scholarly journals Statistical deconvolution for inference of infection time series

2020 ◽  
Author(s):  
Andrew C. Miller ◽  
Lauren Hannah ◽  
Joseph Futoma ◽  
Nicholas J. Foti ◽  
Emily B. Fox ◽  
...  
Keyword(s):  
United States ◽  
Time Series ◽  
Model Misspecification ◽  
Synthetic Data ◽  
The United States ◽  
Mortality Data ◽  
Regularization Scheme ◽  
Censored Observations ◽  
Epidemic Response ◽  
Accuracy And Stability

AbstractAccurate measurement of daily infection incidence is crucial to epidemic response. However, delays in symptom onset, testing, and reporting obscure the dynamics of transmission, necessitating methods to remove the effects of stochastic delays from observed data. Existing estimators can be sensitive to model misspecification and censored observations; many analysts have instead used methods that exhibit strong bias or do not account for delays. We develop an estimator with a regularization scheme to cope with these sources of noise, which we term the Robust Incidence Deconvolution Estimator (RIDE). We validate RIDE on synthetic data, comparing accuracy and stability to existing approaches. We then use RIDE to study COVID-19 records in the United States, and find evidence that infection estimates from reported cases can be more informative than estimates from mortality data. To implement these methods, we release incidental, a ready-to-use R implementation of our estimator that can aid ongoing efforts to monitor the COVID-19 pandemic.

scholarly journals Outcomes for Children and Adolescents With Cancer: Challenges for the Twenty-First Century

2010 ◽  
Vol 28 (15) ◽  
pp. 2625-2634 ◽  
Author(s):  
Malcolm A. Smith ◽  
Nita L. Seibel ◽  
Sean F. Altekruse ◽  
Lynn A.G. Ries ◽  
Danielle L. Melbert ◽  
...  
Keyword(s):  
United States ◽  
Childhood Cancer ◽  
Cancer Mortality ◽  
Lymphoblastic Leukemia ◽  
The United States ◽  
Mortality Rates ◽  
Mortality Data ◽  
Childhood Cancers ◽  
Essential Information ◽  
The Impact

Purpose This report provides an overview of current childhood cancer statistics to facilitate analysis of the impact of past research discoveries on outcome and provide essential information for prioritizing future research directions. Methods Incidence and survival data for childhood cancers came from the Surveillance, Epidemiology, and End Results 9 (SEER 9) registries, and mortality data were based on deaths in the United States that were reported by states to the Centers for Disease Control and Prevention by underlying cause. Results Childhood cancer incidence rates increased significantly from 1975 through 2006, with increasing rates for acute lymphoblastic leukemia being most notable. Childhood cancer mortality rates declined by more than 50% between 1975 and 2006. For leukemias and lymphomas, significantly decreasing mortality rates were observed throughout the 32-year period, though the rate of decline slowed somewhat after 1998. For remaining childhood cancers, significantly decreasing mortality rates were observed from 1975 to 1996, with stable rates from 1996 through 2006. Increased survival rates were observed for all categories of childhood cancers studied, with the extent and temporal pace of the increases varying by diagnosis. Conclusion When 1975 age-specific death rates for children are used as a baseline, approximately 38,000 childhood malignant cancer deaths were averted in the United States from 1975 through 2006 as a result of more effective treatments identified and applied during this period. Continued success in reducing childhood cancer mortality will require new treatment paradigms building on an increased understanding of the molecular processes that promote growth and survival of specific childhood cancers.

scholarly journals Mortality due to Influenza in the United States—An Annualized Regression Approach Using Multiple-Cause Mortality Data

2005 ◽  
Vol 163 (2) ◽  
pp. 181-187 ◽  
Author(s):  
Jonathan Dushoff ◽  
Joshua B. Plotkin ◽  
Cecile Viboud ◽  
David J. D. Earn ◽  
Lone Simonsen
Keyword(s):  
United States ◽  
The United States ◽  
Mortality Data ◽  
Regression Approach

External Validation of the Cardiac Surgery Score in a Quaternary Hospital in the United States of America

2021 ◽  
pp. 088506662110668
Author(s):  
Asha Singh ◽  
Chen Liang ◽  
Stephanie L. Mick ◽  
Chiedozie Udeh
Keyword(s):  
United States ◽  
Cardiac Surgery ◽  
United States Of America ◽  
Predictive Accuracy ◽  
External Validation ◽  
Cleveland Clinic ◽  
Area Under The Curve ◽  
The United States ◽  
Mortality Data ◽  
The Usa

