Abstract
BACKGROUND: Kawasaki disease exhibits a distinct seasonality, and short-term changes in weather may affect its occurrence. METHODS: To investigate the effects of weather variability on the occurrence of this syndrome, we conducted a time-between-events analysis of consecutive admissions for Kawasaki disease to a large pediatric hospital in Chicago. We used gamma regression to model the times between admissions. This is a novel application of gamma regression to model the time between admissions as a function of subject-specific covariates. RESULTS: We recorded 723 admissions in the 18-year (1986-2003) study period, of which 700 had complete data for analysis. Admissions for Kawasaki disease in Chicago were seasonal: The mean time between admissions was 34% shorter (relative time = 0.66, 95% confidence interval 0.54-0.81) from January-March than from July-September. In 1998, we recorded a larger number of admissions for Kawasaki disease (n = 65) than in other years (mean n = 37). January-March months of 1998 were warmer by a mean of 3°C (1.5°C-4.4°C) and the mean time between admissions was 48% shorter (relative time = 0.52, 0.36-0.75) than in equivalent periods of other study years. CONCLUSIONS: Our findings show that atypical changes in weather affect the occurrence of Kawasaki disease and are compatible with a link to an infectious trigger. The analysis of interevent times using gamma regression is an alternative to Poisson regression in modeling a time series of sparse daily counts.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 194-201 |
| Number of pages | 8 |
| Journal | Epidemiology |
| Volume | 20 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 2009 |
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ASJC Scopus subject areas
- Epidemiology
- Medicine(all)
Cite this
Short-term weather variability in chicago and hospitalizations for kawasaki disease. / Checkley, William; Guzman-Cottrill, Judith; Epstein, Leonardo; Innocentini, Nancy; Patz, Jonathan; Shulman, Stanford.
In: Epidemiology, Vol. 20, No. 2, 03.2009, p. 194-201.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Short-term weather variability in chicago and hospitalizations for kawasaki disease
AU - Checkley, William
AU - Guzman-Cottrill, Judith
AU - Epstein, Leonardo
AU - Innocentini, Nancy
AU - Patz, Jonathan
AU - Shulman, Stanford
PY - 2009/3
Y1 - 2009/3
N2 - BACKGROUND: Kawasaki disease exhibits a distinct seasonality, and short-term changes in weather may affect its occurrence. METHODS: To investigate the effects of weather variability on the occurrence of this syndrome, we conducted a time-between-events analysis of consecutive admissions for Kawasaki disease to a large pediatric hospital in Chicago. We used gamma regression to model the times between admissions. This is a novel application of gamma regression to model the time between admissions as a function of subject-specific covariates. RESULTS: We recorded 723 admissions in the 18-year (1986-2003) study period, of which 700 had complete data for analysis. Admissions for Kawasaki disease in Chicago were seasonal: The mean time between admissions was 34% shorter (relative time = 0.66, 95% confidence interval 0.54-0.81) from January-March than from July-September. In 1998, we recorded a larger number of admissions for Kawasaki disease (n = 65) than in other years (mean n = 37). January-March months of 1998 were warmer by a mean of 3°C (1.5°C-4.4°C) and the mean time between admissions was 48% shorter (relative time = 0.52, 0.36-0.75) than in equivalent periods of other study years. CONCLUSIONS: Our findings show that atypical changes in weather affect the occurrence of Kawasaki disease and are compatible with a link to an infectious trigger. The analysis of interevent times using gamma regression is an alternative to Poisson regression in modeling a time series of sparse daily counts.
AB - BACKGROUND: Kawasaki disease exhibits a distinct seasonality, and short-term changes in weather may affect its occurrence. METHODS: To investigate the effects of weather variability on the occurrence of this syndrome, we conducted a time-between-events analysis of consecutive admissions for Kawasaki disease to a large pediatric hospital in Chicago. We used gamma regression to model the times between admissions. This is a novel application of gamma regression to model the time between admissions as a function of subject-specific covariates. RESULTS: We recorded 723 admissions in the 18-year (1986-2003) study period, of which 700 had complete data for analysis. Admissions for Kawasaki disease in Chicago were seasonal: The mean time between admissions was 34% shorter (relative time = 0.66, 95% confidence interval 0.54-0.81) from January-March than from July-September. In 1998, we recorded a larger number of admissions for Kawasaki disease (n = 65) than in other years (mean n = 37). January-March months of 1998 were warmer by a mean of 3°C (1.5°C-4.4°C) and the mean time between admissions was 48% shorter (relative time = 0.52, 0.36-0.75) than in equivalent periods of other study years. CONCLUSIONS: Our findings show that atypical changes in weather affect the occurrence of Kawasaki disease and are compatible with a link to an infectious trigger. The analysis of interevent times using gamma regression is an alternative to Poisson regression in modeling a time series of sparse daily counts.
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UR - http://www.scopus.com/inward/citedby.url?scp=66149092724&partnerID=8YFLogxK
U2 - 10.1097/EDE.0b013e3181961a9b
DO - 10.1097/EDE.0b013e3181961a9b
M3 - Article
C2 - 19129731
AN - SCOPUS:66149092724
VL - 20
SP - 194
EP - 201
JO - Epidemiology
JF - Epidemiology
SN - 1044-3983
IS - 2
ER -