National health facility based surveillance for malaria in Uganda relies primarily on clinical diagnosis without laboratory confirmation. Furthermore, when laboratory tests are performed the results are often not linked to clinical data and there is limited data on treatment practices. National Health Management Information Systems (HMIS) reports are based on aggregate data and one is unable to easily perform sub-group analysis (i.e. age stratification).
UMSP developed the sentinel site malaria surveillance project to complement HMIS surveillance. A sentinel site is a government run health facility which generates data on malaria cases in the local catchment area. The aim of this approach is to obtain high quality individual patient data from a limited number of sites. The project was initiated in June 2006 to provide accurate malaria surveillance data for quick response systems, to support ongoing malaria control activities and strengthen HMIS at the sentinel sites. Sentinel sites were selected to reflect the geographic and ecological diversity of malaria transmission. The local epidemiology of malaria (EIR, drug resistance patterns) is well studied at the sites.
The goal of this surveillance system is to provide high quality and timely malaria surveillance data beyond the scope of the HMIS surveillance system. Individual level patient data are collected using a standardized case record form for all patients who present to the outpatient departments of the sentinel site health facilities. Data include patient demographics, results of laboratory tests, diagnoses given, and treatments prescribed. One of the strengths of the system is an emphasis on laboratory-confirmed diagnosis through training and capacity building for microscopy and rapid diagnostic tests. Data are entered electronically at the sites in real time and sent to a core facility in Kampala at the end of each month using cellular technology. At our core facility in Kampala data are cleaned and merged into a larger database which is accessible through this website. The individual patient database allows one to correlate outcome variables and stratify data across demographic variables (i.e. age) and calendar time. Standardized systems have been developed for automated analyses and graphical presentation of data.