scholarly journals Spatial risk factors for Pillar 1 COVID-19 case counts and mortality in rural eastern England, UK

2020 ◽  
Author(s):  
Julii Brainard ◽  
Steve Rushton ◽  
Tim Winters ◽  
Paul R. Hunter
Keyword(s):  
Risk Factors ◽  
Air Quality ◽  
Population Density ◽  
Urban Areas ◽  
Structural Equation Models ◽  
Care Home ◽  
Age Distribution ◽  
Spatial Risk ◽  
Bed Capacity ◽  
Excess Deaths

Understanding is still developing about risk factors for COVID-19 infection or mortality. This is especially true with respect to identifying spatial risk factors and therefore identifying which geographic areas have populations who are at greatest risk of acquiring severe disease. This is a secondary analysis of patient records in a confined area of eastern England, covering persons who tested positive for SARS-CoV-2 through end May 2020, including dates of death and residence area. For each residence area (local super output area), we obtained data on air quality, deprivation levels, care home bed capacity, age distribution, rurality, access to employment centres and population density. We considered these covariates as risk factors for excess cases and excess deaths in the 28 days after confirmation of positive covid status relative to the overall case load and death recorded for the study area as a whole. We used the conditional autoregressive Besag-York-Mollie model to investigate the spatial dependency of cases and deaths allowing for a Poisson error structure. Structural equation models were also applied to clarify relationships between predictors and outcomes. Excess case counts or excess deaths were both predicted by the percentage of population age 65 years, care home bed capacity and less rurality: older population and more urban areas saw excess cases. Greater deprivation did not correlate with excess case counts but was significantly linked to higher mortality rates after infection. Neither excess cases nor excess deaths were predicted by population density, travel time to local employment centres or air quality indicators. Only 66% of mortality could be explained by locally high case counts. The results show a clear link between greater deprivation and higher COVID-19 mortality that is separate from wider community prevalence and other spatial risk factors.

scholarly journals Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States

2021 ◽  
Vol 18 (13) ◽  
pp. 6832
Author(s):  
Han Yue ◽  
Tao Hu
Keyword(s):  
United States ◽  
Risk Factors ◽  
Mortality Rate ◽  
Population Density ◽  
Potential Risk ◽  
Interactive Effects ◽  
Geographical Detector ◽  
Spatial Risk ◽  
Potential Risk Factors ◽  
The Government

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.

scholarly journals Socio-economic status and population density risk factors for psychosis: prospective incidence study in the Maltese Islands

2010 ◽  
Vol 7 (3) ◽  
pp. 69-71 ◽  
Author(s):  
Nigel Camilleri ◽  
Anton Grech ◽  
Rachel Taylor
Keyword(s):  
Risk Factors ◽  
European Union ◽  
Population Density ◽  
Urban Areas ◽  
Economic Status ◽  
The European Union ◽  
Socio Economic Status ◽  
Maltese Islands ◽  
The World ◽  
Harbour Area

Malta is an archipelago (with three inhabited islands) in the Mediterranean Sea. According to the 2006 census, Malta has a population of just over 400000 and is the eighth most densely populated country in the world (1272 persons/km2) and the most densely populated of the member states of the European Union (EU). The most densely populated town in Malta is Senglea, with 22744 persons/km2 (situated in the Southern Harbour Area). In comparison, Malta's sister island, Gozo, has a density of 422 persons/km2. Over 92% of the population lives in urban areas.

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

2017 ◽  
Vol 68 (4) ◽  
pp. 841-846
Author(s):  
Hai-Ying Liu ◽  
Daniel Dunea ◽  
Mihaela Oprea ◽  
Tom Savu ◽  
Stefania Iordache
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Urban Areas ◽  
Behavioural Change ◽  
Health Assessment ◽  
Warning System ◽  
Personal Exposure ◽  
Mobile Technologies ◽  
Quality Maps

This paper presents the approach used to develop the information chain required to reach the objectives of the EEA Grants� RokidAIR project in two Romanian cities i.e., Targoviste and Ploiesti. It describes the PM2.5 monitoring infrastructure and architecture to the web-based GIS platform, the early warning system and the decision support system, and finally, the linking of air pollution to health effects in children. In addition, it shows the analysis performance of the designed system to process the collected time series from various data sources using the benzene concentrations monitored in Ploiesti. Moreover, this paper suggests that biomarkers, mobile technologies, and Citizens� Observatories are potential perspectives to improve data coverage by the provision of near-real-time air quality maps, and provide personal exposure and health assessment results, enabling the citizens� engagement and behavioural change. This paper also addresses new fields in nature-based solutions to improve air quality, and studies on air pollution and its mental health effects in the urban areas of Romania.

