- Record: found
- Abstract: found
- Article: found
Thrombocytopenia in Plasmodium vivax Malaria Is Related to Platelets Phagocytosis
research-article
Author(s):
Helena Cristina C. Coelho
1
,
2 ,
Stefanie C. P. Lopes
3 ,
João Paulo D. Pimentel
4
,
6 ,
Paulo A. Nogueira
4 ,
Fábio T. M. Costa
3 ,
André M. Siqueira
1
,
2 ,
Gisely C. Melo
1 ,
Wuelton M. Monteiro
2
,
5 ,
Adriana Malheiro
5
,
6 ,
Marcus V. G. Lacerda
1
,
2
,
*
Publication date (Electronic):
28 May 2013
Journal:
PLoS ONE
Publisher:
Public Library of Science
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Abstract
Background
Although thrombocytopenia is a hematological disorder commonly reported in malarial patients, its mechanisms are still poorly understood, with only a few studies focusing on the role of platelets phagocytosis.
Methods and Findings
Thirty-five malaria vivax patients and eight healthy volunteers (HV) were enrolled in the study. Among vivax malaria patients, thrombocytopenia (<150,000 platelets/µL) was found in 62.9% (22/35). Mean platelet volume (MPV) was higher in thrombocytopenic patients as compared to non- thrombocytopenic patients (p = 0.017) and a negative correlation was found between platelet count and MPV (r = −0.483; p = 0.003). Platelets from HV or patients were labeled with 5-chloromethyl fluorescein diacetate (CMFDA), incubated with human monocytic cell line (THP-1) and platelet phagocytosis index was analyzed by flow cytometry. The phagocytosis index was higher in thrombocytopenic patients compared to non-thrombocytopenic patients (p = 0.042) and HV (p = 0.048). A negative correlation was observed between platelet count and phagocytosis index (r = −0.402; p = 0.016). Platelet activation was assessed measuring the expression of P-selectin (CD62-P) in platelets’ surface by flow cytometry. No significant difference was found in the expression of P-selectin between thrombocytopenic patients and HV (p = 0.092). After evaluating the cytokine profile (IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ and IL-17) in the patients’ sera, levels of IL-6, IL-10 and IFN-γ were elevated in malaria patients compared to HV. Moreover, IL-6 and IL-10 values were higher in thrombocytopenic patients than non-thrombocytopenic ones (p = 0.044 and p = 0.017, respectively. In contrast, TNF-α levels were not different between the three groups, but a positive correlation was found between TNF-α and phagocytosis index (r = −0.305; p = 0.037).
Conclusion/Significance
Collectively, our findings indicate that platelet phagocytosis may contribute to thrombocytopenia found in vivax malaria. Finally, we believe that this study opens new avenues to explore the mechanisms involved in platelet dysfunction, commonly found in vivax malaria patients.
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Most cited references43
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- Abstract: found
- Article: found
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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Malaria in Brazil: an overview
Malaria is still a major public health problem in Brazil, with approximately 306 000 registered cases in 2009, but it is estimated that in the early 1940s, around six million cases of malaria occurred each year. As a result of the fight against the disease, the number of malaria cases decreased over the years and the smallest numbers of cases to-date were recorded in the 1960s. From the mid-1960s onwards, Brazil underwent a rapid and disorganized settlement process in the Amazon and this migratory movement led to a progressive increase in the number of reported cases. Although the main mosquito vector (Anopheles darlingi) is present in about 80% of the country, currently the incidence of malaria in Brazil is almost exclusively (99,8% of the cases) restricted to the region of the Amazon Basin, where a number of combined factors favors disease transmission and impair the use of standard control procedures. Plasmodium vivax accounts for 83,7% of registered cases, while Plasmodium falciparum is responsible for 16,3% and Plasmodium malariae is seldom observed. Although vivax malaria is thought to cause little mortality, compared to falciparum malaria, it accounts for much of the morbidity and for huge burdens on the prosperity of endemic communities. However, in the last few years a pattern of unusual clinical complications with fatal cases associated with P. vivax have been reported in Brazil and this is a matter of concern for Brazilian malariologists. In addition, the emergence of P. vivax strains resistant to chloroquine in some reports needs to be further investigated. In contrast, asymptomatic infection by P. falciparum and P. vivax has been detected in epidemiological studies in the states of Rondonia and Amazonas, indicating probably a pattern of clinical immunity in both autochthonous and migrant populations. Seropidemiological studies investigating the type of immune responses elicited in naturally-exposed populations to several malaria vaccine candidates in Brazilian populations have also been providing important information on whether immune responses specific to these antigens are generated in natural infections and their immunogenic potential as vaccine candidates. The present difficulties in reducing economic and social risk factors that determine the incidence of malaria in the Amazon Region render impracticable its elimination in the region. As a result, a malaria-integrated control effort - as a joint action on the part of the government and the population - directed towards the elimination or reduction of the risks of death or illness, is the direction adopted by the Brazilian government in the fight against the disease.
