DHS ANALYTICAL 

STUDIES 43

Measures of Malaria Parasitemia 

Prevalence in National Surveys: 

Agreement between Rapid Diagnostic

Tests and Microscopy

SEPTEMBER 2014

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Lia Florey.

DHS Analytical Studies No. 43

Measures of Malaria Parasitemia Prevalence in National 

Surveys: 

Agreement between Rapid Diagnostic Tests and Microscopy 

Lia Florey 

ICF International 

Rockville, Maryland, USA 

September 2014 

Corresponding author: Lia Florey, International Health and Development, ICF International, 530 Gaither 

Road, Suite 500, Rockville, Maryland 20850, USA; Phone: +1 301-572-0922; Fax: +1 301-407-6501; 

Email: Lia.Florey@icfi.com 
Acknowledgment: The author would like to thank Cameron Taylor for her invaluable assistance in 

producing maps and figures, Fred Arnold and Albert Kilian for careful review and expert advice. 

Editor: Rebecca Winter 

Document Production: Natalie La Roche 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AID-OAA-C-13-00095). The views expressed are 

those of the author and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about the DHS Program contact: 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 

301-407-6500, fax: 301-407-6501, email: reports@dhsprogram.com, Internet: www.dhsprogram.com. 

Recommended citation: 

Florey, Lia. 2014. Measures of Malaria Parasitemia Prevalence in National Surveys: Agreement between 

Rapid Diagnostic Testing and Microscopy. DHS Analytical Studies No. 43. Rockville, Maryland, USA: 

ICF International. 
iii

Contents 

List of Tables ................................................................................................................................................ v

List of Figures ............................................................................................................................................. vii

Preface ......................................................................................................................................................... ix

Abstract ........................................................................................................................................................ xi

Executive Summary ................................................................................................................................... xiii

1 Introduction ........................................................................................................................................... 1

1.1 Microscopy ................................................................................................................................ 2

1.2 Rapid Diagnostic Tests (RDTs)................................................................................................. 4

1.3 Polymerase Chain Reactions (PCR) .......................................................................................... 5

1.4 Loop mediated isothermal amplification (LAMP) .................................................................... 5

1.5 Literature Comparing RDTs and Microscopy ........................................................................... 6

1.6 Parasitemia Testing in The DHS Program ................................................................................ 7

2 Methods................................................................................................................................................. 9

2.1 Data Sources .............................................................................................................................. 9

2.2 Study Population ..................................................................................................................... 11

2.3 Study Variables ....................................................................................................................... 11

2.3.1 Outcome: Agreement between microscopy and RDT results ...................................... 11

2.3.2 Explanatory Variables .................................................................................................. 11

2.4 Analytical Procedures .............................................................................................................. 13

3 Results ................................................................................................................................................ 17

3.1 Section 1: Survey specific results ............................................................................................ 17

3.1.1 Malaria parasitemia prevalence and test agreement ..................................................... 17

3.1.2 Stratified Analyses: Recent Fever ................................................................................ 22

3.1.3 Stratified Analysis: Antimalarial Treatment ................................................................ 30

3.1.4 Stratified Analysis: WHO regional trends ................................................................... 39

3.1.5 Stratified Analysis: subnational regional trends .......................................................... 39

3.2 Change over time..................................................................................................................... 41

3.2.1 National-level change .................................................................................................. 41

3.2.2 Regional-level change .................................................................................................. 42

3.3 Pooled Analyses ...................................................................................................................... 43

3.3.1 Summary statistics ....................................................................................................... 43

3.3.2 Multivariable regression .............................................................................................. 43

4 Discussion........................................................................................................................................... 47

References .................................................................................................................................................. 49
iv
v

List of Tables 

Table 1. Slide-reading capacities, based on a 6-hour working day, not including time to collect 

and stain slides or report results (table from (WHO 2008)). ..................................................... 3

Table 2. Summary of Malaria Diagnostic Tests’ Characteristics ............................................................ 6

Table 3. Summary of malaria parasitemia testing in DHS and MIS ....................................................... 8

Table 4. Summary of explanatory variables .......................................................................................... 13

Table 5. Standard two-by-two table ...................................................................................................... 13

Table 6. Malaria parasitemia prevalence in children 6-59 months from national surveys and 

measures of agreement between microscopy and RDT test results ......................................... 17

Table 7. Summary of malaria parasitemia prevalence in children 6-59 months of age and 

agreement statistics from national surveys, stratified by recent fever status ........................... 26

Table 8. Summary of malaria parasitemia prevalence in children 6-59 months of age and 

agreement statistics from national surveys, stratified by recent treatment with 

antimalarials ............................................................................................................................ 33

Table 9. Pooled, multivariable regression model of discordant test results in children age 6-59 

months who were tested for malaria via microscopy and RDT .............................................. 43

Table 10. Pooled, multivariable regression model of discordant test results in children age 6-59 

months who were tested for malaria via microscopy and RDT .............................................. 44

Table 11. Pooled, multivariable regression model of discordant test results in children age 6-59 

months who were tested for malaria via microscopy and RDT. ............................................. 45
vi 
vii 

List of Figures 

Figure 1. Map of countries with DHS or MIS surveys between 2007 and 2013 in which children 

age 6-59 months were tested for malaria parasites by both RDT and microscopy ................. 10

Figure 2. Timeline of available survey data ............................................................................................ 11

Figure 3. Example of an AUC from ROC analysis ................................................................................ 14

Figure 4. Prevalence of malaria parasitemia in children 6-59 months as measured by A) 

microscopy and by B) RDT in national surveys...................................................................... 18

Figure 5. Prevalence of malaria parasitemia in children 6-59 months as measured by RDT and 

by microscopy in national surveys .......................................................................................... 19

Figure 6. Prevalence of malaria parasitemia in children 6-59 months as measured by RDT and 

by microscopy in: A) national surveys with <30% malaria parasitemia and B) national 

surveys with ≥30% malaria parasitemia .................................................................................. 20

Figure 7. Interrater reliability measures (percent agreement and Cohen’s Kappa coefficient) 

between microscopy and RDT prevalence in children 6-59 months of age from 

national surveys ....................................................................................................................... 21

Figure 8. AUC from ROC analysis: Sensitivity and specificity of RDT tests as compared to 

microscopy for diagnosing malaria parasitemia in children 6-59 months from national 

surveys ..................................................................................................................................... 22

Figure 9. Fever prevalence in children 6-59 months of age with both RDT and microscopy 

results available from national surveys ................................................................................... 23

Figure 10. Malaria parasitemia prevalence as measured by microscopy in children 6-59 months 

of age with and without fever in the past two weeks............................................................... 24

Figure 11. Malaria parasitemia prevalence as measured by RDT in children 6-59 months of age 

with and without fever in the past two weeks ......................................................................... 25

Figure 12. Malaria parasitemia prevalence in children 6-59 months of age with recent fever as 

measured by A) microscopy and B) RDT and in children without recent fever as 

measured by C) microscopy and D) RDT ............................................................................... 27

Figure 13. Malaria parasitemia prevalence in children 6-59 months in national surveys as 

estimated by RDT and by microscopy stratified by recent fever status .................................. 28

Figure 14. Percent agreement between estimates of malaria parasitemia prevalence as measured 

by RDT and by microscopy in children 6-59 months who had recent fever compared 

to those who did not and the difference in percent agreement between the two strata ........... 29

Figure 15. Kappa statistics between estimates of malaria parasitemia prevalence as measured by 

RDT and by microscopy in children 6-59 months who had recent fever compared to 

those who did not and the difference in percent agreement between the two strata................ 29

Figure 16. Area under the curve from ROC analysis between estimates of malaria parasitemia 

prevalence as measured by RDT and by microscopy in children 6-59 months who had 

recent fever compared to those who did not and the difference in percent agreement 

between the two strata ............................................................................................................. 30

Figure 17. Frequency of antimalarial treamtent for recent fever in children 6-59 months of age 

with both RDT and microscopy results available from national surveys ................................ 31
viii 

Figure 18. Malaria parasitemia prevalence as measured by microscopy in children 6-59 months 

of age with fever in the past two weeks by antimalarial treatment status ............................... 32

Figure 19. Malaria parasitemia prevalence as measured by RDT in children 6-59 months of age 

with fever in the past two weeks by antimalarial treatment status .......................................... 32

Figure 20. Malaria parasitemia prevalence in children 6-59 months of age with fever in the past 

two weeks who took antimalarials as measured by A) microscopy and B) RDT; and in 

children who did not take antimalarials as measured by C) microscopy and D) RDT............ 34

Figure 21. Malaria parasitemia prevalence in children 6-59 months with recent fever in national 

surveys as estimated by RDT and by microscopy stratified by antimalarial treatment 

status ........................................................................................................................................ 35

Figure 22. Percent agreement between estimates of malaria parasitemia prevalence as measured 

by RDT and by microscopy in children 6-59 months with recent fever who reported 

antimalarial treatment compared to those who did not and the difference in percent 

agreement between the two strata ............................................................................................ 36

Figure 23. Kappa statistics of estimates of malaria parasitemia prevalence as measured by RDT 

and by microscopy in children 6-59 months with recent fever who reported 

antimalarial treatment compared to those who did not and the difference in Kappa 

statistics between the two strata .............................................................................................. 37

Figure 24. Area Under the Curve from ROC analysis of estimates of malaria parasitemia 

prevalence as measured by RDT and by microscopy (gold standard) in children 6-59 

months with recent fever who reported antimalarial treatment compared to those who 

did not and the difference in AUC between the two strata ...................................................... 38

Figure 25. Malaria parasitemia prevalence as estimated by RDT and microscopy by region of 

survey within Sub-Saharan Africa........................................................................................... 39

Figure 26. Difference in malaria parasitemia prevalence as estimated by RDT and microscopy by 

subnational region ................................................................................................................... 40

Figure 27. Change in malaria parasitemia prevalence over time as estimated by RDT and 

microscopy .............................................................................................................................. 41

Figure 28. Regional RDT and microscopy estimates of malaria prevalence in Liberia from 2009 

and 2011 MIS .......................................................................................................................... 42
ix

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be useful 

to policymakers and program managers in low- and middle-income countries. DHS Analytical Studies serve 

this objective by providing in-depth research on a wide range of topics, typically including several countries 

and applying multivariate statistical tools and models. These reports are also intended to illustrate research 

methods and applications of DHS data that may build the capacity of other researchers. 

The topics in the DHS Analytical Studies series are selected by The DHS Program in consultation with the 

U.S. Agency for International Development. 

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program 
x
xi

Abstract 

This report examines the relationship between two different diagnostic tests used in nationallyrepresentative household surveys to measure malaria parasitemia prevalence: Rapid Diagnostic Tests 

(RDTs) and microscopy. Prevalence estimates from 22 surveys containing both microscopy and RDT 

results were compared using tests of agreement and factors that could be influencing discordance between 

the two tests were identified. Although prevalence estimates from the two tests were highly correlated, RDT 

prevalence estimates were higher than microscopy prevalence estimates in 19 of 22 surveys examined. 

Given the lack of a gold standard diagnostic test for malaria that is practical for use in national survey 

settings, discrepancies in prevalence estimates generated from microscopy testing and from RDT testing 

are difficult to reconcile. Although results of these tests could be useful in identification of high-risk 

populations for targeting of interventions, assessments of impact based on changes in malaria prevalence 

measured with these tests are more problematic. Development of an algorithm for program managers to use 

to best estimate national prevalence given the available diagnostic data would be useful for the future. 
xii 
xiii 

Executive Summary 

This report examines the relationship between two different diagnostic tests used in nationallyrepresentative household surveys to measure malaria parasitemia prevalence: Rapid Diagnostic Tests 

(RDTs) and microscopy. Substantial resources have been invested in malaria control programs in recent 

years, with clear goals of reducing malaria transmission. Yet measuring changes in malaria prevalence has 

proven challenging given the varied quality of available data from health facilities and a paucity of other 

data sources. As more high quality, standardized, national survey data including biological tests for malaria 

infection have become available in recent years, the potential uses of these data merit reexamination. 

