Linking Surveillance to Action: Incorporation of Real-time Regional Data into a Medical Decision Rule
- aDivision of Emergency Medicine, Department of Medicine Children’s Hospital Boston, Boston, MA
- bChildren’s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Boston, MA
- cDepartment of Epidemiology Harvard School of Public Health, Boston, MA
- Correspondence and reprint requests to: Andrew M. Fine, MD, MPH, Division of Emergency Medicine, Children’s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115; (e-mail: <Andrew.Fine{at}childrens.harvard.edu>)
- Received 22 August 2006
- Accepted 5 December 2006
Abstract
Objective Broadly, to create a bidirectional communication link between public health surveillance and clinical practice. Specifically, to measure the impact of integrating public health surveillance data into an existing clinical prediction rule. We incorporate data about recent local trends in meningitis epidemiology into a prediction model differentiating aseptic from bacterial meningitis.
Design and Measurements Retrospective analysis of a cohort of all 696 children with meningitis admitted to a large urban pediatric hospital from 1992 to 2000. We modified a published bacterial meningitis score by adding a new epidemiological context adjustor variable. We examined 540 possible rules for this adjustor, varying both the number of aseptic meningitis cases that needed to be seen, and the recent time window in which they were seen. We performed sensitivity analyses with each of 540 possibilities in order to identify the optimal rule—namely, the one that included the most cases of aseptic meningitis without missing additional cases of bacterial meningitis, as compared with the published prediction model. We used bootstrap methods to validate this new score.
Results The optimal rule was found to be: “at least four cases of aseptic meningitis in the previous 10 days.” The epidemiological context adjustor based on surveillance of recent cases of meningitis allowed the correct identification of an additional 47 cases (7%) of aseptic meningitis without missing any additional cases of bacterial meningitis. The epidemiological context adjustor was validated, showing significance in 84% of 1,000 bootstrap samples.
Conclusion Epidemiological contextual information can improve the performance of a clinical prediction rule. We provide a methodological framework for leveraging regional surveillance data to improve medical decision-making.
Footnotes
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This work was supported by grant 1 R01 LM007677-01 from the National Library of Medicine, National Institutes of Health, by National Research Service Award grant T32 HD40128-01 (Research Training in Pediatric Emergency Medicine), and grant P01 CD000260-01 from the Centers for Disease Control and Prevention.
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The authors gratefully acknowledge Drs. Nathan Kuppermann and Richard Malley for their careful reading and critique of the manuscript, and for sharing of their expert knowledge of the dataset used in this investigation.
