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Molecular Evidence of Plasmodium vivax Mono and Mixed Malaria Parasite Infections in Duffy-Negative Native Cameroonians
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Abstract
The malaria parasite Plasmodium vivax is known to be majorly endemic to Asian and Latin American countries with no or very few reports of Africans infected with this parasite. Since the human Duffy antigens act as receptors for P. vivax to invade human RBCs and Africans are generally Duffy-negative, non-endemicity of P. vivax in Africa has been attributed to this fact. However, recent reports describing P. vivax infections in Duffy-negative Africans from West and Central parts of Africa have been surfaced including a recent report on P. vivax infection in native Cameroonians. In order to know if Cameroonians living in the southern regions are also susceptible to P. vivax infection, we collected finger-prick blood samples from 485 malarial symptomatic patients in five locations and followed PCR diagnostic assays with DNA sequencing of the 18S ribosomal RNA gene. Out of the 201 malaria positive cases detected, 193 were pure P. falciparum, six pure P. vivax and two mixed parasite infections ( P. falciparum + P. vivax). The eight P. vivax infected samples (six single + two mixed) were further subjected to DNA sequencing of the P. vivax multidrug resistance 1 ( pvmdr1) and the P.vivax circumsporozoite ( pvcsp) genes. Alignment of the eight Cameroonian pvmdr1 sequences with the reference sequence showed high sequence similarities, reconfirming P. vivax infection in all the eight patients. DNA sequencing of the pvcsp gene indicated all the eight P. vivax to be of VK247 type. Interestingly, DNA sequencing of a part of the human Duffy gene covering the promoter region in the eight P. vivax-infected Cameroonians to identify the T-33C mutation revealed all these patients as Duffy-negative. The results provide evidence of single P. vivax as well as mixed malaria parasite infection in native Cameroonians and add knowledge to the growing evidences of P. vivax infection in Duffy-negative Africans.
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Most cited references25
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High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction.
G Snounou, S Viriyakosol, X Zhu … (1993)
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A Long Neglected World Malaria Map: Plasmodium vivax Endemicity in 2010
Introduction The international agenda shaping malaria control financing, research, and implementation is increasingly defined around the goal of regional elimination [1]–[6]. This ambition ostensibly extends to all human malarias, but whilst recent years have seen a surge in research attention for Plasmodium falciparum, the knowledge-base for the other major human malaria, Plasmodium vivax, is far less developed in almost every aspect [7]–[11]. During 2006–2009 just 3.1% of expenditures on malaria research and development were committed to P. vivax [12]. The notion that control approaches developed primarily for P. falciparum in holoendemic Africa can be transferred successfully to P. vivax is, however, increasingly acknowledged as inadequate [13]–[17]. Previous eradication campaigns have demonstrated that P. vivax frequently remains entrenched long after P. falciparum has been eliminated [18]. The prominence of P. vivax on the global health agenda has risen further as evidence accumulates of its capacity in some settings to cause severe disease and death [19]–[25], and of the very large numbers of people living at risk [26]. Amongst the many information gaps preventing rational strategies for P. vivax control and elimination, the absence of robust geographical assessments of risk has been identified as particularly conspicuous [9], [27]. The endemic level of the disease determines its burden on children, adults, and pregnant women; the likely impact of different control measures; and the relative difficulty of elimination goals. Despite the conspicuous importance of these issues, there has been no systematic global assessment of endemicity. The Malaria Atlas Project was initiated in 2005 with an initial focus on P. falciparum that has led to global maps [28]–[30] for this parasite being integrated into policy planning at regional to international levels [4], [31]–[36]. Here we present the outcome of an equivalent project to generate a comprehensive evidence-base on P. vivax infections worldwide, and to generate global risk maps for this hitherto neglected disease. We build on earlier work [26] defining the global range of the disease and broad classifications of populations at risk to now assess the levels of endemicity under which these several billion people live. This detailed depiction of geographically varying risk is intended to contribute to a much-needed paradigm shift towards geographically stratified and evidence-based planning for P. vivax control and elimination. Numerous biological and epidemiological characteristics of P. vivax present unique challenges to defining and mapping metrics of risk. Unlike P. falciparum, infections include a dormant hypnozoite liver stage that can cause clinical relapse episodes [37], [38]. These periodic events manifest as a blood-stage infection clinically indistinguishable from a primary infection and constitute a substantial, but geographically varying, proportion of total patent infection prevalence and disease burden within different populations [37], [39]–[41]. The parasitemia of P. vivax typically occurs at much lower densities compared to those of falciparum malaria, and successful detection by any given means of survey is much less likely. Another major driver of the global P. vivax landscape is the influence of the Duffy negativity phenotype [42]. This inherited blood condition confers a high degree of protection against P. vivax infection and is present at very high frequencies in the majority of African populations, although is rare elsewhere [43]. These factors, amongst others, mean that the methodological framework for mapping P. vivax endemicity, and the interpretation of the resulting maps, are distinct from those already established for P. falciparum [28], [29]. The effort described here strives to accommodate these important distinctions in developing a global distribution of endemic vivax malaria. Methods The modelling framework is displayed schematically in Figure 1. In brief, this involved (i) updating of the geographical limits of stable P. vivax transmission based on routine reporting data and biological masks; (ii) assembly of all available P. vivax parasite rate data globally; (iii) development of a Bayesian model-based geostatistical model to map P. vivax endemicity within the limits of stable transmission; and (iv) a model validation procedure. Details on each of these stages are provided below with more extensive descriptions included as Protocols S1, S2, S3, and S4. 10.1371/journal.pntd.0001814.g001 Figure 1 Schematic overview of the mapping procedures and methods for Plasmodium vivax endemicity. Blue boxes describe input data. Orange boxes denote models and experimental procedures; green boxes indicate output data (dashed lines represent intermediate outputs and solid lines final outputs). U/R = urban/rural; UNPP = United Nations Population Prospects. Labels S1-4 denote supplementrary information in Protocols S1, S2, S3, and S4. Updating Estimates of the Geographical Limits of Endemic Plasmodium vivax in 2010 The first effort to systematically estimate the global extent of P. vivax transmission and define populations at risk was completed in 2009 [26]. As a first step in the current study, we have updated this work with a new round of data collection for the year 2010. The updated data assemblies and methods are described in full in Protocol S1. In brief, this work first involved the identification of 95 countries as endemic for P. vivax in 2010. From these, P. vivax annual parasite incidence (PvAPI) routine case reports were assembled from 17,893 administrative units [44]. These PvAPI and other medical intelligence data were combined with remote sensing surfaces and biological models [45] that identified areas where extreme aridity or temperature regimes would limit or preclude transmission (see Protocol S1). These components were combined to classify the world into areas likely to experience zero, unstable (PvAPI 0.9) to have a PvPR1–99 less than 1% were classed as unstable. Areas in which Duffy negativity gene frequency is