Abstract:Thyroid and breast cancers (TC, BC) are common female malignant tumors worldwide. Studies suggest that TC patients have a higher BC risk, and vice versa. However, it has not been investigated quantitatively if there is an association between the space-time TC and BC incidence distributions at the population level. This work aims to answer this question. 5358 TC and 8784 BC (female) cases were diagnosed in Hangzhou (China, 2008–2012). Pearson and Spearman rank correlation coefficients of the TC and BC incidence… Show more
“…The space‐time prediction of nitrate and phosphate concentrations was based on the BME theory (Christakos, ), which has been applied successfully in various fields, including water quality, air pollution, and epidemiology (e.g., Coulliette et al, ; Fei et al, ; Jiang et al, ). In the BME modeling context, let NP ( p ) be the spatiotemporal random field model (Christakos, ) representing nutrient (nitrate or phosphate) pollutant concentrations in China's near seas during 2015, where the vector p = ( s , t ) determines a point within the space‐time domain D , with s denoting geographical location and t time.…”
Human activities interacting with coastal waters lead to large amounts of nutrient loading and severe water pollution in China's near Seas. In this context, a comprehensive quantitative characterization of the spatiotemporal variation of nutrient pollutant concentrations is a key component of any reliable seawater quality assessment and integrated coastal management plan. The present work combines the Bayesian maximum entropy method with stochastic site indicators to estimate monthly nitrate and phosphate concentrations in China's near seas during 2015, explore their spatiotemporal variation, and provide an explicit quantitative assessment of seawater quality in conditions of in situ uncertainty. This makes it the first study of space‐time nutrient pollutant characterization at a national‐scale in a coastal seawater environment. The results showed that nitrate and phosphate distributions exhibit the same spatial trends along China's near seas, whereas high nutrient pollution levels are found in the Yangtze River, Liaohe River, and Pearl River estuaries. Local differences of temporal trends exist between nitrate and phosphate distributions, which suggest that distinct remediation strategies are needed to properly satisfy the required seawater quality standards. The average nitrate and phosphate concentrations across space‐time were found to be equal to 0.271 and 0.015 mg/L, respectively. The nitrate and phosphate concentrations exceeding the fourth grade seawater quality standard during 2015 were about 11% and 2.6%, respectively. The study of both the seasonal changes in human activities along the coastal cities and the temporal marine hydrodynamics can offer a better understanding of the seawater quality and the biogeochemical process of nutrient transport and distribution.
“…The space‐time prediction of nitrate and phosphate concentrations was based on the BME theory (Christakos, ), which has been applied successfully in various fields, including water quality, air pollution, and epidemiology (e.g., Coulliette et al, ; Fei et al, ; Jiang et al, ). In the BME modeling context, let NP ( p ) be the spatiotemporal random field model (Christakos, ) representing nutrient (nitrate or phosphate) pollutant concentrations in China's near seas during 2015, where the vector p = ( s , t ) determines a point within the space‐time domain D , with s denoting geographical location and t time.…”
Human activities interacting with coastal waters lead to large amounts of nutrient loading and severe water pollution in China's near Seas. In this context, a comprehensive quantitative characterization of the spatiotemporal variation of nutrient pollutant concentrations is a key component of any reliable seawater quality assessment and integrated coastal management plan. The present work combines the Bayesian maximum entropy method with stochastic site indicators to estimate monthly nitrate and phosphate concentrations in China's near seas during 2015, explore their spatiotemporal variation, and provide an explicit quantitative assessment of seawater quality in conditions of in situ uncertainty. This makes it the first study of space‐time nutrient pollutant characterization at a national‐scale in a coastal seawater environment. The results showed that nitrate and phosphate distributions exhibit the same spatial trends along China's near seas, whereas high nutrient pollution levels are found in the Yangtze River, Liaohe River, and Pearl River estuaries. Local differences of temporal trends exist between nitrate and phosphate distributions, which suggest that distinct remediation strategies are needed to properly satisfy the required seawater quality standards. The average nitrate and phosphate concentrations across space‐time were found to be equal to 0.271 and 0.015 mg/L, respectively. The nitrate and phosphate concentrations exceeding the fourth grade seawater quality standard during 2015 were about 11% and 2.6%, respectively. The study of both the seasonal changes in human activities along the coastal cities and the temporal marine hydrodynamics can offer a better understanding of the seawater quality and the biogeochemical process of nutrient transport and distribution.
