Analysis of the effect of PM10 on hand, foot and mouth disease in a basin terrain city

Independent and interactive effects of particulate matter and meteorological factors on hand, foot and mouth disease in Fuyang

Previous research has demonstrated the influence of environmental factor on the occurrence of infectious diseases. However, there is insufficient and conflicting evidence regarding the association between Hand, foot and mouth disease (HFMD) and environmental variables, particularly the interaction of environmental variables. This study aims to investigate the individual and interactive effects of particulate matter (PM) and meteorological factors on HFMD incidence in Fuyang. The generalized additive models were combined with distributed lag non-linear models to assess the individual effects between PM and meteorological factor on HFMD incidence in Fuyang. Subsequently, a product term was incorporated into the model to investigate the interaction between PM and meteorological factors. Temperature and PM2.5 were identified as the two primary risk factors for HFMD, with relative risks (RR) of 1.586(1.493,1.685) and 1.349(1.325,1.373), respectively. Furthermore, PM exhibited a synergistic effect with meteorological factors. For instance, the RR values for PM2.5 in relation to HFMD were 1.029 (95% CI: 1.024-1.035) and 1 0.117 (95% CI: 1 0.108 - 11 0.127) under different temperature group categories. Notably, HFMD predominantly affects children under the age of five years old and infants aged between zero to one year old demonstrate heightened susceptibility to environmental variables. The results showed that both PM and meteorological factors were risk factors for HFMD, with evidence of an interaction between these variables. These findings have important implications for local HFMD incidence prediction and the development of effective prevention strategies.

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.

Individual and contextual factors associated with measles infection in Malaysia: a multilevel analysis

BACKGROUND

Despite effective vaccination strategies, measles remains a global public health challenge. The study explored individual and contextual factors associated with measles infection in Malaysia from 2018 to 2022, informing the development of targeted public health interventions.

METHODS

This cross-sectional study utilised data from the Ministry of Health, the Department of Statistics, and the Department of Environment Malaysia. Multilevel logistic regression analysis was employed to examine individual-level factors, including age, sex, ethnicity, nationality, contact history, travel history, and vaccination status. Concurrently, contextual factors were assessed, encompassing district-level determinants such as population density, median household income, urbanisation, the number of health and rural clinics, vaccination rates, fine particulate matter less than 2.5 μm (PM2.5) levels, relative humidity, and temperature, to determine their impact on measles infection risk.

RESULTS

Measles infection was significantly associated with various individual factors. These included age (adjusted odds ratio [aOR], 1.02; 95% confidence interval [CI], 1.02-1.03), ethnicity, non-Malaysian nationality (aOR, 34.53; 95% CI, 8.42- 141.51), prior contact with a measles case (aOR, 2.36; 95% CI, 2.07-2.69), travel history (aOR, 2.30; 95% CI, 1.13-4.70), and vaccination status (aOR, 0.76; 95% CI, 0.72-0.79). Among contextual factors, urbanisation (aOR, 1.56; 95% CI, 1.16- 2.10) and the number of clinics (aOR, 0.98; 95% CI, 0.97-0.99) were significant determinants.

CONCLUSION

This multilevel logistic regression analysis illuminates the complexities of measles transmission, advocating public health interventions tailored to individual and contextual vulnerabilities. The findings highlight the need for a synergistic approach that combines vaccination campaigns, healthcare accessibility improvements, and socioeconomic interventions to effectively combat measles.

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.

Modification effects of long-term air pollution levels on the relationship between short-term exposure to meteorological factors and hand, foot, and mouth disease: A distributed lag non-linear model-based study in Shandong Province, China

The occurrence of hand, foot, and mouth disease (HFMD) is closely related to meteorological factors. However, location-specific characteristics, such as persistent air pollution, may increase the complexity of the impact of meteorological factors on HFMD, and studies across different areas and populations are largely lacking. In this study, a two-stage multisite time-series analysis was conducted using data from 16 cities in Shandong Province from 2015 to 2019. In the first stage, we obtained the cumulative exposure-response curves of meteorological factors and the number of HFMD cases for each city. In the second stage, we merged the estimations from the first stage and included city-specific air pollution variables to identify significant effect modifiers and how they modified the short-term relationship between HFMD and meteorological factors. High concentrations of air pollutants may reduce the risk effects of high average temperature on HFMD and lead to a distinct peak in the cumulative exposure-response curve, while lower concentrations may increase the risk effects of high relative humidity. Furthermore, the effects of average wind speed on HFMD were different at different levels of air pollution. The differences in modification effects between subgroups were mainly manifested in the diversity and quantity of significant modifiers. The modification effects of long-term air pollution levels on the relationship between sunshine hours and HFMD may vary significantly depending on geographical location. The people in age＜3 and male groups were more susceptible to long-term air pollution. These findings contribute to a deepening understanding of the relationship between meteorological factors and HFMD and provide evidence for relevant public health decision-making.

