seroepidiemoloy of malaria in yemen - lshtm - eric garson 2012
TRANSCRIPT
PROJECT REPORT
Candidate Number:105322
MSc: Immunology of Infectious Diseases
Title: Seroepidemiology of Malaria in Yemen
Supervisor: Dr. Chris Drakeley
Word Count: 7645
Submitted in part fulfilment of the requirements for the degree of MSc in Immunology
of Infectious Diseases
For Academic Year 2011-2
“through the battle, through the defeat, moving yet and never
stopping pioneers! O pioneers”
Andrew Balfour
[I]
Abstract
Background
Filter paper blood spots from children were investigated to assess the seroepideimology of malaria in Yemen. They were performed during two household surveys in the Sukhmal Wadi, located in the west of Dhamar governorate, in Yemen. The first survey aimed to assess malaria transmission during the peak malaria season, October 2011. A second survey was conducted to assess the seasonal difference of transmission in March 2012. Both surveys obtained RDTs (Rapid Diagnostic Tests) and giemsa stained microscopy slides from over 2,000 children to show current infection trends. Additionally, filter paper blood spots were dovetailed to the second survey to evaluate the history of infection in the region. The overall aim of the study was to use the results for tailored, safe and effective interventions by LLINs (Long-Lasting Insecticide-treated Nets) and IRS (Indoor Residual Spraying), resulting in a reduction in the malaria in the Sukhmal Wadi.
Methods ELISAs (Enzyme Linked Immunosorbet Assays) were performed against the MSP-1 (Merozoite
Surface Protein-1) epitope of both Plasmodium falciparum and Plasmodium vivax species.
Experiments were conducted on twelve clusters, each cluster containing on average 200 filter paper
blood spots each. Results were input into a MACRO program to check for duplicates before being
statistically analysed by STATA software. Results were merged and compared to RDTs and
microscopy clinical data to provide a holistic picture of the malaria transmission both currently and
historically in the Sukhmal Wadi.
Results
Plasmodium falciparum MSP-1 serology showed heterogeneity between the twelve cluster sites.
There was no presence of Plasmodium vivax. The serological investigation showed the presence of a
current epidemic and past epidemic in the Sukhmal Wadi.
Conclusions
Serological examination of malaria can provide an effective method for assessing malarial
transmission, providing a stepping stone towards eliminating malaria from the Sukhmal Wad
[I]
Contents
Abstract ........................................................................................................................................ I
Background .......................................................................................................................................... I
Methods ............................................................................................................................................... I
Results .................................................................................................................................................. I
Conclusions .......................................................................................................................................... I
List of Figures ............................................................................................................................... II
Abbreviations .............................................................................................................................. II
Acknowledgements..................................................................................................................... IV
Introduction ................................................................................................................................ 1
Global Overview .................................................................................................................................. 1
Plasmodium ........................................................................................................................................ 1
Mosquito ............................................................................................................................................. 2
Life cycle .............................................................................................................................................. 2
Clinical Disease .................................................................................................................................... 4
Clinical Complications ......................................................................................................................... 4
Immunity ............................................................................................................................................. 5
Genetics .............................................................................................................................................. 5
Diagnosing and detecting malaria ...................................................................................................... 6
Control and prevention strategies ...................................................................................................... 6
Treatment ........................................................................................................................................... 7
Serology and Seroconversion ............................................................................................................. 7
Malaria in Yemen and the Gulf region ................................................................................................ 9
Yemen and Current Malaria Status ..................................................................................................... 9
Yemen Study and Objectives ............................................................................................................ 11
Aim of Serological Investigation ................................................................................................. 15
Materials and Methods .............................................................................................................. 16
Reconstitution of Sample .................................................................................................................. 16
ELISAs ................................................................................................................................................ 16
Analysis of Results ............................................................................................................................. 17
Results ....................................................................................................................................... 18
Discussion .................................................................................................................................. 25
Recommendations ..................................................................................................................... 29
References ................................................................................................................................. 30
[II]
Student’s Questionnaire ................................................................................................................... 32
List of Figures Figure 1 ................................................................................................................................................... 3
Figure 2 ................................................................................................................................................. 10
Figure 3 ................................................................................................................................................. 10
Figure 4. ................................................................................................................................................ 11
Figure 5 ................................................................................................................................................. 11
Figure 6 ................................................................................................................................................. 12
Figure 7 ................................................................................................................................................. 12
Figure 8 ................................................................................................................................................. 13
Figure 9 ................................................................................................................................................. 14
Figure 10 ............................................................................................................................................... 16
Figure 11 ............................................................................................................................................... 18
Figure 12. .............................................................................................................................................. 18
Figure 13 . ............................................................................................................................................. 19
Figure 14 ............................................................................................................................................... 19
Figure 15 ............................................................................................................................................... 20
Figure 16 ............................................................................................................................................... 20
Figure 17 ............................................................................................................................................... 21
Figure 18 ............................................................................................................................................... 21
Figure 19 ............................................................................................................................................... 22
Figure 20 ............................................................................................................................................... 22
Figure 21 ............................................................................................................................................... 23
Abbreviations ACT- Artemesinin Combination Treatment
AMA-1- Apical Membrane Antigen-1
CD4 T-Cell- Clusters of Differentiation 4 T-Cell
CD8 T-cell- Clusters of Differentiation 8 T-cell
CSA- Chondroitin Sulphate A
DDT- Dichloro-Diphenly-Trichlorethane
DNA- Deoxyribonucleic Acids
EGF- Epidermal Growth Factor
ELISAs- Enzyme Linked Immunosorbent Assays
[III]
EMRO- Eastern Mediterranean Regional office
EPO- Erythropoetin
GEF- Global Environmental Facility
GPI- Glycosylphosphatidylinositol
H2O- Water
H2SO4- Sulphuric Acid
HRP- Horseradish Peroxidase
ICAM-1- Intercellular Adhesion Molecule-1
IFN-γ- Interferon-gamma
IPT- Intermittent Presumptive Therapy
IRS- Indoor Residual Spraying
LFA-1- Leukocyte Function Antigen-1
LLINs- Long-Lasting Insecticide-treated Nets
LSHTM- London School of Hygiene and Tropical Medicine
MSP-1- Merozoite Surface Protein-1
NK cell- Natural Killer Cell
OD- Optical Density
OPD- o-Phenylenediamine Dihydrochloride
PBS- Phosphate Buffer Saline
PBS-T- Phosphate Buffer Saline-Tween20
PCR- Polymerase Chain Reaction
Pfemp1- Plasmodium falciparum erythrocyte protein 1
RDTs- Rapid Diagnostic Tests
TGF-β- Transforming Growth Factor β
[IV]
TNFα- Tumour Necrosis Factor alpha
UNDP-Unied Nations Develoment Programme
UNEP- United Nations Environmental Programme
WHO- World Health Organization
Acknowledgements To Chris, Ali, Lynn, Lynn, Sophie, Carolyn, Patrick, Sanie, Liz, Ruth, Steve, Immo, Chi, Eleanor, Greg,
David and Chrissy Thank you.
[1]
Introduction
Global Overview
Malaria threatens 3.3 billion lives, causing 216 million cases and 655,000 deaths last year.
Nearly 90% were children under the age of five. However even in the face of these
challenges malaria can be prevented, treated and eradicated. Thus the challenge of
reaching no malaria deaths and 75% reduction in cases by the end of 2015 was championed
by The WHO (World Health Organisation) in 2011 (WHO, 2011).
Malaria is transmitted by the Anopheles mosquito vector. Anopheles requires warm, tropical
climates. Consequently tropical communities are at greatest risk from malaria, in particular
rural, young populations. Malaria affects children the most because they do not develop
immunity till much older in life (Miller et al., 2002, Greenwood et al., 2008).
Plasmodium
Plasmodium species are the cause of malaria. Plasmodium species are unicellular parasites
belonging to the Apicomplexia family of protozoa (Greenwood et al., 2008, Sherman, 2001).
