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

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BACKGROUND

Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.

METHODS

This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.

RESULTS

When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9).

CONCLUSION

This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China

Background

The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue.

Methods

Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence.

Results

We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)).

Conclusion

Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Spatio-temporal pattern and associate factors of intestinal infectious diseases in Zhejiang Province, China, 2008-2021: a Bayesian modeling study

BACKGROUND

Despite significant progress in sanitation status and public health awareness, intestinal infectious diseases (IID) have caused a serious disease burden in China. Little was known about the spatio-temporal pattern of IID at the county level in Zhejiang. Therefore, a spatio-temporal modelling study to identify high-risk regions of IID incidence and potential risk factors was conducted.

METHODS

Reported cases of notifiable IID from 2008 to 2021 were obtained from the China Information System for Disease Control and Prevention. Moran's I index and the local indicators of spatial association (LISA) were calculated using Geoda software to identify the spatial autocorrelation and high-risk areas of IID incidence. Bayesian hierarchical model was used to explore socioeconomic and climate factors affecting IID incidence inequities from spatial and temporal perspectives.

RESULTS

From 2008 to 2021, a total of 101 cholera, 55,298 bacterial dysentery, 131 amoebic dysentery, 5297 typhoid, 2102 paratyphoid, 27,947 HEV, 1,695,925 hand, foot and mouth disease (HFMD), and 1,505,797 other infectious diarrhea (OID) cases were reported in Zhejiang Province. The hot spots for bacterial dysentery, OID, and HEV incidence were found mainly in Hangzhou, while high-high cluster regions for incidence of enteric fever and HFMD were mainly located in Ningbo. The Bayesian model showed that Areas with a high proportion of males had a lower risk of BD and enteric fever. People under the age of 18 may have a higher risk of IID. High urbanization rate was a protective factor against HFMD (RR = 0.91, 95% CI: 0.88, 0.94), but was a risk factor for HEV (RR = 1.06, 95% CI: 1.01-1.10). BD risk (RR = 1.14, 95% CI: 1.10-1.18) and enteric fever risk (RR = 1.18, 95% CI:1.10-1.27) seemed higher in areas with high GDP per capita. The greater the population density, the higher the risk of BD (RR = 1.29, 95% CI: 1.23-1.36), enteric fever (RR = 1.12, 95% CI: 1.00-1.25), and HEV (RR = 1.15, 95% CI: 1.09-1.21). Among climate variables, higher temperature was associated with a higher risk of BD (RR = 1.32, 95% CI: 1.23-1.41), enteric fever (RR = 1.41, 95% CI: 1.33-1.50), and HFMD (RR = 1.22, 95% CI: 1.08-1.38), and with lower risk of HEV (RR = 0.83, 95% CI: 0.78-0.89). Precipitation was positively correlated with enteric fever (RR = 1.04, 95% CI: 1.00-1.08), HFMD (RR = 1.03, 95% CI: 1.00-1.06), and HEV (RR = 1.05, 95% CI: 1.03-1.08). Higher HFMD risk was also associated with increasing relative humidity (RR = 1.20, 95% CI: 1.16-1.24) and lower wind velocity (RR = 0.88, 95% CI: 0.84-0.92).

CONCLUSIONS

There was significant spatial clustering of IID incidence in Zhejiang Province from 2008 to 2021. Spatio-temporal patterns of IID risk could be largely explained by socioeconomic and meteorological factors. Preventive measures and enhanced monitoring should be taken in some high-risk counties in Hangzhou city and Ningbo city.

