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      Success or failure of critical steps in community case management of malaria with rapid diagnostic tests: a systematic review


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          Malaria still causes high morbidity and mortality around the world, mainly in sub-Saharan Africa. Community case management of malaria (CCMm) by community health workers (CHWs) is one of the strategies to combat the disease by increasing access to malaria treatment. Currently, the World Health Organization recommends to treat only confirmed malaria cases, rather than to give presumptive treatment.


          This systematic review aims to provide a comprehensive overview of the success or failure of critical steps in CCMm with rapid diagnostic tests (RDTs).


          The databases of Medline, Embase, the Cochrane Library, the library of the ‘Malaria in Pregnancy’ consortium, and Web of Science were used to find studies on CCMm with RDTs in SSA. Studies were selected according to inclusion and exclusion criteria, subsequently risk of bias was assessed and data extracted.


          27 articles were included. CHWs were able to correctly perform RDTs, although specificity levels were variable. CHWs showed high adherence to test results, but in some studies a substantial group of RDT negatives received treatment. High risk of bias was found for morbidity and mortality studies, therefore, effects on morbidity and mortality could not be estimated. Uptake and acceptance by the community was high, however negative-tested patients did not always follow up referral advice. Drug or RDT stock-outs and limited information on CHW motivation are bottlenecks for sustainable implementation. RDT-based CCMm was found to be cost effective for the correct treatment of malaria in areas with low to medium malaria prevalence, but study designs were not optimal.


          Trained CHWs can deliver high quality care for malaria using RDTs. However, lower RDT specificity could lead to missed diagnoses of non-malarial causes of fever. Other threats for CCMm are non-adherence to negative test results and low referral completion. Integrated CCM may solve some of these issues. Unfortunately, morbidity and mortality are not adequately investigated. More information is needed about influencing sociocultural aspects, CHW motivation and stock supply.


          CCMm is generally well executed by CHWs, but there are several barriers for its success. Integrated CCM may overcome some of these barriers.

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          Criteria list for assessment of methodological quality of economic evaluations: Consensus on Health Economic Criteria.

          The aim of the Consensus on Health Economic Criteria (CHEC) project is to develop a criteria list for assessment of the methodological quality of economic evaluations in systematic reviews. The criteria list resulting from this CHEC project should be regarded as a minimum standard. The criteria list has been developed using a Delphi method. Three Delphi rounds were needed to reach consensus. Twenty-three international experts participated in the Delphi panel. The Delphi panel achieved consensus over a generic core set of items for the quality assessment of economic evaluations. Each item of the CHEC-list was formulated as a question that can be answered by yes or no. To standardize the interpretation of the list and facilitate its use, the project team also provided an operationalization of the criteria list items. There was consensus among a group of international experts regarding a core set of items that can be used to assess the quality of economic evaluations in systematic reviews. Using this checklist will make future systematic reviews of economic evaluations more transparent, informative, and comparable. Consequently, researchers and policy-makers might use these systematic reviews more easily. The CHEC-list can be downloaded freely from http://www.beoz.unimaas.nl/chec/.
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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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              Community Case Management of Fever Due to Malaria and Pneumonia in Children Under Five in Zambia: A Cluster Randomized Controlled Trial

