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Comparison between passive surveillance and active surveillance during follow up from May 2007 to May 2008 in a subset of children aged 5–17months.
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Febrile malaria is the most common clinical manifestation of P. falciparum infection, and is often the primary endpoint in clinical trials and epidemiological studies. Subjective and objective fevers are both used to define the endpoint, but have not been carefully compared, and the relative incidence of clinical malaria by active and passive case...
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Citations
... Most included case studies relied on data obtained from passive surveillance systems in areas with sufficient access to healthcare facilities and for diseases with sufficient symptomatic cases. However, such systems can underestimate the prevalence in periods of low transmission [64,65], and hamper the prediction performance of resilience indicators if the reporting rate increases together with the prevalence of the disease [41]. Low-income countries with circulating diseases of poverty such as cholera or Ebola as well as neglected tropical diseases lack a constant surveillance system in place prior to outbreaks and the surveillance is mostly reactive, making the data unsuitable for resilience indicators [66][67][68][69]. ...
To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so -called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach.
... In such settings, prompt diagnosis and effective treatment of malaria is one of key interventions recommended by the WHO [4]. Fever is a common clinical sign of Plasmodium falciparum infection [3,[5][6][7]. The WHO recommends early malaria diagnosis and treatment i.e., within 24 h of the onset of symptoms [1] to prevent progression from uncomplicated to severe malaria [8,9] and reduce transmission [10]. ...
Background
In Malawi, malaria is responsible for 40% of hospital deaths. Prompt diagnosis and effective treatment within 24 h of fever onset is critical to prevent progression from uncomplicated to severe disease and to reduce transmission.
Methods
As part of the large evaluation of the malaria vaccine implementation programme (MVIP), this study analysed survey data to investigate whether prompt treatment-seeking behaviour is clustered at community-level according to socio-economic demographics.
Results
From 4563 households included in the survey, 4856 children aged 5–48 months were enrolled. Out of 4732 children with documented gender, 52.2% were female and 47.8% male. Among the 4856 children, 33.8% reported fever in the two weeks prior to the survey. Fever prevalence was high in communities with low socio-economic status (SES) (38.3% [95% CI: 33.7–43.5%]) and low in areas with high SES (29.8% [95% CI: 25.6–34.2%]). Among children with fever, 648 (39.5%) sought treatment promptly i.e., within 24 h from onset of fever symptoms. Children were more likely to be taken for prompt treatment among guardians with secondary education compared to those without formal education (aOR:1.37, 95% CI: 1.11–3.03); in communities with high compared to low SES [aOR: 2.78, 95% CI: 1.27–6.07]. Children were less likely to be taken for prompt treatment if were in communities far beyond 5 km to health facility than within 5 km [aOR: 0.44, 95% CI: 0.21–0.92].
Conclusion
The high heterogeneity in prevalence of fever and levels of prompt treatment-seeking behaviour underscore the need to promote community-level malaria control interventions (such as use of long-lasting insecticide-treated nets (LLINs), indoor residual spraying (IRS), intermittent preventive therapy (IPT), presumptive treatment and education). Programmes aimed at improving treatment-seeking behaviour should consider targeting communities with low SES and those far from health facility.
... However, limited human and financial resources are significant obstacles to reaching this goal in low-income-countries [3]. Most malaria surveillance occurs passively, only capturing those cases that reach health facilities, missing the vast majority of infections where access to health care is low [4][5][6]. To help overcome these obstacles, improved methods to predict malaria incidence at local scales could help health care practitioners optimize the distribution of limited resources when and where they are most needed. ...
While much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. High-resolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socio-economic, land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales.
... Logistic regression models are typically used to handle this bias. The model determine the risk of the outcome as a continuous function of parasite density [1,2] and have been widely used to obtain attributable fractions against a range of outcomes with parasitaemia as the exposure variable [1,2,[7][8][9]. Additionally, a Bayesian latent class model of two-component mixture distributions was proposed to improve the estimation of attributable fractions [3]. The latent class model was developed to handle the limitation of imprecise or negative attributable fractions occasionally observed in standard logistic regression models [5]. ...
