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 Hip Fracture-Related Mortality among Older Adults in the United States: Analysis of the CDC WONDER Multiple Cause of Death Data, 1999–2013

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Carlos H. Orces
Keyword(s):  
United States ◽  
Older Adults ◽  
Hip Fracture ◽  
Cause Of Death ◽  
The United States ◽  
Mortality Rates ◽  
Online Data ◽  
Annual Percent Change ◽  
Death Record ◽  
Related Mortality

Objectives. To examine trends in hip fracture-related mortality among older adults in the United States between 1999 and 2013. Material and Methods. The Wide-Ranging Online Data for Epidemiological Research system was used to identify adults aged 65 years and older with a diagnosis of hip fracture reported in their multiple cause of death record. Joinpoint regression analyses were performed to estimate the average annual percent change in hip fracture-related mortality rates by selected characteristics. Results. A total of 204,254 older decedents listed a diagnosis of hip fracture on their death record. After age adjustment, hip fracture mortality rates decreased by −2.3% (95% CI, −2.7%, and −1.8%) in men and −1.5% (95% CI, −1.9%, and −1.1%) in women. Similarly, the proportion of in-hospital hip fracture deaths decreased annually by −2.1% (95% CI, −2.6%, and −1.5%). Of relevance, the proportion of cardiovascular diseases reported as the underlying cause of death decreased on average by −4.8% (95% CI, −5.5%, and −4.1%). Conclusions. Hip fracture-related mortality decreased among older adults in the United States. Downward trends in hip fracture-related mortality were predominantly attributed to decreased deaths among men and during hospitalization. Moreover, improvements in survival of hip fracture patients with greater number of comorbidities may have accounted for the present findings.

Abstract 14: Regional Cardiologist Density and 30-Day and 1-Year Mortality in Acute Myocardial Infarction and Heart Failure

2013 ◽  
Vol 6 (suppl_1) ◽  
Author(s):  
Vivek T Kulkarni ◽  
Joseph S Ross ◽  
Yongfei Wang ◽  
Brahmajee K Nallamothu ◽  
John A Spertus ◽  
...  
Keyword(s):  
United States ◽  
Myocardial Infarction ◽  
Heart Failure ◽  
Acute Myocardial Infarction ◽  
The United States ◽  
Mortality Rates ◽  
Lower Mortality ◽  
Administrative Claims ◽  
Physician Density ◽  
Hospital Referral

Background: Although the distribution of cardiologists and mortality for cardiovascular conditions are both known to vary across regions of the United States, no study has examined the relationship between regional cardiologist density and patient mortality for acute myocardial infarction (AMI) or heart failure (HF). Methods: We used 2010 Medicare administrative claims data for AMI and HF. Pneumonia (PN) was used as a control condition. Primary outcomes were death at 30 days and 1 year from admission. For each Hospital Referral Region (HRR), we used the 2010 Bureau of Health Professionals’ Area Resource File to define cardiologist density (number of cardiologists divided by population aged 65+) and 4 HRR characteristics: primary care physician density, total physician density, unemployment rate, and percent white race. We used 2-level hierarchical logistic regression models to examine the association between cardiologist density by tertile and mortality for each condition adjusting for (Model A) patient age, sex, and condition-specific comorbidities, and (Model B) patient and HRR characteristics. Results: Median (interquartile range) cardiologist density per 100,000 in the low, middle, and high tertiles of HRRs was 26.3 (22.9-29.9), 38.6 (36.5-43.1), and 64.5 (54.4-85.3), respectively. There were 171,126 admissions for AMI, 352,853 for HF, and 343,053 for PN. The 30-day mortality rates were 15.3% (26,290), 11.7% (41,121), and 11.9% (40,906), and 1-year mortality rates were 32.1% (55,292), 40.4% (142,612), and 35.2% (120,666), respectively (Table). For 30-day mortality, while model A showed lower mortality with higher cardiologist density for all conditions (odds ratios (ORs): 0.84-0.95), model B showed no associations. For 1-year mortality, while model A showed lower mortality in the high cardiologist density tertile for AMI (OR=0.93) and HF (OR=0.91) and no associations for PN, model B showed no associations for AMI or HF and higher mortality with higher cardiologist density for PN (ORs=1.04-1.06). Conclusion: After adjusting for patient and HRR characteristics, regional cardiologist density was not associated with 30-day or 1-year mortality for AMI or HF, suggesting that the uneven regional distribution of cardiologists across the United States does not affect patient outcomes.

scholarly journals Association between county level cannabis dispensary counts and opioid related mortality rates in the United States: panel data study