              Introduction Pneumonia and malaria are major causes of morbidity and mortality in children under five in sub-Saharan Africa [1],[2]. In many rural areas in developing countries, health facilities are not readily accessible to much of the population [3],[4], and the health needs of large numbers of sick children are met through the informal sector, including community health workers (CHWs), drug sellers, and traditional healers [5]. Since access to health facilities is limited and most children die at home [5], new and innovative approaches to reducing childhood mortality will require interventions implemented at the community level. The World Health Organization (WHO) and the United Nations Children Fund (UNICEF) now recommend that where malaria and pneumonia are major killers, their treatment should be integrated in community case management activities [6]. Many pneumonia deaths could be prevented through early, appropriate and low-cost community-based treatment [7],[8]. Training CHWs in the management of acute lower respiratory tract infections has been shown to be feasible and effective [9],[10]. Similarly, several studies have shown that CHWs can be trained to provide effective malaria case management at the community level [11],[12]. Following WHO recommendations, artemisinin-based combination therapy (ACT) has been introduced as the first line treatment for uncomplicated malaria in much of sub-Saharan Africa [13],[14]. The use of rapid diagnostic tests (RDTs) for guiding malaria treatment has been found to offer a practical solution to the challenge of malaria diagnostics in Africa [15],[16]. As a result, RDTs are increasingly being utilized to improve malaria case management and reduce unnecessary ACT use [17]. Because the overlap of symptoms between malaria and pneumonia in children makes differential diagnosis in the absence of diagnostic equipment difficult [18],[19], RDT use by CHWs presents a remarkable opportunity to improve the diagnosis of malaria and pneumonia at the community level. The use of RDTs by CHWs has been found to be potentially effective and feasible [20]–[23]. Although ACTs and RDTs are now available at health facilities in Zambia, they have not been fully deployed at the community level. Few studies have evaluated the integrated management of fever due to pneumonia and malaria by CHWs in children [24],[25], and the strategy of having CHWs dispense ACT has not been carefully evaluated. In addition, the potential benefit(s) of RDTs in improving malaria diagnosis before treatment with ACT by CHWs remain unknown. The objective of this study was, therefore, to assess the effectiveness and feasibility of using CHWs to manage pneumonia and malaria in children with the aid of RDTs per our protocol (Text S1). Methods We report here, using the Consort checklist (Text S2), a cluster randomized controlled trial that compared two models for community-based management of malaria and/or nonsevere pneumonia in children in rural Zambia. A cluster design was used instead of individual randomization because it was socially and culturally inappropriate for a CHW to give one patient treatment for nonsevere pneumonia and to refer the next patient to a health facility. Baseline and poststudy household surveys were conducted to assess changes in health-seeking behavior. Study Area and Participants The study was conducted in the Chikankata Mission Hospital catchment area, a geographic area with an estimated population of 70,000 [26] extending across parts of Siavonga and Mazabuka Districts in Zambia's Southern Province. The Siavonga area is predominantly plain and referred to as “the valley”; and the Mazabuka area is hilly and referred to as “the plateau.” Malaria, malnutrition, pneumonia, and diarrhea are the leading causes of morbidity and death in children under five [27],[28]. Transmission of malaria is hyperendemic and highest in the rainy season from November to April [29]. The study area has poor road networks and is served by the mission hospital and five official Zambian Ministry of Health rural health centers, of which only one has a full complement of staff (clinical officer, environmental health technician, and midwife). Most sick children are seen by CHWs who work in a fixed location called the community health post, which serves a number of villages. At the time of study initiation, CHWs did not use ACT, RDTs, or amoxicillin. Instead they would treat malaria with sulfadoxine-pyrimethamine and refer suspected pneumonia cases to the nearest health facility. They also routinely managed children presenting with diarrhea and dehydration with oral rehydration therapy. Two to eight health posts are situated within a health center catchment area. There was no formalized incentive package for the CHWs, but the Chikankata Mission Hospital provided occasional incentives (i.e., bicycles, umbrella, and stationery) when resources were