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

TL;DR

This paper develops an ordinal regression and microsimulation approach to forecast future self-rated health in Ireland at small-area levels, aiding local health planning and policy-making.

Contribution

It introduces a novel microsimulation combined with ordinal regression for spatially disaggregated health prediction, including an alignment technique to improve accuracy.

Findings

Ageing population may slightly decrease average health status.

The model accurately predicts 2022 health distribution in Ireland.

Spatial granularity enables targeted local health interventions.

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.