MALARIA EPIDEMIOLOGY IN DISTRICT BANNU, KHYBER PAKHTUNKHWA: IDENTIFYING GENOTYPES OF PLASMODIUM VIVAX IN THE REGION
PH.D THESIS BY FATIMA JAHAN
DEPARTMENT OF ZOOLOGY UNIVERSITY OF PESHAWAR Session (2009-2010) MALARIA EPIDEMIOLOGY IN DISTRICT BANNU, KHYBER PAKHTUNKHWA: IDENTIFYING GENOTYPES OF PLASMODIUM VIVAX IN THE REGION
THESIS SUBMITTED TO THE DEPARTMENT OF ZOOLOGY, UNIVERSITY OF PESHAWAR, PESHAWAR, PAKISTAN IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
FATIMA JAHAN
DEPARTMENT OF ZOOLOGY UNIVERSITY OF PESHAWAR KHYBER PAKHTUNKHWA, PAKISTAN Session (2009-2010)
AUTHOR’S DECLARATION
I, Fatima Jahan hereby state that my Ph.D thesis titled “Malaria Epidemiology in
District Bannu, Khyber Pakhtunkhwa: Identifying Genotypes of Plasmodium Vivax in the Region” is my own work and has not been submitted to previously by me for taking any degree from this university of Peshawar, Pakistan or anywhere else in the country
/ world.
At any time if my statement is found to be incorrect even after my graduation the university has the right to withdraw my Ph.D degree
Student Name
Fatima Jahan
ACKNOWLEDGEMENTS
I have no words to express my deepest gratitude to Almighty Allah, all praises for him, who guides us in darkness and helps in difficulties. First, I would like to extend my very deep appreciation, gratitude and sincere thanks to research supervisor Prof. Dr.
Naheed Ali for giving me the opportunity to work on this project. I am grateful for her sound professional guidance and for encouraging me in all the stages of my dissertation.
I am very thankful to my co-supervisor Assistant Prof. Dr. Nazma Habib khan, tremendous help during the study, informative advices of writing my dissertation and practical instructions during Lab work. Her approachability and ever inspiring encouragement played a vital role in the accomplishment of this study. Special thanks to Assistant Prof. Dr. Sobia wahid for her useful suggestions and support in this work.
I am also thankful to Dr. Inayat Ali Shahjehan (Ex-chairman) and Dr. Syed Akram Shah
(Chairman) Department of Zoology, University of Peshawar for their full support and valuable guidance.
I would like to thanks Miss Ayesha kauser, Gule Tanzila for their advices and encouragement and always having inspiring words for me even in stressful situations during the study work. Also thankful to Dr. Shumaila Noreen for her useful suggestions.
The field work was a difficult task and it would not have been possible without the cooperation and constant support of the laboratory and hospital staff. I would like to thanks Mr. Ikram Ullah (WCH, Bannu), Mr. Mujtaba (Siddique Lab. Bannu) for their valuable co-operation during sample collection. A special thanks to Mr. Zaki Ullah
(Asst. Prof. Pharmacy Dept., University of Peshawar) for his full support during Lab work. I am grateful to Mr. Safeer Ullah Shah (National Centre of Excellence in Geology, University of Peshawar) for his co-operation. I also wish to thank Dr. Khalid
Masud (Director LRH) for his full co-operation and support.
Thank you to my office staff (Dr. Faisal Shehzad and Dr. Mujeeb Ur Rehman) and colleagues (Mahwish Khan, Marina Iqbal, Uzma Karim, Sikander Wadood, Arif Khan and Rashid Noor) who always support me during this tough route of PhD.
Special gratitude for my friends Nazish Shah (Lecturer Swabi University) Farida Zai
(Lecturer at COBAM, UOP), Humaira liaqat, Baseerat Ayesha and Ayesha Gul who constantly encouraging me during this task.
A special thanks to my husband Ghulam Dastagir (Late) who sacrificed his precious time for my study. May Allah bless him with high Place in Jannah. I am particularly thankful to my brothers in Law (Mr. Imran Ullah khan, Farman Ullah khan and Abdul
Samad) for their kind co-operation in this work.
Finally, I am extremely thankful to my family members. I would like thanks my father
Taj Muhammad Khan, mother, sisters (Surraya Jahan, Sadia Jahan, Bushra Jahan, Dr.
Ruqqaya Jahan, Dr, Shahida Jahan, Bilques Jahan, Maimoona Jahan and Aiman Jahan) for providing constant source of inspiration and encouragement throughout my life and whose moral and financial support, understanding and irreplaceable love enabled me to bring my academic carrier to this stage. Last but not the least I would like to mention special thanks to my younger sister Dr. Asma Jahan for her love and support during my study. I prominently appreciate having you in my life.
Fatima Jahan
Contents TABLE OF CONTENTS ...... i ABSTRACT ...... iv ABBREVIATIONS ...... vi LIST OF FIGURES ...... vii LIST OF TABLES ...... viii Chapter 1 INTRODUCTION ...... 1 1.1 Malaria ...... 1 1.2 History of malaria ...... 2 1.3 Plasmodium; the parasite ...... 4 1.3.1 Plasmodium Genome ...... 5 1.3.2 Evolution of Plasmodium...... 6 1.3.3 Taxonomy ...... 11 1.3.4 Reservoirs of Plasmodium: ...... 12 1.4 Vector of Malaria ...... 12 1.4.1 Distribution of malaria vector ...... 13 1.5 Transmission & pathogenesis ...... 14 1.5.1 Vector and hosts ...... 14 1.5.2 Transmission of malaria: ...... 16 1.5.3 Life cycle: ...... 16 1.5.4 Pathogenesis in vector host: ...... 19 1.5.5 Disease Pathogenesis in Human ...... 20 1.5.6 Pathogenesis of severe malaria ...... 23 1.5.7 Clinical representation of malaria ...... 25 1.6 Recurrent or persistent malaria ...... 29 1.7 Immunity against parasite ...... 30 1.8 Treatment and Prevention of Malaria ...... 30 1.8.1 Treatment ...... 30 1.8.2 Prevention ...... 32 1.9 Malaria vaccines ...... 33 1.10 Antimalarial drug resistance ...... 34 1.11 Worldwide malaria epidemiology ...... 35 1.12 Malaria epidemiology in Pakistan...... 39 1.13 Diagnosis ...... 45 1.13.1 MICROSCOPY ...... 45 1.13.2 Rapid diagnostic Technique (RDT) ...... 48 1.13.3 Polymerase chain reaction (PCR) ...... 50
i 1.13.4 Serology ...... 54 1.14 Risk factors for malaria ...... 55 1.14.1 Environmental factors ...... 55 1.14.2 Vector related factors ...... 58 1.14.3 Human related factors ...... 59 1.15 Genetics of Plasmodium ...... 61 1.16 Rationale of the project: ...... 66 Chapter 2 MATERIALS AND METHODS ...... 69 2.1 Introduction ...... 69 2.2 Ethical approval ...... 71 2.3 Data collection and sampling procedure ...... 71 2.3.1 Microscopy ...... 72 2.3.2 Rapid Diagnostic Technique (RDT) ...... 74 2.3.3 Sample storage ...... 74 2.3.4 Molecular diagnosis ...... 74 2.4 Sequencing of mitochondrial genome ...... 79 2.5 Data analysis ...... 79 2.5.1 Assessment of sensitivity and specificity of diagnostic methods ...... 80 2.5.2 Multivariate and univariate analysis for risk factors ...... 81 2.6 Analysis of mitochondrial genome data ...... 82 2.6.1 Identification of genetic population substructure ...... 82 2.6.2 Population genetic analysis of geographical populations ...... 84 Chapter 3 EPIDEMIOLOGY OF MALARIA IN BANNU REGION ...... 89 3.1 Introduction ...... 89 3.2 RESULTS ...... 91 3.2.1 Techniques used for the diagnosis of malaria: ...... 92 3.2.2 Locality wise distribution of Plasmodium parasite ...... 93 3.2.3 Month wise malaria data in 2014 as recorded by women and children hospital (WCH) Bannu: ...... 102 3.3 Discussion ...... 103 3.4 Conclusion ...... 115 3.5 Recommendations ...... 116 Chapter 4 RISK FACTORS ANALYSIS FOR MALARIA ...... 118 4.1 Introduction ...... 118 4.2 Materials and method; ...... 120 4.2.1 Study site and design: ...... 120 4.2.2 Laboratory method ...... 120 4.3 Risk factors data ...... 121
ii 4.4 Statistical analysis of risk factors data ...... 121 4.5 Results ...... 121 4.5.1 Univariate analysis for Microscopy positive samples ...... 122 4.5.2 Multivariate analysis for Microscopy positive samples ...... 123 4.5.3 Univariate risk factors analysis for RDT positive samples ...... 125 4.5.4 Risk factors analysis for PCR positive samples ...... 127 4.5.5 Univariate risk factors analysis for all positive cases ...... 129 4.5.6 Multivariate analysis for all positive cases (through all techniques) ...... 130 4.6 Discussion ...... 131 4.6.1 Strategies ...... 144 Chapter 5 Population genetics of Plasmodium vivax ...... 146 5.1 Introduction ...... 146 5.2 Results ...... 149 5.2.1 Basic properties of the analysed data ...... 151 5.2.2 Mitochondrial genes analysis ...... 153 5.2.3 Nucleotide substitution pattern ...... 156 5.2.4 Genetic clustering analysis...... 156 5.2.5 Population parameters analysis of geographical clusters ...... 160 5.3 Discussion ...... 167 5.4 Conclusion ...... 174 Chapter 6 GENERAL DISCUSSION ...... 176 Appendix A Peshawar University ethics committee approval ...... 195 Appendix B Questionnaire Form Administered in the study...... 196 References ...... 197
iii ABSTRACT
Malaria is the fourth leading cause of deaths among communicable diseases in Pakistan.
Federally Administered Tribal Areas (FATA), Baluchistan and Khyber Pakhtunkhwa provinces have one of the highest Annual parasite incidence (API) within the country.
Present study aimed at elucidating epidemiology and comparing performance of several diagnostic procedures in Bannu, a highly endemic district (API of 1.6-3.5 per 1000) of
Khyber Pakhtunkhwa.
The aim of this PhD project was to generate current information on parasite prevalence through active antigen detection and also investigated the risk factors of malaria. In addition, genetic diversity among existing P. vivax strain was also investigated. A total of 2033 blood samples of suspected cases were collected and processed through three diagnostic technique including microscopy, RDT and parasite species specific PCR. A questionnaire was administered to collect household and individual based information to determine the potential risk factors of malaria.
The present study was performed from 2012 to 2013. Samples were collected from suspected individuals of rural and urban areas visiting laboratory. Microscopy and
Rapid Diagnostic Test was used on the spot for detection of Plasmodium species. A total of 2033 individuals were recruited, of whom 21.1% (N=429) were positive for malaria by at least one method. Overall, positivity detected by PCR was 30.5% (95/311) followed by 17.7% by microscopy (359/2033) and 16.4% by RDT (266/1618).
Plasmodium vivax (16.9%, N=343) was the detected as the dominant species followed by Plasmodium falciparum (2.3%, N=47) and mixed infections (1.2%, N=39).
Microscopy and RDT (Cohen's kappa k=0.968, 15 p=<0.0001, McNemar test p=0.069)
iv displayed significant agreement with each other, suggesting that RDT may be a useful alternative to microscopy in the field.
Satisfactory health, sleeping inside room, presence of health care facility in vicinity (at an accessible range from home), living in upper middle class and in concrete houses significantly reduced malaria risk. On the other hand, low literacy level, presence of domestic animals indoors and a diagnosis recommended by clinician increased the disease risk.
Mitochondrial genome sequencing of Plasmodium vivax revealed the existence of significant number of SNPs in coding and non-coding region of the genome while population structure analysis shown the presence of strain which are closely related to the strains of other regions. Population genetic study identify the significant mutation in the genome. It also identifies the large evolutionary distance between Pakistani and other regions P. vivax strains.
This data will help to provide beneficial and up-to-date information to manage control activities in the study area. Appropriate management of identified risk factors can benefit in reducing the prevalence of malaria in Bannu and its peripheries.
v ABBREVIATIONS
KPK Khyber Pakhtunkhwa
WHO World Health Organization
RH Relative humidity
Pak Pakistan
PBS Phosphate buffered saline
TBE Tris-Borate EDTA
HWE Hardy Weinberg equilibrium
RBCs Red blood cells
WCH Women and children hospital
CDC Center for Disease and Control
vi LIST OF FIGURE Figure 1.1 Evolutionary relationships of Plasmodium spp...... 11 Figure 1.2 Map of the world showing the distribution of predominant malaria vector (CDC, 2017a)...... 14 Figure 1.3 Life cycle of Plasmodium parasite (Bannister, 2009) ...... 19 Figure 1.4 Cytoadherence and rosetting in post-capillary vasculature (Chen et al., 2000) ...... 25 Figure 1.5 Induction of fever by malaria parasites ...... 29 Figure 1.6 Estimated country share of (a) total malaria cases and (b) P. vivax malaria cases, 2016 (WHO, 2017) ...... 36 Figure 1.7 World map of past and current malaria distribution (WHO, 2017) ...... 38 Figure 1.8 Distribution of Plasmodium vivax in Pakistan (WHO, 2017) ...... 42 Figure 1.9 Distribution of Plasmodium falciparum in Pakistan (WHO, 2017) ...... 42 Figure 1.10 Mechanim of action of Antigen-detecing malaria RDT (WHO, 2011a) .. 50 Figure 2.1 (a) Map of Pakistan showing its provincial divisions and neighboring countries (b) Map showing administrative divisions of KP and FATA ...... 70 Figure 2.1 Thick and thin smear ...... 73 Figure 3.2 District wise origin of patients ...... 91 Figure 3.3 Origin of positive patients shown on Land cover map ...... 92 Figure 3.5 Seasonal fluctuation of Plasmodium infection ...... 100 Figure 5.1 Molecular Phylogenetic analysis by Maximum Likelihood method ...... 158 Figure 5.2 Graphical output of STRUCTURE v2.3.1 for dataset ...... 159
vii LIST OF TABLE Table 2.1 Demographic information of District Bannu ...... 70 Table 2.1 Primers used in PCR ...... 76 Table 2.2 PCR Mix for Nest 2 reaction ...... 77 Table 2.3 cycling conditions for Nest 1 ...... 77 Table 2.4 PCR mix for Nest 2 ...... 78 Table 2.5 cycling conditions for Nest 2 ...... 78 Table 2.6 Distribution of populations according to WHO malaria regions ...... 84 Table 3.2 Plasmodium species distribution through different techniques ...... 93 Table 3.3 Area wise distribution of Plasmodium species ...... 93 Table 3.4 Spearman’s correlation analysis between parasite Species and areas ...... 94 Table 3.5 Tehsil wise distribution of Plasmodium species ...... 94 Table 3.6 Correlation of Plasmodium parasites (vivax and falciparum) with two Tehsils of Bannu ...... 95 Table 3.7 Comparison of the performance of PCR using microscopy as a standard... 96 Table 3.8 Comparison of the performance of RDT using microscopy as a standard .. 96 Table 3.9 Comparing Sensitivity and Specificity of diagnostic methods ...... 97 Table 3.10 Correlation between Plasmodium species and diagnostic methods and Auto correlation between diagnostic means ...... 98 Table 3.11 Symptom wise distribution of Plasmodium species ...... 98 Table 3.12 Correlation between Plasmodium species and symptoms ...... 99 Table 3.13 Month wise distribution of Plasmodium species ...... 100 Table 3.14 Age and Gender wise distribution of Plasmodium species...... 101 Table 3.15 Relation of Blood group with malaria ...... 102 Table 3.16 WCH data collected by analyzed in laboratory ...... 103 Table 4.1 Detail and division of potential risk factors for malaria in the study area . 122 Table 4.2 Risk factors for microscopy ...... 124 Table 4.3 Multivariate table of microscopy positive samples ...... 125 Table 4.4 Univariate risk factors, analysis of RDT positive samples ...... 126 Table 4.5 Univariate risk factors analysis for PCR positive samples ...... 128 Table 4.6 All positive cases risk factors analysis ...... 129 Table 4.7 Multivariate analysis of significant factors ...... 130 Table 5.1 Data distribution into WHO malaria regions ...... 150
viii Table 5.2 General properties of populations ...... 151 Table 5.3 Variable sites in the mitochondrial genome ...... 152 Table 5.4 Cox1 Gene: Amino acid mutations observed in the dataset and their respective positions on the nucleotide sequence ...... 154 Table 5.5 Cytb gene: Amino acid mutations observed in the dataset and their respective positions on the nucleotide sequence ...... 155 Table 5.6 Maximum Composite Likelihood Estimate of the Pattern of Nucleotide Substitution ...... 156 Table 5.7 Composition of clusters observed by STRUCTURE analysis for P. vivax populations ...... 160 Table 5.8 Genetic diversity and inbreeding indices per geographic cluster ...... 161 Table 5.9 Tests for linkage equilibrium/disequilibrium per sub-population ...... 162
Table 5.10 FST estimates of the inter-cluster differentiation in the lower triangular matrix ...... 164 Table 5.11 Estimates of Evolutionary Divergence over Sequence Pairs between Groups ...... 166 Table 5.12 Analysis of molecular variance (AMOVA) undertaken for Plasmodium sub-populations as defined by geography ...... 167
ix Chapter 1 INTRODUCTION
1.1 Malaria
Malaria is a parasitic disease caused by protozoan of the genus Plasmodium of the phylum Apicomplexa which was described in 1885 by Ettore Marchiafava and Angelo
Celli. The five known human malaria species P. falciparum, P. vivax, P. malariae, P. ovale and P. knowlesi which were identified to infect humans (Omar et al., 2001a, Omar et al., 2001b) while Plasmodium vivax is the main cause of malaria morbidity outside
Africa (Carlton et al., 2008) and has historically been mostly neglected in control, relatively because of its lower virulence than P. falciparum (Price et al., 2007).
The most serious and sometimes fatal type of malaria is caused by Plasmodium falciparum. Approximately 80 – 90% of P. vivax burden is concentrated in the Middle
East, Asia and Central & South America (Guerra et al., 2006). Plasmodium falciparum is responsible for severe form of infection and cause approximately 90% of deaths and
80% of human infections, with symptoms arising after approximately 6 to 14 days, including fever, vomiting, arthralgia, shivering, convulsions, hemoglobinuria and anemia caused by hemolysis (Beare et al., 2006).
Other clinical features include splenomegaly, anemia, thrombocytopenia, hypoglycemia and pulmonary or renal dysfunction. In severe infections neurological damage coma and even death can be caused by rosetting of the red blood cells, leading to impaired micro-vascular blood flow in the brain, and the release of cytokines such as TNF which can in turn trigger the release of harmful substances such as nitric oxide which affect the central nervous system (CNS) causing neurologic deficits, cognitive sequelae, damage of brain (Mayxay et al., 2004) and epilepsy (neurocognitive sequelae are defined as impairment of neurologic or cognitive function). The neurologic impairment caused by P. falciparum consists of loss of function in motor, including
1 coordination, speech, vision, and hearing domains, as well as epilepsy) (Omar et al.,
2001a, Omar et al., 2001b).
1.2 History of malaria
During second century, ancient Nei Ching (the canon of Medicine) (about 4700 BCE) the initial malaria-like symptoms were recorded. These writing also consist of various malaria treatment, one of them is the use of Qinghao (Artemisia annua) plant for fever reduction(Hsu, 2006). Artemisinin, the active ingredient of the Qinghao plant, was not isolated until 1971 by Chinese scientists. Artemisinin drug products are used today as antimalarial drugs in areas where there is resistance to chloroquine (MalariaSite., 2017,
CDC, 2017b).
In the early 17th century the Spanish Jesuit missionaries recognized the use of another medication found in the bark of tree in Peru. The tree was named Cinchona after usage of its bark which cured the fever of countess of Cinchon, the wife of viceroy of Peru.
The bark was called Peruvian bark. The recently used antimalarial quinine is the derived from Peruvian bark (CDC, 2017b).
In the 4th century BCE Hippocrates documented (in the sanskrit medical treatise
“Susruta”) the signs of malaria along with time of year and place where the victim fell ill, he was the first to differentiate the recurrent fever of malaria from continual fever of other infectious diseases. For a long time, malaria was associated with swamps and it was a concept that it is transmitted by wind, modern English term malaria is derived from Italian malaria from mala aria, literally “bad air”. An Italian Physician Francisco
Torti (1658-1741), used the term malaria for the first time. In 1880, a French Army
Physician Charles Louis Alphonse Laveran discovered the causative agent of human malaria in the patient blood, received Nobel Prize for medicine and physiology in 1907.
Later, Laveran investigated the presence of malaria parasite outside the human body.
2 He deduced that “the marsh fever parasite must undergo one phase of its development in mosquitoes and be inoculated into humans by their bites” (Bloland, 2001).
In 1886 Camillo Golgi concluded from his study that there are at least two forms of malaria. The one called tertian which produced fever on alternative day. Another type was quartan, produced fever every third day. In 1890 the two Italian investigators,
Giovanni Batista Grassi and Raimondo Filetti introduced the terms Plasmodium vivax and Plasmodium malariae. The third malaria parasite P. falciparum was named by
American William H. Welch in 1897 while reconsidering the Laveran’s work. Finally, in 1922, John W. W. Stephens found a fourth human malaria parasite P. ovale. In 1897 a British officer Ronald Ross revealed that malaria parasite is transmitted by dappled wing brown Anopheles mosquito and demonstrate that parasite can be transferred to mosquito by introducing parasite free mosquitoes into the rooms of malaria infected patients. The Plasmodium life cycle was first described by Giovanni Batista Grassi in
1899 (CDC, 2017b).
Almost 60 years after the discovery of erythrocytic stages of Plasmodium, in 1948 the tissue stage of primates and human Plasmodium were detected in the livers of rhesus monkeys infected with P. cynomolgi Sporozoites by Shortt, Garnham and collegues at the Ross institute in London. Later the Shortt and colleagues describe the complete life history of P. falciparum. With progress of molecular & computer technologies and the discovery of in vitro culturing of falciparum, many studies defined the malaria parasite classification, structure, Physiology, molecular composition, biochemistry, immunology and genetics (Trager and Jensen, 2005, Haynes et al., 1976).
The chief advancement in the present and future malaria research is the whole genome sequencing of some Plasmodium species, first of Plasmodium falciparum (Gardner et al., 2002) and later Plasmodium vivax, Plasmodium knowlesi, Plasmodium berghei,
3 Plasmodium chabaudi & Plasmodium yoelii (Aurrecoechea et al., 2009). The molecular innovation led to a rapid extension of research into the structure and function of proteins expressed during life cycle, use of novel techniques for studying genetic mutation and provides a plenty of data for new drug and vaccine targets.
1.3 Plasmodium; the parasite
Plasmodium shares some of the features with eukaryotes while some of its characteristics are unique to phylum or genus. Like eukaryotes it contain a nucleus with a single copy of genome in almost all parts of life cycle except in the mid gut of the insect host where doubling of genome occurs for a brief sexual exchange (Obado et al.,
2016). Endoplasmic reticulum are attached with nucleus which functions like in other eukaryotes. Proteins are transferred from the endoplasmic reticulum to the Golgi apparatus which generally consists of a single membrane-bound compartment in
Apicomplexans (Jimenez-Ruiz et al., 2016). From here proteins are transported to various cellular compartments or to the cell surface (Jimenez-Ruiz et al., 2016).
In human Plasmodium, there are four developmental stages in humans (hepatic schizonts and then intraerythrocytic trophozoites, schizonts and gamonts) and three stages in Anopheles vector (ookinetes, oocysts and sporozoites). Liver schizonts are clusters of small basophilic bodies (merozoite nuclei), 40-80µm in diameter when mature, present in liver cells. The blood stages consist of small circular trophozoites
(ring form) of 1-2µm diameter, unstructured multinucleate schizonts measuring up to
7-8µm in size, the micro and macro gametocytes ranges from 7-14µm in length. The morphology of blood stages is species specific. Micro-gametocytes have a larger more diffuse nucleus (ready for gamete production) while macro-gametocytes have darker- staining cytoplasm (plentiful ribosomes for protein synthesis) (Qiagen, 2013).
4 In mosquito, the elongated microgametes (15-25µm in length) formed by ex- flagellation fertilize the rounded female gametes and form a motile Ookinetes (15-20 x
2-5µm) which travels through the gut wall form rounded oocysts (up to 50µm in diameter) on the outer surface. Thousands of thin elongated Sporozoites (~15µm long) were formed which migrated to salivary glands (www.parasite.org.au) (Bloland, 2001,
CDC, 2017b)
Morphologically sporozoite is about 10 µm to 15 µm long by 1 µm in diameter and have a pellicle consist of a thin outer membrane, a doubled inner membrane, and a layer of sub-pellicular microtubules. There are three polar rings and rhoptries are long, extending to the mid-portion of the organism while rest of the cytoplasm is occupied by Micronemes. Apparently a nonfunctional cytostome is present and there is a mitochondrion at the posterior end of the sporozoite (Aikawa & Sterling, 1974).
The unusual characteristics of Plasmodium in comparison to general eukaryotes is the presence of specific apical secretory organelles (Rhoptries, Micronemes and dense granules) that secrete special protein required for infecting new host and polar rings near the apical end. The secretion by apical secretory organelles are unique to
Apicomplexans (Counihan et al., 2013).
1.3.1 Plasmodium Genome
Plasmodium have 14 chromosomes in the nucleus along with genetic material in the mitochondrion and in the apicoplast. The chromosomes vary from 500 kb to 3.5 mb in length. The apicoplast is involved in isoprenoid metabolism, Fe-S cluster synthesis, fatty acid synthesis, and phospholipid biosynthesis On a molecular level, the parasite damages red blood cells using plasmepsin enzymes aspartic acid proteases which degrade hemoglobin Evolution of Plasmodium (van Dooren and Striepen, 2013).
5 1.3.2 Evolution of Plasmodium The ancestors of Plasmodium probably existed at least half a billion years ago. The molecular studies provide strong evidence that the pre-parasitic ancestor of
Plasmodium was chloroplast containing free living protozoan which adapted for the gut habitat of aquatic invertebrates (insect larvae, including those of early Dipterans, the taxonomic order to which mosquitoes and other blood-sucking flies belong) and later that single celled organism probably had obligate sexual reproduction in the lumen of mid-gut of host. This pre-malaria parasite acquired an asexual and intracellular reproduction called schizogony (schizogony in the RBCs of humans causes the clinical manifestations of malaria) and increase its division potential. During that period the parasite achieved two host life cycle, an adaptation to the blood feeding habits of insect hosts (Carter et al., 2000).
The complexities of the evolutionary relationships, geographic distribution, prevalence rates, and mammalian host and vector associations of Plasmodium parasites were recently illuminated though it was discovered almost 100 years ago. Though evolutionary origin of human Plasmodium falciparum and P. vivax has now been elucidated but nothing is known about the mechanism that led to their emergence, such info is very essential for understanding how ape parasite crossed the species barrier and whether this process can occur again. The great barrier for efficient analysis of ape malaria parasite is the lack of in vitro culture system for Plasmodium parasite, but the whole genome sequencing of parasite from infected blood is the valuable step towards exploration of biology of ape Plasmodium (Otto et al., 2014, Sundararaman et al.,
2016). This analysis revealed several surprising findings such as horizontal transfer of invasion genes among ape Laverania parasite species (Sundararaman et al., 2016).
6 In past Plasmodium diverged into two groups, one infected mammals and other having bird or reptile hosts. Within mammalian division Plasmodium falciparum and P. reichenowi form a unique subgroup but split off early and diverged to rodents and other primate parasites. It is evident from molecular studies that divergence of Plasmodium falciparum and P. reichenowi occurred about 6-8 million years ago, when the chimpanzee and human lineages were diverged (Martinsen et al., 2008).
One of the old hypothesis suggested that humans and chimpanzees inherited the
Plasmodium falciparum-like infection from the common ancestor and for millions of years these parasites co-evolved with their respective host species (Escalante and
Ayala, 1994) while Plasmodium vivax was believed to evolve several hundred years ago after the species transmission of a parasite from a macaque in southeastern Asia
(Escalante et al., 2005, Jongwutiwes et al., 2005, Mu et al., 2005, Neafsey et al., 2012).
Both theories have recently been contradicted after the characterization of high number of additional malaria parasites from the African apes. Unambiguously it is now proved that Plasmodium falciparum infection is relatively new for human population as it arose after acquiring parasite from a gorilla, probably within past 10,000 years (Liu et al.,
2010, Sundararaman et al., 2016). Likewise, Plasmodium vivax didn’t emerge in Asia but presents a bottleneck lineage that absconded out of Africa afore the spread of Duffy negativity which reduced African humans resistant to P. vivax (Liu et al., 2014).
Based on presence of homozygous hemoglobin C and RBC Duffy negativity alleles that confer protection against P. falciparum and P. vivax respectively, it is believed that most of the present-day Plasmodium population may have their origin in West Africa
(P. falciparum) and West and Central Africa (P. vivax) (Makanga et al., 2016).
As falciparum was found to be very closely related to Plasmodium reichenowi of chimpanzees and both are more closely related to bird’s malaria parasite as compared
7 to other mammals. The ancestry of these parasites probably occurred around 130 million years ago, almost the same period of origin of the two-host life cycle involving blood feeding insects and land vertebrates. The separation of these lines probably occurred only 4-10 million year ago, coinciding the era of human divergence from
African great apes. The modern phylogenetic studies of Plasmodium falciparum reveal that all existing falciparum populations were originated from P. reichenowi probably by a song host transfer, 2-3 million years ago (Rich et al., 2009, Carter and Mendis,
2002) while the present lethal strain of Plasmodium falciparum may have emerged within last 5-10 thousand years, after the development of agriculture in Africa (Carter and Mendis, 2002).
Many of the molecular studies disproved the Plasmodium falciparum origin from chimpanzee, bonobo or ancient human origin. The mitochondrial, apicoplast and nuclear genome proved the monophyletic lineage of human P falciparum within the gorilla parasite radiation (Krief et al., 2010, Liu et al., 2010, Prugnolle et al., 2010).
Another human Plasmodium species (malariae, ovale and vivax) diverged 100 million years ago along the mammalian Plasmodium. P. ovale is the only known surviving representative of its lineage and caused malaria in humans only. The ancestors of both human and African great apes were parasitized by P. malariae and it had the ability to parasitize and cross-infect both host lines as they diverged around five million years ago. P. malariae (naturally infect the West Africa chimpanzees) and P. brazilianum
(infect new world monkeys in central and south America) were morphologically indistinguishable. Like P. ovale, P. malariae is the only surviving member of its line.
P. vivax, closely related to P. shwetzi (parasite of African great apes), belongs to a group of malaria parasites like P. cynomolgi, that infect monkeys (Daneshvar et al., 2009).
8 The divergence time of P. vivax from P. cynomolgi is about 2-3 million years ago.
Several cases of Plasmodium knowlesi infection (zoonotic from macaque monkeys) have been recently reported from Southeast Asia, including Thailand, Malaysia,
Vietnam, Phillippines and Myanmar (Daneshvar et al., 2009, Singh et al., 2004, Cox-
Singh et al., 2008, Peters and Van Noorden, 2009, Rich et al., 2009, Putaporntip et al.,
2011).
The genome sequencing of additional species such as ape P. vivax and P. praefalciparum will provide patterns for systematic studies and in vitro genome manipulation to compare the function of key proteins among the various ape and human
Plasmodium spp. Population genomic studies of ape Laverania parasites may also inform ongoing malaria vaccine development efforts by identifying antigens that are under strong immune selection pressure in apes as well as humans (Ochola et al., 2010,
Amambua-Ngwa et al., 2012). The characterization of transmitting vector of Ape malaria parasites and to find the degree of human exposure to these parasites through the bite of infected mosquitoes, will be of utmost importance. A careful study of antibody responses to pre-erythrocytic stages could provide answer to this question.
Finally, P. ovale and P. malariae like sequences have been detected in African great apes (Duval et al., 2010, Krief et al., 2010, Liu et al., 2010), and additional work is required to ascertain the relationship of these parasites to their human counterparts.
For the past 80 years, 4 Plasmodium species (P. falciparum, P. vivax, P. ovale, and P. malariae) are known to cause human malaria with P. falciparum being responsible for more severe outcomes (Kantele and Jokiranta, 2011). The molecular analysis of small subunit rRNA gene of these species suggested that they are the members of separate phylogenetic lineages of other animal’s malaria parasites. According to this analysis
Plasmodium falciparum is closely related to bird parasites (P. gallinaceum and P.
9 lophurae), P. malariae have its own lineage while P. vivax showed close association to several other primates’ species due to insufficient data the placement of P. ovale in respective lineage is difficult (Waters et al., 1993).
Other study of mitochondrial cytochrome b gene placed the P. falciparum in a clade with P. reichenowi (from chimpanzees) not related to other Plasmodium of mammals.
This study also support placement of Hepatocystsis spp. And Haemoproteus in separate clades with different species of Plasmodium, thus making genus Plasmodium paraphyletic (Perkins and Schall, 2002, Perlmann et al., 2000). Taxonomy in parasitology before DNA based methods was always problematic, and revisions are continuing, leaving many obsolete names for Plasmodium species that infect humans
(Coatney and Roudabush, 1936). Recently, a fifth Plasmodium species P. knowlesi has been documented as a human malaria parasite which was formerly known to cause malaria only in macaques (Kantele and Jokiranta, 2011).
10
Figure 1.1 Evolutionary relationships of Plasmodium spp.
Colours highlight Plasmodium spp. that infect humans (red), chimpanzees (blue) and gorillas (green). Four groups of Plasmodium spp. are shown, with subgenus designations indicated for primate parasites. The phylogeny was estimated by maximum likelihood analysis of 2.4 kb of the mitochondrial genome; the scale bar indicates 0.03 substitutions per site
1.3.3 Taxonomy
Plasmodium belongs family Plasmodiidae (Levine, 1988), order Haemospridia and phylum Apicomplexa. There are 450 known species in the order (www.wikipedia.com).
The genus Plasmodium was generated in 1885 by Marchiafava and Celli (Chavatte et al., 2007). The family Plasmodiidae comprises genera Plasmodium, Haemoproteus and
Leucocytozoon, which are malaria and malaria like organisms. When in host cells
Plasmodium, Haemoproteus commonly produce a Hemozoin pigment from host hemoglobin, differentiate them from the closely related Leucocytozoon, which does not produce Hemozoin (Robert and Janovy, 2009).
11 Genus Plasmodium was divided by Garnham (Garnham, 1966) into nine sub genera, of which 3 occurs in mammals, 4 in birds and 2 in lizards. Most Plasmodium species are parasite of birds and others are parasite of rodents, primates and reptiles (Robert and
Janovy, 2009).
1.3.4 Reservoirs of Plasmodium:
In tropical Africa, Chimpanzees may carry the P. malariae. No other animal reservoir of human Plasmodia is known to exist. Asymptomatic person can be a reservoir who harbors the sufficient number of viable, mature, male and female gametocytes in blood
(Park, 1998).
1.4 Vector of Malaria
There are about 3500 species of mosquitoes which are categorized into 41 genera. The females of genus Anopheles are the vector of human malaria. Among 430 species of
Anopheles only 30-40 are involved in malaria transmission. Malaria transmitting
Anopheline mosquitoes are found both in endemic and non-endemic countries thus the non-endemic countries are always at risk of re-introduction of malaria. Most of the
Anopheles are crepuscular (active at dusk or dawn) or nocturnal (active at night). They may be endophagic (feed indoor) or exophagic (feed outdoor) in feeding habit (CDC,
2017b).
Depending on resting behavior mosquitoes may be exophilic (rest outdoor) or endophilic (rest indoor). The nocturnal and endophagic mosquitoes can be controlled by use of insecticide treated nets (ITNs) or by improving the house construction to prevent the mosquito entry in the houses. Endophilic Anopheles can be reduced by indoor spray of residual insecticides. Breeding sites destruction is the effective way for reducing the exophagic/exophilic mosquitoes. The survival of vector, ambient temperature and humidity play an important role in the developmental of parasite inside
12 the Anopheles. In contrast to humans the parasite doesn’t cause any harm to mosquitoes
(CDC, 2017b, WHO, 2017).
Anopheles feed at night although few also feed at daytime. Anopheline mosquitoes rest with the body sloping forwards. Their breeding sites vary and include permanent or temporary pools, swamps, seepages, rice fields, tree holes, ditches and reservoirs with some requiring sunlight and others shade for breeding (Cheesbrough, 1987).
1.4.1 Distribution of malaria vector
Despite worldwide distribution of Anopheles, it is absent from the Pacific East of
Vanuatu including Polynesia. Species distribution of Anopheles is specific to different geographical areas. Existing vectors in Africa are An. gambiae, An. arabiensis, An. melas, An. merus, An. quaclriannulatus and An. bawambae. Species found in Europe,
North Africa and Middle East are: An. atropurvus, An. labranchiae, An. pharoensis,
An. sacharovi, An. sergentii, An. stephensi and An. superpictus. Common Anopheline vectors found in South-East Asia are: An. culicifacies, An. dirus, An. flavirostris, An. fluviatilis, An. minimus, An. stephensi, An. aconitus, An. balabacensis, An. hyrcanus,
An. letifer, An. maulatus etc. Shared species of America and Australia are: An. albimanus, An. albitarsis, An. aquaslis, An. darlingi, An. punctimacula, An. cruzil, An. bellator, An. nuneztovara, An. farauti and An. Koliensis (Service, 2002).
13
Figure 1.2 Map of the world showing the distribution of predominant malaria vector
(CDC, 2017a).
1.5 Transmission & pathogenesis
1.5.1 Vector and hosts
Principal mode of spread of malaria is by the bites of female Anopheles mosquito. Of more than 500 species of Anopheles, only 80 were reported for malaria transmission, of which about 40 are significant vectors. The female Anopheline mosquito needs a blood meal and habitat for laying eggs. Some vectors have anthropophilic behaviours, which include preferences for humans as a source of blood meal and endophily (the tendency to enter and rest inside houses) (Besansky et al., 2004, Bannister, 2009). Every continent is reported with its own species of these mosquitoes: An. Gambiae, A. arabiensis, A. funestus are found in tropical Africa (Besansky et al., 2004, Coetzee et al., 2000, Lindsay and Martens, 1998), An. freeborni in North America, An. culicifacies,
An. fluviatilis, An. minimus, An. philippinensis, An. stephensi, and An. sundaicus in the
14 Indian subcontinent (CDC, 2017b). An. leucosphyrus, An. latens, An. cracens, An. hackeri, An. dirus etc., have been identified as the vectors for the transmission of P. knowlesi (Van den Eede et al., 2010).
Most of the Anopheline mosquitoes have been characterized as anthropophilic (prefer human blood meal), endophagic (bite indoors), and nocturnal (bite at night) with peak biting at midnight, between 11 pm and 2 am while some shows the endophily (tendency to enter houses and rest inside) (Besansky et al., 2004, CDC, 2017b). The blood meal from a vertebrate host is essential for the female mosquitoes to nourish their eggs.
The mosquitoes find their host by seeking visual, thermal, and olfactory stimuli, the most important mosquito attractants are carbon dioxide, lactic acid, skin temperature, and moisture. As these stimuli varies from person to person, the exposure of individuals to mosquito varies according to strength of these stimuli (Fradin, 1998, Murray et al.,
2003). Females Anopheles transmit the infection through biting the healthy individual
(Robert and Janovy, 2009).
Like all other mosquitoes, each Anopheles species breed in water and each have specific breeding site, feeding pattern and resting place. Ambient temperature is mandatory for completion of life cycle of both vector and parasite (Craig and Sharp, 1997). As the survival of the mosquito is rainfall and temperature dependent (Reiter, 2001, Zhou et al., 2004, Alsop, 2007), therefore any change in the range would affect the mosquito bionomics. A study conducted on temporal correlation between malaria and meteorological factors in Tibet, it was found that the relative humidity (RH) was the greatest influencing factor, which affected the mosquito survival directly (Huang et al.,
2011).
15 1.5.2 Transmission of malaria:
Principal mode of malaria transmission is the bite of Anopheles mosquito. In other modes of transmission includes the direct inoculation of asexual forms into the blood and the pre-erythrocytic development of organism does not occurs in the liver. Other way of transmission is the transfer of parasite from infected mother to the child trans- placentally or either during labor called congenital malaria. According to recent studies the congenital malaria ranges between 8%-33% from both endemic and non-endemic regions. Transfusion is another way for transmission of malaria. The administration of antimalarial drugs to transfusion recipients may help to prevent transmission. Cases of malaria transmission through needle-stick injuries, accidentally among health care professionals (some even fatal) or due to needle sharing among drug addicts, have also been reported (Bartoloni and Zammarchi, 2012)
1.5.3 Life cycle:
The life cycle of Plasmodium alternates between vertebrate and invertebrate hosts with sexual phase in the invertebrate host (mosquito).
1.5.3.1 Invertebrate phase (definitive host)
Along with blood meal Gametocytes are ingested by Female mosquito (Anopheles) and reached up to mid gut, where further development starts. In the mid gut the micro and macro gametocytes escape from the RBCs and develops into male and female gametes.
Male gametocytes multiply rapidly into many motile flagellated microgametes and each can fertilize a female gamete to form a zygote (the only diploid stage of Plasmodium life cycle). After 18 hours of blood meal the zygotes develop in Ookinete (fertilized motile cell). The ookinete penetrates the epithelial layer of gut wall and encysted in the gut wall as rounded oocyst. Within oocyst sporogony occurs in which haploid
16 sporozoites are produced through meiosis (Sinden and Hartley, 1985, Torii et al., 1992).
After maturation, the oocyst rupture and Sporozoites migrate to salivary glands where it completes remaining developmental process (Rosenberg and Rungsiwongse, 1991).
The average duration of sporogony is round about two weeks and ranging between 1-5 weeks (depends on species and temperature). Further continual of life cycle depends on inoculation of sporozoites into human body (Bannister, 2009, Morell, 1997, Ngasala et al., 2008).
1.5.3.2 Vertebrate Phase:
1.5.3.2.1 Inoculation of sporozoites
When infected mosquito bites the human it injects about 10-100 sporozoites per blood meal. Sporozoites moves to liver and invade hepatocytes (liver cells) (CDC, 2017b).
1.5.3.2.2 Tissue Phase (Hepatic stage and Pre-erythrocytic stage)
Sporozoites enter the bloodstream or lymph and circulate to parasitize the liver or spleen of mammals and endothelial cells and macrophages (birds and reptiles). There they proliferate to form hundreds of intrusive merozoites (exo-erythrocytic phase or pre-erythrocytic). These merozoites released into the bloodstream and attack the Red blood cells (Bannister, 2009).
Inside the liver cells the sporozoites converts to trophozoites which grows and divide through Schizogony and produce thousands of merozoites (about 30,000 for
Plasmodium falciparum and 10,000 for Plasmodium vivax). Duration of hepatic schizogony vary with species (about 5 day for P. falciparum, 16 days for P. malariae).
In Plasmodium vivax and P. ovale the sporozoites can remain dormant in liver cells as hypnozoites, which can reactivate at variable interval and cause disease relapse. The liver schizonts rupture on maturation and release merozoites into blood stream and each occupy red blood cells (Gilles, 1933, Tuteja, 2007).
17 1.5.3.2.3 Erythrocytic stage
The attachment of parasite to erythrocyte is mediated by specific erythrocytes surface receptor. Inside red blood cells, the merozoites converts to trophozoites and start feeding host cell contents also form food vacuoles where they digest the host cell hemoglobin. After certain period of growth, the mature trophozoites multiply several times to form more trophozoites and invade non-infected erythrocyte. This cycle of division repeats several times to increase the number of parasite greatly. The erythrocytic cycle duration varies for each species (48 hours for P. vivax, P. falciparum and P. ovale, 72 hours for P. malariae and 24 hours for P. knowlesi) (Nafo Traore,
2005).
At this phase when schizonts rupture releasing merozoites and parasite by-products which cause the clinical symptoms of malaria while the merozoites invades the uninfected RBCs and erythrocytic schizogony repeats again. Some of the merozoites develops into either female gametocyte (macrogametocyte) or male gametocyte
(microgametocyte) (Bannister, 2009, Ngasala, 2010, Tuteja, 2007) Continuation of life cycle now depends on fate of gametocytes being taken into the gut of female mosquito where both gametocyte (micro and macro) escape from their host cells. One of the characteristic feature of Plasmodium falciparum is the sequestration (adherence of infected red blood cells to endothelial cells of various organs), protects the organism from clearance system occurs in spleen (Miller et al., 1994, Treutiger et al., 1992).
18
Figure 1.3 Life cycle of Plasmodium parasite (Bannister, 2009)
The life cycle of malaria parasites. Parasites enter the vertebrate host through a mosquito bite. Sporozoites enter the skin and travel through the bloodstream to the liver, where they multiply into merozoites, which return to the blood. Merozoites infect RBCs, where they develop through several stages to produce either more merozoites, or gametocytes. Gametocytes are ingested by mosquito and become infected, continuing the life cycle.
1.5.4 Pathogenesis in vector host:
As to whether the Plasmodium parasites cause harm to their mosquito hosts is a matter of debate, with several contrasting opinions stressed. It does seem that mosquitoes that are carrying the malaria parasite do experience decreased life expectancy and higher mortality rates than their non-infected counterparts (Kettle, 1995). Plasmodium spp. cause pathogenesis to their vertebrate hosts and apparently effect the fitness of their vectors. Infected mosquitoes survive starvation comparatively better than non-infected
19 mosquitoes, associated with storage of high energy resources that is accumulated by mosquito during oocyst development of Plasmodium (Zhao et al., 2012).
Microarray analysis reported that the metabolism of infected mosquitoes is altered during rapid growth oocysts, due to suppression of several enzymes involved in carbohydrate catabolism. Additionally, enhanced expression of several insulin-like peptides was noted in infected (Plasmodium) mosquitoes. Impaired Plasmodium development due to blocking of insulin-like signaling pathway was also observed.
Shortly the Plasmodium infection disturb the metabolic pathways in mosquitoes, thus thereby conferring a survival advantage to the insects during periods of starvation.
(Zhao et al., 2012).
The viability of the mosquito vector is sometime reduced by Plasmodium infection specifically at the stage when Ookinetes occupied the mosquito mid gut and provoke an energy demanding immune response by the vector (Ferguson and Read, 2002, Maier et al., 1987, Michel and Kafatos, 2005). In general, Plasmodium is thought to reduce the overall fitness of its arthropod vector (Vernick et al., 2005).
1.5.5 Disease Pathogenesis in Human
Malaria is an acute febrile illness with incubation period of 7 days or longer
(Vanderberg, 1980). The frequency of malarial fever varies with the length of schizogony in erythrocyte. With the first attack P. falciparum, irregular pattern of fever is seen unlike repeating patterns seen with tertian fever, usually no relapses occur as there are no hypnozoites are formed in Plasmodium falciparum (Vanderberg, 1980).
The infection of RBCs by asexual forms of Plasmodium results in all manifestations of disease. All type of malaria display common signs such as fever and some patients may progress to the severe malaria. Though severe malaria is usually seen in Plasmodium
20 falciparum but severe cases and even deaths were also reported from non-falciparum malaria (Omer et al., 2003).
The destruction of RBCs results into most of the pathologic outcomes. Red blood cells are destroyed by release of merozoites and by spleen which first sequester the infected
RBCs and then lyse them. The enlargement of spleen (the characteristic feature of malaria) is caused by congestion of sinusoids with erythrocytes along with hyperplasia of lymphocytes and macrophages (Levinson, 2014). During bursting of RBCs
(containing schizonts), along with merozoites numerous substances are released in the blood such as blood cell membrane products, hemozoin pigment and toxic substances
(such as GPI) which activates the macrophages and endothelial cells to secrete cytokines and inflammatory mediators (tumor necrosis factor (TNF), interferon-γ, interleukin-1, IL-6, IL-8, macrophage colony-stimulating factor, and lymphotoxin, as well as superoxide and nitric oxide). GPI tail which is common to several merozoite surface proteins such as MSP-1, MSP-2, and MSP-4, act as a key parasite toxin
(Chakravorty et al., 2008, Clark and Cowden, 2003, Gowda, 2007, Mackintosh et al.,
2004, Nebl et al., 2005, Pichyangkul et al., 1994).
The common manifestation of malaria such as fever, headache, rigors, vomiting and nausea, anorexia, diarrhoea, fatigue, joints and muscles pain, thrombocytopenia, immunosuppression, coagulopathy, and central nervous system manifestations have been largely attributed to the various cytokines released in response to these parasite and red cell membrane products (Clark et al., 2006). Plasmodial DNA is also pro- inflammatory and can induce cytokinemia and fever (Parroche et al., 2007, Schumann,
2007). Hemozoin has also been linked to the induction of apoptosis in developing erythroid cells in the bone marrow, thereby causing anemia (Awandare et al., 2007,
Lamikanra et al., 2009).
21 Malaria infection develops via two phases including exo-erythrocytic and erythrocytic phase. During intake of blood meal, mosquitoes also introduce the sporozoites into blood stream of an individual which migrates to liver and infect hepatocytes. Inside liver cells they multiply asexually and asymptomatically for a period of 8–30 days
(Bledsoe, 2005).
After this dormant period the parasites are differentiated into thousands of merozoites, rupture the hepatocyte and enters the blood stream to infect the RBCs to start the erythrocytic phase of life cycle (Bledsoe, 2005). During migration from liver cells the parasite wrap itself into membrane of infected host liver cell and become undetected
(Vaughan et al., 2008). Within RBCs the parasite multiply asexually and periodically burst the host cell to attack uninfected red blood cells. Several multiplication cycles occur and waves of fever arise from simultaneous merozoites escaping and infecting red blood cells (Bledsoe, 2005).
Some of the sporozoites of P. vivax remain dormant instead of developing in merozoites. These dormant sporozoites produce hypnozoites that remain dormant for several months to several years and these hypnozoites can reactivate and develops into merozoites. Hypnozoites are responsible for late relapses in P. vivax infections (Richter et al., 2010, White, 2011), while their existence in P. ovale is not clear (Richter et al.,
2010).
