CERTIFICATION
This research project of NGOCHE EMMANUEL (MLS/20/263) entitled “comparing
diagnostic performance of histidine rich protein-2 and parasite lactose dehydrogenase in
malaria diagnosis among febrile patients at the Limbe Regional Hospital” submitted to the
Department of Medical Laboratory Sciences, of St Louis University Institute Bonamoussadi,
in partial fulfilment of the requirements of the award of a Higher National Diploma in
Medical Laboratory Sciences is supervised by:
Mr. AMBENE NGOTELO FUNWI
Signature
i
Date
DEDICATION
I dedicate this work to my family, especially to my mother and to all my friends.
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ACKNOWLEDGMENT
I would like to acknowledge my supervisor Mr. Ambene Ngotelo Funwui for constantly
guiding me with precised instructions throughout this proposal writing and for his time
sacrificed. I would also like to appreciate the research department for accurate guide the
provided, the training and preparations they gave me to be able to go through with the
proposal writing. I thank my family for the support they offered me, both financial and moral
during this period. Most especially, I am grateful to GOD Almighty for the strength, guidance
and protection towards me throughout this period of my proposal writing.
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ABSTRACT
Background: Malaria is one of the deadliest infectious diseases and is a leading cause of
death and illness worldwide, with 241 million cases and responsible for about 627 000 deaths
in the year 2020 as reported by WHO, Africa was responsible for 95% of these cases and 96%
of deaths, with Cameroon included. An accurate diagnosis of this infection by either
microscopy or RDT plays a major role in the treatment and eradication plan of the disease.
However, many underdeveloped regions such as face difficulties in diagnosis due to unstable
power (electricity) source and some disagreements on the choice of mRDT to use, based on
their different sensitivities and specificities.
Aim: The aim of this study was to evaluate the analytic performance (sensitivity and
specificity) of HRP-2 and pLDH in the diagnosis of malaria among febrile patients at the
Limbe Regional Hospital.
Methods: This study shall use a descriptive cross-sectional study design, in the town of
Limbe, among 411 febrile patients at the Limbe Regional Hospital. These patients shall the
tested using HRP-2 and PLDH cassettes, and the results shall be entered and analyzed on
Microsoft excel 2019. Sensitivity and specificity of the diagnostic tools shall be calculated in
percentages and represented on tables. Before collecting data, an ethical clearance shall be
gotten from the research department of ST LOUIS HIGHER UNIVERSITY INSTITUTE and
an authorization shall be obtained from the South West Regional Delegation of Public Health.
Also, vital information from participant shall not be included in our study, and their informed
consent shall be obtained before including them in this study.
Results: Out of the 425 participants, microscopy, HRP-2, and PLDG had 31.53%, 25.65%
and 9.41% of positive cases respectively, and HRP-2 had a sensitivity of 70.15%, specificity
of 94.86%, PPV of 86.24%, NPV of 87.34% and efficiency of 87.06% while PLDH had a
sensitivity of 27.61%, specificity of 98.97%, PPV of 92.50%, NPV of 74.81%, and efficiency
of 76.47%.
Keywords: mRDT, HRP-2, PLDH, malaria, febrile, sensitivity, specificity.
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TABLE OF CONTENT
CERTIFICATION ....................................................................................................................... i
ACKNOWLEDGMENT ........................................................................................................... iii
ABSTRACT .............................................................................................................................. iv
LIST OF FIGURES ................................................................................................................... ix
LIST OF TABLES ..................................................................................................................... x
LIST OF ABBREVIATINS ...................................................................................................... xi
CHAPTER ONE
INTRODUCTION
1.1 Background to the study ................................................................................................... 1
1.2 Statement of problem........................................................................................................ 3
1.3 Significance of study ........................................................................................................ 3
1.4 Research question ............................................................................................................. 3
1.4.1 Main research question .............................................................................................. 3
1.4.2 Specific research question.......................................................................................... 4
1.5 Research objectives .......................................................................................................... 4
1.5.1 Main research objective ............................................................................................. 4
1.5.2 Specific research objectives ....................................................................................... 4
1.6 Research scope and delimitations ..................................................................................... 5
1.7 Operational definition of terms and concepts ................................................................... 5
CHAPTER TWO
LITERATURE REVIEW
2.1 An overview on malaria. .................................................................................................. 7
v
2.1.1 Definition of malaria. ................................................................................................. 7
2.1.2 Burden of malaria ...................................................................................................... 8
2.1.4 Life cycle of malaria. ............................................................................................... 11
2.1.5 Signs and symptoms of malaria. .............................................................................. 14
2.1.6 Diagnosis of malaria. ............................................................................................... 15
2.1.7 Management of malaria. .......................................................................................... 19
2.1.8 Complications. ......................................................................................................... 20
2.1.9 Malaria prevention. .................................................................................................. 21
2.2 Diagnostic performance of histidine rich protein-2 in malaria....................................... 22
2.2.1 Sensitivity and specificity of histidine rich protein-2 RDT. .................................... 22
2.3 Diagnostic performance of parasite lactose dehydrogenase in malaria. ......................... 23
2.3.1 Sensitivity and specificity of parasite lactose dehydrogenase RDT ........................ 23
CHAPTER THREE
METHODS AND MATERIALS
3.1 Study area and study setting ........................................................................................... 24
3.2 Study design ................................................................................................................... 24
3.3 Study population ............................................................................................................. 25
3.3.1 Inclusion criteria ...................................................................................................... 25
3.3.2 Exclusion criteria ..................................................................................................... 25
3.4 Sample size ..................................................................................................................... 25
3.5 Sampling procedure ........................................................................................................ 26
3.6 Data collection procedure ............................................................................................... 26
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3.6.1 Study variable .......................................................................................................... 26
3.6.2 Data collection tools ................................................................................................ 27
3.6.3 Pretesting of data collection tool.............................................................................. 27
3.6.4 Data collection procedure ........................................................................................ 27
3.6.5 Data management plan ............................................................................................. 28
3.7 Data analysis plan ........................................................................................................... 28
3.8 Ethical consideration ...................................................................................................... 29
CHEPTER FOUR
RESULTS
4.1 Sociodemographic characteristics of study participants ................................................. 30
4.2 Summary of data ............................................................................................................. 31
4.2.1 The diagnostic performance of HRP-2 .................................................................... 32
4.2.2 The diagnostic performance of PLDH ..................................................................... 33
4.2.3 Malaria prevalence ................................................................................................... 34
CHAPTER FIVE
DISCUSSION CONCLUSION AND RECOMMENDATION
5.1 Discussion ....................................................................................................................... 35
5.2 Conclusion ...................................................................................................................... 38
5.3 Limitations ...................................................................................................................... 38
5.4 Suggestions ..................................................................................................................... 39
REFERENCE ........................................................................................................................... 40
APPENDICES .......................................................................................................................... 57
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INFORM CONSENT FOR PARTICIPANTS ..................................................................... 57
viii
LIST OF FIGURES
Figure 1: life cycle of the malaria causing parasite (CDC, 2020). ........................................... 14
Figure 2: pattern of malaria microscopy (WHO, 2016.). ......................................................... 17
Figure 3: MRDT results interpretation (Mallepaddi et al., 2019) ............................................ 18
Figure 4: Prevalence derived using light microscopy, PLHD (RDT) and HRP-2 (RDT)........ 34
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LIST OF TABLES
Table 1: A table showing the sociodemographic characteristics of study participants ............ 30
Table 2: A table showing a summary of results obtained from the study. ............................... 31
Table 3: A table showing the test results and diagnostic performance of HRP-2. ................... 32
Table 4: A tabular representation of tests results and the diagnostic performance of PLDH
tests cassettes. ........................................................................................................................... 33
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LIST OF ABBREVIATINS
mRDT
malaria rapid diagnostic test
pLDH
parasite lactose dehydrogenase
HRP-2
Histidine rich protein-2
WHO
World Health Organization
IPT
intermittent preventive treatment
ITNs
insecticide treated bed nets
GTS
global technical strategy for malaria
HIV
Human immunodeficiency virus
AIDS
acquired immune deficiency syndrome
LLINs
long lasting insecticidal nets
IRS
indoor residual sprays
SSA
sub-Saharan Africa
NAAT
Nucleic acid amplification test
PCR
Polymerase chain reaction
USD/US$
united states dollars
ACTs
artemisinin-based combination therapies
ARDS
acute respiratory distress syndrome
CDC
center of disease control
PPV
Positive predictive value
NPV
Negative predictive value
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CHAPTER ONE
INTRODUCTION
1.1 Background to the study
Malaria is one of the most deadly infectious diseases and is a leading cause of death and
illness worldwide especially in the tropics and subtropics (Mohammed et al., 2021). Malaria
remains a major public health problem despite the progress on malaria control in the past
decade. The World Health Organization (WHO) in 2019 reported an estimated 228 million
cases and 405,000 deaths related to malaria in the year 2018. African region (especially subSaharan Africa) had 93% of all cases (Makenga et al., 2020). A study conducted by (Payne et
al., 2020) showed that in Cameroon, malaria is responsible for about 40%-45% of medical
consultations, 57% of hospitalization and 40% of mortality among pregnant women and
children less than 5 years of age especially.
