BACKGROUND: The Centers for Disease Control and Prevention is incorporating laboratory data into real-time surveillance systems. When normal patterns of laboratory test orders and results are modeled, aberrations can be detected. Because many test orders are available electronically well before results, atypical patterns of test ordering may signal outbreaks. OBJECTIVES: The authors sought to characterize baseline patterns in the ordering and early results of lumbar punctures, motivated by the possibility of using these data for real-time surveillance for early detection of meningitis or encephalitis outbreaks. METHODS: Retrospective cohorts of pediatric emergency department patients at a single hospital (1993-2003) and from the National Hospital and Ambulatory Medical Care Survey (1992-2000) were used for analysis. RESULTS: Test ordering exhibits seasonal patterns, with monthly peaks in January and August (p 0.0001). For the hospital cohort, the rate of cerebrospinal fluid pleocytosis exhibits seasonal patterns (p 0.0001), with a peak from August to October. This is strongly associated with the rate and pattern of clinical neurologic disease (p 0.0001). A long-term secular decline in daily test ordering is evident, dropping from 5.3 to 2.9 in the hospital sample, and from 371.8 to 185.3 in the national sample (p 0.001). The long-term rate of pleocytosis has declined (p 0.0001), though the yield of testing for pleocytosis has improved (p = 0.0104). CONCLUSIONS: Laboratory test patterns correspond with those of clinical disease and are a promising source of surveillance data. Using such data for real-time monitoring requires specific adjustments for patient age, periodicities, and secular trends.

BACKGROUND: The goal of this study was to evaluate the interactions of mothers with normative or high levels of depressive symptomatology on the Center for Epidemiologic Studies-Depression Scale (CES-D) and their 3-month-old infants. Although successful mutual regulation of affect is critical to children's socio-emotional development, little is known about the factors that influence dyadic processes such as synchrony, matching, mismatching, and bi-directionality during early infancy. Therefore, this study evaluated the effects of maternal depressive symptom status, infant gender, and interactional context on mother-infant affective expressiveness and the dyadic features of their interactions. METHODS: Participants were 133 mothers and their healthy full-term infants. Mothers were classified into three groups on the basis of their total score on the CES-D at 2 months of infant age: a high symptom group (CES-D score > or = 16), a mid symptom control group (CES-D score = 2-12), and a low symptom group (CES-D score = 0-1). Mothers and infants were then videotaped in the Face-to-Face Still-Face paradigm at 3 months of infant age. The mothers' and infants' affect during the interactions prior to (first play) and following the still-face (reunion play) were coded microanalytically using Izard's AFFEX system. RESULTS: Results indicated that male as compared to female infants were more vulnerable to high levels of maternal depressive symptoms and that high symptom mothers and their sons had more difficult interactions in the challenging reunion episode. CONCLUSIONS: The findings suggest that a cycle of mutual regulatory problems may become established between high symptom mothers and their sons, particularly in challenging social contexts. The long-term consequences of this early social interactive vulnerability in terms of later development need to be further investigated.

BACKGROUND: Evaluating surveillance systems for the early detection of bioterrorism is particularly challenging when systems are designed to detect events for which there are few or no historical examples. One approach to benchmarking outbreak detection performance is to create semi-synthetic datasets containing authentic baseline patient data (noise) and injected artificial patient clusters, as signal. METHODS: We describe a software tool, the AEGIS Cluster Creation Tool (AEGIS-CCT), that enables users to create simulated clusters with controlled feature sets, varying the desired cluster radius, density, distance, relative location from a reference point, and temporal epidemiological growth pattern. AEGIS-CCT does not require the use of an external geographical information system program for cluster creation. The cluster creation tool is an open source program, implemented in Java and is freely available under the Lesser GNU Public License at its Sourceforge website. Cluster data are written to files or can be appended to existing files so that the resulting file will include both existing baseline and artificially added cases. Multiple cluster file creation is an automated process in which multiple cluster files are created by varying a single parameter within a user-specified range. To evaluate the output of this software tool, sets of test clusters were created and graphically rendered. RESULTS: Based on user-specified parameters describing the location, properties, and temporal pattern of simulated clusters, AEGIS-CCT created clusters accurately and uniformly. CONCLUSION: AEGIS-CCT enables the ready creation of datasets for benchmarking outbreak detection systems. It may be useful for automating the testing and validation of spatial and temporal cluster detection algorithms.

