Clustering symptoms of non-severe malaria in semi-immune Amazonian patients.
Malaria is a disease that generates a broad spectrum of clinical features. The purpose of this study was to evaluate the clinical spectrum of malaria in semi-immune populations. Patients were recruited in Mâncio Lima, a city situated in the Brazilian Amazon region. The study included 171 malaria cases, which were diagnosed via the use of a thick blood smear and confirmed by molecular methods. A questionnaire addressing 19 common symptoms was administered to all patients. Multiple correspondence analysis and hierarchical cluster analysis were performed to identify clusters of symptoms, and logistic regression was used to identify factors associated with the occurrence of symptoms. The cluster analysis revealed five groups of symptoms: the first cluster, which included algic- and fever-related symptoms, occurred in up to 95.3% of the cases. The second cluster, which comprised gastric symptoms (nausea, abdominal pain, inappetence, and bitter mouth), occurred in frequencies that ranged between 35.1% and 42.7%, and at least one of these symptoms was observed in 71.9% of the subjects. All respiratory symptoms were clustered and occurred in 42.7% of the malaria cases, and diarrhea occurred in 9.9% of the cases. Symptoms constituting the fifth cluster were vomiting and pallor, with a 14.6% and 11.7% of prevalence, respectively. A higher parasitemia count (more than 300 parasites/mm(3)) was associated with the presence of fever, vomiting, dizziness, and weakness (P < 0.05). Arthralgia and myalgia were associated with patients over the age of 14 years (P < 0.001). Having experienced at least eight malaria episodes prior to the study was associated with a decreased risk of chills and fever and an increased risk of sore throat (P < 0.05). None of the symptoms showed an association with gender or with species of Plasmodium. The clinical spectrum of malaria in semi-immune individuals can have a broad range of symptoms, the frequency and intensity of which are associated with age, past exposure to malaria, and parasitemia. Understanding the full spectrum of nonsevere malaria is important in endemic areas to guide both passive and active case detection, for the diagnosis of malaria in travelers returning to non-endemic areas, and for the development of vaccines aimed to decrease symptom severity.
Health Sciences Center, Federal University of Acre , Rio Branco, Acre , Brazil.
Antonio C
AC
Martins
Health Sciences Center, Federal University of Acre , Rio Branco, Acre , Brazil.
Felipe M
FM
Araújo
Health Sciences Center, Federal University of Acre , Rio Branco, Acre , Brazil.
Cássio B
CB
Braga
Health Sciences Center, Federal University of Acre , Rio Branco, Acre , Brazil.
Maria G S
MG
Guimarães
Health Sciences Center, Federal University of Acre , Rio Branco, Acre , Brazil.
Rudi
R
Nogueira
Health Sciences Center, Federal University of Acre , Rio Branco, Acre , Brazil.
Rayanne A
RA
Arruda
Tropical Medicine Institute, University of São Paulo , São Paulo , Brazil.
Lícia N
LN
Fernandes
Tropical Medicine Institute, University of São Paulo , São Paulo , Brazil.
Livia R
LR
Correa
Tropical Medicine Institute, University of São Paulo , São Paulo , Brazil.
Rosely Dos S
Rdos S
Malafronte
Scientific Computation Programm, Oswaldo Cruz Foundation , Rio de Janeiro, Rio de Janeiro , Brazil.
Oswaldo G
OG
Cruz
Scientific Computation Programm, Oswaldo Cruz Foundation , Rio de Janeiro, Rio de Janeiro , Brazil.
Cláudia T
CT
Codeço
Health Sciences Center, Federal University of Acre , Rio Branco, Acre , Brazil.
Mônica
M
da Silva-Nunes
Amazon; Clustering; Malaria; Symtoms