BMJ ◽  
2021 ◽  
pp. m4957 ◽  
Author(s):  
Greta Hsu ◽  
Balázs Kovács
Keyword(s):  
United States ◽  
Confidence Interval ◽  
The United States ◽  
Mortality Rates ◽  
Mortality Data ◽  
Panel Regression ◽  
County Level ◽  
Regression Methods ◽  
Synthetic Opioids ◽  
Related Mortality

Abstract Objective To examine county level associations between the prevalence of medical and recreational cannabis stores (referred to as dispensaries) and opioid related mortality rates. Design Panel regression methods. Setting 812 counties in the United States in the 23 states that allowed legal forms of cannabis dispensaries to operate by the end of 2017. Participants The study used US mortality data from the Centers for Disease Control and Prevention combined with US census data and data from Weedmaps.com on storefront dispensary operations. Data were analyzed at the county level by using panel regression methods. Main outcome measure The main outcome measures were the log transformed, age adjusted mortality rates associated with all opioid types combined, and with subcategories of prescription opioids, heroin, and synthetic opioids other than methadone. The associations of medical dispensary and recreational dispensary counts with age adjusted mortality rates were also analyzed. Results County level dispensary count (natural logarithm) is negatively related to the log transformed, age adjusted mortality rate associated with all opioid types (β=−0.17, 95% confidence interval −0.23 to −0.11). According to this estimate, an increase from one to two storefront dispensaries in a county is associated with an estimated 17% reduction in all opioid related mortality rates. Dispensary count has a particularly strong negative association with deaths caused by synthetic opioids other than methadone (β=−0.21, 95% confidence interval −0.27 to −0.14), with an estimated 21% reduction in mortality rates associated with an increase from one to two dispensaries. Similar associations were found for medical versus recreational storefront dispensary counts on synthetic (non-methadone) opioid related mortality rates. Conclusions Higher medical and recreational storefront dispensary counts are associated with reduced opioid related death rates, particularly deaths associated with synthetic opioids such as fentanyl. While the associations documented cannot be assumed to be causal, they suggest a potential association between increased prevalence of medical and recreational cannabis dispensaries and reduced opioid related mortality rates. This study highlights the importance of considering the complex supply side of related drug markets and how this shapes opioid use and misuse.

scholarly journals 1487. Recent Resurgence of Salmonellosis-Related Mortality in the United States, 1990–2015

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S541-S542
Author(s):  
Palak Panchal ◽  
Frank Sorvillo ◽  
Mark S Dworkin
Keyword(s):  
United States ◽  
Cause Of Death ◽  
Temporal Trends ◽  
The United States ◽  
Mortality Rates ◽  
Comorbid Conditions ◽  
Adjusted Rate ◽  
Asian Pacific ◽  
Substantial Decline ◽  
Related Mortality

Abstract Background Non-typhoidal salmonellosis is one of the most common causes of foodborne illness in the United States. The objective of this study was to update the epidemiology of salmonellosis-related mortality in the United States by examining multiple-cause-of-death data (MCOD). Methods MCOD data from the National Center for Health Statistics (NCHS) for the years 1990–2015 were analyzed. Mortality rates and 95% confidence intervals (CI) were calculated for age, sex, race/ethnicity, year, and state. Poisson regression models were used to examine temporal trends. Logistic regression was used to determine whether selected comorbid conditions were associated with salmonellosis-related deaths. Results Overall, 1,987 salmonellosis-related deaths (3.17%) were identified as an underlying and/or associated cause of death. The average annual age-adjusted mortality rate was 0.027 per 100,000 person-years. Salmonellosis mortality rates were higher among males with an age-adjusted rate ratio (RR) of 1.89 (95% CI, 1.79–2.01) compared with females. Mortality rates were higher among non-Hispanic Blacks and Asian/Pacific Islanders with an age-adjusted RR of 2.46 (95% CI, 2.19–2.77) and 2.06 (95% CI, 1.67–2.55) compared with Whites, respectively. The highest number of salmonellosis deaths were reported among the 75–84 year age group (n = 467; 24% of all cases). A significant decrease in trend was observed in age-adjusted salmonellosis mortality rates from 1990 to 2015. Since 2006, a significant increase of 66% in mortality rates was observed. Among selected comorbid conditions, HIV, acute renal failure, cancers affecting bone marrow, and diseases of the digestive system were associated with salmonellosis deaths with odds ratios of 11.51 (95% CI 8.78–15.10), 3.09 (95% CI, 2.01 = 4.75), 2.81 (95% CI, 1.67–4.74), and 2.82 (95% CI, 2.34–3.40), respectively. Conclusion Salmonellosis is an underlying and/or associated cause of death, especially among those with immune senescence and suppression. Despite a substantial decline in mortality rates, since 2006 rates have increased, a concerning trend. If rates continue to increase, an evaluation of Salmonella prevention efforts will be warranted. Disclosures All authors: No reported disclosures.

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.

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 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.

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.

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