Patient delay in a coronavirus disease 2019 (COVID-19) outbreak in Tianjin, China from January to February 2020.

BACKGROUND: Patient delay of COVID-19 patients occurs frequently, which poses a challenge to the overall epidemic situation. In this study, we aimed to evaluate the extent of patient delay, explore its factors, and investigate the effects of patient interval on epidemic situation. METHODS: A retrospective cohort study was conducted with 136 COVID-19 patients in Tianjin, China. Factors associated with patient delay were explored using logistic regression models. The relationship was investigated by spearman correlation analysis and mean absolute error between patient interval of lagging days and epidemic situation. RESULTS: The factors associated with patient delay of COVID-19 patients were mainly the imported cases, the first presentation to a tertiary hospital, close contacts and spatial accessibility to fever clinic. The longer the patient intervals of lagging days, the greater the number of new-onset and confirmed cases in 3-4 and 5-7 days after the first day symptoms, respectively. CONCLUSION: Identification and quarantine of close contacts, promoting the spatial accessibility to fever clinics and creating public awareness are crucial to shortening patient delays to flat the curve for COVID-19.

Reducing exposure to COVID-19 by improving access to fever clinics: an empirical research of the Shenzhen area of China.

BACKGROUND: The current 2019 coronavirus disease (COVID-19) pandemic is hitting citizen's life and health like never before, with its significant loss to human life and a huge economic toll. In this case, the fever clinics (FCs) were still preserved as one of the most effective control measures in China, but this work is based on experience and lacks scientific and effective guidance. Here, we use travel time to link facilities and populations at risk of COVID-19 and identify the dynamic allocation of patients' medical needs, and then propose the optimized allocation scheme of FCs. METHODS: We selected Shenzhen, China, to collect geospatial resources of epidemic communities (ECs) and FCs to determine the ECs' cumulative opportunities of visiting FCs, as well as evaluate the rationality of medical resources in current ECs. Also, we use the Location Set Covering Problem (LSCP) model to optimize the allocation of FCs and evaluate efficiency. RESULTS: Firstly, we divide the current ECs into 3 groups based on travel time and cumulative opportunities of visiting FCs within 30 min: Low-need communities (22.06%), medium-need communities (59.8%), and high-need communities (18.14%) with 0,1-2 and no less than 3 opportunities of visiting FCs. Besides, our work proposes two allocation schemes of fever clinics through the LSCP model. Among which, selecting secondary and above hospitals as an alternative in Scheme 1, will increase the coverage rate of hospitals in medium-need and high-need communities from 59.8% to 80.88%. In Scheme 2, selecting primary and above hospitals as an alternative will increase the coverage rate of hospitals in medium-need and high-need communities to 85.29%, with the average travel time reducing from 22.42 min to 17.94 min. CONCLUSIONS: The optimized allocation scheme can achieve two objectives: a. equal access to medical services for different types of communities has improved while reducing the overutilization of high-quality medical resources. b. the travel time for medical treatment in the community has reduced, thus improving medical accessibility. On this basis, during the early screening in prevention and control of the outbreak, the specific suggestions for implementation in developing and less developed countries are made.

[Spatial accessibility of fever clinics for multi-tiered prevention and control on COVID-19 in Beijing].