Malaria is a major factor for poverty in resource-poor settings, particularly in tropical regions
throughout the world. Some population groups are at higher risk of this disease than others,
and this including infants, children under 5 years old, pregnant women, and patients with
HIV/AIDS as well as non-immune migrants, individuals in endemic regions, mobile
populations and travelers(Abossie et al., 2020). With a primary focus on ensuring that
everyone has access to malaria prevention, diagnosis, and treatment, the WHO and the roll
back malaria global action plan expect having a malaria-free world by the year 2030. Between
2014 and 2016, 13.6 million of the ITNs delivered in SSA. In Cameroon, malaria prevention
techniques based on the use of ITNs and/or IRS have been quite effective in reducing incident
cases of the disease(Tewara et al., 2018).
In the year 2010, the World Health Organization suggested that, patients suspected to have
malaria should be confirmed with microscopy and/or a rapid diagnostic test before treatment
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(Ekeh et al., 2022). More than 20 million LLINs have already been freely distributed to the
populace through a number of programs. More than 80% of households have one net for two
people. It is estimated that between 2000 and 2015, the country's scale-up of treated bed nets
led to a great decline in the prevalence of malaria reported cases from 41% to 12%(AntonioNkondjio et al., 2019). Prior to 2002, light microscopy was only used as a confirmation for
cases of severe malaria and treatment failure. Most cases of malaria in Cameroon were
diagnosed on a presumptive basis. Presumptive diagnosis was restricted to children under the
age of five, pregnant women, and regions with limited access to light microscopy by 2008,
when the national diagnostic strategy was updated. Despite this choice, the target group had
limited access to universal diagnosis because light microscopy needed a competent technician
to operate it as well as a power source. Malaria fast diagnostic test strips were adopted in
2009, but only 52 health districts countrywide had them implemented after two years of
resource mobilization. The national malaria control program recommended at the time that all
suspected cases of malaria be systematically diagnosed before artemisinin-based combination
therapy (Moyeh et al., 2019).
The trustworthiness of these results is reduced by the inherent limits of both light the mRDTs,
despite the fact that numerous studies have been conducted to assess the accuracy of these
mRDTs, with expert microscopy serving as the gold standard in the majority of them. In order
to evaluate the accuracy of these diagnostic approaches, a method that is more sensitive and
focused is required(Moyeh et al., 2019). Therefore, it is crucial to assess the diagnostic
efficacy (specificity and sensitivity) of these malaria RDTs and compare them to widely used
diagnostic techniques like light microscopy.
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1.2 Statement of problem
Although microscopy is the gold standard of malaria diagnosis, some areas of low resources
in Cameroon (rural areas) still face difficulties in implementing this diagnostic method. This
is partly due to the unavailability/unstable power source and the need for a good parasitologist
to identify plasmodium, especially in low parasitemia. Hospitals in these parts of the country
rely principally on RDTs diagnostic method to confirm suspected malaria cases. There are
still some disagreements on the choice of mRTD to use in this diagnosis. Some healthcare
facilities use histamine rich protein-2 (HRP-2) test kits, while others use parasite lactose
dehydrogenase (pLDH) test kit.
1.3 Significance of study
Participants in this study will benefit of free malaria diagnosis and counseling on the
prevention of malaria at an individual, family and community level. This study shall help
healthcare providers choose the right mRDT diagnostic tool for malaria diagnosis, based on
their sensitivity and specificity. At the end of this study, there will be rate of errors in malaria
diagnosis related to the choice of diagnostic tool will be reduced, as this study will provide
evidence-based results as per which diagnostic tool is more accurate in the diagnosis of
malaria. Also, this study will help in the fight against malaria by suggesting an accurate
diagnostic tool. Future researchers might use these results as the standard to compare other
malaria diagnostic tools.
1.4 Research question
1.4.1 Main research question
The main research question of this study was, what are the analytic performances of HRP-2
and pLDH in malaria diagnosis among febrile patients at the Limbe regional Hospital?
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1.4.2 Specific research question
The specific research questions for this study were:
i.
What is the prevalence of malaria amongst febrile patients at the Limbe Regional
Hospital?
ii.
Which diagnostic tool between HRP-2 and pLDH is more sensitive for the diagnosis
of malaria among febrile patients at the Limbe Regional Hospital?
iii.
Which diagnostic tool between HRP-2 and pLDH is more specific for the diagnosis of
malaria among febrile patients at the Limbe Regional Hospital?
iv.
What is the diagnostic tool that has a more predictive value for the diagnosis of
Malaria amongst febrile patients at the Limbe Regional Hospital?
1.5 Research objectives
1.5.1 Main research objective
The aim objective of this study was to evaluate the analytic performance (sensitivity and
specificity) of HRP-2 and pLDH in the diagnosis of malaria among febrile patients at the
Limbe Regional Hospital.
1.5.2 Specific research objectives
The specific objectives of this research are:
i.
To evaluate the prevalence of malaria among febrile patients at the Limbe Regional
Hospital.
ii.
To evaluate the sensitivity of HRP2 and pLDH in diagnosis of malaria among febrile
patients at the Limbe Regional Hospital.
iii.
To evaluate the specificity of HRP-2 and pLDH in the diagnosis of malaria among
febrile patients at the Limbe Regional Hospital.
iv.
To evaluate the predictive values of HRP-2 and pLDH in the diagnosis of malaria.
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1.6 Research scope and delimitations
Although so many aspects can be explored on malaria, this study fucosed on diagnosis,
precisely on the sensitivity and specificity of HRP-2 and pLDH in the diagnosis malaria.
This study could also be conducted in many different areas (Hospitals) in Cameroon.
However, this study was limited to febrile patients at the Limbe Regional Hospital. The study
shall not include asymptomatic patients.
This study was carried out withing a period of one month (from the 02nd of January to the 09th
of February 2023).
1.7 Operational definition of terms and concepts
Malaria: Malaria is an infectious disease caused by the Plasmodium parasite, transmitted to
humans by an infected female Anopheles mosquito through bites.
Sensitivity: Sensitivity is the ability of a diagnostic tool to correctly test a malaria positive
patient as positive (ability to identify a true positive patient).
Specificity: Specificity if the ability of a diagnostic tool to correctly test a malaria negative
patient as negative (true negative).
Light microscopy: This is a diagnostic technique (gold standard for malaria diagnosis),
which involves the visualization of Giemsa-stained parasites in blood samples. The parasitic
morphology can be appreciated and be counted.
Parasitemia: Parasitemia is the presence of parasites (Plasmodium parasites) in blood.
Febrile: febrile means having or showing symptoms of fever.
Histidine protein rich-2: This a protein produced by Plasmodium falciparum species, but is
not produced by other Plasmodium species.
Plasmodium lactose dehydrogenase: It is an enzyme produced by malaria parasites.
