The authors have declared that no competing interests exist.
Conceived and designed the experiments: JCB OF LH AHT DC EB XR RU DB MT DP. Performed the experiments: JCB OF LH AHT DC EB XR RU DB MT DP. Analyzed the data: JCB OF LH AHT DC EB XR RU DB MT DP. Contributed reagents/materials/analysis tools: JCB OF LH DC EB MT DP. Wrote the manuscript: JCB OF LH AHT DC EB XR RU DB MT DP.
¶ Membership of the Kawasaki Disease Global Climate Consortium is provided in Table S1.
Understanding global seasonal patterns of Kawasaki disease (KD) may provide insight into the etiology of this vasculitis that is now the most common cause of acquired heart disease in children in developed countries worldwide.
Data from 1970-2012 from 25 countries distributed over the globe were analyzed for seasonality. The number of KD cases from each location was normalized to minimize the influence of greater numbers from certain locations. The presence of seasonal variation of KD at the individual locations was evaluated using three different tests: time series modeling, spectral analysis, and a Monte Carlo technique.
A defined seasonal structure emerged demonstrating broad coherence in fluctuations in KD cases across the Northern Hemisphere extra-tropical latitudes. In the extra-tropical latitudes of the Northern Hemisphere, KD case numbers were highest in January through March and approximately 40% higher than in the months of lowest case numbers from August through October. Datasets were much sparser in the tropics and the Southern Hemisphere extra-tropics and statistical significance of the seasonality tests was weak, but suggested a maximum in May through June, with approximately 30% higher number of cases than in the least active months of February, March and October. The seasonal pattern in the Northern Hemisphere extra-tropics was consistent across the first and second halves of the sample period.
Using the first global KD time series, analysis of sites located in the Northern Hemisphere extra-tropics revealed statistically significant and consistent seasonal fluctuations in KD case numbers with high numbers in winter and low numbers in late summer and fall. Neither the tropics nor the Southern Hemisphere extra-tropics registered a statistically significant aggregate seasonal cycle. These data suggest a seasonal exposure to a KD agent that operates over large geographic regions and is concentrated during winter months in the Northern Hemisphere extra-tropics.
The search for the causative agent for Kawasaki disease (KD) has now spanned four decades and the environmental trigger for this self-limited pediatric vasculitis remains elusive [
Collaborating sites were recruited by the following methods: 1) Announcement of the project at the 9th and 10th International KD Symposia held in Taipei, Taiwan and Kyoto, Japan, respectively; 2) E-mail invitation to corresponding authors of English language epidemiologic reports of KD published since 2000 and ascertained through PubMed. Patients included in the time series were those meeting the 2004 American Heart Association (AHA) criteria as follows: 1) ≥ 3 days of fever and 4/5 classical clinical criteria or 2) ≥ 5 days of fever, fewer than 4 criteria, but dilated or aneurysmal coronary arteries as defined by AHA criteria [
Canada | Montreal | 3/1980 - 3/2009 | 1,382 | N. Dahdah, M. Gibbon, R. Scuccimarri | Sainte-Justine University Hospital Center, Montreal Children’s Hospital/McGill University Health Centre |
Ontario | 12/1994 - 12/2009 | 2,884 | B. McCrindle, C. Manlhiot | The Hospital for Sick Children, University of Toronto | |
