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Background: Passive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect clinical data in electronic medical records (EMRs) that could be used for surveillance. This study's primary objective was to identify data streams from EMRs that may perform well for GI outbreak detection. Methods: Zip code-specific daily episode counts in 2009 were generated for 22 syndromic and laboratory-based data streams from Kaiser Permanente Northern California EMRs, covering 3.3 million members. Data streams included outpatient and inpatient diagnosis codes, antidiarrheal medication dispensings, stool culture orders, and positive microbiology tests for six GI pathogens. Prospective daily surveillance was mimicked using the space-time permutation scan statistic in single and multi-stream analyses, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters. Results: Potential outbreaks included a cluster of 18 stool cultures ordered over 5 days in one zip code and a Salmonella cluster in three zip codes over 9 days, in which at least five of six cases had the same rare serotype. In all, 28 potential outbreaks were identified using single stream analyses, with signals in outpatient diagnosis codes most common. Multi-stream analyses identified additional potential outbreaks and in one example, improved the timeliness of detection. Conclusions: GI disease-related data streams can be used to identify potential outbreaks when generated from EMRs with extensive regional coverage. This process can supplement traditional GI outbreak reports to health departments, which frequently consist of outbreaks in well-defined settings (e.g., day care centers and restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included microbiology test results, stool culture orders, and outpatient diagnoses. In particular, clusters of microbiology tests positive for specific pathogens could be identified in EMRs and used to prioritize further testing at state health departments, potentially improving outbreak detection. © Copyright 2012, Mary Ann Liebert, Inc.

Uncontrolled asthma leads to preventable morbidity and increased health care utilization. The authors examined trends, predictors, and costs of uncontrolled asthma over 5 years in a large health plan population.The authors retrospectively examined administrative health claims data from mid-2000 to mid-2007 on patients with asthma aged 1 to 56 years (n = 54,653 patient-years, 28,595 unique patients). Uncontrolled asthma events were defined asor =2 oral steroid fills oror =5 short-acting beta(2)-agonist (SABA) fills over 12 months, or an asthma-related hospitalization or emergency department (ED) visit. Multivariate generalized mixed regression models determined patient- and neighborhood-level predictors for uncontrolled asthma events. The authors compared asthma-related costs for patients with and without uncontrolled asthma events.In 2002-2003, 39% of patients hador =1 uncontrolled asthma event. Most frequent were high use of rescue asthma medications. Asthma-related hospitalizations and ED visits were infrequent. The percentage having uncontrolled asthma events decreased significantly over time to 27% in 2006-2007, due to a decreased rate of frequent SABA fills. Males and adultsor =24 years had higher odds of medication-based uncontrolled asthma events, whereas residence in neighborhoods with more minorities and lower educational attainment was associated with ED visits or hospitalizations. Patients with uncontrolled asthma events had significantly higher asthma-related costs.In this population, the proportion of patients with uncontrolled asthma, particularly as indicated by high SABA fills, decreased over a 5-year period. Several individual- and neighborhood-level characteristics were associated with uncontrolled asthma events. Clinicians and health plans can identify higher-risk patients in order to target asthma management strategies and reduce asthma-related morbidity and its associated costs.

Monitoring ongoing processes of illness to detect sudden changes is an important aspect of practical epidemiology and medicine more generally. Most commonly, the monitoring has been restricted to a unidimensional stream of data over time. In such situations, analytic results from the industrial process monitoring have suggested optimal approaches to monitor the data streams. Data streams including spatial location as well as temporal sequence are becoming available. Monitoring methods that incorporate spatial data may prove superior to those that ignore it. However, analytically, optimal methods for spatial surveil-lance data may not exist. In the present article, we introduce and discuss evaluation metrics that can be used to compare the performance of statistical methods of surveillance. Our general approach is to generalize receiver operating characteristic (ROC) curves to incorporate the time of detection in addition to the usual test characteristics of sensitivity and specificity. In addition to weighting ordinary ROC curves by two measures of timeliness, we describe three three-dimensional generalizations of ROC curves that result in timeliness-ROC surfaces. Working in the context of surveillance of cases of disease to detect a sudden outbreak, we demonstrate these in an artificial example and in a previously described simulation context and show how the metrics differ. We also discuss the differences and under which circumstances one might prefer a given method.

