Human life expectancy has exhibited continual and steady growth over the past decades. Beyond longevity and mortality trends, understanding healthy life expectancy through the modelling and forecasting of disability prevalence rates has become increasingly important for policymakers and financial practitioners. This project utilises two distinct datasets to study the disability prevalence experiences across multiple global regions. First, for a number of European countries, a cluster analysis is conducted based on their age- and sex-specific disability prevalence rates over time. Within each identified cluster, the proposed Bayesian common factor models are then applied to jointly model and forecast prevalence rates across countries and sexes. An out-of-sample analysis is carried out to assess the forecast accuracy of the cluster-based multi-population prevalence modelling and forecasting approach. The results demonstrate that incorporating prior clustering and the Bayesian framework enhances forecasting performance. Second, the proposed method is applied to model and forecast Australian disability prevalence rates of females and males. The potential practical application of these forecasts and simulations is explored in the context of valuing retirement village contracts. Overall, the findings suggest a continuing decline in disability prevalence rates across populations and age groups over the coming decades. These results contribute to the broader understanding of population health dynamics and provide valuable insights for the development of health policy and actuarial practice.