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True positive: A true positive test is a malaria RDT test indicating positive, which was
confirmed to be positive by light microscopy.
True negative: A true negative test is a malaria RDT test showing negative, which is
confirmed to be negative by light microscopy.
False positive: A false positive result is a test result showing positive on mRDT, but negative
on light microscopy the gold standard.
False negative: A false negative result in an RDT result showing negative, but is tested to be
positive using light microscopy.
PPV: The positive predictive value of a tool is the proportion participants who test positive
using that tool, and are actually have malaria confirmed by light microscopy (proportion of
true positive cases)
NPV: The negative predictive value of a diagnostic tool is the proportion of people who test
negative using the tool, who are actually negative as confirmed by light microscopy.
Positive test: A positive test is a test which will produce colored bands both on the control
and test zone of the RDT cassettes.
Negative test: A negative test is a test which will produce a colored band on the control
region of the cassette, show no band on the test zone.
Holoendemic: Holoendemic means there is a high prevalence of malaria in Cameroon, and
many people are infected with malaria at childhood, but infected people show little to no
symptoms of malaria at adulthood.
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CHAPTER TWO
LITERATURE REVIEW
2.1 An overview on malaria.
Malaria is an acute febrile illness caused by Plasmodium parasites, which are spread to people
through the bites of infected female Anopheles mosquitoes. The most common parasite
species that causes malaria in humans is Plasmodium falciparum. It is the cause of the most
common and lethal malaria cases in Africa. The most common malaria parasite outside of
sub-Saharan Africa is Plasmodium vivax (WHO, 2022).
The initial signs of malaria, including fever, headache, and chills, can be mild and challenging
to diagnose. They typically show 10 to 15 days after the infecting insect bite. If Plasmodium
falciparum malaria is not treated, it can proceed to severe sickness and death in less than 24
hours in some cases (WHO, 2022).
Nearly half of the world's population was at risk from malaria in 2020. Most of these cases
included; newborns, young children (less than 5 years old), pregnant women, HIV/AIDS
patients, as well as those with low immunity who travel to regions where malaria transmission
is severe, such as migratory workers, and travelers. These group of people also developed
severe symptoms (WHO, 2022a).
2.1.1 Definition of malaria.
Malaria is a life-threatening disease caused by parasites that are transmitted to people through
the bites of infected female Anopheles mosquitoes(WHO, 2022). Malaria can also be defined
as, an infectious disease caused by a protozoan parasite of the genus Plasmodium which
affects primarily infect liver, and then the red blood cells. The disease is transmitted to man
by the female anopheles Mosquito. Malaria is a severe disease caused by parasites of the
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genus Plasmodium, which is transmitted to humans by a bite of an infected female mosquito
of the species Anopheles. Malaria remains one of the leading causes of mortality around the
world. The five Plasmodium species that infect people are Plasmodium falciparum,
Plasmodium ovale, Plasmodium vivax, Plasmodium malariae, and Plasmodium knowlesi.
Plasmodium falciparum is the most virulent species and is responsible for the majority of
malaria infections and related deaths (Njunda et al., 2016).
Taxonomic classification of Plasmodium.
Kingdom: Protista
Subkingdom: Protozoa
Plylum: Protozoa
Class: Telospora
Sub class: Coccidia
Order: Eicoccidia
Genus: Plasmodium
Species: Plasmodium vivax, Plasmodium falciparum, Plasmodium malariae, Plasmodium
ovale. (CDC, 2020)
2.1.2 Burden of malaria
Malaria prevalence and mortality
Over the decade from 2000 to 2019, the number of malaria deaths worldwide decreased
substantially, from 896 000 in 2000 to 562 000 in 2015 and 558 000 in 2019. WHO estimated
that there were 241 million cases of malaria and 627 000 deaths from malaria in the year
2020. The WHO African Region had a disproportionately large share of the burden of malaria
in the world. In 2020, the region was responsible for 95% of malaria cases and 96% of
malaria deaths. In comparison to 2019, there were 69 000 more fatalities and almost 14
million more cases in 2020 Inadequate malaria prevention, diagnosis, and treatment during
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the pandemic were responsible for about two out of three of these excess deaths. The most
vulnerable age group to malaria are children under 5 years old; in 2020, they made up over
80% of all malaria-related fatalities in the WHO African Region (World Health Organization,
2022).
As the majority of victims, the African continent is severely burdened by malaria. The World
Health Organization (WHO) reported more than 90% of malaria cases and death in 2019
occurred in sub-Saharan Africa (SSA) (Kojom Foko et al., 2021). According to another study
done by (Metoh et al., 2020a), the majority of malaria deaths worldwide (an estimated 90%)
occur in Sub-Saharan Africa, and more than 78% of those deaths involve children under the
age of five.
Out of an estimated 33.8 million pregnancies in the 33 countries with moderate to high
transmission in the WHO African Region in 2020, 11.6 million (or 34%) were exposed to
malaria infection while pregnant. West Africa had the highest rate of exposure to malaria
during pregnancy (39.8%), followed closely by central Africa (39.4%), according to the WHO
subregion (World Health Organization, 2022).
The proportion of all malaria-related deaths among children under the age of five decreased
from 87% in 2000 to 77% in 2020. Globally, the malaria fatality rate (defined as deaths per
100 000 people at risk) decreased by half, from roughly 30 in 2000 to 15 in 2015, and
subsequently slowed down, dropping to 13 in 2019. The mortality rate rose in 2020 to 15
deaths per 1000 (World Health Organization, 2022).
29 countries accounted for 96% of all malaria deaths worldwide. Just over half of all malaria
deaths worldwide in 2020 were caused by six countries: Nigeria (27%), the Democratic
Republic of the Congo (12%), Uganda (5%), Mozambique (4%), Angola (3%) and Burkina
Faso (3%). Malaria-related mortality decreased by 36% in the WHO African Region, from
840 000 in 2000 to 534 000 in 2019, and then rose to 602 000 in 2020. Between 2000 and
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2019, the malaria fatality rate dropped by 63%, from 150 to 56 deaths per 100 000 people at
risk, before increasing to 62 in 2020(World Health Organization, 2022).
Based on a study performed in 2017, the prevalence of malaria in Cameroon had decreased as
a result of the government's continuous efforts to guarantee that every household in the
country has access to insecticide-treated bed nets (ITNs). However, malaria is a leading cause
of morbidity and mortality, particularly in children. It was responsible for 67% of all pediatric
fatalities each year, 30% of morbidity, and 48% of all hospital admissions in Cameroon.
According to records, over 22 million people throughout Cameroon were at risk of the
disease. Additionally, severe malaria cases are common in Cameroon, with cerebral and
severe anemia being two of the main causes of total malaria fatality(Kwenti et al., 2017).
Never the less, as the year 2022, malaria still poses a risk to more than 90% of Cameroonians,
and about 41% of people get malaria at least once a year. Cameroon is amongst the 19 most
affected African countries, at position 11 with 2.9% of the total infection(John Elflein, 2022).
A study performed by Djoufounna showed that, malaria is endemic to varying degrees in
Cameroon, ranging from hypo-endemic to hyper-endemic places, depending on the local
ecological conditions. Malaria is one of the leading causes of consultation at many
Cameroonian health centers, notably for children under the age of five and pregnant women,
with more than 11 million cases annually (Djoufounna et al., 2022).
The Global Technical Strategy for malaria (GTS) gives an estimate of the money needed to
reach 2020, 2025, and 2030 targets. In 2016, it was anticipated that US$ 4.1 billion in yearly
resources will be required, rising to US$ 6.8 billion in 2020. During the years 2021-2030, it is
anticipated that an additional US$ 0.85 billion will be needed annually for global malaria
research and development (R&D) (World Health Organization, 2022).