available. Children aged between 6 mo and 5 y who presented to a CHW at a health post with fever and/or cough/difficult breathing/fast breathing were eligible for participation. Exclusion criteria included age below 6 mo or above 5 y, signs or symptoms of severe illness, or known sensitivity to the study medications. The Intervention Training All study CHWs had previously undergone 6 wk training before becoming community health workers. As part of the study, they participated in an additional 5-d training workshop using modifications of nationally developed training manuals. Members from both the Mazabuka and Siavonga District Health Management Teams (DHMT), study staff, and the principal investigator (KYA) led the training workshop. Training team members were experienced in training Integrated Management of Childhood Illness (IMCI) skills. The workshop had two main parts. In one part, which lasted 4 d, both intervention and control CHWs were trained, using the CHW training manual (Text S3), to classify and manage children with pneumonia and/or malaria, and to manage stocks of drugs and supplies. The training of the CHWs was highly interactive and included a variety of methods including lectures, discussion, role play, demonstrations, case studies, and supervised clinical practice at the hospital. The training emphasized community-based integrated management of febrile children including basic clinical history taking, physical examination skills, counseling of caregivers, and recognition of signs of severe illness requiring referral. A major focus was training CHWs in the use of simplified treatment algorithms developed to aid classification and treatment of malaria and pneumonia, with separate algorithms for the intervention and the control CHWs. Each algorithm had three sections; section A was used for children who presented with fever plus cough and/or shortness of breath (fast breathing, difficult breathing); section B for children who presented with cough and/or shortness of breath (fast breathing, difficult breathing) but without fever; and section C for children who had fever alone, without cough and/or shortness of breath. The identification of danger signs was an important focus of the training, to help ensure that the child, if in danger, was immediately referred to the nearest health center. The CHWs were also trained in the use of simple dosing guidelines based on weight, if available, or age for artemether-lumefantrine (AL). As part of this component of the training, both groups of CHWs were trained on how to manage the drug supplies, including proper documentation of patient complaints, their physical examination, and medications administered to the child. The second part of the workshop involved additional training for the intervention CHWs only. The CHW RDT training manual (Text S4) was used for this half-day session, which focused on performing and interpreting RDTs, with the aid of RDT interpretation guides. The proper interpretation of RDT results was emphasized, including how to react to both positive and negative results. As part of the RDT training, intervention CHWs were trained in infection control measures such as aseptic technique, proper disposal of hazardous biological waste, and avoidance of lancet injuries. The intervention CHWs also received supplemental training in amoxicillin dosing and documentation of RDT results. After completion of training, the instructors assessed the competency of all CHWs to count respiratory rate and follow treatment algorithms, and, for the intervention CHWs, the proper performance and interpretation of RDTs. One month after initial training, the training team completed a follow-up skills assessment to ensure that CHWs retained the necessary skills. All study CHWs completed an additional 2-d refresher course 6 mo after the initial training. Specialized data collectors were recruited and trained in study procedures, research ethics, informed consent protocols, and the use of data collection instruments. Patient management and follow-up Intervention CHWs performed RDTs on children with reported fever and counted the respiratory rate of children with cough and/or difficult/fast breathing using a respiratory timer. They classified children with positive RDT results and normal respiratory rate as malaria and treated with AL and an antipyretic (acetaminophen). Children with positive RDT results and high respiratory rate (≥50 breaths per minute in children 95%). 