This paper provides an overview of methods and systems developed for record linkage. Modern record linkage begins with the pioneering work of Newcombe and is especially based on the formal mathematical model of Fellegi and Sunter. In their seminal work, Fellegi and Sunter introduced many powerful ideas for estimating record linkage parameters and other ideas that still influence record linkage today. Record linkage research is characterized by its synergism of statistics, computer science, and operations research. Many difficult algorithms have been developed and put in software systems. Record linkage practice is still very limited. Some limits are due to existing software. Other limits are due to the difficulty in automatically estimating matching parameters and error rates, with current research highlighted by the work of Larsen and Rubin. Keywords: computer matching, modeling, iterative fitting, string comparison, optimization RsSUMs Cet article donne une vue d'ensemble sur les ...

INTRODUCTION Matching has a long history of uses in statistical surveys and administrative data development. A business register consisting of names, addresses, and other identifying information such as total financial receipts might be constructed from tax and employment data bases (see chapters by Colledge, Nijhowne, and Archer). A survey of retail establishments or agricultural establishments might combine results from an area frame and a list frame. To produce a combined estimator, units from the area frame would need to be identified in the list frame (see Vogel-Kott chapter). To estimate the size of a (sub)population via capture-recapture techniques, one needs to accurately determine units common to two or more independent listings (Sekar and Deming 1949; Scheuren 1983; Winkler 1989b). Samples must be drawn appropriately to estimate overlap (Deming and Gleser 1959). Rather than develop a special survey to collect data for policy decisions, it might be more appropriate t

Bureau of the Census This paper extends techniques for frequency-based matching (see e.g., Fellegi and Sunter 1969). The extended techniques allow table-building under weaker assumptions than those typically used in practice. Although CPU requirements can increase, human intervention can be reduced in some situations.

By combining data on enfities from different sources, researchers are often able to perform anslysas that would not be possible if they were to use data from individual sources separately.

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The relationship between self-reported health and mortality is well documented, but less well understood. This study uses the National Health Interview Survey linked to mortality data from the National Death Index to examine the association between selfreported health and a comprehensive set of underlying cause of death and multiple cause of death categories. We also examined whether gender moderates the relationship between self-reported health and cause-specific mortality risk. Results show that the relationship between self-rated health and mortality differs by cause of death and by number of causes. Deaths due to diabetes, infectious and respiratory diseases, and a higher number of causes are most strongly associated with subjective health. Selfreported health also exhibits a moderately strong association with deaths due to heart disease, stroke, and cancer. In contrast, self-rated health is only weakly or not associated with deaths due to accident, homicide, and suicide. The relationship between selfreported health and mortality risk is also found to be stronger among men for several causes, although not for all. These findings should help researchers and policy-makers to better understand the specific predictive power of this important global measure of health.