Joe Keating and Josh Yukich, Tulane University School of Public Health and Tropical Medicine
A unique sentinel surveillance project in Ethiopia uses mobile phone technology to collect weekly malaria data at the health post level to quickly detect and respond to potential malaria outbreaks.
For nearly two years, researchers have been setting up epidemic detection sites in the Oromia Region in Ethiopia as part of a sentinel surveillance and epidemic detection system to detect microclusters of malaria infection. The project, funded by the United States Agency for International Development (USAID), uses trained health workers, enhanced supervision strategies, and mobile phone technology to get weekly data on malaria transmission. While continuing to capture detailed clinical data that can otherwise take months to collect and analyze, the project also operates at the most local of health system levels, using small health facilities often in rural areas as epidemic detection sites for the rapid collection of aggregate data.
Why collect data about malaria transmission at the micro level? When national parasite prevalence is very low, as it is in Ethiopia, malaria becomes a very focal disease, existing largely in hotspots (i.e., microclusters of infection) that, if left undetected, could turn into a full-scale epidemic. Thus, how quickly you can respond to a potential outbreak is extremely important. Population-based surveys such as the Malaria Indicator Surveys (MIS) take three to six months to complete, and health management information systems (HMIS) sometimes fail to collect data on relevant indicators for epidemic detection. In addition, the current paper-based system requires three weeks for microscopists to record data, take it to Addis Ababa, enter it into a database, and send to the program. Using SMS technology, this takes only one week. Another important component of this project is getting the right data to decision-makers in a timely fashion.
Currently, 10 health centers and their satellite health posts are operating as epidemic detection sites in the Oromia Region (nearly 100 sites will be included when the project scales fully), with one or two Health Extension Workers (HEWs) assigned to each health post. Each HEW collects data, enters it into their phone using text messaging, and once a week sends it to a server that analyzes it, makes sure the numbers add up, and streams it into a database. From that moment, the data are accessible; if a report shows higher than normal transmission levels for a particular area, it immediately triggers a warning to relevant personnel. Facilities that participate in the project also receive technical assistance with microscopy and diagnostic issues, indicator data, and data collection, as well as other aspects of clinical malaria care of patients.
Sentinel surveillance sites used in the past have had issues with quality and timeliness of reported data; data often moved slowly and efforts were not expanded to the community level. Many of these systems worked only at higher health system levels and didn’t add value above and beyond HMIS data. The success of this project depends on the usefulness of the data. In the next few years we hope to prove that this model can rapidly generate high quality data to provide a clear geographic picture of what’s happening in infection microclusters at short timescales and effectively trigger the prevention of large-scale malaria epidemics in Ethiopia.
http://www.macepalearningcommunity.org/forecasting.htm
A unique sentinel surveillance project in Ethiopia uses mobile phone technology to collect weekly malaria data at the health post level to quickly detect and respond to potential malaria outbreaks.
For nearly two years, researchers have been setting up epidemic detection sites in the Oromia Region in Ethiopia as part of a sentinel surveillance and epidemic detection system to detect microclusters of malaria infection. The project, funded by the United States Agency for International Development (USAID), uses trained health workers, enhanced supervision strategies, and mobile phone technology to get weekly data on malaria transmission. While continuing to capture detailed clinical data that can otherwise take months to collect and analyze, the project also operates at the most local of health system levels, using small health facilities often in rural areas as epidemic detection sites for the rapid collection of aggregate data.
Why collect data about malaria transmission at the micro level? When national parasite prevalence is very low, as it is in Ethiopia, malaria becomes a very focal disease, existing largely in hotspots (i.e., microclusters of infection) that, if left undetected, could turn into a full-scale epidemic. Thus, how quickly you can respond to a potential outbreak is extremely important. Population-based surveys such as the Malaria Indicator Surveys (MIS) take three to six months to complete, and health management information systems (HMIS) sometimes fail to collect data on relevant indicators for epidemic detection. In addition, the current paper-based system requires three weeks for microscopists to record data, take it to Addis Ababa, enter it into a database, and send to the program. Using SMS technology, this takes only one week. Another important component of this project is getting the right data to decision-makers in a timely fashion.
Currently, 10 health centers and their satellite health posts are operating as epidemic detection sites in the Oromia Region (nearly 100 sites will be included when the project scales fully), with one or two Health Extension Workers (HEWs) assigned to each health post. Each HEW collects data, enters it into their phone using text messaging, and once a week sends it to a server that analyzes it, makes sure the numbers add up, and streams it into a database. From that moment, the data are accessible; if a report shows higher than normal transmission levels for a particular area, it immediately triggers a warning to relevant personnel. Facilities that participate in the project also receive technical assistance with microscopy and diagnostic issues, indicator data, and data collection, as well as other aspects of clinical malaria care of patients.
Sentinel surveillance sites used in the past have had issues with quality and timeliness of reported data; data often moved slowly and efforts were not expanded to the community level. Many of these systems worked only at higher health system levels and didn’t add value above and beyond HMIS data. The success of this project depends on the usefulness of the data. In the next few years we hope to prove that this model can rapidly generate high quality data to provide a clear geographic picture of what’s happening in infection microclusters at short timescales and effectively trigger the prevention of large-scale malaria epidemics in Ethiopia.
http://www.macepalearningcommunity.org/forecasting.htm
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