OBJECTIVE: To simulate the different prevalence of corona virus disease 2019 (COVID-19) in Beijing as the spreading and the outbreak city and analyze the response capacity of its medical resources of fever clinics, and to provide a scientific basis for optimizing the spatial layout in Beijing under severe epidemics. METHODS: The study obtained epidemiological indicators for COVID-19, factors about medical resources and population movement as parameters for the SEIR model and utilized the model to predict the maximum number of infections on a single day at different control levels in Beijing, simulated as an epidemic spreading city and an epidemic outbreak city respectively. The modified two-step floating catchment area method under ArcGIS 10.6 environment was used to analyze spatial accessibility to fever clinics services for the patients in Beijing. RESULTS: According to the results of the SEIR model, the highest number of infections in a single day in Beijing simulated as an epidemic spreading city at low, medium and high levels of prevention and control were 8 514, 183, and 68 cases, the highest number of infections in a single day in Beijing simulated as an outbreak city was 22 803, 10 868 and 3 725 cases, respectively. The following result showed that Beijing was simulated as an epidemic spreading city: among the 585 communities in Beijing, under the low level of prevention and control, there were 17 communities (2.91%) with excellent accessibility to fever clinics, and that of 41 communities (7.01%) with fever clinics was good. Spatial accessibility of fever clinics in 56 communities (9.57%) was ranked average, and 62 communities' (10.60%) accessibility was fair and 409 communities (69.91%) had poor accessibility; at the medium level of prevention and control, only the west region of Fangshan District and Mentougou District, the north region of Yanqing District, Huairou District and Miyun District had poor accessibility; under the high level of prevention and control, 559 communities' (95.56%) had excellent accessibility. The accessibility in 24 communities (4.10%) was good and in 2 communities (0.34%) was average. In brief, the existing fever clinics could meet the common demand. Beijing was simulated as an outbreak city: under the low level of prevention and control, only 1 community (0.17%) had excellent accessibility to fever clinics, and 5 communities (0.86%) had good accessibility. The accessibility of fever clinics in 10 communities (1.71%) was average and in 12 communities (2.05%) was fair. The accessibility of fever clinics in 557 communities (95.21%), nearly all areas of Beijing, was poor; under the middle and high level of prevention and control, the accessibility of ecological conservation areas was also relatively poor. CONCLUSION: The distribution of fever clinic resources in Beijing is uneven. When Beijing is simulated as an epidemic spreading city: under the high level of prevention and control, the number of fever clinics can be appropriately reduced to avoid cross-infection; at the medium level of prevention and control, the fever clinics can basically meet the needs of patients with fever in Beijing, but the accessibility of fever clinics in ecological conservation areas is insufficient, and priority should be given to the construction of fever clinics in public hospitals above the second level in the ecological conservation areas. When the level of prevention and control is low, the accessibility of fever clinics in ecological conservation areas is poor. Priority should be given to the construction of fever clinics in ecological conservation areas, and temporary fever sentinels can be established to relieve the pressure of fever clinics. When Beijing is simulated as an outbreak city and has low prevention and control, due to a large number of infections, it is necessary to upgrade the prevention and control level to reduce the flow of people to curb the development of the epidemic.

Consideration of the Management of Pediatric Fever Clinics During the Novel Coronavirus Pneumonia Outbreak.

Since the outbreak of 2019 novel coronavirus (2019-nCoV) infection in Wuhan City, China, pediatric cases have gradually increased. It is very important to prevent cross-infection in pediatric fever clinics, to identify children with fever in pediatric fever clinics, and to strengthen the management of pediatric fever clinics. According to prevention and control programs, we propose the guidance on the management of pediatric fever clinics during the nCoV pneumonia epidemic period, which outlines in detail how to optimize processes, prevent cross-infection, provide health protection, and prevent disinfection of medical staff. The present consideration statement summarizes current strategies on the pre-diagnosis, triage, diagnosis, treatment, and prevention of 2019-nCoV infection, which provides practical suggestions on strengthening the management of pediatric fever clinics during the nCoV pneumonia epidemic period.

Hydroxychloroquine Safety Outcome within Approved Therapeutic Protocol for COVID-19 Outpatients in Saudi Arabia.