predicted to exceed 90% [43] are shown in hatching for additional context. Modelling Plasmodium vivax Endemicity within Regions of Stable Transmission We adopt model-based geostatistics (MBG) [47], [48] as a robust and flexible modelling framework for generating continuous surfaces of malaria endemicity based on retrospectively assembled parasite rate survey data [28], [29], [49]. MBG models are a special class of generalised linear mixed models, with endemicity values at each target pixel predicted as a function of a geographically-varying mean and a weighted average of proximal data points. The mean can be defined as a multivariate function of environmental correlates of disease risk. A covariance function is used to characterise the spatial or space-time heterogeneity in the observed data, which in turn is used to define appropriate weights assigned to each data point when predicting at each pixel. This framework allows the uncertainty in predicted endemicity values to vary between pixels, depending on the observed variation, density and sample size of surveys in different locations and the predictive utility of the covariate suite. Parts of the map where survey data are dense, recent, and relatively homogenous will be predicted with least uncertainty, whilst regions with sparse or mainly old surveys, or where measured parasite rates are extremely variable, will have greater uncertainty. When MBG models are fitted using Bayesian inference and a Markov chain Monte Carlo (MCMC) algorithm, uncertainty in the final predictions as well as all model parameters can be represented in the form of predictive posterior distributions [50]. We developed for this study a modified version of the MBG framework used previously to model P. falciparum endemicity [28], [29], with some core aspects of the model structure remaining unchanged and others altered to capture unique aspects of P. vivax biology and epidemiology. The model is presented in full in Protocol S3. As in earlier work [28], [29], [49], we adopt a space-time approach to allow surveys from a wide time period to inform predictions of contemporary risk. This includes the use of a spatiotemporal covariance function which is parameterised to downweight older data appropriately. We also retain a seasonal component in the covariance function, although we note that seasonality in transmission is often only weakly represented in PvPR in part because of the confounding effect of relapses occurring outside peak transmission seasons [51]. A minimal set of covariates were included to inform prediction of the mean function, based on a priori expectations of the major environmental factors modulating endemicity. These were (i) an indicator variable defining areas as urban or rural based on the Global Rural Urban Mapping Project (GRUMP) urban extent product [52], [53]; (ii) a long-term average vegetation index product as an indicator of overall moisture availability for vector oviposition and survival [54], [55]; and (iii) a P. vivax specific index of temperature suitability derived from the same model used to delineate suitable areas on the basis of vector survival and sporogony [45]. Age Standardisation Our assembly of PvPR surveys was collected across a variety of age ranges and, since P. vivax infection status can vary systematically in different age groups within a defined community, it was necessary to standardise for this source of variability to allow all surveys to be used in the same model. We adopted the same model form as has been described [56] and used previously for P. falciparum [28], [29], whereby population infection prevalence is expected to rise rapidly in early infancy and plateau during childhood before declining in early adolescence and adulthood. The timing and relative magnitude of these age profile features are likely distinct between the two parasites in different endemic settings [51], [57], and so the model was parameterised using an assembly of 67 finely age-stratified PvPR surveys (Protocol S2), with estimation carried out in a Bayesian model using MCMC. The parameterised model was then used to convert all observed survey prevalences to a standardised age-independent value for use in modelling, and then further allowed the output prevalence predictions to be generated for any arbitrary age range. We chose to generate maps of all-age infection prevalence, defined as individuals of age one to 99 years (thus PvPR1–99). We excluded infection in those less than one year of age from the standardisation because of the confounding effect of maternal antibodies, and because parasite rate surveys very rarely sample young infants. We deviated from the two-to-ten age range used for mapping P. falciparum [28], [29] because the relatively lower prevalences has meant that surveys are far more commonly carried out across all age ranges. Incorporating Duffy Negativity Since Duffy negative individuals are largely refractory to P. vivax infection [58], high population frequencies of this phenotype have a dramatic suppressing effect on endemicity, even where conditions are otherwise well suited for transmission [26]. The predominance of Duffy negativity in Africa has led to a historical perception that P. vivax is absent from much of the continent, and a dearth of surveys or routine diagnoses testing for the parasite have served to entrench this mantra [59]. However, evidence exists of autochthonous P. vivax transmission across the continent [26], and therefore we did not preclude any areas at risk a priori. Instead, we used a recent map of estimated Duffy negativity phenotypic frequency [43] and incorporated the potential influence of this blood group directly in the MBG modelling framework. The mapped Duffy-negative population fraction at each location was excluded from the denominator in PvPR survey data, such that any P. vivax positive individuals were considered to have arisen from the Duffy positive population subset. Thus in a location with 90% Duffy negativity, five positive individuals in a survey of 100 would give an assumed prevalence of 50% amongst Duffy positives. Correspondingly, prediction of PvPR was then restricted to the Duffy positive proportion at each pixel, with the final prevalence estimate re-converted to relate to the total population. This approach has two key advantages. First, predicted PvPR at each location could never exceed the Duffy positive proportion, therefore ensuring biological consistency between the P. vivax and Duffy negativity maps. Second, where PvPR survey data were sparse across much of Africa, the predictions could effectively borrow strength from the Duffy negativity map because predictions of PvPR were restricted to a much narrower range of possible values. Model Implementation and Map Generation The P. vivax endemic world was divided into four contiguous regions with broadly distinct biogeographical, entomological and epidemiological characteristics: the Americas and Africa formed separate regions, whilst Asia was subdivided into Central and South East sub-regions with a boundary at the Thailand-Malaysia border (see Protocol S2). This regionalisation was implemented in part to retain computational feasibility given the large number of data points, but also to allow model parameterisations to vary and better capture regional endemicity characteristics. Within each region, a separate MBG model was fitted using a bespoke MCMC algorithm [60] to generate predictions of PvPR1–99 for every 5×5 km pixel within the limits of stable transmission. The prediction year was set to 2010 and model outputs represent an annualised average across the 12 months of that year. Model output consisted of a predicted posterior distribution of PvPR1–99 for every pixel. A continuous endemicity map was generated using the mean of each posterior distribution as a point estimate. The uncertainty associated with predictions was summarised by maps showing the ratio of the posterior distribution inter-quartile range (IQR) to its mean. The IQR is a simple measure of the precision with which each PvPR value was predicted, and standardisation by the mean produced an uncertainty index less affected by underlying prevalence levels and more illustrative of relative model performance driven by data densities in different locations. This index was then also weighted by the underlying population density to produce a second map indicative of those areas where uncertainty is likely to be most operationally important. Refining Limits Definition and Population at Risk Estimates In some regions within the estimated limits