Specifically, surveys in which both microscopy and RDTs were used to test for malaria infection were used 

in this study for comparison of prevalence estimates, examination of test agreement, and identification of 

factors that could be influencing discordance between the two tests. 

Objectives 

1) Describe the levels of malaria infection in children 6-59 months of age identified by the two 

diagnostic tests in all Sub-Saharan African countries in which both tests were performed; 

2) Identify factors related to discordant RDT and microscopy test results for malaria infection; 

3) Clarify the challenges to using national survey data on malaria prevalence for demonstrating change 

over time or impact of programs. 

Methods 

With data from the Demographic Health Surveys (DHS) and Malaria Indicator Surveys (MIS), the study 

examines malaria infection prevalence in children 6-59 months of age and different measures of agreement 

between RDT and microscopy diagnostic test results, including Kappa coefficients and Areas Under the 

Curve (AUC) from Receiver Operating Characteristic (ROC) analysis. Results are further stratified by fever 

status, by antimalarial treatment status, by level of malaria prevalence and by region. Change over time in 

prevalence estimates is examined for a subset of countries in which more than one survey with malaria 

testing is available. Additionally, the study examines the factors associated with discordance in RDT and 

microscopy test results in a pooled sample. Household location (urban vs. rural), household wealth, 

children’s age and children’s fever and antimalarial treatment status were all found to significantly affect 

the likelihood of test discordance between RDT and microscopy in pooled models. 

Results 

Although scatter-plots show strong correlation between national-level RDT and microscopy estimates of 

malaria prevalence in children 6-59 months of age in Sub-Saharan African countries, several countries have 

discordant prevalence results depending on the test used. RDT results were higher than microscopy results 

in 19 out of 22 surveys. Agreement between the two diagnostic tests were shown to vary by overall 

prevalence level, by recent fever status of children and by antimalarial treatment status. Regional variation 

was also demonstrated. Household location (urban vs. rural), household wealth, children’s age and 

children’s fever and antimalarial treatment status were all found to significantly affect the likelihood of test 

discordance between RDT and microscopy in pooled models. Finally, changes in malaria prevalence in the 

four countries with two surveys was determined to be difficult to ascertain given the level of discordance 

between tests and the observed sub-national variation in prevalence and in discordance. 
xiv 

Conclusions 

Given the lack of a gold standard diagnostic test for malaria that is practical for use in national survey 

settings, discrepancies in prevalence estimates generated from microscopy testing and from RDT testing 

are difficult to reconcile. Although results of these tests could be useful in identification of high-risk 

populations for targeting of interventions, assessments of impact based on changes in malaria prevalence 

measured with these tests are more problematic and should be conducted with caution. Development of an 

algorithm for program managers to use to best estimate national prevalence given the available diagnostic 

data would be useful for the future. 
1

1 Introduction 

Malaria is a major cause of child morbidity and mortality in much of Sub-Saharan Africa. Significant 

resources have been allocated to prevention and control of the disease by national governments and 

international donors; in 2013 USD 1.97 billion international disbursements for malaria control were made 

and national governments contributed US$ 522 million in 2012 (WHO 2013). Monitoring the effectiveness 

of these investments requires data on the coverage of malaria interventions as well as information on health 

outcomes. Malaria-specific outcomes are notoriously difficult to measure for many reasons, including the 

non-specificity of malaria symptoms and challenges to data reporting common in many malaria-endemic 

countries. As a result, monitoring and evaluation of malaria control programs often depends on non-specific 

health outcomes as proxies for malaria-specific outcomes. For example, the Roll Back Malaria (RBM) 

consortium is currently leading a series of Malaria Impact Evaluations in countries supported by the 

President’s Malaria Initiative (PMI) to determine if the scale-up in malaria interventions over the past 

decade has been effective. All-cause child mortality is being used as the major outcome variable with the 

assumption that a significant proportion of child mortality is caused by malaria. In order to add strength to 

this argument, analyses are supplemented by data on prevalence of malaria parasites (parasitemia) wherever 

possible. 

Five species of Plasmodium parasites infect humans: P. falciparum (Pf), P. vivax (Pv), P. malariae (Pm), 

P. ovale (Po), and P. knowlesi (Pk). Pf is the most dangerous of these parasites to human health. The 2013 

World Malaria Report estimates that Pf is responsible for 91% of all malarial infections worldwide (WHO 

2013) and 90% of the deaths (WHO 2013). While Pf predominates in Sub-Saharan Africa, it is usually 

secondary to other species in other regions (WHO 2013). Pv is widely distributed, causing infection in Latin 

America, Asia, and parts of Africa, and it is responsible for up to 50% of malaria cases outside of SubSaharan Africa. Unlike Pf, Pv is rarely fatal although increasing evidence of severe disease and mortality 

associated with Pv infection has recently come to light, especially when infection occurs concurrently with 

malnutrition. Po and Pm infections are much more rare (<5-10% and 4-20%, respectively, in endemic 

regions (Bruce et al. 2007)) and more benign, with few infections leading to severe disease. These parasites 

are mostly limited to Sub-Saharan Africa, Southeast Asia, Indonesia, parts of the Western Pacific and the 

Amazon Basin (Westling et al. 1997). Pk is the most recently discovered species of human-infecting 

Plasmodium found in Southeast Asia. Pk accounts for up to 70% of malaria cases in endemic zones in 

Borneo and can be fatal due to a short erthyrocytic cycle (24 hours compared to an average of 48 hours for 

the other Plasmodium species) resulting in high density parasitemia (McCutchan, Piper, and Makler 2008). 

Due to the complex nature of malaria parasite transmission and the wide array of factors that influence 

prevalence of infection, monitoring national trends in parasitemia is a challenging task. Two major data 

sources are used for this purpose: household surveys such as malaria indicator surveys (MIS) and health 

management information systems (HMIS). Ideally, national HMIS would provide a rich source of 

longitudinal data useful for monitoring trends in prevalence and incidence of malaria. Unfortunately, many 

countries do not have the resources, infrastructure or skilled personnel necessary to run a reliable HMIS. In 

this case, other data sources are required. National MIS provide a simplified snapshot of parasitemia 

prevalence levels in a population at the time of the survey; however, cross-sectional data are much more 

difficult to interpret due to the temporality of data and to the variation across surveys in other factors likely 

to influence infection levels. 

Testing nationally representative sample populations for Plasmodium infection produces different 

information than monitoring trends via health information systems. First, national surveys represent the 

entire household-based population including those who do not access health facilities, whereas HMIS data 

are derived solely from people accessing care. Second, national surveys produce estimates of asymptomatic 

infections whereas HMIS data are typically restricted to cases of clinical disease. These differences are 
2

important and depending on the goal of data collection, one data source may be more appropriate. 

Monitoring asymptomatic infection is essential when elimination of malaria parasites is the immediate goal. 

However, measuring patterns in clinical disease is a priority in high-prevalence areas where the primary 

goal is reducing malaria-associated mortality and morbidity. 

The frequency of data collection is an important consideration for monitoring and evaluation of malaria 

control. Current evaluation efforts are challenged by a lack of baseline parasitemia prevalence data in most 

malaria-endemic countries. In settings with limited HMIS capacity, periodic national surveys with 

parasitemia testing are required in order to monitor trends. The Roll Back Malaria Monitoring and 

Evaluation Reference Group (RBM 2011) recommends carrying out no more than three to four surveys 

with parasitemia testing per decade. Meaningful changes in national prevalence estimates are difficult to 

detect between closely-spaced surveys which are also unlikely to be cost effective. Maximizing the value 

added from testing for Plasmodium parasites in national surveys requires a balance between the 

interpretability and utility of the data collected and the cost of the testing. As natural variations in malaria 

infection levels occur both annually and seasonally, annual or biannual surveys of malaria parasitemia are 

difficult to interpret; observed gains or failures over short time periods can be due to normal fluctuations in 

rainfall or temperature instead of to prevention and control interventions. 

Cost is also a critical consideration in data collection for malaria M&E. In low endemic countries nationallevel screening is not likely to be cost effective because large sample sizes are required to generate reliable 

estimates of infection prevalence. Also, microscopy and rapid diagnostic tests (RDTs) are not very sensitive 

at detecting parasites at low parasite densities, making results more difficult to interpret. In low endemic 

regions, monitoring trends in malaria using HMIS data is likely to be more cost effective. In contrast, 

monitoring malaria morbidity and mortality take priority over monitoring asymptomatic infection in highly 

endemic Sub-Saharan African countries. In this case, clinical malaria would normally be the outcome of 

primary interest and collecting data from HMIS may be the best use of limited funds; however, a lack of 

reliable HMIS data in many countries leads to reliance on estimates of asymptomatic infection from survey 

data as a proxy measure. 

Once the decision has been made to collect parasitemia prevalence data through a national survey, choosing 

the appropriate method for measuring malaria infection is another critical consideration. Three primary 

diagnostic tools are available for identifying Plasmodium species: microscopy, rapid diagnostic tests 

(RDTs) and polymerase chain reaction (PCR) techniques. A fourth, more recently developed tool, loop 

mediated isothermal amplification (LAMP), is becoming more widely available. Descriptions of these 

techniques and the advantages of each are provided below. 

1.1 Microscopy 

Microscopic techniques have been used for the identification of malaria parasites in blood samples since 

the French physician Charles Louis Alphonse Laveran first observed the protozoan inside red blood cells 

in 1880. Two microscopic techniques are commonly used in malaria diagnosis: reading of thick and thin 

blood smears. Thick smears are used to identify the presence or absence of malaria parasites and can be 

used for identification of Plasmodium species whereas thin smears are used for quantification of malaria 

parasites and for confirmation of Plasmodium species identification (WHO 1991). 

Unfortunately, the processes required to produce high quality blood smears are complicated under field 

conditions. Thin smears must be fixed with methanol as soon as the smear is dry in order for the red blood 

cells to remain intact. As immediate transfer to laboratories is usually not practical in field settings, thin 

slides need to be fixed in the field. Thick smears, conversely, must remain unfixed in order to allow 

hemolysis of red blood cells. Staining of both thick and thin smears is also necessary for proper microscopic 

diagnosis of malaria infection. Staining is a process during which a dye, typically Giemsa stain (a mixture 
3

of methylene blue, eosin, azure B and a solvent), is applied to a blood slide to differentiate between human 

and parasite cells for ease of detection and quantification of parasites. If slides are kept cool and dry, staining 

can be postponed for several days for thin smears, and for longer periods, for thick smears. Care must be 

taken to avoid autofixation (spontaneous fixation of red blood cells which arrests the process of hemolysis) 

which can occur if smears are not fixed in a timely fashion (typically 7-15 days for thick smears) or if 

smears are exposed to excess heat and humidity. Autofixation makes staining difficult or impossible. 

Staining is typically done in the lab; however staining in the field may be necessary on rare occasions. 

Once slides are properly prepared and transported to laboratory facilities, microscopists may begin to stain 

and read the slides. The speed at which identification of malaria parasites can occur depends on multiple 

factors. Slides with high parasite densities typically require less time as identification of a single parasite 

on a thick smear is sufficient for a positive diagnosis. Identifying parasite density takes much longer. The 

amount of additional time required depends on the parasite density on the slide. In samples with few or no 

parasites, technicians must examine 200 or more fields. Similarly, identification of Plasmodium species 

requires more time as approximately one hundred fields of the thick slide must be examined and 

confirmation via thin slide is often required (WHO 1991). Reading thin smears takes 10 times as long as 

reading thick smears, on average (WHO 1991). A summary of World Health Organization (WHO) 

estimates of slide-reading capacities based on slide positivity rates is provided in Table 1. 

Table 1. Slide-reading capacities, based on a 6-hour working day, not including time to collect and 

stain slides or report results (table from (WHO 2008)).

Slide positivity rate 10% 20% 30% 40% 50% 60% 70% 80%

Slides read/hour 7 8 8 8 9 9 10 11

Slides read/day 44 46 48 50 53 56 59 63

In practice, slide reading may require more time than the WHO recommendations suggest. In MIS surveys 

conducted with technical assistance from ICF International, microscopists read slides at a much slower rate, 

ranging from 15 to 30 slides per day. Thus, waiting for microscopy results often delays availability of 

survey data by several months. 

