scholarly journals Nature inequity and higher COVID-19 case rates in less-green neighbourhoods in the United States

2021 ◽  
Author(s):  
Erica N. Spotswood ◽  
Matthew Benjamin ◽  
Lauren Stoneburner ◽  
Megan M. Wheeler ◽  
Erin E. Beller ◽  
...  
Keyword(s):  
United States ◽  
Low Income ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The United States ◽  
Incidence Rates ◽  
Lower Income ◽  
Urban Nature ◽  
Urbanized Areas ◽  
Block Groups

AbstractUrban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic.

scholarly journals Who has nature during the pandemic? COVID-19 cases track widespread inequity in nature access across the United States

2021 ◽  
Author(s):  
Erica Spotswood ◽  
Matthew Benjamin ◽  
Lauren Stoneburner ◽  
Megan Wheeler ◽  
Erin Beller ◽  
...  
Keyword(s):  
Low Income ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The United States ◽  
Incidence Rates ◽  
Demographic Variables ◽  
Urban Nature ◽  
Communities Of Color ◽  
The Us ◽  
Block Groups

Abstract Urban nature can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income and communities of color—the same communities hardest hit by COVID-19. We quantified nature inequality across all urbanized areas in the US and linked nature access to COVID-19 case rates for ZIP Codes in 17 states. Areas with majority persons of color had both higher case rates and less greenness. Furthermore, when controlling for socio-demographic variables, an increase of 0.1 in Normalized Difference Vegetation Index (NDVI) was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9-6.8%). Across the US, block groups with lower-income and majority persons of color are less green and have fewer parks. Thus, communities most impacted by COVID-19 also have the least nature nearby. Given urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic.

scholarly journals Can Parametric Microinsurance Improve the Financial Resilience of Low-Income Households in the United States?

2021 ◽  
Author(s):  
Carolyn Kousky ◽  
Helen Wiley ◽  
Len Shabman
Keyword(s):  
United States ◽  
Low Income ◽  
Large Body ◽  
Developed Countries ◽  
The United States ◽  
Disaster Risk ◽  
Financial Resources ◽  
Lower Income ◽  
Distribution Models ◽  
Low Income Households

AbstractNatural disaster risk is escalating around the globe and in the United States. A large body of research has found that lower-income households disproportionally suffer from disasters and are less likely to recover. Poorer households often lack the financial resources for rebuilding, endangering other aspects of wellbeing. Parametric microinsurance has been used in many developing countries to improve the financial resilience of low-income households. This paper presents a review of the evidence for implementing parametric microinsurance in the U.S., with spillover lessons for other highly developed countries. We discuss the benefits and the challenges of microinsurance in a US context and explore 4 possible distribution models that could help overcome difficulties, with policies being provided: (1) by an aggregator, (2) through a mobile-based technology, (3) by linking to other products or retailers, or (4) through a public sector insurer.

scholarly journals Spatiotemporal patterns of urban mosquitoes are modulated by socioeconomic status and environmental traits in the United States

2021 ◽  
Author(s):  
Senay Yitbarek ◽  
Kelvin Chen ◽  
Modeline Celestin ◽  
Matthew McCary
Keyword(s):  
United States ◽  
Socioeconomic Status ◽  
Low Income ◽  
Household Income ◽  
Meta Analysis ◽  
Spatial Regression ◽  
The United States ◽  
Urban Environments ◽  
Urban Landscapes ◽  
Lower Income

