MOLECULAR EPIDEMIOLOGY OF MALARIA AND

DETECTION OF ANTI-MALARIAL DRUG RESISTANCE-

ASSOCIATED MARKERS (PFCRT, PFMDR-1, PFDHFR

AND PFDHPS) IN HADHRAMOUT GOVERNORATE,

YEMEN

OMAR ABDULLAH ALI BAMAGA

FACULTY OF MEDICINE

UNIVERSITY OF MALAYA

KUALA LUMPUR

2017 Univers

ity of

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MOLECULAR EPIDEMIOLOGY OF MALARIA AND

DETECTION OF ANTI-MALARIAL DRUG RESISTANCE-

ASSOCIATED MARKERS (PFCRT, PFMDR-1, PFDHFR

AND PFDHPS) IN HADHRAMOUT GOVERNORATE,

YEMEN

OMAR ABDULLAH ALI BAMAGA

THESIS SUBMITTED IN FULFILMENT OF THE

REQUIREMENTS FOR THE DEGREE OF DOCTOR OF

PHILOSOPHY

FACULTY OF MEDICINE

UNIVERSITY OF MALAYA

KUALA LUMPUR

2017

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UNIVERSITI MALAYA

ORIGINAL LITERARY WORK DECLARATION

Name of Candidate: Omar Abdullah Ali Bamaga

Registration / Matric No: MHA100059

Name of Degree: DOCTOR OF PHILOSOPHY

Title of Project Paper/Research Report/Dissertation/Thesis (“this Work”):

Molecular epidemiology of malaria and detection of anti-malarial drug resistance-

associated markers (Pfcrt, pfmdr-1, pfdhfr and pfdhps) in Hadhramout

governorate, Yemen

Field of Study: Parasitology

I do solemnly and sincerely declare that:

(1) I am the sole author/writer of this Work;

(2) This Work is original;

(3) Any use of any work in which copyright exists was done by way of fair dealing and

for permitted purposes and any excerpt or extract from,or reference to or

reproduction of any copyright work has been disclosed expressly and sufficiently

and the title of the work and its authorship have been acknowledged in this Work;

(4) I do not have any actual knowledge nor do I ought reasonably to know that the

making of this work constitutes an infringement of any copyright work;

(5) I hereby assign all and every rights in the copyright to this work to the University of

Malaya (“UM”), who henceforth shall be owner of the copyright in this work and

that any reproduction or use in any form or by any means whatsoever is prohibited

without the written consent of UM having been first had and obtained;

(6) I am fully aware that if in the course of making this work I have infringed any

copyright whether intentionally or otherwise, I may be subject to legal action or any

other action as may be determined by UM.

Candidate’s Signature Date

Subscribed and solemnly declared before,

Witness’s Signature Date

Name: Dr. Yvonne Lim Ai Lian

Designation: Professor

Department of Parasitology, Faculty of Medicine, University of Malaya

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ABSTRACT

Malaria, especially Plasmodium falciparum malaria is one of the main causes of

mortality and morbidity worldwide. Yemen is an Eastern Mediterranean country where

68% of its population is at risk of malaria. In 2013, it was estimated that there were

150,000 cases recorded in Yemen with 55 malarial deaths, compared to 900,000 cases

in 2000. The anti-malarial treatment policy in Yemen was changed from chloroquine

(CQ) to artemisinin combination therapy (ACT) in 2005.The present study is the first in

Hadhramout, Yemen which aimed to assess the epidemiology of malaria parasites and

to determine the frequency of mutant alleles and genotypes associated with antimalarial

drug resistance in Plasmodium falciparum isolates. Blood specimens were collected

from seven villages in two different districts of the Hadhramout governorate by house-

to-house visits from July 2011 to May 2012. A total of 735 individuals aged 1 to 75

years with a median of 16 years and 22 interquartile range participated in the study. A

pre-tested questionnaire was used to gather demographic, socioeconomic and

environmental data. Plasmodium species were first identified by microscopy

examination and subsequently genomic DNA was extracted from dried archive blood

spots of P. falciparum isolates and analyzed using nested PCR. Mutation-specific nested

polymerase chain reaction (MS-PCR) and restriction fragment length polymorphism

(PCR–RFLP) methods were used to investigate the mutations in the Pfmdr1 (codons 86

and 1246) and Pfcrt (codons 76, 271, 326, 356 and 371) genes. DNA was also amplified

using nested PCR and subsequently sequenced for Pfdhfr and Pfdhps genes. Sequences

were analyzed for mutations in Pfdhfr at codons 51, 59, 108, and 164 and in Pfdhps at

codons 436, 437, and 540. Results of the overall prevalence of malaria parasites in

Hadhramout governorate, Yemen via microscopy was 18.8% (138 of 735) with

Plasmodium falciparum being the predominant species (99.3%; 137 of 138), followed

by Plasmodium vivax (0.7%; 1). Nested PCR detected P. falciparum in four samples

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that were previously negative using microscopy. The combination of microscopy and

nested PCR detection resulted in three samples being identified as mixed infections of

P. falciparum and P. vivax. The infection rate was higher in Al-Raydah-Qusyer district

(21.8%) compared to Hajer district (11.8%). Fifty two percent of those positive for

Plasmodium were asymptomatic with low parasite density. The adults had a higher

infection rate as compared to children. Univariate analysis identified those whose

household’s heads are fishermen (OR = 11.3, 95% CI: 3.13–40.5) and farmers (OR =

4.84, 95% CI: 1.73–13.6) as high-risk groups. A higher number of positive rates were

observed in people living in houses with uncemented brick walls (OR = 2.1, 95% CI:

1.32–3.30), without access to toilets (OR = 1.6, 95% CI: 1.05–2.32), without a fridge

(OR = 1. 6, 95% CI: 1.05–2.30), or without TV (OR = 1. 6, (95% CI: 1.05–2.30).

People living in houses with water collection points located less than 200 meters away

were also at higher risk of acquiring malaria (OR = 1.6, 95% CI:1.05–2.30). Knowledge

about the importance of using insecticide-treated mosquito nets (ITNs) and indoor

residual spraying (IRS) for prevention of malaria was 7% and 2%, respectively. The

prevalence of Pfcrt mutations at codons 76, 271, 326 and 371 were 50.4%, 58.7%,

54.3% and 44.9%, respectively. All isolates had wild-type Pfcrt 356 allele. The majority

of Pfmdr1 86 alleles (83.3%) and all Pfmdr1 1246 (100%) alleles were also wild type.

There was no association between Pfcrt mutations and symptomatology, gender and age

groups. For Pfdhfr/Pfdhps mutations, each Pfdhfr mutant allele (I51 and N108) in P.

falciparum isolate had a frequency of 84%. Pfdhfr R59 mutant allele was detected in one

isolate. Pfdhps at codon G437 mutant allele was detected in 44.7% of Plasmodium

falciparum malaria isolates. Frequencies of Pfdhfr double mutant genotype

(I51C59N108I164) and Pfdhfr/Pfdhps triple mutant genotype (I51C59N108I164-S436G437K540)

were 82.8% and 40.6%, respectively. It is important to note that there was one isolate

each which harbored Pfdhfr triple mutant genotype (I51, R59, N108, I164) and

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Pfdhfr/Pfdhps quadruple mutant genotype (I51R59N108I164-S436G437K540). In conclusion,

several environmental, socioeconomic and behavioral issues were discovered to be the

contributing factors to the high prevalence of malaria in this southeast Yemen

governorate. High frequencies of point mutations in codons 76, 271, 326 and 371 of P.

falciparum, suggested a sustained high CQ resistance even after 6 years of shifting to

ACTs. High frequencies of Pfdhfr and Pfdhps mutant alleles and genotypes in P.

falciparum isolates from Hadhramout, Yemen, highlight the risk of decreasing efficacy

of sulfadoxine pyrimethamine antimalarial drugs. Novel strategies adapted to local

situations need to be established in order to improve the effectiveness of malaria

control. The current study findings necessitate continuous monitoring of the efficacy of

malaria treatment.

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ABSTRAK

Malaria, terutamanya malaria Plasmodium falciparum merupakan salah satu faktor

utama mortaliti dan morbiditi di seluruh dunia. Yemen adalah sebuah negara yang

terletak di timur Mediterranean di mana 68% daripada penduduknya adalah berisiko

tinggi untuk dijangkiti malaria. Pada tahun 2013, dianggarkan bahawa 150,000 kes

malaria direkodkan di Yemen dengan 55 kematian berbanding dengan 900,000 kes pada

tahun 2000. Polisi rawatan anti-malaria di Yemen telah ditukar daripada chloroquine

(CQ) kepada terapi gabungan artemisinin (ACT) pada tahun 2005. Kajian ini merupakan

yang pertama di Hadhramout, Yemen untuk mengkaji epidemiologi parasit malaria serta

penentuan kekerapan alel mutan dan genotip yang dikaitkan dengan rintangan ubat anti-

malaria dalam pencilan. Plasmodium falciparum. Spesimen darah telah diambil dari

tujuh kampung di dua daerah yang berlainan di wilayah Hadhramout dari rumah ke

rumah dari Julai 2011 hingga Mei 2012. Sejumlah 735 individu berusia di antara 1

hingga 75 tahun dengan median umur 16 tahun and julat interquartile 22 telah terlibat

dalam kajian ini. Borang pra-soalselidik berdasarkan isi rumah telah digunakan untuk

mengumpul demografi, sosio-ekonomi dan data alam sekitar. Pertama sekali, spesies

Plasmodium dikenalpasti melalui pemeriksaan mikroskop. DNA genomik kemudian

diekstrak daripada tompok darah arkib pencilan P. falciparum untuk analisis tindak

balas berantai polimerase (PCR). Kaedah mutase-spesifik reaksi bersarang rantaian

polimerase (MS-PCR) dan sekatan panjang serpihan polymorphism (PCR-RFLP) telah

digunakan untuk mengenalpasti mutasi pada gen Pfmdr1 (kodon 86 dan 1246) dan Pfcrt

(kodon 76, 271, 326, 356 dan 371). DNA juga telah diamplifikasi menggunakan kaedah

PCR bersarang dan kemudiannya gen Pfdhfr dan Pfdhps dijujuk. Jujukan DNA

dianalisis untuk mutasi dalam gen Pfdhfr pada kodon 51, 59, 108, dan 164 dan di gen

Pfdhps pada kodon 436, 437, dan 540. Keputusan keseluruhan prevalen malaria di

Hadhramout, Yemen melalui kaedah pemeriksaan mikroskop adalah 18.8% (138 dari

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735) dengan spesies P. falciparum sebagai spesies utama (99.3%; 137 dari 138) diikuti

dengan Plasmodium vivax (0.7%; 1). PCR bersarang mengesan P. falciparum dalam

empat sampel yang sebelum ini negatif menggunakan mikroskop. Gabungan

penggunaan mikroskop dan pengesanan PCR bersarang menemui tiga sampel yang

dikenalpasti sebagai jangkitan campuran P. falciparum dan P. vivax. Kadar jangkitan

adalah lebih tinggi di daerah Al-Raydah-Qusyer (21.8%) berbanding dengan daerah

Hajer (11.8%). Lima puluh dua peratus daripada individu yang positif untuk

Plasmodium adalah asimptomatik dengan kepadatan parasit yang rendah. Orang dewasa

mempunyai peratusan jangkitan yang lebih tinggi berbanding kanak-kanak. Analisis

univariat mengenalpasti individu di mana ketua rumahnya adalah nelayan (OR = 11.3,

95% CI: 3.13-40.5) dan petani (OR = 4.84, 95% CI: 1.73-13.6) sebagai kumpulan

berisiko tinggi. Peratusan positif didapati lebih tinggi bagi orang yang tinggal di rumah

yang berdinding tidak bersimen (OR = 2.1, 95% CI: 1.32-3.30), tidak mempunyai

tandas (OR = 1.6, 95% CI: 1.05-2.32), tidak mempunyai peti sejuk (OR = 1. 6, 95% CI:

1.05-2.30), atau tidak mempunyai televisyen (OR = 1. 6, 95% CI:. 1.05-2.30). Individu

yang tinggal di rumah di mana sumber pengumpulan airnya terletak kurang daripada

200 meter juga berisiko tinggi untuk dijangkiti malaria (OR = 1.6, 95% CI: 1.05-2.30)

Pengetahuan tentang kepentingan menggunakan insecticide-treated mosquito nets

(ITNs) dan indoor residual spraying (IRS) untuk pencegahan malaria adalah 7% dan

2%, masing-masing. Prevalen mutasi Pfcrt pada kodon 76, 271, 326 dan 371 adalah

50.4%, 58.7%, 54.3% dan 44.9%, masing-masing. Semua pencilan mempunyai alel

Pfcrt 356 jenis liar. Majoriti Pfmdr1 86 alel (83.3%) dan semua alel Pfmdr1 1246

(100%) juga adalah jenis liar. Tidak ada kaitan antara mutasi Pfcrt dengan

simptomatologi, jantina dan kumpulan umur. Untuk mutasi Pfdhfr / Pfdhps, setiap alel

mutan Pfdhfr (I51 and N108) dalam pencilan P. falciparum mempunyai kekerapan 84%.

Alel mutan Pfdhfr R59 ditemui hanya dalam satu pencilan. Alel mutan kodon G437

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dikesan pada 44.7% daripada pencilan malaria Plasmodium falciparum. Kekerapan

Pfdhfr genotip dua mutan (I51C59N108I164) dan Pfdhfr / Pfdhps genotip tiga mutan

(I51C59N108I164-S436G437K540) adalah 82.8% dan 40.6%, masing-masing. Terdapat satu

pencilan setiap satu untuk Pfdhfr genotip tiga mutan (I51, R59, N108, I164) dan Pfdhfr /

Pfdhps genotip empat mutan (I51R59N108I164-S436G437K540). Kesimpulannya, beberapa

isu alam sekitar, sosio-ekonomi dan tingkah laku telah ditemui sebagai faktor yang

menyumbang kepada kes malaria yang tinggi di wilayah tenggara Yemen. Frekuensi

tinggi mutasi titik dalam kodon 76, 271, 326 dan 371 Pfcrt dan Pfdhfr / Pfdhps alel

mutan P. falciparum, menunjukkan rintangan tinggi terhadap CQ adalah berterusan

walaupun telah 6 tahun beralih kepada ACT. Frekuensi alel mutan dan genotip Pfdhfr

and Pfdhps yang tinggi dalam pencilan P. falciparum di Hadhramout, Yemen,

menunjukkan risiko penurunan effikasi ubat anti-malaria sulfadoxine pyrimethamine.

Strategi dan langkah pengawalan yang baru yang bersesuaian dengan keadaan tempatan

perlu diwujudkan dalam usaha untuk meningkatkan keberkesanan kawalan malaria.

Hasil daripada kajian ini memerlukan pemantauan berterusan terhadap tahap

keberkesanan rawatan malaria.

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ACKNOWLEDGEMENTS

First and foremost, I would like to thank Allah for all the wonderful blessings and

giving me the courage, guidance, strength and perseverance throughout the duration of

my whole life.

I would like to express my deepest appreciation and extend my profound gratitude to

my supervisors, Professor Dr. Yvonne Lim Ai Lian and Associate Professor Dr.

Mohammed Mahdy for their support, assistance and guidance during the course of this

study. Their advice, boundless ideas, skills, expertise, comments, criticism,

encouragement and challenges were very much appreciated. You will forever remain an

indelible part of my life as mentors.

Special thanks to the head and staff of Department of Parasitology, Faculty of

Medicine, for their support. I thank the University of Malaya for supporting the research

under the University of Malaya Research Grant (UMRG/RG503-13HTM) and the

University of Malaya High Impact Research Grant UM-MOHE UM.C /62 /1/ HIR /

MOHE/MED/18 from the Ministry of Higher Education Malaysia. I would like to thank

all my lecturers, colleagues, and friends for their unending encouragements.

I also would like to thank all the technical staff in the field study and laboratory

expert groups for their assistance in the molecular laboratory of Parasitology

Department, the National Malaria Control Program in Hadhramout governorate Yemen,

Ministry of Health and communities in Hadhramout for their cooperation during this

study.

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Most importantly, I would like to thank my Mother; my Father, Abdullah Ali; my

wife, my brothers, my sisters for their undivided support, encouragement, assistance and

their prayers and many others who are too numerous to mention here.

I am also thankful to the Hadhramout University of Science and Technology of my

country for the support given to me in terms of scholarship to study in Malaysia.

Finally, I would like to thank everyone for their assistance and prayers. I hope that

this dissertation has provided meaningful ideas and significant contributions that will be

beneficial to the field of malaria and I hope that everyone who reads this dissertation

finds it useful.

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TABLE OF CONTENTS

ABSTRACT iii

ABSTRAK vi

ACKNOWLEDGEMENTS ix

TABLE OF CONTENTS xi

LIST OF FIGURES xvi

LIST OF TABLES xviii

LIST OF SYMPOLS AND ABBREVIATIONS xxi

LIST OF APPENDICES xxiv

CHAPTER 1: INTRODUCTION 1

1.1 BACKGROUND OF STUDY 1

1.2 PROBLEM STATEMENTS 3

1.3 RESEARCH HYPOTHESES 5

1.4 OBJECTIVES 6

1.4.1 General objective 6

1.4.2 Specific objectives 6

1.5 SIGNIFICANCE OF STUDY 7

CHAPTER 2: LITERATURE REVIEW 8

2.1 MALARIA 8

2.1.1 History 8

2.1.2 Biology, etiology and life cycle 8

2.1.3 Clinical manifestations 12

2.1.4 Epidemiology 13

2.1.4.1 Malaria burden and geographical distribution of Plasmodium

species

13

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2.1.4.2 Factors associated with malaria 17

2.1.5 Diagnosis of malaria 22

2.1.6 Treatment 23

2.1.6.1 Anti-malarial drugs 23

2.1.6.2 Methods of anti-malarial drug resistance surveillance 26

A) In vivo methods 26

B) In vitro method 31

C) Molecular markers 32

2.2 MALARIA IN EASTERN MEDITERRANEAN REGION 35

2.2.1 Current status 35

2.2.2 Anti-malarial drug resistance 38

2.3 MALARIA IN YEMEN 43

2.3.1 Plasmodium species and types of vectors 43

2.3.2 Trend of confirmed malaria cases in the last 23 years 46

2.3.3 Risk factors 48

2.3.4 Malaria distribution and intensity of transmission 52

2.3.5 Prevention and control 53

2.3.6 Malaria diagnosis 53

2.3.7 Malaria treatment in Yemen 54

2.3.7.1 The old strategy (from 1999) 54

2.3.7.2 The new strategy (from 2005) 54

2.3.7.3 Monitoring anti-malarial drug resistance 55

A) In vivo studies 55

B) Molecular markers based studies 55

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CHAPTER 3: METHODOLOGY 57

3.1 OVERVIEW OF STUDY METHODS 57

3.6.2 Ethical clearance 59

3.3 STUDY AREA AND STUDY POPULATIONS 59

3.3 DESIGN OF STUDY 62

3.4 SAMPLE SIZE 62

3.5 DESCRIPTION OF VARIABLES 63

3.6 DATA AND SAMPLE COLLECTION 63

3.6.1 Strategy of field work 63

3.6.2 Questionnaire 63

3.6.3 Blood sampling 64

3.7 DETECTION OF MALARIA PARASITE BY MICROSCOPY METHOD 65

3.7.1 Staining blood smears 65

3.7.2 Microscopy examination 65

3.8 MOLECULAR IDENTIFICATION AND GENOTYPING OF MALARIA

SPECIES

66

3.8.1 DNA extraction 66

3.8.2 Molecular identification of malaria species 66

3.8.3 Molecular detection of mutation in Pfcrt gene at codon K76T 70

3.8.4 Molecular detection of point mutations in Pfcrt gene (positions Q271E,

N326S, I356T, R371I) and Pfmdr1 gene (positions N86Y and

D1246Y)

72

3.8.5 Molecular detection of point mutations in Pfdhfr gene at different

codons

72

3.8.6 Molecular detection of point mutations in Pfdhps gene at different 73

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codons

3.9 STATISTICAL ANALYSIS 76

CHAPTER 4: RESULTS 77

4.1 EPIDEMIOLOGICAL RESULTS OF MALARIA IN THE HADHRAMOUT

GOVERNORATE, YEMEN

77

4.1.1 Characteristic of study population 77

4.1.2 Prevalence of malaria and identify the risk factors associated with

malaria in the Hadhramout governorate, Yemen

80

4.1.3 Assessment of knowledge, attitude and practices (KAP) towards

malaria in the Hadhramout governorate, Yemen

84

4.1.4 Clinical manifestations of individuals positive with malaria 86

4.2 MOLECULAR CHARACTERIZATION OF MALARIA IN THE

HADHRAMOUT GOVERNORATE, YEMEN

89

4.2.1 Malaria parasite identification using nested PCR based on 18SSU

rRNA gene

89

4.2.2 Prevalence and distribution of mutations in Pfcrt gene at 76, 271, 326,

356 and 371 and Pfmdr1 gene at 86 and 1246 as molecular markers

of CQ resistance of Plasmodium falciparum isolates in Hadhramout

governorate, Yemen

91

4.2.3 Prevalence of mutations in Pfdhfr and Pfdhps genes at different codons

as molecular markers of SP resistance of Plasmodium falciparum

isolates in Hadhramout governorate, Yemen

94

CHAPTER 5: DISCUSSION 98

5.1 THE EPIDEMIOLOGICAL OF MALARIA IN THE HADHRAMOUT

GOVERNORATE, YEMEN

98

5.1.1 Prevalence of malaria and identify the risk factors associated with 98

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malaria in the Hadhramout governorate, Yemen

5.1.2 Assessment of knowledge, attitude and practices towards malaria in the

Hadhramout governorate, Yemen

100

5.2 MOLECULAR CHARACTERIZATION RESULTS OF MALARIA IN THE

HADHRAMOUT GOVERNORATE, YEMEN

102

5.2.1 Point mutations in Pfcrt gene at 76, 271, 326, 356 and 371 and Pfmdr1

gene at 86 and 1246 as molecular markers of CQ resistance of

Plasmodium falciparum isolates in Hadhramout governorate

102

5.2.2 Point mutations in Pfdhfr and Pfdhps genes at different codons as

molecular markers of sulfadoxine-pyrimethamine resistance of

Plasmodium falciparum isolates in Hadhramout governorate

105

5.3 LIMITATIONS OF STUDY 110

CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 111

6.1 CONCLUSIONS 111

6.2 RECOMMENDATIONS 113

References 115

Appendices 148

List of publications and paper presented 182

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LIST OF FIGURES

Figure 2.1: Life cycle of Plasmodium spp. 11

Figure 2.2: The malaria control stage and the countries contributing to the

global death

15

Figure 2.3: The intensity of malaria transmission worldwide 16

Figure 2.4: Modified map of distribution of Anopheles mosquito in

governorates, Yemen

45

Figure 2.5: Malaria trend in Yemen from 1990 till 2014 47

Figure 3.1: Schematic diagram of samples and data collection and molecular

marker detections

58

Figure 3.2: Map of study area highlighted in the Hadhramout governorate,

Yemen

61

Figure 4.1: Malaria prevalence in endemic areas of the two districts (i.e, Al

Raydah-Qusyer and Hajer) of Hadhramout governorate, Yemen

78

Figure 4.2: Prevalence of parasitemia among populations infected with

malaria in Hadhramout governorate, Yemen

88

Figure 4.3: Agarose gel electrophoresis for identification of Plasmodium

species (Secondary nested PCR-genus specific).