Interactions of particulate matter with temperature, heat index and relative humidity on pediatric hand, foot, and mouth disease in a subtropical city

The associations of hand, foot, and mouth disease (HFMD) with meteorological variables and particulate matter (PM) remain controversial, and limited evidence is available on heat index (HI) and coarse particulate (PM10-2.5). Moreover, temperature and humidity are considered major risk factors but their interaction with PM remains unclear. We combined the distributed lag non-linear and quasi-Poisson models to estimate the non-linear and lagged associations of meteorological variables and PM with HFMD based on reported HFMD during 2015-2019 in Fuzhou, China. The multiplicative term of interaction was used to explore the relationship between HFMD and meteorological variables or PM at different levels of another variable. Stratified analyses were used to identify vulnerable subpopulations. We observed inverted-V-shaped relationships between HFMD and temperature and HI, and the W- and N-shaped for relative humidity (RH) and PM, respectively. Extreme high (i.e., the 95th percentile) temperature, HI and RH increased the HFMD with relative risks (RR) of 4.00 (95% confidence interval, 2.79-5.75), 2.20 (1.71-2.83) and 1.54 (1.35-1.75) referent to the minimum effect value of 10.3 °C, 69.4 and 54.8%, respectively. Higher concentrations of PM rapidly increased the HFMD. Infants under 2 years suffered more from temperature, HI and PM. There were synergistic effects between meteorological variables and PM on HFMD. For instance, the RRs of temperature (30 °C) and RH (40%) on HFMD increased from 3.68 (2.24-6.06) to 6.44 (4.29-9.66) and from 0.45 (0.14-1.47) to 2.15 (0.90-5.12) at low (<25%) and high (>75%) categories of PM2.5, respectively. While the RRs of 70 μg/m3 of PM10 and PM10-2.5 increased from 0.65 (0.32-1.31) to 2.93 (1.63-5.26) and from 0.86 (0.23-3.21) to 3.26 (1.23-8.62) at low and high categories of HI. These findings are essential for the development a prediction and warning systems and prevention and control strategies for HFMD.

Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China

BACKGROUND

Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control.

METHODS

We collected the daily number of HFMD cases among children aged 0-14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks.

RESULTS

From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM10. The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay.

CONCLUSIONS

The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors.

Dynamic modeling and data fitting of climatic and environmental factors and people's behavior factors on hand, foot, and mouth disease (HFMD) in Shanghai, China

Background

Hand, foot, and mouth disease (HFMD) appear to be a multi-wave outbreak with unknown mechanisms. We investigate the effects of climatic and environmental factors and changes in people's behavior factors that may be caused by external factors: temperature, relative humidity, and school opening and closing.

Methods

Distributed lag nonlinear model (DLNM) and dynamic model are used to research multi-wave outbreaks of HFMD. Climatic and environmental factors impact on transmission rate β ( t ) is modeled through DLNM and then substituted into this relationship to establish the dynamic model with reported case data to test for validity.

Results

Relative risk (RR) of HFMD infection increases with increasing temperature. The RR of infection first increases and then decreases with the increase of relative humidity. For the model fitting HFMD dynamic, time average basic reproduction number [ R 0 ] of Stage I (without vaccine) and Stage II (with EV71 vaccine) are 1.9362 and 1.5478, respectively. Temperature has the highest explanatory power, followed by school opening and closing, and relative humidity.

Conclusion

We obtain three conclusions about the prevention and control of HFMD. 1) According to the temperature, relative humidity and school start time, the outbreak peak of HFMD should be warned and targeted prevention and control measures should be taken. 2) Reduce high indoor temperature when more than 31.5 oC, and increase low relative humidity when less than 77.5% by opening the window for ventilation, adding houseplants, using air conditioners and humidifiers, reducing the incidence of HFMD and the number of infections. 3) The risk of HFMD transmission during winter vacations is higher than during summer vacations. It is necessary to strengthen the publicity of HFMD prevention knowledge before winter vacations and strengthen the disinfection control measures during winter vacations in children's hospitals, school classrooms, and other places where children gather to reduce the frequency of staff turnover during winter vacations.

