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Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

ABSTRACT: BACKGROUND: Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. METHODS: Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. RESULTS: Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. CONCLUSIONS: This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors.

SUBMITTER: Loha E

PROVIDER: S-EPMC2898788 | biostudies-other | 2010

REPOSITORIES: biostudies-other

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Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

Loha Eskindir E Lindtjørn Bernt B

Malaria journal 20100616

<h4>Background</h4>Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia.<h4>Methods</h4>Retrospective data fro ...[more]

PMID: 20553590

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Project description:BackgroundEfficient allocation of resources to intervene against malaria requires a detailed understanding of the contemporary spatial distribution of malaria risk. It is exactly 40 y since the last global map of malaria endemicity was published. This paper describes the generation of a new world map of Plasmodium falciparum malaria endemicity for the year 2007.Methods and findingsA total of 8,938 P. falciparum parasite rate (PfPR) surveys were identified using a variety of exhaustive search strategies. Of these, 7,953 passed strict data fidelity tests for inclusion into a global database of PfPR data, age-standardized to 2-10 y for endemicity mapping. A model-based geostatistical procedure was used to create a continuous surface of malaria endemicity within previously defined stable spatial limits of P. falciparum transmission. These procedures were implemented within a Bayesian statistical framework so that the uncertainty of these predictions could be evaluated robustly. The uncertainty was expressed as the probability of predicting correctly one of three endemicity classes; previously stratified to be an informative guide for malaria control. Population at risk estimates, adjusted for the transmission modifying effects of urbanization in Africa, were then derived with reference to human population surfaces in 2007. Of the 1.38 billion people at risk of stable P. falciparum malaria, 0.69 billion were found in Central and South East Asia (CSE Asia), 0.66 billion in Africa, Yemen, and Saudi Arabia (Africa+), and 0.04 billion in the Americas. All those exposed to stable risk in the Americas were in the lowest endemicity class (PfPR2-10 < or = 5%). The vast majority (88%) of those living under stable risk in CSE Asia were also in this low endemicity class; a small remainder (11%) were in the intermediate endemicity class (PfPR2-10 > 5 to < 40%); and the remaining fraction (1%) in high endemicity (PfPR2-10 > or = 40%) areas. High endemicity was widespread in the Africa+ region, where 0.35 billion people are at this level of risk. Most of the rest live at intermediate risk (0.20 billion), with a smaller number (0.11 billion) at low stable risk.ConclusionsHigh levels of P. falciparum malaria endemicity are common in Africa. Uniformly low endemic levels are found in the Americas. Low endemicity is also widespread in CSE Asia, but pockets of intermediate and very rarely high transmission remain. There are therefore significant opportunities for malaria control in Africa and for malaria elimination elsewhere. This 2007 global P. falciparum malaria endemicity map is the first of a series with which it will be possible to monitor and evaluate the progress of this intervention process.

Project description:The epidemiology of malaria makes surveillance-based methods of estimating its disease burden problematic. Cartographic approaches have provided alternative malaria burden estimates, but there remains widespread misunderstanding about their derivation and fidelity. The aims of this study are to present a new cartographic technique and its application for deriving global clinical burden estimates of Plasmodium falciparum malaria for 2007, and to compare these estimates and their likely precision with those derived under existing surveillance-based approaches.In seven of the 87 countries endemic for P. falciparum malaria, the health reporting infrastructure was deemed sufficiently rigorous for case reports to be used verbatim. In the remaining countries, the mapped extent of unstable and stable P. falciparum malaria transmission was first determined. Estimates of the plausible incidence range of clinical cases were then calculated within the spatial limits of unstable transmission. A modelled relationship between clinical incidence and prevalence was used, together with new maps of P. falciparum malaria endemicity, to estimate incidence in areas of stable transmission, and geostatistical joint simulation was used to quantify uncertainty in these estimates at national, regional, and global scales. Combining these estimates for all areas of transmission risk resulted in 451 million (95% credible interval 349-552 million) clinical cases of P. falciparum malaria in 2007. Almost all of this burden of morbidity occurred in areas of stable transmission. More than half of all estimated P. falciparum clinical cases and associated uncertainty occurred in India, Nigeria, the Democratic Republic of the Congo (DRC), and Myanmar (Burma), where 1.405 billion people are at risk. Recent surveillance-based methods of burden estimation were then reviewed and discrepancies in national estimates explored. When these cartographically derived national estimates were ranked according to their relative uncertainty and replaced by surveillance-based estimates in the least certain half, 98% of the global clinical burden continued to be estimated by cartographic techniques.Cartographic approaches to burden estimation provide a globally consistent measure of malaria morbidity of known fidelity, and they represent the only plausible method in those malaria-endemic countries with nonfunctional national surveillance. Unacceptable uncertainty in the clinical burden of malaria in only four countries confounds our ability to evaluate needs and monitor progress toward international targets for malaria control at the global scale. National prevalence surveys in each nation would reduce this uncertainty profoundly. Opportunities for further reducing uncertainty in clinical burden estimates by hybridizing alternative burden estimation procedures are also evaluated.

