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Objectives: The subjectivity and complexity of surveillance definitions for ventilator-associated pneumonia preclude meaningful internal or external benchmarking and therefore hamper quality improvement initiatives for ventilated patients. We explored the feasibility of creating objective surveillance definitions for ventilator-associated pneumonia., Design: We identified clinical signs suitable for inclusion in objective definitions, proposed candidate definitions incorporating these objective signs, and then applied these definitions to retrospective clinical data to measure their frequencies and associations with adverse outcomes using multivariate regression models for cases and matched controls., Setting: Medical and surgical intensive care units in eight U.S. hospitals (four tertiary centers, three community hospitals, and one Veterans Affairs institution)., Patients: Eight thousand seven hundred thirty-five consecutive episodes of mechanical ventilation for adult patients., Interventions: We evaluated 32 different candidate definitions composed of different combinations of the following signs: three thresholds for respiratory deterioration defined by sustained increases in daily minimum positive end-expiratory pressure or FIO2 after either 2 or 3 days of stable or decreasing ventilator settings, abnormal temperature, abnormal white blood cell count, purulent pulmonary secretions defined by neutrophils on Gram stain, and positive cultures for pathogenic organisms., Measurements and Main Results: Ventilator-associated pneumonia incidence, attributable ventilator days, hospital days, and hospital mortality. All candidate definitions were significantly associated with increased ventilator days and hospital days, but only definitions requiring objective evidence of respiratory deterioration were significantly associated with increased hospital mortality. Significant odds ratios for hospital mortality ranged from 1.9 (95% confidence interval 1.2-2.9) to 6.1 (95% confidence interval 2.2-17). Requiring additional clinical signs beyond respiratory deterioration alone decreased event rates, had little impact on attributable lengths of stay, and diminished sensitivity and positive predictive values for hospital mortality., Conclusions: Objective surveillance definitions that include quantitative evidence of respiratory deterioration after a period of stability strongly predict increased length of stay and hospital mortality. These definitions merit further evaluation of their utility for hospital quality and safety improvement programs.

Background: Ventilator-associated pneumonia (VAP) surveillance is time consuming, subjective, inaccurate, and inconsistently predicts outcomes. Shifting surveillance from pneumonia in particular to complications in general might circumvent the VAP definition's subjectivity and inaccuracy, facilitate electronic assessment, make interfacility comparisons more meaningful, and encourage broader prevention strategies. We therefore evaluated a novel surveillance paradigm for ventilator-associated complications (VAC) defined by sustained increases in patients' ventilator settings after a period of stable or decreasing support., Methods: We assessed 600 mechanically ventilated medical and surgical patients from three hospitals. Each hospital contributed 100 randomly selected patients ventilated 2-7 days and 100 patients ventilated >7 days. All patients were independently assessed for VAP and for VAC. We compared incidence-density, duration of mechanical ventilation, intensive care and hospital lengths of stay, hospital mortality, and time required for surveillance for VAP and for VAC. A subset of patients with VAP and VAC were independently reviewed by a physician to determine possible etiology., Results: Of 597 evaluable patients, 9.3% had VAP (8.8 per 1,000 ventilator days) and 23% had VAC (21.2 per 1,000 ventilator days). Compared to matched controls, both VAP and VAC prolonged days to extubation (5.8, 95% CI 4.2-8.0 and 6.0, 95% CI 5.1-7.1 respectively), days to intensive care discharge (5.7, 95% CI 4.2-7.7 and 5.0, 95% CI 4.1-5.9), and days to hospital discharge (4.7, 95% CI 2.6-7.5 and 3.0, 95% CI 2.1-4.0). VAC was associated with increased mortality (OR 2.0, 95% CI 1.3-3.2) but VAP was not (OR 1.1, 95% CI 0.5-2.4). VAC assessment was faster (mean 1.8 versus 39 minutes per patient). Both VAP and VAC events were predominantly attributable to pneumonia, pulmonary edema, ARDS, and atelectasis., Conclusions: Screening ventilator settings for VAC captures a similar set of complications to traditional VAP surveillance but is faster, more objective, and a superior predictor of outcomes.