Associations between ambient air pollutants and childhood hand, foot, and mouth disease in Sichuan, China: a spatiotemporal study

Hand, foot, and mouth disease (HFMD) is a major public health concern in the Asia-Pacific region. Previous studies have implied that ambient air pollution may affect the incidence of HFMD, but the results among different regions are inconsistent. We aimed to deepen the understanding of the associations between air pollutants and HFMD by conducting a multicity study. Daily data on childhood HFMD counts and meteorological and ambient air pollution (PM_2.5, PM₁₀, NO₂, CO, O₃, and SO₂) concentrations in 21 cities in Sichuan Province from 2015 to 2017 were collected. A spatiotemporal Bayesian hierarchical model framework was established, and then a distributed lag nonlinear models (DLNMs) was constructed to reveal exposure-lag-response relationships between air pollutants and HFMD while controlling for spatiotemporal effects. Furthermore, given the differences in the levels and seasonal trends of air pollutants between the basin region and plateau region, we explored whether these associations varied between different areas (basin and plateau). The associations between air pollutants and HFMD were nonlinear, with different lag responses. Low NO₂ concentrations and both low and high PM_2.5 and PM₁₀ concentrations were associated with a decreased risk of HFMD. No significant associations between CO, O₃, and SO₂ and HFMD were found. The associations between air pollutant concentrations and HFMD were different between the basin and plateau regions. Our study revealed associations between PM_2.5, PM₁₀, and NO₂ concentrations and HFMD, deepening the understanding of the relationships between air pollutants and HFMD. These findings provide evidence to support the formulation of relevant prevention measures and the establishment of an early warning system.

Interactive effects of meteorological factors and air pollutants on hand, foot, and mouth disease in Chengdu, China: a time-series study

OBJECTIVES

Hand, foot, and mouth disease (HFMD) is a viral infectious disease that poses a substantial threat in the Asia-Pacific region. It is widely reported that meteorological factors are associated with HFMD. However, the relationships between air pollutants and HFMD are still controversial. In addition, the interactive effects between meteorological factors and air pollutants on HFMD remain unknown. To fill this research gap, we conducted a time-series study.

DESIGN

A time-series study.

SETTING AND PARTICIPANTS

Daily cases of HFMD as well as meteorological and air pollution data were collected in Chengdu from 2011 to 2017. A total of 184 610 HFMD cases under the age of 15 were included in our study.

OUTCOME MEASURES

Distributed lag nonlinear models were used to investigate the relationships between HFMD and environmental factors, including mean temperature, relative humidity, SO2, NO2, and PM10. Then, the relative excess risk due to interaction (RERI) and the proportion attributable to interaction were calculated to quantitatively evaluate the interactions between meteorological factors and air pollutants on HFMD. Bivariate response surface models were used to visually display the interactive effects.

RESULTS

The cumulative exposure-response curves of SO2 and NO2 were inverted 'V'-shaped and 'M'-shaped, respectively, and the risk of HFMD gradually decreased with increasing PM10 concentrations. We found that there were synergistic interactions between mean temperature and SO2, relative humidity and SO2, as well as relative humidity and PM10 on HFMD, with individual RERIs of 0.334 (95% CI 0.119 to 0.548), 0.428 (95% CI 0.214 to 0.642) and 0.501 (95% CI 0.262 to 0.741), respectively, indicating that the effects of SO2 and PM10 on HFMD were stronger under high temperature (>17.3°C) or high humidity (>80.0%) conditions.

CONCLUSIONS

There were interactive effects between meteorological factors and air pollutants on HFMD. Our findings could provide guidance for targeted and timely preventive and control measures for HFMD.

Modification effects of socioeconomic factors on associations between air pollutants and hand, foot, and mouth disease: A multicity time-series study based on heavily polluted areas in the basin area of Sichuan Province, China

BACKGROUND

Hand, foot, and mouth disease (HFMD) is a serious threat among children in China. Some studies have found that air pollution is associated with HFMD incidence, but the results showed heterogeneity. In this study, we aimed to explore the heterogeneity of associations between air pollutants and the number of HFMD cases and to identify significant socioeconomic effect modifiers.

