Using daily syndrome-specific absence data for early detection of school outbreaks: a pilot study in rural China - Tan, Li; Cheng, L.; Yan, W.; Zhang, J.; Xu, B.; Diwan, V. K.; Dong, Hengjin; Palm, L.; Wu, Y.; Long, L.; Tian, Y.; Nie, S. (Public health, 2014)
Objectives - This paper describes and preliminarily evaluates the usefulness of the daily syndrome-specific absenteeism surveillance system (DSSASS) as an early warning system of school outbreaks in rural China. - Study design - We conducted an experimental study in rural areas of Hubei Province from September 19, 2011 to December 31, 2011. - Methods - Nine public elementary schools from two counties were selected as pilot sentinel schools. Daily monitoring data of the absent date and reason, sex, age and class of each absent student was collected and entered into a web database. Reported data were checked daily and field investigation was carried out when there was abnormal absentee aggregation. Descriptive analysis and preliminary evaluation were then conducted after the pilot study. - Results - The findings showed that the total average of daily absenteeism rate was 3%, and the absenteeism rate differed by county, school level and grade level. The daily absenteeism rate in illness absentees was highest (2.74%), followed by business absentees (0.13%) and injury absentees (0.09%). The total timeliness report rate was 64.84% and the total incident report rate was 29.22%. One varicella outbreak and one influenza B outbreak were identified, but neither of them was detected by China Information System for Diseases Control and Prevention (CISDCP). The study shows syndrome-specific absenteeism data would be useful for early detection of unusual public health events or outbreaks in school. However, more efforts are needed to enhance the quality of surveillance data, and longer follow-up and more analysis are required to evaluate the system comprehensively. Our study might provide useful experience and evidence for other developing regions or counties establishing similar systems.
Gesehen am 04.09.2020