Study on association factors of intestinal infectious diseases based-Bayesian spatio-temporal model

Yancen Zhan; Hua Gu; Xiuyang Li

doi:10.1186/s12879-023-08665-3

Study on association factors of intestinal infectious diseases based-Bayesian spatio-temporal model

BMC Infect Dis. 2023 Oct 24;23(1):720. doi: 10.1186/s12879-023-08665-3.

Authors

Yancen Zhan¹, Hua Gu², Xiuyang Li³

Affiliations

¹ Department of Big Data in Health Sciences, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
² The Cancer Hospital, University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, 310022, China.
³ Department of Big Data in Health Sciences, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310058, Zhejiang, China. lixiuyang@zju.edu.cn.

Abstract

Background: Intestinal infectious diseases (IIDs) are a significant public health issue in China, and the incidence and distribution of IIDs vary greatly by region and are affected by various factors. This study aims to describe the spatio-temporal trends of IIDs in the Chinese mainland and investigate the association between socioeconomic and meteorological factors with IIDs.

Methods: In this study, IIDs in mainland China from 2006 to 2017 was analyzed using data obtained from the China Center for Disease Control and Prevention. Spatio-temporal mapping techniques was employed to visualize the spatial and temporal distribution of IIDs. Additionally, mean center and standard deviational ellipse analyses were utilized to examine the spatial trends of IIDs. To investigate the potential associations between IIDs and meteorological and socioeconomic variables, spatiotemporal zero-inflated Poisson and negative binomial models was employed within a Bayesian framework.

Results: During the study period, the occurrence of most IIDs has dramatically reduced, with uneven reductions in different diseases. Significant regional differences were found among IIDs and influential factors. Overall, the access rate to harmless sanitary toilets (ARHST) was positively associated with the risk of cholera (RR: 1.73, 95%CI: 1.08-2.83), bacillary dysentery (RR: 1.32, 95%CI: 1.06-1.63), and other intestinal infectious diseases (RR: 1.88, 95%CI: 1.52-2.36), and negatively associated with typhoid fever (RR: 0.66, 95%CI: 0.51-0.92), paratyphoid fever (RR: 0.71, 95%CI: 0.55-0.92). Urbanization is only associated with hepatitis E (RR: 2.48, 95%CI: 1.12-5.72). And GDP was negatively correlated with paratyphoid fever (RR: 0.82, 95%CI: 0.70-0.97), and bacillary dysentery (RR: 0.77, 95%CI: 0.68-0.88), and hepatitis A (RR: 0.84, 95%CI: 0.73-0.97). Humidity showed positive correlation with some IIDs except for amoebic dysentery (RR: 1.64, 95%CI: 1.23-2.17), while wind speed showed a negative correlation with most IIDs. High precipitation was associated with an increased risk of typhoid fever (RR: 1.52, 95%CI: 1.09-2.13), and high temperature was associated with an increased risk of typhoid fever (RR: 2.82, 95%CI: 2.06-3.89), paratyphoid fever (RR: 2.79, 95%CI: 2.02-3.90), and HMFD (RR: 1.34, 95%CI: 1.01-1.77).

Conclusions: This research systematically and quantitatively studied the effect of socioeconomic and meteorological factors on IIDs, which provided causal clues for future studies and guided government planning.

Keywords: Bayesian framework; Intestinal infectious diseases; Spatio-temporal model.

MeSH terms

Bayes Theorem
China / epidemiology
Communicable Diseases* / epidemiology
Dysentery, Bacillary* / epidemiology
Humans
Incidence
Intestinal Diseases* / epidemiology
Intraabdominal Infections*
Paratyphoid Fever* / epidemiology
Spatio-Temporal Analysis
Typhoid Fever* / epidemiology