10
A total of US$ 3.3 billion was estimated to be in 2020 for the control and eradication of
malaria, up from US$ 3.0 billion in 2019. Recent years have seen a sharp decline in the
amount invested; it went from US$ 2.3 billion in 2018 to US$ 2.6 billion in 2019 and US$ 3.5
billion in 2020 Nearly 1.4 billion (42%) of the US$ 3.3 billion spent in 2020 went through the
Global Fund to Fight AIDS, Tuberculosis, and Malaria (Global Fund). The Global Fund's
payments to nations with high malaria rates increased by around US$ 0.2 billion in 2020
compared to 2019 (World Health Organization, 2022).
More than 79% of the US$ 3.3 billion spent in 2020 went to the WHO African Region. Drug
investments have received the majority of financing for malaria research (226 million USD,
or 37% of total funding), followed by fundamental research (176 USD, or 28% of total
funding), and vaccine R&D (118 USD, or 19 USD). The remaining 10% ($65 million) went
into vector control products. Biologics (US$ 5.3 million, 0.9%), unidentified (US$ 17 million,
2.7%), and all other items received lesser investments including biologics ($5.3 million,
0.9%), nonspecific products ($12 million, 1.9%), and diagnostics ($17 million, 2.7%) (World
Health Organization, 2022).
2.1.4 Life cycle of malaria.
Malaria exhibits a complex life cycle involving alternating cycles of asexual division
(schizogony) occurring in man (intermediate host) and sexual development (sporogony)
occurring in female Anopheles mosquito (definitive host). Therefore, malaria parasite exhibits
alternation of generations and alternation of hosts. An infected female Anopheles mosquito
injects sporozoites (infective stage) into man, during a blood meal (CDC, 2020).
Pre-erythrocytic Cycle
Within one hour all the sporozoites leave the blood stream and enter into liver parenchyma
cells. They multiply and develop into primary exo-erythrocytic schizonts. The duration of this
cycle for P. falciparum is 6 days (varies with different species). When primary
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exoerythrocytic schizogony is complete, the liver cell ruptures and releases merozoites into
the blood stream (CDC, 2020).
Erythrocytic Cycle
The merozoites then enter the blood stream and invade red cells where they pass through the
stages of trophozoites, schizonts and merozoites. Depending on the species, 2-32 nuclei are
produced followed by cytoplasmic division, and the red cell ruptures to release the individual
merozoites, which then infect fresh red blood cells. The parasitic multiplication during the
erythrocytic phase is responsible for bringing on a clinical attack of malaria. Erythrocytic
schizogony may be continued for a considerable period, but in the course of time the infection
tends to die out. Plasmodium falciparum differs from the other forms of malaria parasites in
that developing erythrocytic schizonts aggregate in the capillaries of the brain and other
internal organs, so that only young ring forms are found in peripheral blood. After malaria
parasites have undergone erythrocytic schizogony for a certain period, some merozoites
develop within red cells into male (microgametocytes) and female (macrogametocytes)
gametocytes and they stay and develop in the red blood cells of the capillaries of internal
organs like spleen and bone marrow. Only mature gametocytes are found in the peripheral
blood. They do not cause any febrile conditions in the human host. These are produced for the
propagation and continuation of the species (CDC, 2020)
Sporogonic Cycle
Sexual cycle actually starts in the human host itself by the formation of gametocytes which
are present in the peripheral blood. Both asexual and sexual forms of the parasite are ingested
by the female Anopheles mosquito during another blood meal from the patient. In the stomach
of the mosquito one microgametocyte gives rise to eight thread-like filamentous structures
called microgametes which are formed by the process of exflagellation. It develops into a
macrogamete; its nucleus shifts to the surface, where a projection is formed. Fertilization
12
occurs when a microgamete penetrates this projection. The fertilized macrogamete is known
as the zygote. This occurs in about 20 minutes to 2 hours. In the next 24 hours, the zygote
lengthens and matures into an ookinete, a motile vermiculate stage. It penetrates the epithelial
lining of the stomach of the mosquito and comes to lie between the external border of the
epithelial cell and peritrophic membrane. Here, it develops into an oocyst. It increases in size
sporozoites develop inside this. The number of sporozoites in each oocyst varies from a few
hundreds to a few thousands and number of oocytes in the stomach wall varies from a few to
more than a hundred. By about the 10th day the oocyst is fully mature, ruptures and releases
sporozoites in the body cavity of the mosquito. Through the body fluid the sporozoites are
carried to the salivary glands and ultimately reach maximum numbers in the salivary ducts. At
this stage the, mosquito is capable of transmitting the infection to man when it bites and the
life cycle continues(CDC, 2020).
13
Figure 1: life cycle of the malaria causing parasite (CDC, 2020).
2.1.5 Signs and symptoms of malaria.
Asymptomatic malaria
This implies and individual has the Plasmodium parasite in his or her blood, but does not
show any clinical symptoms of the disease. This is common in malaria endemic countries
such as Cameroon (Phillips et al., 2017).
Uncomplicated malaria
It can be caused by all Plasmodium species. Symptoms generally occur 7-10 days after the
initial mosquito bite. Symptoms are non-specific and can include fever, moderate to severe
shaking chills, profuse sweating, headache, nausea, vomiting, diarrhoea and anaemia, with no
clinical or laboratory findings of severe organ dysfunction(Phillips et al., 2017).
14
Severe (complicated) malaria
Severe malaria is usually caused by infection with Plasmodium falciparum, though less
frequently can also be caused by Plasmodium vivax or Plasmodium knowlesi. Complications
include severe anaemia and end-organ damage, including coma (caused cerebral malaria),
impairment of consciousness, seizures, pulmonary complications (for example, oedema) and
hypoglycaemia or acute kidney injury. Severe malaria is often associated with
hyperparasitaemia and is associated with increased mortality(Phillips et al., 2017b).
2.1.6 Diagnosis of malaria.
There are three major ways of diagnosing malaria parasite; light microscopy, antigen
detection (immunochromatographic test) and molecular diagnosis.
Microscopy
Light microscopy is the WHO gold standard for malaria diagnosis. It allows the detection of
different malaria-causing parasites their various parasite stages, including gametocytes, and
the quantification of parasite density to monitor response to treatment. Through a finger stick
or venipuncture, a patient's blood is drawn in little amounts. To avoid extended exposure to
anticoagulants in the collection tube that can change parasite morphology When blood is
collected via venipuncture, it is advised that the blood be spread onto a slide right away. One
drop of blood is applied on a slide and smeared with an applicator in a circular motion to
create a thick smear. The different malaria parasite blood stages, including trophozoites,
gametocytes, and schizonts, are released once the red blood cells are destroyed. When
preparing thin smears, a drop of blood is smeared across a slide to form a feathery edge that
includes a single layer of cells. These smears are used to identify the morphology of the
parasite species (Metoh et al., 2020a).
The air-dried blood films were stained with a 10% Giemsa solution for 20 minutes. Two
qualified and skilled parasitologists independently evaluate each blood film under a
15
microscope in accordance with established protocols for the identification and detection of
malaria parasites. When Plasmodium schizonts, trophozoites, and/or gametocytes were found
on the blood film, slides were declared positive. By carefully counting the number of parasites
per 200 white blood cells and multiplying the parasite count by the participants' white blood
cell count as determined by the whole blood count analysis, parasite density was calculated on
thick blood films (Teh et al., 2019).
To observe the thick smear, using the 10x telephoto lens;
1. Turn on the microscope, make the best adjustments to the light source, then use the 10x
objective and the ocular to determine the focus.
2. Check the blood film for blood components and parasites. Pick a section of the film that is
uniformly spread throughout and has good staining.
3. Apply an immersion oil droplet to the thick film. Make sure the immersion oil applicator
never touches the slide to prevent cross-contamination.
4. Place the 100x oil immersion objective on the thick film you've chosen. To see the image
clearly, adjust the focus finely. To prevent the slide from being damaged, raise the mechanical
stage.
5. Focus on the cell components with the fine adjustment to make sure the film is suitable for
standard inspection: A suitable film will have between 15 and 20 white blood cells per thick
film field. A more thorough investigation is necessary for films with fewer white blood cells
per field.
