Syndrome-Specific School Absenteeism Data for Public Health Surveillance Shuying Shen, MStat...
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Transcript of Syndrome-Specific School Absenteeism Data for Public Health Surveillance Shuying Shen, MStat...
Syndrome-Specific School Absenteeism Data for
Public Health Surveillance Shuying Shen, MStat
University of Utah
SettingDavis County in Utah
Smallest county in Utah, 3rd largest in population.22.6% of the population is between 5-18 years old
(large families)
School district includes 56 elementary, 14 junior high, 8 high schools, 3 alternative schools, and 8 year-round schools.
Surveillance SystemIn place since fall 2004
Absences reported by homeroom teachers and parents/guardians with:Date, classroom, teacher name, student nameReason for absence (Sick, Other, Unknown)
Entered into District Information System and available in real-time.
EnhancementTen public elementary schools recruited to
participate as sentinel schools in fall 2007.
Reason for absence expanded into syndrome categories:RespiratoryGIRashOtherUnknown
ResultsThe most common reason for absence was
unknown (59%), followed by sick (23%) and other (18%).
Other sickness-related absences accounted for 38% of the sick absences, followed by unknown (26%), GI (18%), respiratory (16%), and rash (0.6%).
Respiratory-related absences correlated well with influenza cases reported to UDOH.
Decrease in GI beginning of fall, otherwise stable
Absence Rate by Reason for Absence
Absence Rate by Syndrome
Other ObservationsNumerous schools not recruited used the
enhanced reporting voluntarily.
Requires minimal resource for enhanced reporting
Principals and school nurses very interested in feedback informationHow we compare to ourselvesHow we compare to other schools
LimitationsData quality needs to be improved
Too many unknowns and others
Potential SolutionsImprove data collection by
Promotion of importance of surveillance to parents/guardians
Implement proactive reporting system for parents/guardians
Statistical modelingMissing data imputation because likely bias in
unknownsAdjustment for misclassification errors using
Bayesian approach