Skip to main content

Ethnic disparities in the uptake of colorectal cancer screening

Colorectal cancer is one of the most common cancers and most common cause of cancer deaths worldwide. Despite the availability of screening, disparities in survival from colorectal continue in certain ethnic minority groups. This may, in part, be secondary to low take-up of bowel cancer screening. Different ethnic groups may have different cultural and health beliefs, different levels of education, understanding and acculturation that negatively impacts upon their use of faecal testing and endoscopic procedures.

In a systematic review published in the journal Perspectives in Public Health, we examined studies that had investigated ethnic differences in the uptake of colorectal cancer screening.

We found that disparities in colorectal cancer screening are multifactorial and complex in their origin and that ethnicity plays an important role. Although seemingly intuitive, this is the first systematic review that summarises the association between uptake of screening in specific ethnic groups and which highlights the presence of significant variations in ethnicity classification globally. 

Further consistent international research is required to understand why specific ethnic groups are less likely to take up colorectal cancer screening to help in the development of more tailored public health messaging to improve screening rates and to reduce disparities in health outcomes. 

DOI: https://doi.org/10.1177%2F17579139221093153

Comments

Popular posts from this blog

What is the difference between primordial prevention and primary prevention?

Primordial prevention and primary prevention are both crucial strategies for promoting health, but they operate at different levels. Primordial prevention aims to address the root causes of health problems and improve the wider determinants of health. It focuses on preventing the emergence of risk factors in the first place by tackling the underlying social, economic, and environmental determinants of health. This involves broad, population-wide interventions such as: Policies that promote healthy food choices: Think about initiatives like taxing sugary drinks to discourage unhealthy consumption, or providing subsidies for fruits and vegetables to make them more accessible. Urban planning that prioritises well-being: This could include creating walkable neighborhoods with safe cycling routes, ensuring access to green spaces for recreation and relaxation, and designing communities that foster social connections. Social programs that address inequality: Initiatives aimed at reducing pov...

What makes a good doctor – and who gets to decide?

What Makes a Good Doctor? This is the question that Waseem Jerjes and I explore in the Journal of the Royal Society of Medicine . It is a key question that underpins the architecture of medical education, clinical practice, regulation, and professional identity. It cannot be answered by regulators, educators, or employers in isolation. It must be answered together – by doctors and patients – revisited throughout a career, and adapted as society and the profession change. Without that shared reflection, the danger is not simply disillusionment, but the erosion of the moral foundations of clinical work. As we enter an era when diagnosis will increasingly involve artificial intelligence and when performance metrics reward volume over value, reclaiming this question as a professional one is imperative. The integrity of our institutions – and of the practitioners within them – depends on reimagining excellence in inclusive, relational terms. A good doctor is not a flawless technician or a f...

Relevance Over Recall: Rethinking How AI Uses Clinical Data

Our article in the Journal of the Royal Society of Medicine argues that safe and effective AI in healthcare must incorporate mechanisms that emulate human judgement - down-weighting old, inaccurate or superseded information and prioritising what is recent, clinically relevant and reaffirmed - so that AI supports, rather than disrupts, high-quality patient care.  Clinicians constantly revise, reinterpret and filter past information so that only what is relevant, accurate and timely shapes present-day management decisions; medical records function as dynamic “working tools” rather than fixed archives. By contrast, many AI systems lack this capacity for selective forgetting and often treat all historical data as equally meaningful.  This can lead to outdated or low-confidence diagnoses being repeatedly resurfaced, persistent labels influencing clinical expectations, and irrelevant, long-resolved events cluttering summaries and decision-support outputs. Such indiscriminate recall...