Heuristic Algorithms for Assigning Hispanic Ethnicity

Details

• Alternative Title:
  PLoS One
• Personal Author:
  Boscoe, Francis P. ; Schymura, Maria J. ; Zhang, Xiuling ; Kramer, Rachel A.
• Description:
  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.
• Subjects:
  Adolescent Adult Aged Aged, 80 And Over Algorithms Ethnicity Female Hispanic Or Latino Humans Middle Aged Neoplasms Registries Young Adult

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• Source:
  PLoS One. 2013; 8(2).
• Pubmed ID:
  23405197
• Pubmed Central ID:
  PMC3566036
• Document Type:
  Journal Article
• Funding:
  U58/DP000783/DP/NCCDPHP CDC HHS/United States
• Place as Subject:
  New York
• Volume:
  8
• Issue:
  2
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:80085736f04c3d4c1b9b52c36e908aeb4a59827dc0c7813bb665aa4af982ecd5
• Download URL:
  https://stacks.cdc.gov/view/cdc/22643/cdc_22643_DS1.pdf
• File Type:
  [PDF - 140.22 KB ]