1. Assessing the performance of the Asian/Pacific islander identification algorithm to infer Hmong ethnicity from electronic health records in California
- Author
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Ly, May Ying N, Kim, Katherine K, and Stewart, Susan L
- Subjects
Health Services and Systems ,Health Sciences ,Health Services ,Clinical Research ,Good Health and Well Being ,Adult ,Algorithms ,Asian ,California ,Electronic Health Records ,Emigrants and Immigrants ,Female ,Health Services Administration ,Health Status Disparities ,Humans ,Male ,Middle Aged ,Native Hawaiian or Other Pacific Islander ,Neoplasms ,Patient Identification Systems ,Self Report ,epidemiology ,ethnicity ,health services administration & management ,immigrants ,name algorithm ,surname classification ,Clinical Sciences ,Public Health and Health Services ,Other Medical and Health Sciences ,Biomedical and clinical sciences ,Health sciences ,Psychology - Abstract
ObjectiveThis study assesses the performance of the North American Association of Central Cancer Registries Asian/Pacific Islander Identification Algorithm (NAPIIA) to infer Hmong ethnicity.Design and settingAnalyses of electronic health records (EHRs) from 1 January 2011 to 1 October 2015. The NAPIIA was applied to the EHR data, and self-reported Hmong ethnicity from a questionnaire was used as the gold standard. Sensitivity, specificity, positive (PPV) and negative predictive values (NPVs) were calculated comparing the source data ethnicity inferred by the algorithm with the self-reported ethnicity from the questionnaire.ParticipantsEHRs indicating Hmong, Chinese, Vietnamese and Korean ethnicity who met the original study inclusion criteria were analysed.ResultsThe NAPIIA had a sensitivity of 78%, a specificity of 99.9%, a PPV of 96% and an NPV of 99%. The prevalence of Hmong population in the sample was 3.9%.ConclusionThe high sensitivity of the NAPIIA indicates its effectiveness in detecting Hmong ethnicity. The applicability of the NAPIIA to a multitude of Asian subgroups can advance Asian health disparity research by enabling researchers to disaggregate Asian data and unmask health challenges of different Asian subgroups.
- Published
- 2019