Spatiotemporal cluster patterns of hand, foot, and mouth disease at the province level in mainland China, 2011–2018

Although three monovalent EV-A71 vaccines have been launched in mainland China since 2016, hand, foot, and mouth disease (HFMD) still causes a considerable disease burden in China. Vaccines’ use may change the epidemiological characters of HFMD. Spatial autocorrelation analysis and space-time scan statistics analysis were used to explore the spatiotemporal distribution pattern of this disease at the provincial level in mainland China. The effects of meteorological factors, socio-economic factors, and health resources on HFMD incidence were analyzed using Geodetector. Interrupted time series (ITS) was used to analyze the impact of the EV-A71 vaccine on the incidence of HFMD. This study found that the median annual incidence of HFMD was 153.78 per 100,000 (ranging from 120.79 to 205.06) in mainland China from 2011 to 2018. Two peaks of infections were observed per year. Children 5 years and under were the main morbid population. The spatial distribution of HFMD was presented a significant clustering pattern in each year (P<0.001). The distribution of HFMD cases was clustered in time and space. The range of cluster time was between April and October. The most likely cluster appeared in the southern coastal provinces (Guangxi, Guangdong, Hainan) from 2011 to 2017 and in the eastern coastal provinces (Shanghai, Jiangsu, Zhejiang) in 2018. The spatial heterogeneity of HFMD incidence could be attributed to meteorological factors, socioeconomic factors, and health resource. After introducing the EV-A71 vaccine, the instantaneous level of HFMD incidence decreased at the national level, and HFMD incidence trended downward in the southern coastal provinces and increased in the eastern coastal provinces. The prevention and control policies of HFMD should be adapted to local conditions in different provinces. It is necessary to advance the EV-A71 vaccination plan, expand the vaccine coverage and develop multivalent HFMD vaccines as soon as possible.

Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities

Regional public attention has been critical during the COVID-19 pandemic, impacting the effectiveness of sub-national non-pharmaceutical interventions. While studies have focused on public attention at the national level, sub-national public attention has not been well investigated. Understanding sub-national public attention can aid local governments in designing regional scientific guidelines, especially in large countries with substantial spatiotemporal disparities in the spread of infections. Here, we evaluated the online public attention to the COVID-19 pandemic using internet search data and developed a regional public risk perception index (PRPI) that depicts heterogeneous associations between local pandemic risk and public attention across 366 Chinese cities. We used the Bayesian Spatiotemporally Varying Coefficients (STVC) model, a full-map local regression for estimating spatiotemporal heterogeneous relationships of variables, and improved it to the Bayesian Spatiotemporally Interacting Varying Coefficients (STIVC) model to incorporate space-time interaction non-stationarity at spatial or temporal stratified scales. COVID-19 daily cases (median contribution 82.6%) was the most critical factor affecting public attention, followed by urban socioeconomic conditions (16.7%) and daily population mobility (0.7%). After adjusting national and provincial impacts, city-level influence factors accounted for 89.4% and 58.6% in spatiotemporal variations of public attention. Spatiotemporal disparities were substantial among cities and provinces, suggesting that observing national-level public dynamics alone was insufficient. Multi-period PRPI maps revealed clusters and outlier cities with potential public panic and low health literacy. Bayesian STVC series models are systematically proposed and provide a multi-level spatiotemporal heterogeneous analytical framework for understanding collective human responses to major public health emergencies and disasters.

Spatial Pattern and Spillover of Abatement Effect of Chinese Environmental Protection Tax Law on PM2.5 Pollution

Particulate matter (PM2.5) pollution is a threat to public health, and environmental taxation is an important regulatory mode controlling PM2.5 pollution. In 2018, China implemented the Environmental Protection Tax Law (EPTL) targeting PM2.5 pollution. Based on in-situ monitoring and emission inventory data, a Bayesian hierarchical spatiotemporal model combining a two-period trends difference method was employed to measure the abatement effects of China’s EPTL on PM2.5 pollution (AEEPTLPM). On this basis, a spatial spillover index (SSI) of the AEEPTLPM is proposed. Applying this index, we calculated the spatial spillover characteristics of the AEEPTLPM in mainland China at a provincial scale in 2018–2019. The results show that the EPTL has had significant abatement effects on both in-situ-monitored PM2.5 concentrations and local total industrial PM2.5 emissions. Additionally, the two types of AEEPTLPM display distinct spatial heterogeneity. A correlation between the AEEPTLPM and the degree of PM2.5 pollution was observed; areas with serious PM2.5 pollution have higher AEEPTLPM levels, and vice versa. The SSI indicates that the AEEPTLPM exhibits significant spatial spillover characteristics, and spatial heterogeneity is also present.