... In the study, the logistic regression method was applied and derived a parasite density threshold of 2500 parasites/µ L of blood as the most appropriate to distinguish malaria-attributable fevers from fevers due to other causes in both settings. Following Ngerenya and Chonyi study, 2500 parasites/µ L threshold has been widely applied in the definition of malaria cases in various studies conducted along the Kenyan coast [7,[10][11][12][13]. ...
... The main assumption in defining correlates of protection to inform vaccine development is that malaria case definition is non-biased. Many of the studies classify the participants into two groups (clinical malaria case and non-case) using a defined parasitaemia threshold plus fever [1,2,[7][8][9]. The optimal parasite density threshold is selected from maximum combined sensitivity and specificity after fitting the case definition models [1]. Additionally, the models estimate the probabilities individual episodes of fever are malaria attributable at a given density of parasitaemia [1,3,17]. ...
Background
Asymptomatic carriage of malaria parasites is common in high transmission intensity areas and confounds clinical case definitions for research studies. This is important for investigations that aim to identify immune correlates of protection from clinical malaria. The proportion of fevers attributable to malaria parasites is widely used to define different thresholds of parasite density associated with febrile episodes. The varying intensity of malaria transmission was investigated to check whether it had a significant impact on the parasite density thresholds. The same dataset was used to explore an alternative statistical approach, using the probability of developing fevers as a choice over threshold cut-offs. The former has been reported to increase predictive power.
Methods
Data from children monitored longitudinally between 2005 and 2017 from Junju and Chonyi in Kilifi, Kenya were used. Performance comparison of Bayesian-latent class and logistic power models in estimating malaria attributable fractions and probabilities of having fever given a parasite density with changing malaria transmission intensity was done using Junju cohort. Zero-inflated beta regressions were used to assess the impact of using probabilities to evaluate anti-merozoite antibodies as correlates of protection, compared with multilevel binary regression using data from Chonyi and Junju.
Results
Malaria transmission intensity declined from over 49% to 5% between 2006 and 2017, respectively. During this period, malaria attributable fraction varied between 27–59% using logistic regression compared to 10–36% with the Bayesian latent class approach. Both models estimated similar patterns of fevers attributable to malaria with changing transmission intensities. The Bayesian latent class model performed well in estimating the probabilities of having fever, while the latter was efficient in determining the parasite density threshold. However, compared to the logistic power model, the Bayesian algorithm yielded lower estimates for both attributable fractions and probabilities of fever. In modelling the association of merozoite antibodies and clinical malaria, both approaches resulted in comparable estimates, but the utilization of probabilities had a better statistical fit.
Conclusions
Malaria attributable fractions, varied with an overall decline in the malaria transmission intensity in this setting but did not significantly impact the outcomes of analyses aimed at identifying immune correlates of protection. These data confirm the statistical advantage of using probabilities over binary data.
... In addition to the treatments and reductions in malaria in Africa, the comeback of malaria infection has resulted in increased illness loads among adults and children as a result of a lengthy period of malaria control (Trape et al., 2011). Despite the increased use of insecticide-treated nets and other treatments, malaria transmission patterns are shifting in some regions of Kenya (Olotu et al., 2010). This incidence has been connected to insecticide resistance, a shift in vector population species, and global warming (Wanjala and Kweka, 2018). ...
