A spatiotemporal mixed model to assess the influence of environmental and socioeconomic factors on the incidence of hand, foot and mouth disease

Development and validation of a potential risk area identification model for hand, foot, and mouth disease in metropolitan China

Maximum Entropy model (MaxEnt), as a machine learning algorithm, is widely used to identify potential risk areas for emerging infectious diseases. However, MaxEnt usually overlooks the influence of the optimal selection of spatial grid scale and the optimal combination of factor information on identification accuracy. Furthermore, the internal level information of factors is closely related to the potential risk of disease occurrence but is rarely applied to enhance MaxEnt's accuracy. In this study, the Optimal Parameters-based Geographical Detectors-Information Value-MaxEnt (OPGD-IV-MaxEnt) was first proposed to identify the potential risk areas of hand, foot, and mouth disease (HFMD) in Shenzhen and compared its identification accuracy with that of OPGD-MaxEnt and MaxEnt. Firstly, the optimal grid scale and optimal combination of factor information were determined by OPGD. Secondly, the contributions of factors' internal level information to the potential risk of HFMD occurrence were quantified and incorporated by IV. Lastly, the spatial patterns of potential risk areas and their main driving factors were elucidated. Results showed that: (i) Area under the curve (AUC) of single MaxEnt were 0.638, 0.688, 0.763, 0.796, and 0.757 at 100 m, 250 m, 500 m, 750 m, and 1000 m scale, respectively, and 750 m were deemed the optimal scale. (ii) At the optimal scale, OPGD-IV-MaxEnt (AUC = 0.868) identified potential risk areas more accurately than MaxEnt (AUC = 0.796) and OPGD-MaxEnt (AUC = 0.827). (iii) Resident (r = 0.61, q = 0.39) and Market (r = 0.61, q = 0.36) were the primary factors affecting the identification of potential risk areas. (iv) Potential high-risk areas of HFMD were mainly distributed in northwestern, southwestern, and central Shenzhen, with dense resident and market distribution. Such insights are instrumental in devising targeted infection prevention and control measures for emerging infectious diseases and provide references for improving the identification accuracy of similar machine learning algorithms.

Air pollution's numerical, spatial, and temporal heterogeneous impacts on childhood hand, foot and mouth disease: a multi-model county-level study from China

BACKGROUND

While stationary links between childhood hand, foot and mouth disease (HFMD) and air pollution are known, a comprehensive study on their heterogeneous relationships (nonstationarity), jointly considering numerical, temporal and spatial dimensions, has not been reported.

METHODS

Monthly HFMD incidence and air pollution data were collected at the county level from Sichuan-Chongqing, China (2009-2011), alongside meteorological and social environmental covariates. Key influential factors were identified using random forest (RF) under the stationary assumption. Factors' numerically, temporally, and spatially heterogeneous relationships with HFMD were assessed using generalized additive model (GAM) and geographically and temporally weighted regression (GTWR).

RESULTS

Our findings highlighted the relatively higher stationary contributions of fine particulate matter (PM2.5) and ozone (O3) to HFMD incidence across Sichuan-Chongqing counties. We further uncovered heterogeneous impacts of PM2.5 and O3 from three nonstationary perspectives. Numerically, PM2.5 showed an inverse 'V'-shaped relationship with HFMD incidence, while O3 exhibited a complex pattern, with increased HFMD incidence at low PM2.5 and moderate O3 concentrations. Temporally, PM2.5's impact peaked in autumn and was weakest in spring, whereas O3's effect was strongest in summer. Spatially, hotspot mapping revealed high-risk clusters for PM2.5 impact across all seasons, with notable geographical variations, and for O3 in spring, summer, and autumn, concentrated in specific regions of Sichuan-Chongqing.

CONCLUSIONS

This study underscores the nuanced and three-perspective heterogeneous influences of air pollution on HFMD in small areas, emphasizing the need for differentiated, localized, and time-sensitive prevention and control strategies to enhance the precision of dynamic early warnings and predictive models for HFMD and other infectious diseases, particularly in the fields of environmental and spatial epidemiology.

Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis

Background

In the past 10 years, the number of hand, foot, and mouth disease (HFMD) cases reported in Guangzhou, China, has averaged about 60,000 per year. It is necessary to conduct an in-depth analysis to understand the epidemiological pattern and related influencing factors of HFMD in this region.