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The Limits and Intensity of Plasmodium falciparum Transmission: Implications for Malaria Control and Elimination Worldwide
Introduction The magnitude of the public health burden posed by malaria worldwide [1] and its connection to poverty [2] has galvanized the international donor community to put malaria control high on the development agenda and helped leverage unprecedented additional financing for malaria endemic countries [3]. Progress toward agreed targets of intervention coverage has been slow [4–6], but recent evidence indicates a precipitous increase in access to effective drugs and prevention strategies in several countries [7–10]. In part, this renaissance in malaria control has served as a catalyst to revisit the possibility of malaria elimination in many regions and countries [11–14]. A changing malaria landscape requires an accurate spatial and dynamic description of malaria risk that maps the spatial extent and need for control and elimination over the coming decades. Such a map is conspicuous by its absence [15]. Here, we present the first detailed description of the global distribution of P. falciparum risk in 40 y [16,17] by using geopositioned assemblies of national surveillance of malaria risk, medical intelligence, biological models of transmission suitability, and surveys of parasite prevalence. The paper focuses on detailing the data sources and their adaptation for the malaria cartography necessary to guide current disease control, with an emphasis on how we define the spatial limits of stable and unstable P. falciparum risk worldwide. Methods Using Medical Intelligence to Define the Limits of P. falciparum Risk Many countries have information assembled from medical intelligence on the distribution of malaria risk within their national borders. This information is documented primarily in reports from national health information systems that define the annual numbers of confirmed parasite-specific local malaria infections by geographic unit, referred to classically as the annual parasite incidence (API) [18–21]. The API is generated from various combinations of active (fever surveys in communities where every person presenting with a fever is tested for parasite infection) and passive (reports from febrile patients attending the local health services) case detection, and usually expresses the combined results as the number infected per 1,000 people per annum (pa) [18–21]. The precision of these estimates of malaria incidence are highly variable, and with the exception of some countries where case identification is a primary control tool [22], these data cannot be used confidently to derive the public health burden posed by malaria [1,23–26]. They can, however, be a useful indicator of where local parasite species-specific malaria risk is likely or absent, and are particularly plausible when triangulated with other sources of medical intelligence, reported in international travel health guidelines or by national malaria control programmes. Malaria coordinating officers in the regional offices of the World Health Organization (WHO), responsible for the collation of national API data from member countries were contacted to obtain data reported nationally to the highest possible geographic administrative unit level on populations at risk and numbers of confirmed P. falciparum cases, for as many years as were available between 2002 and 2006. Among the countries in the American Regional Office, P. falciparum–specific API (PfAPI) data from national surveillance systems in Brazil, Colombia, Peru, and Honduras were obtained directly from personal communication with malaria specialists. The reported cases of confirmed P. falciparum malaria per 1,000 resident population were computed for each year by administrative level and averaged over the number of reporting years. Summary data were categorized as no autochthonous P. falciparum cases reported, <0.1 autochthonous P. falciparum cases per 1,000 people pa, and ≥0.1 autochthonous P. falciparum cases per 1,000 people pa. The threshold around 0.1 cases per thousand pa was used to provide some indication of unstable conditions versus more stable transmission. This threshold is consistent with previous uses of PfAPI during the Global Malaria Eradication Programme [27] and balanced against the confidence in the precision of reported PfAPI values (Protocol S1). Each PfAPI summary estimate was mapped by matching it to its corresponding first-, second-, or third-level administrative unit in a geographic information system (GIS; ArcView GIS 3.2, ESRI, 1999). Mapped PfAPI data were then compared to other sources of medical intelligence, notably national malaria control presentations at regional malaria meetings obtained from regional WHO malaria coordinators and from Web sites, published sources that described national malaria epidemiology, and international travel and health guidelines [28,29]. These combined