Skip to main content

What are the implications of "Make America Healthy Again" (MAHA) movement?

There are many positive elements in the "Make America Healthy Again" (MAHA) movement that would be beneficial for public health. This would include improved physical health through promoting exercise, better nutrition, reducing rates of obesity and managing chronic diseases. Exploring ways to make healthcare more affordable and accessible by the US population is also important as is recognising the importance of mental well-being, reducing stigma, and increasing access to mental healthcare services. 

Another key area is environmental health. This could include cleaner air and water, reducing pollution, and addressing climate change. The USA also suffers from high rates of drug addiction and this needs addressing through prevention, treatment, and harm reduction strategies. These would all be positive steps for public health in the USA (and countries that replicated these approaches). But it is also that the MAHA movement does not undermine effective public health interventions such as vaccination or promote drug treatments for which there is no evidence of benefit and which could be harmful.

A "Make America Healthy Again" movement, if built on a foundation of evidence-based public health principles, a commitment to health equity, and a focus on both individual and systemic changes, could be a powerful force for improving the health and well-being of Americans. However, it's essential that such a movement avoids the pitfalls of promoting misinformation, undermining proven interventions like vaccination, and pushing unproven or harmful treatments.

Comments

Post a Comment

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...
Post a Comment
Read more

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...
Post a Comment
Read more

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...
Post a Comment
Read more