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Use of population health data to refine diagnostic decision-making for pertussis
Andrew M Fine1, Ben Y Reis, Lise E Nigrovic
1Division of Emergency Medicine, Children's Hospital Boston and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts 02115, USA. andrew.fine@childrens.harvard.edu
Insights
A new decision model combining clinical data and local pertussis (whooping cough) incidence significantly improved case identification in infants. This approach enhances public health surveillance and clinical decision-making for pertussis.
Area of Science:
- Infectious Disease Epidemiology
- Clinical Decision Support Systems
- Public Health Surveillance
Background:
- Accurate identification of pertussis (whooping cough) is crucial for timely treatment and public health interventions.
- Traditional diagnostic models often rely solely on clinical data, which can have limitations in sensitivity and specificity.
- Integrating real-time, local disease incidence data can potentially enhance the accuracy of diagnostic models.
Purpose of the Study:
- To develop and evaluate a decision model for identifying pertussis cases in infants.
- To assess the added value of incorporating recent, local, population-level pertussis incidence data into a clinical decision model.
- To compare the performance of models based on clinical data alone, incidence data alone, and a combination of both.
Main Methods:
- Retrospective cohort analysis of 443 infants tested for pertussis between 2003 and 2007.
- Development of three predictive models: clinical data only, local incidence data only, and combined clinical and incidence data.
- Model performance was evaluated using sensitivity, specificity, and area under the receiver-operating characteristics (ROC) curve (AUC).
Main Results:
- The clinical data-only model achieved 89% sensitivity and 27% specificity (AUC 0.80).
- The local incidence-only model performed best when regional pertussis positivity exceeded 10%, yielding 13% sensitivity and 53% specificity (AUC 0.65).
- The combined model, incorporating clinical variables (cyanosis, cough, absence of fever) and local incidence data, demonstrated superior performance with 100% sensitivity and 38% specificity (AUC 0.82).
Conclusions:
- Incorporating recent, local population-level pertussis incidence significantly improves the accuracy of decision models for identifying infant pertussis cases.
- The findings support enhanced collaboration between public health agencies and clinical practice.
- This study validates a method for integrating large-scale public health data with clinical information to enhance diagnostic capabilities and public health outcomes.
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.
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