Concordance of cancer registry and self-reported race, ethnicity, and cancer type: a report from the American Cancer Society’s studies of cancer survivors

Layne, Tracy M.; Ferrucci, Leah M.; Jones, Beth A.; Smith, Tenbroeck; Gonsalves, Lou; Cartmel, Brenda

doi:10.1007/s10552-018-1091-3

Cited by 21 publications

(30 citation statements)

References 38 publications

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“…All articles identified how research aims emerged, therefore fulfilling the prioritization domain. Research aims emerged from community-based organizations[ 24 ] and empiric evidence [ 23 , 25 , 26 , 28 – 39 ]. However, the inclusion of Indigenous stakeholders in the prioritization process was not identified.…”

Section: Resultsmentioning

confidence: 99%

Indigenous health equity in health register ascertainment and data quality: a narrative review

Wright

Tapera

Stott

et al. 2022

Int J Equity Health

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Background Health registers play an important role in monitoring distribution of disease and quality of care; however, benefit is limited if ascertainment (i.e., the process of finding and recruiting people on to a register) and data quality (i.e., the accuracy, completeness, reliability, relevance, and timeliness of data) are poor. Indigenous peoples experience significant health inequities globally, yet health data for, and about, Indigenous peoples is often of poor quality. This narrative review aimed to (i) identify perceived barriers for the ascertainment of Indigenous peoples on health registers, and (ii) collate strategies identified and used by health registers to support comprehensive ascertainment and high-quality data for Indigenous peoples. Methods A Kaupapa Māori theoretical framework was utilized to guide this work. Four electronic databases were systematically searched for original articles and screened for eligibility. Studies involving health registers with Indigenous population(s) identified were included if either ascertainment or data quality strategies were described. Data extraction focused on the reporting of research involving Indigenous peoples using the CONSIDER checklist domains, ascertainment, and data quality. Results Seventeen articles were included spanning publication between 1992 and 2020. Aspects of four of eight CONSIDER domains were identified to be included in the reporting of studies. Barriers to ascertainment were themed as relating to ‘ethnicity data collection and quality’, ‘systems and structures’, ‘health services/health professionals’, and ‘perceptions of individual and community-level barriers’. Strategies to support ascertainment were categorized as ‘collaboration’, ‘finding people’, and ‘recruitment processes’. Categorized strategies to support data quality were ‘collaboration’, ‘ethnicity data collection and quality’, ‘systems-level strategies’, and ‘health service/health professional-level strategies’. Conclusions Poor-quality data for Indigenous peoples in health registers prevents the achievement of health equity and exemplifies inaction in the face of need. When viewed through a critical structural determinants lens, there are visible gaps in the breadth of strategies, particularly relating to the inclusion of Indigenous peoples in health register and research governance, and actions to identify and address institutional racism. Indigenous led research, meaningful collaboration, and a sharing of knowledge and experiences between health registers is recommended to enable research and health registers that support Indigenous self-determination and health equity.

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Section: Resultsmentioning

confidence: 99%

Indigenous health equity in health register ascertainment and data quality: a narrative review

Wright

Tapera

Stott

et al. 2022

Int J Equity Health

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“…Individuals were classified as American Indian if their race was identified in SEER as “American Indian/Alaska Native” and their data came from a SEER registry other than the Alaska Native Tumor Registry. To minimize potential racial misclassification, we further excluded from the study those individuals whose race was identified as American Indian but whose incidence records could not be linked with the Indian Health Service files 17,18 . We excluded women whose cancers were not histologically confirmed, those who were diagnosed by death certificate or at autopsy, or those whose cases were reported only by a nursing home.…”

Section: Methodsmentioning

confidence: 99%

“…To minimize potential racial misclassification, we further excluded from the study those individuals whose race was identified as American Indian but whose incidence records could not be linked with the Indian Health Service files. 17,18 We excluded women whose cancers were not histologically confirmed, those who were diagnosed by death certificate or at autopsy, or those whose cases were reported only by a nursing home. Age was categorized into 3 categories (18-44 years, 45-64 years, and ≥65 years).…”

Section: Data and Patientsmentioning

confidence: 99%

“…Less restrictive cutoff values were used in the TAILORx study than traditionally had been used to minimize the risk of undertreatment. In the TAILORx trial, the 3 risk groups were categorized as low risk (<11), intermediate risk (RS,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), and high risk (>25) according to the results of the 21-gene RS. We have reported our findings and analyses based on the TAILORx cutoff values, which likely will be the clinically relevant RS cutoff values given the recent publication of TAILORx trial results.…”