Plasmodium requires two hosts for its lifecycle. It fluctuates between intracellular and
extracellular forms within vertebrates and mosquito species (Sherman, 2001).
Historically Plasmodium was first identified in 1880 by Charles Laveron, the discovery lead
to the observation of the parasites destroying erythrocytes by Camile Golgi in 1886.
Meanwhile Ronald Ross found the mosquito vector responsible for its transmission
Anopheles. In the mid 20thcentury Henry Shortt showed the parasite affected the liver. These
principles built one upon the other have continued to this day offering a prospect of
alleviating the suffering of millions from malaria (Sherman, 2001).
Human malaria is caused by five main species of Plasmodium.
Plasmodium ovale is responsible for latent infections in humans but causes relatively low
numbers of reported cases (Stevenson and Riley, 2004). It can reside within the liver as a
dormant hypnozoite whereupon it can reactivate months or years post bite or initial infection,
thus acting as a reservoir of infection. This is replicated in Plasmodium vivax species
(Sherman, 2001).
Plasmodium vivax can survive in lower temperatures compared to its counterparts and
therefore affects a greater swath of the globe. It causes limited febrile illness, rarely causing
death (Greenwood et al., 2008, Miller et al., 2002, Stevenson and Riley, 2004).
[2]
Plasmodium knowlesi is a recent zoophilic Plasmodium which has its animal reservoir in
Macaque monkeys. Unlike other Plasmodium species it has a 24 hour life cycle, causing
high acute parasitaemia in humans. However Macaques appear to show chronic infections
with low parasitaemia. The organism transmission is limited to South East Asia, but poses a
significant public health threat for the world at large (Greenwood et al., 2008).
Plasmodium malaria causes disease, but cases are rare. Its erythrocytic stage takes 72
hours (Greenwood et al., 2008).
Plasmodium falciparum causes the largest numbers of deaths and cases globally because it
infects more erythrocytes than other Plasmodium species. Plasmodium falciparum can infect
erythrocytes by Glycophorin A2, B and C/D as opposed to Plasmodium vivax relying solely
upon the Duffy antigen for infection. Moreover Plasmodium falciparum can infect various
forms of differentiated erythrocytes whilst Plasmodium vivax can only infect and reproduce in
reticulocytes. Coupled to these physiological factors there are widespread Duffy negative
populations in West Africa where malaria prevalence (caused by Plasmodium vivax) would
be high due to the environment, consequently malaria caused by Plasmodium vivax is less
reported (Miller et al., 2002).
Mosquito
Plasmodium can infect many vertebrate species. Lizards and birds are infected by Culex
mosquitos. Meanwhile bats, rodents and primates are vulnerable to Anopheles mosquito
bites.
There are over 20 varieties of mosquito which can transmit malaria (Michalakis and Renaud,
2009).When female Anopheles mosquitos are pregnant they take blood meals which can
transmit or ingest Plasmodium species. When ingesting Plasmodium gametes, the parasite
takes 10-14 days to transform to sporozoites ready for the next transmission life cycle.
Anopheles mosquitos lay their eggs on water, the larvae take 2-4 days for development.
Temperature, climate, rainfall and humidity of >60% and temperatures varying between 20-
30°C are ingredients necessary for Plasmodium and Anopheles to coexist sufficiently for
transmission cycles to be appropriate for malarial transmission (Greenwood et al., 2008,
Michalakis and Renaud, 2009, Medicine, 1991).
Life cycle
Anti-coagulants, saliva and infectious sporozoites are injected into the blood or lymphatic
system when Anopheles takes a blood meal. The sporozoites are motile and travel to the
liver sinusoids in 30 minutes and usurp the kupffer cell, using it as a Trojan horse to enter
[3]
hepatocyte cells of the liver. Sporozoites may also enter hepatocytes directly using their
thrombospondin antigens to bind to hepatocytes’ heparin sulphate proteoglycans receptors.
The development of the infection in the liver is asymptomatic. The sporozoites asexually
transform into merozoites which are released from the liver entering general circulation
ready to infect erythrocytes. The infection of erythrocytes causes the clinical symptoms.
Depending on the species of Plasmodium, merozoites transform into ring, trophozoites,
schizonts structures and are then re-released as asexual merozoites which can replicate the
previous erythrocytic life cycle every 48 hours. 1013 multiplications can occur an hour
resulting in high parasitemia.
The multiplication stage within erythrocytes can also be carried out in a sexual reproductive
manner making male and female gametocytes. The asexual process causes the clinical
disease, the sexual process is benign.
The gametocytes are taken up by a feeding Anopheles. Inside the midgut of the mosquito
the gametes fuse by fertilisation to form a zygote and then develop into an Ookinete followed
by an Oocyst. Eventually the Oocycst bursts releasing sporozoites into the salivary gland to
repeat the malarial life cycle. The transformation of parasite within the mosquito is
temperature dependent (Michalakis and Renaud, 2009, Miller et al., 2002, Greenwood et al.,
2008).
Figure 1-Life cycle of malaria
[4]
Clinical Disease
The development of clinical symptoms occurs 7-20 days post Anopheles bite resulting in two
forms of pathology, uncomplicated malaria and severe malaria (Schantz-Dunn and Nour,
2009). Uncomplicated malaria causes flu-like symptoms including headaches, fever,
vomiting, tiredness and general increase in pro-inflammatory cytokines or endogenous
cytokines (Greenwood et al., 2008). Uncomplicated malaria can swing from hot and cold
stages of symptoms reflecting synchronised erythrocytic cell death.
Meanwhile severe malaria is associated with Plasmodium falciparum infections causing
multi-organ, renal failure, cerebral malaria, severe haemolysis of erythrocytes leading to
severe anaemia. Severe anaemia causes a rapid reduction in the oxygen carrying capacity
of the blood needed for respiration. With the toxic mix of a systemic pro inflammatory
cytokine response in full swing and lactic acidosis, hyperglycaemia, hypoxia and coma,
death can be the outcome. Thus severe malaria represents a medical emergency (Schantz-
Dunn and Nour, 2009, Stevenson and Riley, 2004).
Clinical Complications
The destruction of one infected erythrocyte is accompanied by twelve non-infected
erythrocytes being removed by the spleen. The body reacts by overproducing EPO
(Erythropoietin). However it has a limited effect on bone marrow production. It is thought
IFN-γ (Interferon-gamma), TNF-α (Tumour Necrosis Factor-alpha), MIF (migration inhibitory
factor), IL-10 or Hemozoin (a toxic breakdown product of haemoglobin produced by
Plasmodium species) may prevent bone marrow erythropoiesis (Lamikanra et al., 2007).
The following processes of sequestration, resetting and clumping all exacerbate malaria
symptoms.
The process of sequestration causes the adhesion of erythrocytes to endothelium or
vascular walls through the binding of Pfemp1 (Plasmodium falciparum erythrocyte
membrane protein 1) extrusions from erythrocyte cell membranes sticking to adhesion
factors expressed on vascular and endothelial walls, for example CD36, CSA (Chondroitin
Sulphate A) and ICAM-1 (Intracellular Adhesion Molecule 1). Sequestering of Plasmodium
parasites can occur in numerous tissues, brain, kidney, lung and heart. It has been
implicated with cerebral and placental malaria. Both block circulation and deprive the body of
nutrients and oxygen needed for effective respiration and the functioning of the brain and
heart (Miller et al., 2002). Cerebral malaria is common in low intensity localities, while severe
anaemia becomes more apparent in stable endemic regions (Greenwood et al., 2008).
[5]
Rosetting is the sticking together of infected and non-infected erythrocytes. The
phenomenon is caused by Pfemp1 antigens sticking out of the membrane and binding to
adhesion factors of other cells (Miller et al., 2002). Platelets can cause the clumping together
of infected cells. This can block blood circulation and prevent the transportation of oxygen to
cells (Miller et al., 2002).
Immunity
In order to get a grip of the high parasitemia and consequently control malaria, a synergy of
TH1 CD4 T-cells, CD8 T-cells, antibodies, Natural Killer (NK) cells, IFN-γ, IL-12, TNF-α
contribute to the alleviation of malarial symptoms (Stevenson and Riley, 2004).
