scholarly journals Whites’ County-Level Racial Bias, COVID-19 Rates, and Racial Inequities in the United States

2020 ◽  
Vol 17 (22) ◽  
pp. 8695
Author(s):  
Marilyn D. Thomas ◽  
Eli K. Michaels ◽  
Sean Darling-Hammond ◽  
Thu T. Nguyen ◽  
M. Maria Glymour ◽  
...  
Keyword(s):  
United States ◽  
Standard Deviation ◽  
Incidence Rate ◽  
Racial Bias ◽  
The United States ◽  
Mortality Rates ◽  
Incidence Rates ◽  
County Level ◽  
Explicit Bias ◽  
Implicit And Explicit

Mounting evidence reveals considerable racial inequities in coronavirus disease 2019 (COVID-19) outcomes in the United States (US). Area-level racial bias has been associated with multiple adverse health outcomes, but its association with COVID-19 is yet unexplored. Combining county-level data from Project Implicit on implicit and explicit anti-Black bias among non-Hispanic Whites, Johns Hopkins Coronavirus Resource Center, and The New York Times, we used adjusted linear regressions to estimate overall COVID-19 incidence and mortality rates through 01 July 2020, Black and White incidence rates through 28 May 2020, and Black–White incidence rate gaps on average area-level implicit and explicit racial bias. Across 2994 counties, the average COVID-19 mortality rate (standard deviation) was 1.7/10,000 people (3.3) and average cumulative COVID-19 incidence rate was 52.1/10,000 (77.2). Higher racial bias was associated with higher overall mortality rates (per 1 standard deviation higher implicit bias b = 0.65/10,000 (95% confidence interval: 0.39, 0.91); explicit bias b = 0.49/10,000 (0.27, 0.70)) and higher overall incidence (implicit bias b = 8.42/10,000 (4.64, 12.20); explicit bias b = 8.83/10,000 (5.32, 12.35)). In 957 counties with race-specific data, higher racial bias predicted higher White and Black incidence rates, and larger Black–White incidence rate gaps. Anti-Black bias among Whites predicts worse COVID-19 outcomes and greater inequities. Area-level interventions may ameliorate health inequities.

scholarly journals Lifestyle Effects on the Risk of Transmission of COVID-19 in the United States: Evaluation of Market Segmentation Systems

2021 ◽  
Vol 18 (9) ◽  
pp. 4826
Author(s):  
Esra Ozdenerol ◽  
Jacob Seboly
Keyword(s):  
United States ◽  
The United States ◽  
Mortality Rates ◽  
Incidence Rates ◽  
Statistical Testing ◽  
Average Risk ◽  
County Level ◽  
Household Level ◽  
The Impact ◽  
The U.S

The aim of this study was to associate lifestyle characteristics with COVID-19 infection and mortality rates at the U.S. county level and sequentially map the impact of COVID-19 on different lifestyle segments. We used analysis of variance (ANOVA) statistical testing to determine whether there is any correlation between COVID-19 infection and mortality rates and lifestyles. We used ESRI Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes to identify consumers’ lifestyles and preferences. According to the ANOVA analysis, a significant association between COVID-19 deaths and LifeModes emerged on 1 April 2020 and was sustained until 30 June 2020. Analysis of means (ANOM) was also performed to determine which LifeModes have incidence rates that are significantly above/below the overall mean incidence rate. We sequentially mapped and graphically illustrated when and where each LifeMode had above/below average risk for COVID-19 infection/death on specific dates. A strong northwest-to-south and northeast-to-south gradient of COVID-19 incidence was identified, facilitating an empirical classification of the United States into several epidemic subregions based on household lifestyle characteristics. Our approach correlating lifestyle characteristics to COVID-19 infection and mortality rate at the U.S. county level provided unique insights into where and when COVID-19 impacted different households. The results suggest that prevention and control policies can be implemented to those specific households exhibiting spatial and temporal pattern of high risk.

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.

