Increasing evolution, prevalence, and outbreaks for rift valley fever virus in the process of breaking geographical barriers.

BACKGROUND: Rift valley fever (RVF) is listed as one of prioritized diseases by WHO. This study aims to describe RVF virus' landscape distribution globally, and to insight dynamics change of its evolution, prevalence, and outbreaks in the process of breaking geographical barriers. METHODS: A systematic literature review and meta-analyses was conducted to estimate RVF prevalence by hosts using a random-effect model. Molecular clock-based phylogenetic analyses were performed to estimate RVF virus nucleotide substitution rates using nucleotide sequences in NCBI database. RVF virus prevalence, nucleotide substitution rates, and outbreaks were compared before and after breaking geographical barriers twice, respectively. RESULTS: RVF virus was reported from 26 kinds of hosts covering 48 countries from 1930 to 2022. Since RVF broke geographical barriers, (1) nucleotide substitution rates significantly increased after firstly spreading out of Africa in 2000, (2) prevalence in humans significantly increased from 1.92 % (95 % CI: 0.86-3.25 %) to 3.03 % (95 % CI: 2.09-4.12 %) after it broke Sahara Desert geographical barriers in 1977, and to 5.24 % (95 % CI: 3.81-6.82 %) after 2000, (3) RVF outbreaks in humans and the number of wildlife hosts presented increasing trends. RVF virus spillover may exist between bats and humans, and accelerate viral substitution rates in humans. During outbreaks, the RVF virus substitution rates accelerated in humans. 60.00 % RVF outbreaks occurred 0-2 months after floods and (or) heavy rainfall. CONCLUSION: RVF has the increasing risk to cause pandemics, and global collaboration on "One Health" is needed to prevent potential pandemics.

Investigation on the possibility of dynamic COVID-Zero strategy in China: a population-based transmission model analysis and economic evaluation.

OBJECTIVE: To explore the feasible and cost-effective intervention strategies to achieve the goal of dynamic COVID-Zero in China. DESIGN: A Susceptible-Exposed-Infectious-Recovered model combined economic evaluation was used to generate the number of infections, the time for dynamic COVID-Zero and calculate cost-effectiveness under different intervention strategies. The model simulated the 1 year spread of COVID-19 in mainland China after 100 initial infections were imported. INTERVENTIONS: According to close contact tracing degree from 80% to 100%, close contact tracing time from 2 days to 1 day, isolation time from 14 days to 7 days, scope of nucleic acid testing (NAT) from 10% to 100% and NAT frequency from weekly to every day, 720 scenarios were simulated. OUTCOME MEASURE: Cumulative number of infections (CI), social COVID-Zero duration (SCD), total cost (TC) and incremental cost-effectiveness ratio. RESULTS: 205 of 720 scenarios could achieve the total COVID-Zero since the first case was reported. The fastest and most cost-effective strategy was Scenario 680, in which all close contacts were traced within 1 day, the isolation time was 14 days and 10% of the national population was randomly checked for NAT every day. In Scenario 680, the CI was 280 (100 initial infections) and the SCD was 13 days. The TC was ¥4126 hundred million and the cost of reducing one infection was ¥47 470. However, when the close contact tracing time was 2 days and the degree of close contact tracing was 80%-90%, the SCD would double to 24-101 days and the TCs increased by ¥16 505 to 37 134 hundred million compared with Scenario 680. CONCLUSIONS: If all close contact was controlled within 1 day, the rapid social COVID-Zero can be achieved effectively and cost-effectively. Therefore, the future prevention and control of emerging respiratory infectious diseases can focus on enhancing the ability of close contact tracing.

Spatial and temporal distribution of emerging airborne viral infectious diseases outbreaks on a global scale.

Aim: This study aimed to explore the spatial and temporal characteristics of emerging airborne viral infectious diseases outbreaks worldwide. Subject and methods: We conducted a systematic literature review on outbreaks of emerging airborne viral infectious diseases and calculated outbreak number and intensity at the country level. Fisher's exact test was used to compare the viral infectious diseases outbreaks in different income-level regions. To identify the major airborne viral infectious diseases outbreaks, we ranked and extracted the leading viral infectious diseases in outbreak number and intensity in each country by year. Results: A total of 2505 outbreaks were reported from 1873 to 2021 across 2010 studies. There were 47 countries (47/130, 36.15%) with more frequent emerging airborne viral infectious disease outbreaks (more than nine outbreaks), and these countries mainly distributed in high-income regions (22/47 countries, 46.81%, p < 0.05), especially in Western Europe (14/47 countries, 29.79%, p < 0.05). The number of overall outbreaks was more in the United States and China than in other countries in different years. Outbreaks of measles and influenza are always frequent and intense. Highly pathogenic human coronaviruses infection caused short-term pandemics during which their outbreak number and intensity exceeded other viruses. Rift valley fever outbreaks in the human population are spreading outside of Africa through the flow of goods and travelers. Conclusion: Countries in high-income regions reported more emerging airborne viral infectious diseases outbreaks, especially in the Western European region, the United States, and China. It is urgent to strengthen collaborative surveillance of emerging airborne viruses, cross-border flow of goods and travelers, and ecological environment to avoid the spread of viral infectious diseases outbreaks worldwide. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01850-3.

