scholarly journals County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

2020 ◽  
Author(s):  
Jochem O Klompmaker ◽  
Jaime E Hart ◽  
Isabel Holland ◽  
M Benjamin Sabath ◽  
Xiao Wu ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Vegetation Index ◽  
Negative Binomial ◽  
The United States ◽  
Systems Science ◽  
County Level ◽  
Incidence And Mortality ◽  
The Us ◽  
At Home

AbstractBackgroundCOVID-19 is an infectious disease that has killed more than 246,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.ObjectivesWe evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.MethodsWe downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.ResultsAn increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density.DiscussionExposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

Abstract MP38: Intracerebral Hemorrhage Across the United States: Incidence and Mortality Trends From 1990 to 2017

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Augustin DeLago ◽  
Harpreet Singh ◽  
Arashdeep Rupal ◽  
Chinmay Jani ◽  
Arshi Parvez ◽  
...  
Keyword(s):  
United States ◽  
Intracerebral Hemorrhage ◽  
Vitamin K Antagonists ◽  
Oral Anticoagulants ◽  
Direct Oral Anticoagulants ◽  
The United States ◽  
Mortality Trends ◽  
Reversal Agents ◽  
Incidence And Mortality ◽  
The Us

Background: Intracerebral Hemorrhage (ICH) accounts for 10% of strokes annually in the United States (US). Up-to-date trends in disease burden and regional variation remain unknown; especially after a dramatic increase in the use of direct oral anticoagulants (DOACs) since 2010. Our study reports updated incidence, mortality and mortality to incidence ratio (MIR) data related to ICH across the US. Methods: This observational study utilized the Global Burden of Disease database to determine age-standardized incidence (ASIR), death (ASDR) and MIR rates for ICH overall and for each state in the US from 1990-2017. All analyses were stratified by sex. Trends were analyzed using Joinpoint regression analysis, with presentation of estimated annual percentage changes (EAPCs) in ASIRs, ASDRs and MIRs over the observation period. Results: We observed an overall decrease in ASIRs, ASDRs and MIRs in both genders from 1990-2017, apart from female ASIRs and ASDRs in West Virginia and Kentucky. In 2017, the mean ASIR per 100,000 population for men was 25.67 and 19.17 for women. The 2017 mean ASDRs per 100,000 population for men and women were 13.96 and 11.35, respectively. The District of Columbia had the greatest decreases in ASIR EAPCs for males at -41.25% and females at -40.58%, and the greatest decreases in ASDR EAPCs for both males and females at -55.38% and -48.51%, respectively. The overall MIR during the study period decreased in males by -12.12% and females by -7.43%. However, MIR increased in males from 2014-2017 (EAPC +2.2% [95% CI +0.9%-+3.5%]) and in females from 2011-2017 (EAPC +1.0% [CI +0.7%-+1.4%]). Conclusion: This report reveals overall decreasing trends in incidence, mortality and MIR from 1990-2017. Notably, no significant change in mortality was found in the last 6 years of the study period, and MIR worsened in males from 2014-2017 and in females from 2011-2017, suggesting decreased ICH related survival lately. The substitution of vitamin K antagonists with DOACs is one possible explanation for a downtrend in incidence despite an aging population and increased use of anticoagulants. Limited access to reversal agents for DOACs is a potential reason for increase in MIR, however concrete deductions cannot be made owing to the observational nature of the study.

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 The Effect of Stay-at-Home Orders on COVID-19 Infections in the United States

2020 ◽  
Author(s):  
James H. Fowler ◽  
Seth J. Hill ◽  
Remy Levin ◽  
Nick Obradovich
Keyword(s):  
United States ◽  
Temporal Variation ◽  
Fixed Effects ◽  
National Level ◽  
The United States ◽  
Spatial And Temporal Variation ◽  
Full Potential ◽  
County Level ◽  
Fixed Effects Model ◽  
At Home

