Race, Ethnicity, and Language Data Collection by Health Plans: Findings from 2010 AHIPF-RWJF Survey

Nerenz, David R.; Carreón, Rita; Veselovskiy, German

doi:10.1353/hpu.2013.0182

Cited by 10 publications

(11 citation statements)

References 20 publications

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“…[2][3][4] Irrespective of debates over how to best conceptualize and measure race and ethnicity, 5 it is critically important to characterize the quality of the existing data on these demographics to determine whether current data collection standards are being implemented appropriately or need reinforcement. [5][6][7][8][9][10][11][12][13] Large computerized databases such as cancer registries and electronic medical records (EMRs) have changed the landscape of healthcare practice, policy, and research. Increasing emphasis on the "meaningful use" of electronic health information technology has increased the utilization of these data sources for multiple purposes.…”

mentioning

confidence: 99%

Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

Lee

Grobe

Tiro

2015

Journal of the American Medical Informatics Association

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confidence: 99%

Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

Lee

Grobe

Tiro

2015

Journal of the American Medical Informatics Association

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“…This potential is constrained, however, by the lack of reliable race and ethnicity information in APCDs. Despite long-standing recommendations from the Institute of Medicine, the National Quality Forum, and others that health plans systematically collect race and ethnicity data, implementation has been slow due to privacy concerns and resource limitations [6][7][8][9]. Even Medicare and Medicaid plans, which are federally mandated to collect race and ethnicity data by the Affordable Care Act [10], continue to struggle to collect this information completely [11].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Race and Ethnicity using Bayesian Imputation in an All Payer Claims Database

Ibrahimi¹,

Hendricks²,

Hallvik³

et al. 2022

Preprint

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Background: All Payer Claims Databases (APCD) are a rich source of health information, however, race and ethnicity (R&E) data are largely missing. Bayesian Improved Surname Geocoding (BISG) is a common R&E imputation method, yet, validation of BISG in APCDs is lacking. We used the BISG to impute missing R&E in the Oregon APCD. Methods: BISG imputed R&E for Asian Pacific Islanders (API), Blacks, Hispanics and Whites were contrasted to the gold standard (vital statistics) and sensitivity and specificity improvements were assessed. Logistic regression examined whether missing R&E was random across patient characteristics. Results: Among 85,857 individuals in the study, 32.1% (n=27,594) had missing R&E. Missing R&E was not randomly distributed. There were higher odds of missingness among males, Whites, those age 65 and older, and commercially insured individuals. Differences in the percent missing were also found by co-morbid conditions and mortality causes. Imputing the missing R&E with BISG method improved the sensitivity to identify White, Black, API, and Hispanics. Conclusions: APCDs can benefit from enhancing missing R&E with BISG imputation to perform more robust population-health level analyses and identify inequities according to R&E without losing power or dropping non-random records with missing R&E data.

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“…Policymakers and practitioners recognize that in general, data that patients self-report are strongly preferred [24,33], but in practice, providers have struggled to convince most patients to voluntarily self-report. In the interest of generating data necessary to reduce disparities, methods to estimate race and ethnicity have been widely adopted to supplement self-reported data [54]. Early inference methods involved basic geocoding and surname analysis; more advanced probabilistic techniques have since been developed to refine these estimates.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms produce probabilities that individuals belong to a particular racial or ethnic group, which can then be used to assess disparities between subgroups at an aggregate level [29,68]. A number of health plans combine selfreported and estimated data to increase accuracy of their analysis [54].…”

Section: Introductionmentioning

confidence: 99%

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Bogen,

Rieke,

Ahmed

2019

Preprint

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Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted.This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities.This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account.

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Race, Ethnicity, and Language Data Collection by Health Plans: Findings from 2010 AHIPF-RWJF Survey

Abstract: Since 2008, collection and use of REL data continues gradually to increase among health plans, demonstrating the industry's commitment to address racial/ethnic gaps in care.

Cited by 10 publications

References 20 publications

Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

Enhancing Race and Ethnicity using Bayesian Imputation in an All Payer Claims Database

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

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