Spatial Clusters of Condyloma Acuminata and the Regional Risk Factors in South Korea: Bayesian Spatial Regression Analysis

TL;DR

This study maps high-risk areas for genital warts in South Korea and finds that factors like social welfare spending and adult entertainment venues are linked to higher infection rates.

Contribution

The study identifies geographic clusters of condyloma acuminata in South Korea and links them to regional socioeconomic and behavioral factors using Bayesian spatial regression.

Findings

Genital warts cases are clustered in metropolitan and southeastern regions of South Korea.

Higher incidence rates are associated with social welfare spending, single-person households, and adult entertainment venues.

Spatial clustering suggests the need for targeted public health interventions in high-risk areas.

Abstract

Condyloma acuminata (CA), the clinical manifestation of infection with low-risk human papillomaviruses 6 and 11, is a common sexually transmitted infection (STI) with recurrent lesions and notable psychosocial and health system burden. Recent evidence indicates a substantial global burden, with prevalence ranging from 0.5% to 33.1% and incidence ranging from 24 to 2940 per 100,000 person-years, varying by age, sex, time, and geography. In South Korea, national insurance data show sustained increases in patients receiving care for CA during 2010 to 2019. Beyond individual behaviors, spatial proximity and contextual factors can produce clustered STI risk. However, the municipal-level spatial distribution of CA in Korea and its contextual correlates remain understudied.

This study aimed to identify high-risk geographic clusters of CA in South Korea and determine the regional factors associated with its incidence rates.

We conducted an ecological analysis using 2019 municipal-level data from the National Health Insurance Service of Korea. Spatial autocorrelation of CA incidence rates was evaluated using Moran’s I, and clustering was assessed with Getis-Ord Gi* to detect high-risk clusters. We then analyzed potential regional determinants using two Bayesian spatial regression models: the intrinsic conditional autoregressive model and the Besag–York–Mollié model. Key municipal-level variables included health behaviors, socioeconomic indicators, health care access, adult entertainment venue density, and risk of sexual violence. Results are reported as adjusted relative risks (aRRs) with 95% credible intervals (CrIs).

A total of 52,009 CA cases were identified in 2019, 70.03% (36,421/52,009) of which were in men. We found significant positive spatial autocorrelation in CA incidence rates (Moran’s I>0, P<.001), indicating nonrandom spatial clustering. The Getis-Ord Gi* analysis revealed several high-incidence clusters (hotspots) in metropolitan and southeastern regions of South Korea. In the Bayesian spatial models, higher CA incidence rates were associated with a greater share of the municipal budget spent on social welfare (aRR 1.005, 95% CrI 1.001‐1.009), a higher percentage of single-person households (aRR 1.034, 95% CrI 1.025‐1.043), and more adult entertainment establishments per 10,000 people (aRR 1.006, 95% CrI 1.001‐1.012).

We identified significant geographic hotspots of CA and several community-level risk factors driving these patterns in South Korea. These findings highlight the importance of spatial surveillance and targeted public health interventions in high-risk areas. Adapting STI prevention programs to address local social determinants may help reduce the spread of CA in the identified hotspots.

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