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Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region.

ABSTRACT: BACKGROUND: A common challenge to the study of several infectious diseases consists in combining limited cross-sectional survey data, collected with a more sensitive detection method, with a more extensive (but biased) syndromic sentinel surveillance data, collected with a less sensitive method. Our article describes a novel modeling framework that overcomes this challenge, resulting in enhanced understanding of malaria in the Western Brazilian Amazon. METHODOLOGY/PRINCIPAL FINDINGS: A cohort of 486 individuals was monitored using four cross-sectional surveys, where all participants were sampled regardless of symptoms (aggressive-active case detection), resulting in 1,383 microscopy and 1,400 polymerase chain reaction tests. Data on the same individuals were also obtained from the local surveillance facility (i.e., passive and active case detection), totaling 1,694 microscopy tests. Our model accommodates these multiple pathogen and case detection methods. This model is shown to outperform logistic regression in terms of interpretability of its parameters, ability to recover the true parameter values, and predictive performance. We reveal that the main infection determinant was the extent of forest, particularly during the rainy season and in close proximity to water bodies, and participation on forest activities. We find that time residing in Acrelandia (as a proxy for past malaria exposure) decreases infection risk but surprisingly increases the likelihood of reporting symptoms once infected, possibly because non-naïve settlers are only susceptible to more virulent Plasmodium strains. We suggest that the search for asymptomatic carriers should focus on those at greater risk of being infected but lower risk of reporting symptoms once infected. CONCLUSIONS/SIGNIFICANCE: The modeling framework presented here combines cross-sectional survey data and syndromic sentinel surveillance data to shed light on several aspects of malaria that are critical for public health policy. This framework can be adapted to enhance inference on infectious diseases whenever asymptomatic carriers are important and multiple datasets are available.

SUBMITTER: Valle D

PROVIDER: S-EPMC3210805 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region.

Valle Denis D Clark James S JS Zhao Kaiguang K

PloS one 20111108 11

<h4>Background</h4>A common challenge to the study of several infectious diseases consists in combining limited cross-sectional survey data, collected with a more sensitive detection method, with a more extensive (but biased) syndromic sentinel surveillance data, collected with a less sensitive method. Our article describes a novel modeling framework that overcomes this challenge, resulting in enhanced understanding of malaria in the Western Brazilian Amazon.<h4>Methodology/principal findings</h ...[more]

PMID: 22087321

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Project description:Plasmodium vivax parasites are the predominant cause of malaria infections in the Brazilian Amazon. Infected individuals are treated with primaquine, which can induce haemolytic anaemia in glucose-6-phosphate dehydrogenase (G6PD)-deficient individuals and may lead to severe and fatal complications. This X-linked disorder is distributed globally and is caused by allelic variants with a geographical distribution that closely reflects populations exposed historically to endemic malaria. In Brazil, few studies have reported the frequency of G6PD deficiency (G6PDd) present in malaria-endemic areas. This is particularly important, as G6PDd screening is not currently performed before primaquine treatment. The aim of this study was to determine the prevalence of G6PDd in the region of Alto do Juruá, in the Western Brazilian Amazon, an area characterized by a high prevalence of P. vivax infection.Five-hundred and sixteen male volunteers were screened for G6PDd using the fluorescence spot test (Beutler test) and CareStart™ G6PD Biosensor system. Demographic and clinical-epidemiological data were acquired through an individual interview. To assess the genetic basis of G6PDd, 24 SNPs were genotyped using the Kompetitive Allele Specific PCR assay.Twenty-three (4.5%) individuals were G6PDd. No association was found between G6PDd and the number of malaria cases. An increased risk of reported haemolysis symptoms and blood transfusions was evident among the G6PDd individuals. Twenty-two individuals had the G6PDd A(-) variant and one the G6PD A(+) variant. The Mediterranean variant was not present. Apart from one polymorphism, almost all SNPs were monomorphic or with low frequencies (0-0.04%). No differences were detected among ethnic groups.The data indicates that ~1/23 males from the Alto do Juruá could be G6PD deficient and at risk of haemolytic anaemia if treated with primaquine. G6PD A(-) is the most frequent deficiency allele in this population. These results concur with reported G6PDd in other regions in Brazil. Routine G6PDd screening to personalize primaquine administration should be considered, particularly as complete treatment of patients with vivax malaria using chloroquine and primaquine, is crucial for malaria elimination.