SummaryBackgroundIn March and April 2020, public health authorities in the United States acted to mitigate transmission of and hospitalizations from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). These actions were not coordinated at the national level, which raises the question of what might have happened if they were. It also creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy.MethodsWe combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases at the county level in the United States (N = 124,027). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. This enables us to estimate the effect of stay-at-home orders while accounting for local variation in factors like health systems and demographics, and temporal variation in national mitigation actions, access to tests, or exposure to media reports that could influence the course of the disease.FindingsMean county-level daily growth in COVID-19 infections peaked at 17.2% just before stay-at-home orders were issued. Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95% CI 1.2 to 6.6) reduction in the growth rate after one week and a 6.9 percentage point (2.4 to 11.5) reduction after two weeks. By day 27 the reduction (22.6 percentage points, 14.8 to 30.5) had surpassed the growth at the peak, indicating that growth had turned negative and the number of new daily infections was beginning to decline. A hypothetical national stay-at-home order issued on March 13, 2020 when a national emergency was declared might have reduced cumulative infections by 63.3%, and might have helped to reverse exponential growth in the disease by April 10.InterpretationAlthough stay-at-home orders impose great costs to society, delayed responses and piecemeal application of these orders generate similar costs without obtaining the full potential benefits suggested by this analysis. The results here suggest that a coordinated nationwide stay-at-home order might have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19. Future efforts in the United States and elsewhere to control pandemics should coordinate stay-at-home orders at the national level, especially for diseases for which local spread has already occurred and testing availability is delayed. Since stay-at-home orders reduce infection growth rates, early implementation when infection counts are still low would be most beneficial.FundingNone.

scholarly journals Globalization of Ambitions. A Critical Analysis of Donald Trump’s National Security Strategy 2017

2017 ◽  
pp. 86-95
Author(s):  
N. P. Gribin
Keyword(s):  
United States ◽  
National Security ◽  
The United States ◽  
Economic Security ◽  
Security Strategy ◽  
National Security Strategy ◽  
The Us ◽  
The World ◽  
At Home

Under the Goldwater-Nichols Defense Department Reorganization Act of 1986, the President of the United States must submit to Congress each year a report on the national security strategy. This report under the name of “National Security Strategy” is intended to be a comprehensive statement anticipating the worldwide interests, goals and objectives that are deemed crucial to the national security of the United States. The new “National Security Strategy” (December 2017) lays out the strategic vision of the Presidential Administration under Donald Trump about ways and means by which the US seeks to deal with internal and external threats. The authors of the Strategy set themselves the main task of proving that American security is based on the realization that American principles are: “a lasting force for good in the World.”  The authors of the Strategy prioritize the protection of the American way of life and American interests all over the world. In that aspect, they see the main danger from the hostile states and non-states actors who are “trying to acquire different types of weapons”. In addition, the administration is demonstrating concerns about the activity of international terrorist organizations (jihadist), transnational criminal organizations, drug cartels and cybercrime. Different from previous similar documents, Trump’s Strategy makes an evident accent on economic security as an important part of national security. The task in that area is “to rebuild economic strength at home and preserve a fair and reciprocal international system.” In a rather confronting manner, the Strategy assesses the role of China and Russia in the international affairs. It underlines that between the main sets of challengers – “the revisionist powers of China and Russia and the rogue states of Iran and North Korea”, the United States will seek areas of cooperation with competitors but will do so from a position of strength. The Strategy pays great attention to restoring military capability of the US. It is stressed that military strength remains a vital component of the competition for influence. In a certain sense, the authors of the Strategy demonstrate a new approach to the role of diplomacy, and especially in regards to the tools of economic diplomacy, intended to protect the US “from abuse by illicit actors”. Pillar four of the Strategy outlines considerations for expanding US influence on a global scale and for supporting friendly partners. As stated in the Strategy, American assistance to developing countries should help promote national interests and vice versa. The US will use all means, including sanctions, to “isolate states and leaders that pose a threat to the American interests.” The Strategy pays much attention to the regional aspect of national security, and, from these positions, the situation in various parts of the world (the Indo-Pacific region, Europe, the Middle East, etc.) is assessed. The authors emphasize that changes in the balance of power at the world level can cause global consequences and threaten American interests and US security. On the contrary, “stability reduces the threats that Americans face at home.”

scholarly journals Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258308
Author(s):  
Jess A. Millar ◽  
Hanh Dung N. Dao ◽  
Marianne E. Stefopulos ◽  
Camila G. Estevam ◽  
Katharine Fagan-Garcia ◽  
...  
Keyword(s):  
Generalized Linear Model ◽  
Ecological Study ◽  
Negative Binomial ◽  
Case Fatality ◽  
The United States ◽  
Asthma Prevalence ◽  
County Level ◽  
Increased Risk ◽  
The Us ◽  
Level Analysis

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

scholarly journals County-Level Exposures to Greenness and Associations with COVID-19 Incidence and Mortality in the United States

2020 ◽  
Author(s):  
Jochem O. Klompmaker ◽  
Jaime E. Hart ◽  
Isabel Holland ◽  
M. Benjamin Sabath ◽  
Xiao Wu ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
County Level ◽  
Incidence And Mortality

scholarly journals County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model

10.2196/23902 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e23902
Author(s):  
Kevin L McKee ◽  
Ian C Crandell ◽  
Alexandra L Hanlon
Keyword(s):  
United States ◽  
Differential Equation ◽  
Population Density ◽  
Household Income ◽  
The United States ◽  
Policy Impact ◽  
County Level ◽  
Median Household Income ◽  
Social Distancing

