RESUMEN
INTRODUCTION: The coronavirus disease 19 (COVID-19) pandemic has disrupted healthcare delivery including elective endoscopy. We aimed to determine the prevalence of endoscopy cancellations in the COVID-19 era and identify patient characteristics associated with cancellation due to the pandemic. METHODS: Medical charts were reviewed for adults who cancelled an outpatient endoscopic procedure from 5/2020 to 8/2020. The association of patient characteristics with cancellation of endoscopy due to COVID-19 was assessed using logistic regression. RESULTS: There were 652 endoscopy cancelations with 211 (32%) due to COVID-19, 384 (59%) due to non-COVID reasons, and 57 (9%) undetermined. Among COVID-19 related cancellations, 75 (36%) were COVID-19 testing logistics related, 121 (57%) were COVID-19 fear related, and 15 (7%) were other. On adjusted analysis, the odds of cancellation due to COVID-19 was significantly higher for black patients (OR 2.04, 95% CI 1.07-3.88, p = 0.03), while patients undergoing EGD (OR 0.56, 95% CI 0.31-0.99, p = 0.05) or advanced endoscopy (OR 0.18, 95% CI 0.07-0.49, p = 0.001) had lower odds of cancellation. The odds of cancelling due to COVID-19 testing logistics was significantly higher among black patients (OR 3.12, 95% CI 1.03-9.46, p = 0.05) and patients with Medi-Cal insurance (OR 2.89, 95% CI 1.21-6.89, p = 0.02). CONCLUSION: Black race is associated with an increased risk of COVID-19 related cancellation. Specifically, black patients and those with Medi-Cal are at increased risk of cancellation related to logistics of obtaining pre-endoscopy COVID-19 testing. Racial and socioeconomic disparities in access to endoscopy may be further amplified by the COVID-19 pandemic and warrant further study.
RESUMEN
Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.
RESUMEN
Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.
RESUMEN
In this study, dustfall samples were systematically collected in various regions of Shanghai before and after the occurrence of COVID-19 in December 2019 and December 2020. The magnetic response, content and pollution status of relevant heavy metal elements in the samples were analyzed using environmental magnetism, geochemistry, scanning electron microscopy (SEM) and the enrichment factor (EF) method. The results show that the magnetic particles in the dustfall samples are mainly pseudo-single-domain (PSD) and multi-domain (MD) ferrimagnetic minerals, and Fe, Zn, Cr, and Cu are mainly concentrated in the districts with intensive human activities. Due to restrictions on human activities following the COVID-19 epidemic, both the values of magnetic parameters and the heavy metal pollution level in 2019 are more significant than those in 2020, which is consistent with the Air Quality Index (AQI) results. In addition, magnetic susceptibility (χlf), non-hysteresis remanence (χARM) and saturation isothermal remanence (SIRM) have different degrees of correlation with heavy metal elements, and the correlations with Fe, Pb, Cr and Zn are extremely prominent. The magnetic parameters can effectively and quickly reflect the level of particulate matter pollution, making them a useful tool for monitoring urban air quality.