The intersection of racism, discrimination, bias, and homophobia toward African American sexual minority patients with cancer within the health care system

Elk, Ronit

doi:10.1002/cncr.33627

Cited by 6 publications

(8 citation statements)

References 25 publications

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“…One can understand how AI could use features associated with SRR from medical images captured via visible light that incorporate patients' skin tone (eg, melanoma screening). 5,9 This has similarly been observed in color retinal fundus images (RFIs) (Figure 1). 6,7,10 One workaround has been to use preprocessing methods, such as AI-based u-nets, or convolutional neural networks (CNNs) that provide precise segmentation for biomedical images, for automated segmentation of relevant retinal biomarkers, such as the retinal vasculature, hemorrhages, or neovascularization.…”

supporting

confidence: 68%

“…One can understand how AI could use features associated with SRR from medical images captured via visible light that incorporate patients’ skin tone (eg, melanoma screening) . This has similarly been observed in color retinal fundus images (RFIs) (Figure 1).…”

Section: Introductionmentioning

confidence: 87%

“…It has been welldocumented that clinicians can be biased by patients' gender, sexual orientation, weight, race, ethnicity, etc, which can ultimately affect diagnoses and subsequent treatment decisions. [1][2][3][4][5][6] With the number of applications of artificial intelligence (AI) algorithms in clinical medicine increasing, so has the attention paid to their potential biases. [6][7][8] Because AI can be deployed at a higher scale and across more populations than individual clinicians, the transferred harm from their biases is greater.…”

mentioning

confidence: 99%

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Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images

et al. 2023

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ImportanceAlthough race is a social construct, it is associated with variations in skin and retinal pigmentation. Image-based medical artificial intelligence (AI) algorithms that use images of these organs have the potential to learn features associated with self-reported race (SRR), which increases the risk of racially biased performance in diagnostic tasks; understanding whether this information can be removed, without affecting the performance of AI algorithms, is critical in reducing the risk of racial bias in medical AI.ObjectiveTo evaluate whether converting color fundus photographs to retinal vessel maps (RVMs) of infants screened for retinopathy of prematurity (ROP) removes the risk for racial bias.Design, Setting, and ParticipantsThe retinal fundus images (RFIs) of neonates with parent-reported Black or White race were collected for this study. A u-net, a convolutional neural network (CNN) that provides precise segmentation for biomedical images, was used to segment the major arteries and veins in RFIs into grayscale RVMs, which were subsequently thresholded, binarized, and/or skeletonized. CNNs were trained with patients’ SRR labels on color RFIs, raw RVMs, and thresholded, binarized, or skeletonized RVMs. Study data were analyzed from July 1 to September 28, 2021.Main Outcomes and MeasuresArea under the precision-recall curve (AUC-PR) and area under the receiver operating characteristic curve (AUROC) at both the image and eye level for classification of SRR.ResultsA total of 4095 RFIs were collected from 245 neonates with parent-reported Black (94 [38.4%]; mean [SD] age, 27.2 [2.3] weeks; 55 majority sex [58.5%]) or White (151 [61.6%]; mean [SD] age, 27.6 [2.3] weeks, 80 majority sex [53.0%]) race. CNNs inferred SRR from RFIs nearly perfectly (image-level AUC-PR, 0.999; 95% CI, 0.999-1.000; infant-level AUC-PR, 1.000; 95% CI, 0.999-1.000). Raw RVMs were nearly as informative as color RFIs (image-level AUC-PR, 0.938; 95% CI, 0.926-0.950; infant-level AUC-PR, 0.995; 95% CI, 0.992-0.998). Ultimately, CNNs were able to learn whether RFIs or RVMs were from Black or White infants regardless of whether images contained color, vessel segmentation brightness differences were nullified, or vessel segmentation widths were uniform.Conclusions and RelevanceResults of this diagnostic study suggest that it can be very challenging to remove information relevant to SRR from fundus photographs. As a result, AI algorithms trained on fundus photographs have the potential for biased performance in practice, even if based on biomarkers rather than raw images. Regardless of the methodology used for training AI, evaluating performance in relevant subpopulations is critical.

