Name Recognition to Identify Patients of South Asian Ethnicity within the Cancer Registry

Methods for retrospectively improving race/ethnicity data quality: a scoping review

Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases in July 2022. A total of 2 441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis. Six main method types for improving race/ethnicity data were identified: expert review (n = 9; 8%), name lists (n = 27, 23%), name algorithms (n = 55, 46%), machine learning (n = 14, 12%), data linkage (n = 9, 8%), and other (n = 6, 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56, 47%) and White (n = 51, 43%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.

Construction of the Armenian Surname List (ASL) for public health research

BACKGROUND

There are an estimated 460,000 Armenians in the United States, and more than half live in California. Armenian-Americans are generally represented within the 'White' or 'Some Other Race' race categories in population-based research studies. While Armenians have been included in studies focused on Middle-Eastern populations, there are no studies focused exclusively on Armenians due to a lack of standardized collection of Armenian ethnicity in the United States or an Armenian surname list. To fill this research gap, we sought to construct and evaluate an Armenian Surname List (ASL) for use as an identification tool in public health and epidemiological research studies focused on Armenian populations.

METHODS

Data sources for the ASL included the California Public Use Death Files (CPUDF) and the Middle Eastern Surname List (MESL). For evaluation of the ASL, the California Cancer Registry (CCR) database was queried for surnames with birthplace in Armenia and identified by the MESL.

RESULTS

There are a total of 3,428 surnames in the ASL. Nearly half (1,678) of surnames in the ASL were not identified by the MESL. The ASL captured 310 additional Armenian surnames in the CCR than the MESL.

CONCLUSIONS

The ASL is the first surname list for identifying Armenians in major databases for epidemiological research.