scholarly journals United States County-level COVID-19 Death Rates and Case Fatality Rates Vary by Region and Urban Status

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 330
Author(s):  
Rashid Ahmed ◽  
Mark Williamson ◽  
Muhammad Akhter Hamid ◽  
Naila Ashraf
Keyword(s):  
United States ◽  
Case Fatality ◽  
The United States ◽  
Mortality Rates ◽  
Small Sample ◽  
County Level ◽  
Death Rates ◽  
Rural Counties ◽  
Urban Classification ◽  
Population Mortality

COVID-19 is a global pandemic with uncertain death rates. We examined county-level population morality rates (per 100,000) and case fatality rates by US region and rural-urban classification, while controlling for demographic, socioeconomic, and hospital variables. We found that population mortality rates and case fatality rates were significantly different across region, rural-urban classification, and their interaction. All significant comparisons had p < 0.001. Northeast counties had the highest population mortality rates (27.4) but had similar case fatality rates (5.9%) compared to other regions except the Southeast, which had significantly lower rates (4.1%). Population mortality rates were highest in urban counties but conversely, case fatality rates were highest in rural counties. Death rates in the Northeast were driven by urban areas (e.g., small, East Coast states), while case fatality rates tended to be highest in the most rural counties for all regions, especially the Southwest. However, on further inspection, high case fatality rate percentages in the Southwest, as well as in overall US counties, were driven by a low case number. This makes it hard to distinguish genuinely higher mortality or an artifact of a small sample size. In summary, coronavirus deaths are not homogenous across the United States but instead vary by region and population and highlight the importance of fine-scale analysis.

scholarly journals Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States

2020 ◽  
Vol 1 (3) ◽  
pp. 100047 ◽  
Author(s):  
Donghai Liang ◽  
Liuhua Shi ◽  
Jingxuan Zhao ◽  
Pengfei Liu ◽  
Jeremy A. Sarnat ◽  
...  
Keyword(s):  
United States ◽  
Air Pollution ◽  
Urban Air Pollution ◽  
Case Fatality ◽  
The United States ◽  
Mortality Rates ◽  
Urban Air

scholarly journals 694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep &lt;7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

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

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.

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

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.

scholarly journals Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Neighborhood Features

2020 ◽  
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

AbstractWhat is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?BackgroundFollowing 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. We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.MethodsSatellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 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.ResultsPredicted 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). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. 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.ConclusionsThe 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.

scholarly journals Trends in heart failure-related cardiovascular mortality in rural versus urban United States counties, 2011–2018: A cross-sectional study

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246813
Author(s):  
Jacob B. Pierce ◽  
Nilay S. Shah ◽  
Lucia C. Petito ◽  
Lindsay Pool ◽  
Donald M. Lloyd-Jones ◽  
...  
Keyword(s):  
United States ◽  
Heart Failure ◽  
Cause Of Death ◽  
Negative Binomial ◽  
The United States ◽  
Mortality Rates ◽  
County Level ◽  
Physician Density ◽  
Rural And Urban ◽  
Related Mortality

Background Adults in rural counties in the United States (US) experience higher rates broadly of cardiovascular disease (CVD) compared with adults in urban counties. Mortality rates specifically due to heart failure (HF) have increased since 2011, but estimates of heterogeneity at the county-level in HF-related mortality have not been produced. The objectives of this study were 1) to quantify nationwide trends by rural-urban designation and 2) examine county-level factors associated with rural-urban differences in HF-related mortality rates. Methods and findings We queried CDC WONDER to identify HF deaths between 2011–2018 defined as CVD (I00-78) as the underlying cause of death and HF (I50) as a contributing cause of death. First, we calculated national age-adjusted mortality rates (AAMR) and examined trends stratified by rural-urban status (defined using 2013 NCHS Urban-Rural Classification Scheme), age (35–64 and 65–84 years), and race-sex subgroups per year. Second, we combined all deaths from 2011–2018 and estimated incidence rate ratios (IRR) in HF-related mortality for rural versus urban counties using multivariable negative binomial regression models with adjustment for demographic and socioeconomic characteristics, risk factor prevalence, and physician density. Between 2011–2018, 162,314 and 580,305 HF-related deaths occurred in rural and urban counties, respectively. AAMRs were consistently higher for residents in rural compared with urban counties (73.2 [95% CI: 72.2–74.2] vs. 57.2 [56.8–57.6] in 2018, respectively). The highest AAMR was observed in rural Black men (131.1 [123.3–138.9] in 2018) with greatest increases in HF-related mortality in those 35–64 years (+6.1%/year). The rural-urban IRR persisted among both younger (1.10 [1.04–1.16]) and older adults (1.04 [1.02–1.07]) after adjustment for county-level factors. Main limitations included lack of individual-level data and county dropout due to low event rates (<20). Conclusions Differences in county-level factors may account for a significant amount of the observed variation in HF-related mortality between rural and urban counties. Efforts to reduce the rural-urban disparity in HF-related mortality rates will likely require diverse public health and clinical interventions targeting the underlying causes of this disparity.

scholarly journals On the Relationship between Development and Fertility: The Case of the United States

2015 ◽  
Vol 40 (4) ◽  
Author(s):  
Igor Ryabov
Keyword(s):  
United States ◽  
Human Development ◽  
Negative Relationship ◽  
The United States ◽  
Median Income ◽  
County Level ◽  
Cross Sectional ◽  
Development Indicators ◽  
Rural Counties ◽  
The Relationship

