• View in gallery

    Location of Dazhou in Sichuan Province, China.

  • View in gallery

    Distribution of Japanese encephalitis cases (A), distributions of the daily average temperature (B), distributions of the daily average relative humidity (C), and distributions of the daily rainfall (D), Dazhou, China, 2005–2012.

  • View in gallery

    Distribution of Japanese encephalitis cases in various daily average temperatures in Dazhou, China, 2005–2012.

  • View in gallery

    Three-dimensional effect diagram of the daily average temperature on Japanese encephalitis (JE) at a lag of 10–50 days (A), RR of daily average temperature (25°C) on JE (B), RR of daily average temperature (higher than 20°C) on JE at the 25-day lag (C), and cumulative RR of daily average temperature on JE (D), Dazhou, China, 2005–2012.

  • View in gallery

    Three-dimensional effect diagram of daily rainfall on Japanese encephalitis (JE) at the lag of 10–50 days (A), RR of rainfall (100 mm) on JE (B), RR of rainfall on JE at the 30-day lag (C), and cumulative RR of rainfall on JE (D), Dazhou, China, 2005–2012.

  • 1.

    Sahu SS, Dash S, Sonia T, Muthukumaravel S, Sankari T, Gunasekaran K, Jambulingam P, 2018. Entomological investigation of Japanese encephalitis outbreak in Malkangiri district of Odisha state, India. Mem Inst Oswaldo Cruz 113: e170499.

    • Search Google Scholar
    • Export Citation
  • 2.

    Liu B, Gao X, Ma J, Jiao Z, Xiao J, Wang H, 2018. Influence of host and environmental factors on the distribution of the Japanese encephalitis vector Culex tritaeniorhynchus in China. Int J Environ Res Public Health 15: 1848.

    • Search Google Scholar
    • Export Citation
  • 3.

    Ichiro K, Ken-ichi S, Akira K, Yasuaki H, Tomohiko T, 2013. The effect of precipitation on the transmission of Japanese encephalitis (JE) virus in nature: a complex effect on antibody-positive rate to JE virus in sentinel pigs. Int J Environ Res Public Health 10: 18311844.

    • Search Google Scholar
    • Export Citation
  • 4.

    Impoinvil DE, Solomon T, Schluter WW, Rayamajhi A, Bichha RP, Shakya G, Caminade C, Baylis M, 2011. The spatial heterogeneity between Japanese encephalitis incidence distribution and environmental variables in Nepal. PLoS One 6: e22192.

    • Search Google Scholar
    • Export Citation
  • 5.

    Kakoti G, Dutta P, Das BR, Borah J, Mahanta J, 2013. Clinical profile and outcome of Japanese encephalitis in children admitted with acute encephalitis syndrome. Biomed Res Int 2013: e152656.

    • Search Google Scholar
    • Export Citation
  • 6.

    Lannes N, Summerfield A, Filgueira L, 2017. Regulation of inflammation in Japanese encephalitis. J Neuroinflammation 14: 158.

  • 7.

    Kumar K, Arshad SS, Selvarajah GT, Abu J, Toung OP, Abba Y, Yasmin AR, Bande F, Sharma R, Ong BL, 2018. Japanese encephalitis in Malaysia: an overview and timeline. Acta Trop 185: 219229.

    • Search Google Scholar
    • Export Citation
  • 8.

    Duan LL, Shang J, Zhang WM, Ju J, 2019. Analysis of epidemic situation of legal infectious diseases in mainland China in 2010–2017 [in Chinese]. Mod Prev Med 46: 25012506.

    • Search Google Scholar
    • Export Citation
  • 9.

    Baig S, Fox KK, Jee Y, O’Connor P, Hombach J, Wang SA, Hyde T, Fischer M, Hills SL, 2013. Japanese encephalitis surveillance and immunization – Asia and the western Pacific, 2012. MMWR Morb Mortal Wkly Rep 62: 658662.

    • Search Google Scholar
    • Export Citation
  • 10.

    Liang G-D, Huanyu W, 2015. Epidemiology of Japanese encephalitis: past, present, and future prospects. Ther Clin Risk Manag 11: 435448.

  • 11.

    Tao Z, Liu G, Wang M, Wang H, Lin X, Song L, Wang S, Wang H, Liu X, Cui N, 2013. Molecular epidemiology of Japanese encephalitis virus in mosquitoes during an outbreak in China, 2013. Sci Rep 4: 4908.

    • Search Google Scholar
    • Export Citation
  • 12.

    Huanyu W, Yixing L, Xiaofeng L, Guodong L, 2009. Japanese encephalitis in mainland China. Jpn J Infect Dis 62: 331336.

  • 13.

    Zhao X, Cao M, Feng HH, Fan H, Chen F, Feng Z, Li X, Zhou XH, 2014. Japanese encephalitis risk and contextual risk factors in southwest China: a Bayesian hierarchical spatial and spatiotemporal analysis. Int J Environ Res Public Health 11: 42014217.

    • Search Google Scholar
    • Export Citation
  • 14.

    Masuoka P, Klein TA, Kim HC, Claborn DM, Achee N, Andre R, Chamberlin J, Small J, Anyamba A, Dongkyu L, 2010. Modeling the distribution of Culex tritaeniorhynchus to predict Japanese encephalitis distribution in the Republic of Korea. Geospat Health 5: 4557.

    • Search Google Scholar
    • Export Citation
  • 15.

    Wang L et al. 2014. The role of environmental factors in the spatial distribution of Japanese encephalitis in Mainland China. Environ Int 73: 19.

    • Search Google Scholar
    • Export Citation
  • 16.

    Li X, Gao X, Ren Z, Cao Y, Wang J, Liang G, 2014. A spatial and temporal analysis of Japanese encephalitis in Mainland China, 1963–1975: a period without Japanese encephalitis vaccination. PLoS One 9: e99183.

