Using Tribal Data Linkages to Improve the Quality of American Indian Cancer Data in Michigan

Weber, Tess L.; Copeland, Glenn; Pingatore, Noel L.; Schmid, Kendra K.; Jim, Melissa A.; Watanabe‐Galloway, Shinobu

doi:10.1353/hpu.2019.0084

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…[24][25][26] And increasingly the Collective Benefit, Authority to Control, Responsibility, and Ethics 27 (CARE) principles that guide access and use of Indigenous data are being used to review data agreements, linkages, codes and integrated into research proposals. 26 Sustainability of these efforts will come from establishing standards at the national level and within Native nations to guide policy development and best practice. 27 Placing Indigenous systems alignment within the context of the IDS/IDG movement points the way toward future research and policy development.…”

Section: Indigenous Data Sovereignty and Governance For Tribal Data S...mentioning

confidence: 99%

“…A strong cross‐sector tribal data system can redress the fragmented and siloed systems that resulted from lack of funding for Native nations and Indigenous community organizations 23 . Examples exist of working across jurisdictions in partnership to establish the agreements to improve access to reliable data 24–26 . And increasingly the Collective Benefit, Authority to Control, Responsibility, and Ethics 27 (CARE) principles that guide access and use of Indigenous data are being used to review data agreements, linkages, codes and integrated into research proposals 26 .…”

Section: Indigenous Data Sovereignty and Governance For Tribal Data S...mentioning

confidence: 99%

“… 23 Examples exist of working across jurisdictions in partnership to establish the agreements to improve access to reliable data. 24 , 25 , 26 And increasingly the Collective Benefit, Authority to Control, Responsibility, and Ethics 27 (CARE) principles that guide access and use of Indigenous data are being used to review data agreements, linkages, codes and integrated into research proposals. 26 Sustainability of these efforts will come from establishing standards at the national level and within Native nations to guide policy development and best practice.…”

Section: Indigenous Data Sovereignty and Governance For Tribal Data S...mentioning

confidence: 99%

See 2 more Smart Citations

Developing an Indigenous systems alignment framework: A call for direct funding

Oré,

Fong‐Gurzinsky,

Marshall

et al. 2024

Health Services Research

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Section: Indigenous Data Sovereignty and Governance For Tribal Data S...mentioning

confidence: 99%

Section: Indigenous Data Sovereignty and Governance For Tribal Data S...mentioning

confidence: 99%

Section: Indigenous Data Sovereignty and Governance For Tribal Data S...mentioning

confidence: 99%

See 1 more Smart Citation

Developing an Indigenous systems alignment framework: A call for direct funding

Oré,

Fong‐Gurzinsky,

Marshall

et al. 2024

Health Services Research

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“…Trainings should also expand awareness of and linkage with broad data sources, like electronic health registries and tribal records ( 19 , 62 ). However, mortality and cancer registry linkages with Indian Health Service (IHS) patient records are not perfect and can result in non-Native individuals being misclassified as Native, or Native people being missed who do not use, access, or have the ability to attend IHS facilities ( 63 – 66 ). In NC, where most tribes are not federally recognized and some are not state recognized, access and use of these rosters are also likely to present new challenges.…”

Section: Improving Decedent Race and Ethnicity Classificationmentioning

confidence: 99%

Challenges with misclassification of American Indian/Alaska Native race and Hispanic ethnicity on death records in North Carolina occupational fatalities surveillance

McClure

Gartner²,

Bell³

et al. 2022

Front. Epidemiol.

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As frequently segregated and exploitative environments, workplaces are important sites in driving health and mortality disparities by race and ethnicity. Because many worksites are federally regulated, US workplaces also offer opportunities for effectively intervening to mitigate these disparities. Development of policies for worker safety and equity should be informed by evidence, including results from research studies that use death records and other sources of administrative data. North Carolina has a long history of Black/white disparities in work-related mortality and evidence of such disparities is emerging in Hispanic and American Indian/Alaska Native (AI/AN) worker populations. The size of Hispanic and AI/AN worker populations have increased in North Carolina over the last decade, and North Carolina has the largest AI/AN population in the eastern US. Previous research indicates that misidentification of Hispanic and AI/AN identities on death records can lead to underestimation of race/ethnicity-specific mortality rates. In this commentary, we describe problems and complexities involved in determining AI/AN and Hispanic identities from North Carolina death records. We provide specific examples of misidentification that are likely introducing bias to occupational mortality disparity documentation, and offer recommendations for improved data collection, analysis, and interpretation. Our primary recommendation is to build and maintain relationships with local community leadership, so that improvements in the ascertainment of race and ethnicity are grounded in the lived experience of workers from communities of color.

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Misracialization of Indigenous people in population health and mortality studies: a scoping review to establish promising practices

Gartner

Maples

Nash

et al. 2023

Epidemiologic Reviews

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Indigenous people are often misracialized or misclassified as other racial or ethnic identities in population health research. This misclassification leads to underestimation of Indigenous-specific mortality and health metrics, and subsequently, inadequate resource allocation. In recognition of this problem, investigators around the world have devised analytic methods to address racial misclassification of Indigenous people. We carried out a scoping review based on searches in PubMed, Web of Science, and the Native Health Database for empirical studies published after 2000 that include Indigenous-specific estimates of health or mortality and that take analytic steps to rectify racial misclassification of Indigenous people. We then considered the weaknesses and strengths of implemented analytic approaches, with a focus on methods used in the United States (U.S.) context. To do this, we extracted information from 97 articles and compared the analytic approaches used. The most common approach to address Indigenous misclassification is to use data linkage, though other methods include geographic restriction to areas where misclassification is less common, exclusion of some subgroups, imputation, aggregation, and electronic health record abstraction. We identified four primary limitations of these approaches: (1) combining data sources that use inconsistent process and/or sources of race and ethnicity information, (2) conflating race, ethnicity, and nationality, (3) applying insufficient algorithms to bridge, impute, or link race and ethnicity information, and (4) assuming the hyperlocality of Indigenous people. While there is no perfect solution to the issue of Indigenous misclassification in population-based studies, a review of this literature provided promising practices to consider.

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Using Tribal Data Linkages to Improve the Quality of American Indian Cancer Data in Michigan

Cited by 4 publications

References 17 publications

Developing an Indigenous systems alignment framework: A call for direct funding

Developing an Indigenous systems alignment framework: A call for direct funding

Challenges with misclassification of American Indian/Alaska Native race and Hispanic ethnicity on death records in North Carolina occupational fatalities surveillance

Misracialization of Indigenous people in population health and mortality studies: a scoping review to establish promising practices

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