“…2b). Previous studies have shown that the BC incidence distribution was spatially heterogeneous in Hangzhou (revealing an increasing incidence trend from the southern to the northern parts of the city) and temporally stable during the period 2008–2012 11, 21 , thus making it a suitable data set to be studied by the STOK and the STP techniques for the purpose of space-time modeling, estimation and mapping of the BC incidence distribution.Figure 2( a ) BC incidence data histogram and ( b ) BC incidence data histogram after the data have been transformed as .
…”
Section: Methodsmentioning
confidence: 99%
“…Methodologically, the random field BC ( s , t ) is viewed as a collection of realizations (possibilities) of the BC incidence distribution, where the probability that each one of these realizations occurs is expressed by the BC incidence probability law (Gaussian or non-Gaussian). This BC model makes it possible to calculate various space-time properties of incidence distribution with reasonable accuracy 11 , 22 . In particular, the BC ( s , t ) incidence distribution is represented by 18 where is the so-called travelling random field model of BC incidence, and .…”
Section: Methodsmentioning
confidence: 99%
“…During the period 2000–2011, BC incidence in China kept an increasing trend with an annual percentage change (APC) of 3.9 3 . BC, being one of the most important public health issues worldwide, many of its risk factors have been studied by scientists, such as genetic susceptibility 4 , diet and alcohol consumption 5 , 6 , body mass index (BMI) 7 , reproductive, menstrual and hormonal factors 8 – 11 .…”
Breast cancer (BC) is the main cause of death of female cancer patients in China. Mainstream mapping techniques, like spatiotemporal ordinary kriging (STOK), generate disease incidence maps that improve our understanding of disease distribution. Yet, the implementation of these techniques experiences substantive and technical complications (due mainly to the different characteristics of space and time). A new spatiotemporal projection (STP) technique that is free of the above complications was implemented to model the space-time distribution of BC incidence in Hangzhou city and to estimate incidence values at locations-times for which no BC data exist. For comparison, both the STP and the STOK techniques were used to generate BC incidence maps in Hangzhou. STP performed considerably better than STOK in terms of generating more accurate incidence maps showing a closer similarity to the observed incidence distribution, and providing an improved assessment of the space-time BC correlation structure. In sum, the inter-connections between space, time, BC incidence and spread velocity established by STP allow a more realistic representation of the actual incidence distribution, and generate incidence maps that are more accurate and more informative, at a lower computational cost and involving fewer approximations than the incidence maps produced by mainstream space-time techniques.
“…No trabalho [Fei et al 2016], os autores propõem uma relação espaço-temporal dos casos de câncer de tireoide e de mama na cidade de Hangzhou, na China. Eles utilizam o critério integrativo de previsibilidade da doença para observar a relação entre os dois tipos de câncer nas bases de conhecimento que mapeiam a doença.…”
As tecnologias da informação e comunicação vêm proporcionando soluções inovadoras em saúde ao longo dos anos. Neste contexto, este trabalho apresenta um serviço de mapeamento espaço-temporal para sistemas e-Health e m-Health para gestão de dados relacionadosá saúde. A proposta, denominada SININHO, foi incorporada em um sistema de saúde real de diagnóstico, tratamento e acompanhamento do câncer infanto-juvenil. Por meio da otimização de indicadores espaço-temporais, o SININHO gera múltiplas visualizações estratégicas de mapas de marcadores, mapas de densidade e mapas de calor (heat map). Dessa forma, gestores de saúde podem analisar os cenários apresentados e fundamentar as suas tomadas de decisões.
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