The humoral arm of the adaptive immune system provides antibodies which contribute the
best protection and fight against plasmodium and its downstream effects. The main
attachment scenarios during the life cycle of malaria are where antibodies’ properties
provide the best prospects of protection.
For instance, the invasion of hepatocytes from sporozoites, the invasion of erythrocytes by
merozoites, neutralisation and opsonisation of motile parasites in the blood, prevention of
sequestration, antibody mediated phagocytosis by macrophages and the induction of
complement cascade pathways for splicing open of parasites are all properties of antibodies’
(Stevenson and Riley, 2004).
IFN-γ, IL-12 and TNF-α have beneficial properties. However their systemic response can in
reaction to a malarial infection, be deleterious. Consequently their overreaction can impede
their beneficial effects and also the beneficial effects of NK cells, γδ T-cells and Dendritic
Cell (Dc) antigen presentation, which bridges the arms of the innate and adaptive immune
response. However anti-inflammatory cytokines such as TGF-β and IL-10 can balance this
over-enthusiastic response of the immune system (Stevenson and Riley, 2004).
Genetics
Sickle cell anaemia and Duffy negative populations provide an element of resistance to
malarial infections. In the case of sickle cell anaemia, mutations to haemoglobin cause the
erythrocyte to become sickle shaped, therefore making the cell an unpalatable food source
for Plasmodium multiplication and development. The sickle shape also makes the mutated
erythrocyte susceptible to removal from circulation by the spleen. Regarding Duffy antigen,
as reiterated before, Plasmodium vivax needs the Duffy receptor to infect erythrocytes since
it is the sole receptor for invasion (Williams et al., 2005, Greenwood et al., 2008).
[6]
Diagnosing and detecting malaria
Malaria can be mistaken for flu-like symptoms and thus misdiagnosis is a crucial barrier to
controlling the disease and its sequelae. Therefore prompt diagnosis is vital. In rural
communities, microscopy by Giemsa staining remains the best option for malaria diagnosis
and detection due to its simplicity and low cost. However the expertise, experience and
validity of observationn can vary depending on the setting. Thus the introduction of Rapid
Diagnostic Tests (RDTs) which search for Histidine Rich Protein 2 (HRP2) provides a quick,
simple and large scale alternative in rural field settings.
To accompany these field setting methods, laboratory diagnosis by ELISA (Enzyme Linked
Immunosorbent Assay), PCR (Polymerase Chain Reaction) and immunofluorescence can be
implemented. However due to cost, limited resources and time these are more appropriate
for population-wide studies (Greenwood et al., 2008, Guerin et al., 2002).
Control and prevention strategies
Direct control of malaria involves treatment and vaccination, whilst indirect methods revolve
around inhibiting or killing mosquito vector populations (Michalakis and Renaud, 2009).
Insecticides are used to destroy vector population’s habitats capable of supporting
Anopheles populations. Sadly mosquitoes can breed in small water sources and human and
domestic consumption of insecticide-treated water sources can create health hazards.
Secondly, insecticides can be used in IRS (Indoor Residual Spraying) interventions, where
households are sprayed with chlorinated hydrocarbons such as DDT (dichloro-diphenly-
trichlorethane). This intervention must be used wisely, firstly to prevent resistance and
,secondly, to prevent “contact avoidance” where mosquitos have learned to avoid
households because of IRS control measures (Medicine, 1991, Michalakis and Renaud,
2009).
ITN’s (Insecticide Treated Nets) can keep children alive, reducing the number of cases and
deaths attributed to malaria. However their longevity reduces with time. Hence the
introduction of LLINs (Long-Lasting Insecticide-treated Nets) provides a barrier, sprayed with
a safe pyrethroid insecticide which can kill the mosquito. These last for three years and
contribute to protecting users against malaria and other vector-borne conditions such as
Chagas disease and African Sleeping Sickness (Greenwood et al., 2008, WHO, 2011).
Pyrethroid insecticides prevent neuronal transmission of action potentials by inhibiting
voltage gated channels. Pyrethroids are the only chemical safe enough for human use and
potent enough against mosquitoes. However, their use can cause resistance which has
[7]
been reported in 41 nations (WHO, 2011, Greenwood et al., 2008). The use of LLINs may
not necessarily be the best option in unstable transmission (hyperendemic) regions such as
Yemen, where mosquitoes bite not just at night but morning and evening (Guerin et al.,
2002, Medicine, 1991).
IPT (Intermittent Presumptive Therapy) provides anti-malarial treatment to high-risk groups
such as mothers who have recently given birth (20 weeks post birth of their child) and to
infants (Schantz-Dunn and Nour, 2009, WHO, 2011).
To implement an effective intervention, an understanding of the types of mosquito, their
number, habitat, peak rainfall and temperature of the region, the link between agricultural
practices and malaria levels, fresh or free standing water sources and potential benefit
versus harm of the intervention must be taken into consideration (Greenwood et al., 2008,
Najera, Raghavendra et al., 2011).
Treatment
All suspected patients with malaria should be given a diagnostic test before implementation
of a treatment strategy. Plasmodium falciparum is treated with Artemesinin Combination
Treatment (ACT’s), while Plasmodium vivax requires chloroquine and primaquine medication
strategy. The primaquine (a quinolone) prevents relapse whilst chloroquine prevents “haem-
detoxification” (WHO, 2011).
ACT is the first line anti-malarial medication of choice because of its limited side effects, a
95% efficacy and the limited development of resistance, only reported in regions of South
East Asia such as Thailand and Cambodia (Greenwood et al., 2008).
Serology and Seroconversion
Anti-malarial antibodies can provide evidence of an exposure to a malarial infection. They
take time to develop due to somatic hypermutation, affinity maturation and the repeated
exposure to an antigenic source. As a consequence children are more vulnerable to malarial
infections than adults who will have generated a level of immunity partly conferred by
antibodies and various other arms of the innate and adaptive immune system (Drakeley and
Cook, 2009).
Antibodies in collaboration with genetic, immunological (innate and adaptive) and age-
dependent factors provides a holistic level of protection from malaria (Drakeley and Cook,
2009).
[8]
After two weeks malaria antibodies are present. Thus, although not an ideal indicator of
current infection they provide a history of infection. With memory-B or plasma-B cell
responses antibodies are produced faster upon re-exposure to malaria. Dispute remains as
to the longevity of this response, however this is a better option for detecting malaria
exposure than infection itself which may disappear (Corran et al., 2007, Drakeley and Cook,
2009).
Serological measurements of antibodies for epidemiological studies can provide a robust
observation of malarial transmission in a locality or population. Serology provides a time
machine for detecting malaria. However they cannot provide an indication of a cessation of
malarial transmission when no exposure to infection is present (Corran et al., 2007).
The intensity of the infection and transmission can be investigated by looking at the
seroconversion rate λ (the rate of developing antibodies in a given time frame and their
longevity). It tells us how likely you are to experience the disease and the “immunogenicity”
of the given infection (Corran et al., 2007).
Testing these principles and observing a transmission trend in real life requires a robust,
sustainable and “high-through put” or large scale analysis. ELISAs (Enzyme Linked
Immunosorbent Assays) have these advantages. These test the presence of a sample for
antibodies against a given antigen stuck to a micro-titre plate. If the antibody binds
sufficiently without cross reactivity then a secondary antibody detection system using
Horseradish Peroxidase (HRP) or histidine tags can result in a colour change (Drakeley and
Cook, 2009).
This methodology must be tailored to the location. Yemen has epidemics of malaria
(hyperendemic) as opposed to endemic malaria transmission. Thus analysis by AMA-1
(Apical Membrane Antigen-1) would not be ideal in this location, meanwhile MSP-1
(Merozoite Surface Protein 1) is less immunogenic, providing a bone-fide response to
malaria in low transmission regions (Corran et al., 2007, Medicine, 1991).
