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A longitudinal cohort study of malaria exposure and changing serostatus in a malaria endemic area of rural Tanzania
research-article
Author(s):
Ryan A. Simmons
1
,
2
,
,
Leonard Mboera
3 ,
Marie Lynn Miranda
4 ,
Alison Morris
5 ,
Gillian Stresman
5 ,
Elizabeth L. Turner
1
,
2 ,
Randall Kramer
2 ,
Chris Drakeley
5 ,
Wendy P. O’Meara
2
Publication date (Electronic):
2 August 2017
Journal:
Malaria Journal
Publisher:
BioMed Central
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Abstract
Background
Measurements of anti-malarial antibodies are increasingly used as a proxy of transmission intensity. Most serological surveys are based on the use of cross-sectional data that, when age-stratified, approximates historical patterns of transmission within a population. Comparatively few studies leverage longitudinal data to explicitly relate individual infection events with subsequent antibody responses.
Methods
The occurrence of seroconversion and seroreversion events for two Plasmodium falciparum asexual stage antigens (MSP-1 and AMA-1) was examined using three annual measurements of 691 individuals from a cohort of individuals in a malaria-endemic area of rural east-central Tanzania. Mixed-effect logistic regression models were employed to determine factors associated with changes in serostatus over time.
Results
While the expected population-level relationship between seroprevalence and disease incidence was observed, on an individual level the relationship between individual infections and the antibody response was complex. MSP-1 antibody responses were more dynamic in response to the occurrence and resolution of infection events than AMA-1, while the latter was more correlated with consecutive infections. The MSP-1 antibody response to an observed infection seemed to decay faster over time than the corresponding AMA-1 response. Surprisingly, there was no evidence of an age effect on the occurrence of a conversion or reversion event.
Conclusions
While the population-level results concur with previously published sero-epidemiological surveys, the individual-level results highlight the more complex relationship between detected infections and antibody dynamics than can be analysed using cross-sectional data. The longitudinal analysis of serological data may provide a powerful tool for teasing apart the complex relationship between infection events and the corresponding immune response, thereby improving the ability to rapidly assess the success or failure of malaria control programmes.
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Most cited references36
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Serology: a robust indicator of malaria transmission intensity?
Patrick Corran, Paul A. Coleman, Eleanor M. Riley … (2007)
To estimate the burden of malarial disease, and evaluate the likely effects of control strategies, requires reliable predictions of malaria transmission intensity. It has long been suggested that antimalarial antibody prevalences could provide a more accurate estimate of transmission intensity than traditional measures such as parasite prevalence or entomological inoculation rates, but there has been no systematic evaluation of this approach. Now, the availability of well characterized malarial antigens allows us to test whether serological measurements provide a practical method for estimating transmission. Here we present a suggested methodology, highlight the advantages and shortcomings of serological measurements of malaria transmission and identify areas in which further work is desirable.
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Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya
Philip Bejon, Thomas N Williams, Anne Liljander … (2010)
Introduction Many infectious disease show marked heterogeneity of transmission [1]. Mathematical models predict that this heterogeneity reduces the efficacy of disease control strategies [2], and intensifying control measures on foci of high transmission is predicted to be very effective in reducing overall transmission [1]. Marked spatial heterogeneity of malaria transmission on the household level is consistently detected when analysed [3]–[9], and results from both genetic and environmental factors [10],[11]. It is unclear how stable hotspots are in longitudinal data. Malaria risk is related to environmental factors [12] including altitude [13], cultivation practices [14], urbanization [15], and distance from bodies of water [16]. However, ecological analyses to guide malaria control have been limited by the following factors: the overall models are complex [17]–[19], the same ecological feature may not have a consistent effect in different settings [20],[21], and there is marked residual variation in malaria risk despite models with detailed ecological data [22]. Furthermore, the resolution of multitemporal remote sensing satellite data (i.e. data based on more than a single snap-shot) for environmental monitoring is rarely