We evaluated absentee data for MDCPS during September 8–October 21, 2009. Eighty-seven charter and special education schools were excluded because of consistently unstable absenteeism levels. On the basis of MDCPS’s previous year’s mean of 4.9% absenteeism, we used 8.0% as the threshold level (4,5). The mean and standard deviation were estimated in countywide and individual school levels. Alerts were automatically generated 1) for an absentee rate >8% and 2) when the percentage was at least 1.0 SD beyond the mean of the previous 30 days in countywide or an individual school, compared with their own value. At 1.0 SD, a warning was signaled, and when the standard deviations were 1.96 and 2.58 beyond the mean, yellow and red alerts were triggered, respectively. These cutoffs were set to alert at the 95th and 99th percentiles, assuming the percentage of absenteeism was normally distributed.

Only the yellow and red alerts were applied to countywide absenteeism trends by age group; however, all alerts were applied to the individual school trends. Combining absentee rates with an alert status helped exclude schools with percentage of absenteeism >8% without an alert that, on the basis of historical data, typically have high absentee rates because of low attendance. SAS version 9.13 (SAS, Cary, NC, USA), Visual Basic (Microsoft, Redmond, WA, USA), and ArcGIS 9.3 (www.esri.com) were used to design an SBASS that created 4 reports. These comprised 1) a figure with the percentage of countywide absentee trends by age group (<5, 6–11, 12–14, 15–17, and >18 years of age); 2) a table with the countywide absentee percentages by mean, ratio, standard deviation, and alert status (red alert, yellow alert, or warning); 3) a list of the alerted schools; and 4) a geographic information system map with the alerted school locations.

The applied epidemiology and research team of the MDCHD Epidemiology, Disease Control and Immunization Services (EDC-IS) performed daily school absentee surveillance. Protocol dictated contacting attendance offices when the system detected an alert. The school calendar was used to ascertain dates that would have high absenteeism because of teacher planning days, early release days, holidays, and other events. Daily school absentee reports were sent to MDCPS offices through email.

If clustering of influenza-like illness (ILI) was identified, MDCHD initiated an investigation. ILI clusters were deemed outbreaks when >2 students or staff with a clear association, such as classmates or sharing of similar activities, had symptoms of fever along with cough or sore throat within a specified period.

In our report, passive surveillance and direct reporting refer to school-initiated reporting of public health events to MDCHD. Direct reporting comprises public and private schools; the SBASS comprises public schools only.

Figure

Figure. Epidemic curve of persons with influenza-like illness (ILI) identified through a school-based absentee surveillance system, Miami-Dade County Public Schools, Miami, Florida, USA, September 8–October 21, 2009.

The SBASS gave 61 red alerts, 28 yellow alerts, and 67 warnings during the study period (Table). After active investigation, 9 of 89 alerted schools were confirmed to have influenza outbreaks, and 71 persons with ILI were identified (Figure). Two of these 9 schools had simultaneously initiated reporting of outbreaks directly to MDCHD. Additionally, MDCHD received reports of suspected ILI activity from 24 public schools, none of which were confirmed outbreaks. Thus, 2 (8%) of 26 schools that directly reported to MDCHD had confirmed ILI outbreaks. Regardless of how ILI outbreaks were detected, all were investigated in accordance with EDC-IS protocol.

The SBASS detected all influenza-related outbreaks among public schools and proved useful in conjunction with traditional surveillance methods. Pandemic (H1N1) 2009 was a novel disease with unknown implications; therefore, implementation of an aggressive surveillance approach was needed to better characterize and understand its public health effects, particularly among school-aged children. Schools are ideal settings for detecting influenza outbreaks, and the epidemiology of influenza has shown that children play an important role in the acquisition and spread of ILI (6).

As of July 13, 2009, a total of 90% of positive influenza specimens in Florida tested positive for pandemic (H1N1) 2009 virus. Subsequently, the Florida Department of Health declared that identified clusters of ILI were assumed to be pandemic (H1N1) 2009 (7).

The inherent design of the SBASS sets it apart from other school-absentee systems, which use only percentages to determine absentee rates. Major advantages of an SBASS include an ability to identify schools with higher than normal absenteeism. The system assesses absenteeism against a historic baseline for each school. Schools with consistently high levels alone did not trigger an alert; only schools with higher than normal levels generated alerts and required follow-up. Use of an SBASS has also helped in the development of stronger partnerships between MDCHD and the school system. Frequent communication increased public health awareness and emphasized the vital role schools play in preventing and controlling disease. Additionally, the SBASS geographic information system mapping feature enabled better detection of geographic clustering when multiple schools had alerts.

Limitations of an SBASS still include an inability to capture reasons for absenteeism and its exclusion of private school attendance information. Furthermore, manual entry on the part of schools’ attendance offices may lead to a lag in data submission time, and data may contain typographical errors. Future studies should aim to extend the study period and compare influenza trends over multiple years. Research using an SBASS to detect other infectious disease outbreaks, not only in the event of a known source as was the case with pandemic (H1N1) 2009, should also be considered.