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      Mapping the Aetiology of Non-Malarial Febrile Illness in Southeast Asia through a Systematic Review—Terra Incognita Impairing Treatment Policies

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          Abstract

          Background

          An increasing use of point of care diagnostic tests that exclude malaria, coupled with a declining malaria burden in many endemic countries, is highlighting the lack of ability of many health systems to manage other causes of febrile disease. A lack of knowledge of distribution of these pathogens, and a lack of screening and point-of-care diagnostics to identify them, prevents effective management of these generally treatable contributors to disease burden. While prospective data collection is vital, an untapped body of knowledge already exists in the published health literature.

          Methods

          Focusing on the Mekong region of Southeast Asia, published data from 1986 to 2011 was screened to for frequency of isolation of pathogens implicated in aetiology of non-malarial febrile illness. Eligibility criteria included English-language peer-reviewed studies recording major pathogens for which specific management is likely to be warranted. Of 1,252 identified papers, 146 met inclusion criteria and were analyzed and data mapped.

          Results

          Data tended to be clustered around specific areas where research institutions operate, and where resources to conduct studies are greater. The most frequently reported pathogen was dengue virus (n = 70), followed by Orientia tsutsugamushi and Rickettsia species (scrub typhus/murine typhus/spotted fever group n = 58), Leptospira spp. (n = 35), Salmonella enterica serovar Typhi and Paratyphi (enteric fever n = 24), Burkholderia pseudomallei (melioidosis n = 14), and Japanese encephalitis virus (n = 18). Wide tracts with very little published data on aetiology of fever are apparent.

          Discussion and Conclusions

          This mapping demonstrates a very heterogeneous distribution of information on the causes of fever in the Mekong countries. Further directed data collection to address gaps in the evidence-base, and expansion to a global database of pathogen distribution, is readily achievable, and would help define wider priorities for research and development to improve syndromic management of fever, prioritize diagnostic development, and guide empirical therapy.

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          Most cited references 23

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          Estimating the Global Clinical Burden of Plasmodium falciparum Malaria in 2007

