Skip to main content

Deprivation, risk of emergency readmission and inpatient mortality in people with sickle cell disease

Sickle cell disease (SCD) is a frequent cause of emergency readmissions. In a paper published in the Journal of Public Health, Ghida Aljuburi and colleages examined trends in SCD emergency readmissions and inpatient mortality in England in relation to socio-economic status.

Data from Hospital Episode Statistics were extracted for all SCD patients admitted in 2005/06. The financial year 2005/06 was taken as the index year for analysis. We calculated readmission rates and inpatient mortality for patients admitted with a primary or secondary diagnosis of sickle cell anaemia with crisis and without crisis in the index year during the subsequent 5 years (2006/07–2010/11). Charlson Score was used to measure comorbidity. Using Cox proportional hazards models, we also examined the relationship between patient characteristics and both emergency readmissions and inpatient mortality.

In 2005/06, there were 7679 SCD index admissions. Over the subsequent 5-year period, patients living in the most socio-economically deprived areas were at highest risk of readmission (54.2% readmitted over the study period compared with 28% of the least deprived group). Inpatient mortality amongst readmissions was  also highest in patients living in the most deprived areas [hazard ratio (HR) 2.34, 95% CI 1.41–3.90].

We concluded that SCD patients from the most socio-economically deprived areas and with comorbidities are at highest risk of both SCD readmissions and in-hospital mortality, suggesting that there are inequalities in healthcare access and health outcomes amongst people with SCD.

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...