10.1371/journal.pmed.1000340.t003 Table 3 Presenting complaints and signs. Complaint or Sign Intervention (n = 1,017) Control (n = 2,108) RR (95% CI) Fever 94.7% 98.9% 0.45 (0.39–0.53) Fever with temperature ≥37.5°C 45.5% 50.8% 0.87 (0.78–0.96) Cough 67.8% 63.3% 1.15 (1.03–1.28) Difficult breathing 16.8% 6.9% 1.80 (1.60–2.02) Fast breathing by history 35.8% 10.2% 2.45 (2.24–2.68) Fast breathing based on respiratory rate counted by the community health worker 37.6% 9.7% 2.61 (2.31–2.85) Caregiver visited community health post on the same day as the first symptom onset 12.1% 10.1% 1.14 (0.98–1.33) RDT Results and Treatment for Malaria Of the 975 children in the intervention arm who presented with a history of fever and had RDTs performed, 27.8% had a positive RDT result. RDT positivity varied by geographical area and rural health center catchment area, and was higher in children seen at community health posts located in the “valley” (Table 4). Of the 975 children with reported fever, 460 had a measured temperature of ≥37.5°C. The proportion of children with a positive RDT in this subgroup was 28.5%; hence there was no difference in the RDT positivity rate whether the child had reported fever or had a measured temperature of ≥37.5°C. 10.1371/journal.pmed.1000340.t004 Table 4 Proportion of children with positive rapid diagnostic tests. Children n RDTs done n RDT positives Percent Positive All children 975 271 27.8 Children with temperature ≥37.5°C 460 131 28.5 Geographic Location Valley (Siavonga) 487 219 45.0 Plateau (Mazabuka) 488 52 10.7 Community Health Post Sianyoolo 239 88 36.8 Chaanga 248 131 52.8 Chikankata 66 1 1.5 Nameembo 88 6 6.8 Nadezwe 134 6 4.5 Chikombola 200 39 19.5 The proportion of children presenting with a history of fever that received AL in the intervention arm was 27.5% compared to 99.1% in the control arm (RR 0.23, 95% CI 0.14–0.38). Only three of the 704 children with negative RDT results were given AL by the CHW. Caregivers of five children with negative RDT results sought and received antimalarials from other sources after the CHW did not provide them. Early and Appropriate Treatment for Pneumonia Of the children classified as nonsevere pneumonia in the intervention arm, 78.8% sought treatment (consulted a CHW) within 24–48 h of onset of first symptom and 68.2% received early and appropriate treatment. In the control arm, 75.4% of children classified as nonsevere pneumonia sought treatment within 24–48 h of onset of first symptom and only 13.3% received early and appropriate treatment. While there was no significant difference between the two arms in the proportions of children who sought treatment within 24–48 h of onset of first symptom (RR 1.06, 95% CI 0.91–1.23), the difference in the proportions that received early and appropriate treatment for nonsevere pneumonia was significant (RR 5.32, 95% CI 2.19–8.94). Several factors, including age of the child, presenting complaints, maternal age and education, were examined either as promoters of or barriers to early and appropriate treatment. Children ≤11 mo tended to be less likely to receive early and appropriate treatment compared to older children (RR 0.84, 95% CI 0.69–1.02). Children of mothers with primary or secondary education tended to receive early and appropriate treatment compared to children of women without any education (RR 1.18, 95% CI 0.97–1.45). However, maternal age and type of presenting complaint did not influence the achievement of early and appropriate treatment. Treatment Failure There was no difference in the overall treatment failure rates among patients enrolled in the intervention (9.3%) and control (10.0%) arms (Table 5). Similarly, there was no significant difference between arms in treatment failure rate among children classified as having malaria (Table 6). However, children in the intervention group who were classified as having nonsevere pneumonia were significantly less likely to experience treatment failure (RR 0.44, 95% CI 0.21–0.92). The most common reasons for treatment failure in both arms were persistent fever and fast/difficult breathing at follow up. Hospitalization was also an important reason for treatment failure in the control arm (Table 7). Two patients in the intervention arm and one in the control arm died. The final outcomes of hospitalized patients were not determined nor were verbal autopsies performed to ascertain the cause of death. 10.1371/journal.pmed.1000340.t005 Table 5 Treatment failure for all patients. Variable Intervention Control RRa (95% CI) Treatment failure at day 5–7 95/1,017 (9.3%) 211/2,108 (10.0%) 0.68 (0.39–1.19) Persistent fever, fast/difficult breathing at follow-up 73/975 (7.5%) 159/2,052 (7.7%) 0.74 (0.42–1.29) Lower chest in-drawing at follow-up 1/973 (0.1%) 9/2,052 (0.4%) 0.17 (0.01–2.11) Received additional antibiotics 13/975 (1.3%) 25/2,054 (1.2%) 0.94(0.19–4.79) Received additional antimalarials 4/975 (0.4%) 8/2,054 (0.4%) 1.24 (0.41–3.57) Hospitalization 4/1,017 (0.4%) 14/2,108 (0.7%) 0.25 (0.04–1.50) Death 2/1,017 (0.2%) 1/2,108 (0%) — a Adjusted for baseline fast breathing and fever. 