Effective interventions for potentially modifiable risk factors for late-onset dementia: a costs and cost-effectiveness modelling study

Abstract

Background The potential economic value of interventions to prevent late-onset dementia is unknown. We modelled this for potentially modifiable risk factors for dementia.Methods For this modelling study, we searched PubMed and Web of Science from inception to March 12, 2020, and included interventions that: successfully targeted any of nine prespecified potentially modifiable risk factors (hypertension, diabetes, hearing loss, obesity, physical inactivity, social isolation, depression, cigarette smoking, and less childhood education); had robust evidence that the intervention improved risk or risk behaviour; and are feasible to enact in an adult population. We established when in the life course each intervention would be delivered. We calculated dementia incidence reduction from annual incidence of dementia in people with each risk factor, and population attributable fraction for each risk, corrected for risk factor clustering, and how effectively the intervention controls the risk factor. We calculated the discounted value of lifetime health gain and effect on cost (including NHS, social care and carer costs) per person eligible for treatment. We estimated annual total expenditure on the fully operational intervention programme in England. Findings We found effective interventions for hypertension, smoking cessation, diabetes prevention, and hearing loss.Treatments for stopping smoking and provision of hearing aids reduced cost. Treatment of hypertension was costeffective by reference to standard UK thresholds. The three interventions when fully implemented would save £1•863 billion annually in England, reduce dementia prevalence by 8•5%, and produce quality-adjusted life-year gains. The intervention for diabetes was unlikely to be cost-effective in terms of effect on dementia alone.Interpretation There is a strong case for implementing the three effective interventions on grounds of costeffectiveness and quality-of-life gains, as well as for improvements in general health. The interventions have the potential to remain cost-saving or cost-effective even with variations in dementia incidence and costs and effectiveness of interventions.

Differences in the potential for dementia prevention between major ethnic groups within one country: A cross sectional analysis of population attributable fraction of potentially modifiable risk factors in New Zealand

“…24 This might be a relatively simple but cost-effective intervention for New Zealand that may result in the more equitable reduction of dementia risk across all major ethnic groups living in New Zealand. Indeed, a recent cost modelling study in the UK 47 demonstrated not only a reduction in dementia prevalence and quality adjusted life year gains for the individual associated with the provision of hearing aids but also a net cost saving to the care system.…”

The art of simulation

“…The Article by Naaheed Mukadam and colleagues 8 in this issue of The Lancet Healthy Longevity must be seen in light of this situation. This paper is a valuable contribution to the scarce literature of the health economics of dementia prevention.…”

“…There are different ways to integrate modification of several risk factors to reflect their adjusted effect. The use of weighted population-attributable fractions (used in Mukadem and colleagues' Article 8 ) is one interesting approach. Another approach is to use a multivariate risk model such as the CAIDE index, 9 which was used in the FINGER project.…”

Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

“…As shown in a very recent UK study, effective intentions for potentially modifiable risk factors of dementia would save £1863 billion annually in England, reduce dementia prevalence by 8.5% and produce gains in quality-adjusted life year. 7 In societies where the proportions of undiagnosed dementia are particularly high, risk-predictive algorithms may even serve as a valuable tool to support early diagnosis of dementia.…”