Table 1 |
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Examples of Syndromic Surveillance Systems in Developing Countries |
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|
Type of surveillance |
Country |
Type of Data |
Data collection and recording methods |
Data centralization methods |
Analysis Frequency |
Aberration Detection Method |
Potential and limitations of the system for early detection of outbreaks |
|
|
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|
Malaria |
Uganda |
Incidence rates |
Health facilities |
District level |
Weekly |
Anomaly measure provides index of deviation from expected weekly incidence rates |
Early detection documented [20] |
|
Malaria |
Eritrea |
Outpatient cases and climate datasets |
242 districts via computerized access database |
Central database |
Monthly |
Principal component analysis/non-hierarchical clustering |
2–3 month lead time of peak malaria Climate variables only accurate in El Nino years [21] |
|
Malaria |
Jamaica |
Active fever surveillance |
Fever cases recorded at sentinel sites |
Analysis at local level, then transmitted centrally |
Daily then decreased over time |
Not available |
Active door to door surveillance [24] |
|
Dengue Fever "2SE FAG" |
French Guiana |
Fever, dengue fever and malaria cases |
Collected by medical provider at individual sites Recorded on IT system with syndromic software |
Reported to French health authorities |
Data converted to weekly format Reported immediately in case of alarm, weekly in normal operation |
Automated alarm based on current past experience graph (CPEG) |
Potential: 60 minutes between case presentation and system detection Improved detection of dengue Limitations: Sensitivity high but specificity low [30,31,33] |
|
Foodborne disease |
Egypt |
Hospital based syndromic surveillance |
Case reports |
Passive reports from hospital providers |
Passive surveillance |
Not available |
Limitations: Missed outpatients compared to laboratory surveillance [46] |
|
Food-borne disease |
Pacific Island Countries and Territories |
Varies: reports of diarrheal disease; laboratory surveillance |
Data collected by health care providers, reporting of laboratories |
Pacific Public Health Surveillance Network to organize resources and facilitate centralized data collection and sharing |
Monthly reports |
Not available |
No laboratory surveillance in use except for Samoa [45] Limitation: No uniform definition for foodborne disease |
|
STI's |
Burkina Faso |
Prevalence studies, sentinel surveillance, population based surveys |
Various methods |
Not available |
Not available |
Not available |
Decrease in incidence of gonorrhea, chlamydia and syphilis [53] |
|
STIs |
Ivory Coast |
Data from three STI syndromes |
Community and public clinic and hospital data computerized at district level, compiled at regional level |
Data collated by districts and region then centralized nationally |
Monthly |
Annual incidence rates |
Data provide trends of STI's and are used to estimate quantity of drugs[54] |
|
Various Diseases: Alerta DISAMAR |
Peru, operated in conjunction DOD-GEIS |
Suspected or lab-confirmed cases of diseases/syndromes |
Medical record review for reporting |
Medical officer transmits site data to Alerta DISAMAR central hub |
Daily or twice weekly |
Voxiva software converts data to common format Graphs of weekly counts |
|
|
Various Diseases EWORS (Early Warning Outbreak Recognition System) |
Southeast Asia and Peru |
Standardized questionnaire at clinical sites |
Questionnaire filled out on computer terminal with EWORS software |
EWORS data files sent by email to EWORS hub for analysis |
Once daily; monthly report to each participating hospital Varying degrees of centralization |
Automated statistical outbreak detection algorithm |
Potential: detection of large cholera outbreak in Indonesia [48]; Limitations: mechanisms for linking suspected outbreaks to response; lack of standardization of procedures (15) |
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May et al. BMC Public Health 2009 9:242 doi:10.1186/1471-2458-9-242 |
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