, 95% CI 1.02–1.27) were more concerned about heat exposure compared to other workers. Most respondents (63.5%) stated that their work efficiency declined during extremely hot weather. The factors significantly associated with a reduction of work efficiency included undertaking physically demanding jobs (OR = 1.28, 95% CI 1.07–1.54) and witnessing other workers’ injuries during high temperatures (OR = 1.26, 95% CI 1.11–1.43). More than half of the workers were willing to adjust their work habits to adapt to the impact of high temperatures (81.6%). The internet was the most common method to obtain heat prevention information (44.7%), and the most frequently used heat prevention measure was the provision of cool drinking water (64.8%). Conclusions: Chinese construction workers lack heat risk awareness and are not well prepared for the likely increasing heat exposure in the workplace due to global warming. Therefore, there is a need to improve their awareness of heat-related injuries, strengthen high temperature related education and training, and update the current heat prevention policies to ensure compliance and implementation.

Global warming is raising increasing concerns about its impact on worker productivity, particularly in industries like construction where outdoor physically demanding jobs are prevalent. This study aimed to perform a meta-analysis to assess the existing evidence on the impact of heat exposure on productivity loss among construction workers. We conducted a comprehensive literature search across six databases—Web of Science, PubMed, Embase, Scopus, ScienceDirect, and IEEE—covering the period from database inception to September 18, 2024. The Joanna Briggs Institute (JBI) critical appraisal checklist was used for quality assessment. A random-effect model meta-analysis was performed, and publication bias was evaluated by Egger’s and Begg’s tests. From an initial pool of 1209 studies, 14 met the inclusion criteria, representing data from 2387 workers. Our findings indicate that 60% (95% CI: 0.48–0.72, p < 0.01) of construction workers exposed to elevated temperatures experienced significant productivity loss. Productivity loss was more pronounced when the Wet Bulb Globe Temperature (WBGT) exceeded 28 °C or when ambient temperatures surpassed 35 °C. Furthermore, workers aged over 38 (proportion = 0.61, 95% CI: 0.49–0.72) and teams with female workers (ratio = 0.74, 95% CI: 0.60–0.87) were more susceptible to productivity loss. This review highlights heat exposure as a significant factor affecting productivity in the construction industry. We recommend prioritizing the protection of vulnerable groups such as women and older workers, developing innovative technologies and equipment for working in hot conditions, and improving the working environment to safeguard workers’ health and productivity. Further research is needed to investigate the long-term health impacts of heat exposure and develop strategies for optimizing microclimate management in construction settings.

Does ambient temperature exposure affect outcomes including clinical pregnancy and live birth in women undergoingIVF? Both extreme cold and hot ambient temperatures were significantly associated with adverse pregnancy outcomesof IVF cycles. Heat exposure has been linked to adverse pregnancy outcomes worldwide. However, the effect of ambienttemperature on infertile women undergoing IVF treatment is unclear. A retrospective cohort study was conducted from a database of 3452 infertile women who underwenttheir first fresh or frozen embryo transfer in the Shanghai First Maternity and Infant Hospital from April 2016 to December 2020. Daily mean ambient temperature exposure for each patient was obtained based on their residential address. Temperature-stratified multiple logistic regression analysis was performed to investigate associations between temperature exposure and pregnancy outcomes after controlling for confounders. Vulnerable sub-groups were identified using forest plots. The clinical pregnancy rate and live birth rate were 45.7% and 37.1%, respectively. Regarding clinical pregnancy, a higher temperature during cold weather was significantly associated with a higher pregnancy rate in the period about 11 weeks before ovarian stimulation (adjusted odds ratio (aOR) = 1.102, 95% CI: 1.012–1.201). Regarding live birth, an increased temperature during cold weather was significantly related to a higher live birth rate in the period after confirmation of clinical pregnancy or biochemical pregnancy, with the aORs of 6.299 (95% CI: 3.949–10.047) or 10.486 (95% CI: 5.609–19.620), respectively. However, a higher temperature during hot weather was negatively associated with the live birth rate in the periods after confirmation of clinical pregnancy or biochemical pregnancy, with the aORs at 0.186 (95% CI: 0.121–0.285) or 0.302 (95% CI: 0.224–0.406), respectively. Moreover, the decline in live birth rates during cold and hot weather was accompanied by increased rates of early miscarriage (P < 0.05). Stratified analyses identified susceptibility characteristics among the participants. Climate monitoring data were used to represent individual temperature exposure levels according to the patient’s residential address in the study. We were not able to obtain information of personal outdoor activity and use of indoor air conditioners in this retrospective study, which may affect actual temperature exposure. This study highlights that the ambient temperature exposure should be taken into account during IVF treatment and afterwards. There is a need to be alert to extremes in cold and hot ambient temperatures, especially during the period of follicle development and pregnancy. With this knowledge, clinicians can scientifically determine the timing of IVF treatment and reinforce patients’ awareness of self-protection to minimize adverse pregnancy outcomes associated with extreme temperatures. This work was supported by a grant from the Clinical Research Plan of Shanghai Hospital Development Center [SHDC2020CR4080], a grant from the Science and Technology Commission of Shanghai Municipality [19411960500], and two grants from the National Natural Science Foundation of China [81871213, 81671468]. B.W.M. is supported by a NHMRC Investigator grant (GNT1176437). B.W.M. reports consultancy for ObsEva, and research grants from Merck KGaA, Ferring and Guerbet. The other authors have no conflict of interest to declare. N/A.

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients’ quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan–Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.

Workplace may not only increase the risk of heat-related illnesses and injuries but also compromise work efficiency, particularly in a warming climate. This study aimed to utilize machine learning (ML) and deep learning (DL) algorithms to quantify the impact of temperature discomfort on productivity loss among petrochemical workers and to identify key influencing factors. A cross-sectional face-to-face questionnaire survey was conducted among petrochemical workers between May and September 2023 in Fujian Province, China. Initial feature selection was performed using Lasso regression. The dataset was divided into training (70%), validation (20%), and testing (10%) sets. Six predictive models were evaluated: support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), Gaussian Naive Bayes (GNB), multilayer perceptron (MLP), and logistic regression (LR). The most effective model was further analyzed with SHapley Additive exPlanations (SHAP). Among the 2393 workers surveyed, 58.4% (1,747) reported productivity loss when working in high temperatures. Lasso regression identified twenty-seven predictive factors such as educational level and smoking. All six models displayed strong prediction accuracy (SVM = 0.775, RF = 0.760, XGBoost = 0.727, GNB = 0.863, MLP = 0.738, LR = 0.680). GNB model showed the best performance, with a cutoff of 0.869, accuracy of 0.863, precision of 0.897, sensitivity of 0.918, specificity of 0.715, and an F1-score of 0.642, indicating its efficacy as a predictive tool. SHAP analysis showed that occupational health training (SHAP value: -3.56), protective measures (-2.61), and less physically demanding jobs (-1.75) were negatively associated with heat-attributed productivity loss, whereas lack of air conditioning (1.92), noise (2.64), vibration (1.15), and dust (0.95) increased the risk of heat-induced productivity loss. Temperature discomfort significantly undermined labor productivity in the petrochemical sector, and this impact may worsen in a warming climate if adaptation and prevention measures are insufficient. To effectively reduce heat-related productivity loss, there is a need to strengthen occupational health training and implement strict controls for occupational hazards, minimizing the potential combined effects of heat with other exposures.