Racial and Geographical Disparities in Colorectal Cancer Incidence in Mississippi, 2003-2018

Purpose: The aim of this study is to investigate racial and geographical disparities of colorectal cancer incidence in Mississippi. Methods: Incidence data from 2003-2018 were obtained at the county-level from the Mississippi Cancer Registry. Incidence rate difference and 95% confidence intervals between age-adjusted colorectal cancer incidence for whites and blacks were calculated and mapped using ArcGIS. Results: The black incidence rate for colorectal cancer was 59.8 per 100,000 while the white incidence rate was 48.9 per 100,000. Blacks experienced significantly higher incidence rates than whites in 39 counties throughout much of Mississippi. These areas of higher racial disparities did not cluster in a specific region of the state. In the southern part of the state an 8-county cold-spot region was detected without racial disparities; incidence rate for blacks was 41.3 per 100,000 in this region. Conclusions: There are racial disparities throughout Mississippi except for an 8-county region towards the southern part of the state. Additional research should be conducted to identify what factors are responsible for the lower incidence rates among blacks in this region, and to implement effective interventions statewide to reduce racial disparities in colorectal cancer incidence.

The Caucasus Territory Hot-Cold Spots Determination And Description Using 2D Surface Waves Tomography

In order to identify and describe Hot-Cold spots inside the earth based on increasing and decreasing wave velocity anomalies, this paper attempts to generate the first 2D tomographic maps of Rayleigh surface wave velocity dispersion curves, by using ~1200 local-regional earthquake data and ~30000 vertical (Z) components of earthquake data waveform energy with magnitude M≥4 from 1999 to 2018 in a periods range of 5 to 70 seconds and a grid spacing of 0.2º×0.5º for a depth of ~200 km. To conduct this, a generalized 2D linear inversion procedure developed by Yanovskaya and Ditmar has been applied to construct the first 2D Rayleigh tomography velocity maps in order to understand better the regional tectonic activities in the enigmatic ongoing collision-compressed edge zone of the Eurasian-Arabic plates. In this study, we assumed that low-velocity (slow) region with dark red shade is hot spot and high-velocity (fast) region with dark blue-green-yellow is a cold spot. In short and medium periods were determined the number of 15 and 2 hot spots with a depth of 7 to 108 km, respectively. In long-periods and a depth of ~200 km, most part of the area study has covered by low-velocity anomaly.

Spatial Disparities and Correlated Variables of Community Care Facility Accessibility in Rural Areas of China

With the increasing aging of the world’s population, research on the equitable allocation of elderly care facilities has received increasing attention, but measuring the accessibility of community care facilities (CCFs) in rural areas has received little attention. In this study, which covered 7985 CCFs in 223,877 villages, we measured the accessibility of CCFs in rural areas of Hubei Province by using the nearest distance method. Based on the accessibility calculation, the spatial disparities and agglomeration characteristics of spatial accessibility were analyzed, and the correlated variables related to the accessibility were analyzed from both natural environment and socioeconomic aspects by employing a geographically weighted regression (GWR) model. Our results show that 87% of villages have a distance cost of less than 7121 m and 81% of townships have a distance cost of less than 5114 m; good spatial accessibility is present in the eastern and central regions, while poor spatial accessibility is shown in a small number of areas in the west. The results from the clustering analysis show that the hot spot areas are mainly clustered in the western mountainous areas and that the cold spot areas are mainly clustered around Wuhan city. We also observed that area, elevation, population aged 65 and above, and number of villages are significantly correlated with accessibility. The results of this study can be used to provide a reference for configuration optimization and layout planning of elderly care facilities in rural areas.