6. Examine the slide in a systematic manner. Start at the top left of the film and begin at the
periphery of the field, then move horizontally to the right, field by field. When the other end
of the film is reached, move the slide slightly downwards, then to the left, field by field, and
so on(WHO, 2016.).
16
Figure 2: pattern of malaria microscopy (WHO, 2016.).
Microscopy requires a well-trained parasitologist to be able to correctly identify and quantify
parasite. Also, regions with unstable power as supply such as rural areas of Cameroon (health
centers) find it difficult to carry out microscopy.
Antigen detection (mRDTs)
The WHO also recommend the diagnosis of febrile patients using RDTs prior to treatment
using artemisinin-based combination therapy, especially when microscopy is not available.
This test method is based on the detection of malarial antigens present in peripheral blood
using monoclonal antibodies coated on dipstick or cassette test. A commercialized RDT
cassette is used to diagnose malaria infection in the peripheral blood samples, following
manufacturer’s instructions. Briefly, five microliters (a small drop of whole blood) of
17
anticoagulated blood is applied to the RDT cassette. Then, two drops of the buffer added to
form a blood-buffer mixture and the result is read within 10–15 minutes, and immediately
recorded (Megnekou et al., 2018)
The test is interpreted as follows:
Negative results: The appearance of only one coloured (red) band on the control window
indicated a negative result.
Positive results: The appearance of two coloured (red) bands, one in the test window and the
other in the control window indicates the test is positive.
Invalid: the absence of a colour band on the control control region indicates an invalid
result(Megnekou et al., 2018).
Despite the rapidity of this test, its reliability is minimal, as parasitemia (quantifying parasite)
cannot be known.
Figure 3: MRDT results interpretation (Mallepaddi et al., 2019)
18
Molecular diagnosis (Nucleic acid amplification)
Nucleic acid amplification tests (NAATs) enable sensitive detection of low-density malaria
infections (below 1 parasite/µL). Polymerase chain reaction (PCR); including nested (n),
quantitative (q) or real-time reverse transcription (RT-PCR), loop mediated isothermal
amplification (LAMP), and quantitative nucleic acid sequence-based amplification (QTNASBA) are among the key NAATs that have been developed to detect malaria. NAATs can
offer quantitative as well as qualitative output, and many tests have also been developed to
enable specific detection of both sexual- and asexual-stage parasites(WHO, 2022).
Although this diagnostic method is highly sensitive, it is not widely used as it is slow,
expensive and not readily available in every health setting.
2.1.7 Management of malaria.
Treatment of uncomplicated malaria
Artemisinin-based combination is recommended by WHO for the management of
uncomplicated malaria. Treat children and adults with uncomplicated P. falciparum malaria
(except pregnant women in their first trimester) with one of the following ACTs:
Artesunate + amodiaquine
Artemether + lumefantrine
Artesunate + mefloquine
Dihydroartemisinin + piperaquine
Artesunate + sulfadoxine–pyrimethamine (SP)
Artesunate + pyronaridine
These medications are administered orally, with an antipyretic (WHO, 2022).
Management of severe malaria
In Cameroon there are three medications used for the management of severe malaria;
injectable artesunate (fist line), injectable artemether (second line), and injectable quinine
19
given as third line medication. Treatment is relayed with oral medications as soon as patients
are able to eat and drink (MINSANTE 2019.).
2.1.8 Complications.
Cerebral malaria. If parasite-filled blood cells block small blood vessels to your brain
(cerebral malaria), swelling of your brain or brain damage may occur. Cerebral malaria may
cause seizures and coma (MayoClinic, 2021).
Breathing problems. Accumulated fluid in your lungs (pulmonary edema) can make it
difficult to breathe (MayoClinic, 2021).
Organ failure. Malaria can damage the kidneys or liver or cause the spleen to rupture. Any of
these conditions can be life-threatening (MayoClinic, 2021).
Anemia. Malaria may result in not having enough red blood cells for an adequate supply of
oxygen to your body's tissues (anemia), caused by destruction of red blood cells by
Plasmodium parasites (MayoClinic, 2021)
Low blood sugar. Severe forms of malaria can cause low blood sugar (hypoglycemia), as can
quinine; a common medication used to combat malaria. Very low blood sugar can result in
coma or death(Myoclonic, 2021).
pulmonary oedema – a build-up of fluid in the lungs.
Acute respiratory distress syndrome (ARDS)
Low blood sugar (hypoglycemia), due to increased glucose use by the parasite.
Kidney failure.
Swelling and rupturing of the spleen.
Dehydration.
Black water fever (passing out reddish black urine). This is caused by intravascular
hemolysis from heavy parasitemia. It is commonly associated to Plasmodium falciparum
infections.
20
Algid fever. A rare complication of tropical malaria and it occurs in 0.37% of cases. Algid
malaria is characterized by hemodynamic disorders as shock with pronounced metabolic
changes and hypothermia (CDC, 2019).
Pregnancy-related malaria infections can have harmful effects on both the mother and the
fetus, including;
Maternal anemia
Stillbirth (death of baby before or during delivery).
Premature birth (birth before 37 weeks of pregnancy).
Intrauterine growth restriction
Spontaneous abortion (involuntary loss of pregnancy before 20 weeks of pregnancy)
Low birth weight babies (birth weight below 2.5kg).
Death of the mother(CDC, 2019).
2.1.9 Malaria prevention.
In Cameroon as well as other parts of the world, long-lasting insecticidal nets (LLINs) and
indoor residual sprays are frequently employed to combat malaria. The use of LLINs and/or
indoor residual spraying (IRS) alone, however, is insufficient to stop the spread of malaria. In
addition to the use of LLINs, chemoprophylaxis or IPT administered to women of all parity
groups lowers the prevalence of prenatal parasites and placental malaria. Using larvicidal
pesticides, removing shrubs from surrounding homes, draining and filling of larval breeding
sites are all methods for controlling vectors. Additionally, using sprays, lotions, or creams are
some other effective methods of preventing malaria (World Health Organization, 2022).
Nearly 2.3 billion LLINs were distributed globally between 2004 and 2020, according to
manufacturer delivery data, of which 2 billion (86%) were distributed to sub-Saharan Africa.
In sub-Saharan Africa, 65% of homes have at least one LLIN by 2020, up from 5% in 2000.
From 1% in 2000 to 34% in 2020, more households will have at least one LLIN for every two
21
occupants. Between 2000 and 2020, the percentage of people who slept under an LLIN
climbed significantly across the board (from 2% to 43%), among children under the age of
five (from 3% to 49%), and among pregnant women (from 3% to 49%) (World Health
Organization, 2022).
2.2 Diagnostic performance of histidine rich protein-2 in malaria.
2.2.1 Sensitivity and specificity of histidine rich protein-2 RDT.
According to a study done in Nigeria, the malaria RDT's specificity and sensitivity were
96.08% and 97.95%, respectively, with positive and negative predictive values (PPV and
NPV) of 96.08% and 97.95%. In this study, the youngest age group appeared to have better
malaria RDT sensitivity and specificity (15-25 years) (Metoh et al., 2020a). The sensitivity of
96% and specificity 93% of HRP2 RDT were obtained during a study performed in Ethiopia
(Lopez et al., 2020).
Another study performed in Rwanda gave sensitivity of RDT (HRP-2) to be 95.0%, and
specificity of RDT (HRP-2) was calculated and found to be 59.2% (Niyibizi & Gatera, 2020).
Also, a study conducted in the year 2019 on malaria diagnosis showed that, HRP-2 had a
99.70% sensitivity, 100% specificity, 100% PPV and 99.88% NPV (Mallepaddi et al., 2019).
Based on a 2018 study, it was discovered that HRP-2 had an 87.83% sensitivity and a 100%
specificity (Bahk et al., 2018). A specificity of 100% and sensitivity of 73% were determined
during a study in the Northwest region of Cameroon. In this same study, the positive
predictive and negative predictive values were 100% and 70%, respectively (Fru et al.,
2020b). After a research on malaria performed in Buea using HRP-2 test kits, the sensitivity
was 87.5%, specificity 99.41%, positive and negative predictive values were 97.71% and
96.55% respectively (Sylvie et al., 2019).