Microscopy has long been considered the “gold standard” in malaria diagnosis. However, major challenges 

to using microscopy in national surveys in developing countries have forced specialists to reconsider this 

position. The time required for diagnosis for treatment purposes via microscopy can be prohibitive; it is 

impractical to transport slides to a laboratory for reading and to return to the household to treat any infected 

individuals. Since identifying infected individuals and withholding treatment is ethically unacceptable, 

household surveys currently use RDTs administered in the field to determine if respondents need treatment, 

followed by microscopy for confirmation of infection status. Blood smears must be protected from dust, 

wind, uneven surfaces, rain, excess heat and humidity to avoid contamination and autofixation. Smears 

must also be transported to a central laboratory for reading, and this must be done within 7 to 15 days 

depending on humidity and temperature conditions, a necessity which is often expensive. Another common 

challenge is lack of skilled microscopists (Mwanziva et al. 2008, Castelli and Carosi). Finding technicians 

only for short-term survey work is difficult, and unemployed technicians often lack expertise, which affects 

the quality of diagnosis. Where well-trained technicians are available, they are often engaged in other vital 

projects which have to stop for the survey work. The range of microscopy skills and often adverse field 

conditions in which blood smears are made influence the reliability and validity of parasite diagnosis by 

microscopy in many countries. This reality, combined with recent improvements in diagnostic capacity of 

RDTs, has led some experts to conclude that microscopy may not always be the gold standard in malaria 

diagnostics (RBM 2011). 
4

1.2 Rapid Diagnostic Tests (RDTs) 

Rapid diagnostic tests for identifying malaria infection have greatly improved over the last 20 years. In 

order to help rank RDT performance and guide procurement decisions, the World Health Organization 

(WHO)-Foundation for Innovative New Diagnostics (FIND) Malaria RDT Evaluation Programme was 

established (WHO 2009). FIND’s website provides information on performance of specific RDTs based on 

rigorous testing at low and high parasite densities. RDTs are typically used to identify infection and provide 

immediate treatment to positive subjects in the field (or referral for follow-up treatment in a health facility) 

in conditions under which microscopy is impractical. RDTs have also been shown to be cost-effective in a 

range of settings accounting for treatment costs and alternate diagnostic options (Mosha et al. 2010, Lubell 

et al. 2007, Shillcutt et al. 2008). 

RDTs detect parasite antigens instead of actual parasites. The most commonly used RDTs test for histidinerich protein- 2 (HRP2) or species-specific plasmodium lactate dehydrogenase (pLDH), which are antigens 

that can persist following parasite clearance causing false positive test results. This difference limits the 

direct comparability of microscopy and RDT results, as recently treated infections will often appear as 

positive by RDTs but not by microscopy. HRP2-based RDTs have been shown to be sensitive and stable 

diagnostic tests for P. falciparum infection; however, residual antigens can produce positive tests for several 

weeks after successful treatment of infection (Swarthout et al. 2007, Mayxay 2001). As much as 74% of 

patients remain HRP2 positive 35 days post clearance of infection (Swarthout et al. 2007, Houze et al. 2009, 

Kyabayinze et al. 2008). LDH-based RDTs may not be as sensitive or heat-stable as the HRP2 tests; 

however, they are more specific as LDH does not persist in the blood after successful treatment of infection 

(Perkins and Bell 2008, Iqbal et al. 2004). Improvements in the sensitivity and heat stability of LDH tests 

to the level of HRP2 tests have been recently documented (Ratsimbasoa et al. 2007, Ashley et al. 2009, 

Gerstl et al. 2010, WHO 2011b) and these tests are therefore likely to be more widely used in the future. 

A major advantage of RDTs is their relative simplicity. The test requires only a single drop of blood from 

a finger or heel stick and do not require skilled technicians or access to laboratory equipment. RDTs are 

becoming increasingly available in rural settings in which microscopy is not a viable diagnostic option 

(Masanja et al. 2012). The top 10-15% of RDTs reviewed in the FIND study perform well, with sensitivity 

similar to that of a skilled microscopist (using Polymerase Chain Reaction diagnosis as the gold standard) 

(RBM 2011). MIS surveys carried out with support from The DHS Program typically use RDTs from the 

group with the highest levels of sensitivity and specificity. Another advantage to RDTs is that they produce 

results rapidly, within 15 minutes, whereas obtaining microscopy data usually requires several months after 

the completion of interviews. 

Average costs for RDT testing in MIS surveys implemented by The DHS Program are lower than 

microscopy. Average cost of microscopy testing in an MIS, including materials (glass slides, methanol, 

Giemsa stain, etc.), and shipping costs are USD 1.50 per test compared to an average cost of USD 1.23 per 

test for RDT1. A previous comparative cost analysis of malaria diagnostic testing in MIS found RDTs to be 

much more cost effective than microscopy (USD 0.71 vs. USD 1.53 for materials alone, unpublished data). 

Part of the reason for reduced relative cost effectiveness is the increasing use of HRP2-pLDH combo RDTs 

which are more costly than the HRP2 only tests. 

In summary, in a national household survey setting, diagnosing malaria infection using a high quality RDT 

is less expensive than using microscopy. Material costs alone are less expensive for RDTs than for 

microscopy and microscopy requires additional resources for transporting samples to laboratories and for 

 

1 Assuming 5000 samples with 100% response rate, 25% added for pretest/ training and 15% for loss/breakage. 

Excludes costs of general supplies such as lancets, gauze, gloves, biohazard bags, etc. Also excludes salaries of 

microscopists and field workers and costs to transport samples to laboratory facilities.
5

salaries for microscopists. In addition, surveys that include microscopy testing must also use RDTs to test 

for infection in the field in order to enable treatment of infected individuals, an ethical obligation; whereas 

RDT testing can be done without microscopy. However, RDTs are limited in their ability to identify 

Plasmodium species and cannot be used for quantification of parasites; microscopy is still required in 

countries where those data are needed. 

1.3 Polymerase Chain Reactions (PCR) 

PCR is a molecular test in which Plasmodium DNA is detected from a patient’s blood. Typically, blood 

samples are collected on a filter paper card from a finger stick or heel stick and allowed to dry. When 

protected from humidity, the resulting dried blood spots (DBS) can be stored at room temperature (for 

months if necessary) until processing in the laboratory takes place. Carrying out a PCR test requires special 

reagents and equipment, as well as skilled technicians. As a result, PCR is costly, between USD 0.50 and 

USD 2.50 per assay for the materials with an additional USD 10,000 to USD 25,000 for the PCR machine 

(Malhotra 2005, Bass et al. 2008). One study found quantitative PCR to be six-fold more expensive than 

microscopy for identification of malaria parasites with a USD 14.80 per sample cost of reagents and a onetime cost for the purchase of a thermocycler at USD 36,000 (Khairnar et al. 2009). 

PCR works by amplifying parasite DNA (by orders of magnitude) to the point at which it can be detected. 

PCR is extremely sensitive; theoretically a single parasite can be detected from a blood sample. In practice 

PCR has been shown to detect five parasites or less per microliter of blood (Abba et al. 2011). This contrasts 

with RDTs and thick smear microscopy, which have limits of detection of 100 parasites per microliter of 

blood and 50 parasites per microliter blood, respectively (Moody 2002). Because of this extreme sensitivity, 

prevalence estimates from surveys using PCR as a diagnostic tool should not be compared with those using 

microscopy or RDT. For example, Taylor and colleagues compared microscopy results with a new costsaving method of testing pooled PCR samples and found microscopy to have a 68% sensitivity and a 92% 

specificity as compared to PCR results (Taylor et al. 2010). 

PCR has several other strengths. It can be used to determine the specific Plasmodium species (e.g., diagnosis 

of infection with P. knowlesi requires PCR due to morphological similarities to P. malariae species (Singh 

et al. 2004)) and for testing for drug resistance markers. Although its cost and required expertise make its 

use currently impractical in most malaria-endemic countries, PCR is a useful tool in countries in the 

elimination or pre-elimination phase of malaria control where very sensitive diagnostic tools are required. 

For example, both Djibouti and Swaziland have completed MIS surveys in which PCR was used to estimate 

prevalence of infection (Kingdom of Swaziland Ministry of Health 2011, Noor et al. 2011). As laboratory 

technologies are rapidly advancing, the feasibility of using PCR in MIS should be regularly revisited. 

1.4 Loop mediated isothermal amplification (LAMP) 

Another molecular diagnostic tool has recently been developed for malaria: Loop mediated isothermal 

amplification (LAMP). Highly sensitive and specific, this test produces results faster than PCR and requires 

less laboratory expertise and equipment (Hopkins et al. 2013, Polley et al. 2013). The LAMP assay detects 

sequences of mitrochondrial DNA of Plasmodium parasites, either Pf specifically or sequences common to 

all Plasmodium species that infect humans (Pf, Pm, Po, Pv, or Pk, also known as pan-Plasmodium) (FIND 

2012). The test is able to detect as few as one parasite per microliter of blood in one hour from fresh or 

frozen blood samples or from dried blood spots (FIND 2012). Although in some ways an improvement on 

PCR (less expensive, less technically demanding, less time-consuming), LAMP is currently best suited to 

detecting low density infections. As such, LAMP could be useful in screening programs in areas slated for 

malaria elimination, and in serving as a reference standard against which other diagnostic tools can be 

tested. Other potential uses include antenatal screening, diagnosis in returned travelers, antimalarial drug 

efficacy trials, resistance monitoring programs and vaccine trials. 
6

Table 2. Summary of Malaria Diagnostic Tests’ Characteristics 

Ease of Use Cost

Training 

Required Sensitivity†

Quantification 

possible

Speciation 

possible

Results 

Immediate

Microscopy Low to Moderate Moderate Moderate to High ~ 50-100 parasites/μL Yes Yes No

RDT High Low Low ~100 parasites/μL No Some* Yes

PCR Low High Extensive ~5 parasites/μL Yes** Yes No***

LAMP Low Moderate/High Moderate ~1 parasite/μL No Some†† Within 1 hour

*Some RDTs are designed to be species specific and others will detect multiple species; however, only the following combinations 

of parasite detection are available: Pf only, Pf/pan, Pf/Pv, Pf/Pvom, Pv only and pan only (where pan= Pf, Pv, Pm and Po and 

Pvom = Pv, Po and Pm) (WHO 2011a). 

**Nested PCR and traditional multiplex PCR do not allow for quantification of parasites. Real time PCR does. 

***Innovative rapid PCR diagnostic tools are under development for use in the field(FIND). 

†See reference (Moody 2002) 

†† Pf-LAMP can specifically detect Pf parasites, pan-LAMP can detect the presence of any species of Plasmodium parasite.

1.5 Literature Comparing RDTs and Microscopy 

Few formal studies have compared sensitivity or specificity of these diagnostic tests in the context of 

national, population-based household surveys. The few that have are plagued by challenges such as lack of 

a comparable gold standard across studies. Measures of sensitivity and specificity are dependent on the 

choice of gold standard. Typically, microscopy is considered the gold standard, but the quality of 

microscopy results depends heavily on the expertise of the technicians, and microscopy is less sensitive at 

low parasite densities (<200 parasites per microliter of blood). Thus, using microscopy as a gold standard 

can be misleading. 

Some research has found RDTs to produce more sensitive measures of parasite infection than microscopy. 

Hopkins and colleagues (Hopkins 2007) used PCR-adjusted microscopy as a gold standard in a study set in 

seven sentinel sites of varied malaria transmission intensity in Uganda. Results from two different types of 

RDTs (HRP2 and pLDH) and microscopy were compared. Samples that were positive by RDT but negative 

by microscopy were confirmed with PCR. HRP2-based RDTs performed better than both pLDH-based 

RDTs and routine microscopy at detecting cases (PCR confirmed) across all study sites. Another study 

looking at samples from travelers returning to the United States from malaria endemic areas found RDTs 

to have higher sensitivity and higher negative predictive value than microscopy using PCR as the gold 

standard. This finding held for both falciparum and non-falciparum cases (Stauffer et al. 2009). 

