Religious Environments and the Distribution of Anti-Poverty Nonprofit Organizations

2016 ◽  
Vol 46 (1) ◽  
pp. 156-174 ◽  
Author(s):  
Edward C. Polson
Keyword(s):  
Nonprofit Organizations ◽  
Regression Models ◽  
Spatial Regression ◽  
The United States ◽  
County Level ◽  
Geographic Concentration ◽  
Religious Traditions ◽  
Level Data ◽  
Spatial Regression Models ◽  
Previous Scholarship

Previous scholarship highlights the effect that religious environments have on community-level outcomes such as neighborhood stability, economic development, and crime. In the present study, I extend work on the contextual effects of religion by examining how the religious composition of U.S. counties is related to the distribution of anti-poverty nonprofit organizations. Anti-poverty nonprofits represent an important source of support for communities across the United States, and history suggests that religious people and groups have played a significant role in their development. Still, it is unclear whether some religious environments may be more nurturing of these organizations than others. Utilizing spatial regression models and county-level data, I seek to address this question. I find that the geographic concentration of some religious traditions is related to a more robust presence of anti-poverty nonprofit organizations than others.

scholarly journals Reduced Lung Cancer Mortality With Lower Atmospheric Pressure

Dose-Response ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 155932581876948 ◽  
Author(s):  
Ray M. Merrill ◽  
Aaron Frutos
Keyword(s):  
United States ◽  
Lung Cancer ◽  
Cancer Mortality ◽  
Atmospheric Pressure ◽  
Regression Models ◽  
Spatial Regression ◽  
Lung Cancer Mortality ◽  
The United States ◽  
Mortality Rates ◽  
County Level

Background: Research has shown that higher altitude is associated with lower risk of lung cancer and improved survival among patients. The current study assessed the influence of county-level atmospheric pressure (a measure reflecting both altitude and temperature) on age-adjusted lung cancer mortality rates in the contiguous United States, with 2 forms of spatial regression. Methods: Ordinary least squares regression and geographically weighted regression models were used to evaluate the impact of climate and other selected variables on lung cancer mortality, based on 2974 counties. Results: Atmospheric pressure was significantly positively associated with lung cancer mortality, after controlling for sunlight, precipitation, PM2.5 (µg/m3), current smoker, and other selected variables. Positive county-level β coefficient estimates ( P < .05) for atmospheric pressure were observed throughout the United States, higher in the eastern half of the country. Conclusion: The spatial regression models showed that atmospheric pressure is positively associated with age-adjusted lung cancer mortality rates, after controlling for other selected variables.

scholarly journals Timing of social distancing policies and COVID-19 mortality: county-level evidence from the U.S.

2021 ◽  
Author(s):  
Catalina Amuedo-Dorantes ◽  
Neeraj Kaushal ◽  
Ashley N. Muchow
Keyword(s):  
New York ◽  
Political Ideology ◽  
Death Rate ◽  
The United States ◽  
County Level ◽  
Level Data ◽  
Level Information ◽  
Alternative Measures ◽  
The U.S ◽  
Pharmaceutical Interventions

AbstractUsing county-level data on COVID-19 mortality and infections, along with county-level information on the adoption of non-pharmaceutical interventions (NPIs), we examine how the speed of NPI adoption affected COVID-19 mortality in the United States. Our estimates suggest that adopting safer-at-home orders or non-essential business closures 1 day before infections double can curtail the COVID-19 death rate by 1.9%. This finding proves robust to alternative measures of NPI adoption speed, model specifications that control for testing, other NPIs, and mobility and across various samples (national, the Northeast, excluding New York, and excluding the Northeast). We also find that the adoption speed of NPIs is associated with lower infections and is unrelated to non-COVID deaths, suggesting these measures slowed contagion. Finally, NPI adoption speed appears to have been less effective in Republican counties, suggesting that political ideology might have compromised their efficacy.

scholarly journals 694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep &lt;7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

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.

Spatial Regression Models

2017 ◽  
pp. 299-404
Author(s):  
Oliver Schabenberger ◽  
Carol A. Gotway
Keyword(s):  
Regression Models ◽  
Spatial Regression ◽  
Spatial Regression Models

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2012 ◽  
pp. 308-320
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Hedonic Pricing ◽  
Spatial Lag Model ◽  
Hedonic Pricing Method ◽  
Spatial Weights Matrix ◽  
Spatial Regression Models ◽  
Gis Environment ◽  
Value Of Irrigation Water

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2010 ◽  
Vol 1 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Software Tool ◽  
Hedonic Pricing ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

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