    • Search Google Scholar
    • Export Citation
  • 17.

    Chowdhury FR, Ibrahim QSU, Bari MS, Alam MMJ, Dunachie SJ, Rodriguez-Morales AJ, Patwary MI, 2018. The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh. PLoS One 13: e0199579.

    • Search Google Scholar
    • Export Citation
  • 18.

    Yang H, Luo P, Wang J, Mou C, Mo L, Wang Z, Fu Y, Lin H, Yang Y, Bhatta LD, 2015. Ecosystem evapotranspiration as a response to climate and vegetation coverage changes in Northwest Yunnan, China. PLoS One 10: e0134795.

    • Search Google Scholar
    • Export Citation
  • 19.

    Zhang F, Liu Z, Zhang C, Jiang B, 2016. Short-term effects of floods on Japanese encephalitis in Nanchong, China, 2007–2012: a time-stratified case-crossover study. Sci Total Environ 563–564: 11051110.

    • Search Google Scholar
    • Export Citation
  • 20.

    Zhang FF, 2017. Short-Term Impacts of Floods and Meteorological Factors on Japanese Encephalitis in Sichuan Province, China, 2005–2012 in Chinese. Jinan, People’s Republic of China: Shandong University.

    • Search Google Scholar
    • Export Citation
  • 21.

    Figueiras A, Roca-Pardinas J, Cadarso-Suarez C, 2005. A bootstrap method to avoid the effect of concurvity in generalised additive models in time series studies of air pollution. J Epidemiol Community Health 59: 881884.

    • Search Google Scholar
    • Export Citation
  • 22.

    Beaumont DC, 1981. Regression diagnostics—identifying influential data and sources of collinearity. J Oper Res Soc 32: 157158.

  • 23.

    Gasparrini A, Armstrong B, Kenward MG, 2010. Distributed lag non-linear models. Stat Med 29: 22242234.

  • 24.

    Li R, Lin H, Liang Y, Zhang T, Luo C, Jiang Z, Xu Q, Xue F, Liu Y, Li X, 2016. The short-term association between meteorological factors and mumps in Jining, China. Sci Total Environ 568: 10691075.

    • Search Google Scholar
    • Export Citation
  • 25.

    Bai Y et al. 2014. Regional impact of climate on Japanese encephalitis in areas located near the three gorges dam. PLoS One 9: e84326.

  • 26.

    Krishnan B, Antonio G, Shakoor H, Liam S, Ben A, 2013. Time series regression studies in environmental epidemiology. Int J Epidemiol 42: 11871195.

  • 27.

    Solomon T, Dung NM, Kneen R, Gainsborough M, Vaughn DW, Khanh VT, 2015. Japanese encephalitis. J Neurol Sci 357: e463.

  • 28.

    Zhang MX, Wang N, Du CL, Li XS, 2017. Application of the distributed lag non-linear model in relation between epidemic encephalitis B and meteorological factors [in Chinese]. Mod Prev Med 44: 17451749 ; 1769.

    • Search Google Scholar
    • Export Citation
  • 29.

    Gasparrini A, 2011. Distributed lag linear and non-linear models in R: the package dlnm. J Stat Softw 43: 120.

  • 30.

    Zhang S, Hu W, Qi X, Zhuang G, 2018. How socio-environmental factors are associated with Japanese encephalitis in Shaanxi, China-A Bayesian spatial analysis. Int J Environ Res Public Health 15: 608.

    • Search Google Scholar
    • Export Citation
  • 31.

    Borah J, Dutta P, Khan SA, Mahanta J, 2013. Association of weather and anthropogenic factors for transmission of Japanese encephalitis in an endemic area of India. Ecohealth 10: 129136.

    • Search Google Scholar
    • Export Citation
  • 32.

    Lin CL, Chang HL, Lin CY, Chen KT, 2017. Seasonal patterns of Japanese encephalitis and associated meteorological factors in Taiwan. Int J Environ Res Public Health 14: 1317.

    • Search Google Scholar
    • Export Citation
  • 33.

    Gu PQ, Min JG, Gu ZQ, Huang PX, Song HL, 2003. The relationship between the first appearance in spring and seasonal distribution of Culex tritaeniorhynchus and the meteorological conditions in Shanghai. Acta Entomol Sin 46: 325332.

    • Search Google Scholar
    • Export Citation
  • 34.

    Chai C, Wang Q, Cao S, Zhao Q, Wen Y, Huang X, Wen X, Yan Q, Ma X, Wu R, 2018. Serological and molecular epidemiology of Japanese encephalitis virus infections in swine herds in China, 2006–2012. J Vet Sci 19: 151155.

    • Search Google Scholar
    • Export Citation
  • 35.

    Bi P, Zhang Y, Parton KA, 2007. Weather variables and Japanese encephalitis in the metropolitan area of Jinan city, China. J Infect 55: 551556.

    • Search Google Scholar
    • Export Citation
  • 36.

    Krzyzewska A, Wereski S, Dobek M, 2020. Summer UTCI variability in Poland in the twenty-first century. Int J Biometeorol (Epub ahead of print). doi: 10.1007/s00484-020-01965-2.

    • Search Google Scholar
    • Export Citation
  • 37.

    Mansfield KL, Hernandez-Triana LM, Banyard AC, Fooks AR, Johnson N, 2017. Japanese encephalitis virus infection, diagnosis and control in domestic animals. Vet Microbiol 201: 8592.

    • Search Google Scholar
    • Export Citation
  • 38.

    Oya A, Kurane I, 2007. Japanese encephalitis for a reference to international travelers. J Trav Med 14: 259268.

  • 39.