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

Heterogeneous effects of hospital competition on inpatient expenses: an empirical analysis of diseases grouping basing on conditions' complexity and urgency

Background

Multiple pro-competition policies were implemented during the new round of healthcare reform in China. Differences in conditions’ complexity and urgency across diseases associating with various degrees of information asymmetry and choice autonomy in the process of care provision, would lead to heterogeneous effects of competition on healthcare expenses. However, there are limited studies to explore it. This study aims to examine the heterogeneous effects of hospital competition on inpatient expenses basing on disease grouping according to conditions’ complexity and urgency.

Methods

Collecting information from discharge data of inpatients and hospital administrative data of Sichuan province in China, we selected representative diseases. K-means clustering was used to group the selected diseases and Herfindahl-Hirschman Index (HHI) was calculated based on the predicted patient flow to measure the hospital competition. The log-linear multivariate regression model was used to examine the heterogeneous effects of hospital competition on inpatient expenses.

Results

We selected 19 representative diseases with significant burdens (more than 1.1 million hospitalizations). The selected diseases were divided into three groups, including diseases with highly complex conditions, diseases with urgent conditions, and diseases with less complex and less urgent conditions. For diseases with highly complex conditions and diseases with urgent conditions, the estimated coefficients of HHI are mixed in the direction and statistical significance in the identical regression model at the 5% level. For diseases with less complex and less urgent conditions, the coefficients of HHI are all positive, and almost all of them significant at the 5% level.

Conclusions

We found heterogeneous effects of hospital competition on inpatient expenses across disease groups: hospital competition does not play an ideal role in reducing inpatient expenses for diseases with highly complex conditions and diseases with urgent conditions, but it has a significant effect in reducing inpatient expenses of diseases with less complex and less urgent conditions. Our study offers implications that the differences in condition’s complexity and urgency among diseases would lead to different impacts of hospital competition, which would be given full consideration when designing the pro-competition policy in the healthcare delivery system to achieve the desired goal.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07331-1.

Socioeconomic and Environmental Impacts on Regional Tourism across Chinese Cities: A Spatiotemporal Heterogeneous Perspective

Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impacts of socioeconomic and environmental drivers on city-level tourism from a spatiotemporal heterogeneous perspective. We collected the total tourism revenue indicator and 30 potential influencing factors from 343 cities across China during 2008–2017. Three mainstream regressions and an emerging local spatiotemporal regression named the Bayesian spatiotemporally varying coefficients (Bayesian STVC) model were constructed to investigate the global-scale stationary and local-scale spatiotemporal nonstationary relationships between city-level tourism and various vital drivers. The Bayesian STVC model achieved the best model performance. Globally, eight socioeconomic and environmental factors, average wage (coefficient: 0.47, 95% credible intervals: 0.43–0.51), employed population (−0.14, −0.17–−0.11), GDP per capita (0.47, 0.42–0.52), population density (0.21, 0.16–0.27), night-time light index (−0.01, −0.08–0.05), slope (0.10, 0.06–0.14), vegetation index (0.66, 0.63–0.70), and road network density (0.34, 0.29–0.38), were identified to have nonlinear effects on tourism. Temporally, the main drivers might have gradually changed from the local macro-economic level, population density, and natural environment conditions to the individual economic level over the last decade. Spatially, city-specific dynamic maps of tourism development and geographically clustered influencing maps for eight drivers were produced. In 2017, China formed four significant city-level tourism industry clusters (hot spots, 90% confidence), the locations of which coincide with China’s top four urban agglomerations. Our local spatiotemporal analysis framework for geographical tourism data is expected to provide insights into adjusting regional measures to local conditions and temporal variations in broader social and natural sciences.