Exploring Implicit and Explicit Racial Bias in OT Professionals

2021 ◽  
Vol 75 (Supplement_2) ◽  
pp. 7512500020p1-7512500020p1
Author(s):  
Alaa Abou-Arab ◽  
Rochelle Mendonca
Keyword(s):  
United States ◽  
Exploratory Study ◽  
Racial Bias ◽  
The United States ◽  
Further Education ◽  
Negative Evaluation ◽  
Racial Biases ◽  
Implicit And Explicit ◽  
Primary Author

Abstract Date Presented 04/13/21 Racial bias is defined as the negative evaluation of a group and its members relative to another and can exist on explicit and implicit levels. This is an exploratory study to examine the presence of implicit and explicit racial bias among OT professionals across the United States. The results (N = 201) highlight the presence of implicit and explicit racial biases among OT professionals in the United States and the need for further education on racial bias. Primary Author and Speaker: Alaa Abou-Arab Additional Authors and Speakers: Alee Leteria, Kristina Zanayed, and Susanne Higgins

scholarly journals 1164. County-Level Geographic Distribution of Extended-Spectrum Cephalosporin-Resistant Enterobacteriaceae Across Outpatient Settings of the Veterans Health Administration, 2000–2017

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S350-S351
Author(s):  
Michihiko Goto ◽  
Rajeshwari Nair ◽  
Daniel Livorsi ◽  
Marin Schweizer ◽  
Michael Ohl ◽  
...  
Keyword(s):  
United States ◽  
Veterans Health Administration ◽  
The United States ◽  
Incidence Rates ◽  
County Level ◽  
Veterans Health ◽  
Health Administration ◽  
Geographic Variability ◽  
Extended Spectrum ◽  
Outpatient Settings

Abstract Background Extended-spectrum cephalosporin resistance (ESCR) among Enterobacteriaceae has emerged globally over the last two decades, with increased prevalence in the community. Data from European countries and healthcare-associated isolates in the United States have demonstrated substantial geographic variability in the prevalence of ESCR, but community-onset isolates in the United States have been less studied. We aimed to describe geographic distribution and spread of ESCR among outpatient settings across the Veterans Health Administration (VHA) over 18 years. Methods We analyzed a retrospective cohort of all patients who had any positive clinical culture specimen for ESCR Enterobacteriaceae collected in an outpatient setting; ESCR was defined by phenotypic nonsusceptibility to at least one extended-spectrum cephalosporin agent or detection of an extended-spectrum β-lactamase. Patient-level data were grouped by county of residence, and the total number of unique patients who received care within VHA for each county was used as a denominator. We aggregated data by time terciles (2000–2005, 2006–2011, and 2012–2017), and overall and county-level incidence rates were calculated as the number of unique patients in each year with ESCR Enterobacteriaceae per person-year. Results During the study period, there were 1,980,095 positive cultures for Enterobacteriaceae from 870,797 unique patients across outpatient settings of VHA, from a total of 107,404,504 person-years. Among those, 136,185 cultures (6.9%) from 75,500 unique patients (8.7%) were ESCR. The overall incidence rate was 9.0 cases per 10,000 person-years, which increased from 6.3 per 10,000 person-years in 2000 to 14.6 per 10,000 person-years in 2017. County-level incidence rates ranged widely but increased overall (interquartile range [IQR] in 2000–2005: 0–6.7; 2006–2011: 0–9.1; 2012–2017: 3.1–14.3 per 10,000 person-years), with some geographic clustering (figure). Conclusion This study demonstrates that there has been geographic variation both in incidence rates and trends of ESCR Enterobacteriaceae in outpatient settings of VHA, which suggests the importance of tailoring local antibiotic-prescribing guidelines incorporating geographic variability in epidemiology. Disclosures M. Ohl, Gilead Sciences, Inc.: Grant Investigator, Research grant.

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 AIDS-related Burkitt lymphoma in the United States: what do age and CD4 lymphocyte patterns tell us about etiology and/or biology?