Plasmodium parasite is somewhat protected from immune response of the body as it spends most of its human cycle inside liver and blood cells and is relatively undetectable to immune surveillance. However, circulating infected RBCs are destroyed in the spleen. To avoid the destruction of infected RBCs
P. falciparum parasite displays adhesive proteins on the surface of the infected blood cells which adhere the infected blood cells to the walls of small blood vessels, thereby
22 sequestering the parasite from passing through general circulation and spleen (Tilley et al., 2011). The blockage of the small vessels causes symptoms such as in placental malaria (Mens et al., 2012). Sequestered red blood cells can breach the blood–brain barrier and cause cerebral malaria (Renia et al., 2012).
1.5.6 Pathogenesis of severe malaria
When malarial parasite (specifically Plasmodium falciparum) invade the RBCs, it causes structural, biochemical and mechanical changes of the cell that can worsen if left untreated. P. falciparum is reported for most of the severe malaria cases and related mortalities. In recent studies, several cases of severe malaria and even deaths have been reported with Plasmodium vivax and Plasmodium knowlesi (Price et al., 2007, Tjitra et al., 2008, Kochar et al., 2005, Yale et al., 1993, Cox-Singh et al., 2010, Daneshvar et al., 2009).
In P. falciparum infection, the temperature may rise at 48 hour intervals. The ability to cause infected red blood cells (RBCs) to adhere to the linings of small blood vessels is restricted to P. falciparum, this sequestration of the parasites cause considerable blockage to blood vessels which may lead to the severity of malaria (Miller et al., 2002).
The basic reason of severe and complicated malaria is the delay in treating an uncomplicated attack of P. falciparum. The patient suffers from headache, fever and aches and pains all over the body, diarrhea and sometimes abdominal pain. Spleen and liver are often palpable on clinical examination. The misdiagnosis in non-endemic areas can worsen the clinical symptoms (Schulman et al., 1980).
A patient with severe and complicated malaria will often present with impaired consciousness, weakness, and jaundice. Other complications are cerebral malaria (non- rousable coma), generalized convulsions, normocytic anaemia, renal failure,
23 hypoglycaemia, fluid, electrolyte and acid-base disturbances, pulmonary edema, circulatory collapse, shock, disseminated intravascular coagulation, hyperpyrexia, hyperparasitaemia, and malarial haemoglobulinurea. These features may occur singly or in combinations (Cheesbrough, 1987).
Multiple pathophysiological factors such as parasite biomass, toxins, inflammatory responses, cytoadherence, sequestration, rosetting, RBC membrane rigidity and deformability, endothelial activation, and altered thrombostasis have been found to be involved in development of severe malaria. All these phenomena are commonly found in Plasmodium falciparum as compared to other malarial infections. As a result all severe complications such as hypoglycemia, cerebral malaria, metabolic acidosis, renal failure and respiratory distress with exception of severe anemia are related to P. falciparum infection (Chen et al., 2000, Miller et al., 2002).
The development of severe malaria maybe due to combination of parasite-specific factors such as adhesion and sequestration in small vessels and the release of bio-active molecules along with host inflammatory responses. The sequestration of red cells containing mature forms of the parasite (trophozoites and meronts) in the microvasculature is believed to cause the major complications of falciparum malaria, particularly cerebral malaria (Miller et al., 2002).
The sequestration of parasitized red blood cells (PRBCs) in venous beds (relatively hypoxic) allows optimal parasite growth and prevents the PRBCs destruction by spleen.
Sequestration is believed to be a specific interaction between PRBCs and the vascular endothelium. Cytoadherence (infected RBCs adhesion to the vascular endothelium) reduces the blood flow in small vessels which may led to organ and tissue dysfunction such as coma. Rosetting (The adherence of non-parasitized red blood cell (NPRBCs) to
PRBCs) and agglutination (PRBCs to PRBCs) are also involved in the pathogenesis of
24 cerebral malaria. Pro-inflammatory cytokines concentration of blood raised in cerebral malaria. Tumor necrosis factor α (TNF-α) up-regulates endothelial cytoadherence receptors and can cause hypoglycemia and dyserythropoiesis (defective development of RBCs), which are features of severe disease (Mohanty et al., 2006, Anstey et al.,
2009, Crabb and Cowman, 2002, Garg et al., 1999).
As compare to pathophysiology of falciparum malaria there is low knowledge for vivax malaria. Because P. vivax preferentially infects young RBCs, parasitemia occasionally exceed 2% of circulating RBCs and high parasite burdens are not the characteristics of severe disease. As all stages of P. vivax are visible in peripheral blood, P. vivax is not supposed to sequester or cause end-organ dysfunction. But, cytokine production during
P. vivax infections is higher than P. falciparum infections of similar parasite biomass
(Anstey et al., 2009).
Figure 1.4 Cytoadherence and rosetting in post-capillary vasculature (Chen et al.,
2000)
1.5.7 Clinical representation of malaria
Disease begins only once the asexual parasite multiplies in RBCs. This is the only gateway to disease (Miller et al., 2002).
25 Most major clinical manifestations of malaria may be attributed to two general factors:
(1) The host inflammatory response, which produces the characteristic chills and fever as well as other related phenomena, and
(2) Anemia, arising from the enormous destruction of red blood cells. Severity of the disease is related to the species producing it: Falciparum malaria is most serious while quartan and ovale are the least dangerous.
The typical presentation of malaria consists of spasms of fever alternating with periods of fatigue but otherwise relative wellness. Before the paroxysm a patient may feel sickness, muscle ache, headache, slight fever, loss of appetite and paroxysm can occur without prior symptoms. Symptoms associated with febrile attacks include headache, high fever, rigors, sweat and as well as muscle pain, backache, abdominal ache, nausea, vomiting, diarrhea, pallor, and jaundice (Robert and Janovy, 2009, Krause, 2007).
Though, classical presentation is seen in only 50%–70% of the cases with the rest having atypical symptoms. In endemic regions, unusual feature of malaria can be seen due to development of immunity, indiscriminate use of antimalarial drugs and the increasing resistance to antimalarial drugs (Singh et al., 1994).
The attack of benign tertian or quartan malaria begins with a feeling of intense cold as the hypothalamus (the body’s thermostat) is activated and there is fast rise in temperature to 104°F to 106°F. Shivering and chattering with nausea and vomiting are common symptoms. The hot stage begins within one half to one hour later, with intense headache and feeling of intense heat. Often a mild delirium stage lasts for several hours.
As copious perspiration signals the end of the hot stage, the temperature drops back to normal within two to three hours, and the entire paroxysm is over within 8 to 12 hours.
A person may sleep for a while after an episode and feel well until the next paroxysm.
Time periods for stages are usually somewhat shorter in quartan malaria, and
26 paroxysms recur every 72 hours. In vivax malaria periodicity is often quotidian early in the infection because two populations of merozoites usually mature on alternate days.
“Double” and “triple” quartan infections also are known. Only after one or more groups drop out does fever become tertian or quartan, and a patient experiences the classical good and bad days (Bruce-Chwat, 1980, Robert and Janovy, 2009).
Generalized seizures are associated specifically with falciparum malaria and might be followed by coma (cerebral malaria). Most patients with uncomplicated infections have few abnormal physical findings other than fever, mild anemia, and, after several days, a palpable spleen. The liver can become enlarged, especially in young children, whereas mild jaundice is more likely in adults. Literature on the hepatic involvement in malaria has largely shown severe infection with P. falciparum infection. There have been occasional reports of mixed infection with P. vivax (Joshi et al., 1986) and hepatitis E
(Bansal et al., 2002) along with P. falciparum, resulting in malarial hepatitis.
In young children living in regions in which transmission is stable, recurrent infections cause chronic anemia and splenomegaly. The manifestations of severe falciparum malaria, depend on age (Cordery and Urban, 2009). Severe anemia and hypoglycemia are more common in children, whereas acute pulmonary edema, acute kidney injury, and jaundice are more common in adults; coma (cerebral malaria) and acidosis occur in all age groups. Mortality rises when the proportion of infected erythrocytes
(parasitemia) exceeds 2%, although the relation between parasite density and prognosis in falciparum malaria is very variable. When treated promptly with effective antimalarial drugs, uncomplicated falciparum malaria has a mortality of roughly 0.1%.
The main causes of the anemia are destruction of both infected and non-infected erythrocytes, inability of the body to recycle the iron bound in hemozoin and an insufficient erythropoietic response of the bone marrow. Why such large numbers of
27 non-parasitized red cells are destroyed is still not understood, but some evidence has indicated complement mediated, autoimmune hemolysis. In acute malaria, the spleen removes substantial numbers of non-parasitized red cells from the blood, an effect that may persist beyond the time of parasite clearance (Chang and Stevenson, 2004). Both the splenic removal of red cells and the defective bone marrow response may be due in part to TNF toxicity (Clark, 1987, Mendis and Carter, 1995).
Destruction of RBCs leads to an increase in blood bilirubin (breakdown product of hemoglobin). Excessive accumulation of bilirubin cause jaundice (yellows the skin).
Circulating leukocytes taken up the hemozoin and deposited in the reticulo-endothelial system. In severe cases the viscera (especially the liver, spleen, and brain) become blackish or slaty as the result of pigment deposition. Ingestion of hemozoin impair the phagocytic ability of macrophages (Turrini et al., 1993).
Hypoglycemia (reduced concentration of blood glucose) is a common symptom in falciparum malaria. It is usually found in women with uncomplicated or severe malaria who are pregnant or have recently delivered as well as in other cases of severe falciparum malaria(WHO, 1999, Clark, 1987). Coma produced by hypoglycemia has commonly been misdiagnosed as cerebral malaria. This condition is usually associated with quinine treatment. Pancreatic islet cells are stimulated by quinine to increase insulin secretion, thus lowering blood glucose and this effect may also be due to excessive TNF (Clark, 1987).
28
Figure 1.5 Induction of fever by malaria parasites
1.6 Recurrent or persistent malaria
The blood stage of Plasmodium infection can persist for a long period (months or years or even decades in P. malariae infection). In tropical areas, the Plasmodium vivax relapse occurs every 3-4 weeks or every 6-8 weeks in case of treatment with slowly eliminated drugs that suppress the 1st relapse). Blood-stage infection can persist for months or years (or decades in P malariae infections) when untreated. Waves of asexual parasitaemia and gametocytaemia result from antigenic variation. In tropical regions, P vivax relapses typically every 3–4 weeks (or every 6–8 weeks after treatment with slowly eliminated drugs, which suppress the first relapse). In temperate areas, P vivax can remain latent for 8–10 months between primary infection and first relapse
(Simpson et al., 2002). Recurrent falciparum and vivax malaria have pronounced
29 adverse effects in young children, and interfere with growth, development, and schooling.
1.7 Immunity against parasite
Partial immunity based on humoral antibodies that block merozoites from invading the red cells occurs in infected individuals. A low level of parasitemia and low grade symptoms results; this condition is known as premunition. In contrast, a non-immune person, such as a first-time traveler to an area where falciparum malaria is endemic, is at risk severe, life-threatening disease (Levinson, 2014).
Cell-mediated and humoral immunity play active roles in immune-competent individuals living in malaria endemic areas. More immunoglobins especially IgG is raised when the immune cells are activated by specific antigens, for instance, circumsporozoite proteins found on sporozoites. Vaccines are currently being developed against three stages of the parasite: gametocytes, sporozoites, and intra- erythrocytic merozoites (Vanderberg, 1980, Rogerson et al., 2007). The most important part of host defense seems to be antibody production; hence non-immune individuals visiting endemic areas are very vulnerable.
1.8 Treatment and Prevention of Malaria
1.8.1 Treatment
Extracts of bark from Peruvian trees were used with varying success to treat malaria, but alkaloids from the bark of certain species of Cinchona proved dependable and effective (Honigsbaum, 2001, Robert and Janovy, 2009). The most widely used of these alkaloids was quinine, discovered in the 16th century. The alkaloid of D. febrifuga, febrifugine, is now considered too toxic for human use, but the terpene from
A. annua, called qinghaosu (artemisinin), which has recently been “rediscovered,” and its derivatives are valuable drugs (Robert and Janovy, 2009) . P. vivax infection is
30 commonly treated with chloroquine, but parasites are often treated with sulphadoxine- pyrimethamine, the commonly used drug for chemoprophylaxis and treating uncomplicated falciparum malaria (WHO, 2017).
The most important drug was chloroquine but due to emergence of chloroquine resistance new drugs were developed including primaquine, mefloquine, pyrimethamine, proguanil, sulfonamides such as sulfadoxine, and antibiotics such as tetracycline. Among all these only Primaquine is effective against all stages of all species as the other drugs vary in efficacy according to species and stages of parasites.
Primaquine and chloroquine drugs of choice for Plasmodium vivax and Plasmodium ovale malarias and chloroquine alone for Plasmodium malariae infections. Chloroquine is still advised for P. falciparum strains which are sensitive to this drug (Geary and
Jensen, 1986). P. falciparum chloroquine resistant strains has now spread through
Africa, Asia, and South America (D'Alessandro and Buttiens, 2001) and resistance to other drugs also persist. Sulfadoxine and pyrimethamine (Fansidar) combination was used for chloroquine-resistant falciparum malaria, but Fansidar-resistant P. falciparum is now present in many regions. Mefloquine (Lariam) is still effective for multidrug- resistant P. falciparum, but resistance to mefloquine is developed in several endemic areas (Mockenhaupt, 1995). Artemisinin and its derivatives are effective for drug- resistant P. falciparum, both in severe and uncomplicated malaria (Sowunmi and
Oduola, 1996). The artemisinin derivative is commonly given in combination with other drugs (Artemisin-based combination therapies, ACTs) (D'Alessandro and
Buttiens, 2001). Dihydroartemisin with Piperaquine (Artekin) is an ACT with the advantage that it is also low cost (Mutabingwa, 2005).
31 1.8.2 Prevention
There are two ways to prevent malaria. One is vector control and other is use of antimalarial drugs. The main approach for preventing and decreasing malaria transmission is the vector control. WHO recommended the protection against malaria with effective vector control measures for all people who are at risk. Insecticide treated nets and indoor residual spraying are the most effective way for vector control. The long-Lasting insecticidal nets are recommended by WHO in most of the settings (WHO,
2017). For effective vector control it is necessary to spread awareness among individuals, use of efficient insecticide usage and destruction or elimination of vector breeding sites.
The use of prophylactic drugs has been generally effective for travelers and people living in endemic areas. Several aspects should be kept in mind while prescribing chemoprophylactic drugs, such as parasite species, duration of travel, parasite transmission intensity in the specific area and existing antimalarial drug resistant strains in the area. It is very hard to recommend prophylactic drugs for South East Asia due to the high degree of resistance there (Bradley, 2001). The use of antimalarial drugs can also be useful to prevent malaria. Before travelling the chemoprophylaxis is helpful in suppressing the blood stages of Plasmodium. In moderate to high transmission areas,
WHO recommends preventive treatment for pregnant females with sulfadoxine- pyrimethamine at each scheduled antenatal visit after the 1st trimester. Similarly, in high transmission areas three doses of intermittent treatment with sulfadoxine- pyrimethamine are recommended for infants, delivered alongside routine vaccinations
(WHO, 2017).
32 In areas of low endemicity especially in non-African regions the human-mosquito contact can be reduced by appropriate construction and siting of housing and local environmental improvement. Inexpensive, high-technology approaches will apply computerized information management and Geographic Information Systems to identify locations where targeted attack on malaria transmission is called for.
Monitoring, including by satellite, of all aspects and features of a malarious situation will be important to the timing and targeting of antimalarial interventions (Carter et al.,
2000, Thomson and Connor, 2001).
The management of malaria also aim to reduce the number of individuals at risk of malarial infection and to reduce human mosquito contact by all possible means. These means will certainly include, especially in Africa, provision and use of insecticide- treated materials such as bed nets and curtains (Guillet et al., 2001, N'Guessan et al.,
2001, Lengeler, 2000) for those at highest risk mainly infants, young children and pregnant women.
1.9 Malaria vaccines
RTS,S/AS01 (RTS,S) known as Mosquirix is an injectable vaccine that provide partial immunity against malaria in childrens. This vaccine is evaluated in sub Saharan Africa as a complementary malaria control tool. In 2015, a positive opinion was given in favor of this vaccine by European medicine Agency (Medicine regulatory Authority). In the same year WHO recommended this vaccine to be implemented in a limited number of
African countries. In 2016, WHO announced pilot project to be implemented in three countries of sub-Saharan Africa for which funding have been secured. This project would be further implemented on broad scale on the basis of success of pilot projects.
(WHO, 2017).
33 1.10 Antimalarial drug resistance
In Pakistan, chloroquine resistance was first reported in the mid-1980s, which spread throughout the country (Shah et al., 1997, Rowland et al., 1997a). During the last decades, Chloroquine resistance is the key reason for high prevalence of P. falciparum malaria in Pakistan (Shah et al., 1997), Afghanistan (Rab et al., 2001), and neighboring
Tajikistan (Pitt et al., 1998), whereas conflict and population disturbance are the factors responsible for the overall upsurge in malaria in the region (Kazmi and Pandit, 2001).
In previous studies, it was found that about 90% of the P. falciparum cases among
Afghan refugees in western Pakistan were sensitive to sulfadoxine-pyrimethamine
(Rowland et al., 1997b) & P. vivax malaria is still sensitive to chloroquine in this population (Rowland et al., 1999).
P. falciparum has acquired resistance to earlier generations of antimalarial drugs
(chloroquine and sulfadoxine-pyrimethamine) thus has led to increased child mortality and destability of the malaria control programs. WHO encourages the routine monitoring of antimalarial drug resistance and recommends countries to enhance their efforts in this regard. An artemisinin-based combination therapy (ACT) contains both the drug artemisinin and a partner drug. Recently the Plasmodium resistance to artemisinin has been detected in five countries of the Greater Mekong sub-region:
Cambodia, Myanmar, Vietnam, Thailand and Lao People’s Democratic Republic.
Studies have confirmed that artemisinin resistance has emerged independently in many areas of this sub-region. In 2013, Emergency response to artemisinin resistance (ERAR) was launched by WHO in the greater Mekong region but when this project was underway, another independently emerged spots were reported in the same sub-region.
Similarly, increased resistance to ACT partner drug was also reported in the same
34 locales. Thus a new approach is required to cope with changing malaria scenario
(WHO, 2017).
1.11 Worldwide malaria epidemiology
Malaria is a major public health problem, and is endemic in approximately 130 countries and territories including those countries that have not reported malaria recently, but they reported cases in the period 1990-2003 (WHO, 2009). The global burden of malaria is only imprecisely known, because only a minority of cases are recorded in health facilities, yet vital registration of death causes is incomplete and unreliable in countries with highest malaria burden and available community-based studies often over sample areas of relatively intense malaria transmission, usually conducted during peak transmission season (Snow et al., 2005, Korenromp, 2005).
Malaria is the leading cause of mortality in children (< years age) and pregnant women in developing countries (Martens and Hall, 2000, Lagerberg, 2008). In 2010 there were an estimated 216 million cases of malaria worldwide, of which 91% were due to P. falciparum. The large number of cases (81%) were in the African Region followed by
South-East Asia (13%) and Eastern Mediterranean Regions (5%) (WHO, 2011a). The disease remains one of the most important cause of human morbidity and mortality with enormous medical, economic and emotional impact in the world.
Globally the number of malaria cases are increasing due to increasing transmission risk in areas where malaria control has declined, increasing frequency of drug resistant strains of parasite and in a comparatively few cases, considerable increases in international travel and migration (Pasvol, 2005). In 2013, there are 97 countries and territories with ongoing malaria transmission and 7 countries in the prevention of reintroduction phase, making a total of 104 countries and territories in which malaria is presently considered endemic. Globally, an estimated 3.4 billion people are at risk of
35 malaria. WHO estimates that 207 million cases of malaria occurred globally in 2012
(uncertainty range 135–287 million) and 627,000 deaths (uncertainty range 473 000–
789 000). Most of the malaria cases (80%) and deaths (90%) occurred in Africa and most deaths (77%) were in children under 5 years of age (WHO, 2014a).
According to recent reports by World Health Organization (WHO) there are about
438,000 people died because of malaria in 2015 while the Institute of Health Metrics and Evaluation (IHME) and Global Burden of Disease (GBD) puts this estimate at
720,000. The major victims are children and about 72% mortality was reported in children under 5 years of age (WHO, 2017).
Figure 1.6 Estimated country share of (a) total malaria cases and (b) P. vivax malaria cases, 2016 (WHO, 2017)
36 About 216 million malaria cases were reported to occur worldwide in comparison with
211 million cases in 2015 and 273 million cases in 2010. These figures shown the reduction in global malaria prevalence. The WHO African region (90%) is reported with most of the cases which is followed by South-East Asia region (7%) and Eastern
Mediterranean region (2%). Malaria incidence rate was estimated to have decreased by
18% globally, show reduction from 76 to 63 cases per 1000 population (at risk) from
2010 to 2016. Largest decline was documented from WHO South-East Asia region
(48%) followed by Americas (22%) and African region (20%) (WHO, 2017).
Plasmodium falciparum was found most prevalent in sub-Saharan Africa, responsible for 99% of estimated malaria cases in 2016. P. vivax is predominant species outside
Africa and responsible for 64% cases in WHO Americas region, above 30% in the
WHO South-east Asia and 40% in the Eastern Mediterranean regions (WHO, 2017). In
2016, about 445,000 deaths were reported globally while 446,000 estimated cases in
2015. Reduction in mortality was recorded in 2016 when compared to 2010, with exception of WHO Eastern Mediterranean where mortality is remain unchanged in that period. While largest reduction was documented in WHO regions of South-East regions
(44%), Africa (37%) and the America (27%) (WHO, 2017). Many countries are moving towards malaria elimination: 44 countries reported fewer than 10, 000 malaria cases
(2016) and 37 countries in 2010. WHO certified Kyrgyzstan and Sri Lanka as malaria free in 2016 while 21 countries were identified with elimination potential by the year
2020. WHO is working in collaboration with government of these countries which were termed as “E-2020 countries” to support their elimination efforts (WHO, 2017).
37
Figure 1.7 World map of past and current malaria distribution (WHO, 2017)
In 1955 the World Health organization started the efforts to eradicate endemic malaria which included the spraying of houses with residual insecticides, anti-malarial drug treatment, and continual surveillance (CDC, 2017b). Successful elimination was done in countries (including Europe and Australia) with temperate climate and seasonal transmission of malaria. Many other countries (India and Sri Lanka) initially showed marked reduction in cases, however these and other countries including all of sub-
Saharan Africa, had little long term success. Many factors were involved in failure of long term elimination including lack of funds, wars and population disturbance due to wars, emergence of drug resistant strains and arrival of insecticide resistant vector
(WHO, 2009).
38 1.12 Malaria epidemiology in Pakistan
Pakistan shares the highest burden of major vector-borne diseases as malaria, leishmaniasis, dengue, Chikungunya and Crimean-Congo haemorrhagic fever. One of the leading cause of mortality and morbidity in Pakistan is malaria. Malaria in Pakistan remains the fourth largest cause of death among communicable diseases. WHO (2006) places Pakistan in category 3 countries (with moderate endemicity) in the eastern
Mediterranean Region. During 2006, a total of 127,825 microscopically confirmed cases were reported from public sector malaria microscopy centers all over the country.
The overall API (annual parasite index) of Sindh, Baluchistan, and FATA and KPK provinces in 2006 was 4.0 per thousand population. However, the actual API may be 5 times higher than the reported figures, as the public sector FLCFs report only 20% of malaria cases. HMIS report 2006 shows malaria as the 2nd commonest illness after ARI.
HMIS reported 4.3 million clinical and confirmed cases treated at 10,000 public sector health outlets. The slide positivity rate (total number of positive slides per 100 slides examined) for malarial parasite was highest in Baluchistan than other provinces. More than 90% of disease burden in the country is shared by the 56 highly endemic districts, mostly located along the international borders with Iran and Afghanistan. More than 40
% of the reported cases from these districts are due to P. falciparum malaria (DMOC,
2012).
Pakistan has a population of 180 million inhabitants of which 177 million are at risk of malaria with 3.5 million presumed and confirmed malaria cases annually. Among the reported cases 37% of malaria are from the districts and agencies of Federally
Administered Tribal Areas (FATA) and Baluchistan bordering Afghanistan and Iran.
(DMC, 2017).
39 Plasmodium vivax and Plasmodium falciparum are the only prevalent species of parasites detected so far, with P. vivax being the major parasite species responsible for
>80% reported confirmed cases in the country (DMC, 2017, WHO, 2011a, Ghanchi et al., 2010). Malaria transmission pattern is unstable and seasonal, with peaks in summer
(June-September) for vivax malaria and late-summer and winter (August- November) for falciparum malaria (DMC, 2017, Gill, 1938, Rowland et al., 1997a) (Macdonald,
1956, Bouma et al., 1996a). Most of the spring cases remain dormant in the liver and appear after the monsoon season especially P. vivax infection (MOH, 2010, Asif, 2008,
Khan et al., 2006). At the northern part of Pakistan, P. falciparum showed unstable pattern and prominent fluctuations from year to year depending on climatic variations.
High rainfall in autumn or raise of temperature than average range in November &
December are the significant factors that contributes to extend the transmission period in Pakistan (Bouma et al., 1996a).
The major epidemics of malaria in Pakistan was recorded before Pakistani independence in 1908, which was the most devastating outbreak with over 300,000 deaths were documented in an estimated population of 20 million while last epidemic was reported in the mid-1970s (de Zulueta et al., 1980).
The estimated burden of malaria in Pakistan is 1.6 million cases per year, grouped into
Group-3 countries of Eastern Mediterranean (countries with highest malaria burden) along with Afghanistan, Sudan and Yemen. 98% of the malaria cases were reported to be contributed by these four countries in this region (WHO, 2017). Huge population movements within the Pakistan and across international borders (Iran and Afghanistan), natural catastrophes and civil unrest, unpredictable transmission pattern due to climate changes, poor socio economic status, declining health infrastructure due to internally displaced population, resource limitations, limited access to health facilities (preventive
40 and curative services) and lack of monitoring drug resistance in parasites and insecticide resistance in vectors has added in making malaria a serious public health problem. Malaria is usually unstable in Pakistan and all age groups are prone to acquire infection. However, children (5-year age) and pregnant women are more susceptible to malaria (Directorate of malaria control, 2014). In Pakistan, among 24 species and sub species of Anopheles, only two are the primary vectors involved in malaria transmission
(Castro et al., 2010, Alemu et al., 2012). Earlier studies reported the A. culicifacies as the important vector in rural settings (mehmood & MacDonald, 1985; Pervez & Shah,
1989) while A. stephensi in the urban areas (Rehman and Muttalib, 1967). Both primary vectors are endophilic in diurnal resting habit, breed in clean water and chiefly zoophilic.
Malaria control program is implemented in 72 malaria endemic districts by Government of Pakistan while in 19 highly endemic districts were supported from Global Fund. The highly endemic districts are the main target for malaria control program and target population in these 38 districts is 28.84 million individuals. The aim of malaria control program is to reduce the disease burden in these high risk districts by elevation appropriate intervention including improving case management, treating all confirmed
Plasmodium infected cases according to national guidelines and vector control through
IRS and LLINs (Directorate of malaria control, 2014, DMC, 2017).
41
Figure 1.8 Distribution of Plasmodium vivax in Pakistan (WHO, 2017)
Figure 1.9 Distribution of Plasmodium falciparum in Pakistan (WHO, 2017)
In Pakistan, Afghan refugees are at high risk of malaria infection rather than that they brought a high infection load with them from Afghanistan. Therefore, malaria should be controlled on preventive base in Afghan refugees’ camps in Pakistan (Suleman,
1988a).
42 Factors associated with the upsurge include of chloroquine resistance across the country
(Shah et al., 1997), warmer autumns favoring prolonged transmission (Bouma et al.,
1996b, Shah et al., 1997) and a chronic decline in vector control activities.
Anopheles culicifacies, the purported primary vector in the Punjab Province (Reisen and Boreham, 1982) was found disappeared by September whereas A. stephensi was found more abundant and more common in KPK Province than A. culicifacies. In
Pakistan, the primary vector species are A. culicifacies and A. stephensi and in Quetta
Balochistan also (Yasinzai and Kakarsulemankhel, 2003, Yasinzai and
Kakarsulemankhel, 2004).
In 2016, 3.1 million cases were diagnosed in public sector facilities. The malaria indictor survey (MIS) was conducted in 2013 in thirty-eight endemic districts of
Pakistan. Federally Administered Tribal Areas (FATA) presented highest prevalence rates (13.9%) (DMC, 2017). While in 2015 high Annual parasite incidence (8.60/1000
Population) in FATA was reported which is followed by (5.52/1000 Population) in KP,
(4.47/1000 Population) in Baluchistan and (2.11/1000 Population) in Sindh (DMC,
2017).
Based on the reported API, FATA followed by Baluchistan and Khyber Pakhtunkhwa are the highest endemic provinces/regions. According to the latest “Stratification” 66 districts and agencies have been categorized in “high endemicity stratum” where API is >5/1000 population (WHO, 2017).
In the last decade, there has been a six-fold increase in Falciparum malaria which comprised 42% of all malaria cases recorded by National Malaria Control Program
(Shah et al., 1997) Many factors are involved in increase of malaria cases such as warm autumns favoring prolonged transmission (Bouma et al., 1996a) emergence of chloroquine resistance across the country (Shah et al., 1997) and a chronic dropping of
43 vector control activities. Migrated population can play a vital role in malaria transmission in various ways (Rowland et al., 2002). An increasing population in malarious regions, compounded by weak public health systems in developing countries, climate changes, new agriculture practices such as irrigation and dam construction
(Sachs and Malaney, 2002), increased resistance to antimalarial treatments and insecticides (Bozdech et al., 2003) and the complexity and flexibility of the genetics
(Gardner et al., 2002) have all contributed to increase in malaria (Eze and Mazeli,
2001).
In KP malaria (particularly P. falciparum malaria) is unstable and fluctuates from year to year due to climatic variations and malaria outbreaks as reported in past (Bouma et al., 1996a) P. falciparum transmission season starts in the summer monsoon (July) when the temperature and humidity is suitable and remains until the end of the year when the temperature falls below the critical value (December). P. vivax has usually two peaks of transmission in the year, one is its early transmission period during the wet months of spring due to its lower temperature requirement (probably facilitated by true relapses) and the other is with the P. falciparum (monsoon) (Bouma et al., 1996a).
Malaria remains a serious health problem in all districts of KP but two geographical clusters including one in the North East (Malakand, Buner, Shangla, and Dir) and the other in the South West (Kohat, Lakki Marwat, Hangu, Bannu, Tank, Dera Ismail
Khan) were recorded as high risk zone of malaria (Asif, 2008). In district Bannu the annual parasite incidence (API) is 1.6 - 3.5 per 1000 population, which is substantially above the national average (0.8 per 1,000 population). The district has great economic significance, being the central market of the Southern Region in addition to serving as a safe shortcut to markets in Central Asia (Bouma et al., 1996a, MOH, 2010). The falciparum malaria having fatal complications is reported as serious illness in southern
44 and northern districts of KP and delay in treating falciparum malaria may leads to serious consequences (Raziq and Khan, 1995).
1.13 Diagnosis
Accurate and early detection of Plasmodium infection is necessary for effective management and malaria surveillance. To avoid morbidity and mortality from malaria, the availability of standard malaria diagnostics is essential in all settings.
1.13.1 MICROSCOPY
Microscopic detection of malaria parasite from blood smear is used as a gold standard for any new malaria diagnostic tool as it can detect and differentiate each species of
Plasmodium (Alam et al., 2011, Batwala et al., 2011, Ouattara et al., 2011, Tangpukdee et al., 2009, Wongsrichanalai et al., 2007). However, microscopy have several drawbacks such as requiring a visual or light microscope with 1000× magnification and depends on expert and well-trained microscopists. Microscopic detection of malaria parasite is morphology based method so there are chances of incorrect identification of morphologically close species of Plasmodium such as P. knowlesi and P. malariae, even by an expert microscopists. To avoid human errors, an image processing technique is recently introduced for microscopy. Still the problem of detecting low parasitemia by microscopy is not resolved (Das et al., 2015, Tek et al., 2009). This problem is due to the average ability of parasite detection of microscope which is around 10 parasite/
µL for a research setting and in the range of 50–100 parasites/µL for outside a research setting or less sensitive in inadequate resource setting (Payne, 1988).
Fluctuation in parasite density during infection limits the detection power of microscope and all other direct diagnostic approaches (Hawkins et al., 2014). In remote
45 rural settings, the lack of microscopy might be resolved in this near future due to the development of an origami-based paper microscope (Cybulski et al., 2014). This device was deliberately developed for malaria control in a poor country.
For microscopy Patient’s blood drop collected by finger pricking is spread on glass slide to make a smear. Smear is dipped in a dilute Giemsa stain solution (for staining the malarial parasite) and examined under microscope at 1000x magnification.
Morphology of altered RBCs (altered by parasite) and physical features of malarial parasite are used for diagnosis of malarial infection. It is a simple and established technique that is familiar for most laboratorians. Correct and confirmed laboratory diagnosis of malaria needs complete knowledge about morphological features of different blood stages of all plasmodia species. For correct diagnosis, careful observation of different morphological features that draw a well-defined image of the species and at least 100 microscopic fields observation should be done before classifying the slide as negative. A single image of parasite does not reflect the real cause of infection as the possibility of mixed infection can’t be rule out for example a crescent-shaped gametocyte in peripheral blood does not mean that a P. falciparum infection is the cause of the clinical complaint as the possibility of mixed infection always exist (PARN, 2016).
Microscopy is the most widely tool used to diagnose malaria at peripheral levels. In capable hands, it is very sensitive for parasitemia ≤50/µL (0.001%) (Nandwani et al.,
2005) and it can give information of species, parasites stages and parasite density.
However, implementation of good quality of microscopy is difficult. As it is labor intensive, requires highly skilled personnel and regular quality control. Despite of all
46 these short comings it is still a gold standard for malaria diagnosis (Nandwani et al.,
2005).
Currently, microscopic examination of thick and thin blood smear is the conventional method for detecting acute malaria. Although it is cheap, convenient, rapid, inexpensive and relatively accurate diagnostic method but requires highly skilled technician, labor intensive and not sensitive enough to detect low parasitemia level. Therefore, this technique is inappropriate for large scale epidemiological studies. Sensitivity limit of the thick smear examination is estimated at ~5 parasites/ µL (100 microscope field’s examination) (Barker et al., 1992). Parasite densities of 4-40 parasites/µL (1/200 to
1/2,000 parasites per leukocytes) is rarely detected (Gilles, 1933) and in busy, routine examination, the sensitivity level is 10 time lesser and ratio of false negative detection in oligo-parasitemic patients and false positive detections due to artifacts can’t be neglected (Grant et al., 1960). Keeping in mind these limitations, there is need to develop alternative malaria diagnostic techniques. Alternative method to microscopy is immunologic test which is also less sensitive and cannot differentiate past and present infection. Concentration of malaria parasite–infected red blood cells by centrifugation coupled with Acridine orange staining and fluorescence microscopy (quantitative buffy coat (QBC) System, Becton Dickinson) is easier to use and more sensitive (Rickman et al., 1989), but its cost and need for special equipment (Centrifuge, Fluorescene microscope and capillary tubes) is its main limitations (Avila and Ferreira, 1996). DNA hybridization methods show a modest improvement in sensitivity compared with blood films (Waters and McCutchan, 1989). To minimize the risk of malaria transmission more sensitive technique is required to replace microscopy.
47 1.13.2 Rapid diagnostic Technique (RDT)
Early diagnosis and prompt treatment is one of the key strategies in controlling malaria.
For areas where laboratory facilities are not available, clinical diagnosis is widely used
(WHO, 1999). Due to difficulties in maintaining standard microscopy in peripheral health facilities, the introduction of rapid diagnostic tests (RDT) for malaria is a major step for case detection, management and reduction of unnecessary treatment. RDT could be used in malaria diagnosis during population-based surveys and to provide immediate treatment based on the results (WHO, 2009). These tests use a drop of blood from finger, take only 10 to 15 minutes to complete and do not require a laboratory.
Non-clinical staff can easily learn to perform the test and interpret the results
(Wongsrichanalai et al., 2007).
The test is based on the detecion of parasite antigen from the pheripheral blood using monoclonal antibodies (against malaria antigen target) and conjugated to either a gold particles or liposome contaning selenium dye in a mobile phase. Or reversed where istead of monoclonal antibody to capture the antigen, antigen is incorporated into the cellulose to capture the antibody in the serum or plasma (Azikiwe et al., 2012).
RDTs commonly available in two forms. One is antigen based and normally requires the use of haemolyzed red blood cells while the other is antibody based and normally requires the use of extracted serum. Antibodies are better expressed in serum otherwise plasma could also stand in place of serum for antibody based method (Moody, 2002).
Rapid diagnostic technique is expected to play significant role in malaria control. It immunologically detects different malaria antigens such as Histidine-rich protein-2
(HRP-2), lactate dehydrogenase (LDH) and Aldolase and in a few microliter of blood
(typically 5–15 μL) (Piper et al., 1996, Oner et al., 2004). The RDT strip has been extensively tested for its impacts on malaria diagnosis outside research settings for the
48 past few years (Sudhinaraset et al., 2015, Hansen et al., 2015, Boyce et al., 2015), where use of RDTs increased the proportion of patients with a parasite-based diagnosis of malaria compared to microscopy alone, leading to a higher accuracy, timely clinical case management and better cost effectiveness (Boyce et al., 2015, Hansen et al., 2015).
Some RDTs can detect a single species (either P. falciparum or P. vivax) while others can detect multiple species (P. falciparum, P. vivax, P. malariae and P. ovale) (Foster et al., 2014). Although this technique is timely and easy to use, it is relatively expensive and subjected to false-positive responses due to the persistence of malaria antigens in the blood for up to 2 weeks after the parasite clearance from the patient’s circulation.
In addition, limits of detection (LOD) of these RDTs rely on an amount of antigen
(equivalent to 200 infected RBCs/µL or 2000–5000 parasites/µL of blood) (WHO,
2014a).
HRP-2 based tests may be misleading in areas of high transmission because they remain positive for several days or weeks after an infection, even if treated, thus a positive result with a history of a recently treated infection is difficult to interpret. Another limitation of HRP-2 based tests is their geographically variable sensitivity, attributed to polymorphisms in HRP-2 (Baker et al., 2005). Tests based on detection of pLDH or aldolase allow parasite speciation, do not appear to show geographical variability in their ability to detect malaria and revert to negative more quickly than HRP2 based tests, although production of pLDH from gametocytes after elimination of asexual stages means some will stay positive for several days (Tjitra et al., 2001, Pattanasin et al., 2003, van den Broek et al., 2006). However, to date the sensitivity of these tests under field conditions has been reported frequently as falling below 90% (Pattanasin et al., 2003). There are concerns about the stability of all types of tests if transportation
49 and storage conditions are not controlled, but pLDH tests appear to be particularly vulnerable (Jorgensen et al., 2006).
Figure 1.10 Mechanim of action of Antigen-detecing malaria RDT (WHO, 2011a)
1.13.3 Polymerase chain reaction (PCR)
Molecular techniques such as Polymerase Chain Reaction (PCR) and Nucleic Acid
Sequence-Based Amplification (NASBA) have been proposed since 1990 for malaria diagnosis by using various kinds of primers, DNA extraction and detection methods.
PCR is said to detect less than 5 parasites per µl of blood (Snounou et al., 1993b). PCR is more sensitive and specific than all other techniques. However, it is a lengthy procedure that requires specialized and costly equipment and reagents as well as laboratory conditions that are often not available in the field (Snounou et al., 1993a). It is the most sensitive and specific technique yet measured as lengthy procedure with
50 advanced and expensive equipment not favorable for clinical use. Laboratory conditions are also not applicable in the field (Hanscheid and Grobusch, 2002).
PCR is a scientific method for amplification of a single or multiple genes to generate thousand to million copies of a DNA sequences. It is common and essential technique used. There are three major steps involved in the PCR technique: denaturation, annealing, and extension (Joshi and Deshpande, 2010).
The low level parasitaemia (in case of asymptomatic individual) is reported with significant contribution in malaria transmission (Shekalaghe et al., 2009). Due to sub- microscopic infection, the estimated cases of malaria infection detected through microscope was found lower than actual number of cases existing in the population. To reduce the parasite transmission, it is essential to quantify sub-microscopic cases in a population (Okell et al., 2009). Many studies documented the detection of low density infection through PCR due to its higher sensitivity for low level infection as compare to microscopy (Okell et al., 2009, Snounou et al., 1993b, Musalika, 2010a).
Due to high sensitivity and species level identification, PCR is used to compare the sensitivity and specificity of other Plasmodium detection techniques (Mikhail et al.,
2011). Presently PCR is not used as routine diagnostic approach due to its time consumption and technical expertise requirements. The major drawback of PCR based detection is the expensiveness as compared to microscopy, RDT and serology (Wahid,
2013).
Most of the PCR assays used for diagnostic purpose targets the genes 18S subunit rRNA gene or single stranded rRNA. (Gunderson et al., 1987, Kawamoto et al., 1996, Singh et al., 1999a, Snounou et al., 1993a). Amplification of these genes is useful for detecting
Plasmodium species in case of low parasitaemia or clinical samples (filter paper or from slide) which have low parasite DNA content. The basic disadvantage of PCR is the
51 contamination and specificity as it need very specific conditions and slight variation can lead to dramatic reduction in PCR products (Coleman et al., 2006b).
Real time PCR is new advancement in molecular techniques serving as most sensitive and time efficient assay (Lee et al., 2002). It can detect 0.01 to 0.02% level of parasitemia, differentiate species and recognize mixed infection (Mangold et al., 2005).
It is economical in term of time as single probe is used with results based on melting curve analysis instead of gel based analysis (Boonma et al., 2007). However, it is also very expensive method and needs skill and experience, (Brown et al., 1979, Mangold et al., 2005). Most of the real-time PCR assays target the SU 18s RNA (Andrews et al.,
2005, Elsayed et al., 2006, Hermsen et al., 2001, Vo et al., 2007). The main drawback of real time PCR is use of expensive consumables. However, duplex real-time PCR is cost effective up to as it can detect P. falciparum and P. vivax in the same tube (Veron et al., 2009).
The conventional nested PCRs are highly sensitive and specific. Commonly used diagnostic method developed by Snounou and colleagues uses the SU 18s rRNA nested primers that differentiate between four Plasmodium species i.e. Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae and Plasmodium ovale (Snounou et al., 1993a). However, nested PCRs have long running time and there is high risk of contamination while Real-time PCR have the advantage of low contamination chances due to absence of post-PCR handling. Loop-mediated isothermal amplification
(LAMP) is a simple, rapid and sensitive method with high specificity. LAMP uses Bst
DNA polymerase and a set of four primers that bind to six distinct regions of the target gene that produce a specific double hairpin DNA template. The target gene is amplified isothermally for about one hour. The amplified products can be achieved, either by visually comparing the turbidity of magnesium pyrophosphate (a byproduct of DNA
52 synthesis) or by real-time detection through real-time turbidimeter (Notomi et al., 2000,
Nagamine et al., 2001, Mori et al., 2001).
LAMP is found more suitable in filed settings for malaria diagnosis as the conventional microscopy suffer from low sensitivity in case of low parasitaemia and inexperienced personnel (Sirichaisinthop et al., 2011). The specificity and sensitivity of this method is shown to be comparable with that of nested PCR (Han et al., 2007). However, previous studies report the false positive and false negative results by this method.
LAMP is prone to errors for visual of interpretation of turbidity to test positivity are subject to individual variation in interpretation (Paris et al., 2007).
PCR-based malaria diagnosis is the most efficient technique which have high specificity and sensitivity in species level detection. PCR method can be categorized into nested PCR, semi-nested PCR, single step multiplex PCR and real-time or quantitative PCR assays (Ongagna-Yhombi et al., 2013, Lee et al., 2015). Among them, the simplest and least technically demanding is a loop-mediated isothermal amplification (LAMP) assay (Cook et al., 2015a). Usually, PCR can detect low parasitemia (5 parasites/μL) for all human malaria parasites (Moody and Chiodini,
2002, Cook et al., 2015b). Tests based on polymerase chain reaction for species-specific
Plasmodium genome are more sensitive and specific than are other tests, detecting as few as 10 parasites/μl blood (Hanscheid and Grobusch, 2002). It is recently reported that saliva, fecal and urine samples of Plasmodium vivax and Plasmodium falciparum infected patients contain Plasmodial DNA that can be amplified through PCR
(Putaporntip et al., 2011, Jirku et al., 2012).
PCR efficiency depends on the complimentary nucleotide sequences between the primer (known sequence) and its counterpart target DNA (unknown sequence), thus parasites with genetically modified sequences at primer’s target region are subjected to
53 detection failure or to a lower amplification efficiency that ultimately reduce the sensitivity of PCR. Additionally, the detection power of PCR can be effected by copy number of target gene or nucleic acid sequence in the Plasmodium genome. Small subunit 18S ribosomal RNA gene (18S rDNA) is the most commonly used target in
PCR (Kawamoto et al., 1996). This gene is highly conserved across Plasmodium species but to limited number of parasite in the infected cells the amplification of mitochondrial DNA is preferred for parasite detection. As mitochondrial DNA is abundant in parasite (30-100 copies per parasite) than nuclear
DNA.
Another favorable target is the mitochondrial DNA due to its more abundance (30-100 copies per parasite) than nuclear DNA. Cytochrome b gene in mitochondrial genome is utilized in many studies and not only in human Plasmodium (Steenkeste et al., 2009), but recently proven successful in microscopy negative samples in a newly rediscovered
Plasmodium species of ungulates (Templeton et al., 2016). More PCR-based approaches including wide variety of target DNA or RNA transcripts and their limitations can be seen in recent reviews including Refs (Okell et al., 2012a, Bousema et al., 2014, Zimmerman and Howes, 2015).
1.13.4 Serology
There are several techniques for detecting anti-malaria antibodies in the serum. For each of the four Plasmodium species there are specific serological markers. A positive serological test is generally the indication of past infection. However, serology is not useful for detecting acute infection as detectable level of antibodies does not develop before week of infection and it persist long after the parasitaemia is cleared. In addition, serology is comparatively expensive and not widely available (WHO, 2011a).
54 1.14 Risk factors for malaria
The epidemiology of malaria is very complex, involving factors pertaining to the malaria parasites, the insect vectors, the human hosts, and the environment (van der
Hoek et al., 1998). An understanding of the link between malaria transmission, climatic variables, and other human related factors is therefore necessary for developing appropriate measures that will significantly reduce transmission and perhaps eliminate malaria in endemic areas. In most cases these human related risk factors are known to aggravate the extent of climate related problems. Small variations in human related or environmental factors can have intense consequences for malaria transmission due to the variable status of immune status of the human population (Lindsay and Martens,
1998, Shankar, 2000). Different factors can drive these changes by influencing the vector's transmission capacity and the malaria prevalence.
Malaria risk factors can be grouped into three classes:
1.14.1 Environmental factors
Environmental factors such as the presence of stagnant water around homes, presence of bushes, low altitude rainfall and high temperatures facilitate the breeding of malaria vectors, as well as parasite reproduction within vector (Messina et al., 2011), while increased urbanization tends to reduce the rate of Anopheles breeding.
Climate and environmental conditions greatly affect the transmission and incidence of malaria, by influencing primarily the abundance and survival of vectors and parasites, and exposure of humans and other hosts (Lafferty, 2009, Snow et al., 1999).
1.14.1.1 Temperature
Temperature effects the several stages of malaria life cycle. Many studies reported that optimal larval development (at 25°C-30°C), longevity and feeding frequency of
55 mosquito and stable malaria transmission are all temperature sensitive. The larval mortality increases when water temperature fells below 20°C (Bayoh and Lindsay,
2004). Speed of Plasmodium development within mosquito is also temperature dependent. The developmental cycle of malaria parasite within mosquitoes normally lasts for 9-10 days. The minimum temperature for mosquito’s development is between
8-10ºC, the minimum temperatures for parasite development are between 14- 19ºC. The optimum temperature for mosquitoes development is 25-27ºC, and the maximum temperature for both vectors and parasites development is 40ºC (McMichael et al.,
1998). There are some areas with optimal climate for malaria parasite and mosquitoes but found malaria free. This can be attributed to the elimination of the Plasmodium or feeding habit of mosquitoes which does not feed on human blood. Any change in such areas that introduced the vector for human malaria can lead to destructive malaria outbreak due to lack of immunity in that population (Reid, 2000). In addition, higher temperatures increase the number of blood meals taken and the number of times mosquitoes lay eggs (Martens et al., 1995). An epidemic outbreak in Kenya was reported due to increased temperature (Kigotho, 1997).
1.14.1.2 Rainfall
Specific amount of precipitation is required for successful reproduction of Anopheles mosquito. Heavy rainfall or rainfall with storm condition can flush the breeding larvae
(Paaijmans et al., 2007). Along with amount and intensity the time of rainfall in the year also affect the parasite survival. Malaria transmission is also affected by rainfall due to increase in relative humidity and change in temperature as it suggests where and in what quantities mosquito breeding can occur. The increase in humidity may be leading to an increase in adult mosquitoes longevity and consequently the vectorial
56 capacity (Warrell, 2002). A study conduct in highlands of Kenya reported the epidemics of malaria due to increased rainfall (Kigotho, 1997).
1.14.1.3 Altitude
A study to rank malaria risk factors in African highlands, suggested 1800–2000 meters as the upper limit at which malaria transmission could occur (Protopopoff et al., 2009,
Warrell, 2002). Previously the upper limit of malaria in Pakistan was suggested to be
1,500 meters but recent malaria limit maps have shown malaria endemicity in areas between 1500 and 2000 meters (Baluchistan and KP) (MAP, 2013).
1.14.1.4 Topography
The distribution of hills, valleys, plateaus, rivers, streams, swamps, vegetation and poor drainage affects the spatial distribution of breeding sites (Minakawa et al., 2005), irrigation and swamp drainage for cultivation, can create new habitats for malaria vectors (Protopopoff et al., 2009). One of the reasons for the epidemic of malaria in afghan refugee camp in Khyber Pakhtunkhwa (KP), Pakistan was the borrow pits that were dugout for taking mud for building households, which may have been extra habitats for larvae breeding (Leslie et al., 2009a). In the Kenyan highland, most of the breeding habitats of the malaria vector in the hilly highlands were observed in the bottom of the valley, as the hillside gradients offer efficient drainage (Minakawa et al.,
2004).
