A Bayesian Spatiotemporal Analysis of Pediatric Group A Streptococcal Infections.
Background
Pharyngitis due to group A Streptococcus (GAS) is a common pediatric infection. Physicians might diagnose GAS pharyngitis more accurately when given biosurveillance information about GAS activity. The availability of geographic GAS testing data may be able to assist with real-time clinical decision-making for children with throat infections.


Methods
GAS rapid antigen testing data were obtained from the records of 6086 children at Boston Children's Hospital and 8648 children at Duke University Medical Center. Records included children tested in outpatient, primary care settings. We constructed Bayesian generalized additive models, in which the outcome variable was the binary result of GAS testing, and predictor variables included smoothed functions of patient location data and both cyclic and longitudinal time data.


Results
We observed a small degree of geographic heterogeneity, but no convincing clusters of high risk. The probability of a positive test declined during the summer months.


Conclusions
Future work should include geographic data about school catchments to identify whether GAS transmission clusters within schools.
['Wang|Angela|A|', 'Fine|Andrew M|AM|', 'Buchanan|Erin|E|', 'Janko|Mark|M|', 'Nigrovic|Lise E|LE|', 'Lantos|Paul M|PM|0000-0003-2339-1481']
pediatrics pharyngitis Streptococcus modeling Bayesian statistics epidemiology geographic information systems