    Ciota A, Kramer LD, 2013. Vector-virus interactions and transmission dynamics of West Nile virus. Virus 5: 30213047.

  • 40.

    Bashar K, Rahman MS, Nodi IJ, Howlader AJ, 2016. Species composition and habitat characterization of mosquito (Diptera: Culicidae) larvae in semi-urban areas of Dhaka, Bangladesh. Pathog Glob Health 110: 4861.

    • Search Google Scholar
    • Export Citation
  • 41.

    Miller RH, Masuoka P, Klein TA, Kim HC, Somer T, Grieco J, 2012. Ecological niche modeling to estimate the distribution of Japanese encephalitis virus in Asia. PLoS Negl Trop Dis 6: 119121.

    • Search Google Scholar
    • Export Citation
  • 42.

    Mordecai EA et al. 2019. Thermal biology of mosquito-borne disease. Ecol Lett 22: 16901708.

  • 43.

    Xu Y, Ramanathan V, Victor DG, 2018. Global warming will happen faster than we think. Nature 564: 3032.

  • 44.

    Rossati A, 2017. Global warming and its health impact. Int J Occup Environ Med 8: 720.

  • 45.

    Li SC, Xue H, Su Y, Sun H, Zhao FH, 2010. Survey of underreporting of notifiable communicable diseases and network direct reporting quality in Ganzhou district in Zhangye, Gansu province [in Chinese]. Dis Surveill 25: 816819.

    • Search Google Scholar
    • Export Citation
  • 46.

    Liu ZT, Li QF, Wang RH, Huang T, Yu JX, 2011. Underreporting of notifiable communicable diseases in medical institutions in Yunnan province [in Chinese]. Dis Surveill 26: 565567.

    • Search Google Scholar
    • Export Citation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

 

Nonlinear and Threshold Effect of Meteorological Factors on Japanese Encephalitis Transmission in Southwestern China

View More View Less
  • 1 Department of Personnel, Qilu Hospital of Shandong University, Jinan, People’s Republic of China;
  • | 2 Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China;
  • | 3 Cheeloo College of Medicine, Shandong University Climate Change and Health Center, Jinan, People’s Republic of China;
  • | 4 School of Public Health, The University of Adelaide, Adelaide, Australia;
  • | 5 School of Public Health, China Studies Centre, The University of Sydney, Sydney, Australia;
  • | 6 School of Public Health, Fujian Medical University, Fuzhou, People’s Republic of China;
  • | 7 National Meteorological Center, China Meteorological Administration, Beijing, People’s Republic of China

ABSTRACT

Although previous studies have reported that meteorological factors might affect the risk of Japanese encephalitis (JE), the relationship between meteorological factors and JE remains unclear. This study aimed to evaluate the relationship between meteorological factors and JE and identify the threshold temperature. Daily meteorological data and JE surveillance data in Dazhou, Sichuan, were collected for the study period from 2005 to 2012 (restricting to May–October because of the seasonal distribution of JE). A distributed lag nonlinear model was used to analyze the lagged and cumulative effect of daily average temperature and daily rainfall on JE transmission. A total of 622 JE cases were reported over the study period. We found JE was positively associated with daily average temperature and daily rainfall with a 25-day lag and 30-day lag, respectively. The threshold value of the daily average temperature is 20°C. Each 5°C increase over the threshold would lead to a 13% (95% CI: 1–17.3%) increase in JE. Using 0 mm as the reference, a daily rainfall of 100 mm would lead to a 132% (95% CI: 73–311%) increase in the risk of JE. Japanese encephalitis is climate-sensitive; meteorological factors should be taken into account for the future prevention and control measure making, especially in a warm and rainy weather condition.

INTRODUCTION

Japanese encephalitis (JE) or encephalitis B, caused by JE virus (JEV), is one of the most important viral encephalitides in the world, especially in Asia.1 The principal vector is Culex tritaeniorhynchus, and other mosquitos such as Aedes and Anopheles can also transmit the disease.2 Pigs and aquatic birds act as the main natural reservoir and amplifier of the virus,3 whereas humans are the “dead-end hosts.”4 Japanese encephalitis infection can result in acute encephalitis syndrome, which the WHO defined as fever or recent fever with a change in mental status and/or new onset of seizures (excluding simple febrile seizures).5 So far, there is no effective antiviral treatment for JE. Supportive care is usually used to relieve symptoms and stabilize the patients.6 All over the world, the case fatality rate can be as high as 30%. Of those who survived JE, 20–30% suffered permanent intellectual, behavioral, or neurological sequelae.7 Because of its severe outcomes, JE has been classified as a national notifiable infectious disease in China since 1951.8

According to the report from Baig et al.,9 the JEV could cause an estimated 67,900 JE cases annually in Asia. China is one of the main epidemic regions of JE.10 During our study period, the national maximum of annually reported cases is 7,643. For the past few years, the overall trend of JE epidemics in China has been in decline. However, the national incidence is still at a high level with an average incidence of 0.3 per 100,000 population.11 In southwest China, the average incidence can reach up to 1 per 100,000 population,12 and the east part of southwest China is a high-risk area.13 Located in the east part of southwest China, Sichuan Province has more than 15% of all JE cases reported between 2005 and 2012 in China.2 Specifically, 12.8% of JE cases of Sichuan were reported in Dazhou city.

As a vector-borne infectious disease, JE has an obvious seasonal fluctuation with relatively more cases in late spring, summer, and early autumn. Therefore, it is plausible to hypothesize that meteorological factors may play an important role in JE transmission. Some epidemiologic studies have roughly explored the impact of meteorological factors on JE and suggested that JE was sensitive to meteorological factors.14,15 However, there are differences between those discoveries; a study in China reported that abundant rainfall and high temperature in summer could contribute to the occurrence of JE,16 whereas another study conducted in Bangladesh found temperature was inversely associated with JE cases.17 The inconsistency regarding the temperature–JE relationship indicated the associations might vary in regions with various climatic and socioeconomic characteristics. Hence, it is valuable to get specific quantitative relationship between meteorological factors and JE in each specific location so that relevant actions could be implemented.

