Paper Info
Title
Research paper: 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. Design Retrospective cohort analysis of 443 infants tested for pertussis (2003-7). Measurements 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 I week, and absence of fever, and was 89% sensitive (95% Cl 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 I 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% Cl 92 to 100), 38% specific (p<0.001, 95% Cl 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.
Year
DOI
Venue
2010
10.1197/jamia.M3061
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Keywords
DocType
Volume
roc curve,decision models,data model,public health,receiver operator characteristic
Journal
17
Issue
ISSN
Citations 
1
1067-5027
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Andrew M. Fine101.35
Ben Y. Reis29317.53
Lise E. Nigrovic351.15
Donald A. Goldmann401.69
Tracy N. LaPorte500.34
Karen L. Olson693.23
Kenneth D. Mandl727567.17