METHODS

We collected daily surveillance data on HFMD cases in those aged less than 15 years, air pollution variables and meteorological variables from 2015 to 2017 in the basin area of Sichuan Province. We also collected socioeconomic indicator data. We conducted a two-stage multicity time-series analysis. In the first stage, we constructed a distributed lag nonlinear model (DLNM) to obtain cumulative exposure-response curves between each air pollutant and the numbers of HFMD cases for every city. In the second stage, we carried out a multivariable meta-regression to merge the estimations in the first stage and to identify significant socioeconomic effect modifiers.

RESULTS

We found that PM10, NO2 and O3 concentrations were associated with the number of HFMD cases. An inverted V-shaped association between PM10 and the number of HFMD cases was observed. The overall NO2-HFMD association was a hockey-stick shape. For the relationships of PM10, SO2, NO2, O3 and CO with HFMD counts, approximately 58.5%, 48.4%, 51.0%, 55.6% and 52.5% of the heterogeneity could be explained, respectively. The proportion of primary school students, population density, urbanization rate, number of licensed physicians and number of hospital beds explained part of the heterogeneity and modified the relationships.

CONCLUSION

Our study explored the heterogeneity of associations between air pollutants and HFMD counts. The proportion of primary school students, population density, urbanization rate, number of licensed physicians and number of hospital beds could modify the relationships. The results can serve as a reference for relevant public health decision making.

Long-term air pollution levels modify the relationships between short-term exposure to meteorological factors, air pollution and the incidence of hand, foot and mouth disease in children: a DLNM-based multicity time series study in Sichuan Province, China

BACKGROUND

Epidemiological studies have investigated the short-term effects of meteorological factors and air pollution on the incidence of hand, foot, and mouth disease (HFMD). Several meteorological indicators, such as relative humidity and the diurnal temperature range (DTR), significantly modify the relationship between short-term exposure to temperature and HFMD incidence. However, it remains unclear whether (and how) long-term air pollution levels modify the short-term relationships of HFMD incidence with meteorological factors and air pollution.

METHODS

We obtained daily data on meteorological factors, air pollutants, and HFMD counts in children from 21 prefecture-level cities in Sichuan Province in Southwest China from 2015 to 2017. First, we constructed a distributed lag nonlinear model (DLNM) at each prefecture-level site to evaluate the short-term impacts of meteorological variables and air pollutants on HFMD incidence. Then, we assessed the pooled effects of the exposures and incorporated long-term city-specific air pollutant indicators as meta-predictors to examine their potential modification effects by performing multivariate meta-regression models.

RESULTS

We found that long-term SO₂ and CO concentrations significantly modified the short-term relationships between climatic variables and HFMD incidence. Specifically, high concentrations of CO (P = 0.027) and SO₂ (P = 0.039) reduced the risk of HFMD at low temperatures. The relationship between relative humidity and HFMD incidence was weakened at high SO₂ concentrations (P = 0.024), especially when the relative humidity was below the median level. When the minimum relative humidity (32%) was compared to the median relative humidity (77%), the risk ratio (RR) was 0.77 (95% CI: 0.51-1.17) in the 90^th percentile of SO₂ (19.6 μg/m³) and 0.41 (95% CI: 0.27-0.64) in the 10^th percentile of SO₂ (10.6 μg/m³).

CONCLUSION

Our results indicated that long-term SO₂ and CO levels modified the short-term associations between HFMD incidence in children and meteorological variables. These findings may inform health authorities to optimize targeted public health policies including reducing ambient air pollution and reinforcing self-protective actions to weaken the adverse health impacts of environmental factors on HFMD incidence.

Effects of Meteorological Factors and Atmospheric Pollution on Hand, Foot, and Mouth Disease in Urumqi Region

Background

Hand, foot, and mouth disease (HFMD) is a febrile rash infection caused by enteroviruses, spreading mainly via the respiratory tract and close contact. In the past two decades, HFMD has been prevalent mainly in Asia, including China and South Korea, causing a huge disease burden and putting the lives and health of children at risk. Therefore, a further study of the factors influencing HFMD incidences has far-reaching implications. In existing studies, the environmental factors affecting such incidences are mainly divided into two categories: meteorological and air. Among these studies, the former are the majority of studies on HFMD. Some scholars have studied both factors at the same, but the number is not large and the findings are quite different.