178

Figure 4.4: Agarose gel electrophoresis for identification of Plasmodium

falciparum (Secondary nested PCR-species specific)

178

Figure 4.5: Agarose gel electrophoresis for identification of Plasmodium

vivax (Secondary nested PCR-species specific)

178

Figure 4.6: Secondary mutant specific nested PCR of Pfcrt 76 using

restriction enzymes

179

Figure 4.7: Digestion of secondary nested PCR for Pfcrt 271 using 179

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restriction enzymes

Figure 4.8: Digestion of secondary nested PCR for Pfcrt 326 using

restriction enzymes

179

Figure 4.9: Digestion of secondary nested PCR for Pfcrt 356 using

restriction enzymes

179

Figure 4.10: Digestion of secondary nested PCR for Pfcrt 371 using

restriction enzymes

180

Figure 4.11: Digestion of secondary nested PCR for Pfmdr1 86 using

restriction enzymes

180

Figure 4.12: Digestion of secondary nested PCR for Pfmdr1 1246 using

restriction enzymes

180

Figure 4.13: Secondary nested PCR for Pfdhfr 181

Figure 4.14: Secondary nested PCR for Pfdhfr 181

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LIST OF TABLES

Table 2.1: Socioeconomic factors, behavior factors and environmental

factors of malaria

20

Table 2.2: Classification of antimalarial drug and brief outline of mechanism

of action

25

Table 2.3: Definitions of parasitological response to drug in in vivo

therapeutic efficacy studies

29

Table 2.4: Definitions of parasitological and clinical response to drug in in

vivo therapeutic efficacy studies

30

Table 2.5: Most commonly used antimalarial drugs along with their

molecular markers to determine their drug susceptibility /

resistance

34

Table 2.6: Malaria cases in countries with high transmission areas at the

Eastern Mediterranean region in 2013

37

Table 2.7: Summary of some previous studies on mutations of crt, mdr1,

dhfr and dhps genes in Plasmodium isolates and their role in

antimalarial drugs resistance in Mediterranean countries

39

Table 2.8 Socio-economic, behavioral and environmental risk factors

associated with acquiring malaria in four governorates in Yemen

50

Table 3.1: Protocol for the detection of Plasmodium malarial species based

on 18SSU rRNA gene

69

Table 3.2: Detection of point mutations in Pfcrt , Pfmdr1, Pfdhfr, and Pfdhps

genes at different codons

71

Table 3.3: Forward and reverse primers sequences for Pfcrt , Pfmdr1,

Pfdhfr, and Pfdhps genes at different codons

75

Table 4.1: Demographic characteristics of study populations in Hadhramout 79

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governorate, Yemen

Table 4.2: Prevalence and distribution of malaria among population in

Hadhramout governorate, Yemen according to age, gender and

areas

82

Table 4.3: Risk factors associated with malaria in Hadhramout governorate,

Yemen

83

Table 4.4: KAPs of malaria in the rural areas of Hadhramout governorate,

Yemen

85

Table 4.5: Clinical manifestations among humans infected with malaria in

Hadhramout governorate, Yemen

87

Table 4.6: Detection of Plasmodium species using nested PCR among

populations infected with malaria in Hadhramout governorate,

Yemen

90

Table 4.7: Frequency and distribution of Pfcrt and Pfmdr1 alleles in P.

falciparum isolates from populations in Hadhramout governorate,

Yemen

92

Table 4.8: Frequency and distribution of Pfcrt and Pfmdr1 allels according

to symptomatology in P. falciparum isolates from populations in

Hadhramout governorate, Yemen

93

Table 4.9: Prevalence of mutant alleles of Pfdhfr and Pfdhps genes in P.

falciparum isolates from populations in Hadhramout governorate,

Yemen

96

Table 4.10: Prevalence of genotypes of Pfdhfr, Pfdhps and combined Pfdhfr–

Pfdhps genes in P. falciparum isolates from populations in

Hadhramout governorate, Yemen

97

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Table 6.1: Morphological characteristics of the Plasmodium species

infecting human

173

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LIST OF SYMPOLS AND ABBREVIATIONS

% Percentage

< Less than

> More than

µg/l Microgram per liter

µg/ml Microgram per milliliter

µL Microliter

ACPR Adequate clinical and parasitological response

ACT: Artemisinin-based combination therapy

Ala (A): Alanine

AL: Artemether lumefantrine

Arg (R): Arginine

Asn (N): Asparagine

Asp (D): Aspartate

MS-PCR Mutant-specific nested polymerase chain reaction

AS Artesunate

bp Base pair

C° Degree Celsius

CDC: Centers for Disease Control and Prevention

CI: Confidence interval

Cys: Cysteine

Cyt-b The mitochondrial cytochrome b gene

Dhfr-ts Dihydrofolate reductase-thymidylate synthase

Endo. Dig: Endonuclease digestion

ETF Early treatment failure

g Gram

g/dl Gram per deciliter

Gln (Q): Glutamine

Glu (E): Glutamate

Gly (G): Glycine

Hb: Haemoglobin

IL: Interleukin

Ile (I): Isoleucine

IRS: Indoor Residual Spraying

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ITNs: Insecticide-treated Nets

KAP: Knowledge, Attitude and Practices

kb: Kilobase

LCF Late clinical failure

LLINs: Long Lasting Insecticide-treated Nets

LPF: Late parasitological failure

Lys (K): Lysine

mg/dl Milligram per deciliter

min Minute

ml Milliliter

mM Millimolar

MQ: Mefloquine

n Sample size

nM Nano mole

NMCP: National Malaria Control Programme

OR: Odds-ratio

P: Level of significance

PCR: Polymerase Chain Reaction

Pfcrt :

Plasmodium falciparum chloroquine resistance

transporter

Pfdhfr: Plasmodium falciparum dihydrofolate reductase

Pfdhps: Plasmodium falciparum dihydropteroate synthase

Pfmdr-1: Plasmodium falciparum multidrug resistance gene-1

Pfmrp Plasmodium falciparum multidrug resistance-associated

protein

pfnhe Plasmodium falciparum Sodium Hydrogen Exchanger

Phe (F): Phenylalanine

Post-Dig: Post Digestion

RBC: Red blood cell

RE: Restriction enzyme

RFLP: Restriction fragment length polymorphism

SD: Standard deviation

sec Seconds

Ser (S): Serine

SERCA: Sarco/endoplasmic reticulum Ca2+-ATPase

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SNP: Single nucleotide polymorphism

SP: Sulfadoxine-pyrimethamine

SPSS: Statistical Package for Social Science

SSU-rRNA: Small subunit ribosomal RNA

TAE: Tris- acetate EDTA

Thr (T): Threonine

TNF: Tumor necrosis factor

Tyr (Y): Tyrosine

U Unit

V Voltage

WHO: World Health Organization

χ2: Chi-square

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LIST OF APPENDICES

Appendix A: Ethical clearance form (English) 148

Appendix A: Ethical clearance form (Arabic) 151

Appendix B: Consent form (English) 153

Appendix B: Consent form (Arabic) 154

Appendix C: Photography of field study and specimens collection 155

Appendix D: Defination of variables 160

Appendix E: Questionnaire (English) 162

Appendix E: Questionnaire (Arabic) 167

Appendix F: Wright-giemsa stain 172

Appendix G: Key morphological differences between the blood stages of

human Plasmodium species.

173

Appendix G: Photograph of diagnostic stages 174

Appendix H: DNA extraction protocol 175

Appendix I: Gel electrophoresis of PCR products 178

Appendix J: The SNPs alignment of dhfr gene sequences of Plasmodium

falciparum isolates in Hadhramout governorate, Yemen

182

Appendix K: The SNPs alignment of dhps gene sequences of Plasmodium

falciparum isolates in Hadhramout governorate, Yemen

183

Appendix L: List of Publications and Presenting Papers 184

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CHAPTER 1: INTRODUCTION

1.1 Background of study

Malaria, especially Plasmodium falciparum malaria is one of the main causes of

mortality and morbidity worldwide where 3.3 billion people are at risk of malaria

transmission and 1.2 billion individuals are at high risk of being infected with malaria

(Dyer et al., 2007; Joubert et al., 2009; WHO, 2011). Globally, the transmission of

malaria mostly occurs in tropical and subtropical countries, particularly, in sub-Saharan

Africa and South Asia, affecting 124 to 283 million people and resulting in an estimated

584,000 deaths due to complications, mostly among children less than five years of age

in the African region (Waitumbi et al., 2000; WHO, 2014b). Almost all deaths were

caused by Plasmodium falciparum (Färnert et al., 2001; Snow et al., 2013). In endemic

countries, the people at higher risk of infection with malaria and those severely affected

reside in the poorest communities, with limited or without proper access to effective

prevention, diagnosis and treatment. Thus, combating and elimination of malaria are

related to strengthening of health system, development of infrastructure and reduction of

poverty (WHO, 2014b).

In the WHO Eastern Mediterranean region, which consists of 12 countries,

approximately 280 million people in eight countries including Yemen are at risk of

malaria transmission. Of these, 104 million people are at high risk of malaria

transmission. Six countries (i.e., Sudan, Pakistan, Yemen, Afghanistan, Somalia and

Djibouti) have areas with high incidence of malaria with an estimated 1,027 deaths,

occurred mostly in Sudan and Pakistan (WHO, 2011, 2014b).

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Yemen is a Mediterranean country where 62% of its population (about 24 million) are at

risk of malaria. In 2013, there were more than 25% of the population at high risk of

acquiring the infection, with 149,451 confirmed cases (WHO, 2014b). Most cases of

malaria in Yemen belongs to the afro-tropical type with the predominance of P.

falciparum which is accountable for nearly 99% of malaria cases with only minimal

cases caused by Plasmodium vivax and with Anopheles arabiensis as the predominant

vector (WHO, 2014b). However, the malaria parasite vector in Socotra Island and the

eastern governorate of Al-Maharah belongs to the oriental type with Anopheles

culicifacies as the predominant vector (NMCP, 2011). The National Malaria Control

Program (NMCP) in Yemen is proactive in controlling malaria through prompt

diagnosis and proper treatment, distribution of insecticide-treated mosquito nets (ITN),

indoor residual spraying (IRS), and active case surveillance (WHO, 2012). However,

Yemen is placed in the control phase and was not on track to achieve the Global Malaria

Action Plan (GMAP)’s objective which was to reduce malaria cases by 75% by the end

of 2015 (WHO, 2012, 2014b). By contrast, Saudi Arabia, the northern neighbouring

country of Yemen, showed more than 75% reduction in malaria case incidence placing

it in the elimination phase, and Oman, the eastern neighbouring country of Yemen is

now in the prevention of re-introduction phase (WHO, 2012).

Socio-economic, environmental and human behavioral factors might contribute to

the slow progress of malaria control in Yemen. Previous malaria indicator survey,

conducted in Yemen between 2008 and 2009, reported that only 4.2% of people and 7%

of children under 5 years slept under long lasting insecticide-treated net (LLINs) (Noor,

2009). Several factors have been identified to increase the malaria transmission in

different countries including house condition, education level, occupation, usage of bed

net, spraying of insecticide inside the house, agriculture, knowledge, beliefs and

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practices toward malaria disease (Worrall et al., 2003; Yé et al., 2006; Tipmontree et

al., 2009; Ayele et al., 2012; Liu et al., 2014).

Following the emergence of chloroquine (CQ) resistance, the antimalarial

treatment policy in Yemen has shifted to artemisinin-based combination therapy (ACT)

in 2005, where artesunate plus sulphadoxine-pyrimethamine (SP) has been used as the

first line, and artemether lumefantrine (AL) as the second line therapy for

uncomplicated malaria (NMCP, 2006). However, CQ and SP are still being prescribed

as monotherapy by clinicians in both public and private health facilities because they

have limited knowledge of the newer treatment policy (Bashrahil et al., 2010; Bin

Ghouth, 2013). Despite a highly efficacious current antimalarial dug policy in Yemen

(Adeel et al., 2015), several related dihydrofolate reductase (dhfr) gene mutations which

is a molecular marker for SP failure, the partner drug of AS have been reported recently

among Plasmodium falciparum isolates from different governorates in Yemen,

suggesting that the emergence and spread of SP resistance will expose the parasite to

AS monotherapy, which has the potential to contribute to the emergence of ACT

resistance in Yemen. These mutations include double mutant genotype of Pfdhfr

(I51/N108) in Taiz, Dhamar, and Hodeidah governorates in western Yemen (Al-

Hamidhi et al., 2013) and single mutant genotype of Pfdhfr (N108) in Hodeidah

governorate (Abdul-Ghani et al., 2014).

1.2 PROBLEM STATEMENTS

Based on WHO reports, malaria is still a public health threat in Yemen and this

infection contributes a high proportion to the total cases of malaria reported from WHO

Mediterranean region. Besides, it is a challenge to achieve a reduction of malaria cases

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by 75% by the end of 2015 as stated in the Global Malaria Action Plan (GMAP) (WHO,

2014b). Barriers and factors that are challenging the success of malaria control in

Yemen should be identified in order to develop an effective control strategy. Firstly,

there is a scarcity of data on malaria predictors in this country. Thus, the present study

aimed to determine the prevalence and risk factors of malaria in the southeastern part of

Yemen, and to explore the residents’ knowledge, attitude and practices (KAP) toward

malaria.

In Yemen, malaria treatment is also another challenge. The national antimalarial

drug policy in Yemen was formulated in 1999, included CQ as first-line and SP as a

second line monotherapies for treating uncomplicated falciparum malaria (NMCP,

2006). As mentioned above, CQ is still being prescribed as monotherapy by clinicians

in both public and private health facilities because they have limited knowledge of the

newer treatment policy (Bashrahil et al., 2010; Bin Ghouth, 2013). Continued use of CQ

sustains the selection of CQ resistant mutations leading to persistence of mutant

parasite. The complete withdrawal of CQ use may enhance the emergence of CQ

sensitive parasite over time and make CQ possible to be re-introduced for malaria

treatment (Kublin et al., 2003; Laufer et al., 2006). However, the persistence of CQ

resistance will be prolonged if the shift to ACT and the simultaneous withdrawal of CQ

are not rigorously implemented. The aim of the current survey is to also determine the

prevalence of CQ resistant mutations, since they will be important for future monitoring

and assessment of antimalarial drug policy in Yemen.

In 2005, due to the increased CQ resistance, antimalarial drug policy shifted to a

combination of AS and SP as the first-line therapy and AL as a second-line treatment

for uncomplicated falciparum malaria (WHO, 2011). Various factors such as continued

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use of SP in the new policy, availability of this drug in the private sector, and poor

knowledge of the national policy among physicians (Bin Ghouth, 2013) may have

increased the usage of monotherapy of SP against P. falciparum, which is likely to

compromise drug efficacy. However, the efficacy of AS + SP as first-line treatment for

uncomplicated falciparum malaria was rated at 97% ACPR in a recent clinical drug

efficacy trail carried out in 2013 (Adeel et al., 2015). It is noteworthy that currently

used routine clinical efficacy trail is the gold standard for the assessment of the

efficiency of the combined antimalarial drugs, although it does not differentiate between

the efficacy of AS and its partner drug. The evolution of SP resistant parasite will

expose the malaria to AS monotherapy and speed the emergence of resistance to

artemisinins. Molecular markers are practical for tracking the resistance toward

antimalarial drugs. Quintuple mutant of combined dhfr and dhps genes (Pfdhfr I51, R59,

T108 plus Pfdhps G437, E540) is significantly associated with in vivo resistance to SP, as

reported in a systematic review in 2009 (Picot et al., 2009). Therefore, the current study

will investigate mutations in dhfr and dhps genes associated with SP resistance and

findings from this study will be used to predict and monitoring the development of SP

resistance in Hadhramout governorate, Yemen.

1.3 RESEARCH HYPOTHESES

1- There are foci with high prevalence of malaria in the Hadhramout governorate,

Yemen.

2- There is a significant association between socio-economic, human behavioural and

environmental factors and malaria in the Hadhramout governorate, Yemen.

3- There is a high prevalence of Pfcrt 76 mutant allele in the Hadhramout governorate,

Yemen.

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4- There is an existence of Pfmdr1 mutant allele at different loci (86 and 1246) in the

Hadhramout governorate, Yemen.

5- There is an existence of mutants in Pfdhfr and Pfdhps genes in the Hadhramout

governorate, Yemen.

1.4 OBJECTIVES

1.4.1 General objective

The general objective of this study is to determine the epidemiology of malaria and to

detect the frequency of alleles and genotypes of genes associated with antimalarial drug

resistance (Pfcrt, Pfmdr1, Pfhdfr, and Pfdhps) in the Hadhramout governorate, Yemen.

1.4.2 Specific objectives

1. To determine the prevalence of malaria in the Hadhramout governorate, Yemen.

2. To identify the risk factors associated with malaria in the Hadhramout

governorate, Yemen.

3. To assess the knowledge, attitude and practices towards malaria in the

Hadhramout governorate, Yemen.

4. To detect the point mutations of Pfcrt gene at 76, 271, 326, 356 and 371 positions

as molecular markers of CQ resistance of P. falciparum in the Hadhramout

governorate, Yemen

5. To detect the point mutations of Pfmdr1 gene at 86 and 1246 positions as

molecular markers of antimalarial drug resistance of P. falciparum in Hadhramout

governorate, Yemen.

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6. To ascertain the point mutations of Pfdhfr and Pfdhps genes at different positions

as molecular markers of SP resistance of P. falciparum in Hadhramout

governorate, Yemen.

1.5 SIGNIFICANCE OF STUDY

The present study is the first in Hadhramout governorate, Yemen which will apply new

molecular technologies based on DNA to determine the frequency of mutant alleles and

genotypes associated with antimalarial drug resistance in P. falciparum. The study also

determine the prevalence of malaria and identify environmental, socioeconomic and

behavioural factors associated with the high prevalence of malaria in Hadhramout

governorate, Yemen. Information from this study would help public health local

authorities to develop an effective malaria control strategy based on better

understanding of malaria epidemiology. The study highlight the importance of the

continuous surveying of P. falciparum population for molecular markers as an early

alarming tool for the emergence of antimalarial drug resistance.

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CHAPTER 2: LITERATURE REVIEW

2.1 Malaria

2.1.1 History

Malaria is an infectious disease and is caused by protozoan parasites of the genus

Plasmodium (Barillas‐Mury & Kumar, 2005). The name malaria is derived from the

Italian ‘mal’ aria,’ which means bad air, derived from the belief that the illness occurs

due to inhalation of bad air around marshy area. Plasmodium was discovered by Charles

Louis Alphonse Laveran at the end of the 19th century. He noticed the parasites in the

blood film of a patient suffering from malaria and for this discovery, he was awarded

the Nobel Prize in 1907 (Launiala & Kulmala, 2006). Later, Dr. Ronald Ross, a British

medical officer in the Indian Medical Service, was the first to discover that mosquitoes

transmit malarial parasites to human and then an Italian professor Giovanni Battista

Grassi identified that only female Anopheline mosquitoes are able to transmit malarial

parasites (Launiala & Kulmala, 2006).

2.1.2 Biology, etiology and life cycle

Out of two hundred species of Plasmodium, five species can cause human malaria and

these include Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae,

Plasmodium ovale. and Plasmodium knowlesi (Singh et al., 1999; Singh et al., 2004a;

Chavatte et al., 2007; Cox-Singh et al., 2008; White, 2008a; Piekarski, 2012).

Although malaria transmission is anthroponotic from human to human via the bites of

infected female Anopheles mosquito, monkeys have been implicated as a source of P.

knowlesi which had been considered as a monkey malaria (Singh et al., 2004a; Cox-

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Singh et al., 2008; Lee et al., 2011). About 465 Anopheles mosquitoes have been

identified (Harbach, 2011; Raghavendra et al., 2011) Of these, 70 species are able to

transmit malaria parasite to human (Warrell & Gilles, 2002) with 41 species being

reported as dominant natural malaria vectors (Joy et al., 2008; Coutinho-Abreu et al.,

2010; Hay et al., 2010; Dhandapani et al., 2011; Cholewiński et al., 2015).

Transmission of malaria is affected by the mosquito behaviour such as night or day

feeding behaviour, indoor or outdoor, and human or animal preference. The presence of

these species depends on the geographical region and the environmental conditions

(Subbarao & Sharma, 1997).

Plasmodium of mammalian hosts has a complex life cycle, sexual (in Anopheles

as definitive host) and asexual (in human as the intermediate host) life cycles. The

typical life cycle of Plasmodium malaria is demonstrated in Figure 2.1. The asexual life

cycle in human starts with a single bite of infected female Anopheles mosquito

containing 20-30 sporozoites which are able to initiate the malaria disease (Satoskar et

al., 2009). The sporozoites travel though the blood stream to liver cells in 30 minutes

where asexual multiplications occur inside the hepatocytes to form mature schizont

which contains 2000 - 40,000 merozoites. This phase is called exo-erythrocytic

schizogony which usually takes 7-16 days based on the Plasmodium species (Satoskar

et al., 2009). The mature schizonts rupture releasing numerous merozoites that enter

blood stream and intiate erythrocytic schizogony phase. Some sporozoites of P. vivax

and P. ovale may remain inside liver as dormant form called hypnozoite for months or

years prior to the development into mature schizonts that then cause malaria relapse. In

the erythrocytic phase, merozoites invade red blood cells (erythrocytes) to form early

trophozoites (ring form) which have cytoplasm, nucleus and vacuole. The mature

trophozoites develop into erythrocytic schizonts which rupture releasing merozoites.

