Month: February 2025

The Role of Virtual Consultations in People with Type 2 Diabetes

Living with Type 2 Diabetes (T2D) can feel like a full-time job. Regular check-ups, monitoring blood sugar levels, adjusting medications, and managing lifestyle changes all demand time and effort. For the 460 million people worldwide dealing with diabetes (a number projected to climb to 700 million by 2045), finding convenient and effective healthcare solutions is more critical than ever. Enter virtual consultations (VC); a modern approach to healthcare delivery that is transforming how people with T2D manage their condition. Our recent systematic review and meta-analysis published in the Journal of Diabetes Science and Technology sheds light on just how powerful this tool can be. Let’s examine the benefits of virtual consultations for people with T2D and why they’re becoming more important in the care of people with long-term conditions like T2D.

Convenience Without Compromise: Effectiveness Matches In-Person Care

One of the key findings from the review is that virtual consultations are just as effective as traditional face-to-face visits when it comes to managing T2D. The meta-analysis, which included 15 studies and over 821,000 participants, showed no significant difference in HbA1c reduction (a key marker of blood sugar control in people with T2D) between virtual and in-person care. Whether it’s monitoring blood pressure, lipid profiles, or glycaemic levels, VCs hold their own. This means you can get appropriate care from the comfort of your home without sacrificing results. For busy people juggling work, family, or mobility challenges, this is very positive news – good quality care no longer always requires a trip to the clinic.

Saving Time and Money: Efficiency at Its Best

Time is money, and virtual consultations save both. The review highlighted how VCs cut down on the need for frequent in-person visits, reducing travel time and costs. One study found that treatment costs dropped by an average of $135 per patient with VCs—a significant saving for both individuals and healthcare systems. Another showed that while face-to-face care often required visits every one to two weeks, virtual care patients needed only one or two appointments over four months. VCs make diabetes management more efficient, freeing up time for what matters most.

Care When You Need It: Timeliness That Counts

When it comes to long-term conditions like T2D, timely adjustments to treatment can make all the difference. Virtual consultations have a role here as well. The review noted that the average time to an initial visit dropped from 106 days to just 46 days after implementing VCs. Faster access to healthcare providers means quicker feedback, medication adjustments, and support, helping keep your condition in check. Patients also reported shorter wait times and more flexible scheduling, making it easier to fit care into busy lives.

A Personal Touch: Boosting Patient Satisfaction

You might think remote care feels impersonal, but the data says otherwise. The review found high levels of patient satisfaction with VCs, with 83% of participants in one study rating them as helpful and convenient. Another 76% felt that tools like FaceTime made their care more interactive and engaging. Patients especially appreciated the flexibility of VCs during the Covid-19 pandemic, and many expressed a desire to stick with virtual care long-term. It’s not just about convenience. It is also about feeling heard and supported in a way that fits your lifestyle.

Bridging Gaps: Improved Access for Some

For people in rural areas or those with mobility issues, getting to a clinic can be a hurdle. Virtual consultations break down those barriers. The review pointed out that VCs can increase access to care, particularly for rural patients or those who struggle with transportation. One study even noted higher rates of preventive measures – like statin prescriptions – with VCs, suggesting that remote care can enhance certain aspects of diabetes management. While there is still work to be done to ensure everyone benefits equally (more on that later), VCs are a step toward making healthcare more accessible.

A Few Caveats: Safety and Equity Challenges

No solution is perfect, and the review flagged some areas to watch. Safety-wise, VCs can not fully replace physical examinations – such as checking for foot ulcers or neuropathy – which could miss some issues if VCs are not paired with in-person visits. On the equity front, younger, patients tend to thrive with VCs, showing greater HbA1c improvements and higher satisfaction. Older adults or those with lower digital literacy, however, faced challenges like technical difficulties or lower comfort with the technology, leading to less impressive outcomes. The digital divide is real, but it’s not insurmountable. But policies to boost digital literacy and access are essential.

Conclusions

Virtual consultations aren’t just a stopgap. They are a viable, long-term option for managing people with Type 2 Diabetes. They match in-person care in effectiveness, save time and money, deliver timely support, and keep patients happy. While there is room to improve safety and ensure everyone can benefit, the potential is clear. As the review suggests, integrating VCs into routine care, paired with efforts to bridge the digital divide, could improve how we support people with T2D to manage their condition.

The Hidden Cost of Cheaper NHS Contracts: Losing Community Trust

NHS budgets are under considerable pressure. It is therefore unsurprising that many NHS Integrated Care Boards (ICBs) In England will aim to prioritise price in contract awards, But this approach is a significant threat to community-centred healthcare. While competitive tendering is a legally required, an excessive focus on costs in awarding NHS contracts risks overshadowing key factors such as established community trust, local expertise, and the long-term impact on continuity of care. This shift towards cheaper, often external, commercial providers threatens to cut the links between communities and their local health services. The argument that competitive tendering is solely about legal compliance, and not cost, is undermined by the very nature of such tendering, which by design encourages the lowest bid. This approach risks eroding the social fabric of local healthcare provision, where established relationships and understanding of specific community needs are essential.

