Identification of the spatial-temporal cluster and risk factors of hepatitis E from 2017 to 2022 in Shanghai, China

Identification of the spatial-temporal cluster and risk factors of hepatitis E from 2017 to 2022 in Shanghai, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of the spatial-temporal cluster and risk factors of hepatitis E from 2017 to 2022 in Shanghai, China Ma Zhi-Tao, Qu Ling-Xiao, Chen Kai-Yun, Shen Xin, Xu Di, Li Zhao-he, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6062452/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 17 You are reading this latest preprint version Abstract Background Hepatitis E, caused by the Hepatitis E virus (HEV), is a global infectious liver disease primarily transmitted through fecal-oral and zoonotic routes. Despite the launch of a vaccine in China in 2011, hepatitis E remains a public health concern due to its diverse transmission routes and increasing sporadic cases. Herein, we aim to identify the potential case clusters and risk events at community-level in Shanghai to inform tailored strategy. Methods Data on HEV cases from 2017 to 2022 in Shanghai were collected from the National Notifiable Disease Reporting System (NNDRS) and supplemented with population and socio-economic data from Shanghai's communities. Descriptive and temporal analysis were applied to describe the epidemiological patterns, spatial-temporal scan analysis was used to identify potential case clusters, and binary logistic regression tests were conducted to explore the associations between risk factors and potential clusters. Results A total of 4,668 HEV cases were analyzed, with an average annual notification rate of 3.14 per 100,000 population in Shanghai from 2017 to 2022. Temporal analysis identified a significant temporal cluster from January 1st of 2017 to May 31th of 2019(RR = 1.73, LLR = 135.74, P < 0.001), and a seasonal cluster from December to May. Spatial-temporal analysis revealed the most likely cluster in urban areas, with additional clusters in suburban towns. Binary logistic regression indicated positive associations between the risk rank of the clusters and population density (OR PD =7.367, P PD <0.001), as well as the count of malls (OR COM =1.531, P COM =0.050), and a negative association with the distance to river (OR DTR =0.742, P DTR =0.048). Conclusions The spatial-temporal pattern of HEV in Shanghai during 2017 to 2022 suggested that although the communities with higher notification rate distribute in the southeast of Shanghai, the most likely cluster through spatial-temporal analysis per year locates in urban area, which is corresponding with the one scanned over 6 years. Investigation about HEV infectious cases related to potential transmission routes could be further operated to testify the causal effects and inform a more focused strategy to prevent the transmission of HEV. Hepatitis E Infection Spatial-temporal distribution Cluster Risk factors Figures Figure 1 Figure 2 Figure 3 Background Hepatitis E is an infectious liver disease caused by the hepatitis E virus (HEV) which is the primary etiologic agent of acute hepatitis and jaundice worldwide [ 1 ], and mainly transmitted via the fecal-oral and zoonotic routes [ 2 , 3 ]. According to the all-cause mortality estimation from Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2021), HEV caused about 1.94 million new infections and led to 3,400 deaths per year [ 4 ]. In 2016, the World Health Assembly (WHA) endorsed the Global Health Sector Strategy on Viral Hepatitis aimed to eliminate viral hepatitis as a public health threat by 2030, with elimination defined as a 90% reduction in incidence and a 65% reduction in mortality [ 5 ]. Recently, due to the HEV diversified transmission routes, low coverage of vaccine immunization which was originally launched in China since 2011 [ 6 ] and increasing number of reported sporadic cases, hepatitis E became a public health rising concern not only in developing countries, but also in developed ones [ 7 ]. China is a vast country with uneven economic development. From 2004 to 2023, the reported incidence of hepatitis E show an upward trend from 1.27/100,000 to 2.11/100,000, additionally, the sero-epidemiological investigations revealed an existence of subclinical infectious status among residents [ 8 ]. To reduce the incidence rate of HEV infection and its potential disease burden, the establishment of early warning algorithm and identification of the potential case cluster and risk events to prevent the spread of epidemic would be of great importance. Previous studies have identified spatial clustering of HEV in coastal provinces of China, with a discernible trend of shifting from the northeastern coastal regions towards the southern coastal areas such as the Yangtze River Delta (YRD) region [ 9 ]. Shanghai is a mega-city in the YRD region, the age-standardized prevalence rate of anti-HEV IgG in the general population was 31.94% in 2018 [ 10 ], whereas, the epidemic characteristics of hepatitis E were mainly sporadic, which was close to the developed countries. To look for the epidemiological associations of these sporadic cases, a surveillance system has been established and a series of studies of HEV genotype, transmission route, risk factors for infection, forecasting mathematical algorithm and spatial-temporal analysis have been conducted in Shanghai since 1997 [ 11 , 12 ]. Therefore, taking measures in Shanghai would play a pivotal role in achieving the targets of incidence reduction for the whole China and other developed countries and regions. To establish a proper algorithm of early warning upon HEV and raise the sensitivity and efficiency of the existing HEV surveillance system, we aim to identify the potential case clusters and risk events at spatial or hierarchical levels such as city, district or community to inform priorities and tailored strategy. Our research team had applied hotspot analysis and spatial-temporal statistics to detect the hepatitis E spatial clusters and spatial-temporal clusters from 2006 to 2016 and identified “high-high cluster” located in the downtown of Shanghai [ 13 ]. Herein, on the base of the former studies we analyzed the epidemiological characteristics of hepatitis E to grasp distribution variation in Shanghai, and further combine regression model with risk factors from zoonotic, waterborne and population density at community level to reveal potential transmission patterns. Method Data source The data of HEV cases involved were reported by the specialized medical staff in submitting and notification of HEV to the National Notifiable Disease Reporting System (NNDRS) which was established by China Centers for Disease Control and Prevention (CDC) for surveillance of disease nationwide, and confirmed with criteria announced by the Ministry of Health of the People’s Republic of China, which were infected by hepatitis E virus and diagnosed with laboratory tests [ 14 ]. The cases were resided in Shanghai during 2017 to 2022, which were gathered and screened from the system. The characters of every confirmed HEV case contained age, gender, residence address, current address, onset date, notifiable date. The data of population and social-health-economy distributed in the communities of Shanghai were gathered from the Statistical Yearbooks of Shanghai. Every HEV case had a unique code according to the community of residential addressed, which was consistent with the area code from polygon map based on a 1:1,000,000 scale layer, with which we aggregated and matched the cases to polygon map. Community’s central point was selected as the representation of potential cluster created by toolbox of Geoprocessing in ArcGIS v10.1(ESRI Inc, Redlands, CA, USA), whose longitude and latitude coordinates were determined through the Google geocoding service. Descriptive and temporal analysis The specific values of day, month and year were respectively extracted from the notification date of HEV cases and