China | Jilin | 12/1999 - 12/2010 | 1,016 | C-J. Jin, L-H. Jin, Z-Y Jin, J-H Piao, Y Zhou | Department of Pediatrics, First Affiliated Hospital, Jilin University |
Shaanxi | 8/2007 - 4/2011 | 1,047 | F. Jiao | Dept. of Pediatrics, The Shaanxi Provincial People’s Hospital of Xi | |
Shanghai | 1/1998 - 3/2011 | 1,748 | G-Y. Huang | Pediatric Heart Center, Children’s Hospital of Fudan University | |
Finland | 5/1982 - 12/2010 | 948 | E. Salo | Helsinki University Central Hospital | |
France | Lyon | 6/1979 - 4/2007 | 211 | R. Cimaz, S Di-Filippo, J-C. Lega | Department of Pediatric Cardiology, Louis Pradel Hospital |
India | Chandigarh | 5/1994 - 1/2011 | 259 | R. Aulakh, S. Singh, D. Suri | Pediatric Allergy Immunology Unit, Advanced Pediatrics Centre, Post Graduate Institute of Medical Education and Research, Chandigarh |
Israel | 1/1996-12/2009 | 764 | M. Bar-Meir | Pediatrics and Infectious diseases, Shaare-Zedek Medical Center, Jerusalem | |
Italy | Florence | 7/1997 - 6/2009 | 162 | R. Cimaz | Department of Pediatric Rheumatology, Anna Meyer Children’s Hospital |
Japan | 1/1970 - 10/2010 | 271,475 | Y. Nakamura, R. Uehara | Department of Public Health Jichi Medical University | |
Korea | Daejeon | 1/1987 - 12/2008 | 772 | K-Y. Lee | Department of Pediatrics, The Catholic University of Korea, Daejeon St. Mary’s Hospital |
Uijeoungbu | 1/1993 - 12/2008 | 723 | J-W Han | Department of Pediatrics, The Catholic University of Korea, Uijeongbu St. Mary’s Hospital | |
Seoul | 12/1993 - 4/2009 | 473 | M-K. Han, Y-M. Hong*, G-Y. Jang**, D-S. Kim***, H-D. Lee****, J-K. Lee and I-S. Park*****, M-S. Song******, S-W. Yun******* | Department of Pediatrics, University of Ulsan, Gangneung Asan Hospital,*Department of Pediatrics, Ewha Womans University Hospital,**Department of Pediatrics, Korea University Hospital,***Department of Pediatrics, Yonsei University College of Medicine, Severance Children’s Hospital,****Department of Pediatrics, Pusan National University Hospital,*****University of Ulsan, Asan Medical Center,******Department of Pediatrics, Inje Univeristy, Paik Hospital,*******Department of Pediatrics, Chung-Ang Univeristy Hospital | |
Netherlands | Amsterdam | 1/1987 - 12/2010 | 501 | W.B. Breunis, T.W. Kuijpers, C.E. Tacke | Division of Pediatric Hematology, Immunology and Infectious diseases, Emma Children’s Hospital, Academic Medical Center |
Russia | Moscow | 1/2008 - 12/2010 | 64 | G. Lyskina, O. Shirniskaya, A. Torbyak | Sechenov First Moscow State Medical University |
Irkutsk | 1/2003 - 5/2011 | 103 | L. Bregel, T. Soldatova, V. Subbotin, | Irkutsk State Academia of Continuing Medical Education, Kawasaki Disease Center, Irkutsk Regional Children"s Hospital, Irkutsk Regional Hospital | |
Spain | Barcelona | 6/1984 - 1/2009 | 158 | J. Anton, F. Prada*, S. Ricart, R. Bou | Pediatric Rheumatology Unit and * Cardiology Department, Hospital Sant Joan de Déu and Universitat de Barcelona. |
Turkey | Ankara | 3/1994 - 2/2011 | 30 | S. Atalay*, E. Çiftçi, E. İnce, A. Karbuz, H. Özdemir | Department of Pediatric Infectious Diseases and * Department of Pediatric Cardiology, Ankara University Medical School |
UK | UK | 8/1982 - 3/2005 | 2,009 | A. Harnden, M. Levin*, R. Mayon-White, R. Tulloh**, C. Michie***, V. Wright* | Department of Primary Health Care Sciences, University of Oxford, Oxford *Pediatrics Faculty of Medicine, Imperial College, London,**Bristol Royal Hospital for Children, Bristol ***Ealing Hospital NHS Trust, London |
United States of America | Boston, Massachusetts | 4/1976 - 12/2010 | 1,407 | A.L. Baker, J.W. Newburger | Children’s Hospital Boston |
Chicago, Illinois | 1/2004 - 12/2010 | 420 | N. Innocentini, S. T. Shulman | Division of Infectious Diseases, Ann and Robert H. Lurie Children’s Hospital of Chicago | |