Project description:Anopheles nuneztovari sensu lato comprises cryptic species in northern South America, and the Brazilian populations encompass distinct genetic lineages within the Brazilian Amazon region. This study investigated, based on two molecular markers, whether these lineages might actually deserve species status.Specimens were collected in five localities of the Brazilian Amazon, including Manaus, Careiro Castanho and Autazes, in the State of Amazonas; Tucuruí, in the State of Pará; and Abacate da Pedreira, in the State of Amapá, and analysed for the COI gene (Barcode region) and 12 microsatellite loci. Phylogenetic analyses were performed using the maximum likelihood (ML) approach. Intra and inter samples genetic diversity were estimated using population genetics analyses, and the genetic groups were identified by means of the ML, Bayesian and factorial correspondence analyses and the Bayesian analysis of population structure.The Barcode region dataset (N = 103) generated 27 haplotypes. The haplotype network suggested three lineages. The ML tree retrieved five monophyletic groups. Group I clustered all specimens from Manaus and Careiro Castanho, the majority of Autazes and a few from Abacate da Pedreira. Group II clustered most of the specimens from Abacate da Pedreira and a few from Autazes and Tucuruí. Group III clustered only specimens from Tucuruí (lineage III), strongly supported (97 %). Groups IV and V clustered specimens of A. nuneztovari s.s. and A. dunhami, strongly (98 %) and weakly (70 %) supported, respectively. In the second phylogenetic analysis, the sequences from GenBank, identified as A. goeldii, clustered to groups I and II, but not to group III. Genetic distances (Kimura-2 parameters) among the groups ranged from 1.60 % (between I and II) to 2.32 % (between I and III). Microsatellite data revealed very high intra-population genetic variability. Genetic distances showed the highest and significant values (P = 0.005) between Tucuruí and all the other samples, and between Abacate da Pedreira and all the other samples. Genetic distances, Bayesian (Structure and BAPS) analyses and FCA suggested three distinct biological groups, supporting the barcode region results.The two markers revealed three genetic lineages for A. nuneztovari s.l. in the Brazilian Amazon region. Lineages I and II may represent genetically distinct groups or species within A. goeldii. Lineage III may represent a new species, distinct from the A. goeldii group, and may be the most ancestral in the Brazilian Amazon. They may have differences in Plasmodium susceptibility and should therefore be investigated further.

Project description:BackgroundLarge-scale forest conservation projects are underway in the Brazilian Amazon but little is known regarding their public health impact. Current literature emphasizes how land clearing increases malaria incidence, leading to the conclusion that forest conservation decreases malaria burden. Yet, there is also evidence that proximity to forest fringes increases malaria incidence, which implies the opposite relationship between forest conservation and malaria. We compare the effect of these environmental factors on malaria and explore its implications.Methods and findingsUsing a large malaria dataset (~1,300,000 positive malaria tests collected over ~4.5 million km(2)), satellite imagery, permutation tests, and hierarchical Bayesian regressions, we show that greater forest cover (as a proxy for proximity to forest fringes) tends to be associated with higher malaria incidence, and that forest cover effect was 25 times greater than the land clearing effect, the often cited culprit of malaria in the region. These findings have important implications for land use/land cover (LULC) policies in the region. We find that cities close to protected areas (PA's) tend to have higher malaria incidence than cities far from PA's. Using future LULC scenarios, we show that avoiding 10% of deforestation through better governance might result in an average 2-fold increase in malaria incidence by 2050 in urban health posts.ConclusionsOur results suggest that cost analysis of reduced carbon emissions from conservation efforts in the region should account for increased malaria morbidity, and that conservation initiatives should consider adopting malaria mitigation strategies. Coordinated actions from disparate science fields, government ministries, and global initiatives (e.g., Reduced Emissions from Deforestation and Degradation; Millenium Development Goals; Roll Back Malaria; and Global Fund to Fight AIDS, Tuberculosis and Malaria), will be required to decrease malaria toll in the region while preserving these important ecosystems.

Project description:BACKGROUND:There is a growing body of evidence linking micronutrient deficiencies and malaria incidence arising mostly from P. falciparum endemic areas. We assessed the impact of micronutrient deficiencies on malaria incidence and vice versa in the Brazilian state of Amazonas. METHODOLOGY/PRINCIPAL FINDINGS:We evaluated children <10 years old living in rural communities in the state of Amazonas, Brazil, from May 2010 to May 2011. All children were assessed for sociodemographic, anthropometric and laboratory parameters, including vitamin A, beta-carotene, zinc and iron serum levels at the beginning of the study (May 2010) and one year later (May 2011). Children were followed in between using passive surveillance for detection of symptomatic malaria. Those living in the study area at the completion of the observation period were reassessed for micronutrient levels. Univariate Cox-proportional Hazards models were used to assess whether micronutrient deficiencies had an impact on time to first P. vivax malaria episode. We included 95 children median age 4.8 years (interquartile range [IQR]: 2.3-6.6), mostly males (60.0%) and with high maternal illiteracy (72.6%). Vitamin A deficiencies were found in 36% of children, beta-carotene deficiency in 63%, zinc deficiency in 61% and iron deficiency in 51%. Most children (80%) had at least one intestinal parasite. During follow-up, 16 cases of vivax malaria were diagnosed amongst 13 individuals. Micronutrient deficiencies were not associated with increased malaria incidence: vitamin A deficiency [Hazard ratio (HR): 1.51; P-value: 0.45]; beta-carotene [HR: 0.47; P-value: 0.19]; zinc [HR: 1.41; P-value: 0.57] and iron [HR: 2.31; P-value: 0.16]). Upon reevaluation, children with al least one episode of malaria did not present significant changes in micronutrient levels. CONCLUSION:Micronutrient serum levels were not associated with a higher malaria incidence nor the malaria episode influenced micronutrient levels. Future studies targeting larger populations to assess micronutrients levels in P. vivax endemic areas are warranted in order to validate these results.

Dataset Information

Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region.

Publications

Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region.

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