Other researchers believe that microscopy is preferable to RDT testing in survey settings. Keating and 

colleagues (Keating et al. 2009) compared microscopy and RDT results in a national-level household survey 

in Zambia and concluded that microscopy results should continue to be used as the gold standard. The 

authors argue that RDT results represent measures of prevalence over a period of approximately two weeks 

compared to the point prevalence estimate generated by microscopy and that comparison of national 

parasitemia levels over time will be complicated by changing recommended diagnostic procedures. They 

also posit that the multiple brands of RDTs used limits comparability of survey results, especially where 

trends in national parasitemia prevalence are of interest for programming, monitoring and evaluation 

purposes. 

Comparisons of these diagnostic methods are also important in clinical settings. A review of RDTs and 

microscopy for the WHO guidelines for the management of common illnesses in hospitals with limited 

resources conducted by Allen Cheng and David Bell concluded that diagnostic recommendations should be 

country specific (Cheng and Bell 2006). After assessing 145 studies, Cheng and Bell concluded that uptake 
7

of RDTs in health facilities should take into consideration local realities such as existing quality of 

microscopy services, availability of skilled personnel, baseline malaria prevalence, predominant 

Plasmodium species, costs and supervisory capacity. Recommendations from this review include use of 

RDTs in countries where cost-effectiveness has been established at the local level based on local conditions. 

Specific guidelines on type of RDT were also provided: HRP2-based RDTs are advised where P. 

falciparum (Pf) predominates whereas combination HRP2/pLDH or pLDH-based RDTs are recommended 

in countries endemic to multiple Plasmodium species as HRP2 tests will detect Pf only and pLDH will 

detect all Plasmodium species. In other settings, microscopy may still be preferred. Thus country-specific 

conditions play an important role in determining appropriate diagnostic recommendations. 

Despite the lack of clear results in the malaria literature, experts working on the FIND study state that the 

highest quality RDTs are as sensitive and specific as expert microscopy with less variability in results, 

especially when microscopy is being conducted by newly trained technicians. Supporting this, a recent 

Cochrane Review article assessed the diagnostic performance of RDTs detecting HRP2 and pLDH antigens 

from 74 unique studies and concluded that all of the RDTs tested had sufficient sensitivity and specificity 

compared to microscopy to replace other diagnostic methods for detecting uncomplicated Pf malaria (Abba 

et al. 2011). 

1.6 Parasitemia Testing in The DHS Program 

Since 2000, The DHS Program has collected data on mosquito net ownership and use, treatment of fever 

in children, and intermittent preventive treatment of malaria in pregnant women. In 2006, starting with 

Angola, The DHS Program began incorporating parasitemia testing into some surveys. To date, The DHS 

Program has provided technical assistance for 32 DHS and Malaria Indicator Surveys with malaria 

parasitemia testing in 24 countries (Table 3). 
8

Table 3. Summary of malaria parasitemia testing in DHS and MIS 

Included in Analyses

Survey Months of fieldwork RDT type

Angola 2011 MIS Jan - May SD Biolone Malaria AG Pf/Pv

Benin 2011-12 DHS May - Jan Parcheck Pf

Burkina Faso 2010 DHS Dec - March Parcheck Pf

Burundi 2012 MIS Nov - Jan SD Bioline Malaria Ag Pf/pan 

Cote d’Ivoire 2011-12 Dec - May SD Bioline Malaria Ag Pf

Ghana 2011 MICS Sept - Dec CareStart Malaria HRP2/pLDH (pf/pan) Combo

Guinea 2012 DHS June - Oct First Response Malaria Ag Combo

Kenya 2010 MIS July - Sept CareStart Malaria HRP2/pLDH (pf/pan) Combo

Liberia 2009 MIS Dec - March Paracheck Pf

Liberia 2011 MIS Sept - Dec First Response Malaria Ag HRP2

Madagascar 2011 MIS March - June Paracheck Pf

Madagascar 2013 MIS April - June CareStart Malaria HRP2/pLDH (pf/pan) Combo

Malawi 2012 MIS April - May SD Bioline Malaria Ag Pf

Mali 2010 A&P Aug - Oct Paracheck Pf

Mozambique 2011 DHS May - Nov SD Bioline Malaria Ag Pf/Pv

Nigeria 2010 MIS Oct - Dec Paracheck Pf

Rwanda 2007-08 IDHS Dec - April Optimal-IT

Rwanda 2010 DHS Sept - March First Response Pf

Senegal 2008-09 MIS Dec - Jan Paracheck Pf

Senegal 2010-11 DHS Oct - April Paracheck Pf

Tanzania 2011-12 AIS/MIS Dec - May SD Bioline Malaria Ag Pf/pan

Uganda 2009 MIS Nov - Jan Paracheck Pf

Not Included - No Microscopy Data

Survey Months of fieldwork RDT type

Angola 2006 MIS Jan - April Paracheck Pf

Cameroon 2011 DHS Jan - Aug Malaria Ag Pf

Tanzania 2007-08 MIS/AIS Oct - Feb Paracheck Pf

Not Included - Data Not Available by April 2014

Survey Months of fieldwork RDT type

DRC 2013 DHS Aug - Dec SD Bioline Malaria Ag Pf

Equatorial Guinea 2011 DHS July - Nov ICT Malaria Combo

Gambia 2013 DHS Feb - April SD Bioline Malaria Ag Pf/pan

Mali 2013 DHS Nov - Feb SD Bioline Malaria Ag Pf

Senegal Cont. DHS 2012-13 All Year SD Bioline Malaria Ag Pf/Pv

Sierra Leone 2013 MIS Feb - March First Response Malaria Ag HRP2

Togo 2014 DHS Oct - Feb First Response Malaria Ag HRP2
9

2 Methods 

2.1 Data Sources 

Data included in our analysis come from DHS and MIS surveys conducted in malaria-endemic countries in 

Sub-Saharan Africa that tested children age 6-59 months for Plasmodium parasites by RDT and by 

microscopy. As of April 2014, 29 nationally representative household surveys implemented with technical 

assistance from The DHS Program have measured parasitemia in children aged 6-59 months using both 

RDTs and microscopy (Table 3). Of these 29 surveys, data were available from 22 at the time of data 

analysis (Figure 1, Figure 2). Data are nationally representative estimates generated from responses to 

household questionnaires and to individual questionnaires asked of women of reproductive age 15–49. 

Responses to child health questions were collected for all children born to interviewed women during the 

five years immediately preceding the survey. Malaria parasite testing was done on all (or a representative 

subsample of) children 6-59 months of age from interviewed households.
10

Figure 1. Map of countries with DHS or MIS surveys between 2007 and 2013 in which children age 

6-59 months were tested for malaria parasites by both RDT and microscopy 
11

Figure 2. Timeline of available survey data 

2.2 Study Population 

Children age 6–59 months at the time of the survey living or staying the night before in interviewed 

households, who were tested for malaria parasites by both microscopy and RDT comprised the study 

population. Children under age 6 months were excluded because infants are protected by maternal 

antibodies. For some analyses the study population is further restricted to children born to interviewed 

women age 15-49 for whom data were collected on recent fever and on treatment seeking behavior in 

response to fever. 

2.3 Study Variables 

2.3.1 Outcome: Agreement between microscopy and RDT results 

National-level summaries of each survey were compiled to look at agreement between microscopy and 

RDT test results. Several measures of agreement were used including Kappa coefficients and Area Under 

the Curve (AUC) estimates from Receiver Operating Characteristic (ROC) analyses. Models of RDTmicroscopy discordance were also run. 

2.3.2 Explanatory Variables 

Agreement between microscopy and RDT results was also assessed in several stratified analyses. Separate 

analyses were run for children with a fever in the two weeks preceding each survey and for children without 

a recent fever. This stratification was done due to the likely correlation between a positive malaria 

parasitemia test result at the time of data collection and fever status, as fever is a common symptom of 

malaria. However, questions on fever are asked about children 0-59 months of age of interviewed women 

for a period of two weeks before the survey: 

whereas malaria parasitemia testing is done on all eligible, consented children in the household whether or 

not his/her mother is interviewed. Thus analyses including fever status were limited to children born to 

interviewed mothers who had valid test results for both microscopy and RDT. 

As HRP2-based RDTs detect Plasmodium spp. antigens for a period of up to several weeks post clearance 

of infection (Mayxay 2001, Swarthout et al. 2007), discrepancies in results between HRP2-based RDT and 

microscopy are likely to be higher in settings of moderate to high malaria transmission with high levels of 

care seeking and appropriate antimalarial treatment. In order to determine if antimalarial treatment could 

2007

• Rwanda Interim DHS

2008

• Liberia MIS

• Senegal MIS

2009

• Uganda

2010

• Angola MIS

• Burkina Faso DHS

• Kenya MIS

• Mali A&P

• Nigeria MIS

• Rwanda DHS

• Senegal DHS

2011

• Benin DHS

• Cote d'Ivoire DHS

• Ghana MICS

• Liberia MIS

• Madagascar MIS

• Mozambique DHS

• Tanzania MIS/AIS

2012

• Burundi MIS

• Guinea DHS

• Malawi MIS

2013

• Madagascar MIS
12

be influencing the concordance of test results, analyses were further stratified by child’s recent antimalarial 

treatment status. The standard DHS/MIS Women’s questionnaires ask: 

of children of interviewed women who were reported to have fever in the two weeks directly preceding the 

survey. For this analysis, any of the response options under “Antimalarial Drugs” were considered valid 

antimalarials. As these questions were only asked of children with fever, the sample size in this stratified 

analysis is smaller than those of the previous sections. 

Other variables that might influence agreement in malaria diagnostic test results were used in analyses. 

These variables included household wealth, location of the household in an urban or rural setting, age of 

the child in months, sex of the child, in addition to recent fever status, and self-reported treatment with 

antimalarials in response to recent fever that have been previously discussed. Table 4 contains a summary 

of these variables. 
13

Table 4. Summary of explanatory variables

Variable Type Details of Measurement

Wealth Index Categorical with 5 ordinal categories Asset-based principal component analysis

Residence Categorical with 2 categories Household classified as being in an urban or rural setting

Child’s Age Categorical with 3 categories: 6-11 

months; 12-17 months, and 18-23 months

Based on date of birth and date of interview

Child’s Sex Categorical with 2 categories: male, female From women’s interview

Recent Fever Categorical with 2 categories: yes/no Whether or not the child had a fever in the 2 weeks 

preceding interview by mother’s report.

Antimalarial 

Treatment

Categorical with 2 categories: yes/no Whether or not a child with a reported recent fever took 

antimalarial medication by mother’s report.

2.4 Analytical Procedures 

Separate, national-level estimates of malaria parasitemia prevalence in children 6-59 months of age were 

generated for the twenty-two surveys that collected both microscopy data and RDT data using the subset 

of records containing results for both tests. Estimates were weighted to adjust for the sample design of each 

survey. Agreement between the two estimates generated by RDT testing and by microscopy was further 

examined using Kappa coefficients and ROC analysis. 

Cohen’s Kappa coefficient is used as a measure of rater agreement, which, although controversial for 

quantifying the level of agreement, is appropriate for evaluation of whether or not agreement exceeds levels 

expected by chance for binary ratings (tests of rater independence). Kappa coefficients are affected by the 

distribution of outcomes in the study population and are thus not comparable across populations (Thompson 

and Walter 1988, Feinstein and Cicchetti 1990). Kappa coefficients may be low even with high levels of 

agreement and despite accurate individual ratings. This depends on what assumptions are made about raters’ 

decision-making. In short, while Kappa can be used to measure a departure from levels of agreement 

between tests that could be expected by chance alone, it does not determine whether or not the raters 

correctly classify the cases (Uebersax 2010). Kappa values are calculated using estimates of the observed 

and expected agreement among raters. 

