scholarly journals Long-term air pollution and other risk factors associated with COVID-19 at the census-tract-level in Colorado

2021 ◽  
Author(s):  
Kevin Berg ◽  
Paul Romer Present ◽  
Kristy Richardson
Keyword(s):  
Relative Risk ◽  
Census Tract ◽  
State Level ◽  
The United States ◽  
American People ◽  
Public Health Response ◽  
Effective Response ◽  
Bayesian Hierarchical ◽  
Census Tract Level

AbstractAn effective response to the COVID-19 pandemic requires identification of the factors that affect the severity and mortality of the disease. Previous nationwide studies have reported links between long-term PM2.5 concentrations and COVID-19 infection and mortality rates. In order to translate these results to the state level, we use Bayesian hierarchical models to explore potential links between long-term PM2.5 concentrations and census tract-level rates of COVID-19 outcomes (infections, hospitalizations, and deaths) in Colorado. We explicitly consider how the uncertainty in PM2.5 estimates affect our results by comparing four different PM2.5 surfaces from academic and governmental organizations. After controlling for 20 census tract level covariates including race/ethnicity, socioeconomic status, social distancing, age demographics, comorbidity rates, meteorology, and testing rate, we find that our results depend heavily on the choice of PM2.5 surface. Using PM2.5 estimates from the United States EPA, we find that a 1 µg/m3 increase in long term PM2.5 is associated with a statistically significant 25% increase in the relative risk of hospitalizations and a 35% increase in mortality. Results for all other surfaces and outcomes were not statistically significant. At the same time, we find a clear association between communities of color and COVID-19 outcomes at the Colorado census-tract level that is minimally affected by the choice of PM2.5 surface. A per-interquartile range (IQR) increase in the percent of non-African American people of color was associated with a 31%, 44%, and 59% increase in the relative risk of infection, hospitalization, and mortality respectively, while a per-IQR increase in the proportion of non-Hispanic African Americans was associated with a 4% and 7% increase in the relative risk of infections and hospitalizations. These results have strong implications for the implementation of an equitable public health response during the crisis and suggest targeted areas for additional air monitoring in Colorado.

scholarly journals Associations between Greenspace and Gentrification-Related Sociodemographic and Housing Cost Changes in Major Metropolitan Areas across the United States

2021 ◽  
Vol 18 (6) ◽  
pp. 3315
Author(s):  
Leah H. Schinasi ◽  
Helen V. S. Cole ◽  
Jana A. Hirsch ◽  
Ghassan B. Hamra ◽  
Pedro Gullon ◽  
...  
Keyword(s):  
United States ◽  
Census Tract ◽  
Household Income ◽  
The United States ◽  
Effect Modification ◽  
Ethnic Composition ◽  
Housing Cost ◽  
Census Tract Level ◽  
Specific Distribution ◽  
Meta Analyses

Neighborhood greenspace may attract new residents and lead to sociodemographic or housing cost changes. We estimated relationships between greenspace and gentrification-related changes in the 43 largest metropolitan statistical areas (MSAs) of the United States (US). We used the US National Land Cover and Brown University Longitudinal Tracts databases, as well as spatial lag models, to estimate census tract-level associations between percentage greenspace (years 1990, 2000) and subsequent changes (1990–2000, 2000–2010) in percentage college-educated, percentage working professional jobs, race/ethnic composition, household income, percentage living in poverty, household rent, and home value. We also investigated effect modification by racial/ethnic composition. We ran models for each MSA and time period and used random-effects meta-analyses to derive summary estimates for each period. Estimates were modest in magnitude and heterogeneous across MSAs. After adjusting for census-tract level population density in 1990, compared to tracts with low percentage greenspace in 1992 (defined as ≤50th percentile of the MSA-specific distribution in 1992), those with high percentage greenspace (defined as >75th percentile of the MSA-specific distribution) experienced higher 1990–2000 increases in percentage of the employed civilian aged 16+ population working professional jobs (β: 0.18, 95% confidence interval (CI): 0.11, 0.26) and in median household income (β: 0.23, 95% CI: 0.15, 0.31). Adjusted estimates for the 2000–2010 period were near the null. We did not observe evidence of effect modification by race/ethnic composition. We observed evidence of modest associations between greenspace and gentrification trends. Further research is needed to explore reasons for heterogeneity and to quantify health implications.

scholarly journals Year-round monitoring reveals prevalence of fatal bird-window collisions at the Virginia Tech Corporate Research Center

