Abstract P636: Social Determinants of Stroke Hospitalization and Mortality in the United States

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Randhir Sagar Yadav ◽  
Durgesh Chaudhary ◽  
Shima Shahjouei ◽  
Jiang Li ◽  
Vida Abedi ◽  
...  
Keyword(s):  
United States ◽  
Income Inequality ◽  
Social Determinants ◽  
Hemorrhagic Stroke ◽  
The United States ◽  
Care Physician ◽  
County Level ◽  
Medicare Beneficiaries ◽  
Level Data ◽  
Control And Prevention

Introduction: Stroke hospitalization and mortality are influenced by various social determinants. This ecological study aimed to determine the associations between social determinants and stroke hospitalization and outcome at county-level in the United States. Methods: County-level data were recorded from the Centers for Disease Control and Prevention as of January 7, 2020. We considered four outcomes: all-age (1) Ischemic and (2) Hemorrhagic stroke Death rates per 100,000 individuals (ID and HD respectively), and (3) Ischemic and (4) Hemorrhagic stroke Hospitalization rate per 1,000 Medicare beneficiaries (IH and HH respectively). Results: Data of 3,225 counties showed IH (12.5 ± 3.4) and ID (22.2 ± 5.1) were more frequent than HH (2.0 ± 0.4) and HD (9.8 ± 2.1). Income inequality as expressed by Gini Index was found to be 44.6% ± 3.6% and unemployment rate was 4.3% ± 1.5%. Only 29.8% of the counties had at least one hospital with neurological services. The uninsured rate was 11.0% ± 4.7% and people living within half a mile of a park was only 18.7% ± 17.6%. Age-adjusted obesity rate was 32.0% ± 4.5%. In regression models, age-adjusted obesity (OR for IH: 1.11; HH: 1.04) and number of hospitals with neurological services (IH: 1.40; HH: 1.50) showed an association with IH and HH. Age-adjusted obesity (ID: 1.16; HD: 1.11), unemployment (ID: 1.21; HD: 1.18) and income inequality (ID: 1.09; HD: 1.11) showed an association with ID and HD. Park access showed inverse associations with all four outcomes. Additionally, population per primary-care physician was associated with HH while number of pharmacy and uninsured rate were associated with ID. All associations and OR had p ≤0.04. Conclusion: Unemployment and income inequality are significantly associated with increased stroke mortality rates.

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 Unraveling US National COVID-19 Racial/Ethnic Disparities using County Level Data Among 328 Million Americans

2020 ◽  
Author(s):  
Daniel Li ◽  
Sheila M. Gaynor ◽  
Corbin Quick ◽  
Jarvis T. Chen ◽  
Briana J.K. Stephenson ◽  
...  
Keyword(s):  
Health Disparities ◽  
Social Determinants ◽  
Ethnic Disparities ◽  
The United States ◽  
Vulnerable Groups ◽  
County Level ◽  
Racial And Ethnic Disparities ◽  
Racial Health Disparities ◽  
Personal Protection Equipment ◽  
Level Data

ABSTRACTRacial and ethnic disparities in COVID-19 outcomes reflect the unequal burden experienced by vulnerable communities in the United States (US). Proposed explanations include socioeconomic factors that influence how people live, work, and play, and pre-existing comorbidities. It is important to assess the extent to which observed US COVID-19 racial and ethnic disparities can be explained by these factors. We study 9.8 million confirmed cases and 234,000 confirmed deaths from 2,990 US counties (3,142 total) that make up 99.8% of the total US population (327.6 out of 328.2 million people) through 11/8/20. We found national COVID-19 racial health disparities in US are partially explained by various social determinants of health and pre-existing comorbidities that have been previously proposed. However, significant unexplained racial and ethnic health disparities still persist at the US county level after adjusting for these variables. There is a pressing need to develop strategies to address not only the social determinants but also other factors, such as testing access, personal protection equipment access and exposures, as well as tailored intervention and resource allocation for vulnerable groups, in order to combat COVID-19 and reduce racial health disparities.

scholarly journals Explaining COVID-19 Outbreaks with Reactive SEIRD Models

2021 ◽  
Author(s):  
Kunal Menda ◽  
Lucas Laird ◽  
Mykel J. Kochenderfer ◽  
Rajmonda S. Caceres
Keyword(s):  
Neural Network ◽  
United States ◽  
Infection Rate ◽  
Expectation Maximization ◽  
The United States ◽  
County Level ◽  
Training Set ◽  
Single Model ◽  
Partial Observability ◽  
Level Data

AbstractCOVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. In this work, we seek to explain the diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of time and of the prevalence of the disease. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization in order to overcome the challenge of partial observability—that the system’s state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it is capable of exhibiting both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We also numerically compare the error of simulations from our model with a standard SEIRD model, showing that the proposed extensions are necessary to be able to explain the spread of COVID-19.

