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Objectives: Opioids play a significant role in the effective management of cancer-related pain. The COVID-19 lock down may have reduced access to opioids and caused a decline in the use of prescription of opioids among cancer survivors. This study compared opioid prescription rates among cancer survivors before and after the onset of COVID-19 pandemic using real-world electronic health records (EHR). Method(s): Cohort analyses of cancer patients using data from EHR database from the TriNetX, a global federated health research network across 76 healthcare organizations. We analyzed changes in prescription opioid use before (March 1, 2018, through March 1, 2019) and after onset of COVID-19 (April 01, 2020, through March 2021) among cancer survivors. The key outcome variable was any opioid prescription within 1 year of cancer diagnosis. One-to-one propensity score matching was used to balance the characteristics (age, sex, race, diagnoses including diabetes, hypertensive diseases, overweight, mood disorders, and visual disturbances) of the two cohorts. Data were analyzed using the TriNetX platform. Result(s): There were 1,502,143 cancer survivors before COVID-19 and 1,412,599 cancer survivors after the onset of COVID-19. The one-to-one propensity-score match yielded 1,382,561 cancer patients, mean age 64 at cancer diagnosis, and 73% were white. Percentage of opioid use among cancer patients declined from 35.6% before the COVID-19 to 35.1% after the onset of the pandemic (OR=0.976, 95% CI 0.971-0.981). Average number of opioid prescriptions within 1 year of cancer diagnosis declined from 5.7 before to 5.3 after the COVID-19 onset (p<0.001). Conclusion(s): Among cancer survivors, a small decline in prescription opioid use was observed after the onset of COVID-19 pandemic. Future studies are needed to distinguish the impact of revised guidelines, opioid prescription policy changes, and COVID-19 lock down on lower rates of prescription opioid use among cancer survivors.Copyright © 2023
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Background: During the first months of the COVID-19 pandemic presentations to emergency psychiatric services sharply declined, despite no significant change in the incidence of psychosis. Aim(s): To investigate the impact of COVID-19 on the duration of untreated psychosis (DUP) in a first-episode service. Method(s): Data was collected by the specialized treatment early in psychosis (STEP) clinic to compare the DUP pre vs. early and late pandemic stages. The onset of the pandemic was defined as the 15th of March 2020, based on an analysis of case numbers and the advent of restrictions. Outcome measures were DUP total (the time elapsed between onset of psychosis and enrolment in the STEP clinic), DUP demand (the time from onset of psychosis to first antipsychotic prescription), and DUP supply (the time from first antipsychotic prescription to enrolment into STEP). Result(s): DUP total decreased significantly (p = .008) during the early pandemic compared with pre-pandemic from a median of 208 (IQR, 24-1020.0) to 55.5 days (IQR, 8.0-560.0). During the late pandemic stage, DUP total increased back to a median of 153.5 days (IQR, 1.0- 885.0). DUP demand decreased significantly (p = .001) during the early pandemic compared to pre-pandemic from a median of 117 (IQR, 17.0-714.0) to 35 days (IQR, 2.0-541.0) and then reduced further to 27.5 (IQR, 0.0-690.0) days during the late pandemic. No significant changes were found in DUP supply (p = .24) across the different stages of the pandemic. This is the first study to show a reduction in DUP associated with the pandemic.
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Coronavirus disease 2019 (COVID-19), as a public health emergency, is a serious threat to human health. Cancer patients have a high risk of being infected with COVID-19. As one of important means of cancer treatment, radiotherapy has become an important alternative to surgery during the epidemic of COVID-19. The radiotherapy department of Peking Union Medical College Hospital ensured the smooth development of radiotherapy work on the setup of prevention and control systems for COVID-19 by establishing admission strategies for cancer patients, disinfection, isolation, daily management measures, scientific exploration, and clinical practice. In this paper, the associated strategies are summarized and analyzed, which can provide experience and reference for radiotherapy treatment under public health emergencies.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.