of stable transmission, PvPR1–99 was predicted to be extremely low, either because of a dense abundance of survey data reporting zero infections or, in Africa, because of very high coincident Duffy negativity phenotype frequencies. Such areas are not appropriately described as being at risk of stable transmission and so we defined a decision rule whereby pixels predicted with high certainty (probability >0.9) of being less than 1% PvPR1–99 were assigned to the unstable class, thereby modifying the original transmission limits. These augmented mapped limits were combined with a 2010 population surface derived from the GRUMP beta version [52], [53] to estimate the number of people living at unstable or stable risk within each country and region. The fraction of the population estimated to be Duffy negative [43] within each pixel was considered at no risk and therefore excluded from these totals. Model Validation A model validation procedure was implemented whereby 10% of the survey points in each model region were selected using a spatially declustered random sampling procedure. These subsets were held out and the model re-fitted in full using the remaining 90%. Model predictions were then compared to the hold-out data points and a number of different aspects of model performance were assessed using validation statistics described previously [28], [29]. The validation procedure is detailed in full in Protocol S4. Results Model Validation Full validation results are presented in Protocol S4. In brief, examination of the mean error in the generation of the P. vivax malaria endemicity point-estimate surface revealed minimal overall bias in predicted PvPR with a global mean error of −0.41 (Americas −1.38, Africa 0.03, Central Asia −0.43, South East Asia −0.43), with values in units of PvPR on a percentage scale (see Protocol S4). The global value thus represents an overall tendency to underestimate prevalence by just under half of one percent. The mean absolute error, which measures the average magnitude of prediction errors, was 2.48 (Americas 5.05, Africa 0.53, Central Asia 1.52, South East Asia 3.37), again in units of PvPR (see Protocol S4). Global Plasmodium vivax Endemicity and Populations at Risk in 2010 The limits of stable and unstable P. vivax transmission, as defined using PvAPI, biological exclusion masks and medical intelligence data are shown in Figure 2A. The continuous surface of P. vivax endemicity predicted within those limits is shown in Figure 2B. The uncertainty map (posterior IQR:mean ratio) is shown in Figure 3A and the population-weighted version in Figure 3B. 10.1371/journal.pntd.0001814.g003 Figure 3 Uncertainty associated with predictions of Plasmodium vivax endemicity. Panel A shows the ratio of the posterior inter-quartile range to the posterior mean prediction at each pixel. Large values indicate greater uncertainty: the model predicts a relatively wide range of PvPR1–99 as being equally plausible given the surrounding data. Conversely, smaller values indicate a tighter range of values have been predicted and, thus, a higher degree of certainty in the prediction. Panel B shows the same index multiplied by the underlying population density and rescaled to 0–1 to correspond to Panel A. Higher values indicate areas with high uncertainty and large populations. We estimate that P. vivax was endemic across some 44 million square kilometres, approximately a third of the Earth's land surface. Around half of this area was located in Africa (51%) and a quarter each in the Americas (22%) and Asia (27%) (Table 1). However, the uneven distribution of global populations, coupled with the protective influence of Duffy negativity in Africa, meant that the distribution of populations at risk was very different. An estimated 2.48 billion people lived at any risk of P. vivax in 2010 (Table 1), of which a large majority lived in Central Asia (82%) with much smaller fractions in South East Asia (9%), the Americas (6%), and Africa (3%). Of these, 1.52 billion lived in areas of unstable transmission where risk is very low and case incidence is unlikely to exceed one per 10,000 per annum. The remaining 964 million people at risk lived in areas of stable transmission, representing a wide diversity of endemic levels. The global distribution of populations in each risk class was similar to the total at risk, such that over 80% of people in both classes lived in Central Asia (Table 1). 10.1371/journal.pntd.0001814.t001 Table 1 Area and populations at risk of Plasmodium vivax malaria in 2010. Region Area (million km2) Population (millions) Unstable Stable Any risk Unstable Stable Any risk America 1.38 8.08 9.46 87.66 49.79 137.45 Africa+ 20.60 1.86 22.46 48.72 37.66 86.38 C Asia 5.60 3.63 9.24 1,236.92 812.55 2,049.47 SE Asia 0.96 1.78 2.74 150.17 64.90 215.07 World 28.55 15.35 43.90 1,523.47 964.90 2,488.37 Risk is stratified into unstable risk (PvAPI 6,000/uL among inpatients classified as having not serious, serious, and fatal illness with a diagnosis of P. vivax compared to P. falciparum [24]. Further, the majority of case reports describing severe and fatal illness with a diagnosis of vivax malaria typically show parasitemia >5,000/uL. In contrast, the World Health Organization threshold for severe illness attributable to hyperparasitemia with P. falciparum is >200,000/uL [73]. In brief, the relationship between prevalence and risk of disease and transmission for P. vivax is distinct from that for P. falciparum, and it is weighted more heavily towards substantial risks at much lower parasite densities and levels of prevalence of microscopically patent parasitemia. The capacity of P. vivax hypnozoites to induce relapsing infections has a number of important implications. First, because dormant liver stage infections are not detectable in routine parasite rate surveys, our maps do not capture the potentially very large reservoir of asymptomatic infections sequestered in each population. Evidence is emerging that this hidden reservoir may be substantially larger than previously thought, with long-latency P. vivax phenotypes both prevalent and geographically widespread [37]. Whilst not contributing to clinical disease until activated, these dormant hypnozoites ultimately play a vital role in sustaining transmission since they are refractory to blood-stage antimalarial chemotherapy and interventions to reduce transmission. Hypnozoites also ensure an ability of P. vivax to survive in climatic conditions that cannot sustain P. falciparum transmission. Second, the P. vivax parasite rates observed in population surveys detect both new and relapsing infections, although the two are almost never distinguishable. This confounds the relationship between observed infection prevalence and measures of transmission intensity such as force of infection or the entomological inoculation rate. This, in turn, has implications for the use of transmission models seeking to evaluate or optimise control options for P. vivax [2], [9], [27], [74]. The current unavailability of any diagnostic method for detecting hypnozoites [75] and our resulting ignorance about the size and geographic distribution of this reservoir therefore remain critical knowledge gaps limiting the feasibility of regional elimination [9]. It is also worth noting that conventional parasite rate data do not measure multiplicity of infection which is an additional potential confounding effect between observed infection prevalence and transmission intensity. P. vivax in Africa and Duffy Polymorphism Our map of P. vivax endemicity and estimates of populations at risk in Africa are heavily influenced by a single assumption: that the fraction of the population estimated to be negative for the Duffy antigen [43] is refractory to infection with P. vivax. A body of empirical evidence is growing, however that P. vivax can infect and cause disease in Duffy negative individuals, as reported in Madagascar [76] and mainland sub-Saharan Africa [77]–[80] as well as outside Africa [81], [82]. Whether the invasion of erythrocytes via Duffy antigen-independent pathways is a newly evolved mechanism, or whether this capacity has been overlooked by the misdiagnosis of P. vivax in Africa as P. ovale remains unresolved [9], [42], [59]. Whilst this accumulated evidence stands contrary to our simplifying assumption of complete protection in Duffy negative individuals, there is currently no evidence to suggest that such infections are anything but rare and thus are unlikely to have any substantive influence on the epidemiology or infection