approaches were particularly useful to identify mapped descriptions of risk defined at higher spatial resolution than those described by the PfAPI reported across large first-level administrative units. Details of all sources used are provided in Protocol S1. Defining the Biological Limits of P. falciparum Transmission Within the limits of risk described through PfAPI, environmental conditions suitable for transmission vary enormously. These variations can be captured at much higher spatial resolution than it is possible to define by stratifying risk at administrative unit levels. Climate-based determinants of parasite and vector development and survival were developed that impose biological constraints on the geographical limits of P. falciparum transmission. First, we used a combination of the temperature-dependant relationship between P. falciparum sporogony and the longevity of the main dominant vectors to estimate the proportion of vectors surviving parasite development (Protocol S2). Using mean monthly temperature records from a 30-arcsec (∼1 km) spatial resolution climate surface [30], the duration of P. falciparum sporogony was estimated for each synoptic calendar month, and those pixels where the duration of sporogony was 31 d or less were identified. The exception was small areas that potentially support the longer-lived Anopheles sergentii and A. superpictus, where 62 d were considered more appropriate biologically (Protocol S2). This resulted in 12 images with a binary outcome: P. falciparum sporogony could or could not be completed in the month. These images were then combined to identify the number of suitable months for P. falciparum transmission in a synoptic year. All pixels where the duration of sporogony exceeded 1 mo, or 2 mo for areas within the range of A. sergentii and A. superpictus, were masked since it was highly unlikely that transmission would occur. Second, there are areas within several malaria endemic countries where, despite temperature being suitable for sporogony, arid conditions restrict Anopheles development and survival [31]. Limited surface water reduces the availability of water bodies for oviposition. Moreover, low ambient humidity in arid environments further affects egg and adult survival through the process of desiccation [32]. The ability of adult vectors to survive long enough to contribute to parasite transmission and of preadult stages to ensure minimum population abundance is, therefore, dependent on the levels of aridity and species-specific resilience to arid conditions. To capture the influence of aridity on transmission we used the enhanced vegetation index (EVI) derived from the bidirectional reflectance-corrected MODerate-resolution Imaging Spectroradiometer (MODIS) sensor imagery, available at approximately 1-km spatial resolution [33,34] (Protocol S2). Temporal Fourier–processed, monthly EVI images were used to develop 12 monthly surfaces that reclassified EVI ≤ 0.1, assuming this corresponded to a good proxy for arid conditions [35,36]. Pixels were classified as suitable for transmission if their EVI values were higher than 0.1 for at least two consecutive months in an average year. This definition was based on the biological requirement, at optimum temperatures, of at least 12 d to complete vector development from egg to adult [37] and on the assumption that a second month is required for a sufficient vector population to establish and transmit malaria [38]. These reclassified aridity images were then overlaid in a GIS to produce 12 paired images. The 12 pairs were then combined to define pixels where conditions were suitable for transmission. The aridity mask was treated differently from the temperature-limiting mask to allow for the possibility, in arid environments, of highly over-dispersed transmission due to man-made water collection points and nomadic human populations transporting vectors and parasites [39–41]. A more conservative approach was taken, therefore, which down-regulated PfAPI risk by one class. In other words, extremely arid areas defined originally as at stable risk were stepped down to unstable risk and those classified initially as unstable to malaria free. Estimating Populations at P. falciparum Transmission Risk in 2007 The Global Rural Urban Mapping Project alpha 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 [42]. We used the adjusted population counts for the year 2000 and projected them to 2007 by applying national, medium variant, intercensal growth rates by country [43], using methods previously described [44]. This resulted in a contemporary population density surface of approximately 1-km spatial resolution, which was combined with the climate-adjusted PfAPI risk surface to extract population at risk estimates using ArcView GIS 3.2 (ESRI, 1999). Describing Global Patterns of Parasite Prevalence We have described previously the rigorous process of identifying, assembling, and geolocating community-based survey estimates of parasite