Disparities in gestational age–specific fetal mortality rates in the United States, 2009–2013

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.

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Birth Weight by Gestational Age for 76,710 Twins Born in the United States as a Result of In Vitro Fertilization: 2006 to 2010

American Journal of Perinatology ◽

10.1055/s-0036-1584137 ◽

2016 ◽

Vol 34 (01) ◽

pp. 31-37

Author(s):

Richard Dickey ◽

Xu Xiong ◽

Monica Klempel ◽

Gabriella Pridjian

Keyword(s):

United States ◽

Birth Weight ◽

In Vitro Fertilization ◽

Gestational Age ◽

The United States ◽

Vitro Fertilization

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Adult sports-related traumatic brain injury in United States trauma centers

Neurosurgical FOCUS ◽

10.3171/2016.1.focus15613 ◽

2016 ◽

Vol 40 (4) ◽

pp. E4 ◽

Cited By ~ 25

Author(s):

Ethan A. Winkler ◽

John K. Yue ◽

John F. Burke ◽

Andrew K. Chan ◽

Sanjay S. Dhall ◽

...

Keyword(s):

United States ◽

Traumatic Brain Injury ◽

Brain Injury ◽

The United States ◽

Mortality Rates ◽

Hospital Length Of Stay ◽

Health Concern ◽

Medical Complications ◽

Equestrian Sports ◽

Prolonged Hospital

OBJECTIVE Sports-related traumatic brain injury (TBI) is an important public health concern estimated to affect 300,000 to 3.8 million people annually in the United States. Although injuries to professional athletes dominate the media, this group represents only a small proportion of the overall population. Here, the authors characterize the demographics of sports-related TBI in adults from a community-based trauma population and identify predictors of prolonged hospitalization and increased morbidity and mortality rates. METHODS Utilizing the National Sample Program of the National Trauma Data Bank (NTDB), the authors retrospectively analyzed sports-related TBI data from adults (age ≥ 18 years) across 5 sporting categories—fall or interpersonal contact (FIC), roller sports, skiing/snowboarding, equestrian sports, and aquatic sports. Multivariable regression analysis was used to identify predictors of prolonged hospital length of stay (LOS), medical complications, inpatient mortality rates, and hospital discharge disposition. Statistical significance was assessed at α < 0.05, and the Bonferroni correction for multiple comparisons was applied for each outcome analysis. RESULTS From 2003 to 2012, in total, 4788 adult sports-related TBIs were documented in the NTDB, which represented 18,310 incidents nationally. Equestrian sports were the greatest contributors to sports-related TBI (45.2%). Mild TBI represented nearly 86% of injuries overall. Mean (± SEM) LOSs in the hospital or intensive care unit (ICU) were 4.25 ± 0.09 days and 1.60 ± 0.06 days, respectively. The mortality rate was 3.0% across all patients, but was statistically higher in TBI from roller sports (4.1%) and aquatic sports (7.7%). Age, hypotension on admission to the emergency department (ED), and the severity of head and extracranial injuries were statistically significant predictors of prolonged hospital and ICU LOSs, medical complications, failure to discharge to home, and death. Traumatic brain injury during aquatic sports was similarly associated with prolonged ICU and hospital LOSs, medical complications, and failure to be discharged to home. CONCLUSIONS Age, hypotension on ED admission, severity of head and extracranial injuries, and sports mechanism of injury are important prognostic variables in adult sports-related TBI. Increasing TBI awareness and helmet use—particularly in equestrian and roller sports—are critical elements for decreasing sports-related TBI events in adults.

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Explanations for the Decrease in Ischemic Heart Disease Mortality Rates in the United States from 1970 to 2010

The Coronary Heart Disease Pandemic in the Twentieth Century ◽

10.1201/9780203704226-9 ◽

2017 ◽

pp. 95-101

Author(s):

William G. Rothstein

Keyword(s):

United States ◽

Heart Disease ◽

Ischemic Heart Disease ◽

The United States ◽

Mortality Rates ◽

Ischemic Heart ◽

Disease Mortality ◽

Ischemic Heart Disease Mortality

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Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study

Frontiers in Public Health ◽

10.3389/fpubh.2021.766707 ◽

2021 ◽

Vol 9 ◽

Author(s):

Joshua J. Levy ◽

Rebecca M. Lebeaux ◽

Anne G. Hoen ◽

Brock C. Christensen ◽

Louis J. Vaickus ◽

...

Keyword(s):

United States ◽

Deep Learning ◽

Mortality Rate ◽

Satellite Images ◽

The United States ◽

Mortality Rates ◽

Image Features ◽

County Level ◽

Crude Mortality Rate ◽

Crude Mortality

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

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