Are the Fitzpatrick Skin Phototypes Valid for Cancer Risk Assessment in a Racially and Ethnically Diverse Sample of Women?

Sommers, Marilyn S.; Fargo, Jamison D.; Regueira, Yadira; Brown, Kathleen M.; Beacham, Barbara; Perfetti, Angela Ross; Everett, Janine S.; Margolis, David J.

doi:10.18865/ed.29.3.505

Cited by 27 publications

(22 citation statements)

References 28 publications

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“… 23 Other investigators also have found limitations such as using self-reported skin appearance. 24 – 26 Other methods to measure sun sensitivity objectively such as spectrographic readings of melanin index or spectrophotometry to determine constitutive (untanned) skin color have been proposed. 26 …”

Section: Discussionmentioning

confidence: 99%

Validity of the Fitzpatrick Skin Phototype Classification in Ecuador

Fors

González

Viada

et al. 2020

Adv Skin Wound Care

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BACKGROUND The Fitzpatrick Skin Phototype Classification (FSPC) is the most common tool used to assess skin phototype in White populations according to the amount of pigment the skin has and its reaction to sun exposure. Scientific evidence about the use of this scale for persons with darker skin is limited. OBJECTIVE To assess the internal consistency, reliability, and construct validity of the FSPC for Ecuadorians. METHODS This observational cross-sectional study recruited participants of both sexes between 40 and 90 years of age living in a rural area of Quito, Ecuador. Cronbach α values were used to assess the internal consistency of the scale. Construct validity was assessed with confirmatory factor analysis. RESULTS The internal consistency coefficients indicated that the reliability of the responses to the scale was fair. Total α value was .515, whereas the α values of the two factors were .42 and .67. Most item-to-factor correlations had a low to moderate magnitude, ranging from r = 0.30 to 0.37. Confirmatory factor analysis supported a two-factor solution and achieved good overall fit as indicated by root mean square error of approximation = 0.08, and nonnormed fit index = 0.88 was mediocre. Goodness-of-fit χ 2 = 177.10, P < .001. The factor loads were greater than 0.30, ranging from 0.30 to 0.99. CONCLUSIONS The FSPC showed an acceptable construct validity and a fair internal consistency. The five-item scale could potentially be used as an effective instrument for assessing skin phototype in non-White people.

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Section: Discussionmentioning

confidence: 99%

Validity of the Fitzpatrick Skin Phototype Classification in Ecuador

Fors

González

Viada

et al. 2020

Adv Skin Wound Care

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“…In later studies, doctors estimated FST after an in-person inspection [29] [31]. However, even with access to the physical subject, the FST system is known to be generally unreliable estimator of skin pigmentation [17] and FST types are known to be specifically less reliable for non-White individuals [18], [19], [20], [21]. Interestingly, physician-assessed FST types have been demonstrated to correlate with race, but when FSTs are self reported, the relationship between FST and race is not consistent [31], [32], [33].…”

Section: Fitzpatrick Skin Type Classificationmentioning

confidence: 99%

“…No direct assessment or self-report was performed as part of these studies, despite being the documented method of arriving at an FST classification, as laid out in the medical literature [30] [29] [31]. Additionally, these studies did not attempt to validate that their thirdparty, remote assessments of FST were accurate representations of actual FST measures and did not address the well documented concerns of the medical community with the FST approach to skin color classification [17], [18], [19], [20], [21]. It is well established that human perception of face color is known to be affected by race [34] and by the color of other face features, such as lips [35].…”

Section: Fitzpatrick Skin Type Classificationmentioning

confidence: 99%

“…Also, in regards to FST, existing behavioral literature has found humans do not always accurately determine FST [13] [14] [15] [16]. Finally, the fundamental appropriateness of FST as a skin type metric has not been explored in the context of computer science tasks, despite documented concerns from the medical community as to the effectiveness of the FST measure [17], [18], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

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Reliability and Validity of Image-Based and Self-Reported Skin Phenotype Metrics