          Introduction Estimating the disease burden posed by malaria is an important public health challenge [1]–[9]. The clinical consequences of Plasmodium falciparum infection have several features that confound traditional approaches to disease burden and disability measurement [10],[11]. First, not all infections result in progression to disease, notably in areas of stable transmission [12], where populations have acquired clinical immunity [13]. The overall risk of clinical disease has a curvilinear and uncertain association with the risk of infection as a combined function of age at first infection and immunity [13]–[18]. Second, the dominant symptom of fever, or other symptoms, does not distinguish malaria from other locally prevalent infections [19]–[23]. As a consequence, the routine reporting of “malaria” can overestimate disease rates by assuming that most fevers are malaria [24],[25] and that fevers associated with an infection are causally linked to that infection [20],[26]. Third, with few exceptions across malaria-endemic countries, fevers or other malaria-like syndromes are often self-medicated and may resolve regardless of cause before reaching formal health systems [27]. Fourth, inaccurate diagnoses [21],[25],[28] might be used to report disease rates, and these errors may be compounded through inadequate and incomplete national reporting systems [29]–[38]. To circumvent some of the clinical, treatment, and reporting problems inherent in malaria burden estimation, we previously computed the global incidence of P. falciparum clinical disease [5] for 2002, using assemblies of epidemiological data and a modified categorical map of historical malaria endemicity [39]. The publication of (i) the revised global spatial limits of P. falciparum transmission [40], (ii) a contemporary geostatistical description of P. falciparum malaria endemicity within these limits [41], and (iii) updates of the modelled relationship between clinical incidence and prevalence [42] have resulted in a substantially improved evidence base from which to revisit estimates of the clinical burden of P. falciparum, defined as the primary acute clinical event resulting from malaria infection at all ages. Most significantly, a geostatistical space–time joint simulation framework [43] is combined with these improved cartographic and epidemiological data sources to quantify uncertainty in the mapped outputs and to propagate it appropriately into the derived burden estimates. Using these joint simulation procedures we have built upon previous approaches to produce the first continuous map of global clinical P. falciparum incidence, and we use this to estimate the global clinical burden of P. falciparum malaria in 2007. These estimates are then compared with those available from surveillance, and the opportunity for the further hybridization of these techniques is discussed. Methods Analysis Outline A schematic overview of the analysis procedures is provided in Figure 1. In brief, of the 87 countries classified as endemic for P. falciparum malaria [40], seven had sufficiently reliable health information systems for case report data to be used directly to enumerate clinical burden for 2007. We divided the population at risk (PAR) in the remaining 80 countries into regions of unstable and stable risk of transmission [40] (Figure 2). In unstable regions, a uniform clinical incidence rate was adopted of 0.1 case per 1,000 per annum (PA). This rate was multiplied by a population surface [44] for 2007 (Figure 3) and aggregated to obtain country and regional case estimates for these unstable areas. Upper and lower bounds were defined using uniform rates of zero and one case, respectively, per 1,000 PA. In stable regions, we used a previously defined Bayesian geostatistical model that took an assembly of space–time distributed P. falciparum parasite rate (PfPR) surveys and generated realisations of continuous age-standardized prevalence within the limits of stable transmission [41]. We then used a Bayesian nonparametric model [42] of a collection of all-age active case detection studies, to describe the uncertain relationship between the clinical incidence rate and the underlying age-standardized parasite prevalence. These two models were integrated in a geostatistical space–time joint simulation framework to generate joint realisations of clinical attack rate for every pixel as a function of the predicted underlying prevalence [43] (Protocol S1). These attack rates were then multiplied by the corresponding pixel population totals to yield joint realisations of a clinical burden surface (Figures 4 and 5). This joint simulation framework supported the aggregation of per-pixel burden estimates into defined spatial units, whilst preserving a space–time uncertainty structure, allowing country and regional estimates of burden to be made with appropriate credible intervals (Table 1, Protocol S2). Each of these analytical components are now discussed in more detail. 10.1371/journal.pmed.1000290.g001 Figure 1 Schematic diagram showing the procedure for burden estimation. Blue boxes describe input data, orange boxes models and experimental procedures, dashed green rods intermediate output, and solid green rods the final output. The seven countries with reliable national reporting were Belize, Iran, Kyrgyzstan, Panama, Saudi Arabia, South Africa, and Tajikistan. The areas of unstable and stable transmission are defined as having less or more than one case per 10,000 PA, respectively [40],[41]. 10.1371/journal.pmed.1000290.g002 Figure 2 Global limits and endemicity of P. falciparum in 2007. The land area was defined as no risk (light grey), unstable risk (medium grey areas, where PfAPI 0.1‰ PA) [40] with endemicity (PfPR in the 2- up to 10-year age group, PfPR2–10) displayed as a continuum of yellow to red between 0% and 100%. The dashed lines separate the Americas, Africa+, and the CSE Asia region, respectively, from left to right. The seven countries with thick blue borders have very low P. falciparum burden and reliable national health information systems. 10.1371/journal.pmed.1000290.g003 Figure 3 Global human population density in 2007. Human population density [44] in persons per km2 is displayed on a logarithmic colour scale within the limits of P. falciparum transmission. No malaria risk is shown in light grey. 10.1371/journal.pmed.1000290.g004 Figure 4 Global clinical burden of P. falciparum in 2007. Bayesian geostatistical estimates (posterior means) of the number of all-age clinical cases per 5×5 km pixel displayed on a logarithmic colour scale between 0 and 10,000 cases, within the stable limits of P. falciparum transmission. Dark and light grey areas are as described in Figure 2. 10.1371/journal.pmed.1000290.g005 Figure 5 Uncertainty in the global clinical burden of P. falciparum in 2007. Bayesian geostatistical model-based prediction uncertainty (posterior standard deviations) on a logarithmic colour scale between 0 and 20,000 cases, within the stable limits of P. falciparum transmission. No model-based uncertainty metrics were produced for areas of unstable transmission. Dark and light grey areas are as described in Figure 2. 10.1371/journal.pmed.1000290.t001 Table 1 Numbers of Plasmodium falciparum clinical attacks by region globally in 2007. Category Americas (16 countries) Africa+ (47 countries) CSE Asia (19 countries) Total Reliable reporting (casesa) 32 (Panama, Belize) 2,717b (Saudi Arabia, South Africa) 618 (Kyrgyzstan, Tajikistan, Iran) 3,367 Unstable riskc (casesa) 5,455 (0–54,550) 1,892 (0–18,920) 98,049 (0–980,490) 105,395 (0–1,053,950) Stable riskc (millions of casesa) 3.04 (1.17–6.70) 270.88 (241.13–300.56) 176.90 (89.21–269.58) 450.83 (348.76–552.22) Total (millions of casesa) 3.05 (1.17–6.76) 270.89 (241.13–300.58) 177.00 (89.21–270.56) 450.93 (348.76–553.27) The regional groupings are illustrated in Figure 1. a Case numbers from countries with reliable reporting and areas of unstable risk are presented directly whilst those from areas of stable risk are presented in millions of cases, rounded to the nearest 10,000, reflecting the larger numbers and lower precision associated with these model-based estimates. b Presumed to be all P. falciparum, although autochthonous case reports did not specify. c Excluding countries with reliable case data. Defining Populations and Global Regions The Global Rural Urban Mapping Project (GRUMP) alpha version [44] provides gridded population counts and population density estimates for the years 1990, 1995, and 2000, adjusted to the United Nations' national population estimates. Population counts for the year 2000 were projected to 2007 by applying national, medium variant, intercensal growth rates [45] by country using methods previously described [46] (Figure 3). We have modified the World Health Organization (WHO) regional country groupings, recognizing that these geopolitical boundaries do not conform to the biogeographical determinants of malaria risk and thus disease burden [41],[47],[48]. For the purposes of disease risk estimation we have used three malaria regional groupings: Africa+ (including Yemen and Saudi Arabia, which share the same dominant Anopheles vectors as mainland Africa [49]), the Americas, and the combined regions of Near East, Asia, and the Pacific that we refer to as Central and South East (CSE) Asia (Figure 2). To facilitate comparison with other estimates, however, we have also shown the results aggregated by the regional groupings of the WHO (Protocol S2). Defining the Limits of Stable and Unstable P. falciparum Transmission To define the global spatial limits of P. falciparum transmission, we previously assembled confirmed P. falciparum clinical case data for 41 P. falciparum malaria-endemic countries (PfMECs) outside of Africa [40]. National case reported data were expressed as P. falciparum annual parasite incidence (PfAPI) derived from various combinations of active case detection (fever surveys in communities where every person presenting with a fever is tested for parasite infection) and passive case detection (reports from febrile patients attending the local health services) and usually expressed together as the number infected per 1,000 PA [50]–[52]. These data were provided by malaria coordinating officers in the WHO regional offices of the Eastern Mediterranean (EMRO), Europe (EURO), South East Asia (SEARO), and the Western Pacific (WPRO) at the highest available administrative level unit between 2002 and 2007. Among the countries in the American Regional Office (AMRO), PfAPI data from national surveillance systems in Brazil, Colombia, Peru, and Honduras were obtained directly from personal communication with national malaria specialists. The PfAPI data were mapped to first, second, or third administrative level units and used to classify areas as no risk (zero cases) and either unstable or stable risk if the number of confirmed cases was lower or higher than 0.1 case per 1,000 PA, respectively [40]. The unstable/stable classification was based on a review of the statistical, logistical, and programmatic reasons underpinning the PfAPI levels used to define phases and action points during the Global Malaria Eradication Program [12],[53]–[55]. In addition, no transmission was assumed where medical intelligence from international travel advisories or national malaria control programmes stated no malaria risk or where the temperature was too low for sporogony to complete within the average lifespan of the local dominant vector species [49]. Measures of aridity were used to define areas in which transmission is biologically plausible in isolated manmade breeding sites, but overall transmission in surrounding areas is limited by its effects on anopheline survival, and the clinical incidence is likely to be less than 0.1 case per 1,000 PA. The spatial extents of stable and unstable risk defined using these inputs are shown (Figure 2). Defining P. falciparum Clinical Incidence in Areas of Reliable Case Detection Paradoxically, where the incidence of clinical malaria events are rare, their rapid detection and notification becomes increasingly important as part of national malaria control strategies, demanding more sophisticated surveillance [51],[55]–[57]. This is particularly true for countries