Classification of Canadian immigrants into visible minority groups using country of birth and mother tongue.

Open medicine : a peer-reviewed, independent, open-access journal Pub Date : 2013-10-01 eCollection Date: 2013-01-01
Mohammad R Rezai, Laura C Maclagan, Linda R Donovan, Jack V Tu
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Abstract

Background: The Permanent Resident Database of Citizenship and Immigration Canada (CIC) contains sociodemographic information on immigrants but lacks ethnic group classifications. To enhance its usability for ethnicityrelated research, we categorized immigrants in the CIC database into one of Canada's official visible minority groups or a white category using their country of birth and mother tongue.

Methods: Using public data sources, we classified each of 267 country names and 245 mother tongues in the CIC data into 1 of 10 visible minority groups (South Asian, Chinese, black, Latin American, Filipino, West Asian, Arab, Southeast Asian, Korean, and Japanese) or a white group. We then used country of birth alone (method A) or country of birth plus mother tongue (method B) to classify 2.5 million people in the CIC database who immigrated to Ontario between 1985 and 2010 and who had a valid encrypted health card number. We validated the ethnic categorizations using linked selfreported ethnicity data for 6499 people who responded to the Canadian Community Health Survey (CCHS).

Results: Among immigrants listed in the CIC database, the 4 most frequent visible minority groups as classified by method B were South Asian (n = 582 812), Chinese (n = 400 771), black (n = 254 189), and Latin American (n = 179 118). Methods A and B agreed in 94% of the categorizations (kappa coefficient 0.94, 95% confidence interval [CI] 0.93-0.94). Both methods A and B agreed with self-reported CCHS ethnicity in 86% of all categorizations (for both comparisons, kappa coefficient 0.83, 95% CI 0.82-0.84). Both methods A and B had high sensitivity and specificity for most visible minority groups when validated using self-reported ethnicity from the CCHS (e.g., with method B, sensitivity and specificity were, respectively, 0.85 and 0.97 for South Asians, 0.93 and 0.99 for Chinese, and 0.90 and 0.97 for blacks).

Interpretation: The use of country of birth and mother tongue is a validated and practical method for classifying immigrants to Canada into ethnic categories.

Abstract Image

用出生国和母语将加拿大移民划分为可见的少数群体。
背景:加拿大公民和移民永久居民数据库(CIC)包含移民的社会人口统计信息,但缺乏种族分类。为了增强其对种族相关研究的可用性,我们将CIC数据库中的移民根据其出生国和母语分为加拿大官方的少数族裔或白人类别。方法:利用公共数据源,我们将CIC数据中的267个国家名称和245种母语分别分类为10个可见少数群体(南亚、中国、黑人、拉丁美洲、菲律宾、西亚、阿拉伯、东南亚、韩国和日本)中的一个或一个白人群体。然后,我们单独使用出生国(方法A)或出生国加母语(方法B)对CIC数据库中1985年至2010年间移民到安大略省并拥有有效加密健康卡号的250万人进行分类。我们使用6499名响应加拿大社区健康调查(CCHS)的人的相关自我报告的种族数据验证了种族分类。结果:在CIC数据库中列出的移民中,用B方法分类的4个最常见的少数族裔是南亚(n = 582 812)、华人(n = 400 771)、黑人(n = 254 189)和拉丁美洲(n = 179 118)。方法A和B在94%的分类上一致(kappa系数0.94,95%可信区间[CI] 0.93-0.94)。A和B两种方法在86%的分类中与自我报告的CCHS种族一致(两种比较的kappa系数为0.83,95% CI为0.82-0.84)。当使用CCHS中自我报告的种族进行验证时,方法A和B对大多数可见的少数群体都具有很高的敏感性和特异性(例如,方法B对南亚人的敏感性和特异性分别为0.85和0.97,对中国人的敏感性和特异性分别为0.93和0.99,对黑人的敏感性和特异性分别为0.90和0.97)。解释:使用出生国和母语是将加拿大移民划分为种族类别的有效和实用的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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