A description of malaria sentinel surveillance: a case study in Oromia Regional State, Ethiopia
1 Center for Applied Malaria Research and Evaluation, Department of Global Health Systems and Development, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2200, New Orleans, LA 70112, USA
2 U.S. President’s Malaria Initiative, Malaria Branch, U.S. Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, Georgia 30333, USA
3 Bill and Melinda Gates Foundation, 500 Fifth Avenue North, Seattle, WA 98102, USA
4 Addis Continental Institute of Public Health, Addis Ababa, Ethiopia
5 U.S. President’s Malaria Initiative, U.S. Centers for Disease Control and Prevention, Addis Ababa, Ethiopia
6 U.S. President’s Malaria Initiative, U.S. Agency for International Development, Addis Ababa, Ethiopia
7 RTI International, Washington, DC, USA
Malaria Journal 2014, 13:88 doi:10.1186/1475-2875-13-88Published: 11 March 2014
In the context of the massive scale up of malaria interventions, there is increasing recognition that the current capacity of routine malaria surveillance conducted in most African countries through integrated health management information systems is inadequate. The timeliness of reporting to higher levels of the health system through health management information systems is often too slow for rapid action on focal infectious diseases such as malaria. The purpose of this paper is to: 1) describe the implementation of a malaria sentinel surveillance system in Ethiopia to help fill this gap; 2) describe data use for epidemic detection and response as well as programmatic decision making; and 3) discuss lessons learned in the context of creating and running this system.
As part of a comprehensive strategy to monitor malaria trends in Oromia Regional State, Ethiopia, a system of ten malaria sentinel sites was established to collect data on key malaria morbidity and mortality indicators. To ensure the sentinel surveillance system provides timely, actionable data, the sentinel facilities send aggregate data weekly through short message service (SMS) to a central database server. Bland-Altman plots and Poisson regression models were used to investigate concordance of malaria indicator reports and malaria trends over time, respectively.
This paper describes three implementation challenges that impacted system performance in terms of: 1) ensuring a timely and accurate data reporting process; 2) capturing complete and accurate patient-level data; and 3) expanding the usefulness and generalizability of the system’s data to monitor progress towards the national malaria control goals of reducing malaria deaths and eventual elimination of transmission.
The use of SMS for reporting surveillance data was identified as a promising practice for accurately tracking malaria trends in Oromia. The rapid spread of this technology across Africa offers promising opportunities to collect and disseminate surveillance data in a timely way. High quality malaria surveillance in Ethiopia remains a resource intensive activity and extending the generalizability of sentinel surveillance findings to other contexts remains a major limitation of these strategies.