aiming to attain or maintain WHO accredited elimination status [58]–[60]. Of the 87 PfMECs, we have identified seven countries that are relatively wealthy and have specified a goal of P. falciparum elimination where case-detection systems are an integral part of the control strategies [58]–[60]: Panama, Belize, Tajikistan, Kyrgyzstan, Iran, Saudi Arabia, and South Africa (Figure 2). For these seven countries, we have used the national reports for 2007 of all notified, locally acquired infections submitted to regional WHO offices (see Acknowledgments) as the definitive estimate of case burden. These countries are characterised by having a small number of annual cases, with a large proportion of the population living in areas of no risk or unstable transmission and are therefore likely to represent a very small proportion of the global P. falciparum malaria burden [40]. Defining Malaria Incidence in Areas of Unstable P. falciparum Malaria Transmission We estimate that almost one billion people were living in areas where P. falciparum transmission was unstable in 2007 [40] (Figure 2). Defining annualized disease risk in these areas from empirical data is difficult, as epidemiological investigations for research or survey purposes are rare. Nevertheless, in computing disease burdens it is important to impute some measure of completeness of formal malaria reporting within these marginal, unstable transmission areas. A number of malaria treatment-seeking behaviour studies and qualitative examinations of routine malaria reporting frequency suggest large inadequacies in a range of national reporting systems from a variety of causes that can act multiplicatively: Cambodia (actual number of cases 2.7× greater than reported) [35], India (9–50×) [28],[61]–[65], Mozambique (2.7×) [32], Pakistan (5.9×) [30], Peru (4.3×) [34], Solomon Islands (4.7×) [38], Sri Lanka (1.9×) [29], and Syria (4.5×) [31]. There are remarkably few specific investigations of the completeness of malaria case notification systems in different settings. Only four reports provide an estimate of the numbers of cases likely to be missed by routine health system surveillance compared to more aggressive, active case detection methods in the same communities over the same time period. In the Yanomami area of Brazil, approximately 1.25 more events were detected by active detection than were reported to the routine health system [57]. Across different years at different sites the ratio of active to routine, passive detection varied from 4.5 to 42.1 in Vietnam [66], with similar under-reporting rates documented in Cambodia [67]. A 5-fold difference in survey-to-passive rates of case detection has been reported in Yunnan Province in China [68]. It is not possible to provide an evidence-based under-reporting correction factor that is specific for every national malaria information system. We have therefore elected to use a single worst-case rate of 10-fold under-reporting across all countries. We hence assume for all unstable areas a uniform incidence of 0.1 case per 1,000 PA, with a lower confidence bound of zero and an upper confidence bound assuming a 10-fold under-reporting rate; equating to one case per 1,000 PA. Defining Malaria Incidence in Stable Endemic Areas We estimated that in 2007, approximately 1.4 billion people lived in areas of stable P. falciparum transmission [40] (Figure 2). In these areas, we considered that case-reporting through routine health information systems was too unreliable for the calculation of incidence due to inadequate reporting coverage (see above), widespread self-medication [27], and poor diagnosis [21],[25]. Instead, we developed a model-based cartographic method for deriving estimates in the areas of stable transmission in which clinical incidence was modelled as a function of the underlying endemicity (parasite prevalence). This procedure required: (i) a spatially continuous model for endemicity; (ii) a further model to predict incidence as a function of endemicity; (iii) reliable data on 2007 population distribution; and (iv) a technique for combining these components so that the uncertainty inherent in the component models was propagated into the resulting burden estimates. These components are now outlined in turn, with additional statistical details provided in Protocol S1. To estimate stable transmission intensity, a Bayesian space-time geostatistical modelling framework was developed to interpolate empirical estimates of age-corrected parasite prevalence derived from 7,953 community surveys undertaken between 1985 and 2008 across 83 malaria-endemic countries. This model has been described in detail elsewhere [41] and its output allows for a continuous, urban-adjusted, contemporary estimate of parasite prevalence in children aged from 2 up to 10 years (PfPR2–10) at a pixel spatial resolution of 5×5 km for the year 2007 (Figure 2). To estimate clinical incidence, formal literature searches were conducted for P. falciparum malaria incidence surveys undertaken prospectively through active case detection at least every 14 days [42]. The incidence surveys were time–space matched with estimates of parasite prevalence derived from the geostatistical model described above [41]. Potential relationships between all-age clinical incidence and age-standardized parasite prevalence were then specified in a nonparametric Gaussian process model with minimal, biologically informed, prior constraints. A temporal volatility model was incorporated to describe the variance in the observed data and Bayesian inference was used to choose between the candidate models [42]. Separate relationships were preferred for each of the three regions defined globally (Figure 2) to accommodate regional-specific differences in the dominant vector species [47],[49],[69], the impact of drug resistance on recrudescent clinical attacks [70], the possible modification of P. falciparum clinical outcomes in areas of P. vivax co-infection [71],[72], and the genetic contribution to disease risk of inherited haemoglobin disorders [73]. Due to the sparse data in the Americas, however, this region was combined with CSE Asia. In the Africa+ region and the combined Americas and CSE Asia region, clinical incidence increased slowly and smoothly as a function of infection prevalence (Figures 6, 7, 8, and 9). In the Africa+ region, when infection prevalence exceeded 40%, clinical incidence reached a maximum of 500 cases per 1,000 PA (Figure 6). In the combined Americas and CSE Asia regions this maximum was reached at 250 cases per 1,000 PA (Figure 7). 10.1371/journal.pmed.1000290.g006 Figure 6 The posterior distribution of the prevalence-incidence relationship ( , see Methods) in the Africa+ region. The relationship is plotted between malaria endemicity (PfPR in the 2- up to 10-year age group, PfPR2–10) and all-age incidence (clinical cases per thousand of the population PA) [42]. Please see reference [42] for a full description of the data, methods, and techniques used to define this relationship. The light grey, medium grey and dark grey regions define the 95%, 50%, and 25% credible intervals, respectively. The solid black line is the median and the data are shown as red dots. 10.1371/journal.pmed.1000290.g007 Figure 7 The posterior distribution of the prevalence-incidence relationship ( , see Methods) in the combined CSE Asia region and the Americas. The techniques and colours used are identical to Figure 6. 10.1371/journal.pmed.1000290.g008 Figure 8 The predictive distribution of the incidence that would actually be observed by weekly surveillance over a two-year period in the Africa+ region. Please see reference [42] for a full description of the data, methods, and techniques used to define this relationship. The light grey, medium grey, and dark grey regions define the 95%, 50%, and 25% credible intervals, respectively. The solid black line is the median and the data are shown as red dots. Note that the data points were collected using different surveillance intervals over different time periods, and therefore should not be expected to follow the distribution predicted by the model exactly. The observed incidences are included in the figure as a visual aid only. 10.1371/journal.pmed.1000290.g009 Figure 9 The predictive distribution of the incidence that would actually be observed by weekly surveillance over a two-year period in the combined CSE Asia region and the Americas. The techniques and colours used are identical to Figure 8. Both the geostatistical endemicity and the endemicity–incidence models were specified in a fully Bayesian framework. The output of the former was a large set of realisations (n = 250,000): possible maps that, together, represented the modelled uncertainty in endemicity at each location. Similarly, the output of the endemicity–incidence model was a large set (n = 250,000) of possible forms of the endemicity-incidence curve that encompassed the modelled uncertainty in this relationship (Figures 6, 7, 8, and 9). To combine the uncertainty from both models, each realisation of the uncertainty map was used as input into a realisation of the endemicity–incidence model to obtain a realisation of a 5×5 km resolution incidence map. This was downscaled to 1×1 km resolution and multiplied with the 2007 population surface to obtain, for every grid square, a realisation of the number of clinical cases in 2007. By repeating this procedure for every model realisation, a set of 250,000 burden values was generated for every grid square, approximating a complete posterior distribution for the estimates. Because each realisation of the endemicity map was jointly simulated, rather than calculated on a pixel-by-pixel basis, each realisation of burden could be aggregated spatially or temporally, whilst maintaining the correct variance structure. This allowed burden realisations at each pixel to be combined spatially to generate estimates of national and regional burdens with appropriate credible intervals. Joint simulation at this scale is enormously computationally intensive and a bespoke algorithm was developed to implement this stage of the analysis. The algorithm is presented elsewhere [43] and the statistical details are summarised in Protocol S1. Results The combined clinical burden of the seven nations with comprehensive reporting was 3,367 cases in 2007 (Table 1, Protocol S2). Multiplying the population surface (Figure 3) by the assumed incidence rate in unstable areas (see Methods) produced an estimate of 105,395 clinical cases of P. falciparum malaria in areas of unstable transmission (Table 1, Protocol S2), with a plausible range between zero and 1,053,950. The modelling procedures in the stable areas generated an estimate of 451 million cases (lower 95% credible interval 349 million and upper 95% credible interval 552 million) of P. falciparum malaria in areas of stable transmission in 2007, of which 271 (241–301) million were estimated to have occurred in the Africa+ region, 177 (89–270) million in the CSE Asia region and 3 (1–7) million in the Americas (Table 1). Combining our estimates from the seven countries with comprehensive case reporting with those from areas of unstable and stable transmission in the remaining 80 PfMECs, we estimate that in 2007 there were 451 (349–553) million clinical cases of P. falciparum malaria. A continuous map of these incidence predictions is provided (Figure 4), with an additional map of the pixel-specific uncertainty (Figure 5). In addition to the regional summaries presented (Table 1), estimates of clinical burden are summarized for each country and for each of the WHO global regions (Figure 10 and Protocol S2). It is notable that more than half (51%) of the world's estimated P. falciparum clinical cases derive from just four countries: India, Nigeria, DRC, and Myanmar (Burma) (Figure 4 and Protocol S2) and that, in addition, these nations contribute 48% of the uncertainty (Figure 5) in the global incidence estimates. 