Modeling the Spatiotemporal Association Between COVID-19 Transmission and Population Mobility Using Geographically and Temporally Weighted Regression

The ongoing Coronavirus Disease 2019 (COVID-19) has posed a serious threat to human public health and global economy. Population mobility is an important factor that drives the spread of COVID-19. This study aimed to quantitatively evaluate the impact of population flow on the spread of COVID-19 from a spatiotemporal perspective. To this end, a case study was carried out in Hubei Province, which was once the most affected area of COVID-19 outbreak in Mainland China. The geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal association between COVID-19 epidemic and population mobility. Two patterns of population flows, including the population inflow from Wuhan and intra-city population movement, were considered to construct explanatory variables. Results indicate that the GTWR model can reveal the spatial-temporal-varying relationships between COVID-19 and population mobility. Moreover, the association between COVID-19 case counts and population movements presented three stages of temporal variation characteristics due to the virus incubation period and implementation of strict lockdown measures. In the spatial dimension, evident geographical disparities were observed across Hubei Province. These findings can provide policymakers useful knowledge about the impact of population movement on the spatio-temporal transmission of COVID-19. Thus, targeted interventions, if necessary in certain time periods, can be implemented to restrict population flow in cities with high transmission risk.

Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Its Response to Climate Factors in the Ili River Valley Region of China

Background: As the global climate changes, the number of cases of hand-foot-and-mouth disease (HFMD) is increasing year by year. This study comprehensively considers the association of time and space by analyzing the temporal and spatial distribution changes of HFMD in the Ili River Valley in terms of what climate factors could affect HFMD and in what way. Methods: HFMD cases were obtained from the National Public Health Science Data Center from 2013 to 2018. Monthly climate data, including average temperature (MAT), average relative humidity (MARH), average wind speed (MAWS), cumulative precipitation (MCP), and average air pressure (MAAP), were obtained from the National Meteorological Information Center. The temporal and spatial distribution characteristics of HFMD from 2013 to 2018 were obtained using kernel density estimation (KDE) and spatiotemporal scan statistics. A regression model of the incidence of HFMD and climate factors was established based on a geographically and temporally weighted regression (GTWR) model and a generalized additive model (GAM). Results: The KDE results show that the highest density was from north to south of the central region, gradually spreading to the whole region throughout the study period. Spatiotemporal cluster analysis revealed that clusters were distributed along the Ili and Gongnaisi river basins. The fitted curves of MAT and MARH were an inverted V-shape from February to August, and the fitted curves of MAAP and MAWS showed a U-shaped change and negative correlation from February to May. Among the individual climate factors, MCP coefficient values varied the most while MAWS values varied less from place to place. There was a partial similarity in the spatial distribution of coefficients for MARH and MAT, as evidenced by a significant degree of fit performance in the whole region. MCP showed a significant positive correlation in the range of 15–35 mm, and MAAP showed a positive correlation in the range of 925–945 hPa. HFMD incidence increased with MAT in the range of 15–23 °C, and the effective value of MAWS was in the range of 1.3–1.7 m/s, which was positively correlated with incidences of HFMD. Conclusions: HFMD incidence and climate factors were found to be spatiotemporally associated, and climate factors are mostly non-linearly associated with HFMD incidence.