Objective

This study aims to describe the epidemiological characteristics and spatiotemporal distribution of HFMD cases in Guangzhou from 2013 to 2022 and explore the relationship between sociodemographic factors and HFMD incidence.

Methods

The data of HFMD cases in Guangzhou come from the Infectious Disease Information Management System of the Guangzhou Center for Disease Control and Prevention. Spatial analysis and space-time scan statistics were used to visualize the spatiotemporal distribution of HFMD cases. Multifactor ordinary minimum regression model, geographically weighted regression, and geographically and temporally weighted regression were used to analyze the influencing factors, including population, economy, education, and medical care.

Results

From 2013 to 2022, a total of 599,353 HFMD cases were reported in Guangzhou, with an average annual incidence rate of 403.62/100,000. Children aged 5 years and younger accounted for 93.64% (561,218/599,353) of all cases. HFMD cases showed obvious bimodal distribution characteristics, with the peak period from May to July and the secondary peak period from August to October. HFMDs in Guangzhou exhibited a spatial aggregation trend, with the central urban area showing a pattern of low-low aggregation and the peripheral urban area demonstrating high-high aggregation. High-risk areas showed a dynamic trend of shifting from the west to the east of peripheral urban areas, with coverage first increasing and then decreasing. The geographically and temporally weighted regression model results indicated that population density (β=-0.016) and average annual income of employees (β=-0.007) were protective factors for HFMD incidence, while the average number of students in each primary school (β=1.416) and kindergarten (β=0.412) was a risk factor.

Conclusions

HFMD cases in Guangzhou were mainly infants and young children, and there were obvious differences in time and space. HFMD is highly prevalent in summer and autumn, and peripheral urban areas were identified as high-risk areas. Improving the economic level of peripheral urban areas and reducing the number of students in preschool education institutions are key strategies to controlling HFMD.

Spatiotemporal patterns and risk mapping of provincial hand, foot, and mouth disease in mainland China, 2014-2017

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.

Combined impacts of environmental and socioeconomic covariates on HFMD risk in China: A spatiotemporal heterogeneous perspective

BACKGROUND

Understanding geospatial impacts of multi-sourced influencing factors on the epidemic of hand-foot-and-mouth disease (HFMD) is of great significance for formulating disease control policies tailored to regional-specific needs, yet the knowledge is very limited. We aim to identify and further quantify the spatiotemporal heterogeneous effects of environmental and socioeconomic factors on HFMD dynamics.

METHODS

We collected monthly province-level HFMD incidence and related environmental and socioeconomic data in China during 2009-2018. Hierarchical Bayesian models were constructed to investigate the spatiotemporal relationships between regional HFMD and various covariates: linear and nonlinear effects for environmental covariates, and linear effects for socioeconomic covariates.

RESULTS

The spatiotemporal distribution of HFMD cases was highly heterogeneous, indicated by the Lorenz curves and the corresponding Gini indices. The peak time (R2 = 0.65, P = 0.009), annual amplitude (R2 = 0.94, P<0.001), and semi-annual periodicity contribution (R2 = 0.88, P<0.001) displayed marked latitudinal gradients in Central China region. The most likely cluster areas for HFMD were located in south China (Guangdong, Guangxi, Hunan, Hainan) from April 2013 to October 2017. The Bayesian models achieved the best predictive performance (R2 = 0.87, P<0.001). We found significant nonlinear associations between monthly average temperature, relative humidity, normalized difference vegetation index and HFMD transmission. Besides, population density (RR = 1.261; 95%CI, 1.169-1.353), birth rate (RR = 1.058; 95%CI, 1.025-1.090), real GDP per capita (RR = 1.163; 95%CI, 1.033-1.310) and school vacation (RR = 0.507; 95%CI, 0.459-0.559) were identified to have positive or negative effects on HFMD respectively. Our model could successfully predict months with HFMD outbreaks versus non-outbreaks in provinces of China from Jan 2009 to Dec 2018.

CONCLUSIONS

Our study highlights the importance of refined spatial and temporal data, as well as environmental and socioeconomic information, on HFMD transmission dynamics. The spatiotemporal analysis framework may provide insights into adjusting regional interventions to local conditions and temporal variations in broader natural and social sciences.

Association of neighborhood greenness with severity of hand, foot, and mouth disease

Background

Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Relationship of neighborhood greenness with human health has been widely studied, yet its association with severe HFMD has not yet been established.

