TY - JOUR
T1 - Tribal Linkage and Race Data Quality for American Indians in a State Cancer Registry
AU - Johnson, Jennifer C.
AU - Soliman, Amr S.
AU - Tadgerson, Dan
AU - Copeland, Glenn E.
AU - Seefeld, David A.
AU - Pingatore, Noel L.
AU - Haverkate, Rick
AU - Banerjee, Mousumi
AU - Roubidoux, Marilyn A.
N1 - Funding Information:
We want to especially thank Dr. David Espey and Melissa Jim of the CDC and Indian Health Service for their training, support, and technical assistance on this project. Jennifer Johnson was supported by the Cancer Epidemiology Education in Special Populations Program of the University of Michigan (R25-CA 12383). We express our gratitude to the Network for Cancer Control Research Among American Indian and Alaska Native Populations and the Mayo Clinic for their mentorship, and to Kathy Welch at the University of Michigan Center for Statistical Research and Consultation, for statistical assistance. We thank Dr. Hal Morgenstern and Dr. Sandro Galea at the University of Michigan School of Public Health for their comments on an earlier version of the manuscript.
PY - 2009/6
Y1 - 2009/6
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.
AB - 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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U2 - 10.1016/j.amepre.2009.01.035
DO - 10.1016/j.amepre.2009.01.035
M3 - Article
C2 - 19356888
AN - SCOPUS:67349262348
SN - 0749-3797
VL - 36
SP - 549
EP - 554
JO - American Journal of Preventive Medicine
JF - American Journal of Preventive Medicine
IS - 6
ER -