How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM₁₀, SO₂ and NO₂ were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R², average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

Spatiotemporal analysis of hand, foot and mouth disease data using time-lag geographically-weighted regression

Hand, Foot, and Mouth Disease (HFMD) is a common and widespread infectious disease. Previous studies have presented evidence that climate factors, including the monthly averages of temperature, relative humidity, air pressure, wind speed and Cumulative Risk (CR) all have a strong influence on the transmission of HFMD. In this paper, the monthly time-lag geographically- weighted regression model was constructed to investigate the spatiotemporal variations of effect of climate factors on HFMD occurrence in Inner Mongolia Autonomous Region, China. From the spatial and temporal perspectives, the spatial and temporal variations of effect of climate factors on HFMD incidence are described respectively. The results indicate that the effect of climate factors on HFMD incidence shows very different spatial patterns and time trends. The findings may provide not only an indepth understanding of spatiotemporal variation patterns of the effect of climate factors on HFMD occurrence, but also provide helpful evidence for making measures of HFMD prevention and control and implementing appropriate public health interventions at the county level in different seasons.

Application of Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model

BACKGROUND

Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open-source software for spatial epidemiological analysis and demonstrated its applicability and quality.

METHODS

Based on standardized geocode and observational health data, the Application of Epidemiological Geographic Information System (AEGIS) provides two spatial analysis methods: disease mapping and detecting clustered medical conditions and outcomes. The AEGIS assesses the geographical distribution of incidences and health outcomes in Korea and the United States, specifically incidence of cancers and their mortality rates, endemic malarial areas, and heart diseases (only the United States).

RESULTS

The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with previous reports. The incidence of liver cancer in women with the highest Moran's I (0.44; p < 0.001) was 17.4 (10.3-26.9). The malarial endemic cluster was identified in Paju-si, Korea (p < 0.001). When the AEGIS was applied to the database of the United States, a heart disease cluster was appropriately identified (p < 0.001).

CONCLUSIONS

As an open-source, cross-country, spatial analytics solution, AEGIS may globally assess the differences in geographical distribution of health outcomes through the use of standardized geocode and observational health databases.

An alternative comprehensive index to quantify the interactive effect of temperature and relative humidity on hand, foot and mouth disease: A two-stage time series study including 143 cities in mainland China

BACKGROUND

Comprehensive indices have been used to quantify the interactive effect of temperature and humidity on hand, foot and mouth disease (HFMD). The majority of them reflect how weather feels to humans. In this study, we propose an alternative index aiming to reflect the impacts of weather on HFMD and compare its performance with that of previous indices.

METHODS

We proposed an index defined as the product of temperature and a weight parameter raised to the rescaled relative humidity, denoted by THIa. We then compared its model fit and heterogeneity with those of previous indices (including the humidex, heat index and temperature) by a multicity two-stage time series analysis. We first built a common distributed lag nonlinear model to estimate the associations between different indices and HFMD for each city separately. We then pooled the city-specific estimates and compared the average model fit (measured by the QAIC) and heterogeneity (measured by I2) among the different indices.

RESULTS

We included the time series of HFMD and meteorological variables from 143 cities in mainland China from 2009 to 2014. By varying the weight parameter of THIa, the results suggested that 100% relative humidity can amplify the effects of temperature on HFMD 1.6-fold compared to 50% relative humidity. By comparing different candidate indices, THIa performed the best in terms of the average of the model fits (QAIC = 9449.37), followed by humidex, heat index and temperature. In addition, the estimated exposure-response curves between THIa and HFMD were consistent across climate regions with minimum heterogeneity (I2 = 65.90), whereas the others varied across climate regions.

CONCLUSIONS

This study proposed an alternative comprehensive index to characterize the interactive effects of temperature and humidity on HFMD. In addition, the results also imply that previous human-based indices might not be sufficient to reflect the complicated associations between weather and HFMD.