Methods

Individual HFMD cases that occurred in Guangdong province in 2010 were recruited and were categorised into mild and severe cases. Residential greenness was assessed using global land cover data. We used a case-control design (i.e., severe versus mild cases) with logistic regression models to assess the association between neighborhood greenness and HFMD severity. Effect modification was also examined.

Results

A total of 131,606 cases were included, of whom 130,840 were mild cases and 766 were severe cases. In an unadjusted model, HFMD severity increased with higher proportion of neighborhood greenness (odds ratio, OR = 1.029, 95%CI: 1.009–1.050). The greenness-HFMD severity association remained (OR = 1.031, 95%CI: 1.006–1.057) after adjusting for population density, demographic variables and climate variables. Both population density (Z = 4.148, P < 0.001) and relative humidity (Z = -4.297, P < 0.001) modified the association between neighborhood greenness and HFMD severity. In the stratified analyses, a protective effect (OR = 0.769, 95%CI: 0.687–0.860) of greenness on HFMD severity were found in the subgroup of population density being lower than and equal to 5 ln(no.)/km². While in both the subgroups of population density being higher than 5, the greenness had hazard effects (subgroup of > 5 & ≤7: OR = 1.071, 95%CI: 1.024–1.120; subgroup of > 7: OR = 1.065, 95%CI: 1.034–1.097) on HFMD severity. As to relative humidity, statistically significant association between greenness and HFMD severity was only observed in the subgroup of being lower than and equal to 76% (OR = 1.059, 95%CI: 1.023–1.096).

Conclusions

Our study found that HFMD severity is associated with the neighborhood greenness in Guangdong, China. This study provides evidence on developing a prevention strategy of discouraging the high-risk groups from going to the crowded green spaces during the epidemic period.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12444-7.

Association between Meteorological Parameters and Hand, Foot and Mouth Disease in Mainland China: A Systematic Review and Meta-Analysis

Background:

This study reports a systematic review of association between meteorological parameters and hand, foot and mouth disease (HFMD) in mainland China.

Methods:

Using predefined study eligibility criteria, three electronic databases (PubMed, Web of Science, and Embase) were searched for relevant articles. Using a combination of search terms, including “Hand foot and mouth disease,” “HFMD,” “Meteorological,” “Climate,” and “China,” After removal of duplicates, our initial search generated 2435 studies published from 1990 to December 31, 2019. From this cohort 51 full-text articles were reviewed for eligibility assessment. The meta-analysis was devised in accordance with the published guidelines of the Cochrane Collaboration and Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). Effect sizes, heterogeneity estimates and publication bias were computed using R software and Review Manager Software.

Results:

The meta-analysis of 18 eligible studies showed that the meteorological parameters played an important role in the prevalence of HFMD. Lower air pressure may be the main risk factor for the incidence of HFMD in Chinese mainland, and three meteorological parameters (mean temperature, rainfall and relative humidity) have a significant association with the incidence of HFMD in subtropical regions.

Conclusion:

Lower air pressure might be the main risk factor for the incidence of HFMD in Chinese mainland. The influence of meteorological parameters on the prevalence of HFMD is mainly through changing virus viability in aerosols, which may be different in different climate regions. In an environment with low air pressure, wearing a mask that filters the aerosol outdoors may help prevent HFMD infection.

Development and Validation of Diagnostic Models for Hand-Foot-and-Mouth Disease in Children

Objective

To find risk markers and develop new clinical predictive models for the differential diagnosis of hand-foot-and-mouth disease (HFMD) with varying degrees of disease.

Methods

19766 children with HFMD and 64 clinical indexes were included in this study. The patients included in this study were divided into the mild patients' group (mild) with 12292 cases, severe patients' group (severe) with 6508 cases, and severe patients with respiratory failure group (severe-RF) with 966 cases. Single-factor analysis was carried out on 64 indexes collected from patients when they were admitted to the hospital, and the indexes with statistical differences were selected as the prediction factors. Binary multivariate logistic regression analysis was used to construct the prediction models and calculate the adjusted odds ratio (OR).

Results

SP, DP, NEUT#, NEUT%, RDW-SD, RDW-CV, GGT, CK/CK-MB, and Glu were risk markers in mild/severe, mild/severe-RF, and severe/severe-RF. Glu was a diagnostic marker for mild/severe-RF (AUROC = 0.80, 95% CI: 0.78-0.82); the predictive model constructed by temperature, SP, MOMO%, EO%, RDW-SD, GLB, CRP, Glu, BUN, and Cl could be used for the differential diagnosis of mild/severe (AUROC > 0.84); the predictive model constructed by SP, age, NEUT#, PCT, TBIL, GGT, Mb, β2MG, Glu, and Ca could be used for the differential diagnosis of severe/severe-RF (AUROC > 0.76).