prevalence undertaken since 1985 [45]. These data were used here to define the ranges of P. falciparum parasite prevalence rates (PfPR) in areas of stable and unstable malaria risk by WHO region. We acknowledge that these geopolitical boundaries do not necessarily conform to ecological or biological spatial representations of malaria [46,47]. They do, however, represent coherent regions of collective planning and cooperation for malaria control. In an attempt to minimize epidemiologically unrealistic divides for summary purposes, we have combined the Southeast Asian (SEARO) and Western Pacific (WPRO), as well as the Eastern Mediterranean (EMRO) and European (EURO) regions. The American WHO region (AMRO) and the African WHO region (AFRO) were considered separately. PfPR estimates were reported in various age groupings. To standardize to a single, representative age range of 2–10 y, we applied an algorithm based on catalytic conversion models first adapted for malaria by Pull and Grab [48] and described in detail elsewhere [49]. The geolocated and age-standardized prevalence data (PfPR2−10) [45] were overlaid on the PfAPI risk surface to extract a corresponding PfAPI value. Results PfAPI Data and Medical Intelligence to Define Spatial Limits of Transmission The PfAPI data identified 87 countries at risk of P. falciparum transmission between 2002 and 2006, which we now consider as P. falciparum endemic countries (PfMEC) in 2007 (Protocol S1). PfAPI data were mapped to first, second, or third administrative level units across 41 PfMECs covering a total of 8,789 unique polygons. These data incorporate complete years between 2002 and 2006, including summaries of three consecutive years for 16 countries, two consecutive years for eight countries, and the most recent complete year for 17 countries (Protocol S1). No information was available for 46 countries; mostly those in Africa. The spatial representation of no risk, unstable (PfAPI < 0.1 per 1,000 people pa), and stable risk (PfAPI ≥ 0.1 per 1,000 people pa) of P. falciparum transmission globally is shown in Figure 1, top panel. Figure 1 P. falciparum Malaria Risk Defined by Annual Parasite Incidence (top), Temperature, and Aridity (bottom) Areas were defined as stable (dark-red areas, where PfAPI ≥ 0.1 per thousand pa), unstable (pink areas, where PfAPI < 0.1 per thousand pa), or no risk (light grey). The few areas for which no PfAPI data could be obtained, mainly found in India, are coloured in dark grey. The borders of the 87 countries defined as P. falciparum endemic are shown. Highland areas where risk was excluded due to temperature appear in light grey. The aridity mask excluded risk in a step-wise fashion, reflected mainly in the larger extents of unstable (pink) areas compared to the top panel, particularly in the Sahel and southwest Asia (southern Iran and Pakistan). Temperature and Aridity Masks to Constrain Limits of Transmission Within the PfAPI limits of stable transmission (PfAPI ≥ 0.1 per 1,000 pa) on the African continent, the areas with no temperature-suitable months for transmission were congruent with the high altitude areas in Ethiopia, Eritrea, western Kenya, eastern Tanzania, Rwanda, Burundi, eastern Democratic Republic of the Congo, the Malagasy highlands, Mount Cameroon, and the eastern highland ranges in Zimbabwe (Figure 1, bottom panel). Outside of Africa, there was a close correspondence between the areas masked by the absence of reported autochthonous cases and areas classified as unsuitable for transmission based on low temperature in Andean and Himalayan areas (Figure 1, bottom panel). The application of the temperature mask provided a finer spatial resolution constraint to PfAPI data, particularly for the island of New Guinea and the highlands neighbouring the city of Sana'a, Yemen. Important reductions in the spatial areas of risk were also evident in some administrative units in Afghanistan, Bhutan, China, India, and Kyrgyzstan. The aridity mask constrained the mapped P. falciparum transmission risk to small pockets in large administrative boundaries from southern areas of Hilmand and Kandahar, in Afghanistan, the municipality of Djibouti, in Djibouti, and the south-eastern provinces of Iran. The risk areas along the Red Sea coast of Saudi Arabia were also reduced further using the aridity mask. Additional areas constrained within their spatial margins to no risk using the aridity mask included administrative units in India (n = 4), Pakistan (n = 9), Peru (n = 3), Kyrgyzstan (n = 2), Tajikistan (n = 1), and the low risk areas of Namibia bordering the Namib desert. Large areas covered by the aridity mask were reduced from stable (PfAPI ≥ 0.1 per 1,000 pa) to unstable risk (PfAPI < 0.1 per 1,000 pa) in the Sahel. The transmission reducing effects of aridity were also evidenced in Djibouti, Eritrea, northwest Kenya, northeast Ethiopia, northern Somalia, central and coastal areas of Yemen, and southern Pakistan. Importantly, these areas