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 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 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.

Craniotomy for Resection of Pediatric Brain Tumors in the United States, 1988 to 2000: Effects of Provider Caseloads and Progressive Centralization and Specialization of Care

Neurosurgery ◽  
2004 ◽  
Vol 54 (3) ◽  
pp. 553-565 ◽  
Author(s):  
Edward R. Smith ◽  
William E. Butler ◽  
Fred G. Barker
Keyword(s):  
United States ◽  
Brain Tumor ◽  
Mortality Rate ◽  
Multivariate Analyses ◽  
Pediatric Brain Tumor ◽  
High Volume ◽  
The United States ◽  
Mortality Rates ◽  
Discharge Disposition ◽  
Pediatric Brain

Abstract OBJECTIVE Large provider caseloads are associated with better patient outcomes after many complex surgical procedures. Mortality rates for pediatric brain tumor surgery in various practice settings have not been described. We used a national hospital discharge database to study the volume-outcome relationship for craniotomy performed for pediatric brain tumor resection, as well as trends toward centralization and specialization. METHODS We conducted a cross sectional and longitudinal cohort study using Nationwide Inpatient Sample data for 1988 to 2000 (Agency for Healthcare Research and Quality, Rockville, MD). Multivariate analyses adjusted for age, sex, geographic region, admission type (emergency, urgent, or elective), tumor location, and malignancy. RESULTS We analyzed 4712 admissions (329 hospitals, 480 identified surgeons) for pediatric brain tumor craniotomy. The in-hospital mortality rate was 1.6% and decreased from 2.7% (in 1988–1990) to 1.2% (in 1997–2000) during the study period. On a per-patient basis, median annual caseloads were 11 for hospitals (range, 1–59 cases) and 6 for surgeons (range, 1–32 cases). In multivariate analyses, the mortality rate was significantly lower at high-volume hospitals than at low-volume hospitals (odds ratio, 0.52 for 10-fold larger caseload; 95% confidence interval, 0.28–0.94; P = 0.03). The mortality rate was 2.3% at the lowest-volume-quartile hospitals (4 or fewer admissions annually), compared with 1.4% at the highest-volume-quartile hospitals (more than 20 admissions annually). There was a trend toward lower mortality rates after surgery performed by high-volume surgeons (P = 0.16). Adverse hospital discharge disposition was less likely to be associated with high-volume hospitals (P < 0.001) and high-volume surgeons (P = 0.004). Length of stay and hospital charges were minimally related to hospital caseloads. Approximately 5% of United States hospitals performed pediatric brain tumor craniotomy during this period. The burden of care shifted toward large-caseload hospitals, teaching hospitals, and surgeons whose practices included predominantly pediatric patients, indicating progressive centralization and specialization. CONCLUSION Mortality and adverse discharge disposition rates for pediatric brain tumor craniotomy were lower when the procedure was performed at high-volume hospitals and by high-volume surgeons in the United States, from 1988 to 2000. There were trends toward lower mortality rates, greater centralization of surgery, and more specialization among surgeons during this period.

scholarly journals Deaths from necrotizing fasciitis in the United States, 2003–2013

2015 ◽  
Vol 144 (6) ◽  
pp. 1338-1344 ◽  
Author(s):  
N. ARIF ◽  
S. YOUSFI ◽  
C. VINNARD
Keyword(s):  
United States ◽  
Necrotizing Fasciitis ◽  
Incidence Rate ◽  
Racial Differences ◽  
Cause Of Death ◽  
The United States ◽  
Crude Mortality Rate ◽  
Crude Mortality ◽  
Icd 10 ◽  
Life Threatening

SUMMARYNecrotizing fasciitis (NF) is a life-threatening infection requiring urgent surgical and medical therapy. Our objective was to estimate the mortality burden of NF in the United States, and to identify time trends in the incidence rate of NF-related mortality. We obtained data from the National Center for Health Statistics, which receives information from death certificates from all states, including demographic information and cause of death. The U.S. Multiple Cause of Death Files were searched from 2003 to 2013 for a listing of NF (ICD-10 code M72.6) as either the underlying or contributing cause of death. We identified a total of 9871 NF-related deaths in the United States between 2003 and 2013, corresponding to a crude mortality rate of 4·8 deaths/1 000 000 person-years, without a significant time trend. Compared to white individuals, the incidence rate of NF-associated death was greater in black, Hispanic, and American Indian individuals, and lower in Asian individuals. Streptococcal infection was most commonly identified in cases where a pathogen was reported. Diabetes mellitus and obesity were more commonly observed in NF-related deaths compared to deaths due to other causes. Racial differences in the incidence of NF-related deaths merits further investigation.

