Dataset Information

The color of health: skin color, ethnoracial classification, and discrimination in the health of Latin Americans.

ABSTRACT: Latin America is one of the most ethnoracially heterogeneous regions of the world. Despite this, health disparities research in Latin America tends to focus on gender, class and regional health differences while downplaying ethnoracial differences. Few scholars have conducted studies of ethnoracial identification and health disparities in Latin America. Research that examines multiple measures of ethnoracial identification is rarer still. Official data on race/ethnicity in Latin America are based on self-identification which can differ from interviewer-ascribed or phenotypic classification based on skin color. We use data from Brazil, Colombia, Mexico, and Peru to examine associations of interviewer-ascribed skin color, interviewer-ascribed race/ethnicity, and self-reported race/ethnicity with self-rated health among Latin American adults (ages 18-65). We also examine associations of observer-ascribed skin color with three additional correlates of health - skin color discrimination, class discrimination, and socio-economic status. We find a significant gradient in self-rated health by skin color. Those with darker skin colors report poorer health. Darker skin color influences self-rated health primarily by increasing exposure to class discrimination and low socio-economic status.

SUBMITTER: Perreira KM

PROVIDER: S-EPMC4117723 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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The color of health: skin color, ethnoracial classification, and discrimination in the health of Latin Americans.

Perreira Krista M KM Telles Edward E EE

Social science & medicine (1982) 20140602

Latin America is one of the most ethnoracially heterogeneous regions of the world. Despite this, health disparities research in Latin America tends to focus on gender, class and regional health differences while downplaying ethnoracial differences. Few scholars have conducted studies of ethnoracial identification and health disparities in Latin America. Research that examines multiple measures of ethnoracial identification is rarer still. Official data on race/ethnicity in Latin America are base ...[more]

PMID: 24957692

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Project description:BackgroundWhile skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial intelligence (AI) models can potentially improve early detection of skin cancers; however, the lack of skin color diversity in training datasets may only widen the pre-existing racial discrepancies in dermatology.ObjectiveThe aim of this study was to systematically review the technique, quality, accuracy, and implications of studies using AI models trained or tested in populations with skin of color for classification of pigmented skin lesions.MethodsPubMed was used to identify any studies describing AI models for classification of pigmented skin lesions. Only studies that used training datasets with at least 10% of images from people with skin of color were eligible. Outcomes on study population, design of AI model, accuracy, and quality of the studies were reviewed.ResultsTwenty-two eligible articles were identified. The majority of studies were trained on datasets obtained from Chinese (7/22), Korean (5/22), and Japanese populations (3/22). Seven studies used diverse datasets containing Fitzpatrick skin type I-III in combination with at least 10% from black Americans, Native Americans, Pacific Islanders, or Fitzpatrick IV-VI. AI models producing binary outcomes (e.g., benign vs. malignant) reported an accuracy ranging from 70% to 99.7%. Accuracy of AI models reporting multiclass outcomes (e.g., specific lesion diagnosis) was lower, ranging from 43% to 93%. Reader studies, where dermatologists' classification is compared with AI model outcomes, reported similar accuracy in one study, higher AI accuracy in three studies, and higher clinician accuracy in two studies. A quality review revealed that dataset description and variety, benchmarking, public evaluation, and healthcare application were frequently not addressed.ConclusionsWhile this review provides promising evidence of accurate AI models in populations with skin of color, the majority of the studies reviewed were obtained from East Asian populations and therefore provide insufficient evidence to comment on the overall accuracy of AI models for darker skin types. Large discrepancies remain in the number of AI models developed in populations with skin of color (particularly Fitzpatrick type IV-VI) compared with those of largely European ancestry. A lack of publicly available datasets from diverse populations is likely a contributing factor, as is the inadequate reporting of patient-level metadata relating to skin color in training datasets.

Dataset Information

The color of health: skin color, ethnoracial classification, and discrimination in the health of Latin Americans.

Publications

The color of health: skin color, ethnoracial classification, and discrimination in the health of Latin Americans.

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