Our study aimed to assess the relationship between meteorological factors and JE and calculate the lagged and cumulative effects based on the disease surveillance data in Dazhou, China. Furthermore, the threshold for the effect of daily average temperature on JE was explored. The results of this study may provide evidence for developing preventive measures and strategies for the epidemic control of JE.

MATERIALS AND METHODS

Study area.

Located at 107°50′E longitude and 31°22′N latitude, Dazhou, a city of Sichuan Province in southwest China, covers an area of about 16,591 km2 with a population of 6.9 million (Figure 1). This city features a subtropical humid monsoon climate with average annual temperature and rainfall varying from 17.35°C to 18.14°C and 989.7 to 1,555 mm over the study period.18

Figure 1.
Figure 1.

Location of Dazhou in Sichuan Province, China.

Citation: The American Journal of Tropical Medicine and Hygiene 103, 6; 10.4269/ajtmh.20-0040

Data collection.

Japanese encephalitis surveillance data.

Daily reported cases of JE, from January 1, 2005 to December 31, 2012 in Dazhou, were collected from the National Notifiable Disease Surveillance System (NNDSS). As a national statutory Class B infectious disease, JE cases must be reported directly to the local CDC through the NNDSS within 24 hours.19 Japanese encephalitis cases were diagnosed according to the unifying diagnostic criteria and principles of the National Health Commission of China (WS 271-2008).20 Considering the seasonal distribution of JE, we have restricted our analysis to warm months (May–October) to reduce the impact of seasonality.

Meteorological factors.

Meteorological data for Dazhou including daily rainfall, sunshine duration, daily average temperature, daily maximum temperature, daily minimum temperature, wind speed, air pressure, and daily average relative humidity for the study period were downloaded from the website of China Meteorological Administration Climatic Dataset Center (http://cdc.cma.gov.cn/home.do).

Statistical analysis.

A descriptive analysis was conducted to describe the characteristics of JE cases and meteorological factors over the study period. A scatter plot was drawn to crudely explore the threshold effect between daily average temperature and JE cases.

Considering those meteorological factors as highly correlated and bringing all of the factors into one model may lead to a problem of multicollinearity or/and concurvity.21 Meteorological factors that were highly correlated, that is, variables with coefficient values greater than 0.8, were not included in the same model.22 Comprehensive consideration of the correlation and previous report, daily average temperature, daily relative humidity, and daily rainfall were chosen as independent variables in the model.17 The distributed lag nonlinear model (DLNM) is a modeling framework that can simultaneously represent nonlinear exposure–response dependencies and delayed effects based on a “cross-basis” function.23 Recently, the DLNM is widely used in environmental epidemiology to explore the relationship between meteorological factors and diseases.24 In our study, a DLNM was used to analyze the nonlinear and lag effects for daily average temperature and daily rainfall, and daily relative humidity was treated as a potential confounding factor. Quasi-Poisson distribution was selected to deal with the over-dispersion of JE cases. Based on a previous study, the relationship between JE and the daily average temperature is linear.25 A cross-basis function with a linear threshold function for daily average temperature and a cross-basis function with a natural cubic spline for daily average temperature and daily rainfall were built, respectively. As raw data were likely to be dominated by seasonal patterns and long-term trends, it is necessary for us to control and separate them out from the short-term associations between exposure and outcome, which we are interested in. We have chosen the time-stratified model to control those trends. The time-stratified model can capture those patterns by putting calendar year and month as a categorical variable in the regression model and splitting the study period into intervals and estimating a different baseline for each interval.26

According to previous studies, the incubation period of JE was 4–14 days27 and the period of infection was 2–7 weeks.28 Thus, a lag period of 10–50 days was applied in our regression models.

The DLNM can be specified as follows:
Log[E(Yt)]=β+cb(tem)+ns(hum)+cb(rain)+factor(strata),
in which cb (temp) and cb (rain) indicate the cross-basis functions for JE cases with daily average temperature and daily rainfall, respectively. ns (hum) denotes the natural cubic spline. The factor (strata) which was formed by factor (year) and factor (month) to be represented in the time-stratified model.

Sensitivity analyses.

Residuals of an autocorrelation function (ACF) and partial ACF (PACF) were drawn to diagnose whether the autocorrelation was well controlled. Because the parameters, variables, and the way of controlling the long-term trend and seasonality of our models were chosen empirically, sensitivity analyses were needed to verify whether our results were robust. Different lag periods of meteorological factors were attempted from 0–50 to 10–60 days, and threshold values were changed from 20°C to 15°C accordingly. A different way with second (time) which using smooth splines to control the time trend took the place of factor (strata) in our model. To avoid the problem of over-fit, we have left annual data out one by one to evaluate the performance of the model with those missing data; meanwhile, we applied our model at Bazhong city, which is also located in Sichuan Province, to evaluate the fitness of the model in the near region.

All statistical analyses were performed with R software (v. 3.6.1, The R Project for Statistical Computing, Vienna, Austria), using functions from the packages “mgcv” and “dlnm.”29 All statistical tests were two-sided, with a statistical significance level of 0.05.

RESULTS

Descriptive analysis.

From 2005 to 2012, a total of 622 JE cases were reported in Dazhou city. There were more male cases, with a male-to-female ratio of 1.4:1. Most of the cases were children and adolescents. Specifically, cases younger than 6 years accounted for approximately 84% of cases.

