Bayesian evaluation of the performance of three diagnostic tests for Plasmodium falciparum infection in a low-transmission setting in Kilifi County, Kenya [version 3; peer review: 2 approved]

Abstract

Background: Central to the successful elimination of Plasmodium falciparum malaria, are tests with superior capability of diagnosing low-density parasitaemias. Empirical evidence on the performance of the commonly available diagnostics (light microscopy (LM), rapid diagnostic tests (RDT) and polymerase chain reaction (PCR)) is needed to better inform case management and surveillance activities within primary health care settings where elimination of falciparum malaria is targeted. The objective of this study was to estimate the sensitivity (Se) and specificity (Sp) and predictive values of LM, RDT and PCR tests for P. falciparum infection in children, while evaluating the effect of specific covariates on the accuracy of the tests.

Methods: The study enrolled 1,563 children presenting with fever (axillary temperature ≥ 37.5⁰C) to the Ngerenya dispensary, Kilifi County between March and December 2014. A Bayesian latent class model (BLCM) was fitted to the participants’ diagnostic data obtained from blood samples that were screened for the presence of P. falciparum using the three tests.

Results: The PCR assay registered a higher Se (97.6% [92.0; 99.7]) than LM (84.0% [74.8; 91.0]) but similar to RDT (92.2% [84.4; 97.0]). However, the assay showed a similar Sp (98.9% [98.2; 99.4]) to both RDT (99.4% [98.9; 99.7]) and LM (99.5% [99.0; 99.8]). Regarding predictive values, the tests yielded statistically similar estimates of positive and negative predictive values (PPV and NPV). A serial interpretation of the results of RDT and LM raised the PPVs and NPVs to >98%.

Conclusions: LM and RDT afford high Se and Sp in symptomatic care-seeking children in this low P. falciparum prevalence setting. A serial combination of the tests assures high PPV and NPV estimates. These elements, coupled with the wide deployment and affordability of the tests, lend the tests useful for guiding clinical care and surveillance activities for P. falciparum within elimination settings.

Introduction

Malaria persists as a leading cause of morbidity and mortality globally¹ and in Kenya, close to 70% of the population is at risk of the disease. Plasmodium falciparum is the most preponderant malaria parasite in the country associated with over 99% of malaria infections². However, studies report a declining trend of P. falciparum prevalence particularly along the Kenyan coastal region^3,4.

The Kenya national guidelines for diagnosis and treatment of malaria dictate that malaria treatment should be informed by parasitological diagnosis⁵. Light microscopic (LM) examination of thin or thick blood smears is held as the standard method for malaria diagnosis⁵. The test is inexpensive, generally exhibits high sensitivity (Se) and specificity (Sp) and permits parasite quantification which is a vital attribute in evaluating disease severity and guiding appropriate therapy⁶. Nevertheless, the test is fraught with some challenges: it requires good lab equipment and well trained microscopists that are often lacking in poor settings and it may display poor Se especially in cases of low parasitaemia given its detection limit of approximately 50 – 100 parasites per µl of blood under field conditions⁶.

Rapid diagnostic tests (RDTs) are immunochromatographic tests that detect specific parasite antigens⁷: the P. falciparum-specific histidine-rich protein II (HRP–II) or lactate dehydrogenase (LDH) and Plasmodium aldolase, which is pan-specific⁸. RDTs are hailed for their rapidity, usage simplicity, suitability for use in remote settings with limited equipment and trained staff and display high Se and Sp under field conditions⁹. However, their Se may be limited in situations of low parasitaemia¹⁰.

With declining P. falciparum transmission rates and thus its prevalence within the country^3,11, low parasite densities in the population are anticipated that may compromise the Se of LM and RDTs⁹. This situation may warrant alternative tests capable of detecting diminished levels of parasitaemia. Molecular-based techniques such as polymerase chain reaction (PCR) assays are touted as being less subjective and affording high Se and Sp in low parasite density settings^12,13. Nevertheless, since PCRs may detect non-viable parasites – quite sensibly common in elimination settings – their utility in guiding national guidelines for clinical case management is vague. Empirical evidence on the performance of these diagnostics in low-transmission settings is thus necessary to better inform management and surveillance efforts for P. falciparum malaria.