Table 5. Standard two-by-two table 

Test A + Test A - TOTAL

Test B + a b a+b

Test B - c d c+d

TOTAL a+c b+d N = a+b+c+d

From the standard two-by-two table (Table 5), Observed agreement is: 

Pr(a) = (a+d)/N 

and Expected agreement based on chance is: 

Pr(e) = ((a+b)*(a+c))+((c+d)*(b+d))/ N2
14

Cohen’s Kappa is calculated using both of these estimates:

κ = (Pr(a) – Pr(e))/(1-Pr(e)) 

Unlike the previous tests, using ROC analysis to look at interrater reliability requires determination of a 

gold standard. Results of a second diagnostic test are compared to the gold standard and the Area Under 

the Curve (AUC) of the sensitivity versus 1-specificity for various cutpoints can be generated: 

Z = a1-a0/SE a1-a0 ~N(0,1) 

Where a1 = area under the diagnostic test curve, a0 = 0.5, SE a1 = standard error of the area, and SE a0 = 

0.00 

AUC can be interpreted as the probability that a randomly selected abnormal individual will produce a 

diagnostic test result greater than the result of the same diagnostic test from a randomly selected normal 

subject. Thus, higher AUC values are indicative of better diagnostic tests. A value of 0.5 for the AUC

implies no agreement between tests and poor predictive power. A high value (significantly higher than 0.5) 

implies a good test for predicting the outcome and a value significantly lower than 0.5 implies that the test 

identifies inverse predictors of the outcome. The N(0,1) distribution is used as a reference for an individual 

curve. Figure 3 provides an example of an AUC from ROC analysis. 

Figure 3. Example of an AUC from ROC analysis 

The AUC is a measure of the discriminating ability of the test and is equal to the probability that the test 

correctly classifies patients as true positives or true negatives. In the case of an imperfect gold standard, the 

measure gives an indication on the similarities or dissimilarities between the two tests. 

Following these descriptive analyses, additional agreement tests were generated stratified by recent fever 

history in the study population (children 6-59 months) and by use of antimalarials in response to recent 

fever as reported by mothers during interview. This was done to investigate whether measures of interrater 

reliability varied between strata. As HRP2-based RDTs measure antigen that can persist for several weeks 

following effective treatment of malaria parasites and cause positive test results, some discrepancies 
15

between RDT and microscopy would be expected (higher RDT prevalence). It should be noted that the type 

of RDT used was not consistent across surveys. The specific RDT used in each survey is noted in Table 3. 

Scatter-plots were made of national malaria parasitemia prevalence as measured by microscopy on the xaxis and RDT on the y-axis for each survey. Best-fit lines were plotted and used to compare correlation 

between the two measures across surveys with equality lines. This was done for the population of children 

6-59 months of age. In this figure, each dot represents a survey. An additional pair of scatter-plots were 

constructed stratified into lower and higher levels of parasitemia prevalence (30% as measured by 

microscopy was used as the cut-off). Another pair of scatter-plots were constructed stratified by recent fever 

status in children, and a final pair of plots were constructed stratified by antimalarial treatment status in 

children tested for malaria during the survey who had reported recent fever. Scatter-plots comparing RDT 

and microscopy estimates of malaria prevalence were also produced for survey data stratified by WHO 

regions within Sub-Saharan Africa. West African, Central African and East African countries were 

represented. 

Sub-national trends were examined using a map of discordance in RDT and microscopy estimates of 

malaria parasitemia prevalence which was generated at the regional level for the most recent survey in each 

country. The regional stratifications defined in each survey were used. 

Differences in malaria prevalence as measured by the two diagnostic tests were also compared over time in 

the four countries with more than one survey containing the necessary data. Data from the two surveys in 

Liberia were used to illustrate regional changes over time in the prevalence estimates generated from the 

two diagnostic tests. 

Datasets were pooled and multivariable logistic models were run to investigate predictors of discordance 

in test results. Discordance was defined as either a positive RDT result and a negative microscopy result or 

a negative RDT result and a positive microscopy result. Models were adjusted for the household residence 

(urban/rural) of the child, the household wealth quintile, the child’s age categorized into 6-23 months of 

age or 24-59 months, and the child’s sex. In addition, the first set of models were adjusted for the child’s 

history of fever in the two weeks preceding the survey (Y/N). The second set of models were limited to 

children who had reported recent fever and were adjusted for self-reported treatment with antimalarials 

(Y/N). Dummy variables for survey were included to control for country-level variation. 
16
17

3 Results 

3.1 Section 1: Survey specific results 

3.1.1 Malaria parasitemia prevalence and test agreement 

In surveys conducted in malarious countries in Sub-Saharan Africa between 2007 and 2013 in which 

malaria parasitemia was measured by both RDT and by microscopy, prevalence of parasitemia in children 

age 6-59 months ranged from 1.4% in Rwanda in 2010 to 66% in Burkina Faso in 2010 (Figure 4; Table 

6). Prevalence as measured by RDT ranged from 2.2% in Rwanda in 2007/8 to 72% in Burkina Faso in 

2010. In almost every survey, RDT estimates of malaria parasitemia prevalence were higher than 

microscopy estimates. The exceptions were the Benin 2011-12 DHS, the Rwanda 2007-08 IDHS and the 

Senegal 2010-11 DHS. 

Table 6. Malaria parasitemia prevalence in children 6-59 months from national surveys and measures of 

agreement between microscopy and RDT test results 

Survey

Microscopy 

Prevalence

RDT 

Prevalence % Agreement Kappa ROC Sensitivity Specificity

Angola 2011 MIS 10.1 13.5 0.93 0.65 0.87 94.6 93.1

Burkina Faso 2010 DHS 65.9 71.8 0.76 0.43 0.70 89.7 50.6

Benin 2011-12 DHS 28.5 24.7 0.72 0.31 0.65 48.5 82.0

Burundi 2012 MIS 17.3 22.0 0.93 0.76 0.92 91.2 93.1

Cote d’Ivoire 2011-12 18.0 41.5 0.66 0.26 0.72 81.1 62.2

Ghana 2011 MICS 27.5 47.5 0.75 0.51 0.79 95.9 62.2

Guinea 2012 DHS 43.9 46.9 0.74 0.51 0.74 72.9 75.4

Kenya 2010 MIS 8.2 12.5 0.94 0.69 0.93 91.7 93.8

Liberia 2009 MIS 31.7 36.5 0.85 0.65 0.84 82.3 85.5

Liberia 2011 MIS 27.7 44.7 0.75 0.50 0.79 91.0 67.2

Madagascar 2011 MIS 6.3 8.7 0.96 0.61 0.88 78.4 96.9

Madagascar 2013 MIS 9.0 10.0 0.96 0.72 0.88 78.8 97.6

Malawi 2012 MIS 37.5 42.5 0.82 0.65 0.86 91.8 79.4

Mali 2010 A&P 27.7 43.3 0.84 0.60 0.84 82.6 84.8

Mozambique 2011 DHS 35.1 38.2 0.84 0.64 0.83 80.6 85.8

Nigeria 2010 MIS 42.0 51.5 0.74 0.48 0.75 78.1 71.9

Rwanda 2007-08 IDHS 2.5 2.2 1.00 0.89 0.91 81.0 100.0

Rwanda 2010 DHS 1.4 2.6 0.98 0.54 0.89 78.4 98.6

Senegal 2008-09 MIS 5.7 11.8 0.92 0.56 0.88 82.7 93.2

Senegal 2010-11 DHS 2.9 2.7 0.96 0.44 0.71 43.3 98.3

Tanzania 2011-12 AIS/MIS 4.1 9.2 0.93 0.46 0.85 76.1 93.4

Uganda 2009 MIS 44.7 54.9 0.82 0.65 0.83 92.2 74.1
18

Figure 4. Prevalence of malaria parasitemia in children 6-59 months as measured by A) microscopy 

and by B) RDT in national surveys.

Note: Data from the most recent survey is presented in countries with more than one national survey 

(Liberia, Madagascar, Rwanda, and Senegal) 

The maps in Figure 4 show microscopy (map A) and RDT (map B) estimates of malaria parasitemia for the 

study populations in each survey. The darker colors indicate higher malaria prevalence. The higher RDT 

estimates are evident comparing map B to map A, especially in West Africa. 

A scatter-plot of national microscopy estimates versus national RDT estimates among the study population 

shows fairly strong agreement between the two measures with an R2 of 0.89 (Figure 5). The best-fit line is 

above the equality line, indicating that RDT results reported higher prevalence than did microscopy results. 

The slope of the best-fit line is greater than 1.0 suggesting that the difference between the RDT and 

microscopy results is larger in countries with higher malaria prevalence. 
19

Figure 5. Prevalence of malaria parasitemia in children 6-59 months as measured by RDT and by 

microscopy in national surveys

Note: Small dotted line is the equity line (microscopy prevalence = RDT prevalence) and the larger dotted 

line is the regression line whose equation is specified. 

Another set of scatter-plots showing national malaria prevalence estimates from microscopy versus RDT 

among the study population were made, stratified by level of malaria parasitemia (<30%, ≥30%) (Figure 

6). The Malaria Atlas Project uses a cut-off of 40% Plasmodium falciparum prevalence rate in children 2-

10 years of age to define high transmission areas. Thirty percent prevalence was used as a cut-off in this 

analysis as too few of the surveys included in these analyses had parasitemia prevalence greater than 40%. 

Results show that the correlation between microscopy and RDT results is higher in high malaria prevalence 

countries than in low malaria prevalence countries (low: R2 = 0.83, high: R2 = 0.95). The slope of the bestfit line in low prevalence surveys is positive (m=1.5) indicating that the difference between prevalence 

estimates measured by RDT and microscopy increases as the prevalence increases, with RDT values being 

higher than microscopy estimates. In high prevalence surveys, the slope of the best-fit line is positive but 

very close to one (m=1.05) indicating that the linear correlation between the two diagnostic tests is very 

high. RDT prevalence estimates are higher than microscopy estimates in both low prevalence and high 

prevlance surveys with the exception of Benin 2011 DHS. It should be noted that these results are sensitive 

to the prevalence cut-off chosen, especially since four surveys in the mid-prevalence range have very 

discordant results (Cote d’Ivoire 2011-12 DHS, Ghana 2011 MICS, Liberia 2011 MIS, and Mali 2009 

A&P). 

y = 1.0957x + 4.2678

R2 = 0.8915

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70

Prevalence via RDT result

Prevalence via microscopy result
20

Figure 6. Prevalence of malaria parasitemia in children 6-59 months as measured by RDT and by 

microscopy in: A) national surveys with <30% malaria parasitemia and B) national surveys with 

≥30% malaria parasitemia

Note: Small dotted line is the equity line (microscopy prevalence = RDT prevalence) and the larger dotted 

line is the regression line whose equation is specified. 

Interrater reliability measures such as the observed percent agreement and the Cohen’s Kappa coefficient 

comparing results of the two diagnostic tests are summarized in Figure 7 and Table 6. The observed percent 

agreement between the tests ranged from a low of 65.5% in Cote d’Ivoire 2011-12 to a high of 99.5% in 

Rwanda 2007-08. Cohen’s Kappa coefficient adjusts the observed agreement by a level of agreement that 

would be expected based on chance and is therefore lower than the observed agreement, ranging from 26% 

in Cote d’Ivoire 2011-12 to 89% in Rwanda 2007-08. Kappa statistics are affected by baseline prevalence 

levels which accounts for the greater variation in values as compared to the observed percent agreement. 

y = 1.4581x + 0.6527

R2 = 0.8323

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30

Prevalence via RDT

Prevalence via microscopy A

y = 1.0542x + 3.6004

R2 = 0.946

0

10

20

30

40

50

60

70

80

30 40 50 60 70

Prevalence via RDT

Prevalence via microscopy B
21

Figure 7. Interrater reliability measures (percent agreement and Cohen’s Kappa coefficient) 

between microscopy and RDT prevalence in children 6-59 months of age from national surveys 

ROC analyses produced values of the area under the curve (AUC) for each survey which ranged from 65% 

in Benin 2011 to 93% in Kenya 2010. The AUC is a product of both the sensitivity and the specificity of 

the RDT test for detecting the same result as the microscopy test (gold standard). Figure 8 presents surveys 

in which the specificity is higher than the sensitivity on the left hand side and surveys in which the 

sensitivity is higher than the specificity on the right hand side. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Agreement measure (%)

% Agreement Kappa
22

Figure 8. AUC from ROC analysis: Sensitivity and specificity of RDT tests as compared to 

microscopy for diagnosing malaria parasitemia in children 6-59 months from national surveys 

 

Note: Left side bars represent countries in which specificity is higher than sensitivity and right side bars 

represent countries in which sensitivity is higher than specifcity. 

