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 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.

Epidemiology and Trends of Hereditary Hemorrhagic Telangiectasia in the United States

2019 ◽  
Vol 34 (2) ◽  
pp. 230-237
Author(s):  
Andrew M. Ferry ◽  
Alex E. Wright ◽  
Gwen Baillargeon ◽  
Yong-Fang Kuo ◽  
Mohamad R. Chaaban
Keyword(s):  
United States ◽  
Hereditary Hemorrhagic Telangiectasia ◽  
Cerebral Hemorrhage ◽  
Multidisciplinary Treatment ◽  
Pulmonary Hemorrhage ◽  
The United States ◽  
National Study ◽  
Increased Risk ◽  
Icd 10 ◽  
Life Threatening

Background To our knowledge, no national studies have investigated the epidemiology of hereditary hemorrhagic telangiectasia (HHT) in the United States since the incorporation of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10 CM). Objective Our objective is to analyze the epidemiology of HHT from 2013 to 2017 and to determine the relationships between epistaxis and other associated complications of this rare disease. Methods We analyzed the epidemiology of HHT between 2013 and 2017 by accessing data of 87 709 738 patients from the Clinformatics Data Mart using ICD-9 CM and ICD-10 CM codes. Variables analyzed included age, gender, region, clinical setting of diagnosis, hospitalizations, and complications. Bivariate analyses using generalized linear models were conducted to determine the likelihood of HHT patients with epistaxis enduring associated life-threatening complications such as cerebral hemorrhage, thrombosis, and pulmonary hemorrhage compared to HHT patients without epistaxis during the study period. Results The prevalence of HHT increased from 6.1 to 12.1 per 100 000 persons, with patients of ages 18 to 29 years and those older than 60 years seeing the greatest percent increase. The prevalence of HHT in the Southern United States saw a 147% increase. Compared to HHT patients without epistaxis, HHT patients with epistaxis were 3.4 times more likely to experience pulmonary hemorrhage, 3.3 times more likely to have pulmonary emboli, 2.8 times more likely to experience cerebral hemorrhage, and 2.0 times more likely to have thrombosis during the study period. Conclusion Our national study has provided the first incidence and prevalence rates of HHT in the United States since the incorporation of the ICD-10 CM. HHT patients with epistaxis require prompt multidisciplinary treatment of their condition due to their increased risk of life-threatening complications.

Commercial Fishing as an Occupational Determinant of Opioid Overdoses and Deaths of Despair in Two Massachusetts Fishing Ports, 2000–2014

2021 ◽  
pp. 104829112110234
Author(s):  
Scott Fulmer ◽  
Shruti Jain ◽  
David Kriebel
Keyword(s):  
United States ◽  
Cause Of Death ◽  
Death Certificate ◽  
The United States ◽  
Opioid Overdose ◽  
Commercial Fishing ◽  
Opioid Epidemic ◽  
Death Certificate Data ◽  
Deaths Of Despair

The opioid epidemic has had disproportionate effects across various sectors of the population, differentially impacting various occupations. Commercial fishing has among the highest rates of occupational fatalities in the United States. This study used death certificate data from two Massachusetts fishing ports to calculate proportionate mortality ratios of fatal opioid overdose as a cause of death in commercial fishing. Statistically significant proportionate mortality ratios revealed that commercial fishermen were greater than four times more likely to die from opioid poisoning than nonfishermen living in the same fishing ports. These important quantitative findings suggest opioid overdoses, and deaths to diseases of despair in general, deserve further study in prevention, particularly among those employed in commercial fishing.

scholarly journals Stroke Declines From Third to Fourth Leading Cause of Death in the United States

Stroke ◽  
2011 ◽  
Vol 42 (8) ◽  
pp. 2351-2355 ◽  
Author(s):  
Amytis Towfighi ◽  
Jeffrey L. Saver
Keyword(s):  
United States ◽  
Cause Of Death ◽  
The United States

scholarly journals Racial differences in nasopharyngeal carcinoma in the United States

2013 ◽  
Vol 37 (6) ◽  
pp. 793-802 ◽  
Author(s):  
Yu Wang ◽  
Yawei Zhang ◽  
Shuangge Ma
Keyword(s):  
United States ◽  
Nasopharyngeal Carcinoma ◽  
Racial Differences ◽  
The United States

scholarly journals Suicide in prison and after release: a 17-year national cohort study