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State and local governments have imposed health policies to contain the spread of COVID-19 since it had a serious impact on human daily life. However, the public stance on these measures may be time-varying. It is likely to escalate the infection in the area where the public is negative or resistant. To take advantage of the correlation between public stance on health policies and the COVID-19 statistics, we propose a novel framework, Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement (MP3), which is composed of three modules: (1) Stance awareness module to make stance detection on health policies from users' tweets in social media and convert them into a stance time series. (2) Temporal feature extraction module that applies Convolution Neural Network and Recurrent Neural Network to extract and fuse local patterns and long-term correlations from COVID-19 statistics. Moreover, a Stance Latency-aware Attention is proposed to capture dynamic social effects and fuse them with temporal features. (3) Multi-task prediction module to adopt Graph Convolution Network to model the spread of pandemic and employ multi-task learning to simultaneously predict COVID-19 statistics and the trend of public stance on health policies. The proposed framework outperforms state-of-the-art baselines on both confirmed cases and deaths prediction tasks. © 2022 IEEE.
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread worldwide and threatened human's health. With the passing of time, the epidemiology of coronavirus disease 2019 evolves and the knowledge of SARS-CoV-2 infection accumulates. To further improve the scientific and standardized diagnosis and treatment of maternal SARS-CoV-2 infection in China, the Chinese Society of Perinatal Medicine of Chinese Medical Association commissioned leading experts to develop the Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection under the guidance of the Maternal and Child Health Department of the National Health Commission. This recommendations includes the epidemiology, diagnosis, management, maternal care, medication treatment, care of birth and newborns, and psychological support associated with maternal SARS-CoV-2 infection. It is hoped that the recommendations will effectively help the clinical management of maternal SARS-CoV-2 infection.Copyright © Wolters Kluwer Health, Inc. All rights reserved.
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News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic. © 2022 Association for Computational Linguistics.
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Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal information into account to better explore spatial-temporal dependencies and makes regions aware of related regions' epidemic situations. The RAGL can also combine with external resources, such as human mobility, to further improve prediction performance. Comprehensive experiments on five real-world epidemic-related datasets (including influenza and COVID-19) demonstrate the effectiveness of our proposed method and show that EpiGNN outperforms state-of-the-art baselines by 9.48% in RMSE. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Macau's urban development model has many unique characteristics, including expansion of the city through sea reclamation, increasing population mainly through immigration, and economic development driven by the gaming industry. Based on data from the Macau Statistics and Census Service, this study uses the Error Correction representation of the Autoregressive Distributed Lag model (ARDL-ECM) to analyze the impact of urban development on the trends of immigration and labor migration in Macau between 1992 and 2019. Results show that both land area and wage level have positive effects on the number of migrant workers and negative effects on the number of immigrants, indicating that Macau is over-dependent on short-term migrant workers. Macau's land and human resources are tilted towards the gaming industry, resulting in a decreasing living environment and resident carrying capacity as the city develops. Therefore, this paper suggests that Macau should reduce the cost of city expansion and improve economic diversity through strengthening cooperation with neighboring mainland cities, hence sparing resources to absorb non-local talent and ensuring sustainable urban development.
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News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic.(1)
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Objectives: The US is amid a national opioid crisis before and during the COVID-19 pandemic. The Food and Drug Administration has approved methadone, buprenorphine, and naltrexone as medications for opioid use disorder (MOUD). This study examined the real-world dispensing of MOUD. Methods: All dispensing pharmacies, clinics, or other dispensers of Schedule II-V controlled substances in California report to the Controlled Substance Utilization Review and Evaluation System (CURES) on the day of prescriptions refills. Leveraging the data of buprenorphine (schedule III) and methadone (Schedule II) prescriptions from Mar 2019-Mar 2021 employing California’s deidentified CURES database, this study examined real-world dispensing of methadone and buprenorphine before (03/19/2019-03/18/2020) and during the pandemic (03/19/2020-03/18/2021). We did not review naltrexone dispensing, which is not a controlled substance. Results: In Mar 2019-Mar 2021, 182,367 patients≥18 in California obtained 875,051 buprenorphine and methadone prescriptions: Before the pandemic, there were 482,965 MOUD prescriptions dispensed to 116,644 patients;since the pandemic, 97,887 patients received 392,086 prescriptions, of which 32,164 patients(as “non-naïve” patients) started their MOUD before Mar 2020. On average, patients refilled their prescriptions 4.1 times/year before the pandemic and 4.0 times/year since the pandemic. The MOUD non-naïve patients (n=32,164) received 8.1 prescriptions/year before Mar 2020 and 7.4 refills/year afterward. The MOUD medications most widely prescribed in Mar 2019-Mar 2021 were buprenorphine (473,206 (98.0%) and 383,297 (97.8%), respectively, before and after the pandemic), which included 802,936 counts of buprenorphine alone and 53,567 combination medications of buprenorphine and naloxone. The number of methadone prescriptions declined from 9,759 before Mar 2020 to 8,789 during the pandemic. Conclusions: Buprenorphine is the leading MOUD prescribed for patients in California. The decline in MOUD dispensing for non-naïve patients may indicate restricted access to medication-assisted treatment under the pandemic. Policymakers should maintain or modify the policy strategies to help support medication access.