The diagnostic performance of RDTs has previously been evaluated using LM/PCR as a reference test^14–18. A drawback of this approach is that, given the imperfection of these references, the index tests’ characteristics are subject to bias. Moreover, for index tests presumed to have superior accuracy to the existing reference test, their evaluation based on the reference test is impractical. In the absence of a reliable reference test, latent class models (LCMs) allow for the simultaneous estimation of Se and Sp of two or more tests without any assumption about the underlying true disease status of each individual¹⁹. LCMs can be fit using either maximum likelihood or Bayesian methods²⁰. Essentially, Bayesian methods are preferable when observed data are insufficient²¹.

In heterogeneous populations, when information on some covariates perceived to influence the performance of index tests is available, stratified estimates of Se and Sp are computable and, reasonably, are more relevant than singular estimates. Of note, estimates of Se and Sp are characteristics specific to a test. However, once the test is applied in any given population, our interest rests on predictive values since the present concern is whether a particular tested individual has/does not have the disease in question given his/her test status. Therefore, the objective of this study was to estimate (within a Bayesian framework) the Se and Sp and predictive values of LM, RDT and PCR diagnostic tests for P. falciparum infection in children, while evaluating the effect of specific covariates on the performance of the tests.

Methods

Study area and population

The study participants comprised children aged <15 years presenting with fever (axillary temperature ≥ 37.5°C) to the Ngerenya dispensary, Kilifi County between March and December, 2014. This facility is situated within the Kilifi Health and Demographic Surveillance System (KHDSS) area on the Kenyan coast where, among other infectious diseases, malaria screening is conducted routinely²². Notably, the KHDSS area spans a population of approximately 260,000 persons in an area of about 891km²²². The area experiences a bimodal rainfall pattern, with long rains coming in the months of April to July and short rains in October and November. Malaria is endemic and transmission occurs throughout the year, with annual entomological inoculation rates ranging between 30 and 100².

Sample collection

At the dispensary, blood samples were collected from the study participants for immunological examinations and assessment of P. falciparum infections. Specifically, a 50 µl finger prick blood sample was collected for RDT, a slide for LM and an EDTA for PCR. Rapid diagnosis was carried out by trained health workers at the facility while slide and EDTA blood samples were stored in cooler boxes and transported to the KEMRI-Wellcome Trust Research laboratory, Kilifi where the LM and PCR tests were run by trained laboratorians. Examiners for parasitaemia using any of the diagnostics were blinded to the results of the other tests. Besides the samples, the participants’ socio-demographic characteristics (area of residence, age, sex and visit date) were captured.

Ethical considerations

Parents/guardians of enrolled children provided written informed consent agreeing to their children’s participation in the study. In addition, assent was secured from the participating minors. Approval for the study was obtained from the Kenya Medical Research Institute (KEMRI) Ethical Review Committee (SSC No. 2617).

Target condition

The latent (unobserved) infection status (referred to here as parasitaemia) targeted for detection by the three tests: LM, RDT and PCR, represents a blood sample containing either the live P. falciparum parasite or its HRP-II antigens/products or debris at any concentration level.

Light microscopy (LM)

Malaria microscopy was performed as per standard guidelines²³. Briefly, thick and thin blood films were stained in 3% Giemsa solution for 45 to 60 minutes and examined under a light microscope at 1000× magnification for malaria parasites. Parasite quantification was achieved by counting the number of malaria parasites per 200 leucocytes. Parasite density per µl of blood was estimated assuming 8000 leucocytes per µl of blood and reported by species i.e. P. falciparum, Plasmodium malariae and Plasmodium ovale. For analytical purposes, P. falciparum densities above zero constituted a positive result; otherwise negative.

Rapid diagnostic test (RDT)

A CareStart™ Malaria RDT kit (Cat No. G0141, AccessBio Inc.) was used to test for the presence of P. falciparum specific HRP-II antigens in the collected blood samples in accordance with instructions contained in the CareStart™ Malaria manual (AccessBio). A total of 5 µl of blood was added into sample wells followed by 60 µl of assay buffer solution added to assay wells. The blood-buffer mixture was then allowed to flow towards the test and control windows. The presence of two colour bands denoted a positive result; one band (the control line) indicating a negative result.