1.14.1.5 Wind
Wind may have both negative and positive impact on malaria cycle as very strong winds can decrease biting or ovipositing by Anopheles, while at the same time it can extend the flight length of the mosquito. During monsoon, wind can change the geographic distribution of mosquitoes (Reid, 2000).
57 1.14.1.6 Relative humidity
Relative humidity can disturb the malaria transmission by affecting the activity and survival of the Anopheline mosquitoes. If the average monthly relative humidity is below 60%, it is believed that the life of the mosquito is so shortened that malaria transmission is almost impossible. An epidemic outbreak in highlands of Kenya due to increased humidity was reported previously (Kigotho, 1997). Deforestation in Uttar
Pradesh lead to reduction of humidity which results in short lifespan of Anopheles and clearance of Plasmodium malariae from that area as it requires long lifespan of vector host for its developmental period (Sharma, 1996).
1.14.2 Vector related factors
1.14.2.1 Vector Biology
High sporozoites rate and number of infectious bites are greatly influenced by the lifespan of the mosquitoes and mosquito density, which may lead to high transmission intensity (Paul et al., 2004, Protopopoff et al., 2009). The spread and survival of the malaria vectors are highly dependent on environmental factors (Adimi et al., 2010).
Other important factors which can affect transmission of malaria in an area include; vector species e.g., zoophilic mosquitos which have preference for biting animals over humans (Bruce-Chwatt, 1960) (in Pakistan most of malaria vector species are zoophilic), climatic suitability for vector breeding and survival (Craig and Sharp, 1997).
Number of different vectors, vector density, biting activity and entomologic inoculation rates (EIRs) are also the contributing factors for malaria transmission (Ndenga et al.,
2006, Dev et al., 2004). Changes in vector biting behavior (Mboera and Kitua, 2001) and development of insecticide resistance in vectors (DMC, 2017) is also reported with increased risk of malaria.
58 1.14.3 Human related factors
1.14.3.1 Population immunity (ability to suppress infections)
Immunity is dependent on exposure to infection (Bodker et al., 2006), health condition of the individual and malnutrition (Shankar, 2000). Similarly, pregnant women are more susceptible to malaria. Plasmodium falciparum, which can sequester in the placenta causes severe morbidity to pregnant women and contributing significantly to maternal and infant mortality (Menendez, 1995). The other more susceptible group is
HIV positive individual which are immune-compromised group and they might increase malaria transmission and malaria parasite biomass (Protopopoff et al., 2009).
Previous study also reported that malaria incidence is quite low at first year of life due to the presence of maternal antibodies which provide partial immunity. After first year, the malaria incidence increase sharply and reached to maximum at 2-3 years of age. At this stage immunity seems to develop against both Plasmodium species but appears to mainly protective against P. vivax relapses (Stefani et al., 2011).
1.14.3.2 Personal Vector control
The personal protection (use of bed nets, mosquito repellent, and avoidance of mosquitoes during the peak biting times of the Anopheles mosquito) from vector bite can be very effective if followed on daily basis (Martens and Hall, 2000). However, the degree of efficacy differs individual wise depend on wide range of other factors.
1.14.3.3 Population migration
Huge population movement leads to population growth which results into socio- economic pressure, reduced access to health facilities and poor quality of health services (Martens and Hall, 2000, Hay et al., 2004) and all these circumstances are favorable for malaria outbreaks. Previous studies in Thailand and in the Philippines, had suggested periodic population movements into potential transmission sites in
59 endemic areas was the most important risk factor of increasing transmission (Fungladda et al., 1987, Sornmani et al., 1983). Population movement is also associated with the spread of drug resistance Plasmodium strains to areas previously free of drug resistance
(WHO, 1999, Bloland, 2001).
1.14.3.4 Antimalarial drug resistance
Development of drug resistance is significant barrier in malaria control and it play an important role in spread of malaria to the new areas and in the occurrence and severity of epidemics (WHO, 2011a, Bloland, 2001). One of doubtless reason for resurgence of malaria in several countries was the appearance and spread of drug resistant strains
(Bodker et al., 2000, Trape, 2001). Factors involved in emergence of drug resistance are human behavior, vector and parasite biology, pharmacokinetics and economics. In south Asian countries, the self-medication practice (Oemijati, 1992, Arasu, 1992,
Fungladda and Sornmani, 1986) availing variety of health care practices and improper dosage intake have been suggested causes of drug resistance development (Fungladda and Sornmani, 1986). Drugs resistance leads to economic burden as resources are invested in development of new drugs (Foster, 1991, Ridley, 1997).
Plasmodium vivax and Plasmodium falciparum were reported with drug resistance to almost all existing antimalarial drugs while geographical distribution of resistance to any single drug varies greatly. P. vivax was found to acquire resistance to Chloroquine and/or primaquine in some areas (Murphy et al., 1993, Looareesuwan et al., 1997).
1.14.3.5 Supra-Individual Level Risk Factors
Malaria transmission is also influenced by culture, regional and household economics, geographic location, and community support mechanism (Diez-Roux, 1998, Mauny et al., 2004). These supra-individual level factors can provide the background for malaria
60 existence in a population and help in understanding that why malaria affect the individuals differently.
The distance between houses and vector breeding sites (Staedke et al., 2003, Vittor et al., 2006, Tilaye and Deressa, 2007, Graves et al., 2009, Vittor et al., 2009), socioeconomic status of individuals (Somi et al., 2007, Gamage-Mendis et al., 1991), regional conflicts (Messina et al., 2011), number of family members, houses building material (Ghebreyesus et al., 2000, Butraporn et al., 1986, Arasu, 1992, Sornmani,
1992), literacy level of family members or head of the family and presence of domestic animals inside house or near the house (Bogh et al., 2001) and human travel (Siri et al.,
2010) are few examples of supra-individual factors. Community level risk factors included access to health facilities (Kreuels et al., 2008, Tanner and Vlassoff, 1998) proportion of individuals over 45 years age, community-wide fumigation campaigns
(Peterson et al., 2009). Along with individual level factor the supra-individual level factors also provide significant information about malaria transmission as it is reported that social context can greatly influence the ones’ health and his health outcomes
(Duncan et al., 1993).
1.15 Genetics of Plasmodium
Genetic structure of any parasite is useful for understanding of evolution of parasite virulence, change in parasitic characteristics, development of vaccines and assessing the impact of malaria control measures (Thakur et al., 2008, Mzilahowa et al., 2007).
Unfortunately, there is lack of knowledge about genetic structure of existing
Plasmodium species (P. vivax and P. falciparum) in Pakistan (Khatoon et al., 2010).
The study of genetic diversity can provide significant evidence on a parasite’s response to human interventions for example drugs or vaccines, as directional selection will favor the fixation of advantageous alleles in population thus reducing genetic diversity (Paul
61 et a., 1995). A very limited information is available on genetic structure of Plasmodium vivax and Plasmodium falciparum. One recent study analyze the genetic diversity of
Plasmodium vivax in FATA region of Pakistan (Zakeri et al., 2010d).
Among human malarial parasites, P. vivax causes maximum illness outside Africa
(WHO, 2014a). P. vivax differs from the more widely studied P. falciparum in aspects of its geographic distribution, evolutionary history, life cycle, disease severity, ecology and raising concerns that gaps in our knowledge about its basic biology may compromise its control (Galinski et al., 2013, Prajapati et al., 2013, Schneider and
Escalante, 2013, Otten et al., 2009, Escalante et al., 2005, Prugnolle et al., 2013,
Mueller et al., 2009). The control and ultimate eradication of P. vivax are global health priorities. Genomic research contributes to this objective by improving our understanding of the biology of P. vivax and through the development of new genetic markers that can be used to monitor efforts to reduce malaria transmission (Winter et al., 2015).
Arrival of drug resistant strains indicates the genetic mutation of parasite. Drug resistance results into increase virulence of the Plasmodium vivax which warns the attention of medical practitioners to both species. The genetic variations play an important role in parasite’s survival strategies including the potential for recombination, clonal expansion, gametocyte production drug resistance and escape from host immune response (Nassir et al., 2005, Lee et al., 2006). The best way of finding genetic diversity is to analyse more than one marker gene, in such manner the probability that different clones share the same genotype by chance is considerably reduced (Schoepflin et al., 2009).
Due to high mortality rate and morbidity, the Plasmodium falciparum put the extreme selective pressure on human genome in recent past. Several genetic factors were
62 reported to show resistance to Plasmodium infection such as sick cell traits, absence of
Duffy antigens on red blood cells and Glucose-6-phosphae dehydrogenase deficiency
(Kwiatkowski, 2005, Hedrick, 2011).
The migration of Plasmodium parasite from one geographical region to other have become very frequent due to increased population movement (within the country, cross country and cross-continent). Such migratory phenomena of human malaria pathogen is generally associated with disease epidemics (Engering et al., 2013) and addition of new and recombined genotypes in the local population. These genetically recombined parasite genomes often give rise to new virulent and drug-resistant phenotypes (Darch et al., 2015).
In case of sudden malaria outbreak or unexpected emergence of anti-malarial drug resistance to a working drug, the identification of parasite genotype is of utmost importance in controlling the drug resistance and outbreaks. The exact geographical origin and migration route of pathogen should be exactly known for effective intervention and control measures. For successful treatment, it is necessary to know whether the patient is parasitized with single or multiple clones of parasite, as in high transmission region or season the chances of multiple clonal infection is quite high.
Accurate characterization of Plasmodium species is also very important to monitor the frequency and distribution of specific species in population and differentiate recurrent infection from re-infection in drug trials (Tripathi and Das, 2015).
In such circumstances the genotyping of parasite is considered the only way to distinguish between various clones. Initially parasite clones were differentiated by nested Polymerase chain reaction (PCR)(Snounou et al., 1993b). On the basis of length polymorphisms at two merozoite surface protein genes (msp-1 and msp-2), one gene encodes for glutamine-rich protein (glurp) and other is circumsporozoite protein-
63 encoding gene (csp). Recently SNPs (located in the conserved regions of nuclear or extra-nuclear genomes) approach provide a new way for future genotyping of malarial parasites (Tripathi and Das, 2015).
Genomic approaches also make easier the study of parasites which are hard to culture such as Plasmodium vivax. For example, genome-wide scans performed on samples from a natural parasite population can identify regions of the genome subject to strong selection (Mu et al., 2010, Ochola et al., 2010, Amambua-Ngwa et al., 2012, Park et al., 2012). Previous studies of population genetic have reported that P. vivax populations in many regions of the Americas are less diverse than those from Asia or
Oceania (Jongwutiwes et al., 2005, Cornejo and Escalante, 2006, Imwong et al., 2007,
Chenet et al., 2008, Culleton et al., 2011). Similarly, a current whole genome study documented limited genetic diversity in a population of Amazon basin of Peru
(Flannery et al., 2015). Genomic studies can also support efforts to control and eliminate malaria from a given region by identifying genetic markers that will be informative for fine-scale population genetic studies in that region. Molecular epidemiological investigations rely on multilocus genotyping of SNPs or microsatellites (Escalante et al., 2015) to investigate patterns of population structure and gene flow.
Whole genome study helps in identification of new polymorphic loci which helps in the pattern of transmission and can be used to distinguish the local cases from those introduced from other regions. Limited sequence analyze of protein coding regions of nuclear origin have revealed the difference between local and imported pathogens as well as between parasite strains imported from different endemic regions (Severini et al., 2002).
64 As a source of information on P. vivax polymorphisms, Plasmodium mitochondrial genome (6-kb) is comparatively more useful than currently available nuclear markers for epidemiological studies. For epidemiological studies the mitochondrial genome of the P. vivax is more useful than currently available marker as a source of information on polymorphism due to some advantages such as; a). The mitochondrial genome does not recombine with each other due to uni-parental inheritance, b). Mitochondrial haplotypes can be clustered per their geographic origin, c). Most of the polymorphisms in mitochondrial genome are evolutionary neutral and they reflect the population history of lineage more effectively than polymorphism in coding region (Conway et al.,
2000, Joy et al., 2003).
Mitochondrial genome sequencing was proved to be useful at population level studies in many organisms specifically in case of Plasmodium genus. Mitochondrial genome consists of many genes encoding for ribosomal RNA and three genes for cellular respiration i.e. Cox I (cytochrome c oxidase gene I), Cox III (cytochrome c oxidase gene III) and Cytb (cytochrome b). The later three genes are crucial for cellular activities such as oxidative phosphorylation, heme and coenzyme Q biosynthesis and maintenance of membrane potential (Hughes, 2004).
65 1.16 Rationale of the project:
The aim of this study is to generate current information on malaria infection through prevalence of malaria infection among the suspected individuals of District Bannu
(where transmission of malaria is perennial) in Khyber Pakhtunkhwa (KP) province of
Pakistan formerly called North West Frontier Province (NWFP).
P. falciparum and P. vivax malaria are the major health issue in Pakistan. At least 39 districts of the two southern provinces of Balochistan and Sindh have been classified as high risk area. In different areas of Pakistan studies have been conducted on prevalence of Plasmodium (Khatoon et al., 2009, Yasinzai and Kakarsulemankhel,
2004, Niazmani et al., 1995) but few studies were conducted in the study area (Bannu) related to malaria prevalence. To fulfill this gap the present study was conducted to assess the epidemiology of malaria in the region. The disease is endemic in different parts of Khyber Pakhtunkhwa, including the study site. The present study was designed with the main aim of investigating the prevalence of malaria infection among the suspected individuals of Bannu district, Khyber Pakhtunkhwa, Pakistan and creating awareness of malaria disease in them.
The epidemiology of malaria is very complex, involving factors pertaining to the malaria parasites, the insect vectors, the human hosts, and the environment (van der
Hoek et al., 1998). An understanding of the link between malaria transmission, climatic variables, and other human related factors is therefore necessary for developing appropriate measures that will significantly reduce transmission and perhaps eliminate malaria in endemic areas. This study was also aimed to recognize the possible risk factors of malaria, its relationship with infection and exposure which will be helpful in the prevention and control of the disease in this area and will also assist decision makers in better targeting malaria intervention and control efforts. Identified risk factors and
66 malaria incidence patterns can be utilized in devising localized disease control initiatives.
Additionally, the clinical isolates obtained from District Bannu, Khyber Pakhtunkhwa were typed for mitochondrial genome to elucidate the status of P. vivax epidemiology in south region of Khyber Pakhtunkhwa province of Pakistan in relation to the global samples.
In case of sudden malaria outbreak or unexpected emergence of anti-malarial drug resistance to a working drug, the identification of parasite genotype is of utmost importance in controlling the drug resistance and outbreaks. The exact geographical origin and migration route of pathogen should be exactly known for effective intervention and control measures. For successful treatment, it is necessary to know whether the patient is parasitized with single or multiple clones of parasite, as in high transmission region or season the chances of multiple clonal infection is quite high.
Accurate characterization of Plasmodium species is also very important to monitor the frequency and distribution of specific species in population and differentiate recurrent infection from re-infection in drug trial (Tripathi and Das, 2015). Simple genetic markers from apicoplast and mitochondrial genomes can be utilized in studying genetic profiles or haplotypes from P. vivax that can be specific to their geographic origin and thus can be a valuable public health tool for locating the source of outbreaks. Such haplotypes for P. vivax can have prospects in monitoring drug resistance and epidemiology of these specific profiles in a region and identification of specific genotypes will help to map out import routes and thus implement proper disease general control measures by the Health department.
67 Specific Objectives:
The major goals of this research are to:
1. Estimate incidence of malaria in Bannu district, using three diagnostic methods i.e.
Microscopy, RDT, and PCR.
2. Evaluate the specificity and sensitivity of the employed diagnostic means in the
study area.
3. Assess behavioral and household risk factors for malaria in Bannu, district.
4. Genotype and sequence apicoplast and mitochondria geographical-specific markers
in P. vivax.
68 Chapter 2 MATERIALS AND METHODS
2.1 Introduction
Bannu (32°43′–33°06′ N; 70°22′–57′ E) is one of the 26 districts in the south of Khyber
Pakhtunkhwa province of Pakistan with an area of about 1227 sq. km and a population of 1,167,892 (Pakistan Bureau of Statistics, 2017) majority of which is rural. Bannu is divided into two tehsils Domel/Bannu-1 and Bannu-II, comprising 40 union councils
(Government of Khyber Pukhtunkhwa, 2017). About 80 public health centers including
Medical Teaching Institutes (MTI), District Headquarter Hospitals (DHQ), Regional
Health Centers (RHC), Basic Health Units (BHU), Civil Dispensaries (CDs) and
Mother & Child Health Centers (MCH) along with 15 private centers operate in Bannu
District. In these health centers, first line treatment for unconfirmed malaria is
Chloroquine, for P. vivax is Chloroquine-Primaquine, for uncomplicated P. falciparum malaria is Artesunate/ Sulfadoxine-Pyrimethamine (AS+SP), whereas Artesunate,
Artemether or quinine is recommended for treating severe P. falciparum malaria or cases with treatment failures (WHO, 2012). Control interventions being conducted here are run and sponsored by “Integrated vector control/ malaria control program Khyber
Pakhtunkhwa” (IVC/MCP-KP) and “Frontier Primary Health Care” (FPHC). These include regular trainings for focal persons in basic microscopy, RDTs use, malaria case management training, outbreak training etc. Other chief prevention strategies include indoor residual spray, mass distribution of bed nets to every household, antenatal care
(ANC) bed net distribution (only pregnant women receive a bed net during her visit to hospital for antenatal care) and community education and mobilization campaigns
(Personal communication with IVC/MCP-KP).
69 Table 2.1 Demographic information of District Bannu
Area 1,227 km2 (474 sq mi) Population - 2017 1,167,892 persons Male 593492 Female 574391 Population growth rate 2.99 From 1998 to 2017. Population Density 950/km2 (2,500/sq mi) Urban Population 49965 Rural Population 1117927 Literacy Ratio (2017) 66.02 Male 50.79 % Female 12.18 % Population - 1981 423018 persons Total Housing Units 119993 Administrative Units Tehsils 02 Union Councils 40 Mauzas 229 Municipal Committees 01 Cantonment 01 (Government of Khyber Pukhtunkhwa, 2017)
Figure 2.1 (a) Map of Pakistan showing its provincial divisions and neighboring countries (b) Map showing administrative divisions of KP and FATA *Encircled region showed study area
70 2.2 Ethical approval
Patients were dually informed about the purpose of sample collection and written consent was obtained for inclusion in the study. Results of the different malaria diagnostic tests were communicated to the patients and were further guided about any consequent recommended medication. The study procedures have been approved by
Peshawar University (local Ethics committee) and copy of ethics clearance is attached in appendix A.
2.3 Data collection and sampling procedure
Blood samples were collected from suspected malaria patients (belonging to different parts of Bannu district) visiting Malaria Model Laboratory, Malaria Lab (Women &
Children Hospital Bannu) and Siddique Laboratory Bannu for diagnosis or treatment purposes. The Labs catchment area covers the entire district. The flow of patients was greater in these labs as among above mentioned labs two (MML and Malaria Lab
WCH) are govt. labs which provide free services with collaboration of UNHCR malaria control program. While the later one has good reputation for satisfactory diagnostic facility. Samples were collected from March to October 2013. Sample collection was performed thrice in a week. Patients, suspected of malaria, visiting the above- mentioned labs were taken in for sampling. Primary data was collected from the patients through a questionnaire for acquiring demographic and other relevant information including gender, literacy level, locality, socioeconomic status etc. for assessing risk factors for malaria in the study area (Appendix B). As questionnaire was in English, the scale was translated into Urdu & Pushto. For children, consent was requested from a parent or an adult guardian. Each patient (or guardian) was interviewed in person by principal investigator. Anonymization was maintained by omission of names of participants and their replacement with ID numbers.
71 Ethyl alcohol, glass slides, RDT kit, filter papers (for blood spot collection) and cooling
box with ice packs were carried along in each visit to the laboratory. Filter papers were
prepared in laminar flow hood (Department of Zoology, University of Peshawar) by
cutting about 2cm pieces and each piece was stapled between card sheets.
Patient finger was sanitized with alcohol, allowed to dry and later pricked with blood
lancet. Blood was processed with RDT (First response® Malaria Ag, pLDH/HRP2 Combo
Card test kit, Cat. No 116FRC30), microscopy (thick and thin smears) (WHO, 2009) and
PCR for the confirmation of Plasmodium species. Few drops of blood were placed on glass
slide for making smears utilized for microscopy, while one drop was taken by disposable
pipette and placed on RDT. To detect the malaria parasite using PCR about 1 micro liter
of blood was spotted (2-3 spots) on coarse-porosity filter papers. These filter papers were
stored in small zip lock bags with silica beads at low temperature (4°C). The serial number
assigned to the subject and collection date was written on a card cover placed along the
filter paper. These samples were transported to the molecular lab of Department of
Zoology; University of Peshawar. These filter papers were utilized in identifying
Plasmodium species using PCR.
Results of the different malaria diagnostic tests was communicated to the patients and they were further guided about any consequent recommended medication.
2.3.1 Microscopy
Malaria parasite is found in the red blood cells of human beings. The diagnosis of
malaria is dependent upon the presence of malaria parasite in the peripheral blood film.
The essential requirements for a precise diagnosis of a case of malaria are: Making a
proper blood smear from a suspected malaria patient, high quality staining of the smear
and examination of the stained slide by a skilled microscopist.
72 Two types of smears were prepared from the peripheral blood i.e. thin and thick smear.
Thick film examination is sensitive for the presence of parasite. Thin film examination was made to identify the species of Plasmodium. For preparation of blood smear the patient finger was cleaned with alcohol and blood drops (3 for thick smear and 1 for thin smear) were taken on slide, 1cm apart from each other. With the help of another slide (spreader) thick smear was made by joining the 3 drops of blood and spreading over an area of 10 mm diameter. Thin smear was made by bringing the spreader in contact with the drop of blood at an angle of 30-45º from the horizontal and pushing the spreader gradually down the surface of the slide drawing the blood behind till the smear was formed. Slides were number after drying and later fixed with methanol. All slides were fixed with Giemsa stain (mixture of eosin and methylene blue) by placing in staining trough for 30-45 minutes. Stain was poured off gently and rinse the slides with clean water for a few seconds and then allowed for drying. Thick film was examined first and thin film was examined only when the thick film gets auto fixed or when it is necessary to confirm the identification of a species. The examination continued, for at least l00 fields to determine whether the blood film is positive or negative for malaria (WHO, 2009).
\
Figure 2.2 Thick and thin smear
73 2.3.2 Rapid Diagnostic Technique (RDT)
Rapid tests were conducted by using rapid diagnosis kits. (First response® Malaria Ag, pLDH/HRP2 Combo Card test kit, Cat. No 116FRC30). The CSPfPan test was suggested the most appropriate for this P. vivax predominant region and was found to be the most sensitive of the RDTs compared which was not unduly affected by heat
(Mikhail et al., 2011). 5ul fresh blood was taken through company provided pipette and released into sample well and put two drops of buffer solution (amount required per type of RDT). The RDT strip was left for 20 minutes to progress as suggested by the manufacturers. After 20 minutes’ results were read and interpreted according to RDT results interpretation chart. All positive cases were further confirmed by microscopy.
2.3.3 Sample storage
3-4 spots were taken on filter paper and left for few minutes to become air dry then labelled with patient ID and date of collection. Disposable gloves were used during sample collection. At the end of the day the filter papers were wrapped individually in an airtight re-sealable bag with silica gel. These filter papers were carried in cold storage boxes and then stored in the fridge, later transferred to the -20°C at the Dept. of
Zoology, University of Peshawar. All blood samples collected on filter paper were packed and transferred in ice box containing ice packs to department of zoology university of Peshawar and preserved in -20˚C.
2.3.4 Molecular diagnosis
PCR was used to identify malaria infections. Filter paper samples including all microscopy positive and negative samples were processed by PCR (Snounou et al.,
1993a).
74 DNA Extraction
A resin based method (employing Chelex) was used for DNA extraction described by
Plowe, et al., 1995. DNA was extracted from the blood samples on filter paper using the resin-based Chelex method as follows. Sections of the filter papers were punched using Harris Uni‐ Core hole punch (at least 2mm diameter). About 1-2 discs were collected in each separate autoclaved Eppendorf tube (marked by sample ID). The punch hole and the forceps were treated with 70% alcohol and flame between each sample. In each batch about 30-40 samples were processed. Spots were immersed in
1ml freshly prepared 0.5% Saponin in 1 X Phosphate buffered saline (PBS). All tubes were tightly closed and then incubated at 37˚C overnight to release the haemoglobin in to the PBS, leaving the DNA on the paper. Each sample was briefly centrifuged (4000 rpm for 2 min). Saponin and debris were removed by micropipette with a new tip for each sample. 1ml fresh 1 X PBS was added to each tube and centrifuged (4000 rpm for
2 min) and washing step was repeated until no haemoglobin was seen on the filter papers. 150μl of 6% Chelex-100 suspension in nuclease‐ free water (using a trimmed pipette tip) was added. All samples were boiled for 30 minutes in a water bath, ensuring that the level of water covers the samples in the tubes. Each tube was spun at 4000 rpm for 2 minutes to pellet down the Chelex and the filter papers. The DNA supernatant
(approximately 120μl of the top layer) was transferred into sterile pre-labelled tubes.
Each tube was spun down again (4000rm for 10mins) to sediment any Chelex taken in with the DNA. Labelled Eppendorf tubes containing DNA were stored at ‐ 20oC and
Chelex and filter paper was discarded. All samples of one batch were stored in one pack labelled with Date, time, number of samples at -20˚C.
75 Diagnostic PCR for identification of Plasmodium species
Nested PCR targeting conserved rRNA genes was utilized for Plasmodium species discrimination using genus and species specific primers as described by (Snounou et al., 1993a). A 130 bp fragment of the Plasmodium spp. 18S genes was amplified using
10 pmoles of each Genus-specific primer.
For first amplification cycle genus-specific primers rPLU5 and rPLU6 were used. The first amplification cycle product was then used for a second amplification cycle, in which each parasite species is identified separately using specie- specific primers.
Table 2.2 Primers used in PCR
Purpose Primer Forward primer (5’-3’) Primer Reverse primer (3’-5’) Genus TTAAAATTGTTGCAGTTA CCTGTTGTTGCCTTAAAC specific PLU 6 AAACG PLU 5 TTC ACTTCCAAGCCGAAGCAA CGCTTCTAGCTTAATCCA rVIV2 AGAAAGTCCTTA rVIV1 CATAACTGATAC Species ACACAATGAACTCAATCA TTAAACTGGTTTGGGAA specific Rfal2 TGACTACCCGTC Rfal1 AACCAAATATATT
Nest 1 Reaction
The PCR was nested using sets of primers as described by Snounou et al. (Snounou et al., 1993a). The Nest 1 PCR mix and cycling conditions were as follows
76 Table 2.3 PCR Mix for Nest 2 reaction
Working concentration Final concentration Reaction volume 20 µL
dH2O - 10.5 µl
10XNH4SO4buffer 1X 2µl
50 mMMgCl2 2 mM 0.8µl 10 mMdNTPs 250 μM 0.5µl 5 μM Primer mix (genus specific) 250 nM 1µl each Taq DNA polymerase 0.2μl (1unit) DNA - 5µl
The cycling parameters for the first amplification reaction were as follows:
The cycling conditions for N1 are as follows,
Table 2.4 cycling conditions for Nest 1
Step Temp (°C) Time (min.) Cycles
Step-1 95 5 1
Step-2 Annealing 58 2 25 cycles
Step-3 Extension 72 2
Step-4 Denaturation 94 1 Step-2 Annealing 58 2 1
Step-3 Extension 72 5
25 10 1
Nest 2 Reaction
PCR mix and cycling conditions for N2 are as follows.
PCR Mix (PCR mix for N2 was prepared according to the method as mentioned above).
77 Table 2.5 PCR mix for Nest 2
Working concentration Final concentration Quantity per 20 µL dH2O - 14.5 μl 10XNH4SO4buffer 1X 2 μl 50 mMMgCl2 2 mM 0.8 μl 10 mMdNTPs 250 μM 0.5 μl
5 μM Primer mix (P. falciprum or P. vivax 250 nM 1 μl specific) Taq: 1 unit 0.2 μl DNA (N1 product) - 1 μl
Cycling conditions: The cycling parameters for the second amplification reaction were as follows:
Table 2.6 cycling conditions for Nest 2
Steps o Time (min.) Cycles Temp ( C) Step-1 95 5 1 Step-2 Annealing 58 1 30 cycles Step-3 Extension 72 1 Step-4 Denaturation 94 1 Step-2 Annealing 58 1 1 Step-3 Extension 72 5 25 10 1
For preparation of 2% agarose gel, 2 mg of agarose was dissolved in 100ml of 1X Tris-
Borate-EDTA (TBE). Heated the mixture in microwave to dissolve agarose particles then added 2µl of Ethidium Bromide (10mg/ml) and mixed it. Then boiled the mixture until transparent solution was obtained. Cooled up to 50˚C before pouring into gel tray.
Allowed the gel to set for 30 minutes at room temperature and then combs were removed. N2 samples (10µl), each were added into respective vials along with loading dye (2µl). 6µl ladder (100bp) was added into 1st and another selected veil. Power supply was set at 90 volts for 60 minutes. After separation of nest 2 products, gel was visualized
78 in gel doc under UV light to see the Plasmodium species bands. Plasmodium falciparum produces 205 bp PCR product and for Plasmodium vivax the PCR product size produced is 120bp.
2.4 Sequencing of mitochondrial genome
Mitochondrial genome was sequenced for Plasmodium vivax. For this purpose, a set of
DNA samples identified as P. vivax from the present study (N=60). These samples were sent to London School of Hygiene and Tropical Medicine (LSHTM) for sequencing where 11 novel primers were utilized for sequencing of mitochondrial genomes
(approx. 6Kb) of the Pakistani P. vivax samples (table 2.7).
Table 2.7. List of primers used for mitochondrial genome sequencing
No. Forward Reverse 1 1F:AGCTGTAGATGGATGCTTCGA 1R:ACCCTAGAACATTAGAACAGGAG 2 2F:TGGCATCTCTCTCGATTTCCA 2R:ACAGGCTGAGTCTCTATGCC 3 3F:TTCCCGGGTATCCAATCCAG 3R:AATGGCGAGAAGGGAAGTGT 4 4F:GACCGGTCAAAACGGAATCA 4R:TTGCATCACTTCAAACAACTGA 5 5bF:GAACGGTGATTTTGTGTGCC 5bR:GATCCATACAGTCCCAGCGA 6 6F:GCTGGGACTGTATGGATCGA 6R:TCTCCAGCAAATGTAGGATCAA 7 7F:TGTCTTTATCTCCTGTTGCAGT 7R:TGCTCTTGTATCTACTTCTAAACCTG 8 8F:AGGAAGTGTTGTATGGGCTCA 8R:CCATCCATTTAAAGCGTCTGGA 9 9F:TGTAATGCCTAGACGTATTCCTGA 9R:CCCCAATAACTCATTTGTCCCC 10 10F:GGGGACAAATGAGTTATTGGGG 10R:CGGTCTGTATTGTTCTGCTCA 11 11F:ATGGATTGGATGTCAGTTACCA 11R:GTTTAGCCAGGAAGTCAGCG
2.5 Data analysis
Questionnaire data was entered into Microsoft excel and were analyzed in IBM SPSS
Statistics 20. The data was analyzed with Stata software, version 14. Cross tabulation and different tests for comparison of diagnostic techniques including Cohen’s kappa,
McNemar marginal homogeneity test were carried out in IBM SPSS Statistics 20.
79 2.5.1 Assessment of sensitivity and specificity of diagnostic methods
Agreement of results between the two methods was assessed using Cohen’s k-test
(Cohen 1960) for concordance and McNamara’s test for discordance. Kappa statistic is frequently used to test interrater reliability. It represents the extent to which the data collected in the study are correct representations of the variables measured. Cohen’s kappa, symbolized by the lower case Greek letter, κ (Marston, 2010) is a robust statistic useful for either interrater or intra-rater reliability testing. Like correlation coefficients, it can range from −1 to +1, where 0 represents the amount of agreement that can be expected from random chance, and 1 represents perfect agreement between the raters.
While kappa values below 0 are possible, Cohen notes they are unlikely in practice
(Marston, 2010). Kappa result be interpreted as follows: Values ≤ 0 as indicating no agreement, 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–
0.80 as substantial and 0.81–1.00 as almost perfect agreement (Landis and Koch, 1977).
McNemar's test is a statistical test used on paired nominal data. It is applied to
2 × 2 contingency tables with a dichotomous trait, with matched pairs of subjects, to determine whether the row and column marginal frequencies are equal (that is, whether there is "marginal homogeneity"). It is named after Quinn McNemar, who introduced it in 1947 (McNemar, 1947).
For the different diagnostic methods used in the study sensitivity (Probability of a patient being test-positive when disease present i.e. sensitivity=true positive/true positive + false negative), specificity (Probability of being test-negative when disease absent i.e. Specificity = true negative/true negative + false positive), positive predictive value (PPV) (Probability of patient having disease when test is positive i.e. PPV=true positive/true positive + false positive) and negative predictive value (NPV) (Probability
80 of patient not having disease when test is negative i.e. NPV=true negative/false negative
+ true negative) were calculated. Sensitivity and specificity are inversely proportional, meaning that as the sensitivity increases, the specificity decreases and vice versa
(Parikh et al., 2008). All samples were considered confirmed positives if they were positive parasitologically by at least two diagnostic methods. Samples were considered confirmed negatives if they were negative by one method.
2.5.2 Multivariate and univariate analysis for risk factors
Among Two thousand and thirty-three individuals, positive cases by all diagnostic means were matched by gender and age categories (<5, 5-20, >20 year) to derive 359 matched pairs in Stata version 13 (Statacorp, 2013). Using this paired data, risk factor assessment was performed through univariable and step-wise multivariable conditional logistic regression in Stata v13.
Univariate and multivariate logistic regression model was used, to investigate whether socio-economic and demographic factors as a risk factors of malaria prevalence of infection and exposure in the studies population. Potential risk factors tested in the models were: age in years, sex, literacy level, malaria fever history, intake of antimalarial drugs, previous malaria history, present symptoms, water reservoir near to house, house construction material, window glazing, recent or regular bed net use, and use of repellents or coils and socio-economic status (SES) as an indicator of household wealth. To determine the effect of each exposure variable on the outcome variable univariate analysis was performed. Any exposure variable significant with p≤ 0.05 in univariate analysis was used in multivariate analysis (Wald test). The final multivariate modal includes only significant exposure variable with p≤ 0.05.
81 2.6 Analysis of mitochondrial genome data
Mitochondrial sequences from 11 primers generated from P. vivax samples were merged and aligned against complete genome of P. vivax reference strain Salvador I.
Missing data between and within sequences were represented N. All sequences were aligned in MEGA v7 using Clustal W alignment and all ambiguous site with insertion or deletion were removed. All SNPs were identified and made compatible with secondary SNP data of 210 Plasmodium isolates from 17 countries (kindly provided by
Dr. Taane Clark, LSHTM) that worked as backdrops for genetic analysis of Pakistani samples.
Composite mitochondrial sequences from Pakistan were translated in GENEIOUS v10.2.3 to identify as synonymous and non-synonymous mutations in mitochondrial genes (Cytochrome B, COX-1 and COX3). Translation was done with genetic code of translation table-4 of Mold protozoan mitochondrial genome. By comparing our sample sequences with reference sequence of Salvador I, SNPs were exported to Excel sheet along with those other countries.
2.6.1 Identification of genetic population substructure
Identification of the number of clusters within the SNP dataset was approached by 2 methods i.e. STRUCTURE and NJ tree (Neighbor Joining tree).
2.6.1.1 STRUCTURE approach
STRUCTURE V2.3.1 was used. The program structure uses a Bayesian model-based clustering method for inferring population structure using MCMC (Markov chain
Monter Carlo) scheme (Pritchard et al., 2000). The model assumes that within the populations the marker loci are unlinked and at linkage equilibrium with one another.
It also assumes the populations are in Hardy-Weinberg equilibrium (HWE). It is thus
82 not considered appropriate to be used for diploid organisms although, the versions 2.0 onwards have been modified to some extent to deal with weakly linked markers
(Pritchard et al., 2009).
The model also does not assume a mutation process and thus can be applied for commonly used markers as microsatellites, RFLPs or SNPs etc. The method can be used to identify population structure and migration or admixture events. Isolates are assigned probabilistically to one population or more (in case of admixed genotypes) based on a set of allele frequencies characteristic for each population.
K (number of clusters) may be predefined or calculated by the software. To calculate
K, the number of iterations were set to 100 000 iterations with additional 500 000
MCMC iterations. Admixture ancestry model was used to infer Alpha (α) set between
1.0-10.0 with a standard deviation of 0.025. Same value of alpha was used for all sub- populations. The settings for frequency model were (a) Allele frequencies correlated among sub-populations (b) Different values of FST for different sub-populations (c)
Constant value of the parameter for allele frequencies (λ). (d) The frequency of
Metropolis update for Q (estimated membership coefficient) as 10runs for each value of K. The value of K was allowed to vary between 1-20 for 5 independent runs. The mean of LnP(D) (estimated log likelihood values ln Pr(K/X), where X is the number of genotypes) for each K was then plotted against the number of clusters (K). The value of optimum K, is chosen where the graph ceases to follow a Gaussian distribution so that K value increases and more-or-less plateaus at some point. If two or more values of K give almost similar log Pr(X|K) figures then the smallest of these is chosen as the optimum number of sub-populations (Pritchard et al., 2009).
83 2.6.1.2 Neighbor joining phylogenetic tree
The NJ tree building method is heuristic approach that uses genetic distances to infer the cladistic clustering of similar isolates. This algorithm assumes different rates of evolution for different lineages. However, if the evolutionary rate varies greatly among sites or if some distances are large then accurate estimation of the optimal tree becomes difficult. Otherwise the method is well known for its robustness(Page and Holmes,
1998b).
The fasta format file created from MEGA v5.05 was run in jMODELTEST v1.0 to select a best fit nucleotide substitution model from a list of 88 available (Posada, 2008).
The best model selected for the dataset was used to construct a Maximum Likelihood tree (ML) tree in MEGA V5.05 with 1000 bootstrap re-samplings.
2.6.2 Population genetic analysis of geographical populations
The all available populations were divided into geographical clusters on the basis of
WHO malaria regions (WHO, 2017). Several sub-population parameters were calculated. This analysis was carried out on geographically assigned clusters/sub- populations in table 2.7.
Table 2.7 Distribution of populations according to WHO malaria regions
Cluster name Counties included (number of samples)
Americas Colombia, Peru, Brazil, Panama, Mexico Western Pacific China, Cambodia, Malaysia, Vietnam, PNG South East Asia India, Thailand, Myanmar, Indonesia, Sri Lanka Eastern Mediterranean Pakistan African Region Madagascar
84 2.6.2.1 Population diversity testing for Hardy Weinberg Equilibrium (HWE) and linkage dis-equilibrium (LD) Mean number of alleles (MNA) as measure of gene diversity was calculated for each geographical cluster in GeneAEx v.6.5 (Park, 2001). A global test across loci per cluster was implemented in Arlequin v3.5 to calculate mean observed and expected heterozygosities (Excoffier and Lischer, 2010). Observed heterozygosity (HO) is the number of heterozygotes per locus in a sample from a real population. Expected heterozygosity (HE) in this case relates to sample corrected gene diversity calculated by
Nei (1973) as the probability of choosing two non-identical alleles at random at a locus per sub-population (Hedrick, 2005, Nei, 1973).
풌 ퟐ HE=ퟏ − ∑풊=ퟏ 퐏퐢
The above equation is used to calculate HE for a locus with multiple alleles. HE is derived by subtracting sum of expected frequency of homozygotes in the population from the total frequency of alleles in the population (Hedrick, 2005).
Under the assumptions of HWE (Hardy Weinberg Equilibrium), the association between alleles at different loci within a population should be random or in other words they should be in linkage equilibrium (LE) (Hedrick, 2005, Page and Holmes, 1998b).
If two alleles are linked physically, there is deviation from LE, and they are in linkage dis-equilibrium (LD). LD was measured for multi-allelic population data (MLMT) using two statistics in MULTILOCUS v1.3. First one was Maynard-Smith’s (1993) index of association (IA). IA can be calculated from a comparison between observed
(VO) and expected (VE) values of variance in the number of alleles among individuals.
VE is calculated on the assumption that alleles are in LE whereas VO is observed from the dataset. Deviation of IA from 0 shows linkage between loci. MULTILOCUS v1.3
85 generated a p-value for each IA value by random permutation assuming infinite recombination.
IA = VO/VE -1
Presence of LD in a cluster is indicative of a truly clonal mode of propagation, where recombination or genetic exchange is so rare that it cannot break the clonal population structure. However, there are some genetic phenomena that can produce LD in an otherwise recombining population e.g. genetic drift in small populations, population sub-division (Walhund effect), sampling from biologically and ecologically separated populations and physical linkage of genetic markers used (Page and Holmes, 1998b,
Hedrick, 2005, Maynard Smith et al., 1993). MULTILOCUS v1.3, however, can prevent over-estimation of linkage by giving options for pre-defining linkage groups and sub-divisions within clusters.
Since IA is reported to be dependent on the number of loci tested (Brown et al., 1980,
Maynard Smith et al., 1993), another statistic rbard (or rd) was calculated in
MULTILOCUS v1.3. Statistic rbard is independent of number of loci tested and calculated under the same theoretical principal as IA. IA and rbard are related across the randomization steps and thus identical p-value is produced for both the statistics by
MULTILOCUS v1.3 (Agapow and Burt, 2001).
2.6.2.2 Population differentiation or gene flow
Gene flow between any two pairs of sub-populations is one of the mechanisms by which genetic variations can be introduced. The process tends to homogenize gene frequencies between population pairs to limit or prevent differentiation while also increasing heterozygosity of the recipient population. The level of differentiation or gene flow between populations or clusters was assessed using Wright’s F-statistic FST.
86 FST could be calculated as follows,
FST= H̅ T-H̅ S/H̅ T
Where, H̅ s is the average expected heterozygosity within a cluster/sub-population over loci and H̅ T is the average heterozygosity in a total population over loci (Hedrick, 2005,
Nei, 1977). FST conforms to IAM (infinite allele model). Under this model a novel allele is produced at a rate u, with each mutation. It thus, does not permit homoplasy (Kimura and Crow, 1964). However, authors have criticized the suitability of this statistic for microsatellites since it assumes a low mutation rate and removes memory of allelic state prior to mutation (Balloux and Lugon-Moulin, 2002).There is evidence that suggests that size of mutated allele depends on its size prior to mutation.
FST for each cluster or sub-population pair was calculated in Arlequin v3.5 (Excoffier and Lischer, 2010) and p-values were generated by randomly per mutating genotypes between each pair. The statistic was interpreted according to the guidelines provided by Wright (1978) and Hartl & Clark (1997). They suggest that FST<0.05 indicated little or no differentiation, 0.05-0.15 moderate differentiation, 0.15-0.25 great differentiation, and >0.25 indicated very great differentiation (only limited gene flow).
(Wright, 1978, Hartl and Clark, 1997).
FST was also estimated across all sub-populations in context of AMOVA (Analysis of molecular variance) in Arlequin v3.5. The method tested appropriateness of geographical clustering in explaining the overall variance of the data (Excoffier et al.,
1992). FST is designed to measure sub-division that result from deviation from HWE and thus do not strictly rely on HW allele frequencies (De Meeus et al., 2006).
87 2.6.2.3 Evolutionary distance
Estimate of evolutionary divergence were also calculated in MEGA v7. Evolutionary distance, the number of substitutions per site separating a pair of homologous sequences as they diverged from their common ancestral sequence, is particularly important measure in molecular evolution and comparative genomics. It is used for a wide range of purposes, ranging from phylogenetic analysis (Saitou and Nei, 1987, Edward and
Cavalli-Sforza, 1963), to estimating times of divergence (Kumar & Hedges, 1998;
Hedges & Kumar, 2003), the tempo and mode of evolutionary change and functional constraints (Hare and Palumbi, 2003). Evolutionary distance estimation is one of the first steps in high-throughput sequence analysis, errors in these estimates may have wide-ranging consequences on downstream analyses and conclusions (Rosenberg,
2005).
88 Chapter 3 EPIDEMIOLOGY OF MALARIA IN BANNU REGION
3.1 Introduction
Due to changes in both human and parasite population in Pakistan, monitoring the distribution and burden of Plasmodium species is necessary to ensure appropriate treatment, particularly in situations where diagnosis by microscopy or species-specific rapid diagnostic tests is not available (Khattak et al., 2013).
Bannu was considered one of the most malarious area of Indo-Pak region as it is traversed by Innumerable irrigation channels traverse the entire area of the cantonment and its neighborhood and are the main mosquito breeding places. Anopheles species (A. stephensi, A. culicifacies and A. subpictus) are found as common vector in the region.
Malaria incidence used to peak at the end of August, reach their maximum in October and November, and then decrease rapidly per hospital records. Plasmodium vivax and
P. falciparum are endemic in Pakistan. Bannu (study area) is also reported among the highest malaria incidence areas of Pakistan (Asif, 2008).
Malaria burden increased with displaced population as they belong to malaria endemic regions as Federally Administered Tribal Areas (FATA) was reported to have the highest prevalence of malaria (13.9%) in Pakistan followed by Baluchistan (6.2%), and
Khyber Pakhtunkhwa (KP) (3.8%) (DMC, 2017). Cumulative displaced population in
2014 was 435,429 people (males: 114596, females: 137301 and children 183532).
Moreover, 73% of the total displaced population are children and women. 17 families have already moved into Bakakhel Camp as of June 22 (FATA disaster managment authority, 2014).
Almost all aspect of malaria epidemiology including community prevalence and age- profile of infection, the incidence and type of disease syndromes and total malaria mortality are affected by malaria transmission intensity (Hay and Snow, 2006, Hay et
89 al., 2008). Transmission rate also affect the results of malaria control programs because transmission intensity varies geographically. Maps that describe this variation are necessary to identify populations at different levels of risk to compare and interpret malaria interventions conducted in different places, to objectively evaluate options for disease control (Hay et al., 2008).
Incorrect malaria diagnosis and symptomatic diagnosis is the common practice in most endemic countries (Derua et al., 2011). Symptomatic diagnosis results in over- diagnosis of malaria which leads to unnecessary use of antimalarial drugs and possible amplification of disease burden. Misdiagnosis can lead to delay of proper treatment and sometimes death. Therefore, laboratory diagnosis is an important component of case management and control of malaria (Derua et al., 2011).
Malaria diagnostic techniques can be divided roughly into two methods i.e. Direct and indirect methods. Simple microscopy by observing stained slides, rapid diagnostic test
(RDT) for parasite antigens and polymerase chain reaction (PCR) for Plasmodial DNA is included in direct diagnostic tests. Indirect tests detect host reaction to infection, like immune-fluorescent antibody test (IFAT), enzyme immunoassay (EIA) and iron pigment detection (Hemozoin) (Seed et al., 2005). World health organization recommends direct visualization of parasites using microscopy or malaria-specific rapid diagnostic test (RDT) before treatment to confirm diagnosis for all clinically suspected malaria cases (CDC, 2017b).
The present study was designed with the main aim of investigating the prevalence of malaria infection among the suspected individuals of Bannu district, Khyber
Pakhtunkhwa, Pakistan. In this chapter the applied diagnostic means are compared to assess the specificity and sensitivity of the techniques. Kappa and McNemar’s tests are
90 applied to find the degree of agreement between different tests. Spearman’s rank correlation analysis was also done to find the correlation among different entities.
3.2 RESULTS
Samples were collected from 2033 malaria suspected individuals visiting malaria model lab, Siddique lab and malaria lab of women and children hospital Bannu. Among them most of the patients were not recommended by doctors for malaria investigation but they came there on their own behalf. A total of 2033 individuals were recruited, of whom 429 (21.1%) were positive for malaria by at least one diagnostic technique.
Figure 3.1 District wise origin of patients
Most of the patients were locals of Bannu region and FR Bannu. Some patients also belong to neighboring districts as diagnostic services of Health facilities of those areas are comparatively not satisfactory (Fig. 3.2).
91
Figure 3.2 Origin of positive patients shown on Land cover map
Number of Plasmodium infected patients belonging to different areas are shown on land cover map (Fig. 3.3). Highest flow of patients was observed in agricultural areas including Bannu, Sarai Naurang, Domel and in areas located near sample collection centers.
3.2.1 Techniques used for the diagnosis of malaria:
Table 3.2 shows the results obtained by analyzing blood samples using various diagnostic techniques. Highest number of samples were analyzed by using microscopy technique, showing 17.66% positive results, followed by RDT with 16.32% positivity results. Highest percentage of positive result of 30.54% obtained by using PCR, although least number of samples were screened through this method. Among PCR positive samples 25.40% were P. vivax, 5.14% were P. falciparum while no mixed infection was found.
92 Table 3.1 Plasmodium species distribution through different techniques
Diagnostic Method No. of malarial infections (% positivity) No. of total No. of patients positive (% screened for each P. vivax P. falciparum Mixed positivity) method Microscopy 290/2033 30/2033 39/2033 359/2033 N=2033 (14.26%) (1.48%) (1.92%) (17.66%) RDT 195/1618 30/1618 39/1618 264/1618 N=1618 (12.11%) (1.85%) (2.41%) (16.32%) PCR 79/311 16/311 0/311 95/311 N=311 (25.40%) (5.14%) (0%)* (30.54%) *Subset of samples screened for PCR had no positive mixed infection by microscopy
3.2.2 Locality wise distribution of Plasmodium parasite
The highest number of suspected individuals were from Bannu as the laboratories were located in the Bannu region and accessible to residents of the study area. Highest percentage of P. vivax (18.4%) infection was recorded from Bannu when compared to other areas. Similarly, more number of P. falciparum (2.17%) and mixed (1.7%) infection was recorded from the same region, followed by FATA and Lakki Marwat, while least number was recorded from Karak (Table 3.3).