3.1.2 Stratified Analyses: Recent Fever 

The prevalence of fever in children in the study population varied from 9% in Benin 2011 DHS to 55% in 

the Liberia 2011 MIS (Figure 9). 

0

10

20

30

40

50

60

70

80

90

100

Benin 2011-12 DHS

Senegal 2010-11 DHS

Guinea 2012 DHS

Mozambique 2011 DHS

Mali 2010 A&P

Liberia 2009 MIS

Tanzania 2011-12 AIS/MIS

Madagascar 2011 MIS

Senegal 2008-09 MIS

Madagascar 2013 MIS

Rwanda 2010 DHS

Rwanda 2007-08 IDHS

Burundi 2012 MIS

Kenya 2010 MIS

Burkina Faso 2010 DHS

Cote d'Ivoire 2011-12

Nigeria 2010 MIS

Ghana 2011 MICS

Liberia 2011 MIS

Uganda 2009 MIS

Malawi 2012 MISAngola 2011 MIS

Sensitivity>Specificity Sensitivity<Specificity

Percent

ROC Sensitivity Specificity
23

Figure 9. Fever prevalence in children 6-59 months of age with both RDT and microscopy results 

available from national surveys 

Malaria parasitemia prevalence and agreement statistics were generated for children who were reported to 

have had fever in the two weeks preceding the interview and for those without (Table 7, Figure 12). In 

almost all surveys, microscopy prevalence estimates were higher in children who reported a recent fever 

than in those who did not (Figure 10). Burkina Faso 2011-12 DHS, Liberia 2009 MIS and Liberia 2011 

MIS are the exceptions. In all cases, RDT prevalence estimates were higher in children who reported a 

recent fever than in those who did not (Figure 11), and the difference between the RDT prevalence estimate 

and the microscopy prevalence estimate was greater in children with recent fever than in those without in 

all surveys (Table 7, Figure 12). 

0

10

20

30

40

50

60

Fever prevalence (%)
24

Figure 10. Malaria parasitemia prevalence as measured by microscopy in children 6-59 months of 

age with and without fever in the past two weeks 

Note: Sorted by difference in microscopy prevalence with higher prevalence in the non-fever strata on the 

left to higher prevalence in the fever strata on the right. 

0

2

4

6

8

10

12

14

16

0

10

20

30

40

50

60

70

80

Burkina Faso 2010 DHS

Liberia 2009 MISLiberia 2011 MIS

Senegal 2010-11 DHS

Benin 2011-12 DHS

Rwanda 2010 DHS

Rwanda 2007-08 IDHS

Cote d'Ivoire 2011-12

Angola 2011 MIS

Kenya 2010 MIS

Senegal 2008-09 MIS

Tanzania 2011-12 AIS/MIS

Madagascar 2011 MISMadagascar 2013 MIS

Nigeria 2010 MIS

Mozambique 2011 DHS

Guinea 2012 DHS

Ghana 2011 MICS

Uganda 2009 MIS

Malawi 2012 MIS

Burundi 2012 MIS

Diffrence in prevalence

Prevalence by microscopy

No Fever Fever Difference
25

Figure 11. Malaria parasitemia prevalence as measured by RDT in children 6-59 months of age 

with and without fever in the past two weeks 

Note: Sorted by difference in RDT prevalence between no fever and fever strata, from smaller to larger, 

left to right. 

0

5

10

15

20

25

30

0

10

20

30

40

50

60

70

80

Difference in Prevalence

Prevalence by RDT

No Fever Fever Difference
26

Table 7. Summary of malaria parasitemia prevalence in children 6-59 months of age and agreement statistics f

stratified by recent fever status 

Microscopy 

Prevalence RDT Prevalence Difference % Agreement Kappa 

No 

Fever Fever

No 

Fever Fever

No 

Fever Fever

No 

Fever Fever

No 

Fever Fev

Angola 2011 MIS 8.4 11.1 10.5 17.5 2.1 6.4 94.0 91.4 0.64 0.

Benin 2011-12 DHS 27.8 28.4 23.1 31.2 -4.7 2.8 72.4 72.2 0.61 0.

Burkina Faso 2010 DHS 66.9 62.4 71.8 72.8 4.9 10.4 76.5 73.8 0.44 0.4

Burundi 2012 MIS 10.3 24.8 12.2 33.5 1.9 8.7 95.9 88.3 0.79 0.

Cote d’Ivoire 2011-12 16.9 18.8 40.9 42.2 24.0 23.4 67.0 62.3 0.27 0.2

Ghana 2011 MICS 25.4 35.5 42.2 67.7 16.8 32.2 77.7 65.1 0.56 0.

Guinea 2012 DHS 41.9 50.4 40.5 59.7 -1.4 9.3 76.1 71.6 0.51 0.4

Kenya 2010 MIS 7.0 9.9 10.6 18.2 3.6 8.3 94.6 89.6 0.71 0.

Liberia 2009 MIS 31.1 28.6 32.9 36.5 1.8 7.9 85.2 84.4 0.66 0.

Liberia 2011 MIS 28.1 26.3 41.5 47.4 13.4 21.1 77.7 72.7 0.55 0.4

Madagascar 2011 MIS 5.1 11.3 6.7 18.0 1.6 6.7 96.7 93.6 0.58 0.

Madagascar 2013 MIS 7.6 14.5 8.1 18.8 0.5 4.3 97.1 93.4 0.73 0.

Malawi 2012 MIS 23.0 36.2 32.8 61.1 9.8 24.9 86.8 74.0 0.64 0.

Mozambique 2011 DHS 34.2 41.9 36.3 48.9 2.1 7.0 84.6 81.6 0.64 0.

Nigeria 2010 MIS 38.6 45.8 45.8 58.9 7.2 13.1 76.1 71.5 0.50 0.4

Rwanda 2007-08 IDHS 2.3 3.4 2.0 3.1 -0.3 -0.3 99.5 99.5 0.89 0.

Rwanda 2010 DHS 1.0 1.6 2.0 3.6 1.0 2.0 98.6 97.1 0.52 0.

Senegal 2008-09 MIS 4.5 7.6 11.1 13.4 6.6 5.8 92.9 91.0 0.51 0.

Senegal 2010-11 DHS 2.7 2.8 2.7 2.9 0.0 0.1 96.2 96.6 0.40 0.

Tanzania 2011-12 AIS/MIS 3.3 6.7 6.3 17.4 3.0 10.7 94.6 86.3 0.48 0.4

Uganda 2009 MIS 38.5 50.3 42.6 67.2 4.1 16.9 86.5 77.2 0.73 0.
27

Figure 12. Malaria parasitemia prevalence in children 6-59 months of age with recent fever as 

measured by A) microscopy and B) RDT and in children without recent fever as measured by C) 

microscopy and D) RDT 

Note: Data from the most recent survey is presented in countries with more than one national survey 

(Liberia, Madagascar, Rwanda, and Senegal) 
28

The maps in Figure 12 compare malaria prevalence estimates by diagnostic test and by fever status in 

children 6-59 months of age. Darker colors indicated higher prevalence estiamtes. Comparing map A and 

map B shows spatially the countries in which microscopy estimates were lower than RDT estimates for 

malaria prevalence in children with fever. Map C compared to Map D shows the difference in microscopy 

and RDT estimates for malaria prevalence in children without recent fever. Looking at Map A and Map C, 

comparisons of microscopy estimates of malaria prevalence in children with and without recent fever is 

possible. A similar comparison can be made for RDT estimated malaria prevalence using Map B and Map 

D. 

A scatter-plot of national microscopy estimates versus national RDT estimates among the study population 

stratified by recent fever status shows that the diagnostic tests were more similar among children without 

recent fever than among those with recent fever (Figure 13). The best-fit lines in the figures show that RDT 

results reported higher prevalence than did microscopy in all children (i.e.the best-fit lines are above the 

equality line), but that the difference was more extreme in children who were recently febrile. At a national 

level, the correlation between microscopy prevalence and RDT prevalence was similarly strong with R2

estimates of 0.89 for both. 

Figure 13. Malaria parasitemia prevalence in children 6-59 months in national surveys as 

estimated by RDT and by microscopy stratified by recent fever status 

Note: Thin dotted line represents equality in RDT and microscopy results 

Observed percent agreement was lower in all surveys in children with a recent fever than in those without 

in all but a few surveys (Table 2, Figure 14). Kappa coefficients were also lower in children with a recent 

fever than in those without (Table 2, Figure 15). The largest differences in kappa statistics between children 

with recent fever and those without were in Benin 2011-12 DHS (0.606 vs. 0.366), and in Ghana 2011 

MICS (0.564 vs. 0.350) and Uganda 2009 MIS (0.7252 vs. 0.544). Patterns in AUC from ROC analysis are 

more difficult to determine. The largest differences between children with and without recent fever were 

observed in the Ghana 2011 MICS and the Uganda 2009 MIS (Figure 16). The dotted lines in Figures 14-

16 show the difference between the various agreement measures in children without recent fever compared 

to those who had fever within the past two weeks. 

y = 1.0439x + 3.7792

R2 = 0.8852

y = 1.2071x + 5.4463

R2 = 0.8885

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

Prevalence via RDT

Prevalence via microscopy

No Fever Fever
29

Figure 14. Percent agreement between estimates of malaria parasitemia prevalence as measured 

by RDT and by microscopy in children 6-59 months who had recent fever compared to those who 

did not and the difference in percent agreement between the two strata 

Figure 15. Kappa statistics between estimates of malaria parasitemia prevalence as measured by 

RDT and by microscopy in children 6-59 months who had recent fever compared to those who did 

not and the difference in percent agreement between the two strata 

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Difference in Agreement

% agreement

No Fever Fever Difference

0

0.05

0.1

0.15

0.2

0.25

0.3

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Difference in Kappa

Kappa

No Fever Fever Difference
30

Figure 16. Area under the curve from ROC analysis between estimates of malaria parasitemia 

prevalence as measured by RDT and by microscopy in children 6-59 months who had recent fever 

compared to those who did not and the difference in percent agreement between the two strata

3.1.3 Stratified Analysis: Antimalarial Treatment 

The prevalence of antimalarial treatment of fever in children in the study population varied from 6% in 

Rwanda in the 2007/08 Interim DHS to 67% in the Liberia 2009 MIS (Figure 17). 

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Difference in AUC

Area under the curve

No Fever Fever Difference
31

Figure 17. Frequency of antimalarial treamtent for recent fever in children 6-59 months of age with 

both RDT and microscopy results available from national surveys 

Malaria parasitemia prevalence and agreement statistics were generated for children who had fever in the 

two weeks preceding the interview who took any antimalarial and for those who did not (Table 8). In almost 

all surveys, microscopy prevalence estimates were higher in children with a recent fever who did not report 

treatment with an antimalarial compared to those who did report treatment (Figure 18, Table 8). Madagascar 

2013 MIS and 2011 MIS, Burundi 2012 MIS, Mozambique 2011 DHS and Rwanda 2007-08 Interim DHS 

are the exceptions. RDT prevalence estimates varied substantially between children with a recent fever who 

reported antimalarial treatment and those who did not (Figure 19, Table 8), with some surveys reporting 

higher prevalence in the treated children and others with higher prevalence in the untreated children. 

