scholarly journals Developing a COVID-19 mortality risk prediction model when individual-level data are not available

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Noam Barda ◽  
Dan Riesel ◽  
Amichay Akriv ◽  
Joseph Levy ◽  
Uriah Finkel ◽  
...  
Keyword(s):  
Case Fatality ◽  
Operating Characteristics ◽  
Hybrid Strategy ◽  
Individual Level ◽  
Level Data ◽  
Receiver Operating Characteristics Curve ◽  
Severe Respiratory Infection ◽  
Baseline Predictor ◽  
Risk Predictor ◽  
Risk Predictors

Abstract At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.

scholarly journals Performing risk stratification for COVID-19 when individual level data is not available – the experience of a large healthcare organization

2020 ◽  
Author(s):  
Noam Barda ◽  
Dan Riesel ◽  
Amichay Akriv ◽  
Joseph Levi ◽  
Uriah Finkel ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Prediction Models ◽  
Case Fatality ◽  
Healthcare Organization ◽  
Operating Characteristics ◽  
Good Discrimination ◽  
Hybrid Strategy ◽  
Individual Level ◽  
Level Data

AbstractWith the global coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need for risk stratification tools to support prevention and treatment decisions. The Centers for Disease Control and Prevention (CDC) listed several criteria that define high-risk individuals, but multivariable prediction models may allow for a more accurate and granular risk evaluation. In the early days of the pandemic, when individual level data required for training prediction models was not available, a large healthcare organization developed a prediction model for supporting its COVID-19 policy using a hybrid strategy. The model was constructed on a baseline predictor to rank patients according to their risk for severe respiratory infection or sepsis (trained using over one-million patient records) and was then post-processed to calibrate the predictions to reported COVID-19 case fatality rates. Since its deployment in mid-March, this predictor was integrated into many decision-processes in the organization that involved allocating limited resources. With the accumulation of enough COVID-19 patients, the predictor was validated for its accuracy in predicting COVID-19 mortality among all COVID-19 cases in the organization (3,176, 3.1% death rate). The predictor was found to have good discrimination, with an area under the receiver-operating characteristics curve of 0.942. Calibration was also good, with a marked improvement compared to the calibration of the baseline model when evaluated for the COVID-19 mortality outcome. While the CDC criteria identify 41% of the population as high-risk with a resulting sensitivity of 97%, a 5% absolute risk cutoff by the model tags only 14% to be at high-risk while still achieving a sensitivity of 90%. To summarize, we found that even in the midst of a pandemic, shrouded in epidemiologic “fog of war” and with no individual level data, it was possible to provide a useful predictor with good discrimination and calibration.

scholarly journals Estimates of COVID-19 case-fatality risk from individual-level data

2020 ◽  
Author(s):  
Simona Bignami-Van Assche ◽  
Daniela Ghio ◽  
Ari Van Assche
Keyword(s):  
Case Fatality ◽  
Event History ◽  
Canadian Province ◽  
Fatality Risk ◽  
Individual Level ◽  
Level Data ◽  
The World ◽  
Simple Ratio ◽  
Case Fatality Risk ◽  
True Measure

ABSTRACTWhen calculated from aggregate data on confirmed cases and deaths, the case-fatality risk (CFR) is a simple ratio between the former and the latter, which is prone to numerous biases. With individual-level data, the CFR can be estimated as a true measure of risk as the proportion of incidence for the disease. We present the first estimates of the CFR for COVID-19 by age and sex based on event history modelling of the risk of dying among confirmed positive individuals in the Canadian province of Ontario, which maintains one of the few individual-level datasets on COVID-19 in the world.

scholarly journals COVID-19 Fatality and Comorbidity Risk Factors among Diagnosed Patients in Mexico

2020 ◽  
Author(s):  
Patricio Solís ◽  
Hiram Carreño
Keyword(s):  
Case Fatality Rate ◽  
Case Fatality ◽  
Fatality Rate ◽  
Cross Sectional ◽  
Fatality Risk ◽  
Individual Level ◽  
Level Data ◽  
Younger Age ◽  
High Prevalence ◽  
Right Censorship

