Measuring the population attributable fraction and the potential impact fraction of dementia risk factors in Malta : a population based secondary data analysis

Abstract

Background: The 2024 Lancet Commission report estimated that up to 45% of global dementia cases could be prevented by addressing modifiable risk factors. However, these estimates are based on international data and may not reflect national differences in risk factor prevalence or intervention feasibility. This study uses a population-based secondary data analysis to estimate both the population attributable fraction (PAF) and the potential impact fraction (PIF) for 14 dementia risk factors specific to the Maltese population.

Design and methods: Secondary data were used to estimate the prevalence of each risk factor from Maltese peer-reviewed studies or reputable international datasets where national data were unavailable. Unweighted PAFs were calculated using Levin’s formula and adjusted for overlap between risk factors. PIFs were derived by applying evidence-based relative risk reductions from published intervention studies for each weighted PAF, representing more realistic outcomes of targeted interventions.

Results: The weighted PAF for Malta was 40.47%, indicating the theoretical maximum proportion of preventable dementia cases if all risk factors were eliminated. The total PIF was 33.82%, reflecting the estimated reduction in dementia incidence if feasible interventions were implemented. The risk factors contributing most to preventable cases were high LDL cholesterol (6.15%), loneliness (4.29%), and untreated vision loss (4.25%).

Conclusions: Up to one-third of dementia cases in Malta could be reduced through population-level strategies targeting modifiable risk factors. These PIF estimates provide realistic, evidence-informed guidance for national dementia prevention planning and prioritisation.