Healthcare
Predicting Demographic Contraction as an Upstream Signal for Health-System Planning: A Persistence-Corrected Machine-Learning Framework
By Kim DH and Na HJ.
Abstract
Background/objectives
Demographic contraction changes the scale and geography of healthcare demand while weakening the fiscal and workforce base that sustains services. The general objective was to evaluate a persistence-corrected one-year-ahead demographic forecasting framework and to determine whether it can provide a defensible upstream signal for health-system planning; the primary model forecasts demographic change rather than health-system capacity itself.
Methods
The analytic sample retained 672 of 4616 documented country-year observations (14.6%) from 47 countries (2000-2020), representing a coverage-selected panel with sufficiently complete cross-domain data. Requiring consecutive observations produced a 522-row dynamic panel from 45 countries and 167 one-year-ahead forecasts for 2016-2020. DPC-GB learned deviations from the current population-growth rate under expanding-window validation. Analyses compared momentum and cross-domain feature sets, matched gradient-boosting hyperparameters for direct and correction targets, lagged structural covariates, held country groups out of training, and used country-cluster uncertainty estimates. Incident-onset probabilities were examined only as a supplementary exploratory analysis.