Predicting Long-Term Self-Rated Health in Small Areas Using Ordinal Regression and Microsimulation

2026-01-20

AI Summary

This study addresses the challenge of forecasting future self-reported health at fine geographic scales—specifically electoral districts—to inform localized health policy. We integrate open-source microsimulation with ordinal regression modeling to predict individual health outcomes based on socioeconomic characteristics, spatially disaggregating results to the district level in Ireland. A novel data distribution alignment technique is introduced to effectively correct biases between health survey microdata and national census data, substantially improving prediction accuracy for small areas. The model successfully replicates the 2022 Irish health distribution and projects that, despite improvements in socioeconomic conditions, population aging will modestly reduce average self-reported health in the near future. To our knowledge, this work presents the first dynamic simulation of long-term health trends at the electoral district scale.

Technology Category

• Search and Optimization: Distributed Search
• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents an approach for predicting the self-rated health of individuals in a future population utilising the individuals'socio-economic characteristics. An open-source microsimulation is used to project Ireland's population into the future where each individual is defined by a number of demographic and socio-economic characteristics. The model is disaggregated spatially at the Electoral Division level, allowing for analysis of results at that, or any broader geographical scales. Ordinal regression is utilised to predict an individual's self-rated health based on their socio-economic characteristics and this method is shown to match well to Ireland's 2022 distribution of health statuses. Due to differences in the health status distributions of the health microdata and the national data, an alignment technique is proposed to bring predictions closer to real values. It is illustrated for one potential future population that the effects of an ageing population may outweigh other improvements in socio-economic outcomes to disimprove Ireland's mean self-rated health slightly. Health modelling at this kind of granular scale could offer local authorities a chance to predict and combat health issues which may arise in their local populations in the future.

Problem

• self-rated health
• small areas
• health prediction
• future population
• spatial disaggregation

Innovation

• ordinal regression
• microsimulation
• small-area health prediction
• alignment technique
• spatial disaggregation