Denver, Colorado | 11/1997 - 12/2010 | 459 | M. Anderson, S. Dominguez, M. Glode | Pediatric Infectious Disease Children’s Hospital, Colorado | |
Orange, California | 12/1993 - 11/2010 | 731 | A. Arrieta | Pediatric Infectious Diseases, Children’s Hospital of Orange, County | |
Los Angeles, California | 1/2000 - 7/2009 | 345 | C. Dozal, W. Mason | Children’s Hospital of Los Angeles | |
Riverside, California | 1/1991 - 1/2011 | 491 | J. Beck | Department of Pediatrics, Loma Linda University Medical Center | |
San Diego, California | 10/1978 - 1/2012 | 1,274 | J. C. Burns, A. Tremoulet, S. Fernandez | Department of Pediatrics, UCSD School of Medicine | |
Brazil | Brasilia | 2/2002 - 2/2011 | 154 | C.M. Magalhaes, R. Pratesi | Dept. of Pediatrics, Brasilia University School of Medicine |
China | Hong Kong | 6/2006 - 4/2011 | 52 | Y-F. Cheung | Department of Pediatrics and Adolescent Medicine, Queen Mary Hospital |
Colombia | Bogota | 2/2006 - 12/2008 | 13 | M. Reyes | Departamento de Microbiología, Pontificia Universidad Javeriana |
Indonesia | Jakarta | 2/2001 - 7/2009 | 210 | N. Advani | University of Indonesia, Jakarta Pusat |
Jamaica | 12/1986 - 10/2005 | 102 | O. Olugbuyi, R. Pierre | Department of Child and Adolescent Health, University of the West Indies | |
Panama | 2/2011 - 6/2011 | 10 | E. Castaño, D. Estripeaut and X. Sáez-Llorens | University of Panama School of Medicine, Hospital del Niño | |
Singapore | 1/1998 - 12/2008 | 1,306 | C. K. Chen, T. L. J. Choo, T. H. Tan, K. Y. Wong | Cardiology Service, Department of Pediatric SubspecialtiesKK Women’s & Children’s Hospital | |
Taiwan | Taipei+Kaohsiung | 1/1999 - 12/2010 | 652 | H-C. Kuo, M-T. Lin*, M-H. Wu* | Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital,*Department of Pediatrics, National Taiwan University Hospital |
Thailand | Chiangmai | 1/2000-12/2010 | 237 | R. Sittiwangkul | Division of Cardiology, Department of Pediatrics, Chiang Mai University |
United States of America | Honolulu, Hawaii | 2/1996 - 3/2011 | 652 | M. Melish | Department of Pediatrics, Kapiolani Medical Center |
Australia | Perth | 3/1977 - 7/2009 | 418 | D. Burgner, M. Odam | Murdoch Childrens Research Institute and Department of Pediatrics, University of Melbourne |
Chile | 5/2004 - 12/2009 | 44 | A. Salgado, G. Soza | Dr. Hernan Henriquez Aravena Hospital | |
New Zealand | 7/1995 - 6/2006 | 441 | J. Doran, P. Heaton*, N. Wilson** | Taranaki Base Hospital,*Paediatric Department, Yeovil District Hospital,**Department of Pediatric Cardiology, Starship Hospital | |
South Africa | Cape Town +Johannesburg | 5/2004 - 11/2010 | 52 | B. Eley, D. Moore* | Pediatric Infectious Diseases Unit, Red Cross War Memorial Children’s Hospital and Department of Pediatrics and Child Health, University of Cape Town,*Respiratory and Meningeal Pathogens Research Unit, University of the Witwatersrand |
For sites with only the country name listed, the KD cases were collected from the entire country over the time period specified.
Colors indicate number of cases reported in the entire time series from each location.
For each of the individual time series, the mean number of KD cases for each of the 12 months of the year was obtained by averaging all the cases in that month and dividing by the number of times that month appeared in the time series. First, the time series were plotted and evaluated visually to detect sampling artifacts and other issues. The presence of seasonal variation in the KD records was tested using three different techniques: comparing the fit of seasonal versus non-seasonal time series models, spectral analysis, and a Monte Carlo sampling exercise.