retained small pockets of higher, more-suitable transmission conditions, corresponding to river tributaries and irrigated land where higher transmission risk is supported [50]. Populations at Risk Table 1 provides a summary of the spatial extents and the projected 2007 populations at risk (PAR) within areas of assumed unstable (PfAPI < 0.1 per 1,000 pa) and stable P. falciparum transmission (PfAPI ≥ 0.1 per 1,000 pa) globally and by WHO region. Country PAR estimations are also provided (Table S1). We estimate that there are 2.37 billion people at risk of P. falciparum transmission worldwide, 26% located in the AFRO region and 62% in the combined SEARO-WPRO regions (Table 1). The definition of unstable risk outlined here is the predominant feature of exposure to transmission in the EMRO-EURO region (Table 1). Low-risk areas in AFRO were also coincident with arid, low population density areas. Globally, 42% of the population exposed to some risk of P. falciparum was classified as inhabiting areas of unstable transmission; the total population in these areas was 0.98 billion people. Table 1 Area and Population at Risk of P. falciparum Malaria in 2007 Global and Regional Summary of P. falciparum Parasite Prevalence The summary data on age-corrected PfPR are presented without adjustments for biological and climatic covariates, urbanization, congruence with dominant Anopheles vector species, or any sampling issues inherent in an opportunistic sample of this kind. This is the subject of ongoing work. The summarized data, however, do provide important new insights into the ranges of infection prevalence reported between regions of the world within the P. falciparum spatial limits of stable and unstable transmission. A total of 4,278 spatially unique cross-sectional survey estimates of PfPR were assembled as part of the activities of the Malaria Atlas Project (MAP) by 01 September 2007. These included 186 (4.4%) surveys that were not possible to geolocate and are not considered further in the analysis. Of the positioned survey data, 3,700 (90.4%) were derived from individual communities (about 10 km2 or less), 131 from wide areas (more than about 10 km2 and about 25 km2 or less), 145 from small polygons (more than about 25 km2 and about 100 km2 or less), and 116 from large polygons (more than about 100 km2) [45]. A total of 406 surveys were undertaken outside the defined spatial limits of P. falciparum transmission, of which 46 reported presence of P. falciparum infection in the populations surveyed and 360 reported zero prevalence after allowing for a 10-km buffer around the limits. Thus, the overall sensitivity adjusting for plausible positioning errors [51] was 98.5%. There were 611 surveys falling inside the limits that reported zero prevalence. Even using the 10-km buffer the specificity of the limits was low (37.1%). This reflects the difficulties in estimating zero prevalence without large sample sizes [52], as well as the over-dispersed nature of infection risks between communities within small spatial scales [53]. The global diversity of the age-corrected PfPR2–10 estimates within the limits of transmission is shown in Figures 2–5. A total of 253 surveys reported zero prevalence among 2,121 surveys undertaken in AFRO (Figure 2). Outside of Africa, 358 surveys reported zero prevalence among 1,565 surveys undertaken within the defined limits of transmission. Over 92% and 95% of surveys reporting PfPR2–10 ≥ 50% and ≥ 75%, respectively, were located in AFRO and concentrated mostly between 15° latitude north and south, areas inhabited by Anopheles gambiae s.s. [54] (Figure 2). Conversely lower estimates of PfPR2–10 were described among those surveys conducted in areas occupying the A. arabiensis–dominant regions along the Sahel, horn, and southern areas of Africa [54] (Figure 2). In AMRO (Figure 3) and EMRO-EURO (Figure 4), 87% and 65% of surveys reported PfPR2–10 below 10%, respectively, referred to classically as hypoendemic. Over 65% of PfPR2–10 survey estimates in the combined SEARO-WPRO region reported infection prevalence below 10% (Figure 5), including 218 surveys reporting zero prevalence. Figure 2 Community Surveys of P. falciparum Prevalence Conducted between 1985 and 2007 in AFRO Other regions are shown in Figures 3–5. Of the 4,278 surveys reported globally, 4,092 could be geopositioned of which 3,686, shown in these figures, fell within the predicted limits of P. falciparum malaria risk. A total of 406 records, not shown in the figures, were found outside the limits, of which 46 reported presence of P. falciparum. Data shown are age-standardized (PfPR2–10) and represented as a continuum from zero to 100%. Table 2 and Figure 6 present detailed descriptive statistics for these data. Figure 3 Community Surveys of P. falciparum Prevalence Conducted between 1985 and 2007 in AMRO Figure 4 Community Surveys of P. falciparum Prevalence Conducted between 1985 and 2007 in EMRO-EURO Figure 5 Community Surveys of P. falciparum