Table 3.2 Area wise distribution of Plasmodium species
District No. of positive P. vivax P. falciparum Mixed infections cases (%) Total No. of patients screened N (%) N (%) N (%)
Bannu (N=1566) 349 (22.3%) 288 (18.4%) 34 (2.17%) 27 (1.7%)
FATA (N=320) 52 (16.3%) 40 (12.5%) 10 (3.1%) 2 (0.6%)
Karak (N=19) 4 (21.1%) 2 (1.5%) 0 2 (10.5%)
Lakki Marwat (N=128) 24 (18.7%) 13 (10.2%) 3 (2.3%) 8 (6.3%)
Total (N=2033) 429(21.1%) 343 (16.9%) 47 (2.3%) 39 (1.9%)
Spearman’s correlation analysis was conducted to find correlation between
Plasmodium species and Localities. The positive correlation was found among Vivax infection with District Bannu (0.074%, p=0.004), Lakki Marwat (0.046%, p=0.036) and
93 FATA (0.05%, p=0.023). No significant correlation was found between falciparum and
Districts. Mixed infection also showed statistically significant positive correlation with
District Lakki Marwat (0.082, p=0.00) and Karak (0.061, p=0.006).
Table 3.3 Spearman’s correlation analysis between parasite Species and areas
Species Bannu FATA Lakki Marwat Karak
P. vivax 0.074** 0.050* 0.046* -0.016 (0.004) (0.023) (0.036) (0.458) P. falciparum 0.017 0.023 0.0001 -0.015 (0.44) (0.292) (0.98) (0.51) Mixed -0.026 -0.041 0.082** 0.061** (0.243) (0.066) (0.00) (0.006) * Correlation significant at the 0.01 level ** Correlation is significant at the 0.05 level
Most of the individuals visiting the health facilities belonged to Bannu’s two Tehsils i.e., Bannu 1 and Bannu 2. Among 1566 individual, 468 were from Bannu 1 and 1098 were from Bannu 2. Among positive cases 23.5% infected cases were recorded from
Bannu 2 and 19.4% were from Bannu 1. Highest prevalence of vivax (19.5%), falciparum (2.3%) and mixed infection (1.7%) were also found in Bannu 2 while Bannu
1 presented comparatively low values. (Table 3.5)
Table 3.4 Tehsil wise distribution of Plasmodium species
Tehsil No. positive cases P. vivax P. falciparum Mixed Total No. of N (%) N (%) N (%) infections patients N (%) screened Bannu 1 91 (19.4) 74 (15.8) 9 (1.9) 8 (1.7) N=468 Bannu 2 258 (23.5) 214 (19.5) 25 (2.3) 19 (1.7) N=1098 Total 349 (22.3) 288 (18.4) 34 (2.2) 27 (1.7) N=1566
The spearman’s correlation analysis for Plasmodium species and tehsils of Bannu
District showed statistically significant correlation between vivax infection and Bannu
94 2 tehsil of District Bannu. No other significant correlation value was found among other entities (table 3.6).
Table 3.5 Correlation of Plasmodium parasites (vivax and falciparum) with two
Tehsils of Bannu
Species Bannu 1 Bannu 2 P. vivax -0.015 0.076** (0.486) (0.001) P. falciparum -0.014 -0.003 (0.524) (0.909) Mixed -0.008 -0.015 (0.707) (0.503) * Correlation significant at the 0.01 level ** Correlation is significant at the 0.05 level
Out of 2033 samples examined through microscopy, 1618 samples were analyzed using
RDT kit while 311 samples were run through PCR. The results showed 266 (16.4%) samples were positive through RDT, out of which 15.94% were also microscopy positive cases while rest of the cases (0.49%) were microscopy negative. 83.18% samples were declared negative through both RDT and microscopy where as 0.37% were RDT negative while microscopically positive.
A total of 95 (30.54%) samples were detected positive through PCR among which significantly high number 64(20.58%) of samples were microscopy negative showing significant difference between both diagnostic procedures. About 9.96% samples were positive by both methods. The same samples when screened through microscopy confirmed positivity for 45 (14.46%) samples while 31(9.96%) could not be detected by using microscopy.
95 Samples showing negative results for both RDT and microscopy were 1346(83.18%), however, 6(0.37%) were RDT negative but microscopically positive. While 8(0.49%) samples were RDT positive but microscopically negative (Table 3.7 and table 3.8).
Table 3.6 Comparison of the performance of PCR using microscopy as a standard
Mic Total PCR Neg. Pos. 171 45 216 Neg. (54.98%) (14.46%) (69.4%) 64 31 95 Pos. (20.58%) (9.96%) (30.54%) Total 235 76 311
Table 3.7 Comparison of the performance of RDT using microscopy as a standard
Mic Total
RDT Neg. Pos. 1346 6 Neg. 1352(83.5%) (83.18%) (0.37%) 8 258 Pos. 266(16.4%) (0.49%) (15.94%) Total 1354(83.7%) 264(16.3%) 1618
Sensitivity, specificity, PPV and NPV of microscopy were calculated using PCR as the reference test. Sensitivity of the microscopy was 41% with confidence interval (CI)
95% while specificity was 73% (CI=95%). Cohen’s kappa was run to determine agreement between PCR and microscopy for parasite diagnosis. There was moderate agreement between the two diagnostic procedures, k=0.14, p=0.026. Whereas
McNemar test p=0.069, shows no significant difference between two raters (diagnostic methods).
96 PCR and RDT sensitivity, specificity, PPV and NPV were calculated using Microscopy as the reference test. The sensitivity and specificity for PCR analysis was 32% and 79% respectively. The positive predictive value was 41% and negative predictive value was
73%. Kohen’s kappa value was 0.125 (p=0.026) which indicates slight agreement between microscopy and PCR.
Using microscopy as a reference test the sensitivity and specificity of RDT was 79%
(CI=95%) and 99% (CI=95%) respectively while positive PPV value was 98% and
NPV value was 99%. The kappa value was 0.968 (p=<0.001) which shows the perfect agreement between microscopy and RDT for detection of Plasmodium parasite (Table
3.9).
Table 3.8 Comparing Sensitivity and Specificity of diagnostic methods
Sensitivity Specificity PPV NPV McNemar marginal Cohen’s homogeneity test (p Kappa Value value) (p value) PCR 32% 79% 41% 73% 0.069 0.125 (0.026) RDT 79% 99% 98% 99% 0.593 0.968 (<0.001)
Spearman correlation analysis test was conducted to examine the association between different species and diagnostic methods.
The Spearman's correlation coefficients between species shows negative relation among vivax, falciparum and mixed infection and showed positive relation between species and diagnostic methods i.e. Microscopy, RDT and PCR and also positive relation among these diagnostic methods. The correlation value between P. vivax vs. P. falciparum (γ= -0.0693, p=0.0018), P. vivax vs. Mixed infection (γ=-0.063, p=0.00) are
97 negatively significant. The statistically positive correlation was found among
Plasmodium species and diagnostic techniques (Table 3.10).
Table 3.9 Correlation between Plasmodium species and diagnostic methods and
Auto correlation between diagnostic means
P. falciparum Mix Microscopy RDT PCR
P. -0.0693* -0.0630* 0.7696* 0.8199* 0.5794* vivax (0.0018) (0.00) (0.00) (0.00) (0.00) P. falciparum -0.0215 0.2378* 0.3043* 0.3538* (0.332) (0.00) (0.00) (0.00) Mixed 0.3020* 0.3559* - (0.00) (0.00) * Correlation significant at the 0.01 level
Among 429 positive patients, 56.4% individuals were showing symptomatic fever and were found positive for mixed infection while 46.65% individuals with symptomatic fever were vivax positive. Other noticeable percentages was found among individuals presented with body aches (Table 3.11)
Table 3.10 Symptom wise distribution of Plasmodium species
Symptoms Total positive P. falciparum (%) Mixed (%) P. vivax (%) cases Symptomatic fever 197 (79.9) 15 (31.91) 22 (56.41) 160 (46.65)
Chill fever 57 (13.3) 3 (6.38) 6 (15.38) 48 (13.99) Headache 22 (5.1) 4 (8.51) 3 (7.69) 15 (4.37) Body ache 84 (19.6) 8 (17.02) 14 (35.9) 62 (18.02) Diarrhea 4 (0.93) 0 0 4 (1.17) Vomiting 19 (4.43) 2 (4.26) 3 (7.69) 14 (14.08) Anemia 5 (1.16) 0 0 5 (1.46) RTI 6 (1.39) 0 0 6 (1.75)
98 Spearman’s correlation analysis showed statistically significant negative correlation
(γ=-0.09, p=0.00) between Plasmodium vivax and body aches while positive correlation
(γ =0.05, p=0.02) was found between falciparum and fever (Table 3.12).
Table 3.11 Correlation between Plasmodium species and symptoms
Species Fever Chill Headache Body Diarrhea Vomiting Anemia RTI fever ache P. vivax -0.013 0.0122 -0.029 -0.09* 0.008 0.013 0.005 - (0.54) (0.58) (0.18) (0.00) (0.71) (0.53) (0.82) 0.012 0.61) P. 0.05* -0.03 0.017 -0.03 -0.015 -0.008 -0.018 0.226 falciparum (0.02) (0.17) (0.44) (0.10) (0.49) (0.71) (0.42) (0.31) Mixed 0.02 0.009 0.01 0.026 -0.014 0.0137 -0.016 - (0.29) (0.66) (0.63) (0.22) (0.53) (0.53) (0.46) 0.021 (0.35) * Correlation significant at the 0.01 level Monthly variation of Plasmodium species is shown in table (3.13) and Fig 3.5. High frequency of vivax is in month of August (30%) followed by July (19.8%), September
(17%) and October (14%). Highest peak of falciparum was in October (7.6%) followed by May (1.5%) and April (1.2%). No case was reported in August (0%) and September
(0%). Fig 3.7 shows that from March to July, the falciparum prevalence decreases and it disappears in August and September. After disappearance in the month of August and
September, the falciparum reaches to the peak in in the month of October. Mixed (Vivax
+ Falciparum) infection shows peak in October (7.6%) and 2nd high prevalence value in April (3.1%). After April, it decreases and disappears in September (0%), then shows second highest peak in October (26%).
Highest peaks of all three types of infections i.e. Vivax, Falciparum and Mixed were observed in August (30%), October (7.6%) and October (7.6%) respectively. Lowest prevalence of all three types of infections i.e. Vivax, Falciparum and Mixed were
99 observed in March (3.8%), August & September (0%) and March & September (0%) respectively.
Table 3.13 shows falciparum appearance in the month of March, with gradual increase in the following two months of April and May but decline in the month of July and complete disappearance in August and September. Abrupt increase with the highest value of 7.6% was recorded in the month of October.
Table 3.12 Month wise distribution of Plasmodium species
Months Total P. vivax (%) P. falciparum Mixed (%) (%) March 132 3.8 0.75 0 April 324 5.5 1.2 3.1 May 396 8.6 1.5 0.5 July 806 19.8 0.74 1.6 August 110 30 0 0.9 September 94 17 0 0 October 171 14 7.6 7.6 Total 2033 14.3 1.5 1.9
Seasonal variation of Plasmodium Species 35
30
25
20
% Prevalence % 15
10
5
0 March April May July August September October Total Months Vivax (%) Falciparum(%) Mixed (%)
Figure 3.3 Seasonal fluctuation of Plasmodium infection
100 This study was carried out to determine malaria prevalence in clinically suspected individuals at three different laboratories of Bannu. A total of 2033 samples were collected from 1299 male and 734 female individuals. Plasmodium species was detected in 21.24% of the samples from male with 2.54% of Plasmodium falciparum,
2.16% mixed infection and 16.55% were Vivax positive. Samples from females showed 20.84% positivity for Plasmodium species including 1.91% P. falciparum,
1.50% mixed infection and 17.44% P. vivax infection (Table 3.13).
All individuals were divided into three age categories (age in years) i.e. Category-I (0 and above), Category-II (5 and above) and Category-III (25 and above) consisting of
691, 757 and 585 individuals respectively. 20.69% of the Plasmodium infected individuals belonged to category-I, 22.46% belonged to category-II and 19.83% belonged to category-III. Among Falciparum positive cases 2.60% belonged to
Category-I, 2.11% were of category-II and 2.22% were of category-III. Mixed infection comprised of 1.16% of category-I, 1.85% of category-II and 2.91% of category-III.
16.91% of the Vivax cases belonged to Category-I, 18.94% and 14.70% belonged to category-II and category-III respectively (Table 3.14).
Table 3.13 Age and Gender wise distribution of Plasmodium species
Variable Total Positive P. vivax P. falciparum Mixed 276/1299 215/1299 33/1299 28/1299 Male (21.24%) (16.55%) (2.54%) (2.16%) 153/734 128/734 14/734 11/734 Gender Female (20.84%) (17.44%) (1.91%) (1.50%) 143/691 117/692 18/691 8/691 0> (20.69%) (16.91%) (2.60%) (1.16%) Age 170/757 140/757 16/757 14/757 categories 5> (22.46%) (18.49%) (2.11%) (1.85%) (age in 116/585 86/585 13/585 17/585 years) 25> (19.83%) (14.70%) (2.22%) (2.91%)
101
Blood group was asked from patients but many of them have no information about blood group as most of the individuals were illiterate. Among males, highest number of cases were recorded in O positive (22.7%) group followed by A positive (22.2%).
Among females O positive have highest percentage i.e. 25%.
Table 3.14 Relation of Blood group with malaria
Blood group Positive cases/total Positive cases/total Total (%) Male (%) Female (%) A+ 6/27 (22.2) 0/2 (0) 6/29 (20.7) A- 2/4 (50 ) - 2/4 (50) B+ 5/33 (15.2) 1/12 (8.3) 6/45 (13.3) B- 1/4 (25) - 1 /4 (25) AB+ 0/6 (0) 0/2 (0) 0/8 O+ 5/22 (22.7) 1/4 (25) 6/26 (23.1) O- 0/2 (0) 0/1 (0) 0/3 TOTAL 19/98 (19.4) 2/21 (9.5) 21/119 (17.6)
3.2.3 Month wise malaria data in 2014 as recorded by women and children
hospital (WCH) Bannu:
The data shown in table 3.16 represents the Plasmodium species percentage during
January-June 2014. Samples analyzed by laboratory technicians through microscopy.
Table represent the data for six months, PCR processed samples were collected in July.
Highest percentage of P. vivax was recorded in June followed by January and March.
No mixed infection was recorded in these months while very low percentage of P. falciparum, was recorded in April, May and January.
102
Table 3.15 WCH data collected by analyzed in laboratory
Months Total samples P. vivax P. falciparum Mixed Total +ve screened (%) (%) infection% % January 994 15.69 0.20 0 15.89 February 1536 3.05 0 0 3.05 March 1484 10.17 0 0 10.17 April 1452 9.71 0.14 0 9.85 May 1872 9.51 0.05 0 9.56 June 1710 17.13 0.17 0 17.31
3.3 Discussion
This chapter aimed to present malaria epidemiology and to assess the efficiency/sensitivity of different diagnostic methods.
Overall 21.1% cases were positive by at least one method. Bannu was considered as one of the most malarious areas of the province (Asif, 2008). Internal displacement of
FATA population in 2012 (about 700,000 people from neighbouring North Waziristan) was suspected to have added to the malaria burden in Bannu since FATA regions are known to exhibit highest rates of disease incidence in the country (FATA disaster managment authority, 2014, Khattak et al., 2013, Munir et al., 2014). Corroborating previous studies, P. vivax infections were more prevalent compared to P. falciparum and mixed infections (Razzaq et al., 2014, Khan et al., 2016, Khan, 2014). Dominance of one malaria species over the other is primarily determined by the parasite’s biology and by an area’s climatic and seasonal variations (Checchi et al., 2006). Larger influx of P. vivax is possible because true relapses do not occur in P. falciparum whereas in
P. vivax relapses are common due to prolonged survival of Vivax hypnozoites in liver cells (Robert and Janovy, 2009, Garnham, 1987, Garnham, 1967).
103 According to the 2012 national malaria disease surveillance annual report, 9% of
Pakistani population were living in high transmission districts (34) with an annual parasitic index (API) ranging from 5 to 28, 20% are living in moderately endemic districts (41 districts) with API ranging from 1 to 5, and 71% living in low endemic districts with an API below 1/1000 population. The national API for the entire country was averaging 1.69, which classifies Pakistan as a moderate malaria endemic country.
Malaria mappings show clearly that the highly endemic districts are located mainly in the provinces of Baluchistan, FATA, Sindh, and KP. The lowest malaria incidence was reported in two provinces – Punjab and AJK with combined population of more than
56% of total population of the country (MPR, 2012).
Studies in different districts of Khyber Pakhtunkhwa reported high prevalence of P. vivax as compared to P. falciparum. In Kohat (P. vivax as 86.34% and P. falciparum as
13.65%), Buner (P. vivax (5.78%) and 1.08% P. falciparum), Abbotabad (P. vivax as
72.4% and P. falciparum 24.1% (Khatta et al., 2012, Muhammad and Hussain, 2003,
Idris et al., 2007) are all reported with high Vivax prevalence. Malarial infection is more common in rural areas as compared to urban regions of the study area (Khan et al.,
2016, Khan et al., 2013a). Agricultural activities, irrigation system, poor sewage management, ignorant behavior towards protective measures, sleeping habit are the factors contributing to increase prevalence in rural population.
Other studies in different parts of Pakistan also reported high prevalence of P. vivax
(6%-71%) as compare to P. falciparum (0.5%-37%) (Yar et al., 1998, Jan and Kiani,
2001, Mehmood, 2005, Yasinzai, 2008). The highest burden of vivax malaria was reported from Khyber Pakhtunkhwa and the Federally Administered Tribal Areas, illustrating the need for greater programmatic and health system strengthening in these regions (Murtaza et al., 2009). Pakistan is almost in the middle of malaria belt around
104 the globe among tropical and subtropical countries where majority of population is living in rural areas (Soomro et al., 2009). In towns, the defaulted sewerage system, stagnant water, improper dumping of garbage contributes to the spread of malaria.
In our study, P. vivax cases peaked in the summer month (August) while P. falciparum and mixed infections in winter (October). Similar distribution trends are known to exist in certain areas of neighboring Afghanistan that share similar climatic settings (Zakeri et al., 2006, MOH, 2010, WHO, 2011b). Generally, in Pakistan P. falciparum transmission starts in the summer monsoon (July) when the temperature and humidity is optimum and it prevails until the end of the year when the temperature falls below the critical value (December). P. vivax usually observes two transmission peaks, one is its early transmission period during the wet months of spring (probably facilitated by true relapses) and the other is with the P. falciparum (monsoon) (Bouma et al., 1996a,
MOH, 2010, Asif, 2008, Khan et al., 2006). The seasonal abundance of vector as irrigation system and rain fall pattern of Pakistan provides ideal conditions for mosquito breeding. (MPR, 2012, Chaves and Koenraadt, 2010). In District Bannu number of malaria cases rises towards ends of August, maximum in October and November then decreases onward (Dogra, 1938).
Similar findings were reported from different parts of KP including Karak, shangla,
Buner, Lower Dir, Mardan, Peshawar (Daud et al., 2014, Rahman et al., 2017,
Muhammad and Hussain, 2003, Zeb et al., 2015, Majid et al., 2016), FATA (Khan et al., 2016) and other regions (Amodu et al., 1998, Lathia and Joshi, 2004). The recent malaria cases were reported from Bannu, Hangu and Charsadda while no malaria case was reported from nine districts including Abbottabad, Haripur, Kohistan, Mansehra,
Battagram, Torghar, Lower Dir, Upper Dir and Shangla (Associated Press of Pakistan,
2016).
105 Possible reasons for this high prevalence of malaria may be, raining during monsoon and favorable temperature providing suitable conditions for the growth of mosquitoes thus increased in malaria cases (Nizamani et al., 2006, Macdonald, 1956, Hussain et al., 2013, Mehmood, 2005, Farogh et al., 2009, Jahan and Sarwar, 2013, Strickland et al., 1987, Yaseen and Ali, 2015). Low temperature and dry season during winter hinders the sporogonic development (Trape et al., 1992). Currently climatic change, behavior of different vectors and Plasmodium parasite according to Schmalhausen’s law is under consideration for understanding seasonality of malaria in certain parts of the world
(Basurko et al., 2011).
Yaseen & Nafisa, (2015) reported same ratio of P. vivax and P. falciparum cases in post monsoon months and winter (October- February) (Yaseen and Ali, 2015), while seasonal variation of both species during post-monsoon and dry season has been reported (Anya, 2004, Zacarias and Andersson, 2010). P. falciparum emerged as predominant species during 1981-1985 (Rafi et al., 1994). Outbreaks of P. falciparum malaria frequently occur, particularly during the rainy season (April–September) in
India (Dev and Dash, 2007). Immigrants from Afghanistan (P. falciparum endemic) are responsible for its spread in Southern Punjab, Baluchistan and Sindh provinces (Atif,
2009).
The dominance of one malaria species over the other at a certain period might vary from one area to another not only depending on climatic and seasonal factors but also owing to variation in geographical localities (Checchi et al., 2006). In the present study, the infection rate of P. vivax was higher than P. falciparum, because there seems to be no second exothermic cycle and true relapses do not occur in P. falciparum whereas in
P. vivax relapses occurs due to prolonged survival of Plasmodium vivax hypnozoites in liver cells (Garnham, 1967, Garnham, 1987) and these relapses are also responsible for
106 outbreak of the disease (Feighner et al., 1998, Chadee et al., 1992). Moreover the longevity of P. falciparum in man seldom exceeds one year and P. vivax usually die- out within three years (Bruce-Chwat, 1980). It is observed that controlled P. falciparum transmission led to an increase in relapsed P. vivax cases because of suppressive effect of P. falciparum on P. vivax prevalence (Rowland, 2001).
Unlike the findings of our study, age has earlier been associated with malaria acquisition (Dondorp et al., 2008, Ferede et al., 2013) where children ≤ 5 years are shown to carry a comparatively higher risk (Bodker et al., 2003, Brooker et al., 2004,
Peterson et al., 2009). The acquired immunity is both exposure- and age-dependent, and the older children are likely to have developed some degree of immunity because of repeated infections (Bodker et al., 2006, Woyessa et al., 2013). There also exists a possibility of extended exposure due to inattentiveness of parents/guardians towards protective/treatment measures (Peterson et al., 2009). Further, we documented no significant association with gender. Although, several earlier studies in Khyber
Pakhtunkhwa and elsewhere prove males to be at a higher risk of malaria compared to females (Khan et al., 2013a, Awan and Jan, 2008a, Razzaq et al., 2014, Awan and Jan,
2008b, Rahman et al., 2017, Irshad et al., 2013, Daud et al., 2014, Sahar et al., 2010,
Rehman et al., 1994, Kondrachine, 2008, Khan et al., 2016) suggesting increased exposure to mosquito bites since males are more likely to work outdoors and are not traditionally well-covered as females (usually veiled) (Khan et al., 2006). Cultural and social norms might have stemmed low influx of female patients to health care facilities.
A comparison of age-specific parasite rates of malaria in Afghan refugees and nearby local population at Karachi shows that Afghan refugees are susceptible to malaria even in later age-groups while infections in the local population were limited to younger age-
107 groups. Similarly, prevalence of malaria over the years is much higher in Afghan refugees than in the local population which is due to the low herd immunity in Afghan refugees (Suleman, 1988b). The children have low immune system as compared to the adults (Khan et al., 2016).
In contrast to earlier studies from Buner (KP) and (Muhammad and Hussain, 2003) and different areas of Baluchistan (Yasinzai and Kakarsulemankhel, 2003, Yasinzai and
Kakarsulemankhel, 2004, Yasinzai and Kakarsulemankhel, 2007), in the present study mixed infections (1.92%) were detected by microscopy only, unfortunately conclusive statement couldn’t be given about authenticity of these positive mixed infection as PCR was not carried out for these samples. Mixed infections are frequently detected through
PCR as microscopy usually fails to detect mixed infections due to presence of higher numbers of parasites of one species relative to the other (Singh et al., 1999a).
There is no report from Pakistan for detection of mixed infection by microscopic examination, although high percentage of mixed malaria infection was reported through
PCR. Mixed infection rate of 6.5 %, 22%, and 23.5 % was found in Iran, Afghanistan and Pakistan respectively by PCR (Zakeri et al., 2010a). Due to poor quality microscopy practices, mixed infections are rarely diagnosed and reported, less number of mixed infection (0.77%) was reported from endemic areas of Iran while no mixed infection was reported from Afghanistan and Pakistan through the same technique
(Zakeri et al., 2010d). A study conducted in India reported 45.5% of mixed infections by PCR assay while no case of mixed infection was detected through microscopy
(Gupta et al., 2010). The main challenge is the detection of P. falciparum parasitaemia when it is superimposed by P. vivax. In such circumstances, additional tests such as
PCR or ELISA are essential for an accurate diagnosis (Noedl et al., 2006). A study conducted in Flores, Indonesia, reported more sensitivity of PCR in comparison with
108 microscopy in case of sub microscopic infections in low transmission areas (Kaisar et al., 2013).
In the present study, no mixed infection was found among PCR processed samples either through PCR or Microscopy as collection was done in July, during this month falciparum infection does not exist except those having previous dormant infection
(Bouma et al., 1996a). During routine laboratory investigation, no mixed infection was found through microscopy in previous months as falciparum is prevalent from
September to December in the study area. Mixed malaria species infections are often underestimated in surveys from Asia (3%) therapeutic studies showed that the estimated rate is much higher (up to 30%) (Mayxay et al., 2004), 29% in Africa (Rubio et al.,
1999) and Venezuela (Postigo et al., 1998) and 5% in Pakistan (Khatoon et al., 2010).
Absolute parasite identification is an important factor for eradication of any disease.
There is lack of accurate diagnostic procedures for Plasmodium identification in many malaria endemic regions, where dependence on the appearance of clinical signs and symptoms alone are insufficient and vague pointers of specific disease (Fransisca et al.,
2015). In the current study, we also compare the results of different diagnostic techniques used for clinically malaria suspected individuals in malaria endemic region.
Microscopy positive cases were 17.66% including 14.26%, 1.84% and 1.92% were P. vivax, P. falciparum and mixed infection respectively. One of the actual challenge for microscopy is mixed infection as similarities exist between developmental stages of different Plasmodium species. Some individuals were detected as vivax positive through microscopy while co-infection was indicated by RDT. In accordance with this study, in Thailand patients (∼8%) treated for P. vivax malaria are later found to be co- infected with P. falciparum (Mayxay et al., 2001).
109 Among 1618 samples screened through RDT, 264 (16.32%) were detected positive for
Plasmodium infection among which percentage of vivax, falciparum and mixed infection was 12.11%, 1.85% and 2.41% respectively. Some microscopy missed cases were identified by RDT which might be individuals who had been treated but antigen remain persistent in them (Moody, 2002) but possibility of false positive detection by
RDT remain exist.
RDT shows more sensitivity to falciparum and mixed parasitaemia as compared to non- falciparum infection which may be attributed to longer persistence of falciparum antigen (HRP-2) after treatment or resolution as compared to other Plasmodial antigen
(pLDH) which quickly disappear after treatment (Bell et al., 2005, Hopkins et al.,
2007).
WHO recommends RDTs and microscopy as primary methods for diagnosis of clinically suspected malaria in all epidemiological settings, including low transmission areas like Khyber Pakhtunkhwa (WHO, 2014b). In our study, among 1618 samples screened through RDT, 266 (16.4) were detected positive for Plasmodium infection.
RDT presented satisfactory sensitivity (79%) when compared to microscopy. The results of RDT was also compared with microscopy (gold standard), RDT shows perfect agreement with microscopy kappa value (k =0.968) (Landis and Koch, 1977). These results match with the results of Kim et al (2013) showing greater agreement between
RDT and microscopy with high kappa value (0.896-0.991) (Kim et al., 2013). The specificity, PPV and NPV values of RDT against microscopy were 99%, 98% and 99% respectively. High positive predictive value recorded in this study is higher than previous studies that was in the range of 64% to 71% (Ashton et al., 2010, Singh et al.,
2010) and 90.2% by Ameh et al (Ameh, 2012). The finding of the present study is similar to the study conducted in Nigeria where the antigen based method has a better
110 correlation with both the gold standard i.e. microscopy and the clinical settings. The antibody based method as anticipated showed good level of sensitivity but, highly unspecific (Azikiwe et al., 2012). These results provide basis for use of RDT as screening tool and on the basis of its results the clinicians can proceed with the treatment of malaria positive individuals.
Overall findings from the study provide reasonable basis for use of RDT as a screening tool in field and for clinicians to proceed with timely treatment of malaria patients.
Studies also support the superior performance of RDT compared to microscopy in routine clinical settings and especially in remote locales where medical units face deficit in resources like contained labs, electricity, trained microscopists etc (Ohrt et al., 2002, Erdman and Kain, 2008). RDTs can serve as a cost-effective method for clinical diagnosis, mainly due to improved treatment and health outcomes for non- malaria febrile disease (Shillcutt et al., 2008). Incorrect diagnoses are a common observation in labs like the ones selected in our study, largely due to huge numbers of patients availing for free diagnosis. Furthermore, we observed that many of the patients visited the labs for diagnosis without being referral by clinicians.
Similar finding was reported in previous studies with minor differences between results of microscopy and RDT which may be due to the use of antimalarial drugs. Rapid device did not detect P. vivax trophozoites while seen under microscope were due to use of anti-malarial drugs that kill the parasite and are no more able to secrete
Plasmodium lactate dehydrogenase enzyme (PLDH) as secreted by the living forms only (Palmer et al., 1998, Zeb et al., 2015).
Operative study comparing traditional blood smear to rapid antigen capture test demonstrates that the RDT, performed in a routine clinical setting is superior to a single
111 set of Giemsa-stained blood smears for quickly evaluating a patient for malaria.
Importantly, the rapid antigen capture assay had a 100% NPV for P. falciparum malaria. Light microscopy can routinely detect parasitemia level as low as 40 parasites/µl, and experienced microscopists can detect as low as 5-10 parasites/µl of blood (WHO, 2009).
Sensitivity and specificity of RDTs are measured on the basis of HRP-2, pLDH
(parasite lactate dehydrogenase) and PL-aldo (Plasmodium aldolase) antigens targeted by RDT, HRP-2 is secreted by P. falciparum while later are secreted by another species
(Moody, 2002). For detection of low parasitaemia level there should be high sensitivity and specificity of the RDT. The decrease specificity from WHO recommended values can be a result of drug intake before diagnosis which clears the parasitaemia from circulating blood however HRP-2 (Falciparum antigen) remain circulates in blood even post treatment (Chinkhumba et al., 2010).
Different factors related to parasite and host can also influence the RDT performance while its sensitivity is influenced by density of parasitaemia (Shakya et al., 2012, Kim et al., 2008). Variation in antigen structure (Baker et al., 2005, Lee et al., 2006), gene deletion (Gamboa et al., 2010) and prozone effect (prevent the stimulation of antigen- antibody complex which is essential for visibility of test lines, specifically associated with HRP-2 antigen) (Gillet et al., 2009, Luchavez et al., 2011) are the reported factors for false negative results. Previous studies reported 58% sensitivity of RDT in case of
≤100/µL malaria parasite concentration, sensitivity drops to almost zero at concentration of 0.001% (≤50/µL) where microscopy also showed negative result
(Maltha et al., 2011).
112 Incorrect malaria diagnosis leads to increased illness and mortality, drug resistance due to administration of wrong drugs and ignorance of causes of fever such as bacterial infection, leads to prolonged and ineffective treatment. As in study area PCR cannot be used as a routine diagnostic method, chances of correct diagnosis should be increased by use of RDT along with microscopy (Ngasala et al., 2008, de Oliveira et al., 2009,
Hopkins et al., 2008).
Among PCR processed samples, 30.54% (95) were positive for Plasmodial infection, among which 25.40% and 5.14% were vivax and falciparum positive respectively.
Although all quality standards were assured during PCR experiment in this study, there exist a risk for contamination in such a sensitive technique. In the present study, nested
PCR detect 20.58% (64 of 95) more cases which were microscopically declared negative. Among P. falciparum positive cases, ten were diagnosed as P. vivax infection through microscopy but through PCR they were detected vivax negative. This shows either poor quality of the microscopy in the study but the risk of contamination in PCR cannot be excluded.
In the present study, microscopy displayed poor sensitivity when compared to rRNA
PCR. The results of microscopy diagnostics were compared with PCR (Gold standard).
The microscopy shows slight agreement as kappa value (k =0.14) with PCR with the sensitivity and specificity of Microscopy evaluated against PCR (gold standard). The sensitivity, specificity, PPV and NPV values were 41%, 73%, 32% and 79% respectively. Similar results were found in previous studies conducted in Ethiopia, microscopy shows low sensitivity (52%) when compared with Nested PCR for P. falciparum and P. Vivax identification (WHO, 2014a). Studies suggest that microscopy, when compared to molecular diagnostic methods like PCR; often exhibit considerable discrepancies (usually showing lower sensitivity to mixed infections), especially in
113 submicroscopic infections (Mayor et al., 2012, Arango et al., 2013, Kaisar et al., 2013);
(Ohrt et al., 2002). Parasite densities measured by microscopy correlate with parasite gene copy number in quantitative PCRs and studies indicate that in submicroscopic infections lower copy numbers are expected in the low transmission settings than those in the high transmission. These deviations eventually impact diagnostic outcomes (Lo et al., 2015, Okell et al., 2012b). Moreover, mixed infections pose challenges to microscopists since similarities exist between developmental stages of different
Plasmodium species. Microscopy and RDT are prone to miss infections with density less than 100 parasites/μL, while the detection limit of PCR is generally < 5 parasites/μL (Cordray and Richards-Kortum, 2012, Vasoo and Pritt, 2013). Lack of expertise and factors like lack of standard good quality blood films also often contribute to such incongruities (Rantala et al., 2010, Ohrt et al., 2002). On the other hand, PCR will rarely miss microscopy positives due to poor DNA quality, poor quality standards in techniques or missed priming due to mutations (Coleman et al., 2006a). Although studies demonstrate superior performance of PCR traditional diagnostics means in field
(Haghdoost et al., 2006, Zakeri et al., 2010c); (Rodulfo et al., 2007). PCR cannot be used as a routine diagnostic tool due to its technologically advanced lab requirements and expenditures (Ngasala et al., 2008, de Oliveira et al., 2009, Hopkins et al., 2008).
For collecting more accurate epidemiological data from unapproachable areas of world,
PCR is more sensitive technique in combination with filter paper blood spot collection as compared to other methods (Singh et al., 1999b). The high sensitivity of PCR than microscopy for parasite detection is attained by nested method and previous results confirm these observations that there is a sharp cut-off point for the nested PCR assay resulting in an all or none detection of parasites (Snounou et al., 1993b).
114 Sensitivity of genus specific PCR amplification is more than species specific amplification as for primers targeting at least two ssrRNA genes are present in
Plasmodial genome (McCutchan et al., 1988, Gunderson et al., 1987, Li et al., 1997).
Primers for identification of Plasmodium species i.e. P. vivax and P. falciparum are specific only for expressed C-type ssrRNA genes of both species (McCutchan et al.,
1988, Li et al., 1997) thus, failure of PCR in specie identification might be due to low parasitemia (Kawamoto et al., 1996).
In opted laboratories, the technicians were trained for microscopy but still they lack the professional skills for correct diagnosis of species identification. Incorrect diagnosis is a common practice in these labs due to patient’s overflow as among these three labs, two were government facilities where patients were not charged for malaria test so many people visited the lab just for no reason or without any symptoms. Another reason for overflow is the location of the facilities, located in the center of the study area.
3.4 Conclusion
1. Malaria infections in Pakistan are largely credited to P. vivax but P.
falciparum and mixed species infections are also prevalent. In addition,
regional difference in the prevalence and species composition of malaria is
high.
2. The data obtained was checked according to the gender, age, season and
species wise in the human population of Bannu. Males (up to 24 years’ age)
are more susceptible to malaria as compared to females. The seasonal
prevalence pattern is almost similar to the previously reported studies
conducted in the study area, country and in other regions of world,
Plasmodium vivax and Plasmodium falciparum are the prevalent species in
District Bannu with P. vivax being the most prevalent.
115 3. In this study, we followed the questionnaire based data collection from
different patients through local health facilities and diagnostic labs. It is
concluded that load of malaria is high in rural centers as compared to urban
centers where large population belonged to lower class from economic
point of view.
4. RDT provided good test sensitivity, specificity, positive and negative
predictive values as well as inter-procedure agreement for detecting
parasite as compared to Microscopy. RDT can become the first screening
test for the diagnosis of malaria in the laboratory
5. Current evidence indicated that no single method for the diagnosis of
malaria is perfect and none of them be a stand-alone accurate and effective
diagnostic criterion. Both symptoms/signs and laboratory-based malaria
diagnostic methods should be employed for an accurate and effective
diagnosis of malaria. Various factors including malaria endemicity, the
urgency of the diagnosis, transmission pattern, the experience of the health
worker, effectiveness of the health care system and available budget
resources greatly affect the priority of any of the malaria diagnostic
method.
3.5 Recommendations
1. A well designed, longitudinal and multi-centered study is suggested to explore
the seasonality of malaria and its drivers in our environment.
2. Accurate knowledge of this common illness can help the community and the
malaria control program authorities to focus on eradication of malaria. To
control the disease, awareness about the disease, early treatment and preventive
measure are required.
116 3. Evaluation and comparison of different environmental factors and climate or
vector drivers for malaria.
4. Awareness campaigns to encourage the use of repellents, protective clothing,
screening, bed-nets and other forms of personal protection against the bite of
mosquitoes are important and widely recommended (Valecha et al., 1996).
5. The community should be informed and educated about the importance of early
diagnosis and prompt treatment with effective antimalarial.
6. Improvement in the quality of microscopic techniques in clinical laboratories
and provision of PCR and RDT to those health facilities are needed where
microscopy standard is not up to the mark (Cuadros et al., 2015).
117 Chapter 4 RISK FACTORS ANALYSIS FOR MALARIA
4.1 Introduction
Among the Pakistani population, roughly 60% live in malaria endemic regions
(Williams and Meek, 2011). After eradication efforts in the 1960s, malaria surged back to an epidemic level in the 1970s. In recent years, an increase in malaria can be partially attributed to floods that affected approximately 20 million people in over 60 districts
(Williams and Meek, 2011). About 0.5 million cases of malaria and close to fifty thousand deaths are attributed to malaria in Pakistan per year (Mukhtar, 2006).
Pakistan being a tropical and agricultural country where majority of population is poor and lives in the rural areas. The incidence of Increase of malaria cases in this part of the world is due to several factors including increased poverty, environmental deterioration and particularly the spread of chloroquine resistance. Warmer autumns which favor prolonged transmission and a chronic decline in vector control activities are also contributing in the spread of malaria (Rowland et al., 1997b, Tasawer et al., 2003).
Similarly, the prevailing extensive agricultural practices, an expansive irrigation network, and the monsoon rains act together to promote a favorable environment for malaria transmission in many areas of Pakistan (Ghanchi et al., 2011).
The study area (Bannu) of Pakistan consists of agricultural bushes where better sites for malaria vectors growth are linked with the crop cultivation, increasing subsequent infection risks. The poor hygienic condition, no use of antimalarial sprays, use of irrigated land, sharing the house with livestock, improper diagnosis, high temperature of the area and load shading also play a key role for the spread of malaria in the Bannu
District (Khan et al., 2013a).
118 Like other infectious diseases malaria has very complex epidemiology regarding the parasites, its vectors, the human hosts and the environment (van der Hoek et al., 1998).
For designing an effective control and prevention strategy it is essential to focus on the relationship among malaria transmission, human related factors and climatic variables to reduce the burden and transmission intensity of the disease. Human related factors are known to intensify the range of climatic factors. In malaria endemic regions, risk of infection to human population varies with geographical conditions of the areas with in the same country. Malaria incidence, transmission rate and vector distribution can vary between neighboring villages and small settlements within same village, due to variation in risk factors (Lindsay et al., 1990, Drakeley et al., 2003, Snow et al., 1997,
Reyburn et al., 2005).
Apart from rainfall and river-flow velocities other factors are also identified as risk factors such as utilization of control measures (van der Hoek et al., 1998), age and gender (Mendis et al., 1990), human migration (Klinkenberg, 2001), as well as type and location of housing (Gamage-Mendis et al., 1991, Gunawardena et al., 1998, van der
Hoek et al., 1998). Because of geographical and chronological variation of malaria transmission it is essential to analyze the basic risk factors, so that control efforts can be targeted to the high-risk areas (Klinkenberg et al., 2004).
It is epidemiologically proved that in low transmission areas the malaria infection is restricted within communities. Malaria risk varies from person to person due to individual characteristics (Ayele et al., 2012). In areas with stable transmission, age (<5 years) is reported as a high risk of malaria morbidity and mortality (Snow et al., 2005).
Kreuels et al. analyzed the distribution of sex, ethnic background, infant’s birth season, sickle cell trait (HbAS compared with HbAA), mother’s education and occupation, mother’s knowledge of malaria transmission and use of protective measures (Kreuels
119 et al., 2008). Malaria risk factors studies mostly failed to examine the wide range of both household and individual level factors as omission of several factors leads to unsatisfactory results (Mauny et al., 2004). Further research is needed to clarify the different effects of socioeconomic and environmental risk factors on severe malaria occurrence.
The current study was designed to examine the prevalence of malaria infection and associated risk factors in Bannu District, where transmission of malaria is perennial.
The disease is endemic in different parts of Khyber Pakhtunkhwa, including the study site. This study was aimed to recognize the possible risk factors of malaria, its relationship with infection and exposure which will be helpful in the prevention and control of the disease in this area. Individual and household characteristics (fever history, previous malarial illness and protection from mosquitoes, construction material, vicinity to mosquito breeding sites and socio economic status) were studied.
The relationship of these factors to the number of malaria cases was analyzed.
4.2 Materials and method;
4.2.1 Study site and design:
The present study was carried out in three laboratories of Bannu District (32º 43' - 33º
06' N; 70° 22' - 57' E), Khyber Pakhtunkhwa Province. Rivers Kurram and Gambila passing through Bannu serve the area for irrigation and provides breeding grounds for
Anopheles (Malaria vector). The study area has two rain season, in March and summer moon soon during July and August (MOHP, 2017).
4.2.2 Laboratory method
Three diagnostic procedure (Microscopy, RDT, PCR) were used as described in chapter
2.
120 4.3 Risk factors data
The risk factors questionnaire data collected individual and household information
(Appendix B). The data from each individual were taken either from patient or guardian of the patient. Questionnaire recorded information about living area, previous illness, presence of domestic animals, housing type, proximity to vector breeding site, use of preventive methods (e.g., coils, house spraying), bed net use socioeconomic status, sleeping habit, accessibility to health facility, intake of antimalarial drugs etc.
4.4 Statistical analysis of risk factors data
A database was created in Ms Excel and transferred to Stata 14. Univariate and multivariate logistic regression model was used, to investigate whether socio-economic and demographic factors as a risk factors of malaria prevalence of infection and exposure in the studies population. Odds ratios (ORs) and 95% confidence intervals
(95% CIs) were computed. Risk factors were analyzed for three diagnostic techniques separately. Distribution maps of Plasmodium species in study areas and nearby districts were produced by using ARC GIS. Data was paired with positive and control cases through stata 14 (age and gender-wise pairing of positives). To determine the effect of each exposure variable on the outcome variable (prevalence of infection) univariate analysis was performed. Any exposure variable significant with p≤ 0.05 in univariate analysis was used in multivariate analysis (Wald test). The final multivariate modal includes only significant exposure variable with p≤ 0.05.
4.5 Results
Total 2033 samples were analyzed for the detection of Plasmodium infection.
121 Table 4.1 Detail and division of potential risk factors for malaria in the study area
Individual and Household/residential risk Vector control Behavioral risk factors factors Age Locality Insecticide residual spray (IRS) Sex Distance of water body Use of bed nets Education level House construction Fever history Windows glazing Personal Mosquitoes Protection Treatment history socio-economic status (SES) Sleeping habit, Blood Animals ownership group, General Health, Access to health providers Symptoms
4.5.1 Univariate analysis for Microscopy positive samples
A full list of factors investigated for association with the prevalence of Plasmodium infection (detected by Microscopy) are shown in table. 2033 individuals including 359 positive cases and 1674 controls, were matched by gender and age categories to derive
359 matched pairs in Stata Statistical Software: Release 13 (StataCorp LP, 2013). Using this case-control data, risk factor assessment was performed through univariable and step-wise multivariable conditional logistic regression in Stata v13.
To find the association with potential risk factors and disease univariate odds ratios
(OR) were estimated by logistic regression with confidence interval (95%). The univariate analysis showed that satisfactory health condition (OR=0.24, P=0.00, 95%
CI), sleeping inside the room (OR=0.62, P=0.002, 95%CI), presence of health care facility in living area (OR=0.06, P=0.006, 95%CI) and the persons economically belongs to upper middle class (OR=0.68, P=0.04, 95% CI) are statistically significant for reducing risk of malaria.
122 4.5.2 Multivariate analysis for Microscopy positive samples
The details of Multivariate logistic regression analysis for risk factors and prevalence of Plasmodium infection detected by microscopy are given in table 4.2. The multivariate regression model showed a significant effect of apparently satisfactory health condition (OR=0.24, P=0.00, 95% CI), sleeping inside the room (OR=0.69, 0.02,
95% CI) and health care facility in living area (OR=0.61, P=0.003, 95%CI) in reducing risk for malarial infection. This model showed these factors as a protective factor as having odds ratio less than 1 (OR<1).
123 Table 4.2 Risk factors for microscopy
Variable Odd ratio (confidence P value interval) Age group (compared to <5) >5 1 (95%) 1 >20 1 (95%) 1 Sex (female compared to male) 1 (95%) 1 Literacy (compared to illiterate) Primary & above 1.330619 (95%) 0.325 Secondary & above .8828151 (95%) 0.464 Duration of previous malaria illness (compared to no illness) <15 Months 1.056139 (95%) 0.645 >15 Months - - Previous illness 0.9666325 (95%) 0.821 Recurrent malaria (No/yes) 1.913154 (95%) 0.144 Fever history - - General health (compared with poor health) Weak .8216903 (95%) 0.35 Satisfactory .2355209 (95%) 0 Domestic animals (No/yes) 1.312242 (95%) 0.071 Screening against malaria (No/yes) 1.168308 (95%) 0.378 House type (compared with Mud) Concrete .7988062 (95%) 0.138 Tent 1.671007 (95%) 0.322 Water reservoir in vicinity (No/yes) 1.44321 (95%) 0.179 Sleeping habit (compared to outside) Inside .6256993 (95%) 0.002 Blood group (compared to A blood group) B .875 (95%) 0.858 AB - - O .65625 (95%) 0.573 Health care facility in living area (No/yes) .6466535 (95%) 0.006 Anti-malarial drugs intake (No/yes) 1.057517 (95%) 0.708 Socio economic status (compared with lower class) Lower middle .8449726 (95%) 0.368 Upper middle .6766382 (95%) 0.046 Upper class .5713833 (95%) 0.09 Symptomatic fever (No/yes) .8944738 (95%) 0.455 Chill fever (No/Yes) 1.48448 (95%) 0.078 Headache (No/yes) 1.053107 (95%) 0.872 Body ache (No/yes) .7667148 (95%) 0.123 Diarrhea (No/yes) .6629128 (95%) 0.527 Vomiting (No/yes) .8425439 (95%) 0.613 Anemic (No/yes) 1 1 Respiratory tract infection (No/yes) .5932011 (95%) 0.317 Treatment recommended by doctor (No/yes) 1.260712 (95%) 0.128 Number of family members having malaria (No/yes) - -
124 Table 4.3 Multivariate table of microscopy positive samples
Variables Odds ratio P value (confidence interval) General health (compared with poor health) Weak .8698489 (95%) 0.515 Satisfactory .2413133 (95%) 0.000 Sleeping habit (compared to outside) Inside .6898804 (95%) 0.022 Health care facility in living area (No/yes) .6086407 (95%) 0.003
4.5.3 Univariate risk factors analysis for RDT positive samples
1618 individuals including 264 positive cases and 1354 controls, were matched by gender and age categories to derive 264 matched pairs in Stata Statistical Software:
Release 13 (StataCorp LP, 2013). Using this case-control data, risk factor assessment for RDT was performed through univariable and step-wise multivariable conditional logistic regression in Stata v13.
Univariate risk factors analysis for RDT detected samples showed general health condition (satisfactory OR=0.58, P=0.04, 95%CI and weak OR=0.71, p=0.00, 95%CI) have significantly protective effect for malaria infection.
125 Table 4.4 Univariate risk factors analysis of RDT positive samples
Variable Odd ratio (confidence P value interval) Age group (compared to <5) >5 .9866667(95%) 0.951 >20 .9758242(95%) 0.912 Sex (female compared to male) 1 1 Literacy (compared to illiterate) Primary & above 1.294421(95%) 0.536 Secondary & above 1.017045(95%) 0.931 Duration of previous malaria illness (compared to no illness) <15 .8990181(95%) 0.554 >15 .4253687(95%) 0.073 Previous illness (yes/No) .8548892(95%) 0.376 Recurrent malaria (No/yes) 1.391304(95%) 0.485 Fever history - - General health (compared with poor health) Weak .5815387(95%) 0.048 Satisfactory .175814(95%) 0 Domestic animals (No/yes) 1.276169(95%) 0.163 Screening against malaria (No/yes) 1.043564(95%) 0.836 House type (compared with Mud) Concrete 0.9699454(95%) 0.861 Tent - - Water reservoir in vicinity (No/yes) 1.176(95%) 0.688 Sleeping habit (compared to outside) Inside 0.7123756(95%) 0.05 Blood group (compared to A blood group) B 0.4242424(95%) 0.231 AB 1 - O 3.888889(95%) 0.26 Health care facility in living area (No/yes) 0.9466738(95%) 0.774 Anti-malarial drugs intake (No/yes) 1.200354(95%) 0.296 Socio economic status (compared with lower class) Lower middle 0.7501209(95%) 0.187 Upper middle 0.7168911(95%) 0.143 Upper class 1.088778(95%) 0.817 Symptomatic fever (No/yes) 1.095278(95%) 0.601 Chill Fever 1.197183(95%) 0.428 Headache (No/yes) 1.18107(95%) 0.618 Body ache (No/yes) 0.7584077(95%) 0.138 Diarrhea (No/yes) 4.4046154(95%) 0.213 Vomiting (No/yes) 0.8396761(95%) 0.609 Anemic (No/yes) 2.007634(95%) 0.57 Respiratory tract infection (No/yes) 0.6177606(95%) 0.404 Treatment recommended by doctor (No/yes) 1.297297(95%) 0.137 Number of family members having malaria (No/yes) - -
126 4.5.4 Risk factors analysis for PCR positive samples
Among 311 individuals including 95 positive cases and 216 controls, were matched by gender and age categories to derive 95 matched pairs in Stata Statistical Software:
Release 13 (StataCorp LP, 2013). Using this case-control data, risk factor assessment for PCR was performed through univariable and step-wise multivariable conditional logistic regression in Stata v13.
