Lindbergatkinson0657

This study suggests that Poisson regression using GWR and GLM can address unobserved spatial non-stationary relationships. Findings of this study can promote current understanding of the demographic and socioeconomic variables impacting the spatial patterns of COVID-19 in Oman, allowing local and national authorities to adopt more appropriate strategies to cope with this pandemic in the future and also to allocate more effective prevention resources.This study aimed to demonstrate the use of geographic information systems (GIS) in identifying factors contributing to schistosomiasis endemicity and identifying high-risk areas in a schistosomiasis- endemic municipality in the Philippines, which was devastated by Typhoon Haiyan in 2013. Data on schistosomiasis determinants, obtained through literature review, the Philippine Department of Health, and concerned local government units, were standardized and incorporated into a GIS map using ArcGIS. Data gathered included modifiable [agriculture, poverty, sanitation, presence of intermediate and reservoir hosts, disease prevalence and mass drug administration (MDA) coverage] and nonmodifiable (geography and climate) determinants for schistosomiasis. Results showed that most barangays (villages) are characterized by favourable conditions for schistosomiasis transmission which include being located in flood-prone areas, presence of vegetation, low sanitary toilet coverage, presence of snail intermediate host, high carabao (water buffalo) population density, previously reported ≥1% prevalence using Kato-Katz technique, and low MDA coverage. Similarly, barangays not known to be endemic for schistosomiasis but also characterized by the same favourable conditions for schistosomiasis as listed above and may therefore be considered as potentially endemic, even if not being high-risk areas. Dactolisib in vitro This study demonstrated the importance of GIS technology in characterizing schistosomiasis transmission. Maps generated through application of GIS technology are useful in guiding program policy and planning at the local level for an effective and sustainable schistosomiasis control and prevention.The aim of this study was to estimate the territory at risk of establishment of influenza type A (EOITA) in Mexico, using geospatial models. A spatial database of 1973 outbreaks of influenza worldwide was used to develop risk models accounting for natural (natural threat), anthropic (man-made) and environmental (combination of the above) transmission. Then, a virus establishment risk model; an introduction model of influenza A developed in another study; and the three models mentioned were utilized using multi-criteria spatial evaluation supported by geographically weighted regression (GWR), receiver operating characteristic analysis and Moran's I. The results show that environmental risk was concentrated along the Gulf and Pacific coasts, the Yucatan Peninsula and southern Baja California. The identified risk for EOITA in Mexico were 15.6% and 4.8%, by natural and anthropic risk, respectively, while 18.5% presented simultaneous environmental, natural and anthropic risk. Overall, 28.1% of localities in Mexico presented a High/High risk for the establishment of influenza type A (area under the curve=0.923, P less then 0.001; GWR, r2=0.840, P less then 0.001; Moran's I =0.79, P less then 0.001). Hence, these geospatial models were able to robustly estimate those areas susceptible to EOITA, where the results obtained show the relation between the geographical area and the different effects on health. The information obtained should help devising and directing strategies leading to efficient prevention and sound administration of both human and financial resources.Exposure to asbestos causes a wide range of diseases, such as asbestosis, malignant mesothelioma (MM) and other types of cancer. Many European countries have reduced production and use of asbestos and some have banned it altogether. Based on data derived from the World Health Organisation (WHO) Cancer Mortality Database, we investigated whether some regions in Europe could have a higher relative risk of MM incidence than others. The data were compared, including the number of MM deaths per million inhabitants and aged-standardized mortality rates. Applying Moran's I and Getis-Ord Gi statistic on the agedstandardized mortality rates of MM cases assisted the spatial analysis of the occurrence of health events leading to an assessment of the heterogeneity of distribution and cluster detection of MM. We found a statistically significant positive autocorrelation for the male population and also the general population, while there was no statistically significant positive one for the female population. Hotspots of relative risk of developing MM were found in northwestern Europe. For the general population, Great Britain and the Netherlands stood out with high levels at the 99% and 95% confidence levels, respectively. For the male population, the results were similar, but with addition of risk also in Belgium and Switzerland. However, in many European countries with high asbestos use per capita, the MM incidence was found to still be low. The reasons for this are not yet clear, but part of the problem is certainly due to incomplete data in registers and databases. The latency time can be longer than 40 years and is related to the intensity and time of exposure (occupational, para-occupational and environmental). In Europe, even though peak production occurred in the 1960s and 1970s, a significant decrease in production did not occur until 25 years later, which means that the impact will continue for as late as The mid 2030s.To decrease diabetes morbidity and mortality rates, early interventions are needed to change lifestyles that are often cemented early, making school-based interventions important. However, with limited resources and lack of within-county diabetes data, it is difficult to determine which local areas require intervention. To identify at-risk school districts, this study mapped diabetes prevalence and related deaths by school district using geographic information systems (GIS). The 2010-2014 records of diabetes-related deaths were identified for 13 cities in Michigan, USA. Diabetes prevalence was estimated using the weighted average of population by school district from the '500 Cities Project' of the Centres of Disease Control and prevention (CDC). Prevalence and mortality rates were mapped by school district and the correlation between diabetes prevalence and mortality rate analysed using the Spearman's rank correlation. Years of potential life lost (YPLL) were calculated using a 75-year endpoint. The result indicated there were geographic variations in diabetes prevalence, mortality and YPLL across Michigan.