Skip to main content

Table 1 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

From: The usefulness of school-based syndromic surveillance for detecting malaria epidemics: experiences from a pilot project in Ethiopia

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 10 % 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 0.5 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 2.4 weeks compared to 12 weeks lag)

[54]