Established local healthcare organisations – such as general practices and GP Federations – deeply rooted within their regions – possess an invaluable understanding of the intricate web of local health needs, existing healthcare networks, and the importance of continuity of care. This knowledge, developed over many years, allows these local healthcare providers to deliver care that is not only clinically effective but also culturally sensitive and responsive to the unique circumstances of the populations they serve. Distant, commercially driven firms, regardless of their operational efficiency, are unlikely to have this nuanced understanding. The potential exclusion of these local providers, who have built strong relationships with the populations they serve, could disrupt established care pathways, diminish the social value inherent in community-based healthcare, and ultimately lead to a fragmentation of services that undermines a holistic approach to patient care.

It is essential that ICBs adopt a more balanced and holistic approach to commissioning; one that transcends the narrow focus just on financial efficiency. This approach must recognise and value the long-term impact on community well-being, the preservation of essential local expertise, and the safeguarding of established relationships between healthcare providers and patients. A wider definition of ‘value’ needs to be adopted, one that includes social value, rather than simply financial cost. A system that truly prioritises patient outcomes and community health must consider the benefits of local knowledge and continuity of care, thereby ensuring that commissioning decisions are guided by a commitment to the long-term health and well-being of the communities they serve.

Why Indirect Costs on Research Grants are Essential for Universities

In recent days, there has been discussion about the “overheads” or “indirect” costs that universities add on to the cost of research projects. This has been driven by a decision by the US government to reduce the indirect costs of research on grants awarded by the US National Institutes of Health (NIH) from the current 60% to 15%. Comments from people such as Elon Musk has suggested these costs are wasteful and can therefore be easily cut from research grants. In this blog, I make the case for retaining a fair amount of indirect costs on research grants.

Without the indirect costs that universities receive on government research grants, universities would struggle to provide the essential support and infrastructure required for high-quality research to take place. While direct research costs (such as staff salaries, laboratory equipment, travel and consumables) are essential, they are only part of the funding needed. Research relies heavily on a wide array of indirect resources that ensure long-term sustainability, efficiency, and the proper functioning of universities.

Indirect costs include funding for essential services, such as maintaining research facilities and buildings, providing IT infrastructure and support, managing financial systems, and ensuring compliance through administrative and monitoring processes. Without adequate funding to cover these areas, research projects would be more difficult to complete successfully.

To address this challenge and ensure that universities receive adequate funding beyond direct project expenses, the UK government introduced the Full Economic Costs model. The Full Economic Costs model is designed to fairly and transparently allocate funding that covers the full range of costs associated with research activities.

Under this system, universities are able to recover a more realistic portion of the actual costs incurred in hosting and conducting research, helping to bridge the gap between the direct funding provided by grants and the true expenses they face. This model recognises that indirect costs, although not always visible at the project level, are vital to the successful completion and long-term sustainability of research projects.

The issue of indirect cost recovery is not unique to the UK. In the United States, for example, universities receive indirect cost reimbursements through a negotiated rate with federal agencies, but this system now also faces scrutiny over transparency and fairness. Comparisons like these highlight the importance of continually refining models such as the Full Economic Costs model to ensure they remain fair value for governments, taxpayers and universities.

The successful delivery of research projects relies on more than just securing grants for individual projects. It requires a support system that includes well-maintained buildings and other facilities, appropriate technology, efficient administrative processes, and skilled personnel; all of which are sustained by indirect funding.

Predicting COVID-19 Hospital Bed Occupancy: A Pragmatic Approach for Effective Healthcare Planning

Effective management of hospital resources was a critical component of the response to the COVID-19 pandemic. With fluctuating waves of infection and emerging virus variants, accurately predicting the demand for hospital beds has proven to be a complex but essential task. Our recent study, led by Derryn Lovett and published in BMJ Health Care Informatics, evaluates a pragmatic approach to forecasting COVID-19-positive hospital bed occupancy using simple, accessible methods.

Why Predicting Bed Occupancy Matters

During the COVID-19 pandemic, healthcare systems around the world faced unprecedented challenges, with surges in demand for acute care beds due to severe cases of the virus. The ability to predict future bed occupancy is vital for several reasons:

  1. Resource allocation: Effective forecasting helps healthcare leaders plan staffing, equipment needs, and additional capacity.
  2. Crisis management: Accurate predictions enable health systems to anticipate surges and manage overflow by opening additional care facilities when needed.
  3. Cost efficiency: Reducing over- or under-preparation minimizes waste of resources and ensures that patients receive timely care.

However, many prediction models require complex statistical knowledge, making them difficult to deploy at local or regional levels. This study sought to evaluate a simpler, more pragmatic model suitable for use by typical health system teams.

The Study: A Pragmatic Approach

The research focused on North West London (NWL) during two major COVID-19 waves, driven by the Delta variant in summer 2021 and the Omicron variant in winter 2021-2022. The model used observational data from community testing, vaccination records, and hospital admissions, with linear regression as the primary tool for prediction.

