1
|
Wei Y, Ma Y, Zhang T, Luo X, Yin F, Shui T. Spatiotemporal patterns and risk mapping of provincial hand, foot, and mouth disease in mainland China, 2014-2017. Front Public Health 2024; 12:1291361. [PMID: 38344231 PMCID: PMC10853440 DOI: 10.3389/fpubh.2024.1291361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Background Hand, foot, and mouth disease (HFMD) has remained a serious public health threat since its first outbreak in China. Analyzing the province-level spatiotemporal distribution of HFMD and mapping the relative risk in mainland China will help determine high-risk provinces and periods of infection outbreaks for use in formulating new priority areas for prevention and control of this disease. Furthermore, our study examined the effect of air pollution on HFMD nationwide, which few studies have done thus far. Methods Data were collected on the number of provincial monthly HFMD infections, air pollution, meteorological variables, and socioeconomic variables from 2014 to 2017 in mainland China. We used spatial autocorrelation to determine the aggregate distribution of HFMD incidence. Spatiotemporal patterns of HFMD were analyzed, risk maps were developed using the Bayesian spatiotemporal model, and the impact of potential influencing factors on HFMD was assessed. Results In our study, from 2014 to 2017, the HFMD annual incidence rate in all provinces of mainland China ranged from 138.80 to 203.15 per 100,000 people, with an average annual incidence rate of 165.86. The temporal risk of HFMD for 31 Chinese provinces exhibited cyclical and seasonal characteristics. The southern and eastern provinces had the highest spatial relative risk (RR > 3) from 2014 to 2017. The HFMD incidence risk in provinces (Hunan, Hubei, and Chongqing) located in central China increased over time. Among the meteorological variables, except for the mean two-minute wind speed (RR 0.6878; 95% CI 0.5841, 0.8042), all other variables were risk factors for HFMD. High GDP per capita (RR 0.9922; 95% CI 0.9841, 0.9999) was a protective factor against HFMD. The higher the birth rate was (RR 1.0657; 95% CI 1.0185, 1.1150), the higher the risk of HFMD. Health workers per 1,000 people (RR 1.2010; 95% CI 1.0443, 1.3771) was positively correlated with HFMD. Conclusions From 2014 to 2017, the central provinces (Hunan, Hubei, and Chongqing) gradually became high-risk regions for HFMD. The spatiotemporal pattern of HFMD risk may be partially attributed to meteorological and socioeconomic factors. The prevalence of HFMD in the central provinces requires attention, as prevention control efforts should be strengthened there.
Collapse
Affiliation(s)
- Yuxin Wei
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yue Ma
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Tao Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelian Luo
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Fei Yin
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Tiejun Shui
- Yunnan Center for Disease Control and Prevention, Kunming, China
| |
Collapse
|
2
|
Zheng J, Shen G, Hu S, Han X, Zhu S, Liu J, He R, Zhang N, Hsieh CW, Xue H, Zhang B, Shen Y, Mao Y, Zhu B. Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review. BMC Infect Dis 2022; 22:723. [PMID: 36064333 PMCID: PMC9442567 DOI: 10.1186/s12879-022-07669-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/03/2022] [Indexed: 11/20/2022] Open
Abstract
Background The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases. Methods We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors. Conclusions Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07669-9.
Collapse
Affiliation(s)
- Junyao Zheng
- China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China.,School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China
| | - Guoquan Shen
- School of Public Administration and Policy, Renmin University of China, Beijing, China
| | - Siqi Hu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Xinxin Han
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Siyu Zhu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Jinlin Liu
- School of Public Policy and Administration, Northwestern Polytechnical University, Xi'an, China
| | - Rongxin He
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Ning Zhang
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.,MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College, London, UK
| | - Chih-Wei Hsieh
- Department of Public Policy, City University of Hong Kong, Hong Kong, China
| | - Hao Xue
- Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA
| | - Bo Zhang
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yue Shen
- Laboratory for Urban Future, School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Ying Mao
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Bin Zhu
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
| |
Collapse
|
3
|
Hu B, Qiu W, Xu C, Wang J. Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease. BMC Public Health 2020; 20:479. [PMID: 32276607 PMCID: PMC7146977 DOI: 10.1186/s12889-020-08607-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 03/27/2020] [Indexed: 01/16/2023] Open
Abstract
Background Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic. Methods In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence. Results The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation. Conclusions The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD.
Collapse
Affiliation(s)
- Bisong Hu
- School of Geography and Environment, Jiangxi Normal University, Nanchang, 330022, China.,State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Wenqing Qiu
- School of Geography and Environment, Jiangxi Normal University, Nanchang, 330022, China
| | - Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
| |
Collapse
|
4
|
Huang R, Ning H, He T, Bian G, Hu J, Xu G. Impact of PM 10 and meteorological factors on the incidence of hand, foot, and mouth disease in female children in Ningbo, China: a spatiotemporal and time-series study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:17974-17985. [PMID: 29961907 DOI: 10.1007/s11356-018-2619-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 06/19/2018] [Indexed: 06/08/2023]
Abstract
Hand, foot, and mouth disease (HFMD) is a viral illness that is considered a critical public health challenge worldwide. Previous studies have demonstrated that meteorological parameters are significantly related to the incidence of HFMD in children; however, few studies have focused only on female children. This study quantified the associations of HFMD incidence with meteorological parameters and PM10 (particulate matter with an aerodynamic diameter of 10 μm) among female children. Data were collected on daily HFMD cases, meteorological variables, and PM10 levels in Ningbo, China, from January 2012 to December 2016. Data were assessed using a distributed lag nonlinear model (DLNM) with Poisson distribution. A total of 59,809 female children aged 0-15 years with HFMD were enrolled. The results showed that highest relative risk (RR) of HFMD for temperature was 3 °C and the lag effect was 3 days. The highest RR for PM10 was 80 mg/m3 and the lag effect was 5 days. Spatial analysis showed that female HFMD incidence was mainly concentrated in the suburban of Ningbo city indicating that female children in this area should be more paid attention on avoiding this disease outbreak. Our findings suggest that HFMD prevention strategies should focus more attention on local meteorological parameters.
