Education on cancer risk assessment and genetic counseling to address cancer health disparities among racial/ethnic groups and rural populations: Implementing culturally tailored outreach through community health educators

2020 ◽  
Vol 29 (2) ◽  
pp. 243-246
Author(s):  
Sandra L. San Miguel‐Majors ◽  
Damiya E. Whitaker ◽  
Brian C. Davis ◽  
LeeAnn O. Bailey ◽  
Sanya A. Springfield
Keyword(s):  
Risk Assessment ◽  
Health Disparities ◽  
Genetic Counseling ◽  
Rural Populations ◽  
Cancer Risk Assessment ◽  
Health Educators ◽  
Cancer Health Disparities ◽  
Culturally Tailored ◽  
Community Health Educators ◽  
Racial Ethnic

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

2019 ◽  
Vol 29 (3) ◽  
pp. 505-512 ◽  
Author(s):  
Marilyn S. Sommers ◽  
Jamison D. Fargo ◽  
Yadira Regueira ◽  
Kathleen M. Brown ◽  
Barbara L. Beacham ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Cancer Risk ◽  
Skin Color ◽  
Strong Relationship ◽  
Criterion Validity ◽  
Ethnic Composition ◽  
Cancer Risk Assessment ◽  
Entire Sample ◽  
Race Ethnicity ◽  
Racial Ethnic

The Fitzpatrick Skin Phototypes (FSP) were developed to classify skin color and response to ultraviolet radiation. FSP are used clinically to assess risk for sunburn and skin cancer. Our aim was to determine the criterion-related validity of self-reported FSP when compared with skin color and sunburn history, controlling for age, race/ ethnicity, and seasonality/geography. We performed a secondary analysis of data (N=466) from an observational study. The racial/ethnic composition of the sample was 45% White/White Hispanic (WWH), 40% Black/Black Hispanic (BBH), and 15% Other Identities. Outcome measures were self-reported FSP and sunburn history, as well as physiological measures of skin color (L* lightness/darkness, a* redness/greenness, b*yellowness/blueness). Correlation between FSP and L* was -.77 (95% CI -.81, -.73; P<.001). Although 60% of the variance in FSP was accounted for by L* values for the entire sample, only 5% of the variance was accounted for among BBH participants (r=-.23), and up to 30% for WWH/Other Identity participants (r=-.48 and -.52). Mul­tiple regression analysis indicated L* and b* values, sunburn history, and race/ethnicity, but not geography/seasonality or a* values significantly and collectively accounted for 72% of the variance in FSP. While the criterion validity of FSP was established by the strong relationship between L* values and FSP for the entire sample, when exam­ined at the level of individual racial/ethnic subgroups, criterion validity of FSP was not demonstrated. When self-reported FSP are used for clinical skin assessment and sun cancer screening, they provide a restricted range of options for people with darker skin that does not capture variations in their skin color. Inaccuracy of clinical data may lead to unequal treatment or inadequate cancer risk assessment. Ethn Dis. 2019;29(3):505-512. doi:10.18865/ed.29.3.505

Identification, Management, and Evaluation of Children with Cancer-Predisposition Syndromes

2012 ◽  
pp. 576-584 ◽  
Author(s):  
Sara Knapke ◽  
Kristin Zelley ◽  
Kim E. Nichols ◽  
Wendy Kohlmann ◽  
Joshua D. Schiffman
Keyword(s):  
Risk Assessment ◽  
Genetic Counseling ◽  
Cancer Surveillance ◽  
Cancer Risk Assessment ◽  
Childhood Cancers ◽  
Children With Cancer ◽  
Genetic Syndrome ◽  
Inherited Susceptibility ◽  
Counseling And Testing ◽  
Increased Risk

Overview: A substantial proportion of childhood cancers are attributable to an underlying genetic syndrome or inherited susceptibility. Recognition of affected children allows for appropriate cancer risk assessment, genetic counseling, and testing. Identification of individuals who are at increased risk to develop cancers during childhood can guide cancer surveillance and clinical management, which may improve outcomes for both the patient and other at-risk relatives. The information provided through this article will focus on the current complexities involved in the evaluation and management of children with cancer-predisposing genetic conditions and highlight remaining questions for discussion.

scholarly journals Breast Cancer Risk Assessment Among Low-Income Women of Color in Primary Care: A Pilot Study

2015 ◽  
Vol 11 (4) ◽  
pp. e460-e467 ◽  
Author(s):  
Emily E. Anderson ◽  
Silvia Tejeda ◽  
Kimberly Childers ◽  
Melinda R. Stolley ◽  
Richard B. Warnecke ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Primary Care ◽  
Genetic Counseling ◽  
Low Income ◽  
Women Of Color ◽  
Task Force ◽  
Breast Cancer Mortality ◽  
Cancer Risk Assessment ◽  
Breast Cancer Risk Assessment

Incorporation of US Preventive Services Task Force genetic counseling recommendations as part of primary care is feasible, and warrants further investigation as a strategy for addressing disparities in breast cancer mortality.

scholarly journals Identifying the Causes of Cancer Health Disparities

2010 ◽  
Vol 8 (SI) ◽  
pp. 101-111 ◽  
Author(s):  
Keola K. Beale
Keyword(s):  
Health Disparities ◽  
Cultural Practices ◽  
Underserved Populations ◽  
Geographic Origin ◽  
Pacific Islanders ◽  
Racial Groups ◽  
Cancer Health Disparities ◽  
Clinical Trial Research ◽  
Ethnic Classification ◽  
Racial Ethnic

The causes of cancer health disparities amongst Pacific Islanders and other racial groups are complex and multifactorial. Both biologic and non biologic determinants have been identified as causal factors. Racial/ethnic classification can be used as a surrogate for non biologic determinants such as place of geographic origin, socioeconomic status, cultural practices, and diet. Given that non biologic and biologic determinants are not mutually exclusive, using racial/ethnic classification may be hypothesis generating and assist in the identification of biologic determinants such as infections, toxins, and/or environmental exposures that lead to carcinogenesis. This commentary provides several examples of cancer specific biologic determinants that may lead to cancer health disparities. It also discusses specific non biologic determinants of cancer health disparities that must be overcome in order to increase participation of underserved populations in clinical trial research. Taken together, these examples demonstrate the need to further our understanding of the determinants of cancer health disparities that can lead to the enactment of preventive measures and/or targeted therapies.

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