22
2.3 Diagnostic performance of parasite lactose dehydrogenase in malaria.
2.3.1 Sensitivity and specificity of parasite lactose dehydrogenase RDT
The sensitivity of One Step malaria pLDH RDTs was and 61.53% and the specificity was
100% in a study performed in Yaoundé Cameroon(Megnekou et al., 2018). Another study
demonstrated that, the sensitivity of PLDH RDTs was 89% using a microscope as a reference
test. The associated specificity was 95%. Additionally, the PLDH RDT had a 97% positive
predictive value (PPV) and a 76% negative predictive value (Alemayehu et al., 2020). The
results of a study conducted in Ethiopia showed that, the sensitivity and specificity of the
PLDH RDT were 89% and 99%, respectively (Lopez et al., 2020).
In 2019, a study revealed that pLDH was 92.81% sensitive and 94.13% specific, with a
84.02% PPV and 97.52% NPV in the diagnosis of malaria (Mallepaddi et al., 2019). During a
2018 research on malaria in Uganda, the sensitivity and specificity of PLDH were 89.57%
and 100% respectively (Bahk et al., 2018). A study was carried out at the Yaoundé Central
Hospital, and results showed that the sensitivity of the malaria RDT using PLDH was 91.67%
while the specificity was 53.13% (Ebong et al., 2022).
23
CHAPTER THREE
METHODS AND MATERIALS
3.1 Study area and study setting
This study was conducted at the Regional Hospital Limbe. The Hospital is located in the
coastal town of Limbe, in the South West Region of Cameroon. Precisely about 1.1km from
Half Mile (the central town). The Hospital was built in the year 1940 and was accorded the
status of a Provincial Hospital in the year 1972. Since 2008, with the change of name from
Provence to Region, the hospital officially became a Regional Hospital. This hospital has the
following functional departments; the radiology, surgical unit, gynecology, obstetrics,
dentistry, ophthalmology, pediatrics, neonatal, and general medicine. That laboratory has the
following functional departments; bacteriology, parasitology, biochemistry, virology,
serology and blood bank.
The study was carried out at the Limbe Regional Hospital because of its high patient inflow.
Also, malaria is holoendemic in the South West Region of Cameroon, and Limbe being a
touristic zone, it attracts a lot of people from around the country. The climatic condition of
Limbe (warm temperature, high rainfall, and humid air), favors the growth of malaria
transmitting mosquitos. The above reasons therefore increase the chances of obtaining a high
sample size.
3.2 Study design
A descriptive cross-sectional study was be use, where the study participants were be met just
once throughout the study.
24
3.3 Study population
Our study population was all febrile patients attending the Regional Hospital Limbe who were
prescribed malaria diagnosis. Over the recent years, the town of Limbe has been rapidly
growing in population. Also, Limbe receives people from all over the country mainly for
touristic reasons. These increases the chances of high malaria prevalence which is common in
this Region of the country.
3.3.1 Inclusion criteria
Participants of this study included and satisfy the following criteria:
i.
All febrile patients attending the Regional Hospital Limbe, who were present during the
period of the study (from the 02nd of January to the 09th of February 2022).
ii.
All febrile patients who were present at the Hospital, and were prescribed malaria
examination.
iii.
All febrile patients who gave their consent to participate in the study.
3.3.2 Exclusion criteria
Although this study targeted all febrile patients, individuals were excluded from this study, in
cases where they did not satisfy the following criteria:
i.
All febrile patients not present at the Regional Hospital Limbe during the period of the
study.
ii.
Febrile patients who were present at the Limbe Regional Hospital, but were not
prescribed malaria examination.
iii.
All febrile patients present at the Regional Hospital Limbe, but did not give their
consent to participate in the study.
3.4 Sample size
The sample size for this study was determined using the Crocran’s formular, which states that;
𝐬𝐚𝐦𝐩𝐥𝐞 𝐬𝐢𝐳𝐞 (𝐧) =
𝐙²𝐩𝐪
𝐞²
.
25
Where n= the sample size
z= 1.96 (from the z table)
p= expected proportion in the population based on previous studies. From a study done in
Cameroon, the prevalence of malaria was 42% (Bamou et al., 2021). Hence, our p value was
= 42% (0.42).
q= 1-p, which is = 1-0.42. therefore, q=0.58
e= Absolute error or precision, which 0.05 (5%).
This implies our estimated sample size was, 𝒏 =
(𝟏.𝟗𝟔)²∗(𝟎.𝟒𝟐)(𝟎.𝟓𝟖)
(𝟎.𝟎𝟓)²
=374.32 approximately 374
people.
Taking into account a 10% non-response rate, which is 10% of the calculated participant size,
𝟏𝟎
𝟏𝟎𝟎
∗ 𝟑𝟕𝟒 = 𝟑𝟕 people. This therefore implies our actual sample size was be 374+37= 411
people.
3.5 Sampling procedure
A purposive sampling technique was used in this study, whereby only patients who were sent
to the laboratory for malaria microscopy were recruited for the study. This sampling
technique was employed because of its convenience and ease to apply in this study. Although
this sampling technique reduced the chances of obtaining a large sample size, every patient
who was sent to the laboratory for malaria microscopy was included in the study in order to
maximize the sample size. This also helped prevent selection bias.
3.6 Data collection procedure
3.6.1 Study variable
Sensitivity:
Sensitivity of the diagnostic tool is the ability of the tool to correctly detect a true positive
patient. This implies, the proportion of patients tested positive using RDT who were
26
confirmed positive by light microscopy, to the total number of participants. The percentage of
malaria positive patients using our diagnostic tool (HRP-2 and pLDH).
Specificity:
The specificity of a test is the ability of the RDT test cassettes to correctly detect true negative
patients (results). That is, the proportion of true negative cases compared to the total sample
size. The true positive, false positive, true negative and false negative results were obtained by
comparing the results of the RDTs to those obtained from light microscopy.
3.6.2 Data collection tools
The data collection tool for this study were; Hospital register, patients’ Hospital booklets, pen,
ruler, logbook, and a laptop.
3.6.3 Pretesting of data collection tool.
The test kits used were pretested on a small sample size, to ensure that the cassettes are
functional. A few blood samples of patients with known malaria status were ran on HRP-2
and PLDH cassette and observed to see if the cassettes work correctly. That is, the cassettes
are able to produce red/pink bands on the control and test window on for positive patients, and
red lines on the control window alone for negative patients. Cassettes producing red bands on
the test windows only, or nor bands at all on both the test and control windows were discarded
since they produced invalid results.
3.6.4 Data collection procedure
During data collection, participants were made to understand that participation is voluntary,
and small quantities of capillary blood were be collected for testing.
During the data collection procedure, patients’ information (names, age, and sex) was
collected. Next, patients were greeted and asked to seat comfortably, and reasons for blood
collection were explained to participants. Then after, we proceeded with preparation of blood
collection materials. After this, puncture sites (preferably the middle fingers for older children
27
and adult participants, or the sides of the heels for pediatric patients) disinfected using a clean
cotton damped with 70% alcohol, and allowed to air dry. When they dried, the puncture sites
were pricked using a sterile lancet, and small quantities of blood was collected using small
plastic pipettes, while squeezing the fingers gently. After collecting, mild pressure was
applied on pricked sites using a dry cotton, to stop bleeding. When the above was done,
participant’s blood was transferred onto the glass slides to prepare thick blood smears for
microscopy. Also, small quantities (drops) added in the sample window on RDT cassettes for
testing. The results obtained (from light microscopy and the RDTs) were documented in the
hospital register to be later analyzed. RDT results were interpreted as follows;
Positive: The appearance of a red band both on the control and test window of the cassette.
Negative results: The appearance of a red band just on the control window of the cassette.
Invalid: Results with no color band on both the control and test window, or the appearance of
colored bands just on the test window will be invalid results.