finer than 0.5–1 km [23]. Since vector dispersion occurs on average over 0.5–1 km distances [24]–[29], this is the scale at which “hotspots” of transmission need to be identified in order to impact overall transmission. Malaria episodes have been found to cluster at this scale to form hotspots in high resolution geo-spatial analyses in Mali [6], Uganda [7], Ethiopia [30], and the highlands of Kenya [31],[32]. Here, we have conducted an analysis of malaria episodes and parasitaemia over 12 y, a substantially longer time period than has been reported previously, across three different cohorts without conspicuous variations in geography, such as nearby water bodies. We examine febrile episodes, asymptomatic parasitaemia, serological markers of exposure, environmental remote sensing data, and molecular studies of parasite clones to describe the spatial and temporal limits of hotspots, and to examine whether heterogeneity can be predicted. Methods The approval for human participation in these cohorts was given by Kenya Medical Research Institute Scientific committee and National Review and Ethical Committee of Kenya Medical Research Institute, and was conducted according to the principles of the declaration of Helsinki. Surveillance for Malaria The cohorts under surveillance for malaria episodes were located in Chonyi, Ngerenya, and Junju sublocations of Kilifi District, on the coast of Kenya between January 1998 and June 2009 (Figure 1). Concurrent entomological studies and parasite prevalence rates suggest that the transmission intensity is higher in Junju and Chonyi than in Ngerenya [33],[34], but transmission has been falling throughout the period of study [35]. 10.1371/journal.pmed.1000304.g001 Figure 1 The distribution of homesteads monitored in the three cohorts is shown within Kilifi District. The field methods used to identify episodes of febrile malaria and asymptomatic parasitaemia have previously been described [36],[37]. Weekly active surveillance was undertaken, and children with fever had blood slides for malaria parasites. In Chonyi and Ngerenya, children with either subjective (i.e., reported) or objective fever (temperature ≥37.5°C) had blood smears performed for estimating the parasite density. In Junju blood smears were done only on children with an objective fever, but children with subjective fever were seen again 6–12 h later, and the temperature measurement repeated. Blood smears were made if objective fever was confirmed at this measurement. The parents of the children in Chonyi and Ngerenya were instructed to report to Kilifi District Hospital 20 km away if the child had any symptoms of disease at any time (and reimbursed for travel expenses), and in Junju trained field workers were available at all times in the villages for passive surveillance. Antimalarials were supplied for proven episodes of malaria by the study team in accordance with government of Kenya guidelines; this was sulfadoxine-pyrimethamine until 2004, and co-artemether thereafter. Study participants may have used private clinics or bought antimalarials without the study team's knowledge, but given the availability of free treatment this was probably infrequent. Surveys for asymptomatic parasitaemia were undertaken once yearly, immediately before the rainy season. All individuals recruited to the study were asked to attend for blood sampling, and microscopy results were available for 70%–88% of participants for each survey. The Geographic Positioning System coordinates from the Kilifi Demographic Surveillance Survey were linked to each homestead in the study. In Ngerenya and Chonyi, all the residents at individual homesteads were recruited, but in Junju only children under 8 y of age were recruited. The homesteads in Ngerenya and Junju were evenly spaced throughout the study location, but in Chonyi the homesteads were distributed along a central road through the study area. Children born in the study homesteads during the period of monitoring were recruited, and so the average age of the cohort did not increase over time (Table 1). 10.1371/journal.pmed.1000304.t001 Table 1 Cohort summary characteristics. Characteristics Chonyi Junju Ngerenya Asymptomatic parasitaemia prevalence rate 35% 32% 14% Incidence of febrile malaria (episodes per child year) 0.82 0.55 0.49 Average population 818 462 428 Median length of follow-up per child (y) 2.8 3.5 5.0 Median age of child (y) 5.5 4.2 5.7 Years of longitudinal monitoring in cohort 3 5 11 Homesteads 59 149 48 Participants per homestead 13.9 3.1 8.7 Average distance between adjacent sampled homesteads (km) 0.08 0.08 0.35 Age range (y) 0–80 0–12 0–90 Area including cohort dimensions N to S (km) 7 7.6 6 Area including cohort dimensions E to W (km) 9.4 7.6 8.4 The asymptomatic parasitaemia prevalence includes both adults and children. The incidence of febrile malaria is given for participants 2,500/µl [36]. Episodes of febrile malaria were censored for 21 d after the last episode. The incidence per homestead per year was calculated