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The erythrocytic cycle takes 24 -72 hours based on the species of Plasmodium; one day

for P. knowlesi (Bronner et al., 2009), three days for P. malariae, and two days for P.

vivax, P. ovale and P. falciparum (Satoskar et al., 2009). The released merozoites

invade new erythrocytes and start a new erythrocytic cycle. The gametogenesis starts

after several asexual erythrocytic cycles which produces male (microgametocytes) and

female gametocytes (macroga metocytes) that are infective for female anopheline

mosquito (Lacroix et al., 2005). The circulating gametocytes can be observed for

months or years in the absence of treatment (Bousema et al., 2004). The sexual life

cycle in mosquito starts when the mosquito picks up blood meal with infective

transmission stages (gametocytes) of malaria. The male microgamete and female

macrogamete fuse in the mosquito midgut to form a zygote which undergoes maturation

forming a motile stage called ookinete which will be able to penetrate the gut wall and

further develop into oocysts that contain numerous spindle shapes sporozoites. The

oocysts undergo maturation and asexual multiplications of sporozoites leading to

oocysts rupture and the release of large numbers of sporozoites which travel to the

salivary glands of the mosquito. This process of parasite development is called

sporogony which takes 10-18 days depending on the species of Plasmodium and the

infected Anopheles mosquito which may remain infectious for 1-2 months (Day et al.,

1998; Barry, 2005; Liljander, 2010).

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Figure 2.1: Life cycle of Plasmodium spp.

Source: Centers of Disease Control and Prevention (CDC, 2014)

www.cdc.gov/malaria/about/biology/index.html

Sporogonic cycle

Exo-erythrocytic cycle

Erythrocytic cycle

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2.1.3 Clinical manifestations

The first appearance of the symptoms which varies depending on species of

Plasmodium; 9-14 days for P. falciparum, 12-16 days for P. vivax, 16-18 for P. ovale,

18-40 or more for P. malariae and 10-12 days for P. knowlesi (Warrell & Gilles, 2002).

The longest incubation periods reported previously are 4 years for P. falciparum and 30

years for P. vivax (Moody & Chiodini, 2000; Moody, 2002; Trampuz et al., 2003;

Tangpukdee et al., 2009). The long incubation period could be due to the level of

immunity acquired via previous infection, chemoprophylaxis of malaria and prior

partial treatment (Ohrt et al., 2008). Paroxysms of malaria fever occurs as a result of

schizonts rupture within two or three days according to the type of Plasmodium species

(tertian or quartan fever). The ruptured erythrocytic schizont releases into blood stream

Plasmodium pigments, toxins, antigens, and a series of pathological factors such as

cytokines, interleukin 1 (IL-1), interleukin 6 (IL-6), and tumor necrosis factor-alpha

(TNF-α), which lead to the common symptoms of malaria; high fever, profuse sweating,

chills, headache, fatigue, vomiting, nausea, diarrhea and anaemia (Miller et al., 1994;

Miller et al., 2002). Severe malaria may cause serious complications including cerebral

malaria, severe anaemia, hepatosplenomegaly, pulmonary oedema, jaundice,

haemoglobinuria, acute kidney injury, acute respiratory distress syndrome,

hypoglycemia, acidosis, hypotension and brain inflammation that may lead to coma

(Mackintosh et al., 2004; Fritsche & Selvarangan, 2011). The high risk groups for

severe malaria are young children, pregnant women and travellers to malaria endemic

areas (Bejon et al., 2009; Phillips et al., 2009; Mali et al., 2010). Malaria is classified as

severe based on the following criteria; hyperparasitemia (>100,000 parasites/µl in

hypoendemic areas and >200,000 parasites/µl in hyperendemic areas), impaired

consciousness, respiratory distress, or severe anaemia (White, 1996; WHO, 2003;

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Mackintosh et al., 2004; Crawley et al., 2010; White et al., 2014; WHO, 2015a).

Partially protective immunity is developed in individuals living in a malaria endemic

area after frequent exposure to malaria infections. The individuals with the partial

immunity may be asymptomatic carriers or develop mild signs and symptoms (Bousema

et al., 2014; WHO, 2014b).

2.1.4 Epidemiology

2.1.4.1 Malaria burden and geographical distribution of Plasmodium species

Malaria is a major health problem worldwide with 3.2 billion individuals (representing

about half of the world’s population) at risk of being infected with malaria, and 1.2

billion people are at high risk (WHO, 2014b). In 2013, 198 million cases of malaria

were reported globally with an estimated 584 000 deaths, of them 90% occurred among

children less than five years old in the African region (Dyer et al., 2007; Joubert et al.,

2009; WHO, 2011, 2014b). Approximately 90% of total malaria deaths are due to

falciparum malaria (WHO, 2014b). The countries that have contributed to malaria-

related death are illustrated in Figure 2.2. As a result of the scale up of malaria control

in the period from 2000 to 2012, malaria incidence rate dropped by 31% and mortality

rate by 49% in the WHO African Region (WHO, 2013). Several factors have challenged

the achievement of the goals designed by the Roll Back Malaria (RBM) partnership and

the World Health Organization to decrease the cases of malaria and death recorded in

2000 by 50% and 70% by the end of 2010 and 2015, respectively, in the poor countries.

These factors included climate changes, emergence and spread of antimalarial drugs and

insecticide resistance, lack of infrastructure, international travels to endemic areas,

political instability, civil war, poverty, low-income, weak or unavailability of public

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health services, and outdoor and indoor biting habits of mosquitoes (Tatem et al., 2010;

WHO, 2012; Cotter et al., 2013).

WHO has classified the malaria endemic areas into six WHO regions including 97

countries; three countries in the Europe region, 10 in the Southeast Asian region, 10 in

the Western pacific region, 8 in the Eastern Mediterranean region, 21 in the America

region and 45 countries in the African region. Majority of malaria cases (82%)

occurred in the WHO African region, followed by the WHO Southeast Asian region

(12%) and the WHO Eastern Mediterranean region (5%) (WHO, 2014b). The

transmission of malaria can be either stable with continuous seasonal or non-seasonal

transmission for many years or unstable malaria transmission with fluctuation variations

(Kiszewski & Teklehaimanot, 2004). In high transmission areas, infants and young

children are the most infected groups, while in low transmission areas, most malaria

cases occur in older children and adults (Carneiro et al., 2010). The intensity of malaria

transmission is indicated in Figure 2.3.

The geographical distribution of malaria species in the world is distinct with most

overlapping in certain geographical areas; P. vivax occurs in many parts of the world

and is predominant in the Asia region, P. falciparum is widespread in tropical and

subtropical regions and highly prevalent in Africa, P. ovale is found in limited parts of

Africa particularly in the western coast, P. malariae is much less frequent in South

America, Asia, and Africa, and P. knowlesi is most commonly reported from Southeast

Asia (Baird, 2007; Mueller et al., 2007; Gupta et al., 2009; Snow et al., 2013).

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Figure 2.2: The malaria control stage and the countries contributing to the

global death (http://www.rbm.who.int/) (Alonso & Tanner, 2013)

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(http://www.rbm.who.int/) (WHO, 2014b)

Figure 2.3: The intensity of malaria transmission worldwide

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2.1.4.2 Factors associated with malaria

The identification of predictors of malaria in an endemic area is a key factor for an

effective control strategy in the area. Multiple risk factors of malaria have been

identified including socioeconomic, environmental and behavioral factors (Table 2.1).

Climate change has a great effect on malaria. Rainfall, temperature and humidity

have influence on vector multiplication or differentiation rate (Longstreth & Wiseman,

1989; Caminade et al., 2014), therefore, high density of malaria vectors increases the

vectors biting rate and consequently increases the prevalence of malaria. The increase in

the environmental temperature may shorten the duration of the sporogonic cycle in the

malaria vector and affect the dynamic of human-vector contact and vector longevity

(Craig et al., 1999; Teklehaimanot et al., 2004). Climate changes, in particular rainfall,

temperature and humidity affect malaria transmission and consequently influence vector

multiplication or differentiation rate (Kiszewski & Teklehaimanot, 2004; Paaijmans et

al., 2009; Blanford et al., 2013; Caminade et al., 2014).

Travelling to malaria endemic areas is a significant risk factor of malaria

(Prothero, 1965; Singhanetra-Renard, 1993; Martens & Hall, 2000). In the same vein,

migration of people due to wars or disasters can caused malaria outbreaks. About

30,000 malarial cases were documented in Tajikistan as a result of civil war in southern

Azerbaijan (Sabitinelli, 2002). Furthermore, as a result of the Soviet Union breakup in

central Europe, about 60,000 malaria epidemic cases were reported (Sabitinelli, 2002).

Occupation may represent a significant predictor of malaria by exposing human to

mosquito bites. Higher prevalence of malaria was reported among soldiers and gem-

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mining workers in the Thai-Cambodian border. Working in the rural areas was also

identified as a significant factor of getting malaria in Colombia (Luxemburger et al.,

1996; Luxemburger et al., 1997; Mendez et al., 2000; Kitvatanachai et al., 2003).

Additional working activities were identified as risk factors of malaria in endemic areas

such as agricultural, logging, ore-digging and harvesting activities in the farm or forest

(Butraporn et al., 1986; Marques, 1987; Martens & Hall, 2000; Singh et al., 2004b).

Poor housing has been associated with the increased occurrence of malaria in

Gambia, Ethiopia, western of Kenya, Tanzania, Malaysia, Eritrea and Thailand

(Pichainarong & Chaveepojnkamjorn, 2004; Atieli et al., 2009; Peterson et al., 2009;

Ahmad et al., 2014) which increases the indoor density of mosquitoes and subsequently

increases the rate of mosquito bites. Incorrect knowledge about transmission of malaria,

etiology and prevention (Fungladda et al., 1987; Arasu, 1991) and not using insecticide-

treated bed nets have been associated with the high prevalence of malaria (Butraporn et

al., 1986; Fungladda & Sornmani, 1986; Singhanetra-Renard, 1986).

In addition, many malaria risk factors have been identified such as sleeping

outdoors, stagnant water nearby the house and wearing insufficient protective clothes

(Arasu, 1991; Ghebreyesus et al., 2000; Sintasath et al., 2005; Graves et al., 2009;

Alemu et al., 2011), age and gender (Mendez et al., 2000; Van Der Hoek et al., 2003;

Incardona et al., 2007; Winskill et al., 2011). Vector ecology is another factor which

may affect the malaria occurrence. Malaria vector in Asia are described as zoophilic.

Thus, the presence of alternative hosts such as cattles, goat and sheep may reduce the

human exposure to mosquito bites and decreases the incidence of malaria (Habtewold et

al., 2004; Do Manh et al., 2010). Moreover, delayed treatment of malaria cases has been

identified as predictor of developing severe malaria which often occur in rural endemic

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areas, among people staying far away from health centers, or among people who treat

themselves with traditional drugs (Fungladda & Sornmani, 1986; Arasu, 1991;

Oemijati, 1992; Alemu et al., 2011).

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Table 2.1: Socioeconomic factors, behavior factors and environmental factors of

malaria

Factors Countries References

Socio-economic factors:-

Family size (more than 5)

Low education

Low income

Age of individuals

(20-39 years/rural area)

(below 17 years)

Pregnant women

Occupation

Poorly constructed houses

Mud walls

Material of the roof

Open of house windows

Some bedrooms without ceiling

Household own cattle

Traveling to another endemic area

Thialand

Sudan

French Guiana

Thailand

Burkina Faso

Thailand

Burkina Faso

Colombia

Srilanka

Thialand

China

Sudan

Brazil

Malawi

India

Thialand

Kenya

Sri lanka

Sri Lanka

Gambia

Eritrea

Gambia

Sri Lanka

Ethiopia

Gambia

Ethiopia

Ethiopia

Burkina Faso

(Butraporn et al., 1986)

(El Samani et al., 1987)

(Hustache et al., 2007)

(Butraporn et al., 1986)

(Baragatti et al., 2009)

(Butraporn et al., 1986)

(Baragatti et al., 2009)

(Mendez et al., 2000)

(van der Hoek et al., 1998)

(Nosten et al., 1991)

(Moore et al., 2008)

(Adam et al., 2005)

(Martínez-Espinosa et al., 2004)

(Brabin et al., 1993)

(Sharma et al., 2015a)

(Inchana et al., 2013)

(Mutero et al., 2000)

(Yapabandara et al., 2001)

(Campaign, 1991)

(Koram et al., 1995)

(Sintasath et al., 2005)

(Adiamah et al., 1993)

(Konradsen et al., 2003)

(Ghebreyesus et al., 2000)

(Adiamah et al., 1993)

(Ghebreyesus et al., 2000)

(Ghebreyesus et al., 2000)

(Baragatti et al., 2009)

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Behavioral factors:

Sleeping or staying outdoor

Non sleeping under bed nets

Some bedrooms without ceiling

Non residual spraying of the

walls in a house

Non or Partial coverage of the

body with clothing

Malaysia

Indonesia

China

Thialand

Burkina Faso

Sri Lanka

Gambia

Ethiopia

Thialand

Sri Lanka

Gambia

India

(Arasu, 1991)

(Oemijati, 1992)

(Moore et al., 2008)

(Butraporn et al., 1986)

(Baragatti et al., 2009)

(van der Hoek et al., 1998)

(Adiamah et al., 1993)

(Ghebreyesus et al., 2000)

(Honrado & Fungladda, 1994)

(van der Hoek et al., 1998)

(Koram et al., 1995)

(Lwin et al., 2014)

Environmental factors:

Distance from mosquitoes

breeding sites

Water collections nearby

Presence of stream

Swamp existence

Man-made water tank

Rainy season

Agricultural and irrigation area

Workers outdoor or forest

Colombia

Thailand

Gambia

Kenya

Uganda

Sri Lanka

Sudan

Pakistan

Sri lanka

Thailand

Uganda

Guiana

Ethiopia

Sudan

Eritrea

Burkina Faso

Ghana

Burkina Faso

Ethiopia

Vietnam

Bangladesh

(Mendez et al., 2000)

(Butraporn et al., 1986)

(Clarke et al., 2002)

(Minakawa et al., 2002)

(Staedke et al., 2003)

(Konradsen et al., 2003)

(El Samani et al., 1987)

(Klinkenberg et al., 2004)

(Van Der Hoek et al., 2003)

(Butraporn et al., 1986)

(Staedke et al., 2003)

(Hustache et al., 2007)

(Yewhalaw et al., 2009)

(Ranson & Lissenden, 2016;

Rayah et al., 2016)

(Sintasath et al., 2005)

(Baragatti et al., 2009)

(Klinkenberg et al., 2008)

(Baragatti et al., 2009)

(Ghebreyesus et al., 2000)

(Erhart et al., 2005)

(Haque et al., 2011)

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2.1.5 Diagnosis of malaria

Accurate and rapid diagnosis of malaria is a key control measure in the strategy of

malaria control which depends on detecting malaria parasite or its antigens or DNA in

the blood of the patient (Fritsche & Selvarangan, 2011). Microscopy is the most

common technique used for detecting and identifying the blood stages of malaria and

has remained the gold standard for malaria diagnosis. Besides, microscopy is the

suitable technique for estimating the parasite density and the assessment of anti-malaria

drug efficacy. However, it is laborious and time consuming, needs expertise and has low

sensitivity in low parasitaemia cases (Kawamoto, 1991; Milne et al., 1994; Coleman et

al., 2006; Ohrt et al., 2008; Hassan et al., 2010). Rapid diagnostic tests (RDTs) were

developed to overcome the drawbacks of microscopy. It is rapid, easy to use, storable at

room temperature and has shown similar or superior sensitivity compared to microscopy

(Azikiwe et al., 2012). Therefore, it has been introduced as alternative tool for malaria

diagnosis in areas where good microscopy cannot be maintained. In contrast, the low

specificity of RDTs has been reported (Bell & Peeling, 2006; Cunningham & Gatton,

2014). Several malaria RDTs of different manufacturers are commercially available

with a variation in their reliability which therefore necessitate selection criteria and in

sometimes field evaluation for decision on procurement and implementation

(Cunningham & Gatton, 2014). Although molecular approaches have shown high

sensitivity and specificity for detecting malaria and identifying Plasmodium species,

they are sophisticated techniques, limited to reference laboratories and therefore not

practical in low income countries (Coleman et al., 2002; Bates et al., 2004; Mitiku et

al., 2004; Azikiwe et al., 2012; Adams et al., 2015)

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2.1.6 Treatment

2.1.6.1 Anti-malarial drugs

Several antimalarial drugs have been developed and the prescription depends on the

Plasmodium species and the severity of disease. The anti-malarial drug policy is

different from one country to another according to the emergence and spread of anti-

malarial drug resistance. Guidelines for malaria treatment are published and available

from the World Health Organization (WHO, 2015a). The most widely used antimalarial

drugs are classified into classes; 4-aminoquinolines and 8-aminoquinoline (quinolones),

diaminopyrimidine and aminosulfonamide (antifolate drugs), sesquiterpene lactone

endoperoxides (Artemisinin’s drugs) and antibiotics, depending on the chemical

structure and/or mechanism of action (Table 2.2).

Quinine was the first and the oldest antimalarial drug which has been introduced

as pure form for treating malaria since 1820 (Sullivan & Krishna, 2006), but the

resistance of P. falciparum to quinine has been documented in many endemic areas

(Cowman & Foote, 1990; Pukrittayakamee et al., 1994; Jelinek et al., 1995; Segurado et

al., 1997). Chloroquine was the drug of choice for the treatment of uncomplicated

malaria for more than 40 years, and sulfadoxine-pyrimethamine was the second line for

the treatment of uncomplicated malaria in CQ resistance endemic areas. Up to date, P.

falciparum, P. vivax, and P. malariae have shown resistance to antimalarial drugs

(Reyburn, 2010). P. falciparum has evolved resistance to many antimalarial drugs such

as chloroquine (WHO, 2010; Klein, 2013), sulfadoxine-pyrimethamine (Clyde & Shute,

1957; Nair et al., 2003; Roper et al., 2003) and recently artemisinin derivatives in

Southeast-Asia and Western Cambodia (Ashley et al., 2014; WHO, 2014a). The

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evolution of P. vivax strains resistant to chloroquine and sulfadoxine-pyrimethamine has

been reported in many endemic areas (Reyburn, 2010; WHO, 2015a). As a result, WHO

has recommended the shifting from monotherapy of malaria to combination drugs;

artemisinins combination therapy.

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Table 2.2: Classification of antimalarial drug and a brief outline of the mechanism of

action

Class/chemical

family

Name of drug Mechanism of action References

4-aminoquinolines

(Quinoline

derivatives)

Chloroquine,

Hydroxychloro

-quine,

Amodiaquine,

Piperaquine

Inhibiting

detoxification of Haem

(Sullivan et al.,

1996; Pagola et al.,

2000; Pandey et al.,

2001; Lvova et al.,

2016)

4-

methanolquinolines

(Quinoline

derivatives)

Mefloquine,

Lumefantrine,

Halofantrine,

Quinine,

Quinidine

Inhibiting

detoxification of Haem

(Kumar &

Bandyopadhyay,

2005; Kumar et al.,

2007; Lvova et al.,

2016)

Diaminopyrimidine

(Antifolates

derivatives)

Pyrimethamine,

Cycloquanil,

Proguanil

(Chloroguanide)

Inhibiting plasmodial

dihydrofolate reductase

(DHFR)

(Zhang & Meshnick,

1991; Cunha‐Rodrigues et al.,

2006)

Aminosulfonamide

(Antifolate

derivatives)

Sulfadoxine,

Dapsone,

Sulfametho-

pyrazine

Inhibiting plasmodial

dihydropteroate

synthase (DHPS)

(Ferone et al., 1969;

Olliaro, 2001)

Sesquiterpine

lactones

(Artemisinin

derivatives)

Artemether,

Arteether,

Artesunate,

Arterolane,

Artemisinin

Free-radical induced

damage or Inhibition of

Sarcoplasmic reticulum

Calcium-dependent

ATPase 6 (SERCA)

(Terkuile et al.,

1993; White, 2008b;

Fidock, 2010)

8-aminoquinoline

(Quinoline

derivatives)

Primaquine,

Tafenoquine,

Bulaquine

Inhibits electron

transport chain in

Plasmodium

(Butterworth et al.,

2013)

Naphthoquinone

(Quinoline

derivatives)

Atovaquone

Inhibiting

mitochondrial electron

transport chain in

Plasmodium and

mimicking ubiquinone

(Fry & Beesley,

1991; Fry & Pudney,

1992; Hudson, 1993;

Srivastava et al.,

1997)

Antibiotics Tetracycline

Doxycycline

Clindamycin

Azithromycin

Inhibiting protein

synthesis in apicoplast

(Cunha‐Rodrigues et

al., 2006; van Eijk &

Terlouw, 2011)

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2.1.6.2 Methods of anti-malarial drug resistance surveillance

The emerging resistance of malaria parasites has resulted in reduced efficacy of

antimalarial drugs and drug combinations in some areas. Surveillance of antimalarial

drug efficacy is required for effective malaria management of cases and early detection

of resistance to antimalarial drugs. Three main approaches have been used to evaluate

the effectiveness of several antimalarial drugs: in vivo drug efficacy testing, in vitro

assay susceptibility testing, and molecular methods (WHO, 2003). These methods have

advantages and disadvantages. It is essential to distinguish the resistance of malaria

parasites from treatment failure, which is unable to clear malarial parasitaemia and/or

resolve clinical symptoms of the disease after treatment. The susceptibility of malarial

parasites is just one factor that determines the outcome of the antimalarial drug

treatment. Other factors that contribute to treatment failure include incorrect dose of

treatment, poor treatment persistence and compliance, poor quality of drug, inadequate

drug absorption and/or interaction with other drugs (Laufer, 2009).