A four-generation family transmission chain of COVID-19 along the China-Myanmar border in October to November 2021.

Background: Foreign imported patients and within-household transmission have been the focus and difficulty of coronavirus disease 2019 (COVID-19) prevention and control, which has also posed challenges to border areas' management. However, household transmission caused by foreign imported cases has not been reported in China's border areas. This study aimed to reveal a clear family clustering transmission chain of COVID-19 caused by contact with Myanmar refugees along the China-Myanmar border during an outbreak in October to November 2021. Methods: During the outbreak, detailed epidemiological investigations were conducted on confirmed patients with COVID-19 and their close contacts in daily activities. Patients were immediately transported to a designated hospital for treatment and quarantine, and their close contacts were quarantined at designated sites. Regular nucleic acid testing and SARS-CoV-2 antibody testing were provided to them. Results: A clear four-generation family clustering transmission involving five patients with COVID-19 was found along the China-Myanmar border. The index case (Patient A) was infected by brief conversations with Myanmar refugees across border fences during work. His wife (Patient B) and 9-month-old daughter (Patient C) were second-generation cases infected by daily contact with him. His 2-year-old daughter (Patient D) was the third-generation case infected by her mother and sister during quarantine in the same room and then transmitted the virus to her grandmother (Patient E, the fourth-generation case) who looked after her after Patients B and C were diagnosed and transported to the hospital. The household secondary attack rate was 80.0%, the average latent period was 4 days, and the generation time was 3 days. Ten of 942 close contacts (1.1%) of this family had positive IgM antibody during the medical observation period. In total 73.9% (696/942) of them were positive for IgG antibody and 8.3% (58/696) had IgG levels over 20 S/CO (optical density of the sample/cut-off value of the reagent). Conclusion: This typical transmission chain indicated that it is essential to strengthen COVID-19 prevention and control in border areas, and explore more effective children care approaches in quarantine sites.

Development and evaluation of a predictive nomogram for survival in heat stroke patients: a retrospective cohort study.

BACKGROUND: This study aimed to establish an effective nomogram to predict the survival of heat stroke (HS) based on risk factors. METHODS: This was a retrospective, observational multicenter cohort study. We analyzed patients diagnosed with HS, who were treated between May 1 and September 30, 2018 at 15 tertiary hospitals from 11 cities in Northern China. RESULTS: Among the 175 patients, 32 patients (18.29%) died before hospital discharge. After the univariate analysis, mechanical ventilation, initial mean arterial pressure <70 mmHg, maximum heart rate, lab results on day 1 (white blood cell count, alanine aminotransferase, creatinine), and Glasgow admission prediction score were included in multivariate analysis. Multivariate Cox regression showed that invasive ventilation, initial mean arterial pressure <70 mmHg (1 mmHg=0.133 kPa), and Glasgow admission prediction score were independent risk factors for HS. The nomogram was established for predicting 7-d and 14-d survival in the training cohort. The nomogram exhibited a concordance index (C-index) of 0.880 (95% confidence interval [95% CI] 0.831-0.930) by bootstrapping validation (B=1,000). Furthermore, the nomogram performed better when predicting 14-d survival, compared to 7-d survival. The prognostic index cut-off value was set at 2.085, according to the operating characteristic curve for overall survival prediction. The model showed good calibration ability in the internal and external validation datasets. CONCLUSION: A novel nomogram, integrated with prognostic factors, was proposed; it was highly predictive of the survival in HS patients.

Influence of frailty and its interaction with comorbidity on outcomes among total joint replacement.

