Geographic clustering of residence in early life and subsequent risk of breast cancer (United States)

Han, Daikwon; Rogerson, Peter A.; Nie, Jing; Bonner, Matthew R.; Vena, John E.; Vito, Dominica; Muti, Paola; Trevisan, Maurizio; Edge, Stephen B.; Freudenheim, Jo L.

doi:10.1007/s10552-004-1675-y

Cited by 29 publications

(32 citation statements)

References 24 publications

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“…In previous analyses in the WEB study data, we found evidence of more clustering of premenopausal breast cancer cases than for controls for residence at earlier life, i.e., at birth and menarche [19]. We also found that TSP exposures at birth residence were associated with increased postmenopausal breast cancer risk [18].…”

Section: Discussionsupporting

confidence: 58%

“…While we were not able to examine selection bias for the analyses of exposure in the past, we did examine it for current residence. There was some tendency for both participating cases and controls to have current residences closer to our study center than non-participants [19]. We did not find differences in the major characteristics between subjects who were included and excluded in the study, except that excluded women were older, particularly in the earlier time periods analyses, because women whose menarche or first birth was before 1960 were excluded.…”

Section: Discussionmentioning

confidence: 72%

“…Included were women with no previous history of cancer other than non-melanoma skin cancer. Details of the study have been described elsewhere [18,19]. Briefly, cases had incident, primary, histologically-confirmed breast cancer.…”

Section: Methodsmentioning

confidence: 99%

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Exposure to traffic emissions throughout life and risk of breast cancer: the Western New York Exposures and Breast Cancer (WEB) study

Nie

Beyea

Bonner

et al. 2007

Cancer Causes Control

Self Cite

131

137

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Objective We previously reported that total suspended particulates exposure (a measure of air pollution) at the time of birth was related to increased postmenopausal breast cancer risk. In this study, we examined breast cancer risk in relation to exposure to air pollution from traffic emissions throughout life. Methods We conducted a case-control study of breast cancer. Participants were women, aged 35-79, residents of Erie and Niagara Counties. Cases had incident, primary, histologically confirmed breast cancer. Controls were randomly selected from the population, frequency-matched on age and race. Using lifetime residential histories, exposure to traffic emissions was modeled for each woman using her residence as a proxy. Estimates were calculated for residence at menarche, her first birth, and 20 and 10 years before interview. Unconditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI). Results Higher exposure to traffic emissions at the time of menarche was associated with increased risk of premenopausal breast cancer (OR 2.05, 95% CI 0.92-4.54, p for trend 0.03); and at the time of a woman's first birth for postmenopausal breast cancer (OR 2.57, 95% CI 1.16-5.69, p for trend 0.19). Statistically significant associations were limited to lifetime non-smokers; there was a significant interaction between exposure at time of menarche and smoking for premenopausal women. Conclusion Our findings add to accumulating evidence that early life exposures impact breast cancer risk and provide indication of potential importance of traffic emissions in risk of breast cancer.

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Section: Discussionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 72%

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Exposure to traffic emissions throughout life and risk of breast cancer: the Western New York Exposures and Breast Cancer (WEB) study

Nie

Beyea

Bonner

et al. 2007

Cancer Causes Control

Self Cite

131

137

View full text Add to dashboard Cite

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“…The primary use of K-function analysis was exploring the presence and scale of spatial clustering of the selected exposure variables (Austin et al, 2005;Hillier et al, 2009;Day and Pearce, 2011). The K-function was also used to assess the spatial structure of a distribution before conducting local analyses of spatial clustering (Han et al, 2004;Broman et al, 2006;Wheeler, 2007;Epp et al, 2010;Ngowi et al, 2010;Poljak et al, 2010). Knowing the scale and structure of the spatial dependency among data helps the user confirm whether local analyses are required as well as provide an approximation of spatial weight specifications.…”

Section: K-functionmentioning

confidence: 99%

“…Over half of papers in this review applied the spatial scan statistic to examine the spatial patterns of address location data (Andrade et al, 2004;Brooker et al, 2004;Han et al, 2004;Polack et al, 2005;Bautista et al, 2006;Chaix et al, 2006;Ernst et al, 2006;Pollack et al, 2006;Sarkar et al, 2007;Wheeler, 2007;Pasma, 2008;Warden, 2008;Huang et al, 2009;Meliker et al, 2009;Tanser et al, 2009;Epp et al, 2010;Ngowi et al, 2010;Poljak et al, 2010;Westercamp et al, 2010;Winskill et al, 2011). Of the reviewed articles, 83% applied a Bernoulli model spatial scan statistic to case-control data; two of those articles also used other models in SatScan that can be applied to address location data, the discrete normal continuous model ) and the discrete poisson continuous model (Ngowi et al, 2010), ordinal model (Westercamp et al, 2010) and the multinomial model (Westercamp et al, 2010).…”

Section: Spatial Scan Statisticmentioning

confidence: 99%

A scoping review of spatial cluster analysis techniques for point-event data

et al. 2013

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Abstract. Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

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Theoretical properties of tests for spatial clustering of count data

Jung

Kulldorff

2007

Can J Statistics

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Absrracf: Testing for spatial clustering of count data is an important problem in spatial data analysis. Several procedures have been proposed to this end but despite their extensive use, studies of their fundamental theoretical properties are almost non-existent. The authors suggest two conditions that any reasonable test for spatial clustering should satisfy. The latter are based on the notion that the null hypothesis should be rejected almost surely as the amount of spatial clustering tends to infinity. The authors show that the chisquared test and the Potthoff-Whittinghill V have both properties but that other classical tests do not. ProprStes thhriques des tests de regroupement spatial base de frequencesRlswni : La detection de regroupements spatiaux A I'aide de fr6quences observ6es revst une certaine importance en analyse spatiale. Plusieurs tests ont 1915 proposts cet effet mais en dCpit de leur usage rtpandu, leurs proprittis fondamentales ont 1915 fort peu ttudites au plan thtorique. Les auteurs suggtrent deux conditions auxquelles devrait dpondre tout bon test de regroupement spatial. Ce!les-ci traduisent le fait que I'hypoWse nulle devrait &re rejet& presque sQrement quand le degrt de regroupement spatial tend vers I'infini. Les auteurs montrent que le test du khideux et le V de Potthoff-Whittinghill possedent les deux propritt6s mais qu'il en va autrement pour certains autres tests classiques. JUNG 8 KULLDORFF Vol. 35, No. 3 H. W. Venema (1988). Spatial distribution of fiber types in skeletal muscle: Test for a random distribution.

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Geographic clustering of residence in early life and subsequent risk of breast cancer (United States)

Abstract: This study provides evidence that early environmental exposures may be related to breast cancer risk, especially for pre-menopausal women.

Cited by 29 publications

References 24 publications

Exposure to traffic emissions throughout life and risk of breast cancer: the Western New York Exposures and Breast Cancer (WEB) study

Exposure to traffic emissions throughout life and risk of breast cancer: the Western New York Exposures and Breast Cancer (WEB) study

A scoping review of spatial cluster analysis techniques for point-event data

Theoretical properties of tests for spatial clustering of count data

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