Prevalence Conducted between 1985 and 2007 in SEARO-WPRO Table 2 Summaries of the P. falciparum Parasite Rate Data Reported between 1985 and 2007 and Mapped within the Spatial Limits of P. falciparum Malaria Figure 6 Box and Whisker Plots of PfPR2–10 by Period and WHO Regions Thick black lines are the medians, and the light-blue boxes represent interquartile ranges; whiskers show extreme, non-outlier observations. Empty circles represent mild and/or extreme outliers. Sample sizes correspond to those shown in Table 2. Despite notable gaps in the coverage of PfPR2–10 data in many areas (Figures 2–5), a summary of the ranges of prevalence survey estimates is provided in Table 2 and Figure 6. These data are presented for the whole time period (Figure 6, top panel) and stratified by time (Figure 6, middle and bottom panels). We stress that these data are not spatially congruent and therefore should not be viewed as representing secular changes in PfPR2–10 estimates by WHO region. The data used for the bottom panel of Figure 6 are potentially of greater value, however, when describing the endemicity characteristics of malaria within the spatial limits shown in Figure 1, as they represent the most contemporary summary of malaria endemicity judged by PfPR2–10. Discussion We have triangulated as much information as we could assemble from exhaustive searches to provide an improved evidence-based description of the limits of P. falciparum transmission globally. The spatial referencing of health statistics, medical intelligence, and national expert opinion represents, to our knowledge, the most complete, current framework to understand the global distribution of P. falciparum risk in 2007. The use of plausible biological constraints upon transmission, based on long-term temperature data and remotely sensed correlates of vegetation cover, improved the spatial precision of the limits and categories of risk. We estimate that there were 2.37 billion people at risk of P. falciparum worldwide in 2007, and 40.1 million km2 of the world might be able to support P. falciparum transmission. Assembling geographic information on disease risk is an iterative process, building on new data and identifying gaps in our knowledge. We have presented previously the distribution of P. falciparum using historical descriptions of risk [1,16] and through the reconciliation of information in multiple travel advisories [55,56]. None have been perfect representations of contemporary malaria distributions worldwide, but such work has initiated a dialogue on the importance of providing an evidence base to malaria cartography and in the sharing of this information [15]. We have not considered the spatial distribution of P. vivax in this paper for a number of methodological reasons. First, the accuracy of health reporting systems for P. vivax clinical cases in areas of coincidental P. falciparum risk is notoriously poor [57]. Second, the climatic constraints on parasite–vector survival are less well defined and thus harder to predict using standardized regional-specific vector bionomics [58]. Third, the combined effects of a prolonged liver stage and the consequences upon natural and drug-resistant recrudescence make the interpretation of prevalence data considerably harder for P. vivax compared to P. falciparum [59]. We are acutely aware that the spatial extent and disease burden of P. vivax merits more attention than it has received, but to achieve an informed evidence-based map similar to that of P. falciparum demands a more fundamental construction of the basic biology of transmission and clinical epidemiology before this can be attempted effectively. We have been cautious in the use of the PfAPI data reported at national levels, recognizing the inadequacies and incompleteness of malaria surveillance [1,23–26]. The intention has been to identify administrative reporting areas that had not detected cases of P. falciparum malaria between 2002 and 2006. It was also recognized that there existed a wide range of reported PfAPI estimates, from one case per 100,000 people pa to reports of confirmed cases in almost 50% of the population every year, which presents a problem for the classification of risk. We therefore applied threshold criteria that would distinguish areas of low clinical risk (i.e., those areas reporting few cases and likely to support unstable transmission conditions) from areas with higher reported case incidence and probably more stable in their P. falciparum transmission characteristics. Our use of a distinction between unstable and stable transmission at 0.1 per thousand pa, while conservative is not without precedent. During the era of the Global Malaria Eradication Programme, epidemiologists proposed a variety of criteria to describe malaria risk in concert with preparatory, active, consolidation, and maintenance phases of elimination and ultimate “eradication” [60–63]. Parasite prevalence was the metric of choice for defining baseline