by episodes of febrile malaria per homestead divided by the number of children who were 0.05). The most significant parameters from each channel were then combined in a final multivariable logistic regression model, with further exclusion of nonsignificant parameters (p>0.05). There are approximately 120 MODIS picture elements (pixels) widths/heights to each degree of latitude/longitude at the equator (i.e., approximately 1 km squared per pixel), so several homesteads are included in each pixel. The logistic regression models were therefore adjusted to take account of the nonindependence of observations by using the sandwich estimator to group observations by pixel [50]. The coefficients and constant from the final model (i.e., with p 1.0 OD, but only 37% of the whole cohort's malaria episodes would occur in these homesteads. In order to include 50% of the malaria episodes, a cut-off of 0.85 would be required, and this would require 32% of the homesteads in the study area to be treated. Table 8 shows the results of monitoring episodes of febrile malaria during 1 or 2 mo of surveillance to predict febrile malaria episodes during the following year, on the basis of the assumption that any homestead with at least one episode of febrile malaria during the monitoring period would be targeted for treatment. 10.1371/journal.pmed.1000304.t008 Table 8 Predicted performance if all homesteads with one or more episode of febrile malaria during 1-mo monitoring are targeted for measures to interrupt transmission over the following year. Monitoring Percent Homesteads Targeted Percent Malaria Covered January (short rains) 28 73 March (dry) 17 48 May (start long rains) 26 68 September (dry) 20 65 November (dry) 17 56 Discussion We identified stable hotspots of asymptomatic parasitaemia, and unstable hotspots of febrile disease in each of three cohorts in Kilifi District. The hotspots of asymptomatic parasitaemia were characterized by nonsignificantly higher mean antibody titres, a lower mean age at febrile episodes, and lower overall incidences of febrile disease. There may have been an increase in the incidence of febrile malaria in the penumbra around the perimeter of the hotspots of asymptomatic parasitaemia (Figure 5). Furthermore, hotspots of asymptomatic parasitaemia were stable over the full 7 y of monitoring, whereas hotspots of febrile malaria were not stable past 3 y of monitoring. Taken together, these observations suggest the following explanation: that a rapid acquisition of immunity in stable high transmission hotspots offsets the high rates of febrile malaria that would otherwise result. Instead, a high prevalence of asymptomatic parasitaemia is seen [51]. Unstable hotspots are not associated with prior exposure, and hence relatively low levels of immunity, and therefore produce higher incidences of febrile disease. Although the unstable hotspots are directly associated with more febrile disease, hotspots of asymptomatic parasitaemia may be critical in maintaining transmission [52]. The satellite remote sensing data suggest a primarily environmental cause for stable hotspots, but it was unclear why some hotspots are unstable. It was not simply a seasonal effect, since there was no indication of a seasonal specificity of hotspots in pooled analysis by season. Potential explanations might be a changing prevailing wind direction [53] or the temporary presence of “superspreaders” as seen in other infectious diseases [54]. Insecticide-treated net (ITN) use has expanded in Kilifi generally [35] and in the Junju cohort specifically [55], but there was no evidence that there were hotspots of ITN use in our dataset (p = 0.9). The study is based on active case detection in a sample of the total population (5%–10%). Data from the missing homesteads might alter the dimensions and frequency of the hotspots identified, and clarify whether clustering is stronger at the level of individual homestead or at the level of groups of homesteads that form “hotspots.” The resolution of remote sensing data results in an average of six homesteads per pixel. The remote sensing data predicted hotspots, but heterogeneity in risk by individual homestead was also observed, and higher resolution remote sensing data are needed to further investigate this. The instability of hotspots of febrile malaria was not simply due to variations in age of the children monitored (Table 3). Furthermore, stable hotspots of asymptomatic parasitaemia were observed in Junju, where only children and not adults were recruited to the cohort. Although the three cohorts monitored were from geographically different areas, the study is limited by presenting data from a single district, and so cannot represent the great diversity of ecology that will be seen across sub-Saharan Africa. Other cohorts undergoing longitudinal surveillance should be examined to confirm our findings. Malaria transmission varies by geographical features such as altitude [13], cultivation practices [14], streams and dams [16], house construction [56], socioeconomic factors [57], and ITN use [58]. Data on these factors were not available for the analysis presented here, although there were no large water bodies in the study area. However, a previous analysis in one of the study areas demonstrated that both environmental factors at the homestead level and host genetic factors contributed substantially to variation in malaria risk [10]. Our analysis suggests that environmental factors identified by remote sensing are associated with stable hotspots of asymptomatic parasitaemia. The environment in Kenya is seasonal, with two rainy seasons per year. The strongest individual factors from remote sensing were not the means of any index, but rather the minimum, and phases for a vegetation index and indicators of temperature, consistent with previous studies that have demonstrated the importance of temporal monitoring [12]. The remote sensing data show strong cross-correlation, and significant individual factors are likely to be proxies for more complex environmental determinants of transmission. 48 different measures of remote sensing were tested, but after a Bonferroni correction p = 1.4×10−5 and p = 2×10−4 for the two most significant individual factors. The evidence for environmental causation of the unstable hotspots of febrile malaria was less strong. Nevertheless, p = 0.006 after a Bonferroni correction for the single individual factor retained in the final model. On principle component analysis the second and third components were significantly associated with hotspots of asymptomatic parasitaemia, but the first component was associated with hotspots of febrile malaria. This finding is consistent with a complex environmental causation of hotspots rather than a single factor, and different environmental causes for the two types of hotspot. Anti-AMA antibody titres were associated with hotspots, but seroprevalence and seroconversion rates did not predict hotspots. This association contrasts with previous findings on a larger geographical scale [59]. The effects of individual variation may become more noticeable on a smaller scale [60], and smaller scale hotspots are unlikely to be as stable as environmental features such as altitude that operate on a larger scale [61]. There was substantial heterogeneity of transmission inside and outside hotspots. This heterogeneity was not simply random variation, since the previous year's incidence per homestead was strongly predictive of subsequent risk (r = 0.57). Variation in malaria risk at the homestead level has been shown to be due to a mixture of genetic and environmental factors [10],[11]. Irrespective of the cause, hotspots have a substantial effect on overall transmission in the community [2]. Here, we demonstrate two levels of clustering; clustering at the homestead level, and hotspots of groups of high risk homesteads in 1.3-km radius areas. The “gradient” effect away from the perimeter of a hotspot is consistent with transmission in the majority of the cohort being maintained by transmission from within the hotspot of homesteads. Hence, targeting the 73% of febrile malaria within the 28% highest risk homesteads is likely to benefit the wider community, since the highest risk homesteads will increase transmission in the surrounding area [1]. The genotyping data suggest that individual parasite clones are associated with a hotspot of asymptomatic parasitaemia. This finding may be because particular hosts more frequently transmit their parasites within a hotspot [54], or because particular parasite clones have adapted to a geographical micro-environment, determined, for instance, by the vector species [62]. Genotypes were not available from febrile disease episodes. Clustering of episodes by individual is reported in many infectious diseases [1], and has been relevant to the control of diverse pathogens such as Escherichia coli [54], tuberculosis [63], gonorrhea [64], SARS [65], and leishmaniasis [66]. Clustering of malaria episodes by homestead [10] and larger geographical hotspots including groups of homesteads is well described [3]–[9],[67], but there are few data on the temporal stability of hotspots. Targeted strategies for malaria control need to consider two kinds of hotspot; stable hotspots of asymptomatic parasitaemia and unstable hotspots of febrile disease. One might argue against intervening in hotspots of asymptomatic parasitaemia, since rates of febrile disease are not high, and intervention might reduce host immunity. However, where transmission has fallen in areas of high transmission, substantial direct and indirect mortality and public health gains have been described in the short and long term [35],[68],[69]. Furthermore, these stable hotspots probably feed transmission in their penumbrae for a distance of several kilometres because of vector dispersion [24]–[29]. Hotspots of asymptomatic parasitaemia can be identified by parasite surveys, serological surveys, or, more conveniently, remote sensing. Hotspots of febrile disease may