Tribal Linkage and Race Data Quality for American Indians in a State Cancer Registry

Jennifer C. Johnson, Amr S. Soliman, Dan Tadgerson, Glenn E. Copeland, David A. Seefeld, Noel L. Pingatore, Rick Haverkate, Mousumi Banerjee, Marilyn A. Roubidoux

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Background: Racial misclassification of American Indian and Alaska Native (AI/AN) individuals as non-AI/AN in cancer registries presents problems for cancer surveillance, research, and public health practice. The aim of this study was to investigate the efficiency of tribal linkages in enhancing the quality of racial information in state cancer registries. Methods: Registry Plus™ Link Plus 2.0 probabilistic record linkage software was used to link the Michigan state cancer registry data (1985-2004; 1,031,168 cancer cases) to the tribal membership roster (40,340 individuals) in July of 2007. A data set was created containing AI/AN cancer cases identified by the state registry, Indian Health Service (IHS) linkages, and tribal linkage. The differences between these three groups of individuals were compared by distribution of demographic, diagnostic, and county-level characteristics using multilevel analysis (conducted in 2007-2008). Results: From 1995 to 2004, the tribal enrollment file showed linkages to 670 cancer cases (583 individuals) and the tribal linkage led to the identification of 190 AI/AN cancer cases (168 individuals) that were classified as non-AI/AN in the registry. More than 80% of tribal members were reported as non-AI/AN to the registry. Individuals identified by IHS or tribal linkages were different from those reported to be AI/AN in terms of stage at diagnosis, tumor confirmation, and characteristics of the county of diagnosis, including contract health services availability, tribal health services availability, and proportion of AI/AN residents. Conclusions: The data linkage between tribal and state cancer registry data sets improved racial classification validity of AI/AN Michigan cancer cases. Assessing tribal linkages is a simple, noninvasive way to improve the accuracy of state cancer data for AI/AN populations and to generate tribe-specific cancer information.

Original languageEnglish (US)
Pages (from-to)549-554
Number of pages6
JournalAmerican Journal of Preventive Medicine
Volume36
Issue number6
DOIs
StatePublished - Jun 1 2009

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North American Indians
Registries
Neoplasms
United States Indian Health Service
Data Accuracy
Contract Services
Alaska Natives
Public Health Practice
Multilevel Analysis
Health Services Accessibility
Information Storage and Retrieval
Population Groups
Health Services
Software

ASJC Scopus subject areas

  • Epidemiology
  • Public Health, Environmental and Occupational Health

Cite this

Johnson, J. C., Soliman, A. S., Tadgerson, D., Copeland, G. E., Seefeld, D. A., Pingatore, N. L., ... Roubidoux, M. A. (2009). Tribal Linkage and Race Data Quality for American Indians in a State Cancer Registry. American Journal of Preventive Medicine, 36(6), 549-554. https://doi.org/10.1016/j.amepre.2009.01.035

Tribal Linkage and Race Data Quality for American Indians in a State Cancer Registry. / Johnson, Jennifer C.; Soliman, Amr S.; Tadgerson, Dan; Copeland, Glenn E.; Seefeld, David A.; Pingatore, Noel L.; Haverkate, Rick; Banerjee, Mousumi; Roubidoux, Marilyn A.

In: American Journal of Preventive Medicine, Vol. 36, No. 6, 01.06.2009, p. 549-554.

