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.
Tuberculosis (TB) remains one of the leading causes of infectious disease mortality worldwide, with an estimated 1.3 million deaths annually 1,2 . Despite long-term global efforts to control the disease, substantial regional disparities in TB incidence and outcomes persist 3,4 . Within the Organization for Economic Co-operation and Development (OECD), South Korea presents a notable case: it consistently reports over 10,000 new TB cases annually and ranks second in TB incidence and fifth in TB-related mortality among OECD countries 5 . This combination of persistently high burden and comprehensive public health surveillance distinguishes Korea from most high-income countries with small numbers.
South Korea maintains a fully digitized public health reporting system in which annual statistics on TB patients, fatalities, and healthcare infrastructure are collected and released at the administrative district level 6,7 , covering over 200 districts nationwide. In such data-rich settings, where disease surveillance is systematically curated and released with high spatial coverage, the integration of population and geographic information enables detailed analyses of spatial inequality and accessibility in urban systems 3,4,[8][9][10] . South Korea provides a representative example of this environment, allowing us to examine hospital distribution and accessibility patterns in urban infrastructure using established spatial and optimization-based approaches 3,[11][12][13][14] .
More broadly, the spatial distribution of public infrastructure has been studied as a generic feature of urban systems, where facility density scales with population density 10,12,13,15,16 . In the context of healthcare, such spatial organization can influence not only average accessibility but also the robustness of service provision under uneven demand or localized constraints, as observed in other infrastructure systems 9,17 . However, in practice, privacy constraints prevent most existing analyses from accessing disease data that are jointly disaggregated by age and administrative district at fine spatial resolution, limiting their ability to capture demographic heterogeneity in disease outcomes.
Age is a critical determinant of TB disease severity 5,18 . Older individuals are more likely to experience fatal outcomes and may face structural barriers to early diagnosis or continuous treatment 19,20 . In South Korea, TB fatalities are heavily concentrated among individuals aged 70 years and older [Fig. 1(a)], whereas the general population is predominantly concentrated in middleaged groups, indicating a pronounced mismatch between demographic structure and TB mortality risk that cannot be captured by age-agnostic models. However, as mentioned above, due to privacy regulations, Korean public health statistics do not provide TB data that are jointly disaggregated by age and higher-spatial-resolution administrative districts (e.g., specific age groups within specific districts) 6 . While province-level age disaggregation is available at lower spatial resolution, this limitation creates a structural data gap that hinders age-aware spatial analysis and equity assessments at the district level, where policy interventions are typically implemented 6 .
To address this gap, we reconstruct a district-level, age-disaggregated dataset of TB patients and fatalities spanning 2014 to 2022. Our method integrates publicly available province-level age distributions with district-level totals through an upscaling procedure, thereby introducing age resolution while preserving spatial fidelity. Because the reconstruction is based solely on aggregated statistics and does not involve individual-level records, it does not increase privacy risks beyond those present in the original data. The reconstructed dataset can also be used as a testbed for evaluating age-aware spatial analyses under realistic administrative constraints. The full reconstruction pipeline and source code are released alongside the dataset, enabling reproducibility and reuse in spatial epidemiology, healthcare accessibility, and infrastructure optimization studies that require both fine spatial resolution and demographic detail.
Furthermore, hospital accessibility plays a central role in disease outcomes, as effective treatment requires early diagnosis and repeated visits to secondary care facilities over extended periods 2,19,20 . Previous optimization studies have shown that increasing hospital density and accessibility is associated with reduced TB fatalities 11 . These approaches, however, typically assume uniform vulnerability across patients and do not incorporate age-specific fatality risk, despite strong empirical evidence that TB outcomes vary sharply with age.
Building on this line of work, we extend an existing hospital relocation optimization framework by embedding an ageweighted fatality minimization objective. By explicitly accounting for age-specific vulnerability, the model a
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