MSP-1 is required in the invasion of erythrocytes by merozoites. They are involved in the
general reorientation of the merozoite to infect erythrocytes (Sherman, 2001). MSP-1 is a
GPI (glycosylphosphatidylinositol) antigen which forms the majority of the Plasmodium
membrane surface. MSP-1 (230kd) is proteolytically cleaved to MSP-1 (33kd) confirmation
leaving EGF (Epidermal Growth Factor) one and two domains to bind to erythrocytes
(Cowman and Crabb, 2006).
[9]
By using a serological measurement of MSP-1 by ELISA, it is possible to monitor malarial
transmission over a given time frame, meanwhile providing an insight into the effectiveness
of intervention strategies such as IRS or LLINs (Corran et al., 2007). Furthermore, such
observations can determine malaria prevalence hotspots in heterogeneous localities such as
Yemen where pre-elimination strategies are beginning to cut the head of the snake of
malaria (Bousema et al., 2012).
Malaria in Yemen and the Gulf region
Some 60% of the Yemeni population live in malaria prevalent sites (Alkadi et al., 2006).
Plasmodium falciparum is the main species which affects Yemen, particularly children and
women in pregnancy. Plasmodium vivax and Plasmodium malariae rarely cause reported
cases of malaria (Al-Maktari et al., 2003, Assabri and Muharram, 2002, Bassiouny and Al-
Maktari, 2005). Chloroquine is still used against Plasmodium falciparum in Yemen and the
effect of such treatment strategies has resulted in resistance to the medicine (Alkadi et al.,
2006). Poor information provided to people regarding malaria treatment and prevention
hampers efforts to control the disease (Abdo-Rabbo, 2003).
Surrounding nations in the Arabian Peninsula such as Saudi Arabia are also experiencing
problems with malaria control. Saudi Arabia has reported drug resistant strains of
Plasmodium falciparum in the Jazan district on the north-west border of Yemen. The Jazan
region experiences Anopheles arabiensis mosquito populations distributing malaria. The
majority of rainfall comes during “November to March” period (Al-Farsi et al., 2012).
Countries on the other side of the Gulf of Aden such as Somalia experience malaria at
endemic levels. Whether this has implications for the spread of malaria in Yemen needs
investigation (Al-Farsi et al., 2012).
Yemen and Current Malaria Status
Yemen lies at the southern end of the Arabian Peninsula sticking out into the Gulf of Aden
and Red Sea. It has a population of 24 million (WHO, 2011). Nearly half the population
(45.4%) are under the age of 15 (UNDP, 2012 ). The majority of the population live in rural
communities (76%) (UNDP, 2012 ) and 14.9 million or 62% live in high malaria transmission
areas (WHO, 2011). Some 52% of the Yemeni people have inadequate water supply and the
country is projected to run out of water in the near future (UNDP, 2012 , Yahia, 2011,
Clements, 2011). Yemen is the poorest Arab state, experiencing poor literacy and education
rates contributing to a food crisis, a humanitarian displacement and political insecurity
(Clements, 2011, UNDP, 2012 , EMRO, 2012).
[10]
Some 99% of malaria is caused by Plasmodium falciparum and transmitted by Anopheles
arabiensis, the main vector mosquito of the region (WHO, 2011). Yemen has adopted
WHO’s recommendation for the free distribution of bed nets to all people regardless of age.
IRS policy interventions have been implemented since 2001, as well as free diagnostic tests
before receiving ACT for malaria (WHO, 2011).
Figure 2- Satellite Image of Yemen. Yemen sits at the southern end of the Arabian Peninsula. Its geography
ranges from deserts in the north east bordering Saudi Arabia, lush mountainous valleys in the interior and shorelines sticking out into the Gulf of Aden and Red Sea across from Somalia and Eritrea. Reproduced from
Science Photo Library. PLANETOBSERVER/SCIENCE PHOTO LIBRARY. Downloaded 03/09/2012 http://www.sciencephoto.com/media/463256/view (NASA and LIBRARY, 2000).
Figure 3- Governorate Map of Yemen. Yemen is split up into governorates. The study site (red dot) is
situated in the Dhamar governorate of Yemen, specifically the Sukhmal valley of the Wusab As Safil district. Which lies south west of the capital Sana’a. Reproduced from Vascular plant flora and phytogeography of the southern governorates of Yemen, Freie Universitat Berlin. Downloaded 03/05/2012 http://www.bgbm.org/BGBM/research/areas/arabia/flora_sy.htm (Botanischer Garten und Botanisches Museum Berlin-Dahlem, 2009).
[11]
Figure 4- Current Profile of Malaria Epidemiology in Yemen. The map highlights the west of Yemen
being greatest effected by malaria. This coincides with figure one describing the geography of the country and in particular, the Sukhmal valley as hilly terrain. The Sukhmal valley varies from 1-50 cases of malaria per 1000 people. Whether the coincidence of higher malaria transmission coincides with terrain or closeness to the Red Sea coast remains a significant question, particularly in light of almost 15 million Yemenis living in high transmission regions. Reproduced from WHO World Malaria Report 2011. World Health Organisation. Downloaded 03/05/2012 http://www.who.int/malaria/world_malaria_report_2011/en/ (WHO, 2011).
Yemen Study and Objectives
The goal was to use the two household survey data sets for implementing LLINs alone or
LLINs and IRS interventions together in the cluster sites in order to reduce malaria
transmission in the region as part of Yemen’s pre-
elimination strategies (Othman, 2012).
Two studies were performed in the Sukhmal Wadi
(valley) of the west of Dhamar governorate in
Yemen. The studies were conducted by the National
Program for Combating Malaria, EMRO (Eastern
Mediterranean Regional Office) of the WHO in
collaboration with GEF (Global Environmental
Facility) and UNEP (United Nations Environmental
Programme) (Othman, 2012).
The valley has a population of 29,547 people. It is an
agricultural region growing bananas, Qat (a mild narcotic), coffee, cotton and mangoes. This
is supported by a “perennial flow” water stream. The land is hilly and lush, at an altitude of
between 600-1,000 metres above sea level (Othman, 2012).
Figure 5-The Sukhmal Stream. The
“Perennial flow” snaking its way through the Sakhmal valley, illustrating the lush
vegetation, swamp lands and habitat for Anopheles mosquito development
(Othman, 2012)
[12]
A preliminary assessment was conducted in
October 2011 (peak transmission) to evaluate the
baseline malaria transmission in the region on 1,935
children. Twelve clusters were chosen with 100-200
children aged between 0.5-15 years. Each child had
an RDT test and a drop of blood taken for
microscopy slide detection of malaria. Approval for
the survey was granted by the National Committee
for Health Research (Othman, 2012).
The second assessment was implemented in March
2012 (the dry season) on 2,254 children. The same 12 cluster sites were assessed by a
team of five scientists. Each cluster had on average
200 children aged between 2 months-15 years old.
Three days were taken to sample each cluster.
Whilst obtaining samples, investigators employed
standard aseptic techniques to prevent and avoid
transmission of blood-borne infections.
Current infections were assessed by RDTs and
microscopy analysis. The microscopy tests were
performed on microscopy slides and repeated twice
for each child by Giemsa staining protocols. The
microscopy analysis took place in Sana’a.
Meanwhile, previous exposure to malaria was assessed by filter paper blood spots on thin
3mm Whatman chromatography paper which would be analysed by serological (ELISA) and
molecular biology (PCR) methods at LSHTM (London School of Hygiene and Tropical
Medicine).
Some, 11.5% were positive for malaria by microscopy whilst RDTs showed 12.6% of the
children had malaria, 93% being Plasmodium falciparum positive cases and 7% of the
children being Plasmodium malariae positive.
Filter paper blood spots were sealed in plastic bags with desiccant to prevent deterioration of
sample. Samples were placed in brown envelopes relating to cluster number and stored at
4°C until and during experimentation. To accompany the above surveys further work was
conducted to establish that Anopheles arabsiensi was the main vector in the Sukhmal valley
at 69.8%. Steps were and are being taken to improve health providers’ knowledge of
Figure 6- The Sukhmal Valley. The
valley varies in altitude between 600-100metres above sea level (Othman, 2012)
Figure 7- The First Malaria Survey in October 2011. Obtaining RDT’s and
microscopy samples from children to assess malaria prevalence during peak transmission period (Othman, 2012).