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The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed the global Twitter data from January 1st to June 30(th), 2020, to answer two research questions. What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media? Does significant association exist between the changing Covid-19 awareness and the pandemic outbreak? We established a Twitter data mining framework calculating the Ratio index to quantify and track awareness. The lag correlations between awareness and health impacts were examined at global and country levels. Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh. Asian countries showed more disparities in awareness than European countries, and awareness in Eastern Europe was higher than in central Europe. Finally, the Ratio index had high correlations with global mortality rate, global case fatality ratio, and country-level mortality rate, with 21-31, 35-42, and 13-18 leading days, respectively. This study yields timely insights into social media use in understanding human behaviors for public health research.
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The outbreak of COVID-19 in 2020 greatly impacted China's transportation industry. This paper aims to analyze this impact and the indispensable role of public transport control measures in preventing the spread of the epidemic. The impact of SARS and COVID-19 was compared. Taking Hebei Province as an example, the impact of the epidemic on the public transport industry was analyzed. The information of Jincheng public transport network and bus IC card data were selected for analysis. The parameters of bus network density and bus line repetition coefficient were calculated. The number of bus departures of each line before and after Jincheng epidemic was counted. Based on the data of bus IC card, the dynamic network of passenger contact was constructed, and the passenger contact network diagram was drawn. The analysis results can inform and enable urban public transport departments to take control measures when public health emergencies occur. © 2021 CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals. All rights reserved.
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The flood of the Yangtze River has the characteristics of high peak, large quantity and long duration. The Yangtze River Hydrology Bureau summarizes and combs the complete business process chain of flood hydrological monitoring, and gradually constructs the Yangtze River flood hydrological monitoring system. Including station network layout, early warning response, monitoring technology, information processing, results output and other dimensions. The hydrological monitoring system of the Yangtze River flood has been gradually constructed and has been successfully applied in many flood basins. Especially under the special situation of COVID-19 epidemic situation in 2020 and the severe flood situation in the Yangtze River Basin, the scientific and practical nature and practicability of the hydrological monitoring system of the Yangtze River flood are further verified. In view of the shortcomings existing in the existing monitoring system, this paper looks forward to the frontier technologies involved in flood monitoring, and has a certain reference function for flood hydrological emergency monitoring. © 2021 The authors and IOS Press.
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Coronavirus disease 2019 (COVID-19), as a public health emergency, is a serious threat to human health. Cancer patients have a high risk of being infected with COVID-19. As one of important means of cancer treatment, radiotherapy has become an important alternative to surgery during the epidemic of COVID-19. The radiotherapy department of Peking Union Medical College Hospital ensured the smooth development of radiotherapy work on the setup of prevention and control systems for COVID-19 by establishing admission strategies for cancer patients, disinfection, isolation, daily management measures, scientific exploration, and clinical practice. In this paper, the associated strategies are summarized and analyzed, which can provide experience and reference for radiotherapy treatment under public health emergencies. © 2021, Peking Union Medical College Hospital. All rights reserved.