Howard¹,

Sirotin²,

Tipton³

et al. 2021

Preprint

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With increasing adoption of face recognition systems, it is important to ensure adequate performance of these technologies across demographic groups, such as race, age, and gender. Recently, phenotypes such as skin tone, have been proposed as superior alternatives to traditional race categories when exploring performance differentials. However, there is little consensus regarding how to appropriately measure skin tone in evaluations of biometric performance or in AI more broadly. Biometric researchers have estimated skin tone, most notably focusing on face area lightness measures (FALMs) using automated color analysis or Fitzpatrick Skin Types (FST). These estimates have generally been based on the same images used to assess biometric performance, which are often collected using unknown and varied devices, at unknown and varied times, and under unknown and varied environmental conditions. In this study, we explore the relationship between FALMs estimated from images and ground-truth skin readings collected using a colormeter device specifically designed to measure human skin. FALMs estimated from different images of the same individual varied significantly relative to ground-truth FALMs. This variation was only reduced by greater control of acquisition (camera, background, and environmental conditions). Next, we compare ground-truth FALMs to FST categories obtained using the standard, in-person, medical survey. We found that there was relatively little change in ground-truth FALMs across different FST category and that FST correlated more with self-reported race than with ground-truth FALMs. These findings show FST is poorly predictive of skin tone and should not be used as such in evaluations of computer vision applications. Finally, using modeling, we show that when face recognition performance is driven by FALMs and independent of race, noisy FALM estimates can lead to erroneous selection of race as a key correlate of biometric performance. These results demonstrate that measures of skin type for biometric performance evaluations must come from objective, characterized, and controlled sources. Further, despite this being a currently practiced approach, estimating FST categories and FALMs from uncontrolled imagery does not provide an appropriate measure of skin tone.

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“…There is also growing consensus that the language used in FSPT questions may not be broadly applicable outside of the original study population it was designed for (lightly pigmented individuals of European ancestry). As a measure of photosensitivity, FSPT performs poorly in populations from Eastern, Northeastern, and Southern Asia, South Africa, as well as in African Americans and Hispanics (Chan et al, 2005; Choe, Jang, Jo, Ahn, & Youn, 2006; Eilers et al, 2013; Pichon et al, 2010; Robinson, Joshi, Ortiz, & Kundu, 2011; Robinson, Penedo, Hay, & Jablonski, 2017; Sommers et al, 2019; Stanford, Georgouras, Sullivan, & Greenoak, 1996; Wright, Wilkes, du Plessis, & Reeder, 2015). Pichon et al (2010) observed that FSPT did not correlate well with sun‐reactivity in African‐American populations, and that in fact 59% of individuals reported that they could not even answer FSPT questions about sun sensitivity because terms such as “burning” and “tanning” did not describe their experiences.…”

Section: Dermatologymentioning

confidence: 99%

The color of normal: How a Eurocentric focus erases pigmentation complexity

Norton

2020

American J Hum Biol

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Objectives Skin pigmentation is both a highly variable and highly visible human phenotypic trait. Investigations into the biology and origins of this variation have been the focus of research in the fields of dermatology, anthropology, and forensic science, among others. This manuscript explores how much of what we know about the biology, genetics, and evolutionary origins of pigmentation has been strongly influenced by investigations and applications that focus on lighter skin. Methods I reviewed literature from the fields of dermatology, anthropology and evolutionary genetics, and forensic science to assess how perceptions of lighter skin as the “normal” state in humans can shape the ways that knowledge is gathered and applied in these fields. Results This normalization of lighter skin has impacted common tools used in dermatology and shaped the framework of dermatological education. A strong Eurocentric bias has shaped our understanding of the genetic architecture of pigmentary traits, which influences the ways in we understand the evolutionary processes leading to modern pigmentation diversity. Finally, I discuss how these biases in pigmentation genetics work in combination with phenotypic systems that privilege predicting lighter pigmentation variation to impede accurate prediction of intermediate phenotypes, particularly in individuals with ancestry from multiple populations. This can lead to a disproportionate targeting of already over‐policed populations with darker skin. Conclusions Potential changes to how we conceptualize clinical and basic pigmentation research may help to reduce existing health disparities and improve understanding of pigmentation genetic architecture and how this knowledge is applied in forensic contexts.

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Are the Fitzpatrick Skin Phototypes Valid for Cancer Risk Assessment in a Racially and Ethnically Diverse Sample of Women?

Cited by 27 publications

References 28 publications

Validity of the Fitzpatrick Skin Phototype Classification in Ecuador

Validity of the Fitzpatrick Skin Phototype Classification in Ecuador

Reliability and Validity of Image-Based and Self-Reported Skin Phenotype Metrics

The color of normal: How a Eurocentric focus erases pigmentation complexity

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