10.1371/journal.pmed.1000290.g010 Figure 10 Pie chart of P. falciparum clinical cases in 2007. The pie chart shows the fraction of the 451 million cases of total clinical burden in each of the World Health Organization regions (Protocol S2). In the pie the regions are ordered counterclockwise starting at the top, from highest to lowest burden. The plotted area representing the EURO region is too thin to be visible. The thumbnail map shows the country composition of the WHO regions for all 87 P. falciparum endemic countries. Regional summary estimates of P. falciparum malaria cases in unstable and stable transmission areas are summarized in Table 1 and are also shown for the WHO regions in Figure 10. It is clear that African populations suffered the largest proportion (60%) of the 451 million clinical cases of P. falciparum estimated globally in 2007 (Figure 10, Table 1 and Protocol S2). The highest-burden countries in Africa are Nigeria and DRC, both countries with extensive regions of high endemicity (Figure 2) and large populations (Figure 3). These two countries account for 23% of the world's P. falciparum disease burden (Protocol S2). Less than 1% of the global P. falciparum burden occurred in the Americas, where transmission intensity is almost universally low or unstable (Figure 2). We estimate that the remaining 39% of global burden in 2007 occurred in the CSE Asia region (Table 1). In this region, the immense population living at risk of P. falciparum malaria means that, despite a low prevalence [41] (Figure 2) and the lower endemicity–incidence relationship [42] (Figure 7), cases in CSE Asia add substantially to the global disease burden (Table 1). At a country level, India and Myanmar contribute 22.6% and 5.8%, respectively, of the total number of clinical cases due to P. falciparum worldwide (Protocol S2). Discussion We have used a combination of methods, including a joint simulation of incidence in areas of stable transmission, to estimate 451 (349–552) million clinical cases of P. falciparum malaria in 2007: 3 (1–7) million in the Americas, 271 (241–301) in the Africa+ region, and 177 (89–270) in the CSE Asia region. Morbidity in Areas of Unstable Transmission We have accepted as accurate the surveillance reports of seven relatively high income and low burden PfMECs, all nations with credible plans for malaria elimination [59],[60],[74]–[76]. We have further attempted to describe clinical disease incidence in areas of the world that we classify as unstable risk [40], which were home to almost a billion people in 2007. We know relatively little about the epidemiology of P. falciparum in the 40% of the global PAR of P. falciparum malaria living in unstable transmission areas. These areas are notoriously difficult to define in terms of potential disease outcomes; they may go several years without a single autochthonous case, transmission is extremely focal and, importantly, investigation of the clinical epidemiology is prohibitively expensive because of the rarity of the disease [77]. We have, therefore, defaulted to national reporting systems as an entry point to the definition of risk and have used surveys of under-reporting rates to define plausible ranges of the disease burden in these marginal transmission zones. We estimate that there were 105,395 (0–1,053,950) cases of P. falciparum in unstable transmission areas in 2007. Despite being relatively crudely defined, these sums represent only 0.02% of the global clinical P. falciparum burden. Therefore, while these cases are of significant concern to those nations with large populations at unstable risk and to those considering elimination [59],[60],[74]–[76], they make a very small contribution to the estimation of the global P. falciparum burden. Morbidity in Stable Areas We have improved upon a P. falciparum disease burden estimation rubric that has been used several times previously for Africa [1],[3],[4],[6],[7] and once before globally [5]. This method requires an understanding of the basic clinical epidemiology of P. falciparum malaria, its relationship to transmission intensity and the use of empirical, longitudinal observations in populations exposed to different conditions of transmission. However, these empirical studies of clinical incidence are not without their own caveats [42]. Longitudinal surveillance over a complete annual malaria transmission cycle within the same cohort is likely to underestimate the “natural” risk of disease given the ethical need to treat effectively all detected infections or clinical events. These studies are also conducted throughout a range of region-specific co-species infection [78], HIV/AIDS prevalence [79], and drug resistance [80] conditions. The number of studies meeting our inclusion criteria remains low, so these covariate determinants of clinical risk cannot be adequately modelled or controlled for in this series [42]. We have considered all infections that are associated with a reported or measured febrile event as clinical malaria. This seems appropriate under conditions of low transmission intensity, but as transmission intensity increases, the proportion of fevers that can be causally linked to malaria infection declines [26],[81]. Consequently, our estimates of clinical attack rates at the highest levels of transmission are likely to be overestimates of true P. falciparum clinical incidence. Locally derived age- and transmission-dependent aetiological fraction estimates were not available for the majority of studies in order to allow the application of meaningful corrections. Conversely, the use of fever and any level of peripheral infection to define a malaria case corresponds closely to the criteria recommended for case treatment across the world [82],[83] and thus has congruence with disease burdens that should be managed with appropriate medicines. Finally, we have not considered the impact of scaled or partial coverage of interventions aimed at preventing infection, because we feel this is reflected in the parasite prevalence surface [41]. The one exception is the use of failing monotherapy because recrudescent cases will not be reflected in our endemicity–incidence relationship based on active case detection with effective treatment and thus, where this poses a significant threat, our estimates will be even greater underestimates. Despite the caveats, we believe that this approach to P. falciparum disease burden estimation provides an alternative and, in nations with inadequate surveillance, the only existing approach to estimating the true global risk of malaria. Robust Estimates of Uncertainty We have used joint simulations from an established Bayesian geostatistical model for P. falciparum parasite prevalence in the 2- up to 10-year age group (PfPR2–10) (Figure 2), integrated with a second Bayesian model for the endemicity-incidence relationship (Figures 6 and 7), to generate spatially distributed estimates of the clinical burden of P. falciparum malaria worldwide with associated uncertainty. This reflects the uncertainty in measures of risk that results in a range of possible estimates globally from 349 to 553 million cases in 2007; similar to the range size in other malaria burden estimations [1],[3],[5],[7],[84]. This elaborate modelling framework has allowed the incorporation of uncertainty in our knowledge of the intensity of transmission at any given location with uncertainty in our knowledge of how this intensity influences the rate of clinical episodes at that location, allowing the net uncertainty to be propagated into final estimates of clinical burden. Crucially, the joint simulation framework allows modelled uncertainty to be aggregated across regions to provide our final credible intervals for country and region-specific burden estimates, a procedure that is not possible using the per-pixel prediction approaches currently pervasive in disease mapping. The WHO has recently used surveillance-based techniques to estimate the combined burden of P. falciparum and P. vivax to be 247 million cases in 2006 (189–287) [8]. The WHO placed greater reliance on data reported routinely through national health management information systems (HMIS), which were subjected to a range of evidence-based adjustments for nonattendance, reporting rates, and diagnostic practices. These HMIS data were used for national estimates in 77 of 107 countries considered worldwide (Protocol S2). The fidelity of these estimates and their sensitivity to assumptions underlying the suite of adjustment factors was dependent on the quality and completeness of the HMIS data from each country. In the 30 countries with the least reliable national data, a predecessor of the prevalence-based modelling protocol presented in this study was used [8],[85]. The results are shown for individual countries in Protocol S2. These estimates were revised in 2009 but data have not been made available for all countries [9]. Uncertainty in India India is a country of considerable diversity in its current and historic malaria ecology, a country which suffered in excess of a million deaths PA during the colonial era [86]. Since its independence in 1947, India has achieved remarkable malaria control gains, reducing morbidity to 100,000 cases and mortality to zero in 1965 [87] at the peak of the Global Malaria Eradication Programme [53]. Since this time malaria resurgence has been widely reported in the country [87]–[89]. The contemporary burden is unknown [90]–[97] and is probably exacerbated by the unique problem of urban malaria, maintained by Anopheles stephensi [49],[88],[98]. India remains a massive source of uncertainty in our cartography-based estimates (Results and Protocol S2), contributing over three-quarters (76%) of the uncertainty range in the global incidence estimates. It is therefore important to explore ancillary evidence for the plausibility of these cartographic estimates of 102 (31–187) million compared to the much smaller estimate derived from surveillance-based techniques: 10.65 (9.00–12.41) million [8]. A wide range of factors can reduce the accuracy of surveillance data. Low rates of care-seeking for malaria in the formal health sector, unreliable diagnoses, poor record keeping, and inefficient data transfer and collation systems can all combine to make the number of cases formally reported a small fraction of the true number of cases in a population. To mitigate these substantial sources of bias in raw surveillance data, the approach taken by WHO is to modify the raw data using a number of adjustment parameters, which can include the proportion of people with fever seeking formal-sector care, the reporting rate by facilities, and the likely positivity rates amongst non-attending and non-slide–confirmed cases of fever [8],[85]. Such adjustments are essential, but the validity of the final estimate is entirely dependent on the values used for each parameter, which are drawn from a mixture of health-system reported figures, secondary data of varying fidelity, and ad-hoc decision rules. A key weakness of this approach is that, in many cases, the true uncertainty around key parameter values is not captured adequately. In the case of India, raw surveillance data for 2006 reported 1.8 million malaria cases. Adjustments were made for care-seeking behaviour and reporting rate by health facilities, which combined to increase the estimate by a factor of 5.0–6.9, to the final figure of 10.65 (9.00–12.41) million [8], with the confidence range primarily reflecting differing assumptions for positivity rate amongst nonpresenting fevers. Assessing the validity of either the individual adjustment parameters or the final estimate is difficult since, by definition, gold-standard values for comparison do not exist. However, numerous studies in India have compared case numbers detected via routine surveillance with parallel community-based longitudinal surveys and found disparities much larger than the factor of approximately six used by the WHO. For example, malaria incidence in the Kichha Primary Health Centre (PHC) and Kharkhoda PHC were 23.5 and 38.9 times under-reported, respectively [61]. Large discrepancies were also reported in Gadarpur PHC (53.5×) [62], Nichlaul PHC (20.3×) [64] and Ahmedabad City (9×) [65]. For India, the WHO estimate makes no allowance for misdiagnosis within the formal health sector, although studies have shown that this can be substantial. In the PHCs of ten districts in Uttar Pradesh, 75% of slide-confirmed infections were missed when the slides were checked by a reference centre [28], and an estimated 58% were missed in Bisra PHC when fortnightly rather than weekly