A) In vivo methods

This method is based on the WHO standardized observation period of 7, 14, or 28 days

and subsequent follow-up of parasitological outcomes (S/RI/RII/RIII levels of

resistance) (Table 2.3) or parasitological and clinical signs and symptoms (adequate

clinical response, early or late treatment failure) (Table 2.4) and treatment of a

symptomatic patient with a standard dose of an antimalarial drug (WHO, 2003;

Stepniewska et al., 2004). The in vivo therapeutic efficacy method remains the gold

standard for monitoring antimalarial drug efficacy and guiding drug policy. It is a

straightforward method, provides indicator results of the efficacy of an antimalarial drug

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and requires minimal training (except microscopy), equipment and supplies in contrast

to the in vitro method (WHO, 2003). The in vivo therapeutic efficacy method was

introduced as a result of CQ resistance in 1965 and the protocols were developed,

revised and standardized for the assessment and monitoring antimalarial drug efficacy

in children and infants in high transmission areas of malaria (WHO, 1996). Recently,

the methodology of in vivo test has been developed and has undergone many

modifications to render it suitable to apply in areas with low to moderate transmission

of malaria (WHO, 2002, 2003). However, this method has many disadvantages such as

long periods of monitoring leading to possible high patient loss, does not necessarily

reflect the level of true antimalarial drug resistance due to many factors such as

treatment outcome interference with patient immunity, previous drug intake,

metabolism and drug absorption variations, also misclassification of reinfection as

recrudescence.

Following the WHO recommendation, genotyping to detect populations of

malaria parasite can be a useful tool for examining a number of infecting parasite clones

and diversity of infection due to host immunity and transmission intensity. In

antimalarial drug clinical trials, the PCR based parasite genotyping can be used as a

correction method to differentiate between new infections and recrudescence in

Plasmodium malaria (WHO, 2008b). Furthermore, the characterisation of length

polymorphism of the merozoite surface proteins (MSP-1, MSP-2 ) and the glutamate

rich protein (GLURP) genes in samples collected at day zero and on the day

reappearance of parasitemia was present. Recrudescence infection was defined when at

least one identical length polymorphism for each genotype markers (MspI,Msp2, and

Glurp) was present between samples collected on the same period. Wherase, a new

infection was occured when, the length polymorphisms were different for one or more

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genotype markers between the sample collected on the same period (Mugittu et

al.,2006; WHO 2008b).

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Table 2.3: Definitions of parasitological response to drug in in vivo therapeutic efficacy

studies

Parasitological response outcomes (WHO, 1996) a

Sensitive Reduce of asexual parasitemia to 25% within 48 hours after

initiation of treatment and complete clearance of parasitemia on

day 7, without subsequent recrudescence up to day 28.

RI Reduce of asexual parasitemia to < 25% within 48 hours after

initiation of treatment, but reappears between day 7 and day 28.

RII Reduce of asexual parasitemia to > 25% but < 75% within 48

hours after initiation of treatment, without complete clearance on

day 7.

RIII Absence or reduce level of parasitemia to < 25% or an increase

in parasitaemia after 48 hours from initiation of treatment.

a Outcomes for extended test protocol (i.e., 14 Day or 28 Day follow-up); R, resistance.

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Table 2.4: Definitions of parasitological and clinical response to drug in in vivo

therapeutic efficacy studies (WHO, 2003)a

WHO in vivo test follow up protocol

ETF Signs of complicated severe malaria on day 1, 2, or 3, or level of parasitaemia

on day 2 higher than on day 0, or level of parasitaemia on day 3 ≥ 25% of day

0, or parasitaemia with axillary temperature ≥ 37.5°C on day 3.

LCF Signs of complicated severe malaria after day 3, or parasitaemia with axillary

temperature ≥37.5°C from day 4 to day 28 without meeting any of the criteria

for ETF.

LPF Parasitaemia without axillary temperature ≥37.5°C from day 7 to day 28

without meeting any of the criteria for ETF or LCF.

ACPR Absence of parasitaemia on day 28, without meeting any of the criteria for

ETF, LCF or LPF.

a 14 day follow-up protocol for high transmission areas and 28 day follow-up for low to

moderate transmission areas.

ETF; Early treatment failure, LCF; Late clinical failure, LPF; Late parasitological

failure, ACPR; Adequate clinical and parasitological response.

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B) In vitro method

In vitro assays measure the intrinsic sensitivity of malaria parasites to a range of

antimalarial drug concentrations. There are several in vitro assay methods, which differ

in respect to the measured effect and period of exposure to the test compound. These

include the WHO mark III test (assessment of schizont maturation or replication by

using microscopic examination of blood films), the radioisotopic test (Incorporation of

radiolabelled nucleotide precursors), the enzyme-linked immunosorbent assay (ELISA)

test together with antibodies against Plasmodium lactate dehydrogenase or histidine-rich

protein 2 (Noedl et al., 2002; Olliaro, 2005) and fluorometric assays with DNA binding

fluorescent dyes (Noedl et al., 2003; Corbett et al., 2004). The in vitro method has many

advantages, such as multiple analyses can be carried out with the same isolated sample

and numerous drug sensitivities can be evaluated simultaneously, including drugs that

are undergoing experiments. It also removes host-confounding factors which influence

in vivo tests as a result of isolated parasites from the host and placed into the controlled

environment. It can accurately detect true drug resistance and provides quantitative

results. The in vitro and molecular markers monitoring could help as complementary

methods for in vivo studies and provide early alarm tools for antimalarial drug

resistance (Shah et al., 2011). However, the disadvantages of in vitro method include

being costly and time-consuming, require expensive equipment and supplies, proper

training and advanced skills for parasite cultures (Basco & Ringwald, 2000).

Furthermore, there is a lack of standardized in vitro protocols, long-term in vitro

adaptation may lose parasites (LeRoux et al., 2009), threshold values for resistance are

not validated, and the correlation with therapeutic response studies is not yet fully

reliable and understood. Therefore, they are not readily amenable to large scale

epidemiological mapping, especially in poor countries, and are recommended to be used

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with in vivo efficacy studies (Miller et al., 2005). Also, once it has been identified that

the antimalarial drug resistance is associated with genetic changes, the assessment of

drug resistance needs to be confirmed using molecular techniques.

C) Molecular markers

Table 2.5 indicates the commonly used antimalarial drugs and their corresponding

molecular markers that predict the evolution of resistant parasite. Screening the parasite

population for mutations at these genetic loci has been considred essential tool for

antimalarial drug resistance surveillance in malaria endemic areas (Plowe et al., 2007).

This molecular approach has many advantages, such as multiple tests can be performed

on one isolated sample on filter paper and numerous drugs can be evaluated

simultaneously. In addition, numerous samples can be easily collected, transported,

stored, assessed, and examined within a short period. This approach is also not affected

by host-confounding factors that are usually controlled in the routine clinical efficacy

trails (WHO, 2003; Ekland & Fidock, 2008). Furthormore, screening for mutations in

molecular markers corresponding to combined drugs in a combined based antimlarial

drug policy may provide early indications for evloution of a resistance to the partner

drug in the combination therapy, avoiding the expose of the malaria parasite to

monotherapy which can not be achieved using the routine therapeutic efficacy methods

(Plowe et al., 1995; Su et al., 1997; Kublin et al., 2003; Picot et al., 2009). However,

the presence of the molecular marker is not necessary correlated with the failure of the

corresponding treatment (Sidhu et al., 2002). Beside the evolution of mutations

associated with developing antimalarial drug resistance, the possibility of treatment

failure depends on host-confounding factors such as the immune response of the host,

drug dose, drug absorption variation and metabolism (Hastings, 2007).

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Many methods are available for monitoring and assessment of antimalarial drug

resistance genes which include PCR-RFLP (polymerase chain reaction-restriction

fragment length polymorphism) (Djimdé et al., 2001a; Djimdé et al., 2001b), a nested

mutation-specific PCR (Djimdé et al., 2001a; Lopes et al., 2002; Ranford-Cartwright et

al., 2002; Sangster et al., 2002). These methods have disadvantages, which include

SNPs analysis for a limited number of samples usually limited by high cost. Other

techniques such as pyrosequencing (Nair et al., 2002), real-time PCR (de Monbrison et

al., 2003; Alker et al., 2004), molecular beacons (Durand et al., 2000), or clamped-

probe PCR (Senescau et al., 2005), are also complex and expensive. Most of these

technologies have disadvantages such as a lack of standardised protocols for specimen

collection, processing and DNA extraction. Also, these technologies involve expensive

infrastructure, equipment, and supplies, especially in the poorest countries.

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Table 2.5: Most commonly used antimalarial drugs along with their molecular

markers to determine their drug susceptibility/resistance

Anti-

malarial

derivatives

Class/

Chemical

family

Name of

drug

Presence

of

resistance

Corres-

ponding

genetic

markers

References

Quinoline

derivatives

4-amino-

quinolines

Chloroquine,

Amodiaquine

Yes Point

mutations

in Pfcrt , Pfmdr1,

Pfmrp1 and

copy

number variation

in Pfmdr1

Djimdé et al., 2001b;

Sidhu et al., 2002; Mu

et al., 2003; Duraisingh, & Cowman 2005;

Humphreys et al., 2007;

Nawaz et al., 2009; Ecker et al., 2012;

Gupta et al., 2014.

4-methanol-

quinolines

(Amino

alcohol)

Mefloquine,

Lumefantrin,

Halofantrine,

Quinine

Yes Point

mutations

in Pfcrt ,

Pfmdr1, Pfmrp,

Pfnhe-1

and copy number

variation in

Pfmdr 1

Duraisingh et al., 2000;

Mu et al., 2003; Ferdig

et al.,2004; Price et al.,

2004; Sidhu et al., 2006; Humphreys et al.,

2007; Preechapornkul

et al., 2009; Koenderink et al., 2010.

8-amino-

quinoline

Primaquine Yes Not known Murphy et al., 1993;

Baird & Hoffman 2004;

Thomas et al., 2016

Naphtho-

quinone

Atovaquone

Yes Point

mutation in

Cyt-b gene

Korsinczky et al., 2000;

Fivelman et al., 2002.

Antifolate

derivatives

Diamino-

pyrimidine

Pyrimethami

n,

Proguanil

Yes Point

mutation in

Pfdhfr

Plowe et al., 1997;

Gregson et al., 2005;

Gama et al., 2009; Alifrangis et al., 2014;

Sharma et al., 2015b.

Amino-

sulfonamide

Sulfadoxine,

sulfene

Yes Point

mutation in Pfdhps

Yuvaniyama et al.,

2003; Pearce et al., 2003; Gama et al.,

2009; Lumb et al., 2011;

Alifrangis et al., 2014; Sharma et al., 2015b

Artemisinin

derivatives

Sesquiterpine

lactones

Artemether,

Artesunate,

Artemisinin

Yes Polymorphi

sm in

Kelch 13 protein

Noedl et al., 2008;

Dondorp et al., 2009;

Ariey et al., 2014; Ashley et al.,2014;

Takala-Harrison et al.,

2015.

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2.2 Malaria in Eastern Mediterranean region

2.2.1 Current status

In 2013, approximately 280 million people were at risk of malaria in eight countries in

the Eastern Mediterranean. P. falciparum is responsible for the majority of malaria

cases in all countries except in Iran, Afghanistan, and Pakistan, where P. vivax is the

most common species responsible for malaria cases. The majority of malaria cases were

reported from Sudan, Pakistan, Afghanistan, Yemen, Somalia and Djibouti with more

than 55% of the cases occurring in Sudan. Most malaria attributed death cases occurred

in Sudan (67%) and Pakistan (24%) (Table 2.6) (WHO, 2014).

All endemic countries in the Eastern Mediterranean have reported a decline in the

number of confirmed malaria cases and deaths due to the strengthening of malaria

control programmes which include the use of effective artemisinin-based combination

therapies (WHO, 2014b), and vector control such as long-lasting insecticidal nets and

indoor residual spraying. Improved health services especially in urban areas have also

contributed to progress and improved outcomes. The number of malaria cases dropped

from two million to one million during the period from 2000 to 2013. The incidence of

malaria in six countries (Saudi Arabia, Oman, Syria, Afghanistan, and Iran) decreased

more than 75% during the same period. Only 519 and 34 indigenous cases were

reported from Iran and Saudi Arabia in 2013, respectively, and zero indigenous cases

were reported from Iraq since 2009 (WHO, 2014b).

Seven countries are still in control phase including Yemen, Sudan, South Sudan,

Pakistan, Somalia, Afghanistan and Djibouti, while two countries have limited

transmission of malaria which are the Kingdom of Saudi Arabia and Iran and have been

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classified in the elimination phase. Four countries are in the prevention of reintroduction

phase. They are Egypt, Oman, the Syrian Arab Republic, Iraq, since 1998, 2004; 2005,

and 2009, respectively, however, few local malaria cases may be recorded due to

imported cases, such as in Aswan governorate, Egypt has reported 22 local malaria

cases in 2014 (WHO, 2014b). in addition, the countries that have been certified free of

malaria including Morocco (2010) and the United Arab Emirates (2007) and Tunisia

(2012) (WHO, 2012, 2013). The remaining countries belong to Eastern Mediterranean

region that have eliminated local transmission of malaria long time ago or have a few

imported cases include Qatar (1970), Bahrain (1979), Kuwait (1979), Libya (1973),

Lebanon (1963), Jordan (1970), Palestine (1965) and Cyprus (1953), but they have not

request certification from WHO (WHO, 2001; Malaria, 2002; Atta & Zamani, 2008).

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Table 2.6: Malaria cases in countries with high transmission areas at the Eastern

Mediterranean region in 2013

Countries Confirmed

Cases

Deaths cases Total estimated

cases

Pakistan 3,472,727 244 7,752,797

Sudan 989,946 685 2,197,653

Afghanistan 319,742 24 787,624

Yemen 149,451 55 924,821

Somalia 60,199 - 119,752

Djibouti 1,684 17 7,934

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2.2.2 Anti-malarial drug resistance

There have been various drug-resistant molecular markers identified for P. falciparum

in Mediterranean countries and these are summarised in Table 2.7. All studies have

shown a predominance of Pfcrt 76T allele suggesting the spread of CQ resistance (Al-

Mekhlafi et al., 2011b; Afsharpad et al., 2012; Al-Farsi et al., 2012; Dajem et al., 2012;

Khatoon et al., 2013). For mutations in Pfdhfr and Pfdhps genes, the single mutant

allele R59 has been reported from Saudi Arabia (Al Harthi, 2007; Dajem & Al-Qahtani,

2010; Zakai et al., 2013), Iran (Zakeri et al., 2003; Heidari et al., 2007; Zakeri et al.,

2007; Afsharpad et al., 2012; Rouhani et al., 2015) and Yemen (Mubjer et al., 2011; Al-

Hamidhi et al., 2013). The double mutant 51I/108N was reported from Saudi Arabia

(Al-Farsi et al., 2012; Dajem et al., 2012), Pakistan (Ghanchi et al., 2011), Iran (Zakeri

et al., 2003; Zakeri et al., 2007; Afsharpad et al., 2012) and Yemen (Al-Hamidhi et al.,

2013). The triple mutant 51I/59R/108N was not seen in Saudi Arabia (Al-Farsi et al.,

2012) but was reported in Sudan (A-Elbasit et al., 2008). The single mutant allele

Pfdhps 437G was reported from Saudi Arabia and Iran (Afsharpad et al., 2012; Al-Farsi

et al., 2012; Dajem et al., 2012; Rouhani et al., 2015). In addition, 20% isolates

harboring a single mutant 540E of the Pfdhps gene were recorded in 1999 in Iran

(Eskandarian et al., 2002). It is noteworthy that the absence of the quintuple mutant

genotype (Pfdhfr I51R59N108 and Pfdhps G437 E540) which has a significant association

with SP failure (Picot et al., 2009) shows that SP is still justified as AS-partner drug in

the ACTs drug policy in the Mediterranean region. However, the emergence of the

triple mutant 51I/59R/108N genotype in the region necessitate the continued monitoring

of the efficacy of ACTs and the AS partner drugs.

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Table 2.7: Summary of some previous studies on mutations of Pfcrt, Pfmdr1, Pfdhfr and Pfdhps genes in Plasmodium isolates and their role in

antimalarial drugs resistance in Mediterranean countries.

Country Mutations in gene markers Notes Reference

Pfcrt Pfmdr1 Pfdhfr Pfdhps

Saudi

Arabia

76T

76T

76T

76T

76T

86Y,184F

86Y

86Y

86Y,184F

59R

51I,108N

59R

59R

51I,108N

437G

437G

-Low level of mutations in Pfdhfr against SP drug

indicates that SP is still an effective treatment.

-No significant difference in distribution of drug

resistance genes was observed between Saudis and

expatriates.

-Showed high prevalence of K76T and N86Y

mutations in the Pfcrt and Pfmdr1 genes by

pyrosequencing.

-Association of CQR with Pfcrt 76T and Pfmdr1 86Y

mutations.

-Showed initial phase of SP resistance in Jazan district.

-Showed prevalence of mutations was associated with

CQR and evolution and spread of SP resistance.

(Zakai et al., 2013)

(Dajem et al., 2012)

(Dajem et al., 2011)

(Dajem & Al-

Qahtani, 2010)

(Al Harthi, 2007)

(Al-Farsi et al.,

2012)

Iran

76T

76T

86Y

86Y

59R, 108N

15I, 59R, 108N

15I, 59R, 108N

59R, 108N,164L

437G

437G

437G

436A,437G

- No association between the clinical outcome of SP

treatment and presence of single or double mutations.

- No correlation between in vivo resistance and the

presence of mutations.

-Association between in vivo SP resistance and

mutations in Pfdhfr and Pfdhps genes before adoption

of SP + AS as the first-line, fixed level of Pfcrt 76T

was observed.

-No correlation between in vivo resistance and the

quintuple mutant, but A437G showed correlation.

(Rouhani et al.,

2015)

(Afsharpad et al.,

2012)

(Zakeri et al., 2003;

Zakeri et al., 2007)

(Heidari et al., 2007)

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Pakistan*

(P.v &P.f)

76T

76T

1076L*

1076L

51I*, 117 N*

58R*, 117N*

57L*, 58R*,

117N/T*

59R, 108N, 57L*,

58R*, 117N*

58R*, 117N*,

51I*, 93H*

58R*, 117N*

59R, 108N

58R*, 117N*

119K*

383G*,

553G*

383G*,

553G*

437G

383G*

437G

-Association between SP resistance and both 117 N

and 50I mutations.

-Low association between SP resistance and both 117

N and 50I mutations in P.vivax but with high Pvmdr1

1076L indicate low efficacy of chloroquine plus

primaquine.

- Showed association of mutations with SP drug

resistance in clinical isolates of P.v

- Showed CQ resistance but other drugs such as SP has

not reached an alarming threshold yet.

-Showed low prevalence of mutations associated with

SP resistance and high Pvmdr1 1076L mutant indicate

an alarming emergence of chloroquine-resistant.

-Showed that more than half of P.v isolates had mutant

haplotype indicating an emergence of SP drug

resistance.

-Showed an emerging multi-drug resistance

problem in P. v and P. f malaria.

(Waheed et al.,

2015)

(Khattak et al.,

2013b)

(Raza et al., 2013)

(Khatoon et al.,

2013)

(Khattak et al.,

2013b)

(Zakeri et al., 2011)

(Khatoon et al.,

2009)

Pakistan

(P.f)

76T

76T

76T

86Y

86Y

59R, 108N

59R, 108N, 57L,

58R, 117N

51I, 59R, 108N

437G,

K540E

A581G

437G

437G, 540E

-Showed complete fixation of CQ resistance genotype

with emerging multiple resistance alleles in Pfdhfr and

Pfdhps indicating a warrant to assess whether SP

remains efficacious as a partner drug.

- Showed CQ resistance but other drugs such as SP has

not reached an alarming threshold yet.

-Showed high prevalence of in vivo CQ resistance

against P.f but the high level SP resistance was not

recorded.

(Khattak et al.,

2013a)

(Khatoon et al.,

2013)

(Ghanchi et al.,

2011)

Afghanist-

an

76T

86Y, 184F

59R, 108N

59R, 108N

437G,

-Showed that AS-SP drug still maintain efficacy

therapy for uncomplicated P.f.

-Showed high level of SP resistance than previously

(Awab et al., 2016)

(Awab et al., 2013)

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76T

76T

86Y,184F

86Y,184F

58R*, 117N*

540E, 581G

detected.

-Showed mutations associated with CQ resistance.

-Showed in vitro correlation of Pfcrt mutations and

high level of amodiaquine resistance.

-Showed low level of Pvdhfr mutations.

(Howard et al., 2011)

(Beshir et al., 2010)

(Zakeri et al., 2010a)

Somalia 51I, 108N, 59R 437G, 540E -Showed association of SP treatment failure with

double, quadruple and quintuple mutations in the dhfr

and dhps genes and with younger age.

(Warsame et al.,

2015)

Djibouti

76T

51I, 108N, 59R

51I, 108N, 59R

436A, 437G

540E

-Showed Pfdhfr mutations associated with low level of

pyrimethamine sensitivity.

-Showed low level of mutations in Pfdhfr and Pfdhps

but high in Pfcrt indicating high prevalence of CQ

resistance.

(Khaireh et al.,

2013)

(Rogier et al., 2005)

Sudan

76T

76T

51I, 108N, 59R

51I, 108N, 59R

51I, 108N

51I, 108N

51I, 59R, 108N

113L*, 58R*,

117N*

437G,

540E,

581G

436A 437G,

540E,581G

437G, 540E

437G, 540E

-Showed high prevalence of quadruple, quintuple or

sextuple dhfr/dhps haplotype mutations with only nine

treatment failures.

-Showed association between Pfdhps haplotype

SGEGA and SP treatment failures.

-Showed strong association between resistant dhfr and

dhps genes, indicate high rate of SP resistance.

-Showed no association between dhfr and dhps

haplotypes with SP plus CQ resistance.

-Showed Pfcrt and Pfdhfr mutations with CQ and SP

resistance.

-Showed high Pvdhfr double mutations associated with

low level of SP resistance in Eastern and Central

Sudan.

(Adeel et al., 2016)

(Gadalla et al., 2013)

(Al-Saai et al., 2009)

(A-Elbasit et al.,

2008)

(Hamour et al.,

2005)

(Pirahmadi et al.,

2014)

Yemen

108N

-Showed association of high level of Pfdhfr 108N

mutation with frequent antimalarial drug intake and

history of malaria infection previously.

(Abdul-Ghani et al.,

2014)

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* point mutations are shown for genes in Plasmodium vivax at different codons.