The meteorological factors during the study period were summarized in Table 1. The mean value of daily temperature, relative humidity, and daily rainfall is 17.6°C, 76%, and 3.5 mm, respectively. Figures 2 and 3 show the time series plots of JE cases and meteorological factors from 2005 to 2012. The number of JE cases, daily average temperature, and rainfall have obvious seasonal trends with a peak in summer.

Table 1

Daily summary of meteorological factors in Dazhou, China, 2005–2012

VariableMeanSDMinimumMaximumQuantile
255075
Daily maximum temperature (°C)29.09.211.740.725.029.535.3
Daily average temperature (°C)24.28.110.234.121.024.527.8
Daily minimum temperature (°C)20.87.57.429.018.021.023.8
Relative humidity (%)75.610.944.099.068.076.083.3
Rainfall (mm)5.511.20.0140.80.00.02.5
Figure 2.
Figure 2.

Distribution of Japanese encephalitis cases (A), distributions of the daily average temperature (B), distributions of the daily average relative humidity (C), and distributions of the daily rainfall (D), Dazhou, China, 2005–2012.

Citation: The American Journal of Tropical Medicine and Hygiene 103, 6; 10.4269/ajtmh.20-0040

Figure 3.
Figure 3.

Distribution of Japanese encephalitis cases in various daily average temperatures in Dazhou, China, 2005–2012.

Citation: The American Journal of Tropical Medicine and Hygiene 103, 6; 10.4269/ajtmh.20-0040

Regression analysis.

The distribution of JE cases at different daily average temperatures was shown in Figure 3. The number of JE cases started to increase when daily average temperature was roughly over 20°C.

According to Figure 3, we set 20°C as the threshold value of daily average temperature. The exposure–lag–response relationship between daily average temperature and JE cases was shown in Figure 4A and B. By fitting the DLNM, we found that the RR of JE was positively associated with the increase in daily average temperature. RR of JE reached the maximum at lag 25 days. Figure 4C showed the exposure–response relationship between daily average temperature and JE at lag 25 days. Each 5°C increase in daily average temperature over the threshold value led to a 13% (95% CI: 1–17.3%) increase in JE. Figure 4D shows the cumulative effect of temperature on JE at lag 25 days (RR = 10.33, 95% CI: 5.31–20.11).

Figure 4.
Figure 4.

Three-dimensional effect diagram of the daily average temperature on Japanese encephalitis (JE) at a lag of 10–50 days (A), RR of daily average temperature (25°C) on JE (B), RR of daily average temperature (higher than 20°C) on JE at the 25-day lag (C), and cumulative RR of daily average temperature on JE (D), Dazhou, China, 2005–2012.

Citation: The American Journal of Tropical Medicine and Hygiene 103, 6; 10.4269/ajtmh.20-0040

The exposure–lag–response relationship of daily rainfall on JE was shown in Figure 5A and B. The RR of JE was positively associated with daily rainfall, and the RR of JE reached the maximum at lag 30 days. Using 0 mm as the reference, at 100 mm it would lead to 132% (95% CI: 73–311%) increase in JE.

Figure 5.
Figure 5.

Three-dimensional effect diagram of daily rainfall on Japanese encephalitis (JE) at the lag of 10–50 days (A), RR of rainfall (100 mm) on JE (B), RR of rainfall on JE at the 30-day lag (C), and cumulative RR of rainfall on JE (D), Dazhou, China, 2005–2012.

Citation: The American Journal of Tropical Medicine and Hygiene 103, 6; 10.4269/ajtmh.20-0040

Sensitivity analysis.

Residuals of the ACF and PACF for residuals of the DLNM of temperature and rainfall are shown in the Supplemental Figures S1 and S2. There was no apparent autocorrelation of model residuals. The results of sensitivity analyses are shown in the Supplemental Material. After changing the threshold value of daily average temperature (from 20°C to 15°C), the effects were similar (see Supplemental Figure S3). At a different way of controlling the long-term trend, we found that the effect of both daily average temperature and rainfall on JE did not change substantially (see Supplemental Figure S5). When changing the lag days, the effects of daily rainfall on JE were consistent (Supplemental Figure S6). The effects of daily rainfall on JE transmission have been changed when the 2005 and 2011 rainfall data were removed, which could be because of the inconstant annual rainfall amount, although the associations still exist (see Supplemental Figures S7 and S8). The relationship between daily average temperatures and JE transmission did not change substantially when the 2-year data were removed (see Supplemental Figures S9 and S10). While applying our model to a nearby region, Bazhong city, the results were similar to these from Dazhou (see Supplemental Figures S11 and S12). This indicated our model is robust.

DISCUSSION

A positive relationship between daily average temperature, daily rainfall, and JE with a 25-day lag and 30-day lag was detected in Dazhou, southwest China, respectively. The results are in line with previous studies in China. For example, a study conducted in Shaanxi, a warm temperate monsoon climate zone, reported that monthly average rainfall was positively associated with the incidence of JE with a 1- to 2-month lagged effect.30 Another study including data from 438 counties of southeast China also found rainfall and temperature were positively associated with JE incidence and had a similar time-lagged effect.13 Moreover a similar result was also reported at an endemic area of India, which found monthly minimum temperature and rainfall were associated with JE transmission at a 1-month lagged effect.31

Moreover, a threshold value of the daily average temperature of 20°C was identified in our study, which aligns with the previous study demonstrating the number of JE cases started to increase at the temperature of 22°C.32 A study conducted in Shanghai, a city with the same latitude as Dazhou, reported the number of C. tritaeniorhynchus increased substantially when the temperature was higher than 18°C.33 This finding supports our result on the threshold of 20°C for JE. A study on pigs, the main amplifying host of JEV, has found the increase in the JEV-positive rate among pig shared a similar trend with the daily average temperature.34 Higher optimum temperatures may also boost the increase in mosquito population, which allows JEV to spread quickly among mosquitoes. A study has shown that only 14% of mosquitoes were infected with JEV when temperatures were 18–22°C, whereas the figure reached up to 80% of mosquitoes when temperatures were between 26°C and 30°C.35 Besides, the warmer temperature may also change people’s behavior, that is, dressing short clothes and spending more time outdoors,36 which would increase the contact opportunities with mosquitoes and lead to a higher incidence of JE.