utilized to present the distribution from 2017 to 2022, and characteristics such as report date and registered residence were described. The purely temporal scan analysis was employed to detect the possible temporal cluster and seasonal cluster, whose statistically significances appear when a larger log likelihood ratio (LLR) compared to the Standard Monte Carlo Critical Value (SMCCV) accompany with P value less than 0.05. Spatial-temporal scan analysis The dimensions of purely temporal scan analysis and spatial distribution per year were confined to temporal and spatial respectively. In order to merge the spatial dimension and the temporal dimension, we utilized Kulldorff’s space-time scan statistic for spatial-temporal scan analysis, which could be realized by SaTScan v10.1(Martin Kulldorff, Information Management Services Inc)[ 15 ]. Kulldorff’s space-time scan statistic belongs to an method for detecting and evaluating localized clusters in study area with three dimensions, which could produce a cylindrical window contains a circular geographic base linking to space scanning and a height linking to time scanning[ 16 ]. The cylindrical window moves in space along with time for searching likely clusters through 999 times replicated simulation. The hypothesis was shaped in this study: whether the risk of being a confirmed HEV case among HEV cases in certain communities inside the cluster( p ) appear larger compared to the case outside of the communities( q ). The spatial-temporal risk can be assessed through the null hypothesis(H0: p = q ) and the alternative hypothesis(H1: p > q )[ 17 ]. The relative risk ( RR ) was utilized to estimate the risks inside and outside the cluster which is calculate with the equation RR = p / q . Log likelihood ratio (LLR) was employed to calculated in each simulative cylindrical window, and the sum of the LLRs whose observed cases were larger than the one with expected cases among 999 simulative replications was applied to assess the probability of the cluster appeared. The maximum sum of LLRs ( Max \(\:\sum\:_{i}^{}LLRi\) ) with more expected HEV cases indicated the most likely cluster, the second largest LLR indicated the second likely cluster[ 18 ]. The relative likelihood (RL) aims to evaluate the significance of HEV cluster, which is the value of LLR inside the cluster to overall LLR(L0), the RL were produced as the cylindrical window move in this study and the rank of the RL was generated to assess statistical significance of HEV cluster at 0.05 level(α = 0.05). In our space-time statistical analysis, limits were set that the expected HEV cases were no more than the total HEV cases and scan time range were no more than half of the study period, in case unable to reflect the difference between the risk of infection inside of the cluster and the one outside for overlapping most communities and study period, whose time aggregation length was set as 1.5 months in aligned with the average incubation period of HEV. Binary logistic regression test Based on the fact that the HEV transmission usually links with the water contamination and the consumption of infected pork [ 19 , 20 ]. The indicators of nearest distance to river (DTR) and count of mall (COM) covered by a circular area with a radius of 1 kilometer base on center of polygons and population density (PD) of every community were involved as independent variables, and the risk rank of clusters (RRC) as dependent variable. The distance to river (DTR) is defined as the straight distance from the community’s central point to nearest river, and the count of mall (COM) is defined as count of malls covered by a circle area with a radius of 1 km and centered at community’s central point. The population density (PD) means the population (10,000) per square kilometers. the risk rank of every community was defined as 0 and 1 values, which depends on every community’s central location including outside or inside the most likely cluster through space-time scan statistical analysis over 6 years span. The binary logistic regression test was employed to explore the correlation between the risk rank (RR) and distance to river (DTR), count of mall (COM), population density (PD) with SPSS 18.0.0 software. Results Descriptive analysis Totally 4,668 HEV cases were included in the study. The average notification annual rate in Shanghai was 3.14 per 100,000 population during 2017 to 2022. The ratio of male and female increased by years from 2017 to 2022 (Table 1 ). The first peak of distribution mostly presented in 2018 in Shanghai from 2017 to 2019, and the second peak appeared in 2021 during the period time of 2020–2022. Table 1 The number and notification rate of HEV case (per 100,000 population) with characteristics from 2017 to 2022 in Shanghai. Year Gender Gender rate(M:F) Total Incidence rate (/10 5 ) Male Female 2017 462 373 1.24 835 0.34 2018 505 397 1.27 902 0.36 2019 502 379 1.32 881 0.36 2020 318 196 1.62 514 0.21 2021 552 303 1.82 855 0.34 2022 425 256 1.66 681 0.28 Total 2764 1904 1.45 4668 -- Temporal analysis Through temporal analysis, we found a pure temporal cluster started from Jan 1st, 2017 to May 31st, 2019, whose relative risk (RR), log likelihood ratio (LLR) was respectively 1.35, 52.18 ( P = 0.001) (Fig. 1 ). A seasonal temporal ranged from December to May, and the RR, LLR was respectively 1.44, 76.28( P = 0.001). Spatial-temporal analysis The spatial temporal analysis of every year revealed that the most likely cluster focus at urban area. And there are two extra likely clusters far away beyond the most likely cluster, one of which covered two towns (Jiading and Juyuanxinqu) both located in Jiading district, and another one covers Nanhuixincheng town locates in Pudongxinqu district in 2018. In 2020, the second likely cluster covers Hudongxincun town in Pudong district except the most likely cluster around central area. (Fig. 2 and Table 2 ) Through spatial-temporal analysis with 6 years span at community-level from 2017 to 2022, one statistically significant cluster with time frame during Jan 1st, 2017 to Jun 30th, 2019 ( P < 0.001) were found, whose Relative Risk (RR), Log likelihood ratio ( LLR ) was respectively 1.73, 135.74.(Fig. 3 ) Table 2 The S-T cluster* scanned per year at community-level in Shanghai, 2017–2022. Scan Period Time frame Count of covered communities Relative risk ( RR ) Log likelihood ratio ( LLR ) P -value 2017 2017.1.1-2017.6.30 36 1.87 35.97 < 0.001 2018 2018.1.1-2018.6.30 90 1.99 42.82 < 0.001 2018.1.1-2018.3.31 2 11.14 16.45 < 0.001 2018.4.1-2018.9.30 1 9.08 10.50 0.035 2019 2019.1.1-2019.6.30 92 2.02 44.92 < 0.001 2020 2020.7.1-2020.12.31 83 1.79 16.39 < 0.001 2020.1.1-2020.3.31 1 14.88 12.33 0.006 2021 2021.1.1-2021.6.30 98 1.90 36.82 < 0.001 2022 2022.1.1-2022.3.31 109 2.45 44.42 < 0.001 *S-T cluster: Spatial-Temporal cluster Table 3 The binary logistic regression test between COM, DTR, PD and RRC respectively Risk factors β value OR value 95% CI P -value COM 0.426 1.531 0.999–2.345 0.050 DTR − .0.299 0.742 0.551–0.998 0.048 PD 1.997 7.367 2.629–20.638 < 0.001 Binary logistic regression test The risk rank of every community was defined as 0 and 1 values base on every community’s location outside and inside the most likely cluster through space-time scan statistical analysis over 6 years span. The result of binary logistic regression test revealed that, PD (OR = 7.367, P < 0.001) and COM (OR = 1.531, P = 0.050) have positive associations with RRC except DTR (OR = 0.742, P = 0.048) has negative association. The equation of model created by logistic regression test is Y (exp) = 1.997X (PD) + 0.426X (COM) -0.299X (DTR) -2.114. Discussion This study aimed to explore the spatial-temporal character and clustered trend of HEV and investigate the factors of clustered risk by utilizing the incidence rate of HEV from 2017 to 2022. In our research, we found a most likely cluster, and indicates the count of mall, the population density and the distance to river were