Background The Cardiac Surgery Score (CASUS) was developed to assist in predicting post-cardiac surgery mortality using parameters measured in the intensive care unit. It is calculated by assigning points to ten physiologic variables and adding them to obtain a score (additive CASUS), or by logistic regression to weight the variables and estimate the probability of mortality (logistic CASUS). Both additive and logistic CASUS have been externally validated elsewhere, but not yet in the United States of America (USA). This study aims to validate CASUS in a quaternary hospital in the USA and compare the predictive performance of additive to logistic CASUS in this setting. Methods Additive and logistic CASUS (postoperative days 1-5) were calculated for 7098 patients at Cleveland Clinic from January 2015 to February 2017. 30-day mortality data were abstracted from institutional records and the Death Registries for Ohio State and the Centers for Disease Control. Given a low event rate, model discrimination was assessed by area under the curve (AUROC), partial AUROC (pAUC), and average precision (AP). Calibration was assessed by curves and quantified using Harrell's Emax, and Integrated Calibration Index (ICI). Results 30-day mortality rate was 1.37%. For additive CASUS, odds ratio for mortality was 1.41 (1.35-1.46, P <0.001). Additive and logistic CASUS had comparable pAUC and AUROC (all >0.83). However, additive CASUS had greater AP, especially on postoperative day 1 (0.22 vs. 0.11). Additive CASUS had better calibration curves, and lower Emax, and ICI on all days. Conclusions Additive and logistic CASUS discriminated well for postoperative 30-day mortality in our quaternary center in the USA, however logistic CASUS under-predicted mortality in our cohort. Given its ease of calculation, and better predictive accuracy, additive CASUS may be the preferred model for postoperative use. Validation in more typical cardiac surgery centers in the USA is recommended.

scholarly journals Time-series analysis for porcine reproductive and respiratory syndrome in the United States

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0195282 ◽  
Author(s):  
Andréia Gonçalves Arruda ◽  
Carles Vilalta ◽  
Pere Puig ◽  
Andres Perez ◽  
Anna Alba
Keyword(s):  
United States ◽  
Time Series ◽  
Time Series Analysis ◽  
The United States ◽  
Series Analysis

scholarly journals Volunteers’ Felt Respect and Its Associations With Volunteering Retention, Daily Affect, Well-being, and Mortality

2018 ◽  
Vol 75 (8) ◽  
pp. 1625-1636 ◽  
Author(s):  
Dwight C K Tse
Keyword(s):  
United States ◽  
Negative Affect ◽  
Positive Affect ◽  
Well Being ◽  
The United States ◽  
National Sample ◽  
Retention Rates ◽  
Mortality Data ◽  
Volunteer Work ◽  
Daily Affect

Abstract Objectives Volunteering is associated with improved physical and psychological well-being; volunteers feeling more respect for their work may have better well-being than their counterparts. Methods This study investigated the effects of felt respect for volunteer work on volunteering retention, daily affect, well-being (subjective, psychological, and social), and mortality. The study analyzed survey and mortality data from a national sample of 2,677 volunteers from the Midlife in the United States Study over a 20-year span. Daily affect data were obtained from a subsample of 1,032 volunteers. Results Compared to volunteers feeling less respect from others, those feeling more respect (a) were more likely to continue volunteering 10 and 20 years later, (b) had higher levels of daily positive affect and lower levels of daily negative affect, and (c) had higher levels of well-being over a 20-year period. The effect of felt respect on mortality was not statistically significant. Discussion Greater level of felt respect for volunteer work is positively related to volunteers’ retention rates, daily affective experience, and well-being.

Projected All-Cause Deaths Attributable to COVID-19–Related Unemployment in the United States

2021 ◽  
Vol 111 (4) ◽  
pp. 696-699
Author(s):  
Ellicott C. Matthay ◽  
Kate A. Duchowny ◽  
Alicia R. Riley ◽  
Sandro Galea
Keyword(s):  
United States ◽  
The United States ◽  
Mortality Data ◽  
Economic Policies ◽  
Gender And Education ◽  
Hazard Ratios ◽  
Low Education ◽  
Unemployment Data ◽  
Bureau Of Labor Statistics ◽  
Excess Deaths

Objectives. To project the range of excess deaths potentially associated with COVID-19–related unemployment in the United States and quantify inequities in these estimates by age, race/ethnicity, gender, and education. Methods. We used previously published meta-analyzed hazard ratios (HRs) for the unemployment–mortality association, unemployment data from the Bureau of Labor Statistics, and mortality data from the National Center for Health Statistics to estimate 1-year age-standardized deaths attributable to COVID-19–related unemployment for US workers aged 25 to 64 years. To accommodate uncertainty, we tested ranges of unemployment and HR scenarios. Results. Our best estimate is that there will be 30 231 excess deaths attributable to COVID-19–related unemployment between April 2020 and March 2021. Across scenarios, attributable deaths ranged from 8315 to 201 968. Attributable deaths were disproportionately high among Blacks, men, and those with low education. Conclusions. Deaths attributable to COVID-19–related unemployment will add to those directly associated with the virus and will disproportionately burden groups already experiencing incommensurate COVID-19 mortality. Public Health Implications. Supportive economic policies and interventions addressing long-standing harmful social structures are essential to mitigate the unequal health harms of COVID-19.

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