Rainfall also plays an important role in JE transmission. We have found that abundant rainfall (at 100 mm rainfall) would lead to a 132% increase in the risk of JE by using 0 mm as reference. The previous study has reported that suitable rainfall could provide a favorable environmental condition to maintain the C. tritaeniorhynchus breeding sites and facilitate the mosquito bites.37 In particular, a study has found that adequate rainfall has a statistically significant positive association with the JEV activity.3,38 The higher JEV activity shortens the time of occurrence of viremia among pigs and accelerates the transmission of virus between pigs and mosquitoes.39

Our results were contrary to the findings of a study from Bangladesh, which found an inverse association between JE and increased temperature,17 as Bangladesh is a tropical country with annual average maximum temperature reaching as high as above 38°C,40 whereas the optimum temperature C. tritaeniorhynchus is between 22.8°C and 34.5°C41 and the thermal optima of the virus is no more than 29°C.42 Such high temperature would reduce the activity of both mosquito and virus and suppress the transmission of JE. Our study area is located in the temperate zone. The mean of daily maximum temperature during our study period was 29°C, which is within the optimal temperature range.

The global average surface temperature has increased by 0.6°C since the late 1950s.43 It may lead to the expansion of mosquito activity regions and the extension of the activity time in the future,44 thereby facilitating the transmission of JE. A better understanding of the impact of meteorological factors on JE in a high-prevalence and potential epidemic areas may provide solid evidence to support public health policymaking. Our findings may contribute to developing an early warning system of JE. The high-risk weather conditions for JE including high temperature and adequate rainfall should be considered in disease prevention and control. After entering the epidemic season of JE, local health authorities should propose some precautionary health actions, such as issuing a community public health warning for possible JE transmission, especially after rainfall and when the temperature threshold has been reached to encourage community members to have personal protection and to remove the larval habitat timely.

Limitations of this study should be acknowledged. First, non-meteorological factors such as immunization rate, mosquito density, rice cultivation area, and pig density were not taken into account when modeling the association between meteorological factors and JE because of the unavailability of relevant data. Second, as the disease data were collected from a passive surveillance system, underreporting is an inevitable limitation for the analysis, although the underreporting rate was low and remained stable.45,46 Last, because only one weather station was chosen to represent the regional meteorological conditions, the findings may not be appropriate to be generalized to different climatic regions.

CONCLUSION

Japanese encephalitis is sensitive to climate. Meteorological factors could be taken into account in the process of making prevention and control measures against JE, especially in warm and rainy weather conditions.

Supplemental material

ACKNOWLEDGMENTS

We would like to sincerely thank the Chinese CDC and China Meteorology Administration for providing the disease notification data.

REFERENCES

  • 1.

    Sahu SS, Dash S, Sonia T, Muthukumaravel S, Sankari T, Gunasekaran K, Jambulingam P, 2018. Entomological investigation of Japanese encephalitis outbreak in Malkangiri district of Odisha state, India. Mem Inst Oswaldo Cruz 113: e170499.

    • Search Google Scholar
    • Export Citation
  • 2.

    Liu B, Gao X, Ma J, Jiao Z, Xiao J, Wang H, 2018. Influence of host and environmental factors on the distribution of the Japanese encephalitis vector Culex tritaeniorhynchus in China. Int J Environ Res Public Health 15: 1848.

    • Search Google Scholar
    • Export Citation
  • 3.

    Ichiro K, Ken-ichi S, Akira K, Yasuaki H, Tomohiko T, 2013. The effect of precipitation on the transmission of Japanese encephalitis (JE) virus in nature: a complex effect on antibody-positive rate to JE virus in sentinel pigs. Int J Environ Res Public Health 10: 18311844.

    • Search Google Scholar
    • Export Citation
  • 4.

    Impoinvil DE, Solomon T, Schluter WW, Rayamajhi A, Bichha RP, Shakya G, Caminade C, Baylis M, 2011. The spatial heterogeneity between Japanese encephalitis incidence distribution and environmental variables in Nepal. PLoS One 6: e22192.

    • Search Google Scholar
    • Export Citation
  • 5.

    Kakoti G, Dutta P, Das BR, Borah J, Mahanta J, 2013. Clinical profile and outcome of Japanese encephalitis in children admitted with acute encephalitis syndrome. Biomed Res Int 2013: e152656.

    • Search Google Scholar
    • Export Citation
  • 6.

    Lannes N, Summerfield A, Filgueira L, 2017. Regulation of inflammation in Japanese encephalitis. J Neuroinflammation 14: 158.

  • 7.

    Kumar K, Arshad SS, Selvarajah GT, Abu J, Toung OP, Abba Y, Yasmin AR, Bande F, Sharma R, Ong BL, 2018. Japanese encephalitis in Malaysia: an overview and timeline. Acta Trop 185: 219229.

    • Search Google Scholar
    • Export Citation
  • 8.

    Duan LL, Shang J, Zhang WM, Ju J, 2019. Analysis of epidemic situation of legal infectious diseases in mainland China in 2010–2017 [in Chinese]. Mod Prev Med 46: 25012506.

    • Search Google Scholar
    • Export Citation
  • 9.