Collapse
Affiliation(s)
- Ruixue Huang
- Department of Occupational and Environmental Health, Xiangya School of Public Health, Central South University, Changsha, 410078, Hunan Province, China
| | - Huacheng Ning
- Department of Occupational and Environmental Health, Xiangya School of Public Health, Central South University, Changsha, 410078, Hunan Province, China
| | - Tianfeng He
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, 315010, China
| | - Guolin Bian
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, 315010, China
| | - Jianan Hu
- Department of Occupational and Environmental Health, Xiangya School of Public Health, Central South University, Changsha, 410078, Hunan Province, China.
| | - Guozhang Xu
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, 315010, China.
| |
Collapse
|
5
|
Xu C, Zhang X, Xiao G. Spatiotemporal decomposition and risk determinants of hand, foot and mouth disease in Henan, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 657:509-516. [PMID: 30550914 DOI: 10.1016/j.scitotenv.2018.12.039] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 12/04/2018] [Accepted: 12/04/2018] [Indexed: 06/09/2023]
Abstract
Hand, foot and mouth disease (HFMD) remains an increasing public health concern. The spatiotemporal variation of HFMD can be represented from multiple-perspectives, and it may be driven by different dominant factors. In this study, the HFMD cases in children under the age of five years in each county in Henan province, China, from 2009 to 2013 were assessed to explore the integrative spatiotemporal patterns of HFMD and investigate their driving factors. The empirical orthogonal function was applied to identify representative spatiotemporal patterns. Then, GeoDetector was used to quantify the determinant powers of driving factors to the disease. The results indicated that the most prominent spatiotemporal pattern explained 56.21% of the total variance, presented in big cities, e.g. capital city and municipal districts. The dominant factors of this pattern were per capita gross domestic product and relative humidity, with determinant powers of 62% and 42%, respectively. The secondary spatiotemporal pattern explained 10.52% of the total variance, presented in the counties around big cities. The dominant factors for this pattern were the ratio of urban to rural population and precipitation, with determinant powers of 26% and 41%, respectively. These findings unveiled the key spatiotemporal features and their determinants related to the disease; this will be helpful in establishing accurate spatiotemporal preventing of HFMD.
Collapse
Affiliation(s)
- Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiangxue Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; The School of Earth Science and Resources, Chang'an University, Xi'an 710054, China
| | - Gexin Xiao
- China National Center for Food Safety Risk Assessment, Beijing 100022, China.
| |
Collapse
|
6
|
Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15071476. [PMID: 30002344 PMCID: PMC6069258 DOI: 10.3390/ijerph15071476] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 07/07/2018] [Accepted: 07/10/2018] [Indexed: 12/16/2022]
Abstract
Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health.
Collapse
|
7
|
ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan. Public Health 2016; 134:72-85. [PMID: 26880489 DOI: 10.1016/j.puhe.2016.01.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Revised: 12/31/2015] [Accepted: 01/11/2016] [Indexed: 01/19/2023]
Abstract
OBJECTIVES Globally, disease surveillance systems are playing a significant role in outbreak detection and response management of Infectious Diseases (IDs). However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems. Our research is intended to formulate an integrated service-oriented visual analytics architecture for ID surveillance, identify key constituents and set up a baseline for easy reproducibility of such systems in the future. STUDY DESIGN This research focuses on development of ID-Viewer, which is a visual analytics decision support system for ID surveillance. It is a blend of intelligent approaches to make use of real-time streaming data from Emergency Departments (EDs) for early outbreak detection, health care resource allocation and epidemic response management. METHODS We have developed a robust service-oriented visual analytics architecture for ID surveillance, which provides automated mechanisms for ID data acquisition, outbreak detection and epidemic response management. Classification of chief-complaints is accomplished using dynamic classification module, which employs neural networks and fuzzy-logic to categorize syndromes. Standard routines by Center for Disease Control (CDC), i.e. c1-c3 (c1-mild, c2-medium and c3-ultra), and spatial scan statistics are employed for detection of temporal and spatio-temporal disease outbreaks respectively. Prediction of imminent disease threats is accomplished using support vector regression for early warnings and response planning. Geographical visual analytics displays are developed that allow interactive visualization of syndromic clusters, monitoring disease spread patterns, and identification of spatio-temporal risk zones. RESULTS We analysed performance of surveillance framework using ID data for year 2011-2015. Dynamic syndromic classifier is able to classify chief-complaints to appropriate syndromes with high classification accuracy. Outbreak detection methods are able to detect the ID outbreaks in start of epidemic time zones. Prediction model is able to forecast dengue trend for 20 weeks ahead with nominal normalized root mean square error of 0.29. Interactive geo-spatiotemporal displays, i.e. heat-maps, and choropleth are shown in respective sections. CONCLUSION The proposed framework will set a standard and provide necessary details for future implementation of such a system for resource-constrained regions. It will improve early outbreak detection attributable to natural and man-made biological threats, monitor spatio-temporal epidemic trends and provide assurance that an outbreak has, or has not occurred. Advanced analytics features will be beneficial in timely organization/formulation of health management policies, disease control activities and efficient health care resource allocation.
Collapse
|