3.6.5 Data management plan
Invalid test results were discarded, and tests repeated. Multiple copies of tests results were
made, to minimize the risk of loosing data. Also, softs copies of results were made and saved
on different drives and on google cloud.
3.7 Data analysis plan
Data collected was analyzed using Microsoft excel 2019. Only descriptive statistics was used
in analysis. The sensitivity, specificity and predictive values of the test cassettes were
calculated and expressed in percentages and presented on tables and histograms. The
𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇𝒕𝒓𝒖𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒗𝒆 𝒄𝒂𝒔𝒆𝒔 𝒅𝒆𝒕𝒆𝒄𝒕𝒆𝒅
sensitivities was calculated as, 𝒔𝒆𝒏𝒔𝒊𝒕𝒊𝒗𝒊𝒕𝒚 = 𝒔𝒖𝒎 𝒐𝒇 𝒕𝒓𝒖𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒗𝒆𝒔 𝒂𝒏𝒅 𝒇𝒂𝒍𝒔𝒆 𝒏𝒂𝒈𝒂𝒕𝒊𝒗𝒆𝒔 ∗ 𝟏𝟎𝟎.
The sensitivity of the diagnostic tools was obtained, and these results were compared with
results obtained from light microscopy (gold standard). These results were graded as; poor,
sensitive, very sensitive and excellent. Specificity was calculated using the formula;
28
𝒔𝒑𝒆𝒄𝒊𝒊𝒇𝒊𝒄𝒊𝒕𝒚 =
𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒕𝒓𝒖𝒆 𝒏𝒆𝒈𝒆𝒕𝒊𝒗𝒆 𝒑𝒂𝒕𝒊𝒆𝒏𝒕𝒔 𝒅𝒆𝒕𝒆𝒄𝒕𝒆𝒅
𝒔𝒖𝒎 𝒐𝒇 𝒕𝒓𝒖𝒆 𝒏𝒆𝒈𝒂𝒕𝒊𝒗𝒆𝒔 𝒂𝒏𝒅 𝒇𝒂𝒍𝒔𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒗𝒆𝒔
*100. The specificities obtained were
compared to results obtained from light microscopy and graded as; poor, specific, very
specific, and excellent.
3.8 Ethical consideration
Before carrying out any scientific research, it is very important to consider some ethical rules,
especially when dealing with humans.
For this study, an ethical clearance was obtained from the research community of ST. LOUIS
UNIVERSITY INSTITUTE CAMEROON upon deposition of research proposal. An
authorization was gotten from the South West Regional Delegation of public Health. Also, an
authorization was obtained from the Director of the Limbe Regional Hospital, permitting the
principal investigator to carry out the study. Finally, a form of informed consent was signed
by all participants, without any influence on them from the principal investigator. This gave
participants the right to withdraw when they want. Also, signing this form increased trust
between the participant and principal investigator. Although very young participants were
unable to give an informed consent, the principal investigator ensured that their parents or
guardian give their consent. Sensitive information for participants were not be included nor
considered in this study. Participants’ results were handed to them, except in cases of children
and disabled, which was handed to their parents or guardian. Participation was free and
participants were not influenced by any means to partake in this study, and also, participants
had free malaria tests and counselling on malaria prevention.
29
CHEPTER FOUR
RESULTS
4.1 Sociodemographic characteristics of study participants
Table 1: A table showing the sociodemographic characteristics of study participants
Variable
Category
Age
0-5 years
47
11.06
6-18 years
176
41.41
18 years and above
202
47.53
Total
425
100
Male
189
44.47
Female
236
55.53
Total
425
100
Pregnancy
Pregnant women
30
12.71
status for
Non-pregnant
206
87.29
female
Total
236
100
Sex
Frequency
Percentage (%)
Most of our study participants, 202 (47.5%) were adults above 18years of age, and the
smallest proportion, 47 (11%) of the study participants were children between the ages of zero
(0) to five (5) years of age. More than half, 236 (55.5%) of the study participants were female,
most of which, 206 (87%) of them were not pregnant.
30
4.2 Summary of data
Table 2: A table showing a summary of results obtained from the study.
VARIABLE
CATEGORY
FREQUENCY PERCENTAGE
(%)
MICROSCOPY Number of positive cases
134
31.53
Number of negative cases
291
68.47
Total
425
100
Prevalence
134
32
Number of positive cases
109
25.65
Number of negative cases
316
74.35
Total
425
100
Number of true positives
94
86.24
Number of false positives
15
12.76
Total
109
100
Number of true negatives
276
87.34
Number of false negatives
40
12.66
Total
316
100
Number of positive cases
40
9.41
Number of negative cases
385
90.51
Total
425
100
Number of true positives
37
92.50
Number of false positives
3
7.50
Total
40
100
Number of true negatives
288
74.81%
Number of false negatives
97
25.19%
Total
385
100
HRP-2
PLDH
31
4.2.1 The diagnostic performance of HRP-2
Table 3: A table showing the test results and diagnostic performance of HRP-2.
Microscopy
Positive
Negative
Total
Positive
94
15
109
Negative
40
276
316
Total
134
291
425
94
94
Sensitivity= 134 ∗ 100 = 70.15%
PPV= 109 ∗ 100 = 86.24%
276
276
Specificity= 291 ∗ 100 = 94.85%
NPV= 316 ∗ 100 = 87.34%
109
Prevalence= 425 ∗ 100 = 25.65%
Efficiency=
94+276
425
∗ 100 = 87.06%
The HRP-2 test cassettes produced a total of 109 positive tests, of which 94 were true positive
results confirmed by microscopy, and 15 were false positive results. The test also had 316
negative tests, where 276 were true negative tests and the other 40 were false negatives. HRP2 showed higher specificity (94.85%) than sensitivity (70.15%), with a higher NPV than PPV.
This test cassettes also gave a malaria prevalence of 25.65% different from that obtained by
microscopy (31.53%), and a test efficiency of 87.06%.
32
4.2.2 The diagnostic performance of PLDH
Table 4: A tabular representation of tests results and the diagnostic performance of PLDH
tests cassettes.
Microscopy
Positive
Negative
Total
Positive
37
3
40
Negative
97
288
385
Total
134
291
425
37
37
Sensitivity= 134 ∗ 100 = 27.61%
PPV= 40 ∗ 100 = 92.50%
288
288
Specificity= 291 ∗ 100 = 98.97%
40
Prevalence= 425 ∗ 100 = 9.41
NPV= 385 ∗ 100 = 74.81%
`
Efficiency=
37+288
425
∗ 100 = 76.47%
The results of PLDH had 40 positive tests results, with 37 true positive and only 3 false
positive results. These tests cassettes also produced 385 negative results, 288 of them were
true negative and 97 were false negative results. The test showed a higher specificity, 98.97%
than sensitivity (27.61%), with a higher PPV of 92.50% than NPV (74.81%). The calculated
prevalence by this test was 9.41% compared to the microscopy prevalence of 31.53%, with a
test efficiency of 76.47%.
33
4.2.3 Malaria prevalence
PERCENTAGE
Malaria Prevalence
35
31,53
30
25,65
25
20
15
9,41
10
5
0
Malaria light microscopy
HRP-2 (RDT)
PLDH (RDT)
TEST METHOD
Malaria Prevalence
Figure 4: Prevalence derived using light microscopy, PLHD (RDT) and HRP-2 (RDT).
Malaria light microscopy had the relatively highest prevalence of 31.53%, with PLDH (RDT)
having the lowest prevalence of 9.41%.
34
CHAPTER FIVE
DISCUSSION CONCLUSION AND RECOMMENDATION
5.1 Discussion
The main objective of this study was to compare the analytic performance of HRP-2 and
PLDH in the diagnosis of malaria among febrile patients at the Limbe Regional Hospital. 425
participants prescribed malaria light microscopy were tested using both the HRP-2 and PLDH
test cassettes, and the results were recorded.
