Use of population health data to refine diagnostic decision-making for pertussis
- Andrew M Fine1,
- Ben Y Reis1,2,
- Lise E Nigrovic1,
- Donald A Goldmann3,
- Tracy N LaPorte4,
- Karen L Olson1,2,
- Kenneth D Mandl1,2,5
- 1Division of Emergency Medicine, Children's Hospital Boston and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
- 2Children's Hospital Informatics Program at the Harvard-MIT, Division of Health Sciences and Technology, Boston, Massachusetts, USA
- 3Division of Infectious Diseases, Children's Hospital Boston and Harvard Medical School, Boston, Massachusetts, USA
- 4Massachusetts Department of Public Health, Jamaica Plain, Massachusetts, USA
- 5The Manton Center for Orphan Disease Research, Children's Hospital Boston, Boston, Massachusetts, USA
- Correspondence to Dr A M Fine, Division of Emergency Medicine—Main 1, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA; andrew.fine{at}childrens.harvard.edu
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Contributors All authors made substantial contributions to conception, design, analysis, and interpretation of data. AMF and KDM drafted the manuscript, and all authors were involved in revising it critically for important intellectual content and final approval. AMF is guarantor.
- Received 12 May 2008
- Accepted 23 August 2009
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 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.
Footnotes
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Funding This work was supported by grants K01HK000055 and 1 P01 HK000088 from the Centers for Disease Control and Prevention and by G08LM009778 and R01 LM007677 from the National Library of Medicine.
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Competing interests None.
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Ethics approval The Committee on Clinical Investigation of Children's Hospital Boston approved the study.
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Provenance and peer review Not commissioned; externally peer reviewed.
