Trends in Mortality Due to Cancer in the United States by Age and County-Level Income, 1999–2015

Abstract Disparities in cancer mortality by county-level income have increased. It is unclear whether these widening disparities have affected older and younger adults equally. National death certificate data were utilized to ascertain cancer deaths during 1999–2015. Average annual percent changes in mortality rates and mortality rate ratios (RRs) were estimated by county-level income quintile and age (25–64 vs ≥65 years). Among 25- to 64-year-olds, cancer mortality rates were 30% higher (RR = 1.30, 95% confidence interval [CI] = 1.29 to 1.31) in the lowest-vs the highest-income counties in 1999–2001 and 56% higher (RR = 1.56, 95% CI = 1.55 to 1.57) in 2013–2015; the disparities among those 65 years and older were smaller but also widened over time (RR1999–2001 = 1.04, 95% CI = 1.03 to 1.05; RR2013–2015 = 1.14, 95% CI = 1.13 to 1.14). Widening disparities occurred across cancer sites. If all counties had the mortality rates of the highest-income counties, 21.5% of cancer deaths among 25- to 64-year-olds and 7.3% of cancer deaths in those 65 years and older would have been avoided in 2015. These results highlight an ongoing need for equity-focused interventions, particularly among younger adults.

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The evolution of infant mortality inequality in the United States, 1960–2016

Science Advances ◽

10.1126/sciadv.aba5908 ◽

2020 ◽

Vol 6 (29) ◽

pp. eaba5908

Author(s):

Nick Turner ◽

Kaveh Danesh ◽

Kelsey Moran

Keyword(s):

United States ◽

Infant Mortality ◽

The United States ◽

Mortality Rates ◽

County Level ◽

Level Data ◽

Antipoverty Programs ◽

The Relationship ◽

Over Time ◽

Mortality Inequality

What is the relationship between infant mortality and poverty in the United States and how has it changed over time? We address this question by analyzing county-level data between 1960 and 2016. Our estimates suggest that level differences in mortality rates between the poorest and least poor counties decreased meaningfully between 1960 and 2000. Nearly three-quarters of the decrease occurred between 1960 and 1980, coincident with the introduction of antipoverty programs and improvements in medical care for infants. We estimate that declining inequality accounts for 18% of the national reduction in infant mortality between 1960 and 2000. However, we also find that level differences between the poorest and least poor counties remained constant between 2000 and 2016, suggesting an important role for policies that improve the health of infants in poor areas.

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REGIONAL CONVERGENCE OF CANCER MORTALITY RATES OVER TIME IN THE UNITED STATES, 1940–1960

American Journal of Epidemiology ◽

10.1093/oxfordjournals.aje.a121237 ◽

1971 ◽

Vol 93 (2) ◽

pp. 79-83 ◽

Cited By ~ 1

Author(s):

DEWARD E. WAGGONER ◽

GUY R. NEWELL

Keyword(s):

United States ◽

Cancer Mortality ◽

The United States ◽

Mortality Rates ◽

Regional Convergence ◽

Over Time

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Reduced Lung Cancer Mortality With Lower Atmospheric Pressure

Dose-Response ◽

10.1177/1559325818769484 ◽

2018 ◽

Vol 16 (2) ◽

pp. 155932581876948 ◽

Cited By ~ 1

Author(s):

Ray M. Merrill ◽

Aaron Frutos

Keyword(s):

United States ◽

Lung Cancer ◽

Cancer Mortality ◽

Atmospheric Pressure ◽

Regression Models ◽

Spatial Regression ◽

Lung Cancer Mortality ◽

The United States ◽

Mortality Rates ◽

County Level

Background: Research has shown that higher altitude is associated with lower risk of lung cancer and improved survival among patients. The current study assessed the influence of county-level atmospheric pressure (a measure reflecting both altitude and temperature) on age-adjusted lung cancer mortality rates in the contiguous United States, with 2 forms of spatial regression. Methods: Ordinary least squares regression and geographically weighted regression models were used to evaluate the impact of climate and other selected variables on lung cancer mortality, based on 2974 counties. Results: Atmospheric pressure was significantly positively associated with lung cancer mortality, after controlling for sunlight, precipitation, PM2.5 (µg/m3), current smoker, and other selected variables. Positive county-level β coefficient estimates ( P < .05) for atmospheric pressure were observed throughout the United States, higher in the eastern half of the country. Conclusion: The spatial regression models showed that atmospheric pressure is positively associated with age-adjusted lung cancer mortality rates, after controlling for other selected variables.

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133: Comparisons of Prostate Cancer Mortality Rates with Changes in Dietary Practices in the United States from 1930 to 2000

The Journal of Urology ◽

10.1016/s0022-5347(18)32400-5 ◽

2006 ◽

Vol 175 (4S) ◽

pp. 42-43

Author(s):

Jan L. Colli ◽

Albert Colli

Keyword(s):

Prostate Cancer ◽

United States ◽

Cancer Mortality ◽

The United States ◽

Mortality Rates ◽

Dietary Practices ◽

Prostate Cancer Mortality

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

Journal of Clinical Oncology ◽

10.1200/jco.2009.27.0421 ◽

2010 ◽

Vol 28 (15) ◽

pp. 2625-2634 ◽

Cited By ~ 593

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.

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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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