BACKGROUND: Global healthcare is challenged following the COVID-19 pandemic, since late 2019. Multiple approaches have been performed to relieve the pressure and support existing healthcare. The Saudi Arabian Ministry of Health (MOH) launched an initiative to support the National Healthcare System. Since the 5th of June 2020, 238 outpatient fever clinics were established nationwide. This study aimed to assess the safety outcome and reported adverse events from hydroxychloroquine use among suspected COVID-19 patients. METHOD: A cross-sectional study included 2,733 patients subjected to MOH treatment protocol (hydroxychloroquine) and followed-up within 3-7 days after initiation. Data was collected through an electronic link and cross-checked with the national database (Health Electronic Surveillance Network, HESN) and reports from the MOH Morbidity and Mortality (M&M) Committee. RESULTS: 240 patients (8.8%) discontinued treatment because of side effects (4.1%) and for non-clinical reasons in the remaining (4.7%). Adverse effects were reported among (6.7%) of all studied participants, including mainly cardiovascular (2.5%, 0.15% with QTc prolongation), and gastrointestinal (2.4%). No Intensive Care Unit admission or death were reported among these patients. CONCLUSION: Our results show that hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.

A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics.

BACKGROUND: Currently, the need to prevent and control the spread of the 2019 novel coronavirus disease (COVID-19) outside of Hubei province in China and internationally has become increasingly critical. We developed and validated a diagnostic model that does not rely on computed tomography (CT) images to aid in the early identification of suspected COVID-19 pneumonia (S-COVID-19-P) patients admitted to adult fever clinics and made the validated model available via an online triage calculator. METHODS: Patients admitted from January 14 to February 26, 2020 with an epidemiological history of exposure to COVID-19 were included in the study [model development group (n=132) and validation group (n=32)]. Candidate features included clinical symptoms, routine laboratory tests, and other clinical information on admission. The features selection and model development were based on the least absolute shrinkage and selection operator (LASSO) regression. The primary outcome was the development and validation of a diagnostic aid model for the early identification of S-COVID-19-P on admission. RESULTS: The development cohort contained 26 cases of S-COVID-19-P and seven cases of confirmed COVID-19 pneumonia (C-COVID-19-P). The final selected features included one demographic variable, four vital signs, five routine blood values, seven clinical signs and symptoms, and one infection-related biomarker. The model's performance in the testing set and the validation group resulted in area under the receiver operating characteristic (ROC) curves (AUCs) of 0.841 and 0.938, F1 scores of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and precision of 0.400 and 0.500, respectively. The top five most important features were age, interleukin-6 (IL-6), systolic blood pressure (SYS_BP), monocyte ratio (MONO%), and fever classification (FC). Based on this model, an optimized strategy for the early identification of S-COVID-19-P in fever clinics has also been designed. CONCLUSIONS: A machine-learning model based solely on clinical information and not on CT images was able to perform the early identification of S-COVID-19-P on admission in fever clinics with a 100% recall score. This high-performing and validated model has been deployed as an online triage tool, which is available at https://intensivecare.shinyapps.io/COVID19/.

"An integrated system, three separated responsibilities", a new fever clinic management model, in prevention and control of novel coronavirus pneumonia.

BACKGROUND: Since December 2019, there have been many new cases of coronavirus pneumonia in Wuhan, Hubei Province, which has gradually spread throughout the country. AIM: To explore our hospital's innovative management system to ensure the efficient operation of fever clinics during the epidemic, since controlling the spread of disease is an important way to prevent and control the epidemic. METHODS: In total, 200 outpatients with fever at our hospital between November 2019 and July 2020 were selected and allocated into two groups. RESULTS: The fever clinic in our hospital operated smoothly, and infection with the novel coronavirus disease (COVID-19) has not been reported in our hospital. Additionally, we did not have any cases of missed diagnosis. The awareness regarding COVID-19 infection sources, transmission routes, early symptoms, and preventive measures was significantly higher in our fever clinic than in those of the pre-management group. CONCLUSION: "An integrated system, three separate responsibilities" ensured the efficient functioning of our fever outpatient clinic and early screening of COVID-19 cases, which effectively curbed the transmission of COVID-19 and hence prevented COVID-19 pneumonia epidemic in our hospital, ultimately achieving the maximum effect of epidemic prevention and control.