Existence Of Spatial Clusters Research Articles

Mortality due to noninfectious lower respiratory diseases: A spatiotemporal, cross-sectional study.

Background and AimsGeography plays an important role in the incidence of respiratory diseases. The aim of this study was to investigate the epidemiology and geographical distribution of death due to noninfectious lower respiratory diseases (NILRDs).MethodsData related to all death due to NILRD in Kerman Province between 2012 and 2018 were extracted from the National Mortality Registry. The underlying causes of death were extracted from the registry based on the assigned codes from ICD‐10 (International Classification of Diseases 10th Revision) classification. The existence of spatial clusters and outliers was evaluated using local indicators of spatial association statistics.ResultsThe frequency of death due to NILRD was 8005 persons during the 7 years of the study. The main cause of death was chronic lower respiratory disease (54.2%). Other causes of death were, respectively, lung diseases due to external agents (1.09%), other respiratory diseases mainly affecting the interstitium (1.16%), other diseases of pleura (0.57%), and other diseases of the respiratory system (42.13%). The age‐ and sex‐adjusted mortality rates due to NILRD in the north and center of the province increased significantly from 2012 to 2018. Also, the results of cluster analysis identified northern regions as the clustered areas of NILRD.ConclusionsOur findings showed a significant increase in mortality due to NILRD in Kerman Province during the 7 years of the study. To reduce this type of death, health policymakers should have environmental health plans and basic solutions, such as a warning system to reduce the commuting on highly air‐polluted days and to control pollutants, especially in the industrial areas of the north of this province.

Atividade leiteira no Paraná: uma análise espacial e econométrica

O presente estudo buscou verificar a configuração espacial da produção de leite nos municípios do Paraná. Foi realizada uma Análise Exploratória de Dados Espaciais – AEDE, que possibilitou identificar a existência de clusters espaciais nas mesorregiões Oeste, Sudoeste, Centro Oriental e Norte Pioneiro. Isso mostra que municípios que possuem alto valor do Valor Bruto de Produção (VBP) do leite (reais/Km2) no Paraná são rodeados por municípios de similar alto valor do VBP do leite (reais/Km2), assim como municípios com baixos valores para a variável. Utilizando os dados preliminares do Censo Agropecuário 2017, da RAIS e do IPARDES estimou-se um modelo econométrico, do tipo Durbin Espacial (SDM), que inclui a defasagem espacial no valor bruto de produção e nas variáveis independentes, a fim de identificar quais, dentre fatores econômicos e técnicos, são os determinantes do valor bruto de produção de leite. Como principais resultados observou-se que a orientação técnica, produtividade, pastagem, ocupações técnico-científicas e energia elétrica são alguns dos fatores que estão associados ao valor bruto de produção do leite.

Spatial and spatio-temporal analysis of malaria cases in Zimbabwe

BackgroundAlthough effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016.MethodsGeographical information system (GIS) and spatial scan statistic were applied on passive malaria data collected from health facilities and aggregated at district level to detect existence of spatial clusters. The global Moran’s I test was used to infer the presence of spatial autocorrelation while the purely spatial retrospective analyses were performed to detect the spatial clusters of malaria cases with high rates based on the discrete Poisson model. Furthermore, space-time clusters with high rates were detected through the retrospective space-time analysis based on the discrete Poisson model.ResultsResults showed that there is significant positive spatial autocorrelation in malaria cases in the study area. In addition, malaria exhibits spatial heterogeneity as evidenced by the existence of statistically significant (P < 0.05) spatial and space-time clusters of malaria in specific geographic regions. The detected primary clusters persisted in the eastern region of the study area over the six year study period while the temporal pattern of malaria reflected the seasonality of the disease where clusters were detected within particular months of the year.ConclusionsGeographic regions characterised by clusters of high rates were identified as malaria high risk areas. The results of this study could be useful in prioritizing resource allocation in high-risk areas for malaria control and elimination particularly in resource limited settings such as Zimbabwe. The results of this study are also useful to guide further investigation into the possible determinants of persistence of high clusters of malaria cases in particular geographic regions which is useful in reducing malaria burden in such areas.

A Spatiotemporal Analysis of Schistosomiasis in Hunan Province, China.

Based on annual parasitological data recently collected at county and village levels, this article presents a multiscale spatiotemporal analysis of transmission risk of schistosomiasis japonica in Hunan Province during 2001 to 2015 in a geographic information system environment. The study shows that the incidence and prevalence rate of human Schistosoma japonicum infection in Hunan Province decreased after 2001. A spatial autocorrelation analysis reveals the existence of spatial clusters of human Schistosoma japonicum infection and a growing tendency of spatial clustering over time. The identification of high-risk areas (hot spots) helps find areas of priority for future implementation of control strategies. The research demonstrates the importance of spatial scale in public health studies.

Job Search Channels, Neighborhood Effects, and Wages Inequality in Developing Countries: The Colombian Case

This paper analyzes the relationship between social networks and the probability of finding a job. We explore geographic closeness as the social interaction to explain the job search function. Using data from Colombia in 2009, we calculate how neighborhoods have an effect on the channel used to find a job (social network versus no social network). In addition, we study how wage premium relates to using a social network in finding a job, exploring the inequality that can arise using a different job search method. Our results show that neighborhood affects the individual's job search method and that referred workers earn less at the bottom of the wage distribution with respect to non‐referred workers. Colombia presents persistent high levels of informality and inequality with the existence of spatial clusters that impose important social and economic costs with strong informational asymmetries on the job market.