endemicity in the preparatory phase and was useful as an indicator of control progress in the attack phase [52,64], until it became impossible to measure with cost-efficient sampling at very low levels of endemicity. At this juncture, it was proposed that malaria risk be measured through incidence metrics such as the PfAPI [65]. We identified very few PfPR surveys (n = 233) undertaken in areas where reported PfAPI was below 0.1 per thousand pa, 70 (30%) of which reported zero prevalence (Figures 2–5); and the median parasite prevalence was 1.4% (Table 2). It seems appropriate, practical, and feasible to consider multiple metrics during the assembly of malaria risk maps, and we have combined two common malariometric measures of risk: the PfAPI and PfPR. The mathematical relationship between these measures and other traditional epidemiological measures, such as the basic reproduction rate of infection and the entomological inoculation rate, is the subject of ongoing research [61]. Stratification of these risk areas by dominant vector species to enable a more informed assessment of the appropriate suites of intervention measures is also being pursued actively [15]. The PfPR data have been assembled from peer-reviewed literature, unpublished ministry of health sources, postgraduate theses and provision of raw data from malaria scientists in all malaria endemic regions [45]. They do not derive from nationally representative, random-sample surveys. Their coverage might, therefore, be subject to bias toward areas thought to be more malarious. The inclusion of 971 geopositioned surveys reporting zero prevalence (including 523 [53.8%] from Africa), however, does not support this view. Future investigation of the ecological and climatic covariates of PfPR2–10 will need to move from the categorical descriptions of over-dispersed endemicity data presented here, to geostatistically robust estimates of risk that are cognisant of the many potential biases in these data across the entire limits of stable transmission shown in Figure 1. We note, however, that as infection prevalence responds to increased intervention coverage and access to effective medicines, the use of traditional biological covariates might prove less effective in predicting the distribution of P. falciparum transmission intensity. Spatial models of PfPR distribution are being developed and tested as part of MAP's ongoing research to more accurately reflect the ranges of malaria transmission intensity within the margins of stable endemicity. Moreover, the PfAPI and PfPR data described in the present paper will change with time, and future data assemblies need to be maintained in a world with a rapidly changing malaria epidemiology. The supporting geostatistical models used to predict the spatial distribution of endemicity must also therefore facilitate rapid updates. The annual revision of the spatial limits of stable and unstable malaria, based upon new medical intelligence, PfAPI summaries, and the increasingly available contemporary PfPR information will iteratively redefine the cartography of malaria and be hosted on the MAP website (http://www.map.ox.ac.uk) as a public domain resource [15]. Assuming some degree of fidelity in the descriptions of unstable malaria used here, we estimate that one quarter (∼26%) of the malaria-endemic areas of the world are exposed to some degree of unstable P. falciparum transmission and home to approximately one (0.98) billion people. Even within the regions with more stable transmission, the available empirical evidence from contemporary PfPR2–10 survey data is that outside of AFRO, the intensity of transmission is best described as hypoendemic [66] (Figure 6). This observation has important implications for how we view malaria control and broader development goals at a global scale over the next decade. The provisional categorical descriptions of global P. falciparum malaria risk are shown in Figure 1 and suggest that, at a global scale, an aggressive approach to P. falciparum elimination might be reconsidered as a more ambitious and achievable objective in many areas. Regional initiatives aimed at elimination have begun [11–14]. In the Americas, elimination is considered in the most recent 5-y regional strategic plan [12]. In the European region, the two PfMECs (Tajikistan and Kyrgyzstan) are targeted for P. falciparum elimination within the next 5 y [11,13]. Detailed plans have been developed in the Eastern Mediterranean region to consider targeted P. falciparum elimination strategies in Iran and Saudi Arabia, while strengthening maintenance phases of elimination in currently P. falciparum–free countries [14]. With the exception of EURO, detailed maps of the spatial extents of risk in these various regions are not available. Where elimination is considered a viable strategy, resource requirements, targets, and maps become a regional and sub-regional public good and are no longer solely national concerns. Saudi Arabia is providing