0

10

20

30

40

50

60

70

80

Antimalarial treatment (%)
32

Figure 18. Malaria parasitemia prevalence as measured by microscopy in children 6-59 months of 

age with fever in the past two weeks by antimalarial treatment status 

Figure 19. Malaria parasitemia prevalence as measured by RDT in children 6-59 months of age with 

fever in the past two weeks by antimalarial treatment status 

0

5

10

15

20

25

30

0

10

20

30

40

50

60

70

80

Difference in Prevalence

Prevalence by microscopy

No Treatment Treatment Difference

0

5

10

15

20

25

30

0

10

20

30

40

50

60

70

80

Difference in Prevalence

Prevalence via RDT

No Treatment Treatment Difference
33

Table 8. Summary of malaria parasitemia prevalence in children 6-59 months of age and agreement statistics from natio

by recent treatment with antimalarials 

Microscopy 

Prevalence RDT Prevalence Difference % Agreement Kappa 

No 

TreatmentTreatment

No 

TreatmentTreatment

No 

TreatmentTreatment

No 

TreatmentTreatment

No 

TreatmentT

Angola 2011 MIS 12.1 8.3 18.6 14.4 6.5 6.1 91.6 91.0 0.67 

Benin 2011-12 DHS 32.4 20.8 40.1 14.7 7.7 -6.1 70.4 75.8 0.38 

Burkina Faso 2010 DHS 66.9 54.7 73.8 71.1 6.9 16.4 76.2 69.6 0.42 

Burundi 2012 MIS 23.5 28.7 27.2 51.8 3.7 23.1 92.6 75.6 0.80 

Cote d’Ivoire 2011-12 19.3 16.5 42.3 42.1 23.0 25.6 62.5 61.3 0.25 

Ghana 2011 MICS 45.0 18.8 70.5 62.6 25.5 43.8 72.0 51.7 0.43 

Guinea 2012 DHS 52.0 46.6 61.4 55.7 9.4 9.1 72.4 69.4 0.45 

Kenya 2010 MIS 12.5 4.9 17.8 18.9 5.3 14.0 92.8 83.4 0.74 

Liberia 2009 MIS 31.5 27.2 38.0 35.7 6.5 8.5 85.2 84.0 0.68 

Liberia 2011 MIS 29.4 24.1 48.7 46.5 19.3 22.4 73.5 72.2 0.48 

Madagascar 2011 MIS 11.0 12.4 15.3 28.5 4.3 16.1 94.6 89.4 0.67 

Madagascar 2013 MIS 13.6 22.5 18.5 21.7 4.9 -0.8 93.7 90.2 0.69 

Malawi 2012 MIS 43.6 21.0 61.2 60.9 17.6 39.9 81.5 57.9 0.64 

Mozambique 2011 DHS 40.7 44.6 46.7 53.8 6.0 9.2 84.2 75.6 0.66 

Nigeria 2010 MIS 49.5 42.0 61.9 55.8 12.4 13.8 72.8 70.1 0.46 

Rwanda 2007-08 IDHS 3.4 4.0 3.0 4.3 -0.4 0.3 99.4 100.0 0.91 

Rwanda 2010 DHS 1.7 1.4 2.4 12.0 0.7 10.6 98.2 89.2 0.49 

Senegal 2008-09 MIS 7.8 6.0 13.5 11.5 5.7 5.5 91.0 91.3 0.58 

Senegal 2010-11 DHS 3.0 1.0 2.9 3.2 -0.1 2.2 96.8 94.6 0.52 

Tanzania 2011-12 

AIS/MIS

7.7 5.9 15.8 18.7 8.1 12.8 90.2 82.8 0.53 

Uganda 2009 MIS 53.4 48.4 63.1 69.8 9.7 21.4 84.5 72.8 0.68 
34

Figure 20. Malaria parasitemia prevalence in children 6-59 months of age with fever in the past two 

weeks who took antimalarials as measured by A) microscopy and B) RDT; and in children who did 

not take antimalarials as measured by C) microscopy and D) RDT 

Note: Data from the most recent survey is presented in countries with more than one national survey 

(Liberia, Madagascar, Rwanda, and Senegal) 
35

The maps in Figure 20 compare malaria prevalence estimates by diagnostic test and by antimalarial 

treatment history in children 6-59 months of age with recent fever. Darker colors indicated higher 

prevalence estiamtes. Comparing map A and map B shows spatially the countries in which microscopy 

estimates were lower than RDT estimates for malaria prevalence in children with recent fever who took 

antimalarials. Map C compared to Map D shows the difference in microscopy and RDT estimates for 

malaria prevalence in children with recent fever who did not take antimalarials. Looking at Map A and Map 

C, comparisons of microscopy estimates of malaria prevalence in children with recent fever who took 

antimalarials and those who did not is possible. A similar comparison can be made for RDT estimated 

malaria prevalence using Map B and Map D. 

A scatter-plot of national malaria parasitemia estimates as diagnosed by microscopy versus by RDT among 

the study population stratified by antimalarial treatment status among those with recent fever shows that 

the diagnostic tests were more similar among children who had not received antimalarials than among those 

with who did (Figure 21). The best-fit lines in the figures show that RDT results reported higher malaria 

parasitemia prevalence than did microscopy in all children (shown by best-fit lines being above the equality 

line), but that the difference was more extreme in children who recently took antimalarials for fever than in 

those who did not. The correlation between microscopy prevalence and RDT prevalence was lower in 

treated children than in untreated children at the national level (R2 =0.71 vs. R2 =0.93). 

Figure 21. Malaria parasitemia prevalence in children 6-59 months with recent fever in national 

surveys as estimated by RDT and by microscopy stratified by antimalarial treatment status 

Note: Thin dotted line represents equality in RDT and microscopy results 

Observed percent agreement was lower in all but three surveys in children with a recent fever who took 

antimalarial treatment than in those who did not (Figure 22, Table 8): Benin 2011-12 DHS, Rwanda 2007-

08 IDHS, and Senegal 2008-09 MIS. Kappa statistics were also lower in children with a recent fever who 

took antimalarial treatment than in those who did not in all but Senegal 2008-09 MIS and Rwanda 2007-08 

IDHS (Figure 23, Table 8). The largest differences in Kappa statistics between children with recent fever 

y = 1.1668x + 4.251

R2 = 0.9289

y = 1.1286x + 11.179

R2 = 0.7073

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

Prevalence via RDT

Prevalence via microscopy

No treatment Treatment
36

who took antimalarials and those who did not were observed in Burundi 2012 MIS (0.29), Rwanda 2010 

DHS (0.31), Malawi 2012 MIS (0.39) and Kenya 2010 MIS (0.39). Patterns in AUC from ROC analysis 

are more difficult to determine (Figure 24, Table 8). The largest differences between children with recent 

fever who took antimalarials and those who did not were observed in the Senegal 2010-11 DHS (0.22) and 

the Rwanda 2010 DHS (0.14); however, in this case the treated children had higher AUC than the untreated 

children. In the majority of surveys, AUC was larger in the untreated strata than in the treated strata, with 

the greatest differences in Malawi 2012 MIS (0.13) and Burundi 2012 MIS (0.11). 

Figure 22. Percent agreement between estimates of malaria parasitemia prevalence as measured 

by RDT and by microscopy in children 6-59 months with recent fever who reported antimalarial 

treatment compared to those who did not and the difference in percent agreement between the two 

strata 

0

5

10

15

20

25

0

10

20

30

40

50

60

70

80

90

100

Difference in % Agreement

% Agreement

No Treatment Treatment Difference
37

Figure 23. Kappa statistics of estimates of malaria parasitemia prevalence as measured by RDT and 

by microscopy in children 6-59 months with recent fever who reported antimalarial treatment 

compared to those who did not and the difference in Kappa statistics between the two strata 

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0

0.1

0.2

0.3

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0.5

0.6

0.7

0.8

0.9

1

Difference in Kappa

Kappa

No Treatment Treatment Difference
38

Figure 24. Area Under the Curve from ROC analysis of estimates of malaria parasitemia prevalence 

as measured by RDT and by microscopy (gold standard) in children 6-59 months with recent fever 

who reported antimalarial treatment compared to those who did not and the difference in AUC 

between the two strata 

0

0.05

0.1

0.15

0.2

0.25

0

0.1

0.2

0.3

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1

Difference in AUC

Area Under the Curve

No Treatment Treatment Difference
39

3.1.4 Stratified Analysis: WHO regional trends 

Regional differences in agreement between diagnostic tests were also examined. Each survey was 

categorized by WHO region (WHO 2014). In this analysis surveys from the Central (2), the East (9) and 

the West (11) regions of Sub-Saharan Africa (SSA) were included. Due to the small number of Central 

African surveys the Central and East African surveys were combined. Figure 25 shows the correlation 

between microscopy results and RDT results by region. The diagnostic test results from the Central and 

East regions were highly correletated (R2 = 0.96), whereas the two tests were less strongly correleted in the 

West African surveys (R2 = 0.80). 

Figure 25. Malaria parasitemia prevalence as estimated by RDT and microscopy by region of survey 

within Sub-Saharan Africa 

3.1.5 Stratified Analysis: subnational regional trends 

Although the datasets used for these analyses come from nationally-representative surveys, most are 

sampled to produce representative estimates at the regional level as well. Figure 26 presents a visual 

depiction of the difference between RDT and microscopy malaria prevalence estimates at the regional level. 

As regional sample sizes are smaller than national samples, the estimates of malaria prevalence are less 

precise; however the map highlights some interesting trends. In general, the difference in RDT and 

microscopy estimates is highest in West Africa, in areas along the coast. Large differences are also seen in 

one coastal region in Tanzania. Similarly, the regions in which microscopy estimates of malaria prevalence 

are greater than RDT estimates are focused in West Africa. It should be noted that these are absolute 

differences and are thus likely to be smaller in areas with low malaria prevalence. This map helps to 

highlight areas in which further quality assurance of test results may be necessary. 

y = 1.2038x + 1.5266

R2 = 0.9611 y = 0.9666x + 9.2602

R2 = 0.8039

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70

Prevalence by RDT

Prevalence by microscopy

East & Central West
40

Figure 26. Difference in malaria parasitemia prevalence as estimated by RDT and microscopy by 

subnational region 
41

3.2 Change over time 

3.2.1 National-level change 

Four of the countries included in these analyses have had two surveys in which both microscopy and RDT 

have been used to test for malaria parasitemia. It is therefore possible to look at trends over time in these 

four countries. Figure 27 compares the microscopy estimates and the RDT estimates of malaria prevalence 

in two surveys from Liberia, Madagascar, Rwanda and Senegal. 

Figure 27. Change in malaria parasitemia prevalence over time as estimated by RDT and microscopy 

These trends are difficult to interpret. In Senegal, it appears that malaria parasitemia has declined 

significantly between the two surveys (despite a very short time period between the two). However, these 

two surveys were conducted during different periods in the malaria transmission cycle; the 2008-09 MIS 

was fielded between December and January whereas the 2010 DHS was conducted from October to April. 

In Liberia, different conclusions can be drawn depending on whether RDT or microscopy data are used to 

estimate malaria parasitemia. From RDT results it appears that parasitemia prevalence has increased 

significantly between 2009 and 2011; however microscopy results do not indicate any significant change. 

The Liberia surveys were also conducted during different periods, with the 2009 survey occuring between 

December and March and the 2011 survey between September and December. No change in parasitemia is 

apparent in Madagascar, where the two surveys were fielded during the same months (April-June and 

March-June). Rwanda also did not show any significant changes in parasitemia between the two surveys; 

however, the malaria prevalence is so low in Rwanda that a very large sample size would be required in 

0

10

20

30

40

50

60

2009 2011 2011 2013 2007-08 2010 2008-09 2010

Liberia Madagascar Rwanda Senegal

RDT Microscopy
42

order to detect meaningful change. For this reason the Rwanda 2013 MIS did not collect blood for malaria 

parasitemia testing. Another factor that may influence the difference in prevalence estimates is the specific 

RDT used in each survey. In both Liberia and both Senegal surveys HRP2-based tests were used. In 

Madagascar, an HRP2-based test was used in 2011 and a combo test in 2013. In Rwanda an LDH-based 

test was used in 2007-08 followed by an HRP2 test in 2010. 

3.2.2 Regional-level change 

Looking at patterns in regional RDT and microscopy test results in one country over the course of two 

surveys permits a more nuanced examination. Comparing the 2009 MIS and the 2011 MIS in Liberia we 

see that patterns in test results varied significantly by region (Figure 28). Malaria parasitemia prevalence 

as estimated by RDT increased significantly between surveys in the South regions but nowhere else. 