    Baig S, Fox KK, Jee Y, O’Connor P, Hombach J, Wang SA, Hyde T, Fischer M, Hills SL, 2013. Japanese encephalitis surveillance and immunization – Asia and the western Pacific, 2012. MMWR Morb Mortal Wkly Rep 62: 658662.

    • Search Google Scholar
    • Export Citation
  • 10.

    Liang G-D, Huanyu W, 2015. Epidemiology of Japanese encephalitis: past, present, and future prospects. Ther Clin Risk Manag 11: 435448.

  • 11.

    Tao Z, Liu G, Wang M, Wang H, Lin X, Song L, Wang S, Wang H, Liu X, Cui N, 2013. Molecular epidemiology of Japanese encephalitis virus in mosquitoes during an outbreak in China, 2013. Sci Rep 4: 4908.

    • Search Google Scholar
    • Export Citation
  • 12.

    Huanyu W, Yixing L, Xiaofeng L, Guodong L, 2009. Japanese encephalitis in mainland China. Jpn J Infect Dis 62: 331336.

  • 13.

    Zhao X, Cao M, Feng HH, Fan H, Chen F, Feng Z, Li X, Zhou XH, 2014. Japanese encephalitis risk and contextual risk factors in southwest China: a Bayesian hierarchical spatial and spatiotemporal analysis. Int J Environ Res Public Health 11: 42014217.

    • Search Google Scholar
    • Export Citation
  • 14.

    Masuoka P, Klein TA, Kim HC, Claborn DM, Achee N, Andre R, Chamberlin J, Small J, Anyamba A, Dongkyu L, 2010. Modeling the distribution of Culex tritaeniorhynchus to predict Japanese encephalitis distribution in the Republic of Korea. Geospat Health 5: 4557.

    • Search Google Scholar
    • Export Citation
  • 15.

    Wang L et al. 2014. The role of environmental factors in the spatial distribution of Japanese encephalitis in Mainland China. Environ Int 73: 19.

    • Search Google Scholar
    • Export Citation
  • 16.

    Li X, Gao X, Ren Z, Cao Y, Wang J, Liang G, 2014. A spatial and temporal analysis of Japanese encephalitis in Mainland China, 1963–1975: a period without Japanese encephalitis vaccination. PLoS One 9: e99183.

    • Search Google Scholar
    • Export Citation
  • 17.

    Chowdhury FR, Ibrahim QSU, Bari MS, Alam MMJ, Dunachie SJ, Rodriguez-Morales AJ, Patwary MI, 2018. The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh. PLoS One 13: e0199579.

    • Search Google Scholar
    • Export Citation
  • 18.

    Yang H, Luo P, Wang J, Mou C, Mo L, Wang Z, Fu Y, Lin H, Yang Y, Bhatta LD, 2015. Ecosystem evapotranspiration as a response to climate and vegetation coverage changes in Northwest Yunnan, China. PLoS One 10: e0134795.

    • Search Google Scholar
    • Export Citation
  • 19.

    Zhang F, Liu Z, Zhang C, Jiang B, 2016. Short-term effects of floods on Japanese encephalitis in Nanchong, China, 2007–2012: a time-stratified case-crossover study. Sci Total Environ 563–564: 11051110.

    • Search Google Scholar
    • Export Citation
  • 20.

    Zhang FF, 2017. Short-Term Impacts of Floods and Meteorological Factors on Japanese Encephalitis in Sichuan Province, China, 2005–2012 in Chinese. Jinan, People’s Republic of China: Shandong University.

    • Search Google Scholar
    • Export Citation
  • 21.

    Figueiras A, Roca-Pardinas J, Cadarso-Suarez C, 2005. A bootstrap method to avoid the effect of concurvity in generalised additive models in time series studies of air pollution. J Epidemiol Community Health 59: 881884.

    • Search Google Scholar
    • Export Citation
  • 22.

    Beaumont DC, 1981. Regression diagnostics—identifying influential data and sources of collinearity. J Oper Res Soc 32: 157158.

  • 23.

    Gasparrini A, Armstrong B, Kenward MG, 2010. Distributed lag non-linear models. Stat Med 29: 22242234.

  • 24.

    Li R, Lin H, Liang Y, Zhang T, Luo C, Jiang Z, Xu Q, Xue F, Liu Y, Li X, 2016. The short-term association between meteorological factors and mumps in Jining, China. Sci Total Environ 568: 10691075.

    • Search Google Scholar
    • Export Citation
  • 25.

    Bai Y et al. 2014. Regional impact of climate on Japanese encephalitis in areas located near the three gorges dam. PLoS One 9: e84326.

  • 26.

    Krishnan B, Antonio G, Shakoor H, Liam S, Ben A, 2013. Time series regression studies in environmental epidemiology. Int J Epidemiol 42: 11871195.

  • 27.

    Solomon T, Dung NM, Kneen R, Gainsborough M, Vaughn DW, Khanh VT, 2015. Japanese encephalitis. J Neurol Sci 357: e463.

  • 28.

    Zhang MX, Wang N, Du CL, Li XS, 2017. Application of the distributed lag non-linear model in relation between epidemic encephalitis B and meteorological factors [in Chinese]. Mod Prev Med 44: 17451749 ; 1769.

    • Search Google Scholar
    • Export Citation
  • 29.

    Gasparrini A, 2011. Distributed lag linear and non-linear models in R: the package dlnm. J Stat Softw 43: 120.

  • 30.

    Zhang S, Hu W, Qi X, Zhuang G, 2018. How socio-environmental factors are associated with Japanese encephalitis in Shaanxi, China-A Bayesian spatial analysis. Int J Environ Res Public Health 15: 608.

    • Search Google Scholar
    • Export Citation
  • 31.

