A qualitative study of Black breast cancer previvors' and survivors' experiences after positive genetic testing.
Malika SudErika StallingsCatharine WangLillian T SosaPublished in: Journal of genetic counseling (2024)
Black women have a disproportionately high mortality rate from breast cancer, which is likely influenced by an intersection of environmental, cultural, economic, and social factors. Few published studies capture the experiences of Black women after a genetic diagnosis associated with increased risk for breast cancer. This study aims to explore the perspectives and experiences of Black women who carry a pathogenic variant associated with increased breast cancer risk and identify barriers to care for this population. We conducted semi-structured interviews with 16 participants with and without histories of breast cancer. The sample included representation across a range of demographic groups (e.g., income level, employment status, insurance status, and education level). Reflexive thematic analysis was the methodology used to analyze data. Five major themes emerged from participants' descriptions of their experiences during and after genetic testing: (1) searching for representation; (2) information enabling agency; (3) healthcare providers as facilitators or barriers to care; (4) self-identity impacting disclosure; and (5) evolving mental health and coping strategies. Participants identified barriers to care including challenging or misinformed healthcare providers, medical racism, and a lack of Black representation in the cancer community. This work deepens our understanding of the nuanced experiences of Black women across the continuum of cancer care, illustrates unmet needs, and provides a foundation for future research that includes the perspectives of Black women.
Keyphrases
- healthcare
- breast cancer risk
- mental health
- polycystic ovary syndrome
- palliative care
- pregnancy outcomes
- quality improvement
- cervical cancer screening
- pain management
- cardiovascular disease
- insulin resistance
- depressive symptoms
- current status
- cardiovascular events
- systematic review
- machine learning
- risk factors
- social support
- health information
- climate change
- data analysis
- deep learning
- genome wide