Univariate risk factors analysis for PCR showed significant association between general health condition of individual (satisfactory OR=0.5, P=0.008, 95%CI and weak
OR=0.33, P=0.04, 95%CI) and decrease in malarial infection risk.
127 Table 4.5 Univariate risk factors analysis for PCR positive samples
Variable Odd ratio (confidence P value interval) Age group (compared to <5) >5 1 1 >20 1 1 Sex (female compared to male) 1 1 Literacy (compared to illiterate) Primary & above 1.894345 0.12 Secondary & above 1.515476 0.286 Previous illness (Yes/No) 1.241026 0.463 Duration of previous malaria illness (compared to no illness) <15 1.32 0.356 >15 0.55 0.501 Recurrent malaria (No/yes) 1.348148 0.701 Fever history - - General health (compared with poor health) Weak 0.5 0.038 Satisfactory 0.336 0.008 Domestic animals (No/yes) 1.102901 0.754 Screening against malaria (No/yes) 1.447619 0.295 House type (compared with Mud) Concrete 0.542005 0.054 Tent 0.569801 0.231 Water reservoir in vicinity (No/yes) 0.489923 0.125 Sleeping habit (compared to outside) Inside 1.101075 0.756 Blood group (compared to A blood group) B - - AB O Health care facility in living area (No/yes) 0.649123 0.143 Anti-malarial drugs intake (No/yes) 0.894085 0.738 Socio economic status (compared with lower class) Lower middle 1.244444 0.533 Upper middle 1.46087 0.359 Upper class 1.217391 0.891 Symptomatic fever (No/yes) 1.349876 0.344 Headache (No/yes) - - Chill fever (No/yes) 1.516484 0.653 Body ache (No/yes) 0.586813 0.475 Diarrhea (No/yes) - - Vomiting (No/yes) - - Anemic (No/yes) - - Respiratory tract infection (No/yes) 1 1 Treatment recommended by doctor (No/yes) - - Number of family members having malaria (No/yes)
128 4.5.5 Univariate risk factors analysis for all positive cases
In univariate logistic regression model for cases detected by three diagnostic means
(microscopy, PCR and RDT), the exposure variables which were statistically significant for malaria outcome included literacy level (OR=1.97 P=0.01, 95% CI), animals domesticated within houses (OR=1.35, P=0.027, 95%CI) and treatment recommendation by doctor (OR=1.84, P=0.00, 95%CI).
Table 4.6 All positive cases risk factors analysis
Variable Odd ratio P value (confidence interval) Age group (compared to <5) >5 1 1 >20 1 Sex (female compared to male) 1 1 Literacy (compared to illiterate) Primary & above 1.973333 0.01 Secondary & above 1.062534 0.705 Duration of previous malaria illness (compared to no illness) <15 months 1.042077 0.786 >15 months 0.497462 0.068 Previous Illness (No/yes) 0.990654 0.945 Recurrent malaria (No/yes) 1.401459 0.362 Fever history 1 1 General health (compared with poor health) Weak 0.51024 0.001 Satisfactory 0.156651 0 Domestic animals (No/yes) 1.356831 0.027 Screening against malaria (No/yes) 1.212341 0.23 House type (compared with Mud) Concrete 0.857802 0.27 Tent 2.17734 0.093 Water reservoir in vicinity (No/yes) 1.091667 0.717 Sleeping habit (compared to outside) Inside 0.659492 0.003 Blood group (compared to A blood group) B 0.466667 0.275 AB 1 - O 2.4 0.354 Health care facility in living area (No/yes) 1.021853 0.883 Anti-malarial drugs intake (No/yes) 0.884773 0.373 Socio economic status (compared with lower class)
129 Lower middle 1.025686 0.881 Upper middle 0.762584 0.129 Upper class 0.975645 0.94 Symptomatic fever (No/yes) 0.927839 0.584 Chill fever (No/Yes) 1.041935 0.893 Headache (No/yes) 0.873514 0.653 Body ache (No/yes) 0.541876 0 Diarrhea (No/yes) 1.33647 0.705 Vomiting (No/yes) 0.639193 0.14 Anemic (No/yes) 1.252948 0.738 Respiratory tract infection (No/yes) 0.539007 0.228 Treatment recommended by doctor (No/yes) 1.841656 0 Number of family members having malaria (No/yes)
4.5.6 Multivariate analysis for all positive cases (through all techniques)
Multivariate analysis of the samples detected through all techniques showed Literacy level (OR=1.98, P=0.017, 95%CI), treatment recommended by doctor (OR=1.47,
P=0.012, 95%CI) are the significant risk factors for malaria infection. While Apparent health status (weak OR=0.56, P=0.005, 95%CI and satisfactory OR=0.17, P=0.00,
95%CI) and Sleeping inside the room (OR=0.71, P=0.028, 95%CI), body ache
(OR=0.66, P=0.019, 95%CI) are the protective factors which shows statistical significance in preventing malaria.
Table 4.7 Multivariate analysis of significant factors
Variables Odds ratio P value (confidence interval) Literacy (compared to illiterate) Primary & above 1.981171 0.017 Secondary & above 1.251425 0.201 General health (compared with poor health) Weak 0.5601284 0.005 Satisfactory 0.1766944 0.000 Sleeping habit (compared to outside) Inside 0.7152879 0.028 Body ache (No/yes) 0.6640837 0.019 Treatment recommended by doctor (No/yes) 1.475297 0.012
130 4.6 Discussion
The study was conducted to find the relationship between malaria prevalence and environmental and socioeconomic variables. For this purpose, multilevel analysis of individual and household level factors was used to find their significance with risk of malaria.
Domestic and individual level malaria risk factors were reported in many studies, included access to health facility, house type, closeness to the vector breeding sites, vector abundance, socio economic status, occupation, gender, travel history, migration, frequent change of residence, presence of domestic animals, use of bed nets and other protective measures etc. (Kreuels et al., 2008, Ghebreyesus et al., 2000, Staedke et al.,
2003, Vittor et al., 2006, Tilaye and Deressa, 2007, Somi et al., 2007, Siri et al., 2010,
Bogh et al., 2001). Ethnicity is also recognized as an independent risk factor for malaria
(Hustache et al., 2007). The existence of mosquito breeding sites also determined by the variations in the geography of land parts (Balls et al., 2004). The risk to malaria infection is also varies with the distance to the mosquito breeding sites (Trape et al.,
1992, Gamage-Mendis et al., 1991, Gunawardena et al., 1998). The interaction of these factors with malaria prevalence is variable in different geographical settings which needs to be investigated in each locality along with correlated climatic factors. The impact of these parameters differs with fluctuations of seasonal transmission of the localities (Carter et al., 2000, Antonio-Nkondjio et al., 2005, Cano et al., 2006).
Researchers have since seen that age-specific human attack rates peaked in adult males, suggesting that occupation may be an important risk factor in malaria transmission
(Roper et al., 2000). The same study found that agriculturists were 3.2 times as likely to have a malaria episode compared to individuals working in commerce. The travelling history is also considered as important risk factor for malaria (Chuquiyauri et al., 2012).
131 Keeping livestock indoors was seen as a risk factor for acquiring malaria as described previously (Khan et al., 2013; Bouma & Rowland, 1995). Cattle may attract mosquitoes, either by serving as bait or by creating mosquito breeding and resting sites near livestock pens (Tuyishimire, 2013; Mahande et al., 2007; Peterson et al., 2009;
Siri et al., 2010). Cattle provide an easy source of blood to mosquitoes, which results into increased vector populations, a significant proportion of which is attracted to feed on individuals sleeping outdoors near animals (Wahid, 2013). It is most likely that the risk of mosquito bites could have been reduced due to zoo-prophylaxis (Kaburi et al.,
2009) especially in situations where vector species predominantly display zoophagic foraging tendencies. However, the benefit of keeping livestock close to human dwellings has been refuted by many authors (Bogh et al., 2001; Ghebreyesus et al.,
2000). In Pakistan, Anopheles culicifacies and A. stephensi are confirmed as malaria vectors (Jahan & Sarwar, 2013) and are chiefly Pakistan’s zoophilic (Naz et al., 2013).
A. culicifacies is seen as the primary vector in rural areas while A. stephensi considered to an important vector for malaria transmission in urban areas (Carmichael, 1972;
Reisen & Boreham, 1982). Blood meal analyses conducted by Reisen and Boreham
(1982) in Punjab revealed just 1% of blood meals in Anopheles stephensi and 5% in An. culicifacies to be of human origin. Livestock are thought to be largely responsible for generating the high mosquito densities in the region, a strong positive correlation between cattle:man ratio and malaria incidence was reported in northern KP province of Pakistan (Bouma & Rowland, 1995).
Concrete houses appeared to reduce risk of malaria since they can decrease mosquito contact by limiting their entry and reducing their resting places (Ayele et al., 2012).
Higher malaria incidence rate is commonly reported from poorly constructed houses
(incomplete, mud, or palm walls and palm thatched roofs) compared to well-
132 constructed houses (bricks/stones) since poor housing likely leads to an increase in human-vector contact (Gamage-Mendis et al., 1991; Keiser et al., 2004; Ayele et al.,
2012; Ghebreyesus et al., 2000; Gunawardena et al., 1998; Koram et al., 1995). On the contrary, several others suggest no such association between malaria incidence and housing quality (Peterson et al., 2009; Kimbi et al., 2013; Van Der Hoek et al., 1998;
Wolff et al., 2001; Lenz, 1988; Snow et al., 1998).
The poorly constructed houses or mud built houses in rural settings contributes more in malaria infection by providing more resting places and entrance points into houses for mosquitoes in malaria prevalent areas, other studies also reported high prevalence of disease in rural areas (Kimbi et al., 2013, Chirebvu et al., 2014)) where poorly constructed houses and keeping domesticated animal inside mud built houses can be one of the major reason by serving as bait or by creating mosquito breeding and resting sites near livestock pens, rural travel was strongly associated with adult malaria incidence while urban travel was not found to be associated with malaria risk (Peterson et al., 2009). Similarly, in Southeast Asian countries, living in poorly built houses (mud, palm walls, and palm thatched roofs) were associated with increased risk of malaria that was suggested to increase vector-human contact (Arasu, 1992, Oemijati, 1992). For example, a study conducted in Thailand demonstrated that poor housing conditions
(mud, palm walls and palm thatched roofs) increased the risk of malaria 19-fold
(Butraporn et al., 1986). Similarly, in India better housing conditions (bricks walls/cemented or iron sheet roof with no eaves) in town was associated with low risk of malaria prevalence than rural areas that were thought to limit access to vector mosquitoes (Dev et al., 2004).
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
133 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 (Bamaga et al., 2014).
Other studies also reported significant association between house type and malaria outcome (Woyessa et al., 2013, Coleman et al., 2010). People with poor housing quality are at great risk of malaria as it provides entrance to the mosquitoes inside houses. The houses construction pattern is reported as greater risk associated with malaria infection
(Chanon et al., 2003, Schofield and White, 1984, Mendis et al., 1990, Konradsen et al.,
2003).
Living in an upper middle class was protective against malaria as these households have the amenities to adopt better preventive measures compared to the poor and underprivileged (Koram et al., 1995; Guthmann et al., 2001; Ong'echa et al., 2006; El
Samani et al., 1987; Banguero, 1984; Sintasath et al., 2005; Macintyre et al., 2002).
Most of the people of rural areas belongs to poor class which are the major victims as they are less privileged and economically downtrodden people, who often have dirty environments and most often have no means of acquiring better mosquito-bite preventive measures, and no access to modern treatment and found to be at greater risk of malaria disease because they are poorer and face higher transmission rates than their urban counterparts as Social and economic factors aggravate the contribution of climate related factors in influencing the malaria burden (Macintyre et al., 2002, Somi et al.,
2007, IndexMundi., 2013, Robert et al., 2003, Henry et al., 2003). Socioeconomic factors may directly or indirectly affect malaria transmission (Temel, 2004; Brooker et al., 2004; Mcmichael et al., 2006) and failure to consider effects of socioeconomic elements might jeopardize the success of control programs (Al-Taiar et al., 2009).
134 According to the World Health Organization sedentary communities living in malaria prone areas who cannot afford to move out of flood affected areas due to low economic status have an increased chance of acquiring infection. This is particularly true for children, pregnant women, and older people, who form the high-risk groups (WHO,
2011a). Other studies also reported no significant associations (Luckner et al., 1998,
Niringiye and Douglason, 2010) which contradicts the assertion that poverty undermines the coping mechanisms that could help poor people reduce their vulnerability to malaria (Yanda et al., 2006.).
The low socio economic status (SES) was reported significant for increasing malaria risk in previous studies (Thang et al., 2008, Cook, 2010, Feachem et al., 2010, Ayele et al., 2012, Chirebvu et al., 2014). This may be variety of factors such as repellent affordability, poor hygiene, poor house construction which contribute to increased risk of malaria in lower income class as compare to higher status. It has also been reported that the wealthy and educated households often live in clean environments, and can afford better mosquito-bite preventive measures, and good medical attention when afflicted with malaria. The major victims are usually the poor, less privileged and economically downtrodden people, who often have dirty environments, and most often, have no means of acquiring better mosquito-bite preventive measures, and no access to modern treatment (Robert et al., 2003, Kimbi et al., 2013).
The risk of being infected with malaria was up to 2.5 times greater for people in poorly constructed houses, with thatched roofs and mud walls than for people in better- constructed houses (Charlwood et al., 2015, Obaldia, 2015, Gamage-Mendis et al.,
1991, Gunawardena et al., 1998, Konradsen et al., 2003, Pinikahana, 1992, van der
Hoek et al., 1998). Higher socio-economic level (according to equipment and education levels) was associated with a significantly lower risk of infection as the higher socio-
135 economic status makes easier the access to anti-parasite interventions, i.e. anti-malarial treatment, more than to anti-vector measures independent of bed net use (Baragatti et al., 2009).
Dale et al. found that low-middle income and lower education levels were significantly associated with malaria in Indonesia (Dale et al., 2005). A study in Thailand showed a longer residence duration (adjusted OR = 0.36, 95% CI) and the use of anti-malarial self-medication (adjusted OR = 0.08, 95% CI) were significantly associated with protection from severe malaria (Hustache et al., 2007). Koram et al. also found that children living in poor quality housing and crowded dwellings were infected with malaria more frequently than other children living in better housing conditions in peri- urban areas in Gambia (Koram et al., 1995). Ijumba et al. demonstrated that the disappearance of malaria in some areas of Europe was associated with economic development and there is enough historical evidence to support that economic development has a positive impact on health.
Commonly, individuals availing government health services belong to lower socioeconomic backgrounds generally with low education levels. These have been associated with poor knowledge regarding utilization of preventative methods and malaria treatment among them (Tuyishimire, 2013). Achieving higher education is known to be protective against malaria (Dike et al., 2006). In Bannu, acquiring only primary education by patients or guardian of patients seemed to increase the risk of malaria. There is a dire need to disseminate information for preventive and control strategies in the study area as 1588 individuals denied using any precautionary measures.
Better education also helps the individuals to explain their illness to health workers as compared to illiterate ones (Krause et al., 1998). High level of education was found
136 significantly protective against malaria and the possible reason could be that people who have high level of education are better able to protective themselves as most of them belongs to higher class. However, the high level of education is not necessarily related to appropriate knowledge as in the same study there were only 20% of the individuals which had gone through beyond primary level of education while 55% of the individuals had better knowledge of protection against mosquito’s bites (Guthmann et al., 2001). The similar finding was reported in Gambia where 10% of the mothers had formal education beyond primary level but they had fair knowledge about malaria
(Koram et al., 1995). Delayed diagnosis and treatment seeking attitudes, conditioned by the perception of the disease, could explain the increased risk of malaria in people who have poor knowledge of malaria (Hla et al., 1998, Weber et al., 2003).
In third world countries, the higher illiteracy level is reported among females as compared to males having better educational opportunity, experience, and unrestricted mobility, leads to less knowledge of illness and treatment of malaria while literate women have significantly higher demand for the utilization of malaria treatment and preventative methods than their illiterate counter-parts (Lampietti et al., 1999).
Apparent health status of the individual was indicative of malaria possibly because many patients were referred by clinicians for malaria tests due to their anemic status expected in malaria infections (Ekvall et al., 2001; Sumbele et al., 2010; Kimbi et al.,
2013; Tarimo, 2007). If left untreated (or inadequately treated), malaria may result in several weeks or months of poor health following repeated attacks of fever and anemia
(UNICEF, 2000).
Sutcliffe et al. reported 10-20% of the positive individuals with fever at the time of testing while many of the positive individuals were asymptomatic which poses a significant challenge to malaria elimination as many control strategies rely on the
137 identification and treatment of symptomatic individuals seeking care at health centers
(Sutcliffe et al., 2011, Moonen et al., 2010).
Prompt and accurate diagnosis of malaria is the key to effective disease management and therefore it is one of the main interventions of the global malaria control strategy.
Identification of the species of malarial parasite is very important for its effective and curative treatment as resistance to chloroquine and other anti-malarial drugs has been reported previously (Rahim and Younas, 2003, Atif, 2009). For accurate diagnosis and treatment easy access to health care and antimalarial drugs, and greater willingness to take sanitary measures can play a vital role in reducing malaria burden (Robert et al.,
2003, Klinkenberg et al., 2004). In study area, many of the patients lacking access to the public health services which leads to increasing incidence of malaria as many of them use self-medication which fails in curing malaria, the self-intake of antimalarial drugs leads to incorrect dosage consumption which cause the increase resistance of
Plasmodium to routine dosage. The rapid increase of both anti-malarial drug resistance and insecticide resistance is a major problem which restrains the control of malaria.
Therefore, the WHO introduced two powerful and broadly used intervention tools, i.e. insecticide-treated nets (ITN) and indoor residual spraying (IRS) to reduce the intensity of local malaria transmission. Natural disasters are considered to accelerate the spread of malaria infection. The improved access to health care facility can be one of the effective malaria control strategies (Reiter, 2001, Suh et al., 2004).
Frequent outdoor activities also increased probability of infection as mosquitoes are generally active between dusk and dawn (Dale et al., 2005). Risk of malaria transmission always exists where mosquito host seeking behavior coincides with place and times of human presence (Ndoen et al., 2011). In the study, herein, most of
138 individuals had evening activities while sleeping in open grounds was a common practice.
Sleeping habits especially clustering in sleeping rooms was established as a crucial risk factor. Clustering at household level appears to increase malaria risk probably due to increased release of human chemo-attractants (Torres-Estrada & Rodriguez, 2003;
Danis-Lozano et al., 2007). The study area experiences long hours of power outage in summers that aggravates the biting rate. Sleeping of more than one member on a single bed reflects the lower economic condition of the family, while the increased amount of human released chemo-attractants can possibly increase the mosquito attraction inside the rooms can increase the risk of malaria. Similarly sleeping of more individuals inside single room can also increase mosquito biting rate as human chemo-attractants increased (Torres-Estrada and Rodriguez, 2003, Danis-Lozano et al., 2007). In the urban center of study area most of the members sleeping in single room faced severe load shedding problem with no alternative which lead to high influx of mosquitoes inside the room and chemo-attractants may also contributes to increased biting rate.
Infected houses members also increased the risk of spreading malaria.
In study area, most of the population belongs to lower socioeconomic class who can’t afford the good quality repellents and other protective measures. Illiteracy also contributes to ignorant behavior about protective measure. While locally made affordable repellents are of low quality which didn’t fulfill he need. In poorly constructed houses the sleeping and cooking is also performed in same room, smoke could act as repellent but not useful when meal is prepared outside room or when room windows are unglazed and cracks in door or in walls (Ghebreyesus et al., 2000). Study done in Kenya showed that sleeping in a room with sprayed insecticides reduce the risk of infection by 75% while using ITN can reduced the risk by 63% (Guyatt et al., 2002).
139 Dale et al. found that outdoor activities are a higher risk behavior as mosquitoes are most active between dusk and dawn (Dale et al., 2005). The highest risk of malaria transmission is where mosquito host seeking behavior coincides with places and times of human presence (Ndoen et al., 2011). In the study area, most of the individuals have evening activities while sleeping in open grounds at night is a common practice which may increase the risk of malaria.
Many of the previous studies reported positive relation between apparent symptoms and malaria outcome. Each attack of malaria may last several hours with several symptoms, often begins with shivering (body shaking), fever and profuse sweating.
During an attack, the patient often complains of headache and pain in the back, joints and all over the body. Other symptoms included loss of appetite, vomiting and diarrhoea. While the child may present with symptoms of severe malaria such as loss of consciousness, drowsiness and/or convulsions, diarrhoea, dark urine and reduced urine output (anuria). While many of the anti-malarial drugs have side-effects including headache and body aches (UNICEF, 2000). The present study also showed significant relation of body aches and malaria. Malarial infection leads to ischemia due to sequestration of red blood cells, inflammation and oxidative stress, which in turn damages skeletal and cardiac muscles. Malaria induces a combination of inflammation and oxidative destruction in skeletal and cardiac muscles leading to the increased degradation of contractile proteins which results in compromised muscle function
(Marrelli and Brotto, 2016).
Statistically significant relation was not found between headache and malaria outcome in this study but previously it is reported as important presentation of malaria before and during treatment. Headache profile in patients with malaria focusing on its mechanism. Headache is an important presentation in malaria, either cerebral type or
140 not. The cytokine is believed to be an important factor leading to headache in acute malaria. Some antimalarial drugs can cause headaches. In addition, headache is one of the symptoms of post malaria neurologic syndrome (Wiwanitkit, 2009).
According to the WHO guidelines, Mozambique is in the process of adopting a case management policy that recommends all cases of suspected malaria be confirmed by laboratory testing (Tiago et al., 2011). This policy serves at least two purposes: reduce unnecessary use of artemisinin-based combination therapy and exclude malaria among non-malaria febrile patients, allowing for other causes of fever to be investigated and treated. Overtreatment of malaria is not only an economic concern; it has been proposed that restricting antimalarial use to laboratory-confirmed cases will also delay the emergence and spread of resistance to artemisinin derivatives and their partner drugs
(Malisa et al., 2010). Of even more concern is the issue of labeling febrile patients with malaria. Some studies have shown a reduction in the prevalence of malaria parasitaemia among febrile patients over the last decades (D'acremont et al., 2010; Waitumbi et al.,
2010; Reyburn et al., 2004). The labeling of all febrile patients as having malaria can have severe consequences as the underlying disease would not be properly identified and treated (Reyburn et al., 2004). In our study, we observed that although many of the patients were recommended by clinicians for diagnosis, there were several patients that visited the facility on their own behalf. Many had a history of malaria with self- medication while others were prescribed anti-malarials without diagnosis. Many of the private practitioners and labs also prescribed antimalarial drugs without confirmatory malaria tests. The cases of improper intake of anti-malarial drugs were also observed during study as lack of awareness and careless attitude towards health is a common practice in the area. For prevention of all these ill practices, government should make
141 sure that without doctor’s recommendation and reliable diagnostic results the antimalarial drugs are not provided to individuals.
Previous malarial infections lead to rapid acquisition of protection by developing immunity against malaria which is reported in previous studies (Gupta et al., 1999,
Phillips, 2001). Patients having no previous history of malaria showed more severe disease condition compared to those having 1-3 episodes of clinical malaria. Little exposure to malaria can lead to acquiring immunity against disease which can protect against severe disease while in children it leads to rapid acquisition of protection against infection (Gupta et al., 1999, Mackinnon et al., 2005).
In this study, we also ask about previous history of malarial infection and many of individuals have previous infection history but in contrast to other studies significant association between malarial infection and previous history of malaria was not found in the present study.
The previous clinical infection play an important role in secondary infection as the
Vivax hypnozoites survive in liver cells for long time period (Garnham, 1967,
Garnham, 1987) while the reactivation of these parasites leads to relapse of infection in individual (Hankey et al., 1953) and the role of these relapses in malaria outbreaks are documented (Feighner et al., 1998, Chadee et al., 1992). In Mexico, the collection of new and secondary cases together results in 80% of malaria cases in endemic regions.
The family member having clinical episode of malaria are the greater risk for other healthy individuals of the house as he can harbor the parasite for several years and can also present symptomatic and asymptomatic relapses (Danis-Lozano et al., 2007).
Age is another factor reported as significant in many of previous studies (Dondorp et al., 2008, Ferede et al., 2013) while in this study age is not found statistically significant.
Children in the youngest age (≤ 6 years) had significantly high malaria parasite
142 prevalence when compared to the older ones (Bodker et al., 2003, Brooker et al., 2004,
Peterson et al., 2009). The acquired immunity is both exposure- and age-dependent, and the older children are likely to have developed some degree of immunity, because of repeated infections (Bodker et al., 2006) conducted in Butajira area of Ethiopia reported highly significant statistical relation between low age and malaria incidence(Woyessa et al., 2013). The possible reason could be the illiteracy of the guardians towards protective measures and overlapping of different activities of children and Anopheles biting behavior (Peterson et al., 2009).
Surprisingly gender as significant factor was not found in this study while previous studies reported significant association between male gender and risk of malaria as males expose their bodies more often than females, especially when the weather is hot, thus, increasing their chances of being bitten by mosquitoes, males engage in activities which make them more prone to infective mosquito bites as compared to females’ counterparts which are mostly at home and protected from such infective bites (Kimbi et al., 2013, Ferede et al., 2013, Alemu et al., 2012, Mandel et al., 1984). While in contrast other studies reported female at greater risk of malaria because of their schedule than their male counter parts. Women are often at work before dawn and continue late into the night when the mosquitoes are active (Tanner and Vlassoff, 1998,
Ghebreyesus et al., 2000). They wake up during early mornings to prepare food and stay up late to clean and to take care of other household chores in the evening. In addition, women fetch water from rivers, dams, and water reservoirs, in most cases early in the morning. Women also wash family clothes and household items by going to these rivers and water reservoirs. These water sources harbor a large population of mosquitoes increasing women’s chances of getting malaria. In most cases men usually do not involve in household chores.
143 The use of protective measures such as topical repellents, domestic insecticides, smoke, bed nets etc. are reported significant in many studies (Stefani et al., 2011, Pajot et al.,
1977) but statistically significant association could not be confirmed in this study. The possible reason can be the improper usage and quality of the protective substances and measures. Poor knowledge about disease and its prevention could be the greater risk for increasing malaria incidence (Valle and Torres, 1965). Intensity of malaria transmission, poor housing condition and poor protection against malaria can be the possible causes of the increase malaria risk (Danis-Lozano et al., 2007).
The movement of infected people from areas where malaria is endemic to areas with low rates or malaria-free can lead to resurgence of disease (Dale et al., 2005).
Conversely, non-immune or low immune people are prone to be infected by malaria if they travel or move to malaria endemic regions (Reiter, 2001). Huge population movement which increases the risk of malaria transmission (Zhou et al., 2005). Many internally displaced population (IDPs) was settled in Bannu in the study duration which was an important factor for contributing increased malaria incidence in Bannu. But statistically significance between migrants and malaria outcome in the region was not found.
4.6.1 Strategies
For controlling malaria it is necessary to adopt new control strategies such as reducing mosquito abundance by treating larval breeding sites. Controlling the relapses of Vivax by repeated use of chloroquine and primaquine is another way for reducing burden.
Other strategies including involvement of community participation and periodic clearing of vegetation around houses, raising education level to improve knowledge about malaria diagnosis and seeking treatment, use of protective measure while travelling, increase use of treated bed nets are useful (Chanon et al., 2003, Rozendaal
144 et al., 1989, Burkot et al., 1990, D'Alessandro et al., 1995, Lindblade et al., 2004,
Charlwood et al., 2003, Rhee et al., 2005).
145 Chapter 5 Population genetics of Plasmodium vivax
5.1 Introduction
Current research is mainly focused on Vivax and falciparum malaria and little efforts are contributed towards other human malaria species. Beside mono-infection by these species, mixed infection within one individual can occur which play an important role in progression and outcomes of disease (Richie, 1988, Bruce et al., 2000).
The species identification is performed through microscopy but its sensitivity and specificity is related with several factors. Species involved in multiple infections can be confirmed through molecular techniques while there several genotypes or strains of same species can also be confirmed through molecular methods (Luxemburger et al.,
1997, May et al., 2000). Naturally significant number of multiple infections are caused by different genotypes of same species within the single host which is referred as multiplicity of infection (MOI). In case of falciparum, MOI is related with several factors such as age of the host, clinical severity and transmission intensity.
Determination of within-host diversity may be a useful marker to assess the impact of interventions (Smith et al., 1993, Mayengue et al., 2009, Juliano et al., 2010).
P. vivax is widely spread human malaria species and half of the world population is estimated to be at risk of vivax malaria (Price et al., 2007, Price et al., 2009, Guerra et al., 2006). In previous studies, it was reported that genetic variability also exists among members of multi-genes families of P. vivax (Neafsey et al., 2012). Mutation can help the parasite to evade the human immune response and to tolerate the drugs or stabilize the structure and function of proteins, proved by analyzing the field isolates (Wang et al., 2014, Chan et al., 2015). Novel sequencing techniques can help to find genetic diversity of P. vivax which is significantly important to monitor genes involved in drugs
146 susceptibility and to identify potential vaccine candidates (Amambua-Ngwa et al.,
2012, Mobegi et al., 2014, Conway, 2015, Ariey et al., 2014).
Due to increased prevalence of P. vivax there is stronger need for new drug development, several genome projects are underway for this purpose but still there is low published data of vivax genome. Most of the sequencing projects are focused on the regional characteristics of P. vivax isolates (Winter et al., 2015), (Dharia et al.,
2010).
The origin and evolutionary history of human malaria parasites, P. falciparum and P. vivax, the mitochondrial genome of Whole mt genome analyses of most common human malaria parasites, Plasmodium falciparum and Plasmodium vivax, have provided evidence of the origin and evolutionary histories of these species (Joy et al.,
2003, Mu et al., 2005). Mitochondrial genome (partial or complete sequence) is useful marker for population studies in wide range of organisms (Avise and Walker, 1998,
Jorde et al., 1998, Saccone, 1994). In Plasmodium genus, mitochondrial genome is specifically convenient for population studies as it is only 6kb in length and include only three genes i.e.; cox3, cox1 and cytb (Wilson and Williamson, 1997).
Mitochondrial genome was used to find out the age of most recent common ancestor
(MRCA) of falciparum (Hughes and Verra, 2002). As compared to falciparum there is less knowledge regarding population genetics of P. vivax. According to recent SNP analysis of nuclear genome, the polymorphism in vivax is similar or slightly higher than falciparum (Joy et al., 2003). The evolutionary studies of falciparum were aided by available isolates of P. reichenowi (chimpanzee malaria parasite), so it can be assumed that MRCA of falciparum and reichenowi occurred around the time that human and chimpanzee lineages diverged (5-7 million years ago). Thus P. reichenowi provides a calibration point for estimating divergence time within falciparum. Such a calibration
147 reference is not available for P. vivax (Escalante et al., 1998, Hughes and Verra, 2001),
(Joy et al., 2003).
Mitochondria are ubiquitous organelles in all eukaryotes that are essential for a range of cellular processes and cellular signaling. Nearly all mitochondria have their own
DNA or mitochondrial (mt) genome, which varies considerably in size, structure and organization. The phylum Apicomplexa includes a variety of unicellular eukaryotes, some of which are parasites of clinical or economic importance. Recent studies have demonstrated that apicomplexan mt genomes, which include the smallest 6 kb genome of the malaria parasites, exhibit remarkably diverse structures (Hikosaka et al., 2013).
The genomes of Plasmodium mitochondria have only three protein-coding genes
(cytochrome c oxidase subunits I and III: cox1and cox3 and cytochrome b: cytb) and highly fragmented rRNA genes have been identified. Plasmodium has the smallest mt genome in the form of a circular and/or tandemly repeated linear element of 6 kb
(Preiser et al., 1996). Copy numbers for this element are approximately 20-fold and
150-fold of the nuclear genomes in P. falciparum (Preiser et al., 1996) and P. yoelii
(Vaidya and Arasu, 1987), respectively. The 6-kb element encodes only three mt protein-coding genes (cox1, cox3 and cob) in addition to the large subunit (LSU) and small subunit (SSU) ribosomal RNA (rRNA) genes (Feagin et al., 1997). The two rRNA genes are highly fragmented with 20 rRNA pieces having been identified (Feagin et al., 1997, Hikosaka et al., 2011). Very recently, eight additional fragmented rRNA candidates have been identified in the P. falciparum mt genome (Feagin et al., 2012).
Curiously, no transfer RNA genes have been identified.
There is little literature about P. vivax genome sequencing due to the reason it is not amenable to culture in vitro. Genetic diversity of P. vivax is reported from Pakistan is poorly studied and diversity based on whole genome sequences has not been reported
148 to best of our knowledge. The mitochondrial genome sequence of prevalent malarial species in Pakistan is yet to be analyzed and compared to global mt genome sequences.
To complete the evolutionary history of globally prevalent malarial parasite, the
Pakistani genome data is necessary to be included.
There is high diversity of both P. vivax and P. falciparum populations in Bannu district.
High prevalence of mixed specie and mixed-clone infections in P. falciparum and P. vivax in Bannu may be attributed to introduction of parasite (through frequent human travel) and high transmission rate. Bannu is basin like, and is intersected by two major rivers whose banks and associated irrigation activities provide ideal breeding grounds for malaria vectors. Secondly, besides being a regional commercial hub for Central
Asia, the area has a high influx of Afghan refugees who may also facilitate the introduction of parasite clones from Afghanistan (Yang et al., 2006, Anderson et al.,
2000, Vafa et al., 2008). Such heterogeneity might thus cause differences in parasite virulence, transmissibility and responses to chemotherapy, with important implications for malaria control measures in this populous region (Khatoon et al., 2010).
In this study, clinical isolates were obtained from District Bannu, Khyber Pakhtunkhwa were typed for mitochondrial genome to elucidate the status of P. vivax epidemiology in south region of Khyber Pakhtunkhwa province of Pakistan in relation to the global samples.
5.2 Results
Participants were recruited from the three labs of Bannu. The hospital's catchment area covers the entire district. Inclusion criterion was suspected malaria patients visiting the labs, irrespective of age or sex. PCR results of these samples showed that 25%, 5.14% and 69.4% of the samples (n=311) had P. vivax, P. falciparum and parasite-free respectively, while 14.46% were misdiagnosed by microscopy method. The same
149 sample were utilized to study the population structure of the P. vivax in the Bannu district of Pakistan. The positive samples were sent to LSHTM (London school of
Hygiene and tropical medicine) for mitochondrial genome sequencing. Eleven different primers were designed and used for sequencing of mitochondrial DNA. Complete mitochondrial genome sequence was not obtained as clinical isolated were used in this study. In addition, data from Preston et al et al. provided dataset for other geographical areas. Unfortunately, due to missing nucleotides sequences the number of haplotypes in the study area could not reported. Three mitochondrial genes i.e., cox1, cox3 and cytb were analyzed and mutation on different sites were recorded (Preston et al., 2014).
Two hundred and thirty-one isolates from 18 countries were included in SNP analysis.
Along with Pakistani samples all other countries SNP data was provided by LSHTM laboratory. Samples related to one country was considered as single population thus 18 population were included in this study for different analysis. These 18 populations were further categorized into five geographical regions described by WHO (WHO, 2017).
Table 5.1 Data distribution into WHO malaria regions
Cluster name Number of Counties included (number of samples) isolates Americas 20 Colombia (3), Peru (10), Brazil (4), Panama (1), Mexico (2)
Western Pacific 92 China (1), Cambodia (57), Malaysia (6), Vietnam (12), PNG (16) South East Asia 94 India (1), Thailand (86), Myanmar (1), Indonesia (5), Sri Lanka (1) Eastern 21 Pakistan (21) Mediterranean African Region 1 Madagascar (1)
150 5.2.1 Basic properties of the analysed data
Table 5.2 shown the details of all populations. Highest number of samples were from
Thailand (88) and Cambodia (57). The total length of alignment utilized is 6Kb. The
Salvador 1 strain was used as a reference. The missing data is represented by alphabet
“N”.
Highest number of polymorphic loci was detected in PNG (78) among 208 usable loci, followed by Thailand (28) with 201 usable loci. The highest number of usable loci was found in Pakistan i.e. 224. The overall mean of polymorphic sites was 9.6.
Table 5.2 General properties of populations
No. of gene copies No. of usable Loci No. of polymorphic loci Salvador 1 180 0 Brazil 4 182 2 Cambodia 57 203 23 China 1 181 0 Colombia 3 181 0 India 1 181 0 Indonesia 5 188 7 Madagascar 1 181 0 Malaysia 6 181 8 Mexico 2 181 0 Myanmar 1 181 0 Pakistan 21 224 11 Panama 1 181 0 Peru 10 181 1 PNG 16 208 78 Sri Lanka 1 181 0 Thailand 88 201 28 Vietnam 12 189 15 Mean 12.8 188.1 9.611 SD 23.1 12.6 19.1
Table 5.3 shows the variables sites found in Pakistani sample alignment. The 32 variable sites (SNP) were reported from Pakistani samples including 28 private SNPs.
Most of the SNPs are noted at non-coding region of the genome.
151 Table 5.3 Variable sites in the mitochondrial genome
Position
240 667 668 676 704 958 1054 1270 1291 1292 1293 1295 1950 1973 3301 3302 3313 3476 3490 3616 3632 3700 3778 3863 3982 4352 4353 4511 5367 5519 5911 5931
Ref allele A G A A A G G T A C A T T G C A A G G G G G G G G A T G T C A A PAK1 A G A A A G G T A C A T N N N N N N N N N N N N N N N N N N N N PAK5 N T T T A A G N N N N N N N N N N N N N N N N N N N N N N N N N PAK17 N G A A A G A T A C A T T G C A - G G G G G G G G N N N T C T A PAK19 A G A A A G G T - - A G T G C A A A A A A A A A A A T C T C T A PAK22 A G A A A G G T A C A T T G C A - A G A A A A A A A T C T C T A PAK23 N G A A A G G T A C A T T G T C A G G G G G G G G A T C T C T A PAK25 A N N N N N N T A C A T T G C A A G G G G G G A G A C C G C T A PAK26 A G A A A G G N N N N N N N C A - A G G G G G G G A T C T C T A PAK27 N N N N N N N N N N N N T G N N N N N N N N N G G N N N N N N N PAK28 A G A A A G G T A C A T T T N N N N N N N N N G G A T C N N N N PAK29 A N N N N N N T A C A T T G C A A G G G G G G G G A T C T C T AA PAK30 A G A A A G G T A C A T T G C A A G G G G G G G G A T C N N N N PAK36 N G A A A G G N N N N N T G C A A G G G G G G G G N N N T C T A PAK38 A G A A A G G N N N N N T G N N N N N N N N N N N N N N N A T A PAK39 A G A A A G G T A C A T T G N N N N N N N N N N N A T C T C A A PAK47 N N N N N N N N N N N N T G N N N N N N N N N N N N N N T C T A PAK48 N N N N N N N T A C A T N N N N N N N N N N N N N N N N T C T A PAK50 A T T T G G G T A C A T T G C A A G G G G G G G G N N N N C T A PAK52 A N N N N N N G A C G T T G N N N N N N N N N N G A T C T C T A PAK54 C G T A A G G N N N N N TA G N N N N N N N N N N G N N N T C A A PAK58 A N N N N N N T A A A - T T N N N N N N N N N N G G C C N C T A Reference sequence based on the most common genotype observed i.e. Salvador1 Grey highlighted cells are private SNPS reported from Pakistan N represent the missing data
152 5.2.2 Mitochondrial genes analysis
The three genes (cox1, cytb, cox3) present on mitochondrial genome was analyzed individually to assess the mutations in polypeptide chains.
5.2.2.1 Cox 1 gene
The table 5.4 represents the amino acid mutation in protein chain due to mutation in nucleotides at different regions of cox1 gene. Total 10 amino acid mutations were observed due to variation at 11 nucleotides position. Among 10 amino acids six were replaced by “K”, two were replaced by “N”, one is replaced by “A”. At position 324 of amino acid two consecutive nucleotides mutation was found but there is no effect on amino acid.
153 Table 5.4 Cox1 Gene: Amino acid mutations observed in the dataset and their respective positions on the nucleotide sequence
Position of amino acid
mutation on
50 55 97
102 125 151 179 219 324 395 polypeptide sequence Corresponding positions of mutation
on the nucleotide 3676 3490 3616 3632 3700 3778 3863 3982 4352 4352 4511 sequence aReference R E E R E D R D H G PAK19 K K K K K N K N . A PAK22 K . K K K N K N . A PAK23 ...... A PAK25 ...... K . . A PAK26 K ...... A PAK28 ------. A PAK29 ...... A PAK30 - ...... A PAK36 ...... - - A PAK39 ------. A PAK52 ------. A PAK58 ------. A aReference amino acid sequence
154 5.2.2.2 Cytb gene
Table 5.5 shows the mutation pattern in Cytb gene located on Plasmodium mitochondrial genome.
Four SNPs were found on Cytb gene. The mutation at base 5367, 5911 and 5931 cause no changes in polypeptide sequence while SNP found at position 5519 replaced the amino acid “A” to “D” in sample Pak 36.
Table 5.5 Cytb gene: Amino acid mutations observed in the dataset and their respective positions on the nucleotide sequence
Position of amino acid mutation on 195 246 377 383 polypeptide sequence Corresponding positions of mutation 5367 5519 5911 5931 on the nucleotide sequence Reference G A K * PAK17 . . . . PAK19 . . . . PAK22 . . . . PAK23 . . . . PAK25 . . . . PAK26 . . . . PAK28 - - - - PAK29 . . . . PAK30 - - - - PAK36 . D . . PAK38 - . . . PAK39 . . . . PAK47 . . . . PAK48 . . . . PAK50 . . . . PAK52 . . . . PAK54 . . . . PAK58 - . . . *unknown codon
155 5.2.3 Nucleotide substitution pattern
The analysis involved 226 nucleotide sequences. All ambiguous positions were removed for each sequence pair. There was a total of 223 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 (Kumar et al., 2016).
Table 5.6 Maximum Composite Likelihood Estimate of the Pattern of Nucleotide
Substitution
A T C G A - 8.41 4.95 7.04 T 8.65 - 9.75 6.93 C 8.65 16.58 - 6.93 G 8.78 8.41 4.95 -
Each entry shows the probability of substitution (r) from one base (row) to another base (column) (Tamura et al., 2004). For simplicity, the sum of r values is made equal to 100. Rates of different transitional substitutions are shown in bold and those of transversionsal substitutions are shown in italics. The nucleotide frequencies are 29.88% (A), 29.06% (T/U), 17.10% (C), and 23.96% (G).
5.2.4 Genetic clustering analysis
Genetic clusters were analyzed by utilizing 224 markers, 18 populations with in global samples of 231 P. vivax isolates. Genetic clusters were identified utilizing STRUCTURE and neighbor joining tree NJ. These approaches would provide a comparative overview of applicability and robustness of these methods to interpret the genetic structure of P. vivax. All samples or populations were grouped into priori geographical clusters according to WHO malaria regions.
5.2.4.1 Neighbor joining phylogenetic tree
An un-rooted NJ tree was constructed for SNP data. One thousand random haploid re-samplings of the dataset were made and the mean of the values (Bootstraps>50%) for main branches are shown on the NJ tree. The tree is later edited by figtree v1.4.3. Significant bootstrap values (>50) were added in the tree. All populations were grouped into clusters based on their geographical
156 location. Each cluster is represented by specific color in the tree. Nine clades were identified on
NJ tree which are labeled as A to I.
The clade A consist of two samples of PNG (Papua New Guinea) with significant bootstraps value.
The clade B consisted of 10 samples of eastern Mediterranean region (Pakistani samples) with significant bootstrap value (83). Clade C was observed with strains from Eastern Mediterranean,
Southeast Asia and Americas region while no significant bootstrap value was found. On the other hand, clade D consist of 2 samples of belonging to Americas region (Peru) (bootstrap=65).
Clade E comprised samples of two regions including Americas and Southeast Asia. One of sub- clade consist of two Southeast Asian strains was found with significant bootstrap value (65). Clade
“F” consist of only two regions i.e., Western Pacific and Southeast Asia. The sub branches were found with significant bootstrap values (>50) including samples of Western Pacific regions.
Another clade “G” consist of samples of three regions including Western Pacific, Southeast Asia and Eastern Mediterranean (Pakistan). In this clade, a sub branch was well-supported by significant bootstrap value (50). The sub-clade of china and Vietnam was found with well- supported bootstrap (75) and along with another sample of Cambodia the three were found with significant bootstrap (76). Another well supported (bootstrap value=52) small sub branch consist of Western Pacific and Southeast Asia samples of Plasmodium vivax. The two isolates of Western
Pacific were also observed together with significant bootstrap value (62).
Another main branch of the tree was sub divided into two sub-branches which were categorized into Clade “H” and “I”. The clade H consist of two regions Western pacific and Southeast Asia.
The largest clade “I” had sub-clades of significant bootstrap values for Western Pacific (82 and
57), Southeast Asia (59) and eastern Mediterranean (Pakistan) (bootstraps=64).
157
Figure 5.1 Molecular Phylogenetic analysis by Maximum Likelihood method
ML tree was concluded from 1000 bootstrap replicates. Branches with bootstraps >50% are shown next to the branches. 5.2.4.2 STRUCTURE
The program STRUCTURE uses a Bayesian model-based clustering method to infer population structure using MCMC (Markov chain Monter Carlo) scheme. It assumes linkage equilibrium
(HWE) and non-linkage between marker loci within populations. The method also identifies migration or admixture events. (Pritchard et al., 2000). For details see section 2.6.1.1 of chapter
2.
158 The Bayesian approach implemented in STRUCTURE was used to infer population sub-structure
P. vivax in the dataset with 21 Pakistani isolates and 210 samples of other countries. Value for optimal number of clusters (K) was allowed to vary between 1 to 20 for 5 replicate consecutive runs (for settings see section 2.6.1.1). The mean of the –ln likelihoods (ln Pr(K/X)) for 5 independent runs was observed to plateau at K=6 (Figure 5.2).
The STRUCTURE plot based on this optimum cluster or sub-population number (Table 5.7) showed a more condensed genetic structure of the dataset. The delta K method identified six sub- populations with 4 admixtures evident between subpopulations. These admixtures were evident between the STRUCTURE population 1, 2, 3 and one at the end of population.
Pakistani Plasmodium vivax population was observed between population 1, 5 and Admixture 1,
2 and 4.
Figure 5.2 Graphical output of STRUCTURE v2.3.1 for dataset
159 Table 5.7 Composition of clusters observed by STRUCTURE analysis for P. vivax populations
Populations Number Country of origin (Number of samples) of samples 55 Salvador (1), Brazil (4), Colombia (3), India (1), Indonesia Population 1 (3), Madagascar (1), Malaysia (1), Mexico (2), Pakistan (7), Panama (1), Peru (10), PNG (13), Sri Lanka (1), Thailand (7) Population 2 55 Cambodia (21), Thailand (30), Vietnam (4) Population 3 14 Cambodia (6), China (1), Indonesia (1), Malaysia (3), Thailand (1), Vietnam (2) Population 4 67 Cambodia (23), Myanmar (1), Thailand (42), Vietnam (1) Population 5 1 Pakistan (1) Population 6 2 Papua New Guinea (2) Admixture 1 11 Indonesia (1), Malaysia (1), Pakistan (7), Thailand (2) Admixture Admixture 2 9 Malaysia (1), Pakistan (4), Thailand (2), Vietnam (2) Admixture 3 6 Cambodia (3), PNG (1), Thailand (1), Vietnam (1) Admixture 4 11 Cambodia (4), Pakistan (2), Thailand (3), Vietnam (2)
5.2.5 Population parameters analysis of geographical clusters
Several parameters were studies for all the geographical clusters by utilizing the SNP data produced from 224 sequencing markers.
5.2.5.1 Genetic diversity and inbreeding indices per geographic cluster
Genetic diversity and inbreeding indices were calculated as described in section 2.5.2.5.3 of chapter 2.
Genetic diversity indices varied across the geographic clusters (Table 5.8). The mean number of alleles was highest in the Malaysia (MNA=2.13) followed by Peru, Brazil, Indonesia and Thailand
(2), PNG & Vietnam (1.91), Cambodia (1.87) and Pakistan (1.33). However, a statistic like MNA is subject to sample bias (e.g. estimates are higher for populations with larger sample size). Thus, another sample corrected HE (Nei’s genetic diversity) was used. It suggested that Malaysia
(HE=0.52) and Brazil (HE=0.5) were the most genetically diverse groups. Whereas, Cambodia
(HE=0.11) and Pakistan (HE=0.06) were the least diverse groups. These results were only for clusters with polymorphic loci all those with no polymorphic loci were excluded from analysis.
160 Table 5.8 Genetic diversity and inbreeding indices per geographic cluster
a b c d N MNA MNASD* LT HE HESD* Sal 1 - - 180 - - Br 4 2 0 182 0.5 0.00 Cam 57 1.87 0.34 203 0.11 0.12 Chi 1 - - 181 - - Col 3 - - 181 - - Ind 1 - - 181 - - Ino 5 2 0.00 188 0.44 0.08 Mdg 1 - - 181 - - Mly 6 2.13 0.35 181 0.52 0.12 Mex 2 - - 181 - - Myn 1 - - 181 - - Pak 21 1.33 0.55 224 0.06 0.11 Pan 1 - - 181 - - Per 10 2 0.00 181 0.35 0.00 PNG 16 1.91 0.30 206 0.198 0.09 Srl 1 - - 181 - - Tha 88 2 0.00 201 0.08 Vie 12 1.91 0.29 189 0.26 0.14
*SD=Standard deviation a Number of samples per-sub-population b MNA=Mean number of alleles c Total number of loci tested. The test was not performed on loci that were monomorphic or not typed for that group d HE=Mean unbiased (sample corrected) expected heterozygosity (Nei, 1973) calculated overall loci in Arlequin v3.5
Linkage disequilibrium (LD) tests were carried out for non-random association of alleles at different loci) for all loci per sub-population. A statistic called Index of association (IA) was used for this purpose. Since statistic IA is sensitive to the number of loci used in the study, an alternative analogous statistic rbard was also calculated. Deviation of IA and rbard from zero can be taken as an evidence of LD. A p-value is generated by a random permutation procedure to test the significance of any deviation from zero. Both IA and rbard provided clear evidence of significant allelic disequilibrium in Cambodia, Thailand, PNG, Indonesia, Malaysia and Pakistan (p<0.05).