Methods

We collect monthly cases of HFMD in children, meteorological factors and atmospheric pollution in Urumqi from 2014 to 2020. Trend plots are used to understand the approximate trends between meteorological factors, atmospheric pollution and the number of HFMD cases. The association between meteorological factors, atmospheric pollution and the incidence of HFMD in the Urumqi region of northwest China is then investigated using multiple regression models.

Results

A total of 16,168 cases in children are included in this study. According to trend plots, the incidence of HFMD shows a clear seasonal pattern, with O3 (ug/m3) and temperature (°C) showing approximately the same trend as the number of HFMD cases, while AQI, PM2.5 (ug/m3), PM10 (ug/m3) and NO2 (ug/m3) all show approximately opposite trends to the number of HFMD cases. Based on multiple regression results, O3 (P = 0.001) and average station pressure (P = 0.037) are significantly and negatively associated with HFMD incidences, while SO2 (P = 0.102), average dew point temperature (P = 0.072), hail (P = 0.077), and thunder (P = 0.14) have weak significant relationships with them.

Relationship between meteorological factors, air pollutants and hand, foot and mouth disease from 2014 to 2020

Background

Meteorological factors and air pollutants have been reported to be associated with hand, foot, and mouth disease (HFMD) epidemics before the introduction of vaccine. However, there is limited evidence for studies with long-term dimensions.

Methods

We collected the daily HFMD counts, weather and air pollution data from 2014 to 2020 in Chengdu. Distributed lag non-linear models (DLNM) were used to assess the associations of meteorological factors and air pollutants on HFMD cases.

Results

From 2014–2020, high relative humidity and precipitation and extremely high and low levels of PM₁₀, O₃, SO₂ and CO increased the risk of HFMD. In pre-vaccination period, extreme high and low temperatures, PM₁₀ and NO₂, low precipitation and high concentrations of PM_2.5 and O₃ significantly increase the risk of HFMD; In post-vaccination period, high relative humidity and low level of CO can significantly increase the incidence of HFMD; During the period of COVID-19, only low temperature will significantly increase the risk of HFMD; Low concentration of air pollutants has the greatest impact on the 6–14 age group, while the high concentration of air pollutants has the greatest impact on the 0–1 age group.

Conclusions

Our study suggest that high relative humidity and precipitation and extremely high and low levels of PM₁₀, O₃, SO₂ and CO increased the risk of HFMD from 2014 to 2020. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13365-9.

PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona

Coccidioidomycosis (Valley fever) is a disease caused by the fungal pathogens Coccidioides immitis and Coccidioides posadasii that are endemic to the southwestern United States and parts of Mexico and South America. Throughout the range where the pathogens are endemic, there are seasonal patterns of infection rates that are associated with certain climatic variables. Previous studies that looked at annual and monthly relationships of coccidioidomycosis and climate suggest that infection numbers are linked with precipitation and temperature fluctuations; however, these analytic methods may miss important nonlinear, nonmonotonic seasonal relationships between the response (Valley fever cases) and explanatory variables (climate) influencing disease outbreaks. To improve our current knowledge and to retest relationships, we used case data from three counties of high endemicity in southern Arizona paired with climate data to construct a generalized additive statistical model that explores which meteorological parameters are most useful in predicting Valley fever incidence throughout the year. We then use our model to forecast the pattern of Valley fever cases by month. Our model shows that maximum monthly temperature, average PM10, and total precipitation 1 month prior to reported cases (lagged model) were all significant in predicting Valley fever cases. Our model fits Valley fever case data in the region of endemicity of southern Arizona and captures the seasonal relationships that predict when the public is at higher risk of being infected. This study builds on and retests relationships described by previous studies regarding climate variables that are important for predicting risk of infection and understanding this fungal pathogen. IMPORTANCE The inhalation of environmental infectious propagules from the fungal pathogens Coccidioides immitis and Coccidioides posadasii by susceptible mammals can result in coccidioidomycosis (Valley fever). Arizona is known to be a region where the pathogen is hyperendemic, and reported cases are increasing throughout the western United States. Coccidioides spp. are naturally occurring fungi in arid soils. Little is known about ecological factors that influence the growth of these fungi, and a higher environmental burden may result in increases in human exposure and therefore case rates. By examining case and climate data from Arizona and using generalized additive statistical models, we were able to examine the relationship between disease outbreaks and climatic variables and predict seasonal time points of increased infection risk.