10.1371/journal.pmed.1000340.t006 Table 6 Treatment failure for patients classified as malaria. Variable Intervention Control RRa (95% CI) Treatment failure at day 5–7 20/272 (7.4%) 207/2,082 (9.9%) 0.68 (0.38–1.19) Persistent fever, fast/difficult breathing at follow up 17/255 (6.7%) 155/2,026 (7.7%) 0.86 (0.51–1.45) Lower chest in-drawing at follow-up 0/253 (0%) 9/2,026 (0.4%) Received additional antibiotics 2/255 (0.8%) 25/2,028 (1.2%) 0.58 (0.09–3.96) Received additional antimalarials 1/255 (0.4%) 8/2,025 (0.4%) — Hospitalization 0/272 (0%) 14/2,082 (0.7%) — Death 0/272 (0%) 1/2,082 (0%) — a Adjusted for baseline fever. 10.1371/journal.pmed.1000340.t007 Table 7 Treatment failure for patients classified as pneumonia. Variable Intervention Control RRa (95% CI) Treatment failure day 5–7 41/362 (11.3%) 41/203 (20.2%) 0.44 (0.21–0.93) Persistent fever, fast/difficult breathing at follow up 36/344 (10.5%) 32/193 (16.6%) 0.50 (0.22–1.17) Lower chest in-drawing on presentation at follow-up 0/344 (0%) 2/193 (1.0%) Received additional antibiotics 3/344 (0.9%) 1/193 (0.5%) 1.71 (0.18–16.2) Received additional antimalarials 1/344 (0.3%) 0/193 (0) Hospitalization 2/362 (0.6%) 7/203 (3.4%) 0.13 (0.02–0.75) Death 1/362 (0.3%) 0/203 (0%) a Adjusted for baseline fast breathing. Among children classified as having nonsevere pneumonia in the control arm, 14 (6.8%) were not referred by the CHWs as per standard of care and training. Of those who were referred to a health center, 22% did not comply with the referral. The major reason for noncompliance was that the caregiver did not believe the child's illness was serious enough to warrant referral, particularly when the child had been given treatment for malaria. Health-Seeking Practices During the household surveys, 439 and 441 women were interviewed in the baseline and postintervention surveys, respectively. Table 8 shows the changes in health-seeking practices that occurred between the beginning and end of the 1-y study period. There was a significant shift in where sick children sought care between the preintervention (baseline) and the postintervention surveys in both arms. In the postintervention survey, the proportion that sought care from CHWs increased while there was a corresponding decrease in the proportion that sought care at the rural health centers or resorted to home care. However, for children with fast/difficult breathing, the same shift only occurred in the intervention arm. The most common reasons for not seeking care with a CHW were unavailability of the CHW (45%), sickness perceived to be too severe for the CHW to handle (12%), and being nearer to the rural health center than to the community health post (10%). 10.1371/journal.pmed.1000340.t008 Table 8 Proportion of children seeking care for all illnesses and fast breathing during the baseline and poststudy household surveys. Source of care Intervention Baseline Intervention Poststudy Control Baseline Control Poststudy All illnesses ( n  = 174) ( n  = 190) ( n  = 163) ( n  = 203) Home 12.7% 2.6% 7.4% 4.9% CHW 47.1% 78.9% 50.9% 77.3% RHC/CMH 40.2% 18.5% 41.7% 17.8% Fast breathing ( n  = 61) ( n  = 66) ( n  = 59) ( n  = 34) Home 6.6% 3.0% 6.8% 8.8% CHW 50.8% 77.3% 54.2% 55.9% RHC/CMH 42.6% 19.7% 39.0% 35.3% CMH, Chikankata Mission Hospital; RHC, rural heath center. Discussion This study has demonstrated the feasibility and effectiveness of using CHWs to provide integrated management of pneumonia and malaria at the community level. Allowing CHWs to dispense amoxicillin to children with nonsevere pneumonia and AL for malaria after the use of RDTs resulted in a significant increase in the proportion of appropriately timed antibiotic treatments for nonsevere pneumonia and in a significant decrease in inappropriate use of antimalarials. Our study showed a 5-fold increase in the proportion of children with nonsevere pneumonia who received early and appropriate treatment when treated by CHWs in the community instead of the existing system of referral to health centers. This finding adds to the growing evidence of the important role of community-based workers in the management of pneumonia, which has been well documented in South East Asia, as described below, but to a much lesser extent in sub-Saharan Africa. A community-based pneumonia management program in Nepal using female community health volunteers resulted in almost 70% of Nepal's under-five population having access to pneumonia treatment and a reduction of under-five mortality by almost 50% [34]. In Pakistan, case management of acute lower respiratory infections by village level CHWs backed by local health center staff resulted in the reduction of pneumonia-specific and all-cause mortality in children under five [35]. A community-based intervention project in which village heath workers and traditional birth attendants were trained to give mass education about pneumonia and to recognize and treat childhood pneumonia with cotrimoxazole in India also resulted in significant reductions of pneumonia-specific and all-cause mortality [36]. An operational research evaluation project that used nonrandomized design in Senegal showed that CHWs can correctly classify acute