Factors effecting the morbidity in populations living in the vicinity of atomic industry plants

Dyomin, Sergei N.; Buldacov, Lev A.; Ternovsky, I.A.; Tokarskaya, Z.B.; Fomina, Tatyana P.; Tretyakov, Fedor D.; Ivanova, Galina; Shevchenko, V. N.; Kolmogortsev, Veniamin A.; Уральшин, А. Г.; Suslova, I V

doi:10.1016/0048-9697(94)90078-7

Cited by 3 publications

(3 citation statements)

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“…Studies commonly use multivariate linear regression analyses to determine the relationships between environmental factors and diseases, including cancers [30,31]. However, for the spatial data, the fundamental assumptions of the classical linear model (seen in matrix form, y = X β + ɛ, where y is an n × 1 vector of observation on the dependent variable, X is an n × 1 vector of observation on the explanatory variable, β is an regression coefficient for the explanatory variable, and ɛ is an n × 1 vector of random error term) are violated, due to the spatial autocorrelation among regression residuals.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Trends in Oral Cancer Mortality and Potential Risks Associated with Heavy Metal Content in Taiwan Soil

Chiang

Lian

et al. 2010

IJERPH

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Central and Eastern Taiwan have alarmingly high oral cancer (OC) mortality rates, however, the effect of lifestyle factors such as betel chewing cannot fully explain the observed high-risk. Elevated concentrations of heavy metals in the soil reflect somewhat the levels of exposure to the human body, which may promote cancer development in local residents. This study assesses the space-time distribution of OC mortality in Taiwan, and its association with prime factors leading to soil heavy metal content. The current research obtained OC mortality data from the Atlas of Cancer Mortality in Taiwan, 1972–2001, and derived soil heavy metals content data from a nationwide survey carried out by ROCEPA in 1985. The exploratory data analyses showed that OC mortality rates in both genders had high spatial autocorrelation (Moran’s I = 0.6716 and 0.6318 for males and females). Factor analyses revealed three common factors (CFs) representing the major pattern of soil pollution in Taiwan. The results for Spatial Lag Models (SLM) showed that CF1 (Cr, Cu, Ni, and Zn) was most spatially related to male OC mortality which implicates that some metals in CF1 might play as promoters in OC etiology.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal Trends in Oral Cancer Mortality and Potential Risks Associated with Heavy Metal Content in Taiwan Soil

Chiang

Lian

et al. 2010

IJERPH

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show abstract

Section: Spatial Regression Analysismentioning

confidence: 99%

Spatiotemporal Analysis and Mapping of Oral Cancer Risk in Changhua County (Taiwan): An Application of Generalized Bayesian Maximum Entropy Method

Chiang

Lin

et al. 2010

Annals of Epidemiology

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“…Many studies frequently focus on determining the correlation between potential environmental risk factors and diseases of concern through using multivariate linear regression analyses (Ashley, 1969;Dyomin et al, 1994). However, for analysis of observational data with spatial dependence, the classical linear regression model with spatial auto-correlated residuals violates the independence assumption for error.…”

Section: Spatial Regression Analysismentioning

confidence: 99%

Environmental Factors Identified in the Etiology of Oral Cancers in Taiwan

Chiang¹,

Chang²,

Lian³

et al. 2012

Oral Cancer

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Factors effecting the morbidity in populations living in the vicinity of atomic industry plants

Cited by 3 publications

References 1 publication

Spatiotemporal Trends in Oral Cancer Mortality and Potential Risks Associated with Heavy Metal Content in Taiwan Soil

Spatiotemporal Trends in Oral Cancer Mortality and Potential Risks Associated with Heavy Metal Content in Taiwan Soil

Spatiotemporal Analysis and Mapping of Oral Cancer Risk in Changhua County (Taiwan): An Application of Generalized Bayesian Maximum Entropy Method

Environmental Factors Identified in the Etiology of Oral Cancers in Taiwan

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