surveillance was used [63]. In completely independent work, the final estimate for malaria mortality in India in 2006, taken from the “million deaths” verbal autopsy study was approximately 200,000 deaths (Dhingra N, et al., unpublished data). Assuming a conservative case fatality rate of only one per 1,000 [99],[100], this would lead to a morbidity estimate much closer to those retrieved using cartographic techniques—somewhere in the region of 200 million cases. Similar arguments of plausible morbidity totals can be made using other recent mortality estimates of 50,000 deaths in 1998 in 15 of 38 States and Union Territories [90],[93]. In sum, we find that cartography-based estimates are supported by, and resonate most closely with, the findings in the recent literature [90]–[96], although it should be acknowledged that there is likely to be a publication bias in reports of problems over progress. There is no perfect post-hoc correction to compensate for poor malaria surveillance. Both methods using routine HMIS adjusted for nonattendance, poor reporting, and inadequate diagnostics, and those presented here, have limitations with respect to coverage and quality of the input data for each model, and with respect to underlying modelling assumptions. Both approaches to burden estimation result in wide margins of confidence and the inevitable plea from any such analysis is for accurate national reporting systems or more empirical epidemiological data. It can be seen clearly from these analyses that improvements in basic malariometric information in only four countries would radically reduce uncertainty in the global estimates of the malaria burden. Additionally, the approach presented does provide a standardized method across all malaria-endemic countries, using a set of transparent epidemiological rules allowing countries to be compared without concerns about differences in national health information quality or coverage. A Hybrid Approach? To allay some of the concerns about the use of cartographic techniques in low-endemicity settings [101], we have also investigated the possibility of combining the two burden estimation processes for the 87 PfMECs. Seven countries have “gold-standard” reporting systems requiring no adjustment by either technique. These are in the African Regional Office (AFRO): South Africa; in AMRO: Belize and Panama; EMRO: Iran and Saudi Arabia; and EURO: Kyrgyzstan and Tajikistan (7/87). In many PfMECs in the Africa+ region, an outdated cartographic technique was used by WHO [8]. Since the new methods outlined here are an unambiguous improvement, these were adopted for the following PfMECs: in AFRO: Angola, Burkina Faso, Cameroon, Central African Republic, Chad, Congo, Côte d'Ivoire, DRC, Equatorial Guinea, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria, Sierra Leone, Togo, Uganda, and Zimbabwe; and in EMRO: Yemen (25/87). In addition, Mayotte in AFRO and French Guiana in AMRO have no WHO estimates, so we default to the cartographic approach (2/87). Conversely there are two small island nations in AFRO (Cape Verde and the Comoros) for which we had no contemporary PfPR data and the spatial resolution of mapping was not ideal, so the WHO estimates were used (2/87). We then calculated, for all countries, the ratio of the width of the 95% credible interval to the point estimate obtained using the cartographic method and ranked this relative uncertainty metric by nation (Protocol S2). For those countries where this cartography-based uncertainty ranked in the bottom half (i.e., the least uncertain, corresponding to a ratio of <40), we adopted our cartographic-based estimates. They were in AFRO: Benin, Burundi, Ethiopia, Gabon, Kenya, Madagascar, Rwanda, Senegal, United Republic of Tanzania, and Zambia; in EMRO: Somalia and Sudan; in SEARO: India, Indonesia, and Myanmar; and in WPRO: Papua New Guinea (16/87). Conversely, in countries where cartography-based uncertainty was ranked in the top half (ratio ≥40) we defaulted to the WHO estimate. They were in AFRO: Botswana, Eritrea, Namibia, São Tomé and Príncipe, and Swaziland; in AMRO: Bolivia, Brazil, Colombia, Dominican Republic, Ecuador, Guatemala, Guyana, Haiti, Honduras, Nicaragua, Peru, Suriname, and Venezuela; in EMRO: Afghanistan, Djibouti, and Pakistan; in SEARO: Bangladesh, Bhutan, Nepal, Sri Lanka, Thailand, and Timor-Leste; and in WPRO: Cambodia, China, Lao People's Democratic Republic, Malaysia, Philippines, Solomon islands, Vanuatu, and Viet Nam (35/87). This hybrid approach resulted in seven countries using gold standard national reports, 43 nations using cartographic techniques and 37 using the surveillance-based methods of WHO. The percentage of the global burden estimated by each technique was 0.001%, 97.722%, and 2.277%, respectively. Using a hybrid approach therefore makes very little difference to the global clinical burden estimate for 2007, although it has a significant impact on the absolute number of cases estimated for each country (Protocol S2). Interpreting Estimates These estimates improve upon previous efforts, which used epidemiological approaches to estimate the global burden of P. falciparum clinical attacks in 2002 (515 million, interquartile range 300–660 million) [5], and more recent efforts to estimate paediatric clinical events due to high parasite densities of P. falciparum in Africa in 2000 (116 million, uncertainty interval 91–258 million) [7]. The differences between these results and previous efforts are not primarily due to differences in the base year of analysis or definitions of a clinical attack, but stem largely from differences in estimation of the endemicity-structured PARs. In our previous global estimates [5], we adapted a historical, categorical description of malaria endemicity, whilst in Africa we [1],[3],[4] and others [6],[7] have previously used a climate suitability model of the likelihood of stable transmission as an index of differences in transmission intensity [102],[103]. The single largest difference between previous work and the present iteration of P. falciparum disease burden estimation is that neither previous approach was based upon an empirically defined risk map of malaria transmission [41]. Comparing estimates derived using these different techniques, over various time periods, is not a sound basis for investigating trends and should be avoided. It is clear that investing in radically improved surveillance and/or nationally representative malariometric surveys would substantially increase the fidelity of national and, by extension, global burden estimates. Because there are regional differences in the uncertain relationship between transmission intensity and disease outcome [42], more information derived from active case detection studies would improve the precision in our estimates of disease incidence within these transmission ranges. This information, while welcome, is likely to make only small differences to the computed risk in most scenarios of malaria transmission defined here. As a consequence, we believe that until there is a universally reliable reporting system for malaria cases worldwide to support comprehensive surveillance-based estimates, a concerted effort to map the changing spatial extents and intensity of transmission will remain a valuable contribution to the future estimations of a changing disease burden worldwide. In the short term, measuring how the “denominator” changes with time is clearly easier and cheaper than improving the global state of health information systems. Future Directions Many improvements will be possible with further work. We have not stratified incidence by age nor considered any of the consequential morbid events, sequelae, or mortality. Systematic biases in the identification of the extent of stable and unstable transmission would clearly impact estimates, and developing the datasets and techniques to address this problem is an important avenue for future work. Nor have we modelled uncertainty in HMIS reporting in unstable and low-stable transmission zones, and this might be possible with a methodological hybrid combining higher spatial resolution HMIS facility data with geostatistical techniques [37]. Moreover, we have not been able to consider some sources of uncertainty in the current framework; for example, those concerning the enumeration of the underlying population, based on collated census data; urban extent maps; and UN population projections. Finally, we have not considered the morbid burden posed by P. vivax. There are important differences in the biology of P. vivax [104] which make its control [105], and thus cartography-based burden estimation, problematic: its tendency to cause relapses [106], the routine reliability of parasite diagnosis when coincidentally prevalent with P. falciparum [107],[108] and the less well-defined relationship between transmission intensity and disease outcome. These all make an informed cartography of P. vivax distribution and estimations of disease burden considerably more complex than for P. falciparum. We do not underestimate the likely disease burden of P. vivax malaria [109]–[112], but new, innovative approaches based on an understanding of the clinical epidemiology and better cartography are required to improve upon current efforts to define the burden due to P. vivax. It is worth reiterating that if the international community wishes to demonstrate progress in malaria control, then the quantity and timeliness of prevalence information and parasite-specific surveillance records must dramatically improve. This is true for all countries but is particularly important in India, Nigeria, DRC, and Myanmar because of the large populations at risk and the paucity of existing malariometric information. These improvements in information collection and provision are as important across space (to be geographically representative of all transmission settings and intervention scenarios) as they are through time, so that impact can be evaluated in a timely manner. Conceptually, we also envisage that significant progress will be made in improving the accuracy of these estimates by hybridising cartographic and surveillance-based approaches. This would be best achieved by combining geopositioned HMIS facility data with geostatistical model outputs [37], so that the relative uncertainty of each can be compared and complementary information from both sources combined in a single coherent spatial framework. Globally, this is likely to be of particular utility in those areas of low and unstable transmission where surveillance capabilities are often more robust and correspondingly where prevalence data are often rare as the number of people needed to be sampled to find infections is prohibitive [12]. The malaria clinical burden estimates presented in this paper are driven by the underlying model of global prevalence [41]. This global malaria map is, to our knowledge, the first evidence-based attempt to define populations at risk of different levels of parasite transmission. It is needed in order to define the ranges of disease outcomes at a global scale and can serve as the benchmark for malaria disease burden estimations. The map will inevitably change with time as new information on the spatial extents of transmission and new PfPR2–10 data become increasingly available with the scale-up of interventions. The time–space functionality of the geostatistical model will increasingly capture the effects of scaled intervention efforts to reduce transmission, causing the size of the PfPR used to compute disease burden to change. Revising the limits and endemicity maps from this baseline and propagating these changes through to revised enumerations of clinical burden thus represents a useful complementary technique to assessing the impact of financing [113] on our progress towards international development targets for reducing malaria burden [59],[114]. Supporting Information Protocol S1 Supplemental methods. (1.39 MB DOC) Click here for additional data file. Protocol S2 A comparison of cartographic and surveillance-based estimates of national clinical incidence. (0.34 MB DOC) Click here for additional data file.
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            A Research Agenda for Malaria Eradication: Diagnoses and Diagnostics