BACKGROUND: Patients with frailty get more and more attention in clinical practice. Yet, no large-scale studies have explored the impact of frailty on the perioperative acute medical and surgical complications following TJA. what is more, comorbid diseases may lead, at least additively, to the development of frailty. There also no studies to find the possible interaction between comorbidity and frailty on the postoperative complications after TJA. METHODS: Discharge data of 2,029,843 patients who underwent TJA from 2005 to 2014 from the National Inpatient Sample (NIS) database, which was analyzed using cross-tabulations and multivariate regression modeling. Frailty was defined based on frailty-defining diagnosis clusters from frailty-defining diagnosis indicator of Johns Hopkins Adjusted Clinical Groups. RESULTS: Among patients who underwent total joint replacement surgeries, 50,385 (2.5%) were identified as frail. Frailty is highly associated with old age, especially for those over the age of 80, meanwhile females and black races have a high Charlson comorbidity index (CCI) of ≥ 3, together with emergency/urgent admission and teaching hospital. While comorbidity is associated with greater odds of acute medical complications, and frailty has a better predictive effect on in-hospital deaths, acute surgical complications. Furthermore, frailty did not show an enhancement in the predictive power of the Charlson comorbidity score for postoperative complications or in-hospital deaths but postoperative LOS and hospitalization costs. CONCLUSION: Frailty can be used to independently predicted postoperative surgical and medical complications, which also has a synergistic interaction with comorbidity for patients who are preparing to undergo TJA.

Cost-effectiveness analysis on COVID-19 surveillance strategy of large-scale sports competition.

BACKGROUND: Nucleic acid test (NAT) could effectively control the spread of COVID-19 caused by large-scale sports competitions. However, quantitative analysis on the appropriate frequency of NAT is scarce, and the cost-effectiveness and necessity of high-frequency NAT remain to be fully explored and validated. This study aims to optimize the COVID-19 surveillance strategies through cost-effectiveness analysis for the Tokyo 2020 Olympic Games and the upcoming Beijing 2022 Olympic Winter Games. METHODS: A total of 18 scenarios were designed regarding the NAT frequency, symptom monitoring, and strengthening close-contact control. An agent-based stochastic dynamic model was used to compare the cost-effectiveness of different NAT scenarios and optimize the surveillance strategies. The dynamics of the proposed model included the arrival and departure of agents, transmission of the disease according to Poisson processes, and quarantine of agents based on regular NATs and symptom onset. Accumulative infections, cost, and incremental cost-effectiveness ratio (ICER) were simulated in the frame of the model. ICER was used to compare the cost-effectiveness of different scenarios. Univariate sensitivity analysis was performed to test the robustness of the results. RESULTS: In Scenario 16, where the competition-related personnel (CRP) received NAT daily and national sports delegation (NSD) with quarantined infections accepted an additional NAT daily, accumulative infection was 320.90 (90 initial infections), the total cost was (United States Dollar) USD 8 920 000, and the cost of detecting out each infection was USD 27 800. Scenario 16 would reduce the total cost by USD 22 570 000 (avoid 569.61 infections), USD 1 420 000 (avoid 47.2 infections) compared with Scenario 10 (weekly NAT, strengthened close contact control) and Scenario 7 (daily NAT, no strengthened close contact control), respectively. Sensitivity analysis showed that the result was most sensitive to the change in basic reproductive number. CONCLUSIONS: High-frequency NATs such as bidaily, daily, and twice a day were cost-effective. NAT daily for CRP with strengthening close-contact control could be prioritized in defense against COVID-19 at large-scale sports competitions. This study could assist policymakers by assessing the cost-effectiveness of NAT scenarios and provide the host country with an optimal COVID-19 surveillance strategy.

A Landscape Analysis on Virus: based on NCBI Database.

What is already known about this topic?: Studies indicate that viruses could spread across species, but it is difficult to know when and where such small probability events occur because it is almost impossible to design an observational study on the whole landscape. What is added by this report?: We did a comprehensive analysis on the National Center for Biotechnology Information database and tried to find the time, place, and host that the viruses stayed in their long evolutionary history. What are the implications for public health practice?: Public databases are helpful to understand the risk of virus infection in humans and also a cost-effective method for monitoring public health and safety events.

Impact of valvular heart disease on hip replacement: a retrospective nationwide inpatient sample database study.

