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首页> 外文期刊>American journal of public health >Racial misclassification of American Indians and Alaska natives by Indian health service contract health service delivery area
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Racial misclassification of American Indians and Alaska natives by Indian health service contract health service delivery area

机译:美国印第安人医疗服务合同提供地区对美洲印第安人和阿拉斯加原住民的种族错误分类

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Objectives. We evaluated the racial misclassification of American Indians and Alaska Natives (AI/ANs) in cancer incidence and all-cause mortality data by Indian Health Service (IHS) Contract Health Service Delivery Area (CHSDA). Methods. We evaluated data from 3 sources: IHS-National Vital Statistics System (NVSS), IHS-National Program of Cancer Registries (NPCR)/Surveillance, Epidemiology and End Results (SEER) program, and National Longitudinal Mortality Study (NLMS). We calculated, within each data source, the sensitivity and classification ratios by sex, IHS region, and urban-rural classification by CHSDA county. Results. Sensitivity was significantly greater in CHSDA counties (IHS-NVSS: 83.6%; IHS-NPCR/SEER: 77.6%; NLMS: 68.8%) than non-CHSDA counties (IHSNVSS: 54.8%; IHS-NPCR/SEER: 39.0%; NLMS: 28.3%). Classification ratios indicated less misclassification in CHSDA counties (IHS-NVSS: 1.20%; IHS-NPCR/SEER: 1.29%; NLMS: 1.18%) than non-CHSDA counties (IHS-NVSS: 1.82%; IHSNPCR/SEER: 2.56%; NLMS: 1.81%). Race misclassification was less in rural counties and in regions with the greatest concentrations of AI/AN persons (Alaska, Southwest, and Northern Plains). Conclusions. Limiting presentation and analysis to CHSDA counties helped mitigate the effects of race misclassification of AI/AN persons, although a portion of the population was excluded.
机译:目标。我们通过印第安人健康服务(IHS)合同健康服务提供地区(CHSDA)评估了美洲印第安人和阿拉斯加原住民(AI / AN)在癌症发病率和全因死亡率数据中的种族错误分类。方法。我们评估了3个来源的数据:IHS国家生命统计系统(NVSS),IHS国家癌症登记计划(NPCR)/监视,流行病学和最终结果(SEER)计划以及国家纵向死亡率研究(NLMS)。我们在每个数据源中计算了按性别,IHS区域以及CHSDA县进行的城乡分类的敏感性和分类比率。结果。 CHSDA县(IHS-NVSS:83.6%; IHS-NPCR / SEER:77.6%; NLMS:68.8%)的敏感性显着高于非CHSDA县(IHSNVSS:54.8%; IHS-NPCR / SEER:39.0%; NLMS :28.3%)。分类比率表明,与非CHSDA县(IHS-NVSS:1.82%; IHSNPCR / SEER:2.56%; CHSDA县(IHS-NVSS:1.20%; IHS-NPCR / SEER:1.29%; NLMS:1.18%)相比,分类错误较少。 NLMS:1.81%)。在农村县和AI / AN人员最集中的地区(阿拉斯加,西南和北部平原),种族分类错误较少。结论尽管仅一部分人口被排除在外,但仅限于CHSDA县的展示和分析有助于减轻AI / AN人员种族分类错误的影响。

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