Vietnam and Brazil showed non-significant values for IA and rbard (P>0.05). the LD test couldn’t
161 be applied to Salvador, China, India, Madagascar, Mexico, Myanmar, Panama and Sri-Lanka due to small sample size (N=2 or <2).
Table 5.9 Tests for linkage equilibrium/disequilibrium per sub-population
a a c Geographical clusters IA rbard P-value Cambodia 1.04 0.05 0.001 Colombia - - - Thailand 0.20 0.009 0.049 Vietnam 0.39 0.03 1 Peru 0.00 - - PNG 59.5 0.78 0.001 Brazil -0.33 -0.33 1 Indonesia 1.40 0.23 0.01 Malaysia 1.83 0.26 <0.001 Pakistan 2.12 0.08 <0.001 Salvadord * * * China d * * * Indiad * * * Sri Lankad * * * Madagascard * * * Mexicod * * * Panamad * * * Myanmard * * * Grey highlighted cells represent sub-populations in significant LD aMeasures of multilocus linkage disequilibrium calculated by comparison to 1000 randomizations of null distribution in Multilocus v1.3. dTest not done on these clusters
5.2.5.2 Gene flow and genetic differentiation across the sub-populations
Analysis of population divergence using the FST statistic corroborates genetic differentiation at regional scale and further finds significant genetic differentiation between the different regions.
The magnitude of significant FST values are varies for different regions. Moderate genetic differentiation was found among samples of Western Pacific (Cam & Mly, FST=0.13) compared to those from Southeast Asia (Cam & Tha, FST= 0.07, Tha & Viet, FST=0.13).
Great genetic differentiation (FST =0.15-0.25) was observed between samples of America and. western pacific (Br & Cam, Br & Vie, Per & Cam, Fst=0.17-0.23), America and Southeast Asia
162 (Br & Ino, FST=0.15 Br & Tha, FST = 0.21). Highest level of differentiation was observed between
Mexico and Peru FST =0.86, though both belongs to same region (America), whereas the lowest differentiation (FST= -0.78) was between Panama and Peru belonging to same region, this represent the free interbreeding of the Plasmodium population located in the same geographical region.
Pakistani strains showed great to very great differentiation with strains from America (Mexico
Brazil, Colombia, Peru), Western pacific (Malaysia, Cambodia and Vietnam), South East Asia
(Myanmar, Indonesia, Thailand). Another greater genetic differentiation was observed among samples of same geographical cluster i.e. Southeast Asia (Indonesia and Thailand, FST=0.39), though located in the same geographical region.
163 Table 5.10 FST estimates of the inter-cluster differentiation in the lower triangular matrix
Distance method: pair wise difference Sal Br Cam Chi Col Ind Ino Mdg Mly Mex Myn Pak Pan Per PNG Srl Tha Vie Sal - Br 0.33 - * * * * * * Cam 0.15 0.18 - * * * * * * Chi 1.00 0.81 0.26 - * Col 1.00 -0.09 0.13 1.00 - * * Ind 1.00 0.45 -0.04 1.00 1.00 - Ino -0.03 0.15 0.27 0.28 0.09 -0.19 - * * * * Mdg 1.00 0.14 -0.06 1.00 1.00 1.00 -0.48 - Mly 0.24 0.29 0.13 0.16 0.25 0.07 0.08 -0.20 - * * * Mex 1.00 0.61 0.18 1.00 1.00 0.00 0.17 1.00 0.31 - * * * Myn 1.00 0.45 -0.62 1.00 1.00 1.00 -0.03 1.00 -0.05 1.00 - * Pak 0.12 0.37 0.57 0.23 0.34 0.12 0.37 0.09 0.40 0.30 0.15 - * * * * Pan 1.00 -1.00 -0.25 1.00 0.00 1.00 -0.55 1.00 -0.20 1.00 1.00 0.11 - * Per 0.84 0.15 0.23 0.95 -0.09 0.84 0.36 0.70 0.47 0.86 0.84 0.46 -0.78 - * * PNG -0.56 -0.02 0.28 -0.14 -0.07 -0.49 -0.02 -0.60 0.07 -0.07 -0.49 0.27 -0.71 0.10 - * * Srl 1.00 0.14 -0.06 1.00 1.00 1.00 -0.48 0.00 -0.20 1.00 1.00 0.09 1.00 0.70 -0.60 - Tha 0.32 0.21 0.07 0.58 0.15 0.28 0.39 0.11 0.26 0.39 -0.68 0.67 -0.19 0.25 0.35 0.11 - * Vie 0.05 0.17 0.02 0.09 0.11 -0.11 0.22 -0.18 0.07 0.16 -0.57 0.42 -0.35 0.29 0.15 -0.18 0.13 -
The upper matrix contain P generated from 1000 random permutations leading to an FST value larger than or equal to that is observed. Dark grey highlighted cells represent FST values >0.15. Light grey highlighted cells represent FST values 0.05< FST<0.15. Significant FST values (p<0.05) are in bold italics. *Significant P values Sal: Salvador Ind: India Myn: Myanmar PNG: Papua New Guinea Col: Colombia Br: Brazil Ino: Indonesia Pak: Pakistan Srl: Siri Lanka Mex: Mexico Cam: Cambodia Mdg: Madagascar Pan: Panama Tha: Thailand Chi: China Mly: Malaysia Per: Peru Vie: Vietnam
164 5.2.5.3 Estimation of evolutionary divergence by number of differences method
Among all populations PNG showed the highest evolutionary distance with other populations (9-17).
Highest divergence value was found between china and PNG (17) followed by Vietnam & PNG
(14.08). Pakistani Plasmodium population also showed the high divergence with other populations including Vietnam, PNG, Indonesia, Brazil, Malaysia, Thailand, China and Myanmar (3-9).
165 Table 5.11 Estimates of Evolutionary Divergence over Sequence Pairs between Groups
Viet PNG Srl Ino Ind Br Mly Tha Mdg Chi Per Myn Pan Col Mex Pak Cam Viet 1.73 1.54 1.68 1.68 1.44 1.28 0.93 1.54 1.79 1.37 0.97 1.36 1.36 1.68 1.21 1.00 PNG 14.08 1.37 1.06 1.78 1.21 1.56 1.53 1.37 2.61 1.17 1.80 1.16 1.16 1.78 1.06 1.66 Srl 4.58 11.44 1.14 1.07 1.03 1.39 1.29 0.00 2.47 0.93 1.58 0.94 0.94 1.07 0.40 1.44 Ino 5.93 11.41 2.80 1.72 1.11 1.49 1.43 1.14 2.33 1.08 1.69 1.07 1.07 1.72 0.95 1.64 Ind 4.92 12.44 1.00 3.80 1.54 1.85 1.61 1.07 2.17 1.45 1.88 1.44 1.44 0.00 0.94 1.48 Br 4.42 10.88 1.50 3.10 2.50 1.45 1.12 1.03 2.68 0.36 1.48 0.30 0.30 1.54 0.82 1.36 Mly 4.96 13.07 3.50 4.23 4.50 4.00 1.23 1.39 1.70 1.40 1.43 1.39 1.39 1.85 1.14 1.30 Tha 3.86 12.51 3.01 4.63 3.83 2.61 4.21 1.29 2.24 1.02 0.51 1.02 1.02 1.61 1.04 0.79 Mdg 4.58 11.44 0.00 2.80 1.00 1.50 3.50 3.01 2.47 0.93 1.58 0.94 0.94 1.07 0.40 1.44 Chi 6.17 17.00 6.00 7.20 5.00 7.50 5.00 6.99 6.00 2.63 2.39 2.63 2.63 2.17 1.93 1.84 Per 4.12 10.76 1.20 2.80 2.20 0.70 3.70 2.33 1.20 7.20 1.39 0.19 0.19 1.45 0.78 1.27 Myn 3.25 12.44 3.00 4.60 4.00 2.50 4.00 1.38 3.00 7.00 2.20 1.40 1.40 1.88 1.21 0.73 Pan 3.92 10.56 1.00 2.60 2.00 0.50 3.50 2.13 1.00 7.00 0.20 2.00 0.00 1.44 0.78 1.26 Col 3.92 10.56 1.00 2.60 2.00 0.50 3.50 2.13 1.00 7.00 0.20 2.00 0.00 1.44 0.78 1.26 Mex 4.92 12.44 1.00 3.80 0.00 2.50 4.50 3.83 1.00 5.00 2.20 4.00 2.00 2.00 0.94 1.48 Pak 5.21 9.39 2.12 4.27 2.94 3.12 4.77 4.16 2.12 6.65 2.94 4.12 2.77 2.77 2.94 1.15 Cam 4.35 13.65 4.18 5.53 4.28 3.92 4.80 3.20 4.18 6.33 3.62 2.44 3.42 3.42 4.28 5.06 The number of base differences per sequence from averaging over all sequence pairs between groups are shown. Standard error estimate(s) are shown above the diagonal. The analysis involved 226 nucleotide sequences. All ambiguous positions were removed for each sequence pair. There was a total of 223 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 (Kumar et al., 2016). Upper matrix shows the standard error while lower matrix shows the distance estimation value. 100 bootstrap replications were run for this analysis.
Viet: Vietnam Br Brazil Per: Peru PNG Papua New Guinae Mly Malaysia Myn: Myanmar Srl Sri Lanka Tha Thailand Pan Panama Ino: Indonesia Mdg Madagascar Col Colombia Ind India Chi China Mex Mexico Pak Pakistan Cam Cambodia
166 5.2.5.4 Analysis of Molecular Variance (AMOVA)
AMOVAs was undertaken in the Plasmodium groups to test the proportion of total genetic variance accounted for geographic population sub-division. In this case 32.58% variations occurred at intra-population level while 67.42% variation occurred at inter-population level.
Table 5.12 Analysis of molecular variance (AMOVA) undertaken for Plasmodium sub- populations as defined by geography
Averag
D.F Sum of Variance Percentage of e FST Source of variation Pc . squares components variation over all loci Grouped by
Geography Among populations 17 345.792 1.61514 Vaa 32.58 0.3258 0.0 Within populations 213 711.845 3.34199 Vbb 67.42 2 0 Total 230 1057.636 4.95714 aVa =Variance component among populations bVb =Variance component within populations c P value for FST calculated by comparison to a null distribution of 1023 permutations in ARLEQUIN v3.5
5.3 Discussion
Partial or complete sequences of mitochondrial genomes have proved to be useful markers for population studies in a wide variety of organisms (Avise and Walker, 1998, Jorde et al., 1998,
Saccone, 1994). In the malaria parasites of the genus Plasmodium, the mitochondrial genome is particularly convenient for such studies, since it is only about 6 kb in length and includes only three protein-coding genes: cox3, cox1, and cytb (Wilson and Williamson, 1997). Although P. vivax is the main causative agent of malaria outside of Africa, remarkably little is known about the infection dynamics of this pathogen under natural circumstances. A genotyping system that can be used in large-scale studies are urgently needed to strengthen the research on control of P. vivax (Koepfli et al., 2009).
167 The present study analyzed mitochondrial genome data from 224 SNP markers for 231 isolates
(including the clinical filter paper Pakistani samples). These included samples typed at LSHTM
(N=231 including 21 Pakistani clinical filter paper samples).
The mitochondrial genome organization is perfectly conserved among 23 Plasmodium species
(Hikosaka et al., 2011), and there is high (84%-99%) pairwise sequence similarity between these
23 species. In general, mitochondrial genomes show a tendency toward size reduction or deletional bias (Andersson and Kurland, 1998). The high degree of conservation of these mitochondrial genome structures may attributed to structural constraints which avoid the deleterious effects of reduction and rearrangement on their genome functions. In addition, the high structural conservation may be due to short evolutionary distances, i.e., not enough time for accumulating rearrangements (Hikosaka et al., 2013).
The SNP were observed with comparing the sequences with References Strain Salvador I. Among
224 SNPs data of 18 populations, 37 were found in cox1 gene while 20 and 17 were found in Cytb and Cox3 gene respectively. Among Pakistani sequences 32 SNPs 28 were found as private SNPs.
Among private SNPs 4 deletions and only one insertion was found. P. vivax has a highly polymorphic genome that may present some challenges for drug and vaccine development. In fact,
SNPs appear to be present at a higher frequency in the P. vivax genome than in P. falciparum genome (Avise and Walker, 1998).
Several characteristics features; such as high mutation rate (Brown et al., 1979), maternal inheritance (Creasey et al., 1994), and lack of recombination (Elson et al., 2001) have made the mt genome an ideal extra-nuclear genome to reconstruct evolutionary histories of the species.
Moreover, the mt genome of parasites evolves neutrally and shows no signs of recombination or selection (Joy et al., 2003), hence the whole genome behaves as a single locus and all sites share a common genealogy, which makes it ideal for studying within species variations and phylogenetic analysis. Amino acid mutation among three mitochondrial genes i.e., cox1, cox3 and cytb was observed in this study. In cox1 gene amino acid Arginine (R) is replaced by Lysine (K)
168 and Glutamic Acid (E) is replaced by Lysine (K) at three positions. Aspartic Acid (D) is replaced by Asparagine (N) at two places, and Glycine (G) is replaced by Alanine (A) at one place. In Cytb gene only one nucleotide was found to be responsible for substitution of amino acid Alanine (A) by Aspartic acid (D). In cox3 gene 17 SNPs were observed but no changes were found in polypeptide chain. In malaria parasites, mt genome is of relevance, due to (i) its small size (~6 kb), (ii) haploid, and (iii) contains 3 protein-coding genes, cytochrome c oxidase I (cox1), cytochrome c oxidase III (coxIII) and cytochrome b (cytb) (Feagin et al., 2012). Three mitochondrial genes are vital for many processes most importantly cytb which is a probable target for Atovaquine resistance (Vaidya et al., 1993).
Deficiency of genetic markers for P. vivax genome has hindered elaborate studies of its population structure avoiding significant contributions in different phenotypes of the parasite such as drug resistance (Rieckmann et al., 1989, Kain et al., 1993, Hastings and Sibley, 2002, Nayar et al.,
1997, Cogswell, 1992). SNPs have received considerable attention recently because of their potential as markers for genetic mapping and for studying molecular evolution and population dynamics (Sachidanandam et al., 2001, Reich et al., 2001).
The genetic variation among Plasmodium species reflects the population history demography and geographic distance, though recombination of nuclear genome disturbs the differentiation pattern while absence of recombination in mitochondrial and apicoplast genome is uniquely informative in tracing of dispersal pattern (Fiser Pecnikar and Buzan, 2014). The geographic differentiation seen in organelle genomes may also be subject to evolutionary forces in addition to genetic drift and migration (Preston et al., 2014).
The geographic origin of Plasmodium isolates has significant role in identifying drug resistance and eliminating malaria. The potential limitation of current mitochondrial bar code is the lack of representation of the Indian sub-continent, Central America, southern Africa and the Caribbean, due to insufficient sequence data from these regions. In addition, there is a need to sample more intensively from East Africa, a region of high genetic diversity, high migration and poor predictive
169 ability. Once the data gaps are filled, the barcode can be rectified to increase its accuracy in assigning sample origin (Preston et al., 2014). Due to closely related mitochondrial lineages of south Asia, central Asia, Africa, and the Middle East, the tracking of geographic origin of these regions parasites was reported challenging. Extensive genetic admixture of South Asian and east
Africa parasite was assumed to be due to high percentage of Plasmodium vivax strains which are presently circulating in east Africa has been introduced by South Asian migrants. The availability of dense sampling is recommended for tracing geographic origin of imported infections (Mali et al., 2012, Culleton et al., 2011, Gupta et al., 2012). To determine the continental origin of the isolates, nearly full length mitochondrial genome sequencing is required as informative polymorphism is scattered over the 6-kb mitochondrial sequence (Ferreira et al., 2007, Orjuela-
Sanchez et al., 2010, Van den Eede et al., 2010).
In the present study two different computational approaches were applied to reveal the genetic structuring within Plasmodium vivax including STRUCTURE and Neighbor joining tree NJ.
In neighbor joining tree some of the clades were found well supported with significant bootstrap values. One of the clade consisting of two samples of PNG were well-supported and strongly differentiated from rest of the populations. This showed the genetic isolation of PNG population from rest of the others. Possibly strains from Papua New Guinea own genetically distinct population due to long-term isolation and lack of gene flow. Some of the Pakistani samples were found in separate group which showed their genetic isolation and limited exchange of genetic material with other population. This differentiation can be result from genetic, spatial or temporal isolation of the isolates. There was also no information about the site of collection of these samples within their countries, thus it is possible that the genetic structuring in time might just be because of large spatial distances between the strains (Rougeron et al., 2017).
The populations belonging to distinct geographical regions were grouped together in same clades which depicts the genetic similarities among different populations while some of the populations of same region were found in different clades displaying the dispersal of population and only
170 possible way of dispersal is the human migration. A noticeable observation was that clades consisted of American and Southeast Asia strains which are geographically far distant from each other. Global movement of parasites threatens elimination and treatment efficacy. By identifying the global patterns of organelles genome polymorphism, we can explore the level to which
Plasmodium populations worldwide are inter-connected by international malaria migration
(Preston et al., 2014).
Routes of import and origin of Pakistani strains cannot be confidently identified owing to missing geographical links in the data. Although some of its strains were genetically similar those from regions located adjacent or bordering Pakistan. It is very difficult to track the geographic origin of samples from south Asia, central Asia, Africa, and the Middle East as they contained the closely related mitochondrial lineages. Mitochondrial genome from these regions failed to provide population specific DNA barcodes for epidemiological studies. One can assume that a high percentage of Plasmodium vivax strains which are presently circulating in east Africa have been probably introduced by South Asian migrants, contributing in an extensive genetic admixture of parasites of these regions. It is recommended that availability of dense sampling is required for geographic racking of imported infections (Mali et al., 2012, Culleton et al., 2011, Gupta et al.,
2012). Nearly full length mitochondrial genome sequencing is required to determine the continental origin of these isolates. Despite the relatively low levels of local malaria transmission, populations of P. vivax are diverse genetically in the North of Pakistan. High influx of Afghan population in the study area significantly contributed to enhancing the number of genetically modified strain into the area as Afghanistan was visited by multiple countries due to war and conflict situation. Analysis of mitochondrial lineages of P. vivax showed moderate levels of genetic diversity and sub-structuring of local parasite populations. These data are consistent with the introduction of P. vivax in this Pakistani region from multiple sources and at different times.
171 This may explain why some parasites cluster together with the lineages from south Asia. However, definitive conclusions may require an additional sampling effort from a vast geographical range.
NJ method is a simple heuristic clustering (strategy designed to explore a sub-set of all possible trees, in the hope that the subset will contain the optimal tree) and thus lacks the ability to optimize a criterion of fit between tree and data (Page and Holmes, 1998a). It uses a so-called “greedy algorithm” that carries out a heuristic search of tree space, where at each step it makes a locally optimal choice to ultimately reach a global optimum tree. However, this greedy approach is weighted towards locally optimal solutions, and these often do not approximate a global optimal solution (which in this case is the best fit NJ tree) due to which NJ method may not produce a best data fitted tree (Guindon and Gascuel, 2003, Eickmeyer et al., 2008).
Another approach was used for genetic clustering of the P. vivax population. The STRUCTURE analysis of the 224 SNPs used, detected 6 populations. Two populations were found with strains of diverse geographical origin. The K1 population comprised of more diverged isolates relative to
K2 population. STRUCTURE output was in congruence to the NJ tree constructed from the same data. The topology within the tree was not well supported for the major clades suggesting weak genetic partitioning within Plasmodium vivax and with its out-groups. The other reason for low bootstrap values in the output from NJ might be the low resolution or diversity of SNPs utilized
(low sensitivity).
The genetic diversity index, MNA (mean number of alleles) was highest for Malaysia (table 5.8) followed by Brazil, Indonesia, PNG and Vietnam. This index is subject to sample bias, however,
Malaysia, Peru and Brazil having a smaller sample size (N=6, 10 and 4 respectively) had higher
MNA values, supporting the genetic richness of these group. These regions have high chances of imported cases as noticeable migration or travel occurs to these countries. Rapid human migration pattern increase the existence of diverse genetic strain in a region (FUNDYGA et al., 2002,
172 Machado et al., 2010, Thiele et al., 2008). Increased genetic diversity due to imported infections were reported in previous study (Gunawardena et al., 2014).
Gene flow and genetic differentiation between populations was estimated used statistic FST
(Table 5.10). Highest level of gene flow was observed between Peru & Panama, belongs to similar region indicates free gene flow among closely located populations. Moderate differentiation was found among population of Western Pacific and between the Western Pacific and Southeast Asia population indicating the significant gene flow among vivax population of both regions. No definitive assumptions can be made about the route of import as the connecting links were not present in this study.
Pakistani Plasmodium vivax population showed strongest genetic differentiation with other region’s P. vivax populations including America (Brazil, Colombia, and Mexico), Western Pacific
(Cambodia, Malaysia) and South East Asia (Myanmar and Indonesia) suggesting highly differentiated population of P. vivax in Pakistan. American region is geographically distant from
Pakistan but genetic differentiation with less distant regions (western pacific and Southeast Asia) was also observed. This differentiation showed limited gene flow between Pakistan and other mentioned regions, which may be attributed to presence of genetically isolated population in
Pakistan. These findings are similar with the results of the study compared the genetic structure of Asian and South American P. vivax population. They reported the greatest genetic differentiation among different continents (Imwong et al., 2007). The lack of similarity between two genotypic phylogenies can also be due to differences in molecular clocks of the markers used e.g. faster mutation rate of mitochondrial DNA as compared to nuclear DNA, in general) (Bastos-
Silveira et al., 2012, Tibayrenc, 1999, Gomez-Zurita and Vogler, 2003). Moderate differentiation value was observed between Pakistan and Madagascar suggesting the route of import from Africa to Pakistan. Temporal factor cannot be excluded in this case, as the provided data sets other than
Pakistan were may earlier than Pakistani P. vivax population (collected in 2014).
173 The cluster pairs with least gene flow included; America & western Pacific, Americas & Southeast
Asia and Western Pacific & Southeast Asia. Those population with less number of samples showed complete isolation, can probably be explained as sampling bias. Given the small size of some of the clusters, it is unclear whether the lack of genetic difference reflects minimal gene flow or merely low power of the test to detect any exchange for small size clusters.
Estimates of evolutionary divergence over sequence pair between groups was also calculated in this study. The populations of western pacific showed highest divergence estimate with other populations. Eastern Mediterranean region (consist of only on type of population i.e. Pakistani samples) showed high divergence estimate with other regions. China and PNG grouped in same cluster showed highest divergence value as compared other population’s estimates. This corroborated the findings from gene flow analysis by FST.
5.4 Conclusion
Marked genetic differences were found among Pakistani P. vivax population. The genetic differentiation show the genetic isolation of Pakistani population and evolutionary divergence also confirm these findings. This study concludes the significant mutation in mitochondrial genome sequences of Pakistani Plasmodium vivax population. This study also corroborates the use of filter papers as a source of clinical material for population genetic analysis studies. However, major drawback associated with this method were the allele drop outs due to insufficient DNA in these samples. Future studies would require optimizing these methods and having more appropriate sample collection and storage medium techniques which could enhance the quality of DNA for population genetic studies. Future research (nuclear as well mitochondrial genome studies) should ideally identify the extent of heterogeneity and recombination of P. vivax within this region. These observations also emphasize the need to scrutinize the genetic structure of P. vivax within Pakistan in general, using new sampling sites. The import route can also be find by exploring the genetic strains found in this region. Further Population genetics analysis of the Plasmodium vivax strains
174 of study area as well as Khyber Pakhtunkhwa and Pakistan would be useful in identifying the imported drug resistant strains of different regions which will be helpful in reducing the over use of anti-malarial drugs as well as reduced the economic burden by introduction of effective drugs against these drug resistant strains.
175 Chapter 6 GENERAL DISCUSSION
This PhD study was aimed to generate latest information on malaria infection by finding parasite prevalence using different diagnostic methods. The study area is among endemic districts of KP with high API index. The new cases and relapse cases leads to increase prevalence in the area.
Most of the individuals suffering from malaria remained suffered for several years with no confirmation that either they are relapse or re-infected cases. The proper follow-up study of that cases is necessary as it contributes to disease elimination process. On each visit, they are prescribed with same antimalarial drugs with no effect their illness. The resistance development, prescription of wrong antimalarial drugs, improper dosage prescription, use of inefficient drugs and improper intake of medicine dosage due to illiteracy are the main possible causes.
To control the diseases, it is necessary to map out the correct situation of the disease burden foci and to search out the dominant Plasmodium species in the area. In finding the species prevalence, one of the limitation is the timing of sample collection as some of the months are correlated with the presence of particular species: P. vivax peaks between April and September while P. falciparum peaks between August and December (Williams and Meek, 2011, DMC, 2007,
Mukhtar, 2009), (Khatoon et al., 2010). Highest P. vivax infections and lowest P. falciparum infection were observed in March for Baluchistan (Yasinzai and Kakarsulemankhel, 2008). The village “Hani kala” in the study area is well known for Falciparum cases (Personal communication) but unfortunately no study has been done yet in that area. Such areas need serious attention of the health authorities to eradicate the fatal Plasmodium species. These areas contribute in transmission of fatal malaria infection to the adjacent areas.
Specific targeting foci of infection in the malaria endemic areas most likely to hinder transmitting but also save money and time by only uses control in these areas (Fegan et al., 2007; Bhattarai et al., 2007). Complete and accurate data of the previous malaria situation, a good record of malaria transmission and its burden are key elements of a successful programme. The decision to change
176 to the next phase of the malaria control or elimination programme is made on the basis of progress made in epidemiological indicators. Monitoring changes in transmission intensity and identification of residual malaria foci is therefore very essential for successful malaria control programme and to concentrate intervention efforts (Wahid, 2013).
In this study, it was difficult to locate the main foci of Plasmodium in the study area as most of the patients visited for health services were belonged to catchment areas of the health facilities where collection was done due to easy accessibility. One of the restriction for finding actual disease burden is the treatment seeking behavior and access to the health facilities. In present study, it was commonly observed that many patients also took medicine for their ill family members by just telling symptoms. Many of the individuals didn’t bring their females to the facilities due to social customs and just rely on symptomatic treatment. Treatment taken prior to diagnosis was also asked and most of them have taken antimalarial drugs, this may also the reason of negative diagnostic results as antimalarial drugs hampers the parasite from peripheral blood.
The clinical study is not sufficient for finding the actual malaria burdened area identification. For this purpose, well-planed field survey is required which is not done yet in the District Bannu.
The health infrastructure at periphery consist of Rural health centers (RHCs), Basic health units
(BHUs), Civil dispensaries (CDs) which provides diagnostic service such as Microscopy for malaria suspected individuals and they also provided antimalarial drugs. Unfortunately, these facilities do not fulfill the purpose for which they are established. The population of the related areas visits these facilities just for taking medicines. Due to lack of essential resources and skilled personnel’s these facilities are utilized just for symptomatic treatment while in some diagnostic facilities where diagnosis of the disease is not appropriate due to lack of expertise which leads to over use or unnecessary intake of antimalarial drugs causing health impairment and economic burden. Some of the cases of misdiagnosis with very poor health condition and low hemoglobin level was observed in this study with false positive or negative results as well as wrong
177 Plasmodium species diagnosis. It was also observed during this study that the lab technicians recommend some of the cases positive or negative based on physical appearance of the patients.
According to them most individuals are IDPs and they came to hospital for free medicine without any disease. In government health facilities of KP almost the same situation exists. Most of the malaria burden can be reduce by enhancing the standard of diagnostic facilities at primary health facilities.
In Bannu region one of the cause of high malaria burden is the war and conflict situation in
Afghanistan and FATA region. IDPs from FATA region excessively increased the malaria burden.
One of the possible reason of increased transmission during IDPs stay in study area is the presence of asymptomatic carriers among that population as they are immuned for malaria antigen so in case of infection symptoms do not develop in them as they had come from malaria endemic region where they develop immunity due to prolong infection. Owing to the breakdown of health systems, mass population displacements, and resettlement of vulnerable refugees in camps or locations prone to vector breeding, malaria is often a major health problem during war and the consequences of war. According to Rowland, during the initial acute phase of the emergency, before health services become properly established, mortality rates may rise to alarming levels
(Rowland, 2001). Clinical diagnosis of malaria is unreliable, microscopy or rapid diagnostic tests must be established as quickly as possible, to improve case management and surveillance
(Luxemburger et al., 1997).
In this study, high prevalence of P. vivax in comparison with P. falciparum was observed.
Comparable situation is reported from different parts of Pakistan (Khattak et al., 2013, WHO,
2017, Khatoon et al., 2009, Khatoon et al., 2010) other studies conducted in FATA (Khan et al.,
2016) KP (Wahid et al., 2016, Khan et al., 2016, Razzaq et al., 2014, Murtaza et al., 2009, Daud et al., 2014), Sindh (Uttra et al., 2010, Soomro et al., 2009, Jan and Kiani, 2001), Punjab (Majeed et al., 2016), Baluchistan (Ahmad et al., 2013). In earlier studies, high prevalence of Plasmodium
178 vivax was reported in temperate areas and as predominant species while small outbreaks of
Plasmodium falciparum were also reported from KP province and larger outbreak were reported from Punjab province (Hehir, 1927, Swaroop, 1949).
There is insufficient epidemiological data available from Pakistan to accurately assess the prevalence of various types of malaria (Khadim, 2002). Malaria transmission is seasonal, with peaks in summer (June-September) for P. vivax malaria and late-summer and winter (August-
November) for P. falciparum malaria. The malaria endemicity in Pakistan has a negative impact on its socio-economic growth and productivity, as the main transmission season coincides with the harvesting and sowing of the main crops (wheat, rice, sugar cane). The Government of
Pakistan is implementing Malaria Control Program (MCP) in 72 malaria endemic districts of
Pakistan with the public-sector resources and in 19 highly endemic districts with the support from the Global Fund. The interventions of the current project are based on the National strategic framework for Malaria control. Association for Community Development (ACD) is one of the sub-recipient responsible for project implementation in three districts of Khyber Pakhtunkhwa and five FATA agencies namely; districts Nowshera, Charsadda and Mardan, along with Mohmand,
Bajaur, Orakzai, Kurram and South Waziristan Agencies (ACD, 2015).
Along with high malaria prevalence in Pakistan, Baluchistan also accounts for high proportion P. falciparum cases due to its dominance in Quetta (Khattak et al., 2013, Yasinzai and
Kakarsulemankhel, 2003) and other regions including Zhob, East Baluchistan and Khuzdar
(Yasinzai and Kakarsulemankhel, 2007, Yasinzai and Kakarsulemankhel, 2008, Farooq et al.,
2008). Greater regional variation exists within Baluchistan province for falciparum prevalence,
15% in Zhob to nearly 90% in Quetta, representing high geographic variation among districts.
Different studies conducted in Sindh also reported the regional variation (Noor and Akbar, 2003,
Beg et al., 2008, Hayyat et al., 2009). Other studies conducted in KP region showed fluctuating levels of P. falciparum, 16% in Buner (Noor and Akbar, 2003) to 25% in Bannu and Abbottabad
(Idris et al., 2007, Khatoon et al., 2009).
179 Migrations across the border added to malaria cases in those areas especially the influx of Afghan refugees between 1979 and 1982 (WHO, 2011a). The movement of Afghan refugees into
Baluchistan province, Iran, was estimated to result in a 24-36% increase in the number of malaria cases (Basseri et al., 2010). Due to poor security conditions over the past few years in the region an upsurge in internal displacement may have been a contributing factor (Kakar, 2009, WFP,
2009). This large-scale dispersion of susceptible populations has to some extent transformed the global epidemiology of malaria (WFP, 2009), leading to epidemics. One such epidemic was reported in 2003 in an refugee camp of Pakistan (incidence > 100 cases/1,000 persons/year)
(Leslie et al., 2009b). P. vivax dominance in the region is confirmed by many other studies. As per 1999 World Health Report estimation, there are about 72 - 80 million cases of P. vivax malaria each year with highest burden observed in South and East Asia (52%), Eastern Mediterranean
(15%) and South America (13%) (Mendis et al., 2001). These statistics are reviewed by an analysis using a combination of malaria epidemiology, historical maps, geographic information systems and information on population densities, environment, and vector limits (Guerra et al., 2006).
In South and Southeast Asia, where most P. vivax malaria occurs, P. vivax accounts for up to 50% of malaria cases with prevalence rates between 1% and 6% of the population (Luxemburger et al.,
1996, Tjitra et al., 2008, Zhou et al., 2005). The proportional burden of P. vivax is even greater in
Central and South America, reaching 71–81% of all malaria cases. In eastern and southern Africa, the P. vivax burden is comparatively lower, accounts for 5% (6 to 15 million cases annually) of the malaria cases (Mendis et al., 2001). With arrival of Chloroquine Resistant P. falciparum, the number P. vivax cases were reduced but absolute number of P. vivax remain high (Singh et al.,
2004). On the other hand, the effective control measures reduce the ratio of P. falciparum to P. vivax infections (Carrara et al., 2006).
During earliest study, Hay and colleagues found 25-40% global burden of non-falciparum malaria
(predominantly P. vivax) with 132-391 million cases annually. With the consideration of population density of areas endemic for P. vivax, the number of peoples at risk of infection reaches
180 to 2.6 billion which is slightly greater than that for P. falciparum. True burden of P. vivax is greatly underappreciated as 25 million new cases are reported annually (Guerra et al., 2006).
Some studies reported the highest disease burden in young children and infants’ immunity usually developing by 10 to 15 years of age (Cattani et al., 1987). A study conducted in Thailand reported that incidence rates varied from 800-200 per 100person/years among children (under 5 years) and older adults. Symptomatic and asymptomatic carriage occur in low transmission settings,
(Roshanravan et al., 2003) although overall 82% of patients with P. vivax parasitemia were still found to be symptomatic (Luxemburger et al., 1997).
In this study, some of the mixed infection cases were detected through RDT while diagnosed as single infection through microscopy. Later their confirmation through PCR was couldn’t performed. Due to poor quality microscopy practices, mixed infections are rarely diagnosed and reported, as confirmed by a recent study carried out in the bordering regions of Afghanistan,
Islamic Republic of Iran and Pakistan (Zakeri et al., 2010a). Comparatively low and highly heterogeneous transmission was reported for both P. vivax and P. falciparum in five Afghan refugee camps in Pakistan (Wahid et al., 2016).
Mixed infections are common in the regions where P. vivax and P. falciparum coexist yet reported rarely. Most of the surveys usually reports less than 2% and careful studies recorded up to 30% and this percentage can be raised with use of PCR (Mayxay et al., 2004). The host response and development of cross species immunity may significantly be affected by mixed infection with different Plasmodium species (Maitland et al., 1997). The severity of the disease caused by P. falciparum infection is reduced by P. vivax when coexisted together in the same host thus reducing the risk of sever malaria (Luxemburger et al., 1997), decreasing the risk of treatment failure (Price et al., 1997), lowering gametocyte carriage (Price et al., 1999), and reducing the prevalence of anemia (Price et al., 2001). However, in higher-transmission areas and areas of emerging drug resistance, the additive burden of severe malarial anemia and maternal malaria has not been addressed in detail. The potential for P. vivax to attenuate falciparum malaria requires further
181 characterization, and has significant implications for P. vivax-only vaccination strategies, and the deployment of drugs such as chloroquine, which have lost efficacy against P. falciparum but still retain it against P. vivax.
Consistent with what has been in East Africa, the Middle East, and other parts of Asia (Bell et al.,
2005), microscopy failed to detect mixed P. vivax and falciparum infections, which PCR successfully detected. These findings highlight the challenge of diagnosing and treating malaria, more so in areas where mixed species occur. These findings highlight the challenge of diagnosing and treating malaria, more so in areas where mixed species occur. Models predict that treatment of P. vivax alone caused by failed diagnosis of a co-infecting, more lethal P. falciparum can lead to a rapid surge in P. falciparum parasitemia (Mason and McKenzie, 1999). Inappropriate diagnosis will thus cause higher morbidity and mortality from malaria, enhance the development of drug resistance because of administration of the wrong drugs, and prevent appropriate management of serious fever– causing illness such as bacterial infections, resulting in poor treatment outcomes (Ngasala et al., 2008). Because PCR is untenable as a routine confirmatory diagnostic method in such settings, diagnostic accuracy in this area of mixed endemicity could be improved by supplementing microscopy with rapid diagnostic tests (de Oliveira et al., 2009,
Hopkins et al., 2008).
FATA region in KP is at high risk of malaria because of internal displacement due to political unrest, people of such areas face a lot of problems regarding diagnosis and treatment. Lack of knowledge about malaria, unhygienic condition and seasonal variation increase the prevalence of malaria in these areas. Most of the population of FATA and FR region was migrated towards
Bannu which were declared as IDPs (internally displaced population) and all health and other facilities were provided in Bannu and nearby districts on emergency basis. But usually patient burden is more in Bannu as in normal routine individuals from FR and FATA regions comes to
Bannu for availing health services.
182 Malaria is endemic in 101 countries, 45 are WHO’s African Region, particularly sub- Saharan
Africa, with 41% malaria cases, 20 in WHO’s Americas Region with 3% cases, 14 in Eastern
Mediterranean Region with6 % cases, 4 in Europe with 3% cases, 8 in South Asia Region with
23% cases and 9 in WHO’s Western Pan Pacific region, with 24% cases. Malaria is also returning to Central Asia due to poor socio-economic conditions and civil war (Mukhtar, 2009). Pakistan is supposed to be at a high risk of infection due to widespread irrigation, agriculture and prolonged monsoon season (WHO, 2011b). Pakistan is among moderately endemic countries but some districts of Sindh, Baluchistan, FATA and KP are categorized among high endemic areas with high API index. Most prevalent is P. vivax malaria but in some areas or districts where falciparum is reported as more prevalent than P. vivax (WHO, 2017).
Per recent reports of WHO, the number of confirmed cases reached up to 3 million per year in
Pakistan. Still Pakistan is categorized with Afghanistan, Somalia, Sudan and Yemen in the east
Mediterranean region which contributes more than 95% of total regional malarial burden (WHO,
2017). Per WHO world malaria report (2017), there is >40% decrease in estimated incidence rate while 20-40% decrease in mortality rate (WHO, 2017). Epidemiological data from different regions of Pakistan are unsatisfactory to exactly charge the occurrence of various types of malaria
(Khadim, 2002).
In this study, greater number of microscopy positive samples were found positive through PCR while P. falciparum infection was detected through PCR which was not detected through microscopy. Greater difference was found between PCR and Microscopy results and little variation was found between RDT and microscopy. Previous studies conducted to compare PCR and microscopy performance shown the high sensitivity of PCR over microscopy (Okell et al.,
2012a, Okell et al., 2009). Three to four time more detection rate was reported by previous studies
(Adegnika et al., 2006, Malhotra et al., 2005). Submicroscopic infections in the general population are significant in serving as a reservoir of parasites to drive transmission intensity (Okell et al.,
2009). In this study the low sensitivity of PCR as compared to microscopy may be due to some
183 false positive cases detected through microscopy. This study agrees with previous studies reported that false-negative results by microscopy for P. vivax are probably due to very low parasitaemia which is very difficult to detect by routine microscopic methods (Postigo et al., 1998). A study conducted in Tanzania in 16 health facilities, showed wide variation in results (only 65% concordance with national reference Laboratory). False positive smears were commonly reported reflecting the tendency of laboratory staff in detecting Plasmodium through microscopy as they report smear as positive when they are in doubt especially in case of symptomatic case (Ngasala et al., 2008). Same tendency was observed in the laboratory staff from where data was collected in this study. The inappropriate cleaning of re-usable slides was previously reported in the study area while also observed in the present study. Due to improper cleaning or artifacts results in the diagnostic errors (Garcia, 2007).
Giemsa microscopy is still regarded as the gold standard and the most suitable diagnostic instrument for malaria control because it is inexpensive to perform, able to differentiate malaria species and quantify parasites (Jonkman et al., 1995). However, microscopy is labour-intensive, time-consuming, requires well-trained (Reyburn et al., 2004), competent microscopists and rigorous maintenance of functional infrastructures plus effective quality control (QC) and quality assurance (QA) (Wongsrichanalai et al., 2007). Microscopic diagnosis has been shown to be insensitive and nonspecific, especially when parasitaemia are low or mixed infections are present
(Amexo et al., 2004). Sensitivities and specificities as low as 71–72% have been reported (Arai et al., 1996, Snounou et al., 1993b).
Though all diagnostics means have their own advantages and disadvantages in contributing the parasite information but all used means were compared to find the most effective diagnostic way in high endemic areas. The specificity and sensitivity of the diagnostic means were also calculated to find their effectiveness in parasite identification. The correct diagnosis is the key point in identifying the true malaria cases, their treatment and control of disease. To prevent the development of resistance against antimalarial drugs and misuse of drugs, implantation of
184 corrective diagnostics in all public and private facilities is essential need. At present time, there is non-serious attitude of public towards the malaria treatment as they do not have knowledge about the outcomes of malarial illness. In the study area malaria is taken as a common disease due to lack of education and awareness.
Data from this survey showed overall 17.66% (359/2033) parasite prevalence detected through microscopy and almost similar percentage was found through RDT. A noticeable difference was found between results of microscopy and PCR results. In this survey PCR results showed 30.54%
(95/311) parasite prevalence while for the same samples microscopy showed 24.4% parasite prevalence. Among microscopy positive samples some were PCR negative. While microscopy negative samples were detected positive by PCR indicating sub microscopic infection reported in other studies. While among microscopy and PCR processed samples, only PCR detect falciparum infection. These PCR detected falciparum cases were detected as P. vivax in laboratory and prescribed with antimalarial drugs used for P. vivax. Historically microscopy has been the backbone of the malarial parasite identification and remains be the reference standard even with its disadvantages and due to some limitations of this technique, easy and quick as well as cheaper alternative techniques requires to be evaluated (Gilles, 1933).
Over 70% of malaria cases in Africa were reported with preliminary self-diagnosis and management at home with traditional remedies and self-prescribed medicine (Amexo et al., 2004).
After failure of self-treatment they attend the health facility (Chandramohan et al., 2002) which might affect the performance of the test methods and leads o development of parasite resistance against drugs. Same situation was observed in this study.
It is previously reported that Plasmodial DNA persist for up to 8 days remains after the intake of antimalarial drugs (Kain et al., 1994) which could result into false positive PCR diagnosis. Due to its high cost, complexity, lack of electricity, labor intensiveness and log running time the PCR is not suitable for rural endemic settings (Hanscheid and Grobusch, 2002).
185 A study conducted in afghan refugees also reported the comparatively high detection rate of PCR as compared to RDT (Wahid, 2013). Four times greater parasite carriage was detected through
PCR as compared to RDT which suggested the presence of asymptomatic carriers (carriers with low level of parasite without symptoms) or sub-RDT infection (parasite level lower than sensitivity of RDT but picked up by PCR). In endemic areas, due to continuous exposure results into asymptomatic carrier which may lead to partial immunity (Staalsoe and Hviid, 1998,
Shekalaghe et al., 2007, Sutherland et al., 2007, Roper et al., 1996). A review on the submicroscopic infections hypothesized that in lower transmission areas infections on average are older and more likely developed to sub-microscopic phase, while in high-transmission areas repeated infections and super-infection may results in the increase of average parasite density in infected individuals (Okell et al., 2012a).
Sub microscopic Plasmodium infections is also reported from low endemic regions including
Yemen (Bin Mohanna et al., 2007), Solomon Island (Harris et al., 2010), Colombia (Cucunuba et al., 2008), Amazon region of Brazil (Suarez-Mutis et al., 2007), Peru (Roper et al., 2000) and
Principe (Lee et al., 2010). Asymptomatic carrier were also reported from high transmission area including Nigeria (EKE and NWACHUKWU, 2006, Achidi et al., 1995), Uganda (Njama-Meya et al., 2004), Ghana (Crookston et al., 2010, Owusu-Agyei et al., 2001), Kenya (Bousema et al.,
2004), Senegal (Males et al., 2008, Le Port et al., 2008), Gabon (Klein Klouwenberg et al., 2005,
Nkoghe et al., 2011) and from intermediate transmission area such as India, Burma (Richards et al., 2007), Thailand (Coleman et al., 2002) also reported with asymptomatic malaria individuals.
The gametocyte prevalence among parasite carrier may be as high as 90% (Bousema et al., 2006).
Many of the previous studies also have shown that microscopy and RDT can misdiagnose the considerable number of individuals with low parasitemia which is considered as a great challenge for malaria elimination and control programmes as the submicroscopic infections can act as a
186 carrier of malaria transmission through gametocytes thus can act as significant barrier in implementation of elimination and control strategies (Shekalaghe et al., 2007, Sutherland et al.,
2007, Roper et al., 1996). The submicroscopic infection can contribute in a similar way as microscopic infection can do (Schneider et al., 2007). Therefor for successful elimination and control programs it is necessary to conduct surveys on asymptomatic individuals in diverse malaria settings has been suggested.
Poor microcopy practice including training and skills maintenance, technique of slide preparation, workload, quality and condition of microscope and standard of essential laboratory supplies can lead to false positive results, false negative results and errors in species identification (Durrhelm et al., 1997, Maguire et al., 2006). Even in the developed countries, expert microscopists are scarce and impaired microscopy based diagnosis in hospital laboratories is common (Milne et al., 1994,
Thomson et al., 2000, Johnston et al., 2006).
Poor specificity of microscopy is commonly report in comparison with expert microscopy. Use of low standard chemicals and poor slide preparation generates artifacts (bacteria, fungi, stain precipitation, and dirt and cell debris) which can be misguided for Plasmodium parasite. The normal blood components can also confuse the parasite identification (Stow et al., 1999,
Wongsrichanalai et al., 2007). To avoid this situation, improved training, high quality smear preparation and staining is essential. Another factor responsible for false diagnosis is the parasite density (Maguire et al., 2006, McKenzie et al., 2003), which needs microscopists proficiency and increased time and number of fields as the recommended number of fields to be observed is 100-
400 to declare slide negative (Trape, 1985). Another aspect of false diagnosis is the errors in species identification. Expert microscopists should be able to differentiate between species even at low parasite density. Sometime a thorough observation of think smear is required for morphologic, differential-diagnostic details such as erythrocyte size, shape, and crenation, characteristic dots in the erythrocyte stroma, pigment structure and color, as well as schizonts.
187 The errors in identification of P. vivax and P. ovale is commonly reported (Singh et al., 2004) while failure in identification of two most common species i.e., P. vivax and P. falciparum is frequent but underreported (Milne et al., 1994, McKenzie et al., 2003). Underreporting mixed species infection is also common (Johnston et al., 2006).
Cross reactivity with rheumatoid factor in blood can results in RDT false positive result but its chances can be reduced by replacement of IgG with IgM (Laferi et al., 1997, Grobusch et al.,
1999, Mishra et al., 1999). Cross reactivity can also occur with heterophile antibodies (Moody and Chiodini, 2002). Rare false negative results may be due to mutation or deletion of the Hrp-2 gene. The presence of anti-HRP-2 antibodies in human can also lead to negative results despite of significant parasitemia (Biswas et al., 2005). Prevention of development of control line due to presence of inhibitor in patient’s blood is also reported. Several factors in manufacturing process as well as environmental conditions (humidity, temperature) may affect the performance of RDT
(Bell et al., 2006, Mboera et al., 2006).
This study was also aimed to describe the risk factors for malaria exposure and transmission of
Plasmodium infection and to map out the most significant factors contributing the disease or parasite prevalence in the Bannu district. The factors found significant in this study were Socio economic status, sleeping habit, literacy level, availability of health care facility in living area, apparent health condition, body aches, treatment recommended by doctor, presence of domestic animals. All these factors are also reported significant previously.
In this study, it was asked from patients that either they are recommended for test by doctors or they come by themselves. Treatment recommended by doctor was found significant for malaria outcome and possible reason is that doctor advice malaria test on the basis of their clinical symptoms related to malaria or which are the apparent symptoms of malaria.
Apparent health condition was found significant for reducing risk of infection. The possible explanation for it is that people with good health status indicates the better living, nutritional and
188 health indicators which showed their superior economic condition and these might explain their lower malaria risk. In contrast to satisfactory health condition the poor health status make the individual prone to infections. In present study, significant number of the individuals visited the government laboratory was with poor health appearance and most of them were also diagnosed with low HB by main pathology lab of the hospital. Most of individuals among them were belonged to lower socio economic status indicating poor living, nutritional and health indicator which explain their higher malaria risk.
Among symptoms body ache was found significant in this study. As the study was clinical so all patients were with complaint of the aches. The possible explanation is that it is one of the symptom of malaria illness. The body ache should be considered significant when diagnosing patients in the study area.
In this study the availability or nearness of health facility was found significant for reducing risk of malaria infection. The possible explanation can be the early detection and effective treatment for malaria infected individuals provided by nearby health facilities. While in some cases the inappropriate intake of antimalarial drugs did not eradicate parasite from the body but just removed from the blood and the intake of drugs were prior to the present diagnosis so parasite was could not detected in the blood which may signify the presence of health care facility in vicinity. In primary health facilities of the study area Artheget (Artemether+Lumafantrine) and chloroquine is provided for malaria treatment. In previous studies dormancy of parasites is reported with Artemisinin derivatives. The asexual blood stage parasites may become temporarily inert or dormant and so survive therapeutic concentrations of anti-malarial drugs (Teuscher et al.,
2010, Mok et al., 2015). Some asexual parasites become temporarily dormant within their infected erythrocytes, and these may regrow after anti-malarial drug concentrations decline. Generally, the dormant parasites are present at densities below the level of microscopy detection (White, 2017).