76T

76T

76T

76T

86Y, 184F

51I, 108N, 59R

59R

-Showed high level of mutations in Pfcrt, Pfmdr1 and

Pfdhfr with high diversity of P. falciparum, indicating

a large parasite reservoir.

-Showed association of Pfcrt 76T mutation with

parasitemia and treatment seeking behaviors.

-Showed association of Pfcrt 76T mutation with

moderate/low parasitaemi, the age group > 10 years

and low household income in Hodeidah and Taiz

governorates, Yemen.

-Showed an emerging resistance to SP and haplotype

associated with CQ resistance P. falciparum parasites

from Lahj governorate, Yemen.

(Al-Hamidhi et al.,

2013)

(Abdul-Ghani et al.,

2013)

(Al-Mekhlafi et al.,

2011b)

(Mubjer et al., 2011)

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2.3 Malaria in Yemen

2.3.1 Plasmodium species and types of vectors

Malaria is still a major public health problem in Yemen. The epidemiology is classified

as an Afrotropical type with 99% of malaria cases being caused by P. falciparum as the

predominant species and the remainder are P. vivax and P. malariae (NMCP, 2002; Al-

Maktari et al., 2003; Azazy & Raja'a, 2003; Bassiouny & Al-Maktari, 2005; Al-Taiar et

al., 2006; Alkadi et al., 2006; Abdulsalam et al., 2010). The epidemiology of

falciparum malaria in the Arabian Peninsula including Yemen can be divided according

to topographical criteria into three eco-epidemiological zones of malaria; Oriental,

Palaearctic and Afrotropical, leading to a wide variation in vectors and transmissions of

malaria parasites and the subsequent increased risk of malaria (Zahar, 1974;

Kouznetsov, 1976; Kravchenko, 1979; Snow et al., 2013).

In Yemen, many species of anopheline mosquitoes have been reported to be

responsible for malaria transmission (Figure 2.4). The most common vectors include

Anopheles arabiensis which has been reported as the main vector within the country,

Anopheles culicifacies which is an important vector in the coastal areas and it is the

predominant vector in Socotra Island and the eastern governorate of Al Maharah,

Anopheles sergenti has been reported to be a vector in the mountainous hinterland and

highland areas, and recently An. algeriensis (Sinka et al., 2010; Snow et al., 2013).

Understanding the time of biting and indoor or outdoor resting behaviours, distribution

of Yemeni anopheline mosquitoes, the susceptibility of vectors to insecticides and their

role are important in the control and transmission of malaria and can assist in planning

for vector control strategies in Yemen. Increase or decrease of malaria incidence

depends on the densities of mosquitoes. Peak transmission of malaria in endemic areas

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is mostly related to rainy seasons as there are plentiful of breeding sites available for

mosquitoes. Yemen shares many similarities of the ecology of mosquitoes with Saudi

Arabia, Jazan Provine. In addition, high transmission of malaria in Yemen as a result of

illegal immigration of people from the malaria endemic countries in the horn of Africa

such as Eritrea, Somalia and Ethiopia due to conflict, political instability, civil wars and

poverty (Soucy, 2011).

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Source: (NMCP, 2006)

Figure 2.4: Modified map of distribution of Anopheles mosquito in governorates,

Yemen

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2.3.2 Trend of confirmed malaria cases in the last 23 years

The estimate of malaria cases in Yemen fluctuates from year to year, making estimation

of malaria cases incidences difficult (WHO, 2013). For example, there were 2.7 million

cases in 1999 and 3.2 million cases in 2001 after which the malaria cases declined

gradually between 800,000 -900,000 cases in 2006 with 1% estimated related deaths

(Figure 2.5). According to a WHO report, malaria cases in Yemen in 2009 were

265,074 cases with 779 related deaths (WHO, 2008a; NMCP, 2011). The weather and

climate of Yemen varies from one region to another due to diverse topography, changes

in climates especially in rainfall, temperature and humidity leading to variation in the

rates of malaria transmission.

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Figure 2.5: Malaria trend in Yemen from 1990 till 2014

0

150000

300000

450000

600000

750000

900000

1050000

1200000

1350000

1500000

1650000

1800000

1950000

2100000

2250000

2400000

2550000

2700000

2850000

3000000

1990

1991

1992

1993

1994

1995

1996

1997

1999

2000

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Mal

aria

ca

ses

Years

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2.3.3 Risk factors

Yemen is the remaining country in the Arabian Peninsula with high transmission rate of

malaria because of unstable political issues and civil wars for many years. The country

undergoes numerous environmental and social stresses such as food and water

insecurity, and severe depletion in water resources, weak institutions and health system,

rapid population growth and climate change (Husain & Chaudhary, 2008).

The research on risk factors for malaria in Yemen is still very scant as there were

only four studies in particular governorates such as Taiz, Hodiedah, Dhamar and

Raymah that have determined some factors that could be associated with the increased

risk of acquiring malaria (Table 2.8). For example, Al‐Taiar et al. (2008) study found

that socioeconomic factors (distance to nearest health center >2 km, driving time to

reach health center > 10 min and house with earth roof), behavior factors (spray

insecticides at home, delay of treatment > 3 days, burning mosquito coils) and

environmental factors (nearby the water pump to house, nearby the man-made water

collection/tank and more than 2 km of the distance from health center) were risk factors

for malaria. In addition, Al-Taiar et al. (2009) have also demonstrated that

socioeconomic factors (house with earth roof with or without opening in the roof, and

traveling to another endemic area in the last 2 month), behavior factors (burning animal

dung) and environmental factors (water collections nearby, presence of water

stream/spring, presence of water pump, swamp existence/marshy land, presence of

latrine outside the house or non and water storage at home:in jerry cans) could be risk

factors that lead to increase the chance of acquiring malaria in the country.

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A more recent study in 2011 found a significance risk factors for malaria include

children ≤ 12 year, large family size, gender, living in rural area, low income, not

working, not sleeping under bed nets, burning mosquito coils, spray insecticides at

home, wear short clothes and water collections nearby the houses (Al-Mekhlafi et al.,

2011a) (Table 2.8).

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Table 2.8: Socio-economic, behavioral and environmental risk factors associated with

acquiring malaria in four governorates in Yemen

Factors Governorates References

Socio-economic factors:-

Family size (more than 5)

Gender

Male

Female

Low income

Rural area

Age of individuals

age ≤ 12 year

Occupation (not working)

Distance to nearest health center >2

km

Driving time to reach health center

> 10 min

Poorly constructed houses

Material of the roof (earth)

Presence of opening in the roof

Presence of latrine:

Outside the house

Not at all

Traveling to another endemic area

In the last 2 month

Dhamar,and Raymah

Dhamar,and Raymah

Taiz, Hodiedah

Taiz, Hodiedah,

Dhamar,and Raymah

Dhamar,and Raymah

Taiz, Hodiedah

Taiz, Hodiedah

Taiz

Taiz

Taiz, Hodiedah,

Taiz

Taiz

Taiz

Taiz

Taiz

(Al-Mekhlafi et al.,

2011a)

(Al-Mekhlafi et al.,

2011a)

(Al-Taiar et al., 2006)

(Al-Mekhlafi et al.,

2011a)

(Al-Mekhlafi et al.,

2011a)

(Al-Mekhlafi et al.,

2011a)

(Al-Mekhlafi et al.,

2011a)

(Al‐Taiar et al., 2008)

(Al‐Taiar et al., 2008)

(Al-Taiar et al., 2006)

(Al‐Taiar et al., 2008)

(Al-Taiar et al., 2009)

(Al-Taiar et al., 2009)

(Al-Taiar et al., 2009)

(Al-Taiar et al., 2009)

(Al-Taiar et al., 2009)

Behavioral factors:

Not sleeping under bed nets

Burning mosquito coils

Spray insecticides at home

Taiz

Taiz, Hodiedah,

Dhamar,and Raymah

Taiz

Taiz

(Al-Mekhlafi et al.,

2011a)

(Al-Mekhlafi et al.,

2011a)

(Al‐Taiar et al., 2008)

(Al‐Taiar et al., 2008)

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Burning animal dung

delay of treatment >3days

Wear short clothes

Dhamar,and Raymah

Taiz

Taiz

Taiz, Hodiedah,

Dhamar,and Raymah

(Al-Mekhlafi et al.,

2011a)

(Al-Taiar et al., 2009)

(Al‐Taiar et al., 2008)

(Al-Mekhlafi et al.,

2011a)

Environmental factors:

Water collections nearby

Presence of water stream/spring

Presence of water pump

Swamp existence/marshy land

Man-made water collection/tank

Water sources for house:

Well

Stream/spring

Water truck

Water storage at home:

In jerry cans

Distance from health center>2km

Taiz, Hodiedah,

Taiz

Taiz, Hodiedah,

Dhamar,and Raymah

Taiz

Taiz

Taiz

Taiz, Hodiedah,

Dhamar,and Raymah

Taiz

Taiz

Taiz

Taiz

Taiz

Taiz

Taiz

(Al-Mekhlafi et al.,

2011a)

(Al-Taiar et al., 2009)

(Al-Mekhlafi et al.,

2011a)

(Al-Taiar et al., 2009)

(Al‐Taiar et al., 2008)

(Al-Taiar et al., 2009)

(Al-Mekhlafi et al.,

2011a)

(Al-Taiar et al., 2009)

(Al‐Taiar et al., 2008)

(Al-Taiar et al., 2009)

(Al-Taiar et al., 2009)

(Al-Taiar et al., 2009)

(Al-Taiar et al., 2009)

(Al‐Taiar et al., 2008)

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2.3.4 Malaria distribution and intensity of transmission

In Yemen, malaria is seasonal and unstable, the malaria endemicity ranges from

mesoendemic in the south to hyperendemic in the north, especially the coastal plains,

including Tehama areas, foothill regions and coastal districts in the Hadhramout

governorate which is characterised by extensive wadis and seasonal rainfall (Yahia,

2005; Mohanna et al., 2007). From the past to present, the prevalence of malaria in

Yemen fluctuates and is a major health problem with prevalence ranging from 12.8% to

18.6% (Alkadi et al., 2006; Mohanna et al., 2007; Abdulsalam et al., 2010; Othman et

al., 2015).

Yemen has four major epidemiological stratification of malaria, and the

classification was based on the attitude, rainfall, and topography (Table 2.8) (NMCP,

2011; Adeel et al., 2015). The areas of the first stratum has an attitude of 0-600 meters

with an average of 4-6 months of rain. The transmission season mainly occur in winter

from November to April and is characterised by occurrence of malaria infection in

wadis along the coastal areas whereas the desert areas are malaria free zones (e.g

Hadhramout). The second stratum consists of an attitude of 601-1000 meters above the

sea level, and malaria transmission occurs in the winter season from November to April.

In addition, there is partial transmission in summer from May to September and is

characterised by occurrence of malaria infection in the Wadis (valleys), and in the

foothills (e.g. Tihama region). The third stratum has an attitude above 1001-2000

meters, malaria transmission occurs in the summer season, especially in the foothills

and wadis of the central highlands. The four stratum is the areas above 2000 meters and

the desert areas which are usually free from malaria. Hadhramout governorate is divided

into three zones which are, coastal plain, mountains and foothill areas (Yahia, 2005).

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The area is classified by the NMCP as belonging to stratum one and peak malaria

transmission occurs in winter between October and April.

2.3.5 Prevention and control

The National Control Malaria Program (NCMP) in Yemen, is proactive in combating

malaria through the implementation of several interventions that include distribution of

insecticide-treated mosquito nets (ITNs), indoor residual spraying (IRS), proper

diagnosis, proper treatment, and reactive and proactive case surveillance.

2.3.6 Malaria diagnosis

In Yemen, the diagnosis of malaria depends on clinical examination and confirmation

by microscopic detection of malaria parasites in blood smear. The microscopic

examination is usually conducted in the main hospitals and health centers where trained

technicians are present. However, this technique may not be available in the rural areas

due to the lack of required facilities and qualified health workers. Furthermore, WHO

reported that the standard of microscopy examination in Yemen is poor due to the lack

of effective national standards; poor quality of blood films, poor quality stains and

staining techniques, generally unsatisfactory laboratory equipment; and the absence of

an effective quality assurance program (WHO, 2009). Therefore, malaria rapid

diagnostic test (RDT) was introduced as alternative tool for malaria diagnosis in areas

where good quality microscopy is not available or cannot be carried out (McMorrow et

al., 2010; WHO, 2015b).

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2.3.7 Malaria treatment in Yemen

2.3.7.1 The old strategy (from 1999)

The national antimalarial drug policy in Yemen was formulated in 1999, consisting of

chloroquine (CQ) as first-line, sulphadoxine-pyrimethamine (SP) as a second line and

the third line is mefloquine and primaquine as a gametocytocidal treatment

monotherapy for treating uncomplicated falciparum malaria. However, quinine

intravenous infusion was used for treating complicated and severe falciparum malaria.

In addition, chloroquine and primaquine treatment drug were used for treating non-

falciparum malaria as anti-relapse treatment for Plasmodium vivax and as

gametocytocidal treatment for Plasmodium malariae (NMCP, 2006).

2.3.7.2 The new strategy (from 2005)

In November 2005, following the emergence of chloroquine resistance and the WHO

recommendation, the antimalarial treatment policy shifted to artemisinin-based

combination therapy (ACT) with artesunate (AS) plus sulphadoxine-pyrimethamine

(SP) as the first-line, and artemether-lumefantrine (AL) as the second line therapy for

treating uncomplicated falciparum malaria (Adeel et al., 2015). However, this new

policy was only implemented four years later in 2009 after proper training and

distribution of the national guideline for antimalarial drugs were carried out (Bin

Ghouth, 2013). Artemether or quinine infusion therapy was used for treating

complicated and severe falciparum malaria. In addition, the treatment of non-falciparum

malaria is still chloroquine and primaquine as an anti-relapse treatment for P. vivax

(NMCP, 2010a, 2010b; WHO, 2012; Bin Ghouth, 2013).

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2.3.7.3 Monitoring anti-malarial drug resistance

A) In vivo studies

Monitoring antimalarial drug efficacy in Yemen started in 2002 to 2005 following the

WHO protocol for in vivo assessment in four sentinel sites that found 39% to 57% of

chloroquine resistance. In 2004, three in vivo studies on the efficacy of SP showed

success rate ranging from 95% to 100%. Four years later, after launching the new

policy, in vivo efficacy trials were conducted in three monitoring sites and they reported

of 97.6–100 % adequate clinical and parasitological response (ACPR) for AS plus SP

(NMCP, 2010b). The efficacy of AS plus SP as first-line treatment for uncomplicated

falciparum malaria was also rated at 97% ACPR in a recent clinical drug efficacy trial

carried out in 2013 (Adeel et al., 2015). It is noteworthy that the currently used routine

clinical efficacy trial is the gold standard for the assessment of the efficiency of the

combined antimalarial drugs, although it does not differentiate between the

effectiveness of AS and its partner drug.

B) Molecular markers based studies

For many years, CQ had been the first line treatment in Yemen. The first case of the

indigenous chloroquine resistance (CQR) in Yemen was reported in 1989 in Taiz

(Mamser, 1989; Alkadi et al., 2006), and then in Hodeidah (Al-Shamahy et al., 2006).

In addition, recent studies have shown high prevalence of CQR marker Pfcrt 76T in

Hodeidah, Dhammar, Rymah and Taiz (Al-Mekhlafi et al., 2011b; Abdul-Ghani et al.,

2013; Al-Hamidhi et al., 2013). Although antimalarial drug policy in Yemen has

changed from CQ to ACT, previous studies conducted in Hadhramout governorate

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reported that CQ is still commonly prescribed (18 out of 42 prescriptions) and some

clinicians were not aware and had poor knowledge about the new national drug policy

(Bashrahil et al., 2010; Bin Ghouth, 2013). Continued use of CQ sustains the selection

of CQ resistant mutations leading to persistence of mutant parasite. The complete

withdrawal of CQ use may enhance the emergence of CQ sensitive parasite over time

and make CQ possible to be re-introduced for malaria treatment (Kublin et al., 2003;

Laufer et al., 2006). However, the persistence of CQ resistance will be prolonged if the

shift to ACT and the simultaneous withdrawal of CQ are not rigorously implemented.

Molecular markers are practical for monitoring SP resistance. Quintuple mutant of

combined dihydrofolate reductase (dhfr) and dihydropteroate synthase (dhps) genes

(Pfdhfr I51, R59, N108 plus Pfdhps G437, E540) was significantly associated with in

vivo resistance to SP (Picot et al., 2009). Several studies have also been conducted for

the screening of P. falciparum population for molecular markers associated SP

resistance in Yemen. The mutant allele R59 of Pfdhfr was detected in 5 % of P.

falciparum isolates (5/99) in Lahj governorate, southern Yemen (Mubjer et al., 2011).

Double mutant genotype of Pfdhfr (I51/N108) was reported in 54 % of P. falciparum

isolates in Taiz, Dhamar, and Hodeidah governorates in western Yemen (Al-Hamidhi et

al., 2013). Pfdhfr mutant allele (N108) was also reported in 53.2 % of P. falciparum

isolates collected from Hodeidah governorate (Abdul-Ghani et al., 2014). The continued

use of SP in the new policy, availability of this drug in the private sector, and poor

knowledge of the national policy among physicians (Bashrahil et al., 2010) may

increase the monotherapy of SP against P. falciparum, which is likely to compromise

drug efficacy. It is noteworthy that the data on molecular markers associated with CQ

and SP resistance are not available from the Hadhramout governorate, Yemen where

this study is being carried out.

.

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CHAPTER 3: METHODOLGY

3.1 OVERVIEW OF STUDY METHODS

A cross–sectional household survey was carried out in Hadhramout governorate, which

is located in the southeast of Yemen. A total of 735 participants aged 1-75 years and

genders were enrolled in this study. Participants were from three villages in Hajer

district and four villages in Al-Raydah-Qusyer district. Questionnaire data and blood

samples were collected during transmission seasons from July 2011 to May 2012. These

data were then analysed using the Statistical Package for Social Sciences for Windows

(SPSS) version 23.0. Blood from each individual was smeared on a glass slide as well

as spotted on a Whatman filter paper 3MM (Whatman International Ltd., Maidstone,

England). Blood smears were brought back from the field and stained as soon as

possible with 10% diluted Giemsa stain and screened under a microscope for the

presence of malaria parasites in the laboratory. Then, the positive specimens for

Plasmodium falciparum were used for molecular identification and genotyping studies

(Figure 3.1).

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Figure 3.1: Schematic diagram of samples and data collection and molecular

marker detections

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3.6.2 Ethical clearance

The study protocol was approved by the Faculty of Medicine, Hadhramout University

for Science and Technology, the Ministry of Health and Population, Yemen, and the

Malaria National Control Program division in Hadhramout governorate, Yemen.

Informed consent was obtained from each participant, and for children, consent was

obtained from their parents after a clear explanation of the study objectives

(APPENDIX B).

3.3 STUDY AREAS AND STUDY POPULATION

The study was conducted in the Hadhramout governorate in the southeast Yemen, the

largest governorate, accounting for half of the country’s surface area. The population of

this governorate was estimated at 1,028,556 (CSO, 2004). Hadhramout has a humid and

hot climate which is characterised by humidity levels ranging from 18 to 93% and

temperature ranging from 18 to 38°C. In general, Hadhramout climate is unstable and

changes from time to time. For example, Hadhramout governorate faced tropical storm

and flash flood in October of 2008 (Breisinger et al., 2012) and tropical cyclone

Chapala which caused heavy flooding in the southeastern governorates of Hadhramout,

Shabwa and along the coast of Arabian Sea in November of 2015. These events might

affect household health, densities of mosquito vectors, reduce incomes and subsequently

food security levels (Hubálek & Halouzka, 1999; McMichael et al., 2006; Breisinger et

al., 2012).

Malaria transmission in Yemen differs between the regions. Hadhramout has

coastal plain region and mountainous area. The coastal plain region (with an altitude of

0–600 m) is characterised by hot climate throughout the year with irregular rainfall

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ranging between 50 and 100 mm per annum. A total of 735 participants aged 1-75 years

and genders were enrolled in this study; 221 participants from three villages in Hajer

district and 514 participants from four villages in Al-Raydah-Qusyer district, houses

were selected randomly (Figure 3.2), and some of population in rural area lived in

poorly constructed houses of mud or uncemented brick walls with man-made water tank

located beside the house for water storage (APPENDIX B). These villages were

selected because they are endemic malaria areas with more than 99 % of cases being

caused by P. falciparum and few cases of Plasmodium vivax.

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Figure 3.2: Map of study area highlighted in the Hadhramout governorate, Yemen

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3.3 DESIGN OF STUDY

A community-based cross-sectional study was carried out in seven endemic villages,

three in Hajer district and four villages in Al-Raydah-Qusyer district.

3.4 SAMPLE SIZE

Sample size required for this study was estimated according to the previous prevalence

of malaria reported in four recent studies in Yemen (Al-Taiar et al., 2006; Alkadi et al.,

2006; Mohanna et al., 2007; Abdulsalam et al., 2010) which was rated at 18 %. By

using the formula developed by Lwanga and Lemeshow (1991) and according to the

following parameters: 18% as the expected malaria prevalence, 95% confidence level

and P ≤ 0.05, the minimum number of sample size needed for this study was 196-246

subjects. The formula is as the following:

n =z2 𝑃 (1−𝑃)

𝑑2 (Eq. 3.1)

where n= sample size,

Z = Z statistic for a level of confidence,

P = expected prevalence or proportion (in proportion of one; if 20%, P = 0.2), and

d= precision (in proportion of one; if 5%, d = 0.05).

Z statistic (Z): For the level of confidence of 95%, which is conventional, Z value is

1.96.

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3.5 DESCRIPTION OF VARIABLES

This study has a dependent variable, which takes the form of yes or no responses to

malaria infection and main outcomes such as point mutations of Pfcrt gene at 76, 271,

326, 356 and 371 positions, Pfmdr-1 gene at 86 and 1246 positions and point mutations

of Pfdhfr and Pfdhps genes at different positions. The independent variables such as

age, gender, education level, occupation, socio- economic factors, behaviour factors,

environmental factors, level of parasitemia. The definition of variables are listed in

APPENDIX D.