Epidemiological characteristics and spatiotemporal analysis of hand-foot-mouth diseases from 2010 to 2019 in Zibo city, Shandong, China

Background

Hand-foot-mouth disease (HFMD) is a global public health issues, especially in China. It has threat the health of children under 5 years old. The early recognition of high-risk districts and understanding of epidemic characteristics can facilitate health sectors to prevent the occurrence of HFMD effectively.

Methods

Descriptive analysis was used to summarize epidemic characteristics, and the spatial autocorrelation analysis and space-time scan analysis were utilized to explore distribution pattern of HFMD and identify hot spots with statistical significance. The result was presented in ArcMap.

Results

A total of 52,095 HFMD cases were collected in Zibo city from 1 Jan 2010 to 31 Dec 2019. The annual average incidence was 129.72/100,000. The distribution of HFMD was a unimodal trend, with peak from April to September. The most susceptible age group was children under 5 years old (92.46%), and the male-to-female ratio is 1.60: 1. The main clusters were identified in Zhangdian District from 12 April 2010 to 18 September 2012. Spatial autocorrelation analysis showed that the global spatial correlation in Zibo were no statistical significance, except in 2012, 2014, 2015, 2016 and 2018. Cold spots were gathered in Boshan county and Linzi district, while hot spots only in Zhangdian District in 2018, but other years were no significance.

Conclusion

Hot spots mainly concentrated in the central and surrounding city of Zibo city. We suggest that imminent public health planning and resource allocation should be focused within those areas.

Approaching precision public health by automated syndromic surveillance in communities

Background

Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named “Sentinel plus” which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians.

Methods

Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019.

Results

The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM_2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM_2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC.

Conclusions

This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.

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.

Influence of coarse particulate matter on chickenpox in Jiading District, Shanghai, 2009-2018: A distributed lag non-linear time series analysis

Although the link between ambient air pollution and some infectious diseases has been studied, few studies have explored so far, the relationship between chickenpox and particulate matter. Daily chickenpox counts in Jiading District, Shanghai, were collected from 2009 to 2018. Time series analysis was conducted to describe the trends of the daily number of chickenpox cases and the concentration of particulate matter 10 μm or less (PM10). The distributed lag non-linear model (DLNM) was developed to assess the lag and non-linear relationship between the number of chickenpox cases and PM10 concentration adjusting for meteorological factors and other pollutants. Spatiotemporal scanning was used to detect the clustering of chickenpox cases. There was a concomitant relationship between the number of chickenpox cases and PM10 concentration, especially in the period of high PM10 concentration. DLNM results showed a nonlinear relationship between the number of chickenpox cases and PM10 concentration with the maximum effect of PM10 being lagged for 13-14 days, which was consistent with the average incubation period of chickenpox. PM10 was significantly associated with the daily number of chickenpox cases when above 300 μg/m³. The risk of chickenpox increased with increasing PM10 concentration and the association was strongest at the lag of 14 day (RR = 1.13, 95% CI: 1.04-1.23) for PM10 concentration of 500 μg/m³ versus 50 μg/m³. The study provides evidence that high PM10 concentration increases the risk of chickenpox spreading.

Short-term effects of meteorological factors, air pollution, and sunspot on childhood hand, foot, and mouth disease in Tianjin, China: a new time series regression, 2014-2018

This study is aimed at defining the relationship between a set of environmental factors and childhood HFMD and then at estimating the related effect. The 16 environmental factors included meteorological, air pollution, and sunspot. A traditional TSR modified by using susceptible-infectious-recovery models and distribution lag nonlinear model was applied to estimate the short-term effects of daily environmental factors on children HFMD occurrence in 2014-2018 with adjustment of potential confounding factors. A total of 70,027 children aged 0-15 years with HFMD were enrolled. No significant effect was observed for daily sunspot numbers and average visibility. We found positive effects of the ambient average temperature, with an approximately m-shaped curve of the overall cumulative relationship, peaking at 25.6 °C with a relative risk (RR) of 1.45 (95% confidence intervals 1.21-1.73). The largest RR value of hot effect was achieved on the current day and then decreased by 2 days (total group, male group, and scatter group) or 1 day (female group and nursery group), and the effect lasted about 6 to 8 days from the lag 4 or lag 6 day. A greater association of temperature with HFMD for the female group and the scattered group was observed. This study suggests that ambient average temperature might be a risk factor for children HFMD in Tianjin. Further studies are warranted to confirm these findings.