respiratory infection and appropriately treat with cotrimoxazole [37]. We found that adequately trained and appropriately resourced CHWs can perform and interpret RDTs, and give treatment for malaria. This finding is consistent with a recent study in Cambodia [23]. Basing treatment on RDT results led to a 4-fold reduction in the use of AL in this study area; the reduction was as high as 10-fold in the dry months when malaria transmission was quite low. This finding has major implications for malaria treatment since the consequences of malaria overdiagnosis may include poor health outcome due to missed diagnosis of alternative causes of symptoms, exposure to unnecessary medication, wastage of essential medicines, and unnecessary expenditure at both the household and health system levels [38]–[40]. Msellam and colleagues in Zanzibar also found that RDT use was associated with lower prescription rates of antimalarials than symptom-based clinical diagnosis alone [41]. Overdiagnosis of malaria without laboratory support has been widely reported [42]–[45], and the findings of this study support the likelihood that RDT use could substantially reduce the inappropriate use of antimalarials if prescribers adhere to the RDT results. Adherence to the results of the RDTs was very high in this study. This suggests that CHWs are more willing to restrict the use of antimalarials to RDT positive patients [46],[47] than health workers, who frequently opt to treat RDT-negative patients [48]–[50]. However, one study has reported high adherence to RDT results when health workers prescribe AL [51]. In the present study, the refresher training after 6 mo, frequent review and assessment of performance of the CHWs at the RHCs, and a relatively high level of education (68% of the CHWs had secondary education) may have contributed to the high level of adherence to treatment guidelines, which were simple and easy to read and interpret (Figures 2 and 3). Lemma and colleagues in Ethiopia have also shown that the use of AL and RDTs by CHWs is not only feasible but has the potential of reducing malaria transmission and case burden for health facilities [52]. 10.1371/journal.pmed.1000340.g002 Figure 2 Treatment algorithm for Intervention Community Health Workers. 10.1371/journal.pmed.1000340.g003 Figure 3 Treatment algorithm for Control Community Health Workers. This study is, to our knowledge, the first randomized, controlled trial of the management of both malaria and pneumonia in children at the community level by CHWs using RDTs to differentiate between malaria and pneumonia. In a Care International community-integrated multiple disease management project in Siaya, Kenya, only the performance of CHWs in managing the multiple diseases was evaluated [25]. Degefie and colleagues have just reported the findings of an evaluation of a project using volunteers to provide treatment for childhood diarrhea, malaria, and pneumonia in a remote district in Ethiopia. The volunteers in this project did not use RDTs and the investigators used a pre–post study design [24]. With the use of RDTs in this study, less than 30% of children with a clinical diagnosis of nonsevere pneumonia were confirmed to also be infected with malaria, compared to almost 90% of the children in the control arm, in which malaria and nonsevere pneumonia diagnoses were syndromic. Without the use of RDTs, most children diagnosed with pneumonia will also be classified as having malaria and will receive antimalarial drugs [19]. Ansah and colleagues in Ghana showed that using RDTs led to a significant reduction in overprescription of antimalarials and better targeting of antibiotics [53]. Providing effective and safe oral treatment for the community-based treatment of malaria will substantially improve access to care for children in malaria-endemic areas and will undoubtedly save lives. However, because of the overlap in clinical presentation for malaria and pneumonia, providing CHWs with malaria-specific treatment (AL) but no effective antibiotics for treating pneumonia or a means to distinguish the two, will undoubtedly lead to pneumonia treatment delays. Failure to comply with referral because caregivers did not think that the child was very sick or has been presumptively treated for malaria was seen in the present study; this has been documented elsewhere [54],[55]. Providing CHWs with the means to treat malaria but not pneumonia increases the risk of treatment delay and progression to more severe disease for children with pneumonia. Our study has a number of strengths including the cluster randomized design, large sample size, accounting for clustering in analysis, additional training program and supervision of CHWs, use of a simple algorithm for diagnosis of the two diseases, and a 12-mo duration, which allowed for seasonal variation of childhood illnesses. A major limitation of this study was an imbalance in the number of individuals