Skip to main content

Does higher quality primary health care reduce stroke admissions?


Hospital admission rates for stroke are strongly associated with population factors. The supply and quality of primary care services may also affect admission rates, but there is little previous research on this association. In a paper published recently in the British Journal of General Practice, Michael Soljak and colleagues from the Department of Primary Care & Public Health at Imperial College London investigated whether the hospital admission rate for stroke is reduced by effective primary and secondary prevention in primary care.

This was a national cross-sectional study in an English population (52 763 586 patients registered with 7969 general practices in 152 primary care trusts). They found that mean annual stroke admission rates per 100 000 population varied from zero to 476.5 at practice level. In a practice-level multivariable Poisson regression, observed stroke prevalence, deprivation, and smoking prevalence were all risk factors for hospital admission. Protective healthcare factors included the percentage of stroke or transient ischaemic attack patients whose last measured total cholesterol was ≤5 mmol/l, and ability to book an appointment with a GP.

They concluded that the associations of stroke admission rates with deprivation and smoking highlight the need for effective smoking-cessation services. Patient experience of access to primary care may also be clinically important. In countries with well-developed primary healthcare systems, the potential to reduce hospital admissions by further improving the clinical quality of primary healthcare may be limited unless more rigorous quality improvement measures than those currently being used are implemented.

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...