prevalence of P. vivax at the population scale throughout most of Africa. We also make no provision in our model for a protective effect in Duffy-negative heterozygotes, although such protection has been observed in some settings [83]–[86]. The movement and mixing within Africa of human populations from diverse ethnographic backgrounds complicates contemporary patterns of Duffy negativity and, in principle, could yield local populations with substantially reduced protection from P. vivax infection in the future. Indeed, the implications for our map of population movement go beyond the effect of Duffy negativity: the carriage of parasites from high to low endemic regions, for example by migratory workers, may play an important role in sustaining transmission in some regions and further research is required to investigate such processes. Mapping to Guide Control There exists for P. falciparum a history of control strategies linked explicitly to defined strata of endemicity, starting with the first Global Malaria Eradication Programme [18], [87], [88] and undergoing a series of refinements that now feature in contemporary control and elimination efforts. Most recently, stratification has been supported by insights gained from mathematical models linking endemic levels to optimum intervention suites, control options, and timelines for elimination planning [2], [89]–[95]. In stark contrast, control options for P. vivax are rarely differentiated by endemicity, and there is little consensus around how this may be done. In part, the absence of agreed control-oriented strata of P. vivax endemicity stems from the biological complexities and knowledge gaps that prevent direct interpretation of infection prevalence as a metric for guiding control. It is also to some extent inevitable that the dogma of unstratified control becomes self-propagating: risk maps are not created because control is not differentiated by endemicity, but that differentiation cannot proceed without reliable maps. As well as providing a basis for stratified control and treatment, the endemicity maps presented here have a number of potential applications in combination with other related maps. First, there is an urgent need to better identify regions where high P. vivax endemicity is coincident with significant population prevalence of glucose-6-phosphate dehydrogenase deficiency (G6PDd). This inherited blood disorder plays a key role in chemotherapy policy for P. vivax because primaquine, the only registered drug active against the hypnozoite liver stage is contra-indicated in G6PDd individuals in whom it can cause severe and potentially fatal haemolytic reactions [96], [97]. A new global map of G6PDd prevalence is now available (Howes et al, submitted) which can be combined with the endemicity maps presented here to provide a rational basis for estimating adverse outcomes and setting appropriate testing and treatment protocols. Moreover, in practice most clinical infections are managed without differentiating the causative parasite species: combining the endemicity maps for P. vivax and P. falciparum may therefore inform unified strategies for malaria control programs and policy [28]. It has been proposed, for example, that artemesinin-based combination therapy (ACT) be adopted for all presumptively diagnosed malaria in areas coendemic for both species, as opposed to a separate ACT/chloroquine treatment strategy [98]. Further, in some regions more than 50% of patients diagnosed with falciparum malaria go on to experience an attack of vivax malaria in the absence of risk of reinfection [99]. This high prevalence of hypnozoites may also justify presumptive therapy with primaquine against relapse with any diagnosis of malaria where the two species occur at relatively high frequencies. Such geographically specific cross-parasite treatment considerations hinge on robust risk maps for both species. Future Challenges in P. vivax Cartography Numerous research and operational challenges remain unaddressed that would provide vital insights into the geographical distribution of P. vivax and its impacts on populations. Perhaps the highest priority is to improve understanding of the link between infection prevalence and clinical burden in both P. vivax mono-endemic settings and where it is coendemic with P. falciparum. Official estimates of national and regional disease burdens for P. vivax remain reliant on routine case reporting of unknown fidelity and are only crudely distinguished from P. falciparum [100]. It is illuminating that only 53 of the 95 P. vivax endemic countries were able to provide vivax-specific routine case reporting data, and there is a clear mandate for strengthening the routine diagnosis and reporting of P. vivax cases. Cartographic approaches to estimating P. vivax burden can therefore play a crucial role in triangulating with these estimates to provide insight into the distribution of the disease independent of health system surveillance and its attendant biases [27], [101]–[105]. There is also a particular need to define burden and clinical outcomes associated with P. vivax in pregnancy [9], [106] and other clinically vulnerable groups, most notably young children. Linking infection prevalence to clinical burden implies the need to better understand the contribution of relapsing infections to disease. Whilst the magnitude of this contribution is known to be highly heterogeneous, its geographical pattern is poorly measured and causal factors only partially understood [39], [41]. Further challenges lie in understanding how P. falciparum and P. vivax interact within human hosts and how these interactions manifest at population levels. Comparison of the maps for each species reveals a complete spectrum from areas endemic for only one parasite through to others where both species are present at broadly equal levels. Whilst identifying these patterns of coendemicity is an important first step, the implications in terms of risks of coinfection and clinical outcomes, antagonistic mechanisms leading to elevated severe disease risk, or cross-protective mechanisms of acquired immunity remain disputed [20], [107]–[109]. Conclusions To meet international targets for reduced malaria illness and death, and to progress the cause of regional elimination, the malaria research and control communities can no longer afford to neglect the impact of P. vivax. Its unique biology and global ubiquity present challenges to its elimination that greatly surpass those of its higher-profile cousin, P. falciparum. Making serious gains against the disease will require substantive strengthening of the evidence base on almost every aspect of its biology, epidemiology, control and treatment. The maps presented here are intended to contribute to this effort. They are all made freely available from the MAP website [110] along with regional and individual maps for every malaria-endemic country. Users can access individual map images or download the global surfaces for use in a geographical information system, allowing them to integrate this work within their own analyses or produce bespoke data overlays and displays. We will also make available, where permissions have been obtained, all underlying P. vivax parasite rate surveys used in this work. Supporting Information Protocol S1 Updating the global spatial limits of Plasmodium vivax malaria transmission for 2010. S1.1 Overview. S1.2 Identifying Countries Considered P. vivax Malaria Endemic. S1.3 Updating National Risk Extents with P. vivax Annual Parasite Incidence Data. S1.4 Biological Masks of Transmission Exclusion. S1.5 Risk Modulation Based on Medical Intelligence. S1.6 Assembling the P. vivax Spatial Limits Map. S1.7 Refining Regions of Unstable Transmission after MBG Modelling. S1.8 Predicting Populations at Risk of P. vivax in 2010. (DOC) Click here for additional data file. Protocol S2 The Malaria Atlas Project Plasmodium vivax parasite prevalence database. S2.1 Assembling the PvPR Data. S2.2 Database Fidelity Checks. S2.3 Data Exclusions. S2.4 The PvPR Input Data Set. S2.5 Age-Standardisation. S2.6 Regionalisation. (DOC) Click here for additional data file. Protocol S3 Bayesian model-based geostatistical framework for predicting Pv PR1–99. S3.1 Bayesian Inference. S3.2 Model Overview. S3.3 Formal Presentation of Model. (DOC) Click here for additional data file. Protocol S4 Model validation procedures and additional results. S4.1 Creation of Validation Sets. S4.2 Procedures for Testing Model Performance. S4.3 Validation Results. (DOC) Click here for additional data file.