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

The Epidemiological and Clinical Characteristics of Hand, Foot, and Mouth Disease in Hangzhou, China, 2016 to 2018

Hand, foot, and mouth disease (HFMD) is most frequently caused by several serotypes of human enterovirus (EV) including Enterovirus 71 (EV-A71), coxsackievirus A16 (CV-A16), or other types of EV. The aim of this study was to determine the epidemiological characteristics of HFMD and to describe the epidemiologic characteristics of HFMD among severe and mild cases. We collected 4760 HFMD cases in Hangzhou from 2016 to 2018. Specimens from these cases were collected and tested for EV-A71, CV-A16, CV-A6, CV-A10, CV-A2, and CV-A5 by reverse transcriptase polymerase chain reaction. From 2016 to 2018, the prevalence of HFMD was seasonal each year. Among the 4760 probable HFMD cases, 3559 cases were confirmed (74.8%), including 426 cases of EV-A71 infections (8.9%), 249 cases of CV-A16 infections (5.2%), and 2884 cases of other EV infections (60.6%). The percentage of other EV infections was more than 80%, which increased year by year. Random selection of samples for detection of other EV infections in 2017 and 2018, among the 1297 cases, showed there were 835 (64.4%) cases of CV-A6 infections, 177 (13.6%) cases of CV-A10 infections, 100 (7.7%) cases of CV-A2 infections, 40 (3.1%) cases of CV-A5 infections, 3 (0.02 %) cases of mixed infections, and 11.0% untyped EV infections. Preschool children were still the primary population susceptible to HFMD. In severe cases, EV-A71 infection was the main cause. Characterizing the epidemiology and the relationship between severe and common cases of HFMD would provide relevant evidences for the prevention and treatment of HFMD.

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.

Epidemiological and aetiological characteristics of hand, foot, and mouth disease in Sichuan Province, China, 2011-2017

Hand, foot, and mouth disease (HFMD) remains a threat to the Asia-Pacific region. The epidemiological characteristics and pathogen spectrum of HFMD vary with space and time. These variations are crucial for HFMD interventions but poorly understood in Sichuan Province, China, particularly after the introduction of the EV-A71 vaccine. Using descriptive methods, regression analyses, spatial autocorrelation analysis, and space-time scan statistics, we analysed the epidemiological and aetiological characteristics of HFMD surveillance data in Sichuan Province between 2011 and 2017 to identify spatio-temporal variations. The dominant serotypes of HFMD have changed from enterovirus 71 and coxsackievirus A16 to other enteroviruses since 2013. The seasonal pattern of HFMD showed two peaks generally occurring from April to July and November to December; however, the seasonal pattern varied by prefecture and enterovirus serotype. From 2011 to 2017, spatio-temporal clusters were increasingly concentrated in Chengdu, with several small clusters in northeast Sichuan. The clusters observed in southern Sichuan from 2011 to 2015 disappeared in 2016–2017. These findings highlight the importance of pathogen surveillance and vaccination strategies for HFMD interventions; future prevention and control of HFMD should focus on Chengdu and its vicinity.

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.

Spatial-temporal variation and risk factor analysis of hand, foot, and mouth disease in children under 5 years old in Guangxi, China

BACKGROUND

Hand, foot and mouth disease (HFMD) incidence is a critical challenge to disease control and prevention in parts of China, particularly Guangxi. However, the association between socioeconomic factors and meteorological factors on HFMD is still unclear.

METHODS

This study applied global and local Moran's I to examine the spatial pattern of HFMD and series analysis to explore the temporal pattern. The effects of meteorological factors and socioeconomic factors on HFMD incidence in Guangxi, China were analyzed using GeoDetector Model.

RESULTS

This study collected 45,522 cases from 87 counties in Guangxi during 2015, among which 43,711 cases were children aged 0-4 years. Temporally, there were two HFMD risk peaks in 2015. One peak was in September with 7890 cases. The other appeared in May with 4687 cases of HFMD. A high-risk cluster was located in the valley areas. The tertiary industry, precipitation and second industry had more influence than other risk factors on HFMD incidence with explanatory powers of 0.24, 0.23 and 0.21, respectively. The interactive effect of any two risk factors would enhance the risk of HFMD.

CONCLUSIONS

This study suggests that precipitation and tertiary industry factors might have stronger effects on the HFMD incidence in Guangxi, China, compared with other factors. High-risk of HFMD was identified in the valley areas characterized by high temperature and humidity. Local government should pay more attention and strengthen public health services level in this area.

