scholarly journals Why do per capita COVID-19 Case Rates Differ Between U.S. States?

2020 ◽  
Author(s):  
Lloyd Chambless
Keyword(s):  
Population Density ◽  
Political Party ◽  
The State ◽  
Median Income ◽  
Poverty Level ◽  
Explanatory Variables ◽  
State Population ◽  
Case Rate ◽  
Socio Economic Variables ◽  
Party Differences

AbstractBackgroundThe popular press has explored the differences among U.S. states in rates of COVID-19 cases, mostly focusing on political party differences, and often mentioning that political party differences in health outcomes are confounded by demographic and socio-economic differences between Democratic areas and Republican areas. The purpose of this paper is to present a thorough analysis of these issues.Design and MethodsState-specific COVID-19 cases per 100,000 people was the main outcome studied, with explanatory variables from Bureau of Census surveys, including percentages of the state population that were Hispanic, black, below poverty level, had at least a bachelor’s degree, or were uninsured, along with median age, median income, population density, and degree of urbanization. We also included political party in power as an explanatory variable in multiple linear regression. The units of analysis in this study are the 50 U.S. states.ResultsAll explanatory variables were at least marginally statistically significantly associated with case rate in univariate regression analysis, except for population density and urbanization. All the census characteristics were at least marginally associated with party in power in one factor analysis of variance, except for percentage black. In a forward stepwise procedure in a multivariable model for case rate, percentages of the state population that were Hispanic or black, median age, median income, population density, and (residual) percentage poverty were retained as statistically significant and explained 62% of the variation between states in case rates. In a model with political party in power included, along with any additional variables that notably affected the adjusted association between party in power and case rate, 69% of the variance between states in case rates was explained, and adjusted case rates per 100,000 people were 2155 for states with Democratic governments, 2269 for states with mixed governments, and 2738 for Republican-led states. These estimates are based on data through October 8, 2020.ConclusionsU.S. state-specific demographic and socio-economic variables are strongly associated with the states’ COVID-19 case rates, so must be considered in analysis of variation in case rates between the states. Adjusting for these factors, states with Democrats as the party in power have lower case rates than Republican-led states.

PANCASILA DAN IDENTITAS KE-INDONESIA-AN: Sebuah Catatan Krisis

2018 ◽  
Vol 16 (2) ◽  
pp. 43
Author(s):  
Muchid Albintani
Keyword(s):  
Political Party ◽  
Direct Democracy ◽  
Identity Crisis ◽  
The State ◽  
The Future ◽  
Electoral Regime ◽  
National Consensus

The term there is no legislation under development of Pancasila as the basis of the state, but theposition of Pancasila is unshakeable. The anti-Pancasila attitude must also be anti-diversity that can live as a nation and a state [national crises]. Without affirmation or not in the legislation, Pancasila is the ‘foundation and ideology of the state’. Based on the fact that there is irrelevant when the question arises, whether Pancasila is still needed as the basis of state and nation, or is Pancasila still needed as a source of national law that explicitly needs to be affirmed into the1945 Constitution and the sanctions of Pancasila tabulatively? This paper is an assertion of [reinforcement] of the Pencasila as an ideology into the 1945 Constitution or not, highly dependent on the winning electoral regime and the ‘election-winning political party’. Pancasila as ‘the foundation and ideology of the state’ becomes the determinant of ‘as close as the regime of the results of the practice of direct democracy’. Therefore, the affirmation of the essentials in building a lasting and harmonious life of fellow children of the nation in the future. Recognizing the reintroduction of the Indonesia’s identity of essence of Pancasila as the ideology of nation and state is based on ‘national consensus’. This awareness is resilient, so that a country that has been established for more than 73 years does not experience an identity crisis. 

scholarly journals Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Stefan Nickel ◽  
Winfried Schröder
Keyword(s):  
Land Use ◽  
Population Density ◽  
Atmospheric Deposition ◽  
Vegetation Structure ◽  
Urban Areas ◽  
Explanatory Power ◽  
Spatial Density ◽  
Area Index ◽  
Explanatory Variables ◽  
Tree Layer

Abstract Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

Abstract W P48: Marked Regional Variation in Acute Stroke Treatment in Medicare Beneficiaries

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
James F Burke ◽  
Lesli E Skolarus ◽  
Eric E Adelman ◽  
Phillip A Scott ◽  
William J Meurer
Keyword(s):  
Population Density ◽  
Acute Stroke ◽  
Census Data ◽  
Combined Therapy ◽  
Local Health ◽  
Median Income ◽  
Medicare Beneficiaries ◽  
Stroke Treatment ◽  
Acute Stroke Treatment ◽  
Iv Tpa