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The International Limits and Population at Risk of Plasmodium vivax Transmission in 2009
Carlos Guerra, Rosalind E. Howes, Anand P Patil … (2010)
Introduction The bulk of the global burden of human malaria is caused by two parasites: Plasmodium falciparum and P. vivax. Existing research efforts have focussed largely on P. falciparum because of the mortality it causes in Africa [1], [2]. This focus is increasingly regarded as untenable [3]–[6] because the following factors indicate that the public health importance of P. vivax may be more significant than traditionally thought: i) P. vivax has a wider geographical range, potentially exposing more people to risk of infection [7], [8]; ii) it is less amenable to control [9], [10]; and, most importantly, iii) infections with P. vivax can cause severe clinical syndromes [5], [11]–[16]. A key research priority for P. vivax malaria is to improve the basic understanding of the geographical distribution of risk, which is needed for adequate burden estimation [6]. Recent work by the Malaria Atlas Project (MAP; www.map.ox.ac.uk) [17] has shown P. falciparum malaria mapping to be a fundamental step in understanding the epidemiology of the disease at the global scale [18], [19], in appraising the equity of global financing for control [20] and in forming the basis for burden estimation [21], [22]. The benefits of a detailed knowledge of the spatial distribution of P. vivax transmission, and its clinical burden within these limits, are identical to those articulated for P. falciparum: establishing a benchmark against which control targets may be set, budgeted and monitored. Such maps do not exist for P. vivax, making any strategic planning problematic. In addition, information about the global extent of P. vivax transmission and population at risk (PAR) is crucial for many nations that are re-evaluating their prospects for malaria elimination [23], [24]. This paper documents the global spatial limits of P. vivax malaria using a combination of national case-reporting data from health management information systems (HMIS), biological rules of transmission exclusion and medical intelligence combined in a geographical information system. The output is an evidence-based map from which estimates of PAR are derived. The resulting map also provides the global template in which contemporary P. vivax endemicity can be estimated and it contributes to a cartographic basis for P. vivax disease burden estimation. Methods Analyses Outline A schematic overview of the analyses is presented in Figure 1. Briefly, P. vivax malaria endemic countries (PvMECs) were first identified and the following layers were progressively applied within a geographical information system to constrain risk areas and derive the final P. vivax spatial limits map: i) a P. vivax annual parasite incidence (PvAPI) data layer; biological exclusion layers comprising of ii) temperature and iii) aridity data layers; iv) a medical intelligence exclusion layer; and v) a predicted Duffy negativity layer. A detailed description of these steps follows. 10.1371/journal.pntd.0000774.g001 Figure 1 Flow chart of the various data and exclusion layers used to derive the final map. The pink rectangle denotes the surface area and populations of PvMECs, whilst the pink ovoid represents the resulting trimmed surface area and PAR after the exclusion of risk by the various input layers, denoted by the blue rhomboids. Orange rectangles show area and PAR exclusions at each step to illustrate how these were reduced progressively. The sequence in which the exclusion layers are applied does not affect the final PAR estimates. Identifying PvMECs Those countries that currently support P. vivax transmission were first identified. The primary sources for defining national risk were international travel and health guidelines [25], [26] augmented with national survey information, pertinent published sources and personal communication with malariologists. Nations were grouped into three regions, as described elsewhere [19]: i) America; ii) Africa, Saudi Arabia and Yemen (Africa+); and iii) Central and South East (CSE) Asia. To further resolve PAR estimates, the CSE Asia region was sub-divided into West Asia, Central Asia and East Asia (Protocol S1). Mapping case-reporting data Methods described previously for mapping the global spatial limits of P. falciparum malaria [18] were used to constrain the area defined at risk within the PvMECs using PvAPI data (the number of confirmed P. vivax malaria cases reported per administrative unit per 1,000 people per annum (p.a.)). The PvAPI data were obtained mostly through personal communication with individuals and institutions linked to malaria control in each country (Protocol S1). The format in which these data were available varied considerably between countries. Ideally, the data would be available by administrative unit and by year, with each record presenting the estimated population for the administrative unit and the number of confirmed autochthonous malaria cases by the two main parasite species (P. falciparum and P. vivax). This would allow an estimation of species-specific API. These requirements, however, were often not met. Population data by administrative unit were sometimes unavailable, in which cases these data were sourced separately or extrapolated from previous years. An additional problem was the lack of parasite species-specific case or API values. In such cases, a parasite species ratio was inferred from alternative sources and applied to provide an estimate of species-specific API. There was, thus, significant geographical variation in the ability to look at the relative frequency of these parasites between areas and this was not investigated further. Finally, although a differentiation between confirmed and suspected cases and between autochthonous and imported cases was often provided, whenever this was not available it was assumed that the cases in question referred to confirmed and autochthonous occurrences. The aim was to collate data for the last four years of reporting (ideally up to 2009) at the highest spatial resolution available (ideally at the second administrative level (ADMIN2) or higher). A geo-database was constructed to archive this information and link it to digital administrative boundaries of the world available from the 2009 version of the Global Administrative Unit Layers (GAUL) data set, implemented by the Food and Agriculture Organization of the United Nations (FAO) within the EC FAO Food Security for Action Programme [27]. The PvAPI data were averaged over the period available and were used to classify areas as malaria free, unstable (<0.1 case per 1,000 p.a.) or stable (≥0.1 case per 1,000 p.a.) transmission, based upon metrics advised during the Global Malaria Eradication Programme [28]–[30]. These data categories were then mapped using ArcMAP 9.2 (ESRI 2006). Biological masks of exclusion of risk To further constrain risk within national territories, two “masks” of biological exclusion were implemented (Protocol S2). First, risk was constrained according to the relationship between temperature and the duration of sporogony, based upon parameters specific to P. vivax [31]. Synoptic mean, maximum and minimum monthly temperature records were obtained from 30-arcsec (∼1×1 km) spatial resolution climate surfaces [32]. For each pixel, these values were converted, using spline interpolation, to a continuous time series representing a mean temperature profile across an average year. Diurnal variation was represented by adding a sinusoidal component to the time series with a wavelength of 24 hours and the amplitude varying smoothly across the year determined by the difference between the monthly minimum and maximum values. For P. vivax transmission to be biologically feasible, a cohort of anopheline vectors infected with P. vivax must survive long enough for sporogony to complete within their lifetime. Since the rate of parasite development within anophelines is strongly dependent on ambient temperature, the time required for sporogony varies continuously as temperatures fluctuate across a year [31]. For each pixel, the annual temperature profile was used to determine whether any periods existed in the year when vector lifespan would exceed the time required for sporogony, and hence when transmission was not precluded by temperature. This was achieved via numerical integration whereby, for cohorts of vectors born at each successive 2-hour interval across the year, sporogony rates varying continuously as a function of temperature were used to identify the earliest time at which sporogony could occur. If this time exceeded the maximum feasible vector lifespan, then the cohort was deemed unable to support transmission. If sporogony could not complete for any cohort across the year, then the pixel was classified as being at zero risk. Vector lifespan was defined as 31 days since estimates of the longevity of the main dominant vectors [33] indicate that 99% of anophelines die in less than a month