              (2011)
            Summary Points New and improved screening tools and strategies are required for detection and management of very low-density parasitemia in the field Improved quality control is required for rapid diagnostic tests (RDTs) and microscopy in the field, to ensure confidence in diagnosis for case management More sensitive tests are required for Plasmodium vivax for case management Field-ready glucose-6-phosphate dehydrogenase (G6PD) deficiency tests and strategies for use to allow safe use of drugs against P. vivax liver stages are needed New strategies to manage parasite-negative individuals are needed to justify the continued inclusion of malaria diagnostics in febrile disease management in very low transmission areas. Introduction As malaria transmission declines across much of its range and the possibility of elimination (reduction of transmission to zero in a defined geographical area) is increasingly considered [1],[2], accurate diagnosis and case identification through the demonstration of malaria parasites in sick patients presenting to health workers (“passive case detection”) is ever more important. During case management in all settings, all symptomatic patients with demonstrated parasitemia should be considered to be malaria cases, and all parasitemic patients should be given definitive antimalarial treatment. Accurate diagnosis is essential both to target antimalarial drugs and to enable effective management of the frequently fatal nonmalarial febrile illnesses [3] that share signs and symptoms with malaria [4]–[13]. However, the very low levels of transmission now being attained in many countries present new challenges that will demand new diagnostic tools and strategies, in particular, a change from passive case detection to “active” case detection. That is, as the elimination agenda is increasingly followed [14], improvements in current field diagnostics (microscopy and rapid diagnostic tests [RDTs]) for case management and new diagnostics that can detect very low levels of Plasmodium in the blood of asymptomatic individuals (and, in the case of P. vivax, in the blood of symptomatic individuals) who may contribute to continuing malaria transmission [15]–[21] will become essential. Furthermore, novel strategies will be needed to incorporate these new and improved diagnostics into routine health service activities. More specifically, to avoid onward transmission, elimination programs for malaria will increasingly need to focus on detecting the highest possible fraction of infections in the general population through active rather than passive case detection. This change of focus will be essential because Plasmodium infections can persist at low densities for different lengths of time with no significant symptoms [16],[22],[23], and, in the case of P. vivax and Plasmodium ovale, as a latent stage in the liver that is not directly detectable. The contributions of these unseen reservoirs to the maintenance of transmission will depend on the success of detection and management of new cases and the coverage of vector and other control measures in the area [24],[25]. Thus, the usefulness of active case detection will vary with the epidemiology and health resources in an area and is itself a subject requiring further research [26]. Countries with successful “sustained control,” (the reduction of malaria transmission to a locally acceptable and sustained level through intensive use of vector control and effective case management) [14], will also need to adjust their diagnostic strategies as transmission declines to low levels and as they consider elimination. Importantly, until eradication of malaria (the reduction of transmission to zero worldwide) is achieved (and diagnostics therefore no longer required), efforts to eliminate malaria will continue to require diagnostics strategies as reintroduction will remain possible. This article, which summarizes the deliberations of the malERA Consultative Group on Diagnoses and Diagnostics, proposes a research agenda for the tools required for this process; related articles address broader issues of health service requirements and case management that will arise from their use [26],[27]. Figure 1 shows the position of different diagnostic approaches/tests in relation to morbidity, parasite prevalence, and densities and the different stages towards malaria elimination. Given the changing priorities for diagnoses and diagnostics as transmission reduces, in our discussion of the research needs for diagnostics, we distinguish between the two broad but overlapping areas of case management and surveillance/screening. This distinction is reflected in the target product profiles presented in Table 1. In both areas, sustainability will require integration with the general health system, and as much commonality as possible between diagnostics for different diseases. Thus we discuss priority setting in the context of the approaches already in use, or in the pipeline, for other diseases managed at the same levels of the health system. Because P. falciparum and P. vivax are the most prevalent plasmodia, the following discussion concentrates on these species, which most commonly present as mono-species infections. However, as P. falciparum infections decline, P. ovale may become relatively more prominent in areas where it is endemic, with implications for detection and management similar to those for P. vivax. Similarly, only time will tell whether transmission of Plasmodium malariae, which is transmitted across a broad geographical range, but at low prevalence, can be reduced using the measures applied to P. falciparum, or whether it will require specific strategies and tools. Notably, however, elimination of the zoonotic Plasmodium knowlesi is likely to require unique strategies (Figure 1). 10.1371/journal.pmed.1000396.g001 Figure 1 The position of different diagnostic approaches/tests in relation to morbidity, parasite prevalence, densities, and different stages towards malaria elimination. Image credit: Fusión Creativa. 10.1371/journal.pmed.1000396.t001 Table 1 Target product profiles for malaria diagnostics. Characteristic Case Management in Elimination Settings Screening/Surveillance (District Level or Below) Technical specifications Analytic sensitivity (parasite/µl)a E, 100–200, D 95%, D≥99% E>95%, D≥99% Analytic specificity Negative all pathogens, common blood disorders Negative all pathogens, common blood disorders Diagnostic specificity E>90%, D>95% E>99% surveillance low-transmission areas, E>95% screening Temperature stability E>35°C, D>45°Cc (2 y) E, 30°C; D, 45°C for short periods Integrity of packaging E, Moisture proof E, Moisture proof Species detection/differentiation: Pf predominant areasd E, Pf; D, Pf/pan E, Pf; D, Pf/pan Pf and non-Pf areas E, Pf/pan E, Pf/pan; D, differentiation all species Genotyping No No/Oe Ability to detect gametocytes No O Ability to detect hypnozoites No D Health systems and technical specifications Packaging of tests or reagentsf D, individual; D, all required consumables enclosed; D, bulk packaging displays temperature violations D, all required consumables enclosed; D, bulk packaging displays temperature violations Field stability/shelf life of consumablesg E, 2 y from manufacture (≥18 mo in country) E, 12 mo (6 mo since country); D, 2 y from manufacture (≥18 mo in country) Training requirements D, half-day of community-level health worker D, 0.9 Kappa>0.9 Instrumentation and laboratory infrastructure requirements E, no external power source; D, all provided with test D, all provided with test D, desirable; E, essential; O, optional. a Analytic sensitivity: detection threshold against the marker of the infective agent (target) in controlled conditions. Diagnostic sensitivity: proportion (percent) of target cases detected by the test in the setting of intended use. The sensitivity required for P. vivax is generally at least that required for P. falciparum, and the parameters here should be applied to both. To achieve the required diagnostic sensitivity in low-prevalence settings, a greater analytic sensitivity (lower threshold of detection) may be required in some cases. b Not required for febrile case management, but in an elimination setting, it would be desirable to detect incidental parasitemia at this level. c Essential where stored in the field in ambient temperatures that frequently reach this level. Ambient temperature of prolonged storage in place of use should be considered the essential temperature stability requirement for a particular product. d Areas in which infections are almost exclusively monospecies or mixed species P. falciparum infections. It is likely that many such infections have subpatent coinfections with other species. Where this represents a minority of infections, treatment on the basis of P. falciparum alone is likely to be acceptable from a programmatic and public health point of view. Non-P. falciparum infections are likely to become relatively more prominent as P. falciparum infections decline in prevalence, making the detection of non-P. falciparum species more desirable. e May be of importance in areas undergoing certification for elimination. f All inner (individual test) packaging should display, at a minimum: manufacturer name, product name, expiry date, lot number, target use (malaria). g Outcome of temperature stability and integrity of packaging (ability to exclude moisture). h Rapidity of results: For case management, results must be available before a patient is likely to leave the clinic. For surveillance, result availability in time for finding and managing cases is highly desirable. Diagnostic Strategies for Programs in the Intensified Control Phase Identification of parasitemia in febrile patients is essential in all of the programmatic phases of the continuum from malaria control to elimination, although the challenges for health systems in maintaining this activity in areas where malaria has become rare will be more prominent, as will the importance of detecting asymptomatic infections of low parasite density. The ongoing role of other routine interventions, such as intermittent preventive treatment in pregnancy, needs reevaluating as elimination is approached. Moreover, because the distribution of malaria transmission is often highly heterogeneous within a country, strategies may need to vary at a subnational level. Analyses of past experiences and operations research are required to guide decisions on when these changes in emphasis should take place as control progresses [27],[28]. Although programs in areas of higher transmission will be less likely to engage in active case finding of individuals with low parasite densities, surveillance is nevertheless necessary to detect trends and the impact of interventions, and requires appropriate, high-throughput diagnostic tools. In addition to the diagnosis of malaria, it will be critical to have diagnostic capabilities for other causes of presenting illness, particularly fever. A sick adult or parent of a febrile child may not be satisfied with a diagnosis of “not malaria,” and both patients and providers require guidance on the integrated management of childhood illnesses, to ensure that appropriate alternative and specific treatment is available and provided. Experience in eliminating malaria and maintaining elimination (or very low transmission) in sub-Saharan Africa is lacking, but experience from other areas suggests that resource requirements may be prohibitive and long-term maintenance of very low transmission and prevention of rebound unachievable using conventional management [29],[30]. Innovative approaches are therefore required. Diagnostic tools capable of detecting very low parasite densities (1 parasite/µl blood) in asymptomatic individuals will increasingly be required for active case detection and population surveillance to obtain a true picture of the prevalence of parasitemia and probability of transmission (as distinct from symptomatic malaria) [16]–[21]. Active case detection and treatment will be required whenever ongoing transmission is suspected and in high-risk populations (including those crossing borders), if the likelihood of ongoing transmission is to be eliminated. In these circumstances, test specificity is of increased importance because the absence of false positive results is critical in understanding the presence or absence of transmission [26]. Diagnostic Strategies for Programs in Areas Where Elimination Has Taken Place Once malaria is eliminated in a given area, considerable resources will be required to detect reintroduction through surveillance and to maintain capacity for rapid management and investigation of any cases found, as long as the risk factors that support transmission are still in place. Screening of migrant populations, screening of populations around detected cases, and case management tools for screening suspected patients, such as recent travelers or geographical associates of malaria cases may be needed. The tools to achieve these activities must be readily available in an environment where technicians are likely to be unskilled in the use of malaria diagnostic tests, particularly microscopy [27]. Thus, the requirements for surveillance and screening in areas where malaria has been eliminated, but risk of transmission is present, are similar to those of programs in an elimination phase. However, case management tools that are minimally dependent on previous technician experience in diagnosing malaria will be of particular importance. Diagnostic Tools for Case Management in an Elimination Setting In settings where there is risk of autochthonous or imported malaria, diagnostics must be capable of rapidly and accurately detecting and quantifying parasitemia in febrile patients, and identifying species. In addition, highly sensitive diagnostic tools are needed for passive case detection and case management at health care facilities (public or private) that report to the national health information or disease surveillance systems. The issues around diagnostics in both case management and surveillance and control settings have a large impact on, and are impacted by, monitoring and evaluation requirements and health systems implementation issues such as the development of improved supply lines and logistics management, reporting of results and commodity consumption, and adherence of health workers and patients to management consistent with diagnostic results. These are all important areas where pooling of knowledge and sometimes operational research is required to maximize the impact of the diagnostic tools discussed below [26],[27]. Light Microscopy When performed to a high standard, light microscopy is capable of accurately identifying and quantifying Plasmodium parasites with sufficient rapidity for case management in most settings. It remains the operational gold standard in both control and elimination settings. However, the quality of light microscopy in the field is often inadequate [31]–[36] and limited by factors