Usefulness of clinical algorithm as screening process to detected malaria in low-to-moderate transmission areas of scarce health related resources.

BACKGROUND

In areas of low-to-moderate risk of malaria transmission, the World Health Organization recommends parasitic confirmation before treatment. Such areas have usually low budget for health care and malaria diagnosis is mostly based on clinical assumption. Algorithms have been developed to improve health care providers' identification of clinical malaria and could be used as screening to reduce the number of individuals requiring parasitic confirmation before treating.

METHODS

Prospective clinical and parasitological data were collected from inhabitants of four villages from March 1984 through March 1985. Symptoms and signs recorded by physicians were used in multivariate models to test the best predictors of malaria. Sensitivity and specificity were calculated for various cut-offs of scores and compared to clinical diagnosis.

RESULTS

A total of 8.941 individuals were evaluated during the 1-year period of data collection. The overall prevalence of malaria parasitemia was 19.7% (n=1762). Of the 4280 people evaluated during the high season period, 24% (n=1024) presented any parasitemia, 55.3% (566/1024) due to Plasmodium falciparum. The final clinical algorithm included history of fever, rigors, headache, absence of myalgia, backache or cough, nausea or vomiting, and splenomegaly on examination as predictable variables. At a cut-off score of 2.0, the sensitivity of the algorithm was higher for the entire sample (57% vs. 43%), for high season period (70% vs. 53%), for children less than 6 years of age (59% vs. 40%), for individuals with parasitemia due to P. falciparum (65% vs. 48%), and for high P. falciparum parasitemic individuals at high season (84% vs. 68%). However, specificity was usually lower unless a higher cut off was used, in which case the gain in sensitivity by using the algorithm was reduced.

CONCLUSIONS

In low-to-moderate transmission areas in which health related resources are scarce, a clinical algorithm increases the identification of real cases of malaria and could be used as screening for further parasitic identification.