associated with clustered risk at statistical significance in Shanghai. The findings from our study presented the average annual incidence rate in Shanghai was 3.14 per 100,000 population during 2017 to 2022, which is higher than the average annual incidence rate(1.65/10 5 ) nationwide from 2011 to 2021[ 21 , 22 ], but lower than Zhejiang province from 2005 to 2023[ 23 ]. The average annual incidence rates of Shanghai and Zhejiang are both higher than an inland province along the Yangtze River[ 24 ]. The incidence rates ranged from 2020 to 2022 is relatively lower compared to those before 2020, which may be influenced by lock-down policies during the Covid-19 pandemic, contribute to the change from dining out to eating at home among the residents[ 25 ]. The demographic characters of HEV in Shanghai showed that the overall ratio of the male-female is lower than the one nationwide(M:F = 2.09)[ 26 ], and the provinces along Yangtze River[ 23 , 24 ]. While the ratio of male-female increased by years from 2017 to 2022 in this study, and previous study present the same pattern that male being more susceptible to HEV than female due to occupational orientation, high rate of alcohol overdose and sex-related biological factors[ 27 ]. The character of pure temporal cluster of HEV started from Jan 1st, 2017 to May 31st, 2019, and the trend may be interrupted by the epidemic Covid-19 pandemic. The seasonal cluster started from December to May which falls the Spring Festival during 2017–2022 in Shanghai, which was aligned with the pattern of Zhejiang province as a part of YRD [ 28 ]. The analysis of spatial-temporal has been widely used to identify the pattern of cluster in spatial and temporal dimensions.[ 24 , 28 – 33 ]. The outbreak of infectious disease usually hints with origin of clustered trend, and the cluster of a specific genotype may lead to an outbreak[ 34 ]. Clustered trend enhanced the probability of outbreak, even pandemic. Hence the analysis on the cluster trend merged with spatial and temporal dimension broaden the insight for epidemic of infectious disease [ 35 ]. And more studies apply the spatial temporal analysis for simulating cluster trend of HEV from country level to province level in China[ 24 , 30 – 32 ].The spatial temporal trend on hepatitis virus all over China revealed that the most likely cluster trend of HEV located in the southeastern provinces, including Fujian, Zhejiang, Anhui, Shanghai, etc. And the second likely cluster located in Liaoning, which is another coastal province[ 31 ]. The southeastern provinces around YRD region present cluster trend since 2004 and the feature present more noticeable with passing years, more attention the epidemic trend of HEV need to be focus on in YRD region[ 29 ]. Recent studies presented that the trend of high risk distributed in downtown cities in Zhejiang Province from 2008 to 2021[ 32 ]. Whose pattern was in accordance with clustered trend from 2006 to 2016 in Shanghai, which revealed that the most likely cluster trend of HEV located at the downtown of Shanghai from 2006 to 2016 [ 13 ] . The result through spatial-temporal scan revealed that most likely spatial-temporal cluster over 6 years distributed around urban area, whose pattern is corresponding with the one scanned by every year in this study. Though the high incidence rate of HEV appeared at central area and perimeter zone, the higher cluster risk mainly appears around urban communities at spatial and temporal dimension. The communities with higher notification rate distributed in the southeast of Shanghai, but the most likely spatial-temporal cluster scanned by every year locates in urban area, noticing that the outbreak seemed to appear in downtown more easily. The extra two clusters in 2018 and another cluster in 2020 hint the risk source may appear including high-density mall with raw pork on sale around suburb, which leads to scattered cluster trend. The evidence in this study showed that the clustered risk rank of HEV is associated with the count of mall. There are evidences shows raw pork in the mall is a remarkable factor of HEV transmission in other study [ 36 ], which is the common flesh food on sale among the mall in Shanghai, hence the mall selling raw pork may indicate a factor associate with clustered risk rank. Another crucial finding in our research hint the population density a another positively related factor lead to higher clustered risk, which is in accordance with the result of previous study[ 32 ]. The density of population facilitates the higher probability of contacting the potential source of HEV and covers more people. There’ s study found the factor of river distribution is accordance with the pattern of clustering in the counties around the basins of three rivers [ 28 ]. But the results showed negative correlation with river distribution with statistical significance in this study, based on the fact that Shanghai is a modern city where pipe water supplied and covered with network system, the results of negative correlation with river distribution indicated the possibility of waterborne transmission with pipe method instead of natural stream system. The limitations of the study should be acknowledged. Firstly, since this study was confined in Shanghai and variables such as temperature, cultural and economic status at community-level show small disparities, thus, the variables in the regression model preferred the risk factors from the HEV surveillance system. Secondly, the results of this binary logistic regression showed real association, however, further consideration of the spatial autocorrelation and spatial heterogeneity would reduce the standard error of deviation of the model. Conclusions This study conducts the analysis on spatial, temporal, spatial-temporal pattern of HEV in Shanghai during 2017 to 2022. The incidence rate of HEV among male is higher than female and the ratio of the male-female is increasing from 2017 to 2022. The character of pure temporal cluster of HEV started from Jan 1st, 2017 to May 31st, 2019, and the seasonal cluster started from December to May. Though the communities with higher notification rate distribute in the southeast of Shanghai, the most likely cluster through spatial-temporal analysis per year locates in urban area, which is corresponding with the one scanned over 6 years. Through binary logistic regression, the count of mall, population density, distance to river is respectively found being associated with cluster risk. Despite we only analyzed the association between community-level factors with clusters of higher risk level, investigation about HEV infectious cases related to potential transmission routes could be further operated to testify the causal effects and inform a focused strategy towards the prevention and control of HEV. Abbreviations HEV Hepatitis E virus NRDRIS National Infectious Disease Reporting Information System CDC Centers for Disease Control and Prevention WHA the World Health Assembly RR Relative Risk LLR Log likelihood ratio SMCCV Standard Monte Carlo Critical Value YRD Yangtze River Delta RRC Relative Risk of Clusters DTR Distance to River COM Count of Mall PD Population Density Declarations Ethics approval and consent to participate Human data involved in this study were anonymized to protect privacy. Ethics approval was granted by the Human Research Ethics Committee of Shanghai Municipal Center for Disease Control and Prevention (2025-18), and the study was exempted from consent by the committee. All authors confirm that the study carried out in accordance with relevant guidelines and regulations in the Helsinki declaration. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The research reported in this publication was supported by a grant from Shanghai three-year action plan to strengthen the public health system (grant# GWVI-9, SX), Shanghai Municipal Health Commission Youth Project (grant# 20224Y0333, QLX), and China Liver Health Project of HEV Prevention and Control (grant# CLH2023-F-HEV-15, CKY). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Authors' contributions All authors attest they meet the ICNJE criteria for authorship. MZT and QLX contributed equally to data analysis and drafted the article. CKY and XD contributed to data acquisition and interpretation. LYH contributed to data interpretation and revised the manuscript. SX, FJL and LZH contributed to revised the manuscript. ZZ and RH conceived of study design, contributed to the interpretation of data and revised the manuscript for important intellectual content. All authors gave final approval of the manuscript to be published and agreed to act as guarantors of the work. Acknowledgments We thank the participants and staffs of the HEV surveillance system in Shanghai. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6062452","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446506075,"identity":"a9ec21ea-3e52-4fc5-a1cc-3483ed022ba9","order_by":0,"name":"Ma Zhi-Tao","email":"","orcid":"","institution":"Jingan Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Ma","middleName":"","lastName":"Zhi-Tao","suffix":""},{"id":446506076,"identity":"8efd82cc-b59c-422b-8f1b-2403f00227b5","order_by":1,"name":"Qu Ling-Xiao","email":"","orcid":"","institution":"Shanghai Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Qu","middleName":"","lastName":"Ling-Xiao","suffix":""},{"id":446506077,"identity":"b1186fd3-98b0-410e-8e2d-0b2e6437dd88","order_by":2,"name":"Chen Kai-Yun","email":"","orcid":"","institution":"Shanghai Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Kai-Yun","suffix":""},{"id":446506078,"identity":"5f630d33-a489-451f-a59e-91168cdbe272","order_by":3,"name":"Shen Xin","email":"","orcid":"","institution":"Shanghai Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Shen","middleName":"","lastName":"Xin","suffix":""},{"id":446506079,"identity":"daf0f408-1b11-4b56-b1b8-8dc457ab8e6e","order_by":4,"name":"Xu Di","email":"","orcid":"","institution":"Shanghai Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Di","suffix":""},{"id":446506080,"identity":"2dc37cbe-3049-4ad6-ac06-69e8ed426946","order_by":5,"name":"Li Zhao-he","email":"","orcid":"","institution":"Chinese Center for Disease Control and Prevention (CDC)","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhao-he","suffix":""},{"id":446506081,"identity":"952b74fe-74d2-4368-8539-c6d66881820b","order_by":6,"name":"Lu Yi-Han","email":"","orcid":"","institution":"Department of Epidemiology, Fudan University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yi-Han","suffix":""},{"id":446506082,"identity":"869e0a51-5a2d-4810-a72f-860c442c2988","order_by":7,"name":"Fang Jia-Lie","email":"","orcid":"","institution":"Jingan Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Jia-Lie","suffix":""},{"id":446506083,"identity":"1bc0fdb1-6ae6-4ead-ab5b-d7f30dd81a56","order_by":8,"name":"Ren Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYBACAxDxgYGBsYGBgY14LYwzSNbCzEOSFnPpw8ce2/yxk+1vYH/2gKHmDmEtln1p6ca5bcnGMw7wmBswHHtGhMPO8JhJ5zYcSNzAwMMmwdhwmBgt/N+kLf6AtLA/I1YLD5s0AxtIC4MZsVrYzCR7QX45zGMmkXCMKC3MzyR+gEKsvf2ZxIcaIrQgADMQJ5CiYRSMglEwCkYBbgAAaEs0nFNxWU8AAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Municipal Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Ren","middleName":"","lastName":"Hong","suffix":""},{"id":446506084,"identity":"9feb5965-2afd-42cc-b5c2-69ee3d190690","order_by":9,"name":"Zhou Zhou","email":"","orcid":"","institution":"Jingan Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-02-19 09:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6062452/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6062452/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81690479,"identity":"f5f1d6fe-d98d-4975-a771-bd1da3dde78d","added_by":"auto","created_at":"2025-04-30 11:29:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329363,"visible":true,"origin":"","legend":"\u003cp\u003ePurely temporal scan analysis with SatScan software in Shanghai from 2017 to 2022. The graph showing the mostly likely cluster of hepatitis E in time dimension, which ranges from Jan 1\u003csup\u003est\u003c/sup\u003e, 2017 to May 31\u003csup\u003est\u003c/sup\u003e, 2019. Blue square represents the count of clustered case and black one the non-clustered observe case.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6062452/v1/8b2c4975985971a099fc301e.png"},{"id":81690475,"identity":"fe74f100-54fd-498a-99d6-aeb4726c0008","added_by":"auto","created_at":"2025-04-30 11:29:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":236304,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of HEV per year range from 2017 to 2022. The notification rates (the number of HEV cases per 100,000 population) present peak in 2018 during 6 years, and the high ones appear at central and southeast area annually. The most likely cluster through spatial temporal scan locates in core zone per year at community-level.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6062452/v1/92c6b99ccbd00636960b5966.png"},{"id":81692990,"identity":"4cb1971a-7593-46dd-b7f5-e62c0623bef7","added_by":"auto","created_at":"2025-04-30 11:45:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149015,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial-temporal cluster area at community-level in Shanghai from 2017 to 2022.\u003c/p\u003e\n\u003cp\u003ei) The yellow area represents the most likely cluster covered areas, which located in urban area;\u003c/p\u003e\n\u003cp\u003eii) The green spots hint mall spot covered by a circular area with a radius of 1 kilometer base on center of community;\u003c/p\u003e\n\u003cp\u003eiii) The blue lines represent the main streams in Shanghai\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6062452/v1/ddff5582a5cc51d4c47447f8.png"},{"id":81703865,"identity":"541d51ce-de83-45e7-90b4-c34e87457220","added_by":"auto","created_at":"2025-04-30 13:12:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2707206,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6062452/v1/0d3a369b-1489-47f1-b9a0-d2635663e917.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of the spatial-temporal cluster and risk factors of hepatitis E from 2017 to 2022 in Shanghai, China","fulltext":[{"header":"Background","content":"\u003cp\u003eHepatitis E is an infectious liver disease caused by the hepatitis E virus (HEV) which is the primary etiologic agent of acute hepatitis and jaundice worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and mainly transmitted via the fecal-oral and zoonotic routes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the all-cause mortality estimation from Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2021), HEV caused about 1.94\u0026nbsp;million new infections and led to 3,400 deaths per year [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2016, the World Health Assembly (WHA) endorsed the Global Health Sector Strategy on Viral Hepatitis aimed to eliminate viral hepatitis as a public health threat by 2030, with elimination defined as a 90% reduction in incidence and a 65% reduction in mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recently, due to the HEV diversified transmission routes, low coverage of vaccine immunization which was originally launched in China since 2011 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and increasing number of reported sporadic cases, hepatitis E became a public health rising concern not only in developing countries, but also in developed ones [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChina is a vast country with uneven economic development. From 2004 to 2023, the reported incidence of hepatitis E show an upward trend from 1.27/100,000 to 2.11/100,000, additionally, the sero-epidemiological investigations revealed an existence of subclinical infectious status among residents [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To reduce the incidence rate of HEV infection and its potential disease burden, the establishment of early warning algorithm and identification of the potential case cluster and risk events to prevent the spread of epidemic would be of great importance.