Previous review study of KAB (knowledge, attitude and beliefs) about malaria, suggested the
189 importance of increasing health awareness, mobilizing the local or community healthcare professionals, for prevention as well as early detection and effective treatment of malaria among people who are at risk. They also emphasize on strengthening health system’s technical and managerial capabilities at all level of primary healthcare systems (Regmi et al., 2016).
In contrast of this study previous studies reported the efforts of government health authorities against malaria as generally poor or inadequate mainly due to poor infrastructure, less qualified healthcare practitioners, lack of faith in government medicines, expensive and poor quality treatment, non-compliant or incomplete treatment (irrational and inappropriate treatment, lack of efficacy of drugs, inadequate surveillance and resource-poor health systems) (Anand et al., 2014,
Das and Ravindran, 2010, Joshi and Banjara, 2008, Sundararajan et al., 2013).
Literacy level is found significant in this study as low literacy contributes to vector friendly environment (Coker H et al., 2001) including unhygienic conditions, ignorance of using repellents.
Due to lack of knowledge most of the people do not know how to prevent themselves from getting malaria and in case of illness do not know about proper intake of prescribed drugs. Inappropriate usage of antimalarial drugs leads to development of resistant strains of Plasmodium and increase the number of asymptomatic carriers. The development of resistant strains is not only harmful for a single individual but also hazardous for the whole population as drug resistant strains does not respond to the antimalarial drugs which are used in routine which put unpleasant impact on health and economic condition of individuals and their families. In previous studies the wide range of antimalarial drug resistance was reported and the chloroquine resistance was reported from
Southeast Asia and South America in the late 1950s and in the late 1970s in Africa (Kain, 1995,
Warhurst, 2001). Chloroquine resistance malaria is also reported from Pakistan (Khan et al.,
2004).
Sleeping inside the room was found significant in this study and the possible explanation for this is the limited access of mosquitoes to the human and the use to repellent and sprays can reduce the biting rate of mosquitoes. In some cases, due to insufficient space the same room is used for
190 cooking and sleeping, smoke produce during cooking expel the mosquitoes from room. Those who slept outside the room and having domestic animals in the same house are at more risk as the number of vector bite is increased with animal’s presence.
The upper middle class Socio economic status of the individuals was found significant for reducing risk of malarial infection as this class lives in concrete build houses and can afford the preventive measures and treatment expenses. There are number of studies which support the relation of low socio economic condition with increasing malaria risk (Woyessa et al., 2013,
Thang et al., 2008, Cook, 2010, Feachem et al., 2010, Arasu, 1992, Oemijati, 1992, Butraporn et al., 1986, Dev et al., 2004, Gamage-Mendis et al., 1991). The explanation of low socio economic status to increasing malaria risk is the ignorance of disease due to poverty (do not visit hospital or physician) which lead to increase of asymptomatic infection which acts as a carrier for transmission of disease. Other possible explanation is living in a house which are more likely to increase contact with the mosquitoes as compared to the houses (cemented floor, iron roof, brick constructed houses) in high socio economic status to limit contact with mosquitoes. Moreover, individual with low SES may be less likely to use antimalarial control measures (to reduce favored access of the mosquito vector to man) due to cost. Previously malaria was regarded as disease of poverty and all poverty related factors (availability of electricity, main source of drinking water, time taken to collect water, toilet facilities and construction material of walls, roof and floor of house) were associated with malaria (Abegunde and Stanciole, 2006, Mendis et al., 2009, Hay et al., 2004). Previous studies showed non-glazed windows as significant factor (Ayele et al., 2012) while in this study it was not found significant.
Keeping animals at home was found significant for increasing malaria risk in this study. The association between malaria outcome and domestic animals was also reported in a study conducted in Afghan refugee camp at Jalozai, KP (Wahid et al., 2016). This finding is also in line with studies conducted in Afghan refugees (Bouma and Rowland, 1995) and Ethiopia (Deressa et
191 al., 2007, Graves et al., 2009), they shown that sharing home with livestock increased the risk of malaria prevalence. The simple explanation for this increase in Plasmodium prevalence is due to increased amount of CO2 and cattle odors that attract malaria vectors (Wahid, 2013).
This study was the first ever study of P. vivax mitochondrial genome in Pakistan as selected studies has been conducted on some of the nuclear genes of P. vivax. Previous studies conducted in
Pakistan were the genetic study on nuclear genome of P. falciparum (Khatoon et al., 2009).
Many of the isolates were found in the same clades despite of their geographic origin. PNG (Papua
New Guinea) was found with highly differentiated genetic material indicated the maximum mutation or gene flow in the population. Studies carried out on the genetic arrangements of P. vivax populations have revealed noticeable polymorphism of P. vivax populations associated with movement of populations from border countries. Similar studies on P. falciparum in Pakistan show many infections are monoclonal with allelic and genetic diversity like those reported from other low endemic south-east Asian countries (Ghanchi et al., 2010).
In this study P. vivax population from Pakistan showed from great to very great differentiation from rest of the populations which showed the limited gene flow. High number of SNPs (single nucleotide polymorphisms) were observed in these isolates which showed the mutation within population.
Conclusion
It is recommended on the basis of these results, the use of both microscopy and PCR for the characterization of Plasmodium spp. (McManus and Bowles, 1996). Despite of all limitation microscopy should remain a routine diagnostic tool for malaria due to its high specificity as previously observed (Musalika, 2010b) and cost and time effectiveness. However, for malaria epidemiological studies in low endemic area a more sensitive diagnostic tool PCR is recommended.
192 Based on current study, we also recommend PCR for identifying the species in population-based studies which will assist in establishing of improved associations among epidemiological dynamics. We also recommend the inclusion of PCR technique into national reference centers institutions accountable for malaria control.
There is the need therefore, to expand malaria diagnostic services as part of a greater framework of health system strengthening within resource-limited settings. Increasingly, countries and implementing partners have identified that limited diagnostic capacity represents a major barrier to implementation and sustainability of prevention, treatment and care programs for malaria
(Maputo., 2008).
The present findings confirmed the use of RDT for general use when the number of patients is large. However, for more reliable results microscopy and particularly PCR could be employed.
In summary, the data presented here recommend that efficient and continued surveillance is needed to sufficiently capture the current situation on a population level, to reliably assess submicroscopic levels of parasitaemia and to provide information on transmission variation over time. Malaria indicators surveys to measure the prevalence and burden of the disease should be initially population and health facility-based, then narrow down to foci and ultimately to individual cases as malaria control programme gradually shift to elimination programme, as recommended by WHO (WHO., 2007).
Malaria control effectiveness may be improved by identifying foci of ongoing transmission, which can be targeted for more frequent and intensive care. Some of the high burden areas were identified in this study. The information about mosquitoes breeding sites is not obtained which may be one of the reasons of these areas to be in the hot spot. Therefore, it will be useful for control programs to check for potential mosquitoes breeding sites and might apply vector control measures to target outdoor biting vectors for example larviciding of mosquito breeding sites and adult vector
(Fillinger et al., 2008).
193 The areas reported with high malaria prevalence (especially Baluchistan and tribal areas of KP) needs MIS (malaria indicator survey) or other community based survey (to locate actual burden and transmission) as these areas are reported with high malaria burden, as the reported prevalence is on basis of hospital records or passive case detection which may be unreliable and outdated.
The study area reported with moderate (66.08%) literacy rate (rank 62) in Pakistan (Naz et al.,
2016), therefore awareness about protection from malaria, health seeking behavior, prompt diagnosis and treatment can lead to significantly improvement in malaria control and finally elimination.
Moreover, health status, body aches, availability of health care center, sleeping habit, literacy level, Socio economic status and keeping animals at home was found to play significant role in spread of malaria. The correction or improvement of all these factors in study area can help in reducing the risk and transmission of malaria.
194 APPENDICIES
Appendix A Peshawar University ethics committee approval
195 Appendix B Questionnaire Form Administered in the study
Information will be collected from patients suspected malaria. Questioners will be based on the following requirements:
Date: Name:
Gender: Locality:
Age: Education:
Population displacement: (Residents / IDPS / others)…..
Duration of previous illness: …………………………..
Previous illness of malaria: Yes/No Previous Fever history:
General health: Poor/Weak/Satisfactory
Domestic animals: Yes/No Screening against malaria: Yes/No
House type: Mud/ Concrete/ Tent
Water reservoir: Yes/No
Sleeping habit: Inside room/Outside room
Blood group: ………………………….
Health care facility: Yes/No
Antimalarial drugs intake: Yes/No
Socio economic status: Lower Class/Lower Middle/Upper Middle/Upper class
Symptoms: ……………………………………………
Test diagnosed by doctor: Yes/No
Family members having malaria:
Microscopy result: +ve/-ve Species
RDT Result: +ve/-ve Species
196 References
ABEGUNDE, D. & STANCIOLE, A. 2006. An estimation of the economic impact of chronic non- communicable diseases in selected countries. Department of Chronic Diseases and Health Promotion (CHP): World Health Organization. ACD 2015. Association for community development. ACHIDI, E. A., PERLMANN, H. & BERZINS, K. 1995. Asymptomatic malaria parasitaemia and seroreactivities to Plasmodium falciparum antigens in blood donors from Ibadan, south-western Nigeria. Ann Trop Med Parasitol, 89, 601-10. ADEGNIKA, A. A., VERWEIJ, J. J., AGNANDJI, S. T., CHAI, S. K., BREITLING, L. P., RAMHARTER, M., FROLICH, M., ISSIFOU, S., KREMSNER, P. G. & YAZDANBAKHSH, M. 2006. Microscopic and sub- microscopic Plasmodium falciparum infection, but not inflammation caused by infection, is associated with low birth weight. Am J Trop Med Hyg, 75, 798-803. ADIMI, F., SOEBIYANTO, R. P., SAFI, N. & KIANG, R. 2010. Towards malaria risk prediction in Afghanistan using remote sensing. Malar J, 9, 125. AFRANE, Y. A., KLINKENBERG, E., DRECHSEL, P., OWUSU-DAAKU, K., GARMS, R. & KRUPPA, T. 2004. Does irrigated urban agriculture influence the transmission of malaria in the city of Kumasi, Ghana? Acta Trop, 89, 125-34. AGAPOW, P. M. & BURT, A. 2001. Indices of multilocus linkage disequilibrium. Molecular Ecology Notes, 1, 101-102. AHMAD, T., HUSSAIN, A. & AHMAD, S. 2013. Epidemiology of malaria in Lal Qilla. Int. J. of Sci. Tech. Res, 2, 11-15. AIKAWA M. & STERLING C. R. 1974. Intracellular parasitic Protozoa. New York: Academic Press, Inc. AL-TAIAR, A., ASSABRI, A., AL-HABORI, M., AZAZY, A., ALGABRI, A., ALGANADI, M., WHITTY, C. J. & JAFFAR, S. 2009. Socioeconomic and environmental factors important for acquiring non-severe malaria in children in Yemen: a case-control study. Trans R Soc Trop Med Hyg, 103, 72-8. ALAM, M. S., MOHON, A. N., MUSTAFA, S., KHAN, W. A., ISLAM, N., KARIM, M. J., KHANUM, H., SULLIVAN, D. J., JR. & HAQUE, R. 2011. Real-time PCR assay and rapid diagnostic tests for the diagnosis of clinically suspected malaria patients in Bangladesh. Malar J, 10, 175. ALEMU, A., MULUYE, D., MIHRET, M., ADUGNA, M. & GEBEYAW, M. 2012. Ten year trend analysis of malaria prevalence in Kola Diba, North Gondar, Northwest Ethiopia. Parasit Vectors, 5, 173. ALSOP, Z. 2007. Malaria returns to Kenya's highlands as temperatures rise. Lancet, 370, 925-6. AMAMBUA-NGWA, A., TETTEH, K. K., MANSKE, M., GOMEZ-ESCOBAR, N., STEWART, L. B., DEERHAKE, M. E., CHEESEMAN, I. H., NEWBOLD, C. I., HOLDER, A. A., KNUEPFER, E., JANHA, O., JALLOW, M., CAMPINO, S., MACINNIS, B., KWIATKOWSKI, D. P. & CONWAY, D. J. 2012. Population genomic scan for candidate signatures of balancing selection to guide antigen characterization in malaria parasites. PLoS Genet, 8, e1002992. AMEH, J., AHMAD, R.M., EKEH, N., LINGA, P., MANGORO, Z., ASIYA UMAR IMAM, A. U., PETER AKEREDOLU, P., AND HUDU, S. 2012. Laboratory diagnosis of malaria: Comparing giemsa stained thick blood films with rapid diagnostic test (RDT) in an endemic setting in North-west Nigeria. Journal of Medical Laboratory and Diagnosis, 3(2), 10-15. AMEXO, M., TOLHURST, R., BARNISH, G. & BATES, I. 2004. Malaria misdiagnosis: effects on the poor and vulnerable. Lancet, 364, 1896-8. AMODU, O. K., ADEYEMO, A. A., OLUMESE, P. E. & GBADEGESIN, R. A. 1998. Intraleucocytic malaria pigment and clinical severity of malaria in children. Trans R Soc Trop Med Hyg, 92, 54-6. ANAND, T., KUMAR, R., SAINI, V., MEENA, G. & INGLE, G. 2014. Knowledge and use of personal protective measures against mosquito borne diseases in a resettlement colony of delhi. Ann Med Health Sci Res, 4, 227-32. ANDERSON, T. J., HAUBOLD, B., WILLIAMS, J. T., ESTRADA-FRANCO, J. G., RICHARDSON, L., MOLLINEDO, R., BOCKARIE, M., MOKILI, J., MHARAKURWA, S., FRENCH, N., WHITWORTH, J., VELEZ, I. D., BROCKMAN, A. H., NOSTEN, F., FERREIRA, M. U. & DAY, K. P. 2000. Microsatellite markers
197 reveal a spectrum of population structures in the malaria parasite Plasmodium falciparum. Mol Biol Evol, 17, 1467-82. ANDERSSON, S. G. & KURLAND, C. G. 1998. Reductive evolution of resident genomes. Trends Microbiol, 6, 263-8. ANDREWS, L., ANDERSEN, R. F., WEBSTER, D., DUNACHIE, S., WALTHER, R. M., BEJON, P., HUNT-COOKE, A., BERGSON, G., SANDERSON, F., HILL, A. V. & GILBERT, S. C. 2005. Quantitative real-time polymerase chain reaction for malaria diagnosis and its use in malaria vaccine clinical trials. Am J Trop Med Hyg, 73, 191-8. ANSTEY, N. M., RUSSELL, B., YEO, T. W. & PRICE, R. N. 2009. The pathophysiology of vivax malaria. Trends Parasitol, 25, 220-7. ANTONIO-NKONDJIO, C., SIMARD, F., AWONO-AMBENE, P., NGASSAM, P., TOTO, J. C., TCHUINKAM, T. & FONTENILLE, D. 2005. Malaria vectors and urbanization in the equatorial forest region of south Cameroon. Trans R Soc Trop Med Hyg, 99, 347-54. ANYA, S. E. 2004. Seasonal variation in the risk and causes of maternal death in the Gambia: malaria appears to be an important factor. Am J Trop Med Hyg, 70, 510-3. ARAI, M., KUNISADA, K., KIM, H., MIYAKE, H., MIZUKOSHI, C., KAKUTANI, T., YAMANE, A., NAKAGAMI, S., KAWAI, S. & NAKANO, H. 1996. A colorimetric DNA diagnostic method for falciparum malaria and vivax malaria: a field trial in the Solomon Islands. Nucleosides, Nucleotides and Nucleic Acids, 15, 719-731. ARANGO, E. M., SAMUEL, R., AGUDELO, O. M., CARMONA-FONSECA, J., MAESTRE, A. & YANOW, S. K. 2013. Molecular detection of malaria at delivery reveals a high frequency of submicroscopic infections and associated placental damage in pregnant women from northwest Colombia. The American journal of tropical medicine and hygiene, 89, 178-183 %@ 0002-9637. ARASU, G. D. 1992. Risk behavior in malaria in Malaysia. Southeast Asian J Trop Med Public Health, 23 Suppl 1, 51-6. ARIEY, F., WITKOWSKI, B., AMARATUNGA, C., BEGHAIN, J., LANGLOIS, A. C., KHIM, N., KIM, S., DURU, V., BOUCHIER, C., MA, L., LIM, P., LEANG, R., DUONG, S., SRENG, S., SUON, S., CHUOR, C. M., BOUT, D. M., MENARD, S., ROGERS, W. O., GENTON, B., FANDEUR, T., MIOTTO, O., RINGWALD, P., LE BRAS, J., BERRY, A., BARALE, J. C., FAIRHURST, R. M., BENOIT-VICAL, F., MERCEREAU-PUIJALON, O. & MENARD, D. 2014. A molecular marker of artemisinin-resistant Plasmodium falciparum malaria. Nature, 505, 50-5. ASHTON, R. A., KEFYALEW, T., TESFAYE, G., COUNIHAN, H., YADETA, D., CUNDILL, B., REITHINGER, R. & KOLACZINSKI, J. H. 2010. Performance of three multi-species rapid diagnostic tests for diagnosis of Plasmodium falciparum and Plasmodium vivax malaria in Oromia Regional State, Ethiopia. Malar J, 9, 297. ASIF, S. A. 2008. Departmental audit of malaria control programme 2001-2005 north west frontier province (NWFP). J Ayub Med Coll Abbottabad, 20, 98-102. ATIF, S. H., FARZANA, M., NAILA, S., AND ABDUL, F. D. 2009. Incidence and pattern of malarial infection at a tertiary care Hospital of Hyderabad. World Journal of Medical Sciences, 4. AURRECOECHEA, C., BRESTELLI, J., BRUNK, B. P., DOMMER, J., FISCHER, S., GAJRIA, B., GAO, X., GINGLE, A., GRANT, G., HARB, O. S., HEIGES, M., INNAMORATO, F., IODICE, J., KISSINGER, J. C., KRAEMER, E., LI, W., MILLER, J. A., NAYAK, V., PENNINGTON, C., PINNEY, D. F., ROOS, D. S., ROSS, C., STOECKERT, C. J., JR., TREATMAN, C. & WANG, H. 2009. PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res, 37, D539-43. AVILA, S. L. & FERREIRA, A. W. 1996. Malaria diagnosis: a review. Braz J Med Biol Res, 29, 431-43. AVISE, J. C. & WALKER, D. 1998. Pleistocene phylogeographic effects on avian populations and the speciation process. Proc Biol Sci, 265, 457-63. AWAN, Z. & JAN, A. 2008a. Rice fields in relation to malaria in District Bannu. Pak J Zool, 28, 11-21. AWAN, Z. U. R. & JAN, A. H. 2008b. Rice field in relations to the malaria in district Bannu NWFP. Proceedings of Pakistan Congress of Zoology, 28, 11-21. AWANDARE, G. A., OUMA, Y., OUMA, C., WERE, T., OTIENO, R., KELLER, C. C., DAVENPORT, G. C., HITTNER, J. B., VULULE, J., FERRELL, R., ONG'ECHA, J. M. & PERKINS, D. J. 2007. Role of
198 monocyte-acquired hemozoin in suppression of macrophage migration inhibitory factor in children with severe malarial anemia. Infect Immun, 75, 201-10. AYELE, D. G., ZEWOTIR, T. T. & MWAMBI, H. G. 2012. Prevalence and risk factors of malaria in Ethiopia. Malar J, 11, 195. AZIKIWE, C. C., IFEZULIKE, C. C., SIMINIALAYI, I. M., AMAZU, L. U., ENYE, J. C. & NWAKWUNITE, O. E. 2012. A comparative laboratory diagnosis of malaria: microscopy versus rapid diagnostic test kits. Asian Pac J Trop Biomed, 2, 307-10. BAKER, J., MCCARTHY, J., GATTON, M., KYLE, D. E., BELIZARIO, V., LUCHAVEZ, J., BELL, D. & CHENG, Q. 2005. Genetic diversity of Plasmodium falciparum histidine-rich protein 2 (PfHRP2) and its effect on the performance of PfHRP2-based rapid diagnostic tests. J Infect Dis, 192, 870-7. BALLOUX, F. & LUGON-MOULIN, N. 2002. The estimation of population differentiation with microsatellite markers. Mol Ecol, 11, 155-65. BALLS, M. J., BODKER, R., THOMAS, C. J., KISINZA, W., MSANGENI, H. A. & LINDSAY, S. W. 2004. Effect of topography on the risk of malaria infection in the Usambara Mountains, Tanzania. Trans R Soc Trop Med Hyg, 98, 400-8. BAMAGA, O. A., MAHDY, M. A., MAHMUD, R. & LIM, Y. A. 2014. Malaria in Hadhramout, a southeast province of Yemen: prevalence, risk factors, knowledge, attitude and practices (KAPs). Parasit Vectors, 7, 351. BANGUERO, H. 1984. Socioeconomic factors associated with malaria in Colombia. Soc Sci Med, 19, 1099-104. BANNISTER, L. H. A. S., IRWIN W 2009. Plasmodium. In: Encyclopedia of Life Sciences (ELS). BANSAL, R., KADHIRAVAN, T., AGGARWAL, P., HANDA, R., BISWAS, A. & WALI, J. P. 2002. Plasmodium vivax and hepatitis E co-infection--a rare cause of malarial jaundice. Indian J Gastroenterol, 21, 207-8. BARAGATTI, M., FOURNET, F., HENRY, M. C., ASSI, S., OUEDRAOGO, H., ROGIER, C. & SALEM, G. 2009. Social and environmental malaria risk factors in urban areas of Ouagadougou, Burkina Faso. Malar J, 8, 13. BARKER, R. H., JR., BANCHONGAKSORN, T., COURVAL, J. M., SUWONKERD, W., RIMWUNGTRAGOON, K. & WIRTH, D. F. 1992. A simple method to detect Plasmodium falciparum directly from blood samples using the polymerase chain reaction. Am J Trop Med Hyg, 46, 416-26. BARTOLONI, A. & ZAMMARCHI, L. 2012. Clinical aspects of uncomplicated and severe Malalria. Mediterr J Hematol Infect Dis, 4. BASSERI, H. R., RAEISI, A., HOLAKOUIE, K. & SHANADEH, K. 2010. Malaria prevention among Afghani refugees in a malarious area, southeastern Iran. Bull Soc Pathol Exot, 103, 340-5. BASTOS-SILVEIRA, C., SANTOS, S. M., MONARCA, R., MATHIAS MDA, L. & HECKEL, G. 2012. Deep mitochondrial introgression and hybridization among ecologically divergent vole species. Mol Ecol, 21, 5309-23. BASURKO, C., HANF, M., HAN-SZE, R., ROGIER, S., HERITIER, P., GRENIER, C., JOUBERT, M., NACHER, M. & CARME, B. 2011. Influence of climate and river level on the incidence of malaria in Cacao, French Guiana. Malar J, 10, 26. BATWALA, V., MAGNUSSEN, P. & NUWAHA, F. 2011. Comparative feasibility of implementing rapid diagnostic test and microscopy for parasitological diagnosis of malaria in Uganda. Malar J, 10, 373. BAYOH, M. N. & LINDSAY, S. W. 2004. Temperature-related duration of aquatic stages of the Afrotropical malaria vector mosquito Anopheles gambiae in the laboratory. Med Vet Entomol, 18, 174-9. BEARE, N. A., TAYLOR, T. E., HARDING, S. P., LEWALLEN, S. & MOLYNEUX, M. E. 2006. Malarial retinopathy: a newly established diagnostic sign in severe malaria. Am J Trop Med Hyg, 75, 790- 7. BEG, M. A., SANI, N., MEHRAJ, V., JAFRI, W., KHAN, M. A., MALIK, A., MENEZES, E., HUSSAIN, R. & SMEGO, R., JR. 2008. Comparative features and outcomes of malaria at a tertiary care hospital in Karachi, Pakistan. Int J Infect Dis, 12, 37-42.
199 BELL, D., PEELING, R. W. & PACIFIC/TDR, W. H.-R. O. F. T. W. 2006. Evaluation of rapid diagnostic tests: malaria. Nat Rev Microbiol, 4, S34-8. BELL, D. R., WILSON, D. W. & MARTIN, L. B. 2005. False-positive results of a Plasmodium falciparum histidine-rich protein 2-detecting malaria rapid diagnostic test due to high sensitivity in a community with fluctuating low parasite density. Am J Trop Med Hyg, 73, 199-203. BESANSKY, N. J., HILL, C. A. & COSTANTINI, C. 2004. No accounting for taste: host preference in malaria vectors. Trends Parasitol, 20, 249-51. BHALLI, M. & SAMIULLAH 2001. falciparum malaria. A review of 120 cases. J COll PhysSurg, 11, 300-303. BIN MOHANNA, M. A., BIN GHOUTH, A. S. & RAJAA, Y. A. 2007. Malaria signs and infection rate among asymptomatic schoolchildren in Hajr Valley, Yemen. East Mediterr Health J, 13, 35-40. BISWAS, S., TOMAR, D. & RAO, D. N. 2005. Investigation of the kinetics of histidine-rich protein 2 and of the antibody responses to this antigen, in a group of malaria patients from India. Ann Trop Med Parasitol, 99, 553-62. BLEDSOE, G. H. 2005. Malaria primer for clinicians in the United States. South Med J, 98, 1197-204; quiz 1205, 1230. BLOLAND, P. B. 2001. Drug Resistance in malaria. World Health Organization. Switzerland. BODKER, R., AKIDA, J., SHAYO, D., KISINZA, W., MSANGENI, H. A., PEDERSEN, E. M. & LINDSAY, S. W. 2003. Relationship between altitude and intensity of malaria transmission in the Usambara Mountains, Tanzania. J Med Entomol, 40, 706-17. BODKER, R., MALIMA, R., MSANGENI, H. & LINDSAY, S. 2000. Resurgence of malaria in the Usambara mountains, Tanzania, an epidemic of drug-resistant parasites. Glob Change Hum Health, 134- 153. BODKER, R., MSANGENI, H. A., KISINZA, W. & LINDSAY, S. W. 2006. Relationship between the intensity of exposure to malaria parasites and infection in the Usambara Mountains, Tanzania. Am J Trop Med Hyg, 74, 716-23. BOGH, C., CLARKE, S. E., PINDER, M., SANYANG, F. & LINDSAY, S. W. 2001. Effect of passive zooprophylaxis on malaria transmission in The Gambia. J Med Entomol, 38, 822-8. BOONMA, P., CHRISTENSEN, P. R., SUWANARUSK, R., PRICE, R. N., RUSSELL, B. & LEK-UTHAI, U. 2007. Comparison of three molecular methods for the detection and speciation of Plasmodium vivax and Plasmodium falciparum. Malar J, 6, 124. BOUMA, M. & ROWLAND, M. 1995. Failure of passive zooprophylaxis: cattle ownership in Pakistan is associated with a higher prevalence of malaria. Trans R Soc Trop Med Hyg, 89, 351-3. BOUMA, M. J., DYE, C. & VAN DER KAAY, H. J. 1996a. Falciparum malaria and climate change in the northwest frontier province of Pakistan. Am J Trop Med Hyg, 55, 131-7. BOUMA, M. J., PARVEZ, S. D., NESBIT, R. & WINKLER, A. M. 1996b. Malaria control using permethrin applied to tents of nomadic Afghan refugees in northern Pakistan. Bull World Health Organ, 74, 413-21. BOUSEMA, J. T., GOUAGNA, L. C., DRAKELEY, C. J., MEUTSTEGE, A. M., OKECH, B. A., AKIM, I. N., BEIER, J. C., GITHURE, J. I. & SAUERWEIN, R. W. 2004. Plasmodium falciparum gametocyte carriage in asymptomatic children in western Kenya. Malar J, 3, 18. BOUSEMA, J. T., SCHNEIDER, P., GOUAGNA, L. C., DRAKELEY, C. J., TOSTMANN, A., HOUBEN, R., GITHURE, J. I., ORD, R., SUTHERLAND, C. J., OMAR, S. A. & SAUERWEIN, R. W. 2006. Moderate effect of artemisinin-based combination therapy on transmission of Plasmodium falciparum. J Infect Dis, 193, 1151-9. BOUSEMA, T., OKELL, L., FELGER, I. & DRAKELEY, C. 2014. Asymptomatic malaria infections: detectability, transmissibility and public health relevance. Nat Rev Microbiol, 12, 833-40. BOYCE, R. M., MUIRU, A., REYES, R., NTARO, M., MULOGO, E., MATTE, M. & SIEDNER, M. J. 2015. Impact of rapid diagnostic tests for the diagnosis and treatment of malaria at a peripheral health facility in Western Uganda: an interrupted time series analysis. Malar J, 14, 203. BOZDECH, Z., LLINAS, M., PULLIAM, B. L., WONG, E. D., ZHU, J. & DERISI, J. L. 2003. The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLoS Biol, 1, E5.
200 BRADLEY, T. 2001. Malaria and drug resistance: MB CHB Special study model project report. Department of Microbiology and Immunology, University of Leicester. . BROOKER, S., CLARKE, S., NJAGI, J. K., POLACK, S., MUGO, B., ESTAMBALE, B., MUCHIRI, E., MAGNUSSEN, P. & COX, J. 2004. Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya. Trop Med Int Health, 9, 757-66. BROWN, A. H., FELDMAN, M. W. & NEVO, E. 1980. Multilocus Structure of Natural Populations of HORDEUM SPONTANEUM. Genetics, 96, 523-36. BROWN, W. M., GEORGE, M., JR. & WILSON, A. C. 1979. Rapid evolution of animal mitochondrial DNA. Proc Natl Acad Sci U S A, 76, 1967-71. BRUCE, M. C., DONNELLY, C. A., ALPERS, M. P., GALINSKI, M. R., BARNWELL, J. W., WALLIKER, D. & DAY, K. P. 2000. Cross-species interactions between malaria parasites in humans. Science, 287, 845-8. BRUCE-CHWAT, L. 1980. Essential malariology, London, William Heinemann Medical Books Ltd. BRUCE-CHWATT 1960. A study of the blood-feeding patterns of Anopheles mosquitos through precipitin tests: Results of collaborative work for the period 1955-59 and their application to malaria eradication programmes. Bull World Health Organ, 22, 685-720. BURKOT, T. R., GARNER, P., PARU, R., DAGORO, H., BARNES, A., MCDOUGALL, S., WIRTZ, R. A., CAMPBELL, G. & SPARK, R. 1990. Effects of untreated bed nets on the transmission of Plasmodium falciparum, P. vivax and Wuchereria bancrofti in Papua New Guinea. Trans R Soc Trop Med Hyg, 84, 773-9. BUTRAPORN, P., SORNMANI, S. & HUNGSAPRUEK, T. 1986. Social, behavioural, housing factors and their interactive effects associated with malaria occurrence in east Thailand. Southeast Asian J Trop Med Public Health, 17, 386-92. CANO, J., DESCALZO, M. A., MORENO, M., CHEN, Z., NZAMBO, S., BOBUAKASI, L., BUATICHE, J. N., ONDO, M., MICHA, F. & BENITO, A. 2006. Spatial variability in the density, distribution and vectorial capacity of anopheline species in a high transmission village (Equatorial Guinea). Malar J, 5, 21. CARLTON, J. M., ADAMS, J. H., SILVA, J. C., BIDWELL, S. L., LORENZI, H., CALER, E., CRABTREE, J., ANGIUOLI, S. V., MERINO, E. F., AMEDEO, P., CHENG, Q., COULSON, R. M., CRABB, B. S., DEL PORTILLO, H. A., ESSIEN, K., FELDBLYUM, T. V., FERNANDEZ-BECERRA, C., GILSON, P. R., GUEYE, A. H., GUO, X., KANG'A, S., KOOIJ, T. W., KORSINCZKY, M., MEYER, E. V., NENE, V., PAULSEN, I., WHITE, O., RALPH, S. A., REN, Q., SARGEANT, T. J., SALZBERG, S. L., STOECKERT, C. J., SULLIVAN, S. A., YAMAMOTO, M. M., HOFFMAN, S. L., WORTMAN, J. R., GARDNER, M. J., GALINSKI, M. R., BARNWELL, J. W. & FRASER-LIGGETT, C. M. 2008. Comparative genomics of the neglected human malaria parasite Plasmodium vivax. Nature, 455, 757-63. CARRARA, V. I., SIRILAK, S., THONGLAIRUAM, J., ROJANAWATSIRIVET, C., PROUX, S., GILBOS, V., BROCKMAN, A., ASHLEY, E. A., MCGREADY, R., KRUDSOOD, S., LEEMINGSAWAT, S., LOOAREESUWAN, S., SINGHASIVANON, P., WHITE, N. & NOSTEN, F. 2006. Deployment of early diagnosis and mefloquine-artesunate treatment of falciparum malaria in Thailand: the Tak Malaria Initiative. PLoS Med, 3, e183. CARTER, R. & MENDIS, K. N. 2002. evolutionary and historical aspects of the burden of malaria. Clin. Microbiol Rev, 15, 564-594. CARTER, R., MENDIS, K. N. & ROBERTS, D. 2000. Spatial targeting of interventions against malaria. Bull World Health Organ, 78, 1401-11. CASTRO, M. C., KANAMORI, S., KANNADY, K., MKUDE, S., KILLEEN, G. F. & FILLINGER, U. 2010. The importance of drains for the larval development of lymphatic filariasis and malaria vectors in Dar es Salaam, United Republic of Tanzania. PLoS Negl Trop Dis, 4, e693. CATTANI, J. A., GIBSON, F. D., ALPERS, M. P. & CRANE, G. G. 1987. Hereditary ovalocytosis and reduced susceptibility to malaria in Papua New Guinea. Trans R Soc Trop Med Hyg, 81, 705-9. CDC 2017a. Malaria. CDC 2017b. Malaria Parasites - About. CDC: Malaria. U.S. Centers for Disease Control and Prevention. CHADEE, D. D., LE MAITRE, A. & TILLUCKDHARRY, C. C. 1992. An outbreak of Plasmodium vivax malaria in Trinidad, W.I. Ann Trop Med Parasitol, 86, 583-90.
201 CHAKRAVORTY, S. J., HUGHES, K. R. & CRAIG, A. G. 2008. Host response to cytoadherence in Plasmodium falciparum. Biochem Soc Trans, 36, 221-8. CHAN, E. R., BARNWELL, J. W., ZIMMERMAN, P. A. & SERRE, D. 2015. Comparative analysis of field- isolate and monkey-adapted Plasmodium vivax genomes. PLoS Negl Trop Dis, 9, e0003566. CHANDRAMOHAN, D., JAFFAR, S. & GREENWOOD, B. 2002. Use of clinical algorithms for diagnosing malaria. Trop Med Int Health, 7, 45-52. CHANG, K. H. & STEVENSON, M. M. 2004. Malarial anaemia: mechanisms and implications of insufficient erythropoiesis during blood-stage malaria. Int J Parasitol, 34, 1501-16. CHANON, K. E., MENDEZ-GALVAN, J. F., GALINDO-JARAMILLO, J. M., OLGUIN-BERNAL, H. & BORJA- ABURTO, V. H. 2003. Cooperative actions to achieve malaria control without the use of DDT. Int J Hyg Environ Health, 206, 387-94. CHARLWOOD, J. D., PINTO, J., FERRARA, P. R., SOUSA, C. A., FERREIRA, C., GIL, V. & DO ROSARIO, V. E. 2003. Raised houses reduce mosquito bites. Malar J, 2, 45. CHARLWOOD, J. D., TOMAS, E. V., BRAGANCA, M., CUAMBA, N., ALIFRANGIS, M. & STANTON, M. 2015. Malaria prevalence and incidence in an isolated, meso-endemic area of Mozambique. PeerJ, 3, e1370. CHAVATTE, J. M., CHIRON, F., CHABAUD, A. & LANDAU, I. 2007. [Probable speciations by "host-vector 'fidelity'": 14 species of Plasmodium from magpies]. Parasite, 14, 21-37. CHAVES, L. F. & KOENRAADT, C. J. 2010. Climate change and highland malaria: fresh air for a hot debate. Q Rev Biol, 85, 27-55. CHECCHI, F., COX, J., BALKAN, S., TAMRAT, A., PRIOTTO, G., ALBERTI, K. P. & GUTHMANN, J. P. 2006. Malaria epidemics and interventions, Kenya, Burundi, southern Sudan, and Ethiopia, 1999-2004. Emerg Infect Dis, 12, 1477-85. CHEESBROUGH, M. 1987. Medical Laboratory Manual for Tropical Countries, 2nd Edition Volume I. Butterworths & Co (Publishers) Limited, Borough Green, London. 23: 1-4, 15, 221-251. CHEN, Q., SCHLICHTHERLE, M. & WAHLGREN, M. 2000. Molecular aspects of severe malaria. Clin Microbiol Rev, 13, 439-50. CHENET, S. M., BRANCH, O. H., ESCALANTE, A. A., LUCAS, C. M. & BACON, D. J. 2008. Genetic diversity of vaccine candidate antigens in Plasmodium falciparum isolates from the Amazon basin of Peru. Malar J, 7, 93. CHINKHUMBA, J., SKARBINSKI, J., CHILIMA, B., CAMPBELL, C., EWING, V., SAN JOAQUIN, M., SANDE, J., ALI, D. & MATHANGA, D. 2010. Comparative field performance and adherence to test results of four malaria rapid diagnostic tests among febrile patients more than five years of age in Blantyre, Malawi. Malar J, 9, 209. CHIREBVU, E., CHIMBARI, M. J. & NGWENYA, B. N. 2014. Assessment of risk factors associated with malaria transmission in tubu village, northern botswana. Malar Res Treat, 2014, 403069. CHUQUIYAURI, R., PAREDES, M., PENATARO, P., TORRES, S., MARIN, S., TENORIO, A., BROUWER, K. C., ABELES, S., LLANOS-CUENTAS, A., GILMAN, R. H., KOSEK, M. & VINETZ, J. M. 2012. Socio- demographics and the development of malaria elimination strategies in the low transmission setting. Acta Trop, 121, 292-302. CLARK, I. A. 1987. Cell-mediated immunity in protection and pathology of malaria. Parasitol Today, 3, 300-5. CLARK, I. A., BUDD, A. C., ALLEVA, L. M. & COWDEN, W. B. 2006. Human malarial disease: a consequence of inflammatory cytokine release. Malar J, 5, 85. CLARK, I. A. & COWDEN, W. B. 2003. The pathophysiology of falciparum malaria. Pharmacol Ther, 99, 221-60. COATNEY, G. R. & ROUDABUSH, R. L. 1936. A catalog and host-index of the genus Plasmodium. J. Parasitol. , 22, 338-353. COETZEE, M., CRAIG, M. & LE SUEUR, D. 2000. Distribution of African malaria mosquitoes belonging to the Anopheles gambiae complex. Parasitol Today, 16, 74-7. COGSWELL, F. B. 1992. The hypnozoite and relapse in primate malaria. Clin Microbiol Rev, 5, 26-35.
202 COKER H, A. B., CHUKWUANI C.M., IFUDU H.D. & AINA B.A. 2001. The malaria scourge. Concepts in disease management. Nigeria. JR of pharma 32, 19-48. COLEMAN, M., COLEMAN, M., MABASO, M. L., MABUZA, A. M., KOK, G., COETZEE, M. & DURRHEIM, D. N. 2010. Household and microeconomic factors associated with malaria in Mpumalanga, South Africa. Trans R Soc Trop Med Hyg, 104, 143-7. COLEMAN, R. E., MANEECHAI, N., RACHAPHAEW, N., KUMPITAK, C., MILLER, R. S., SOYSENG, V., THIMASARN, K. & SATTABONGKOT, J. 2002. Comparison of field and expert laboratory microscopy for active surveillance for asymptomatic Plasmodium falciparum and Plasmodium vivax in western Thailand. Am J Trop Med Hyg, 67, 141-4. COLEMAN, R. E., SATTABONGKOT, J., PROMSTAPORM, S., MANEECHAI, N., TIPPAYACHAI, B., KENGLUECHA, A., RACHAPAEW, N., ZOLLNER, G., MILLER, R. S. & VAUGHAN, J. A. 2006a. Comparison of PCR and microscopy for the detection of asymptomatic malaria in a Plasmodium falciparum/vivax endemic area in Thailand. Malaria Journal, 5, 121 %@ 1475-2875. COLEMAN, R. E., SATTABONGKOT, J., PROMSTAPORM, S., MANEECHAI, N., TIPPAYACHAI, B., KENGLUECHA, A., RACHAPAEW, N., ZOLLNER, G., MILLER, R. S., VAUGHAN, J. A., THIMASARN, K. & KHUNTIRAT, B. 2006b. Comparison of PCR and microscopy for the detection of asymptomatic malaria in a Plasmodium falciparum/vivax endemic area in Thailand. Malar J, 5, 121. CONWAY, D. J. 2015. Paths to a malaria vaccine illuminated by parasite genomics. Trends Genet, 31, 97- 107. CONWAY, D. J., FANELLO, C., LLOYD, J. M., AL-JOUBORI, B. M., BALOCH, A. H., SOMANATH, S. D., ROPER, C., ODUOLA, A. M., MULDER, B., POVOA, M. M., SINGH, B. & THOMAS, A. W. 2000. Origin of Plasmodium falciparum malaria is traced by mitochondrial DNA. Mol Biochem Parasitol, 111, 163-71. COOK, J. 2010. The use of serological markers for evaluation of malaria and transmission dynamics. Ph.D, london school of hygiene and tropical medicines. COOK, J., AYDIN-SCHMIDT, B., GONZALEZ, I. J., BELL, D., EDLUND, E., NASSOR, M. H., MSELLEM, M., ALI, A., ABASS, A. K., MARTENSSON, A. & BJORKMAN, A. 2015a. Loop-mediated isothermal amplification (LAMP) for point-of-care detection of asymptomatic low-density malaria parasite carriers in Zanzibar. Malar J, 14, 43. COOK, J., XU, W., MSELLEM, M., VONK, M., BERGSTROM, B. & GOSLING, R. 2015b. Mass screening and treatment on the basis of results of a Plasmodium falciparum-specific rapid diagnostic test did not reduce malaria incidence in Zanzibar. J Infect Dis, 211. CORDERY, D. V. & URBAN, B. C. 2009. Immune recognition of Plasmodium-infected erythrocytes. Adv Exp Med Biol, 653, 175-84. CORDRAY, M. S. & RICHARDS-KORTUM, R. R. 2012. Emerging nucleic acid–based tests for point-of-care detection of malaria. The American journal of tropical medicine and hygiene, 87, 223-230 %@ 0002-9637. CORNEJO, O. E. & ESCALANTE, A. A. 2006. The origin and age of Plasmodium vivax. Trends Parasitol, 22, 558-63. COUNIHAN, N. A., KALANON, M., COPPEL, R. L. & DE KONING-WARD, T. F. 2013. Plasmodium rhoptry proteins: why order is important. Trends Parasitol, 29, 228-36. COX-SINGH, J., DAVIS, T. M., LEE, K. S., SHAMSUL, S. S., MATUSOP, A., RATNAM, S., RAHMAN, H. A., CONWAY, D. J. & SINGH, B. 2008. Plasmodium knowlesi malaria in humans is widely distributed and potentially life threatening. Clin Infect Dis, 46, 165-71. COX-SINGH, J., HIU, J., LUCAS, S. B., DIVIS, P. C., ZULKARNAEN, M., CHANDRAN, P., WONG, K. T., ADEM, P., ZAKI, S. R., SINGH, B. & KRISHNA, S. 2010. Severe malaria - a case of fatal Plasmodium knowlesi infection with post-mortem findings: a case report. Malar J, 9, 10. CRABB, B. S. & COWMAN, A. F. 2002. Plasmodium falciparum virulence determinants unveiled. Genome Biol, 3, Reviews1031. CRAIG, M. H. & SHARP, B. L. 1997. Comparative evaluation of four techniques for the diagnosis of Plasmodium falciparum infections. Trans R Soc Trop Med Hyg, 91, 279-82.
203 CREASEY, A., MENDIS, K., CARLTON, J., WILLIAMSON, D., WILSON, I. & CARTER, R. 1994. Maternal inheritance of extrachromosomal DNA in malaria parasites. Mol Biochem Parasitol, 65, 95-8. CROOKSTON, B. T., ALDER, S. C., BOAKYE, I., MERRILL, R. M., AMUASI, J. H., PORUCZNIK, C. A., STANFORD, J. B., DICKERSON, T. T., DEARDEN, K. A., HALE, D. C., SYLVERKEN, J., SNOW, B. S., OSEI-AKOTO, A. & ANSONG, D. 2010. Exploring the relationship between chronic undernutrition and asymptomatic malaria in Ghanaian children. Malar J, 9, 39. CUADROS, J., PEREZ-TANOIRA, R., PRIETO-PEREZ, L., MARTIN-MARTIN, I., BERZOSA, P., GONZALEZ, V., TISIANO, G., BALCHA, S., RAMOS, J. M. & GORGOLAS, M. 2015. Field Evaluation of Malaria Microscopy, Rapid Malaria Tests and Loop-Mediated Isothermal Amplification in a Rural Hospital in South Western Ethiopia. PLoS One, 10, e0142842. CUCUNUBA, Z. M., GUERRA, A. P., RAHIRANT, S. J., RIVERA, J. A., CORTES, L. J. & NICHOLLS, R. S. 2008. Asymptomatic Plasmodium spp. infection in Tierralta, Colombia. Mem Inst Oswaldo Cruz, 103, 668-73. CULLETON, R., COBAN, C., ZEYREK, F. Y., CRAVO, P., KANEKO, A., RANDRIANARIVELOJOSIA, M., ANDRIANARANJAKA, V., KANO, S., FARNERT, A., AREZ, A. P., SHARP, P. M., CARTER, R. & TANABE, K. 2011. The origins of African Plasmodium vivax; insights from mitochondrial genome sequencing. PLoS One, 6, e29137. CYBULSKI, J. S., CLEMENTS, J. & PRAKASH, M. 2014. Foldscope: origami-based paper microscope. PLoS One, 9, e98781. D'ACREMONT, V., LENGELER, C. & GENTON, B. 2010. Reduction in the proportion of fevers associated with Plasmodium falciparum parasitaemia in Africa: a systematic review. Malar J, 9, 240. D'ALESSANDRO, U. & BUTTIENS, H. 2001. History and importance of antimalarial drug resistance. Trop Med Int Health, 6, 845-8. D'ALESSANDRO, U., OLALEYE, B. O., MCGUIRE, W., THOMSON, M. C., LANGEROCK, P., BENNETT, S. & GREENWOOD, B. M. 1995. A comparison of the efficacy of insecticide-treated and untreated bed nets in preventing malaria in Gambian children. Trans R Soc Trop Med Hyg, 89, 596-8. DALE, P., SIPE, N., ANTO, S., HUTAJULU, B., NDOEN, E., PAPAYUNGAN, M., SAIKHU, A. & PRABOWA, Y. T. 2005. Malaria in Indonesia: a summary of recent research into its environmental relationships. Southeast Asian J Trop Med Public Health, 36, 1-13. DANESHVAR, C., DAVIS, T. M., COX-SINGH, J., RAFA'EE, M. Z., ZAKARIA, S. K., DIVIS, P. C. & SINGH, B. 2009. Clinical and laboratory features of human Plasmodium knowlesi infection. Clin Infect Dis, 49, 852-60. DANIS-LOZANO, R., RODRIGUEZ, M. H., BETANZOS-REYES, A. F., HERNANDEZ-AVILA, J. E., GONZALEZ- CERON, L., MENDEZ-GALVAN, J. F., VELAZQUEZ-MONROY, O. J. & TAPIA-CONYER, R. 2007. Individual risk factors for Plasmodium vivax infection in the residual malaria transmission focus of Oaxaca, Mexico. Salud Publica Mex, 49, 199-209. DARCH, S. E., MCNALLY, A., HARRISON, F., CORANDER, J., BARR, H. L., PASZKIEWICZ, K., HOLDEN, S., FOGARTY, A., CRUSZ, S. A. & DIGGLE, S. P. 2015. Recombination is a key driver of genomic and phenotypic diversity in a Pseudomonas aeruginosa population during cystic fibrosis infection. Sci Rep, 5, 7649. DAS, A. & RAVINDRAN, T. S. 2010. Factors affecting treatment-seeking for febrile illness in a malaria endemic block in Boudh district, Orissa, India: policy implications for malaria control. Malar J, 9, 377. DAS, D. K., MAITI, A. K. & CHAKRABORTY, C. 2015. Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. J Microsc, 257, 238-52. DAUD, M., ULLAH, N., KHAN, N. & ULLAH, I. 2014. Prevalence of malaria cases in general population of Mithakhel district Karak Pakistan. Reviews of Progress, 2, 1-4. DE MEEUS, T., LEHMANN, L. & BALLOUX, F. 2006. Molecular epidemiology of clonal diploids: a quick overview and a short DIY (do it yourself) notice. Infect Genet Evol, 6, 163-70. DE OLIVEIRA, A. M., SKARBINSKI, J., OUMA, P. O., KARIUKI, S., BARNWELL, J. W., OTIENO, K., ONYONA, P., CAUSER, L. M., LASERSON, K. F., AKHWALE, W. S., SLUTSKER, L. & HAMEL, M. 2009.