AbstractAs of April 18, 2020, 2.16 million patients in the world had been tested positive with Coronavirus (COVID-19) and 146,088 had died, which accounts for a case fatality rate of 6.76%. In Mexico, according to official statistics (April 18), 7,497 cases have been confirmed with 650 deaths, for a case fatality rate of 8.67%. These estimates, however, may not reflect the final fatality risk among COVID-19 confirmed patients, because they are based on cross-sectional counts of diagnosed and deceased patients, and therefore are not adjusted by time of exposure and right-censorship. In this paper we estimate fatality risks based on survival analysis methods, calculated from individual-level data on symptomatic patients confirmed with COVID-19 recently released by the Mexican Ministry of Health. The estimated fatality risk after 35 days of onset of symptoms is 12.38% (95% CI: 11.37-13.47). Fatality risks sharply rise with age, and significantly increase for males (59%) and individuals with comorbidities (38%-168%, depending on the disease). Two reasons may explain the high COVID-19 related fatality risk observed in Mexico, despite its younger age structure: the high selectivity and self-selectivity in testing and the high prevalence of chronic-degenerative diseases.

scholarly journals CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

2021 ◽  
pp. 1-15
Author(s):  
Subrato Bharati ◽  
Prajoy Podder ◽  
M. Rubaiyat Hossain Mondal ◽  
V.B. Surya Prasath
Keyword(s):  
Classification Problem ◽  
Operating Characteristics ◽  
X Ray ◽  
Level Data ◽  
Receiver Operating Characteristics Curve ◽  
Multi Level ◽  
Novel Coronavirus ◽  
Normal Lungs ◽  
Healthy Lung

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.

scholarly journals Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk

2021 ◽  
Vol 18 (14) ◽  
pp. 7561
Author(s):  
Jingjing Wang ◽  
Xueying Wu ◽  
Ruoyu Wang ◽  
Dongsheng He ◽  
Dongying Li ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Built Environment ◽  
Longitudinal Research ◽  
Empirical Studies ◽  
Infection Risk ◽  
Design Guidelines ◽  
Individual Level ◽  
Transit Accessibility ◽  
Level Data ◽  
Transmission Pathways

The coronavirus disease 2019 pandemic has stimulated intensive research interest in its transmission pathways and infection factors, e.g., socioeconomic and demographic characteristics, climatology, baseline health conditions or pre-existing diseases, and government policies. Meanwhile, some empirical studies suggested that built environment attributes may be associated with the transmission mechanism and infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, no review has been conducted to explore the effect of built environment characteristics on the infection risk. This research gap prevents government officials and urban planners from creating effective urban design guidelines to contain SARS-CoV-2 infections and face future pandemic challenges. This review summarizes evidence from 25 empirical studies and provides an overview of the effect of built environment on SARS-CoV-2 infection risk. Virus infection risk was positively associated with the density of commercial facilities, roads, and schools and with public transit accessibility, whereas it was negatively associated with the availability of green spaces. This review recommends several directions for future studies, namely using longitudinal research design and individual-level data, considering multilevel factors and extending to diversified geographic areas.

scholarly journals The Role of Snack Choices, Body Weight Stereotypes and Smoking Behavior in Assessing Risk Factors for Adolescent Overweight and Obesity

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 557
Author(s):  
Elena Raptou
Keyword(s):  
Body Weight ◽  
Smoking Behavior ◽  
Negative Influence ◽  
Overweight And Obesity ◽  
Individual Level ◽  
Level Data ◽  
Prevention Interventions ◽  
Frequent Consumption ◽  
Adolescent Overweight ◽  
Relationship Of