\u003c/p\u003e \u003cp\u003ePrevious studies have identified spatial clustering of HEV in coastal provinces of China, with a discernible trend of shifting from the northeastern coastal regions towards the southern coastal areas such as the Yangtze River Delta (YRD) region [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Shanghai is a mega-city in the YRD region, the age-standardized prevalence rate of anti-HEV IgG in the general population was 31.94% in 2018 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], whereas, the epidemic characteristics of hepatitis E were mainly sporadic, which was close to the developed countries. To look for the epidemiological associations of these sporadic cases, a surveillance system has been established and a series of studies of HEV genotype, transmission route, risk factors for infection, forecasting mathematical algorithm and spatial-temporal analysis have been conducted in Shanghai since 1997 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, taking measures in Shanghai would play a pivotal role in achieving the targets of incidence reduction for the whole China and other developed countries and regions.\u003c/p\u003e \u003cp\u003eTo establish a proper algorithm of early warning upon HEV and raise the sensitivity and efficiency of the existing HEV surveillance system, we aim to identify the potential case clusters and risk events at spatial or hierarchical levels such as city, district or community to inform priorities and tailored strategy. Our research team had applied hotspot analysis and spatial-temporal statistics to detect the hepatitis E spatial clusters and spatial-temporal clusters from 2006 to 2016 and identified \u0026ldquo;high-high cluster\u0026rdquo; located in the downtown of Shanghai [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Herein, on the base of the former studies we analyzed the epidemiological characteristics of hepatitis E to grasp distribution variation in Shanghai, and further combine regression model with risk factors from zoonotic, waterborne and population density at community level to reveal potential transmission patterns.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe data of HEV cases involved were reported by the specialized medical staff in submitting and notification of HEV to the National Notifiable Disease Reporting System (NNDRS) which was established by China Centers for Disease Control and Prevention (CDC) for surveillance of disease nationwide, and confirmed with criteria announced by the Ministry of Health of the People\u0026rsquo;s Republic of China, which were infected by hepatitis E virus and diagnosed with laboratory tests [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The cases were resided in Shanghai during 2017 to 2022, which were gathered and screened from the system. The characters of every confirmed HEV case contained age, gender, residence address, current address, onset date, notifiable date. The data of population and social-health-economy distributed in the communities of Shanghai were gathered from the Statistical Yearbooks of Shanghai.\u003c/p\u003e \u003cp\u003eEvery HEV case had a unique code according to the community of residential addressed, which was consistent with the area code from polygon map based on a 1:1,000,000 scale layer, with which we aggregated and matched the cases to polygon map. Community\u0026rsquo;s central point was selected as the representation of potential cluster created by toolbox of Geoprocessing in ArcGIS v10.1(ESRI Inc, Redlands, CA, USA), whose longitude and latitude coordinates were determined through the Google geocoding service.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescriptive and temporal analysis\u003c/h3\u003e\n\u003cp\u003eThe specific values of day, month and year were respectively extracted from the notification date of HEV cases and utilized to present the distribution from 2017 to 2022, and characteristics such as report date and registered residence were described. The purely temporal scan analysis was employed to detect the possible temporal cluster and seasonal cluster, whose statistically significances appear when a larger log likelihood ratio (LLR) compared to the Standard Monte Carlo Critical Value (SMCCV) accompany with \u003cem\u003eP\u003c/em\u003e value less than 0.05.\u003c/p\u003e\n\u003ch3\u003eSpatial-temporal scan analysis\u003c/h3\u003e\n\u003cp\u003eThe dimensions of purely temporal scan analysis and spatial distribution per year were confined to temporal and spatial respectively. In order to merge the spatial dimension and the temporal dimension, we utilized Kulldorff\u0026rsquo;s space-time scan statistic for spatial-temporal scan analysis, which could be realized by SaTScan v10.1(Martin Kulldorff, Information Management Services Inc)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKulldorff\u0026rsquo;s space-time scan statistic belongs to an method for detecting and evaluating localized clusters in study area with three dimensions, which could produce a cylindrical window contains a circular geographic base linking to space scanning and a height linking to time scanning[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The cylindrical window moves in space along with time for searching likely clusters through 999 times replicated simulation. The hypothesis was shaped in this study: whether the risk of being a confirmed HEV case among HEV cases in certain communities inside the cluster(\u003cem\u003ep\u003c/em\u003e) appear larger compared to the case outside of the communities(\u003cem\u003eq\u003c/em\u003e). The spatial-temporal risk can be assessed through the null hypothesis(H0:\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eq\u003c/em\u003e) and the alternative hypothesis(H1:\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eq\u003c/em\u003e)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The relative risk (\u003cem\u003eRR\u003c/em\u003e) was utilized to estimate the risks inside and outside the cluster which is calculate with the equation \u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ep\u003c/em\u003e/\u003cem\u003eq\u003c/em\u003e. Log likelihood ratio (LLR) was employed to calculated in each simulative cylindrical window, and the sum of the LLRs whose observed cases were larger than the one with expected cases among 999 simulative replications was applied to assess the probability of the cluster appeared. The maximum sum of LLRs (\u003cem\u003eMax\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i}^{}LLRi\\)\u003c/span\u003e\u003c/span\u003e) with more expected HEV cases indicated the most likely cluster, the second largest LLR indicated the second likely cluster[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The relative likelihood (RL) aims to evaluate the significance of HEV cluster, which is the value of LLR inside the cluster to overall LLR(L0), the RL were produced as the cylindrical window move in this study and the rank of the RL was generated to assess statistical significance of HEV cluster at 0.05 level(α\u0026thinsp;=\u0026thinsp;0.05). In our space-time statistical analysis, limits were set that the expected HEV cases were no more than the total HEV cases and scan time range were no more than half of the study period, in case unable to reflect the difference between the risk of infection inside of the cluster and the one outside for overlapping most communities and study period, whose time aggregation length was set as 1.5 months in aligned with the average incubation period of HEV.