The distribution of mosquitoes and associated vector diseases (e.g., West Nile, dengue, and Zika viruses) is likely a function of environmental conditions in the landscape. Urban environments are highly heterogeneous in the amount of vegetation, standing water, and concrete structures covering the land at a given time, each having the capacity to influence mosquito abundance and disease transmission. Previous research suggests that socioeconomic status is correlated with the ecology of the landscape, with lower-income neighborhoods generally having more concrete structures and standing water via residential abandonment, garbage dumps, and inadequate sewage. Whether these socio-ecological factors affect mosquito distributions across urban environments in the United States (US) remains unclear. Here, we present a meta-analysis of 22 paired observations from 15 articles testing how socioeconomic status relates to overall mosquito burden in urban landscapes in the United States. We then analyzed a comprehensive dataset from a socioeconomic gradient in Baltimore, Maryland to model spatiotemporal patterns of Aedes albopictus using a spatial regression model with socio-ecological covariates. The meta-analysis revealed that lower-income neighborhoods (regions making less than $50,000 per year on average) are exposed to 151% greater mosquito densities and mosquito-borne illnesses compared to higher-income neighborhoods (≥$50,000 per year). Two species of mosquito (Ae. albopictus and Aedes aegypti) showed the strongest relationship with socioeconomic status, with Ae. albopictus and Ae. aegypti being 62% and 22% higher in low-income neighborhoods, respectively. In the spatial regression analysis in Baltimore, we found that Ae. albopictus spatial spread of 1.2 km per year was significantly associated with median household income, vegetation cover, tree density, and abandoned buildings. Specifically, Ae. albopictus abundance was negatively correlated with median household income, vegetation cover, and tree density. Ae. albopictus abundance and the cover of abandoned buildings were positively correlated. Together, these results indicate that socio-ecological interactions can lead to disproportionate impacts of mosquitoes on humans in urban landscapes. Thus, concerted efforts to manage mosquito populations in low-income urban neighborhoods are required to reduce mosquito burden for the communities most vulnerable to human disease.

scholarly journals LONG-TERM COSTS OF GRANDPARENT CAREGIVING: RESULTS FROM THREE WAVES OF THE MIDLIFE IN THE UNITED STATES STUDY (MIDUS)

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S797-S798
Author(s):  
Elizabeth Rickenbach ◽  
Elizabeth H Rickenbach ◽  
Chih-Chien Huang ◽  
Jessica Y Allen ◽  
Kelly E Cichy
Keyword(s):  
United States ◽  
Older Adults ◽  
Low Income ◽  
Longitudinal Research ◽  
Well Being ◽  
The United States ◽  
Lower Income ◽  
Cross Sectional ◽  
Grandparent Caregiving

Abstract Cross-sectional studies reveal the health burden of grandparent caregiving. Still, longitudinal, research is needed to understand how grandparent caregiving compromises grandparents’ long-term health. Using three waves of data from the Midlife in the United States Study (MIDUS), we examined sociodemographic factors, health and well-being outcomes between caregiving (CG) and non-caregiving (NCG) grandparents. By wave 3, 12.8% (n = 234) were CG. CG were younger, more likely female, and had lower income and education. MANCOVA adjusted for age, gender, education, and number of children revealed CG reported poorer physical and emotional well-being (e.g. higher depression, anxiety, lower life satisfaction, greater morbidity); CG were consistently less healthy than NCG across all three waves. Lower income and less healthy older adults are more likely to become grandparents, and they remain less healthy over time. Policies and resources to assist grandparents, particularly low-income and vulnerable older adults who are caring for grandchildren, are needed.

scholarly journals Evaluating the Salinity Tolerance of Clonal-type Bermudagrass Cultivars and an Experimental Selection

HortScience ◽  
2017 ◽  
Vol 52 (1) ◽  
pp. 185-191 ◽  
Author(s):  
Mingying Xiang ◽  
Justin Q. Moss ◽  
Dennis L. Martin ◽  
Kemin Su ◽  
Bruce L. Dunn ◽  
...  
Keyword(s):  
United States ◽  
Salinity Tolerance ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Perennial Grass ◽  
Warm Season ◽  
Dry Weight ◽  
The United States ◽  
Clonal Type ◽  
Salinity Levels