Temporal Trend of PM10 and Associated Human Health Risk over the Past Decade in Cluj-Napoca City, Romania

The human health risk associated with PM10 exposure was assessed for the residents of Cluj-Napoca city, Romania, for a best case-scenario based on the monthly average PM10 and for a worst-case scenario based on the monthly 90th percentile of PM10 concentration. As no toxicity value for PM10 was available, for the calculation of the hazard quotient, the toxicity value was considered to be equal to the annual limit value (40 µm/m3) set in the European Union (EU), and to air quality guidelines (20 µm/m3) set by the World Health Organization (WHO). The daily PM10 concentrations for the period 2009–2019, at the four monitoring stations existing in Cluj-Napoca, were obtained from the National Air Quality Monitoring Network. The annual PM10 values ranged between 20.3 and 29.5 µg/m3, and were below the annual limit value (40 µg/m3) set by European and national legislation, but above the annual air quality guideline (20 µg/m3) set by WHO. Generally, the monthly PM10 concentrations were higher from October to March than in the rest of the year. The monthly air quality index (AQI) showed the good to moderate quality of the air during the whole decade; however, there were days when the air quality was unhealthy for sensitive population groups. The air quality was more or less constant during the warm months, and improved significantly for the cold months from 2009 to 2019. In the best-case scenario, calculated using the EU annual limit value for PM10, the potential non-carcinogenic chronic health risk was present only in 2009 and 2010, but in the worst-case scenario, in each year there were periods, especially in the cold months, in which health risk was present. When considering the WHO air quality guidelines in the calculation of the health risk, the potential non-carcinogenic chronic health risk was present between October and March in each year in the best-case scenario, and in most of the months in the worst-case scenario.

Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan

Seasonal influenza epidemics are associated with various meteorological factors. Recently absolute humidity (AH) has garnered attention, and some epidemiological studies show an association between AH and human influenza infection. However, they mainly analyzed weekly surveillance data, and daily data remains largely unexplored despite its potential benefits. In this study, we analyze daily influenza surveillance data using a distributed lag non-linear model to examine the association of AH with the number of influenza cases and the magnitude of the association. Additionally, we investigate how adjustment for seasonality and autocorrelation in the model affect results. All models used in the study showed a significant increase in the number of influenza cases as AH decreased, although the magnitude of the association differed substantially by model. Furthermore, we found that relative risk reached a peak at lag 10–14 with extremely low AH. To verify these findings, further analysis should be conducted using data from other locations.

Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen

BACKGROUND

The epidemic of hand, foot, and mouth disease (HFMD) has become a severe public health problem in the world and has also brought a high economic and health burden. Furthermore, the prevalence of HFMD varies significantly among different locations. However, there have been few investigations of the effects of socioeconomic factors and air pollution factors on the incidence of HFMD.

METHODS

This study collected data on HFMD in Shenzhen, China, from 2012 to 2015. We selected eleven factors as potential risk factors for HFMD. A Bayesian spatiotemporal model was used to quantify the influence of the factors on HFMD and to identify the relative risks in different districts.

RESULTS

The risk factors of HFMD were the population, population density, concentration of SO2, and concentration of NO2. The relative risks (RRs) were 1.00473 (95% CI: 1.00059-1.00761), 1.00010 (95% CI: 1.00002-1.00016), 1.00215 (95% CI: 1.00170-1.00232) and 1.00058 (95% CI: 1.00028-1.00078), respectively. The protective factors against HFMD were the per capita GDP, the number of public kindergartens, the concentration of PM10, and the concentration of O3. The RRs were 0.98840 (95% CI: 0.98660-0.99026), 0.97686 (95% CI: 0.96946-0.98403), 0.99108 (95% CI: 0.98551-0.99840) and 0.99587 (95% CI: 0.99534-0.99610), respectively. The risk of incidence in Longgang district and Pingshan district decreased, while the risk of incidence in Baoan district increased.

CONCLUSIONS

Studies have confirmed that socioeconomic factors and air pollution factors have an impact on the incidence of HFMD in Shenzhen, China. The results will be of great practical significance to local authorities, which is conducive to accurate prevention and can be used to formulate HFMD early warning systems.