Use of population health data to refine diagnostic decision-making for pertussis.

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.

Multistate outbreak of Listeria monocytogenes infection linked to delicatessen turkey meat.

BACKGROUND: Despite a decreasing incidence of listeriosis in the United States, molecular subtyping has increased the number of recognized outbreaks. In September 2000, the New York City Department of Health identified a cluster of infections caused by Listeria monocytogenes isolates with identical molecular subtypes by pulsed-field gel electrophoresis (PFGE) and ribotyping. METHODS: To determine the magnitude of the outbreak and identify risk factors for infection, we notified state health departments and conducted a case-control study. A case was defined as a patient or mother-infant pair infected with Listeria monocytogenes whose isolate yielded the outbreak PFGE pattern. Controls were patients infected with Listeria monocytogenes whose isolate yielded a different PFGE pattern. Patients were asked about food and drink consumed during the 30 days before the onset of illness. RESULTS: Between May and December 2000, there were 30 clinical isolates of Listeria monocytogenes with identical PFGE patterns identified in 11 US states. Cases of infection caused by these isolates were associated with 4 deaths and 3 miscarriages. A case-control study implicated sliced processed turkey from a delicatessen (Mantel-Haenszel odds ratio, 8.0; 95% confidence interval, 1.2-43.3). A traceback investigation identified a single processing plant as the likely source of the outbreak, and the company voluntarily recalled 16 million pounds of processed meat. The same plant had been identified in a Listeria contamination event that had occurred more than a decade previously. CONCLUSIONS: Prevention of persistent L. monocytogenes contamination in food processing plants presents a critical challenge to food safety professionals.