Assessing the performance of the Asian/Pacific islander identification algorithm to infer Hmong ethnicity from electronic health records in California

OBJECTIVE: This 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 SETTING: Analyses 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. PARTICIPANTS: EHRs indicating Hmong, Chinese, Vietnamese and Korean ethnicity who met the original study inclusion criteria were analysed. RESULTS: The 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%. CONCLUSION: The 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.