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.

The surveillance of the epidemiological and serotype characteristics of hand, foot, mouth disease in Neijiang city, China, 2010-2017: A retrospective study

Hand, foot, and mouth disease (HFMD) is well recognized as one of the major threats to children’s health globally. The increasing complexity of the etiology of HFMD still challenges disease control in China. There is little surveillance of the molecular epidemiological characteristics of the enteroviruses (EVs) that cause HFMD in Neijiang city or the Sichuan Basin area in Southwest China. In this study, demographic and epidemiological information for 14,928 probable HFMD cases was extracted and analyzed to describe the epidemic features of HFMD in Neijiang city from Jan 2010 to Dec 2017. The swab samples of select probable HFMD cases from 2012 to 2017 were tested by reverse transcription (RT) real-time PCR to identify the serotype distribution of EVs, and 110 randomly selected RT-real-time PCR positive samples were then amplified and analyzed for the VP1 or VP4 regions of EVs to further analyze the phylogenetic characteristics of the circulating strains in this area. The eight-year average annual incidence was 49.82 per 100,000 in Neijiang. The incidence rates varied between 19.51 and 70.73 per 100,000, demonstrating peaks of incidence in even-number years (2012, 2014 and 2016). The median age of the probable cases was 27 months and the interquartile range (25^th to 75^th percentile) of ages for the probable HFMD cases was between 14 and 42 months. The male-to-female ratio of the probable HFMD cases was 1.47:1, and scattered children were the major population classification (81.7%). Two epidemic peaks were observed: one major peak between April and July and the other lesser peak between October and December. Of 6513 probable cases tested with RT-real-time PCR, 4015 (61.6%) were positive for enterovirus with the serotype distribution as follows: EV71+, 30.1% (n = 1210); CV-A16+, 28.7% (n = 1154) and a sole pan-enterovirus+, 41.1% (n = 1651). A total of 91 cases (82.7%, 91/110) were successfully amplified and underwent phylogenetic analysis: all EV71+ cases were C4a serotype (n = 23/30); all CV-A16+ cases were B2b serotype (n = 24/30); of 42 sole pan-enterovirus+ samples, 20 were CV-A6, 14 were CV-A10 and the rest within this group were CV-A4 (n = 4), CV-A8 (n = 2), CV-A9 (n = 1) and CV-B3 (n = 1). Our findings provide important evidence that aids the improvement of strategies for vaccination against HFMD and comprehensive disease control in China.

Spatiotemporal Distribution of Hand, Foot, and Mouth Disease in Guangdong Province, China and Potential Predictors, 2009⁻2012

Background: Hand, foot, and mouth disease (HFMD) is a common infectious disease among children. Guangdong Province is one of the most severely affected provinces in south China. This study aims to identify the spatiotemporal distribution characteristics and potential predictors of HFMD in Guangdong Province and provide a theoretical basis for the disease control and prevention. Methods: Case-based HFMD surveillance data from 2009 to 2012 was obtained from the China Center for Disease Control and Prevention (China CDC). The Bayesian spatiotemporal model was used to evaluate the spatiotemporal variations of HFMD and identify the potential association with meteorological and socioeconomic factors. Results: Spatially, areas with higher relative risk (RR) of HFMD tended to be clustered around the Pearl River Delta region (the mid-east of the province). Temporally, we observed that the risk of HFMD peaked from April to July and October to December each year and detected an upward trend between 2009 and 2012. There was positive nonlinear enhancement between spatial and temporal effects, and the distribution of relative risk in space was not fixed, which had an irregular fluctuating trend in each month. The risk of HFMD was significantly associated with monthly average relative humidity (RR: 1.015, 95% CI: 1.006–1.024), monthly average temperature (RR: 1.045, 95% CI: 1.021–1.069), and monthly average rainfall (RR: 1.004, 95% CI: 1.001–1.008), but not significantly associated with average GDP. Conclusions: The risk of HFMD in Guangdong showed significant spatiotemporal heterogeneity. There was spatiotemporal interaction in the relative risk of HFMD. Adding a spatiotemporal interaction term could well explain the change of spatial effect with time, thus increasing the goodness of fit of the model. Meteorological factors, such as monthly average relative humidity, monthly average temperature, and monthly average rainfall, might be the driving factors of HFMD.