The present article addresses the question of whether there is a link between the spatial patterns of human development and period fertility in the United States at the county level. Using cross-sectional analyses of the relationship between Total Fertility Rate (TFR) and an array of human development indicators (pertaining to three components of the Human Development Index (HDI) – wealth, health, and education), this study sheds light on the relationship between fertility and human development. The analyses were conducted separately for urban, suburban and rural counties. According to the multivariate results, a negative association between selected human development indicators and TFR exists in suburban and rural counties, as well as in the United States as a whole. However, this is not the case for urban counties, where the results were inconclusive. Some indicators (e.g., median income per capita) were found to be positively, and some (e.g., the share of adults with at least bachelor’s degree) negatively, associated with TFR in urban counties. All in all, our results provide evidence of a negative relationship between human development indicators and period fertility in the United States at the county level, a finding which is consistent with the basic tenets of classic demographic transition theory.

scholarly journals Trends in Premature Mortality From Acute Myocardial Infarction in the United States, 1999 to 2019

2021 ◽  
Author(s):  
Sourbha S. Dani ◽  
Ahmad N. Lone ◽  
Zulqarnain Javed ◽  
Muhammad S. Khan ◽  
Muhammad Zia Khan ◽  
...  
Keyword(s):  
United States ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Mortality Rate ◽  
The United States ◽  
Mortality Rates ◽  
Percentage Change ◽  
Annual Percentage Change ◽  
Rural Counties ◽  
Average Annual Percentage Change

Background Evaluating premature (<65 years of age) mortality because of acute myocardial infarction (AMI) by demographic and regional characteristics may inform public health interventions. Methods and Results We used the Centers for Disease Control and Prevention’s WONDER (Wide‐Ranging Online Data for Epidemiologic Research) death certificate database to examine premature (<65 years of age) age‐adjusted AMI mortality rates per 100 000 and average annual percentage change from 1999 to 2019. Overall, the age‐adjusted AMI mortality rate was 13.4 (95% CI, 13.3–13.5). Middle‐aged adults, men, non‐Hispanic Black adults, and rural counties had higher mortality than young adults, women, NH White adults, and urban counties, respectively. Between 1999 and 2019, the age‐adjusted AMI mortality rate decreased at an average annual percentage change of −3.4 per year (95% CI, −3.6 to −3.3), with the average annual percentage change showing higher decline in age‐adjusted AMI mortality rates among large (−4.2 per year [95% CI, −4.4 to −4.0]), and medium/small metros (−3.3 per year [95% CI, −3.5 to −3.1]) than rural counties (−2.4 per year [95% CI, −2.8 to −1.9]). Age‐adjusted AMI mortality rates >90th percentile were distributed in the Southern states, and those with mortality <10th percentile were clustered in the Western and Northeastern states. After an initial decline between 1999 and 2011 (−4.3 per year [95% CI, −4.6 to −4.1]), the average annual percentage change showed deceleration in mortality since 2011 (−2.1 per year [95% CI, −2.4 to −1.8]). These trends were consistent across both sexes, all ethnicities and races, and urban/rural counties. Conclusions During the past 20 years, decline in premature AMI mortality has slowed down in the United States since 2011, with considerable heterogeneity across demographic groups, states, and urbanicity. Systemic efforts are mandated to address cardiovascular health disparities and outcomes among nonelderly adults.

scholarly journals Changing Spatiotemporal Trends in County‐Level Heart Failure Death Rates in the United States, 1999 to 2018

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Adam S. Vaughan ◽  
Mary G. George ◽  
Sandra L. Jackson ◽  
Linda Schieb ◽  
Michele Casper
Keyword(s):  
United States ◽  
Heart Failure ◽  
Ethnic Group ◽  
The United States ◽  
Local Context ◽  
County Level ◽  
Age Group ◽  
Death Rates ◽  
Spatiotemporal Trends ◽  
Racial Ethnic

Background Amid recently rising heart failure (HF) death rates in the United States, we describe county‐level trends in HF mortality from 1999 to 2018 by racial/ethnic group and sex for ages 35 to 64 years and 65 years and older. Methods and Results Applying a hierarchical Bayesian model to National Vital Statistics data representing all US deaths, ages 35 years and older, we estimated annual age‐standardized county‐level HF death rates and percent change by age group, racial/ethnic group, and sex from 1999 through 2018. During 1999 to 2011, ~30% of counties experienced increasing HF death rates among adults ages 35 to 64 years. However, during 2011 to 2018, 86.9% (95% CI, 85.2–88.2) of counties experienced increasing mortality. Likewise, for ages 65 years and older, during 1999 to 2005 and 2005 to 2011, 27.8% (95% CI, 25.8–29.8) and 12.6% (95% CI, 11.2–13.9) of counties, respectively, experienced increasing mortality. However, during 2011 to 2018, most counties (67.4% [95% CI, 65.4–69.5]) experienced increasing mortality. These temporal patterns by age group held across racial/ethnic group and sex. Conclusions These results provide local context to previously documented recent national increases in HF death rates. Although county‐level declines were most common before 2011, some counties and demographic groups experienced increasing HF death rates during this period of national declines. However, recent county‐level increases were pervasive, occurring across counties, racial/ethnic group, and sex, particularly among ages 35 to 64 years. These spatiotemporal patterns highlight the need to identify and address underlying clinical risk factors and social determinants of health contributing to these increasing trends.

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