Setting Aside Settings: On the Contradictory Dynamics of “Flat Earth,” “Ordinalization,” and “Cold Spot” Education Governing Projects

Place matters, and for schools located in the neighborhoods of towns and cities, place not only holds meaning for individuals, but shapes their experiences of school and education trajectories. This is not simply a question of meaning and identity. Rather, it is that education settings and their opportunity structures shape and are shaped by structural inequalities, in turn reproducing differences. In this paper, I make the case that the state plays an important role in producing inequality by the ways in which it governs, and that contemporary forms of governing on the global level exacerbate these differences whilst erasing the differences that matter. I explore these dynamics by focusing on socio-economic differences between schools in England, UK. I argue that a particular politics of state spatial power is at play, and that the national state and shadow sovereigns manage questions of authority and legitimacy through the use of ideologies (e.g., school effectiveness, social mobility), devices (such as rankings and league tables), and explanations of cause (such as aspiration gaps), with which one can re-express the problem of difference, not as structurally caused, but as a failure of individual effort, expectations, and aspirations.

Study on Spatial Structure Characteristics of the Tourism and Leisure Industry

By taking Beijing as the case site, using open-source Point of Interest data, and employing spatial visualization techniques, this study explores the spatial structural characteristics of the Beijing tourism and leisure industry and its sub-sectors. It has been found that (1) the nearest neighbor indexes of the tourism and leisure industry and its sub-sectors are all less than 1, indicating that the tourism and leisure industry and its sub-sectors in Beijing exhibit a spatial clustering distribution. Scenic spots have the largest R-value of 0.52 and, thus, the lowest degree of clustering. The minimum R-value of 0.15 is found in catering, marking the highest degree of clustering in the industry; (2) the main directional trend of the tourism and leisure industry and its sub-sectors in Beijing is the “northeast-southwest” direction, the south-north directional dispersion is dominant, and scenic spots demonstrate a more noticeable trend of spatial dispersion; (3) within the area from Sanlitun Street in the north to Panjiayuan Street in the south, and from Chaoyangmen Street in the west to Liulitun Street in the east, is situated the largest portion of cluster centers with the highest degree of clustering in Beijing’s tourism and leisure industry. The contiguous high-density cluster center of catering starts from Sanlitun Street in the north to Jinsong Street in the south, and from Chaoyangmen Street in the west to Liulitun Street in the east. The cluster of shopping and entertainment shows a checkerboard pattern in the CZCF and NUDZ. The high-value cluster of accommodation occurs primarily around Sanlitun, Panjiayuan, and Qianmen; (4) the distribution of three grades of hot spot areas and non-significant areas of tourism and leisure, catering, accommodation, and shopping and entertainment in Beijing demonstrates a circular pattern that centers around the CZCF and expands outward in sequence. High-value hot spot streets for this area are dominated by Beixinqiao Street, Hepingli Street, Sanlitun Street, Heping Street, and Tuanjiehu Street; and the high-value cold spot streets of the area are chiefly in Fuzizhuang Township, Wangping Town, Miaofeng Mountain Town, and Tanzhesi Town.

A Spatial Analysis of Tuberculosis Related Mortality in South Africa

Background: South Africa, with an estimated annual tuberculosis (TB) incidence of 360,000 cases in 2019, remains one of the countries with the largest burden of TB in the world. The identification of highly burdened TB areas could support public health policy planners to optimally target resources and TB control and prevention interventions. Objective: To investigate the spatial epidemiology and distribution of TB mortality in South Africa in 2010 and its association with area-level poverty and HIV burden. Methods: The study analysed a total of 776,176 TB deaths for the period 2005–2015. Local and global and spatial clustering of TB death rates were investigated by Global and Local Moran’s Indices methods (Moran’s I). The spatial regression analysis was employed to assess the effect of poverty and HIV on TB mortality rates. Results: There was a significant decrease in TB mortality rate, from 179 per 100,000 population in 2005 to 60 per 100,000 population in 2015. The annual TB mortality rate was higher among males (161.5 per 100,000 male population; (95% confidence interval (CI) 132.9, 190.0) than among females (123.2 per 100,000 female population; (95% CI 95.6, 150.8)). The 35–44 age group experienced higher TB mortality rates, regardless of gender and time. Hot spot clusters of TB mortality were found in the South-Eastern parts of the country, whereas cold spot clusters were largely in the north-eastern parts. Tuberculosis death rates were positively associated with poverty, as measured by the South African Multidimension Poverty Index (SAMPI) as well TB death rates in the neighbouring districts. Conclusion: The findings of this study revealed a statistically significant decrease in TB deaths and a disproportionate distribution of TB deaths among certain areas and population groups in South Africa. The existence of the identified inequalities in the burden of TB deaths calls for targeted public health interventions, policies, and resources to be directed towards the most vulnerable populations in South Africa.

