Abstract
BACKGROUND: Intestinal infectious diseases (IIDs) can impact the growth and development of children and weaken adults. This study aimed to establish a spatial panel model to analyze the relationship between factors such as population, economy and health resources, and the incidence of common IIDs. The objective was to provide a scientific basis for the formulation diseases prevention measures. METHODS: Data on monthly reported cases of IIDs in each district and county of Zhejiang Province were collected from 2011 to 2021. The spatial distribution trend was plotted, and nine factors related to population, economy and health resources were selected for analysis. A spatial panel model was developed to identify statistically significant spatial patterns of influencing factors (P < 0.05). RESULTS: The results revealed that each type of IIDs exhibited a certain level of clustering. Each IIDs had a significant radiation effect, HEV (b = 0.28, P < 0.05), bacillary dysentery (b = 0.38, P < 0.05), typhoid (b = 0.36, P < 0.05), other infectious diarrheas (OIDs) (b = 0.28, P < 0.05) and hand, foot and mouth disease (HFMD) (b = 0.39, P < 0.05), indicating that regions with high morbidity rates spread to neighboring areas. Among the population characteristics, density of population acted as a protective factor for bacillary dysentery (b=-1.81, P < 0.05), sex ratio acted as a protective factor for HFMD (b=-0.07, P < 0.05), and aging rate increased the risk of OIDs (b = 2.39, P < 0.05). Urbanization ratio posed a hazard factor for bacillary dysentery (b = 5.17, P < 0.05) and OIDs (b = 0.64, P < 0.05) while serving as a protective factor for typhoid (b=-1.61, P < 0.05) and HFMD (b=-0.39, P < 0.05). Per capita GDP was a risk factor for typhoid (b = 0.54, P < 0.05), but acted as a protective factor for OIDs (b=-0.45, P < 0.05) and HFMD (b=-0.27, P < 0.05). Additionally, the subsistence allowances ratio was a risk factor for HEV (b = 0.24, P < 0.05). CONCLUSION: The incidence of IIDs in Zhejiang Province exhibited a certain degree of clustering, with major hotspots identified in Hangzhou, Shaoxing, and Jinhua. It would be essential to consider the spillover effects from neighboring regions and implement targeted measures to enhance disease prevention based on regional development.