Section: Data and Patientsmentioning

confidence: 99%

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Genomic expression assay testing among American Indian and Alaska Native women with breast cancer

Marmor

Longacre

Altman

et al. 2020

Cancer

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BACKGROUND: Breast cancer is one of the most common causes of cancer mortality for all women, including American Indian and Alaska Native (AI/AN) women. The use of the 21-gene recurrence score (RS) appears to be predictive of the benefit of chemotherapy for women with estrogen receptor (ER)-positive breast cancer. The objective of the current study was to compare RS testing between AI/AN and non-Hispanic White (NHW) women with breast cancer. Methods: The Surveillance, Epidemiology, and End Results program was used to identify women with ER-positive breast cancer from 2004 through 2015. Multivariable logistic regression was used to evaluate factors associated with RS use, with high-risk RS, and with chemotherapy use among those with a high-risk RS. Results: A total of 363,387 NHW patients and 1951 AI/AN patients with ER-positive breast cancer were identified. AI/AN women were found to be less likely to undergo RS testing and, when tested, were more likely to have a high-risk RS. In the multivariable logistic regression analysis, AI/AN women were found to be significantly more likely to have a high-risk RS (odds ratio,1.28; 95% confidence interval, 1.01-1.66). Among untested women, chemotherapy use was higher for AI/AN women; however, the use of chemotherapy was not found to be significantly different between the groups with a high-risk RS. Using Cox proportional hazards models, AI/AN race was found to be significantly associated with worse overall survival. Conclusions: AI/AN women were less likely to undergo RS testing compared with NHW women and were more likely to have a high-risk RS. Reversing the disparity in genomic expression assay testing is critical to ensure guideline-based breast cancer treatment and improve survival rates for AI/AN women with breast cancer.

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“…The completeness and accuracy of race/ethnicity data is especially problematic for non-Black minority groups (American Indians/Alaska Natives, Asian Americans/Pacific Islanders [AAPIs], and Hispanics/Latinos) that were classified as “other” by the Social Security Administration (SSA) prior to the 1980s. Race/ethnicity data quality issues have been independently identified and reported across cancer registry data (Layne et al, 2019), Ohio Medicaid data (Hartzler & Snyder, 2017), and Medicare administrative data for Veterans Health Administration patients (Hernandez et al, 2019). The limitations of Medicare administrative data on beneficiaries’ race and ethnicity are critically important because this is the primary source of information used to examine disparities in access to and in utilization of physicians’ services and inpatient hospital care at both national and state levels (Center for Medicare and Medicaid Services [CMS], 2020).…”

mentioning

confidence: 99%

Beyond Black and White: Mapping Misclassification of Medicare Beneficiaries Race and Ethnicity

Grafova

Jarrín

2020

Med Care Res Rev

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The Centers for Medicare and Medicaid Services administrative data contains two variables that are used for research and evaluation of health disparities: the enrollment database (EDB) beneficiary race code and the Research Triangle Institute (RTI) race code. The objective of this article is to examine state-level variation in racial/ethnic misclassification of EDB and RTI race codes compared with self-reported data collected during home health care. The study population included 4,231,370 Medicare beneficiaries who utilized home health care services in 2015. We found substantial variation between states in Medicare administrative data misclassification of self-identified Hispanic, Asian American/Pacific Islander, and American Indian/Alaska Native beneficiaries. Caution should be used when interpreting state-level health care disparities and minority health outcomes based on existing race variables contained in Medicare data sets. Self-reported race/ethnicity data collected during routine care of Medicare beneficiaries may be used to improve the accuracy of minority health and health disparities reporting and research.

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Concordance of cancer registry and self-reported race, ethnicity, and cancer type: a report from the American Cancer Society’s studies of cancer survivors

Cited by 21 publications

References 38 publications

Indigenous health equity in health register ascertainment and data quality: a narrative review

Indigenous health equity in health register ascertainment and data quality: a narrative review

Genomic expression assay testing among American Indian and Alaska Native women with breast cancer

Beyond Black and White: Mapping Misclassification of Medicare Beneficiaries Race and Ethnicity

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