Conclusion

By analyzing clinical indicators, we have found the risk markers of HFMD and established suitable predictive models.

Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model

Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents, including malaria, cholera, dengue fever, hand, foot, and mouth disease (HFMD), and the recent Corona-virus pandemic. HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s. The outbreaks may result from a combination of rapid population growth, climate change, socioeconomic changes, and other lifestyle changes. However, the modeling of climate variability and HFMD remains unclear, particularly in statistical theory development. The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling. When dealing with time-series data with clustered variables such as HFMD with clustered states, the independence principle of both modeling approaches may be violated. Thus, a Generalized Additive Mixed Model (GAMM) is used to investigate the relationship between HFMD and climate factors in Malaysia. The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect. This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation. The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia. The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm. Besides, a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances. The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon. The study's outcomes can be used by public health officials and the general public to raise awareness, and thus, implement effective preventive measures.

Spatiotemporal characters and influence factors of hand, foot and mouth epidemic in Xinjiang, China

Hand, foot and mouth (HFM) disease is a common childhood illness. The paper aims to capture the spatiotemporal characters, and investigate the influence factors of the HFM epidemic in 15 regions of Xinjiang province from 2008 to 2017, China. Descriptive statistical analysis shows that the children aged 0-5 years have a higher HFM incidence, mostly boys. The male-female ratio is 1.5:1. Through the scanning method, we obtain the first cluster high-risk areas. The cluster time is usually from May to August every year. A spatiotemporal model is proposed to analyze the impact of meteorological factors on HFM disease. Comparing with the spatial model, the model is more effective in terms of R², AIC, deviation, and mean-square error. Among meteorological factors, the number of HFM cases generally increases with the intensity of rainfall. As the temperature increases, there are more HFM patients. Some regions are mostly influenced by wind speed. Further, another spatiotemporal model is introduced to investigate the relationship between HFM disease and socioeconomic factors. The results show that socioeconomic factors have significant influence on the disease. In most areas, the risk of HFM disease tends to rise with the increase of the gross domestic product, the ratios of urban population and tertiary industry. The incidence is closely related to the number of beds and population density in some regions. The higher the ratio of primary school, the lower the number of HFM cases. Based on the above analysis, it is the key measure to prevent and control the spread of the HFM epidemic in high-risk areas, and influence factors should not be ignored.

Using Geographically Weighted Regression to Study the Seasonal Influence of Potential Risk Factors on the Incidence of HFMD on the Chinese Mainland

Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Its incidence is affected by a variety of natural environmental and socioeconomic factors, and its transmission has strong seasonal and spatial heterogeneity. To quantify the spatial relationship between the incidence of HFMD (I-HFMD) and eight potential risk factors (temperature, humidity, precipitation, wind speed, air pressure, altitude, child population density, and per capita GDP) on the Chinese mainland, we established a geographically weighted regression (GWR) model to analyze their impacts in different seasons and provinces. The GWR model successfully describes the spatial changes of the influence of potential risks, and shows greatly improved estimation performance compared with the ordinary linear regression (OLR) method. Our findings help to understand the seasonally and spatially relevant effects of natural environmental and socioeconomic factors on the I-HFMD, and can provide information to be used to develop effective prevention strategies against HFMD at different locations and in different seasons.

Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Their Influencing Factors in Urumqi, China

Hand, foot, and mouth disease (HFMD) remains a serious health threat to young children. Urumqi is one of the most severely affected cities in northwestern China. This study aims to identify the spatiotemporal distribution characteristics of HFMD, and explore the relationships between driving factors and HFMD in Urumqi, Xinjiang.

METHODS

HFMD surveillance data from 2014 to 2018 were obtained from the China Center for Disease Control and Prevention. The center of gravity and geographical detector model were used to analyze the spatiotemporal distribution characteristics of HFMD and identify the association between these characteristics and socioeconomic and meteorological factors.

RESULTS

A total of 10,725 HFMD cases were reported in Urumqi during the study period. Spatially, the morbidity number of HFMD differed regionally and the density was higher in urban districts than in rural districts. Overall, the development of HFMD in Urumqi expanded toward the southeast. Temporally, we observed that the risk of HFMD peaked from June to July. Furthermore, socioeconomic and meteorological factors, including population density, road density, GDP, temperature and precipitation were significantly associated with the occurrence of HFMD.

