title: Integrating spatial epidemiology into a decision model for evaluation of facial palsy in children.
abstract: OBJECTIVE
  To develop a novel diagnostic algorithm for Lyme disease among children with facial palsy by integrating public health surveillance data with traditional clinical predictors.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  Children's Hospital Boston emergency department, 1995-2007.
  
  
  METHODS
  Two hundred sixty-four children (aged <20 years) with peripheral facial palsy who were evaluated for Lyme disease.
  
  
  METHODS
  Multivariate regression was used to identify independent clinical and epidemiologic predictors of Lyme disease facial palsy.
  
  
  RESULTS
  Lyme diagnosis was positive in 65% of children from high-risk counties in Massachusetts during Lyme disease season compared with 5% of those without both geographic and seasonal risk factors. Among patients with both seasonal and geographic risk factors, 80% with 1 clinical risk factor (fever or headache) and 100% with 2 clinical factors had Lyme disease. Factors independently associated with Lyme disease facial palsy were development from June to November (odds ratio, 25.4; 95% confidence interval, 8.3-113.4), residence in a county where the most recent 3-year average Lyme disease incidence exceeded 4 cases per 100,000 (18.4; 6.5-68.5), fever (3.9; 1.5-11.0), and headache (2.7; 1.3-5.8). Clinical experts correctly treated 68 of 94 patients (72%) with Lyme disease facial palsy, but a tool incorporating geographic and seasonal risk identified all 94 cases.
  
  
  CONCLUSIONS
  Most physicians intuitively integrate geographic information into Lyme disease management, but we demonstrate quantitatively how formal use of geographically based incidence in a clinical algorithm improves diagnostic accuracy. These findings demonstrate potential for improved outcomes from investments in health information technology that foster bidirectional communication between public health and clinical settings.
authors.affiliation: Division of Emergency Medicine, Children's Hospital Boston, Boston, MA 02115, USA. andrew.fine@childrens.harvard.edu
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Fine
authors.forename: John S
authors.initials: JS
authors.lastname: Brownstein
authors.forename: Lise E
authors.initials: LE
authors.lastname: Nigrovic
authors.forename: Amir A
authors.initials: AA
authors.lastname: Kimia
authors.forename: Karen L
authors.initials: KL
authors.lastname: Olson
authors.forename: Amy D
authors.initials: AD
authors.lastname: Thompson
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Mandl
mesh_terms: D000293:Adolescent; D000704:Analysis of Variance; D001900:Boston / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D016001:Confidence Intervals; D003661:Decision Support Techniques; D004636:Emergency Service, Hospital; D005158:Facial Paralysis / Q000175:diagnosis* / Q000453:epidemiology*; D005260:Female; D006776:Hospitals, Pediatric; D006801:Humans; D015994:Incidence; D016014:Linear Models; D008193:Lyme Disease / Q000175:diagnosis* / Q000453:epidemiology*; D008297:Male; D008404:Massachusetts / Q000453:epidemiology; D015999:Multivariate Analysis; D016017:Odds Ratio; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012720:Severity of Illness Index; D055815:Young Adult