scholarly journals Trend and Geographic Disparities in the Mortality Rates of Primary Systemic Vasculitis in the United States from 1999 to 2019: A Population-Based Study

2021 ◽  
Vol 10 (8) ◽  
pp. 1759
Author(s):  
Alicia Rodriguez-Pla ◽  
Jose Rossello-Urgell
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Cause Of Death ◽  
Geographic Distribution ◽  
Systemic Vasculitis ◽  
The United States ◽  
Mortality Rates ◽  
Current Data ◽  
Geographic Disparities ◽  
Primary Systemic Vasculitis

The current data on rates and geographic distribution of vasculitis mortality are limited. We aimed to estimate the mortality rates of primary systemic vasculitis and its geographic distribution using recent population data in the United States. The mortality rates of vasculitis from 1999 to 2019 were obtained from the Center for Disease Control (CDC) Wonder Multiple Cause of Death (MCD). The age-adjusted rates per million for vasculitis as MCD and as an underlying cause of death (UCD) were calculated by state using demographics. A joinpoint regression analysis was applied to evaluate trends over time. The age-adjusted mortality rate of vasculitis as MCD was 4.077 (95% CI: 4.029–4.125) and as a UCD was 1.888 per million (95% CI: 1.855–1.921). Since 1999, mortality rates have progressively decreased. The age-adjusted mortality rate was higher in females than in males. The highest mortality rate for vasculitis as MCD was in White patients (4.371; 95% CI: 4.317–4.424). The northern states and areas with lower populations had higher mortality rates. We found a trend of progressive decreases in the mortality rates of vasculitis, as well as gender, racial, and geographic disparities. Further analyses are warranted to better understand the factors associated with these disparities in order to implement targeted public health interventions to decrease them.

Geographic disparity in systemic sclerosis mortality in the United States: 1999–2017

2019 ◽  
pp. 239719831986956
Author(s):  
Alicia Rodriguez-Pla ◽  
Robert W Simms
Keyword(s):  
United States ◽  
Systemic Sclerosis ◽  
Mortality Rate ◽  
Cause Of Death ◽  
American Indians ◽  
The United States ◽  
Mortality Rates ◽  
Progressive Decrease ◽  
Targeted Interventions ◽  
Underlying Cause Of Death

Introduction: Previous studies reported a progressive decrease in the systemic sclerosis mortality rates in the United States from 1959 to 2002. Identification of areas with clusters of higher mortality rates is important to implement targeted interventions. In this study, we aimed to estimate the mortality rates of scleroderma and to analyze its geographic variability at the state level in the United States. Methods: Mortality rates of scleroderma from 1999 to 2017 were obtained from the CDC Wonder Underlying Cause of Death database and its query system, using International Classification of Diseases, Tenth Revision codes. Age-adjusted rates were calculated by state and demographics. A linear regression model was applied to evaluate trends over time. Results: Over the period studied, a total of 24,525 deaths had scleroderma as the underlying cause of death. The age-adjusted mortality rate was 3.962 per million (95% CI: 3.912–4.012), decreasing progressively from 4.679 (95%CI: 4.423–4.934) in 1999 to 2.993 (95% CI: 2.817–3.170) per million in 2017. The age-adjusted mortality rate was 5.885 (95% CI: 5.802–5.967) and 1.651 (95% CI: 1.604–1.698) per million in females and males, respectively. Per races, the highest age-adjusted mortality rate was in Blacks or African Americans, at 5.703 per million (95% CI: 5.521–5.885), followed by American Indians or Alaska Native at 5.047 per million (95% CI: 4.428–5.667). Clusters of states with higher and lower mortality rates were identified. South Dakota had the highest whereas Hawaii had the lowest mortality rate. Conclusion: We found a trend to a progressive decrease in mortality rates of scleroderma during the years of our study. In addition, we found relevant state-by-state variation in mortality with several geographical clusters with higher mortality rates. Further analyses are warranted in order to better understand the factors associated with the observed geographic disparities.

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.

Abstract 15: Trends in Atrial Fibrillation-related Cardiovascular Mortality Rates in The United States: 1999-2017

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Yoshihiro Tanaka ◽  
Nilay Shah ◽  
Rod Passman ◽  
Philip Greenland ◽  
Sadiya Khan
Keyword(s):  
United States ◽  
Atrial Fibrillation ◽  
Black Men ◽  
Cause Of Death ◽  
Age Groups ◽  
White Men ◽  
The United States ◽  
Mortality Rates ◽  
Men And Women ◽  
Black And White

Background: Atrial fibrillation (AF) is the most common sustained arrhythmia in adults and the prevalence is increasing due to the aging of the population and the growing burden of vascular risk factors. Although deaths due to cardiovascular disease (CVD) death have dramatically decreased in recent years, trends in AF-related CVD death has not been previously investigated. Purpose: We sought to quantify trends in AF-related CVD death rates in the United States. Methods: AF-related CVD death was ascertained using the CDC WONDER online database. AF-related CVD deaths were identified by listing CVD (I00-I78) as underlying cause of death and AF (I48) as contributing cause of death among persons aged 35 to 84 years. We calculated age-adjusted mortality rates (AAMR) per 100,000 population, and examined trends over time estimating average annual percent change (AAPC) using Joinpoint Regression Program (National Cancer Institute). Subgroup analyses were performed to compare AAMRs by sex-race (black and white men and women) and across two age groups (younger: 35-64 years, older 65-84 years). Results: A total of 522,104 AF-related CVD deaths were identified between 1999 and 2017. AAMR increased from 16.0 to 22.2 per 100,000 from 1999 to 2017 with an acceleration following an inflection point in 2009. AAPC before 2009 was significantly lower than that after 2009 [0.4% (95% CI, 0.0 - 0.7) vs 3.5% (95% CI, 3.1 - 3.9), p < 0.001). The increase of AAMR was observed across black and white men and women overall and in both age groups (FIGURE), with a more pronounced increase in black men and white men. Black men had the highest AAMR among the younger decedents, whereas white men had the highest AAMR among the older decedents. Conclusion: This study revealed that death rate for AF-related CVD has increased over the last two decades and that there are greater black-white disparities in younger decedents (<65 years). Targeting equitable risk factor reduction that predisposes to AF and CVD mortality is needed to reduce observed health inequities.

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 &lt; 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 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 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.

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.

Mortality in the US by education level

2020 ◽  
Vol 14 (2) ◽  
pp. 384-419
Author(s):  
Cristian Redondo Lourés ◽  
Andrew J. G. Cairns
Keyword(s):  
United States ◽  
Disease Control ◽  
Cause Of Death ◽  
The United States ◽  
Mortality Rates ◽  
Education Level ◽  
The Us ◽  
Control And Prevention ◽  
Centers For Disease Control ◽  
Death Data

AbstractDifferent mortality rates for different socio-economic groups within a population have been consistently reported throughout the years. In this study, we aim to exploit data from multiple public sources, including highly detailed cause-of-death data from the United States Centers for Disease Control and Prevention, to explore the mortality gap between the better and worse off in the US during the period 1989–2015, using education as a proxy.

Sign in / Sign up

Export Citation Format

Share Document