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Primary care-based post diagnostic dementia care in England: modelling and scaling up evidence-based models for
Magdalena Walbaum1, Derek King1, Louise Robinson2
1Care Policy and Evaluation Centre - The London School of Economics and Political Science, London, United Kingdom.
Background:
The global prevalence of dementia is rising rapidly, placing increasing pressure on health and social care systems to deliver coordinated and equitable post-diagnostic support. In England, service fragmentation and workforce constraints limit the delivery of recommended care models. Non-clinical dementia navigator (DN) models have emerged as a potentially scalable approach to improve care coordination and access, yet evidence on their system-level impact remains limited.
Methods:
We estimated the population of people with diagnosed dementia in England eligible for DN support between 2025 and 2030 using NHS data and projected prevalence trends. Eligibility was adjusted for undiagnosed cases, care home residents, and expected uptake. Costs of delivering DN-led care were derived from existing UK service models and unit costs, while potential savings were based on reductions in health-care utilisation observed in the US Care Ecosystem trial. Probabilistic sensitivity analyses were conducted to assess uncertainty.
Results:
The number of eligible individuals receiving DN support is projected to increase from 300 000 in 2025 to 360 000 in 2030. Delivering this model would require ~940 to 1120 link workers. Annual costs are estimated at £224 million in 2025, rising to £343 million in 2030. These are offset by estimated savings of £1140 million and £1750 million, respectively. Even under conservative assumptions, DN models generate net savings.
Conclusions:
Scaling up DN-led care in England could support large numbers of people with dementia while generating substantial health system savings. These findings suggest that navigator-based models offer a feasible and cost-saving approach to delivering coordinated post-diagnostic dementia care at scale.
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