3.6 DATA AND SAMPLE COLLECTION

3.6.1 Strategy of field work

A household survey from house-to-house was conducted to collect questionnaire data

and blood samples by surveyors who had previous experience with malaria surveys in

the endemic districts in the Hadhramout governorate. Collection was done according to

recommendations from the Malaria National Control Program Unit in Al-Mukalla,

Yemen. All household visits occured during the period from July 2011 to May 2012.

3.6.2 Questionnaire

A pretested standard questionnaire which was developed by the World Health

Organization for malaria prevalence survey was used to collect information about

personal profile, socioeconomic, and environmental background. Firstly, the

questionnaire which was written in English was translated into Arabic, the mother

tongue language of Yemen (APPENDIX E). A checklist was used for malaria clinical

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signs and symptoms, as observed by a team of physicians, and any history of previous

antimalarial treatment was documented. Knowledge, attitude and practices (KAP) were

investigated using a standard questionnaire. The data were collected from the household

members, or from the parents on behalf of children, via face-to-face interviews

conducted by well-trained interviewers. During the interviews, direct observation was

made for the type of household building, wall, floor; for the availability and the type of

toilet facilities, piped water, clothes-wearing habits, electricity, telephone, mosquito

nets, and finally the presence of nearby pools or rivers. The signs and symptoms

recorded included fever and jaundice.

3.6.3 Blood sampling

Blood samples were collected by the finger prick method and thin and thick blood

smears were made, allowed to air-dry (the thin smears were fixed with methanol within

three hours), and then brought back to the laboratory to be stained with Giemsa

(APPENDIX F). Haemoglobin levels were measured in the field directly from capillary

blood using the HemoCue haemoglobinometer (HemoCue, AB, Angelhom, Sweden).

Haemoglobin levels were considered as normal (>11 g/dl), low anaemia (9-11 g/dl),

moderate anaemia (7-8.9 g/dl), and severe anaemia (<7 g/dl). Three drops of blood were

spotted on Whatman filter paper 3MM (Whatman International Ltd., Maidstone,

England) and kept separately in clean, dry and well-sealed plastic bag with silica gel to

reduce humidity in the bag and stored at room temperature until further use for

molecular analysis. The coordinates of each village were recorded using a global

positioning system (GPS) (Garmin GPSMAP 60CSx, Tonopah, AZ, USA).

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3.7 DETECTION OF MALARIA PARASITE BY MICROSCOPY METHOD

3.7.1 Staining blood smears

Thin and thick blood smears were stained with 10% Giemsa stain and examined by

three trained malaria microscopists according to standard procedures (APPENDIX F).

3.7.2 Microscopy examination

Species identification was performed in the laboratory of the National Malaria Control

Programme in Hadhramout governorate by three expert microscopists according to the

the key morphological differences between the blood stages of human Plasmodium

species (APPENDIX G). Parasitaemia per µl of blood was calculated from thick smears

by counting the number of asexual parasites per 200 leukocytes using an assumed

leukocyte count of 8000 WBC/µl (Trape, 1985; Singh et al., 1999; Moody & Chiodini,

2000). A negative result was recorded after screening at least 200 fields under the oil

immersion lens of the light microscope. Parasitaemia was expressed as the total number

of Plasmodium asexual forms per microliter of blood. Parasite levels were classified as

low (1 - 999/μL), moderate (1000 - 9999/μL), or high (>10000/μL) (Bouyou-Akotet et

al., 2003). The positive specimens for Plasmodium were used for molecular studies.

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3.8 MOLECULAR IDENTIFICATION AND GENOTYPING OF MALARIA

SPECIES

Nested PCR was used to detect malaria parasites in blood spot samples. Subsequently,

only falciparum malaria specimens were used for further molecular genotyping

analysis.

3.8.1 DNA extraction

Genomic DNA was extracted from filter paper blood spots. Briefly, by using a sterile-

flamed puncher, a small disc of the filter paper blood spot (approximately 5 mm

diameter) was punched out and transferred into microcentrifuge tubes using a clean and

methanol-flamed forceps. Genomic DNA sample was extracted using Qiagen DNA

Mini Kit for blood and tissue (QIAGEN, DNeasy® blood and tissue kit, Cat. No. 69506,

Germany) by following the manufacturer’s instructions. Extracted DNA was eluted

using 50µL Qiagen AE elution buffer (0.5 mM EDTA, 10 mM Tris-Cl, pH 9.0) and

kept at −20°C until further use (APPENDIX H).

3.8.2 Molecular identification of malaria species

The small subunit ribosomal RNA gene (18SSU rRNA) was targeted in the present

study since it is most commonly used for diagnosis of malaria parasitic infections and

other molecular analysis. The detection of malaria parasites (P. falciparum, P. vivax, P.

malariae, P. ovale and P. knowlesi) was achieved using a nested PCR protocol

developed by Singh et al. (1999). The primary nested PCR reaction was achieved using

genus specific primers sets and yielded an amplicon of about 1.2 kb fragment, whereas,

secondary genus specific nested PCR produced fragment of about 240bp for all

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Plasmodium parasites. Another secondary nested PCR was run by using species specific

primer sets for the identification of P. falciparum (205 bp), P. vivax (117bp), P.

malariae (144 bp), P. ovale (787 bp) and P. knowlesi (153 bp).

Plasmodium species were identified using nested PCR based on small subunit ribosomal

RNA genes (18SSU rRNA). PCR master mix and thermal cycling conditions were

carried out by using five pairs of oligonucleotide primers as reported previously

(Snounou, 1996; Singh et al., 1999). First amplification was to amplify a fragment of

18SSU rRNA genes of different Plasmodium species by using primary and secondary

nested PCR in which genus specific primers were used. Second amplification was to

identify the five Plasmodium species by using a secondary nested PCR in which five

pairs of species specific primers were used (Table 3.1).

PCR reaction for genus specific amplifications was carried out in a final volume

of 50 µL in a PCR tube containing 4 mM MgCl2, 200mM of each deoxynucleoside

triphosphate (dNTPs), 250 nM of rPLU1 primer, 250 nM of rPLU5 primer and 5 µL of

genomic DNA, 1X buffer of PCR (containing 10 mM Tris-HCI, 50 mM KCl), and 1.25

units of Taq polymerase. PCR reagent and primers were from iNtRON (iNtRON

Biotechnology, Inc., Seoul, Republic of Korea). PCR was performed in the thermal

cycler (MyCycler, BioRad Hercules, USA) with the following cycling conditions: initial

denaturation at 94°C for 10 min, 40 cycles of denaturation at 94°C for 30 sec, annealing

at 55°C for 60 sec, extension at 72°C for 60 sec and final extension at 72°C for 5 min.

About 2 µL of primary PCR product was used as template in the secondary nested PCR-

species specific amplifications. PCR reactions contained the same PCR reagent

concentrations as in the primary PCR except using 1.5 mM MgCl2 and amplification

was set for 25 cycles instead of 45 cycles. The cycling conditions and reagents

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concentrations were similar to the primary PCR except the annealing and Taq DNA

polymerase were 58°C and 0.5 units, respectively. The PCR products were loaded in a

2% (w/v) agarose gel with 1X TAE buffer (Tris acetate EDTA), stained with SYBER ®

safe DNA gel stain (Invitrogen, USA). Ten µl from each PCR product were mixed with

2 µl of 6X loading dye and loaded into the wells of agarose gel and the electrophoresis

was run for 30 minutes at 100 Volt. The sizes of amplicons were measured against 100

bp DNA ladder (iNtRON Biotechnology, Inc., Seoul, Republic of Korea) through

visualization through UV transilluminator.

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Table 3.1: Protocol for the detection of Plasmodium malarial species based on 18SSU rRNA gene

C°:degree centigrade, s.: second, m.: minutes, Den.: Denaturation, Ann.: Annealing, Ex.: Extention, Cyc.: Cycle

PCR

reactions

Primer

name Target Sequence (5'-3')

PCR profiles Product

size

Den.

C°/m

Den.

C°/s

Ann.

C°/s

Ex.

C°/s

Final ex.

C°/m

Cyc

.

Primary

PCR

rPLU1 18SSU

rRNA genes

TCA AAG ATT AAG CCA TGC AAG TGA 95/4 94/30 55/60 72/60 72/10 40 1.2 kp

rPLU5 CCT GTT GTT GCC TTA AAC TCC

Genus-

specific

nested PCR

rPLU3 Plasmodium

species

TTT TTA TAA GGA TAA CTA CGG AAA AGC TGT 95/10 94/20 55/20 72/60 72/5 40 240 bp

rPLU4 TAC CCG TCA TAG CCA TGT TAG GCC AAT ACC

Species-

specific

nested PCR

rFAL1 Plasmodium

falciparum

TTA AAC TGG TTT GGG AAA ACC AAA TAT ATT

95/10 94/20 58/20 72/60 72/5 40

205 bp rFAL2 ACA CAA TGA ACT CAA TCA TGA CTA CCC GTC

rVIV1 Plasmodium

vivax

CGC TTC TAG CTT AAT CCA CAT AAC TGA TAC' 117 bp

rVIV2 ACT TCC AAG CCG AAG CAA AGA AAG TCC TTA'

rMAL1 Plasmodium

malariae

ATA ACA TAG TTG TAC GTT AAG AAT AAC CGC 144 bp

rMAL2 AAA ATT CCC ATG CAT AAA AAA TTA TAC AAA

rOVAL1 Plasmodium

ovale

ATC TCT TTT GCT ATC TTT TTT TAG TAT TGG AGA 787 bp

rOVAL2 GGA AAA GGA CAC ATT AAT TGT ATC CTA GTG

Pmk8 Plasmodium

knowlesi

GTT AGC GAG AGC CAC AAAAAA GCG AAT 153 bp

Pmk9 ACT CAA AGT AAC AAA ATCTTC CGT A

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3.8.3 Molecular detection of mutation in Pfcrt gene at codon K76T

A nested mutation-specific PCR was used to detect mutations in Pfcrt K76T (Djimdé et

al., 2001a). Primary PCR was performed using two sets of primer (CRTP1 and CRTP2)

to amplify 537 bp of Pfcrt gene. The secondary PCR was performed using a common

inner primer (CRTP3) together with either CRTP4m or CRTP4w for mutant and wild

types, respectively, resulting in 366 bp of the two alleles (Table 3.2). PCR reaction was

carried out in a final volume of 25µL in a PCR tube containing 2.5 mM MgCl2, 200mM

of each dNTP, 1.25 units of Taq polymerase, 1mM of CRTP1 primer, 1mM of CRTP2

primer and five microliter of genomic DNA. PCR reagent and primers were from

iNtRON (iNtRON Biotechnology, Inc., Seoul, Republic of Korea). PCR was performed

in a thermal cycler (MyCycler, BioRad Hercules, USA) with the following cycling

conditions: initial denaturation at 95°C for 3 min, 45 cycles of denaturation at 94°C for

30 sec, annealing at 56°C for 30 sec, extension at 60°C for 1 min and final extension at

60°C for 5 min. About 1–2µL of primary PCR product was used as template in the

secondary PCR reaction which contained the same PCR reagent concentrations as in the

primary PCR except using 1.5 mM MgCl2 and amplification cycle of 25 cycles instead

of 45 cycles. The cycling conditions were similar to the primary PCR except that the

annealing and extension temperatures were 47°C and 64°C, respectively. The PCR

products were loaded in a 2% (w/v) agarose gel, stained with SYBER ® safe DNA gel

stain (Invitrogen, USA) for electrophoresis and visualized by UV transilluminator.

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Table 3.2: Detection of point mutations in Pfcrt , Pfmdr1, Pfdhfr, and Pfdhps genes at

different codons

Gene codons Analysis

method

Products

Size (N2)

Endo.

Dig.

(R.E)

Post-Dig.

Products

band

Geno-

type

Amino

acids

sub.

(W-M)

Pfcrt K76T MS-Nested

PCR

366 Specific - - Lys-Thr

Pfcrt Q271E Nested

PCR+RFLP

111 XMN1 50+61 Mutant Gln-Glu

Pfcrt N326S Nested

PCR+RFLP

68 Mse1 24+44 Wild Asn-Ser

Pfcrt I356T Nested

PCR+RFLP

100 AlwN1 40+60 Mutant Ile-Thr

Pfcrt R371I Nested

PCR+RFLP

80 Afl II 40+40 Wild Arg-Ile

Pfmdr1 N86Y Nested

PCR+RFLP

291 Afl III 126+165 Mutant Asn-Tyr

Pfmdr1

D1246Y

Nested

PCR+RFLP

203 Bg1 II 113+90 Wild Asp-Tyr

Pfdhfr at

different

codons

Sequencing

and BioEdit

software

700 - - - -

Pfdhps at

different

codons

Sequencing

and BioEdit

software

711 - - - -

MS-Nested PCR: Mutant-Specific Nested Polymerase Chain Reaction.

RFLP: Restriction Fragment Length Polymorphism.

N2: Size of secondary PCR product

Endo. Dig (RE): Endonuclease Digestion Restriction Enzyme.

Post-Dig.: Post Digestion

Amino acids sub. (W-M): Amino acids substitution (wild to mutant)

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3.8.4 Molecular detection of point mutations in Pfcrt gene (positions Q271E, N326S,

I356T, R371I) and Pfmdr1 gene (positions N86Y and D1246Y)

Nested PCR followed by restriction fragment length polymorphism was performed as

previously described (Djimdé et al., 2001a) (Table 3.3). Briefly, the restriction enzymes

XMN1, MSe1, AlwN1 and AflII digest Pfcrt at codons 271, 326, 356 and 371,

respectively. The enzymes AflIII and Bg1II digest Pfmdr1 at codons 86 and 1246,

respectively. The digestion of PCR product was achieved by Fast Digest restriction

enzymes (New England biolabs Inc., USA) according to the instructions of the

manufacturer. Digestion results were analysed by electrophoresis in a 2.5% (w/v)

agarose gel containing SYBER ® safe DNA gel stain (Invitrogen, USA) and visualized

in a UV transilluminator. Genomic DNA from P. falciparum strains HB3, 3D7 and Dd2

(supplied by Malaria Research and Reference Reagents Resources Centre (MR4,

ATCC, ManassasVA, USA) were used as positive controls for mutant and wild types,

whereas nuclease-free water was used as the negative control (Table 3.2).

3.8.5 Molecular detection of point mutations in Pfdhfr gene at different codons

Genomic DNA of Pfdhfr gene was amplified using nested PCR following the method

described previously (Plowe et al., 1995; Tinto et al., 2007). Briefly, an amplicon of

720 bp was amplified using the primers pair AMP 1: (5'-TTTATATTTTCTCCTTTTT

A-3') and AMP 2: (5'-CATTTTATTATTCGTTTTCT-3') in the primary PCR, and an

amplicon of 700 bp was amplified using the primers SP1: (5'-ATGATGGAACAA

GTCTGCGAC-3') and SP2: (5'-ACATTTTATTATTCGTTTTC-3') in the nested PCR

(Table 3.3). The PCR reaction was carried out in a total of 25μl mixture containing 1x

PCR buffer, 3 mM MgCl2, 0.2 mM of dNTPs, 200 nM of each primer, 1 U of Taq

polymerase and 4 μl of genomic DNA. Cycling condition was as follows; initial

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denaturation at 94°C for 3 min, followed by 45 cycles of denaturing for 30 sec at 94°C,

annealing for 1 min at 43.5°C and extension at 72°C for 45 sec, and final extension at

72°C for 5 min. The cycling condition for nested PCR was the same except that

annealing was at 55°C for 45 sec and extension at 74°C for 35 sec, the number of cycles

was decreased to 35 cycles (Table 3.3). PCR reagent and primers were obtained from

iNtRON (iNtRON Biotechnology, Inc., Seoul, Republic of Korea). PCR product was

analyzed by electrophoresis in a 2.5% (w/v) agarose gel containing SYBER® safe DNA

gel stain (Invitrogen, USA) and visualized in a UV transilluminator. PCR products were

purified with Presto™ 96 Well PCR Cleanup Kits and then sequenced in both directions

using the inner primers in the ABI 3730xl DNA analyzer (Applied Biosystems).

Mutations were detected by creating consensus sequences and comparing manually with

the sequences in GenBank (GenBank accession number was XM_001351443 for

Pfdhfr) using BioEdit software (Hall, 2011).

3.8.6 Molecular detection of point mutations in Pfdhps gene at different codons

An amplicon of 711 bp of Pfdhps gene was amplified by nested PCR using the outer

primers pair O1: (5'-GATTCTTTTTCAGATGGAGG-3') and O2: (5'-TTCCTCATGT

AATTCATCTGA-3'), and the nested primers N1 (5'-AACCTAAACGTGCTGTTCAA-

3') and N2: (5'-AATTGTGTGATTTGTCCACAA-3') (Pearce et al., 2003) (Table 3.3).

The PCR mixture was as described above. The cycling conditions for primary and

secondary PCR were as follows: initial denaturation at 94°C for 3 min followed by 25

cycles of denaturing for 1 min at 94°C, annealing for 2 min at 52°C and extension at

74°C for 1 min and final extension at 74°C for 5 min. PCR reagent and primers were

obtained from iNtRON (iNtRON Biotechnology, Inc., Seoul, Republic of Korea). PCR

product was analysed by electrophoresis in a 2.5% (w/v) agarose gel containing

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SYBER® safe DNA gel stain (Invitrogen, USA) and visualized in a UV

transilluminator. PCR products were purified with Presto™ 96 Well PCR Cleanup Kits

and then sequenced in both directions using the inner primers in the ABI 3730xl DNA

analyzer (Applied Biosystems). Mutations were detected by creating consensus

sequences and comparing manually with the sequences in GenBank (GenBank

accession number was Z30654 for Pfdhps) using BioEdit software (Hall, 2011).

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Table 3. 3: Forward and reverse primers sequences for Pfcrt , Pfmdr1, Pfdhfr, and Pfdhps genes at different codons

Gene

codon

Primers

for

primary

PCR

Sequence (5'-3') Primers for

secondary

PCR

Sequence (5'-3')

Pfcrt 76 CRTP1 CCGTTAATAATAAATACACGCAG

Common

CRTP3 TGACGAGCGTTATAGAG

CRTP2 CGGATGTTACAAAACTATAGTTACC Wild

CRTPw GTTCTTTTAGCAAAAATCT

Mutant

CRTP4m GTTCTTTTAGCAAAAATTG

Pfcrt

271 CRT-2A CCCAAGAATAAACATGCGAAAC CRT271a GGCACATTCATTTATTTATTTTTTCTTTCCTAATTAATGAATACGTT

CRT-2B ACAATTATCTCGGAGCAGTT CRT271b GGCTATGGTATCCTTTTTCC

Pfcrt

300s:

(326,

356 &

371

CRT-3a CCTTGGCATTGTTTTCCT CRT326a CCTTTTTATTCTTACATAGCTGGTTATTGAATTATCAC

CRT-3b CCAAAGTTACGAAATCTAATAATCTTGG

CRT326b TGGCATTGTTTTCCTTCT

CRT356a ATATATATGGCTAAGAATTTAAAGTAATAAGCAGTTGCT

CRT356b AATTATCGACAAATTTTCTACC

CRT371a TATTATTTTTACTTTTTAATTTTATAGGGTGATGTCTTAA

CRT371b AAGTTACGAAATCTAATAATCTTGGTTC

Pfmdr1-

86 MDR-A GCGCGCGTTGAACAAAAAGAGTACCGCTG MDR-D1 TTTACCGTTTAAATGTTTACCTGC

MDR-B GGGCCCTCGTACCAATTCCTGAACTCAC MDR-D2 CCATCTTGATAAAAAACACTTCTT

Pfmdr1-

1246 1246-A GGGGGATGACAAATTTTCAAGATTA 1246-D1 AATGTAAATGAATTTTCAAACC

1246-B GGGGGACTAACACGTTTAACATCTT 1246-D2 CATCTTCTCTTCCAAATTTGATA

Pfdhfr Amp1 TTTATATTTTCTCCTTTTTA SP1 ATGATGGAACAAGTCTGCGAC

Amp2 CATTTTATTATTCGTTTTCT SP2 ACATTTTATTATTCGTTTTC

Pfdhps O1 GATTCTTTTTCAGATGGAGG N1 AACCTAAACGTGCTGTTCAA

O2 TTCCTCATGTAATTCATCTGA N2 AATTGTGTGATTTGTCCACAA Univers

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3.9 STATISTICAL ANALYSES

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 23

(SPSS Inc., Chicago, IL, USA). The prevalence of a mutant allele or genotype was

calculated as the percentage of the presence of the mutant allele or the genotype in the

examined P. faclciparum isolates. The difference between proportions of variables was

tested using Pearson Chi-Square test or Fisher’s exact test where applicable. 95%

confidence interval (CI) and odd ratios (OR) were computed. A stepwise conditional

logistic regression model was developed for those variables with P value < 0.05 which

was considered significant.

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CHAPTER 4: RESULTS

4.1 EPIDEMIOLOGICAL RESULTS OF MALARIA IN THE HADHRAMOUT

GOVERNORATE, YEMEN

4.1.1 Characteristic of study population

A total of 735 voluntarily consented individuals from seven villages in two districts of

Hadhramout, Yemen participated in this study. These consisted of 221 participants from

three villages in Hajer district and 514 participants from four villages in Al-Raydah-

Qusyer district, Hadhramout governorate. Of the 735 individuals, 423 (57.6%) were

males and 312 (42.4%) were females. The age of participants ranged from 1 to 75 years

with a median of 16 years and 22 interquartile range. Out of 735, 18.8% (138) were

positive for malaria parasite via microscopy (Figure 4.1). Majority of samples (393;

53.5%) were collected from those above 15 years old (defined as adults), 152 (20.7%)

from those between 5 and 9 years old, 142 (19.3%) from those between 10 – 15 years

old and the least number of samples came from those who were less than five years old

(6.5%). Fourty-seven percent of study population had no formal education and 61.6%

were farmers. Approximately half of the study population had no access to electricity or

communication media such as radio or television. The study population lived in simple

houses of mud or uncemented brick walls and mud or cement floors, with an uncovered

tank located beside the house for water storage (Table 4.1). No association was

observed between the presence of malaria and age and gender of the participants.

