Why Do Ethnic Minorities Have Different Breast Cancer Diagnoses?
Breast cancer is the most common cancer in the UK, with an annual incidence of around 55,000 cases per year, or 1 woman diagnosed every 10 minutes. Recently, concerns have been raised about the apparent differences in diagnosis and treatment and subsequent poorer survival outcomes experienced among ethnic minority groups.
What is the current consensus?
Non-white groups account for approximately 15% of the population in England and Wales, according to the most recent census data available from 2011, and among these, the largest ethnic minority groups are Indians, Pakistanis, Black Caribbeans, and Black Africans. The Race Equality Foundation found that Black patients report more negative experiences of cancer care than white patients and that healthcare providers have a poor understanding of the needs of Black and minority ethnic communities, particularly in cancer awareness. In a recent research article, it was also reported that Black women from Caribbean, African, and Asian backgrounds with breast or ovarian cancer were more likely to be diagnosed at a later stage than white British women. Interestingly, though, in the UK, breast cancer incidence rates are lower in women from ethnically diverse backgrounds when compared to white women. Moreover, women from Black Caribbean and Black African backgrounds are significantly more likely to have more advanced disease at diagnosis than White British women.
Despite such findings, women from these backgrounds may be experiencing differences in breast cancer screening attendance, the stage and age of diagnosis, as well as survival rates. Addressing inequalities in healthcare, wherever they may exist, is extremely important, as is the requirement for future research to address the gaps in current evidence. Ethnic data collection and reliability have improved as a result of the Equalities Act of 2010, which legislates on the public sector equality duty for eight protected characteristics, including race. In parallel, noteworthy progress has been achieved in the completeness and accessibility of national cancer registration data, providing detailed, up-to-date, and comprehensive information on patients, tumour characteristics, and treatment factors.
Is there a disparity in breast cancer risk factor understanding?
Certain factors have been known for a while to increase the risk of breast cancer, including increasing age, harmful use of alcohol, family history of breast cancer, early radiation exposure, reproduction, e.g., age at which menstrual periods begin and age at first live pregnancy, being an active smoker, oral contraceptive use, and hormone treatments. A major challenge in breast cancer risk prediction is to develop a model that incorporates all known and newly discovered risk factors while considering the interactions among them. For example, in recent years, it has become increasingly clear that some exposures that affect breast cancer risk, particularly body mass index (BMI), nulliparity, and age at first live birth, change in magnitude and, in some cases, direction of effect over time.
Nowadays, breast cancer care is largely moving into an era of personalised medicine, where prevention, early detection, and treatment are guided by risk assessment, with the aim of increasing surveillance in high-risk women while sparing lower-risk women the burden of unnecessary imaging procedures (e.g., mammography, etc.). Such personalised medicine has been implemented due to the understanding and inclusion of both modifiable and non-modifiable breast cancer risk factors. That is, age and genetic inheritance are obviously non-modifiable, but smoking and exercise are controllable by the individual and therefore considered changeable.
Recent technological advances could play a crucial role
Currently, machine learning is providing a very compelling alternative approach to standard statistical modelling that can address current limitations and improve the accuracy of risk prediction models. Machine learning is a part of artificial intelligence (AI), which is used in different types of probabilistic and optimisation techniques. Machine learning, developed from earlier studies of pattern recognition and statistical learning, relies on computational algorithms and models to identify complex interactions among multiple heterogeneous features. In the last several years, machine learning has proven to be a great complement to traditional statistical methods for improving cancer diagnosis, detection, prediction, and prognosis.
Due to recent developments in AI, the systematic approach used to investigate new breast cancer risk factors and the creation of specialised treatment and prevention strategies that include them have dramatically improved. Given relevant data, such technology can be used to gain a more detailed understanding of the potential quantifiable relationships between ethnic minorities and the increased risk of breast cancer.
To date, factors that affect the risk of developing breast cancer in different ethnic minority groups have yet to be examined in detail. Further research is required to better understand the patterns and relationships between risk factors and ethnic groups in order to develop effective evidence-based risk management and prevention strategies.
“Early diagnosis is the holy grail of breast cancer treatment.”
— MyBOOBRisk