From the results presented on figure 5 above, microscopy test results showed a malaria
prevalence of 31.53%, Hrp-2 had a prevalence of 25.65%, and PLDH had a 9.41% prevalence
at the end of the test. These results vary significantly from the 42% obtained from the study
on malaria prevalence in Cameroon by Bamou et al 2021. This significant reduction can be
explained by the fact that, these studies were conducted in two different Regions, which are
found in two different malaria strata in Cameroon. Another possible explanation can be the
fact that, these two regions have different level of exposures to information on the prevention
of malaria, and the town of Limbe being relatively cleaner than the south Region of
Cameroon, which this of course plays and important role the transmission level of the malaria
causing mosquito. The prevalence obtained by the RDT were lower than microscopy due to
maybe the fact that most positive cases had a low parasitemia which could not be correctly
detected on the RDT tests cassettes.
At the end of this study, HRP-2 had a sensitivity of 70.15% and specificity of 94.86% with
PPV and NPV of 86.24 and 87.34% respectively. These results differ from those conducted
by Metoh et al, 2020 in Nigeria, with sensitivity of 96.08% and specificity of 97.95%. The
significant in sensitivity could be due to the large number of false negative cases, which has a
35
significant influence on the test’s sensitivity. However, the specificity obtained in these two
studies were similar. The results were lower than those obtained by Lopez et al, in 2020
Ethiopia, with sensitivity (96%) and specificity (93%). The lower sensitivity and was due to
higher rates of tales negative tests results, but the specificities corresponded. In comprism
with the results of a Bahk et al done in Uganda (2018), with sensitivity of 87% and specificity
of 100% there was a slid, e difference in both sensitivity and specificity. On the other hand,
this study showed similar sensitivity and specificity to those obtained by Fru et al in the
North-West Region of Cameroon. When we further compared our results to that of
Mallepaddi et al, but they had higher sensitivity and specificity of 99.7% and 100%
respectively. In a similar study, by Selvie et al (2019), their sensitivity (87.50%) and
specificity (99.41%) were higher than those obtained in our study. Generally, every low
sensitivity id causes by a significant high number of false negative test results obtained. These
false negative results obtain in our study could be as a result of the fact that; most of our
malaria positive cases confirmed by microscopy had very low- parasite densities. Also, gene
deletion might have occurred from the period of sample collection to testing, since our
samples were stored for relatively long periods before testing. Another explanation to the
false negative cases could be a poor performance of the RDT brand we used in testing.
After obtaining our sensitivity of 27.61% and specificity of 98.97% and also PPV of 92.50%
and NPV of 74.81% from PLDH, the results were compared to those obtained by Meguekou
et al (2018) in Yaoundé, whose sensitivity was 61.53% and specificity was 100%. When
compared with results from a study conducted in Ethiopia by Lopez et al in 2020 with
sensitivity of 89% and specificity of 99%, a great difference was noticed in the sensitivities,
though the tests specificities corresponded (were almost the same). The difference in
sensitivity was due to the large number of false negative results obtained during our tests.
Another result from a study performed by Almayehu et al (2020) and a great difference was
36
noticed in both sensitivities, with ours being lowered compared to theirs, and was also due to
the high number of false negative cases we obtained from our tests. However, the specificities
of both tests corresponded, probably to very few false positive results produced by both tests.
The PPV and NPV of both tests were matching, with very little differences, as their results
had PPV of 97% and NPV of 76%. Also, we noticed a large difference in sensitivities when
we compared our results with that of Mallepaddi et al 2019, in India who had a sensitivity of
92.81%, but our specificity corresponded with theirs of 94.13%. the difference in both
sensitivities with ours being lower indicates that our tests produced more false negative results
than theirs. The sensitivity and specificity we obtained was still compared to those of a study
carried out at the Central Hospital in Yaoundé by Ebong et al in 2022, who had a sensitivity
of 91.67% and a specificity of 53.13%. their sensitivity was higher than that of our study due
to the large number of false negatives cases we had, but their specificity was significantly
lower than ours, indicating that they had a lot of false positive results in their study. The false
positive cases can be due to reactions between rheumatoid factor or antigens of
Trypanosomiasis with specific antibodies on the malaria test strips. There was also a variation
in sensitivity of our results from that gotten by Bahk et al during a similar study in Uganda
(2018), who had a sensitivity of 89.57%, but their specificity of 100% was similar to what we
obtained. The lower sensitivity we obtained was because of the many false negative cases we
had. These false negative tests results of PLDH could have been due to the fact that the PLDH
is relatively short lived in blood, resulting in gene loss when sampled were stored (kept) for
relatively long periods before testing. Also, the low parasite densities obtained by microscopy
can also be a reason for difficulties in antigen detection on test strips.
The Prevalence of HRP-2 (25.65%) was higher than that of PLDH (9.41%), which was maybe
due to the fact that,
HRP-2 is produced by Plasmodium falciparum, which is the
predominant (most common) species of Plasmodium in Cameroon. HRP-2 test was more
37
sensitive (70.15%) than PLDH (27.61%), but the PLDH was more specific, with a specificity
of 98.97% compared to the HRP-2 which had a specificity of 94.86%. On the other hand,
PLDH had a higher PPV of 92.50% than HRP-2 with 86.24%, but the NPV was higher
(87.34%) and efficiency (87.06%) in HRP-2 than in PLDH with NPV of 74.81%, and
efficiency of 76.47%. The higher sensitivity of HRP-2 was due to the fact that there were
fewer false negative cases in HRP-2 than in PLDH, which could be explained by the fact that
PLDH antigens are relatively short-lived than the HRP-2, which leads in the loss of PLDH
antigen when samples are stored for long periods before testing. The specificity of both tests
was not significantly different.
5.2 Conclusion
Considering the diagnostic performances (sensitivities and specificities) of the two test kits,
we can conclude that the HRP-2 cassette is a better diagnostic tool for malaria among febrile
patients at the Limbe Regional Hospital. This is because it has a better sensitivity and overall
efficacy than PLDH, though their specificities do not differ much. However, none of these test
cassettes produced the WHO recommended 95% sensitivity, and so the diagnosis of malaria
in this Hospital should not solely depend on RDT.
5.3 Limitations
The principal limitation we encountered during this study was that specimens could not be
tested with RDT immediately after sample collection, since we were only allowed to start
testing after work hours, so as not to interfere with the Hospital’s work flow. Also, malaria
associated risk factors were not documented from patients, which could explain the
prevalence of malaria in this Region.
38
5.4 Suggestions
Further study could be performed on the prevalence of malaria related to socio-demographic
characteristics, their associated risk factors, and the effect of malaria positive results on
patients’ complete blood count (taking into account the hemoglobin level).
39
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56
World
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APPENDICES
INFORM CONSENT FOR PARTICIPANTS
Dear participant, I am Ngoche Emmanuel a level 300 student at St Louis University Institute,
Douala. I am currently conducting a research project entitled “COMPARING DIAGNOSTIC
PERFORMANCE OF HISTIDINE RICH PROTEIN-2 AND PARASITE LACTOSE
DEHYDROGENASE IN THE DIAGNOSIS OF MALARIA AMONG FEBRILE PATIENTS
AT THE LIMBE REGIONAL HOSPITAL”. You are being invited to take part in this
research, by providing answers to some questions related to the study. The information you
will provide will be used only for the purpose of this research and will go a long way to help
solve health problems in Cameroon. Your participation in this study is voluntary. It is up to
you to decide whether to take part in this study. If you decide to take part in this study, you
will be asked to sign this consent form. After you sign the consent form, you are still free to
withdraw at any time and without giving a reason. If you have questions at any time about this
study, or you experience adverse effects as the result of participating in this study, you may
contact the researcher whose contact information is provided below.
CONSENT
I have read, and I understand the provided information and have had the opportunity to ask
questions. I voluntarily agree to take part in this study.
Participant's
signature
_____________________
Date
__________
Investigator's signature _________________ Date __________ Contact--------------------
THANK YOU FOR ACCEPTING TO PARTICIPATE IN THIS STUDY.
57