Mapping Homicide Clusters across the United States Using County Level Data, 2004-2012 with Comparisons to Earlier Studies

2019 ◽  
Vol 42 (2) ◽  
pp. 210-231 ◽  
Author(s):  
Raid Amin ◽  
Hongbo Yang ◽  
Michael J. Lynch
Keyword(s):  
United States ◽  
The United States ◽  
County Level ◽  
Level Data

scholarly journals Tract- and county-level income inequality and individual risk of obesity in the United States

2016 ◽  
Vol 55 ◽  
pp. 75-82 ◽  
Author(s):  
Jessie X. Fan ◽  
Ming Wen ◽  
Lori Kowaleski-Jones
Keyword(s):  
United States ◽  
Income Inequality ◽  
The United States ◽  
Individual Risk ◽  
County Level

scholarly journals Concealed Handgun Licensing and Crime in Four States

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Charles D. Phillips ◽  
Obioma Nwaiwu ◽  
Szu-hsuan Lin ◽  
Rachel Edwards ◽  
Sara Imanpour ◽  
...  
Keyword(s):  
United States ◽  
Policy Issue ◽  
The United States ◽  
Crime Rates ◽  
State Legislators ◽  
County Level ◽  
Contextual Variables ◽  
Research Tradition ◽  
Level Data ◽  
Firearm Policy

Firearm policy in the United States has long been a serious policy issue. Much of the previous research on crime and firearms focused on the effects of states’ passage of concealed handgun licensing (CHL) legislation. Today, given the proliferation of CHL legislation and growing strength of the “pro-gun” movement, the primary policy focus has changed. State legislators now face issues concerning whether and how to increase access to CHLs. Because of this transformation, this research moves away from the research tradition focused on the effect of a legislative change allowing CHLs. Instead, we consider two issues more policy relevant in the current era: What are the dynamics behind CHL licensing? Do increases in concealed handgun licensing affect crime rates? Using county-level data, we found that the density of gun dealers and other contextual variables, rather than changing crime rates, had a significant effect on increases of the rates at which CHLs were issued. We also found no significant effect of CHL increases on changes in crime rates. This research suggests that the rate at which CHLs are issued and crime rates are independent of one another—crime does not drive CHLs; CHLs do not drive crime.

scholarly journals Population Change and Income Inequality in Rural America

2020 ◽  
Author(s):  
Jaclyn L.W. Butler ◽  
Grace Wildermuth ◽  
Brian C. Thiede ◽  
David L. Brown
Keyword(s):  
Income Inequality ◽  
Population Growth ◽  
Rural Areas ◽  
Fixed Effects ◽  
Population Change ◽  
Population Decline ◽  
The United States ◽  
County Level ◽  
Level Data ◽  
The Relationship

This paper examines the effects of population growth and decline on county-level income inequality in the United States from 1980 to 2016. Findings from previous research have shown that income inequality is positively associated with population change, but these studies have not explicitly tested for differences between the impacts of population growth and decline. Understanding the implications of population dynamics is particularly important given that many rural areas are characterized by population decline. We analyze county-level data (n=15,375 county-decades) from the Decennial Census and American Community Survey (ACS), applying fixed effects models to estimate the respective effects of population growth and decline on income inequality, to identify the processes that mediate the links between population change and inequality, and to assess whether these effects are moderated by county-level economic and demographic characteristics. We find evidence that population decline is associated with increased levels of income inequality relative to counties experiencing stable and high rates of population growth. This relationship remains robust across a variety of model specifications, including models that account for changes in counties’ employment, sociodemographic, and ethnoracial composition. We also find that the relationship between income inequality and population change varies by metropolitan status, baseline level of inequality, and region.

scholarly journals A County-Level Examination of the Relationship Between HIV and Social Determinants of Health: 40 States, 2006-2008

2012 ◽  
Vol 6 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Gant Z ◽  
Lomotey M ◽  
Hall H.I ◽  
Hu X ◽  
Guo X ◽  
...  
Keyword(s):  
Income Inequality ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
The United States ◽  
Determinants Of Health ◽  
Physical Factors ◽  
County Level ◽  
Hiv Diagnosis ◽  
Hiv Surveillance ◽  
The Relationship

Background: Social determinants of health (SDH) are the social and physical factors that can influence unhealthy or risky behavior. Social determinants of health can affect the chances of acquiring an infectious disease – such as HIV – through behavioral influences and limited preventative and healthcare access. We analyzed the relationship between social determinants of health and HIV diagnosis rates to better understand the disparity in rates between different populations in the United States. Methods: Using National HIV Surveillance data and American Community Survey data at the county level, we examined the relationships between social determinants of health variables (e.g., proportion of whites, income inequality) and HIV diagnosis rates (averaged for 2006-2008) among adults and adolescents from 40 states with mature name-based HIV surveillance. Results: Analysis of data from 1,560 counties showed a significant, positive correlation between HIV diagnosis rates and income inequality (Pearson correlation coefficient ρ = 0.40) and proportion unmarried – ages >15 (ρ = 0.52). There was a significant, negative correlation between proportion of whites and rates (ρ = -0.67). Correlations were low between racespecific social determinants of health indicators and rates. Conclusions/Implications: Overall, HIV diagnosis rates increased as income inequality and the proportion unmarried increased, and rates decreased as proportion of whites increased. The data reflect the higher HIV prevalence among non-whites. Although statistical correlations were moderate, identifying and understanding these social determinants of health variables can help target prevention efforts to aid in reducing HIV diagnosis rates. Future analyses need to determine whether the higher proportion of singles reflects higher populations of gay and bisexual men.

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