[13]
malarial symptoms and anti-malarial drugs were provided free of charge to diagnosed cases
(Othman, 2012).
Figure 8- Altitude of the Sukhmal Valley. Altitude map showing the twelve cluster locations , the number
of villages per cluster site and their relation to the Wadi or “perennial flow” valley stream. Reproduced PowerPoint Presentation from Dr. Mustafa Abdel Raouf Othman of the National Malaria Control Programme, 2012. (Othman, 2012)
[14]
Figure 9- The Prevalence of Malaria in the Wadi Sukhmal Valley The results of the first control survey in
October 2011, indicating the prevalence of malaria by percentage in each cluster site. The map illustrates malaria prevalence is closely linked to the distance of cluster sites to the stream. Additionally, the further north one looks, the greater the prevalence of malaria in the valley. Paradoxically this is imitated by higher altitude levels. A paradox exists between clusters two and four. Both clusters have similar altitude levels but large differences in malaria prevalence rates (44% difference). The lower altitude clusters have lower malaria prevalence’s such as clusters 6, 9, 7, 10, 11, 12. Reproduced PowerPoint Presentation from Dr. Mustafa Abdel Raouf Othman of the National Malaria Control Programme, 2012. (Othman, 2012).
[15]
Aim of Serological Investigation
The overall serological assessment aimed to understand the history of infection in over
2,000 children in the Sukhmal Wadi (valley) of the Dhamar Governorate of Yemen and,
specifically, to assess the prevalence of MSP-1 antibodies against Plasmodium falciparum
and Plasmodium vivax species.
Specific objectives to achieve this overall goal were firstly, to compare the detection of
malaria by microscopy, RDTs or serology. Secondly, to compare the serological results
against age, sex, cluster site and distance from “perennial flow” or stream. Thirdly, to show
which is more specific or accurate for te detection of current or past infections, since
serological assessments show up exposure to previous infections, while RDTs and
microscopy data provide current infection status. Additionally, which assessment might have
overlooked an infection or provided a false positive? To observe under-fives because such
children can provide an insight into the possibility of a malarial epidemic during the previous
five years. To investigate the possibility of heterogeneity between villages in individual
clusters and improve field site investigators’ ability to target malarial hotspots either by LLINs
alone or by LLINs and IRS interventions combined. What are the immunological implications
of the results? Finally, to conclude what implications this could have for the prevention and
pre-elimination of malaria in the valley itself and Yemen as a whole?
[16]
Materials and Methods
Reconstitution of Sample
Prior to experiments samples were stored in plastic bags with desiccant at 4°C. Using a
leather hole punch a “3.5mm” sized blood spot was pierced from the filter paper and placed
into a “low binding“ 96 well deepwell plate. Note of identification number was corresponded
onto a plate plan. 280μl of reconstitution buffer (0.1% sodium azide, 0.05% PBS-T
(Phosphate Buffered Saline-Tween20) was pipetted to elute sample from filter paper
(providing a 1 in 200 dilution of sample). An overnight incubation began by sealing the plates
and gently rocking them at room temperature. Once the sample had completely eluted,
plates were kept at 4°C till experimentation (Bousema et al., 2010, Rand, 2012).
ELISAs Plasmodium falciparum MSP-1 antigen (CTK
used at 1/2500) was coated to a 96 well
maxisorb plate at 0.5μg/ml and incubated
overnight at 4°C. Coated plates had to be
washed and blocked to remove the chances of
non-specific binding by non-specific antibodies.
Washing used 0.05% PBS-T and blocking used
150μl per well of blocking buffer (1% Marvel milk
powder in 0.05% PBS-T solution). Plates were
incubated at room temperature for 3 hours.
Plates were washed. Primary antibody (Yemen
sample) was added to plates at a final
concentration of dilution 1:1000. Positive and
negative controls were added. Positive controls
were pooled hyperimmune CP3 serum from
Tanzania. Plates were washed. Plates were
incubated overnight at 4°C. Plates were washed.
The conjugate (anti-human rabbit HRP
monoclonal antibody) was added at 1-50,000
dilution, plates were incubated for 3 hours at
room temperature before a final wash and the addition of OPD (o-Phenylenediamine
Dihydrochloride) solution supplied by Sigma fast (LOT#061M8227V) to detect the presence
of antibodies against MSP-1 after 16 minutes 30 seconds of incubation. H2SO4 (2 mol/L)
solution stopped reaction, see figure nine. Plates were analysed at 492nm on plate reader.
Figure 10- Filter paper with blood spots and ELISA. ELISA conducted in duplicates for samples, with controls in the last two lanes of the plate. Positive controls in the rows one to six and negative controls in rows seven to eight. Change in colour is a result of OPD solution showing detection of Plasmodium falciparum MSP-1 antibodies
[17]
The process above for Plasmodium falciparum was used for Plasmodium vivax ELISAs. The
Plasmodium Vivax MSP-1 antigen was supplied by CTK at 1/2500 dilution (Bousema et al.,
2010, Rand, 2012).
Analysis of Results
OD (Optical Densities) results from plate reader spectrophotometer were uploaded into an in
house Microsoft Excel MACRO to check for duplicates, ensure positive controls had worked,
that the background readings were not too high (misreading negative samples as positives)
and that negative controls were functioning as expected. Results were input into a STATA
statistical package (Statistics/Data Analysis/IC 12.1).
Some 2,182 serological measurements were performed by ELISA. Serological data was
cleaned and merged to clinical data from the second pre-elimination survey (March 2012).
Some, 2,087 observations remained after data was merged and cleaned. The discrepancy
was due to 91 serological and 159 clinical data points being omitted, either because samples
they were not received or did not match. There were four genuine repeats.
[18]
Results
Figure 11- Overview of MSP-1 Plasmodium falciparum Serological data. OD provides an indication
of true antibodies against Plasmodium falciparum MSP-1.
Figure 12- Overview of MSP-1 Plasmodium Vivax Serological data. OD provides an indication of true
antibodies against Plasmodium vivax MSP-1.
Plasmodium falciparum MSP-1 Serology
Pe
rce
nta
ge P
osi
tive
(%)
05
10
15
0 .5 1 1.5 2 2.5
Plasmodium falciparum MSP-1 Serology (OD)
Density Fitted_Plasmodium falciparum OD
05
10
15
20
-.1 0 .1 .2 .3Plasmodium vivax MSP-1 Serology (OD)
Density Fitted Plasmodium Vivax OD
Plasmodium vivax MSP-1 Serology
Perc
enta
ge P
osit
ive
(%)
[19]
Figure 13- Percentage of children with antibodies to Plasmodium falciparum MSP-1 distributed
against increasing age-groups.
Figure 14- Percentage of children positive or negative for antibodies against Plasmodium
falciparum MSP-1.
0.1
.2.3
.4
Mea
n P
lasm
od
ium
fal
cip
aru
m
MS
P-1
An
tib
od
ies
%Comparing Plasmodium falciparum
MSP-1 Antibodies Against Age
Age
78.34%
21.66%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
Negative Positive
Pe
rcen
tage
of
Ch
ildre
n w
ith
An
tib
od
eis
A
gain
st P
lasm
od
ium
fal
cpar
um
MSP
-1 (
%)
Negative or Positive for MSP-1 Serology
Total Percentage of Children with Anitbodies Against Plasmodium falciparum MSP-1
[20]
Figure 15- Percentage of children with antibodies against Plasmodium falciparum MSP-1 defined
by cluster site.
Figure 16- Percentage of children with antibodies against Plasmodium falciparum MSP-1 defined
by cluster site under the age of five.