such as the instability and difficult preparation of currently used Romanowsky-based stains [37]–[39], poorly maintained, low quality equipment, and inadequate training, supervision, and quality assurance. Additionally, as malaria transmission decreases, it is likely that light microscopy technician skills may be redeployed elsewhere. Consequently, research into sustainable ways to maintain high-quality light microscopy in field settings, including innovative training, supervisory, and quality-assurance systems, is badly needed. More consistent and stable staining techniques are also required. This area of research has been ignored for the past 60 to 100 years, but has the potential to improve field accuracy significantly and may also improve the potential of the new reading techniques discussed below. Large volumes of slides pose particular challenges with respect to reading, especially in settings with low parasite prevalence where microscopist performance is hard to maintain [26]. Digital Microscopy Computer-assisted analysis of Giemsa-stained slides (possibly combined with automated staining), or digitized image transfer (potentially via mobile telephone) to a reference centre for review by an expert microscopist may enable greater consistency in parasite detection [40]–[44]. Additional research is required to determine whether these techniques will detect lower parasite densities than can be obtained by traditional light microscopy. Related techniques under development use software analysis of the scatter of various wavelengths of light to identify Plasmodium parasites and other pathogens. Although these digital techniques have the potential to improve field detection of malaria parasites, field-ready versions are not yet available, and it is not known whether these tools will meet the requirements for use in resource-poor settings. Fluorescent-Assisted Microscopy Fluorescent-assisted microscopy (FAM)-based methods—for example, the quantitative buffy coat (QBC) method [45], incorporation of a fluorescent probe (fluorescence in situ hybridization [FISH]) or of parasite DNA [46], or antigen staining—has been used to a limited extent in various programs. FAM methods may eventually speed up slide reading and reduce operator error. High-throughput FAM may become possible if high specificity can be maintained by the absence of low artifactual staining. However, at present FAM cannot differentiate between species, a capability considered a major advantage of light microscopy over today's antigen-detection tests, although species-specific markers for FISH assays and fluorescent-tagged monoclonal antibodies are being developed. In addition, the applicability of FAM to parasite quantitation is not clear and FAM requires specialized equipment that will limit where it can be used. Antigen-Detecting RDTs RDTs based on the detection of specific parasite antigens that use a platform design of lateral immunochromatographic flow (dipsticks or plastic cassettes) have started to change the way malaria is diagnosed in endemic settings. RDTs are increasingly being used at the community level and in control programs for case management and in prevalence surveys. Good RDTs reliably detect parasitemia down to 100–200 parasites/µl, which is comparable to the sensitivity of routine well-performed light microscopy [47]. In general, RDTs are simple to use. With training and quality assurance, they can be used by peripheral facility and village health workers to determine whether malaria parasites are present in a patient. However, increasing use in field settings suggests that many commercial RDTs have variable detection thresholds and field stability [48]. Systems for monitoring performance and routine quality control of manufactured product lots are therefore required. Three parasite antigen types are targeted by currently available RDTs. Histidine-rich protein 2 (HRP2)-detecting tests have high sensitivity and specificity for P. falciparum but detectable antigen frequently persists after parasite clearance. The presence of HRP2 deletions in areas of South America also limits the use of these tests [49]. Commercial tests for Plasmodium lactate dehydrogenase (pLDH) have yielded variable results and, in general, have less potential to detect low parasite densities and greater susceptibility to deterioration under storage at high temperature than HRP2-based tests [48],[50]. However, species-specific (P. falciparum and P. vivax) and pan Plasmodium species-specific pLDH-based tests are available. Finally, tests targeting pan-specific parasite aldolase have shown inadequate detection thresholds in recent comparative trials, possibly because of the low concentrations of this target antigen in parasites [48]. The development of RDTs targeting other antigens may improve species identification (critical for elimination of P. vivax) and address some of the deficiencies of the current RDTs. In particular, current tests for P. vivax, which lack consistency in sensitivity and stability, might benefit from the use of monoclonal antibodies that target new antigens or improved manufacturing standards. Quality-Control Methods for Malaria RDTs Standardized quality-control methods for RDTs are important for confirming test quality and ensuring that health workers and patients trust results. As with microscopy [39], quality assurance of RDTs requires a comprehensive, organized program [47],[51]. Such programs are absent in many countries. The development of standardized panels containing known concentrations of target antigens will greatly broaden the reach, applicability, and sustainability of RDT quality-control programs. Parasite-based panels that use cryo-preserved parasite preparations [52] are currently available at a centralized (regional) level, but panels that are easier to standardize and widely available are needed. Likewise, standardized regulatory approval and procurement in keeping with best practices will reduce the requirement for investment by individual procurement agencies in quality control and product evaluation programs. The development of low-cost tools for confirming quality at the national and field level (positive controls [53]) is also necessary to improve reach and sustainability. Finally, novel approaches that use PCR to confirm RDT results might eventually be useful. Diagnostic Tools for Active Case Detection and Community Surveys For use in active surveillance and case finding, a diagnostic tool must be suitable for use in resource-poor field settings. Diagnostic tests must therefore be supportable at the district level or below, be affordable and low-maintenance, require less operator training than current methods, and have a low requirement for consumables. They should also detect very low parasite densities and distinguish between all locally prevalent Plasmodium species, be minimally invasive, and provide sufficiently rapid results to facilitate effective case management when an infection is identified. For use in prevalence surveys, where immediate management of asymptomatic parasitemia is not the aim, testing at a more centralized level may be sufficient. But, even in this context, rapid feedback and case management are desirable. Molecular (DNA) Detection Current methods of detecting circulating parasites by demonstrating parasite DNA through amplification of ribosomal RNA (rRNA) genes by PCR assays represent the overall gold standard of malaria diagnostics. When sample concentration methods are used, 0.5 parasite/µl unconcentrated blood or lower can be detected. Quantitative PCR can be used to determine the concentration of circulating DNA and therefore estimate the density of circulating parasites. Survey and testing techniques, including pooling of samples, can reduce costs [54] but also reduce sensitivity to some extent by diluting samples. At present, the application of PCR-based methods is restricted to well-equipped laboratories with specially trained technicians, partly because the need to avoid contamination (which leads to false-positive results) requires a very high standard of laboratory practice. PCR capacity is consequently limited in resource-poor malaria-endemic countries, where considerable investment would be required to establish and maintain it. PCR capacity-building programs are underway in several African countries through the Malaria Clinical Trials Alliance (MCTA). However, its restriction to well-equipped laboratories limits the applicability of PCR for surveillance and asymptomatic parasitemia case finding because timely feedback to allow the treatment of identified cases is impossible in most endemic areas. The development and field demonstration of high-throughput field-applicable PCR technologies is therefore needed to allow wider use of PCR in endemic settings. Another molecular detection method based on DNA amplification is loop-attenuated isothermal amplification (LAMP). This method, which amplifies DNA (usually mitochondrial) with a single thermal cycle, has the potential to reduce the training and infrastructure requirements of molecular diagnosis [55]–[57], and would allow the timely feedback of results needed for case management. LAMP could also be used for surveillance, for detection of low-density parasitemia, and for monitoring parasite presence in antimalarial drug-efficacy monitoring and drug trials. However, LAMP has not yet been adequately field tested for wide-scale use or developed in a format suitable for the processes of high sample numbers. Hemozoin Detection Hemozoin, a by-product of Plasmodium metabolism, can be detected through refraction/absorbance of laser light of certain frequencies, and has been used to detect malaria and to determine species. Current field-ready technologies are based on flow cytometers. Their application is limited to screening, however, because of low sensitivity at low parasite densities [58]–[62]. Current research activities include the development of transcutaneous hemozoin detection. If sufficiently sensitive and specific, this approach might offer a noninvasive test for malaria for mass-population screening of, for example, individuals moving into a malaria elimination area. Hemozoin detection may find a place in routine case management if appropriate tools can be developed. Antigen-Detection Tests Current antigen-detecting RDTs (see earlier for details) are likely to miss a significant proportion of asymptomatic cases in low-transmission settings [16],[22],[23],[39]. Thus, although the current generation of RDTs can indicate the presence of malaria in a community, they cannot determine the true prevalence of parasite carriage. Research aimed towards increasing the sensitivity of existing RDTs may not change this situation because of the limitations of the currently available technology. Some antigen-detecting ELISAs are more sensitive than RDTs. Furthermore, because they can also be used to quantify antigen, they have been used to monitor drug efficacy. Antigen-detecting ELISAs may also facilitate high-throughput testing. However, their use is currently limited by laboratory and training requirements. Antibody Detection Antibody detection (see also [27]) is currently available in ELISA and RDT formats, and is a sensitive way to demonstrate past exposure to malaria parasites (past infection). Because antibodies may not be detectable in blood-stage infections of very recent onset, these tests are inappropriate for case management. However, they may be useful in detecting established P. falciparum infections in which the blood-stage parasite density has fallen below the limits of light microscopy or antigen-detecting RDTs [63]. Detection of antisporozoite antibodies (so-called anti-CSP antibodies) alone or in combination with antibodies to blood-stage parasites has also been suggested as a surrogate for detecting individuals with a high likelihood of carrying P. vivax hypnozoites (evidence of infection) [64]–[68]. However, anti-CSP antibody responses are usually low and transient, especially in areas of low and moderate transmission, which renders this test unreliable. Because antibody-detecting tests can identify parasite-infected individuals who are undetectable by antigen detection or light microscopy because of low parasite density, they could be used to screen populations such as migrants or blood donors to identify asymptomatic individuals at risk of transmitting malaria. They could also be used for identifying foci of recent transmission in areas that are otherwise malaria free and to determine the presence or absence of recent malaria transmission in specific populations, such as young children. They therefore have potential applications in confirming areas free of transmission during a defined period, provided they are further refined and developed in terms of sensitivity and specificity. Specific Issues for Reduction and Elimination of P. vivax Transmission Detection of Hypnozoites P. vivax detection and management will become increasingly important as control measures reduce P. falciparum transmission. In many programs, P. vivax already causes the majority of clinical malaria episodes. Because P. vivax can remain latent in the liver but produces relapse, its effective management normally requires the use of 8-aminoquinolones to clear hypnozoites from the liver. No current diagnostic technique is capable of detecting P. vivax hypnozoites, and none are in development, although tests that can detect the presence of hypnozoites are a key research and development need wherever and whenever elimination has a chance of becoming a realistic goal. While symptomatic cases of P. vivax can be assumed to harbor liver stages and managed accordingly, a method for detecting hypnozoites would enable populations in P. vivax-endemic areas to be screened during the nontransmission season for asymptomatic individuals likely to have relapses who could then be treated before they become symptomatic and transmit in the following transmission season. Screening could therefore reduce the use of 8-aminoquinolones in mass-treatment programs in P. vivax-endemic areas, which would reduce the probability of drug-related severe side effects in glucose-6-phosphate dehydrogenase (G6PD)-deficient individuals (see next section). At present, compliance issues with the long course of primaquine (generally 14 days) have limited the broad application of this approach, and therefore the need for a diagnostic test for hypnozoites [24]. Potential biomarkers to detect hypnozoites include direct markers of metabolic activity, released antigens, markers of host immune response, and indirect serological markers of other stages (e.g., sporozoites). A lack of known markers of hypnozoite metabolic activity and markers of immunity limits the potential to assess the likely gains from investment in this area, and more knowledge of the biology of hypnozoites, perhaps through the development of liver-stage cultures, is required to determine whether such tests can be developed [69]. Detection of G6PD Deficiency The only drug currently licensed for