Bermudagrass (Cynodon sp.) is a highly productive, warm-season, perennial grass that has been grown in the United States for turfgrass, forage, pasture, rangeland, and roadside use. At the same time, many bermudagrass production and reclamation sites across the United States are affected by soil salinity issues. Therefore, identifying bermudagrass with improved salinity tolerance is important for successfully producing bermudagrass and for reclaiming salt-affected sites with saline irrigated water. In this project, the relative salinity tolerance of seven clonal-type bermudagrass was determined, including industry standards and an Oklahoma State University (OSU) experimental line. The experiment was conducted under a controlled environment with six replications of each treatment. Seven bermudagrass entries were exposed to four salinity levels (1.5, 15, 30, and 45 dS·m−1) consecutively via subirrigation systems. The relative salinity tolerance among entries was determined by normalized difference vegetation index (NDVI), digital image analysis (DIA), leaf firing (LF), turf quality (TQ), shoot dry weight (SW), visual rating (VR), and dark green color index (DGCI). Results indicated that there were variable responses to salinity stress among the entries studied. As salinity levels of the irrigation water increased, all evaluation criterion decreased, except LF. All entries had acceptable TQ when exposed to 15 dS·m−1. When exposed to 30 dS·m−1, experimental entry OKC1302 had less LF than all other entries except ‘Tifway’, while ‘Midlawn’ showed more LF than all the entries. Leaf firing ranged from 1.0 to 2.7 at 45 dS·m−1, where ‘Tifway’ outperformed all other entries. At 45 dS·m−1, the live green cover as measured using DIA ranged from 3.07% to 24.72%. The parameters LF, TQ, NDVI, DGCI, SW, and DIA were all highly correlated with one another, indicating their usefulness as relative salinity tolerance measurements.

scholarly journals Confronting Structural Racism in the Prevention and Control of Tuberculosis in the United States

2020 ◽  
Author(s):  
Emmanuella N Asabor ◽  
Sten H Vermund
Keyword(s):  
United States ◽  
Health Services ◽  
Low Income ◽  
Urban Poor ◽  
Preventive Health Services ◽  
The United States ◽  
Incidence Rates ◽  
Contact Tracing ◽  
Structural Racism ◽  
Tuberculosis Screening

Abstract Tuberculosis incidence in the United States is declining, yet projections indicate that we will not eliminate tuberculosis in the 21st century. Incidence rates in regions serving the rural and urban poor, including recent immigrants, are well above the national average. People experiencing incarceration and homelessness represent additional key populations. Better engagement of marginalized populations will not succeed without first addressing the structural racism that fuels continued transmission. Examples include:(1)systematic underfunding of contact tracing in health departments serving regions where Black, Indigenous, and People of Color (BIPOC) live;(2) poor access to affordable care in state governments that refuse to expand insurance coverage to low-income workers through the Affordable Care Act;(3) disproportionate incarceration of BIPOC into crowded prisons with low tuberculosis screening rates; and(4) fear-mongering among immigrants that discourages them from accessing preventive health services. To eliminate tuberculosis, we must first eliminate racist policies that limit essential health services in vulnerable communities.

scholarly journals Albedo Impacts of Changing Agricultural Practices in the United States through Space-Borne Analysis

2020 ◽  
Vol 12 (18) ◽  
pp. 2887
Author(s):  
Jon Starr ◽  
Jianglong Zhang ◽  
Jeffrey S. Reid ◽  
David C. Roberts
Keyword(s):  
United States ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Practices ◽  
The United States ◽  
Growing Period ◽  
Field Crops ◽  
Sensing Applications ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual

Using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Bidirectional Reflectance Distribution Function (BRDF) with the U.S. Department of Agriculture’s National Agricultural Statistics Service’s Cropland Data Layer (CDL), the daily albedo of homogenous agricultural fields was derived for 51 common United States field crops by wavelength, sky-type, day of year, crop, and hardiness zone from 2015–2018. This study suggests that crop growth can result in changes in reflectivity up to a factor of 2 at most wavelengths and is unique per crop type in timing and range. Additionally, broadband impacts were studied and found to be less conspicuous than the individual wavelengths, but still significant. The results were used to evaluate a common method of cropland albedo estimation, normalized difference vegetation index (NDVI) as a proxy for albedo, and this method was found to have some significant limitations dependent on wavelength and date. Finally, a database of surface albedo variations as a function of growing period is constructed for common field crops to the United States (as well as additional land-cover types). This database can be used to aid both satellite remote-sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors.

