Epidemiology and spatiotemporal analysis of acute hemorrhagic conjunctivitis in Zhejiang province, China, during 2004-2023

doi:10.21203/rs.3.rs-4897072/v1

Background

Acute hemorrhagic conjunctivitis (AHC) has become a significant public health issue in Zhejiang province. However, the analysis of epidemiological characteristics and spatio-temporal aggregation of AHC in Zhejiang province has not been studied yet.

Methods

Information on sporadic cases and clustered epidemics data of AHC from 2004–2023 in Zhejiang province were obtained from the China Information System for Disease Control and Prevention. Global spatial autocorrelation and local spatial autocorrelation analyses were performed using ArcGIS 10.4 software and GeoDa 1.22 software, spatiotemporal hotspot area detection was carried out using SaTScan 10.1.3 software.

Results

Between 2004 and 2023, 52119 AHC cases were reported in Zhejiang province, resulting in an average incidence of 5.37 per 100000. No death cases occurred. The average age of all cases id 25.44 ± 4.37 years old, 16.70% individuals aged between 10–19 years old, August to October was the peak months in each year; students, farmers and kindergarten children were high incidence people. Positive spatial correlation of AHC in Zhejiang province were observed (Moran’I value between 0.075–0.173, P < 0.001), indicating spatial aggregation, mainly in southwest, central-western and eastern coasts counties in Zhejiang province. Spatiotemporal scanning detected one clusterⅠand one cluster Ⅱaggregation areas, with the clusterⅠaggregation area (RR = 212.44, LRR = 53739.20, P < 0.001) locating in northwestern Zhejiang province, comprising 30 counties, and an aggregation time from 1 September 2010 to 30 September 2010. Epidemiological analysis of 3 outbreaks caused by AHC found that the symptoms of the affected students were mainly conjunctival congestion, increased eye secretions, eye swelling, eye pain, photophobia and tearing, and the proportion of fever was relatively low.

Conclusions

Spatial and temporal aggregation of AHC is evident in Zhejiang province, with the incidence rate increased during August to October. It is important to take prevention and control measures such as monitoring, training and health educating in key population in high-incidence epidemic areas.

Acute hemorrhagic conjunctivitis (AHC)

Spatiotemporal analysis

Epidemiology

Clustering

The pathogens causing acute hemorrhagic conjunctivitis (AHC) are diverse, mainly microribonucleic acid viruses, the most common being enterovirus type 70 (EV70), followed by Coxsackie virus A group 24 variant (CA24v) and variants of adenovirus types 3, 7, 8, 11, 19, and 37[1, 2]. Patients infected with AHC mainly manifesting symptoms such as eye redness, stinging, accompanied by increase eye secretion, tearing, and watery discharge[3]. AHC is a self-limited eye infectious disease with incubation period generally ranging from 12 hours to 48 hours, with a maximum of 6 days[4]. Patients are the main source of infection, and the main route of transmission is contact transmission, which is through eye→hand, object, water→eye. Viruses can be transmitted via the fecal-oral route via contaminated water[5].AHC is characterized as rapid onset and strong infectivity, children age between 0 ~ 9 years old are the most common group infected with AHC[6], and it result in global pandemics and local outbreaks commonly in schools[7]. Relevant reports indicated that the inflammatory response of conjunctivitis can spread to the cornea and cause a certain degree of visual impairment that may last over months or years[8].

Since the first occurrence of AHC in Ghana in 1969[9], it has spread rapidly around the world. In recent decades, more than 10 million AHC cases have been reported worldwide[10], including several large outbreaks in Asia and South America. The largest outbreak of AHC was reported in South Korea in 2002, with over 1 million cases[11]. In 2014, Thailand had an outbreak of AHC, with a total of 300000 cases over 3 months[12]. The first outbreak of AHC in China was reported in Hong Kong in 1971[13], and then it has spread to all provinces and cities. Influenza, other infectious diarrhea diseases, hand foot mouth disease, mumps and AHC were ranked as the top 5 incidence of class C infectious diseases in China in 2022[14]. The incidence of AHC varies greatly in different regions in mainland China, Guangxi, Guangdong, Anhui, Hubei are provinces with larger number of cases during year 2013–2020[6]. Currently, AHC is still an important public health issue in China, its’ epidemic feature is worthy of further study.