Many of the time series a showed large number of zero reported cases near the beginning of the record. In the time series and spectral analyses, years at the start of the series with fewer than 24 observations were discarded to reduce the effect of these low-sample years on the analysis. This would have eliminated 8 of the stations in their entirety (noted in
Los Angeles, CA, USA | − | ||
Orange, CA, USA | 99 | ||
San Diego, CA, USA | 99 | ||
Riverside, CA, USA | I | − | 0.6949 |
Denver, CO, USA | I | 90 | |
Chicago, IL, USA | N | 95 | |
Toronto, Canada | 99 | ||
Montreal, Canada | 99 | ||
Boston, MA, USA | 99 | ||
UK | 99 | ||
Amsterdam, Netherlands | N | − | 0.4856 |
Lyon, France+ | 90 | 0.3756 | |
Florence, Italy | N | − | 0.0606 |
Barcelona, Spain+ | I | − | |
Finland | 90 | ||
Moscow, Russia+ | N | − | 0.4141 |
Ankara, Turkey | − | − | 0.7348 |
Israel | 95 | ||
Chandigarh, India | I | 95 | |
Irkutsk, Siberia | I | − | 0.1884 |
Shaanxi, China | 95 | ||
Shanghai, China | 99 | ||
Jilin, China | 90 | ||
Seoul, Korea | I | 90 | |
Daejeon, Korea | I | − | |
Uijeongbu, Korea | 95 | ||
Japan | 99 | ||
Hawaii, USA | N | − | 0.3785 |
Jamaica+ | I | − | 0.2688 |
Brasilia, Brazil+ | 99 | ||
Chiang Mai, Thailand | N | − | 0.3371 |
Singapore | I | − | 0.7492 |
Jakarta, Indonesia | N | − | |
Hong Kong, China+ | N | − | 0.9204 |
Taipei and Kaohsiung, Taiwan | I | − | |
Temuco, Chile+ | N | − | 0.6891 |
Cape Town and Johannesburg, S. Africa+ | I | − | 0.3012 |
Perth, Australia | I | − | 0.8096 |
New Zealand | − |
ARMA model results are reported for locations whose model fits, according to the Akaike Information Criterion, registered as strongly seasonal (SS), seasonal (S), indeterminate (I), or non-seasonal (N). The statistical significance of annual or biannual (6 months period) spectral peaks was reported if they exceeded the 90th, 95th or 99th percent significance level. The statistical significance of the largest monthly mean difference of any two months from the observed series at each location using a ranked set of Monte Carlo simulated monthly mean differences (see text) is reported in bold numerals if the p-value was less than 0.10. + years with as few as 12 cases per year were included at the start of the time series in the ARMA and spectral analysis; for the other stations, only years with 24 or more cases were included.
The individual KD time series were also tested using spectral analysis implemented using the Fourier transformation of the auto-covariance of the time series [
Each individual KD series was also evaluated for seasonality by comparing the observed series against the distribution of a seasonal measure estimated from 10,000 simulated series generated in a Monte Carlo sampling exercise. The possibility that the differences between means of the 12 months were merely the result of random sampling was evaluated using a Monte Carlo test in which each case in a given record was assigned a month according to a random process and this exercise repeated 10,000 times. For each of these simulated time series, we calculated the mean for each month and subtracted the lowest mean from the highest mean as an indication of the seasonal variation. From the 10,000 simulated series, the set of resulting monthly differences was ranked from lowest to highest. The observed maximum monthly mean difference was then assigned a p-value according to its rank within the simulated ordered set. In order to test the consistency of the seasonal patterns over time, the observed KD time series were tested as a whole and as two subsets by dividing the time series into two halves that were independently tested and compared.
To test whether seasonal variation in the cases occurred systematically across a large spatial domain, two additional analyses were conducted: Hewitt’s statistical measure of seasonality [
Hewitt’s statistic for seasonality in monthly data is a non-parametric test wherein one calculates the maximum rank sum, amongst all possible rank sums of the ranks of consecutive 6 month periods [
Under the Monte Carlo random sampling exercise, the normalized monthly means and their relative sequence were preserved, but the 12-month block was randomly shifted. The simulation was conducted independently for each of the normalized time series in the sector. The resulting monthly means from all the locations in the geographic sector were then averaged, and the largest difference between any two months was identified. This same exercise was performed 10,000 times and the resulting set of 10,000 largest monthly differences was ranked from low to high. The largest difference between any pair of the 12 values of the observed aggregated monthly means was placed into this ranked 10,000 member synthesized set of monthly differences in order to establish its statistical significance (p-value). Time series were tested as a whole and as two subsets by dividing the time series into two equal halves that were independently tested.
The largest number of records and the highest number of KD case reports was available in the extra-tropical Northern Hemisphere (north of 23° N). A significant seasonal structure was present in many of the records in this zone.
From the ARMA model fitting analysis, of the 27 Northern Hemisphere time series, 11 registered as strongly seasonal and 4 registered as seasonal (
From the spectral analysis, 8 locations exhibited an annual or semi-annual peak whose amplitude exceeded the 99% significance level; 7 of those were found to be strongly seasonal by the ARMA analysis, and the other was found to be seasonal. Five locations exhibited a spectral peak that was less than the 99% level but exceeded the 95% significance level, and five registered a peak that was less than the 95% level but exceeded the 90% significance level (
90th, 95th and 99th percentile significance levels shown by green, red and purple lines in bottom frame.