Objective: Regionalization of stroke care has occurred sporadically across the U.S, so determining realistic goal treatment rates for individual regions or the nation as a whole is challenging. Studies of a single hospital or region vary widely in estimates of eligibility for acute therapy and may have limited generalizability or biases. We hypothesized that the proportion of U.S. Medicare beneficiaries receiving acute stroke therapy varies by region. Treatment rates in high performing regions may represent realistic national goals and inform policy to increase treatment rates. Methods: All Medicare beneficiaries with a principal diagnosis of ischemic stroke (ICD-9 433.x1, 434.x1, 436) admitted through the emergency department were identified using MEDPAR files from 2007-2010. Receipt of IV tPA (DRG 559, MS-DRG 61-63, ICD-9 procedure code 99.10) or IA thrombolysis (CPT code 37184-6, 37201, 75896 via linked Medicare Carrier files) was determined. Patients were assigned to one of 3,436 Hospital Service Areas (HSA; local health care markets for hospital care) by zip code. Regional acute stroke treatment rates were calculated and the lowest and highest quintiles were compared. Multi-level logistic regression was used to adjust for individual demographics as well as regional population density, education, median income, and unemployment using linked census data. Model-based adjusted regional acute stroke treatment rates were estimated. Results: Of 916,232 stroke admissions 3.6% received IV tPA only and 0.6% received IA or combined therapy. Unadjusted treatment rates by region ranged from 0.8% (minimum) to 14.8% (maximum). Regional rates ranged from 1.7% (quintile 1) to 5.4% (quintile 5). Regions with higher education, population density and income had higher treatment rates (p <= 0.001). After adjustment, regional differences were attenuated slightly _ 1.9% (quintile 1) to 5.1% (quintile 5). Conclusions: Marked variation exists in acute stroke treatment rates by region, even after adjusting for patient and regional characteristics, supporting the perception that a major opportunity exists to improve acute stroke treatment within many HSAs.

scholarly journals Association of Lutzomyia longipalpis (Diptera: Psychodidae) population density with climate variables in Montes Claros, an area of American visceral leishmaniasis transmission in the state of Minas Gerais, Brazil

2009 ◽  
Vol 104 (8) ◽  
pp. 1191-1193 ◽  
Author(s):  
Érika Monteiro Michalsky ◽  
Consuelo Latorre Fortes-Dias ◽  
João Carlos França-Silva ◽  
Marilia Fonseca Rocha ◽  
Ricardo Andrade Barata ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Population Density ◽  
Minas Gerais ◽  
The State ◽  
Lutzomyia Longipalpis ◽  
Climate Variables

scholarly journals Summarizing the state of the terrestrial biosphere in few dimensions

2019 ◽  
Author(s):  
Guido Kraemer ◽  
Gustau Camps-Valls ◽  
Markus Reichstein ◽  
Miguel D. Mahecha
Keyword(s):  
Data Streams ◽  
Land Surface ◽  
Principal Component ◽  
The State ◽  
Climate Indices ◽  
Surface Dynamics ◽  
Terrestrial Biosphere ◽  
Explanatory Variables ◽  
Arctic Regions ◽  
Agricultural Areas

Abstract. In times of global change, we must closely monitor the state of the planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems – i.e. the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73 % of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We assume that this generic approach has great potential for the analysis of land-surface dynamics from observational or model data.

scholarly journals THE OPAQUE PORTRAIT OF CORRUPTION ERADICATION

2016 ◽  
Vol 1 (1) ◽  
pp. 44-54
Author(s):  
Marwan Mas
Keyword(s):  
Political Party ◽  
The State ◽  
Civil Servants ◽  
Deterrent Effect ◽  
Power Elite ◽  
Important Thing ◽  
New Generation

The war against corruption should not only by exposing the various cases of corruption, but the most important thing is to punish the corrupt in an extraordinary way to have a deterrent effect and not replicable by potential criminals that have been queued. Corruption is more structured and systematic, from the center to the regions. In fact, gave birth to a new generation of fat accounts with the discovery of a number of civil servants who are still young. Stop the robbery of money the state cannot just with rhetoric, let alone just a call that seemed hot chicken droppings. Blurred portrait of corruption is characterized by a large number of cases the defendant is acquitted of corruption Anticorruption Court. Similarly, many major cases involving alleged power elite and the ruling political party that is not completed, such as the Bank Century case, the case Hambalang project, as well as allegations of corruption Pensions SEA Games athletes

Probability based models for estimation of wildfire risk

2004 ◽  
Vol 13 (2) ◽  
pp. 133 ◽  
Author(s):  
Haiganoush K. Preisler ◽  
David R. Brillinger ◽  
Robert E. Burgan ◽  
J. W. Benoit
Keyword(s):  
Fire Risk ◽  
Small Region ◽  
The State ◽  
Grouped Data ◽  
Large Fire ◽  
Explanatory Variables ◽  
Time Period ◽  
Monthly Total ◽  
Historic Data ◽  
Large Fires

We present a probability-based model for estimating fire risk. Risk is defined using three probabilities: the probability of fire occurrence; the conditional probability of a large fire given ignition; and the unconditional probability of a large fire. The model is based on grouped data at the 1 km2-day cell level. We fit a spatially and temporally explicit non-parametric logistic regression to the grouped data. The probability framework is particularly useful for assessing the utility of explanatory variables, such as fire weather and danger indices for predicting fire risk. The model may also be used to produce maps of predicted probabilities and to estimate the total number of expected fires, or large fires, in a given region and time period. As an example we use historic data from the State of Oregon to study the significance and the forms of relationships between some of the commonly used weather and danger variables on the probabilities of fire. We also produce maps of predicted probabilities for the State of Oregon. Graphs of monthly total numbers of fires are also produced for a small region in Oregon, as an example, and expected numbers are compared to actual numbers of fires for the period 1989–1996. The fits appear to be reasonable; however, the standard errors are large indicating the need for additional weather or topographic variables.