    Borah J, Dutta P, Khan SA, Mahanta J, 2013. Association of weather and anthropogenic factors for transmission of Japanese encephalitis in an endemic area of India. Ecohealth 10: 129136.

    • Search Google Scholar
    • Export Citation
  • 32.

    Lin CL, Chang HL, Lin CY, Chen KT, 2017. Seasonal patterns of Japanese encephalitis and associated meteorological factors in Taiwan. Int J Environ Res Public Health 14: 1317.

    • Search Google Scholar
    • Export Citation
  • 33.

    Gu PQ, Min JG, Gu ZQ, Huang PX, Song HL, 2003. The relationship between the first appearance in spring and seasonal distribution of Culex tritaeniorhynchus and the meteorological conditions in Shanghai. Acta Entomol Sin 46: 325332.

    • Search Google Scholar
    • Export Citation
  • 34.

    Chai C, Wang Q, Cao S, Zhao Q, Wen Y, Huang X, Wen X, Yan Q, Ma X, Wu R, 2018. Serological and molecular epidemiology of Japanese encephalitis virus infections in swine herds in China, 2006–2012. J Vet Sci 19: 151155.

    • Search Google Scholar
    • Export Citation
  • 35.

    Bi P, Zhang Y, Parton KA, 2007. Weather variables and Japanese encephalitis in the metropolitan area of Jinan city, China. J Infect 55: 551556.

    • Search Google Scholar
    • Export Citation
  • 36.

    Krzyzewska A, Wereski S, Dobek M, 2020. Summer UTCI variability in Poland in the twenty-first century. Int J Biometeorol (Epub ahead of print). doi: 10.1007/s00484-020-01965-2.

    • Search Google Scholar
    • Export Citation
  • 37.

    Mansfield KL, Hernandez-Triana LM, Banyard AC, Fooks AR, Johnson N, 2017. Japanese encephalitis virus infection, diagnosis and control in domestic animals. Vet Microbiol 201: 8592.

    • Search Google Scholar
    • Export Citation
  • 38.

    Oya A, Kurane I, 2007. Japanese encephalitis for a reference to international travelers. J Trav Med 14: 259268.

  • 39.

    Ciota A, Kramer LD, 2013. Vector-virus interactions and transmission dynamics of West Nile virus. Virus 5: 30213047.

  • 40.

    Bashar K, Rahman MS, Nodi IJ, Howlader AJ, 2016. Species composition and habitat characterization of mosquito (Diptera: Culicidae) larvae in semi-urban areas of Dhaka, Bangladesh. Pathog Glob Health 110: 4861.

    • Search Google Scholar
    • Export Citation
  • 41.

    Miller RH, Masuoka P, Klein TA, Kim HC, Somer T, Grieco J, 2012. Ecological niche modeling to estimate the distribution of Japanese encephalitis virus in Asia. PLoS Negl Trop Dis 6: 119121.

    • Search Google Scholar
    • Export Citation
  • 42.

    Mordecai EA et al. 2019. Thermal biology of mosquito-borne disease. Ecol Lett 22: 16901708.

  • 43.

    Xu Y, Ramanathan V, Victor DG, 2018. Global warming will happen faster than we think. Nature 564: 3032.

  • 44.

    Rossati A, 2017. Global warming and its health impact. Int J Occup Environ Med 8: 720.

  • 45.

    Li SC, Xue H, Su Y, Sun H, Zhao FH, 2010. Survey of underreporting of notifiable communicable diseases and network direct reporting quality in Ganzhou district in Zhangye, Gansu province [in Chinese]. Dis Surveill 25: 816819.

    • Search Google Scholar
    • Export Citation
  • 46.

    Liu ZT, Li QF, Wang RH, Huang T, Yu JX, 2011. Underreporting of notifiable communicable diseases in medical institutions in Yunnan province [in Chinese]. Dis Surveill 26: 565567.

    • Search Google Scholar
    • Export Citation

Author Notes

Address correspondence to Baofa Jiang, Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, No. 44 Wenhuaxi Rd., Jinan 250012, People’s Republic of China. E-mail: bjiang@sdu.edu.cn

Disclosure: An ethics exemption from the Ethics Review Committee of Public health Shandong University (20120501) was obtained. All the patient records involved were anonymized and de-identified before receiving.

Financial support: This study was supported by the Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology (Grant No. 2017FY101202) and the National Basic Research Program of China (973 Program) (Grant No. 2012CB955502).

Authors’ addresses: Zhidong Liu, Department of Personnel, Qilu Hospital of Shandong University, Jinan, People’s Republic of China, E-mail: liuzhidong3105@163.com. Yiwen Zhang, Qi Gao, Shuzi Wang, and Baofa Jiang, Department of Epidemiology, School of Public Health, Shandong University, Jinan, People’s Republic of China, and Cheeloo College of Medicine, Shandong University Climate Change and Health Center, Jinan, People’s Republic of China, E-mails: zhangyiwen@mail.sdu.edu.cn, gqi6835@126.com, wangshuzi830@163.com, and bjiang@sdu.edu.cn. Michael Xiaoliang Tong and Peng Bi, School of Public Health, The University of Adelaide, Adelaide, Australia, E-mails: michael.tong@adelaide.edu.au and peng.bi@adelaide.edu.au. Ying Zhang, School of Public Health, China Studies Centre, The University of Sydney, Sydney, Australia, E-mail: ying.zhang@sydney.edu.au. Jianjun Xiang, School of Public Health, Fujian Medical University, Fuzhou, People’s Republic of China, and The University of Adelaide, Adelaide, Australia, E-mail: jianjun.xiang@adelaide.edu.au. Shuyue Sun, National Meteorological Center, China Meteorological Administration, Beijing, People’s Republic of China, E-mail: sunshy@cma.gov.cn.

These authors contributed equally to this work.

Save