How place shapes genital herpes simplex distribution in South Korea: a Bayesian spatial analysis using National Health Insurance Service data

Abstract

Background: Genital herpes simplex (GHS) infections have increased in South Korea over the past decade, yet the geographic distribution of GHS incidence rates remains poorly understood. This study examined the geographic distribution of GHS incidence rates across South Korea and identified place-specific risk factors to inform targeted prevention strategies.

Methods: Using the Korean National Health Insurance Service data from 2019, we analyzed age-standardized GHS incidence rates across 250 municipalities in South Korea. Spatial autocorrelation was assessed using Global Moran's I and hot spots were identified using the Getis-Ord Gi* analysis. Bayesian hierarchical regression models were used to quantify the associations between regional risk factors and GHS risk while accounting for spatial dependence. The area-specific posterior mean was used to map the relative risk (RR) of GHS.

Results: In total, 182,289 patients with GHS were identified. Significant positive spatial autocorrelation was observed (Moran's I = 0.431, p < 0.001), with distinct hot spots concentrated in the Seoul Capital Area. After adjusting for covariates and spatial effects, the proportion of single-person households (RR = 1.026, 95% credible interval (CrI) = 1.017-1.035) and sexual violence risk (2nd quartile: RR = 1.106, 95% CrI = 1.018-1.201; 3rd quartile: RR = 1.095, 95% CrI = 1.003-1.195) were significant place-based risk factors associated with higher GHS incidence rates. High-risk areas for GHS were found in the Seoul Capital Area and some mid-southern cities.

Conclusions: This study reveals distinct geographic patterns in GHS incidence rates that are not fully explained by demographic composition alone. The significant associations with household structure and sexual violence risk suggest that place-specific social contexts influence GHS incidence rates. These findings highlight the importance of spatially targeted interventions to address contextual vulnerabilities in regions with high GHS incidence rates.

Keywords: Bayesian spatial modeling; Genital herpes simplex; Sexually transmitted infections; South Korea; Spatial epidemiology.

Fig. 1

Geographic distribution of standardized genital herpes simplex incidence rates (per 100,000 population) across 250 South Korean municipalities in 2019. Municipalities are color-coded from yellow (low incidence) to red (high incidence)

Fig. 2

Hot spot analysis of standardized genital herpes simplex incidence rates across South Korea in 2019 using the Getis–Ord Gi*. Red areas (high positive z-scores) indicate significant hot spots, whereas blue areas (low negative z-scores) indicate significant cold spots

Fig. 3

Municipality-level posterior mean relative risk (RR) of genital herpes simplex from the Besag–York–Mollié model in South Korea, 2019. Municipalities with significantly higher risk (RR > 1, 95% CrI excludes 1) are shown in red, and those with significantly lower risk (RR < 1) are shown in blue