scholarly journals Decoding State Vaccination Rates Using educational aptitude, Income, and Political Affiliation

2021 ◽  
Author(s):  
Azad Kabir ◽  
Raeed Kabir ◽  
Jebun Nahar ◽  
Ritesh Sengar
Keyword(s):  
United States ◽  
Risk Factors ◽  
Low Income ◽  
The United States ◽  
Political Affiliation ◽  
Lower Income ◽  
Vaccination Rates ◽  
Factors Associated ◽  
The Us ◽  
Per Capita

The objective of the study was to evaluate the risk factors associated with lower COVID-19 vaccination rates in the United States. The study evaluated the effect of red-blue political affiliation and the effect of the US state's average educational aptitude score and per capita income on states' vaccination rates. The study found that states with concomitantly lower income along with lower educational aptitude scores are less vaccinated while the states with higher income have higher vaccination rates even among those with lower educational aptitude scores. These findings stayed significant after adjusting for red-blue political affiliation where states with red political affiliation have lower vaccination rates. Further study is needed to evaluate how to stop online misinformation among states with low income and low educational aptitude scores; and whether such an effort will increase overall vaccination rates in the United States.

Estimating the Urban Bias of Surface Shelter Temperatures Using Upper-Air and Satellite Data. Part II: Estimation of the Urban Bias

1995 ◽  
Vol 34 (2) ◽  
pp. 358-370 ◽  
Author(s):  
David L. Epperson ◽  
Jerry M. Davis ◽  
Peter Bloomfield ◽  
Thomas R. Karl ◽  
Alan L. McNab ◽  
...  
Keyword(s):  
United States ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The United States ◽  
Rural Landscape ◽  
Temperature Record ◽  
Urban Bias ◽  
The Mean ◽  
The Given

Abstract A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.

scholarly journals 55 Malignant primary brain and other central nervous system tumours diagnosed in the Canadian population from 2009 to 2013

2018 ◽  
Vol 45 (S3) ◽  
pp. S16-S17
Author(s):  
EV Walker ◽  
F Davis ◽  
Keyword(s):  
United States ◽  
Central Nervous System ◽  
Nervous System ◽  
Brain Tumour ◽  
Age Distribution ◽  
Paediatric Population ◽  
The United States ◽  
Incidence Rates ◽  
Canadian Population ◽  
Cns Tumours

The Canadian Brain Tumour Registry (CBTR) project was established in 2016 with the aim of enhancing infrastructure for surveillance and clinical research to improve health outcomes for brain tumour patients in Canada. We present a national surveillance report on malignant primary brain and central nervous system (CNS) tumours diagnosed in the Canadian population from 2009-2013. Patients were identified through the Canadian Cancer Registry (CCR); an administrative dataset that includes cancer incidence data from all provinces/territories in Canada. Cancer diagnoses are coded using the ICD-O3 system. Tumour types were classified by site and histology using The Central Brain Tumour Registry of the United States definitions. Incidence rates (IR) and 95% confidence intervals (CI) were calculated per 100,000 person-years and standardized to the 2011 census population age-distribution. Overall, 12,115 malignant brain and CNS tumours were diagnosed in the Canadian population from 2009-2013 (IR:8.43;95%CI:8.28,8.58). Of these, 6,845 were diagnosed in males (IR:9.72;95%CI:9.49,9.95) and 5,270 in females (IR:7.20;95%CI:7.00,7.39). The most common histology overall was glioblastoma (IR:4.06;95%CI:3.95,4.16). Among those aged 0-19 years, 1,130 malignant brain and CNS tumours were diagnosed from 2009-2013 (IR:3.36;95%CI:3.16,3.56). Of these, 625 were diagnosed in males (IR:3.32;95%CI:3.34,3.92) and 505 in females (IR:3.08;95%CI:2.81,3.36). The most common histology among the paediatric population was pilocytic astrocytoma (IR:0.73;95%CI:0.64,0.83). The presentation will include: IRs for other histologies, the geographic distribution of cases and a comparison between Canada and the United States.

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