Polymerase chain reaction (PCR) assay

The PCR analysis was conducted as described elsewhere^24,25. Briefly, 30 µl of blood was used for DNA extraction using a QIAxtractor machine (QIAGEN, Hilden, Germany). In a subsequent step, the extracted DNA was eluted in a volume of 100 µl, after which 5 µl of sample DNA was amplified in a thermal cycler (Applied Biosystems ™ 7500 Real - Time PCR System, Applied Biosystems, Foster City, CA). Specifically, 5 µl of DNA was added to 45 µl of amplification mixture containing TaqMan buffer, 250 µM of each deoxynucleotide triphosphate, 0.125 U Amplitaq Gold polymerase, oligonucleotide primers and TaqMan probe (5’-FAM-AACAATTGGAGGGCAAG-NFQ-MGB-3’)²⁴. In about 10 minutes at 95°C pre-incubation, PCR amplification was carried out for 45 cycles (15s 95°C, 1 min 60°C) using a TaqMan assay for the highly conserved P. falciparum multicopy 18S ribosomal RNA gene. Quantification was achieved based on the Applied Biosystems 7500 software v2.0.6. The method has a quantification limit of 4.5 parasites/µl of blood. Three negative control wells and seven serial dilutions of DNA extracted from an in vitro culture of the P. falciparum 3D7 parasite strain were included on each plate as standards²⁴. Samples with PCR readings beyond zero were considered positive.

Population classification

Organisationally, within the Kenyan health system, a dispensary denotes a primary care unit serving an immediate catchment population²⁶. Arguably, therefore, the facility data represented a sample drawn from a single target population that formed the basis for derivation of Se and Sp of the three tests.

Statistical analysis

Initially, based on the participants’ visit dates, a dichotomous ‘season’ variable (‘wet’: [April – July, October and November] versus ‘dry’: [March, August, September, December]) was generated. A Bayesian latent class model (BLCM) built in OpenBUGS v3.2.2²⁷ but called from R software v3.4.3 via the ‘BRugs’ package²⁸ v0.9-0 was used to infer prevalence, the tests’ characteristics and corresponding predictive values. Notably, the analysis was informed by the guideline for standards for reporting diagnostic accuracy studies that use BLCMs (STARD-BLCM)²⁹. Code used to run this analysis is available as underlying data³⁰.

In fitting a BLCM, three assumptions are necessary: (1) the target population should consist of two or more subpopulations with different prevalences, (2) the Se and Sp of the tests under evaluation should be constant across the subpopulations and (3) the tests should be conditionally independent given the disease status¹⁹. For our situation, the three tests were assumed to be conditionally independent given an individual’s P. falciparum infection status. This is sensible considering that the tests target different aspects of the parasite. As such, statistically, for an individual whose infection status is known, their probability of testing positive (or negative) to one of the tests remains the same regardless of their prior outcomes to the other tests. In order to evaluate the separate effects of ‘season’, ‘sex’ and ‘age’ (dichotomised into <5 yrs and ≥ 5yrs³¹) on the Se and Sp estimates of the tests, we relaxed the assumption on constancy of the tests characteristics by stratifying the single population into subpopulations defined by the levels of the covariates. This allowed for the computation of stratum-specific tests estimates. Hypotheses for the differences between the stratified estimates were evaluated using a Bayesian P – value.

Counts (O_k) of the different test combinations (e.g. +,+,+) were assumed to follow a multinomial distribution of the form:

Ok|SeikSpikPk∼multinomial(probk,nk)

Where Se_ik and Se_ik represent the respective test characteristics for test i (i =1,2,3) in subpopulation k and p_k is the specific prevalence for the k^th (k =1,2) subpopulation. Prob_k is a vector of probabilities of observing the different combinations of test results, and n_k reflects the number of individuals tested for the k^th subpopulation. For instance, in the 1^st subpopulation for an individual testing positive to each of the three tests, prob₁ is given by:

prob1=Pr(T1+T2+T3+|D+)+Pr(T1+T2+T3+|D−)=Se11Se21Se31P1+[1−Sp11][1−Sp21][1−Sp31][1−P1]

For each covariate, the resulting two subpopulations furnished 14 degrees of freedom sufficient to estimate 14 parameters (stratum-specific Se and Sp of the three tests and two subpopulation prevalences) – suggesting identifiability of the model. Of note, model identifiability can at least be justified if the number of subpopulations (k) and tests (i) fulfil the equation: k ≥ i/(2^i–1– 1)³².

Positive and negative predictive values (PPV and NPV respectively) associated with test i and subpopulation k were derived as follows:

ppv=PkSeik/(PkSeik+[1−Pk][1−Seik])npv=[1−Pk]Spik/(Pk[1−Seik]+[1−Pk]Spik)

Non-informative priors (beta(1,1)) were used to fit the Bayesian model since no reliable prior information was available for any of the tests parameters. A separate (non-stratified) model ignoring differences in tests Se and Sp across covariate levels was also fitted and the relative goodness of fit for the nested models compared using the Deviance Information Criterion (DIC) (the model with the smaller DIC value being more preferable).