In recent years, geographic information systems and spatial statistics have been widely applied to study the distribution characteristics of infectious disease[15, 16]. However, there is limited research on the spatial epidemiological characteristics of AHC across a province. In this study, we analyzed the spatio-temporal aggregation characteristics of AHC incidence in the whole Zhejiang province in the past twenty years (from 2004 to 2023). The results of the geographical incidence, hotspots areas and epidemiological features of outbreaks in this article is helpful to provid a basis for a more targeting public health measures and allocation of health resources in disease prevention and control.

Study area

Zhejiang Province is located in the southern wing of the Yangtze River Delta along the southeast coast of China, spanning 27 ° 02 ′ -31 ° 11 ′ N and 118 ° 01 ′ -123 ° 10 ′ E. Zhejiang has a monsoon humid climate, with an average annual temperature is between 15℃-18℃, and an average annual precipitation is between 1100–2000 millimeters. Zhejiang now has 11 prefecture level cities, including Hangzhou, Ningbo, Wenzhou, Jiaxing, Huzhou, Shaoxing, Jinhua, Quzhou, Zhoushan, Taizhou, and Lishui, with 37 municipal districts, 20 county-level cities, and 33 counties (including one autonomous county). At the end of 2023, the permanent population of the province was 66.27 million, with the total GDP of the province in 2023 is 8255.3 billion yuan, which belongs to one of the economically developed coastal provinces of China[17].

Data collection

The data on AHC cases and outbreak in Zhejiang province from January 2004 to December 2023 were obtained from the China Information System for Disease Control and Prevention (CISDCP). Information included the age, gender, occupation, residential address, date of illness onset and date of illness diagnosis of the AHC cases. The diagnosis of AHC refers to the health industry standards for the diagnosis of AHC issued by the National Health Commission of the People's Republic of China (WS 217–2008; WS 217–2021)[18]. The demographical data of Zhejiang province was downloaded from the Zhejiang Statistical Yearbook (https://tjj.zj.gov.cn/col/col1525563/index.html). Vector maps of China and Zhejiang province were obtained from the National Basic Geographic Information System.

Spatial autocorrelation analysis

Spatial autocorrelation analysis is a spatial method used to describe the spatial autocorrelation association based on the location of cases in different study regions. In general, the analysis of spatial autocorrelation can be separated into the “global” and “local” categories, global and local Moran’s I were used to identity whether there are significant spatial autocorrelation regions of AHC in study area. Moran’s I coefficient values range from − 1 to 1, it means when the value is > 0, it indicates a positive spatial correlation, and the larger the value, the stronger the spatial aggregation. When the value is < 0, it means a negative spatial correlation, and the closer the value is to -1, the grater the spatial variability. When Moran’s I is equal to 0, it means spatial irrelevance. Z and P values were used to evaluated the significance of Moran’s I coefficients[19].

Local Spatial autocorrelation analysis

In this process, Local Indicators of Spatial Autocorrelation (LISA) was calculated and the local spatial autocorrelation divide regions into four categories: high-high aggregation, high-low aggregation, low-high aggregation, and low-low aggregation. “High-high” and “low-low” clusters indicate that the observed values in one region are similar to those in their surrounding regions and they are high (low) values. “High-low” and “low-high” clusters indicate that the observed values in one region are larger (smaller) than the observed values in their surrounding areas.

Spatiotemporal cluster analysis

Spatiotemporal aggregation analysis considers both temporal and spatial distribution by creating a scan window in geographic space. The basic principle is that SaTScan software uses a circular scanning window that can dynamically change in size and position. For each scanning window, the theoretical number of cases is calculated based on the actual number of cases and population. Then, using the actual number of cases inside and outside the scanning window and the expected number of cases, a test statistic called log-likelihood ratio (LLR) is constructed to evaluate whether the number of cases inside the window is abnormal. Monte Carlo hypothesis simulation (set to the default value of 999) is used to perform statistical tests and calculate p-value. The larger the LLR value and the statistically significant the difference, the greater the probability that the area contained in the dynamic window is an aggregation area. The most likely cluster is the window with the largest LLR, while other windows with statistically significant LLR are secondary clusters, and so on.

Statistical software

In this study, R software program (v.4.3.1; https://www.R-project.org/) was used in descriptive statistical analysis including investigate the population distribution, occupational composition, seasonal characteristics and diagnosis feature of AHC cases. GeoDa software (version 1.22, Spatial Analysis Laboratory, Urbana, IL, USA) was used to conduct the global and local spatial autocorrelation analysis. The spatiotemporal clusters were detected by SaTScan software (version 10.1.3 Martin Kulldorff, National Cancer Institute, Bethesda, MD, USA). ArcGIS software (version 10.3 ESRI, Redlands, CA, USA) was used for mapping and visualization analysis. All results were considered statistically significant when the p < 0.05 for both sides.