\u003c/p\u003e\n\u003ch3\u003eBinary logistic regression test\u003c/h3\u003e\n\u003cp\u003eBased on the fact that the HEV transmission usually links with the water contamination and the consumption of infected pork [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The indicators of nearest distance to river (DTR) and count of mall (COM) covered by a circular area with a radius of 1 kilometer base on center of polygons and population density (PD) of every community were involved as independent variables, and the risk rank of clusters (RRC) as dependent variable.\u003c/p\u003e \u003cp\u003eThe distance to river (DTR) is defined as the straight distance from the community\u0026rsquo;s central point to nearest river, and the count of mall (COM) is defined as count of malls covered by a circle area with a radius of 1 km and centered at community\u0026rsquo;s central point. The population density (PD) means the population (10,000) per square kilometers. the risk rank of every community was defined as 0 and 1 values, which depends on every community\u0026rsquo;s central location including outside or inside the most likely cluster through space-time scan statistical analysis over 6 years span.\u003c/p\u003e \u003cp\u003eThe binary logistic regression test was employed to explore the correlation between the risk rank (RR) and distance to river (DTR), count of mall (COM), population density (PD) with SPSS 18.0.0 software.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive analysis\u003c/h2\u003e\n \u003cp\u003eTotally 4,668 HEV cases were included in the study. The average notification annual rate in Shanghai was 3.14 per 100,000 population during 2017 to 2022. The ratio of male and female increased by years from 2017 to 2022 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The first peak of distribution mostly presented in 2018 in Shanghai from 2017 to 2019, and the second peak appeared in 2021 during the period time of 2020\u0026ndash;2022.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe number and notification rate of HEV case (per 100,000 population) with characteristics from 2017 to 2022 in Shanghai.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender rate(M:F)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIncidence rate\u003c/p\u003e\n \u003cp\u003e(/10\u003csup\u003e5\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eTemporal analysis\u003c/h3\u003e\n\u003cp\u003eThrough temporal analysis, we found a pure temporal cluster started from Jan 1st, 2017 to May 31st, 2019, whose relative risk (RR), log likelihood ratio (LLR) was respectively 1.35, 52.18 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A seasonal temporal ranged from December to May, and the RR, LLR was respectively 1.44, 76.28(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003eSpatial-temporal analysis\u003c/h3\u003e\n\u003cp\u003eThe spatial temporal analysis of every year revealed that the most likely cluster focus at urban area. And there are two extra likely clusters far away beyond the most likely cluster, one of which covered two towns (Jiading and Juyuanxinqu) both located in Jiading district, and another one covers Nanhuixincheng town locates in Pudongxinqu district in 2018. In 2020, the second likely cluster covers Hudongxincun town in Pudong district except the most likely cluster around central area. (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThrough spatial-temporal analysis with 6 years span at community-level from 2017 to 2022, one statistically significant cluster with time frame during Jan 1st, 2017 to Jun 30th, 2019 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were found, whose Relative Risk (RR), Log likelihood ratio (\u003cem\u003eLLR\u003c/em\u003e) was respectively 1.73, 135.74.(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe S-T cluster* scanned per year at community-level in Shanghai, 2017\u0026ndash;2022.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScan Period\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime frame\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount of covered communities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelative risk (\u003cem\u003eRR\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog likelihood ratio (\u003cem\u003eLLR\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017.1.1-2017.6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018.1.1-2018.6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018.1.1-2018.3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018.4.1-2018.9.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019.1.1-2019.6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020.7.1-2020.12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020.1.1-2020.3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021.1.1-2021.6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022.1.1-2022.3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e*S-T cluster: Spatial-Temporal cluster\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe binary logistic regression test between COM, DTR, PD and RRC respectively\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta; value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u0026ndash;2.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551\u0026ndash;0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.629\u0026ndash;20.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBinary logistic regression test\u003c/h2\u003e\n \u003cp\u003eThe risk rank of every community was defined as 0 and 1 values base on every community\u0026rsquo;s location outside and inside the most likely cluster through space-time scan statistical analysis over 6 years span.\u003c/p\u003e\n \u003cp\u003eThe result of binary logistic regression test revealed that, PD (OR\u0026thinsp;=\u0026thinsp;7.367, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and COM (OR\u0026thinsp;=\u0026thinsp;1.531, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050) have positive associations with RRC except DTR (OR\u0026thinsp;=\u0026thinsp;0.742, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) has negative association. The equation of model created by logistic regression test is Y\u003csub\u003e(exp)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.997X\u003csub\u003e(PD)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.426X\u003csub\u003e(COM)\u003c/sub\u003e-0.299X\u003csub\u003e(DTR)\u003c/sub\u003e-2.114.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to explore the spatial-temporal character and clustered trend of HEV and investigate the factors of clustered risk by utilizing the incidence rate of HEV from 2017 to 2022. In our research, we found a most likely cluster, and indicates the count of mall, the population density and the distance to river were associated with clustered risk at statistical significance in Shanghai.\u003c/p\u003e \u003cp\u003eThe findings from our study presented the average annual incidence rate in Shanghai was 3.14 per 100,000 population during 2017 to 2022, which is higher than the average annual incidence rate(1.65/10\u003csup\u003e5\u003c/sup\u003e) nationwide from 2011 to 2021[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], but lower than Zhejiang province from 2005 to 2023[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The average annual incidence rates of Shanghai and Zhejiang are both higher than an inland province along the Yangtze River[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The incidence rates ranged from 2020 to 2022 is relatively lower compared to those before 2020, which may be influenced by lock-down policies during the Covid-19 pandemic, contribute to the change from dining out to eating at home among the residents[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The demographic characters of HEV in Shanghai showed that the overall ratio of the male-female is lower than the one nationwide(M:F\u0026thinsp;=\u0026thinsp;2.09)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and the provinces along Yangtze River[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. While the ratio of male-female increased by years from 2017 to 2022 in this study, and previous study present the same pattern that male being more susceptible to HEV than female due to occupational orientation, high rate of alcohol overdose and sex-related biological factors[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe character of pure temporal cluster of HEV started from Jan 1st, 2017 to May 31st, 2019, and the trend may be interrupted by the epidemic Covid-19 pandemic. The seasonal cluster started from December to May which falls the Spring Festival during 2017\u0026ndash;2022 in Shanghai, which was aligned with the pattern of Zhejiang province as a part of YRD [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The analysis of spatial-temporal has been widely used to identify the pattern of cluster in spatial and temporal dimensions.