Autoignition of Reacting Hydrocarbon Mixture with Negative Temperature Coefficient Due to the Cold-Spot and Cold Chamber Wall

Autoignition of an n-heptane/air mixture was simulated in non-uniform temperature environments of a Rapid Compression Machine (RCM) and Shock-Tube (ST) with and without the presence of a cold-spot. The simulations were performed to investigate how the presence of a cold-spot and the cold boundary layer of the chamber wall may affect the ignition delay of the hydrocarbon mixture with NTC behavior. The simulations were performed using three models, (1) 3-Dimensional (3D) Computational Fluid Dynamics (CFD) model, (2) Zero-Dimensional (0D) homogenous batch reactor model by including the heat transfer model, and (3) 0D adiabatic homogenous batch reactor model. A detailed n-heptane mechanism was reduced in this work and used for 3D combustion modeling. A cold-spot critical radius of 7 mm was determined, which affects the ignition delay by more than 9%. In addition, two combustion modes were observed in the combustion chamber with a non-uniform temperature environment. With the first combustion mode, combustion starts at the high gas temperature region of the combustion chamber and quickly propagates towards the periphery of the chamber. In this combustion mode, the location of the maximum concentration of hydroxyl radical and the maximum temperature are the same. With the second combustion mode, the combustion starts at the periphery of the chamber, where the temperature is lower than the center of the chamber due to heat transfer to the cold chamber wall. The location of maximum concentration of the hydroxyl radical and maximum temperature are different with this combustion mode. The two observed combustion modes are due to the NTC behavior of the n-heptane mixture. The 0D homogenous batch reactor model (with and without heat transfer models) failed to mimic the ignition delay accurately when the second combustion mode was present. In addition, a propagating combustion has been observed in the simulation which is in agreement with some of the optical autoignition diagnostics of these hydrocarbons. This propagating combustion leads to a gradual pressure rise during autoignition, rather than a sharp pressure rise. The results of this work show that 0D homogenous batch reactor models are unable to simulate autoignition of mixtures with NTC behavior.

Geospatial analysis of salmonellosis and its association with socioeconomic status in Texas

ObjectiveThe study’s objective was to find the association between salmonellosis and socioeconomic status (SES) in hot spot areas and statewide counties.DesignA retrospective cohort study.SettingThe data were recorded regarding salmonellosis in 2017 from the Texas surveillance database. It included assessment of hot spot analysis and SES association with salmonellosis at the county level.ParticipantsPatients with salmonellosis of all age groups in Texas.ResultsThere were a total of 5113 salmonellosis from 254 counties with an unadjusted crude rate of 18 per 100 000 person-years. Seven SES risk factors in the hot spot counties were as follows: low values of the severe housing problem, unemployment, African American and high values of social association rate, fast food/full-service restaurant use, Hispanic and Hispanic senior low access-to-store (p<0.05). A 12% difference existed between local health departments in hot (25%) and cold spot (37%) counties (χ2 (1, n=108)=0.5, p=0.81).Statewide independent risk factors were severe housing problem (incidence rate ratio (IRR)=1.1; 95% CI: 1.05 to 1.14), social association rate (IRR=0.89; 95% CI: 0.87 to 0.92), college education (IRR=1.05; 95% CI: 1.04 to 1.07) and non-Hispanic senior local access-to-store (IRR=1.98; 95% CI: 1.26 to 3.11). The severe housing problem predicted zero occurrences of infection in a county (OR=0.51; 95% CI: 0.28 to 0.95).ConclusionsDisparity exists in salmonellosis and SES. Attention to unmet needs will decrease salmonellosis. Severe housing problem is a notable risk.

District level correlates of COVID-19 pandemic in India during March-October 2020

Background COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. Objective The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India’s socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. Data and methods We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. Results The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran’s- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15–59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. Conclusion The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.

Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods

Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable.