In this issue of CA: A Cancer Journal for Clinicians, Torre and colleagues have published “Cancer Statistics for Asian Americans, Native Hawaiians, and Pacific Islanders, 2016: Converging Incidence in Males and Females.” This is one in a series of articles published regularly by the American Cancer Society Surveillance and Health Services Research group on the cancer burden suffered by various racial and ethnic groups. It is done because the American Cancer Society takes seriously its obligation to serve all Americans and to point out suffering from cancer wherever it might be. These data are gathered from state and local cancer registries; from the US Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute; and from national vital registration. It is important to study and compare data grouped by race/ethnicity to shed light on inequalities in the cancer burden that can be addressed through targeted interventions. However, those of us who study cancer demographics group Americans into racial categories with hesitance. Racial data must be interpreted carefully. When misunderstood, they can truly do an injustice to those who suffer unnecessary disparate health care outcomes by distracting from real issues and taking away from support for efforts to achieve health equity. We worry that the presentation of data by race perpetuates the mistaken beliefs that:

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关于健康监测数据、种族和人口分类的一些想法

在本期《CA：临床医生癌症杂志》中，Torre 及其同事发表了“亚裔美国人、夏威夷原住民和太平洋岛民的癌症统计数据，2016 年：男性和女性的趋同发病率”。这是美国癌症协会监测和健康服务研究小组定期发表的一系列关于不同种族和族裔群体所遭受的癌症负担的文章中的一篇。之所以这样做，是因为美国癌症协会认真履行其为所有美国人服务的义务，并指出无论身在何处都患有癌症。这些数据是从州和地方癌症登记处收集的；来自美国国家癌症研究所的美国监测、流行病学和最终结果 (SEER) 计划；和来自国家人口动态登记。重要的是研究和比较按种族/民族分组的数据，以阐明可以通过有针对性的干预措施解决的癌症负担的不平等。然而，我们这些研究癌症人口统计学的人犹豫不决地将美国人分为种族类别。必须仔细解释种族数据。当被误解时，他们可能会分散对实际问题的注意力，并剥夺对实现健康公平的努力的支持，从而真正对那些遭受不必要的不​​同医疗保健结果的人造成不公正。我们担心按种族呈现数据会延续以下错误信念：必须仔细解释种族数据。当被误解时，他们可能会分散对实际问题的注意力，并剥夺对实现健康公平的努力的支持，从而真正对那些遭受不必要的不​​同医疗保健结果的人造成不公正。我们担心按种族呈现数据会延续以下错误信念：必须仔细解释种族数据。当被误解时，他们可能会分散对实际问题的注意力，并剥夺对实现健康公平的努力的支持，从而真正对那些遭受不必要的不​​同医疗保健结果的人造成不公正。我们担心按种族呈现数据会延续以下错误信念：