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The outbreak of infectious disease usually hints with origin of clustered trend, and the cluster of a specific genotype may lead to an outbreak[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Clustered trend enhanced the probability of outbreak, even pandemic. Hence the analysis on the cluster trend merged with spatial and temporal dimension broaden the insight for epidemic of infectious disease [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. And more studies apply the spatial temporal analysis for simulating cluster trend of HEV from country level to province level in China[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].The spatial temporal trend on hepatitis virus all over China revealed that the most likely cluster trend of HEV located in the southeastern provinces, including Fujian, Zhejiang, Anhui, Shanghai, etc. And the second likely cluster located in Liaoning, which is another coastal province[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The southeastern provinces around YRD region present cluster trend since 2004 and the feature present more noticeable with passing years, more attention the epidemic trend of HEV need to be focus on in YRD region[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Recent studies presented that the trend of high risk distributed in downtown cities in Zhejiang Province from 2008 to 2021[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Whose pattern was in accordance with clustered trend from 2006 to 2016 in Shanghai, which revealed that the most likely cluster trend of HEV located at the downtown of Shanghai from 2006 to 2016 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eThe result through spatial-temporal scan revealed that most likely spatial-temporal cluster over 6 years distributed around urban area, whose pattern is corresponding with the one scanned by every year in this study. Though the high incidence rate of HEV appeared at central area and perimeter zone, the higher cluster risk mainly appears around urban communities at spatial and temporal dimension. The communities with higher notification rate distributed in the southeast of Shanghai, but the most likely spatial-temporal cluster scanned by every year locates in urban area, noticing that the outbreak seemed to appear in downtown more easily. The extra two clusters in 2018 and another cluster in 2020 hint the risk source may appear including high-density mall with raw pork on sale around suburb, which leads to scattered cluster trend.\u003c/p\u003e \u003cp\u003eThe evidence in this study showed that the clustered risk rank of HEV is associated with the count of mall. There are evidences shows raw pork in the mall is a remarkable factor of HEV transmission in other study [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which is the common flesh food on sale among the mall in Shanghai, hence the mall selling raw pork may indicate a factor associate with clustered risk rank. Another crucial finding in our research hint the population density a another positively related factor lead to higher clustered risk, which is in accordance with the result of previous study[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The density of population facilitates the higher probability of contacting the potential source of HEV and covers more people.\u003c/p\u003e \u003cp\u003eThere\u0026rsquo; s study found the factor of river distribution is accordance with the pattern of clustering in the counties around the basins of three rivers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. But the results showed negative correlation with river distribution with statistical significance in this study, based on the fact that Shanghai is a modern city where pipe water supplied and covered with network system, the results of negative correlation with river distribution indicated the possibility of waterborne transmission with pipe method instead of natural stream system.\u003c/p\u003e \u003cp\u003eThe limitations of the study should be acknowledged. Firstly, since this study was confined in Shanghai and variables such as temperature, cultural and economic status at community-level show small disparities, thus, the variables in the regression model preferred the risk factors from the HEV surveillance system. Secondly, the results of this binary logistic regression showed real association, however, further consideration of the spatial autocorrelation and spatial heterogeneity would reduce the standard error of deviation of the model.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study conducts the analysis on spatial, temporal, spatial-temporal pattern of HEV in Shanghai during 2017 to 2022. The incidence rate of HEV among male is higher than female and the ratio of the male-female is increasing from 2017 to 2022. The character of pure temporal cluster of HEV started from Jan 1st, 2017 to May 31st, 2019, and the seasonal cluster started from December to May. Though the communities with higher notification rate distribute in the southeast of Shanghai, the most likely cluster through spatial-temporal analysis per year locates in urban area, which is corresponding with the one scanned over 6 years. Through binary logistic regression, the count of mall, population density, distance to river is respectively found being associated with cluster risk. Despite we only analyzed the association between community-level factors with clusters of higher risk level, investigation about HEV infectious cases related to potential transmission routes could be further operated to testify the causal effects and inform a focused strategy towards the prevention and control of HEV.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHEV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatitis E virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRDRIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Infectious Disease Reporting Information System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe World Health Assembly\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLog likelihood ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMCCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Monte Carlo Critical Value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eYangtze River Delta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative Risk of Clusters\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDistance to River\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCount of Mall\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman data involved in this study were anonymized to protect privacy. Ethics approval was granted by the Human Research Ethics Committee of Shanghai Municipal Center for Disease Control and Prevention (2025-18), and the study was exempted from consent by the committee.\u0026nbsp;All authors confirm that the study carried out in accordance with relevant guidelines and regulations in the Helsinki declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research reported in this publication was supported by a grant from Shanghai three-year action plan to strengthen the public health system (grant# GWVI-9, SX), Shanghai Municipal Health Commission Youth Project (grant# 20224Y0333, QLX), and China Liver Health Project of HEV Prevention and Control (grant#\u0026nbsp;CLH2023-F-HEV-15, CKY). