Special Issue "Spatial Epidemiology"

Guest Editor
Prof. Dr. Peter Congdon
Department of Geography and Centre for Statistics, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
Website: http://www.geog.qmul.ac.uk/staff/congdonp.html
E-Mail: p.congdon@qmul.ac.uk
Phone: +44 2078 828200
Interests: spatial epidemiology; Bayesian modeling of health outcomes; small area disease prevalence estimation; area mortality; geographic and socioeconomic inequalities in chronic disease and mental health; suicidology

Dear Colleagues,

The development of spatial epidemiology has been assisted by conceptual advances (e.g. ecological approaches to health encompassing both individual and contextual influences), and by new methods (e.g. clustering methods, multilevel models, Bayesian approaches). There remains much scope to establish in what way places affect health outcomes, and how important contextual effects are. For example, the operationalisation and measurement of relevant spatial variables, often latent constructs rather than directly observed, is often problematic and can affect levels of explanation of health outcomes attributed to contextual variables. Other issues are the changing impacts on health of area variables according to the area scale adopted, how to represent unmeasured spatially correlated influences on health outcomes, and how to measure spatial clustering in spatial health relativities or area risk factors. This special issue has a broad focus on recent advances in spatial epidemiology, and both theoretical and empirical submissions are welcome.

Prof. Dr. Peter Congdon
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed Open Access monthly journal published by MDPI.

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• spatial clustering
• spatial autocorrelation
• contextual influences on health, area effects
• relevance of scale, modifiable areal unit problem
• spatial regression
• multilevel models
• air pollution and health
• measuring contextual influences/exposures
• spatial latent variable models

Type of Paper: Article
Title: Geographic clustering of breast cancer among long-term residents of Marin County, California accounting for residential mobility, risk factors and covariate
Author: Geoffrey Jacquez
Affiliation: Department of Geography, State University of New York at Buffalo, Buffalo, NY 14260, USA; E-mail: jacquez@biomedware.com
Abstract: Background: Marin County, California, has among the highest incidence of breast cancer in the United States. A case-control study (Wrensch, Chew et al. 2003) found 8 significant risk factors and covariates among 285 cases and 286 controls enrolled between 1997-99. Significant risk factors were being premenopausal, never to have used birth control pills, a lower highest lifetime body mass index, four or more mammograms in 1990-94, beginning drinking after the age of 21, on average drinking two or more drinks per day, the highest quartile of pack-years of cigarette smoking and having been raised in an organized religion. Cases and controls did not significantly differ with regard to having a first-degree relative with breast cancer, a history of benign breast biopsy, previous radiation treatment, age at menarche, parity, use of hormone replacement therapy, age of first living in Marin County, or total years lived in Marin County.
Methods: We used Q-statistics accounting for the significant risk factors reported by Wrensch et al. and residential histories of the study participants obtained from Zero Breast Cancer.  These methods are sensitive to clustering of breast cancer cases after the known risk factors are accounted for, and may be used to identify specific cases, places and times of geographically localized clustering.  Persistent local clusters may be the signature of a risk factor (e.g. environmental) that was not accounted for in the parent case control study design.
Results: We found the study population to be comprised of “movers” and “stayers”.  Statistical analyses found significant global clustering of cases that was localized to specific residential histories and times.  A substantial portion of the observed clustering occurred among the movers, who immigrated to Marin County from New York near Long Island, the upper central Midwest and other parts of California.  However, persistent case-clustering of greater than 15 years duration was found near Greenbrae, San Rafael and Novato.
Conclusions: The finding of significant clustering of breast cancer cases among long-term residents may indicate the role of geographically localized risk factors not accounted for in the parent case-control study design.  Plausible hypotheses include environmental risk factors and differential migration such that cases tend to settle in specific areas. While the burden of statistical evidence indicates the persistent, local clusters identified in this study are statistically unusual, a biologically plausible exposure or risk factor has yet to be identified.