5.98%
43.84%
17.11%
30.65%
36.13%
8.70%
14.81%
22.35%
4.04%
28.73%
3.49%
43.55%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
1 2 3 4 5 6 7 8 9 10 11 12
Pe
rcen
tage
of
Ch
ildre
n w
ith
An
tib
od
ies
Aga
inst
P
lasm
od
ium
fal
cip
aru
m M
SP-1
(%
)
Cluster Site
Percentage of Children with Antibodies Against Plasmodium falciparum MSP-1 in Each Cluster
3.70%
34.04%
10.99% 13.64%
19.78%
3.33% 4.84%
6.94% 4.71%
16.09%
1.49%
22.95%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
1 2 3 4 5 6 7 8 9 10 11 12
Per
cen
tage
of
An
tib
od
ies
Aga
inst
Pla
smo
diu
m
falc
ipa
rum
MSP
-1 (
%)
Cluster Site
Percentage of Children Under Five Years Old With Antibodies Against Plasmodium
falciparum MSP-1 in Each Cluster
[21]
Figure 17- Percentage of children defined by sex, either being positive or negative for antibodies
Plasmodium falciparum MSP-1 antibodies.
Figure 18- Comparing the negative or positive RDT detection of malaria against the percentage of
children with antibodies against Plasmodium falciparum MSP-1 serology.
77.36% 79.23%
22.64% 20.77%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
Female Male
Per
cen
tage
of
Ch
ikd
ren
wit
h A
nti
bo
die
s A
gain
st P
lasm
od
ium
falc
ipa
rum
MSP
-1
Sex
Percentage of Children with Antibodies Against Plasmodium falciparum MSP-1 Defined by Sex
Negative Plasmodium falciparum Serology
Positive Plasmodium falciparum Serology
82.03%
53.73%
17.97%
46.27%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
RDT Negative RDT Positive Pe
rcen
tage
of
An
tib
od
ies
Aga
inst
Pla
smo
diu
m
falc
ipa
rum
MSP
-1 (
%)
Comparison of RDT Against Plasmodium falciparum MSP-1 Serology
Negative Plasmodium falciparum Serology
Positive Plasmodium falciparum Serology
[22]
Figure 19- Comparing the RDT clinical results (%) against the percentage of children with
antibodies against Plasmodium falciparum MSP-1 serology by cluster site.
Figure 20- Comparing the RDT clinical results (%) against the percentage of children with
antibodies against Plasmodium falciparum MSP-1 serology by cluster site in children under five.
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.00 0.10 0.20 0.30 0.40 0.50 Pe
rcen
tage
An
tib
od
ies
Aga
inst
Pla
smo
diu
m
falc
ipa
rum
MSP
-1 (
%)
Rapid Diagnostic Tests (%)
Rapid Diagnostic Tests Compared With Serology By Cluster Site
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Cluster 8
Cluster 9
Cluster 10
Cluster 11
Cluster 12
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.00 0.10 0.20 0.30 0.40 0.50 0.60
An
tib
od
ies
Aga
inst
Pla
smo
diu
m f
alc
ipa
rum
MSP
-1
(%
)
Rapid Diagnostic Tests (%)
Rapid Diagnostic Tests Compared With Serology By Cluster Site in Children Under Five
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Cluster 8
Cluster 9
Cluster 10
Cluster 11
[23]
Figure 21- Comparison of the specificity of analysis by RDTs, microscopy and serology
Serological investigation of Plasmodium falciparum MSP-1 samples were input into a
histogram as seen in figure eleven. To define the cut off, for where a child’s sample
becomes positive for Plasmodium falciparum MSP-1 antibodies detection a cut off of three
standard deviations plus the mean meant any values falling over 0.86086 OD resulted in the
sample being positive for Plasmodium falciparum MSP-1 antibodies. The mixture model is
applicable in this scenario, because there is sufficient numbers of children positive for
Plasmodium falciparum MSP-1 antibodies.
A cut off of three standard deviations plus the mean was performed for figure twelve, any
values that fell above 0.058 OD were positive for Plasmodium vivax MSP-1 antibodies. A
large density of the data points was negative for Plasmodium vivax MSP-1 antibodies. Some
27 children (1.32%) were positive. However, this was not a true reflection of the data
because twenty-one of the samples fell below 0.1 OD. Only one genuine observation was
seen at 0.3 OD. This could be the result of a child travelling to the region, importing
Plasmodium Vivax into the Sukhmal valley rather than an inherent source within the valley.
The mixture model is not truly applicable to Plasmodium vivax serology because it requires a
greater number of results being positive to function. Consequently Plasmodium vivax
transmission is non-existent in the Sukhmal valley.
0.000
0.100
0.200
0.300
0.400
0.500
0.600
1 2 3 4 5 6 7 8 9 10 11 12
Pe
rcen
tage
(%
)
Cluster Site
Comparing RDT, Microscopy and Serology Including Under 5's
Plasmodium All
Plasmodium falciparum Under Five
RDT All
RDT Under Five
Microscopy All
Microscopy Under Five
[24]
Figure thirteen, describes the percentage of children positive being for Plasmodium
falciparum MSP-1 antibodies against defined age groups. As children become older, so their
percentage of MSP-1 antibodies to Plasmodium falciparum increases. Under one year old
infants (<1) show a slight increase, before the proportional increase in seroprevalence. This
can be attributed to maternal antibodies. A slight dip is apparent in 1-2 year old children
before rising to above 30% seroprevalence in nine year olds and older age groups. There is
a dip observed in the trend between 11-12 year old children, this could be attributed to the
cleaning data process, removing data points and perhaps the limited number of children
studied at this age range.
Figure fourteen, illustrates a comparison of children either positive or negative for
Plasmodium falciparum MSP-1 antibodies. The survey shows, 21.66% of the children in the
Sukhmal valley were positive for Plasmodium falciparum MSP-1 antibodies.
Figure fifteen, gives a suggestion of the prevalence of MSP-1 antibodies to Plasmodium
falciparum defined by the twelve cluster sites. Cluster two had the highest seroprevelence at
43.84% and cluster eleven had the lowest at 3.49%.
Observations of Plasmodium falciparum MSP-1 antibodies in under 5’s defined by cluster
site as seen in figure sixteen, matched figure fifteen’s observations overall. However, all
cluster sites had lower seropositives in under fives compared to all groups. Cluster two had
34.04% the highest, meanwhile cluster one had 3.70%, followed by cluster six having 3.33%
and cluster eleven having the lowest at 1.49%. Cluster nine showed an anomaly, under
fives had a higher percentage of Plasmodium falciparum MSP-1 antibodies (4.71%)
compared to all children within that cluster of 3.49%.
Comparing the seroprevelence defined by sex as seen in figure seventeen, resulted in
22.64% of the girls being sero-positive compared to 20.77% of the boys.
Figure eighteen, compared RDT detection with serological detection. Children in the study
that reported RDT negative and serology negative were 82.03% prevalent. However, 17.97
% of the RDT negative children were serologically positive. Some 52.73% of the children
sampled were RDT positive but serologically negative. Meanwhile, 46.27% were RDT
positive and in agreement with being serologically positive also.
Figure nineteen, compares RDT results to serological results for each of the twelve cluster
sites. It shows a proportional trend as RDT detection increases so does seroprevalence
detection of Plasmodium falciparum MSP-1 antibodies increases. Clusters three and four are
[25]
outliers. This is replicated using in under-fives in figure twenty, testing the same parameters.
However cluster four and twelve are the outliers from the proportional trend.
Figure twenty-one, describes the comparison in detection methodologies implemented in the
March 2012 study RDTs, microscopy and serology. It provides an insight into the specificity
of different detection strategies. Cluster two and four illustrated higher observations, in
comparison to lower observations in cluster one and six.
Discussion
Serological investigation of malaria can provide an insight into the history of exposure to the
disease in an individual, population or region (Corran et al., 2007). For instance, 21% of
children in the Sukhmal valley had experienced of malaria, as observed in figure fourteen.