The models were initialised with two Markov Chain Monte Carlo chains with different values. Each chain comprised 70,000 samples, with the first 20,000 being discarded as the burn-in. Convergence of the chains was evaluated by visual appraisal of the time series plots of selected variables and the Gelman-Rubin diagnostic plots. The posterior distribution of the subpopulation prevalences, the Se and Sp of the three tests, as well as the predictive values were reported as the median and the corresponding 95% posterior credible intervals (PCI).

Results

The cross-tabulated counts of the three tests’ outcomes by covariate level are displayed in Table 1. The sample comprised 1563 children, of whom 65.8% (n = 1029) were <5 years of age, 47.3% (n = 739) were female and 34.5% (n = 492) made visits during the dry season.

Table 1. Cross-classified results by stratum for rapid diagnostic test (RDT), light microscopy (LM) and polymerase chain reaction (PCR) tests for diagnosis of P. falciparum infection in the study population in Ngerenya, Kilifi County, Kenya.

Stratum	Tests outcomes combinations (RDT; LM; PCR)								Total (%)
	+++	++-	+-+	-++	+--	-+-	--+	---
Single population	67	1	12	5	9	7	17	1445	1563 (100%)
Age
<5yrs	24	1	6	2	5	4	11	976	1029 (65.8%)
≥5yrs	43	0	6	3	4	3	6	469	534 (34.2%)
Sex
Female	27	0	4	1	2	3	9	693	739 (47.3%)
Male	40	1	8	4	7	4	8	752	824 (52.7%)
Season
Dry	29	0	6	0	3	1	4	449	492 (34.5%)
Wet	38	1	6	5	6	6	13	996	1071 (68.5%)

The stratum-specific estimates of Se and Sp of the three tests for P. falciparum are presented in Table 2. There were no detectable significant differences between the covariate-stratified tests’ estimates as indicated by the Bayesian P-value. Furthermore, the non-stratified model gave better fit (DIC =41.7) to the data than any of the covariate-stratified models (DICs =[72.9; 72.0; 69.9]) and was thus utilised for subsequent analyses.

Table 2. Stratum-specific estimates of sensitivity and specificity of rapid diagnostic test (RDT), light microscopy (LM) and polymerase chain reaction (PCR) tests for P. falciparum infection and a Bayesian P-value for the difference between the stratified estimates.

Test parametera	Covariate		Bayesian P-valuec
	Age
	<5yrs Estimate (95% PCIb)	≥5yrs Estimate (95% PCI)
SeRDT	89.8 (74.4; 97.7)	92.0 (81.9; 97.7)	0.38
SeLM	77.6 (60.5; 90.0)	86.3 (75.0; 94.1)	0.16
SePCR	93.3 (79.5; 99.1)	98.4 (91.6; 99.9)	0.14
SpRDT	99.5 (98.9; 99.9)	99.1 (97.9; 99.7)	0.81
SpLM	99.6 (99.0; 99.9)	99.3 (98.2; 99.8)	0.76
SpPCR	98.9 (98.1; 99.5)	98.7 (97.4; 99.6)	0.61
	Sex
	Female Estimate (95% PCI)	Male Estimate (95% PCI)
SeRDT	94.1 (81.2; 99.3)	89.4 (78.2; 96.2)	0.76
SeLM	84.8 (69.5; 94.5)	81.9 (69.4; 91.0)	0.63
SePCR	97.4 (87.2; 99.9)	96.0 (87.0; 99.5)	0.63
SpRDT	99.6 (99.0; 99.9)	99.0 (98.2; 99.6)	0.92
SpLM	99.5 (98.8; 99.9)	99.4 (98.7; 99.8)	0.57
SpPCR	98.7 (97.6; 99.4)	99.0 (98.0; 99.6)	0.29
	Season
	Dry Estimate (95% PCI)	Wet Estimate (95% PCI)
SeRDT	97.6 (87.8; 99.9)	86.8 (74.5; 94.8)	0.96
SeLM	80.7 (65.6; 91.5)	84.8 (72.3; 93.4)	0.32
SePCR	97.6 (88.0; 99.9)	95.8 (86.3; 99.5)	0.67
SpRDT	99.2 (98.1; 99.8)	99.4 (98.7; 99.8)	0.40
SpLM	99.6 (98.8; 100.0)	99.4 (98.7; 99.8)	0.75
SpPCR	99.0 (97.8; 99.7)	99.8 (97.9; 99.4)	0.67