CONCLUSIONS

HFMD cases occurred in spatiotemporal clusters. Our findings showed strong associations between HFMD and socioeconomic and meteorological factors. We comprehensively considered the spatiotemporal distribution characteristics and influencing factors of HFMD, and proposed some intervention strategies that may assist in predicting the morbidity number of HFMD.

Influencing Indicators and Spatial Variation of Diabetes Mellitus Prevalence in Shandong, China: A Framework for Using Data-Driven and Spatial Methods

To control and prevent the risk of diabetes, diabetes studies have identified the need to better understand and evaluate the associations between influencing indicators and the prevalence of diabetes. One constraint has been that influencing indicators have been selected mainly based on subjective judgment and tested using traditional statistical modeling methods. We proposed a framework new to diabetes studies using data-driven and spatial methods to identify the most significant influential determinants of diabetes automatically and estimated their relationships. We used data from diabetes mellitus patients' health insurance records in Shandong province, China, and collected influencing indicators of diabetes prevalence at the county level in the sociodemographic, economic, education, and geographical environment domains. We specified a framework to identify automatically the most influential determinants of diabetes, and then established the relationship between these selected influencing indicators and diabetes prevalence. Our autocorrelation results showed that the diabetes prevalence in 12 Shandong cities was significantly clustered (Moran's I = 0.328, p < 0.01). In total, 17 significant influencing indicators were selected by executing binary linear regressions and lasso regressions. The spatial error regressions in different subgroups were subject to different diabetes indicators. Some positive indicators existed significantly like per capita fruit production and other indicators correlated with diabetes prevalence negatively like the proportion of green space. Diabetes prevalence was mainly subjected to the joint effects of influencing indicators. This framework can help public health officials to inform the implementation of improved treatment and policies to attenuate diabetes diseases.

Integration of a Kalman filter in the geographically weighted regression for modeling the transmission of hand, foot and mouth disease

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.

Interaction analysis on transmissibility of main pathogens of hand, foot, and mouth disease: A modeling study (a STROBE-compliant article)

Hand, foot, and mouth disease (HFMD) has spread widely and led to high disease burden in many countries. In this study, we aimed to analyze the interaction of the main pathogens of HFMD using a mathematical model.A dataset on reported HFMD cases was collected from April, 2009 to December, 2017 in Changsha City. A long-term etiological surveillance was conducted focusing on the pathogens of the disease including enterovirus A71 (EV71), coxsachievirus A16 (CA16), and other enteroviruses. A susceptible-infectious-recovered model was adopted to calculate the reproduction number during the ascending period of reported cases (defined as Rasc) and the descending period (defined as Rdes).About 214,178 HFMD cases (including clinically diagnosed cases and confirmed cases) were reported in Changsha City, among which 31 were death cases with a fatality of 0.01%. The number of reported HFMD cases increased yearly with a Linear model of "f(t) = 18542.68 + 1628.91t" where f(t) and t referred to number of reported cases and sequence of year, respectively. The fatality of the disease decreased yearly with a linear model of "f(t) = - 0.012 + 0.083/t". About 5319 stool or anal swab specimens were collected from the reported cases. Among them, 1201 were tested EV71 positive, 836 were CA16 positive, and 1680 were other enteroviruses positive. Rasc and Rdes of HFMD was 1.34 (95% confidence interval [CI]: 1.28-1.40) and 0.73 (95% CI: 0.69-0.76), respectively. EV71 and CA16 interacted with each other, and the interaction between EV71 and other enteroviruses and the interaction between CA16 and other enteroviruses were both directional. However, during the reported cases decreasing period, interactions only occurred between EV71 and other enteroviruses and between CA16 and other enteroviruses. These interactions all decreased Rasc but increased Rdes of affected pathogens.The interactions of the pathogens exist in Changsha City. The effective reproduction number of the affected pathogen is adjusted and verges to 1 by the interaction.

The epidemiological characteristics and spatio-temporal analysis of childhood hand, foot and mouth disease in Korea, 2011-2017

Objectives

Hand-foot-mouth disease (HFMD) is a common viral infection in children, with a significant disease burden due to its high contagion rate. This report studied the epidemiological characteristics as well as the chronological and geographical distribution of HFMD in children younger than 6 years of age in Korea.