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Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development and people's daily life. Taking for example the virus transmission that may occur after college students return to school, we analyze the quantitative influence of the key factors on the virus spread, including crowd density and self-protection. One Campus Virus Infection and Control Simulation (CVICS) model of the novel coronavirus is proposed in this article, fully considering the characteristics of repeated contact and strong mobility of crowd in the closed environment. Specifically, we build an agent-based infection model, introduce the mean field theory to calculate the probability of virus transmission, and microsimulate the daily prevalence of infection among individuals. The experimental results show that the proposed model in this article efficiently simulates how the virus spreads in the dense crowd in frequent contact under a closed environment. Furthermore, preventive and control measures, such as self-protection, crowd decentralization, and isolation during the epidemic, can effectively delay the arrival of infection peak, reduce the prevalence, and, finally, lower the risk of COVID-19 transmission after the students return to school. IEEE
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Maintaining hand hygiene has been an essential preventive measure for reducing disease transmission in public facilities, particularly during the COVID-19 pandemic. The large number of sanitizer stations deployed within public facilities, such as on university campuses, brings challenges for effective facility management. This paper proposes an IoT sensor network for tracking sanitizer usage in public facilities and supporting facility management using a data-driven approach. Specifically, the system integrates low-cost wireless sensors, LoRaWAN, and cloud-based computing techniques to realize data capture, communication, and analysis. The proposed approach was validated through field experiments in a large building on a university campus to assess the network signal coverage and effectiveness of sensor operation for facility monitoring. The results show that a LoRaWAN created from a single gateway can successfully connect to sensors distributed throughout the entire building, with the sensor nodes recording and transmitting events across the network for further analysis. Overall, this paper demonstrates the potential of leveraging the IoT-based Sanitizer Station Network to track public health mitigation methods in a large facility, which ultimately contributes to reducing the burden of maintaining public health during and post-pandemic.
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Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge graphs, citation networks to social networks. Graphs of this type are usually large-scale but only a small subset of vertices are related in downstream tasks. Current methods are too expensive to this setting as the complexity is at best linear-dependent on both the number of nodes and edges. In this paper, we propose a new method, namely Dynamic Personalized PageRank Embedding (DynamicPPE) for learning a target subset of node representations over large-scale dynamic networks. Based on recent advances in local node embedding and a novel computation of dynamic personalized PageRank vector (PPV), DynamicPPE has two key ingredients: 1) the per-PPV complexity is O (m d / ϵ) where m, d, and ϵ are the number of edges received, average degree, global precision error respectively. Thus, the per-edge event update of a single node is only dependent on d in average;and 2) by using these high quality PPVs and hash kernels, the learned embeddings have properties of both locality and global consistency. These two make it possible to capture the evolution of graph structure effectively. Experimental results demonstrate both the effectiveness and efficiency of the proposed method over large-scale dynamic networks. We apply DynamicPPE to capture the embedding change of Chinese cities in the Wikipedia graph during this ongoing COVID-19 pandemic. https://en.wikipedia.org/wiki/COVID-19_pandemic. Our results show that these representations successfully encode the dynamics of the Wikipedia graph. © 2021 ACM.
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To prevent the spread of coronavirus disease 2019 (COVID-19), preliminary temperature measurement and mask detection in public areas are conducted. However, the existing temperature measurement methods face the problems of safety and deployment. In this paper, to realize safe and accurate temperature measurement even when a person's face is partially obscured, we propose a cloud-edge-terminal collaborative system with a lightweight infrared temperature measurement model. A binocular camera with an RGB lens and a thermal lens is utilized to simultaneously capture image pairs. Then, a mobile detection model based on a multi-task cascaded convolutional network (MTCNN) is proposed to realize face alignment and mask detection on the RGB images. For accurate temperature measurement, we transform the facial landmarks on the RGB images to the thermal images by an affine transformation and select a more accurate temperature measurement area on the forehead. The collected information is uploaded to the cloud in real time for COVID-19 prevention. Experiments show that the detection model is only 6.1M and the average detection speed is 257ms. At a distance of 1m, the error of indoor temperature measurement is about 3%. That is, the proposed system can realize real-time temperature measurement in public areas. © 2021 IEEE.