This study evaluated the determinants of malaria disease resurgence among the adult residents of Isiolo Sub-County in Kenya. The following specific objectives guided this research; to establish the influence of the level of awareness on malaria disease resurgence among the adult residents of Isiolo Sub-county in Kenya.to evaluate demographic characteristics influencing malaria disease resurgence among the adult residents of Isiolo Sub-county in Kenya, to investigate socio-economic characteristics influencing malaria disease resurgence among adult residents of Isiolo Sub-county in Kenya.to determine the influence of environmental characteristics on malaria disease resurgence among adult residents of Isiolo Sub-county in Kenya, to assess the influence of malaria disease resurgence interventions on malaria disease resurgence among the adult residents of Isiolo Sub-county in Kenya, This was a descriptive study, employing a cross-sectional study design. The researcher used mixed research methodology in this study which employed both qualitative and quantitative methods. Stratified sampling techniques were used for sampling the study respondents. The residents of Isiolo Sub-county, key informant interviewers and NGOs/CBO based focus group discussion constituted the target population. The study respondents were selected from five different wards of Isiolo Sub-county with a Sample size of 392 comprised of 385 respondents randomly selected for quantitative data and 7 key informants and focus group discussion for qualitative data. The study used interview guides to collect data from key informants. Quantitative data were collected using self-administered questionnaires. SPSS version 25.0 was used in quantitative data analysis while qualitative data was analyzed thematically. The study received 100% response rate almost balance by gender as approximately 55% of respondents were male and 45% were female. Education levels of respondents varied because there were respondents without formal education while others had attained a tertiary level of education. Respondents experienced malaria resurgence in the study as evidenced by knowledge of malaria disease symptoms, causative agents of malaria disease, mode of transmission, treatment and intervention strategies among others. This study established that all the characteristics considered in this research had some level of influence on the resurgence of malaria disease in the study site as deduced by regression analysis model whose, R2 = 0.453, F- 1.385 = 316.804, P = 0.000 for socioeconomic, Beta- coefficient = 0.410, p=0.000 for an environmental factor, in all p < 0.05 at CI=95%. The study recommends that policymakers in Isiolo Sub-county should consider formulating policies that support education for the residents since a big percentage 58% had not attended any form of schooling. This study concluded that different malaria intervention strategies were implemented in the research site aimed at taming the resurgence of malaria disease but had mixed results. The researcher recommended that Policy makers and NGOs/CBOs sought to allocate additional resources in support of educating residents about malaria disease and how to control it since prevention is usually cheaper than treatment in their intervention programs to curb malaria resurgence in the study area. The researcher also recommended further research on the reasons behind a big percentage of residents of the Isiolo Sub-county not attending or enrolling in a formal school system.
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... A study conducted in Zambia showed that seeking early and appropriate treatment was suboptimal [12]. In malaria-endemic areas, fever has been used as a proxy for malaria even though the cause could be different [13,14]. World Health Organization (WHO) 2016 report showed that approximately 34% of households of caregivers in sub-Saharan Africa seek early treatment for their febrile children from health care professionals [15]. ...
Background
Early diagnosis and treatment of childhood fever are essential for controlling disease progression and death. However, the Treatment-seeking behaviour of caregivers is still a significant challenge in rural parts of the African region. This study aimed to assess individual and community-level factors associated with treatment-seeking behaviours among caregivers of febrile under-five age children in Ethiopia.
Method
The recent Ethiopian Demographic and Health Survey data (EDHS 2016) was used for the study. The survey collected information among 1,354 under-five children who had a fever within two weeks before the survey. The data were extracted, cleaned, and recoded using STATA version 14. Multilevel logistic regressions were used to determine the magnitude and associated factors of treatment-seeking behaviour among caregivers with febrile children in Ethiopia. Four models were built to estimate both fixed and random effects of individual and community-level factors between cluster variations on treatment-seeking behaviour. The Adjusted Odds Ratios with 95% Confidence Intervals (CI) of the best-fitted model were reported at p<0.05.
Result
This study revealed that 491 (36.26%) caregivers seek treatment for their febrile children. Living in metropolitan and small peripheral regions, delivery at health institutions, being poorer, middle and richer wealth quintiles, having a child with diarrhoea, cough, short rapid breathing, and wasting were positively associated with treatment-seeking behaviour of caregivers.
Conclusion
The caregivers had poor treatment-seeking behaviour for their febrile children in Ethiopia. Health education programmers should emphasise the importance of seeking early treatment, taking action on childhood febrile illness signs.
... Random sampling of individuals was done using the runiform function in STATA/IC version 15.1 (StataCorp College Station, Texas, USA). Fingerprick whole blood samples were collected in EDTA vacutainers from all febrile children for the purpose of malaria rapid diagnostic testing as part of an ongoing malaria surveillance study [27]. The remaining whole blood was stored at − 80°C until the day of viral RNA isolation (see RT-PCR section below). ...
... However, a significant increase in CHIKF incidence over time was noted in Ngerenya while in Pingilikani (located approximately 40 km south of Ngerenya) incidence had been declining. CHIKF incidence was highest during the rainy season in Pingilikani and during the dry season in Ngerenya suggesting potential differences in the ecology of CHIKF in these locations as has been observed for malaria [27]. Aedes spp mosquito vectors for CHIKV are present in Kilifi but their spatial and temporal (by season) distribution in this setting and how this influences risk of CHIKV infection remains to be determined [34]. ...
