Evidence-based public health and prospects for malaria control in Brazil

Abstract

Despite intensive control efforts over the past decades, Brazil still accounts for more than 50% of the malaria burden in the Americas and the Caribbean, with 458,041 slide-confirmed cases reported countrywide in 2007. The reason malaria has proved so difficult to control in this middle-income country with a reasonable health infrastructure remains unclear. Here we examine whether four strategies that were largely successful in other countries (aggressive active case detection, improved anti-relapse therapy for P. vivax infections, distribution of insecticide-treated bed nets, and selective house spraying with residual insecticides) are likely to work in Brazil. We review evidence from field and laboratory studies and identify gaps in our knowledge that require further investigation with well-designed large-scale trials.

Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence

“…Accounts of the historical increase in malaria cases in the Brazilian Amazon region during the late 1970s and 1980s typically attribute this malaria surge to the massive and uncontrolled migration to the region, which led to large-scale deforestation and increased contact of non-immune subjects with forested areas [37,49–52]. Unfortunately, there has been little effort to disentangle the effect of proximity to forest from the effect of deforestation per se .…”

Accuracy of the malaria epidemiological surveillance system data in the state of Amazonas

“…It collects and provides information on the number and distribution of malaria cases and deaths in a territory. It is a critical tool for implementation of governmental malaria control programs (Deane 1988;Ferreira and Silva Nunes 2010;Silva et al 2010). It is needed to determine which areas or population groups are most affected by malaria, so that resources on prevention and control can be targeted to the relevant populations (Martens et al 1999;Tauil and Daniel-Ribeiro 1998).…”

Enhanced Understanding of Infectious Diseases by Fusing Multiple Datasets: A Case Study on Malaria in the Western Brazilian Amazon Region

“…ACD data are obtained by health agents during home visits to symptomatic individuals whereas PCD data come from health facilities, visited by individuals who believe they have malaria [11]. Inherent biases in both datasets make it difficult to determine overall malaria prevalence and the factors that influence it [1], [12]. Aggressive active case detection (AACD) has been proposed as an alternative surveillance technique, consisting of cross-sectional surveys where all individuals are sampled, regardless of symptom status [11].…”

“…Aggressive active case detection (AACD) has been proposed as an alternative surveillance technique, consisting of cross-sectional surveys where all individuals are sampled, regardless of symptom status [11]. AACD data can be used to estimate infection prevalence and its determinants and the size of the reservoir represented by asymptomatic Plasmodium carriers [12]–[14]. Drawbacks of AACD include high costs and the often low acceptability from the population [12], [14], which often limits AACD data to a short time-frame and a small geographical area.…”

“…AACD data can be used to estimate infection prevalence and its determinants and the size of the reservoir represented by asymptomatic Plasmodium carriers [12]–[14]. Drawbacks of AACD include high costs and the often low acceptability from the population [12], [14], which often limits AACD data to a short time-frame and a small geographical area. As a consequence, AACD data might not be as well suited as ACD/PCD data in determining the effect of covariates that change substantially in time and/or space (e.g., precipitation and presence of wetlands).…”