Project description:BackgroundThe global decline of malaria burden and goals for elimination has led to an increased interest in the fine-scale epidemiology of malaria. Micro-geographic heterogeneity of malaria infection could have implications for designing targeted small-area interventions.MethodsTwo-year longitudinal cohort study data were used to explore the spatial and spatio-temporal distribution of malaria episodes in 2040 children aged < 10 years in 16 villages near the Gilgel-Gibe hydropower dam in Southwest Ethiopia. All selected households (HHs) were geo-referenced, and children were followed up through weekly house-to-house visits for two consecutive years to identify febrile episodes of P. falciparum and P. vivax infections. After confirming the spatial dependence of malaria episodes with Ripley's K function, SatScanTM was used to identify purely spatial and space-time clusters (hotspots) of annual malaria incidence for 2 years follow-up: year 1 (July 2008-June 2009) and year 2 (July 2009-June 2010).ResultsIn total, 685 P. falciparum episodes (in 492 HHs) and 385 P. vivax episodes (in 290 HHs) were identified, representing respectively incidence rates of 14.6 (95% CI: 13.4-15.6) and 8.2 (95% CI: 7.3-9.1) per 1000 child-months at risk. In year 1, the most likely (128 HHs with 63 episodes, RR = 2.1) and secondary (15 HHs with 12 episodes, RR = 5.31) clusters of P. vivax incidence were found respectively in southern and north-western villages; while in year 2, the most likely cluster was located only in north-western villages (85 HHs with 16 episodes, RR = 4.4). Instead, most likely spatial clusters of P. falciparum incidence were consistently located in villages south of the dam in both years: year 1 (167 HHs with 81 episodes, RR = 1.8) and year 2 (133 HHs with 67 episodes, RR = 2.2). Space-time clusters in southern villages for P. vivax were found in August-November 2008 in year 1 and between November 2009 and February 2010 in year 2; while for P. falciparum, they were found in September-November 2008 in year 1 and October-November 2009 in year 2.ConclusionHotspots of P. falciparum incidence in children were more stable at the geographical level and over time compared to those of P. vivax incidence during the study period.

Project description:BackgroundPlasmodium falciparum parasite populations in Ethiopia have been experiencing local selective pressures from drugs and immunity, leading to evolutionary adaptation. However, there was a paucity of data on genomic characterization and evolutionary adaptations of P. falciparum isolates from the central area of Ethiopia.MethodsWhole-genome analysis of 25 P. falciparum isolates from central Ethiopia, specifically from West Arsi, were studied to determine their genetic diversity, population structures, and signatures of selection in known drug resistance alleles against global isolates from Cambodia, Thailand, DR Congo, and Malawi.ResultsA total of 18,517 high-quality single-nucleotide polymorphisms (SNPs) were identified in Ethiopian P. falciparum isolates. About 84% of the Ethiopian P. falciparum isolates had a FWS value > 0.95 showing a dominant single genotype infection in most isolates at the time of collection with little potential for out-crossing as expected in areas with low transmission intensity. Within-host diversity of Ethiopian infections was significantly different from East African (p < 0.001), but not Southeast Asian infections (P > 0.05). A significant population structure has been observed by PCA and population differentiation between Ethiopian parasites and East African (Fst ~ 10%) and Southeast Asian populations (Fst ~ 18%), suggesting limited gene flow and the independent evolution of the Ethiopian parasite population. Moreover, a total of 125 genes under balancing selection was found that include ama1, trap, eba175, and lsa3, previously identified as targets of human host immunity. Recent directional selection analysis using integrated standardized haplotype score (IHS) did not detect any selection signatures in the Pfcrt, Pfdhfr, Pfdhps, Pfmdr1, and PfK13 genes. However, known drug resistance-conferring mutations analysis showed that at least one SNP marker was fixed in these genes, but not in Pfdhps and PfK13.ConclusionPlasmodium falciparum populations in the central region of Ethiopia was structurally diverged from both Southeast Asian and other East African populations. Malaria infections in Ethiopia had low within-host diversity, and parasites carry fixed chloroquine resistance markers despite the withdrawal of this drug for the treatment of P. falciparum.

Project description:Malaria and HIV are two major causes of morbidity and mortality among pregnant women in sub-Saharan Africa. Foetal and neonatal outcomes of this co-infection have been extensively studied. However, little is known about maternal morbidity due to clinical malaria in pregnancy, especially malaria-related fever, in the era of generalized access to antiretroviral therapy and anti-malarial preventive strategies.A cohort study was conducted in order to estimate the incidence rate and to determine the factors associated with malaria-related fever, as well as the maternal morbidity attributable to malaria in a high-transmission setting of South Benin among HIV-infected pregnant women. Four-hundred and thirty-two women who participated in a randomized trial testing strategies to prevent malaria in pregnancy were included and followed until delivery, with at least three scheduled visits during pregnancy. Confirmed malaria-related fever was defined as axillary temperature >37.5°C and a concomitant, positive, thick blood smear or rapid diagnostic test for Plasmodium falciparum. Suspected malaria-related fever was defined as an axillary temperature >37.5°C and the concomitant administration of an anti-malarial treatment in the absence of parasitological investigation.Incidence rate for confirmed malaria-related fever was of 127.9 per 1,000 person-year (PY) (95% confidence interval (CI): 77.4-211.2). In multivariate analysis, CD4 lymphocytes (Relative Risk (RR) for a 50 cells/mm3 variation?=?0.82; CI: 0.71-0.96), antiretroviral treatment started before inclusion (RR?=?0.34; CI: 0.12-0.98) and history of symptomatic malaria in early pregnancy (RR?=?7.10; CI: 2.35-22.49) were associated with the incidence of confirmed or suspected malaria-related fever. More than a half of participants with parasitaemia were symptomatic, with fever being the most common symptom. The crude fraction of febrile episodes attributable to malaria was estimated at 91%.This work highlights that malaria is responsible for a substantial morbidity in HIV-infected pregnant women, with cellular immunodepression as a major determinant, and establishes the possible advantage offered by the early initiation of antiretroviral treatment.PACOME Study has been registered under the number NCT00970879.

Dataset Information

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

Publications

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

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