Optimizing the malaria data recording system through a study of case detection and treatment in Sri Lanka.
The potential of using malaria incidence data routinely collected from endemic regions for disease control and research has increased with the availability of advanced computer-based technologies, but will depend on the quality of the data itself. We report here an investigation into the relevance of malaria statistics provided by the routine data collection system in Moneragala, a rural malaria-endemic region in Sri Lanka. All patients (n = 321) treated for malaria in 2 clusters of health care centres (HCCs) of both the private and the public sector in the administrative regions of Moneragala and Buttala Divisional Secretariat (D.S.). Divisions were studied in December 1995/ January 1996. The catchment area of these HCCs included a population resident in 53 Grama Niladhari (GN) areas, the smallest administrative units of the country. Almost equal numbers of malaria patients were detected and treated at Government and private health care institutions, and in 70% of them treatment was based on a diagnosis confirmed by microscopy. The routine data recording system, however, included only statistics from the Government sector, and only of patients whose diagnosis was microscopically confirmed. In compiling data, the origin of a case of malaria is attributed to the D.S. Division in which the institution (at which the patient was treated) was located, rather than the area in which the patient was resident, which was inaccurate because 90% of malaria patients sought health care at institutions located closest to their residence, thus crossing administrative boundaries. It also led to a loss of resolution of spatial data because patients' addresses recorded at the Government HCCs to the village-level are replaced in the statistics by the D.S. Division, which is a coarse spatial unit. Modifications to the system for malaria case recording needed to correct these anomalies are defined here. If implemented, these could result in major improvements to the quality of data, a valuable resource for the future of malaria control. The paper reiterates the call for the use of a standard spatial unit within a country to facilitate exchange of data among health and other sectors for the control of tropical diseases.
Abeysekera
T
T
Department of Parasitology, Faculty of Medicine, University of Colombo, Sri Lanka.
Wickremasinghe
A R
AR
Gunawardena
D M
DM
Mendis
K N
KN
D003625:Data Collection / Q000379:methods; D019992:Databases as Topic; D019353:Endemic Diseases / Q000706:statistics & numerical data; D015994:Incidence; D016247:Information Storage and Retrieval; D008288:Malaria / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy; D011159:Population Surveillance / Q000379:methods*; D013188:Sri Lanka / Q000453:epidemiology