1921
Volume 103, Issue 5
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

Abstract.

The incidence of Japanese encephalitis (JE) has greatly declined in China. However, JE incidence has significantly increased in Gansu in recent years, on the top of ranks among all provinces in China. To explore the spatial spread and resurgence of JE transmission in Gansu in the past 60 years, we collected yearly data on reported JE in each county (1958–2017) and monthly data on JE cases (1968–2017), respectively. We grouped the dataset into six categories, each consisting of a 10-year period between 1958 and 2017. Spatial cluster analysis was applied to identify the potential space–time clusters of JE incidence, and logistic regression models were used to identify the spatial and temporal dispersion of JE. Japanese encephalitis incidence in Gansu showed an upward trend from 1970 to 1977 and peaked in 1974, then declined, and fluctuated over the study period until an outbreak again in 2017. Japanese encephalitis incidence for the first 30-year period (1958–1987) peaked in September each year and thereafter peaked in July and August during 1988–2017. Spatial cluster analysis showed the geographical range of JE transmission fluctuated over the past 60 years. The high-incidence clusters of JE were primarily concentrated in the southeast of Gansu. We found significant space–time clustering characteristics of JE in Gansu, and the geographical range of notified JE cases has significantly expanded over recent years. The potential rebound of JE transmission occurred in 2016–2017 should be placed on the top priority of government work during the control and prevention of JE in Gansu, China.

[open-access] This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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References

