Clinical value of the Toronto inflammatory bowel disease global endoscopic reporting score in ulcerative colitis

BACKGROUND Endoscopic evaluation in diagnosing and managing ulcerative colitis(UC)is becoming increasingly important.Several endoscopic scoring systems have been established,including the Ulcerative Colitis Endoscopic Index of Severity(UCEIS)score and Mayo Endoscopic Subscore(MES).Furthermore,the Toronto Inflammatory Bowel Disease Global Endoscopic Reporting(TIGER)score for UC has recently been proposed;however,its clinical value remains unclear.AIM To investigate the clinical value of the TIGER score in UC by comparing it with the UCEIS score and MES.METHODS This retrospective study included 166 patients with UC who underwent total colonoscopy between January 2017 and March 2023 at the Affiliated Hospital of Qingdao University(Qingdao,China).We retrospectively analysed endoscopic scores,laboratory and clinical data,treatment,and readmissions within 1 year.Spearman’s rank correlation coefficient,receiver operating characteristic curve,and univariate and multivariable logistic regression analyses were performed using IBM SPSS Statistics for Windows,version 26.0(IBM Corp.,Armonk,NY,United States)and GraphPad Prism version 9.0.0 for Windows(GraphPad Software,Boston,Massachusetts,United States).RESULTS The TIGER score significantly correlated with the UCEIS score and MES(r=0.721,0.626,both P<0.001),showed good differentiating values for clinical severity among mild,moderate,and severe UC[8(4-112.75)vs 210(109–219)vs 328(219–426),all P<0.001],and exhibited predictive value in diagnosing patients with severe UC[area under the curve(AUC)=0.897,P<0.001].Additionally,the TIGER(r=0.639,0,551,0.488,0.376,all P<0.001)and UCEIS scores(r=0.622,0,540,0.494,and 0.375,all P<0.001)showed stronger correlations with laboratory and clinical parameters,including C-reactive protein,erythrocyte sedimentation rate,length of hospitalisation,and hospitalisation costs,than MES(r=0.509,0,351,0.339,and 0.270,all P<0.001).The TIGER score showed the best predictability for patients'recent advanced treatment,including systemic corticosteroids,biologics,or immunomodulators(AUC=0.848,P<0.001)and 1-year readmission(AUC=0.700,P<0.001)compared with the UCEIS score(AUC=0.762,P<0.001;0.627,P<0.05)and MES(AUC=0.684,P<0.001;0.578,P=0.132).Furthermore,a TIGER score of≥317 was identified as an independent risk factor for advanced UC treatment(P=0.011).CONCLUSION The TIGER score may be superior to the UCIES score and MES in improving the accuracy of clinical disease severity assessment,guiding therapeutic decision-making,and predicting short-term prognosis.

Summary of Report on Cardiovascular Diseases in China,2012

The prevalence of cardiovascular diseases （CVDs） is associated with the socioeconomic prosperity, lifestyle changes, accelerated process of ageing and urbanization. The prevalence of CVDs is continuously increasing in China and will remain an upward trend in the next 10 years. CVDs are the leading cause of death for Chinese in both urban area and rural area. Nowadays, 41.09% of deaths in rural area and 42.52% of deaths in urban area are caused by CVDs in China. The burden of CVDs remains heavy and has become an important public health problem. Effective strategies should be enforced urgently for the prevention of CVDs under the supervision of the government. In 2012, the Ministry of Health of China and 14 governmental departments jointly issued the Work Plan for Chronic Disease Prevention and Control in China （2012-2015）, a guideline for the prevention of chronic diseases, especially CVDs in China.

A simple modification to the classical SIR model to estimate the proportion of under-reported infections using case studies in flu and COVID-19

Background:Under-reporting and,thus,uncertainty around the true incidence of health events is common in all public health reporting systems.While the problem of underreporting is acknowledged in epidemiology,the guidance and methods available for assessing and correcting the resulting bias are obscure.Objective:We aim to design a simple modification to the Susceptible e Infected e Removed(SIR)model for estimating the fraction or proportion of reported infection cases.Methods:The suggested modification involves rescaling of the classical SIR model producing its mathematically equivalent version with explicit dependence on the reporting parameter(true proportion of cases reported).We justify the rescaling using the phase plane analysis of the SIR model system and show how this rescaling parameter can be estimated from the data along with the other model parameters.Results:We demonstrate how the proposed method is cross-validated using simulated data with known disease cases and then apply it to two empirical reported data sets to estimate the fraction of reported cases in Missoula County,Montana,USA,using:(1)flu data for 2016e2017 and(2)COVID-19 data for fall of 2020.Conclusions:We establish with the simulated and COVID-19 data that when most of the disease cases are presumed reported,the value of the additional reporting parameter in the modified SIR model is close or equal to one,so that the original SIR model is appropriate for data analysis.Conversely,the flu example shows that when the reporting parameter is close to zero,the original SIR model is not accurately estimating the usual rate parameters,and the re-scaled SIR model should be used.This research demonstrates the role of under-reporting of disease data and the importance of accounting for underreporting when modeling simulated,endemic,and pandemic disease data.Correctly reporting the“true”number of disease cases will have downstream impacts on predictions of disease dynamics.A simple parameter adjustment to the SIR modeling framework can help alleviate bias and uncertainty around crucial epidemiological metrics(e.g.:basic disease reproduction number)and public health decision making.

Balancing timeliness of reporting with increasing testing probability for epidemic data

Reporting of epidemiological data requires coordinated action by numerous agencies,across a multitude of logistical steps.Using collated and reported information to inform direct interventions can be challenging due to associated delays.Mitigation can,however,occur indirectly through the public generation of concern,which facilitates adherence to protective behaviors.We utilized a coupled-dynamic multiplex network model with a communication-and disease-layer to examine how variation in reporting delay and testing probability are likely to impact adherence to protective behaviors,such as reducing physical contact.Individual concern mediated adherence and was informed by new-or active-case reporting,at the population-or community-level.Individuals received information from the communication layer:direct connections that were sick or adherent to protective behaviors increased their concern,but absence of illness eroded concern.Models revealed that the relative benefit of timely reporting and a high probability of testing was contingent on how much information was already obtained.With low rates of testing,increasing testing probability was of greater mitigating value.With high rates of testing,maximizing timeliness was of greater value.Population-level reporting provided advanced warning of disease risk from nearby communities;but we explore the relative costs and benefits of delays due to scale against the assumption that people may prioritize community-level information.Our findings emphasize the interaction of testing accuracy and reporting timeliness for the indirect mitigation of disease in a complex social system.