BACKGROUND: In American Indians (AIs), cancer is a leading cause of mortality, yet their disease burden is not fully understood due to unaddressed racial misclassification in cancer registries. This study describes cancer trends among AIs in Michigan, focusing on breast cancer, in a linked data set of Indian Health Service (IHS), tribal, and state cancer registry data adjusted for misclassification. METHODS: AI status was based on reported race and linkage to IHS data and tribal registries. Data with complete linkage on all incident cancer cases in Michigan from 1995 to 2004 was used to calculate age-standardized incidence estimates for invasive all-site and female breast cancers stratified by racial group. For female breast cancers, stage- and age-specific incidence and percent distributions of early- versus late-stage cancers and age of diagnosis were calculated. RESULTS: More than 50% of all AI cases were identified through IHS and/or tribal linkage. In the linked data, AIs had the lowest rates of all-sites and breast cancer. For breast cancers, AI women had a greater late-stage cancer burden and a younger mean age of diagnosis as compared to whites. Although the age-specific rate for whites was greater than for AI women in nearly all age groups, the difference in hazard ratio increased with increasing age. CONCLUSIONS: Our state-specific information will help formulate effective, tailored cancer prevention strategies to this population in Michigan. The data linkages used in our study are crucial for generating accurate rates and can be effective in addressing misclassification of the AI population and formulating cancer prevention strategies for AI nationwide.
BACKGROUND: In American Indians (AIs), cancer is a leading cause of mortality, yet their disease burden is not fully understood due to unaddressed racial misclassification in cancer registries. This study describes cancer trends among AIs in Michigan, focusing on breast cancer, in a linked data set of Indian Health Service (IHS), tribal, and state cancer registry data adjusted for misclassification. METHODS: AI status was based on reported race and linkage to IHS data and tribal registries. Data with complete linkage on all incident cancer cases in Michigan from 1995 to 2004 was used to calculate age-standardized incidence estimates for invasive all-site and female breast cancers stratified by racial group. For female breast cancers, stage- and age-specific incidence and percent distributions of early- versus late-stage cancers and age of diagnosis were calculated. RESULTS: More than 50% of all AI cases were identified through IHS and/or tribal linkage. In the linked data, AIs had the lowest rates of all-sites and breast cancer. For breast cancers, AI women had a greater late-stage cancer burden and a younger mean age of diagnosis as compared to whites. Although the age-specific rate for whites was greater than for AI women in nearly all age groups, the difference in hazard ratio increased with increasing age. CONCLUSIONS: Our state-specific information will help formulate effective, tailored cancer prevention strategies to this population in Michigan. The data linkages used in our study are crucial for generating accurate rates and can be effective in addressing misclassification of the AI population and formulating cancer prevention strategies for AI nationwide.
Authors: Tess L Weber; Glenn Copeland; Noel Pingatore; Kendra K Schmid; Melissa A Jim; Shinobu Watanabe-Galloway Journal: J Health Care Poor Underserved Date: 2019
Authors: Sameer V Gopalani; Amanda E Janitz; Sydney A Martinez; Pamela Gutman; Sohail Khan; Janis E Campbell Journal: Epidemiology Date: 2020-03 Impact factor: 4.860