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

2019 ◽  
Vol 111 (8) ◽  
pp. 863-866 ◽  
Author(s):  
Diana R Withrow ◽  
Amy Berrington de González ◽  
Susan Spillane ◽  
Neal D Freedman ◽  
Ana F Best ◽  
...  
Keyword(s):  
United States ◽  
Cancer Mortality ◽  
Death Certificate ◽  
The United States ◽  
Mortality Rates ◽  
Income Quintile ◽  
County Level ◽  
Younger Adults ◽  
Rate Ratios ◽  
Over Time

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.

The Impact of BMI on Adult Blunt Trauma Outcomes

2019 ◽  
Vol 85 (12) ◽  
pp. 1354-1362
Author(s):  
Rahman Barry ◽  
Milad Modarresi ◽  
Rodrigo Aguilar ◽  
Jacqueline Sanabria ◽  
Thao Wolbert ◽  
...  
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Length Of Stay ◽  
Blunt Trauma ◽  
Nationwide Inpatient Sample ◽  
The United States ◽  
Mortality Rates ◽  
Hospital Length Of Stay ◽  
Obese Patients ◽  
The Impact

Traumatic injuries account for 10% of all mortalities in the United States. Globally, it is estimated that by the year 2030, 2.2 billion people will be overweight (BMI ≥ 25) and 1.1 billion people will be obese (BMI ≥ 30). Obesity is a known risk factor for suboptimal outcomes in trauma; however, the extent of this impact after blunt trauma remains to be determined. The incidence, prevalence, and mortality rates from blunt trauma by age, gender, cause, BMI, year, and geography were abstracted using datasets from 1) the Global Burden of Disease group 2) the United States Nationwide Inpatient Sample databank 3) two regional Level II trauma centers. Statistical analyses, correlations, and comparisons were made on a global, national, and state level using these databases to determine the impact of BMI on blunt trauma. The incidence of blunt trauma secondary to falls increased at global, national, and state levels during our study period from 1990 to 2015, with a corresponding increase in BMI at all levels ( P < 0.05). Mortality due to fall injuries was higher in obese patients at all levels ( P < 0.05). Analysis from Nationwide Inpatient Sample database demonstrated higher mortality rates for obese patients nationally, both after motor vehicle collisions and mechanical falls ( P < 0.05). In obese and nonobese patients, regional data demonstrated a higher blunt trauma mortality rate of 2.4% versus 1.2%, respectively ( P < 0.05) and a longer hospital length of stay of 4.13 versus 3.26 days, respectively ( P = 0.018). The obesity rate and incidence of blunt trauma secondary to falls are increasing, with a higher mortality rate and longer length of stay in obese blunt trauma patients.

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 A survey of naturally occurring diseases of the Syrian hamster

1975 ◽  
Vol 9 (3) ◽  
pp. 179-191 ◽  
Author(s):  
Harland W. Renshaw ◽  
G. L. Van Hoosier ◽  
Norine K. Amend
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Age Groups ◽  
The United States ◽  
Mortality Rates ◽  
Survey Respondent ◽  
Relative Importance ◽  
Review Of The Literature ◽  
Research Activities ◽  
Naturally Occurring

Questionnaires on research activities, mortality rates observed in various age groups, extent of examination of dead hamsters, and natural disease conditions and their relative importance were returned by 24 of 43 organisations surveyed in the United States. The average preweaning mortality rate due to all causes was 11·9%. Comparative data from 6 organisations that raised 87880 hamsters in the calendar year 1971 indicated that 97·5% of total preweaning mortality was due to cannibalism. 13·7% of all animals died before use for experiments. 'Wet-tail' was the most frequently recognized disease (71%), and it was also listed as the most important. Pneumonia was recognized by 43% of the respondents and was most commonly listed as second in importance. A selective review of the literature is presented on those diseases recognized by more than one survey respondent.

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