and, therefore, would be unable to support parasite development in the required time. The exceptions were areas that support the longer-lived Anopheles sergentii and An. superpictus, where 62 days were considered more appropriate (Protocol S2) [18]. The second mask was based on the effect of arid conditions on anopheline development and survival [34]. Limited surface water reduces the availability of sites suitable for oviposition and reduces the survival of vectors at all stages of their development through the process of desiccation [35]. The ability of adult vectors to survive long enough to contribute to parasite transmission and of pre-adult stages to ensure minimum population abundance is, therefore, dependent on the levels of aridity and species-specific resilience to arid conditions. Extremely arid areas were identified using the global GlobCover Land Cover product (ESA/ESA GlobCover Project, led by MEDIAS-France/POSTEL) [36]. GlobCover products are derived from data provided by the Medium Resolution Imaging Spectrometer (MERIS), on board the European Space Agency's (ESA) ENVIronmental SATellite (ENVISAT), for the period between December 2004 and June 2006, and are available at a spatial resolution of 300 meters [36]. The layer was first resampled to a 1×1 km grid using a majority filter, and all pixels classified as “bare areas” by GlobCover were overlaid onto the PvAPI surface. The aridity mask was treated differently from the temperature mask to allow for the possibility of the adaptation of human and vector populations to arid environments [37]–[39]. A more conservative approach was taken, which down-regulated risk by one class. In other words, GlobCover's bare areas defined originally as at stable risk by PvAPI were stepped down to unstable risk and those classified initially as unstable to malaria free. Medical intelligence modulation of risk Medical intelligence contained in international travel and health guidelines [25], [26] was used to inform risk exclusion and down-regulation in specific urban areas and sub-national territories, which are cited as being free of malaria transmission (Protocol S3). Additional medical intelligence and personal communication with malaria experts helped identify further sub-national areas classified as malaria free in Cambodia, Vanuatu and Yemen. Specified urban areas were geo-positioned and their urban extents were identified using the Global Rural Urban Mapping Project (GRUMP) urban extents layer [40]. Rules of risk modulation within these urban extents were as follows: i) risk within urban extents falling outside the range of the urban vector An. stephensi [41] (Protocol S3) was excluded; ii) risk within urban areas inhabited by An. stephensi was down-regulated by one level from stable to unstable and from unstable to free (Protocol S3). Specified sub-national territories were classified as malaria free if not already identified as such by the PvAPI layer and the biological masks. These territories were mapped using the GAUL data set [27]. Duffy negativity phenotype Since Duffy negativity provides protection against infection with P. vivax [42], a continuous map of the Duffy negativity phenotype was generated from a geostatistical model fully described elsewhere (Howes et al., manuscript in preparation). The model was informed by a database of Duffy blood group surveys assembled from thorough searches of the published literature and supplemented with unpublished data by personal communication with relevant authors. Sources retrieved were added to existing Duffy blood group survey databases [43], [44]. The earliest inclusion date for surveys was 1950, when the Duffy blood group was first described [45]. To model the Duffy system and derive a global prediction for the frequency of the homozygous Duffy negative phenotype ([Fy(a-b-)], which is encoded by the homozygous FY*B ES /*B ES genotype), the spatially variable frequencies of the two polymorphic loci determining Duffy phenotypes were modelled: i) nucleotide −33 in the gene's promoter region, which defines positive/negative expression (T-33C); ii) the coding region locus (G125A) determining the antigen type expressed: Fya or Fyb [46]. Due to the wide range of diagnostic methods used to describe Duffy blood types in recent decades, data were recorded in a variety of forms, each providing differing information about the frequency of variants at both loci. For example, some molecular studies sequenced only the gene's promoter region, and thus could not inform the frequency of the coding region variant; serological diagnoses only testing for the Fya antigen could not distinguish Fyb from the Duffy negative phenotype. As part of the larger dataset, however, these incomplete data types can indirectly inform frequencies of negativity. Therefore, despite only requiring information about the promoter locus to model the negativity phenotype, variant frequencies at both polymorphic sites were modelled. This allowed the full range of information contained in the dataset to be used rather than just the subset specifically reporting Duffy negativity frequencies. The model's general architecture and Bayesian framework will be described elsewhere (Howes et al., manuscript in preparation). Briefly, the dataset of known values at fixed geographic locations was used to predict expression frequencies at each locus in all geographic sites where no data were available, thereby generating continuous global surfaces of the frequency of each variant. From the predicted frequency of the promoter region variant encoding null expression (-33C), a continuous frequency map of the Duffy negative population was derived. Estimating the population at risk of P. vivax transmission The GRUMP beta version provides gridded population counts and population density estimates for the years 1990, 1995, and 2000, both adjusted and unadjusted to the United Nations' national population estimates [40]. The adjusted population counts for the year 2000 were projected to 2009 by applying national, medium variant, urban and rural-specific growth rates by country [47]. These projections were undertaken using methods described previously [48], but refined with urban growth rates being applied solely to populations residing within the GRUMP urban extents, while the rural growth rates were applied to the remaining population. This resulted in a 2009 population count surface of approximately 1×1 km spatial resolution, which was used to extract PAR figures. The PAR estimates in Africa were corrected for the presence of the Duffy negativity phenotype by multiplying the extracted population by [1 - frequency of Duffy negative individuals]. Results Plasmodium vivax malaria endemic countries A total of 109 potentially endemic countries and territories listed in international travel and health guidelines were identified [25], [26]. Ten of these countries: Algeria, Armenia, Egypt, Jamaica (P. falciparum only), Mauritius, Morocco, Oman, Russian Federation, Syrian Arab Republic and Turkmenistan have either interrupted transmission or are extremely effective at dealing with minor local outbreaks. These nations were not classified as PvMECs and are all considered to be in the elimination phase by the Global Malaria Action Plan [24]. Additionally, four malaria endemic territories report P. falciparum transmission only: Cape Verde [49], the Dominican Republic [50], Haiti [50], [51] and Mayotte [52]. This resulted in a global total of 95 PvMECs. Figure 1 summarises the various layers applied on the 95 PvMECs in order to derive the limits of P. vivax transmission. The results of these different steps are described below. Defining the spatial limits of P. vivax transmission at sub-national level PvAPI data were available for 51 countries. Data for four countries were available up to 2009. For 29 countries the last year of reporting was 2008, whilst 2007 and 2006 were the last years available for 11 and six countries, respectively. For Colombia the last reporting year was 2005. No HMIS data could be obtained for Kyrgyzstan and Uzbekistan, for which information contained in the most recent travel and health guidelines [25], [26] was used to map risk. With the exception of Namibia, Saudi Arabia, South Africa and Swaziland, which were treated like all other nations, no HMIS data were solicited for countries in the Africa+ region, where stable risk of P. vivax transmission was assumed to be present throughout the country territories. In Botswana, stable risk was assumed in northern areas as specified by travel and health guidelines [25], [26]. Amongst those countries for which HMIS data were available, 16 reported at ADMIN1 and 29 at ADMIN2 level. For Southern China, Myanmar, Nepal and Peru, data were available at ADMIN3 level. Data for Namibia and Venezuela were resolved at ADMIN1 and ADMIN2 levels. In total, 17,591 administrative units were populated with PvAPI data. Protocol S1 describes these data in detail. Figure 2 shows the spatial extent of P. vivax transmission as defined by the PvAPI data, with areas categorised as malaria free, unstable (PvAPI<0.1 case per 1,000 p.a.) or stable (PvAPI≥0.1 case per 1,000 p.a.) transmission [29]. 