Children with chronic HCV infection are generally asymptomatic. By 13 years after infection, 12% of patients had significant fibrosis. Patients enrolled by referral had more severe liver disease than those identified through the look-back program, demonstrating the importance of selection bias in assessing the long-term outcome of HCV infection.

Since the terrorist attacks on September 11, 2001, many state and local health departments around the United States have started to develop syndromic surveillance systems. Syndromic surveillance -a new concept in epidemiology -is the statistical analyses of data on individuals seeking care in emergency rooms (ER) or other health care settings with preidentified sets of symptoms thought to be related to the precursors of diseases. Making use of existing health care or other data, often already in electronic form, these systems are intended to give early warnings of bioterrorist attacks or other emerging health conditions. By focusing on symptoms rather than confirmed diagnoses, syndromic surveillance aims to detect bioevents earlier than would be possible with traditional surveillance systems. Because potential bioterrorist agents such as anthrax, plague, brucellosis, tularemia, Q-fever, glanders, smallpox, and viral hemorrhagic fevers initially exhibit symptoms ("present" 142 Michael A. Stoto et al. in medical terminology) of a flulike illness, data suggesting a sudden increase of individuals with fever, headache, muscle pain, and malaise might be the first indication of a bioterrorist attack or natural disease outbreak. Syndromic surveillance is also thought to be useful for early detection of natural disease outbreaks [Hen04].Research groups based at universities, health departments, private firms, and other organizations have proposed and are developing and promoting a variety of surveillance systems purported to meet public health needs. These include methods for analysis of data from healthcare facilities, as well as reports to health departments of unusual cases. Many of these methods involve intensive, automated statistical analysis of large amounts of data and intensive use of informatics techniques to gather data for analysis and to communicate among physicians and public health officials [WTE01]. Some of these systems go beyond health care data to include nonhealth data such as over-the-counter (OTC) pharmaceutical sales and absenteeism that might indicate people with symptoms who have not sought health care [Hen04].There are a number of technological, logistical, and legal constraints to obtaining appropriate data and effective operation of syndromic surveillance systems [Bue04]. However, even with access to the requisite data and perfect organizational coordination and cooperation, the statistical challenges in reliably and accurately detecting a bioevent are formidable. The object of these surveillance systems, of course, is to analyze a stream of data in realtime and determine whether there is an anomaly suggesting that an incident has occurred. All data streams, however, have some degree of natural variability. These include seasonal or weekly patterns, a flu season that appears at a different time each winter or perhaps not at all, differences in coding practices, sales promotions for OTC medications, and random fluctuations due to small numbers of individuals with particular symptoms. Fur...

Background: Since 2001, the District of Columbia Department of Health has been using an emergency room syndromic surveillance system to identify possible disease outbreaks. Data are received from a number of local hospital emergency rooms and analyzed daily using a variety of statistical detection algorithms. The aims of this paper are to characterize the performance of these statistical detection algorithms in rigorous yet practical terms in order to identify the optimal parameters for each and to compare the ability of two syndrome definition criteria and data from a children's hospital versus vs. other hospitals to determine the onset of seasonal influenza.

BackgroundIn 2003, residents of the District of Columbia (DC) experienced an abrupt rise in lead levels in drinking water, which followed a change in water-disinfection treatment in 2001 and which was attributed to consequent changes in water chemistry and corrosivity.ObjectivesTo evaluate the public health implications of the exceedance, the DC Department of Health expanded the scope of its monitoring programs for blood lead levels in children.MethodsFrom 3 February 2004 to 31 July 2004, 6,834 DC residents were screened to determine their blood lead levels.ResultsChildren from 6 months to 6 years of age constituted 2,342 of those tested; 65 had blood lead levels > 10 μg/dL (the “level of concern” defined by the Centers for Disease Control and Prevention), the highest with a level of 68 μg/dL. Investigation of their homes identified environmental sources of lead exposure other than tap water as the source, when the source was identified. Most of the children with elevated blood lead levels (n = 46; 70.8%) lived in homes without lead drinking-water service lines, which is the principal source of lead in drinking water in older cities. Although residents of houses with lead service lines had higher blood lead levels on average than those in houses that did not, this relationship is confounded. Older houses that retain lead service lines usually have not been rehabilitated and are more likely to be associated with other sources of exposure, particularly lead paint. None of 96 pregnant women tested showed blood lead levels > 10 μg/dL, but two nursing mothers had blood lead levels > 10 μg/dL. Among two data sets of 107 and 71 children for whom paired blood and water lead levels could be obtained, there was no correlation (r2 = –0.03142 for the 107).ConclusionsThe expanded screening program developed in response to increased lead levels in water uncovered the true dimensions of a continuing problem with sources of lead in homes, specifically lead paint. This study cannot be used to correlate lead in drinking water with blood lead levels directly because it is based on an ecologic rather than individualized exposure assessment; the protocol for measuring lead was based on regulatory requirements rather than estimating individual intake; numerous interventions were introduced to mitigate the effect; exposure from drinking water is confounded with other sources of lead in older houses; and the period of potential exposure was limited and variable.

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