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Updated: Feb 10, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Detection of spatial aggregation of cases of cancer from data on patients and health centres contained in the Minimum
Pablo Fernández-Navarro1, Jose-Miguel Sanz-Anquela, Angel Sánchez Pinilla
1Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP). pfernandezn@isciii.es.
Abstract:
The feasibility of the Minimum Basic Data Set (MBDS) as a tool in cancer research was explored monitoring its incidence through the detection of spatial clusters. Case-control studies based on MBDS and marked point process were carried out with the focus on the residence of patients from the Prince of Asturias University Hospital in Alcalá de Henares (Madrid, Spain). Patients older than 39 years with diagnoses of stomach, colorectal, lung, breast, prostate, bladder and kidney cancer, melanoma and haematological tumours were selected. Geocoding of the residence address of the cases was done by locating them in the continuous population roll provided by the Madrid Statistical Institute and extracting the coordinates. The geocoded control group was a random sample of 10 controls per case matched by frequency of age and sex. To assess case clusters, differences in Ripley K functions between cases and controls were calculated. The spatial location of clusters was explored by investigating spatial intensity and its ratio between cases and controls. Results suggest the existence of an aggregation of cancers with a common risk factor such as tobacco smoking (lung, bladder and kidney cancers). These clusters were located in an urban area with high socioeconomic deprivation. The feasibility of designing and carrying out case-control studies from the MBDS is shown and we conclude that MBDS can be a useful epidemiological tool for cancer surveillance and identification of risk factors through case-control spatial point process studies.
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