BACKGROUND: To study the impact of valvular heart disease (VHD) on hip replacement, particularly the clinical impactions of aortic stenosis before total/partial hip arthroplasty. METHODS: This was a retrospective cohort study. Data on patients who had undergone hip replacement from 2005 to 2014 were extracted from the NIS database. Independent t test and chi-square test were used to analyze the essential characteristics of patients. Multivariate regression was used to estimate the correlation among demographics, comorbidities, complications, hospitalization costs, and time. RESULTS: VHD accounted for 5.56% and AS accounted for 0.03% of the patients before hip replacement surgeries. Patients with VHD before hip replacement are related to the following characteristics: female patients (odds ratio [OR] = 1.15 [1.12-1.18]), elective admission (OR = 0.78 [0.76-0.80]), Charlson Comorbidity Index ≥3 (OR = 1.06 [1.03-1.08]), large-volume hospitals (OR = 1.13 [1.1-1.2]), teaching hospitals (OR = 5 4.4 [2.9-6.7]), and hospital location in urban areas (OR = 1.22 [1.2-1.3]). In addition, VHD is a risk factor for mortality and some acute postoperative medical complications, such as acute cardiac event (OR = 2.96 [2.87-3.04]), acute pulmonary edema (OR = 1.13 [1.06-1.21]), acute cerebrovascular event (OR = 1.22 [1.16-1.74]), and acute renal failure (OR = 1.22 [1.17-1.27]). It also has an impact on DVT/PE (OR = 0.89 [0.8-0.99]). Patients with AS before hip replacement have basic demographic characteristics like those of hip replacement patients with valvular disease. Patients with AS are older than those without AS before surgery (OR = 3.28 [2.27-4.75) and are related to the following characteristics: female patients (OR = 1.92 [1.32-2.8]) and elective admission (OR = 0.51 [0.36-0.75]). The perioperative period is limited to acute postoperative complications, such as acute cardiac events (OR = 2.50 [1.76-3.53]) and acute hepatic failure (OR = 7.69 [1.8-32.89]). Both valvular diseases and AS are associated with a higher mortality rate and hospitalization cost. CONCLUSION: VHD independently predicted mortality rate and surgical and medical complications after total/partial hip arthroplasty.

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection.

BACKGROUND: Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters. METHODS: Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches. RESULTS: Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk. CONCLUSIONS: By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.

The Effect of Indoor Daylight Levels on Hospital Costs and Length of Stay of Patients Admitted to General Surgery.

BACKGROUND: Indoor daylight levels can directly affect the physical and psychological state of people. However, the effect of indoor daylight levels on the clinical recovery process of the patient remains controversial. This study was to evaluate the effect of indoor daylight levels on hospital costs and the average length of stay (LOS) of a large patient population in general surgery wards. METHODS: Data were collected retrospectively and analyzed of patients in the Second Affiliated Hospital of Zhejiang University, School of Medicine between January 2015 and August 2020. We measured daylight levels in the patient rooms of general surgery and assessed their association with the total hospital costs and LOS of the patients. RESULTS: A total of 2,998 patients were included in this study with 1,478 each assigned to two daylight level groups after matching. Overall comparison of hospital total costs and LOS among patients according to daylight levels did not show a significant difference. Subgroup analysis showed when exposed to higher intensity of indoor daylight, illiterate patients had lower total hospital costs (CNY ¥13070.0 vs. ¥15210.3, p = 0.018) and shorter LOS (7 vs. 10 days, p = 0.011) as compared to those exposed to a lower intensity. CONCLUSIONS: Indoor daylight levels were not associated with the hospital costs and LOS of patients in the wards of general surgery, except for those who were illiterate. It might be essential to design guidelines for medical staff and healthcare facilities to enhance the indoor environmental benefits of daylight for some specific populations.

Prognostic Nomogram for Patients with Hepatocellular Carcinoma After Thermal Ablation.

PURPOSE: To develop an effective prognostic nomogram for patients with hepatocellular carcinoma (HCC) after thermal ablation. METHODS: A total of 772 patients with intrahepatic primary or recurrent HCC who underwent radiofrequency ablation or microwave ablation between March 2011 and October 2016 were included. 602 patients (mean age, 56.0 ± 11.9 years; 495 male/107 female) were included in the primary cohort to establish a prognostic nomogram. Significant prognostic factors for overall survival (OS) identified by Cox univariate and multivariate regression analyses were used to construct the nomogram. The remaining 170 patients (mean age, 55.9 ± 11.9 years; 145 male/25 female) were used to validate the predictive accuracy of the nomogram. RESULTS: During a mean follow-up period of 26 months (range 1-85 months), the median OS periods were 48.6 months and 44.0 months for the primary and validation cohorts. The 1-, 3-, and 5-year OS rates were 85.5%, 61.4%, and 43.3% in the primary cohort and 84.7%, 59.6%, and 43.3% in the prospective validation cohort, respectively. Multivariate analysis found that pre-ablation treatment, AFP, CEA, CA19-9, ALBI grade, tumor number, and tumor size (hazard ratio > 1, P < 0.05) were independent risk factors for OS. A nomogram was developed based on these seven variables. The calibration curve for predicting the probability of survival showed a good agreement between the nomogram and actual observation both in the primary (concrete index: 0.699) and validation cohorts (concrete index: 0.734). CONCLUSIONS: This simple nomogram based on seven variables including ALBI grade offers personalized prognostic data for HCC patients after ablation. LEVEL OF EVIDENCE: Level 4, case series.