Background
Chikungunya fever (CHIKF) was first described in Tanzania in 1952. Several epidemics including East Africa have occurred, but there are no descriptions of longitudinal surveillance of endemic disease. Here, we estimate the incidence of CHIKF in coastal Kenya and describe the associated viral phylogeny.
Methods
We monitored acute febrile illnesses among 3500 children visiting two primary healthcare facilities in coastal Kenya over a 5-year period (2014–2018). Episodes were linked to a demographic surveillance system and blood samples obtained. Cross-sectional sampling in a community survey of a different group of 435 asymptomatic children in the same study location was done in 2016. Reverse-transcriptase PCR was used for chikungunya virus (CHIKV) screening, and viral genomes sequenced for phylogenetic analyses.
Results
We found CHIKF to be endemic in this setting, associated with 12.7% (95% CI 11.60, 13.80) of all febrile presentations to primary healthcare. The prevalence of CHIKV infections among asymptomatic children in the community survey was 0.7% (95% CI 0.22, 2.12). CHIKF incidence among children < 1 year of age was 1190 cases/100,000-person years and 63 cases/100,000-person years among children aged ≥10 years. Recurrent CHIKF episodes, associated with fever and viraemia, were observed among 19 of 170 children with multiple febrile episodes during the study period. All sequenced viral genomes mapped to the ECSA genotype albeit distinct from CHIKV strains associated with the 2004 East African epidemic.
Conclusions
CHIKF may be a substantial public health burden in primary healthcare on the East African coast outside epidemic years, and recurrent infections are common.
... Most malaria control programs rely on passive surveillance systems via case detection at health facilities. Yet, passive surveillance is known to grossly underestimate the incidence of malaria [13][14][15][16] because only symptomatic patients who seek care at health facilities are recorded. In 2012, the World Health Organization estimated that only 14% of malaria cases worldwide were detected with Conclusions: Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. ...
... Active surveillance systems, on the other hand, can capture a significantly higher proportion of cases and produce more accurate incidence estimates. Unfortunately, in the case of malaria they are too expensive to be used routinely in areas of high transmission, and the results cannot be extrapolated to detect variations in malaria in regions outside of the study area or period [13][14][15][16][17][18]. Thus, our study fills a significant gap for malaria surveillance, which could be applicable to other diseases. ...
... This is consistent with findings from other settings where active and passive malaria surveillance methods were compared. For example, a study in rural Kenya found that the incidence of malaria in children was over three times higher when active surveillance was used compared to passive surveillance [15]. A similar study in central India reported that malaria incidence was almost eight times higher when calculated using active rather than passive surveillance data [16]. ...
Background
Reliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. Yet, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. This is especially true for malaria, for which the World Health Organization estimates that routine surveillance detects only 14% of global cases. The goal of this study was to develop a novel method to obtain accurate estimates of disease spatio-temporal incidence at very local scales from routine passive surveillance, less biased by populations' financial and geographic access to care.
Methods
We use a geographically explicit dataset with residences of the 73,022 malaria cases confirmed at health centers in the Ifanadiana District in Madagascar from 2014 to 2017. Malaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. A benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. Variations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. Separate analyses were carried out for individuals of all ages and for children under five. Cross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. The most plausible sets of estimates were then identified based on these criteria.
Results
Passive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. Adjusted malaria estimates were less biased by differences in populations’ financial and geographic access to care. Average adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. By gathering patient-level data and removing systematic biases in the dataset, the spatial resolution of passive malaria surveillance was improved over ten-fold. Geographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years.
Conclusions
Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.
... Most malaria control programs rely on passive surveillance systems via case detection at health facilities. Yet, passive surveillance is known to grossly underestimate the incidence of malaria [13][14][15][16] because only symptomatic patients who seek care at health facilities are recorded. In 2012, the World Health Organization estimated that only 14% of malaria cases worldwide were detected with routine surveillance 17 . ...