Spatial Model Approach for Deforestation

2013 ◽  
pp. 1901-1912
Author(s):  
Lilik B. Prasetyo ◽  
Chandra Irawadi Wijaya ◽  
Yudi Setiawan
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Forest Cover ◽  
Positive Impact ◽  
Forest Loss ◽  
Road Density ◽  
Policy Makers ◽  
Explanatory Variables ◽  
The Government ◽  
Negative Impacts

Java is very densely populated since it is inhabited by more than 60% of the total population of Indonesia. Based on data from the Ministry of Forestry, forest loss between 2000-2005 in Java was about 800,000 hectares. Regardless of the debate on whether the different methodologies of forest inventory applied in 2005 have resulted in an underestimation of the figure of forest loss or not, the decrease of forest cover in Java is obvious and needs immediate response. Spatial modeling of the deforestation will assist the policy makers in understanding this process and in taking it into consideration, when decisions are made on the issue. Moreover, the results can be used as data input to solve environmental problems resulting from deforestation. The authors of this chapter modeled the deforestation in Java by using logistic regression. Percentage of deforested area was considered as the response variable, whilst biophysical and socioeconomic factors, that explain the current spatial pattern in deforestation, were assigned as explanatory variables. Furthermore, the authors predicted the future deforestation process, and then, for the case of Java, it was validated with the actual deforestation derived from MODIS satellite imageries from 2000 to 2008. Results of the study showed that the impacts of population density, road density, and slope are significant. Population density and road density have negative impacts on deforestation, while slope has positive impact. Deforestation on Java Island tends to occur in remote areas with limited access, low density population and relatively steep slopes. Implication of the model is that the government should pay more attention to remote rural areas and develop good access to accelerate and create alternative non agricultural jobs in order to reduce pressure on the forest.

Spatial Model Approach for Deforestation

2011 ◽  
pp. 376-387 ◽  
Author(s):  
Lilik B. Prasetyo ◽  
Chandra Irawadi Wijaya ◽  
Yudi Setiawan
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Forest Cover ◽  
Positive Impact ◽  
Forest Loss ◽  
Road Density ◽  
Policy Makers ◽  
Explanatory Variables ◽  
The Government ◽  
Negative Impacts

Java is very densely populated since it is inhabited by more than 60% of the total population of Indonesia. Based on data from the Ministry of Forestry, forest loss between 2000-2005 in Java was about 800,000 hectares. Regardless of the debate on whether the different methodologies of forest inventory applied in 2005 have resulted in an underestimation of the figure of forest loss or not, the decrease of forest cover in Java is obvious and needs immediate response. Spatial modeling of the deforestation will assist the policy makers in understanding this process and in taking it into consideration, when decisions are made on the issue. Moreover, the results can be used as data input to solve environmental problems resulting from deforestation. The authors of this chapter modeled the deforestation in Java by using logistic regression. Percentage of deforested area was considered as the response variable, whilst biophysical and socioeconomic factors, that explain the current spatial pattern in deforestation, were assigned as explanatory variables. Furthermore, the authors predicted the future deforestation process, and then, for the case of Java, it was validated with the actual deforestation derived from MODIS satellite imageries from 2000 to 2008. Results of the study showed that the impacts of population density, road density, and slope are significant. Population density and road density have negative impacts on deforestation, while slope has positive impact. Deforestation on Java Island tends to occur in remote areas with limited access, low density population and relatively steep slopes. Implication of the model is that the government should pay more attention to remote rural areas and develop good access to accelerate and create alternative non agricultural jobs in order to reduce pressure on the forest.

Land Use, Economic Welfare and Property Values

2020 ◽  
pp. 414-437
Author(s):  
Pierluigi Morano ◽  
Francesco Tajani ◽  
Marco Locurcio
Keyword(s):  
Private Investment ◽  
Economic Welfare ◽  
Explanatory Variables ◽  
Functional Relationships ◽  
Planning Decisions ◽  
Property Prices ◽  
Socio Economic Variables ◽  
Definition Of ◽  
Investment Choices

In the paper an analysis of functional correlations of property prices with the main locational and socio-economic variables, which generally contribute to define the market value of properties, has been developed. Locational characteristics are represented by the surfaces of soil used for the main functions, borrowing the logic of the system of classification of CORINE Land Cover (European Commission). The analysis has been contextualized to the 258 municipalities of the Apulia region (Southern Italy), and has been referred to two different moments (years 2006 and 2011), and two different market segments (residential and retail). The functional relationships between property prices and explanatory variables considered, estimated through a software that implements a genetic algorithm, are particularly interesting. The methodology outlined constitutes a valuable reference for the definition of models aimed at supporting, in a more rational and convenient way, public planning decisions and private investment choices.

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