Scaling law characteristics and spatiotemporal multicomponent analysis of syphilis from 2016 to 2022 in Zhejiang Province, China

Abstract

Background: Syphilis has caused epidemics for hundreds of years, and the global syphilis situation remains serious. The reported incidence rate of syphilis in Zhejiang Province has ranked first in the province in terms of notifiable infectious diseases for many years and is the highest in China. This study attempts to use the scaling law theory to study the relationship between population size and different types of syphilis epidemics, while also exploring the main driving factors affecting the incidence of syphilis in different regions.

Methods: Data on syphilis cases and affected populations at the county level were obtained from the China Disease Control and Prevention Information System. The scaling relationship between different stages of syphilis and population size was explained by scaling law. The trend of the incidence from 2016 to 2022 was tested by the joinpoint regression. The index of distance between indices of simulation and observation (DISO) was applied to evaluate the overall performance of joinpoint regression model. Furthermore, a multivariate time series model was employed to identify the main driving components that affected the occurrence of syphilis at the county level. The p value less than 0.05 or confidence interval (CI) does not include 0 represented statistical significance for all the tests.

Results: From 2016 to 2022, a total of 204,719 cases of syphilis were reported in Zhejiang Province, including 2 deaths, all of which were congenital syphilis. Latent syphilis accounted for 79.47% of total syphilis cases. The annual percent change (APCs) of all types of syphilis, including primary syphilis, secondary syphilis, tertiary syphilis, congenital syphilis and latent syphilis, were - 21.70% (p < 0.001, 95% CI: -26.70 to -16.30), -16.80% (p < 0.001, 95% CI: -20.30 to -13.30), -8.70% (p < 0.001, 95% CI: -11.30 to -6.00), -39.00% (p = 0.001, 95% CI: -49.30 to -26.60) and - 7.10% (p = 0.008, 95% CI: -11.20 to -2.80), respectively. The combined scaling exponents of primary syphilis, secondary syphilis, tertiary syphilis, congenital syphilis and latent syphilis based on the random effects model were 0.95 (95% CI: 0.88 to 1.01), 1.14 (95% CI: 1.12 to 1.16), 0.43 (95% CI: 0.37 to 0.49), 0.0264 (95% CI: -0.0047 to 0.0575) and 0.88 (95% CI: 0.82 to 0.93), respectively. The overall average effect values of the endemic component, spatiotemporal component and autoregressive component for all counties were 0.24, 0.035 and 0.72, respectively. The values of the autoregressive component for most counties were greater than 0.7. The endemic component of the top 10 counties with the highest values was greater than 0.34. Two counties with value of the spatiotemporal component higher than 0.1 were Xihu landscape county and Shengsi county. From 2016 to 2022, the endemic and autoregressive components of each county showed obvious seasonal changes.

Conclusion: The scaling exponent had both temporal trend characteristics and significant heterogeneity in the association between each type of syphilis and population size. Primary syphilis and latent syphilis exhibited a linear pattern, secondary syphilis presented a superlinear pattern, and tertiary syphilis exhibited a sublinear pattern. This suggested that further prevention of infection and transmission among high-risk populations and improvement of diagnostic accuracy in underdeveloped areas is needed. The autoregressive components and the endemic components were the main driving factors that affected the occurrence of syphilis. Targeted prevention and control strategies must be developed based on the main driving modes of the epidemic in each county.

Keywords: epidemiology; joinpoint regression; multivariate time series model; scaling law; syphilis.

Figure 1

Forest plot of the scaling exponent of syphilis incidence from 2016 to 2022. (A) Forest plot of total syphilis. (B) Forest plot of primary syphilis. (C) Forest plot of secondary syphilis. (D) Forest plot of tertiary syphilis. (E) Forest plot of latent syphilis. (F) Forest plot of congenital syphilis.

Figure 2

The district-specific fitted component of syphilis in Zhejiang Province, China, 2016–2022. (A) The autoregressive component at the county level. (B) The spatiotemporal component at the county level. (C) The endemic component at the county level.

Figure 3

The time series plot of fitted components for the top 16 counties with the highest incidence. The black dots represent the monthly incidence, the blue area shows the endemic component, the green area shows the autoregressive component, and the orange area corresponds to the spatiotemporal component. The region codes represent Wenlin County, Linhai County, Yiwu County, Ninghai County, Yueqing County, Ruian County, Cangnan County, Cixi County, Yuyao County, Fuyang County, Xiaoshan County, Yinzhou County, Yuhang County, Xihu County, Shangcheng County, and Gongshu County from left to right and from top to bottom.

Figure 4

The time series plot of fitted components for the top 16 counties with the lowest incidence. The black dots represent the monthly incidence, the blue area shows the endemic component, the green area shows the autoregressive component, and the orange area corresponds to the spatiotemporal component. The region codes represent Dongtou County, Kaihua County, Qinyuan County, Jingning County, Yunhe County, Longquan County, Pan’an County, Songyang County, Shuichang County, Longyou County, Jindong County, Lanxi County, Shengsi County, Wuyi County, Deqing County, and Xihu Landscape County from left to right and from top to bottom.