Short-term effects of extreme meteorological factors on childhood hand, foot, and mouth disease reinfection in Hefei, China: A distributed lag non-linear analysis

BACKGROUND

Hand, foot, and mouth disease (HFMD) is a major public health issue in China with a high burden of reinfection. Previous studies presented evidence of the relationship between meteorological factors and HFMD incidence, but no study examined the effects of extreme meteorological factors on HFMD reinfection.

METHODS

Daily HFMD reinfection counts and meteorological data of Hefei city were collected from 2011 to 2016. A distributed lag non-linear model was used to quantify the effects of extreme weather (wind speed, sunshine duration, and precipitation) on HFMD reinfection. All effects were presented as relative risk (RR), with 90th or 10th percentiles of meteorological variables compare with their median values. Confounding factors, such as mean temperature, relative humidity, day of week, and long-term trend were controlled.

RESULTS

A total of 4873 HFMD reinfection cases aged 0-11 years were reported. Extremely high precipitation, low wind speed, and low sunshine duration increased HFMD reinfection risk. The effect of extremely high precipitation was greatest at 8 days lag (RR = 1.01, 95%CI: 1.00-1.02). Extremely low wind speed and low sunshine increased 19% (RR = 1.19, 95%CI: 1.09-1.32) and 12% (RR = 1.12, 95%CI: 1.00-1.26) risk at lag 0-12 days, respectively. By contrast, extremely high wind speed and high sunshine duration exerted certain protective effects on HFMD reinfection at lag 0-12 days (RR = 0.76, 95%CI: 0.66-0.88; RR = 0.88, 95%CI: 0.79-0.99, respectively). Subgroup analyses showed that nursery children were the most sensitive people to the extreme wind speed and sunshine duration. Children aged 4-11 years appeared to be more susceptible to extreme sunshine duration than children aged <3 years.

CONCLUSION

The present study provides evidence that extreme meteorological factors exert delayed effects on HFMD reinfection. Developing an early warning system is necessary for the protection of children from harm due to extreme meteorological factors.

Space-time heterogeneity of hand, foot and mouth disease in children and its potential driving factors in Henan, China

BACKGROUND

Hand, foot and mouth disease (HFMD) has become a substantial threat recently. However few studies have quantified spatiotemporal heterogeneity of HFMD and detected spatiotemporal interactive effect of potential driving factors on this disease.

METHODS

Using GeoDetector and Bayesian space-time hierarchy model, we characterized the epidemiology of HFMD in Henan, one of the largest population provinces in China, from 2012 to 2013, and quantified the impacts of potential driving factors.

RESULTS

Notably, 21.43 and 24.60% counties were identified as hot and cold spots, respectively. Spatially, the hotspots were mainly clustered in regions where the economic level was high. Temporally, the highest incidence period of HFMD was discovered to be in late spring and early summer. The impact of meteorological and socio-economic factors on the disease are significant, and this study found that a 1 °C rise in temperature was related to an increase of 4.09% in the HFMD incidence, a 1% increment in relative humidity was associated with a 1.77% increase of the disease, and a 1% increment in ratio of urban to rural population was associated with a 0.16% increase of the disease.

CONCLUSION

Meteorological and socio-economic factors presented significantly association with HFMD incidence, high-risk mainly appeared in large cities and their adjacent regions in hot and humid season. These findings will be helpful for HFMD risk control and disease-prevention policies implementation.