be targeted by monitoring presentations to the local dispensary during the dry season, and targeting the effected homesteads during the subsequent rains. The optimal protocol would be to monitor during the dry season in September and then treat for the following year, which would result in targeting 20% of the homesteads, accounting for 65% of the febrile malaria episodes during the following year. It is optimal to cover 100% of the homesteads with any control intervention (particularly ITN distribution). However, some other interventions are currently not practical on community-wide scale, such as repeated mass drug administration [70],[71], environmental modification [72]–[74], mass vaccination [75], or (in some circumstances) indoor residual spraying (IRS) [76]–[78], but become feasible if targeted on the 20% of homesteads at greatest risk. When transmission has reduced to very low levels, transmission remains geographically clustered [9],[79], and intensified control in these hotspots is key to achieving elimination. In our setting, as in much of sub-Saharan Africa, there is not an immediate prospect of elimination. Additional targeted control measures may be viewed simply as a cost-effective way of ensuring that those most in need get the intervention, but the stronger justification is that reducing transmission in hotspots will reduce transmission on the wider community [1]. Environmental interactions are complex in determining malaria risk per se [12], and so these findings should be validated in other datasets before firm recommendations for malaria control programmes are made. Supporting Information Figure S1 Receiver operator characteristics (ROCs) are shown for AMA-1 antibodies, MSP-2 antibodies, and for the model on the basis of selected remote sensing variables and principle component analysis of the remote sensing variables. The areas under the ROC curves were 0.61, 0.67, 0.68, and 0.68 for prediction of febrile malaria hotspots by AMA-1 and MSP-2 antibodies, and for the model and principal component analyses, respectively. For asymptomatic parasitaemia hotspots the areas under the curves were 0.70, 0.72, 0.82, and 0.73, respectively. (0.61 MB TIF) Click here for additional data file. Figure S2 The incidence of febrile malaria episodes cpy is shown for the three cohorts. More intense green colouring indicates higher incidence. (0.48 MB TIF) Click here for additional data file. Figure S3 The prevalence of asymptomatic malaria is shown for the three cohorts. More intense green colouring indicates higher incidence. (0.47 MB TIF) Click here for additional data file.
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Estimating the Global Clinical Burden of Plasmodium falciparum Malaria in 2007
Simon Hay, Emelda A. Okiro, Peter W. Gething … (2010)
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.
Author and article information
Contributors
Ryan A. Simmons:
ORCID: http://orcid.org/0000-0003-0855-4334
ryan.simmons@duke.edu
Leonard Mboera: lmboera@nimr.or.tz
Marie Lynn Miranda: mlm@rice.edu
Alison Morris: alison.morris@pirbright.ac.uk
Gillian Stresman: gillian.stresman@lshtm.ac.uk
Elizabeth L. Turner: liz.turner@duke.edu
Randall Kramer: kramer@duke.edu
Chris Drakeley: chris.drakeley@lshtm.ac.uk
Wendy P. O’Meara: wendy.omera@duke.edu
Journal
Journal ID (nlm-ta): Malar J
Journal ID (iso-abbrev): Malar. J
Title:
Malaria Journal
Publisher:
BioMed Central
(London
)
ISSN
(Electronic):
1475-2875
Publication date
(Electronic):
2
August
2017
Publication date PMC-release: 2
August
2017
Publication date Collection: 2017
Volume: 16
Electronic Location Identifier: 309
Affiliations
[1
]ISNI 0000000100241216, GRID grid.189509.c, Department of Biostatistics and Bioinformatics, , Duke University Medical Center, ; 2721, Durham, NC 27701 USA
[2
]ISNI 0000 0004 1936 7961, GRID grid.26009.3d, Duke Global Health Institute, , Duke University, ; 310 Trent Drive, Durham, NC 27701 USA
[3
]ISNI 0000 0004 0367 5636, GRID grid.416716.3, , National Institute for Medical Research, ; 3 Barack Obama Drive, P.O. Box 9653, 11101 Dar es Salaam, United Republic of Tanzania
Author information
Ryan A. Simmons
http://orcid.org/0000-0003-0855-4334
Article
Publisher ID: 1945DOI: 10.1186/s12936-017-1945-2
PMC ID: 5539976
PubMed ID: 28764717
SO-VID: c01bb3bc-e0d2-4f2c-9434-3d978d6c1543
Copyright © © The Author(s) 2017
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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
History
Date
received
: 13
April
2017
Date
accepted
: 18
July
2017
Funding
Funded by: FundRef http://dx.doi.org/10.13039/100006108, National Center for Advancing Translational Sciences;
Award ID: UL1TR001117
Award Recipient
:
Ryan A. Simmons
Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
Award ID: R03AI99667
Award Recipient
:
Wendy P. O’Meara
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ScienceOpen disciplines: Infectious disease & Microbiology
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ScienceOpen disciplines: Infectious disease & Microbiology