Mapping tuberculosis fatalities by region and age group in South Korea: A dataset for targeted health policy optimization

TL;DR

This paper introduces a high-resolution, district-level dataset on TB fatalities and hospital accessibility in South Korea, enabling detailed spatial and demographic analyses for targeted health policy optimization.

Contribution

It presents a novel dataset reconstructed from province-level data, extending hospital allocation models to incorporate age-weighted optimization at a fine spatial scale.

Findings

Age-structured optimization yields different hospital allocation patterns.

Incorporating age data influences resource distribution strategies.

The dataset enables temporal and spatial epidemiological analyses.

Abstract

In South Korea, age-disaggregated tuberculosis (TB) data at the district level are not publicly available due to privacy constraints, limiting fine-scale analyses of healthcare accessibility. To address this limitation, we present a high-resolution, district-level dataset on tuberculosis (TB) fatalities and hospital accessibility in South Korea, covering the years 2014 to 2022 across 228 districts. The dataset is constructed using a reconstruction method that infers age-disaggregated TB cases and fatalities at the district level by integrating province-level age-specific statistics with district-level spatial and demographic data, enabling analyses that account for both spatial heterogeneity and age structure. Building on an existing hospital allocation framework, we extend the objective function to an age-weighted formulation and apply it to the reconstructed dataset to minimize TB fatalities under different age-weighting schemes. We demonstrate that incorporating age structure can give rise to distinct optimized hospital allocation patterns, even when the total number of minimized fatalities is similar, revealing trade-offs between efficiency and demographic targeting. In addition, the dataset supports temporal analyses of TB burden, hospital availability, and demographic variation over time, and provides a testbed for spatial epidemiology and optimization studies that require high-resolution demographic and healthcare data.