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A Bayesian Spatiotemporal Analysis of Pediatric Group A Streptococcal Infections
Angela Wang1, Andrew M Fine2, Erin Buchanan3
1Duke University, Durham, North Carolina, USA.
Insights
Geographic data on group A Streptococcus (GAS) testing showed minimal risk clusters in pediatric pharyngitis cases. Positive GAS test probability decreased in summer, suggesting seasonal patterns in this common infection.
Area of Science:
- Pediatric Infectious Diseases
- Epidemiology
- Biostatistics
Background:
- Group A Streptococcus (GAS) pharyngitis is a frequent pediatric illness.
- Accurate diagnosis of GAS pharyngitis can be improved with real-time biosurveillance data.
- Geographic GAS testing data may aid clinical decisions for children with throat infections.
Purpose of the Study:
- To evaluate the utility of geographic and temporal data in understanding group A Streptococcus (GAS) activity for pediatric pharyngitis.
- To identify potential geographic clusters of GAS infections in children.
Main Methods:
- Utilized GAS rapid antigen testing records from 14,734 children across two major medical centers.
- Employed Bayesian generalized additive models incorporating patient location, cyclic, and longitudinal time data.
- Analyzed binary GAS testing results as the outcome variable.
Main Results:
- Observed minor geographic variations in GAS testing results, with no significant high-risk clusters identified.
- Demonstrated a decline in the probability of a positive GAS test during summer months.
- Indicated a seasonal trend affecting GAS prevalence.
Conclusions:
- Geographic heterogeneity in GAS activity was minimal, and no distinct high-risk clusters were found.
- Seasonal variations, specifically a summer decline, were noted in GAS test positivity.
- Future research should incorporate school catchment data to investigate GAS transmission dynamics within educational settings.
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.
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