Generation of Fine Grained Demographic Information for Epidemiological Analysis.

Abstract

Cancer risks may be influenced by local exposures such as working conditions or nuclear waste repositories. To find influences, local accumulations of cancer rates are used, for which finely granulated data should be utilized. In particular, high-resolution demographic data for a reference population are important, but often not available for data protection reasons. Therefore, estimation methods are necessary to approximate small-scale demographic data as accurately as possible. This paper presents an approach to project existing epidemiological and public data to a common granularity with respect to attribute characteristics such as place of residence, age or smoking status to allow for analyses such as local accumulations and consistently falls below an average relative error of 5%.

• Authors: Timo Wolters, Oke Wübbenhorst, Christian Lüpkes, Andreas Hein
• Journal: Studies in health technology and informatics (Stud Health Technol Inform) Vol. 270 Pg. 233-237 (Jun 16 2020) ISSN: 1879-8365 [Electronic] Netherlands
• PMID: 32570381 (Publication Type: Journal Article)

Topics

• Data Analysis
• Data Science
• Databases, Factual
• Demography
• Health
• Humans
• Neoplasms (epidemiology)
• Time Factors