Epidemiological characteristics.

From 2004 to 2023, a total of 52119 AHC cases were reported in Zhejiang province, with an annual reported incidence of 5.37 per 100000 (ranging from 0.77 per 100000 in 2022 to 46.17 per 100000 in 2010). The incidence rate in year 2008 (13.43 per 100000) and year 2011 (6.72 per 100000) reached a small peak, while year 2010 showed a significant outbreak trend (Figure 1(a)). During this study period, there are 30212 male cases and 21912 female cases, no deaths were reported in 20 years. The average gender ratio of male to female is 1.38:1, which is higher than that of the gender ratio of permanent residents in Zhejiang Province in each year (other than year 2004), see Figure 1(b). In terms of age composition, the average age of all study cases is 25.44±4.37 years old, the greatest number of cases were observed in the age group of 10-19 years (18.33%), followed by 30-39 years (16.78%) and 5-9 years (16.70%), see Figure 1(c). The prevalence of AHC had an obvious seasonal high-incidence trend, the peak period of the epidemic is from August to October in each year, see Figure 1(d). The majority of cases were students (14290,27.42%), followed by other occupation (10870, 20.85%), farmers (8998,17.26%) and kindergarten children (5893,11.31%), see Figure 1(e). All cases in this study were clinical diagnosis cases.

Figure 1 Epidemiological characteristics of AHC in Zhejiang province from 2004 to 2023.(a) The annual case number of AHC across male, female and the annual total incidence;(b) The annual male to female ratio in AHC cases and the total population in Zhejiang province; (c) The age distribution of annual AHC cases; (d) The monthly distribution of AHC cases; (e)The proportion of occupation among AHC cases.

Incidence map

The annualized incidence rate of AHC in Zhejiang province was mapped at the county level ranging from 0.00 to 654.49 per 100000 population. The high-incidence area of AHC was changed through these years, the top 10 counties in terms of annual incidence were Dongtou (654.49 per 100000), Pingyang (289.89 per 100000), Yuyao (275.32 per 100000), Longwan (156.02 per 100000), Ninghai (142.37 per 1000001), Lucheng (132.64 per 100000), Yongjia (117.89 per 100000), Tongxiang (110.80 per 1000001), Wucheng (103.82 per 100000) and Sanmen (101.95 per 1000001), a mojority of them are located in the southeast of Zhejiang Province(Figure 2).

Figure 2 Annual incidence maps of AHC in Zhejiang province from 2004 to 2023.

Spatial autocorrelation analyses

The global spatial autocorrelation analysis for the incidence of AHC at the county level in Zhejiang province during the study period revealed the significant positive results of spatial autocorrelation indicating that the geographical distribution of reported cases in Zhejiang province were clustered. The values of Global Moran’s I on the basis of statistical significance ranging from 0.075 to 0.173 demonstrated that the high or low AHC incidence had strong spatial clustering (Table 1).

Table 1 The global spatial autocorrelation of AHC in Zhejiang province from 2004 to 2023.

Year	Moran's I	Z-Score	p-Value	Mean	SD
2004	-0.041	-0.343	0.407	-0.013	0.080
2005	-0.049	-0.749	0.179	-0.010	0.052
2006	-0.013	-0.123	0.451	-0.011	0.019
2007	0.095	2.255	0.039	-0.010	0.047
2008	0.049	0.901	0.161	-0.011	0.067
2009	0.075	2.898	0.031	-0.012	0.030
2010	0.122	1.818	0.052	-0.009	0.072
2011	0.173	2.388	0.034	-0.010	0.076
2012	0.038	0.915	0.178	-0.010	0.053
2013	0.003	0.126	0.261	-0.006	0.071
2014	-0.003	0.173	0.296	-0.009	0.033
2015	0.010	0.460	0.182	-0.010	0.044
2016	0.070	1.124	0.075	-0.009	0.071
2017	0.080	1.240	0.056	-0.010	0.072
2018	0.029	0.832	0.149	-0.010	0.046
2019	0.039	1.891	0.056	-0.011	0.027
2020	0.029	0.598	0.160	-0.013	0.070
2021	0.040	0.871	0.102	-0.010	0.058
2022	-0.012	-0.040	0.318	-0.010	0.032
2023	-0.034	-0.325	0.500	-0.011	0.071

Abbreviation: SD, standard deviation.