This study investigated the relationship of behavioral factors, such as snack choices, obesity stereotypes and smoking with adolescents’ body weight. Individual-level data for 1254 Greek youths were selected via a formal questionnaire. Snack choices seem to be gender specific with girls showing a stronger preference for healthier snacks. Frequent consumption of high-calorie and more filling snacks was found to increase Body Mass Index (BMI) in both genders. Fruit/vegetable snacks were associated with lower body weight in females, whereas cereal/nut snacks had a negative influence in males’ BMI. The majority of participants expressed anti-fat attitudes and more boys than girls assigned positive attributes to lean peers. The endorsement of the thin-ideal was positively associated with the BMI of both adolescent boys and girls. This study also revealed that neglecting potential endogeneity issues can lead to biased estimates of smoking. Gender may be a crucial moderator of smoking–BMI relationships. Male smokers presented a higher obesity risk, whereas female smokers were more likely to be underweight. Nutrition professionals should pay attention to increase the acceptance of healthy snack options. Gender differences in the influence of weight stereotypes and smoking on BMI should be considered in order to enhance the efficacy of obesity prevention interventions.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Etiological Work-Up for Adults with Bronchiectasis: A Predictive Diagnostic Score for Primary Ciliary Dyskinesia and Cystic Fibrosis

2021 ◽  
Vol 10 (16) ◽  
pp. 3478
Author(s):  
Frederic Schlemmer ◽  
Agnes Hamzaoui ◽  
Sonia Zebachi ◽  
Aurelie Le Thuaut ◽  
Gilles Mangiapan ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Primary Ciliary Dyskinesia ◽  
Clinical Score ◽  
Adult Patients ◽  
Operating Characteristics ◽  
Diagnostic Score ◽  
Receiver Operating Characteristics Curve ◽  
Diagnostic Work ◽  
Work Up ◽  
Ciliary Dyskinesia

Background: etiological investigations are not done for all adult patients with bronchiectasis because of the availability and interpretation of tests. The aim of the study was to elaborate a score to identify patients at high risk of having cystic fibrosis or primary ciliary dyskinesia (CF/PCD), which require appropriate management. Methods: diagnostic work-ups were carried out on a French monocenter cohort, and results were subjected to logistic-regression analyses to identify the independent factors associated with CF/PCD diagnosis and, thereby, elaborate a score to validate in a second cohort. Results: among 188 patients, 158 had no obvious diagnosis and were enrolled in the algorithm-construction group. In multivariate analyses, age at symptom onset (8.69 (2.10–35.99); p = 0.003), chronic ENT symptoms or diagnosed sinusitis (10.53 (1.26–87.57); p = 0.03), digestive symptoms or situs inversus (5.10 (1.23–21.14); p = 0.025), and Pseudomonas. aeruginosa and/or Staphylococcus aureus isolated from sputum (11.13 (1.34–92.21); p = 0.02) are associated with CF or PCD. Receiver operating characteristics curve analysis, using a validation group of 167 patients with bronchiectasis, confirmed the score’s performance with AUC 0.92 (95% CI: 0.84–0.98). Conclusions: a clinical score may help identify adult patients with bronchiectasis at higher risk of having CF or PCD.

When Marriage Gets Hard: Intra-Coalition Conflict and Electoral Accountability

2021 ◽  
pp. 001041402110243
Author(s):  
Carolina Plescia ◽  
Sylvia Kritzinger
Keyword(s):  
Economic Performance ◽  
Political Representation ◽  
Economic Evaluations ◽  
Responsibility Attribution ◽  
Electoral Accountability ◽  
Individual Level ◽  
Coalition Governments ◽  
Level Data ◽  
European Election ◽  
Election Studies

Combining individual-level with event-level data across 25 European countries and three sets of European Election Studies, this study examines the effect of conflict between parties in coalition government on electoral accountability and responsibility attribution. We find that conflict increases punishment for poor economic performance precisely because it helps clarify to voters parties’ actions and responsibilities while in office. The results indicate that under conditions of conflict, the punishment is equal for all coalition partners when they share responsibility for poor economic performance. When there is no conflict within a government, the effect of poor economic evaluations on vote choice is rather low, with slightly more punishment targeted to the prime minister’s party. These findings have important implications for our understanding of electoral accountability and political representation in coalition governments.

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