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Role of climate in the spread of shiga toxin-producing Escherichia coli infection among children


Haemolytic-uraemic syndrome (HUS) is a rare disease mainly affecting children that develops as a complication of shiga toxin-producing Escherichia coli (STEC) infection. It is characterised by acute kidney injury, platelet consumption and mechanical destruction of red blood cells (haemolysis). In order to test the working hypothesis that the spread of the infection is influenced by specific climatic conditions, we analysed all of the identified cases of infection occurring between June 2010 and December 2013 in four provinces of Lombardy, Italy (Milano, Monza Brianza, Varese and Brescia), in which a STEC surveillance system has been developed as part of a preventive programme. In the selected provinces, we recorded in few days a great number of cases and clusters which are unrelated for spatially distant or for the disease are caused by different STEC serotypes. In order to investigate a common factor that favoured the onset of infection, we have analysed in detail the weather conditions of the areas. The daily series of temperature, rain and relative humidity were studied to show the common climate peculiarities whilst the correlation coefficient and the principal component analysis (PCA) were used to point out the meteorological variable, maximum temperature, as the principal climate element in the onset of the infection. The use of distributed lag non-linear models (DLNM) and the climate indices characterising heat waves (HWs) has allowed to identify the weather conditions associated with STEC infection. The study highlighted a close temporal correlation between STEC infection in children and the number, duration and frequency of heat waves. In particular, if the maximum temperature is greater than 90th percentile, days classified as very hot, for 3 or more consecutive days, the risk of infection is increasing.

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  • Acquaotta F, Fratianni S (2013) Analysis on long precipitation series in piedmont (North-West Italy). Am J Clim Chang 2:25–33. doi:10.4236/ajcc.2013.21003

    Article  Google Scholar 

  • Acquaotta F, Fratianni S, Cassardo C, Cremonini R (2009) On the continuity and climatic variability of the meteorological stations in Torino, Asti, Vercelli and Oropa. Meteorog Atmos Phys 103:279–287. doi:10.1007/s00703-008-0333-4

    Article  Google Scholar 

  • Acquaotta F, Fratianni S, Garzena D (2015) Temperature changes in the North-Western Italian Alps from 1961 to 2010. Theor Appl Climatol 122:619–634. doi:10.1007/s00704-014-1316-7

    Article  Google Scholar 

  • Aguilar E, Inge Auer I, Brunet M, Peterson TC, Wieringa J (2003) Guidelines on climate metadata and homogenization. World Meteorological Organization WMO/TD No. 1186

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle, in: B.N. Petrov and F. Csaki, eds., 2nd lnternat. Syrup. on Information Theory (Akademia Kiado, Budapest), 267–281

  • Ardissino G, Daccò V, Paglialonga F, Testa S, Loi S, Edefonti A, Cusi D, Sereni F (2003) Weather and hemolytic uremic syndrome. Pediatr Nephrol 18:1195–1196. doi:10.1007/s00467-003-1247-5

    Article  Google Scholar 

  • Ardissino G, Possenti I, Salardi S, Tel F, Colombo E, Testa S, Daprai L, Picicco D, Colombo RM, Torresani E (2014) Co-infection in children with bloody diarrhea caused by shiga toxin–producing Escherichia coli: data of the North Italian HUS network. Journal of Pediatric Gastroenterology & Nutrition 59:218–220

    Article  Google Scholar 

  • Ardissino G, Salardi S, Colombo E, Testa S, Borsa-Ghirardelli N, Paglialonga F, Paracchini V, Tel F, Possenti I, Berlingheri M, Civitillo CF, Sardini S, Ceruti S, Baldioli C, Tommasi P, Parola L, Russo F, Tedeschi S (2016) Epidemiology of haemolytic uremic syndrome in children. Data from the North Italian HUS network. Eur J Pediatr 175:465–473. doi:10.1007/s00431-015-2642-1

    CAS  Article  Google Scholar 

  • Bai L, Ding G, Gu S, Bi P, Su B, Qin D, Xu G, Liu Q (2014) The effects of summer temperatures and heat waves on heat-related illness in a coastal city of China, 2011–2013. Environ Res 132:212–219

    CAS  Article  Google Scholar 

  • Caprioli A, Morabito S, Brugère H, Oswald E (2005) Enterohaemorrhagic Escherichia coli: emerging issues on virulence and modes of transmission. Vet Res 36:289–311. doi:10.1051/vetres:2005002

    CAS  Article  Google Scholar 

  • Cheong YL, Burkart K, Leitao PJ, Lakes T (2013) Assessing weather effects on dengue disease in Malaysia. Int J Environ Res Public Health 10:6319–6334. doi:10.3390/ijerph10126319

    Article  Google Scholar 

  • Colombo N, Giaccone E, Paro L, Buffa G, Fratianni (2016) The recent transition from glacial environment in a high altitude alpine basin (Sabbione basin, North-Western Italian Alps). Preliminary outcomes from a multidisciplinary approach Geografia Fisica e Dinamica Quaternaria, vol 39 (1), 21–36