Key Data Sources:

  • COVID-19-positive test results from NWL’s Whole Systems Integrated Care (WSIC) dataset
  • Vaccination status by age group
  • Daily hospital bed occupancy reports

Model Design:

The team developed two models:

  1. Simple linear regression model: This model used the number of COVID-19 cases among unvaccinated individuals as the main predictor.
  2. Multivariable model: This model incorporated additional variables, such as age bands, recognizing that older populations are more likely to be hospitalized.

Both models accounted for a lag period between positive cases in the community and hospital admissions, allowing for predictions of bed occupancy several days in advance.

Results and Performance

The models were evaluated using mean absolute percentage error (MAPE), a measure of prediction accuracy.

Key Findings:

  • Accuracy before the Omicron wave: The multivariable model performed well, with a MAPE of 10.8% during the Delta-driven wave from July to October 2021.
  • Decline in accuracy during the Omicron wave: The accuracy of predictions deteriorated significantly during the Omicron wave, with MAPE rising to over 110%.
  • Age band considerations: While the multivariable model generally outperformed the simple model, it also faced challenges such as multicollinearity—an issue where variables are highly correlated, leading to unstable predictions.

The rapid spread and distinct characteristics of the Omicron variant, including its lower severity and higher transmissibility compared to Delta, likely contributed to the reduced model performance. The study highlights the importance of continually monitoring prediction errors and adapting models as needed.

Practical Applications

The study demonstrated that even relatively simple models can provide useful predictions during stable periods of a pandemic. Importantly, the predictions generated by the model were shared with healthcare leaders twice a week and used in planning discussions to manage resources effectively.

Key Lessons for Future Pandemics:

  1. Monitoring and adaptation: Prediction models require ongoing monitoring to detect shifts in accuracy and adapt to changing epidemiological conditions.
  2. Collaborative decision-making: The integration of model outputs into strategic meetings allowed for proactive responses to surges in demand.
  3. Scalability: The simplicity of this approach makes it scalable and deployable in other settings, particularly when more sophisticated models are not feasible.
The study also highlights that modelling the impact of COVID-19 was simpler in the initial phases of the pandemic. This was largely due to the uniform susceptibility of the population, as there was no prior exposure to SARS-CoV-2 or any available immunisation. However, as the pandemic progressed, modelling became more complex due to factors such as:
  • Changes in population immunity: Prior infections and vaccination led to varying levels of immunity within the population.
  • Emergence of new variants: The appearance of new SARS-CoV-2 variants with different characteristics (such as the Delta and Omicron variants) further complicated the modelling process.
  •  Changes in government interventions: Public health measures implemented by governments, such as lockdowns and mask mandates, also influenced the spread of the virus and needed to be accounted for in the models.
These factors collectively made modelling more challenging later in the pandemic. The study highlights the need for models that can adapt to these complexities and accurately predict the impact of COVID-19.

Limitations and Future Directions

While the study’s pragmatic approach offers many advantages, several limitations should be addressed in future research:

  • Accounting for prior infections: The current model did not include the protective effect of previous COVID-19 infections, which could improve accuracy.
  • Vaccine efficacy variability: Incorporating dynamic estimates of vaccine efficacy based on variant-specific data and individual factors could enhance predictions.
  • Geographic granularity: Future models could explore more localized predictions by accounting for differences in prevalence and hospital capacity across regions.

Additionally, as COVID-19 testing availability changes over time, alternative data sources, such as primary care or emergency department data, may be required to maintain reliable predictions.

Conclusion: Balancing Simplicity and Accuracy

Our study highlights the value of pragmatic, data-driven models in supporting healthcare system resilience during pandemics. While more complex models may offer greater accuracy, the simplicity and accessibility of this approach make it a valuable tool for rapid response scenarios. The findings underscore the importance of collaboration between research teams and healthcare providers to develop, implement, and refine predictive models tailored to real-world needs.

As we continue to navigate the challenges of COVID-19 and prepare for future health crises, pragmatic prediction models will remain an essential component of effective healthcare planning and delivery.

Reference: Lovett D, Woodcock T, Naude J, et al. Evaluation of a pragmatic approach to predicting COVID-19-positive hospital bed occupancy. BMJ Health Care Inform 2025;32:e101055.

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 poverty, improving education, and promoting social justice can create a more equitable society where everyone has the opportunity to thrive.

In contrast, primary prevention focuses on individuals or groups who are already exposed to risk factors. It aims to prevent the onset of disease by managing those existing risks. This involves measures like:

  • Vaccinations: Protecting individuals from infectious diseases such as polio or measles.
  • Long-term conditions: Reduce the risk of developing long-term conditions such as heart disease through interventions such as encouraging people to take preventive drugs like statins.
  • Lifestyle changes: Encouraging healthy habits like regular exercise, a balanced diet, and avoiding smoking.
  • Health education: Providing information and resources to empower people to make informed choices about their health.

Essentially, primordial prevention works “upstream” by creating a healthier society where risk factors are less likely to develop, while primary prevention works “downstream” by managing existing risks before they lead to disease. Although primordial and primary prevention operate at different levels, they are interlinked. For example, successful primordial prevention can reduce the burden on primary prevention by creating an environment where fewer people are exposed to risk factors and where general health and well-being are improved.

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.