  1. Gao X et al., 2019. Changing geographic distribution of Japanese encephalitis virus genotypes, 1935–2017. Vector Borne Zoonotic Dis 19: 3544.
    [Google Scholar]
  2. Misra UK, Kalita J, 2010. Overview: Japanese encephalitis. Prog Neurobiol 91: 108120.
    [Google Scholar]
  3. Elias C, Okwo-Bele JM, Fischer M, 2009. A strategic plan for Japanese encephalitis control by 2015. Lancet Infect Dis 9: 7.
    [Google Scholar]
  4. Campbell GL et al., 2011. Estimated global incidence of Japanese encephalitis: a systematic review. Bull World Health Organ 89: 774a774e.
    [Google Scholar]
  5. Yin Z et al., 2015. Neurological sequelae of hospitalized Japanese encephalitis cases in Gansu province, China. Am J Trop Med Hyg 92: 11251129.
    [Google Scholar]
  6. Ding D et al., 2007. Long-term disability from acute childhood Japanese encephalitis in Shanghai, China. Am J Trop Med Hyg 77: 528533.
    [Google Scholar]
  7. Zheng Y, Li M, Wang H, Liang G, 2012. Japanese encephalitis and Japanese encephalitis virus in mainland China. Rev Med Virol 22: 301322.
    [Google Scholar]
  8. 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.
    [Google Scholar]
  9. Yin Z et al., 2010. Japanese encephalitis disease burden and clinical features of Japanese encephalitis in four cities in the People’s Republic of China. Am J Trop Med Hyg 83: 766773.
    [Google Scholar]
  10. GBD 2017 DALYs and HALE Collaborators, 2018. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392: 18591922.
    [Google Scholar]
  11. Yin Z et al., 2012. An economic evaluation of the use of Japanese encephalitis vaccine in the Expanded Program of Immunization of Guizhou province, China. Vaccine 30: 55695577.
    [Google Scholar]
  12. Samy AM, Alkishe AA, Thomas SM, Wang L, Zhang W, 2018. Mapping the potential distributions of etiological agent, vectors, and reservoirs of Japanese encephalitis in Asia and Australia. Acta Trop 188: 108117.
    [Google Scholar]
  13. Li X et al., 2016. The spatio-temporal distribution of Japanese encephalitis cases in different age groups in mainland China, 2004–2014. PLoS Negl Trop Dis 10: e0004611.
    [Google Scholar]
  14. Wang L et al., 2014. The role of environmental factors in the spatial distribution of Japanese encephalitis in mainland China. Environ Int 73: 19.
    [Google Scholar]
  15. Wang X, Zhang X, LI J, 2015. Applying periodic graphics method to analyze epidemic periodicity of Japanese encephalitis in Gansu province, 1958–2012. Chin J Vaccines Immun 21: 503505.
    [Google Scholar]
  16. Wang X, Li Y, Gao L, Zhang X, Li H, 2010. Analysis of age distribution characteristic of Japanese encephalitis in Gansu province. Mod Prev Med 37: 33413342.
    [Google Scholar]
  17. Wang X, Li Y, Liang X, Gao L, Zhang X, Li H, 2010. Distribution features of Japanese encephalitis B in Gansu province. China Trop Med 10: 12041243.
    [Google Scholar]
  18. World Health Organization, 2015. Japanese encephalitis vaccines: WHO position paper – February 2015. Wkly Epidemiol Rec 90: 6987.
    [Google Scholar]
  19. Wang LY et al., 2013. Spatiotemporal patterns of Japanese encephalitis in China, 2002–2010. PLoS Negl Trop Dis 7: e2285.
    [Google Scholar]
  20. Impoinvil DE, Baylis M, Solomon T, 2013. Japanese encephalitis: on the One Health agenda. Curr Top Microbiol Immunol 365: 205247.
    [Google Scholar]
  21. Fan W, Ueda T, Sagane Y, 2017. Data on spatiotemporal patterns of the foundation of Japanese companies in China from 1980–2016. Data Brief 15: 10061014.
    [Google Scholar]
  22. Schmittgen TD, Livak KJ, 2008. Analyzing real-time PCR data by the comparative C (T) method. Nat Protoc 3: 11011108.
    [Google Scholar]
  23. Eisen MB, Spellman PT, Brown PO, Botstein D, 1998. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 1486314868.
    [Google Scholar]
  24. ggplot2 Development Team, 2012. Changes and Additions to ggplot2-0.9.0. Available at: https://github.s3.amazonaws.com/downloads/tidyverse/ggplot2/guide-col.pdf. Accessed April 10, 2019.
    [Google Scholar]
  25. Hu W, Clements A, Williams G, Tong S, 2011. Spatial analysis of notified dengue fever infections. Epidemiol Infect 139: 391399.
    [Google Scholar]
  26. Kang SY, McGree J, Mengersen K, 2013. The impact of spatial scales and spatial smoothing on the outcome of Bayesian spatial model. PLoS One 8: e75957.
    [Google Scholar]
  27. Anselin L, 2005. Exploring Spatial Data with GeoDaTM: A Workbook. Available at: http://www.csiss.org/clearinghouse/GeoDa/geodaworkbook.pdf. Accessed August 7, 2020.
    [Google Scholar]
  28. Hafen RP, Anderson DE, Cleveland WS, Maciejewski R, Ebert DS, Abusalah A, Yakout M, Ouzzani M, Grannis SJ, 2009. Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts. BMC Med Inform Decis Mak 9: 21.
    [Google Scholar]
  29. Silawan T, Singhasivanon P, Kaewkungwal J, Nimmanitya S, Suwonkerd W, 2008. Temporal patterns and forecast of dengue infection in northeastern Thailand. Southeast Asian J Trop Med Public Health 39: 9098.
    [Google Scholar]
  30. Cortes F, Turchi Martelli CM, Arraes de Alencar Ximenes R, Montarroyos UR, Siqueira Junior JB, Gonçalves Cruz O, Alexander N, Vieira de Souza W, 2018. Time series analysis of dengue surveillance data in two Brazilian cities. Acta Trop 182: 190197.
    [Google Scholar]
  31. Kulldorff M, 2018. SaTScanTM User Guide for Version 9.6. Available at: https://www.satscan.org/cgi-bin/satscan/register.pl/SaTScan_Users_Guide.pdf?todo=process_userguide_download. Accessed April 10, 2019.
    [Google Scholar]
  32. Banu S, Hu W, Hurst C, Guo Y, Islam MZ, Tong S, 2012. Space-time clusters of dengue fever in Bangladesh. Trop Med Int Health 17: 10861091.
    [Google Scholar]
  33. Tango T, Takahashi K, 2005. A flexibly shaped spatial scan statistic for detecting clusters. Int J Health Geogr 4: 11.
    [Google Scholar]
  34. 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: e1678.
    [Google Scholar]
  35. Zhang H, Wang Y, Li K, Mehmood K, Gui R, Li J, 2019. Epidemiology of Japanese encephalitis in China (2004–2015). Travel Med Infect Dis 28: 109110.
    [Google Scholar]
  36. Hsu SM, Yen AM, Chen TH, 2008. The impact of climate on Japanese encephalitis. Epidemiol Infect 136: 980987.
    [Google Scholar]
  37. 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.
    [Google Scholar]
  38. 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.
    [Google Scholar]
  39. Robertson C, Pant DK, Joshi DD, Sharma M, Dahal M, Stephen C, 2013. Comparative spatial dynamics of Japanese encephalitis and acute encephalitis syndrome in Nepal. PLoS One 8: e66168.
    [Google Scholar]
  40. Wu YC, Huang YS, Chien LJ, Lin TL, Yueh YY, Tseng WL, Chang KJ, Wang GR, 1999. The epidemiology of Japanese encephalitis on Taiwan during 1966–1997. Am J Trop Med Hyg 61: 7884.
    [Google Scholar]
  41. 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.
    [Google Scholar]
  42. Huang XX et al., 2018. The relationship between Japanese encephalitis and environmental factors in China explored using national surveillance data. Biomed Environ Sci 31: 227232.
    [Google Scholar]
  43. Lee HS, Nguyen-Viet H, Lee M, Duc PP, Grace D, 2017. Seasonality of viral encephalitis and associated environmental risk factors in Son La and Thai Binh provinces in Vietnam from 2004 to 2013. Am J Trop Med Hyg 96: 110117.
    [Google Scholar]
  44. Samuel PP, Arunachalam N, Rajendran R, Leo SV, Ayanar K, Balasubramaniam R, Tyagi BK, 2010. Temporal variation in the susceptibility of Culex tritaeniorhynchus (Diptera: Culicidae) to Japanese encephalitis virus in an endemic area of Tamil Nadu, South India. Vector Borne Zoonotic Dis 10: 10031008.
    [Google Scholar]
  45. Li YX et al., 2011. Japanese encephalitis, Tibet, China. Emerg Infect Dis 17: 934936.
    [Google Scholar]
  46. Zhang H et al., 2017. Epidemiologic survey of Japanese encephalitis virus infection, Tibet, China, 2015. Emerg Infect Dis 23: 10231024.
    [Google Scholar]
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  • Received : 09 Mar 2020
  • Accepted : 19 Jul 2020
  • Published online : 28 Sep 2020
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