^aMedian estimates

^bPosterior credible interval

^cValue is considered significant if it lies outside the range 0.025; 0.975

Results of the estimates of Se and Sp of the three tests together with their respective predictive values and prevalence of P. falciparum are shown in Table 3. The PCR assay recorded a higher Se (97.6; 95% PCI [92.0; 99.7]) than LM (84.0; 95% PCI [74.8; 91.0]) but similar to RDT (92.2; 95% PCI [84.4; 97.0]). Nonetheless, the assay registered a similar Sp (98.9; 95% PCI [98.2; 99.4]) to both RDT (99.4; 95% PCI [98.9; 99.7]) and LM (99.5; 95% PCI [99.0; 99.8]). As for predictive values, the tests had statistically similar estimates of PPV and NPV. Since PPVs were comparably lower than NPVs, in a bid to bolster their estimates, a serial interpretation of the results of RDT and LM led to a considerable improvement in PPV (99.9; 95% PCI [99.8;100.0]) at a negligible expense of NPV (98.7 95% PCI [97.9; 99.2]).

Table 3. Estimates of prevalence, sensitivity and specificity of rapid diagnostic test (RDT), light microscopy (LM) and polymerase chain reaction (PCR) tests for P. falciparum infection and their corresponding predictive values (negative and positive predictive values) together with the serial interpretation of the results of RDT and LM.

Parameter	Estimate (95% PCI)
SeRDT	92.2 (84.4; 97.0)
SeLM	84.0 (74.8; 91.0)
SePCR	97.6 (92.0; 99.7)
SpRDT	99.4 (98.9; 99.7)
SpLM	99.5 (99.0; 99.8)
SpPCR	98.9 (98.2; 99.4)
P	5.6 (4.5; 6.8)
NPVRDT	99.5 (99.0; 99.8)
NPVLM	99.1 (98.4; 99.5)
NPVPCR	99.9 (99.5; 100.0)
PPVRDT	89.4 (81.7; 94.9)
PPVLM	90.5 (82.7; 95.8)
PPVPCR	83.5 (75.1; 90.3)
SerialNPV	98.7 (97.9; 99.2)
SerialPPV	99.9 (99.8; 100.0)

Discussion

Using latent class analysis we have estimated the accuracy of LM, RDT and PCR tests for the diagnosis of P. falciparum infection in children along with their associated predictive values. Enøe et al.²⁰ contend that a BLCM framework permits derivation of true estimates of index tests devoid of classification errors that may be introduced by the utilisation of an imperfect reference test. Thus, the findings of this study can be considered readily generalisable to other settings with similar P. falciparum infection burden in children.

Since malaria transmission dynamics have been shown to differ by age³, season³³ and sex³⁴, it is conceivable that the accuracy of malaria diagnostics may be influenced by these covariates. To test for this, the Se and Sp of the three tests were allowed to vary by the aforementioned covariate levels. However, it was shown that neither of the tests’ characteristics differed significantly across the levels of any of the covariates. This implies that the performance of the tests is not influenced by either the prevailing season or the age and sex of the presenting patient. Nonetheless, in São Tomé and Príncipe, LM was reported to have a lower Se in afebrile under-five children suggesting the inadequacy of the test in detection of low-density parasitaemias³⁵.

In the present study setting, the prevalence of P. falciparum infection was estimated to be 5.6% [95% PCI 4.5; 6.8], suggesting a low transmissibility of the parasite in the population. Accordingly, the PCR assay registered a higher Se estimate than LM but similar to RDT upholding its capability in detection of low density infections^10,35–37. In particular, Manning et al.³⁷ recorded comparable Se estimates for both a nested PCR and RDT in the diagnosis of severe falciparum malaria among Papua New Guinean children. The PCR’s superiority to LM in detection of low parasitaemias is owed to its low detection limit of <5 parasites/µl of blood^38,39 compared to an LM’s limit of roughly 20 parasites/µl of blood in research settings⁶. Nonetheless, in some reference laboratories LM may detect parasite densities in the region of 10 parasites/µl⁴⁰. It was further shown that both LM and RDT achieved comparable Se estimates, which coincides with observations of similarity in these estimates at ~100 parasites/µl of blood⁴¹.