Blood ◽  
2010 ◽  
Vol 116 (25) ◽  
pp. 5600-5604 ◽  
Author(s):  
Mercy Guech-Ongey ◽  
Edgar P. Simard ◽  
William F. Anderson ◽  
Eric A. Engels ◽  
Kishor Bhatia ◽  
...  
Keyword(s):  
United States ◽  
Incidence Rate ◽  
Burkitt Lymphoma ◽  
Rate Ratio ◽  
Hiv Transmission ◽  
Incidence Rate Ratio ◽  
The United States ◽  
Incidence Rates ◽  
Cd4 Lymphocyte ◽  
Cd4 Lymphocytes

Abstract Trimodal or bimodal age-specific incidence rates for Burkitt lymphoma (BL) were observed in the United States general population, but the role of immunosuppression could not be excluded. Incidence rates, rate ratios, and 95% confidence intervals for BL and other non-Hodgkin lymphoma (NHL), by age and CD4 lymphocyte count categories, were estimated using Poisson regression models using data from the United States HIV/AIDS Cancer Match study (1980-2005). BL incidence was 22 cases per 100 000 person-years and 586 for non-BL NHL. Adjusted BL incidence rate ratio among males was 1.6× that among females and among non-Hispanic blacks, 0.4× that among non-Hispanic whites, but unrelated to HIV-transmission category. Non-BL NHL incidence increased from childhood to adulthood; in contrast, 2 age-specific incidence peaks during the pediatric and adult/geriatric years were observed for BL. Non-BL NHL incidence rose steadily with decreasing CD4 lymphocyte counts; in contrast, BL incidence was lowest among people with ≤ 50 CD4 lymphocytes/μL versus those with ≥ 250 CD4 lymphocytes/μL (incidence rate ratio 0.3 [95% confidence interval = 0.2-0.6]). The bimodal peaks for BL, in contrast to non-BL NHL, suggest effects of noncumulative risk factors at different ages. Underascertainment or biological reasons may account for BL deficit at low CD4 lymphocyte counts.

scholarly journals Newly incident cannabis use in the United States, 2002–2011: a regional and state level benchmark

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3616 ◽  
Author(s):  
Jacob P. Leinweber ◽  
Hui G. Cheng ◽  
Catalina Lopez-Quintero ◽  
James C. Anthony
Keyword(s):  
United States ◽  
Incidence Rate ◽  
Meta Analysis ◽  
State Level ◽  
The United States ◽  
Incidence Rates ◽  
Cannabis Use ◽  
Border Region ◽  
Community Residents ◽  
Mexico Border

BackgroundCannabis use and cannabis regulatory policies recently re-surfaced as noteworthy global research and social media topics, including claims that Mexicans have been sending cannabis and other drug supplies through a porous border into the United States. These circumstances prompted us to conduct an epidemiological test of whether the states bordering Mexico had exceptionally large cannabis incidence rates for 2002–2011. The resulting range of cannabis incidence rates disclosed here can serve as 2002–2011 benchmark values against which estimates from later years can be compared.MethodsThe population under study is 12-to-24-year-old non-institutionalized civilian community residents of the US, sampled and assessed with confidential audio computer-assisted self-interviews (ACASI) during National Surveys on Drug Use and Health, 2002–2011 (aggregaten ∼ 420,000) for which public use datasets were available. We estimated state-specific cannabis incidence rates based on independent replication sample surveys across these years, and derived meta-analysis estimates for 10 pre-specified regions, including the Mexico border region.ResultsFrom meta-analysis, the estimated annual incidence rate for cannabis use in the Mexico Border Region is 5% (95% CI [4%–7%]), which is not an exceptional value relative to the overall US estimate of 6% (95% CI [5%–6%]). Geographically quite distant from Mexico and from states of the western US with liberalized cannabis policies, the North Atlantic Region population has the numerically largest incidence estimate at 7% (95% CI [6%–8%]), while the Gulf of Mexico Border Region population has the lowest incidence rate at 5% (95% CI [4%–6%]). Within the set of state-specific estimates, Vermont’s and Utah’s populations have the largest and smallest incidence rates, respectively (VT: 9%; 95% CI [8%–10%]; UT: 3%; 95% CI [3%–4%]).DiscussionBased on this study’s estimates, among 12-to-24-year-old US community residents, an estimated 6% start to use cannabis each year (roughly one in 16). Relatively minor variation in region-wise and state-level estimates is seen, although Vermont and Utah might be exceptional. As of 2011, proximity to Mexico, to Canada, and to the western states with liberalized policies apparently has induced little variation in cannabis incidence rates. Our primary intent was to create a set of benchmark estimates for state-specific and region-specific population incidence rates for cannabis use, using meta-analysis based on independent US survey replications. Public health officials and policy analysts now can use these benchmark estimates from 2002–2011 for planning, and in comparisons with newer estimates.

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.

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