Geostatistical Analysis of County‐Level Lung Cancer Mortality Rates in the Southeastern United States. 美国东南部县级肺癌死亡率的地统计学分析

Goovaerts, Pierre

doi:10.1111/j.1538-4632.2009.00781.x

Cited by 25 publications

(28 citation statements)

References 40 publications

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“…These methods have been used for obesity, physical activity, cancer, the pattern for prescribing cardiovascular drugs, and sexually transmitted diseases [14][15][16][17][18][19]. However, few studies of diabetes spatial clustering have been published [8,9,20].…”

Section: Introductionmentioning

confidence: 99%

Spatial Clusters of County-Level Diagnosed Diabetes and Associated Risk Factors in the United States

Shrestha¹

2012

TODIAJ

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Introduction:We examined whether spatial clusters of county-level diagnosed diabetes prevalence exist in the United States and whether socioeconomic and diabetes risk factors were associated with these clusters. Materials and Methods:We used estimated county-level age-adjusted data on diagnosed diabetes prevalence for adults in 3109 counties in the United States (2007 data). We identified four types of diabetes clusters based on spatial autocorrelations: high-prevalence counties with high-prevalence neighbors (High-High), low-prevalence counties with low-prevalence neighbors (Low-Low), low-prevalence counties with high-prevalence neighbors (Low-High), and highprevalence counties with low-prevalence neighbors (High-Low). We then estimated relative risks for clusters being associated with several socioeconomic and diabetesrisk factors.Results: Diabetes prevalence in 1551 counties was spatially associated (p<0.05) with prevalence in neighboring counties. The rate of obesity, physical inactivity, poverty, and the proportion of non-Hispanic blacks were associated with a county being in a High-High cluster versus being a non-cluster county (7% to 36% greater risk) or in a Low-Low cluster (13% to 67% greater risk). The percentage of non-Hispanic blacks was associated with a 7% greater risk for being in a Low-High cluster. The rate of physical inactivity and the percentage of Hispanics or non-Hispanic American Indians were associated with being in a High-Low cluster (5% to 21% greater risk).Discussion: Distinct spatial clusters of diabetes prevalence exist in the United States. Strong association between diabetes clusters and socioeconomic and other diabetes risk factors suggests that interventions might be tailored according to the prevalence of modifiable factors in specific counties.

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Section: Introductionmentioning

confidence: 99%

Spatial Clusters of County-Level Diagnosed Diabetes and Associated Risk Factors in the United States

Shrestha¹

2012

TODIAJ

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“…The delineation of zones of low and high contents in gold was conducted through the application of local cluster analysis (Anselin 1995; Fu et al 2014; Goovaerts et al 2005; Goovaerts 2010). The basic idea is to compute at each grid node a local indicator of spatial autocorrelation (LISA) and test whether this statistic is significantly positive, indicating the existence of an aggregate of grid nodes with similar gold content, either low or high.…”

Section: Methodsmentioning

confidence: 99%

A Multivariate Geostatistical Methodology to Delineate Areas of Potential Interest for Future Sedimentary Gold Exploration

2016

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This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R2=0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold’s paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization.

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“…It is noteworthy that the geostatistical method of kriging with an external drift (KED) accomplishes a similar re-evaluation of local relationships, while accounting for data clustering and pattern of correlation (Wackernagel 1998, Goovaerts 1999). GWR is however easier to implement than KED and empirical comparisons have demonstrated the good correspondence between the results of both methods (Goovaerts 2009a). …”

Section: Correlation Analysismentioning

confidence: 99%

Medical Geography: A Promising Field of Application for Geostatistics

Goovaerts

2009

Math Geosci

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The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. It presents, however, several methodological challenges that arise from the fact that data are typically aggregated over irregular spatial supports and consist of a numerator and a denominator (i.e. population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: estimation of the underlying disease risk, detection of areas with significantly higher risk, and analysis of relationships with putative risk factors. The analysis is illustrated using age-adjusted cervix cancer mortality rates recorded over the 1970-1994 period for 118 counties of four states in the Western USA. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify numerically how the uncertainty about the spatial distribution of health outcomes translates into uncertainty about the location of clusters of high values or the correlation with covariates. Last, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates: the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah.

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Geostatistical Analysis of County‐Level Lung Cancer Mortality Rates in the Southeastern United States. 美国东南部县级肺癌死亡率的地统计学分析

Cited by 25 publications

References 40 publications

Spatial Clusters of County-Level Diagnosed Diabetes and Associated Risk Factors in the United States

Spatial Clusters of County-Level Diagnosed Diabetes and Associated Risk Factors in the United States

A Multivariate Geostatistical Methodology to Delineate Areas of Potential Interest for Future Sedimentary Gold Exploration

Medical Geography: A Promising Field of Application for Geostatistics

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