The usefulness of school-based syndromic surveillance for detecting malaria epidemics: experiences from a pilot project in Ethiopia
Overview of syndromic surveillance approaches
Syndromic surveillance refers to the use of pre-diagnostic health indicators to allow timely detection and investigation of potential infectious disease outbreaks [29] as a supplementary approach to routine public health surveillance, by enabling early identification of clusters of illness before confirmatory data are available. In addition to use of clinical (syndrome) data, syndromic surveillance can be expanded to include surrogate non-clinical data indicating early illness, through mining of available data to track changes in infectious diseases in the population. Surrogate data sources include prescription and over-the-counter drug sales, internet search terms and social media [30–36] and school absenteeism [7–10, 37]. The latter is an alternative indicator of population health that has been applied to monitor influenza outbreaks in high-income countries, but yet to be fully explored as an approach for infectious disease surveillance in resource poor settings. Syndromic surveillance systems piloted in resource poor settings have to date used clinical signs among patients attending health facilities as their indicators [38–42], but two examples of school absenteeism being used as early warning of outbreaks of respiratory and gastrointestinal diseases are available from Cambodia and rural China [11, 13, 43]. The key surveillance studies using school absenteeism for outbreak detection, as well as classic applications of syndromic surveillance utilising data on clinical morbidities are presented in Table 1, to demonstrate the various settings, indicators, temporal resolution and complexity of these syndromic surveillance systems.
Selected syndromic surveillance systems reported in the literature: the setting, target diseases, indicators, system complexity and outcomes of their application. Reported studies are those which use school absenteeism as a key indicator, or systems applied in resource-limited settings for epidemic prone diseases including malaria
Setting
Target disease(s)
Indicators
Reporting frequency
Complexity of system
Surveillance system findings
Ref
Canada
H1N1 influenza
Elementary and high school absenteeism due to influenza-like illness exceeding the defined threshold of 10Â % of total enrolment
Daily analysis of absenteeism, reporting if exceed threshold
Low – schools report data when indicator exceeds the threshold
Absenteeism was well correlated with hospitalisation rates for school age children and PCR positive tests for influenza. Peak absenteeism preceded peaks in hospitalisations by one week
[7]
United Kingdom
H1N1 influenza
School absenteeism in primary and secondary schools, comparing against telephone health hotline, general practitioner sentinel network confirmed influenza data
Weekly mean percentage absenteeism
Low – collation of school % absenteeism data
Weekly school absenteeism peaked concomitantly with existing influenza alert systems, and would not have identified pandemic influenza earlier than other systems. Daily attendance data may have improved timeliness
[8]
Japan
Influenza
School influenza-related absenteeism, where child absent with confirmed diagnosis from physician
Daily school influenza-related absenteeism rate
Low – daily attendance routinely recorded and absent children require doctor’s note
School influenza-related illness can be used to predict outbreaks and determine when a school should close to limit ongoing spread. Thresholds for influenza-related absenteeism proposed.
[9]
China (rural)
Respiratory infections, gastroenteritis
Symptoms reported at health clinics, over-the-counter drug sales at pharmacies and primary school absenteeism
Daily input to web-based system
High – collation and analysis of data at central level
Labour-intensive data entry to electronic system. Presentation of six months’ pilot data, no validation of data from surveillance system against other sources
[11, 43]
Madagascar
Malaria, influenza, dengue, diarrhoeal disease
Malaria case confirmed by RDT, fever respiratory symptoms, fever 2 possible dengue symptoms, diarrhoea.
Daily report by encrypted SMS. Weekly summary paper report.
Moderate – SMS reports entered to database. Temporal spatial analysis by syndrome
Ten cases of fever clusters occurred which weren’t detected by the traditional surveillance system. Five outbreaks identified – two dengue, two influenza and one malaria.
[42]
French Guiana
Dengue
Dengue index: percentage of patients attending the emergency department who had thrombocytopenia but were negative for Plasmodium infection
Weekly generation of indicators
Low – plotting of simple indicators on weekly basis, minimal analysis
Dengue index was specific – increasing during what was confirmed to be a dengue epidemic, but showing no strong increase during two respiratory infection epidemics. Total emergency department attendance with thrombocytopenia but malaria negative was also a specific indicator.
[38]
Pacific island countries and territories
Measles, dengue, rubella, meningitis, leptospirosis, gastroenteritis, influenza, typhoid, malaria
Hospitals report total cases for four syndromes: acute fever rash, diarrhoea, influenza-like illness, prolonged fever
Weekly reporting of data to national level
Moderate – data reported from national to WHO regional level for analysis
The system successfully identified an outbreak of diarrhoeal disease linked to breakdown of water disinfection, and two outbreaks of influenza. The system alert was timely and allowed fast implementation of control measures
[39]
India
Cholera, dysentery, malaria, measles, meningitis, typhoid fever, and 8 others
Suspected cases (clinical diagnosis) of target diseases from public and private health facilities, except malaria, where slide-confirmation required for reporting
As clinical cases identified (daily), using pre-formatted post cards with postage pre-paid
Low – doctors report cases on simple form to central level. Minimal analysis.
Several outbreaks were detected early and interventions applied, the most notable was cholera. Leptospirosis and acute dysentery also commonly reported. Monthly summary of reported diseases distributed to participating facilities for feedback and updates on the surveillance system.
[41]
Cambodia
Respiratory and diarrhoeal diseases
School absenteeism (aggregated daily by schools), compared against overall health facility attendance
Daily SMS report of school absenteeism due to illness, collated at weekly level for analysis
Low – daily data reported by schools to central level, compared against all cause health centre attendance
Illness-specific absenteeism identified two peaks in incidence of illness. Absenteeism data preceded peaks in health centre attendance by 0.5Â weeks on average. Cross correlation analysis indicated moderate correlations between illness specific absenteeism and reference data.
[13]
Papua New Guinea
Influenza, cholera, typhoid, malaria, poliomyelitis, meningitis, measles, dengue
Syndromes relating to target diseases identified in patients presenting to health facilities.
Weekly report by mobile phone, transcription to database
Low – health facilities submit data for analysis at provincial/national level, and automatic generation of feedback reports
System was more sensitive than the reference system for measles, but low sensitivity for malaria, due to poor case definition. Data were more timely than the reference system (mean 2.4Â weeks compared to 12Â weeks lag)
[54]