TY  - JOUR
PY  - 2020//
TI  - An introduction to probabilistic record linkage with a focus on linkage processing for WTC registries
JO  - International journal of environmental research and public health
A1  - Asher, Jana
A1  - Resnick, Dean
A1  - Brite, Jennifer
A1  - Brackbill, Robert M.
A1  - Cone, James
SP  - e6937
EP  - e6937
VL  - 17
IS  - 18
N2  - Since its post-World War II inception, the science of record linkage has grown exponentially and is used across industrial, governmental, and academic agencies. The academic fields that rely on record linkage are diverse, ranging from history to public health to demography. In this paper, we introduce the different types of data linkage and give a historical context to their development. We then introduce the three types of underlying models for probabilistic record linkage: Fellegi-Sunter-based methods, machine learning methods, and Bayesian methods. Practical considerations, such as data standardization and privacy concerns, are then discussed. Finally, recommendations are given for organizations developing or maintaining record linkage programs, with an emphasis on organizations measuring long-term complications of disasters, such as 9/11.<p />  <p>Language: en</p>
LA  - en
SN  - 1661-7827
UR  - http://dx.doi.org/10.3390/ijerph17186937
ID  - ref1
ER  -