title: Use of population health data to refine diagnostic decision-making for pertussis.
abstract: OBJECTIVE
  To improve identification of pertussis cases by developing a decision model that incorporates recent, local, population-level disease incidence.
  
  
  METHODS
  Retrospective cohort analysis of 443 infants tested for pertussis (2003-7).
  
  
  METHODS
  Three models (based on clinical data only, local disease incidence only, and a combination of clinical data and local disease incidence) to predict pertussis positivity were created with demographic, historical, physical exam, and state-wide pertussis data. Models were compared using sensitivity, specificity, area under the receiver-operating characteristics (ROC) curve (AUC), and related metrics.
  
  
  RESULTS
  The model using only clinical data included cyanosis, cough for 1 week, and absence of fever, and was 89% sensitive (95% CI 79 to 99), 27% specific (95% CI 22 to 32) with an area under the ROC curve of 0.80. The model using only local incidence data performed best when the proportion positive of pertussis cultures in the region exceeded 10% in the 8-14 days prior to the infant's associated visit, achieving 13% sensitivity, 53% specificity, and AUC 0.65. The combined model, built with patient-derived variables and local incidence data, included cyanosis, cough for 1 week, and the variable indicating that the proportion positive of pertussis cultures in the region exceeded 10% 8-14 days prior to the infant's associated visit. This model was 100% sensitive (p<0.04, 95% CI 92 to 100), 38% specific (p<0.001, 95% CI 33 to 43), with AUC 0.82.
  
  
  CONCLUSIONS
  Incorporating recent, local population-level disease incidence improved the ability of a decision model to correctly identify infants with pertussis. Our findings support fostering bidirectional exchange between public health and clinical practice, and validate a method for integrating large-scale public health datasets with rich clinical data to improve decision-making and public health.
authors.affiliation: Division of Emergency Medicine, Children's Hospital Boston and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts 02115, USA. andrew.fine@childrens.harvard.edu
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Fine
authors.forename: Ben Y
authors.initials: BY
authors.lastname: Reis
authors.forename: Lise E
authors.initials: LE
authors.lastname: Nigrovic
authors.forename: Donald A
authors.initials: DA
authors.lastname: Goldmann
authors.forename: Tracy N
authors.initials: TN
authors.lastname: Laporte
authors.forename: Karen L
authors.initials: KL
authors.lastname: Olson
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Mandl
mesh_terms: D003661:Decision Support Techniques; D006801:Humans; D015994:Incidence; D007223:Infant; D033181:Information Dissemination; D008404:Massachusetts / Q000453:epidemiology; D015999:Multivariate Analysis; D012372:ROC Curve; D012042:Registries / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014917:Whooping Cough / Q000175:diagnosis* / Q000453:epidemiology