Background: Automated electronic medical records may be useful for syndromic surveillance to quickly detect infectious disease outbreaks. Some syndromic surveillance systems include every encounter in the analysis, whereas others exclude individuals' repeat encounters within the same syndrome occurring within a short period of time, with the rationale that these represent follow-up visits rather than new episodes of illness., Methods: We evaluate the effect of keeping all encounters as compared with removing repeat encounters. Using the prospective space-time permutation scan statistic, we performed daily analyses on all encounters versus on episodes defined as encounters new within 2, 6 or 12 weeks. Data were taken from a Massachusetts Health Maintenance Organization (HMO) for the calendar year 1999 for four different syndromes., Results: We found extensive disagreement in the number of signals detected: 70, 68, 21 and 15 signals when using all encounters versus 15-20, 3, 4-5 and 0 signals when using only new episodes for lower respiratory, lower gastrointestinal, upper gastrointestinal and neurologic syndromes, respectively., Conclusion: Using all encounters in syndromic surveillance may not only create too many signals but may also miss some signals by masking the anomalies generated by actual episodes. However, it is also possible to miss signals when using episodes.

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., (Copyright 2006 John Wiley & Sons, Ltd.)

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.

Introduction: Syndromic surveillance's capability to augment existing surveillance for community-acquired gastrointestinal disease is unknown., Objective: The objective of this study was to evaluate the capability of a syndromic surveillance system to detect outbreaks of gastrointestinal disease., Methods: A retrospective analysis was conducted comparing ambulatory care data from a health plan with a set of 110 gastrointestinal-disease outbreaks identified by the Minnesota Department of Health during 2001-02. Unusual clusters of illness (i.e., signals) in the health-plan data were identified by analyzing daily counts of gastrointestinal illness using an adjusted space-time scan statistic. Concordance was defined as < or =5 km between outbreak and signal and the signal occurring within 1 week of the outbreak., Results: During 104 weeks, the number of signals was roughly what would have been expected by chance, suggesting that the modeling did a good job of estimating the expected counts of illness and that false alarms would not have occurred much more often than the number predicted at the various thresholds. During the same period, the health department identified 110 eligible gastrointestinal outbreaks. Apparent associations of the three statistically most unusual concordant signals with outbreaks of viral or bacterial gastrointestinal illness were ruled out by the health department on the basis of detailed knowledge of the circumstances and low numbers of affected persons seeking medical care., Conclusion: No previously known gastrointestinal outbreaks were identified by this surveillance system. However, relatively few recognized outbreaks resulted in patients seeking medical care, and the sensitivity of this system to detect outbreaks of real significance to public health remains to be determined. Prospective evaluation probably will be required to understand the usefulness of syndromic surveillance systems to enhance existing disease surveillance.

Introduction: Syndromic surveillance systems can be useful in detecting naturally occurring illness., Objectives: Syndromic surveillance performance was assessed to identify an early and severe influenza A outbreak in Denver in 2003., Methods: During October 1, 2003-January 31, 2004, syndromic surveillance signals generated for detecting clusters of influenza-like illness (ILI) were compared with ILI activity identified through a sentinel provider system and with reports of laboratory-confirmed influenza. The syndromic surveillance and sentinel provider systems identified ILI activity based on ambulatory-care visits to Kaiser Permanente Colorado. The syndromic surveillance system counted a visit as ILI if the provider recorded any in a list of 30 respiratory diagnoses plus fever. The sentinel provider system required the provider to select "influenza" or "ILI.", Results: Laboratory-confirmed influenza cases, syndromic surveillance ILI episodes, and sentinel provider reports of patient visits for ILI all increased substantially during the week ending November 8, 2003. A greater absolute increase in syndromic surveillance episodes was observed than in sentinel provider reports, suggesting that sentinel clinicians failed to code certain cases of influenza. During the week ending December 6, when reports of laboratory-confirmed cases peaked, the number of sentinel provider reports exceeded the number of syndromic surveillance episodes, possibly because clinicians diagnosed influenza without documenting fever., Conclusion: Syndromic surveillance performed as well as the sentinel provider system, particularly when clinicians were advised to be alert to influenza, suggesting that syndromic surveillance can be useful for detecting clusters of respiratory illness in various settings.

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.

The National Bioterrorism Syndromic Surveillance Demonstration Program identifies new cases of illness from electronic ambulatory patient records. Its goals are to use data from health plans and practice groups to detect localized outbreaks and to facilitate rapid public health follow-up. Data are extracted nightly on patient encounters occurring during the previous 24 hours. Visits or calls with diagnostic codes corresponding to syndromes of interest are counted; repeat encounters are excluded. Daily counts of syndromes by zip code are sent to a central data repository, where they are statistically analyzed for unusual clustering by using a model-adjusted SaTScan approach. The results and raw data are displayed on a restricted website. Patient-level information stays at the originating health-care organization unless required by public health authorities. If a cluster surpasses a threshold of statistical aberration chosen by the corresponding public health department, an electronic alert can be sent to that department. The health department might then call a clinical responder, who has electronic access to records of cases contributing to clusters. The system is flexible, allowing for changes in participating organizations, syndrome definitions, and alert thresholds. It is transparent to clinicians and has been accepted by the health-care organizations that provide the data. The system's data are usable by local and national health agencies. Its software is compatible with commonly used systems and software and is mostly open-source. Ongoing activities include evaluating the system's ability to detect naturally occurring outbreaks and simulated terrorism events, automating and testing alerts and response capability, and evaluating alternative data sources.