False negative RDT results that may compromise the test’s Se estimate, are reported to occur when P. falciparum HRP-II genes are deleted from a large segment of the parasite population⁴². False negativity may also arise where parasitaemic levels fall below the detection threshold – that is, 100 parasites/µl of blood. Besides, in tropical settings, extreme heat and humidity may degrade the antibodies that bind antigens resulting in negative test outcomes⁴³. As regards Sp, the three tests yielded comparable estimates that concur with findings observed elsewhere^35,44,45. In a low-prevalence setting, false positive test results (that undermine a test(s) Sp) represent a key concern. False positivity in RDTs has been noted due to cross reactions in rheumatoid factor positive patients, gametocytaemia or in situations of persistent antigenaemia with HRP-II antigens in previously treated patients⁴⁶. Especially due to HRP-II antigenaemia, RDTs that target the LDH antigens are evidently more suitable in monitoring treatment efficacy owing to their rapid clearance from blood⁴⁷. False positives by PCR may be attributable to detection of non-viable parasites (parasite debris) in treated patients or contamination in the laboratory process.

In this population, the three tests displayed comparable estimates of PPV and NPV. In particular, these estimates signify a reduced confidence in a positive compared to a negative test result ascribable to the low P. falciparum prevalence. A serial interpretation of the results of both RDT and LM raised the confidence to >98% in both positive and negative test outcomes. Consequently, in this low-transmission setting where false positives are increasingly expected, the most optimal testing strategy should be one that has all individuals initially screened by the more sensitive RDT test, with any resulting positives followed up with the more specific LM. Only those individuals positive to both tests should be eligible for treatment. This multiple-test approach is pivotal to reducing the risk of parasite resistance that can occur when individuals are unnecessarily subjected to artemisinin therapy⁴⁸. The rationale for employing the RDT-LM test combination owes to the wide deployment and affordability of the tests in most primary care settings⁴⁹ granting them promise towards informing clinical care and surveillance activities aimed at eliminating falciparum malaria. Moreover, as quantification of parasitaemias is central to the management of severe malaria and assessment of treatment response³⁶, in this respect, LM affords an added merit. By contrast, as PCRs demand hefty investment in equipment and reagents as well as highly trained personnel, their potential for routine use in low-resource field settings is limited.

The strong confidence realised in a negative test outcome is especially fundamental in a low-transmission setting where the preponderance of low-density infected individuals calls for their accurate detection not only to inform treatment but also to guide successful P. falciparum malaria elimination efforts. Importantly, low-density parasitaemic individuals present as potential reservoirs of infection to uninfected mosquitoes so that, if undetected, transmission may be sustained silently³⁶.

Conclusions

Using a Bayesian approach, we have derived the Se and Sp of LM, RDT and PCR for the diagnosis of P. falciparum infection in symptomatic care-seeking children as well as their associated predictive values. It was shown that the PCR assay’s Se was significantly higher than that of LM but similar to RDT. Nevertheless, the Sp estimates of the three tests were similar. Furthermore, the three tests produced comparable estimates of predictive values. In an elimination setting, a serial interpretation of the results of RDT and LM should guarantee high NPV and PPV; attributes that are indispensable in assuring treatment efficiency and guiding surveillance activities geared towards eliminating falciparum malaria in primary care settings.

Data availability

Underlying data

The raw dataset for the study is stored under restricted access since it contains sensitive participant information. Notwithstanding, accessibility is possible upon placing a formal request to our Data Governance Committee ([email protected]). The replication data and analysis scripts for this manuscript are available from the Harvard Dataverse.

Harvard Dataverse: Replication data for: Bayesian evaluation of the performance of three diagnostic tests for Plasmodium falciparum infection in a low-transmission setting in Kilifi County, Kenya. https://doi.org/10.7910/DVN/Z5RBBT³⁰

This project contains the following underlying data:

BLCM_malaria_tests_code.R (Bayesian Latent Class R code for malaria tests evaluation)

Ngerenya_tests_data.tab (Study dataset)

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Acknowledgements

We wish to express our deepest gratitude to the study participants, field workers and laboratory staff of the KEMRI-Wellcome trust Research Programme for facilitating the success of this work. We are also grateful to the Ngerenya dispensary management committee for graciously availing the facility for this study. This work is published with the permission of the KEMRI Director.

References

1. WHO: World Malaria Report 2018. Geneva; 2018.

2. Kenya Ministry of Health: Kenya Malaria Indicator Survey 2015. Nairobi, Kenya, 2016.

3. Mogeni P, Williams TN, Fegan G, et al.: Age, Spatial, and Temporal Variations in Hospital Admissions with Malaria in Kilifi County, Kenya: A 25-Year Longitudinal Observational Study. PLoS Med. 2016; 13(6): e1002047.

4. von Seidlein L, Knudsen J: Malaria Epidemiology in Kilifi, Kenya during the 21st Century: What Next? PLoS Med. 2016; 13(6): e1002048.

