Ribeiro Teresa Veronica Catonho, Ferreira Luzitano Brandão
Universidade de Brasília, Brazil.
Sao Paulo Med J. 2012;130(2):115-8. doi: 10.1590/s1516-31802012000200008.
Over recent years, the terms race and ethnicity have been used to ascertain inequities in public health. However, this use depends on the quality of the data available. This study aimed to investigate the description of color/race in Brazilian scientific journals within the field of biomedicine.
Descriptive study with systematic search for scientific articles in the SciELO Brazil database.
A wide-ranging systematic search for original articles involving humans, published in 32 Brazilian biomedical scientific journals in the SciELO Brazil database between January and December 2008, was performed. Articles in which the race/ethnicity of the participants was identified were analyzed.
In total, 1,180 articles were analyzed. The terms for describing race or ethnicity were often ambiguous and vague. Descriptions of race or ethnicity occurred in 159 articles (13.4%), but only in 42 (26.4%) was there a description of how individuals were identified. In these, race and ethnicity were used almost interchangeably and definition was according to skin color (71.4%), ancestry (19.0%) and self-definition (9.6%). Twenty-two races or ethnicities were cited, and the most common were white (37.3%), black (19.7%), mixed (12.9%), nonwhite (8.1%) and yellow (8.1%).
The absence of descriptions of parameters for defining race, as well as the use of vague and ambiguous terms, may hamper and even prevent comparisons between human groups and the use of these data to ascertain inequities in healthcare.
近年来，种族和族裔这两个术语已被用于确定公共卫生领域的不平等现象。然而，这种使用取决于现有数据的质量。本研究旨在调查巴西生物医学领域科学期刊中对肤色/种族的描述。
描述性研究，通过系统检索巴西科学电子图书馆（SciELO Brazil）数据库中的科学文章。
对2008年1月至12月期间发表在巴西科学电子图书馆数据库中32种巴西生物医学科学期刊上的涉及人类的原创文章进行广泛的系统检索。对确定了参与者种族/族裔的文章进行分析。
共分析了1180篇文章。描述种族或族裔的术语往往模糊不清。159篇文章（13.4%）中出现了对种族或族裔的描述，但只有42篇（26.4%）描述了如何识别个体。在这些文章中，种族和族裔几乎可以互换使用，定义依据肤色（71.4%）、血统（19.0%）和自我定义（9.6%）。引用了22个种族或族裔，最常见的是白人（37.3%）、黑人（19.7%）、混血（12.9%）、非白人（8.1%）和黄种人（8.1%）。
缺乏对种族定义参数的描述，以及使用模糊不清的术语，可能会妨碍甚至阻止人类群体之间的比较，以及利用这些数据来确定医疗保健方面的不平等现象。
