scholarly journals Simple Accurate Regression-Based Forecasting of Intensive Care Unit Admissions due to COVID-19 in Ontario, Canada

2020 ◽  
Author(s):  
David N. Fisman ◽  
Ashleigh R. Tuite
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Convergent Validity ◽  
Negative Binomial ◽  
External Validation ◽  
Negative Binomial Regression ◽  
Population Level ◽  
Negative Binomial Regression Model ◽  
Icu Length Of Stay ◽  
Short Term Forecasting

AbstractThe pandemic caused by SARS-CoV-2 has proven challenging clinically, and at the population level, due to heterogeneity in both transmissibility and severity. Recent case incidence in Ontario, Canada (autumn 2020) has outstripped incidence in seen during the first (spring) pandemic wave; but has been associated with a lower incidence of intensive care unit (ICU) admissions and deaths. We hypothesized that differential ICU burden might be explained by increased testing volumes, as well as the shift in mean case age from older to younger. We constructed a negative binomial regression model using only three covariates, at a 2-week lag: log10(weekly cases); log10(weekly deaths); and mean weekly case age. This model reproduced observed ICU admission volumes, and demonstrated good preliminary predictive validity. Furthermore, when admissions were used in combination with ICU length of stay, our modeled estimates demonstrated excellent convergent validity with ICU occupancy data reported by the Canadian Institute for Health Information. Our approach needs external validation in other settings and at larger and smaller geographic scales, but appears to be a useful short-term forecasting tool for ICU resource demand; we also demonstrate that the virulence of SARS-CoV-2 infection has not meaningfully changed in Ontario between the first and second waves, but the demographics of those infected, and the fraction of cases identified, have.

scholarly journals Population-level impact of infant 10-valent pneumococcal conjugate vaccination on adult pneumonia hospitalisations in Finland

Thorax ◽  
2017 ◽  
Vol 73 (3) ◽  
pp. 262-269 ◽  
Author(s):  
Omar Okasha ◽  
Hanna Rinta-Kokko ◽  
Arto A Palmu ◽  
Esa Ruokokoski ◽  
Jukka Jokinen ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Population Level ◽  
Interrupted Time Series ◽  
The Elderly ◽  
Negative Binomial Regression Model ◽  
Interrupted Time Series Analysis ◽  
Entire Study ◽  
Before And After ◽  
International Statistical Classification

IntroductionLimited data are available on population-level herd effects of infant 10-valent pneumococcal conjugate vaccine (PCV10) programmes on pneumonia. We assessed national trends in pneumococcal and all-cause pneumonia hospitalisations in adults aged ≥18 years, before and after infant PCV10 introduction in 2010.MethodsMonthly hospitalisation rates of International Statistical Classification of Diseases, 10th revision (ICD-10)-coded primary discharge diagnoses compatible with pneumonia from 2004–2005 to 2014–2015 were calculated with population denominators from the population register. Trends in pneumonia before and after PCV10 introduction were assessed with interrupted time-series analysis. Rates during the PCV10 period were estimated from adjusted negative binomial regression model and compared with those projected as continuation of the pre-PCV10 trend. All-cause hospitalisations were assessed for control purposes.ResultsBefore PCV10, the all-cause pneumonia rate in adults aged ≥18 years increased annually by 2.4%, followed by a 4.7% annual decline during the PCV10 period. In 2014–2015, the overall all-cause pneumonia hospitalisation rate was 109.3/100 000 (95% CI 96.5 to 121.9) or 15.4% lower than the expected rate. A significant 6.7% decline was seen in persons aged ≥65 years (131.5/100 000), which translates to 1456 fewer pneumonia hospitalisations annually. In comparison, hospitalisations other than pneumonia decreased by 3.5% annually throughout the entire study period.ConclusionThese national data suggest that herd protection from infant PCV10 programme has reversed the increasing trend and substantially decreased all-cause pneumonia hospitalisations in adults, particularly the elderly.

Abstract 10874: Evaluation of Intensive Care Unit and Ventilator-free Days as Alternative Outcomes in Out-of-Hospital Cardiac Arrest Trials

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Henry E Wang ◽  
Ashish R Panchal ◽  
Michelle Nassal ◽  
Terry Vanden hoek ◽  
Jing Li ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care ◽  
Competing Risks ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Laryngeal Tube ◽  
Icu Discharge ◽  
Highly Correlated ◽  
Hospital Cardiac Arrest

Objective: Compared with traditional out-of-hospital cardiac arrest (OHCA) trial outcomes such as hospital survival, alternative outcomes such as intensive care unit-free (IFD) and ventilator-free days (VFD) have potential advantages including requiring smaller sample sizes to detect significant differences. Few studies have evaluated these outcomes in OHCA. We sought to evaluate the utility and validity of IFD and VFD as candidate outcomes for OHCA trials. Methods: We analyzed data from the Pragmatic Airway Resuscitation Trial (PART), which tested laryngeal tube (LT) vs. endotracheal intubation (ETI) airway strategies in adult OHCA. We defined IFD as the number of days alive and permanently out of ICU during the first 30 days after the OHCA event. We examined IFD distribution and correlation with Modified Rankin Scale (MRS). To test associations with trial interventions, we applied a range of analytic strategies, including modeling IFD as continuous (Generalized Estimating Equations - GEE), non-parametric (Wilcoxon Rank-Sum test - WRS) and count (zero-inflated negative binomial regression - ZINB) data. We also modeled time-to-ICU discharge using a survival model with death as a competing risk. We repeated the analysis assessing VFD. Results: IFD was available for 2,898 of 3,004 patients. IFD was skewed and J-shaped; 91% IFD=0 and 5.3% IFD≥22. Mean (±SD) IFD was 2.0±6.6 days; 21.2±7.5 days for survivors. IFD varied by intervention (LT 2.4±7.2, ETI 1.6±5.8 days). IFD and VFD strongly correlated with MRS (ρ= -0.88, -0.89). LT was associated with increased IFD using GEE, WRS, and ZINB, but not competing risks model. (Table) LT was associated with increased VFD using GEE and WRS, but not ZINB or competing risks models. Conclusion: IFD and VFD differentiated OHCA interventions, suggesting their utility. IFD and VFD were highly correlated with MRS, suggesting validity. IFD and VFD have important features that may influence OHCA trial design.

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 Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

Bike Count Forecast Model with Multimodal Network Connectivity Measures

2021 ◽  
pp. 036119812110218
Author(s):  
Bingqing Liu ◽  
Divya Bade ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
Negative Binomial ◽  
Network Connectivity ◽  
Negative Binomial Regression ◽  
Forecast Model ◽  
Negative Binomial Regression Model ◽  
Significance Level ◽  
Commute Time ◽  
Improve Model ◽  
Subway Stations

With the rise of cycling as a mode choice for commuting and short-distance delivery, as well as policy objectives encouraging this trend, bike count models are increasingly critical to transportation planning and investment. Studies have found that network connectivity plays a role in such models, but there remains a lack of measure for the connectivity of a link in a multimodal trip context. This study proposes a connectivity measure that captures the importance of a link in connecting the origins of cyclists and nearby subway stations, and incorporates it in a negative binomial regression model to forecast bike counts at links. Representative bike trips are generated with regard to bike-friendliness using the New York City transit trip planner and used to determine the deviation from the shortest path via the designated link. The measure is shown to improve model fitness with a significance level within 10%. Insights are also drawn for income levels, bike lanes, subway station availability, and average commute time of travelers.

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