Evidence map›Paper›PMID 37483944›Full record

ArticleFrontiers in public health2023

Operationalizing racialized exposures in historical research on anti-Asian racism and health: a comparison of two methods.

Marie Kaniecki, Nicole Louise Novak, Sarah Gao, Sioban Harlow, Alexandra Minna Stern

Open access · goldAbstract readComparative Study
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Racism Counterfactuals in Health Research.Journal of racial and ethnic health disparities · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Marie KanieckiUniversity of Michigan, Ann Arbor, MI, United States.
Nicole Louise NovakCollege of Public Health, The University of Iowa, Iowa City, IA, United States.
Sarah GaoUniversity of Michigan, Ann Arbor, MI, United States.
Sioban HarlowSchool of Public Health, University of Michigan, Ann Arbor, MI, United States.
Alexandra Minna SternUniversity of Michigan, Ann Arbor, MI, United States.
University of Michigan · USIowa Policy Project · USUniversity of California, Los Angeles · US

Funding

Demographic Patterns of Eugenic Sterilization: Mixed Methods Investigation of Reproductive Control of the 'Unfit' - Michigan Sterilization Data SupplementR01HG010567 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NOVAK, NICOLE LOUISE, STERN, ALEXANDRA M · 2018 to 2023
$2.8M
NHGRI NIH HHS R01 HG010567
6 · The paper itself

Abstract

Background: Addressing contemporary anti-Asian racism and its impacts on health requires understanding its historical roots, including discriminatory restrictions on immigration, citizenship, and land ownership. Archival secondary data such as historical census records provide opportunities to quantitatively analyze structural dynamics that affect the health of Asian immigrants and Asian Americans. Census data overcome weaknesses of other data sources, such as small sample size and aggregation of Asian subgroups. This article explores the strengths and limitations of early twentieth-century census data for understanding Asian Americans and structural racism. Methods: We used California census data from three decennial census spanning 1920-1940 to compare two criteria for identifying Asian Americans: census racial categories and Asian surname lists (Chinese, Indian, Japanese, Korean, and Filipino) that have been validated in contemporary population data. This paper examines the sensitivity and specificity of surname classification compared to census-designated "color or race" at the population level. Results: Surname criteria were found to be highly specific, with each of the five surname lists having a specificity of over 99% for all three census years. The Chinese surname list had the highest sensitivity (ranging from 0.60-0.67 across census years), followed by the Indian (0.54-0.61) and Japanese (0.51-0.62) surname lists. Sensitivity was much lower for Korean (0.40-0.45) and Filipino (0.10-0.21) surnames. With the exception of Indian surnames, the sensitivity values of surname criteria were lower for the 1920-1940 census data than those reported for the 1990 census. The extent of the difference in sensitivity and trends across census years vary by subgroup. Discussion: Surname criteria may have lower sensitivity in detecting Asian subgroups in historical data as opposed to contemporary data as enumeration procedures for Asians have changed across time. We examine how the conflation of race, ethnicity, and nationality in the census could contribute to low sensitivity of surname classification compared to census-designated "color or race." These results can guide decisions when operationalizing race in the context of specific research questions, thus promoting historical quantitative study of Asian American experiences. Furthermore, these results stress the need to situate measures of race and racism in their specific historical context.

Indexed as

Asian PeopleCensusesEthnicityNamesSystemic RacismAsianCaliforniaHistory, 20th CenturyHumansRacismAsian Americanshistorymisclassification and its errorracializationsensitivity and specificitystructural racismsurnameUnited States census

Identifiers

PMID37483944
PMCPMC10359498
OpenAlexW4383314283

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.