| Titre : | Cancer screening and risk factor rates among American Indians. (2006) |
| Auteurs : | Judith SWAN ; BREEN (Nancy) : USA. Applied Research Program. Division of Cancer Control and Population Sciences. National Cancer Institute. ; BURHANSSTIPANOV (Linda) : USA. Native American Cancer Research. Pine. CO. ; William-W DAVIS ; MCNEEL (Timothy) : USA. Information Management Systems Inc. Silver Spring. MD. ; SATTER (Delight-E) : USA. Center for Health Policy Research. University of California. Los Angeles. ; SNIPP (Cmatthew) : USA. Department of Sociology. Stanford University. Stanford. CA. ; National Cancer Institute. Division of Cancer Control and Population Sciences. Surveillance Research Program. Bethesda. MD. USA |
| Type de document : | Article |
| Dans : | American journal of public health (vol. 96, n° 2, 2006) |
| Pagination : | 340-350 |
| Langues: | Anglais |
| Mots-clés : | Cancer ; Dépistage ; Facteur risque ; Ethnie ; Race ; Homme ; Etats Unis ; Amérique ; Amérique du Nord |
| Résumé : | [BDSP. Notice produite par INIST-CNRS 8R0x7LzI. Diffusion soumise à autorisation]. Objectives. We examined cancer screening and risk factor patterns in California using 4 different statistical tabulations of American Indian and Alaska Native (AIAN) populations. Methods. We used the 2001 California Health Interview Survey to compare cancer screening and risk factor data across 4 different tabulation approaches. We calculated weighted prevalence estimates by gender and race/ethnicity for cancer screening and risk factors, sociodemographic characteristics, and access to care variables. We compared AIAN men and women with members of other racial groups and examined outcomes among AIAN men and women using the 4 tabulation methods. Results. Although some differences were small, in general, screening and risk factor rates among American Indians/Alaska Natives were most similar to rates among Whites when the most inclusive multiracial tabulation approach was used and least similar when the more exclusive US census "single-race" approach was used. Conclusions. Racial misclassification and undercounting are among the most difficult obstacles to obtaining accurate and informative data on the AIAN population. Our analysis suggests some guidelines for overcoming these obstacles. |
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