Skip to main content

General health checks increase the number of new diagnoses

In an article published in the journal Evidence Based Nursing, I comment on a recent Cochrane review of general health checks. These are checks that aim to detect risk factors and diseases in healthy people, with the aim of either preventing a disease from developing, or treating a disease earlier in its course. The systematic review of randomised controlled trials (RCTs) of general health checks found that they did not reduce morbidity or mortality, but did increase the number of new diagnoses. The review did though have several limitations. The trials included differed markedly in their definition of what constituted a ‘general health check’ and in the disease they were aiming to address. They also differed in how any newly identified risk factors or disease would be managed. In many studies, the only intervention offered was brief lifestyle advice. Some of the trials were also old, dating from the 1960s. Many of the currently available risk reduction interventions (such as the widespread use of electronic patient records for targeting specific patient groups, tools for measuring individual cardiovascular risk, and low-cost statins for primary prevention) were unavailable at the time most of these trials were carried out.

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...

UK Covid-19 Inquiry - The Importance of a Strong Primary Healthcare System

In my witness statement for Module 10 of the UK Covid-19 Inquiry, I discuss the pandemic's impact through from the perspectives of  primary care and public health, drawing on my extensive experience as a senior academic at Imperial College London and as a practising GP and NHS Public Health Specialist.  I emphasise that the pandemic disproportionately affected people who were clinically vulnerable, the disabled, ethnic minority communities and those living in deprived areas. The pandemic highlighted how structural inequalities, multigenerational housing, and employment in high-risk frontline roles exacerbated health disparities.  My statement also critiques the weakening of public health infrastructure - particularly for the control of infectious diseases - and the lack of integrated health data systems to identify at-risk groups such as the clinically vulnerable. I also advocated for a more robust preventive healthcare model that prioritises community-based primary care ...

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...