Spatial-temporal analysis of breast cancer in upper Cape Cod, Massachusetts

INTRODUCTION. The reasons for elevated breast cancer rates in the upper Cape Cod area of Massachusetts remain unknown despite several epidemiological studies that investigated possible environmental risk factors. Data from two of these population-based case-control studies provide geocoded residential histories and information on confounders, creating an invaluable dataset for spatial-temporal analysis of participants' residency over five decades. METHODS. The combination of statistical modeling and mapping is a powerful tool for visualizing disease risk in a spatial-temporal analysis. Advances in geographic information systems (GIS) enable spatial analytic techniques in public health studies previously not feasible. Generalized additive models (GAMs) are an effective approach for modeling spatial and temporal distributions of data, combining a number of desirable features including smoothing of geographical location, residency duration, or calendar years; the ability to estimate odds ratios (ORs) while adjusting for confounders; selection of optimum degree of smoothing (span size); hypothesis testing; and use of standard software. We conducted a spatial-temporal analysis of breast cancer case-control data using GAMs and GIS to determine the association between participants' residential history during 1947–1993 and the risk of breast cancer diagnosis during 1983–1993. We considered geographic location alone in a two-dimensional space-only analysis. Calendar year, represented by the earliest year a participant lived in the study area, and residency duration in the study area were modeled individually in one-dimensional time-only analyses, and together in a two-dimensional time-only analysis. We also analyzed space and time together by applying a two-dimensional GAM for location to datasets of overlapping calendar years. The resulting series of maps created a movie which allowed us to visualize changes in magnitude, geographic size, and location of elevated breast cancer risk for the 40 years of residential history that was smoothed over space and time. RESULTS. The space-only analysis showed statistically significant increased areas of breast cancer risk in the northern part of upper Cape Cod and decreased areas of breast cancer risk in the southern part (p-value = 0.04; ORs: 0.90–1.40). There was also a significant association between breast cancer risk and calendar year (p-value = 0.05; ORs: 0.53–1.38), with earlier calendar years resulting in higher risk. The results of the one-dimensional analysis of residency duration and the two-dimensional analysis of calendar year and duration showed that the risk of breast cancer increased with increasing residency duration, but results were not statistically significant. When we considered space and time together, the maps showed a large area of statistically significant elevated risk for breast cancer near the Massachusetts Military Reservation (p-value range:0.02–0.05; ORs range: 0.25–2.5). This increased risk began with residences in the late 1940s and remained consistent in size and location through the late 1950s. CONCLUSION. Spatial-temporal analysis of the breast cancer data may help identify new exposure hypotheses that warrant future epidemiologic investigations with detailed exposure models. Our methods allow us to visualize breast cancer risk, adjust for known confounders including age at diagnosis or index year, family history of breast cancer, parity and age at first live- or stillbirth, and test for the statistical significance of location and time. Despite the advantages of GAMs, analyses are for exploratory purposes and there are still methodological issues that warrant further research. This paper illustrates that GAM methods are a suitable alternative to widely-used cluster detection methods and may be preferable when residential histories from existing epidemiological studies are available.National Cancer Institute (5R03CA119703-02); National Institute of Enviornmental Health (5P42ES007381

US/Canada wheat and barley crop calender exploratory experiment implementation plan

A plan is detailed for a supplemental experiment to evaluate several crop growth stage models and crop starter models. The objective of this experiment is to provide timely information to aid in understanding crop calendars and to provide data that will allow a selection between current crop calendar models

Mathematics, statistics and archaeometry: the past 50 years or so

This review of developments in the use of mathematics and statistics in archaeometry over the past 50 years is partial, personal and 'broad-brush'. The view is expressed that it is in the past 30 years or so that the major developments have taken place. The view is also expressed that, with the exception of methods for analysing radiocarbon dates and increased computational power, mathematical and statistical methods that are currently used, and found to be useful in widespread areas of application such as provenance studies, don't differ fundamentally from what was being done 30 years ago

Bringing the Semantic Web home: a research agenda for local, personalized SWUI

We suggest that by taking the Semantic Web local and personal, and deploying it as a shared "data sea" for all applications to trawl, new types of interaction are possible (even necessitated) with this heterogeneous source integration. We present a motivating scenario to foreground the kind of interaction we envision as possible, and outline a series of associated questions about data integration issues, and in particular about the interaction challenges fostered by these new possibilities. We sketch out some early approaches to these questions, but our goal is to identify a wider field of questions for the SWUI community in considering the implications of a local/social semantic web, not just a public one, for interaction

Nonparametric Hierarchical Clustering of Functional Data

In this paper, we deal with the problem of curves clustering. We propose a nonparametric method which partitions the curves into clusters and discretizes the dimensions of the curve points into intervals. The cross-product of these partitions forms a data-grid which is obtained using a Bayesian model selection approach while making no assumptions regarding the curves. Finally, a post-processing technique, aiming at reducing the number of clusters in order to improve the interpretability of the clustering, is proposed. It consists in optimally merging the clusters step by step, which corresponds to an agglomerative hierarchical classification whose dissimilarity measure is the variation of the criterion. Interestingly this measure is none other than the sum of the Kullback-Leibler divergences between clusters distributions before and after the merges. The practical interest of the approach for functional data exploratory analysis is presented and compared with an alternative approach on an artificial and a real world data set

Analysis of mortality in a pooled cohort of Canadian and German uranium processing workers with no mining experience.

PurposeLong-term health risks of occupational exposures to uranium processing were examined to better understand potential differences with uranium underground miners and nuclear reactor workers.MethodsA cohort study of mortality of workers from Port Hope, Canada (1950-1999) and Wismut, Germany (1946-2008) employed in uranium milling, refining, and processing was conducted. Poisson regression was used to evaluate the association between cumulative exposures to radon decay products (RDP) and gamma-rays and causes of death potentially related to uranium processing.ResultsThe pooled cohort included 7431 workers (270,201 person-years of follow-up). Mean RDP exposures were lower than in miners while gamma-ray doses were higher than in reactor workers. Both exposures were highly correlated (weighted rho = 0.81). Radiation risks of lung cancer and cardiovascular diseases (CVD) in males were increased but not statistically significant and compatible with risks estimated for miners and reactor workers, respectively. Higher RDP-associated CVD risks were observed for exposures 5-14 years prior to diagnosis compared to later exposures and among those employed <5 years. Radiation risks of solid cancers excluding lung cancer were increased, but not statistically significant, both for males and females, while all other causes of death were not associated with exposures.ConclusionsIn the largest study of uranium processing workers to systematically examine radiation risks of multiple outcomes from RDP exposures and gamma-rays, estimated radiation risks were compatible with risks reported for uranium miners and nuclear reactor workers. Continued follow-up and pooling with other cohorts of uranium processing workers are necessary for future comparisons with other workers of the nuclear fuel cycle