Methods

This report established a database by integrating population and geographical data from health insurance claims for HFMD between 2011 and 2017, with an age restriction of ≤6 years, and explored the epidemiological characteristics of both HFMD patients and hospitalized cases in Korea. The relative risk ratio and spatio-temporal scan statistics were calculated by administrative district, using SaTScan.

Results

Over a 7-year period, 1,879,342 children under the age of 6 were diagnosed with HFMD (8.4 of 100 persons younger than 6 years of age). Seasonal incidence tended to increase from week 17 (May) peak between weeks 29 (July) and 39 (September), and increase rapidly in 1- to 2-year cycles. HFMD primarily occurred in children younger than 4 years of age. Furthermore, the greatest proportion of cases occurred at ages 1 (39.2%) and 2 (25.7%). Overall, 92.6% of all cases occurred before the age of 6. The proportion of cases before the age of 6was slightly higher in males. The timing of HFMD epidemics differed over the years. In 2015, the HFMD cumulative incidence was the lowest (5.5/1,000), and the spatio-temporal cluster (RR 2.32) was predominantly located south-central Korea, covering 65 counties for twenty-two weeks. In 2016, however, its cumulative incidence was high (RR 6.34) over a short period (11 weeks) in specific areas such as Ulsan, Daegu, Busan, and Gyeongnam. Also, the southern parts of Korea were found to have a higher rate of hospitalization.

Conclusions

HFMD in Korea is common in children younger than 6 years of age, and it tends to peak in the summer.

Prevalence and Management of Severe Hand, Foot, and Mouth Disease in Xiangyang, China, From 2008 to 2013

Therapeutic strategies for severe hand, foot, and mouth disease (HFMD) are currently either inconsequent or deficient in evidence. We retrospectively surveyed HFMD outbreaks in Xiangyang from June 2008 to December 2013. HFMD is staged from I to V according to clinical severity. Severe HFMD is defined as a case involving the central nervous system (CNS). We analyzed risk factors for fatality of severe cases and compared the efficiency and outcome of some therapies by binary logistic regression. The overall HFMD cases included 637 (1.26%) severe cases and 38 fatalities (0.075%). Analyses indicate that age (<3 years), enterovirus 71 (+), autonomic nervous system dysregulation, pulmonary edema/hemorrhage, C-reactive protein (CRP) (>40 mg/L), and cardiac troponin I (>0.04 ng/ml) are risk factors for fatality (all P < 0.05). Intravenous immunoglobulin (IVIG) and mechanical ventilation applied only in early stage IV significantly improved HFMD progression (both P < 0.05) with odds ratios of 0.24 (95% CI: 0.10-0.57) and 0.01 (95% CI: 0.00-0.10), respectively. Neither methylprednisolone nor milrinone administered in any stage made any significant difference on mortality (all P > 0.05). Precise recognition of the severe HFMD cases in early stage IV and prompt IVIG and mechanical ventilation application may reduce mortality. Mechanical ventilation training programs and dispatch of specialists to hospitals where there is no chance of transferring critical cases to the severe HFMD designated hospitals are two key measures to reduce fatality.

Seasonality of the transmissibility of hand, foot and mouth disease: a modelling study in Xiamen City, China

This study attempts to figure out the seasonality of the transmissibility of hand, foot and mouth disease (HFMD). A mathematical model was established to calculate the transmissibility based on the reported data for HFMD in Xiamen City, China from 2014 to 2018. The transmissibility was measured by effective reproduction number (R_eff) in order to evaluate the seasonal characteristics of HFMD. A total of 43 659 HFMD cases were reported in Xiamen, for the period 2014 to 2018. The median of annual incidence was 221.87 per 100 000 persons (range: 167.98/100,000–283.34/100 000). The reported data had a great fitting effect with the model (R² = 0.9212, P < 0.0001), it has been shown that there are two epidemic peaks of HFMD in Xiamen every year. Both incidence and effective reproduction number had seasonal characteristics. The peak of incidence, 1–2 months later than the effective reproduction number, occurred in Summer and Autumn, that is, June and October each year. Both the incidence and transmissibility of HFMD have obvious seasonal characteristics, and two annual epidemic peaks as well. The peak of incidence is 1–2 months later than R_eff.