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Purpose : Vision Threatening Diseases (VTD) (age-related macular degeneration [AMD], cataract, diabetic retinopathy [DR], and glaucoma) account for 37% of all blindness. Screening and follow-up are crucial in preserving vision. During COVID-19, clinics reduced access, using telemedicine for diagnosis and follow-ups. The efficacy of remote screening and triage in the management of single or multiple VTDs was evaluated. Methods : We screened 41 subjects (19-85 years, 37% male, 17% Caucasian) (20 controls, 21 subjects). Demographics, 45-degree retinal photos, ganglion cell complex (GCC), and optic nerve head (ONH) images were collected using a non-contact puff-tonometer, nonmydriatic retinal camera, and an OCTA. Demographics and images were transmitted to two readers (onsite telemedicine screener [TS] and remote ophthalmologist [RO]) for triage. Triage was categorized: immediate referral to specialist, follow-up in person via clinic or telemedicine visit, or no follow-up necessary during COVID. Results : TS made 19 referrals (46%), 6 in person follow-ups (15%), 15 no follow-ups (37%);RO made 17 referrals (41%), 2 in person follow-ups (5%), 22 no follow-ups (54%). TS identified 12 subjects as possible VTD(s) while RO identified 11 subjects. TS and RO agreed on 8 glaucoma, 7 cataract, 3 DR, and 3 and 2 AMD cases, respectively. Glaucoma was identified using IOPs, retinal fundoscopy, and OCT imaging. Mean intraocular pressures were 12.9 and 15.7 (OD, OS) in glaucoma and 14.2 and 14.0 in controls. Fundoscopy was used for overall retinal health while OCT images were used to analyze GCC, ONH, nerve fiber layer, cup to disc ratio, and anterior chamber angles. AMD and DR were identified by fundoscopy and OCT imaging. 11 of the subjects were known clinic patients;both RO and TS referred all 11 to specialty clinics, matching the in-person clinic management. Conclusions : During the COVID pandemic, triaging patients can minimize person-toperson contact and help control the spread of the virus. Both readers agreed on the management and triage of a variety of patients with TS and RO differing only on 2 referrals and 4 in person follow-ups. Telemedicine is a promising alternative to in-person patient care for management and triage of vision threatening diseases. Further enrollment and follow-up are needed to increase robustness.
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Purpose : The Centers for Disease Control reports 28.2% of surveyed US adults had reduced access to medical care (June/August 2020) due to the COVID-19 pandemic, with 8.9% reporting reduced access to vision care. A non-mydriatic digital retinal camera was piloted for deployment to the Emergency Department (ED) to help address this gap in vision care. Referrals for clinical follow-up in vision threatening diseases (VTDs) such as age-related macular degeneration, cataracts, diabetic retinopathy (DR), and glaucoma were assessed with human readers. Artificial Intelligence (AI) deep learning software was evaluated in known DR cases. Methods : 33 patients with known VTDs (48.48% male, avg 59.33 years) and 36 control subjects (41.67% male, avg 31.33 years) were included in tele-ophthalmology screening. A Canon CR-2 Plus AF non-mydriatic retinal camera captured 45-degree angle color and auto-fluorescence images of the eyes. Images (136 eyes) were graded by a certified telemedicine reader on site and an off-site clinical ophthalmologist following International Clinical Diabetic Retinopathy Disease Severity Scale (ICDRSS). Intergrader agreement between readers was evaluated with Cohen's kappa. An automated deep learning screening software optimized for DR (SELENA+, EyRIS Pte Ltd, Singapore) performed independent validation of readable color fundus images (17 eyes). Results : 5.07% of images were deemed unreadable by graders due to poor quality. Intergrader agreement for subject referral was κ = 0.710 (95% CI 0.545-0.875, p<.0005), with the clinical ophthalmologist generating more referrals than the telemedicine reader. Readers had 96.97% sensitivity (95% CI 91.12-1.028) and 72.22% specificity (95% CI 57.59- 86.85) in detecting referable disease. Positive predictive value was 76.19% (CI 63.31%- 89.07%) and negative predictive value was 96.30% (CI 89.17%- 1.034%). Of the 10 false positives, 6 were referred for rule out of glaucoma. Four had early stage cataracts that were deemed nonurgent. SELENA+ referred 100% of the known 9 DR patients. Conclusions : Tele-ophthalmology deployment in the ED helps limit patient and staff exposure to SARS-CoV-2 without sacrificing evaluation for VTDs. Tele-ophthalmology readers err on the side of caution to avoid missing VTD in a given patient. Use of AI can help keep strict adherence to referral guidelines.