As children get older they acquire Immunity to diseases that in earlier in their lives might
have caused morbidity or even, mortality. Immunity is developed because of increased
encounter with disease, affinity maturation of antibodies becoming more specific and the
acquisition of memory B-cell responses which are able to tackle a secondary infection
quicker and with greater amplification than against the primary infection (Corran et al., 2007,
Drakeley and Cook, 2009). In regards to the Yemen pre-elimination surveys on malaria,
these principles have come vividly to light. Figure thirteen, demonstrates these principles in
action, as the age of the children increases the percentage of MSP-1 specific antibodies
against Plasmodium falciparum also increase. Consequently, they are more able to fight
malarial infections better. It would be of further interest to investigate the rates of morbidity
and mortality caused by malaria in the Sukhmal valley, compared with age groups to show
definitively that humoural immunity is the principle guardian of children against a malarial
infection and perhaps death. Additionally, it would be of interest to investigate the avidity of
the antibodies in infants between one and three years old, compared against the avidity of
antibodies in older age groups, such as nine year olds and older aged groups. Because one
could propose, that the avidity of MSP-1 antibodies against Plasmodium falciparum provides
an element of protection which once passing above a certain threshold of age and/or avidity
prevents the deleterious effects of the erythrocytic life-cycle of malaria and its knock-on
clinical complications. For instance, severe anaemia, cerebral malaria, sequestration,
clumping, and rosetting.
Comparing the RDT data of the second pre-elimination survey of March 2012 referred to in
figure nine, with the serological data in figure fifteen illustrates an insight into the different
specificities of the detection strategies. RDT detection can observe infections in the real
[26]
time, such as during a current epidemic. Meanwhile, serology provides a historical, indirect
and yet, robust suggestion of infection or exposure to malaria during a child’s lifetime
(Bousema et al., 2010, Corran et al., 2007). Both have their advantages and disadvantages.
For instance, figure fifteen and nineteen, show cluster twelve having a seroprevalence of
43.35%. However, it is a mere 13% in the RDT data of figure eight. This illustrates, children
of cluster twelve have experienced high levels of malaria exposure in the past, but are
currently not experiencing malaria exposure of such intensity or immunogenicity today. A
suggestion of an epidemic in the past? This hypothesis was confirmed by Dr. Immo
Kleinschmidt (co-coordinator of the Yemen study), describing anecdotal evidence of a
malaria epidemic in the cluster twelve locality, by the villagers, before the pre-elimination
surveys began.
Meanwhile, cluster two in figure fourteen shows 43.84% of children have made a serological
response to Plasmodium falciparum MSP-1. However, cluster two’s RDT data shows a
prevalance of above 56%. This could be a suggestion of a current epidemic in the cluster.
This is because, antibodies take two weeks to develop to a stage where they can benefit the
holistic responses of the innate and adaptive immune responses contributed by cytokines
TNF-α, IFN-γ and IL-12 cytokines and immunological NK cells, γδ T-cells, TH1 CD4 T-cells
and CD8 T-cells, the serological detection has missed this observation of the RDT data
(Stevenson and Riley, 2004).
The reduction in response seen in figure sixteen could be due to the reduced numbers of
children studied (because of the age bracket) and secondly, because children take a longer
time to develop immunological responses to malaria whilst young (due to age and
exposure), as shown in figure thirteen (Bousema et al., 2010).
However, cluster nine’s RDT data and serology results show an interesting contrast.
Seroprevalence is 4.71% (figure sixteen), meanwhile RDT is 1% (figure ten).What might
account for such a discrepancy? The RDT observation might be showing cluster nine is
currently experiencing low level transmission. However, in the past this might have been for
more forceful infection as observed by the serology (suggesting a history of exposure to
malaria). Secondly the distance from cluster nine to the stream, as with cluster six, may be
the overall reason why malaria prevalence is not so high in these regions.
The slight differences observed between girls and boys in terms of their percentage of MSP-
1 antibodies against Plasmodium falciparum as seen in figure seventeen, could be attributed
to girls acquiring immunity at an earlier age in childhood. Or it could be, girls are more
susceptible to malarial infections then boys, hence the production of antibodies to protect
them. Or, boy’s childhood activities differ slightly to girl’s childhood activities. And, that boys
[27]
are living or playing in an environment that make them less susceptible to Anopheles
mosquito bites compared to girls, consequently antibodies maybe a less essential arm of
protection against malaria.
Figure eighteen, has highlighted 17.97% of children were RDT negative and serologically
positive. This shows the benefit of serological assessments picking up observations of
malarial exposure and transmission which otherwise would have been missed by RDT
detection. It also shows that the Sukhmal valley has had exposure to malaria in the past,
which could not be brought to light otherwise with the two pre-elimination field surveys.
Serology is a time machine.
Figure twenty-one attempts to show the ability of different detection strategies such as
serology, RDTs or microscopy. Serology appears to notice all observations all clusters,
otherwise missed by other detection methods. This fits with the serological investigations of
Corran et al 2007, because looking at a history of infection over a longer period of time to
analyse the extent of malarial transmission results in more observations. Meanwhile, RDT
detection picks up the majority of infections by cluster, but because it’s looking at a smaller
window of exposure as opposed to serology, the number of observations are smaller.
Thirdly, microscopy detection identifies clusters with high prevalence rates (as with other
detection strategies), but due to the smaller window of exposure the number of observations
are the lowest because the microscopy is a less sensitive method. Thus, from a holistic
perspective, serology provides the best yardstick for detecting malaria, however due to time,
expense and limited recourses in the Sukhmal valley. RDTs would provide the best on-field
form of malarial detection.
Cluster two had an RDT prevalence of 56%, the highest recorded value in figure nine. Five
villages within the cluster were chosen to distinguish which village had the highest
seroprevelence. Village number five totalling 26 children had a prevalence of 60%
meanwhile village number one totalling 47 children had a prevalence of 50%. Ideally
confidence intervals would have been included due to the small numbers of children per
villager. Nevertheless, the two villages would represent the best targets or hotspots for the
application of a tailored anti-malarial intervention by LLINs and IRS which could result in the
reduction of malarial transmission in those villages, outside villages in the cluster and
perhaps other clusters beyond in the Sukhmal valley (Bousema et al., 2012).
The stream which runs through the Sukhmal valley appears to be the main reason for high
malarial transmission, as shown in figure nine. Clusters two, three, four, five, eight, ten and
twelve all sit on the stream and have high prevalence’s of malaria by RDT (figure nine) and
by serology (figure fifteen). This outcome is the result of the stream acting as a fertile habitat
[28]
for Anopheles mosquito development. In addition, the stream is used for the irrigation of the
farm land and therefore, human interaction is increased (Greenwood et al., 2008, Michalakis
and Renaud, 2009). Interestingly, the more north and higher in altitude the clusters are as
shown in figure eight, the greater the detection of malaria by serology as shown in figure
fifteen and RDT, figure nine.
Furthermore, there are plans to introduce random allocation of LLINs and IRS interventions
into the Sukhmal valley. It would be interesting to observe what difference this would have
on the cluster sites particularly measuring the peak transmission period during October
2012. Thus,comparing the baseline malaria prevalence with intervention prevalence.
Additionally, an assessment of the benefit of LLINs alone or LLINs with IRS effects on
cluster sites which have the highest malaria transmission rates.
The high level of Plasmodium falciparum detection by serology corroborated The WHO’s
Yemen Profile Report showing Plasmodium falciparum being the main vector cause of
malaria in Yemen (WHO, 2011). However, the non-existence of Plasmodium vivax is a
paradox. The species can survive at lower temperatures, thus should affect a greater swath
of the globe including Yemen, as a consequence one would predict some observation being
present in the Sukhmal valley. Reasons for its absence could be genetic related, mosquito
driven or environmental. Firstly genetic, a high level of children in the valley being Duffy
antigen negative or sickle cell anaemia positive or afflicted with Thalassemia blood disorders
would result in the inhibition of a sustainable life cycle for Plasmodium (Williams et al.,
2005).Secondly, the presence of Anopheles arabiensis being the most abundant mosquito
in the region (Othman, 2012, WHO, 2011) may only allow the spread of Plasmdoium
falciparum species and not Plasmodium vivax species. And lastly, the environment may be
at an altitude incapable of allowing the transmission of Plasmodium vivax species.
