Table 1 Examples of Syndromic Surveillance Systems in Developing Countries
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 |
|---|---|---|---|---|---|---|---|
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) |