Organizing population data into complex family pedigrees: application of a second-order data linkage to state birth defects registries.
Researchers and health officials are increasingly using electronic linkage of large-scale health data systems as a tool for assembling a comprehensive picture of birth defects at a population level. Current linkage and database techniques are limited to first-order linkage--linking information on a single individual in one database with information on that same individual in another database. For example, while current strategies may indicate whether a child with a certain birth defect also has a specific metabolic disorder or risk factor, they are unable to readily determine whether he or she also has any siblings or other relatives with the same pattern. In contrast, the current manuscript proposes a second-order linkage--one that organizes data so that individual-level data can readily be organized into families or extended family pedigrees across an entire population. The ability to link and organize population data into family pedigrees can have significant, broad impact upon health research and service delivery. This can lead to large-scale analysis of genetic factors and, with the linking of environmental data, the potential for large-scale studies of gene-environment interactions. In addition, it expands the potential for epidemiological research by readily allowing the examination of familial effects upon population rates of birth defects, and provides valuable information that can assist in applied public health. An example of a second order database incorporating an electronic birth defects registry is presented.
Tu
Shihfen
S
University of Maine, Orono 04469, USA. shihfen.tu@umit.maine.edu
Mason
Craig A
CA
D000013:Congenital Abnormalities / Q000453:epidemiology / Q000235:genetics; D003625:Data Collection / Q000379:methods*; D005192:Family Health; D006801:Humans; D010375:Pedigree; D011159:Population Surveillance / Q000379:methods*; D012042:Registries / Q000592:standards* / Q000706:statistics & numerical data