Research output: Contribution to journalArticle

Johnson, JC, Soliman, AS, Tadgerson, D, Copeland, GE, Seefeld, DA, Pingatore, NL, Haverkate, R, Banerjee, M & Roubidoux, MA 2009, 'Tribal Linkage and Race Data Quality for American Indians in a State Cancer Registry', American Journal of Preventive Medicine, vol. 36, no. 6, pp. 549-554. https://doi.org/10.1016/j.amepre.2009.01.035
Johnson JC, Soliman AS, Tadgerson D, Copeland GE, Seefeld DA, Pingatore NL et al. Tribal Linkage and Race Data Quality for American Indians in a State Cancer Registry. American Journal of Preventive Medicine. 2009 Jun 1;36(6):549-554. https://doi.org/10.1016/j.amepre.2009.01.035
Johnson, Jennifer C. ; Soliman, Amr S. ; Tadgerson, Dan ; Copeland, Glenn E. ; Seefeld, David A. ; Pingatore, Noel L. ; Haverkate, Rick ; Banerjee, Mousumi ; Roubidoux, Marilyn A. / Tribal Linkage and Race Data Quality for American Indians in a State Cancer Registry. In: American Journal of Preventive Medicine. 2009 ; Vol. 36, No. 6. pp. 549-554.
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abstract = "Background: Racial misclassification of American Indian and Alaska Native (AI/AN) individuals as non-AI/AN in cancer registries presents problems for cancer surveillance, research, and public health practice. The aim of this study was to investigate the efficiency of tribal linkages in enhancing the quality of racial information in state cancer registries. Methods: Registry Plus™ Link Plus 2.0 probabilistic record linkage software was used to link the Michigan state cancer registry data (1985-2004; 1,031,168 cancer cases) to the tribal membership roster (40,340 individuals) in July of 2007. A data set was created containing AI/AN cancer cases identified by the state registry, Indian Health Service (IHS) linkages, and tribal linkage. The differences between these three groups of individuals were compared by distribution of demographic, diagnostic, and county-level characteristics using multilevel analysis (conducted in 2007-2008). Results: From 1995 to 2004, the tribal enrollment file showed linkages to 670 cancer cases (583 individuals) and the tribal linkage led to the identification of 190 AI/AN cancer cases (168 individuals) that were classified as non-AI/AN in the registry. More than 80{\%} of tribal members were reported as non-AI/AN to the registry. Individuals identified by IHS or tribal linkages were different from those reported to be AI/AN in terms of stage at diagnosis, tumor confirmation, and characteristics of the county of diagnosis, including contract health services availability, tribal health services availability, and proportion of AI/AN residents. Conclusions: The data linkage between tribal and state cancer registry data sets improved racial classification validity of AI/AN Michigan cancer cases. Assessing tribal linkages is a simple, noninvasive way to improve the accuracy of state cancer data for AI/AN populations and to generate tribe-specific cancer information.",
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N2 - Background: Racial misclassification of American Indian and Alaska Native (AI/AN) individuals as non-AI/AN in cancer registries presents problems for cancer surveillance, research, and public health practice. The aim of this study was to investigate the efficiency of tribal linkages in enhancing the quality of racial information in state cancer registries. Methods: Registry Plus™ Link Plus 2.0 probabilistic record linkage software was used to link the Michigan state cancer registry data (1985-2004; 1,031,168 cancer cases) to the tribal membership roster (40,340 individuals) in July of 2007. A data set was created containing AI/AN cancer cases identified by the state registry, Indian Health Service (IHS) linkages, and tribal linkage. The differences between these three groups of individuals were compared by distribution of demographic, diagnostic, and county-level characteristics using multilevel analysis (conducted in 2007-2008). Results: From 1995 to 2004, the tribal enrollment file showed linkages to 670 cancer cases (583 individuals) and the tribal linkage led to the identification of 190 AI/AN cancer cases (168 individuals) that were classified as non-AI/AN in the registry. More than 80% of tribal members were reported as non-AI/AN to the registry. Individuals identified by IHS or tribal linkages were different from those reported to be AI/AN in terms of stage at diagnosis, tumor confirmation, and characteristics of the county of diagnosis, including contract health services availability, tribal health services availability, and proportion of AI/AN residents. Conclusions: The data linkage between tribal and state cancer registry data sets improved racial classification validity of AI/AN Michigan cancer cases. Assessing tribal linkages is a simple, noninvasive way to improve the accuracy of state cancer data for AI/AN populations and to generate tribe-specific cancer information.

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