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.

scholarly journals Spatiotemporal patterns and spatial risk factors for visceral leishmaniasis from 2007 to 2017 in Western and Central China: A modelling analysis

2021 ◽  
Vol 764 ◽  
pp. 144275
Author(s):  
Dong Jiang ◽  
Tian Ma ◽  
Mengmeng Hao ◽  
Yushu Qian ◽  
Shuai Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Visceral Leishmaniasis ◽  
Spatiotemporal Patterns ◽  
Central China ◽  
Spatial Risk ◽  
Modelling Analysis

scholarly journals Morpho-Physio-Biochemical Attributes of Roadside Trees as Potential Tools for Biomonitoring of Air Quality and Environmental Health in Urban Areas

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 236
Author(s):  
Ha Na You ◽  
Myeong Ja Kwak ◽  
Sun Mi Je ◽  
Jong Kyu Lee ◽  
Yea Ji Lim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Health ◽  
Urban Areas ◽  
Biochemical Parameters ◽  
Lipid Peroxide ◽  
Platanus Occidentalis ◽  
Physiological Variables ◽  
Sources Of Pollution ◽  
Species Specific

Environmental pollution is an important issue in metropolitan areas, and roadside trees are directly affected by various sources of pollution to which they exhibit numerous responses. The aim of the present study was to identify morpho-physio-biochemical attributes of maidenhair tree (Ginkgo biloba L.) and American sycamore (Platanus occidentalis L.) growing under two different air quality conditions (roadside with high air pollution, RH and roadside with low air pollution, RL) and to assess the possibility of using their physiological and biochemical parameters as biomonitoring tools in urban areas. The results showed that the photosynthetic rate, photosynthetic nitrogen-use efficiencies, and photochromic contents were generally low in RH in both G. biloba and P. occidentalis. However, water-use efficiency and leaf temperature showed high values in RH trees. Among biochemical parameters, in G. biloba, the lipid peroxide content was higher in RH than in RL trees, but in P. occidentalis, this content was lower in RH than in RL trees. In both species, physiological activities were low in trees planted in areas with high levels of air pollution, whereas their biochemical and morphological variables showed different responses to air pollution. Thus, we concluded that it is possible to determine species-specific physiological variables affected by regional differences of air pollution in urban areas, and these findings may be helpful for monitoring air quality and environmental health using trees.

scholarly journals Effectiveness of Government Policies in Controlling COVID-19 in India

2020 ◽  
pp. 002073142098374
Author(s):  
Ashutosh Pandey ◽  
Nitin Kishore Saxena
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Rural Population ◽  
Urban Areas ◽  
Urban Population ◽  
Demographic Factors ◽  
Poverty Rate ◽  
Government Policies ◽  
Cumulative Number ◽  
Population Number

The purpose of this study is to find the demographic factors associated with the spread of COVID-19 and to suggest a measure for identifying the effectiveness of government policies in controlling COVID-19. The study hypothesizes that the cumulative number of confirmed COVID-19 patients depends on the urban population, rural population, number of persons older than 50, population density, and poverty rate. A log-linear model is used to test the stated hypothesis, with the cumulative number of confirmed COVID-19 patients up to period [Formula: see text] as a dependent variable and demographic factors as an independent variable. The policy effectiveness indicator is calculated by taking the difference of the COVID rank of the [Formula: see text]th state based on the predicted model and the actual COVID rank of the [Formula: see text]th state[Formula: see text]Our study finds that the urban population significantly impacts the spread of COVID-19. On the other hand, demographic factors such as rural population, density, and age structure do not impact the spread of COVID-19 significantly. Thus, people residing in urban areas face a significant threat of COVID-19 as compared to people in rural areas.

scholarly journals What Causes Waterlogging?—Explore the Urban Waterlogging Control Scheme through System Dynamics Simulation

2021 ◽  
Vol 13 (15) ◽  
pp. 8546
Author(s):  
Weike Chen ◽  
Jing Dong ◽  
Chaohua Yan ◽  
Hui Dong ◽  
Ping Liu
Keyword(s):  
Risk Factors ◽  
System Dynamics ◽  
Urban Areas ◽  
Model Simulation ◽  
Urban Expansion ◽  
Dynamics Simulation ◽  
Coupling Mechanism ◽  
Data Governance ◽  
Sponge City ◽  
Management Optimization

It is a common phenomenon in cities that waterlogging affects people’s normal life. It is of great significance for targeted transformation and upgrading to identify the risk factors of urban waterlogging. This paper collected the waterlogging data of Tianjin in China, analyzed the coupling mechanism among waterlogging risk factors of urban drainage systems, and then selected the system dynamics theory and the Vensim software as the analysis tools due to the mixing characteristic and the limited availability of data. After that, the sensitive factors were identified by model simulation and sensitivity analysis, and the prominent impact of urban expansion on waterlogging risk was discussed. Then, through the comparison of the three simulation scenarios, it was found that, compared with the urban development focus shifting strategy, the strategies of sponge city reconstruction and management optimization could achieve the risk control goal within a shorter time. On this basis, two kinds of governance schemes with strong operability were put forward, which were the data governance strategy and the sponge city reconstruction strategy of giving priority to old urban areas. According to the simulation results, a city can reverse the increasing trend of waterlogging risk within ten years under the appropriate scheme. Furthermore, the paper puts forward the strategic reimagining of the rural revitalization strategy and the ecological restoration strategy for the long-term sustainable development transformation of Tianjin.

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