Since the intentional dissemination of anthrax through the US postal system in the fall of 2001, there has been increased interest in surveillance for detection of biological terrorism. More generally, this could be described as the detection of incident disease clusters. In addition, the advent of affordable and quick geocoding allows for surveillance on a finer spatial scale than has been possible in the past. Surveillance for incident clusters of disease in both time and space is a relatively undeveloped arena of statistical methodology. Surveillance for bioterrorism detection, in particular, raises unique issues with methodological relevance. For example, the bioterrorism agents of greatest concern cause initial symptoms that may be difficult to distinguish from those of naturally occurring disease. In this paper, the authors propose a general approach to evaluating whether observed counts in relatively small areas are larger than would be expected on the basis of a history of naturally occurring disease. They implement the approach using generalized linear mixed models. The approach is illustrated using data on health-care visits (1996-1999) from a large Massachusetts managed care organization/multispecialty practice group in the context of syndromic surveillance for anthrax. The authors argue that there is great value in using the geographic data.

Several health plans and other organizations are collaborating with the Centers for Disease Control and Prevention to develop a syndromic surveillance system with national coverage that includes more than 20 million people. A principal design feature of this system is reliance on daily reporting of counts of individuals with syndromes of interest in specified geographic regions rather than reporting of individual encounter-level information. On request from public health agencies, health plans and telephone triage services provide additional information regarding individuals who are part of apparent clusters of illness. This reporting framework has several advantages, including less sharing of protected health information, less risk that confidential information will be distributed inappropriately, the prospect of better public acceptance, greater acceptance by health plans, and less effort and cost for both health plans and public health agencies. If successful, this system will allow any organization with appropriate data to contribute vital information to public health syndromic surveillance systems while preserving individuals' privacy to the greatest extent possible.

The advent of domestic bioterrorism has emphasized the need for enhanced detection of clusters of acute illness. We describe a monitoring system operational in eastern Massachusetts, based on diagnoses obtained from electronic records of ambulatory-care encounters. Within 24 hours, ambulatory and telephone encounters recording patients with diagnoses of interest are identified and merged into major syndrome groups. Counts of new episodes of illness, rates calculated from health insurance records, and estimates of the probability of observing at least this number of new episodes are reported for syndrome surveillance. Census tracts with unusually large counts are identified by comparing observed with expected syndrome frequencies. During 1996-1999, weekly counts of new cases of lower respiratory syndrome were highly correlated with weekly hospital admissions. This system complements emergency room- and hospital-based surveillance by adding the capacity to rapidly identify clusters of illness, including potential bioterrorism events.

Background: Gaps in disease surveillance capacity, particularly for emerging infections and bioterrorist attack, highlight a need for efficient, real time identification of diseases., Methods: We studied automated records from 1996 through 1999 of approximately 250,000 health plan members in greater Boston., Results: We identified 152,435 lower respiratory infection illness visits, comprising 106,670 episodes during 1,143,208 person-years. Three diagnoses, cough (ICD9CM 786.2), pneumonia not otherwise specified (ICD9CM 486) and acute bronchitis (ICD9CM 466.0) accounted for 91% of these visits, with expected age and sex distributions. Variation of weekly occurrences corresponded closely to national pneumonia and influenza mortality data. There was substantial variation in geographic location of the cases., Conclusion: This information complements existing surveillance programs by assessing the large majority of episodes of illness for which no etiologic agents are identified. Additional advantages include: a) sensitivity, uniformity and efficiency, since detection of events does not depend on clinicians' to actively report diagnoses, b) timeliness, the data are available within a day of the clinical event; and c) ease of integration into automated surveillance systems. These features facilitate early detection of conditions of public health importance, including regularly occurring events like seasonal respiratory illness, as well as unusual occurrences, such as a bioterrorist attack that first manifests as respiratory symptoms. These methods should also be applicable to other infectious and non-infectious conditions. Knowledge of disease patterns in real time may also help clinicians to manage patients, and assist health plan administrators in allocating resources efficiently.