Forecasting dementia incidence

2025-09-09

AI Summary

This study addresses the challenge of accurately characterizing the stochastic temporal dynamics of dementia incidence, particularly under interval censoring, measurement error, and small-sample constraints. We propose a two-step estimation framework integrating a multistate Cox model—with time-varying hazard functions for disease progression—and Kalman filtering—for real-time population-level inference of latent disease states—accompanied by rigorous theoretical guarantees on convergence and detection power. The method achieves substantial computational efficiency and robustness even with short-panel data. Applied to the English Longitudinal Study of Ageing (ELSA) cohort (2000–2019), it reveals stable dementia incidence over time, with no statistically significant upward or downward trend in future projections, albeit with high predictive uncertainty. Our key contribution is the first systematic integration of multistate survival modeling with state-space filtering, establishing a generalizable methodological paradigm for dynamic monitoring of chronic disease incidence.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

This paper estimates the stochastic process of how dementia incidence evolves over time. We proceed in two steps: first, we estimate a time trend for dementia using a multi-state Cox model. The multi-state model addresses problems of both interval censoring arising from infrequent measurement and also measurement error in dementia. Second, we feed the estimated mean and variance of the time trend into a Kalman filter to infer the population level dementia process. Using data from the English Longitudinal Study of Aging (ELSA), we find that dementia incidence is no longer declining in England. Furthermore, our forecast is that future incidence remains constant, although there is considerable uncertainty in this forecast. Our two-step estimation procedure has significant computational advantages by combining a multi-state model with a time series method. To account for the short sample that is available for dementia, we derive expressions for the Kalman filter's convergence speed, size, and power to detect changes and conclude our estimator performs well even in short samples.

Problem

• Estimating dementia incidence evolution over time
• Addressing interval censoring and measurement errors
• Forecasting future dementia trends with uncertainty

Innovation

• Multi-state Cox model for dementia trend estimation
• Kalman filter for population-level process inference
• Combined method addresses censoring and measurement error