Background Social distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data. Objective We propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices. Methods A differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered. Results Mobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=–0.37 and r=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from –0.12 to –0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect. Conclusions Chronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

scholarly journals Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities

2020 ◽  
Author(s):  
Bhuma Krishnamachari ◽  
Alexander Morris ◽  
Diane Zastrow ◽  
Andrew Dsida ◽  
Brian Harper ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Cumulative Incidence ◽  
Negative Binomial ◽  
State Level ◽  
The United States ◽  
Reverse Causality ◽  
Social Distancing ◽  
School Closing ◽  
Adjusted Rate

AbstractCOVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: “above median value,” and “median value and below” for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

scholarly journals Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States

2018 ◽  
Vol 10 (8) ◽  
pp. 2720 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Yuming Zhang
Keyword(s):  
United States ◽  
Population Density ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
The United States ◽  
Potential Effect ◽  
Transportation Systems ◽  
Transport Systems ◽  
Vehicle Ownership

To improve the sustainability and efficiency of transport systems, communities and government agencies throughout the United States (US) are looking for ways to reduce vehicle ownership and single-occupant trips by encouraging people to shift from driving to using more sustainable transport modes (such as ridesharing). Ridesharing is a cost-effective, sustainable and effective alternative transportation mode that is beneficial to the environment, the economy and society. Despite the potential effect of vehicle ownership on the adoption of ridesharing services, individuals’ ridesharing behaviors and the interdependencies between vehicle ownership and ridesharing usage are not well understood. This study aims to fill the gap by examining the associations between household vehicle ownership and the frequency and probability of ridesharing usage, and to estimate the effects of household vehicle ownership on individuals’ ridesharing usage in the US. We conducted zero-inflated negative binomial regression models using data from the 2017 National Household Travel Survey. The results show that, in general, one-vehicle reduction in households was significantly associated with a 7.9% increase in the frequency of ridesharing usage and a 23.0% increase in the probability of ridesharing usage. The effects of household vehicle ownership on the frequency of ridesharing usage are greater for those who live in areas with a higher population density than those living in areas with a lower population density. Young people, men, those who are unable to drive, individuals with high household income levels, and those who live in areas with rail service or a higher population density, tend to use ridesharing more frequently and are more likely to use it. These findings can be used as guides for planners or practitioners to better understand individuals’ ridesharing behaviors, and to identify policies and interventions to increase the potential of ridesharing usage, and to decrease household vehicle ownership, depending on different contextual features and demographic variables. Comprehensive strategies that limit vehicle ownership and address the increasing demand for ridesharing have the potential to improve the sustainability of transportation systems.

scholarly journals Giardiasis in the United States – an epidemiologic and geospatial analysis of county-level drinking water and sanitation data, 1993–2010

2015 ◽  
Vol 14 (2) ◽  
pp. 267-279 ◽  
Author(s):  
Kerry Schnell ◽  
Sarah Collier ◽  
Gordana Derado ◽  
Jonathan Yoder ◽  
Julia Warner Gargano
Keyword(s):  
United States ◽  
Negative Binomial ◽  
Parasitic Infection ◽  
The United States ◽  
Incidence Rates ◽  
County Level ◽  
Septic System ◽  
Water And Sanitation ◽  
South Central ◽  
Private Wells

Giardiasis is the most commonly reported intestinal parasitic infection in the United States. Outbreak investigations have implicated poorly maintained private wells, and hypothesized a role for wastewater systems in giardiasis transmission. Surveillance data consistently show geographic variability in reported giardiasis incidence. We explored county-level associations between giardiasis cases, household water and sanitation (1990 census), and US Census division. Using 368,847 reported giardiasis cases (1993–2010), we mapped county-level giardiasis incidence rates, private well reliance, and septic system reliance, and assessed spatiotemporal clustering of giardiasis. We used negative binomial regression to evaluate county-level associations between giardiasis rates, region, and well and septic reliance, adjusted for demographics. Adjusted giardiasis incidence rate ratios (aIRRs) were highest (aIRR 1.3; 95% confidence interval 1.2–1.5) in counties with higher private well reliance. There was no significant association between giardiasis and septic system reliance in adjusted models. Consistent with visual geographic distributions, the aIRR of giardiasis was highest in New England (aIRR 3.3; 95% CI 2.9–3.9; reference West South Central region). Our results suggest that, in the USA, private wells are relevant to giardiasis transmission; giardiasis risk factors might vary regionally; and up-to-date, location-specific national data on water sources and sanitation methods are needed.

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