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

Seán Caulfield Curley, Karl Mason, Patrick Mannion

TL;DR

This paper develops a forward-looking framework to predict long-term self-rated health (SRH) at fine geographic scales in Ireland by combining an open-source microsimulation (SEMIPro) with ordinal regression and SRH-alignment techniques. It leverages additive log-ratio transformations and Gaussian Process regression to forecast per-cohort SRH distributions, validated against 2022 census data and extended to 2057 under multiple migration scenarios. The results show strong predictive accuracy (mean $R^2\approx0.9$) and reveal spatially varying health trajectories, with aging capable of slightly reducing national SRH despite favorable socio-economic shifts. The approach provides local authorities with scenario-based, geographically-resolved health forecasts and highlights pathways for extension, including Bayesian or ABM enhancements and incorporation of ethnicity or nationality factors.

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.

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

TL;DR

This paper develops a forward-looking framework to predict long-term self-rated health (SRH) at fine geographic scales in Ireland by combining an open-source microsimulation (SEMIPro) with ordinal regression and SRH-alignment techniques. It leverages additive log-ratio transformations and Gaussian Process regression to forecast per-cohort SRH distributions, validated against 2022 census data and extended to 2057 under multiple migration scenarios. The results show strong predictive accuracy (mean ) and reveal spatially varying health trajectories, with aging capable of slightly reducing national SRH despite favorable socio-economic shifts. The approach provides local authorities with scenario-based, geographically-resolved health forecasts and highlights pathways for extension, including Bayesian or ABM enhancements and incorporation of ethnicity or nationality factors.

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.
Paper Structure (25 sections, 6 equations, 12 figures, 2 tables)

This paper contains 25 sections, 6 equations, 12 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Data and Methods
  4. Microsimulation Populations
  5. Assumptions
  6. Initialisation
  7. Mortality
  8. Migration
  9. Fertility and Marriage
  10. Education and Employment
  11. Ordinal Regression
  12. Aligning Per-Cohort SRH Distributions
  13. Predicting Future SRH Distributions
  14. Experiments
  15. Results
  16. ...and 10 more sections

Figures (12)

  • Figure 1: A graphical summary of how the approach operates. Note that synthetic individuals are created for this example and are not based on individuals from either dataset.
  • Figure 2: A flowchart summarising the workings of the SEMIPro Irish microsimulation model me2025irelandMicrosim.
  • Figure 3: The overall distribution of SRHs from the Healthy Ireland 2023 survey
  • Figure 4: Distribution of $R^2$ values across the 3,417 EDs
  • Figure 5: The effect of each characteristic on the predicted SRH. Characteristics which cannot be assigned a numerical or boolean value are labelled with their overall category.
  • ...and 7 more figures