5. Kenya Ministry of Health: National Guidelines for the Diagnosis,Treatment and Prevention of Malaria in Kenya. Nairobi, Kenya, 2010.

6. Wongsrichanalai C, Barcus MJ, Muth S, et al.: A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT). Am J Trop Med Hyg. 2007; 77(6 Suppl): 119–27.

7. WHO: Malaria rapid diagnosis: Making it work. Philippines. 2003.

8. Tangpukdee N, Duangdee C, Wilairatana P, et al.: Malaria diagnosis: a brief review. Korean J Parasitol. 2009; 47(2): 93–102.

9. WHO: Malaria Rapid Diagnostic Test Performance: Results of WHO product testing of malaria RDTs: round 8 (2016-2018). Geneva; 2018.

10. McMorrow ML, Aidoo M, Kachur SP: Malaria rapid diagnostic tests in elimination settings--can they find the last parasite? Clin Microbiol Infect. 2011; 17(11): 1624–31.

11. Noor AM, Gething PW, Alegana VA, et al.: The risks of malaria infection in Kenya in 2009. BMC Infect Dis. 2009; 9: 180.

12. Abba K, Deeks JJ, Olliaro P, et al.: Rapid diagnostic tests for diagnosing uncomplicated P. falciparum malaria in endemic countries. Cochrane Database Syst Rev. 2011; (7): CD008122.

13. Harris I, Sharrock WW, Bain LM, et al.: A large proportion of asymptomatic Plasmodium infections with low and sub-microscopic parasite densities in the low transmission setting of Temotu Province, Solomon Islands: challenges for malaria diagnostics in an elimination setting. Malar J. 2010; 9: 254.

14. Omondi CJ, Kamau LM, Njaanake HK, et al.: Evaluation of Three Rapid Diagnostic Test Kits in the Diagnosis of Plasmodium Species Infections among Primary School Children in Baringo County, Kenya. Open J Clin Diagn. 2017; 7(1): 31–44.

15. Osanjo GO, Onyango IA, Kimani J, et al.: Evaluation of malaria rapid diagnostic tests among children in a malaria endemic region in coastal Kenya. Afr J Pharmacol Ther. 2017; 6(2): 77–82.

16. Wanja EW, Kuya N, Moranga C, et al.: Field evaluation of diagnostic performance of malaria rapid diagnostic tests in western Kenya. Malar J. 2016; 15: 456.

17. Berzosa P, de Lucio A, Romay-Barja M, et al.: Comparison of three diagnostic methods (microscopy, RDT, and PCR) for the detection of malaria parasites in representative samples from Equatorial Guinea. Malar J. 2018; 17(1): 333.

18. Martin-Diaz A, Rubio JM, Herrero-Martinez JM, et al.: Study of the diagnostic accuracy of microbiological techniques in the diagnosis of malaria in the immigrant population in Madrid. Malar J. 2018; 17(1): 314.

19. Hui SL, Walter SD: Estimating the error rates of diagnostic tests. Biometrics. 1980; 36(1): 167–71.

20. Enøe C, Georgiadis MP, Johnson WO: Estimation of sensitivity and specificity of diagnostic tests and disease prevalence when the true disease state is unknown. Prev Vet Med. 2000; 45(1–2): 61–81.

21. Branscum AJ, Gardner IA, Johnson WO: Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling. Prev Vet Med. 2005; 68(2–4): 145–63.

22. Scott JA, Bauni E, Moisi JC, et al.: Profile: The Kilifi Health and Demographic Surveillance System (KHDSS). Int J Epidemiol. 2012; 41(3): 650–7.

23. WHO: Microscopy for the detection, identification and quantification of malaria parasites on stained thick and thin films in research settings. Geneva; 2015.

24. Mogeni P, Williams TN, Omedo I, et al.: Detecting Malaria Hotspots: A Comparison of Rapid Diagnostic Test, Microscopy, and Polymerase Chain Reaction. J Infect Dis. 2017; 216(9): 1091–8.

25. Hermsen CC, Telgt DS, Linders EH, et al.: Detection of Plasmodium falciparum malaria parasites in vivo by real-time quantitative PCR. Mol Biochem Parasit. 2001; 118(2): 247–51.

26. Kenya Ministry of Health: Kenya Health Sector Strategic and Investment Plan (KHSSPI) July 2013 - June 2017. Nairobi, Kenya, 2013.

27. Lunn D, Spiegelhalter D, Thomas A, et al.: The BUGS project: Evolution, critique and future directions. Stat Med. 2009; 28(25): 3049–67.