Under the individual site Monte Carlo testing, 20 of the 27 records within the Northern Hemisphere extra-tropics registered seasonal change magnitudes with p-values ≤ 0.05 (
Looking further, the timing of seasonal peaks and troughs that occurred in locations across this zone was not randomly distributed (
The normalized amplitude of the seasonal difference (maximum case numbers minus minimum case numbers) is indicated by the size of the dot. Month of maximum case numbers (upper map) and month of minimum case numbers (lower map) is indicated by the color of the dot.
Average of the ratio of normalized monthly mean KD cases to overall number of cases per month over a) Northern Hemisphere extra-tropics, b) Tropics, c) Southern Hemisphere extra-tropics.
No Hemisphere extra-tropics | 291,856 | 56 | ||
Tropics | 3,365 | 49 | 0.48 | 0.67 |
So Hemisphere extra-tropics | 955 | 50 | 0.37 | 0.58 |
The statistical significance of the largest monthly mean difference of any two months from the observed sector-aggregate was assessed from a ranked set of Monte Carlo simulated monthly mean differences.
In the Tropics (23° N to 23° S) and in the Southern Hemisphere extra-tropics (south of 23° S), there were fewer time series and fewer numbers of cases reported. Under each of the three analysis procedures, the KD time series in these zones did not, for the most part, contain distinguishable seasonal variations. Only one of the records registered as strongly seasonal, and one as seasonal, using the ARMA time series analysis. The strongly seasonal location, Brasilia, Brazil, also showed the only significant spectral peak (at the 99% level) of any tropical or Southern Hemisphere location. From the Monte Carlo exercise, 2 of the 8 records in the tropics (including Brasilia) and 1 of the 4 records in the Southern Hemisphere registered seasonal change magnitudes with p-values ≤0.05 (
In a comprehensive analysis of KD cases from across the globe, many time series from the Northern Hemisphere extra-tropics exhibited statistically significant, seasonal variations in KD activity. There was coherence in the seasonal patterns amongst the Northern Hemisphere records. When their normalized long-term the monthly mean occurrences were averaged together, a statistically significant seasonal cycle was apparent, with peak KD occurrence from January through March and a nadir in KD occurrences during the months of August and September. For the Tropics and Southern Hemisphere extra-tropics, a statistically significant seasonal structure was not evident with these techniques, but detecting a seasonal signal was hampered by sparse sampling and fewer numbers of cases in these zones.
Seasonality test results using the three analysis methods operating on estimated KD incidence rates in Japan and in San Diego were essentially identical to those obtained using the original monthly case occurrence data. Thus we concluded that the KD case occurrence data is an adequate form of the KD sample data to investigate for seasonality.
A seasonal occurrence of KD has long been suspected but this is the first analysis in which a dataset with global coverage has been available. Yanagawa and colleagues were the first to note a seasonal peak in Japan beginning in December and tapering off in March [
While environmental exposure appears to play an important role in determining the seasonality of KD, host genetics also influences KD susceptibility. Recent analyses of Asian and European descent populations have uncovered important genetic variation in biologic pathways that shape host susceptibility to KD [
One mechanism that may explain the hemispheric seasonal structure contained in the global KD records is the recent observation that large scale tropospheric wind patterns are associated with fluctuations in KD cases [
We recognize some important strengths and limitations of the current analysis. This is the first spatially comprehensive analysis of KD time series, and the results suggest that it is useful to view KD over a global domain to understand disease mechanisms and to survey trends. Clearly, there is a need for more rigorous reporting of KD cases and the creation of comprehensive time series from a greater number of sites to help refine this type of global analysis. The sparse time series with very low numbers from the Tropics and Southern Hemisphere precluded a more robust statistical analysis of seasonality in these locations. Another limitation is that cases were contributed by individual investigators and under-reporting of cases within a given region is likely. Without a gold standard diagnostic test, there was surely misclassification of cases as well as missed cases in all the time series. That said, the remarkable coherence of KD occurrences in the Northern Hemisphere extra-tropics suggests that many of these time series were sufficiently accurate to register a hemispheric pattern.
In conclusion, the coherence of KD seasonality in the Northern Hemisphere extra-tropics suggests that research efforts should focus on identifying environmental variables that connect the disease across distant regions. Analysis of aerosols and tropospheric wind patterns may be a fruitful avenue of investigation.
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Portions of this work were presented at the 10th International Kawasaki disease Symposium, Kyoto, Japan, February 2012.