Viability of Public Statistics–Based Synthetic Personas in Small-Area Public Health Surveillance: Sociodemographic Benchmark Consistency Study Using Korea Community Health Survey and Korean Statistical Information Service Data

BACKGROUND Synthetic population and persona datasets can provide artificial individual-level records without exposing personal data. However, for surveillance use, they must preserve the joint profiles that define vulnerable groups, not only marginal distributions. OBJECTIVE This study evaluated whether a public statistics–based Korean synthetic persona dataset is consistent with Korea Community Health Survey (CHS) and Korean Statistical Information Service (KOSIS) benchmarks for auxiliary small-area public health surveillance. METHODS We compared 1,000,000 synthetic persona rows with 1,156,122 adult respondents from the 2021-2025 CHS and selected KOSIS municipal indicators. Core variables were age group, sex, marital status, living-alone status, and education; province or municipal area replaced marital status in the regional profiles. Economic activity and occupation were treated as sensitivity domains. We compared marginal and joint distributions using percentage-point differences and total variation distance (TVD) and quantified CHS sampling uncertainty using 500-replicate stratified cluster percentile bootstrap intervals and compared high-dimensional TVD with CHS split-half references. Sparse-cell, KOSIS correlation, and regional composition-standardized analyses assessed robustness and potential downstream use. RESULTS Persona rows were close to CHS for sex (TVD<0.001, 95% bootstrap percentile interval 0.000-0.001), province (TVD 0.006, bootstrap percentile interval 0.005-0.006), living-alone status (TVD 0.008, bootstrap percentile interval 0.007-0.009), and age group (TVD 0.013, bootstrap percentile interval 0.012-0.014). Differences were larger for marital status (TVD 0.039, bootstrap percentile interval 0.038-0.040) and education (TVD 0.059, bootstrap percentile interval 0.058-0.061). The national core profile had a TVD of 0.106 (bootstrap percentile interval 0.105-0.107), compared with a CHS split-half median of 0.009 (2.5th-97.5th percentile 0.008-0.011). The municipal profile had an observed TVD of 0.117, a bootstrap mean of 0.122 (95% bootstrap percentile interval 0.120-0.123), and a split-half median of 0.082 (2.5th-97.5th percentile 0.080-0.084). After restriction to CHS cells with n≥100, the municipal TVD decreased to 0.091. At the municipal level, CHS- and persona-composition standardized unmet health care rates differed by a mean of 0.109 percentage points (Pearson r=0.567), but standardization with national profile-specific probabilities did not reproduce observed local CHS rates (Pearson r=0.004 for persona composition). The conditional KOSIS living-alone comparison was modest (Pearson r=0.547). CONCLUSIONS The synthetic persona data showed close agreement for several basic demographic margins but less consistent agreement for high-dimensional sociodemographic profiles. They may support composition-based scenario development and dashboard prototyping but should not be used as stand-alone sources for local surveillance or health-outcome inference without additional calibration and validation.