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4562 ◽  
Author(s):  
Rebecca M. Schneider ◽  
Christine M. Barton ◽  
Keith W. Zirkle ◽  
Caitlin F. Greene ◽  
Kara B. Newman
Keyword(s):  
Relative Risk ◽  
Land Cover ◽  
Poisson Model ◽  
Credible Interval ◽  
The United States ◽  
Virginia Tech ◽  
Research Center ◽  
Bayesian Hierarchical ◽  
Significant Relationships ◽  
Ornamental Trees

Collisions with glass are a serious threat to avian life and are estimated to kill hundreds of millions of birds per year in the United States. We monitored 22 buildings at the Virginia Tech Corporate Research Center (VTCRC) in Blacksburg, Virginia, for collision fatalities from October 2013 through May 2015 and explored possible effects exerted by glass area and surrounding land cover on avian mortality. We documented 240 individuals representing 55 identifiable species that died due to collisions with windows at the VTCRC. The relative risk of fatal collisions at all buildings over the study period were estimated using a Bayesian hierarchical zero-inflated Poisson model adjusting for percentage of tree and lawn cover within 50 m of buildings, as well as for glass area. We found significant relationships between fatalities and surrounding lawn area (relative risk: 0.96, 95% credible interval: 0.93, 0.98) as well as glass area on buildings (RR: 1.30, 95% CI [1.05–1.65]). The model also found a moderately significant relationship between fatal collisions and the percent land cover of ornamental trees surrounding buildings (RR = 1.02, 95% CI [1.00–1.05]). Every building surveyed had at least one recorded collision death. Our findings indicate that birds collide with VTCRC windows during the summer breeding season in addition to spring and fall migration. The Ruby-throated Hummingbird (Archilochus colubris) was the most common window collision species and accounted for 10% of deaths. Though research has identified various correlates with fatal bird-window collisions, such studies rarely culminate in mitigation. We hope our study brings attention, and ultimately action, to address this significant threat to birds at the VTCRC and elsewhere.

scholarly journals State-level tracking of COVID-19 in the United States

2020 ◽  
Author(s):  
H. Juliette T. Unwin ◽  
Swapnil Mishra ◽  
Valerie C. Bradley ◽  
Axel Gandy ◽  
Thomas A. Mellan ◽  
...  
Keyword(s):  
State Level ◽  
The United States ◽  
Bayesian Hierarchical ◽  
Secondary Infections ◽  
The Us ◽  
Control And Prevention ◽  
Credible Intervals ◽  
Behaviour Changes ◽  
The Impact ◽  
Number Of Individuals

AbstractAs of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly modelled the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We used changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. On 1st June, we estimated that Rt was only below one in 23 states. We also estimated that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.

scholarly journals Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

2020 ◽  
Author(s):  
Sara J Cromer ◽  
Chirag M Lakhani ◽  
Deborah J Wexler ◽  
Sherri-Ann M Burnett-Bowie ◽  
Miriam Udler ◽  
...  
Keyword(s):  
Census Tract ◽  
Private Insurance ◽  
Infected Individual ◽  
The United States ◽  
Older Age ◽  
Census Tract Level ◽  
Male Sex ◽  
Lower Education ◽  
Per Capita ◽  
Household Crowding

Background: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. Methods and Findings: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2. Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. Conclusions: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.

scholarly journals Estimating COVID-19 Hospitalizations in the United States with surveillance data using a Bayesian Hierarchical model

2021 ◽  
Author(s):  
Alexia Couture ◽  
Danielle Iuliano ◽  
Howard H Chang ◽  
Neha N Patel ◽  
Matthew Gilmer ◽  
...  
Keyword(s):  
United States ◽  
Hierarchical Model ◽  
Cumulative Incidence ◽  
Bayesian Hierarchical Model ◽  
The United States ◽  
Surveillance Data ◽  
Age Group ◽  
Covariate Selection ◽  
Bayesian Hierarchical

Introduction: In the United States, COVID-19 is a nationally notifiable disease, cases and hospitalizations are reported to the CDC by states. Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating burden of COVID-19 from established sentinel surveillance systems is becoming more important. We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. Methods: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. We created a model for six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years), separately. We identified covariates from multiple data sources that varied by age, state, and/or month, and performed covariate selection for each age group based on two methods, Least Absolute Shrinkage and Selection Operator (LASSO) and Spike and Slab selection methods. We validated our method by checking sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. Results: We estimated 3,569,500 (90% Credible Interval:3,238,000 - 3,934,700) hospitalizations for a cumulative incidence of 1,089.8 (988.6 - 1,201.3) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 352 - 1,821per 100,000 between states. The age group with the highest cumulative incidence was aged greater than or equal to 85 years (5,583.1; 5,061.0 - 6,157.5). The monthly hospitalization rate was highest in December (183.8; 154.5 - 218.0). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks and timing of peaks between states. Conclusions: Our novel approach to estimate COVID-19 hospitalizations has potential to provide sustainable estimates for monitoring COVID-19 burden, as well as a flexible framework leveraging surveillance data.

