Syphilis remains a persistent public health challenge, and emerging evidence suggests that macro-level socioeconomic determinants contribute to its spatiotemporal heterogeneity. This study analyzed reported syphilis cases from Xining City, China, between 2008 and 2024 to characterize spatiotemporal patterns and quantify the influence of socioeconomic determinants on regional incidence. We first examined the temporal trajectory of syphilis using joinpoint regression to identify significant changes in annual case numbers. Spatial patterns were assessed with global and local Moran’s I, and kernel density estimation was used to generate continuous intensity surfaces of case concentration based on geographic coordinates. To address multicollinearity among twelve socioeconomic indicators while accounting for spatial heterogeneity, we employed geographically weighted principal component analysis (GWPCA) using GWmodelS 1.0. Components with eigenvalues greater than one were retained, capturing 74.6–75.5% of variance for economic status, 81.5–85.2% for educational level, 90.4–91.3% for healthcare development, and 56.1–67.2% for communication infrastructure. These spatially varying component scores were then incorporated as predictors in a geographically and temporally weighted regression (GTWR) model. Finally, an autoregressive integrated moving average (ARIMA) model was developed to forecast incidence trends for 2025 to inform future intervention needs. From 2008 to 2024, syphilis incidence in Xining rose from 1.59 to 6.14 per 100,000. Joinpoint regression identified two phases: a sharp increase during 2008–2013 (APC = 43.46%, p < 0.05) and a slower sustained increase during 2013–2024 (APC = 5.11%, p < 0.05). Incidence rose across all age groups, most rapidly among those aged ≥ 60 years, with sustained increases in both sexes. Spatially, the highest burden concentrated in eastern Xining. Kernel density surfaces revealed a rightward shift and rising peaks, indicating citywide growth and widening regional disparities. Global Moran’s I confirmed significant spatial clustering (I = 0.217, p < 0.05). The Bayesian spatial model further validated this pattern, showing the highest relative risks in the four core urban districts (RR range: 3.17–3.47) and the lowest in Datong County (RR = 1.75), with a spatial random effect variance of 0.32 (95% CI: 0.12–0.55) confirming clustering independent of covariates. GTWR identified positive associations with economic status, healthcare, and communication, while education’s effect varied spatially. The ARIMA model projected a continued decline through 2025, conditional on current efforts. Syphilis incidence in Xining has increased substantially and exhibits pronounced spatial and temporal heterogeneity, with macro-level socioeconomic determinants significantly shaping transmission dynamics. The temporal trends provide the broader context for understanding these spatial patterns, and the projected decline underscores the need for sustained, spatially tailored public health interventions in high-risk areas. Not applicable.
Ren et al. (Fri,) studied this question.