28. Thomas A, O'Hara B, Ligges U, et al.: Making BUGS open. R News. 2006: 12–7.

29. Kostoulas P, Nielsen SS, Branscum AJ, et al.: STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models. Prev Vet Med. 2017; 138: 37–47.

30. Mweu M: Replication Data for: Bayesian evaluation of the performance of three diagnostic tests for Plasmodium falciparum infection in a low-transmission setting in Kilifi County, Kenya. Harvard Dataverse, V1, 2019. http://www.doi.org/10.7910/DVN/Z5RBBT

31. Ngasala BE: Improved malaria case management in under-fives in the era of artemisinin-based combination therapy in Tanzania. Stockholm, Sweden: Karolinska Institutet; 2010.

32. Toft N, Jørgensen E, Højsgaard S: Diagnosing diagnostic tests: evaluating the assumptions underlying the estimation of sensitivity and specificity in the absence of a gold standard. Prev Vet Med. 2005; 68(1): 19–33.

33. Imbahale SS, Mukabana WR, Orindi B, et al.: Variation in malaria transmission dynamics in three different sites in Western kenya. J Trop Med. 2012; 2012: 912408.

34. Zhao Y, Zeng J, Zhao Y, et al.: Risk factors for asymptomatic malaria infections from seasonal cross-sectional surveys along the China-Myanmar border. Malar J. 2018; 17(1): 247.

35. Gonçalves L, Subtil A, de Oliveira MR, et al.: Bayesian Latent Class Models in malaria diagnosis. PLoS One. 2012; 7(7): e40633.

36. WHO: Policy brief on malaria diagnostics in low-transmission settings. Geneva; 2014.

37. Manning L, Laman M, Rosanas-Urgell A, et al.: Rapid antigen detection tests for malaria diagnosis in severely ill Papua New Guinean children: a comparative study using Bayesian latent class models. PLoS One. 2012; 7(11): e48701.

38. Cordray MS, Richards-Kortum RR: Emerging nucleic acid-based tests for point-of-care detection of malaria. Am J Trop Med Hyg. 2012; 87(2): 223–30.

39. Vasoo S, Pritt BS: Molecular diagnostics and parasitic disease. Clin Lab Med. 2013; 33(3): 461–503.

40. Bejon P, Andrews L, Hunt-Cooke A, et al.: Thick blood film examination for Plasmodium falciparum malaria has reduced sensitivity and underestimates parasite density. Malar J. 2006; 5: 104.

41. Bell D, Wongsrichanalai C, Barnwell JW: Ensuring quality and access for malaria diagnosis: how can it be achieved? Nat Rev Microbiol. 2006; 4(9 Suppl): S7–20.

42. Gamboa D, Ho MF, Bendezu J, et al.: A large proportion of P. falciparum isolates in the Amazon region of Peru lack pfhrp2 and pfhrp3: implications for malaria rapid diagnostic tests. PLoS One. 2010; 5(1): e8091.

43. Chiodini PL, Bowers K, Jorgensen P, et al.: The heat stability of Plasmodium lactate dehydrogenase-based and histidine-rich protein 2-based malaria rapid diagnostic tests. Trans R Soc Trop Med Hyg. 2007; 101(4): 331–337.

44. Ochola LB, Vounatsou P, Smith T, et al.: The reliability of diagnostic techniques in the diagnosis and management of malaria in the absence of a gold standard. Lancet Infect Dis. 2006; 6(9): 582–8.

45. Saha S, Narang R, Deshmukh P, et al.: Diagnostic efficacy of microscopy, rapid diagnostic test and polymerase chain reaction for malaria using bayesian latent class analysis. Indian J Med Microbiol. 2017; 35(3): 376–80.

46. Kakkilaya BS: Rapid Diagnosis of Malaria. Lab Med. 2003; 34(8): 602–8.

47. Singh N, Valecha N, Nagpal AC, et al.: The hospital- and field-based performances of the OptiMAL test, for malaria diagnosis and treatment monitoring in central India. Ann Trop Med Parasitol. 2003; 97(1): 5–13.

48. Cui L, Mharakurwa S, Ndiaye D, et al.: Antimalarial Drug Resistance: Literature Review and Activities and Findings of the ICEMR Network. Am J Trop Med Hyg. 2015; 93(3 Suppl): 57–68.

49. ACTwatch Group, Hanson K, Goodman C: Testing times: trends in availability, price, and market share of malaria diagnostics in the public and private healthcare sector across eight sub-Saharan African countries from 2009 to 2015. Malar J. 2017; 16(1): 205.