Residential proximity to petroleum storage tanks and associated cancer risks: Double Kernel Density approach vs. zonal estimates

Zusman, Marina; Dubnov, Jonathan; Barchana, Micha; Portnov, Boris A.

doi:10.1016/j.scitotenv.2012.09.054

Cited by 33 publications

(34 citation statements)

References 30 publications

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“…The incidence of leukemia in our study showed that Eastern Region ranked second which is might be due to presence of ARAMKO, the largest oil company around the world and Jubail Industrial City, global city for chemical industries. Many reports revealed that Exposure to petroleum, petrochemicals and petroleum oil was strongly associated with leukemia 6,7,8,9 however, further studies are needed to determine whether if this association also occurs in Saudi Arabia. Moreover, there was seldom r eports about the incidence of leukemia in specific region which is Northern region reporting that leukemia ranked sixth among all types of cancers by 26 cases registered in 2002.…”

Section: Discussionmentioning

confidence: 99%

Incidence Trend of The Leukemia Reported Cases in The Kingdom of Saudi Arabia, Observational Descriptive Statistic From Saudi Cancer Registry

ALBakr

Khojah

2014

Int Jour of Biomed Res

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Section: Discussionmentioning

confidence: 99%

Incidence Trend of The Leukemia Reported Cases in The Kingdom of Saudi Arabia, Observational Descriptive Statistic From Saudi Cancer Registry

ALBakr

Khojah

2014

Int Jour of Biomed Res

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“…For instance, the observed density of disease patients in each point of space can be divided by the total number of people living in the same location (Kloog et al 2009;Shi 2010;Zusman et al 2012). An additional benefit of this technique, also known as the Double Kernel Density (DKD) estimate, is that it helps to minimize the aforementioned "edge" effect: Since both the events of interest and the general population do not have values outside the study area, the edge effect can be minimized or alleviated by normalization (Gatrell et al 1996;Portnov and Zusman 2014).…”

Section: Double Kernel Density (Dkd) Estimatesmentioning

confidence: 99%

“…However, such aggregation may result in a modifiable areal unit problem (MAUP), according to which different strength of statistical association may emerge from the analysis of data based on different units of spatial resolution (Openshaw 1984;Portnov et al 2009;Swift et al 2014). An additional problem may also arise if the events of interest are highly concentrated in space, and the overall number of areal units, available for aggregation is small, leading to inconclusive results and low statistical power (Li and Lian 2010;Zusman et al 2012). …”

Section: Introductionmentioning

confidence: 99%

“…In recent studies Kloog et al 2009;Zusman et al 2012), a refinement of the KD method, termed the Double Kernel Density (DKD) technique, was proposed. The DKD technique is based on the transformation of continuous KD surfaces into discrete observations, suitable for multivariate analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The DKD technique is based on the transformation of continuous KD surfaces into discrete observations, suitable for multivariate analysis. In these empirical studies, kernel smoothing was used to analyze the concentration of lung cancer cases ), breast cancer cases (Kloog et al 2009) and lung and NHL cancers (Zusman et al 2012), and to associate the observed morbidity with underlying environmental causes, such as air pollution, nighttime artificial illumination or residential proximity to petroleum storage tanks. According to these studies, the DKD technique helped to provide better assessment results than traditional zonal approaches, based on aerial aggregation of original observations into predefined areal units, especially if the number of geographic units available for the analysis was small.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of the double kernel density approach to the multivariate analysis of attributeless event point datasets

Zusman

Broitman

Portnov

2015

Lett Spat Resour Sci

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Attributeless event point datasets (AEPDs) are datasets composed of discrete events or observations defined by their geographical location only and lacking any other additional attributes. Examples of such datasets include spotted criminal events, road accidents and residential locations of disease patients. A commonly used approach to the analysis of such datasets involves their aggregation into predefined areal units, such as neighborhoods or census tracts. However, this approach does not perform effectively when the events of interests are geographically localized and the number of areal units available for aggregation is small. An alternative approach to the analysis of AEPDs is based on double kernel density (DKD) smoothing, according to which events of interest are transformed into continuous density surfaces and then normalized by the density of the entire population from which the events of interest are drawn. In the present study, the applicability of the DKD approach to multivariate analysis is tested for estimation consistency, sensitivity to the number of input observations and potential bias attributed to the spatial dependency of neighboring observations. Our analysis indicates that the DKD approach provides reasonably stable and consistent estimates, if the following three preconditions are met: (a) the kernel estimation parameters are properly defined, (b) the number of reference points, used for transformation of continuous DKD surfaces into discrete observations, is sufficiently large, and (c) the spatial dependency of neighboring observations is taken into account using spatial analysis tools.

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Long‐term exposure to air pollution and risk of non‐Hodgkin lymphoma in Denmark: A population‐based case–control study

Taj

Poulsen

Ketzel

et al. 2020

Intl Journal of Cancer

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There is limited evidence regarding a possible association between exposure to ambient air pollutants and the risk of non‐Hodgkin lymphoma (NHL). Previous epidemiological studies have relied on crude estimations for air pollution exposure and/or small numbers of NHL cases. The objective of our study was to analyze this association based on air pollution modeled at the address level and NHL cases identified from the nationwide Danish Cancer Registry. We identified 20,874 incident NHL cases diagnosed between 1989 and 2014 and randomly selected 41,749 controls matched on age and gender among the entire Danish population. We used conditional logistic regression to estimate odds ratios (ORs) and adjusted for individual and neighborhood level sociodemographic variables. There was no association between exposure to PM2.5, BC, O3, SO2 or NO2 and overall risk of NHL but several air pollutants were associated with higher risk of follicular lymphoma, but statistically insignificant, for example, PM2.5 (OR = 1.15 per 5 μg/m3; 95% CI: 0.98–1.34) and lower risk for diffuse large B‐cell lymphoma (OR = 0.92 per 5 μg/m3; 95% CI: 0.82–1.03). In this population‐based study, we did not observe any convincing evidence of a higher overall risk for NHL with higher exposure to ambient air pollutants.

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Residential proximity to petroleum storage tanks and associated cancer risks: Double Kernel Density approach vs. zonal estimates

Cited by 33 publications

References 30 publications

Incidence Trend of The Leukemia Reported Cases in The Kingdom of Saudi Arabia, Observational Descriptive Statistic From Saudi Cancer Registry

Incidence Trend of The Leukemia Reported Cases in The Kingdom of Saudi Arabia, Observational Descriptive Statistic From Saudi Cancer Registry

Application of the double kernel density approach to the multivariate analysis of attributeless event point datasets

Long‐term exposure to air pollution and risk of non‐Hodgkin lymphoma in Denmark: A population‐based case–control study

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