Analysis of influencing factors of the CO2 emissions in China: Nonparametric additive regression approach

As the maximal carbon dioxide (CO₂) contributor in world, China is embracing severe stress from emission reduction. It is increasingly important to study the factors affecting China's CO₂ emissions. Many researches had extensively researched the driving forces of CO₂ emissions of China. However, majority of the researches adopt a conventional linear method based on time-series or cross-section data for researching the CO₂ emissions as well as nearly neglect nonlinear relationships. To surmount the limitations of extant investigations, this research adopts a data-driven nonparametric additive regression approach to examine primary influencing factors of China's CO₂ emissions. The results manifest that the nonlinear influence of economy on CO₂ emissions is same as the Environmental Kuznets Curve hypothesis. The household consumption level embodies the inverted "U-type" pattern. The industrialization also embodies the overturned "U-type" relationship. Aggregate retail sales of consumer goods present a positive "U-type" effect upon CO₂ emissions. Similarly, the urbanization signifies a positive "U-type" nexus upon CO₂ emissions. Energy intensity indicates a positive "U-type" nexus. The paper ought to exert more attention to the dynamic effects of the driving forces above in order to abate the CO₂ emissions of China. This study will also propose corresponding policies and recommendations according to the dynamic effects.

Spatial-temporal heterogeneity of hand, foot and mouth disease and impact of meteorological factors in arid/ semi-arid regions: a case study in Ningxia, China

Background

The incidence of hand, foot and mouth disease (HFMD) varies over space and time and this variability is related to climate and social-economic factors. Majority of studies on HFMD were carried out in humid regions while few have focused on the disease in arid/semi-arid regions, more research in such climates would potentially make the mechanism of HFMD transmission clearer under different climate conditions.

Methods

In this paper, we explore spatial-temporal distribution of HFMD in Ningxia province, which has an arid/semi-arid climate in northwest China. We first employed a Bayesian space-time hierarchy model (BSTHM) to assess the spatial-temporal heterogeneity of the HFMD cases and its relationship with meteorological factors in Ningxia from 2009 to 2013, then used a novel spatial statistical software package GeoDetector to test the spatial-temporal heterogeneity of HFMD risk.

Results

The results showed that the spatial relative risks in northern part of Ningxia were higher than those in the south. The highest temporal risk of HFMD incidence was in fall season, with a secondary peak in spring. Meteorological factors, such as average temperature, relative humidity, and wind speed played significant roles in the spatial-temporal distribution of HFMD risk.

Conclusions

The study provide valuable information on HFMD distribution in arid/semi-arid areas in northwest China and facilitate understanding of the concentration of HFMD.

Clinical characteristics and managements of severe hand, foot and mouth disease caused by enterovirus A71 and coxsackievirus A16 in Shanghai, China

Background

Hand, foot and mouth disease (HFMD) is a transmissible infectious disease caused by human enteroviruses (EV). Here, we described features of children with severe HFMD caused by EV-A71 or coxsackievirus A16 (CV-A16) in Shanghai, China.

Methods

Severe EV-A71 or CV-A16 caused HFMD children admitted to the Xinhua Hospital from January 2014 and December 2016, were recruited retrospectively to the study. Symptoms and findings at the time of hospitalization, laboratory tests, treatments, length of stay and residual findings at discharge were systematically recorded and analyzed.

Results

Of 19,995 children visited clinic service with probable HFMD, 574 children (2.87%) were admitted, 234 children (40.76%) were confirmed with EV-A71 (90/574) or CV-A16 (144/574) disease. Most (91.02%) of the patients were under 5 years. Initial clinical symptoms of EV-A71 and CV-A16 cases were: fever > 39 °C in 81 (90%) and 119 (82.63%), vomiting in 31 (34.44%) and 28 (19.44%), myoclonic twitching in 19 (21.11%) and 11(7.64%), startle in 21 (23.33%) and 20 (13.69%), respectively. Serum levels of interleukin-1β (IL-1β), IL-2, IL-6, IL-8, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α) were significantly upregulated in severe HFMD subjects. Forty-seven children (20.08%) treated with intravenous gamma globulin (IVIG) showed decreased duration of illness episodes. All children were discharged without complications.

Conclusions

EV-A71 and CV-A16 accounted 40.76% of admitted HFMD during 2014 to 2016 in Xinhua Hospital. IVIG appeared to be beneficial in shortening the duration of illness episodes of severe HFMD.

Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China

BACKGROUND

Pediatric hand, foot, and mouth disease (HFMD) has generally been found to be associated with climate. However, knowledge about how this association varies spatiotemporally is very limited, especially when considering the influence of local socioeconomic conditions. This study aims to identify multi-sourced HFMD environmental factors and further quantify the spatiotemporal nonstationary effects of various climate factors on HFMD occurrence.