Objective: Data are scarce regarding the sociodemographic predictors of antenatal and postpartum depression. This study investigated whether race/ethnicity, age, finances, and partnership status were associated with antenatal and postpartum depressive symptoms. Setting: 1662 participants in Project Viva, a US cohort study. Design: Mothers indicated mid-pregnancy and six month postpartum depressive symptoms on the Edinburgh postpartum depression scale (EPDS). Associations of sociodemographic factors with odds of scoring >12 on the EPDS were estimated. Main results: The prevalence of depressive symptoms was 9% at mid-pregnancy and 8% postpartum. Black and Hispanic mothers had a higher prevalence of depressive symptoms compared with non-Hispanic white mothers. These associations were explained by lower income, financial hardship, and higher incidence of poor pregnancy outcome among minority women. Young maternal age was associated with greater risk of antenatal and postpartum depressive symptoms, largely attributable to the prevalence of financial hardship, unwanted pregnancy, and lack of a partner. The strongest risk factor for antenatal depressive symptoms was a history of depression (OR = 4.07; 95% CI 3.76, 4.40), and the strongest risk for postpartum depressive symptoms was depressive symptoms during pregnancy (6.78; 4.07, 11.31) or a history of depression before pregnancy (3.82; 2.31, 6.31). Conclusions: Financial hardship and unwanted pregnancy are associated with antenatal and postpartum depressive symptoms. Women with a history of depression and those with poor pregnancy outcomes are especially vulnerable to depressive symptoms during the childbearing year. Once these factors are taken in account, minority mothers have the same risk of antenatal and postpartum depressive symptoms as white mothers.

Bioterrorism surveillance systems can be assessed using modeling to simulate real-world attacks., We measured sensitivity and timeliness of a syndromic surveillance system to detect bioterrorism events. A hypothetical anthrax release was modeled by using zip code population data, mall customer surveys, and membership information from HealthPartners Medical Group, which covers 9% of a metropolitan area population in Minnesota. For each infection level, 1,000 releases were simulated. Timing of increases in use of medical care was based on data from the Sverdlovsk, Russia, anthrax release. Cases from the simulated outbreak were added to actual respiratory visits recorded for those dates in HealthPartners Medical Group data. Analysis was done by using the space-time scan statistic. We evaluated the proportion of attacks detected at different attack rates and timeliness to detection. Timeliness and completeness of detection of events varied by rate of infection. First detection of events ranged from days 3 to 6. Similar modeling may be possible with other surveillance systems and should be a part of their evaluation.

To examine how enrollment in high-deductible health plans (HDHPs) affects use of well-child visits relative to traditional plans, when preventive care is exempt from the deductible.Pre-post comparison between groups.We selected children aged18 years enrolled in a large Massachusetts health plan through employers offering only 1 type of plan. Children were in traditional plans for a 12-month baseline period between 2001 and 2004, then were either switched by a decision of the parent's employer to an HDHP or kept in the traditional plan (controls) for a 12-month follow-up period. Preventive and other office visits were exempt from the deductible and subject to copayments, as in traditional plans. The primary outcome was whether the child received well-child visits recommended for the 12-month period. Using generalized linear mixed models, we compared the change in receipt of recommended well-child visits between baseline and follow-up for the HDHP group relative to controls.We identified 1598 children who were switched to HDHPs and 10,093 controls. Between baseline and follow-up, the mean proportion of recommended well-child visits received by HDHP children decreased slightly from 0.846 to 0.841, and from 0.861 to 0.855 for controls. In adjusted models, there was no significant difference in the change in probability that recommended well-child visits were received by HDHP children compared with controls (P = .69).Receipt of recommended well-child visits did not change for children switching from traditional plans to HDHPs that exempt preventive care from the deductible.

