Record Details

Title Heuristic algorithms for assigning Hispanic ethnicity
Author Boscoe, FP
Secondary Authors Schymura MJ, Zhang X, Kramer RA
Publication Type (Help) article
Journal PLoS One
Month
Year 2013
Pages
Volume 8
Number 2
Publisher
Address
Note doi: 10.1371/journal.pone.0055689
URL http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0055689
PubMed ID 23405197
NCI Id
EPub Date 2013 Feb 06
Citation Boscoe FP, Schymura MJ, Zhang X, Kramer RA. Heuristic algorithms for assigning Hispanic ethnicity. PLoS One. 2013;8(2). EPub 2013 Feb 06. PMID 23405197. [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0055689.]
  BibTex    


Abstract

We compared several techniques for assigning Hispanic ethnicity to records in data systems where this information may be missing, variously making use of country of origin, surname, race, and county of residence. We considered an algorithm in use by the North American Association of Central Cancer Registries (NAACCR), a variation of this developed by the authors, a “fast and frugal” algorithm developed with the aid of recursive partitioning methods, and conventional logistic regression. With the exception of logistic regression, each approach was rule-based: if specific criteria were met, an ethnicity assignment was made; otherwise, the next criterion was considered, until all records were assigned. We evaluated the algorithms on a sample of over 500,000 female clients from the New York State Cancer Services Program for whom self-reported Hispanic ethnicity was known. We found that all approaches yielded similarly high accuracy, sensitivity, and positive predictive value in all parts of the state, from areas with very low to very high Hispanic populations. An advantage of the fast and frugal method is that it consists of a small number of easily remembered steps.



Keywords

Keyword
algorithms
ethnicity
heuristic
hispanic