2021 ◽  
Author(s):  
Anne Bukten ◽  
Marianne Riksheim Stavseth
Keyword(s):  
High Risk ◽  
Cause Of Death ◽  
Proportional Hazards ◽  
Prison Population ◽  
Cox Proportional Hazards ◽  
Hazard Ratios ◽  
Crude Mortality ◽  
Icd 10 ◽  
National Cohort ◽  
National Register Data

Abstract Background People in prison have an extremely high risk of suicide. The aim of this paper is to describe all suicides in the Norwegian prison population from 2000 to 2016, during and following imprisonment; to investigate the timing of suicides; and to investigate the associations between risk of suicide and types of crime. Methods We used data from the Norwegian Prison Release study (nPRIS) including complete national register data from the Norwegian Prison Register and the Norwegian Cause of Death Register in the period 1.1.2000 to 31.12.2016, consisting of 96,856 individuals. All suicides were classified according to ICD-10 codes X60-X84. We calculated crude mortality rates (CMRs) per 100,000 person-years and used a Cox Proportional-Hazards regression model to investigate factors associated with suicide during imprisonment and after release reported as hazard ratios (HRs). Results Suicide accounted for about 10% of all deaths in the Norwegian prison population and was the leading cause of death in prison (53% of in deaths in prison). The CMR per 100,000 person years for in-prison suicides was 133.8 (CI 100.5–167.1) and was ten times higher (CMR = 1535.0, CI 397.9–2672.2) on day one of incarceration. Suicides after release (overall CMR = 82.8, CI 100.5–167.1) also peaked on day one after release (CMR = 665.7, CI 0–1419.1). Suicide in prison was strongly associated with convictions of homicide (HR 18.2, CI 6.5–50.8) and high-security prison level (HR 15.4, CI 3.6–65.0). Suicide after release was associated with convictions of homicide (HR 3.1, CI 1.7–5.5). Conclusion There is a high risk of suicide during the immediate first period of incarceration and after release. Convictions for severe violent crime, especially homicide, are associated with increased suicide risk, both in prison and after release.

Medicaid Expansion and Incidence of Kidney Failure among Nonelderly Adults

2021 ◽  
pp. ASN.2020101511
Author(s):  
Rebecca Thorsness ◽  
Shailender Swaminathan ◽  
Yoojin Lee ◽  
Benjamin D. Sommers ◽  
Rajnish Mehrotra ◽  
...  
Keyword(s):  
United States ◽  
Incidence Rate ◽  
Kidney Failure ◽  
The United States ◽  
Early Years ◽  
Relative Reduction ◽  
Medicaid Expansion ◽  
Expansion Time ◽  
Incident Cases ◽  
Medicaid Expansions

BackgroundLow-income individuals without health insurance have limited access to health care. Medicaid expansions may reduce kidney failure incidence by improving access to chronic disease care.MethodsUsing a difference-in-differences analysis, we examined the association between Medicaid expansion status under the Affordable Care Act (ACA) and the kidney failure incidence rate among all nonelderly adults, aged 19–64 years, in the United States, from 2012 through 2018. We compared changes in kidney failure incidence in states that implemented Medicaid expansions with concurrent changes in nonexpansion states during pre-expansion, early postexpansion (years 2 and 3 postexpansion), and later postexpansion (years 4 and 5 postexpansion).ResultsThe unadjusted kidney failure incidence rate increased in the early years of the study period in both expansion and nonexpansion states before stabilizing. After adjustment for population sociodemographic characteristics, Medicaid expansion status was associated with 2.20 fewer incident cases of kidney failure per million adults per quarter in the early postexpansion period (95% CI, −3.89 to −0.51) compared with nonexpansion status, a 3.07% relative reduction (95% CI, −5.43% to −0.72%). In the later postexpansion period, Medicaid expansion status was not associated with a statistically significant change in kidney failure incidence (−0.56 cases per million per quarter; 95% CI, −2.71 to 1.58) compared with nonexpansion status and the pre-expansion time period.ConclusionsThe ACA Medicaid expansion was associated with an initial reduction in kidney failure incidence among the entire, nonelderly, adult population in the United States; but the changes did not persist in the later postexpansion period. Further study is needed to determine the long-term association between Medicaid expansion and changes in kidney failure incidence.

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