substantial financial support for the elimination of malaria in its neighbour, Yemen [67]. If this plan is successful, the reportedly high rates of population inflow from Somalia [68] will pose a continued concern due to the potential reintroduction of the parasite. This situation further highlights the need for a reproducible and evidence-based global map of malaria risk that is maintained as a dynamic platform to estimate and predict cross-border risk. Maintaining the detail necessary to map the spatial extent of malaria risk is paramount to the future of malaria control outside of Africa over the next 5 y. We would also argue, however, that Africa has been labelled inappropriately as a vast expanse of holoendemic transmission, intractable to control. Less than a third of all surveys retrieved from AFRO (29%) reported parasite prevalence above 50%, and, as has been described, these results followed closely the distribution of A. gambiae s.s. [54]. The conditions of hypoendemic and mesoendemic transmission were common in surveys conducted outside of this belt (which are not subject to the ravages of this most efficient vector) and are likely to benefit from approaches to prevention and control specific to the underlying ecologic and epidemiologic conditions [15,69,70]. The descriptions of transmission intensity are dynamic and the PfPR2–10 estimates in Africa (Figure 2) do not correspond to levels of endemicity described four decades ago [17]. In the AFRO region, there has been a recent expansion of insecticide-treated net coverage and provision of effective medicines. These programmatic successes are showing tangible impacts on mortality [8,9,71] and morbidity [8,9,72], and it would seem entirely plausible that similar effects will be operating at the level of transmission. If Africa is undergoing a malaria epidemiological transition, capturing this dynamic through mapped information on infection prevalence, and planning accordingly, should be high on the control agenda. The current focus of the Roll Back Malaria movement is, appropriately, in Africa, as this continent bears the brunt of malaria morbidity and mortality [73,74] and the descriptions presented here reinforce this view. P. falciparum transmission is a global problem, however, requiring a global strategy with regional targets and approaches tailored to what can be achieved within defined intervention periods [61]. This strategic planning demands an epidemiologically consistent map that is constantly updated. The assembly of risk data presented here represents the first attempt to combine disparate sources of malariometric data that should serve as a dynamic platform to define a global strategy and map its progress over the coming decades. The maps and national levels of population at unstable and stable risk are released in the public domain, with the publication of this paper, to further that global effort (MAP, http://www.map.ox.ac.uk). Supporting Information Protocol S1 Sources and Descriptions of Medical Intelligence Used to Describe the PfAPI (346 KB DOC) Click here for additional data file. Protocol S2 Developing Global Biological Limits for P. falciparum Transmission (1.3 MB DOC) Click here for additional data file. Table S1 National Estimates of Population at Risk of P. falciparum Malaria in 2007 (231 KB DOC) Click here for additional data file.
Author and article information
Contributors
Luzia Helena Carvalho: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, USA
)
ISSN
(Electronic):
1932-6203
Publication date Collection: 2013
Publication date
(Electronic):
28
May
2013
Volume: 8
Issue: 5
Electronic Location Identifier: e63410
Affiliations
Author notes
* E-mail:
marcuslacerda.br@
123456gmail.com
Article
Publisher ID: PONE-D-13-07384DOI: 10.1371/journal.pone.0063410
PMC ID: 3665752
PubMed ID: 23723981
SO-VID: 3cd757bf-1c33-431a-9ab7-724d4538e8b3
Copyright statement:
Copyright @
2013
License:
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
History
Date
received
: 18
February
2013
Date
accepted
: 2
April
2013
Page count
Pages: 7
Funding
HCCC received a fellowship from CAPES and SCPL was sponsored by a FAPESP fellowship.
MVGL and FTMC are CNPq fellows. FTMC is also a fellow from Programa Estratégico de
Ciência, Tecnologia & Inovação nas Fundações Estaduais de Saúde (PECTI/AM-Saúde) from
Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM, Amazonas - Brazil). This
work was supported by CNPq and FAPEAM grants. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Categories
Subject:
Research Article
Subject:
Biology
Subject:
Immunology
Subject:
Medicine
Subject:
Clinical Immunology
Subject:
Immune System
Subject:
Cytokines
Subject:
Diagnostic Medicine
Subject:
Pathology
Subject:
Clinical Pathology
Subject:
Hematopathology
Subject:
Hematology
Subject:
Infectious Diseases
Subject:
Parasitic Diseases
Subject:
Malaria
Subject:
Plasmodium Malariae
ScienceOpen disciplines: Uncategorized
Data availability:
ScienceOpen disciplines: Uncategorized