the radical cure of P. vivax infection is primaquine, and the only investigational drug showing promise is tafenoquine, Both these 8-aminoquinolones cause hemolysis in G6PD-deficient individuals, the clinical importance of which varies with the particular G6PD-deficiency phenotype, and the starting hemoglobin concentration, and may depend on how the drugs are administered [70]. Because eliminating P. vivax reservoirs will probably involve the use of a hypnozoiticidal drug [24], unless a non–8-aminoquinolone drug is developed, G6PD testing is likely to be required for wide-scale elimination of P. vivax. The requirements for such a test differ somewhat from those of parasite-detecting RDTs, because testing should only be required once in a lifetime and is not urgently required; the use of hypnozoiticidal drugs can be delayed if necessary. So, for example, a G6PD test does not have the stability requirements of an antigen-detecting RDT. Current tests for G6PD deficiency nevertheless have limitations regarding storage requirements and the complexity of the procedure, so research is needed to develop new tests. Importantly, addressing G6PD deficiency will also involve research into test implementation—how should samples be tested, where should tests be done, and how should results be recorded to facilitate retrieval? Moreover, to decide whether further development of field-applicable G6PD tests is needed also requires more data on the distribution of G6PD phenotypes and on the efficacy and safety of alternatives to the standard hypnozoiticidal primaquine regimen. Other Research Priorities for Future Malaria Diagnostics Noninvasive Sampling Current RDTs detect antigen in peripheral blood samples obtained by finger prick. This method is generally acceptable for case management in the formal health care sector, but it presents some logistical challenges at the community level and in some private sector settings, particularly with regard to the potential risks of blood-borne infection. In addition, invasive tests may not be fully accepted in some settings, particularly when taking samples from asymptomatic individuals, which could diminish access to malaria diagnosis, treatment, and surveillance. Noninvasive sampling (for example, saliva or urine collection) has the potential to overcome these impediments but, at present, the limitations of sensitivity of nonblood sampling are even greater than the limitations of blood sampling combined with antigen-detecting RDTs for screening and surveillance [71]–[73]. Published trials of antigen sampling from saliva and urine, for example, have demonstrated inadequate sensitivity, probably because of the low concentration of available antigen in these samples [71],[74]. Urine sampling may also present practical and cultural constraints. Techniques that concentrate antigen may have potential if they can be made practical for use in low-resource settings, but no such techniques are currently available. Additionally, if quantification is required, these methods would need to incorporate a standard to allow for variations in concentration of saliva or urine. Multiplexing Multiple diagnoses from one assay or “multiplexing” is made possible by, for example, the inclusion of multiple PCR-based nucleic acid probes in a single test or the inclusion of antibodies specific for nonmalarial diseases or of pathological markers of disease severity. The inclusion of antibodies targeting nonmalarial diseases in RDTs in their common format (visually read immunochromatographic tests) increases the technical challenge of achieving the stability needed for sufficient shelf life and makes interpretation of results more complex. The usefulness of such tests is also limited by the ability of the health system to provide appropriate management for each etiological agent that may be identified, and the highly variable prevalence of potential target differential diagnoses within malaria-endemic areas. However, as malaria rates drop through successful control programs, the overall fever rate may not change significantly. Accordingly, it will be increasingly important to integrate management of malaria with that of other febrile diseases, at the point of diagnosis, if the program is to remain credible and sustainable (see also [27]). Nonmalarial fever will need to be diagnosed with sufficient accuracy to allow practitioners to manage the main causes of fever successfully and to at least distinguish major bacterial infections manageable with common antibiotics from nonbacterial infections. Research and development needs for multiplexing include the development of field-ready multiplex tests for malaria and nonmalarial diseases, which are not currently widely available, and research into the inclusion of markers for inflammation or severe disease in malaria tests, which would offer the potential to guide the referral of patients who require urgent management (see also [27]). Finally, the issue of complexity of interpretation in multidisease diagnostics needs to be addressed by the development of automated readers, particularly in combination with technology that allows multiple distinguishable markers to be captured in a single test line. Pooling Samples for Surveillance, Gametocyte Detection, and Genotyping Three other potential research priorities were discussed by the Consultative Group, but the consensus was that research into pooling samples, gametocyte detection, and genotyping was less urgent. Thus, although the idea of pooling individual samples to detect parasitemia in very low transmission settings is intrinsically appealing and could result in cost savings using currently available tests, the Consultative Group felt that the limited quantity of antigen or DNA in pooled samples would severely limit the sensitivity of this approach. Similarly, the group decided that the development of a detection test for gametocytes should not be viewed as a high priority requirement. Finally, although WHO guidelines recommend genotyping of parasites during elimination phases [39], there is debate about whether research into methods for genotyping would be programmatically useful, particularly for P. falciparum. The resource needs to achieve genotyping are massive, and the long feedback time for results is likely to reduce the exercise to one of academic interest only. Genotyping could be useful for P. vivax infections to determine whether a blood-stage infection is new or a relapse. However, it has not yet been possible to develop methods that will reliably distinguish between relapse, recrudescence, and reinfection because of the multiplicity of hypnozoite genotypes present in P. vivax-infected individuals. Genotyping might, however, be useful in suspected outbreak or in new foci of transmission to determine the source of parasites, particularly when elimination in an area is being confirmed [26]. Sustaining the Effort The central importance of active case detection in each programmatic stage towards elimination has been comprehensively dealt with by several of the other malERA Consultative Groups [24]–[27]. However, whether active case detection can be achieved at sufficiently high and sustainable levels will depend to a great extent on the field utility and costs of the diagnostic and other tools eventually adopted for this role and on how these tests are used. Importantly, when malaria is rare and no longer perceived by local health services and the community to be of significant public health concern, ways must be found to maintain the resources needed to test febrile cases for parasitemia to prevent resurgence of infection. Because malaria parasite detection will be competing for resources with other disease priorities with higher mortality, it will be necessary to target diagnostics to those cases more likely to be malaria rather than necessarily screening whole populations (although some form of screening, and the ability to respond rapidly to reintroduction, will continue to be necessary [26]–[28]. It will also be important to integrate malaria detection more fully with other health service activities and, as nonmalarial causes of fever become predominant, it will be critical to provide appropriate diagnosis and management of alternative causes so that compliance is maintained through confidence in the ability of the health system to provide solutions to clinical problems. Conclusions Malaria elimination in the most challenging settings will require improvements in point-of-care tests for case management, and the development of new tests capable of identifying very low parasite densities in asymptomatic individuals in field settings for mass screening and treatment. As a result of our discussions, we propose a research and development agenda for diagnoses and diagnostics that should stimulate and facilitate the development, validation, and use of such tests (see Box 1). Box 1. Summary of the Research and Development Agenda for Diagnosis and Diagnostics Overarching questions What proportion of effort should be directed to screening and surveillance versus early case detection at various time points in elimination? Question to be addressed by modeling and validated in different areas. Do we need microscopy for elimination, or can other tests replace it? Programmatic issues Further data on thresholds of (i) parasite density likely to cause symptoms in low-transmission settings with variable or waning immunity, and (ii) transmission potential of cases with parasitemia below the threshold of microscopy and RDTs Diagnostic tests for nonmalarial febrile illness in malaria-endemic and malaria-elimination settings Distribution of severe G6PD variants Technical issues: case-management tools High priority Stable tests for case management in low-training, low-technology settings with sensitivity sufficient for community-level case management, including: Antigen-detecting RDTs Greater consistency in P. falciparum detection, particularly in the case of nonpersistent antigens More sensitive and stable tests to detect non-P. falciparum parasites Clarification of the programmatic/implementation requirements that will ensure good impact in the field Standardized low-cost positive controls for antigen-detecting RDTs suitable for field use Sustainable tools for quality control of RDTs at a country level. Further investigation of nonblood sampling to determine the potential for detecting recoverable antigen in these samples. More consistent, reliable staining methods for microscopy G6PD deficiency mapping and identification (if 8-amino-quinolones are to be used) Medium priority Multiplexing: Other diseases, markers of severity Field G6PD detection (may be more important if tafenoquine approved), or raised priorities for P. vivax relapse prevention Tools to standardize and improve microscopy interpretation Low priority Hypnozoite detection (becomes a high priority if feasibility can be demonstrated through further research on hypnozoite biology, identifying good biomarkers). Technical issues: surveillance tools High priority Field-applicable tools for detection of low-density parasitemia in a high-throughput manner, suitable for surveys and active detection of parasite carriage in time to allow management of positive cases Tools for minimally invasive, very rapid detection of low-density parasite infections suitable for screening of migrants/travelers Innovation with potential for major operational impact Noninvasive, low-density parasite detection Low-hanging fruit with immediate application for elimination High-throughput field molecular detection, capable of use at district level or below Positive control methods for RDTs Because malaria generally occurs in low-resource settings, the profits likely to be made from malaria diagnostic development and manufacture, particularly in the face of low mortality, are limited. The current market place for malaria rapid tests is dominated by small to medium-sized manufacturers, who are unlikely to be able to make the major investments needed to address these priorities alone. Thus, the role of donor agencies and product development partnerships and research institutions in enabling research and development and in providing the expertise and field access necessary to shape products to meet program needs will be an essential element of diagnostics development. Critically strong and focused, mainly public-private, partnerships will need to built and nurtured.
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              The threat of artemisinin-resistant malaria.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                6 September 2012
                : 7
                : 9
                Affiliations
                [1 ]Malaria/Acute Febrile Syndrome Programme, Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland
                [2 ]Centre for Clinical Vaccinology and Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Churchill Hospital, Oxford, United Kingdom
                [3 ]WorldWide Antimalarial Resistance Network, University of Oxford, Churchill Hospital, Oxford, United Kingdom
                [4 ]Wellcome Trust-Mahosot-Oxford Tropical Medicine Research Collaboration, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
                [5 ]Malaria Molecular Epidemiology Unit, Pasteur Institute of Cambodia, Phnom Penh, Cambodia
                [6 ]Malaria, Other Vector-borne and Parasitic Diseases, WHO Regional Office for the Western Pacific, Manila, The Philippines
                University of Barcelona, Spain
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PNN PJG DB IG NA RC. Performed the experiments: NA IG RC. Analyzed the data: NA IG DB RC PN. Contributed reagents/materials/analysis tools: NA IJG PNN DM RC EC JN DB PJG. Wrote the paper: NA IJG PNN DM RC EC JN DB PJG.

                Article
                PONE-D-12-15053
                10.1371/journal.pone.0044269
                3435412
                22970193

                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.

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                Pages: 15
                Funding
                FIND was supported by the United Kingdom Department For International Development (DFID). Paul Newton is supported by the Wellcome Trust of Great Britain. Didier Ménard is supported by the French Ministry of Foreign Affairs. WWARN is supported by the Bill and Melinda Gates Foundation and DFID. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine
                Clinical Research Design
                Systematic Reviews
                Global Health
                Infectious Diseases
                Neglected Tropical Diseases
                Dengue Fever
                Tropical Diseases (Non-Neglected)
                Malaria
                Viral Diseases
                Dengue
                Non-Clinical Medicine
                Health Care Policy
                Health Systems Strengthening
                Public Health
                Health Screening

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