  • Conrad V, Pollak LV (1962) Methods in climatology. Harvard University, Press – Climatology, 459

  • Douglas AS, Kurien A (1997) Seasonality and other epidemiological features of haemolytic uraemic syndrome and E. coli O157 isolates in Scotland. Scott Med J 42(6):166–171

    CAS  Article  Google Scholar 

  • Fortin G, Acquaotta F, Fratianni S (2016) The evolutionof temperature extremes in the Gaspé Peninsula, Quebec, Canada (1974–2013). Theoretical and Applied Climatology, 1–10. doi: 10.1007/s00704-016-1859-x

  • Garzena D, Fratianni S, Acquaotta F (2015) Temperature analysis on the North-Western Italian Alps through the use of satellite images and ground based meteorological station. Engineering Geology for Society and Territory - Volume 1: Climate Change and Engineering Geology, 77–80. doi 10.1007/978–3–319-09300-0_15

  • Gasparrini A (2013) Modelling exposure-lag-response associations with distributed lag non-linear models. Stat Med 33:881–899. doi:10.1002/sim.5963

    Article  Google Scholar 

  • Gasparrini A, Armstrong B (2011) The impact of heat waves on mortality. Epidemiology 22(1):68–73. doi:10.1097/EDE.0b013e3181fdcd99

    Article  Google Scholar 

  • Gasparrini A, Armstrong B, Kenward MG (2010) Distributed lag non-linear models. Stat Med 29:2224–2234

    CAS  Article  Google Scholar 

  • Giaccone E, Colombo N, Acquaotta F, Paro L, Fratianni S (2015) Climate variations in a high altitude alpine basin and their effects on a glacial environment (Italian Western Alps). Atmosfera 28(2):117–128

    Article  Google Scholar 

  • Hauke J, Kossowski T (2011) Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30:87–93

    Article  Google Scholar 

  • ISTAT (2012) Italian National Statistics Office

  • Karl TR, Nicholls N, Ghazi A (1999) CLIVAR/GCOS/WMO workshop on indices and indicators for climate extremes: workshop summary. Clim Chang 42:3–7

    Article  Google Scholar 

  • Paton AW, Manning PA, Woodrow MC, Paton JC (1998) Translocated intimin receptors (Tir) of Shiga-toxigenic Escherichia coli isolates belonging to serogroups O26, O111, and O157 react with sera from patients with hemolytic-uremic syndrome and exhibit marked sequence heterogeneity. Infect Immun 66(11):5580–5586

    CAS  Google Scholar 

  • Peterson TC, Coauthors (2001) Report on the activities of the working group on climate change detection and related rapporteurs 1998–2001. WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve. 143

  • Peterson TC (2005) Climate change indices. WMO Bull 54(2):83–86

    Google Scholar 

  • Peterson TC, Easterling D, Karl T et al (1998) Homogeneity adjustment of in situ atmospheric climate data: a review. Int J Climatol 18:1493–1517

    Article  Google Scholar 

  • Riviero MA, Pasucci JA, Parma AE (2012) Seasonal variation of HUS occurrence and VTEC infection in children with acute diarrhoea from Argentina. Eur J Clin Microbiol Infect Dis 31(6):1131–1135. doi:10.1007/s10096-011-1418-4

    Article  Google Scholar 

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389

    Article  Google Scholar 

  • Storch HV, Zwiers F (2003) Statistical analysis in climate research. Cambridge University Press, 349

  • Toreti A, Desiato S (2008) Changes in temperature extremes over Italy in the last 44 years. Int J Climatol 28:733–745. doi:10.1002/joc.1576

    Article  Google Scholar 

  • Venables WN, Ripley BD (2002) Modern applied statistics with S. Statistics and Computing, Springer-Verlag New York, p 498

    Book  Google Scholar 

  • Zhang X, Vincent LA, Hogg WD, Niitsoo A (2000) Temperature and precipitation trends in Canada during the 20th century. Atmosphere Ocean 38:395–429

    Article  Google Scholar 

  • Zhang Y, Li S, Pan X, Tong S, Jaakkola J, Gasparrini A, Guo Y, Wang S (2014) The effects of ambient temperature on cerebrovascular mortality: an epidemiologic study in four climatic zones in China. Environ Health 13:1–24. doi:10.1186/1476-069X-13-24

    Article  Google Scholar 

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The authors are thankful to ‘Progetto Alice ONLUS. Associazione per la lotta alla SEU’ for their essential support to the investigation.

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Correspondence to Fiorella Acquaotta.

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The study was approved by the Ethics Committee of Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico on 18 May 2010.

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Acquaotta, F., Ardissino, G., Fratianni, S. et al. Role of climate in the spread of shiga toxin-producing Escherichia coli infection among children. Int J Biometeorol 61, 1647–1655 (2017).

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  • Haemolytic-uraemic syndrome
  • Weather conditions
  • Temperature
  • Epidemiology
  • Children