Running outside the baseline: impact of the 2004 Major League Baseball postseason on emergency department use.
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Baseball↗
Biomedical subjects
Ben Y Reis
Publications and source records attributed to Ben Y Reis.
Syndromic surveillance: the effects of syndrome grouping on model accuracy and outbreak detection.
STUDY OBJECTIVE: Data used by syndromic surveillance systems must be grouped into syndromes or prodromes. Previous studies have examined the accuracy of different methods of syndromic grouping. We seek to study the effects of different syndrome grouping methods on model accuracy, a key factor in the outbreak-detection performance of syndromic surveillance systems. METHODS: Daily emergency department visit rates were analyzed from 2 urban academic tertiary care hospitals for 1,680 consecutive days. During this period, each hospital census totaled approximately 230,000 patient visits. Three methods were used to group the visits into a respiratory-related syndrome category: 1 relying on chief complaint, 1 on diagnostic codes, and 1 on a combination of the two. The different groupings of the syndromic data resulting from these methods were used to build different historical models that were then tested for forecasting accuracy and for sensitivity to detecting simulated outbreaks. RESULTS: For both hospitals, the data grouped according to chief complaints alone yielded the lowest model accuracy and the lowest detection sensitivity. Using diagnostic codes to group the data yielded better results in accuracy and sensitivity. Combining the 2 grouping methods yielded the best results in accuracy and sensitivity. Temporal smoothing of the data was shown to improve sensitivity in all cases, although to various degrees in the different models. CONCLUSION: The methods used to group input data into syndromic categories can have substantial effects on the overall performance of syndromic surveillance systems. The results suggest that incorporating diagnostic data into these systems can improve the modeling accuracy and its detection sensitivity. Furthermore, the best results may be achieved by using a combination of methods to group visits into syndromic categories.
Bioterrorism↗
Use of emergency department chief complaint and diagnostic codes for identifying respiratory illness in a pediatric population.
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. METHODS: 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. RESULTS: 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.
Child↗
Using temporal context to improve biosurveillance.
Current efforts to detect covert bioterrorist attacks from increases in hospital visit rates are plagued by the unpredictable nature of these rates. Although many current systems evaluate hospital visit data 1 day at a time, we investigate evaluating multiple days at once to lessen the effects of this unpredictability and to improve both the timeliness and sensitivity of detection. To test this approach, we introduce simulated disease outbreaks of varying shapes, magnitudes, and durations into 10 years of historical daily visit data from a major tertiary-care metropolitan teaching hospital. We then investigate the effectiveness of using multiday temporal filters for detecting these simulated outbreaks within the noisy environment of the historical visit data. Our results show that compared with the standard 1-day approach, the multiday detection approach significantly increases detection sensitivity and decreases latency while maintaining a high specificity. We conclude that current biosurveillance systems should incorporate a wider temporal context to improve their effectiveness. Furthermore, for increased robustness and performance, hybrid systems should be developed to capitalize on the complementary strengths of different types of temporal filters.
Bioterrorism↗
Time series modeling for syndromic surveillance.
BACKGROUND: Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. METHODS: Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. RESULTS: Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. CONCLUSIONS: Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization.
Bioterrorism↗
Integrating syndromic surveillance data across multiple locations: effects on outbreak detection performance.
Syndromic surveillance systems are being deployed widely to monitor for signals of covert bioterrorist attacks. Regional systems are being established through the integration of local surveillance data across multiple facilities. We studied how different methods of data integration affect outbreak detection performance. We used a simulation relying on a semi-synthetic dataset, introducing simulated outbreaks of different sizes into historical visit data from two hospitals. In one simulation, we introduced the synthetic outbreak evenly into both hospital datasets (aggregate model). In the second, the outbreak was introduced into only one or the other of the hospital datasets (local model). We found that the aggregate model had a higher sensitivity for detecting outbreaks that were evenly distributed between the hospitals. However, for outbreaks that were localized to one facility, maintaining individual models for each location proved to be better. Given the complementary benefits offered by both approaches, the results suggest building a hybrid system that includes both individual models for each location, and an aggregate model that combines all the data. We also discuss options for multi-level signal integration hierarchies.
Bioterrorism↗