... Active surveillance systems, on the other hand, can capture a significantly higher proportion of cases and produce more accurate incidence estimates. Unfortunately, in the case of malaria they are too expensive to be used routinely in areas of high transmission, and the results cannot be extrapolated to detect variations in malaria in regions outside of the study area or period [13][14][15][16][17][18] . Thus, our study fills a significant gap for malaria surveillance, which could be applicable to other diseases. ...
... This is consistent with findings from other settings where active and passive malaria surveillance methods were compared. For example, a study in rural Kenya found that the incidence of malaria in children was over three times higher when active surveillance was used compared to passive surveillance 15 . A similar study in central India reported that malaria incidence was almost eight times higher when calculated using active rather than passive surveillance data 16 . ...
Background: Reliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. Yet, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. This is especially true for malaria, for which the World Health Organization estimates that routine surveillance detects only 14% of global cases. The goal of this study was to estimate the unobserved burden of malaria missed by routine passive surveillance in a rural district of Madagascar to produce realistic incidence estimates across space and time, less sensitive to heterogeneous health care access.
Methods: We use a geographically explicit dataset of the 73,022 malaria cases confirmed at health centers in the Ifanadiana District in Madagascar from 2014 to 2017. Malaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. A benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. Variations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. Separate analyses were carried out for individuals of all ages and for children under five. Cross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. The most plausible sets of estimates were then identified based on these criteria.
Results: Passive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. Adjusted malaria estimates were less biased by differences in populations' financial and geographic access to care. Average adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. Geographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years.
Conclusions: Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.
... Severe malaria was defined as P. falciparum blood-film positivity with one or more of the following clinical or laboratory features: cerebral malaria (CM; Blantyre Coma Score of <3), respiratory distress (RD; abnormal deep breathing), severe malaria anemia (SMA; Hb <5 g/dL) or other features of severity as previously described [17,18]. Children in Group 1 were admitted between 1998 and 2010 and in Group 2 between 2004 and 2005 while Group 3 were recruited during cross-sectional community surveys conducted between 2010-2011 [19], based on the availability of archived samples. ...
... In a subsequent study conducted in Tanzanian children, plasma PfHRP2 was 19 ng/ml (15)(16)(17)(18)(19)(20)(21)(22)(23) in asymptomatic carriers, 163 ng/ml in children with uncomplicated malaria, detected in the community, and 1,510 ng/ml (1,180-1,933) and 1,746 ng/ml (1,577-1,934) among children with severe malaria admitted during two different time periods [26]. A A c c e p t e d M a n u s c r i p t concentration of <200 ng/mL was found to indicate severe febrile illness caused by an alternative diagnosis in >10% of patients. ...
Background:
Most previous studies support a direct link between total parasite load and the clinical severity of Plasmodium falciparum malaria infections.
Methods:
We estimated P. falciparum parasite loads in three groups of children with malaria infections of differing severity: (1) children with WHO-defined severe malaria (n=1,544); (2) children admitted with malaria but without features of severity (n=200) and; (3) children in the community with asymptomatic parasitemia (n=33).
Results:
Peripheral parasitemias were highest in those with uncomplicated malaria (geometric mean 111,064; 95%CI 86,798-141,819 parasites/μl), being almost three times higher than those with severe malaria (39,588; 34,990-44,791 parasites/μl) and >100 times higher than in those with asymptomatic malaria (1,092; 523-2,280 parasites/μl). However, geometric mean PfHRP2 values (95% CI) increased with severity, being 7 (4-12) ng/ml in asymptomatic malaria, 843 (655-1,084) ng/ml in uncomplicated malaria and 1,369 (1,244-1,506) ng/ml in severe malaria. PfHRP2 concentrations were markedly lower in the sub-group of severe malaria patients with concomitant invasive bacterial infections (IBIs) of blood or CSF (GM 312 ng/ml; 95%CI 175-557; p<0.0001) than in those without IBIs (GM 1,439 ng/ml; 1,307-1,584; P<0.001).
Conclusions:
The clinical severity of malaria infections related strongly to the total burden of P. falciparum parasites. A quantitative test for plasma concentrations of PfHRP2 could be useful in identifying children at the greatest clinical risk and to identify critically ill children in whom malaria is not the primary cause.