Background Syndromic surveillance systems can potentially be used to detect a bioterrorist attack earlier than traditional surveillance, by virtue of their near real-time analysis of relevant data. Receiver operator characteristic (ROC) curve analysis using the area under the curve (AUC) as a comparison metric has been recommended as a practical evaluation tool for syndromic surveillance systems, yet traditional ROC curves do not account for timeliness of detection or subsequent time-dependent health outcomes. Methods Using a decision-analytic approach, we predicted outcomes, measured in lives, quality adjusted life years (QALYs), and costs, for a series of simulated bioterrorist attacks. We then evaluated seven detection algorithms applied to syndromic surveillance data using outcomes-weighted ROC curves compared to simple ROC curves and timeliness-weighted ROC curves. We performed sensitivity analyses by varying the model inputs between best and worst case scenarios and by applying different methods of AUC calculation. Results The decision analytic model results indicate that if a surveillance system was successful in detecting an attack, and measures were immediately taken to deliver treatment to the population, the lives, QALYs and dollars lost could be reduced considerably. The ROC curve analysis shows that the incorporation of outcomes into the evaluation metric has an important effect on the apparent performance of the surveillance systems. The relative order of performance is also heavily dependent on the choice of AUC calculation method. Conclusions This study demonstrates the importance of accounting for mortality, morbidity and costs in the evaluation of syndromic surveillance systems. Incorporating these outcomes into the ROC curve analysis allows for more accurate identification of the optimal method for signaling a possible bioterrorist attack. In addition, the parameters used to construct an ROC curve should be given careful consideration.

BackgroundSurveillance for influenza and influenza-like illness (ILI) is important for guiding public health prevention programs to mitigate the morbidity and mortality caused by influenza, including pandemic influenza. Nontraditional sources of data for influenza and ILI surveillance are of interest to public health authorities if their validity can be established.Methods/principal findingsNational telephone triage call data were collected through automated means for purposes of syndromic surveillance. For the 17 states with at least 500,000 inhabitants eligible to use the telephone triage services, call volume for respiratory syndrome was compared to CDC weekly number of influenza isolates and percentage of visits to sentinel providers for ILI. The degree to which the call data were correlated with either CDC viral isolates or sentinel provider percentage ILI data was highly variable among states.ConclusionsTelephone triage data in the U.S. are patchy in coverage and therefore not a reliable source of ILI surveillance data on a national scale. However, in states displaying a higher correlation between the call data and the CDC data, call data may be useful as an adjunct to state-level surveillance data, for example at times when sentinel surveillance is not in operation or in areas where sentinel provider coverage is considered insufficient. Sufficient population coverage, a specific ILI syndrome definition, and the use of a threshold of percentage of calls that are for ILI would likely improve the utility of such data for ILI surveillance purposes.

In modern surveillance of public health, data may be reported in a timely fashion, and include spatial data on cases in addition to the time of their occurrence. This has lead to many recent developments in statistical methods to detect events of public health importance. However, there has been relatively little work into methods to identify how to compare such methods. One powerful rationale for performing surveillance is earlier detection of events of public health significance; previous evaluation tools have focused on metrics which include the timeliness of detection in addition to sensitivity and specificity. However, such metrics have not accounted for the number of persons affected by the events. We re-examine the rationale for this surveillance and conclude that earlier detection is preferred because it can prevent additional morbidity and mortality. Based on this observation, we propose evaluating the number of cases prevented by each detection method, and include this information in assessing the value of different detection methods. Using this approach incorporates more information about the events and the detection and provides a sound basis for making decisions about which detection methods to employ.

