Spatio-temporal pattern and associate factors of intestinal infectious diseases in Zhejiang Province, China, 2008–2021: a Bayesian modeling study

Abstract

Abstract Background Despite significant progress in sanitation status and public health awareness, intestinal infectious diseases (IID) have caused a serious disease burden in China. Little was known about the spatio-temporal pattern of IID at the county level in Zhejiang. Therefore, a spatio-temporal modelling study to identify high-risk regions of IID incidence and potential risk factors was conducted. Methods Reported cases of notifiable IID from 2008 to 2021 were obtained from the China Information System for Disease Control and Prevention. Moran’s I index and the local indicators of spatial association (LISA) were calculated using Geoda software to identify the spatial autocorrelation and high-risk areas of IID incidence. Bayesian hierarchical model was used to explore socioeconomic and climate factors affecting IID incidence inequities from spatial and temporal perspectives. Results From 2008 to 2021, a total of 101 cholera, 55,298 bacterial dysentery, 131 amoebic dysentery, 5297 typhoid, 2102 paratyphoid, 27,947 HEV, 1,695,925 hand, foot and mouth disease (HFMD), and 1,505,797 other infectious diarrhea (OID) cases were reported in Zhejiang Province. The hot spots for bacterial dysentery, OID, and HEV incidence were found mainly in Hangzhou, while high-high cluster regions for incidence of enteric fever and HFMD were mainly located in Ningbo. The Bayesian model showed that Areas with a high proportion of males had a lower risk of BD and enteric fever. People under the age of 18 may have a higher risk of IID. High urbanization rate was a protective factor against HFMD (RR = 0.91, 95% CI: 0.88, 0.94), but was a risk factor for HEV (RR = 1.06, 95% CI: 1.01–1.10). BD risk (RR = 1.14, 95% CI: 1.10–1.18) and enteric fever risk (RR = 1.18, 95% CI:1.10–1.27) seemed higher in areas with high GDP per capita. The greater the population density, the higher the risk of BD (RR = 1.29, 95% CI: 1.23–1.36), enteric fever (RR = 1.12, 95% CI: 1.00–1.25), and HEV (RR = 1.15, 95% CI: 1.09–1.21). Among climate variables, higher temperature was associated with a higher risk of BD (RR = 1.32, 95% CI: 1.23–1.41), enteric fever (RR = 1.41, 95% CI: 1.33–1.50), and HFMD (RR = 1.22, 95% CI: 1.08–1.38), and with lower risk of HEV (RR = 0.83, 95% CI: 0.78–0.89). Precipitation was positively correlated with enteric fever (RR = 1.04, 95% CI: 1.00–1.08), HFMD (RR = 1.03, 95% CI: 1.00–1.06), and HEV (RR = 1.05, 95% CI: 1.03–1.08). Higher HFMD risk was also associated with increasing relative humidity (RR = 1.20, 95% CI: 1.16–1.24) and lower wind velocity (RR = 0.88, 95% CI: 0.84–0.92). Conclusions There was significant spatial clustering of IID incidence in Zhejiang Province from 2008 to 2021. Spatio-temporal patterns of IID risk could be largely explained by socioeconomic and meteorological factors. Preventive measures and enhanced monitoring should be taken in some high-risk counties in Hangzhou city and Ningbo city.

Keywords

• Intestinal infectious diseases
• Spatial clustering
• Climate factors
• Socioeconomic condition
• Hierarchical Bayesian model