Local spatial autocorrelation analysis

The results of local spatial autocorrelation analysis show that there were four types of areas in Zhejiang province during 2004-2023: high-high aggregation, high-low aggregation, low-high aggregation and low-low aggregation. High-high aggregation areas were concentrated in southwest, central-western and eastern coasts districts like Beilun district, Yuyao district, Dongtou district, Fenghua district, Ouhai district, Lucheng district and so on. High-low aggregation located in the central and northwest parts of study area. Low-low aggregation occurred in the central, northwest of Zhejiang province. (Figure 3).

Figure 3 Annual local spatial autocorrelation maps of AHC in Zhejiang province from 2004 to 2023.

Space-time cluster analysis

As summarized in Table 2, the most likely cluster was distributed in the southeast Zhejiang province and covered 30 counties (RR=212.44, LRR=53739.20, P<0.001), and an aggregation time was from 1 September 2010 to 30 September 2010. The secondary likely aggregation areas covered 35 counties (RR=57.23, LRR=13695.73, P<0.001), and an aggregation time was from 1 September 2010 to 30 September 2010 (Figure 4).

Figure 4 Spatiotemporal clusters of AHC cases in Zhejiang province from 2004 to 2023.

Table 2 The cluster results of space-time scan for AHC cases in Zhejiang province from 2004 to 2023.

Cluster type	Time frame	Counties(n)	Radius(km)	Observed cases	Expected cases	LLR	RR	p-value
Most likely	2010/9/1-2010/9/30	30	1.40	12740	79.26	53739.20	212.44	<0.001
Secondary	2010/9/1-2010/9/30	35	128.08	4535	86.65	13695.73	57.23	<0.001

Abbreviation: RR, relative risk; LLR, log likelihood ratio.

Outbreak surveillance

There have been a total of 3 outbreaks caused by AHC, all reported in September 2023, in primary schools(Table 3). A total of 104 clinically confirmed cases have been reported, no death cases reported. In each outbreak, gender ratios of male vs female cases were larger than 1, with an outbreak duration of 8.33 ± 2.42 days. The symptoms of the affected students were mainly conjunctival congestion, increased eye secretions, eye swelling, eye pain, photophobia and tearing, and the proportion of fever was relatively low(Figure 5).

Figure 5 Distribution of clinical symptoms in three AHC outbreaks.

Table 3 Basic information of three outbreaks reported in Zhjiang province from 2004 to 2023.

Name	Date of onset of the first case	Date of outbreak report	County	Place	Total number of students in the school	Number of clinically diagnosed cases	Gender ratio(male:female) of cases	Age range (years old)	Number of classes where cases occurred	Duration of outbreak (days)	Laboratory test results
Outbreak 1	2023/9/5	2023/9/11	Nanxun county	Primary school	2042	10	7：3	6-7	1class	6	Positive universal nucleic acid test for enterovirus
Outbreak 2	2023/9/12	2023/9/15	Kaihua county	Primary school	2058	22	6：5	6-7	5 classes	4	None
Outbreak 3	2023/9/14	2023/9/20	Fenghua county	Primary school	889	72	43：29	6-12	17 classes	15	Positive universal nucleic acid test for enterovirus

In this study, we examined the changes in epidemiology characteristic of AHC cases in Zhejiang province during the 20-year study period from 2004 to 2023. Our study revealed that the incidence remain stable except for 2007,2008, 2010 and 2011, the reason may be associated with flood, the reason for the increase in the incidence rate of AHC caused by flood may be that when flood occurred, it would polluted people's water sources, worsened living environment has led to changes in people's health habits and so on, making AHC easy to spread[20, 21]. According to literature reports, higher incidence rates of AHC were reported in Guangxi, Guangdong, Chongqing, Shandong and other regions in year 2010[7, 22]. 2020-2022 had the minimum number of four kind of hotspots, which may affected by non-intervention measures of COVID-19[23].