METHODS

We propose an innovative method, named spatiotemporally varying coefficients (STVC) model, under the Bayesian hierarchical modeling framework, for exploring both spatial and temporal nonstationary effects in climate covariates, after controlling for socioeconomic effects. We use data of monthly county-level HFMD occurrence and data of related climate and socioeconomic variables in Sichuan, China from 2009 to 2011 for our experiments.

RESULTS

Cross-validation experiments showed that the STVC model achieved the best average prediction accuracy (81.98%), compared with ordinary (68.27%), temporal (72.34%), spatial (75.99%) and spatiotemporal (77.60%) ecological models. The STVC model also outperformed these models in the Bayesian model evaluation. In this study, the STVC model was able to spatialize the risk indicator odds ratio (OR) into local ORs to represent spatial and temporal varying disease-climate relationships. We detected local temporal nonlinear seasonal trends and spatial hot spots for both disease occurrence and disease-climate associations over 36 months in Sichuan, China. Among the six representative climate variables, temperature (OR = 2.59), relative humidity (OR = 1.35), and wind speed (OR = 0.65) were not only overall related to the increase of HFMD occurrence, but also demonstrated spatiotemporal variations in their local associations with HFMD.

CONCLUSION

Our findings show that county-level HFMD interventions may need to consider varying local-scale spatial and temporal disease-climate relationships. Our proposed Bayesian STVC model can capture spatiotemporal nonstationary exposure-response relationships for detailed exposure assessments and advanced risk mapping, and offers new insights to broader environmental science and spatial statistics.

Meteorological factors and its association with hand, foot and mouth disease in Southeast and East Asia areas: a meta-analysis

Since the late 1990s, hand, foot and mouth disease (HFMD) has become a common health problem that mostly affects children and infants in Southeast and East Asia. Global climate change is considered to be one of the major risk factors for HFMD. This study aimed to assess the correlation between meteorological factors and HFMD in the Asia-Pacific region. PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data and Weipu Database were searched to identify relevant articles published before May 2018. Data were collected and analysed using R software. We searched 2397 articles and identified 51 eligible papers in this study. The present study included eight meteorological factors; mean temperature, mean highest temperature, mean lowest temperature, rainfall, relative humidity and hours of sunshine were positively correlated with HFMD, with correlation coefficients (CORs) of 0.52 (95% confidence interval (CI) 0.42–0.60), 0.43 (95% CI 0.23–0.59), 0.43 (95% CI 0.23–0.60), 0.27 (95% CI 0.19–0.35), 0.19 (95% CI 0.02–0.35) and 0.19 (95% CI 0.11–0.27), respectively. There were sufficient data to support a negative correlation between mean pressure and HFMD (COR = −0.51, 95% CI −0.63 to −0.36). There was no notable correlation with wind speed (COR = 0.10, 95% CI −0.03 to 0.23). Our findings suggest that meteorological factors affect the incidence of HFMD to a certain extent.

Bayesian spatiotemporal analysis for association of environmental factors with hand, foot, and mouth disease in Guangdong, China

Hand, foot, and mouth disease (HFMD) remains a significant public health and economic burden in parts of China, particularly Guangdong Province. Although the association between meteorological factors and HFMD has been well documented, significant gaps remain in our understanding of the potential impact of environmental factors. Using county-level monthly HFMD data from China CDC and environmental data from multiple sources, we used spatiotemporal Bayesian models to evaluate the association between HFMD and environmental factors including vegetation index, proportion of artificial surface, road capacity, temperature and humidity, and assessed the spatial and temporal dynamic of the association. Statistically significant correlation coefficients from -0.056 to 0.36 (all P < 0.05) were found between HFMD incidence and all environmental factors. The contributions of these factors for HFMD incidence were estimated to be 16.32%, 12.31%, 14.61%, 13.53%, and 2.63%. All environmental factors including vegetation index (Relative Risk: 0.889; Credible Interval: 0.883-0.895), artificial surface (1.028; 1.022-1.034), road capacity (1.033; 1.028-1.038), temperature (1.039; 1.028-1.05), and relative humidity (1.015; 1.01-1.021) were statistically retained in the final spatiotemporal model. More comprehensive environmental factors were identified as associating with HFMD incidence. Taking these environmental factors into consideration for prevention and control strategy might be of great practical significance.

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

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.