Significant increases in microscopy prevalence were only seen in South eastern B, whereas declines in 

microscopy prevalence were seen in Monrovia. A much greater discrepancy in test results occurred in 2011 

as compared to 2009; none of the regions had RDT and microscopy prevalence results that differed 

significantly in 2009 but all regions except Monrovia had significant discrepancies in 2011. 

Figure 28. Regional RDT and microscopy estimates of malaria prevalence in Liberia from 2009 and 

2011 MIS 

0

10

20

30

40

50

60

70

80

Monrovia North western South central South eastern

A

South eastern

B

North central Total

2009 RDT 2009 Microscopy 2011 RDT 2011 Microscopy
43

3.3 Pooled Analyses 

3.3.1 Summary statistics 

Survey data were pooled and summary statistics were run on the pooled sample. This included estimates of 

malaria parasitemia prevalence as measured by RDT and by microscopy as well as agreement measures, 

such as percent agreement and kappa statistics. Assuming microscopy to be the gold standard, senstivity, 

specificity and positive predictive values were calculated as was the AUC from ROC analysis. Results are 

summarized in Table 9. 

Table 9. Pooled, multivariable regression model of discordant test results in children age 6-59 months 

who were tested for malaria via microscopy and RDT 

Survey

Microscopy 

Prevalence

% Agreement Kappa AUCSensitivitySpecificity PPV NPV

Fever 27% 81.9 0.59 0.84 88.0% 79.7% 61.1% 94.8%

No Fever 19% 87.9 0.65 0.85 80.8% 89.6% 65.2% 95.1%

Antimalarials 28% 75.4 0.49 0.79 87.9% 70.5% 53.7% 93.7%

No antimalarials 26% 85.4 0.65 0.86 88.1% 84.4% 66.3% 95.3%

Total 22% 86.2 0.63 0.85 83.2% 87.1% 64.0% 95.0%

The pooled dataset showed malaria prevalence as measured by microscopy to be higher in children with 

recent fever compared to children without recent fever (27% vs. 19%) but similar in children who had taken 

antimalarials compared to those who had not (28% vs. 26%). Test agreement was lower in children who 

had a recent fever than in those who did not (percent agreement 82% vs. 88%; kappa 0.59 vs. 0.65), but 

AUC values were similar (0.84 vs. 0.85). Agreement was also lower in children who took antimalarials 

than in children who did not (percent agreement 75% vs. 85%; Kappa 0.49 vs 0.65; AUC 0.79 vs. 0.86). 

RDTs were found to be more sensitive to true infection as determined via microscopy in children with 

recent fever than in those without (88% vs. 81%) but less specific (80% vs. 90%). The positive predictive 

value of RDTs was 61% in children with recent fever compared to 65% in children without. The nagtive 

predictive value was similar for both groups (95%). Interestingly the sensitivity of the RDT results 

compared to microscopy for children who had recentely taken antimalarials and those who had not was 

similar (88% for both) but the specificity was significantly lower in the children who had recently taken 

antimalarials (71% vs. 84%). The positive predictive value was lower in this group as well (54% vs. 66%) 

although the negative predictive value was similar (94% vs. 95%). 

3.3.2 Multivariable regression 

The pooled dataset was also used to run an individual-level regression model with test discordance as an 

outcome variable (Table 10). The model revealed that discordant test results were more likely in children 

from rural households than those from urban households (OR = 1.2, p=0.001). The odds of a child having 

discordant test results were significantly lower in children from the highest wealth quintile compared to the 

lowest (OR = 0.46, p<0.001). Children 24-59 months of age had higher odds of discordant test results than 

did younger children (OR = 1.2, p<0.001). The sex of the children did not have a significant effect on 

discordant microscopy and RDT test results. Children who had a fever in the two weeks immediately 

preceding the survey were significantly more likely to have discordant test results than were children with 

no recent history of fever (OR=1.5, p<0.001).
44

Table 10. Pooled, multivariable regression model of discordant test results in children age 6-59

months who were tested for malaria via microscopy and RDT 

Odds Ratio LCI UCI p-value

Residence

Urban ref

Rural 1.20 1.08 1.35 0.001

Wealth Quintile

Poorest ref

Poorer 0.86 0.80 0.93 <0.001

Middle 0.78 0.71 0.85 <0.001

Richer 0.72 0.65 0.79 <0.001

Richest 0.46 0.40 0.53 <0.001

Age

6-23 months ref

24-59 months 1.20 1.14 1.27 <0.001

Sex

Male ref

Female 0.95 0.91 1.01 0.084

Recent Fever Status

No ref

Yes 1.54 1.45 1.63 <0.001

Total N 78388

* Adjusted for a dummy variable for survey. LCI = lower confidence interval; UCI = upper confidence 

interval; N = weighted sample size. Mali is not included as questions permitting calculation of a wealth 

index were not asked.

Another model was run for the subset of children 6-59 months of age who were tested for malaria 

parasitemia by both diagnostic tests and who had reported a history of fever within the two weeks preceding 

the survey (Table 11). This was done in order to look at the affect of antimalarial treatment on discordance 

of test restults. Similar results were found in this model as in the previously described model; discordant 

test results were more likely in children from rural households than those from urban households although 

this affect was only marginally significant (OR = 1.2, p=0.075). The odds of a child having discordant test 

results were significantly lower in children from the highest wealth quintile compared to the lowest (OR = 

0.55, p<0.001). Children 24-59 months of age had higher odds of discordant test results than did younger 

children (OR = 1.2, p<0.001). The sex of the children did not have a significant effect on discordant 

microscopy and RDT test results. Children who received antimalarial treatment in the two weeks 

immediately preceding the survey were significantly more likely to have discordant test results than were 

children with no recent history of fever (OR=1.6, p<0.001).
45

Table 11. Pooled, multivariable regression model of discordant test results in children age 6-59 

months who were tested for malaria via microscopy and RDT. 

Odds Ratio LCI UCI p-value

Residence

Urban ref

Rural 1.16 0.98 1.38 0.075

Wealth Quintile

Poorest ref

Poorer 0.89 0.78 1.00 0.059

Middle 0.83 0.72 0.96 0.015

Richer 0.80 0.69 0.95 0.008

Richest 0.55 0.43 0.69 <0.001

Age

6-23 months ref

24-59 months 1.21 1.10 1.33 <0.001

Sex

Male ref

Female 0.96 0.88 1.05 0.378

Antimalarial Treatment Status

No ref

Yes 1.57 1.42 1.73 <0.001

Total N 21323
46
47

4 Discussion 

This study clarifies the challenges to using estimates of malaria parasitemia prevalence from national 

surveys for demonstrating impact and identifies factors related to discordant RDT and microscopy test 

results. 

The choice of RDT has implications for levels of expected agreement. HRP2-based RDTs detect residual 

antigenemia and can produce false positive results after effective antimalarial treatment. Several studies 

have shown HRP2 RDTs to overestimate malaria parasite prevalence by 1.5% to 10% (Endeshaw et al. 

2008, Keating et al. 2009, Hawkes et al. 2014). Interpretation of these estimates as well as PPV, NPV, AUC 

and other such agreement statistics depends on the chosen gold standard. While microscopy can be the 

more sensitive and specific of the two tests given skilled microscopists, good equipment and high quality 

smears, many studies have found the skills of local microscopists under field conditions to be varied or

subpar (Stow, Torrens, and Walker 1999, Wongsrichanalai et al. 2007, Maguire et al. 2006). Using 

potentially inaccurate test results as gold standards makes interpretation of discordance and other measures 

of agreement challenging to interpret. 

Agreement between microscopy and RDT results varied by prevalence of infection, by recent fever status, 

and by history of antimalarial treatment. Low prevalence settings are challenging environments for 

detection of Plasmodium spp. parasites for both microscopy and RDT tests. Other tools, such as PCR or 

LAMP may be more useful in these settings (Aydin-Schmidt et al. 2014). Recent fever is a non-specific 

symptom of malaria infection. Thus children reporting fever would be more likely than those without fever 

to have suffered from malaria in the previous two weeks. The pooled dataset confirmed this with malaria 

prevalence as measured by microscopy to be 27% in children with recent fever and 19% in children without 

recent fever. Presumably, children with malaria are more likely than those without malaria to be given 

antimalarial treatment but this cannot be tested with survey data as questions on antimalarial treatment are 

only asked about children with recent fever. Test agreement was lower in children who took antimalarials 

than in children who did not. This is expected due to the residual antigenicity that can cause positive HRP2-

based RDT test results following successful antimalarial treatment. Aydin-Schmidt and colleagues found 

the median antigen clearance time to be 28 days for HRP2-based RDTs and 7 days for LDH-based RDTs 

(Aydin-Schmidt et al. 2013). Similarly, specificity was found to be affected by time after treatment, with 

HRP2 tests reaching 87% only after 42 days (Aydin-Schmidt et al. 2013). This could explain the relatively 

low specificity values observed in children who had recently taken antimalarials given that the time frame 

for fever and antimalarial treatment captured in the survey data is 14 days and most surveys used HRP2-

based RDTs. 

Similar to our results, in an analysis of malaria parasitemia diagnostic testing in Luangwa District of 

Zambia, RDT discordance was found to be highest among older children, and in those who reported having 

received antimalarial treatment. Regional variation was also observed (Keating et al. 2009) supporting our 

findings that prevalence and discordance varied substantially on a regional scale. Although Keating and 

colleagues interpreted their study results to mean that RDTs should not be used in place of microscopy for 

surveillance in survey settings, they assume microscopy to produce more accurate measures of true 

infection status, an assumption that may not be true in many survey settings. For example, de Oliveira and 

colleagues found RDTs to be more sensitive and specific than microscopy performed by health facility staff 

as part of routine malaria case management activities (sensitivity: 92% vs. 53%; specificity: 97% vs. 77%) 

(de Oliveira et al. 2009). 

Interpreting results in the absence of a true gold standard is challenging. To the extent that microscopy 

results and RDT results agree, the true disease state is more certain. To the extent that the two tests disagree, 

it becomes challenging to decipher which represents the truth. That discordance is affected by many factors 
48

including the type of RDT, the level of malaria transmission, the age of the study subjects, as well as the 

underlying quality of the training and equipment of the personnel further complicates interpretation. In 

countries with two national surveys collecting RDT and microscopy data, conclusions about whether 

significant change in malaria prevalence had occurred over time depended on which test results were used. 

Even at the regional level in Liberia, the change in malaria parasitemia prevalence depended on the 

diagnostic test used. In the four countries examined in this study, the time between surveys was two to three 

years. It is unlikely that substantial change in national malaria prevalence would be expected over this time 

interval. Given these results, the use of malaria parasitemia data from national surveys should be 

reexamined and a goal clearly defined. Perhaps on a global scale, these data can help assess the overall 

level of malaria infection and could be used at the national level to target resources and interventions to 

high risk areas or populations. However, attempts to use these data to measure the impact of malaria control 

programs at the national level should consider the level of uncertainty and discordance in the prevalence 

estimates. 

This study was constrained by some limitations. Although The DHS Program uses standardized tools for 

training and data collection, some differences across surveys could affect the results of this study. For 

example, there was a range of RDTs used, some HRP2-based and some HRP2/LDH combo tests. In 

addition, the quality of microscopy likely varied between countries. Data were collected from surveys 

fielded during different seasons, which could have an effect on prevalence estimates and on levels of fever 

and antimalarial treatment. Ideally, data would be collected during the high malaria transmission season in 

order to avoid bias from seasonal variation. The magnitude of seasonal variation in parasite prevalence 

within a year can be significant, as was shown in a study in Chikhwawa District in Malawi (Roca-Feltrer 

et al. 2010). Finally, the analyses did not consider the effect of level of malaria transmission. Future analyses 

might include measures of malaria transmission indices to determine the effect of malaria endemicity on 

discordance between different malaria diagnostic tests. These types of analyses would likely need to be 

done on a small geographic scale, due to the often heterogeneous endemicity levels of malaria within many 

countries. Future work might focus on developing decision-making tools for program managers who may 

have data collected with a variety of diagnostic tests or during different seasons. 
49

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