We observed the total number of AHC male cases was higher than female cases in this study, and the sex ratio of male to female were higher than the general population in Zhejiang Province, this finding is in concordance with the results reported in Malaysia and other studies[4, 24]. In addition, AHC is commonly found in the age group under 20 years old and the proportion of cases involving students and farmers is relatively high, which suggests that the reasons for gender differences may be related to the range of activities and hygiene habits of the population. AHC can occur all year round, and the highest incidence was in September, which may be related to climatic conditions, a study from Li Zhang et.al found that a moderate positive correlation was seen between AHC and monthly mean temperature, relative humidity and precipitation[25]. In the meantime, in September, students started go back to school, the gathering of students has exacerbated the spread or even outbreak of AHC[26]. In summary, the epidemiological characteristics of AHC indicate that males, students, farmers, and the population aged 10-19 years old are more likely to be infected with AHC than other populations, which is consistent with the research results on the prevalence patterns of AHC in Chongqing[22], Jinan[27]. This study found that the proportion of laboratory diagnosed cases among AHC infected individuals is very low, and the vast majority are clinical diagnosis cases defined by clinical doctors based on the patient's clinical symptoms. The reason for the low proportion of laboratory confirmed cases may be that the symptoms of AHC infection include unique bulbar conjunctival bleeding, follicular conjunctival reactions, and this infection has a short duration, self-limitation[28]. Symptomatic treatment appears to be as effective as medical regimens for relief of symptoms. Therefore, doctors or patients themselves lack the motivation to retain samples for laboratory testing.

The global autocorrelation analysis results revealed that AHC cases in Zhejiang province were not randomly distributed, indicating spatial clustering. Further LISA analysis results demonstrated that the high-high hotpots varied over time, primarily concentrated in the counties along the southwest, central-western and eastern coasts. These areas have abundant precipitation or a large floating population, which is conducive to the occurrence of AHC. Spatio-temporal scan analysis identified two high-risk aggregation areas, which were generally consistent with local spatial autocorrelation results but also exhibited some difference. The two high-risk aggregation areas include a total of 65 counties, accounting for 73% of the total number of districts in the whole of Zhejiang province. This indicates that there were large-scale outbreaks from September 1 to September 30, 2010. Unfortunately, the monitoring network has not received any information on the outbreak during the same period. Only three outbreaks in school were reported in 2023, clinical symptoms, including conjunctival congestion, increased eye secretions, eye swelling, eye irritation, eyes fear light and shed tears are the main clinical manifestations, which align with prior India and Thailand studies[12, 26, 29]. Notably, 8.66% (9/104) patients exhibited enteroviral infection characteristics like fever, fatigue, dizziness. Unfortunately, due to limitations in experimental conditions and the control of on-site outbreaks, none of these three outbreaks have completed the pathogen classification work. In future work, we will further enhance our awareness and ability of the pathogen classification of AHC, to improve the full chain management of disease control.

Despite our research brings important new knowledge on the epidemiological characteristics of AHC in Zhejiang province, this study was subject to two limitations when interpreting our findings. Firstly, due to the passive monitoring data used in this study, there is inevitably bias. Only cases seeking medical care in hospitals are reported to the local disease control and prevention center for analysis, the quality of data may also be affected by the availability of diagnostic techniques and the work experience of doctors in different hospitals. Therefore, the current analysis may miss reports of clinical cases or subclinical infections that have not undergone healthcare rehabilitation.

Secondly, we failed to obtain the etiological classification of AHC cases, and all cases were clinically diagnosed.

In summary, this study is the first time to analyzed the spatiotemporal dynamics of AHC comprehensively and detailly in Zhejiang province from 2004 to 2023. Our study revealed that a geographical and seasonal variation existed in Zhejiang province, providing pivotal evidence to health authorities, policy-makers and other service providers to devise feasible strategies and issue targeted guidelines. AHC has no specific preventive measures, public health authorities should strength surveillance of the epidemic among students, farmers as well as people in high-risk areas, educating personal protection methods and improving health resource in collaboration with relevant government departments, are highly recommended to prevent and control AHC outbreaks.

Acknowledgments

We thank all of the medical staff and health practitioners in Zhejiang province who have contributed to the reporting of Acute hemorrhagic conjunctivitis cases.

Author contributions

ZM and YC conceived the idea. WS, YC, and ZM collected the data.WS, FL, and SQ analyzed the data. WS, YC wrote the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Declaration of interests

All authors declare no competing interests.

Funding: This study was supported by the Zhejiang medical and health science and technology plan（grant numbers:2024KY901）.

Ethical approval

We used an anonymised database to analyse. It was not possible to connect any data with any personally identifiable information, ethics approval was not sought for this retrospective database study of anonymised data.

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No competing interests reported.

Epidemiology and spatiotemporal analysis of acute hemorrhagic conjunctivitis in Zhejiang province, China, during 2004-2023

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

Study area

Data collection

Spatial autocorrelation analysis

Local Spatial autocorrelation analysis

Spatiotemporal cluster analysis

Statistical software

Results

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1