One area of contention, is the ability of cross reactive antibodies binding to MSP-1 non-
specifically and producing a false positive. One possible method of avoiding this would be to
conduct PCRs on all the samples and to check for discrepancies between the serological
data and the PCR data. PCR is the most sensitive detection system, thus would be
indicative of a true value.
[29]
Recommendations
The second pre-elimination survey observed the of presence Plasmodium malariae samples
in some children of the Sukhamal valley. Plasmodium Malariae is under reported due to its
ability to lie dormant or latently within the liver as a hypnozoite. Hence, performing PCR’s to
confirm this observation would have been significant for the children, the Sukhmal valley and
the scientific literature as a whole. In addition, if ex vivo samples had been obtained during
the study, PCR’s could have been performed to assess the validity of dormant hypnozoites
within the liver and what prospect this could have for the childrens lives.
It appears the prevalence of malarial exposure is linked to the stream. Thus performing
tailored interventions to stream and the interactions of the people who use it could have
beneficial consequences for reducing malarial transmission. This could be implemented by
larviciding the stream with biological interventions such as Bacillus thuringiensis israeliensis
bacteria which kills Anopheles larvae or insecticide treatment of the land in collaboration with
the agricultural community of the Sukhmal valley, who themselves can determine the health
of inhabitants and its land.
Yemen is facing terrible food and water shortages (Clements, 2011). Coupled to fickle
security and civil unrest, it remains to be seen if the malaria intervention in the Sukhmal
Wadi can be begun and sustained to ensure elimination of malaria from the valley. In 1999,
Angola’s warring factions began a cessation of hostilities to one another called the “days of
tranquillity.” It resulted in polio vaccinations being given to over three million children. The
implementation of such an intervention although politically and geographically, very difficult,
perhaps this would be the intervention that could lead to the alleviation of millions suffering
from malaria, starvation and drought in Yemen and thus, reducing morbidity and mortality
(Humanitarian Cease-Fires Project and WHO, 2012).
The Sukhmal serological studies, aid Yemen’s pre-elimination strategies. Secondly, they
contribute to defining the best to use of LLINs and IRS. This will lead to benefitting children
of the Sakhamal valley, the Dhamar governorate, Yemen and its region. Not just for this
generation, but for generations to come.
[30]
References ABDO-RABBO, A. 2003. Household survey of treatment of malaria in Hajjah, Yemen. East Mediterr
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PEREIRA, A., IDRIS, M. A. & BABIKER, H. A. 2012. Source of drug resistant Plasmodium falciparum in a potential malaria elimination site in Saudi Arabia. Infect Genet Evol, 12, 1253-9.
AL-MAKTARI, M. T., BASSIOUNY, H. K., AL-HAMD, Z. S., ASSABRI, A. M., EL-MASSRY, A. G. & SHATAT, H. Z. 2003. Malaria status in Al-Hodeidah Governorate, Yemen: malariometric parasitic survey & chloroquine resistance P. falciparum local strain. J Egypt Soc Parasitol, 33, 361-72.
ALKADI, H. O., AL-MAKTARI, M. T. & NOOMAN, M. A. 2006. Chloroquine-resistant Plasmodium falciparum local strain in Taiz Governorate, Republic of Yemen. Chemotherapy, 52, 166-70.
ASSABRI, A. M. & MUHARRAM, A. A. 2002. Malaria in pregnancy in Hodiedah, Republic of Yemen. East Mediterr Health J, 8, 245-53.
BASSIOUNY, H. K. & AL-MAKTARI, M. T. 2005. Malaria in late pregnancy in Al Hodeidah Governorate, Yemen. East Mediterr Health J, 11, 606-17.
BOTANISCHER GARTEN UND BOTANISCHES MUSEUM BERLIN-DAHLEM, F. U. B. 2009. Available: http://www.bgbm.org/BGBM/research/areas/arabia/flora_sy.htm [Accessed 22/09/2009.
BOUSEMA, T., GRIFFIN, J. T., SAUERWEIN, R. W., SMITH, D. L., CHURCHER, T. S., TAKKEN, W., GHANI, A., DRAKELEY, C. & GOSLING, R. 2012. Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med, 9, e1001165.
BOUSEMA, T., YOUSSEF, R. M., COOK, J., COX, J., ALEGANA, V. A., AMRAN, J., NOOR, A. M., SNOW, R. W. & DRAKELEY, C. 2010. Serologic markers for detecting malaria in areas of low endemicity, Somalia, 2008. Emerg Infect Dis, 16, 392-9.
CLEMENTS, A. J. 2011. Yemen: Fragile lives in hungry times. Oxfam. CORRAN, P., COLEMAN, P., RILEY, E. & DRAKELEY, C. 2007. Serology: a robust indicator of malaria
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Student’s Questionnaire
Candidate No: 105322 MSc Immunology of Infectious Diseases
Project Supervisor: Dr. Chris Drakeley
Project Title: Seroepidemiology of Malaria in Yemen
As part of our assessment procedure for student projects we are asking you to complete the
following short questionnaire. Please tick the most appropriate statements in each section and
bind it into your project. A copy of this questionnaire must be bound into your finished
project report.
(Please ensure you tick the correct box)
Who initiated the project?
x My supervisor
Me
How much help did you get in developing the project?
none: I decided on the design alone
some: I used my initiative but was helped by suggestions from my supervisor
substantial: My supervisor had most say, but I added ideas of my own
x maximal: I relied on the supervisor for ideas at all stages
not applicable: the nature of the project was such that I had minimal opportunity to contribute to the design
How much help did you get in carrying out the work for the project?
none: I worked alone with no supervisor input
minimal: I worked alone with very little supervisor input
appropriate: I asked for help when needed
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x substantial: the supervisor gave me more assistance than expected
excessive: the supervisor had to give me excessive assistance to enable me to get data
What was the degree of technical difficulty involved?
slight: data easily obtained
moderate: data were moderately difficult to obtain
x substantial: data were difficult to obtain
How much help were you given in the analysis and interpretation of any results?
none
x standard: My supervisor discussed the results with the me and advised on statistics and presentation
substantial: My supervisor pointed out the significance of the data and told me how to analyse it
How much help were you given in finding appropriate references?
none
x some: only a few references were provided
substantial: most references were given by my supervisor
maximal: the supervisor supplied all the references used by me
How much help did you get in writing the report?
none: my supervisor did not see the report until it was submitted
minor: my supervisor saw and commented on parts of the report
x standard: my supervisor saw and commented on the first draft of the report
substantial: my supervisor gave more assistance than standard
How much time was spent on the project?
x too little to expect adequate data*
sufficient
too much*
*if too little or too much, were there any reasons for it, e.g. unforeseen technical problems, lack of materials,
etc.?
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Running out of Taq Polymerase enzyme, knowing exactly what data i wished to show but being unable to make
the data because of the technical commands involved in STATA and serology on Plasmodium malaraie samples
would have been interesting.
During the course of the work was your contact with your supervisor
Daily
Weekly
Monthly
x Varied but at regular intervals
Never
Was this contact with your supervisor
too infrequent
x infrequent but sufficient
frequent but not excessive
excessive
Please comment on your experiences during the project
The first meeting with Chris was inspiring.
If feel i let Chris down being unable to do the PCR’s on the Plasmodium Malaraie samples PCR.
I wish i could of gone to Yemen.
It would have been nice to have been given more freedom and independence with respects lab work, thus
allowing me to make my own mistakes and learn from them for the future. (I like to work alone sadly)
Regards project allocation it would have been nice to have had one morning where all the supervisors gave a 2
minute specific explanation (dragons den style) presentation of their work to help in guiding students what
supervisors are working on, supervisors interest, where to find supervisors at LSHTM, how to contact
supervisors, whats expected of students from supervisors and what kind of person the supervisor is.( maybe a
video presentation if unable to come in person).
Lastly it has been sad not to have worked on HIV basic science at LSHTM.
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THIS QUESTIONNAIRE MUST BE BOUND INTO YOUR PROJECT REPORT