BACKGROUND: Public health departments in the United States are beginning to gain timely access to health data, often as soon as one day after a visit to a health care facility. Consequently, new approaches to outbreak surveillance are being developed. When cases cluster geographically, an analysis of their spatial distribution can facilitate outbreak detection. Our method focuses on detecting perturbations in the distribution of pair-wise distances among all patients in a geographical region. Barring outbreaks, this distribution can be quite stable over time. We sought to exemplify the method by measuring its cluster detection performance, and to determine factors affecting sensitivity to spatial clustering among patients presenting to hospital emergency departments with respiratory syndromes. METHODS: The approach was to (1) define a baseline spatial distribution of home addresses for a population of patients visiting an emergency department with respiratory syndromes using historical data; (2) develop a controlled feature set simulation by inserting simulated outbreak data with varied parameters into authentic background noise, thereby creating semisynthetic data; (3) compare the observed with the expected spatial distribution; (4) establish the relative value of different alarm strategies so as to maximize sensitivity for the detection of clustering; and (5) measure factors which have an impact on sensitivity. RESULTS: Overall sensitivity to detect spatial clustering was 62%. This contrasts with an overall alarm rate of less than 5% for the same number of extra visits when the extra visits were not characterized by geographic clustering. Clusters that produced the least number of alarms were those that were small in size (10 extra visits in a week, where visits per week ranged from 120 to 472), diffusely distributed over an area with a 3 km radius, and located close to the hospital (5 km) in a region most densely populated with patients to this hospital. Near perfect alarm rates were found for clusters that varied on the opposite extremes of these parameters (40 extra visits, within a 250 meter radius, 50 km from the hospital). CONCLUSION: Measuring perturbations in the interpoint distance distribution is a sensitive method for detecting spatial clustering. When cases are clustered geographically, there is clearly power to detect clustering when the spatial distribution is represented by the M statistic, even when clusters are small in size. By varying independent parameters of simulated outbreaks, we have demonstrated empirically the limits of detection of different types of outbreaks.

Descriptive statistics for the Neonatal Intensive Care Unit Network Neurobehavioral Scale summary scores are provided for a sample of 125 full-term, healthy 1- to 2-day-old infants. The study sample is described, including demographic characteristics and infant and maternal medical characteristics. Descriptive statistics and percentiles are provided for the Neonatal Intensive Care Unit Network Neurobehavioral Scale summary scores. These tables can be used as quasinorms for comparison with other infants of this age.

OBJECTIVES: (1) To determine the value of emergency department chief complaint (CC) and International Classification of Disease diagnostic codes for identifying respiratory illness in a pediatric population and (2) to modify standard respiratory CC and diagnostic code sets to better identify respiratory illness in children. RESULTS: We determined the sensitivity and specificity of CC and diagnostic codes by comparing code groups with a criterion standard. CC and diagnostic codes for 500 pediatric emergency department patients were retrospectively classified as respiratory or nonrespiratory. Respiratory diagnostic codes were further classified as upper or lower respiratory. The criterion standard was a blinded, reviewer-assigned illness category based on history, physical examination, test results, and treatment. We also modified our respiratory code sets to better identify respiratory illness in this population. METHODS: Four hundred ninety-six charts met inclusion criteria. By the criterion standard, 87 (18%) patients had upper and 47 (10%) had lower respiratory illness. The specificity of CC and diagnostic codes groups was >0.97 [95% confidence interval (CI) 0.95-0.98]. The code group sensitivities were as follows: CC was 0.47 (95% CI 0.38-0.55), upper respiratory diagnostic was 0.56 (95% CI 0.45-0.67), lower respiratory diagnostic was 0.87 (95% CI 0.74-0.95), and combined CC and/or diagnostic was 0.72 (95% CI 0.63-0.79). Modifying the respiratory code sets to better identify respiratory illness increased sensitivity but decreased specificity. CONCLUSIONS: Diagnostic and CC codes have substantial value for emergency department syndromic surveillance. Adapting our respiratory code sets to a pediatric population forced a tradeoff between sensitivity and specificity.