Skip to main content

The Imperial College Obesity Strategy Assessment Framework (IC-OSAF)

Obesity is a major public health issue because of its increasing prevalence and impact on health. For example, as well as its impact on conditions such as high blood pressure and coronary heart disease, obesity is now also an important risk factor for cancer and liver dieases. The management of overweight and obesity has therefore been a government priority for many years. However, overweight and obesity management at a local level has often been ineffective. Although there is a need to examine obesity strategies and policies for local populations, there is currently no readily available framework for evaluating local obesity strategies. Researchers at Imperial College, led by Nik Tuah, therefore developed a framework, the Imperial College Obesity Strategy Assessment Framework (IC-OSAF), for examining the content of local obesity strategies.

The IC-OSAF was developed by adapting two previous policy analysis frameworks (Bardach’s Eightfold Path Framework and Collins’ Health Policy Analysis Framework). These were used these with information from national guidelines to develop an obesity strategy analysis framework. The framewrok was then piloted to evaluate the obesity strategy for one London primary care trust (PCT). The framework was applied successfully and helped identify limitations and omissions in the PCT obesity management strategy. The IC-OSAF is a practical, easy-to-use tool for the analysis of local obesity management strategies. The framework can help identify gaps and limitations in strategies to help reduce variations in obesity management between PCTs. Its use should therefor be considered by other PCTs and GP commissioning groups to assess the completeness of their obesity strategies.

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...