enrolled between the study arms. Since the CHW and child characteristics (including time to health care seeking) were similar in both arms, there is evidence that randomization was not compromised. Clusters were only matched in pairs according to the distance between the community health post and the health center. At the time the study was designed, there were no data available on the size of the catchment areas of the different community health posts. Since utilization of services and health-seeking practices are multifaceted and influenced by many factors [56], it is likely that cluster randomization could not address all of these factors, and hence the resulting imbalance. The fact that more CHWs in the control arm considered themselves as full-time workers and therefore available to see patients most of the day may also have contributed to the larger number of patients seen in the control arm. In favor of this interpretation was the finding that unavailability of CHWs was the most common reason for caregivers not using the services of CHWs. There were twice as many children classified with pneumonia in the intervention arm relative to the control arm. As confirmed by the presenting complaints and postintervention household surveys, caregivers who suspected that their children had pneumonia (due to complaints of fast/difficult breathing) preferentially brought them to the intervention CHWs because they knew that amoxicillin was available. Caregivers in the control arm who suspected that their children had pneumonia bypassed the CHW and went straight to the rural health center. The population of the intervention and control arms was found to be similar; thus the imbalance in the number and cases seen is most likely due to health-seeking practices in response to the intervention and potentially unequal distribution of the catchment population sizes between the two study arms. There was no indication of any significant “contamination” of control caregivers seeking care from intervention CHWs. Improving access to care for remote communities through the implementation of community case management of disease is an important new focus for global health policy. Community case management of pneumonia is an effective approach to reducing child deaths in countries faced with insufficient human resources for health [34],[57] and a feasible, effective strategy to complement facility-based management for areas that lack access to facilities [58]. In addition to optimizing the management of malaria and pneumonia, community case management should also integrate treatment of dehydration due to diarrheal disease with oral rehydration therapy, as was the practice in our study site in rural Zambia, and should also integrate the use of zinc. Future efforts should focus on the incorporation of life-saving interventions for severe disease at the community level including rectal artesunate for severe malaria [59] and amoxicillin for severe pneumonia [60]. With improved point-of-service technologies such as RDTs for malaria, the skills of CHWs can be substantially enhanced. The use of RDTs by CHWs is likely to receive the approval of community members since providers with diagnostic capacity are generally preferred [61]. This study adds to the growing evidence that integrating community case management of pneumonia and malaria is feasible, opening the door to evaluations of the treatment by CHWs of other major diseases of children. Much can be done at the community level to save the lives of children in sub-Saharan Africa [62]. Supporting Information Text S1 Protocol. (0.33 MB DOC) Click here for additional data file. Text S2 CONSORT checklist. (0.19 MB DOC) Click here for additional data file. Text S3 CHW training manual. (0.31 MB DOC) Click here for additional data file. Text S4 CHW RDT training manual. (1.93 MB DOC) Click here for additional data file.

                Author and article information

                Malar J
                Malar. J
                Malaria Journal
                BioMed Central
                12 June 2014
                : 13
                : 229
                [1 ]Royal Tropical Institute/Koninklijk Instituut voor de Tropen (KIT), Amsterdam, The Netherlands
                [2 ]Institute of Tropical Medicine/ Instituut Tropische Geneeskunde (ITG), Antwerp, Belgium
                [3 ]Nagasaki University, School of International Health Development, Nagasaki, Japan
                [4 ]Global Malaria Programme, 20 Avenue Appia, CH 1211 Geneva 27, Switzerland
                Copyright © 2014 Ruizendaal et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

                : 12 February 2014
                : 22 April 2014

                Infectious disease & Microbiology
                malaria,plasmodium falciparum,community health workers,rapid diagnostic tests,sub-saharan africa


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