Since the anthrax attacks of October 2001 and the SARS outbreaks of recent years, there has been an increasing interest in developing surveillance systems to aid in the early detection of such illness. Systems have been established which do this is by monitoring primary health-care visits, pharmacy sales, absenteeism records, and other non-traditional sources of data. While many resources have been invested in establishing such systems, relatively little effort has as yet been expended in evaluating their performance. One way to evaluate a given surveillance system is to compare the signals it generates with known outbreaks identified in other systems. In public health practice, for example, public health departments investigate reports of illness and sometimes track hospital admissions. Comparison of new systems with extant systems cannot generate estimates of test characteristics such as sensitivity and specificity, since the actual number of positives and negatives cannot be known. However, the comparison can reveal whether a new or proposed system's signals match outbreaks detected by the existing system. This could help support or reject the new system as an alternative or complement to the extant system. We propose three methods to test the null hypothesis that the new system does not signal true outbreaks more often than would be expected by chance. The methods differ in the restrictiveness of the assumptions required. Each test may detect weaknesses in the new system, depending on the distribution of outbreaks and can be used to construct confidence limits on the agreement between the new system's signals and the outbreaks, given the distribution of the signals. They can be used to assess whether the new system works in that it detects the outbreaks better than chance would suggest and can also determine if the new systems' signals are generated earlier than an extant system.

The space–time scan statistic is often used to identify incident disease clusters. We introduce a method to adjust for naturally occurring temporal trends or geographical patterns in illness. The space–time scan statistic was applied to reports of lower respiratory complaints in a large group practice. We compared its performance with unadjusted populations from: (1) the census, (2) group-practice membership counts, and on adjustments incorporating (3) day of week, month, and holidays; and (4) additionally, local history of illness. Using a nominal false detection rate of 5%, incident clusters during 1 year were identified on 26, 22, 4 and 2% of days for the four populations respectively. We show that it is important to account for naturally occurring temporal and geographic trends when using the space–time scan statistic for surveillance. The large number of days with clusters renders the census and membership approaches impractical for public health surveillance. The proposed adjustment allows practical surveillance.

Background The spatial and space-time scan statistics are commonly applied for the detection of geographical disease clusters. Monte Carlo hypothesis testing is typically used to test whether the geographical clusters are statistically significant as there is no known way to calculate the null distribution analytically. In Monte Carlo hypothesis testing, simulated random data are generated multiple times under the null hypothesis, and the p-value is r/(R + 1), where R is the number of simulated random replicates of the data and r is the rank of the test statistic from the real data compared to the same test statistics calculated from each of the random data sets. A drawback to this powerful technique is that each additional digit of p-value precision requires ten times as many replicated datasets, and the additional processing can lead to excessive run times. Results We propose a new method for obtaining more precise p-values with a given number of replicates. The collection of test statistics from the random replicates is used to estimate the true distribution of the test statistic under the null hypothesis by fitting a continuous distribution to these observations. The choice of distribution is critical, and for the spatial and space-time scan statistics, the extreme value Gumbel distribution performs very well while the gamma, normal and lognormal distributions perform poorly. From the fitted Gumbel distribution, we show that it is possible to estimate the analytical p-value with great precision even when the test statistic is far out in the tail beyond any of the test statistics observed in the simulated replicates. In addition, Gumbel-based rejection probabilities have smaller variability than Monte Carlo-based rejection probabilities, suggesting that the proposed approach may result in greater power than the true Monte Carlo hypothesis test for a given number of replicates. Conclusions For large data sets, it is often advantageous to replace computer intensive Monte Carlo hypothesis testing with this new method of fitting a Gumbel distribution to random data sets generated under the null, in order to reduce computation time and obtain much more precise p-values and slightly higher statistical power.

Background SaTScan is a software program written to implement the scan statistic; it can be used to find clusters in space and/or time. It must often be run multiple times per day when doing disease surveillance. Running SaTScan frequently via its graphical user interface can be cumbersome, and the output can be difficult to visualize. Results The SaTScan Macro Accessory for Cartography (SMAC) package consists of four SAS macros and was designed as an easier way to run SaTScan multiple times and add graphical output. The package contains individual macros which allow the user to make the necessary input files for SaTScan, run SaTScan, and create graphical output all from within SAS software. The macros can also be combined to do this all in one step. Conclusion The SMAC package can make SaTScan easier to use and can make the output more informative.