Background: Despite South Korea’s achievement of Universal Health Coverage (UHC), regional health inequities persist as a significant public health challenge. Traditional urban-rural classifications often fail to capture the nuanced spatial dynamics of modern settlement patterns. This study aimed to evaluate the prevalence of hypertension (HTN) and diabetes mellitus (DM) across the urbanization gradient-Cities, Urbanized, and Rural areas-using the UN-recommended Degree of Urbanization (DegUrba) classification.
Methods: We analyzed survey-weighted data from the Korean Community Health Survey (KCHS), restricting cross-sectional analyses to adults aged ≥ 18 years residing in Gyeonggi-do in 2020 (n = 41,983), classified into three DegUrba categories (Urban, Urbanized, Rural) using 1-km² population grids. Baseline characteristics were compared using survey-weighted linear regression and the Rao-Scott chi-square test. Three survey-weighted logistic regression models were constructed, with the fully adjusted model (Model 3: age, sex, education, marital status, smoking, and alcohol consumption) as the primary model. A sensitivity analysis excluded municipalities whose DegUrba classification changed during the preceding 10-year period. Age-standardized prevalence estimates from 2008 to 2023 were calculated by direct standardization to the 2020 study population, and temporal trends were assessed using general linear models.
Results: In 2020, the weighted prevalence of hypertension and diabetes mellitus was highest in Rural areas (29.4% and 13.4%) and similar between Urban (19.4% and 8.3%) and Urbanized (20.1% and 8.6%) areas (overall P < 0.001 for both). In the fully adjusted Model 3 with Urban as reference, the rural disadvantage was attenuated and only borderline significant (HTN: OR 1.09, 95% CI 0.99-1.21, P = 0.077; DM: OR 1.11, 95% CI 0.97-1.27, P = 0.143), whereas with Urbanized as reference the association was statistically significant for hypertension (OR 1.14, 95% CI 1.02-1.28, P = 0.026). The Urban-Urbanized comparison was not significant across all models. The sensitivity analysis restricted to municipalities with stable DegUrba classification yielded stronger and more consistent rural effects (HTN Model 3 vs. Urbanized: OR 1.16, P = 0.014; DM: OR 1.18, P = 0.049). From 2008 to 2023, age-standardized prevalence increased significantly for both conditions (year P = 0.002 for HTN, P < 0.001 for DM); Urban and Urbanized prevalences remained statistically indistinguishable (P > 0.999), whereas Rural prevalence was higher than both (all P < 0.005).
Conclusion: The cross-sectional urban-urbanized similarity and persistently higher rural prevalence – particularly in geographically stable rural municipalities – indicate that the conventional urban-rural binary still captures most of the cardiometabolic disparity, but that compositional differences (age, education, marital status) account for a substantial portion of the raw rural penalty. Under universal health coverage, residual rural disadvantage in cardiometabolic disease remains a public-health priority, especially as the rural-urban gap continues to widen over time.
https://link.springer.com/article/10.1186/s12889-026-28174-7
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