10.1371/journal.pntd.0000774.g002 Figure 2 Plasmodium vivax malaria risk defined by PvAPI data. Transmission was defined as stable (red areas, where PvAPI≥0.1 per 1,000 people p.a.), unstable (pink areas, where PvAPI<0.1 per 1,000 p.a.) or no risk (grey areas). The boundaries of the 95 countries defined as P. vivax endemic are shown. Biological masks to refine the limits of transmission Figure 3 shows the limits of P. vivax transmission after overlaying the temperature mask on the PvAPI surface. The P. vivax-specific temperature mask was less exclusive of areas of risk than that derived for P. falciparum [18]. Exclusion of risk was mainly evident in the Andes, the southern fringes of the Himalayas, the eastern fringe of the Tibetan plateaux, the central mountain ridge of New Guinea and the East African, Malagasy and Afghan highlands. There was a remarkable correspondence between PvAPI defined risk in the Andean and Himalayan regions and the temperature mask, which trimmed pixels of no risk at very high spatial resolution in these areas. 10.1371/journal.pntd.0000774.g003 Figure 3 Further refinement of Plasmodium vivax transmission risk areas using the temperature layer of exclusion. Risk areas are defined as in Figure 2. The aridity mask used here [36] was more contemporary and derived from higher spatial resolution imagery than the one used to define the limits of P. falciparum [18]. Figure 4 shows that the effects of the aridity mask were more evident in the Sahel and southern Saharan regions, as well as the Arabian Peninsula. In the western coast of Saudi Arabia, unstable risk defined by the PvAPI layer was reduced to isolated foci of unstable risk by the aridity mask. In Yemen, stable risk was constrained to the west coast and to limited pockets along the southern coast. Similarly, endemic areas of stable risk defined by PvAPI data in southern Afghanistan, southern Iran and throughout Pakistan were largely reduced to unstable risk by the aridity mask. 10.1371/journal.pntd.0000774.g004 Figure 4 Aridity layer overlaid on the PvAPI and temperature layers. Risk areas are defined as in Figure 2. Medical intelligence used to refine risk The two international travel and health guidelines consulted [25], [26] cite 59 specific urban areas in 31 countries as being malaria free, in addition to urban areas in China, Indonesia (those found in Sumatra, Kalimantan, Nusa Tenggara Barat and Sulawesi) and the Philippines (Protocol S3). A total of 42 of these cities fell within areas classified as malarious and amongst these, eight were found within the range of An. stephensi, as were some urban areas in south-western Yunnan, China. Risk in the latter was down-regulated from stable to unstable and from unstable to free due to the presence of this urban vector. In the remaining 34 cities and other urban areas in China, Indonesia and the Philippines, risk was excluded. In addition, 36 administrative units, including islands, are cited as being malaria free (Protocol S3). These territories were excluded as areas of risk, if not already classified as such by the PvAPI surface and biological masks. In addition, the island of Aneityum, in Vanuatu [53], the area around Angkor Watt, in Cambodia, and the island of Socotra, in Yemen [54], were classified as malaria free following additional medical intelligence and personal communication with malaria experts from these countries. Frequency of Duffy negativity From the assembled library of references, 821 spatially unique Duffy blood type surveys were identified. Globally the data points were spatially representative, with 265 in America, 213 in Africa+ (167 sub-Saharan), 207 in CSE Asia and 136 in Europe. The total global sampled population was 131,187 individuals, with 24,816 (18.9%) in Africa+ and 33 African countries represented in the final database. The modelled global map of Duffy negativity (Figure 5) indicates that the P. vivax resistant phenotype is rarely seen outside of Africa, and, when this is the case, it is mainly in localised New World migrant communities. Within Africa, the predicted prevalence was strikingly high south of the Sahara. Across this region, the silent Duffy allele was close to fixation in 31 countries with 95% or more of the population being Duffy negative. Frequencies fell sharply into southern Africa and into the Horn of Africa. For instance, the frequency of Duffy negativity in the South African population was 62.7%, increasing to 65.0% in Namibia and 73.5% across Madagascar. The situation was predicted to be highly heterogeneous across Ethiopia, with an estimated 50.0% of the overall population being Duffy negative. 10.1371/journal.pntd.0000774.g005 Figure 5 The global spatial limits of Plasmodium vivax malaria transmission in 2009. Risk areas are defined as in Figure 2. The medical intelligence and predicted Duffy negativity layers are overlaid on the P. vivax limits of transmission as defined by the PvAPI data and biological mask layers. Areas where Duffy negativity prevalence was estimated as ≥90% are hatched, indicating where PAR estimates were modulated most significantly by the presence of this genetic trait. Populations at risk of P. vivax transmission The estimated P. vivax endemic areas and PAR for 2009 are presented in Table 1, stratified by unstable (PvAPI<0.1 per 1,000 p.a.) and stable (PvAPI≥0.1 per 1,000 p.a.) risk of transmission, globally and by region and sub-region. It was estimated that there were 2.85 billion people at risk of P. vivax transmission worldwide in 2009, the vast majority (91.0%) inhabiting the CSE Asia region, 5.5% living in America and 3.4% living in Africa+, after accounting for Duffy negativity. An estimated 57.1% of the P. vivax PAR in 2009 lived in areas of unstable transmission, with a population of 1.63 billion. 10.1371/journal.pntd.0000774.t001 Table 1 Regional and global areas and PAR of Plasmodium vivax malaria in 2009. Region Area (km2) PAR (millions) Unstable Stable Any risk Unstable Stable Any risk Africa+ 4,812,618 17,980,708 22,793,326 20.1 77.9 98.0 America 1,368,380 8,087,335 9,455,715 99.0 58.8 157.8 CSE Asia 5,848,939 6,127,549 11,976,488 1,509.0 1,084.2 2,593.2 West Asia 2,007,247 2,800,612 4,807,859 653.9 845.2 1,499.2 Central Asia 3,156,574 1,277,219 4,433,793 694.3 129.2 823.4 East Asia 685,118 2,049,717 2,734,835 160.8 109.8 270.6 World 12,029,937 32,195,600 44,225,537 1,628.1 1,220.9 2,849.0 Country level PAR estimates are provided in Protocol S4. The ten countries with the highest estimated PAR, in descending order, were: India, China, Indonesia, Pakistan, Viet Nam, Philippines, Brazil, Myanmar, Thailand and Ethiopia. PAR estimates in India accounted for 41.9% of the global PAR estimates, with 60.3% of the more than one billion PAR (1.19 billion) living in stable transmission areas. The situation in China was different as, according to the PvAPI input data, areas of stable transmission were only found in the southern provinces of Yunnan and Hainan, and in the north-eastern province of Anhui, which reported an unusually high number of cases up to 2007. The latter is in accordance with a recent report documenting the resurgence of malaria in this province [55]. Transmission in the rest of China was largely negligible, with PvAPI values well below 0.1 case per 1,000 people p.a. Given the reported cases, however, these were classified as unstable transmission areas and the total PAR estimated within them, after urban exclusions, was 583 million people. All other countries reporting the highest PAR were in CSE Asia, with the exception of Brazil and Ethiopia. Discussion We present a contemporary evidence-based map of the global distribution of P. vivax transmission developed from a combination of mapped sub-national HMIS data, biological rules of transmission exclusion and medical intelligence. The methods used were developed from those implemented for P. falciparum malaria [18] and can be reproduced following the sequence of data layer assemblies and exclusions illustrated in Figure 1. Plasmodium vivax is transmitted within 95 countries in tropical, sub-tropical and temperate regions, reaching approximately 43 degrees north in China and approximately 30 degrees south in Southern Africa. The fact that P. vivax has a wider range than P. falciparum [18] is facilitated by two aspects of the parasite's biology [56]: i) its development at lower temperatures during sporogony [31]; and ii) its ability to produce hypnozoites during its life cycle in the human host [57]. The sporogonic cycle of P. vivax is shorter (i.e. a lower number of degree days required for its completion) and the parasite's sexual stage is active at lower temperatures than other human malaria parasites (Protocol S2) [31]. Consequently, generation of sporozoites is possible at higher altitudes and more extreme latitudes. In the human host, hypnozoites of P. vivax temperate strains can relapse anywhere between months and years after the initial infection, often temporally coincident with optimal climatic conditions in a new transmission season [10], [57]. The