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Figure 4.1: Malaria prevalence in endemic areas of the two districts (i.e, Al-Raydah-

Qusyer and Hajer) of Hadhramout governorate, Yemen

18.8%

81.2%

Positive

Negative

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Table 4. 1: Demographic characteristics of study populations in Hadhramout

governorate, Yemen

Characteristics

Factors

Number (%)

Gender Male 423 (57.6)

Female 312 (42.4)

Age (years) >15 393 (53.5)

10 – 15 142 (19.3)

5 - 9 152 (20.7)

<5 48 (6.5)

Districts Hajer 221 (30.1)

Al-Raydah and Qusyer 514 (69.9)

Family size >5 members 290 (39.5)

≤5 members

445 (60.5)

Education Not educated 345 (46.9)

Primary 356 (48.4)

Secondary 34 (4.6)

Occupation Not working 180 (24.5)

Farmer 453 (61.6)

Fisherman 26 (3.5)

Government employees

76 (10.3)

Economic status Houses with electricity 379 (51.6)

Availability of TV 295 (40.1)

Availability of telephone 43 (5.9)

Availability of radio 385 (52.4)

Availability of fridge 295 (40.1)

Having motorcycle 148 (20.1)

Having car 212 (28.8)

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4.1.2 Prevalence of malaria and identify the risk factors associated with malaria in the

Hadhramout governorate, Yemen

Based on microscopy examination, the overall malaria prevalence in the individuals

sampled in the Hadhramout governorate, Yemen was 18.8% (138 of 735), where

majority of cases was due to Plasmodium falciparum as the predominant species

(99.3%; 137 of 138) followed by P. vivax (0.7%; 1 of 138). A majority of positive

individuals (57.7%) aged > 15 years old. The overall prevalence in the districts of Al-

Raydah-Qusyer and Hajer were 21.8% and 11.8%, respectively. In addition, this broad

prevalence variation was also noted between the villages, with Qusyer showing the

highest prevalence (31.8%) and Al-Raydah the lowest (5.6%). There were more positive

samples from male (20.3%) as compared to female (16.7%) and the sex ratio was 1.7

males/females (Table 4.2).

In addition, the number of positive individuals was higher in adult males than in

children and adult females. Persons whose household’s head had primary education

were at higher risk of being infected (OR= 10.1, 95% CI: 1.35 – 74.5), as did fishermen

(OR=11.3, 95%CI: 3.13 – 40.5) and farmers (OR= 4.84, 95%CI: 1.73 – 13.6) (Table

4.3). A number of socioeconomic indicators were also associated with increased

prevalence: living in houses with walls made of uncemented bricks (OR= 2.1, 95% CI:

1.32 – 3.30), no access to toilets (OR= 1.6, 95%CI: 1.05 – 2.32), no fridge (OR=1.6,

95%CI: 1.05 – 2.30), or no TV (OR=1.6, (95%CI: 1.05 – 2.30). Individuals living in

houses with a distance of water collection points less than 200 meters were also at

higher risk of acquiring malaria (OR= 1.6, 95%CI: 1.05 – 2.30). Multivariate analysis

using stepwise forward logistic regression confirmed that the significant risk factors

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were living in uncemented brick wall houses, or being a fisherman or a farmer or living

in houses with a distance of water collection points less than 200 meters (Table 4.3).

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Table 4.2: Prevalence and distribution of malaria among population in Hadhramout

governorate, Yemen according to areas

Characteristics

Examined (n)

Infected

(%)

P value

Districts

Hajer 221 26 (11.8) 0.001*

Al-Raydah and Qusyer 514 112 (21.8)

Hajer District Villages

Kunina 83 5 (6) 0.001*

Kinina 47 12 (25.5)

Jol-Bamejah 91 9 (9.9)

Al-Raydah and Qusyer

District Villages

Hadhathim 34 10 (29.4)

Al-Raydah 18 1 (5.6)

Qusyer 22 7 (31.8)

Al-Rahbah 440 94 (21.4)

n: number of subjects

*Significant association at p< 0.05

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Table 4.3: Risk factors associated with malaria in Hadhramout governorate, Yemen.

Characteristics

Factors

Examined

Infected (%)

OR (95%CI)

Age (years) >15 393 79 (20) 1

10 – 15 142 25 (17.6) 0.85 (0.51 – 1.40)

5 - 9 152 30 (19.7) 0.98 (0.61 – 1.56) <5 48 4 (8.3) 0.36 (0.13 – 1.04)

Gender

Female 312 52 (16.7) 1

Male 423 86 (20.3) 1.04 (0.98 – 1.12)

Education

level of

household’s head

Secondary school &

above

34 1 (2.9) 1

Primary school 356 83 (23.3) 10.1 (1.35 – 74.5)

Not educated 345 54 (15.7) 6.12 (0.82 – 45.7)

Occupation of

household’s head*

Government

employees

76 4 (5.3) 1

Not working 180 28 (15.6) 3.31 (1.12 – 9.80)

Farmer 453 96 (21.2) 4.84 (1.73 – 13.6)

Fisherman 26 10 (38.5) 11.3 (3.13 – 40.5)

Family size

>5 members 290 49 (16.9) 1 ≤5 members 445 89 (20) 1.23 (0.84 – 1.81)

House wall* Mud 221 26 (11.8) 1 Uncemented bricks 514 112 (21.8) 2.1 (1.32 – 3.30)

Material of

house floor

Cement 120 19 (15.8) 1

Mud 615 119 (19.3) 1.27 (0.75 – 2.16)

Availability of toilet

Yes 284 42 (14.8) 1

No 451 96 (21.3) 1.6 (1.05 – 2.32)

Distance to the nearest water

collection

> 200 meters 295 44 (14.9) 1 ≤ 200 meters 440 146 (18.6) 1.6 (1.05 – 2.30)

Availability of electricity

Yes 379 66 (17.4) 1

No 356 72 (20.2) 1.04 (0.97 – 1.11)

Availability of fridge

Yes 295 44 (14.9) 1

No 440 94 (21.4) 1.6 (1.05 – 2.30)

Availability of TV

Yes 295 44 (14.9) 1

No 440 94 (21.4) 1.6 (1.05 – 2.30)

Availability of

radio

Yes 385 70 (18.2) 1

No 350 68 (19.4) 1.02 (0.95 – 1.09)

Availability of

telephone

Yes 43 8 (18.6) 1

No 692 130 (18.8) 1.0 (0.87 – 1.16)

*Variables confirmed as significant factors associated with malaria using stepwise

forward logistic regression.

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4.1.3 Assessment of knowledge, attitude and practices (KAP) towards malaria in the

Hadhramout governorate, Yemen

A total number of 735 voluntarily participants enrolled for the survey from 130

housholds in seven villages in two districts, namely Hajer and Al-Raydah-Qusyer

districts. Overall, the survey of the villagers’ knowledge, attitude and practices towards

malaria indicated that although they are all aware of malaria, its mode of transmission,

and its clinical symptoms and severity, their knowledge of and attitude towards malaria

prevention were poor.

For the knowledge about transmission, symptoms, and severity; 100% of the

participants (head of the households) mentioned that malaria is transmitted by mosquito

bites. However, out of these, there were approximately 15% of participants who were

unsure and gave more than one answer. More than half of participants, for example 41%

and 59% recognized fever or fever with shivering as symptoms of malaria, respectively.

Responses about knowledge and attitude towards malaria prevention were poor. Most of

participants (91%) mentioned an ineffective preventive measure. Only 7% and 2% of

study participants knew that sleeping under insecticide-treated mosquito nets (ITNs) or

using indoor residual spraying (IRS) were methods for malaria prevention, respectively.

Furthermore, in all cases, the windows in the houses were kept open at night, 11% of

participants reported using ITNs. When asked what they would do first when they get

malaria, 17% (22/130) mentioned they would go to the clinic or public health center

(Table 4.4).

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Table 4.4: KAPs of malaria in the rural areas of Hadhramout governorate, Yemen

(n=130)

Characteristics

Number (%)

Knowledge and attitudes

Know malaria 130(100)

Malaria can kill

53 (51)

Mode of transmission mentioned

Mosquito bite 130(100)

Lack of sanitation 4(3)

Swamps

19(15)

Causes of malaria mentioned

Flies 42(32)

Sleeping with infected person in the same bed 106(82)

Mosquito bite 130(100)

Drinking or playing in contaminated water 0 (00)

The presence of sewage

9(7)

Symptoms of malaria mentioned

Fever 53(41)

Fever + shivering

77(59)

Serious for adult or children

Children 118(91)

Equally serious

12(9)

Methods of prevention mentioned

Cleaning the house or environment 118(91)

Sleeping under the mosquito net 9(7)

House spraying with insecticides 3(2)

Smoking house

69(53)

Practices

Using insecticide-treated mosquito nets (ITNs)a 14(11)

House spray with insecticide (IRS)# 130(100)

Not closing house windows 130(100)

Closing house doors 130(100)

Going to clinic when having fever 22(17)

Houses with wood roofs 130(100)

Houses with uncemented bricks wall 93(72)

Houses with mud wall 37 (28)

Keeping uncovered water near houses 130 (100)

*KAPs: Knowledge, Attitude and Practices were conducted on the head of the household

# IRS was done by government before one year of the survey a Each house of the 14 houses had one ITNs

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4.1.4 Clinical manifestations of individuals positive with malaria

Clinical manifestations of infected individuals with malaria showed more than half of

malaria cases detected by microscopy were asymptomatic, where 52% (72/138) of the

patients had no fever during the survey, whereas, the symptomatic cases presented with

fever (48%; 66/138), shivering (27.5%; 38/138), jaundice (10%; 14/138) and most of

cases with low anaemia (67%; 92/138). A positive association between clinical

symptoms and parasitaemia was observed (χ2=422, p <0.001) (Table 4.5).

Parasitaemia were recorded as low, moderate and high in 52%, 35% and 13% of

malaria cases, respectively (Figure 4.2). The median of parasite densities was 960

asexual parasite/µl with interquartile range of 560 – 2333 asexual parasite/µl. Fifty-two

percent of the persons positive for Plasmodium were asymptomatic with low parasite

density.

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Table 4.5: Clinical manifestations among humans infected with malaria in Hadhramout

governorate, Yemen

* Fisher exact test was used.

Characteristics

Prevalence N (%)

P value

Presence of fever*

Yes 66 (48) <0.05

No

72 (52)

Presence of shivering*

Yes 38 (27.5) <0.05

No

100 (72.5)

Presence of headache*

Yes 21(15) <0.05

No

117 (85)

Presence of jaundice*

Yes 14(10) <0.05

No

124 (90)

Haemoglobin level

Normal 13 (9) <0.05

Low anaemia 92 (67)

Moderate anaemia

33 (24)

Total 138

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Figure 4.2: Parasitemia among populations infected with malaria in Hadhramout

governorate, Yemen

13%

35%

52% High

Moderate

Low

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4.2 MOLECULAR CHARACTERIZATION OF MALARIA IN THE

HADHRAMOUT GOVERNORATE, YEMEN

4.2.1 Malaria parasite identification using nested PCR based on 18SSU rRNA gene

Nested PCR based on the 18SSU rRNA gene was achieved to detect and confirm the

species of Plasmodium, an additional of four other samples which were negative using

microscopy were found positive together with all 138 microscopy positive. Besides that,

three samples which were reported as having mono-infection of P. falciparum via

microscopy, were confirmed as having mixed infections of P. falciparum and P. vivax

(Table 4.6).

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Table 4.6: Detection of Plasmodium species using nested PCR among populations

infected with malaria in Hadhramout governorate, Yemen

Plasmodium species Microscopy

n (%)

PCR

n (%)

P. falciparum 137 (99.3) 135 (95.07)

P. vivax 1(0.7) 0

Mixed infection (P.f+P.v) 0 3 (2.1)

Non identified by microscopy (P.f) - 4 (2.82)

Total 138 142

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4.2.2 Prevalence and distribution of mutations in Pfcrt gene at 76, 271, 326, 356 and

371 and Pfmdr1 gene at 86 and 1246 as molecular markers of CQ resistance of

Plasmodium falciparum isolates in Hadhramout governorate, Yemen

The results of the point mutations in Pfcrt and Pfmdr1 genes are shown in Table 4.7. Of

138 Plasmodium falciparum isolates, the prevalence of Pfcrt mutant, wild type, mixed

type alleles were detected in 50.7%, 26.1% and 23.1%, respectively. The prevalence of

the mutant alleles of Pfcrt 271E, Pfcrt 326S and Pfcrt 371I were detected in 58.7%,

54.3% and 44.9% of isolates, respectively. However, all isolates harbored wild type

Pfcrt I356 and Pfmdr1 D1246. The majority of isolates (83.3%) had wild type Pfmdr1

Y86.

The survey showed a significant difference in the distribution of Pfcrt 76 alleles

between Hajer and Al-Raydah–Qusyer districts with the Pfcrt 76T allele being higher in

Al-Raydah–Qusyer (52.7%) (p= 0.006). In contrast, the Pfcrt 371I allele had higher

frequency in Hajer (69.2%) compared to Al-Raydah–Qusyer (39.3%). The distribution

of Pfcrt 371 alleles between the two districts was statistically significant (P= 0.006).

Pfmdr1–86 mutant allele was more prevalent in Hajer than Al Raydah–Qusyer while the

wild type allele was higher in Al-Raydah–Qusyer district (p= 0.03) (Table 4.7).

This survey indicated that there is no significant association between the

distribution of Pfcrt and Pfmdr1 alleles with age and gender. Table 4.8 shows no

significant difference in the frequency of Pfcrt and Pfmdr1 alleles between symptomatic

(n=66) malaria cases and asymptomatic (n=72).

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Table 4.7: Frequency and distribution of Pfcrt and Pfmdr1 alleles in P. falciparum

isolates from popualtions in Hadhramout governorate, Yemen

Mutation

Codons

Alleles

Districts n (%)

Total

P value

Hajer

Al-Raydah

–Qusyer

Pfcrt 76

Wild

Mutant

Mutant & Wild

3 (11.5)

11 (42.3)

12 (46.2)

33 (29.5)

59 (52.7)

20 (17.9)

36 (26.1)

70 (50.7)

32 (23.1)

0.006*

Pfcrt 271 Wild

Mutant

10 (38.5)

16 (61.5)

47 (42.0)

65 (58.0)

57 (41.3)

81 (58.7)

0.744

Pfcrt 326 Wild

Mutant

9 (34.6)

17 (65.4)

54 (48.2)

58 (51.8)

63 (45.7)

75 (54.3)

0.210

Pfcrt 356 Wild

Mutant

26 (100.0)

0

112 (100.0)

0

138 (100.0)

NA

Pfcrt 371 Wild

Mutant

8 (30.8)

18 (69.2)

68 (60.7)

44 (39.3)

76 (55.1)

62 (44.9)

0.006*

Pfmdr1-86 Wild

Mutant

18 (69.2)

8 (30.8)

97 (86.6)

15 (13.4)

115 (83.3)

23 (16.7)

0.032

Pfmdr-1246# Wild

Mutant

26 (100.0)

0

111 (100.0)

0

137 (100.0)

NA

*Using Pearson Chi-Square

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Table 4.8: Frequency and distribution of Pfcrt and Pfmdr1 alleles according to

symptomatology in P. falciparum isolates from populations in

Hadhramout governorate, Yemen

Mutation

Codons

Alleles

Symptoms n (%)

P value Symptomatic*

(n=66)

Asymptomatic

(n=72)

Pfcrt 76 Wild

Mutant

Mutant & Wild

17 (25.8)

36 (55.5)

13 (19.7)

19 (26.4)

34 (47.2)

19 (26.4)

0.596

Pfcrt 271 Wild

Mutant

31 (47.0 (

35 (53.0)

26 )36.1 (

46 (63.9)

0.196

Pfcrt 326 Wild

Mutant

26 (39.4)

40 (60.6)

37 (51.4)

35 (48.6)

0.158

Pfcrt 356 Wild

Mutant

66 (100.0)

0

72 (100.0)

0

NA

Pfcrt 371 Wild

Mutant

37 (56.1)

29 (43.9)

39 (54.2)

33 (45.8)

0.823

Pfmdr 1-86 Wild

Mutant

54 (81.8)

12 (18.2)

61 (84.7)

11 (15.3)

0.647

Pfmdr1-1246# Wild

Mutant

65 (100.0)

0

72 (100.0)

0

NA

*Symptomatic was defined by the presence of fever (>37.5 oC) with or without

shivering and headache. # One sample was missing due to PCR failure for this marker.

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4.2.3 Prevalence of mutations in Pfdhfr and Pfdhps genes at different codons as

molecular markers of SP resistance of Plasmodium falciparum isolates in

Hadhramout governorate, Yemen

A total of 138 patients with P. falciparum infections based on nested PCR were

included in the analysis of Pfdhfr mutations at codons 51, 59, 108, and 164, as well as

Pfdhps mutations at codons 436, 437 and 540. Of the 138 P. falciparum isolates,

genomic DNAs from 128 and 114 isolates were successfully sequenced for Pfdhfr and

Pdhps genes, respectively.

Mutant alleles are presented in Table 4.9. Pfdhfr mutations were detected in 84%

(107/128) of P. falciparum isolates for codons 51 (I51) and 108 (N108) and in one isolate

for codon 59 (R59). No mutation was identified at codon 164. Pfdhps mutations were

detected in 44.7% (51/114) of P. falciparum isolates for codon 437 (G437). No

significant difference in the distribution of the mutant alleles between Hajer and Al-

Raydah–Qusyer districts was observed.

Subsequently, genotyping analysis based on sequences for Pfdhfr, Pfdhps, and

combined Pfdhfr–Pfdhps genes was conducted. Double (I51C59N108I164) and triple

(I51R59N108I164) mutant genotypes of Pfdhfr were detected in 82.8% (106/128) isolates

and one isolate (0.8%), respectively. For Pfdhps, single mutant genotype (S436G437K540)

was detected in 44.7% (51/114) of the isolates. Genotyping of 107 P. falciparum

isolates for the combined Pfdhfr–Pfdhps genes showed that 5 (4.7%), 46 (43%), 42

(39.3%), and 1 (0.9) isolates had single, double, triple, and quadruple mutant genotypes,

respectively. Although Al-Raydah–Qusyer district had higher prevalence of mutant

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genotypes than Hajer district, the differences were statistically not significant (Table

4.10).

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Table 4.9: Prevalence of mutant alleles of Pfdhfr and Pfdhps genes in P. falciparum

isolates from populations in Hadhramout governorate, Yemen

Prevalence n (%)

Mutant alleles* Hajer Al-Raydah–

Qusyer

Total P value

Pfdhfr n = 26 n = 102 n = 128

51I 19 (73.1) 88 (86.3) 107 (84) 0.105

59R 0 (00) 1 (1.0) 1 (0.8) 0.797#

108N 19 (73.1) 88 (86.3) 107 (84) 0.105

164L

0 (00) 0 (00) 0 (00) NA

Pfdhps n = 25 n = 89 n = 114

436A 0 (00) 0 (00) 0 (00) NA

437G 9 (36) 42 (47) 51 (44.7) 0.56

540E 0 (00) 0 (00) 0 (00) NA

n; sample size, NA; not applicable

*Mutant alleles are bold and underlined

#The difference was examined using Fisher exact test.

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Table 4.10: Prevalence of genotypes of Pfdhfr, Pfdhps and combined Pfdhfr–Pfdhps

genes in P. falciparum isolates from populations in Hadhramout

governorate, Yemen

n; sample size, NA; not applicable

*Mutant alleles are bold and underlined

#The difference was examined using Fisher exact test.

Gene/Genotype* Prevalence n (%)

Hajer Al-Raydah–

Qusyer

Total P value

Pfdhfr

n = 26 n = 102 n = 128

N51C59S108I164 7 (26.9) 14 (13.7) 21 (17) 0.105

I51C59N108I164 19 (73.1) 87 (85.3) 106 (82.8) 0.140

I51R59N108I164

0 (0) 1 (1) 1 (0.8) 0.797#

Pfdhps

n = 25 n = 89 n = 114

S436A437K540 16 (64) 47 (52.8) 63 (55.3)

S436G437K540

9 (36) 42 (47.2) 51 (44.7) 0.56

Pfdhfr–Pfdhps

n = 25 n = 82 n = 107

N51C59S108I164-S436A437K540 5 (20) 8 (9.8) 13 (12.1) 0.170

N51C59S108I164-S436G437K540 1 (4) 4 (4.8) 5 (4.7) 1.000#

I51C59N108I164-S436A437K540 11 (44) 35 (42.7) 46 (43) 0.907

I51C59N108I164-S436G437K540 8 (32) 34 (41.5) 42 (39.3) 0.396

I51R59N108I164-S436G437K540 0 (00) 1 (1.2) 1 (0.9) 1.000#

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CHAPTER 5: DISCUSSION

5.1 THE EPIDEMIOLOGICAL OF MALARIA IN THE HADHRAMOUT

GOVERNORATE, YEMEN

5.1.1 Prevalence of malaria and identify the risk factors associated with malaria in the

Hadhramout governorate, Yemen

Although Yemen is classified as in the control stage, Hadhramout governorate, located

in the southeast of the country bordering Oman and Saudi Arabia, is considered to be in

the pre-elimination phase and a bilateral collaboration between Yemen and Oman has

been put in place with the aim of making this a malaria-free area (personnel

communication). The purpose of the current study was to evaluate the actual status of

malaria in the Hadhramout community and to investigate associated factors that might

challenge or slow the progress toward malaria elimination.

The overall microscopic prevalence recorded for malaria in the 735 persons

sampled from the seven villages in two districts of Hadhramout governorate was 18.8%.

These high values are inconsistent with a pre-elimination status, and rather placed this

governorate in the control phase. Moreover, the prevalence of malaria in young children

(2-9 years old) exceeded the 10 % level indicative of high to moderate transmission.

This high prevalence could be attributed to several factors including the political

instability in Yemen during the 2011 – 2012 period, which had a direct effect on the

official programs to control and to combat malaria. It was noted that the last IRS was

conducted one year before the field trip. It is also likely that new foci of malaria have

emerged in this area, which was once considered to be of low endemicity. Although the

prevalence of malaria cases is decreasing in Hajer district (NMCP, 2010a), in the

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traditional malaria endemic area in Hadhramout, an increase of prevalence was recorded

in Al-Raydah-Qusyer districts, areas thought to be of low prevalence. This situation

poses a challenge to control efforts. However, there are many factors related to the

increase of malaria prevalence.

Analysis of the data from the survey presented here have identified some factors

that were associated with increased risk of acquiring malaria. These factors should be

taken into consideration when implementing future malaria control strategies. This

study has highlighted that malaria was more prevalent in adults than in children, who

generally constitute the high-risk group. Multivariate analysis confirmed that people

whose household’s head are fishermen and farmers were at higher risk of being malaria

positive.