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors attest they meet the ICNJE criteria for authorship. MZT and QLX contributed equally to data analysis and drafted the article. CKY and XD contributed to data acquisition and interpretation. LYH contributed to data interpretation and revised the manuscript. SX, FJL and LZH contributed to revised the manuscript. ZZ and RH conceived of study design, contributed to the interpretation of data and revised the manuscript for important intellectual content. All authors gave final approval of the manuscript to be published and agreed to act as guarantors of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants and staffs of the HEV surveillance system in Shanghai.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZeng DY, Li JM, Lin S, Dong X, You J, Xing QQ, Ren YD, Chen WM, Cai YY, Fang K\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eGlobal burden of acute viral hepatitis and its association with socioeconomic development status, 1990-2019\u003c/strong\u003e. \u003cem\u003eJ Hepatol\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e75\u003c/strong\u003e(3):547-556.\u003c/li\u003e\n \u003cli\u003eKhuroo MS, Khuroo MS, Khuroo NS: \u003cstrong\u003eTransmission of Hepatitis E Virus in Developing Countries\u003c/strong\u003e. \u003cem\u003eViruses\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e8\u003c/strong\u003e(9).\u003c/li\u003e\n \u003cli\u003eWang B, Meng XJ: \u003cstrong\u003eHepatitis E virus: host tropism and zoonotic infection\u003c/strong\u003e. \u003cem\u003eCurr Opin Microbiol\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e59\u003c/strong\u003e:8-15.\u003c/li\u003e\n \u003cli\u003eEvaluation IfHMa: \u003cstrong\u003eGlobal Burden of Disease Study 2021 (GBD 2021) Results\u003c/strong\u003e. 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associated with endemic and outbreak infections in Bangladesh\u003c/strong\u003e. \u003cem\u003ePloS one\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e16\u003c/strong\u003e(7):e0255054.\u003c/li\u003e\n \u003cli\u003ePujante-Otalora L, Canovas-Segura B, Campos M, Juarez JM: \u003cstrong\u003eThe use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review\u003c/strong\u003e. \u003cem\u003eJournal of biomedical informatics\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e143\u003c/strong\u003e:104422.\u003c/li\u003e\n \u003cli\u003eLewis HC, Wichmann O, Duizer E: \u003cstrong\u003eTransmission routes and risk factors for autochthonous hepatitis E virus infection in Europe: a systematic review\u003c/strong\u003e. \u003cem\u003eEpidemiology and infection\u0026nbsp;\u003c/em\u003e2010, \u003cstrong\u003e138\u003c/strong\u003e(2):145-166.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatitis E Infection, Spatial-temporal distribution, Cluster, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-6062452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6062452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatitis E, caused by the Hepatitis E virus (HEV), is a global infectious liver disease primarily transmitted through fecal-oral and zoonotic routes. Despite the launch of a vaccine in China in 2011, hepatitis E remains a public health concern due to its diverse transmission routes and increasing sporadic cases. Herein, we aim to identify the potential case clusters and risk events at community-level in Shanghai to inform tailored strategy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData on HEV cases from 2017 to 2022 in Shanghai were collected from the National Notifiable Disease Reporting System (NNDRS) and supplemented with population and socio-economic data from Shanghai's communities. Descriptive and temporal analysis were applied to describe the epidemiological patterns, spatial-temporal scan analysis was used to identify potential case clusters, and binary logistic regression tests were conducted to explore the associations between risk factors and potential clusters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 4,668 HEV cases were analyzed, with an average annual notification rate of 3.14 per 100,000 population in Shanghai from 2017 to 2022. Temporal analysis identified a significant temporal cluster from January 1st of 2017 to May 31th of 2019(RR\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;1.73, \u003cem\u003eLLR\u0026thinsp;=\u003c/em\u003e\u0026thinsp;135.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a seasonal cluster from December to May. Spatial-temporal analysis revealed the most likely cluster in urban areas, with additional clusters in suburban towns. Binary logistic regression indicated positive associations between the risk rank of the clusters and population density (OR\u003csub\u003ePD\u003c/sub\u003e=7.367, \u003cem\u003eP\u003c/em\u003e\u003csub\u003ePD\u003c/sub\u003e\u0026lt;0.001), as well as the count of malls (OR\u003csub\u003eCOM\u003c/sub\u003e=1.531, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eCOM\u003c/sub\u003e=0.050), and a negative association with the distance to river (OR\u003csub\u003eDTR\u003c/sub\u003e=0.742, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eDTR\u003c/sub\u003e=0.048).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe spatial-temporal pattern of HEV in Shanghai during 2017 to 2022 suggested that although the communities with higher notification rate distribute in the southeast of Shanghai, the most likely cluster through spatial-temporal analysis per year locates in urban area, which is corresponding with the one scanned over 6 years. Investigation about HEV infectious cases related to potential transmission routes could be further operated to testify the causal effects and inform a more focused strategy to prevent the transmission of HEV.\u003c/p\u003e","manuscriptTitle":"Identification of the spatial-temporal cluster and risk factors of hepatitis E from 2017 to 2022 in Shanghai, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 11:29:52","doi":"10.21203/rs.3.rs-6062452/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T13:24:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T17:18:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T10:42:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T11:36:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T05:18:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188942920257279842603802708270582135780","date":"2026-04-22T12:20:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321066911136811551315490995409116205740","date":"2026-04-19T14:50:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11902729301027146042764915895693501674","date":"2026-04-18T06:48:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51284814020435293805587836251981438246","date":"2026-04-17T11:57:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T18:48:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193749780090915126904669370083455692326","date":"2025-05-08T03:53:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120608245305038556783740218565237166841","date":"2025-04-21T02:50:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-19T18:06:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-10T06:39:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-26T06:34:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T02:27:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-03-26T02:26:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73814da6-4996-4b31-837e-b3e922e54b32","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T13:24:52+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T13:43:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-30 11:29:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6062452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6062452","identity":"rs-6062452","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}