scholarly journals Comparison of Mortality Risk for Dialysis Patients and Cadaveric First Renal Transplant Recipients in Ontario, Canada

2000 ◽  
Vol 11 (5) ◽  
pp. 917-922
Author(s):  
CHRISTIAN G. RABBAT ◽  
KEVIN E. THORPE ◽  
J. DAVID RUSSELL ◽  
DAVID N. CHURCHILL
Keyword(s):  
United States ◽  
Renal Transplantation ◽  
Relative Risk ◽  
Renal Transplant ◽  
The United States ◽  
Older Age ◽  
Survival Advantage ◽  
Time Dependent ◽  
Dialysis Patients

Abstract. In population-based studies, renal transplantation has been shown to improve survival compared to dialysis patients awaiting transplantation in the United States. However, dialysis mortality in the United States is higher than in Canada. Whether transplantation offers a survival advantage in regions where dialysis survival is superior to that in the United States is uncertain. This study examines a cohort of 1156 patients who started end-stage renal disease (ESRD) therapy and were wait-listed for cadaveric renal transplantation in the province of Ontario, Canada between January 1, 1990 and December 31, 1994. Patients were followed from wait-listing for renal transplant (n = 1156), to cadaveric first renal transplant (n = 722), to death, or to study end (December 31, 1995). The annual crude mortality rates for wait-listed dialysis patients and transplanted patients were 5.0 and 3.4%, respectively. In Cox proportional hazards models, mortality in wait-listed patients was associated with increased age and diabetes, but not time from onset of ESRD to wait-listing. Factors associated with death following transplantation include older age, diabetes, and longer time spent on the waiting list before transplantation. In a time-dependent Cox regression model, the relative risk of death after transplantation compared to dialysis varied in a time-dependent manner. Covariates associated with increased risk included older age, diabetes, and time from onset of ESRD to wait-listing. The average relative risk (RR) of dying was 2.91 (95% confidence interval [CI], 1.34 to 6.32) in the first 30 d after transplantation, but was significantly lower 1 yr after transplantation (RR 0.25; 95% CI, 0.14 to 0.42), indicating a beneficial long-term effect when compared to wait-listed dialysis patients. This long-term benefit was most evident in subgroups of patients with diabetes (RR 0.38; 95% CI, 0.17 to 0.87) and glomerulonephritis (RR 0.13; 95% CI, 0.04 to 0.39) as the cause of ESRD. The survival advantage associated with renal transplantation is evident in this cohort of patients with a lower wait-listed dialysis mortality than that reported previously in the United States. The magnitude of the treatment effect is consistent across studies.

scholarly journals State-level tracking of COVID-19 in the United States

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
H. Juliette T. Unwin ◽  
Swapnil Mishra ◽  
Valerie C. Bradley ◽  
Axel Gandy ◽  
Thomas A. Mellan ◽  
...  
Keyword(s):  
State Level ◽  
The United States ◽  
Bayesian Hierarchical ◽  
Secondary Infections ◽  
The Us ◽  
Control And Prevention ◽  
Credible Intervals ◽  
Behaviour Changes ◽  
The Impact ◽  
Number Of Individuals

AbstractAs of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that Rt was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%–4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.

scholarly journals Codifying Cultural Heritage: Why United States Criminal Law Suddenly Treats Our Rare Materials with Respect

2008 ◽  
Vol 9 (2) ◽  
pp. 205-212
Author(s):  
Travis McDade
Keyword(s):  
United States ◽  
Cultural Heritage ◽  
Criminal Law ◽  
The United States ◽  
American People ◽  
Cultural Resources ◽  
Sentencing Guidelines ◽  
Term Benefit

“The Cultural Heritage Guideline will, in my opinion, prove to be one of the most important of all the sentencing guidelines for the long-term benefit of our nation,” said United States Attorney (now federal magistrate judge) Paul Warner. He was testifying before the United States Sentencing Commission, which was about to get serious with cultural crimes. He was also echoing what he had written in a letter to the Commission a mere three months earlier. In that letter, which fully addressed the harm caused to the American people by crimes against cultural resources, he explained that “[f]ew undertakings by the . . .

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