Furthermore, observations on the natural work of household that is important to be

taken into account is that household members in Yemen actively contribute to the work

of the head of the household. Such increased risk of malaria linked to occupational

behavior has been noted in other endemic areas such as Malaysia (Trung et al., 2005),

the Philippines (Lansang et al., 1997) and Latin America (Chuquiyauri et al., 2012;

Hiwat et al., 2012). These observations indicated that exposure to the bite of infective

mosquitoes occured outside the home. Consequently, the traditional vector control

interventions (ITNs and IRS) that protect household members would be insufficient, and

control measures should be implemented to reduce mosquito-human contact during

outdoor activities. Occupation-based vector control interventions have been developed

and have shown reduction in malaria cases in Pakistan (Rowland et al., 2004),

Afghanistan (Rowland et al., 1999) and Vietnam (Thang et al., 2009). Such

interventions include topical repellents such as N,N-diethyl-3-methylbenzamide

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(DEET) (Katz et al., 2008), DEET-based soap (Rowland et al., 2004), plant based

repellant (Hill et al., 2007), long-lasting insecticide-treated hammocks for forest

workers (Sovi et al., 2013) and insecticide-treated personal clothes in refugee areas

(Rowland et al., 1999).

Nonetheless, transmission in and around the house remains significant, as

indicated by the statistical analyses which showed that the type of housing,

unavailability of in-house toilets, and the presence of uncovered water containers close

to the houses are also significant predictors of malaria in Hadhramout. Thus, it will be

important to improve the environment and economic status of the inhabitants if the

government’s efforts to make Hadhramout free of malaria are to be fulfilled. However,

misconceptions, insufficient information, weak or unavailable of health educations

programe about knowledge, attitude and practice against malaria disease especially in

an endemic area will lead to great challenges towards malaria elimination and may

increase malaria transmission in that area.

5.1.2 Assessment of knowledge, attitude and practices towards malaria in the

Hadhramout governorate, Yemen

Communities’ attitudes, beliefs, and knowledge about causes of malaria, well-known

symptom of malaria, proper treatment of malaria, and how to prevent malaria are

helpful and plays an important role in rapid progress toward malaria control and

elimination efforts. In this study, the malaria control strategy in Yemen relies on the

adequate distribution of and use of ITNs, as well as the deployment of the IRS as the

main intervention for vector control. It is, therefore, of concern that in the present study

only 7% of the people expressed the belief that sleeping under ITNs protected them

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from malaria. This unsatisfactory situation is not unique to this district, as a malaria

indicator survey conducted in Yemen in 2008-2009 revealed that 4.2% of people and

7% of children under 5 years slept under long lasting insecticide-treated net (LLINs)

(WHO, 2011). This represents a major challenge that warrants an urgent action. The fact

that more than half of malaria positive persons identified in the current study were

asymptomatic with low parasite densities suggests that it is likely that these cases would

be missed by passive surveillance and would thus remain as a source of malaria

transmission (Bousema et al., 2004; Okell et al., 2012). Furthermore, ensuring that

people in communities understand and know the proper treatment for malaria disease

may help protect them against the drug resistance development as a result of medication

error such as misuse, overdose or under dosing as reported previously (Abuya et al.,

2007).

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5.2 MOLECULAR CHARACTERIZATION OF MALARIA IN THE

HADHRAMOUT GOVERNORATE, YEMEN.

5.2.1 Point mutations in Pfcrt gene at 76, 271, 326, 356 and 371 and Pfmdr1 gene at 86

and 1246 as molecular markers of chloroquine resistance of Plasmodium

falciparum isolates in Hadhramout governorate

In the present study, the prevalence of Pfcrt 76T mutation, the highly sensitive marker

for CQ resistance in Hadhramout was 50.7%. This result is lower than the prevalence of

Pfcrt 76T mutation reported from west of Yemen (81–82%) (Al-Mekhlafi et al., 2011b;

Abdul-Ghani et al., 2013) and Lahj in the south of Yemen (98%) (Mubjer et al., 2011).

In contrast, recently published study from Taiz governorate (south of Yemen) revealed

similar findings (50.9%) (Al-Hamidhi et al., 2013). These differences could be

explained by the time period between the implementation of the new drug policy and

screening for CQ resistance markers. The present study was conducted four years after

the launch of the new drug policy while previous reports were carried out either before

or 1–2 years after the initiation of the new antimalarial drug policy. The decline in

prevalence of CQ resistant parasite after the withdrawal of CQ use has been well

documented (Kublin et al., 2003; Laufer et al., 2006). However, the prevalence of Pfcrt

mutations in this survey, is still considered high which means that there is sustained or

an increased CQ usage or genetic adaptations of parasite in this region. The possible

explanation of the sustainability of CQ resistance is the continued unsupervised use of

CQ for the treatment of malaria in Hadhramout governorate, Yemen (Bashrahil et al.,

2010; Bin Ghouth, 2013). Unfortunately, no previous data are available about the

prevalence of Pfcrt 76T gene mutation in Hadhramout which could be used for

comparison to show the trend of CQ resistance in this area over the years. The

prevalence of mutations in Pfcrt at loci 271, 326 and 371 in this survey, was 58.7%,

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54.3% and 44.9%, respectively. These mutations are more commonly distributed in the

Old World CQ resistant strain and affect the response to CQ in the presence of 76T

mutation (Ibraheem et al., 2014). Findings from this survey necessitate the

implementation of effective control of CQ usage in the Yemeni market especially as CQ

is still necessary for P. vivax infection. However, this has to be done based on accurate

diagnosis.

Although Pfmdr1 plays an important role in modulating levels of antimalarial drug

resistance, CQ resistance has been correlated with the Pfmdr1 86Y (Djimdé et al.,

2001a), while the other point mutations at codons 184, 1034, 1042 and 1246 do not

confer resistance to CQ and are correlated to mefloquine, artesunate, amodiaquine,

halofantrine and quinine resistance (Duraisingh & Cowman, 2005; Danquah et al.,

2010; Gamo, 2014). In the same vein, the Pfmdr1 N86 alleles has been linked to

increased artemether or lumefantrine resistance drugs (Ngo et al., 2003; Lekana-Douki

et al., 2011). In another study, reported lumefantrine usage selected for the Pfmdr1 N86

and 184F alleles (Sisowath et al., 2007; Malmberg et al., 2013). In addition, extensive

use of amodiaquine- artesunate combination therapy selected for the Pfmdr1 N86, 184F

and D1246 alleles (Fröberg et al., 2012). Moreover, many studies reported high

prevalence of wild type Pfmdr1 N86 and D1246 alleles after adopted or increase use of

the artemether/lumefantrine treatment (Zongo et al., 2007; Baliraine & Rosenthal, 2011;

Conrad et al., 2014; Tumwebaze et al., 2015).

However, the mechanism of Pfmdr1 gene mutations in drug resistance is

controversial. In the present study, a prevalence of 16.7% of Pfmdr1 86 mutant type

among P. falciparum isolates was observed, whereas there is no mutation of the Pfmdr1

at codon 1246. This findings are in agreement with recent studies in Yemen and

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neighboring countries such as Kingdom of Saudi Arabia and Iran (Zakeri et al., 2008;

Dajem et al., 2012; Al-Hamidhi et al., 2013). The low prevalence of Pfmdr1 86Y

mutation has been attributed to almost complete withdrawal of CQ in the community

(Vathsala et al., 2004; Mixson-Hayden et al., 2010). In the same vein, recent studies

suggested that some exert opposite selection by antimalarial drugs on genotypes in

parasite, where the parasite selected the wild type Pfmdr1 N86 and 184F instead of

mutant type 86Y and Y184 due to the change in drug policy to ACTs (Humphreys et al.,

2007; Mungthin et al., 2010; Lekana-Douki et al., 2011; Thomsen et al., 2011).

However, this is not the case in our survey since CQ is still prescribed in Hadhramout

governorate (Bashrahil et al., 2010; Bin Ghouth, 2013).

In the present survey, the association of Pfcrt and Pfmdr1 mutations with gender

and age of participants was not significant. Similar findings have been reported from

Malaysia and Iran (Rastaghi et al., 2008; Atroosh et al., 2012). No significant difference

in the prevalence of Pfcrt and Pfmdr1 mutations between subclinical and clinical

infection of malaria parasites was noted in this survey.

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5.2.2 Point mutations in Pfdhfr and Pfdhps genes at different codons as molecular

markers of sulfadoxine-pyrimethamine resistance of Plasmodium falciparum

isolates in Hadhramout governorate

High prevalence (84%) of Pfdhfr mutant alleles I51 and N108 was found among P.

falciparum isolates in Hadhramout. These findings were higher than those from

previous reports from western governorates of Yemen (Al-Hamidhi et al., 2013; Abdul-

Ghani et al., 2014). Pfdhfr mutant allele R59 was detected in one isolate of P.

falciparum in this study. However, a study conducted in Lahj governorate reported four

samples harboring this mutant allele in 99 P. falciparum isolates (Mubjer et al., 2011).

Mutation at codon 437 of Pfdhps (G437) was also detected for the first time in 44.7% of

isolates in Hadhramout governorate. Increased frequency of mutant alleles of Pfdhfr

gene and emergence of new mutant alleles of Pfdhps gene in Yemen are early alarming

signals of the possibility of decreasing in the efficacy of SP. Accumulation of mutations

in Pfdhfr gene starts at codon 108 from serine to asparagine, resulting in low levels of

pyrimethamine resistance followed by mutations I51 and R59, as well as at codon L164

point mutation which is related to high level of resistance (Mita et al., 2009; Antony &

Parija et al., 2016). Similarly, sulphadoxine resistance is associated with mutations in

the dhps gene at codons 436, 437, 540, 581 and 613 that starts initially with mutation at

codon 437 from alanine to glycine, followed by E540 and G581, as well as other

mutations (Cowman et al., 1988; Sibley et al., 2001; Gregson & Plowe, 2005; Antony

& Parija et al., 2016). Emergence of resistant parasite to antimalarial drugs involves

many factors, such as economic effects, human hosts, drug pattern interactions,

characteristics of the drug itself, parasites, vectors, and environmental factors (Warhurst

& Williams, 1996; Holland & Kiechle, 2005; Bharti et al., 2007; Vo et al., 2007; Africa,

2013).

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Drug pressure could have driven the emergence and spreading of the mutant genotypes

in this study. SP had been used as second-line monotherapy for treating uncomplicated

malaria for approximately more than five years before the introduction of ACT drug

policy in 2005 (NMCP, 2006, 2011), which theoretically terminated the use of SP

monotherapy. Moreover, SP is not used for intermittent preventive treatment in

pregnant women in Yemen. However, SP is still available in the private sector with poor

knowledge about the new drug policy among physicians (Bashrahil et al., 2010)

emphasizing the possibility of continued use of SP monotherapy, which may result to

the development of SP resistance (Mohanna et al., 2007). Another possible reason could

be the intensity of transmission; Hadhramout has been classified as low malaria

transmission area and the initiation of pre-elimination phase was suggested (NMCP,

2011). The development and spreading of anti-malarial drug resistance in low

transmission area has been well documented (Roper et al., 2004; Anon et al., 2006;

Menegon et al., 2009). Most patients in this area are usually symptomatic and receive

anti-malarial treatment, which increases the chance of selecting the resistant parasite.

Nevertheless, this classification is not supported by recent studies that have reported

high prevalence of malaria in the community setting and among asymptomatic blood

donors in Hadhramout (Othman et al., 2015).

The present study showed high frequency of double mutant genotype

(I51C59N108I164) among P. falciparum isolates. This genotype has been reported in Sudan

(A-Elbasit et al., 2008; Al-Saai et al., 2009), Saudi Arabia (Al-Farsi et al., 2012),

Angola (Lee et al., 2002), Uganda (Sendagire et al., 2005), Gabon (Bouyou-Akotet et

al., 2010), Iran (Zakeri et al., 2010b) and Afghanistan (Awab et al., 2013). In vitro

studies showed a strong association between the Pfdhfr double mutant (I51 and N108) and

pyrimethamine resistance in Kolkata, West Bengal of India, and Purulia (Das et al.,

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2012; Das et al., 2013). Another study conducted among Colombian children indicated

that double mutant (I51 and N108) is significantly associated with delayed parasite

clearance (Méndez et al., 2002). By contrast, a study in Sudan reported that the presence

of dhfr double mutant I51 and N108 alone is insufficient to induce in vivo pyrimethamine

resistance (Khalil et al., 2002).

In our study, Pfdhfr triple mutant genotype (I51R59N108) was detected in one P.

falciparum isolate in Hadhramout governorate. This genotype has been strongly

associated with in vitro and in vivo SP resistance (Kublin et al., 2002). Mutant genotype

(I51C59N108I164-S436G437K540), which combined Pfdhfr double mutants (I51, N108) and

Pfdhps single mutant (G437), was highly prevalent among P. falciparum isolates in

Hadhramout governorate. Lower frequencies of this mutant genotype compared with the

present study have been reported from southeastern Iran at 2.7% during 2008–2005

(Zakeri et al., 2010b) and again at 1.8% during 2008–2010 (Wongsrichanalai et al.,

2007), as well as in Tanzania at 0.1% (Shiff et al., 1993). Literature review showed that

this genotype is not widely distributed and has not been correlated yet with the efficacy

of SP either in vitro or in vivo. In this study, one isolate of P. falciparum harbored

quadruple mutant genotype combining the triple dhfr mutant (I51R59N108) and single

dhps mutant G437. Significant association between SP resistance and quadruple mutant

genotype has been reported from in vivo studies conducted in Mali (unpublished data)

and in Ghana only after one year of implementation of intermittent preventive treatment

of malaria (Mockenhaupt et al., 2005; Dicko et al., 2010). Low occurrence of this

genotype has been reported from northern Benin (Ogouyèmi-Hounto et al., 2013),

contrary to the high prevalence reported from southern Benin (Bertin et al., 2011),

Ethiopia, (Hailemeskel et al., 2013) and Senegal (Ndiaye et al., 2013). In contrast, high

prevalence of quadruple and quintuple has been reported from Jalpaiguri district, India

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which is associated with AS + SP treatment failure for falciparum malaria (Saha et al.,

2012), also shown association of quadruple mutant genotype with recrudescence

infection in Congo, suggested this genotype could be induced a low SP resistance

among falciparum malaria (Ndounga et al., 2007). In addition,in vivo and molecular

study in Somalia shown association of dhfr/dhps quintuple and quadruple mutant

genotype with SP resistance drug and association of dhps double mutant genotype with

treatment failure (Warsame et al., 2015), suggested that need to change national drug

policy in somalia from (AS+SP) to another effective drug of ACTs.

Anti-malarial drug policy has been designed to combine SP with longer half-live

partner drug which clears the remaining parasite and prevent or delay the emergence of

resistance to AS (WHO, 2015a). In Yemen, SP has been the partner drug combined with

AS for treating uncomplicated falciparum malaria (NMCP, 2011) therefore the

emergence of SP resistance will expose the parasite to AS, which has the potential to

contribute to the emergence of ACT resistance in this country. In 2004, three in vivo

studies on clinical efficacy trails showed that SP monotherapy was highly efficacies

(95% - 100%) for treating falciparum malaria in three districts included Harad district in

Hajja governorate, Al-Odein district in Ibb governorate and Mesemeer district in Lahj

governorate (NMCP, 2011). From the time when antimalarial drug policy had shifted

from SP monotherapy as second-line to AS+SP as first-line for treating uncomplicated

malaria, all in vivo efficacy trials have assessed the drug combination (AS+SP) as still

being effective (Adeel et al., 2015). However, the inability of the routine therapeutic

trails to distinguish between the efficacy of AS and its partner drug put SP efficacy

under uncertainty particularly with the high prevalence of the double mutant genotype,

which has good correlation with decreasing SP efficacy, indicating the discontinued

usage of the combination therapy of AS + SP due to insufficient dose of AS to clear

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parasitemia (Méndez et al., 2002; Das et al., 2013). In contrast, the non-emergence of

quintuple dhfr mutant and triple dhps mutant genotypes that have been associated with

the severe failure of SP (Picot et al., 2009) indicates that SP still provides good

therapeutic response in Hadhramout governorate and Yemen.

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5.3 LIMITATIONS OF STUDY

There are a few limitations were encountrerd in this study. It should be noted that risk

factors of malaria in this study were identified based on a cross – sectional survey which

suggests more robust design to confirm these predictors such as case-control. In

addition, the RLFP- nested PCR was unable to detect a few samples that have low

parasitaemia or non-detected by microscopy method.

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CHAPTER 6...CONCLUSIONS AND RECOMMENDATIONS

6.1 CONCLUSIONS

In conclusion, malaria remains an important public health concern in the southeast

region of Yemen (ie., Hadhramout governorate), where there seems to be an upward

shift in malaria prevalence with the appearance of new endemic foci and occupational

high risk groups. There are many barriers and factors that are challenging to the success

of malaria control in Yemen that should be identified in order to develop an effective

control strategy. However, there is a scarcity of data on malaria predictors, KAP and

molecular markers associated with antimalarial drugs resistance, especially CQ and SP

resistance in Yemen. In the same vein, no data is available on malaria risk factors, KAP

and molecular from the Hadhramout governorate, Yemen where this study is being

carried out.

In the current study, several environmental, socioeconomic and behavioural issues

were discovered to be the contributing factors to the high prevalence of malaria in

Hadhramout governorate, Yemen. Furthermore, in present study, high prevalence of

mutations was detected in Pfcrt, Pfmdr1, Pfdhfr and Pfdhps mutant alleles at different

codons. This suggests the sustainability of CQ resistance and emergence of SP

resistance despite post four years of implementing ACTs as a new drug policy in

Yemen. These findings warrant for effective and stricter control of CQ usage and its

open availability in the Yemeni market. Also, high prevalence of Pfdhfr double mutant

genotype (I51C59N108I164) and triple Pfdhfr-Pfdhps mutant genotype (I51C59N108I164-

S436G437K540) in P. falciparum population in Hadhramout, Yemen were also discovered.

These results highlight the risk of developing resistance to SP, the partner drug of AS.

Essentially, the findings of this study, provide baseline information on the KAP, risk

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factors and molecular markers surveillance of drug-resistant P. falciparum in

Hadhramout governorate, Yemen.

A summary of the main findings:-

1. High prevalence of malaria in two endemic districts in Hadhramout governorate

(18.8%), Yemen, Hajer district (11.8%), and Al-Raydah-Qusyer district (21.8%).

2. High prevalence of malaria has been reported in new foci of malaria named Al-

Rahbah area (21.4%).

3. More than 10% of the prevalence in young children aged 2 to 9 years old is shown

to exceed the level that indicative of high to moderate transmission in

Hadhramout governorate.

4. Several environmental factors, socio-demographic and personal behavioral factors

were discovered in this study to be contributing factors to the high prevalence of

malaria in Hadhramout governorate including: the presence of many ponds and

swamps making this region a suitable breeding ground for Anopheles mosquitoes,

also poor sanitary facilities and political instability in Yemen during the 2011–2012

period. Multivariate analysis using stepwise forward logistic regression confirmed

that the significant associated risk factors in the present study include: living in

uncemented brick wall houses (OR= 2.1, 95% CI: 1.32 – 3.30), or being a

fisherman (OR=11.3, 95% CI: 3.13 – 40.5) or a farmer (OR= 4.84, 95% CI: 1.73 –

13.6), had low level of education (i.e., primary school or not educated) (OR= 10.1,

95% CI: 1.35 – 74.5) or living in houses with the distance of water collection points

less than 200 m (OR= 1.6, 95% CI: 1.05 – 2.32).

5. The predominance of falciparum malaria (99.3%) with high prevalence of

anaemia (67%) highlighted the high risk of developing severe malaria in these

communities.

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6. Only 7% and 2% of study participants were sleeping under the insecticide-treated

mosquito nets (ITNs) or using indoor residual spraying (IRS) as methods of

malaria prevention.

7. Half of malaria positive cases were asymptomatic with low parasite densities

(52%) suggesting a possible miss by passive surveillance and would thus remain

as a source of malaria transmission in the communities.

8. The high prevalence of mutations at Pfcrt K76T, Q271E, N326S and R371I

alleles in the present study suggested the low susceptibility of falciparum malaria

toward CQ after 4 years of shifting to ACTs in Hadhramout governorate. Low

prevalence of Pfmdr1 N86Y mutation has been attributed to susceptibility of

antimalarial drugs other than chloroquine drug toward P. falciparum.

9. High frequencies of markers associated with sulfodoxine pyrimethamine

resistance in the present study include 84% each of Pfdhfr single mutant allele (I51

and N108), 44.7 % of Pfdhps G437, 82.8% of Pfdhfr double mutant genotype

(I51C59N108I164) and 39.3% of triple Pfdhfr-Pfdhps mutant genotype

(I51C59N108I164-S436G437K540).

6.2 RECOMMENDATIONS

Based on the pertinent findings of the current study, it is recommended that the Ministry

of Health programmes in Yemen rectify the misconceptions about malaria prevention,

diagnosis and treatment as well as knowledge about mosquito control by focusing on

health education initiatives. Holistic efforts to improve the environment and economic

status of the inhabitants should also be part of the integral strategic planning if the

government’s efforts to make Hadhramout governorate free of malaria is to be

successful. Occupation-based vector control interventions should be implemented to

reduce mosquito-human contact during outdoor activities. Another crucial aspect is to

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enforce effective strategies to ensure accurate implementation of malarial drug

treatment policies according to the national control programme in Yemen.

Active case detection (ACD) should also be implemented, as this would be crucial

to identify and treat the substantial reservoir of asymptomatic persons in the

community. Of utmost importance, ITNs and IRS must become a priority for malaria

control policy as these methods not only act as prevention and treatment measures, but

are effective tools to reduce drug resistance. A further study to investigate antimalarial

drugs resistance in Plasmodium falciparum in Hadhramout governorate and Yemen

using the in vivo efficacy trails is highly recommended as a continuous effort in

monitoring the efficacy of the national anti-malarial drugs policy in Yemen. The

utilisation of Pfcrt, Pfmdr1, Pfdhfr and Pfdhps genes as molecular markers of CQ and

SP resistance are highly recommended in Yemen as malaria is highly endemic in this

country.

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