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confidence: 68%

Section: Introductionmentioning

confidence: 87%

mentioning

confidence: 99%

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Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images

et al. 2023

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“…Intersectionality and intersectional identities received growing attention during the reviewed publication period. Patients with intersecting identities may face even more complex cancer care challenges than other minority patients and chosen family members [5 ▪▪ ]. For an example, an editorial stated that SGD African Americans face multiple healthcare disparities due to the role racism and discrimination has played over decades in policy making and medical practice [5 ▪▪ ].…”

Section: Resultsmentioning

confidence: 99%

“…Patients with intersecting identities may face even more complex cancer care challenges than other minority patients and chosen family members [5 ▪▪ ]. For an example, an editorial stated that SGD African Americans face multiple healthcare disparities due to the role racism and discrimination has played over decades in policy making and medical practice [5 ▪▪ ]. This notion was confirmed in a recent cross-sectional survey which revealed that 29% of Black sexually diverse (SD) women with breast cancer chose to delay care due to barriers such as past negative healthcare encounters, other priorities or struggles relating to scheduling an appointment [6 ▪ ].…”

Section: Resultsmentioning

confidence: 99%

Sexual and gender diversity in cancer care and survivorship

Schulz-Quach

Kennedy

Lyver

2023

Current Opinion in Supportive &Amp; Palliative Care

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Purpose of the review Sexual and gender diverse (SGD) cancer patients possess unique identities and needs that must be considered during their cancer care. This narrative review explores the current literature on sexual and gender diversity in cancer care and survivorship, in addition to providing recommendations encouraged by the current literature. Recent findings We performed a literature search for articles published in English between January 2021 and June 2022 in Medline ALL and Embase. Fifty-two studies were included in this review. The many identities encapsulated in 2SLGBTQIA+ (2 Spirited, Lesbian, Gay, Bisexual, Transgender, Queer, Intersexual, Asexual, Agender, Aromantic and all gender identities and sexual orientations that are not listed) communities each have their own unique backgrounds, needs and disparities in cancer care and survivorship. However, we also identified specific protective factors in the cancer experience of SGD patients such as reports of higher resiliency and stronger support networks. Much of the recent research features recommendations on improving cancer care by creating inclusive patient questionnaires, improving in-person and online resources, and educating healthcare providers and patient-facing staff on inclusive care. Summary SGD patients have their own specific challenges during and following their cancer care. As the research continues to grow, we gain a better understanding of the needs of these patients and future steps to take to improve SGD patients’ cancer experience.

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Cancer in people who identify as lesbian, gay, bisexual, transgender, queer, or gender‐nonconforming

Kratzer,

Star,

Minihan

et al. 2024

Cancer

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BackgroundIndividuals who identify as lesbian, gay, bisexual, transgender, queer, intersex, or gender‐nonconforming (LGBTQ+) experience discrimination and minority stress that may lead to elevated cancer risk.MethodsIn the absence of population‐based cancer occurrence information for this population, this article comprehensively examines contemporary, age‐adjusted cancer risk factor and screening prevalence using data from the National Health Interview Survey, Behavioral Risk Factor Surveillance System, and National Youth Tobacco Survey, and provides a literature review of cancer incidence and barriers to care.ResultsLesbian, gay, and bisexual adults are more likely to smoke cigarettes than heterosexual adults (16% compared to 12% in 2021–2022), with the largest disparity among bisexual women. For example, 34% of bisexual women aged 40–49 years and 24% of those 50 and older smoke compared to 12% and 11%, respectively, of heterosexual women. Smoking is also elevated among youth who identify as lesbian, gay, or bisexual (4%) or transgender (5%) compared to heterosexual or cisgender (1%). Excess body weight is elevated among lesbian and bisexual women (68% vs. 61% among heterosexual women), largely due to higher obesity prevalence among bisexual women (43% vs. 38% among lesbian women and 33% among heterosexual women). Bisexual women also have a higher prevalence of no leisure‐time physical activity (35% vs. 28% among heterosexual women), as do transgender individuals (30%–31% vs. 21%–25% among cisgender individuals). Heavier alcohol intake among lesbian, gay, and bisexual individuals is confined to bisexual women, with 14% consuming more than 7 drinks/week versus 6% of heterosexual women. In contrast, prevalence of cancer screening and risk reducing vaccinations in LGBTQ+ individuals is similar to or higher than their heterosexual/cisgender counterparts except for lower cervical and colorectal cancer screening among transgender men.ConclusionsPeople within the LGBTQ+ population have a higher prevalence of smoking, obesity, and alcohol consumption compared to heterosexual and cisgender people, suggesting a higher cancer burden. Health systems have an opportunity to help inform these disparities through the routine collection of information on sexual orientation and gender identity to facilitate cancer surveillance and to mitigate them through education to increase awareness of LGBTQ+ health needs.

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The intersection of racism, discrimination, bias, and homophobia toward African American sexual minority patients with cancer within the health care system

Abstract: The creation of a racist‐free, prejudice‐free, bias‐free health care system is our individual and collective responsibility. Applying a health equity lens will guide us toward dismantling inequitable treatment.

Cited by 6 publications

References 25 publications

Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images

Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images

Sexual and gender diversity in cancer care and survivorship

Cancer in people who identify as lesbian, gay, bisexual, transgender, queer, or gender‐nonconforming

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