resulting maps produced an estimate of 2.85 billion people living at risk of P. vivax malaria transmission in 2009. The distribution of P. vivax PAR is very different from that of P. falciparum [18], due to the widespread distribution of P. vivax in Asia, up to northern China, and the high prevalence of the Duffy negativity phenotype in Africa. China accounts for 22.0% of the global estimated P. vivax PAR, although 93.1% of these people live in areas defined as unstable transmission (Protocol S4). An important caveat is that PvAPI data from central and northern China could only be accessed at the lowest administrative level (ADMIN1) (Protocol S1). The very high population densities found in this country exacerbate the problem, inevitably biasing PAR estimates, despite urban areas in China being excluded from the calculations following information from the sources of medical intelligence that were consulted [25], [26]. Malaria transmission in most of these unstable transmission areas in China is probably negligible given the very few cases reported between 2003 and 2007. It is important to stress the necessity to access PvAPI data at a higher spatial resolution from China (i.e. at the county level) in order to refine these estimates and minimise biases. In Africa, the modelled prevalence of Duffy negativity shows that very high rates of this phenotype are present in large swaths of West and Central Africa (Figure 5). One of the functions of the Duffy antigen is being a receptor of P. vivax [46] and its absence has been shown to preclude infection with this parasite [58], [59], although the extent of this has been questioned [60]–[63]. There is no doubt that the African continent has a climate highly conducive to P. vivax transmission (Protocol S2). Moreover, dominant African Anopheles have been shown to be competent vectors of this parasite [62], [64], [65]. In addition, there is a plethora of evidence of P. vivax transmission in Africa, mostly arising from travel-acquired P. vivax infections during visits to malaria endemic African countries (Table 2; Protocol S1). This evidence supports the hypothesis that P. vivax may have been often misdiagnosed as P. ovale in the region due to a combination of morphological similarity and the prevailing bio-geographical dogma driven by the high prevalence of Duffy negativity [60]. Despite the fact that the risk of P. vivax is cosmopolitan, PAR estimates in Africa were modulated according to the high limitations placed on infection by the occurrence of the Duffy negative trait. Consequently, the PAR in the Africa+ region accounts for only 3.5% of the global estimated P. vivax PAR. Although recent work has shown 42 P. vivax infections amongst 476 individuals genotyped as Duffy negative across eight sites in Madagascar [63], we have taken a conservative approach and consider it premature to relax the Duffy exclusion of PAR across continental Africa until this study has been replicated elsewhere. 10.1371/journal.pntd.0000774.t002 Table 2 Published evidence of Plasmodium vivax malaria transmission in African countries. Country References* Angola [68]–[73] Benin [68], [70], [71], [74] Botswana [72] Burkina Faso [68], [71] Burundi [70]–[73] Cameroon [68], [69], [71]–[79] Cen. African Rep. [68] Chad [74] Comoros [68] Congo [68], [70], [71], [73], [74], [76], [77], [80] Côte d'Ivoire [68]–[71], [73], [74], [76], [78] Congo (DR) [68], [81] Djibouti [68], [78] Equatorial Guinea [82] Eritrea [71], [73], [76], [77], [83], [84] Ethiopia [68]–[74], [76]–[79], [85] Gabon [68], [71], [86] Gambia [71], [72], [76], [78] Ghana [69]–[74], [76]–[79] Guinea [68], [69], [71], [76], [77] Kenya [68]–[73], [76]–[79] Liberia [68]–[73], [76]–[79] Madagascar [68]–[73], [76], [78], [87] Malawi [68], [70], [72], [73] Mali [68], [69], [71] Mauritania [68], [69], [71], [72], [76], [77], [88], [89] Mozambique [68]–[71], [73], [76], [79], [90] Namibia [70] Niger [68], [69], [71], [76] Nigeria [69]–[74], [76]–[79], [91] Rwanda [68], [71], [72], [78] São Tomé and Príncipe [68], [92] Senegal [68], [70], [71], [73], [76], [77] Sierra Leone [68], [69], [72]–[74], [76], [78] Somalia [69], [70], [78], [79], [93] South Africa [69]–[71], [76]–[78] Sudan [68]–[74], [76], [77], [79], [94] Togo [70], [71] Uganda [69]–[74], [76]–[79], [95] Tanzania [68]–[72], [76], [77], [79] Zambia [69]–[72], [78], [96] Zimbabwe [68], [69], [71] *The cited references mostly document imported cases from Africa. Evidence of transmission of P. vivax in Guinea Bissau and Swaziland could not be found in the published literature. Mapping the distribution of P. vivax malaria has presented a number of unique challenges compared to P. falciparum, some of which have been addressed by the methods used here. The influence of climate on parasite development has been allowed for by implementing a temperature mask parameterised specifically for the P. vivax life cycle. The question of Duffy negativity and P. vivax transmission has also been addressed by modelling the distribution of this phenotype and by allowing the predicted prevalence to modulate PAR. It is also worth noting that the accuracy of HMIS for P. vivax clinical cases, particularly in areas of coincidental P. falciparum risk, is notoriously poor [66], in part because microscopists are less likely to record the presence of a parasite assumed to be clinically less important. Here, HMIS data were averaged over a period of up to four years and used to differentiate malaria free areas from those that are malarious. Within the latter, a conservative threshold was applied to classify risk areas as being of unstable (PvAPI<0.1 per 1,000 p.a.) or stable (PvAPI≥0.1 per 1,000 p.a.) transmission [29]. We believe that this conservative use of HMIS data balances, to some extent, anomalies introduced by P. vivax underreporting and the correspondence of the biological masks and PvAPI data in many areas is reassuring. The intensity of transmission within the defined stable limits of P. vivax risk will vary across this range and this will be modelled using geostatistical techniques similar to those developed recently for P. falciparum [19]. This modelling work will be cognisant of the unique epidemiology of P. vivax. First, in areas where P. vivax infection is coincidental with P. falciparum, prevalence of the former may be suppressed by cross-species immunity [67] or underestimated by poor diagnostics [66]. Second, there is the ability of P. vivax to generate hypnozoites that lead to relapses. These characteristics render the interpretation of prevalence measures more problematic [5]. Third, the prevalence of Duffy negativity provides protection against infection in large sections of the population in Africa [58], [59]. An appropriate modelling framework is under development and will be the subject of a subsequent paper mapping P. vivax malaria endemicity using parasite prevalence data. These data are being collated in the MAP database, with nearly 9,000 P. vivax parasite rate records archived by 01 March 2010. Supporting Information Protocol S1 Defining risk of transmission of Plasmodium vivax using case reporting data. Document describing more extensively one of the layers used to create the final map. (2.87 MB DOC) Click here for additional data file. Protocol S2 Defining the global biological limits of Plasmodium vivax transmission. Document describing more extensively two of the layers used to create the final map. (0.42 MB DOC) Click here for additional data file. Protocol S3 Risk modulation based upon medical intelligence. Document describing more extensively one of the layers used to create the final map. (0.36 MB DOC) Click here for additional data file. Protocol S4 Country level area and population at risk of Plasmodium vivax malaria in 2009. Country-level table of the estimated area and populations at risk of P. vivax malaria in 2009 (0.16 MB DOC) Click here for additional data file.
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Journal ID (nlm-ta): PLoS One
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Publisher ID: PONE-D-13-53431DOI: 10.1371/journal.pone.0103262
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2013
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June
2014
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This work was supported by the Department of Biotechnology (DBT), New Delhi, India
and the Third World Academy of Sciences (TWAS), Trieste, Italy. The Indian Council
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Categories
Subject:
Research Article
Subject:
Biology and Life Sciences
Subject:
Genetics
Subject:
Genomics
Subject:
Organisms
Subject:
Protozoans
Subject:
Parasitic Protozoans
Subject:
Malarial Parasites
Subject:
Plasmodium Vivax
Subject:
Microbiology
Subject:
Population Biology
Subject:
Medicine and Health Sciences
Subject:
Epidemiology
Subject:
Infectious Disease Epidemiology
Subject:
Infectious Diseases
Subject:
Emerging Infectious Diseases
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Parasitic Diseases
Subject:
Malaria
Subject:
Tropical Diseases
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