204 Performance of malaria rapid diagnostic tests as part of routine malaria case management in Kenya. Am J Trop Med Hyg, 80, 470-4. DE ZULUETA, J., MUJTABA, S. M. & SHAH, I. H. 1980. Malaria control and long-term periodicity of the disease in Pakistan. Trans R Soc Trop Med Hyg, 74, 624-32. DERESSA, W., ALI, A. & BERHANE, Y. 2007. Household and socioeconomic factors associated with childhood febrile illnesses and treatment seeking behaviour in an area of epidemic malaria in rural Ethiopia. Trans R Soc Trop Med Hyg, 101, 939-47. DERUA, Y. A., ISHENGOMA, D. R., RWEGOSHORA, R. T., TENU, F., MASSAGA, J. J., MBOERA, L. E. & MAGESA, S. M. 2011. Users' and health service providers' perception on quality of laboratory malaria diagnosis in Tanzania. Malar J, 10, 78. DEV, V. & DASH, A. P. 2007. Rainfall and malaria transmission in north-eastern India. Ann Trop Med Parasitol, 101, 457-9. DEV, V., PHOOKAN, S., SHARMA, V. P. & ANAND, S. P. 2004. Physiographic and entomologic risk factors of malaria in Assam, India. Am J Trop Med Hyg, 71, 451-6. DHARIA, N. V., BRIGHT, A. T., WESTENBERGER, S. J., BARNES, S. W., BATALOV, S., KUHEN, K., BORBOA, R., FEDERE, G. C., MCCLEAN, C. M., VINETZ, J. M., NEYRA, V., LLANOS-CUENTAS, A., BARNWELL, J. W., WALKER, J. R. & WINZELER, E. A. 2010. Whole-genome sequencing and microarray analysis of ex vivo Plasmodium vivax reveal selective pressure on putative drug resistance genes. Proc Natl Acad Sci U S A, 107, 20045-50. DIEZ-ROUX, A. V. 1998. Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health, 88, 216-22. DIKE, N., ONWUJEKWE, O., OJUKWU, J., IKEME, A., UZOCHUKWU, B. & SHU, E. 2006. Influence of education and knowledge on perceptions and practices to control malaria in Southeast Nigeria. Soc Sci Med, 63, 103-6. DIRECTORATE OF MALARIA CONTROL 2014. Pakistan medical researh council, Save the children. Malaria indicator survey in 38 High risk districts of Pakistan: 2013–14. In: COUNCIL, P. M. R. (ed.). DMC 2007. Malaria case management desk guide for clinicians and health care providers. Islamabad. Directorate of Malaria Control. DMC 2017. Malaria case management desk guide for clinicians and health care providers. Islamabad. Directorate of Malaria Control. DMOC. 2012. Malaria control Program [Online]. Pakistan. Available: http://dmc.gov.pk/ [Accessed 2016]. DOGRA, J. R. A. 1938. Malaria Survey of Bannu Cantonment (1932-33). Journal of the Malaria Institute of India 1, 57-81. DONDORP, A. M., LEE, S. J., FAIZ, M. A., MISHRA, S., PRICE, R., TJITRA, E., THAN, M., HTUT, Y., MOHANTY, S., YUNUS, E. B., RAHMAN, R., NOSTEN, F., ANSTEY, N. M., DAY, N. P. & WHITE, N. J. 2008. The relationship between age and the manifestations of and mortality associated with severe malaria. Clin Infect Dis, 47, 151-7. DRAKELEY, C., SCHELLENBERG, D., KIHONDA, J., SOUSA, C. A., AREZ, A. P., LOPES, D., LINES, J., MSHINDA, H., LENGELER, C., ARMSTRONG SCHELLENBERG, J., TANNER, M. & ALONSO, P. 2003. An estimation of the entomological inoculation rate for Ifakara: a semi-urban area in a region of intense malaria transmission in Tanzania. Trop Med Int Health, 8, 767-74. DUNCAN, C., JONES, K. & MOON, G. 1993. Do places matter? A multi-level analysis of regional variations in health-related behaviour in Britain. Soc Sci Med, 37, 725-33. DURRHELM, D. N., BECKER, P. J., BILLINGHURST, K. & BRINK, A. 1997. Diagnostic disagreement--the lessons learnt from malaria diagnosis in Mpumalanga. S Afr Med J, 87, 609-11. DUVAL, L., FOURMENT, M., NERRIENET, E., ROUSSET, D., SADEUH, S. A., GOODMAN, S. M., ANDRIAHOLINIRINA, N. V., RANDRIANARIVELOJOSIA, M., PAUL, R. E., ROBERT, V., AYALA, F. J. & ARIEY, F. 2010. African apes as reservoirs of Plasmodium falciparum and the origin and diversification of the Laverania subgenus. Proc Natl Acad Sci U S A, 107, 10561-6. EDWARD, A. & CAVALLI-SFORZA, L. 1963. The reconstruction of evolution. Heredity, 18, 553.
205 EICKMEYER, K., HUGGINS, P., PACHTER, L. & YOSHIDA, R. 2008. On the optimality of the neighbor- joining algorithm. Algorithms Mol Biol, 3, 5. EKE , R. C. L. & NWACHUKWU, W. 2006. High prevalence of asymptomatic Plasmodium infection in a suburb of Aba Town, Nigeria. Ann of Afr Med, 5, 42-45. EKVALL, H., ARESE, P., TURRINI, F., AYI, K., MANNU, F., PREMJI, Z. & BJORKMAN, A. 2001. Acute haemolysis in childhood falciparum malaria. Trans R Soc Trop Med Hyg, 95, 611-7. EL SAMANI, F. Z., WILLETT, W. C. & WARE, J. H. 1987. Nutritional and socio-demographic risk indicators of malaria in children under five: a cross-sectional study in a Sudanese rural community. J Trop Med Hyg, 90, 69-78. ELSAYED, S., PLEWES, K., CHURCH, D., CHOW, B. & ZHANG, K. 2006. Use of molecular beacon probes for real-time PCR detection of Plasmodium falciparum and other Plasmodium species in peripheral blood specimens. J Clin Microbiol, 44, 622-4. ELSON, J. L., ANDREWS, R. M., CHINNERY, P. F., LIGHTOWLERS, R. N., TURNBULL, D. M. & HOWELL, N. 2001. Analysis of European mtDNAs for recombination. Am J Hum Genet, 68, 145-153. ENDESHAW, T., GEBRE, T., NGONDI, J., GRAVES, P. M., SHARGIE, E. B., EJIGSEMAHU, Y., AYELE, B., YOHANNES, G., TEFERI, T., MESSELE, A., ZERIHUN, M., GENET, A., MOSHER, A. W., EMERSON, P. M. & RICHARDS, F. O. 2008. Evaluation of light microscopy and rapid diagnostic test for the detection of malaria under operational field conditions: a household survey in Ethiopia. Malar J, 7, 118. ENGERING, A., HOGERWERF, L. & SLINGENBERGH, J. 2013. Pathogen-host-environment interplay and disease emergence. Emerg Microbes Infect, 2, e5. ERDMAN, L. K. & KAIN, K. C. 2008. Molecular diagnostic and surveillance tools for global malaria control. Travel Med Infect Dis, 6, 82-99. ESCALANTE, A. A. & AYALA, F. J. 1994. Phylogeny of the malarial genus Plasmodium, derived from rRNA gene sequences. Proc Natl Acad Sci U S A, 91, 11373-7. ESCALANTE, A. A., CORNEJO, O. E., FREELAND, D. E., POE, A. C., DURREGO, E., COLLINS, W. E. & LAL, A. A. 2005. A monkey's tale: the origin of Plasmodium vivax as a human malaria parasite. Proc Natl Acad Sci U S A, 102, 1980-5. ESCALANTE, A. A., FERREIRA, M. U., VINETZ, J. M., VOLKMAN, S. K., CUI, L., GAMBOA, D., KROGSTAD, D. J., BARRY, A. E., CARLTON, J. M., VAN EIJK, A. M., PRADHAN, K., MUELLER, I., GREENHOUSE, B., PACHECO, M. A., VALLEJO, A. F., HERRERA, S. & FELGER, I. 2015. Malaria Molecular Epidemiology: Lessons from the International Centers of Excellence for Malaria Research Network. Am J Trop Med Hyg, 93, 79-86. ESCALANTE, A. A., FREELAND, D. E., COLLINS, W. E. & LAL, A. A. 1998. The evolution of primate malaria parasites based on the gene encoding cytochrome b from the linear mitochondrial genome. Proc Natl Acad Sci U S A, 95, 8124-9. EXCOFFIER, L. & LISCHER, H. E. L. 2010. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources, 10, 564- 567. EXCOFFIER, L., SMOUSE, P. E. & QUATTRO, J. M. 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479-91. EZE, K. C. & MAZELI, F. O. 2001. Radiological manifestation of malaria . Resid Doc, 5, 41. FAROGH, A., QAYYUM, A., HALEEM, A. & GHAFFAR, A. 2009. Hematological Abnormalities in Malaria. Biomedica, 25. FAROOQ, M. A., SALAMAT, A. & IQBAL, M. A. 2008. Malaria--an experience at CMH Khuzdar (Balochistan). J Coll Physicians Surg Pak, 18, 257-8. FATA DISASTER MANAGMENT AUTHORITY. 2014. FDMA [Online]. Available: http://www.fdma.gov.pk/ [Accessed 2017].
206 FEACHEM, R. G., PHILLIPS, A. A., HWANG, J., COTTER, C., WIELGOSZ, B., GREENWOOD, B. M., SABOT, O., RODRIGUEZ, M. H., ABEYASINGHE, R. R., GHEBREYESUS, T. A. & SNOW, R. W. 2010. Shrinking the malaria map: progress and prospects. Lancet, 376, 1566-78. FEAGIN, J. E., HARRELL, M. I., LEE, J. C., COE, K. J., SANDS, B. H., CANNONE, J. J., TAMI, G., SCHNARE, M. N. & GUTELL, R. R. 2012. The fragmented mitochondrial ribosomal RNAs of Plasmodium falciparum. PLoS One, 7, e38320. FEAGIN, J. E., MERICLE, B. L., WERNER, E. & MORRIS, M. 1997. Identification of additional rRNA fragments encoded by the Plasmodium falciparum 6 kb element. Nucleic Acids Res, 25, 438-46. FEIGHNER, B. H., PAK, S. I., NOVAKOSKI, W. L., KELSEY, L. L. & STRICKMAN, D. 1998. Reemergence of Plasmodium vivax malaria in the republic of Korea. Emerg Infect Dis, 4, 295-7. FEREDE, G., WORKU, A., GETANEH, A., AHMED, A., HAILE, T., ABDU, Y., TESSEMA, B., WONDIMENEH, Y. & ALEMU, A. 2013. Prevalence of malaria from blood smears examination: a seven-year retrospective study from metema hospital, northwest ethiopia. Malar Res Treat, 2013, 704730. FERGUSON, H. M. & READ, A. F. 2002. Why is the effect of malaria parasites on mosquito survival still unresolved? Trends Parasitol, 18, 256-61. FERREIRA, M. U., KARUNAWEERA, N. D., DA SILVA-NUNES, M., DA SILVA, N. S., WIRTH, D. F. & HARTL, D. L. 2007. Population structure and transmission dynamics of Plasmodium vivax in rural Amazonia. J Infect Dis, 195, 1218-26. FILLINGER, U., KANNADY, K., WILLIAM, G., VANEK, M. J., DONGUS, S., NYIKA, D., GEISSBUHLER, Y., CHAKI, P. P., GOVELLA, N. J., MATHENGE, E. M., SINGER, B. H., MSHINDA, H., LINDSAY, S. W., TANNER, M., MTASIWA, D., DE CASTRO, M. C. & KILLEEN, G. F. 2008. A tool box for operational mosquito larval control: preliminary results and early lessons from the Urban Malaria Control Programme in Dar es Salaam, Tanzania. Malar J, 7, 20. FISER PECNIKAR, Z. & BUZAN, E. V. 2014. 20 years since the introduction of DNA barcoding: from theory to application. J Appl Genet, 55, 43-52. FLANNERY, E. L., WANG, T., AKBARI, A., COREY, V. C., GUNAWAN, F., BRIGHT, A. T., ABRAHAM, M., SANCHEZ, J. F., SANTOLALLA, M. L., BALDEVIANO, G. C., EDGEL, K. A., ROSALES, L. A., LESCANO, A. G., BAFNA, V., VINETZ, J. M. & WINZELER, E. A. 2015. Next-Generation Sequencing of Plasmodium vivax Patient Samples Shows Evidence of Direct Evolution in Drug-Resistance Genes. ACS Infect Dis, 1, 367-79. FOSTER, D., COX-SINGH, J., MOHAMAD, D. S., KRISHNA, S., CHIN, P. P. & SINGH, B. 2014. Evaluation of three rapid diagnostic tests for the detection of human infections with Plasmodium knowlesi. Malar J, 13, 60. FOSTER, S. D. 1991. Pricing, distribution, and use of antimalarial drugs. Bull World Health Organ, 69, 349-63. FRADIN, M. S. 1998. Mosquitoes and mosquito repellents: a clinician's guide. Ann Intern Med, 128, 931- 40. FRANSISCA, L., KUSNANTO, J. H., SATOTO, T. B., SEBAYANG, B., SUPRIYANTO, ANDRIYAN, E. & BANGS, M. J. 2015. Comparison of rapid diagnostic test Plasmotec Malaria-3, microscopy, and quantitative real-time PCR for diagnoses of Plasmodium falciparum and Plasmodium vivax infections in Mimika Regency, Papua, Indonesia. Malar J, 14, 103. FUNDYGA, R. E., LOTT, T. J. & ARNOLD, J. 2002. Population structure of Candida albicans, a member of the human flora, as determined by microsatellite loci. Infect Genet Evol, 2, 57-68. FUNGLADDA, W. & SORNMANI, S. 1986. Health behavior, treatment-seeking patterns, and cost of treatment for patients visiting malaria clinics in western Thailand. Southeast Asian J Trop Med Public Health, 17, 379-85. FUNGLADDA, W., SORNMANI, S., KLONGKAMNUANKARN, K. & HUNGSAPRUEK, T. 1987. Sociodemographic and behavioural factors associated with hospital malaria patients in Kanchanaburi, Thailand. J Trop Med Hyg, 90, 233-7. GALINSKI, M., MEYER, E. & BARNWELL, J. 2013. Plasmodium vivax. In: The Epidemiology of Plasmodium vivax. Elsevier BV, 1-26.
207 GAMAGE-MENDIS, A. C., CARTER, R., MENDIS, C., DE ZOYSA, A. P., HERATH, P. R. & MENDIS, K. N. 1991. Clustering of malaria infections within an endemic population: risk of malaria associated with the type of housing construction. Am J Trop Med Hyg, 45, 77-85. GAMBOA, D., HO, M. F., BENDEZU, J., TORRES, K., CHIODINI, P. L., BARNWELL, J. W., INCARDONA, S., PERKINS, M., BELL, D., MCCARTHY, J. & CHENG, Q. 2010. A large proportion of P. falciparum isolates in the Amazon region of Peru lack pfhrp2 and pfhrp3: implications for malaria rapid diagnostic tests. PLoS One, 5, e8091. GARCIA, L. 2007. Diagnostic Medical Parasitology. ASM Press; Washington, DC. GARDNER, M. J., HALL, N., FUNG, E., WHITE, O., BERRIMAN, M., HYMAN, R. W., CARLTON, J. M., PAIN, A., NELSON, K. E., BOWMAN, S., PAULSEN, I. T., JAMES, K., EISEN, J. A., RUTHERFORD, K., SALZBERG, S. L., CRAIG, A., KYES, S., CHAN, M. S., NENE, V., SHALLOM, S. J., SUH, B., PETERSON, J., ANGIUOLI, S., PERTEA, M., ALLEN, J., SELENGUT, J., HAFT, D., MATHER, M. W., VAIDYA, A. B., MARTIN, D. M., FAIRLAMB, A. H., FRAUNHOLZ, M. J., ROOS, D. S., RALPH, S. A., MCFADDEN, G. I., CUMMINGS, L. M., SUBRAMANIAN, G. M., MUNGALL, C., VENTER, J. C., CARUCCI, D. J., HOFFMAN, S. L., NEWBOLD, C., DAVIS, R. W., FRASER, C. M. & BARRELL, B. 2002. Genome sequence of the human malaria parasite Plasmodium falciparum. Nature, 419, 498-511. GARG, R. K., KARAK, B. & MISRA, S. 1999. Neurological manifestations of malaria : an update. Neurol India, 47, 85-91. GARNHAM, P. C. 1966. Malaria Parasites and Other Haemosporidia. Oxford: Blackwell scientific publications Ltd. GARNHAM, P. C. 1967. Relapses and latency in malaria. Protozoology, 2, 55-64. GARNHAM, P. C. 1987. The liver in malaria with special reference to the exoerythrocytic phase. Ann Trop Med Parasitol, 81, 531-7. GEARY , T. G. & JENSEN, J. B. 1986. Protozoan infections of man: Malaria. In Campbell, W. C., and R. S. Rew, eds. Chemotherapy of parasitic diseases. New York: Plenum Press. GHANCHI, N. K., MARTENSSON, A., URSING, J., JAFRI, S., BERECZKY, S., HUSSAIN, R. & BEG, M. A. 2010. Genetic diversity among Plasmodium falciparum field isolates in Pakistan measured with PCR genotyping of the merozoite surface protein 1 and 2. Malar J, 9, 1. GHANCHI, N. K., URSING, J., BEG, M. A., VEIGA, M. I., JAFRI, S. & MARTENSSON, A. 2011. Prevalence of resistance associated polymorphisms in Plasmodium falciparum field isolates from southern Pakistan. Malar J, 10, 18. GHEBREYESUS, T. A., HAILE, M., WITTEN, K. H., GETACHEW, A., YOHANNES, M., LINDSAY, S. W. & BYASS, P. 2000. Household risk factors for malaria among children in the Ethiopian highlands. Trans R Soc Trop Med Hyg, 94, 17-21. GILL, C. A. 1938. The seasonal periodicity of malaria and the mechanism of the epidemic wave. London: Churchill. GILLES, H. 1933. Diagnostic methods in malaria. Gilles HM, Warrell DA, eds. Bruce-Chwatt’s Essential Malariology. Third edition. London: Edward Arnold, 78-95. GILLET, P., MORI, M., VAN ESBROECK, M., VAN DEN ENDE, J. & JACOBS, J. 2009. Assessment of the prozone effect in malaria rapid diagnostic tests. Malar J, 8, 271. GOMEZ-ZURITA, J. & VOGLER, A. P. 2003. Incongruent nuclear and mitochondrial phylogeographic patterns in the Timarcha goettingensis species complex (Coleoptera, Chrysomelidae). J Evol Biol, 16, 833-43. GOVERNMENT OF KHYBER PUKHTUNKHWA. 2017. Bannu District Demographics [Online]. Peshawar, Pakistan. Available: http://kp.gov.pk/page/bannu_district_at_a_glance [Accessed December 2017]. GOWDA, D. C. 2007. TLR-mediated cell signaling by malaria GPIs. Trends Parasitol, 23, 596-604. GRANT, D. B., PERINPANAYAGAM, M. S., SHUTE, P. G. & ZEITLIN, R. A. 1960. A case of malignant tertian (Plasmodium falciparum) malaria after blood-transfusion. Lancet, 2, 469-70. GRAVES, P. M., RICHARDS, F. O., NGONDI, J., EMERSON, P. M., SHARGIE, E. B., ENDESHAW, T., CECCATO, P., EJIGSEMAHU, Y., MOSHER, A. W., HAILEMARIAM, A., ZERIHUN, M., TEFERI, T., AYELE, B., MESELE, A., YOHANNES, G., TILAHUN, A. & GEBRE, T. 2009. Individual, household and
208 environmental risk factors for malaria infection in Amhara, Oromia and SNNP regions of Ethiopia. Trans R Soc Trop Med Hyg, 103, 1211-20. GROBUSCH, M. P., ALPERMANN, U., SCHWENKE, S., JELINEK, T. & WARHURST, D. C. 1999. False-positive rapid tests for malaria in patients with rheumatoid factor. Lancet, 353, 297. GUERRA, C. A., SNOW, R. W. & HAY, S. I. 2006. Mapping the global extent of malaria in 2005. Trends Parasitol, 22, 353-8. GUILLET, P., ALNWICK, D., CHAM, M. K., NEIRA, M., ZAIM, M., HEYMANN, D. & MUKELABAI, K. 2001. Long-lasting treated mosquito nets: a breakthrough in malaria prevention. Bull World Health Organ, 79, 998. GUINDON, S. & GASCUEL, O. 2003. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol, 52, 696-704. GUNAWARDENA, D. M., WICKREMASINGHE, A. R., MUTHUWATTA, L., WEERASINGHA, S., RAJAKARUNA, J., SENANAYAKA, T., KOTTA, P. K., ATTANAYAKE, N., CARTER, R. & MENDIS, K. N. 1998. Malaria risk factors in an endemic region of Sri Lanka, and the impact and cost implications of risk factor-based interventions. Am J Trop Med Hyg, 58, 533-42. GUNAWARDENA, S., FERREIRA, M. U., KAPILANANDA, G. M., WIRTH, D. F. & KARUNAWEERA, N. D. 2014. The Sri Lankan paradox: high genetic diversity in Plasmodium vivax populations despite decreasing levels of malaria transmission. Parasitology, 141, 880-90. GUNDERSON, J. H., SOGIN, M. L., WOLLETT, G., HOLLINGDALE, M., DE LA CRUZ, V. F., WATERS, A. P. & MCCUTCHAN, T. F. 1987. Structurally distinct, stage-specific ribosomes occur in Plasmodium. Science, 238, 933-7. GUPTA, B., SRIVASTAVA, N. & DAS, A. 2012. Inferring the evolutionary history of Indian Plasmodium vivax from population genetic analyses of multilocus nuclear DNA fragments. Mol Ecol, 21, 1597-616. GUPTA, S., SNOW, R. W., DONNELLY, C. A., MARSH, K. & NEWBOLD, C. 1999. Immunity to non-cerebral severe malaria is acquired after one or two infections. Nat Med, 5, 340-3. GUTHMANN, J. P., HALL, A. J., JAFFAR, S., PALACIOS, A., LINES, J. & LLANOS-CUENTAS, A. 2001. Environmental risk factors for clinical malaria: a case-control study in the Grau region of Peru. Trans R Soc Trop Med Hyg, 95, 577-83. GUYATT, H. L., CORLETT, S. K., ROBINSON, T. P., OCHOLA, S. A. & SNOW, R. W. 2002. Malaria prevention in highland Kenya: indoor residual house-spraying vs. insecticide-treated bednets. Trop Med Int Health, 7, 298-303. HAGHDOOST, A. A., MAZHARI, S. & BAHADINI, K. 2006. Comparing the results of light microscopy with the results of PCR method in the diagnosis of Plasmodium vivax. J Vector Borne Dis, 43, 53-7. HAN, E. T., WATANABE, R., SATTABONGKOT, J., KHUNTIRAT, B., SIRICHAISINTHOP, J., IRIKO, H., JIN, L., TAKEO, S. & TSUBOI, T. 2007. Detection of four Plasmodium species by genus- and species- specific loop-mediated isothermal amplification for clinical diagnosis. J Clin Microbiol, 45, 2521- 8. HANKEY, D. D., JONES, R., JR., COATNEY, G. R., ALVING, A. S., COKER, W. G., GARRISON, P. L. & DONOVAN, W. N. 1953. Korean vivax malaria. I. Natural history and response to chloroquine. Am J Trop Med Hyg, 2, 958-69. HANSCHEID, T. & GROBUSCH, M. P. 2002. How useful is PCR in the diagnosis of malaria? Trends Parasitol, 18, 395-8. HANSEN, K. S., GRIEVE, E., MIKHAIL, A., MAYAN, I., MOHAMMED, N., ANWAR, M., BAKTASH, S. H., DRAKE, T. L., WHITTY, C. J., ROWLAND, M. W. & LESLIE, T. J. 2015. Cost-effectiveness of malaria diagnosis using rapid diagnostic tests compared to microscopy or clinical symptoms alone in Afghanistan. Malar J, 14, 217. HARE, M. & PALUMBI, S. 2003. High intron sequence conservation across three mammalian orders suggests functional constraints. Mol Biol Evol, 20, 1960-1963. HARRIS, I., SHARROCK, W. W., BAIN, L. M., GRAY, K. A., BOBOGARE, A., BOAZ, L., LILLEY, K., KRAUSE, D., VALLELY, A., JOHNSON, M. L., GATTON, M. L., SHANKS, G. D. & CHENG, Q. 2010. A large proportion of asymptomatic Plasmodium infections with low and sub-microscopic parasite
209 densities in the low transmission setting of Temotu Province, Solomon Islands: challenges for malaria diagnostics in an elimination setting. Malar J, 9, 254. HARTL, D. L. & CLARK, A. G. 1997. Principles of Population Genetics, 3rd edn. Sinauer Associates, Inc, Sunderland, MA. HASTINGS, M. D. & SIBLEY, C. H. 2002. Pyrimethamine and WR99210 exert opposing selection on dihydrofolate reductase from Plasmodium vivax. Proc Natl Acad Sci U S A, 99, 13137-41. HAWKINS, K., BURTON, R. & LABARRE, P. 2014. Diagnostics to support malaria elimination: choosing an appropriate biomarker to target the subclinical Plasmodium falciparum transmission reservoir. In: IEEE 2014 global humanitarian technology conference. San Jose: IEEE. HAY, S. I., GUERRA, C. A., TATEM, A. J., NOOR, A. M. & SNOW, R. W. 2004. The global distribution and population at risk of malaria: past, present, and future. Lancet Infect Dis, 4, 327-36. HAY, S. I., SMITH, D. L. & SNOW, R. W. 2008. Measuring malaria endemicity from intense to interrupted transmission. Lancet Infect Dis, 8, 369-78. HAY, S. I. & SNOW, R. W. 2006. The malaria Atlas Project: developing global maps of malaria risk. PLoS Med, 3, e473. HAYNES, J. D., DIGGS, C. L., HINES, F. A. & DESJARDINS, R. E. 1976. Culture of human malaria parasites Plasmodium falciparum. Nature, 263, 767-9. HAYYAT, A., MEMON, F., SHAIKH, N. & FATAH DERO, A. 2009. Incidence and pattern of malarial infection at a tertiary care Hospital of Hyderabad. World J Med Sci, 4, 9-12. HEDRICK, P. W. 2005. Genetics of populations (Jones and Bartlet Publishers, Sudbury, Massachussets). HEDRICK, P. W. 2011. Population genetics of malaria resistance in humans. Heredity (Edinb), 107, 283- 304. HEHIR, P. 1927. Malaria in India. London. Humphrey Milford oxford university press. HENRY, M. C., ROGIER, C., NZEYIMANA, I., ASSI, S. B., DOSSOU-YOVO, J., AUDIBERT, M., MATHONNAT, J., KEUNDJIAN, A., AKODO, E., TEUSCHER, T. & CARNEVALE, P. 2003. Inland valley rice production systems and malaria infection and disease in the savannah of Cote d'Ivoire. Trop Med Int Health, 8, 449-58. HERMSEN, C. C., TELGT, D. S., LINDERS, E. H., VAN DE LOCHT, L. A., ELING, W. M., MENSINK, E. J. & SAUERWEIN, R. W. 2001. Detection of Plasmodium falciparum malaria parasites in vivo by real- time quantitative PCR. Mol Biochem Parasitol, 118, 247-51. HIKOSAKA, K., KITA, K. & TANABE, K. 2013. Diversity of mitochondrial genome structure in the phylum Apicomplexa. Mol Biochem Parasitol, 188, 26-33. HIKOSAKA, K., WATANABE, Y., KOBAYASHI, F., WAKI, S., KITA, K. & TANABE, K. 2011. Highly conserved gene arrangement of the mitochondrial genomes of 23 Plasmodium species. Parasitol Int, 60, 175-80. HLA, S., THAN TUN, S., SOE, S., TIN, A., NE, W. & KHIN SAW, A. 1998. The level of knowledge, attitude and practice in relation to malaria in Oo-do village, Myanmar. Southeast Asian J Trop Med Public Health, 29, 546-9. HONIGSBAUM, M. 2001. The fever trail. In search of the cure for malaria. New York: Farrar, Straus and Giroux. HOPKINS, H., BEBELL, L., KAMBALE, W., DOKOMAJILAR, C., ROSENTHAL, P. J. & DORSEY, G. 2008. Rapid diagnostic tests for malaria at sites of varying transmission intensity in Uganda. J Infect Dis, 197, 510-8. HOPKINS, H., KAMBALE, W., KAMYA, M. R., STAEDKE, S. G., DORSEY, G. & ROSENTHAL, P. J. 2007. Comparison of HRP2- and pLDH-based rapid diagnostic tests for malaria with longitudinal follow-up in Kampala, Uganda. Am J Trop Med Hyg, 76, 1092-7. HSU, E. 2006. The history of qing hao in the Chinese materia medica. Trans R Soc Trop Med Hyg, 100, 505-8. HUANG, F., ZHOU, S., ZHANG, S., WANG, H. & TANG, L. 2011. Temporal correlation analysis between malaria and meteorological factors in Motuo County, Tibet. Malar J, 10, 54. HUGHES, A. L. 2004. The evolution of amino acid repeat arrays in Plasmodium and other organisms. J Mol Evol, 59, 528-35.
210 HUGHES, A. L. & VERRA, F. 2001. Very large long-term effective population size in the virulent human malaria parasite Plasmodium falciparum. Proc Biol Sci, 268, 1855-60. HUGHES, A. L. & VERRA, F. 2002. Extensive polymorphism and ancient origin of Plasmodium falciparum. Trends Parasitol, 18, 348-51. HUSSAIN, K., SHAFEE, M., KHAN, N., JAN, S., TAREEN, A. & KHAN, M. 2013. Seroprevalence of pediatric malaria in quetta, balochistan, pakistan. Iran J Parasitol, 8, 342-7. HUSTACHE, S., NACHER, M., DJOSSOU, F. & CARME, B. 2007. Malaria risk factors in Amerindian children in French Guiana. Am J Trop Med Hyg, 76, 619-25. IDRIS, M., SARWAR, J. & FAREED, J. 2007. Pattern of malarial infection diagnosed at Ayub Teaching Hospital Abbottabad. J Ayub Med Coll Abbottabad, 19, 35-6. IMWONG, M., NAIR, S., PUKRITTAYAKAMEE, S., SUDIMACK, D., WILLIAMS, J. T., MAYXAY, M., NEWTON, P. N., KIM, J. R., NANDY, A., OSORIO, L., CARLTON, J. M., WHITE, N. J., DAY, N. P. & ANDERSON, T. J. 2007. Contrasting genetic structure in Plasmodium vivax populations from Asia and South America. Int J Parasitol, 37, 1013-22. INDEXMUNDI. 2013. Percentage of population below national poverty line: Botswana economy. IRSHAD, M., ALI, R., AKBAR, S., MUHAMMAD & AHMAD, T. 2013. Occurrence of malaria in Khwazakhela District Swat Pakistan. PSCI publications, 2, 26-28. JAHAN, N. & SARWAR, M. 2013. Malaria prevalence in district Okara, Punjab, Pakistan. Biologia (Pakistan), 59, 191-195. JAN, A. H. & KIANI, T. A. 2001. Haematozoan parasites in Kashmiri refugees. Pak. J. Med. Res, 40, 10-2. JIMENEZ-RUIZ, E., MORLON-GUYOT, J., DAHER, W. & MEISSNER, M. 2016. Vacuolar protein sorting mechanisms in apicomplexan parasites. Mol Biochem Parasitol, 209, 18-25. JIRKU, M., POMAJBIKOVA, K., PETRZELKOVA, K. J., HUZOVA, Z., MODRY, D. & LUKES, J. 2012. Detection of Plasmodium spp. in human feces. Emerg Infect Dis, 18, 634-6. JOHNSTON, S. P., PIENIAZEK, N. J., XAYAVONG, M. V., SLEMENDA, S. B., WILKINS, P. P. & DA SILVA, A. J. 2006. PCR as a confirmatory technique for laboratory diagnosis of malaria. J Clin Microbiol, 44, 1087-9. JONGWUTIWES, S., PUTAPORNTIP, C., IWASAKI, T., FERREIRA, M. U., KANBARA, H. & HUGHES, A. L. 2005. Mitochondrial genome sequences support ancient population expansion in Plasmodium vivax. Mol Biol Evol, 22, 1733-9. JONKMAN, A., CHIBWE, R. A., KHOROMANA, C. O., LIABUNYA, U. L., CHAPONDA, M. E., KANDIERO, G. E., MOLYNEUX, M. E. & TAYLOR, T. E. 1995. Cost-saving through microscopy-based versus presumptive diagnosis of malaria in adult outpatients in Malawi. Bull World Health Organ, 73, 223-7. JORDE, L. B., BAMSHAD, M. & ROGERS, A. R. 1998. Using mitochondrial and nuclear DNA markers to reconstruct human evolution. Bioessays, 20, 126-36. JORGENSEN, P., CHANTHAP, L., REBUENO, A., TSUYUOKA, R. & BELL, D. 2006. Malaria rapid diagnostic tests in tropical climates: the need for a cool chain. Am J Trop Med Hyg, 74, 750-4. JOSHI, A. B. & BANJARA, M. R. 2008. Malaria related knowledge, practices and behaviour of people in Nepal. J Vector Borne Dis, 45, 44-50. JOSHI, M. & DESHPANDE, J. D. 2010. Polymerase chain reaction: methods, principles and Application. International Journal of Biomedical Research, 1, 81-97. JOSHI, Y. K., TANDON, B. N., ACHARYA, S. K., BABU, S. & TANDON, M. 1986. Acute hepatic failure due to Plasmodium falciparum liver injury. Liver, 6, 357-60. JOY, D. A., FENG, X., MU, J., FURUYA, T., CHOTIVANICH, K., KRETTLI, A. U., HO, M., WANG, A., WHITE, N. J., SUH, E., BEERLI, P. & SU, X. Z. 2003. Early origin and recent expansion of Plasmodium falciparum. Science, 300, 318-21. JULIANO, J. J., GADALLA, N., SUTHERLAND, C. J. & MESHNICK, S. R. 2010. The perils of PCR: can we accurately 'correct' antimalarial trials? Trends Parasitol, 26, 119-24. KABURI, J. C., GITHUTO, J. N., MUTHAMI, L., NGURE, P. K., MUEKE, J. M. & MWANDAWIRO, C. S. 2009. Effects of long-lasting insecticidal nets and zooprophylaxis on mosquito feeding behaviour and density in Mwea, central Kenya. J Vector Borne Dis, 46, 184-90.
211 KAIN, K. C. 1995. Chemotherapy and prevention of drug-resistant malaria. Wilderness Environ Med, 6, 307-24. KAIN, K. C., BROWN, A. E., LANAR, D. E., BALLOU, W. R. & WEBSTER, H. K. 1993. Response of Plasmodium vivax variants to chloroquine as determined by microscopy and quantitative polymerase chain reaction. Am J Trop Med Hyg, 49, 478-84. KAIN, K. C., KYLE, D. E., WONGSRICHANALAI, C., BROWN, A. E., WEBSTER, H. K., VANIJANONTA, S. & LOOAREESUWAN, S. 1994. Qualitative and semiquantitative polymerase chain reaction to predict Plasmodium falciparum treatment failure. J Infect Dis, 170, 1626-30. KAISAR, M., SUPALI, T., WIRIA, A., HAMID, F., WAMMES, L., SARTONO, L. & ET AL 2013. Epidemiology of Plasmodium infection in Flores Island, Indonesia using Realtime PCR. Malar J, 12, 169. KAKAR, Q. 2009. Guidelines for anti-vector interventions in complex emergencies/IDPs. Islamabad. Directorate of Malaria Control, Ministry of Health. KANTELE, A. & JOKIRANTA, T. S. 2011. Review of cases with the emerging fifth human malaria parasite, Plasmodium knowlesi. Clin Infect Dis, 52, 1356-62. KAWAMOTO, F., MIYAKE, H., KANEKO, O., KIMURA, M., NGUYEN, T. D., NGUYEN, T. D., LIU, Q., ZHOU, M., LE, D. D., KAWAI, S., ISOMURA, S. & WATAYA, Y. 1996. Sequence variation in the 18S rRNA gene, a target for PCR-based malaria diagnosis, in Plasmodium ovale from southern Vietnam. J Clin Microbiol, 34, 2287-9. KAZMI, J. H. & PANDIT, K. 2001. Disease and dislocation: the impact of refugee movements on the geography of malaria in NWFP, Pakistan. Soc Sci Med, 52, 1043-55. KEISER, J., UTZINGER, J., CALDAS DE CASTRO, M., SMITH, T. A., TANNER, M. & SINGER, B. H. 2004. Urbanization in sub-saharan Africa and implication for malaria control. Am J Trop Med Hyg, 71, 118-27. KETTLE, D. 1995. Medical and Veterinary Entomology. University Press, Cambridge,UK, 27, 568-576. KHADIM, M. T. 2002. Malaria a menace at Zhob Garrison. . Pak Armed For Med J, 52, 203-207. KHAN, F., AWAN, Z. U. R. & BETTANI, M. R. 2016. Plasmodium Infection among the Hospitalized Pregnant Women in District Bannu Khyber Pakhtunkhwa, Pakistan. International Journal of Advanced Medical Sciences, 1, 20-22. KHAN, H. U., KHATTAK, A. M., KHAN, M. H., MAHSUD, I. U. & SHAH, S. H. 2006. A study of prevalence of malaria in adult population of D. I. Khan, Pakistan. Biomedica, 22, 99-104. KHAN, I. K., SHAH, A. H. & RAHMAN, Z. U. 2013a. Epidemiology of Malaria in Urban and Rural Areas of Bannu District Khyber Pakhtunkhwa, Pakistan. International Journal of Modern Biology and Medicine, 4, 30-39. KHAN, M. A., SMEGO, R. A., JR., RAZI, S. T. & BEG, M. A. 2004. Emerging drug--resistance and guidelines for treatment of malaria. J Coll Physicians Surg Pak, 14, 319-24. KHAN, N. U. 2014. Frequency and seasonal variation of Plasmodium Species in Southern Districts of Khyber Pakhtunkhwa. Pak Armed Forces Med J, 64, 518-23. KHAN, S. N., AYAZ, S., KHAN, S., ATTAULLAH, S., KHAN, M. A., ULLAH, N., KHAN, M. A. & ALI, I. 2013b. Malaria: Still a Health Problem in the General Population of Bannu District, Khyber Pakhtunkhwa, Pakistan Annual Review & Research in Biology, 3, 835-845. KHATOON, L., BALIRAINE, F. N., BONIZZONI, M., MALIK, S. A. & YAN, G. 2009. Prevalence of antimalarial drug resistance mutations in Plasmodium vivax and P. falciparum from a malaria-endemic area of Pakistan. Am J Trop Med Hyg, 81, 525-8. KHATOON, L., BALIRAINE, F. N., BONIZZONI, M., MALIK, S. A. & YAN, G. 2010. Genetic structure of Plasmodium vivax and Plasmodium falciparum in the Bannu district of Pakistan. Malar J, 9, 112. KHATTA, K. A., KHAN, J., BATOOL, N. & KHAN, H. 2012. The magnitude and pattern of malaria in district Kohat. Gomal J Med Sci, 11, 208-11. KHATTAK, A. A., VENKATESAN, M., NADEEM, M. F., SATTI, H. S., YAQOOB, A. & STRAUSS, K. 2013. Prevalence and distribution of human Plasmodium infection in Pakistan. Malar J, 12. KIGOTHO, A. W. 1997. Services stretched as malaria reaches Kenyan highlands. Lancet, 350-422.
212 KIM, J. Y., JI, S. Y., GOO, Y. K., NA, B. K., PYO, H. J., LEE, H. N., LEE, J., KIM, N. H., VON SEIDLEIN, L., CHENG, Q., CHO, S. H. & LEE, W. J. 2013. Comparison of rapid diagnostic tests for the detection of Plasmodium vivax malaria in South Korea. PLoS One, 8, e64353. KIM, S. H., NAM, M. H., ROH, K. H., PARK, H. C., NAM, D. H., PARK, G. H., HAN, E. T., KLEIN, T. A. & LIM, C. S. 2008. Evaluation of a rapid diagnostic test specific for Plasmodium vivax. Trop Med Int Health, 13, 1495-500. KIMBI, H. K., NANA, Y., SUMBELE, I. N., ANCHANG-KIMBI, J. K., LUM, E., TONGA, E., NWEBOH, M. & LEHMAN, L. G. 2013. Environmental Factors and Preventive Methods against Malaria Parasite Prevalence in Rural Bomaka and Urban Molyko, Southwest Cameroon. J Bacteriol Parasitol, 4, 162. KIMURA, M. & CROW, J. F. 1964. THE NUMBER OF ALLELES THAT CAN BE MAINTAINED IN A FINITE POPULATION. Genetics, 49, 725-38. KLEIN KLOUWENBERG, P. M., OYAKHIROME, S., SCHWARZ, N. G., GLASER, B., ISSIFOU, S., KIESSLING, G., KLOPFER, A., KREMSNER, P. G., LANGIN, M., LASSMANN, B., NECEK, M., POTSCHKE, M., RITZ, A. & GROBUSCH, M. P. 2005. Malaria and asymptomatic parasitaemia in Gabonese infants under the age of 3 months. Acta Trop, 95, 81-5. KLINKENBERG, E. 2001. Malaria risk mapping in Sri Lanka: Results from the Uda Walawe area. Proceedings of a health workshop in Embilipitiya, Sri Lanka, March 29th 2001. IWMI Working Paper 21. Colombo, Sri Lanka: International Water Management Institute. KLINKENBERG, E., VAN DER HOEK, W. & AMERASINGHE, F. P. 2004. A malaria risk analysis in an irrigated area in Sri Lanka. Acta Trop, 89, 215-25. KOCHAR, D. K., SAXENA, V., SINGH, N., KOCHAR, S. K., KUMAR, S. V. & DAS, A. 2005. Plasmodium vivax malaria. Emerg Infect Dis, 11, 132-4. KOEPFLI, C., MUELLER, I., MARFURT, J., GOROTI, M. & ET AL 2009. Evaluation of Plasmodium vivax genotyping markers for molecular monitoring in clinical trials. The Jounal of Infectious Disease, 199, 1074-1080. KONDRACHINE, A. 2008. Situation analysis of malaria in the province of Punjab 1–21st September 2008. Islamabad, Pakistan, Country Office. World Health Organization. KONRADSEN, F., AMERASINGHE, P., VAN DER HOEK, W., AMERASINGHE, F., PERERA, D. & PIYARATNE, M. 2003. Strong association between house characteristics and malaria vectors in Sri Lanka. Am J Trop Med Hyg, 68, 177-81. KORAM, K. A., BENNETT, S., ADIAMAH, J. H. & GREENWOOD, B. M. 1995. Socio-economic risk factors for malaria in a peri-urban area of The Gambia. Trans R Soc Trop Med Hyg, 89, 146-50. KORENROMP, E. 2005. Malaria Incidence Estimates at County Level for the Year 2004: Proposed Estimates and Draft Report. World Health Organization. KRAUSE, G., BENZLER, J., HEINMULLER, R., BORCHERT, M., KOOB, E., OUATTARA, K. & DIESFELD, H. J. 1998. Performance of village pharmacies and patient compliance after implementation of essential drug programme in rural Burkina Faso. Health Policy Plan, 13, 159-66. KRAUSE, P. 2007. Malaria (Plasmodium), Philadelphia, PA: WB Saunders. KREUELS, B., KOBBE, R., ADJEI, S., KREUZBERG, C., VON REDEN, C., BATER, K., KLUG, S., BUSCH, W., ADJEI, O. & MAY, J. 2008. Spatial variation of malaria incidence in young children from a geographically homogeneous area with high endemicity. J Infect Dis, 197, 85-93. KRIEF, S., ESCALANTE, A. A., PACHECO, M. A., MUGISHA, L., ANDRE, C., HALBWAX, M., FISCHER, A., KRIEF, J. M., KASENENE, J. M., CRANDFIELD, M., CORNEJO, O. E., CHAVATTE, J. M., LIN, C., LETOURNEUR, F., GRUNER, A. C., MCCUTCHAN, T. F., RENIA, L. & SNOUNOU, G. 2010. On the diversity of malaria parasites in African apes and the origin of Plasmodium falciparum from Bonobos. PLoS Pathog, 6, e1000765. KWIATKOWSKI, D. P. 2005. How malaria has affected the human genome and what human genetics can teach us about malaria. Am J Hum Genet, 77, 171-92. LAFERI, H., KANDEL, K. & PICHLER, H. 1997. False positive dipstick test for malaria. N Engl J Med, 337, 1635-6. LAFFERTY, K. D. 2009. The ecology of climate change and infectious diseases. Ecology, 90, 888-900.
213 LAGERBERG, R. E. 2008. Malaria in pregnancy: a literature review. J Midwifery Womens Health, 53, 209- 15. LAMIKANRA, A. A., THERON, M., KOOIJ, T. W. & ROBERTS, D. J. 2009. Hemozoin (malarial pigment) directly promotes apoptosis of erythroid precursors. PLoS One, 4, e8446. LAMPIETTI, J., POULOS, C., MAUREEN, L., CROPPER, MITIKU, H. & WHITTINGTON, D. 1999. Gender and preferences for malaria prevention in Tigiray, Ethiopia. Policy and Research Report on Gender and Development. The world Bank Development and Research Group/Poverty Reduction and Economic Management Network. LANDIS, J. R. & KOCH, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics, 33, 159-74. LATHIA, T. B. & JOSHI, R. 2004. Can hematological parameters discriminate malaria from nonmalarious acute febrile illness in the tropics? Indian J Med Sci, 58, 239-44. LE PORT, A., COT, M., ETARD, J. F., GAYE, O., MIGOT-NABIAS, F. & GARCIA, A. 2008. Relation between Plasmodium falciparum asymptomatic infection and malaria attacks in a cohort of Senegalese children. Malar J, 7, 193. LEE, P. C., CHONG, E. T., ANDERIOS, F., AL LIM, Y., CHEW, C. H. & CHUA, K. H. 2015. Molecular detection of human Plasmodium species in Sabah using PlasmoNex multiplex PCR and hydrolysis probes real-time PCR. Malar J, 14, 28. LEE, P. W., LIU, C. T., RAMPAO, H. S., DO ROSARIO, V. E. & SHAIO, M. F. 2010. Pre-elimination of malaria on the island of Principe. Malar J, 9, 26. LEE, S. A., YEKA, A., NSOBYA, S. L., DOKOMAJILAR, C., ROSENTHAL, P. J., TALISUNA, A. & DORSEY, G. 2006. Complexity of Plasmodium falciparum infections and antimalarial drug efficacy at 7 sites in Uganda. J Infect Dis, 193, 1160-3. LEE, S. H., KARA, U. A., KOAY, E., LEE, M. A., LAM, S. & TEO, D. 2002. New strategies for the diagnosis and screening of malaria. Int J Hematol, 76 Suppl 1, 291-3. LENGELER, C. 2000. Insecticide-treated bednets and curtains for preventing malaria. Cochrane Database Syst Rev, Cd000363. LENZ, R. 1988. Jakarta kampung morbidity variations: some policy implications. Soc Sci Med, 26, 641-9. LESLIE, T., KAUR, H., MOHAMMED, N., KOLACZINSKI, K., ORD, R. L. & ROWLAND, M. 2009a. Epidemic of Plasmodium falciparum malaria involving substandard antimalarial drugs, Pakistan, 2003. Emerg Infect Dis, 15, 1753-9. LESLIE, T., KAUR, H., MOHAMMED, N., KOLACZINSKI, K., ORD, R. L. & ROWLAND, M. 2009b. Epidemic of Plasmodium falciparum malaria involving substandard antimalarial drugs, Pakistan, 2003. Emerg Infect Dis, 15. LEVINSON, W. 2014. Review of medical microbiology and immunology, McGraw-Hill Education. LI, J., GUTELL, R. R., DAMBERGER, S. H., WIRTZ, R. A., KISSINGER, J. C., ROGERS, M. J., SATTABONGKOT, J. & MCCUTCHAN, T. F. 1997. Regulation and trafficking of three distinct 18 S ribosomal RNAs during development of the malaria parasite. J Mol Biol, 269, 203-13. LINDBLADE, K. A., EISELE, T. P., GIMNIG, J. E., ALAII, J. A., ODHIAMBO, F., TER KUILE, F. O., HAWLEY, W. A., WANNEMUEHLER, K. A., PHILLIPS-HOWARD, P. A., ROSEN, D. H., NAHLEN, B. L., TERLOUW, D. J., ADAZU, K., VULULE, J. M. & SLUTSKER, L. 2004. Sustainability of reductions in malaria transmission and infant mortality in western Kenya with use of insecticide-treated bednets: 4 to 6 years of follow-up. Jama, 291, 2571-80. LINDSAY, S. W., CAMPBELL, H., ADIAMAH, J. H., GREENWOOD, A. M., BANGALI, J. E. & GREENWOOD, B. M. 1990. Malaria in a peri-urban area of The Gambia. Ann Trop Med Parasitol, 84, 553-62. LINDSAY, S. W. & MARTENS, W. J. 1998. Malaria in the African highlands: past, present and future. Bull World Health Organ, 76, 33-45. LIU, W., LI, Y., LEARN, G. H., RUDICELL, R. S., ROBERTSON, J. D., KEELE, B. F., NDJANGO, J. B., SANZ, C. M., MORGAN, D. B., LOCATELLI, S., GONDER, M. K., KRANZUSCH, P. J., WALSH, P. D., DELAPORTE, E., MPOUDI-NGOLE, E., GEORGIEV, A. V., MULLER, M. N., SHAW, G. M., PEETERS, M., SHARP, P. M., RAYNER, J. C. & HAHN, B. H. 2010. Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature, 467, 420-5.
214 LIU, W., LI, Y., SHAW, K. S., LEARN, G. H., PLENDERLEITH, L. J., MALENKE, J. A., SUNDARARAMAN, S. A., RAMIREZ, M. A., CRYSTAL, P. A., SMITH, A. G., BIBOLLET-RUCHE, F., AYOUBA, A., LOCATELLI, S., ESTEBAN, A., MOUACHA, F., GUICHET, E., BUTEL, C., AHUKA-MUNDEKE, S., INOGWABINI, B. I., NDJANGO, J. B., SPEEDE, S., SANZ, C. M., MORGAN, D. B., GONDER, M. K., KRANZUSCH, P. J., WALSH, P. D., GEORGIEV, A. V., MULLER, M. N., PIEL, A. K., STEWART, F. A., WILSON, M. L., PUSEY, A. E., CUI, L., WANG, Z., FARNERT, A., SUTHERLAND, C. J., NOLDER, D., HART, J. A., HART, T. B., BERTOLANI, P., GILLIS, A., LEBRETON, M., TAFON, B., KIYANG, J., DJOKO, C. F., SCHNEIDER, B. S., WOLFE, N. D., MPOUDI-NGOLE, E., DELAPORTE, E., CARTER, R., CULLETON, R. L., SHAW, G. M., RAYNER, J. C., PEETERS, M., HAHN, B. H. & SHARP, P. M. 2014. African origin of the malaria parasite Plasmodium vivax. Nat Commun, 5, 3346. LO, E., ZHOU, G., OO, W., AFRANE, Y., GITHEKO, A. & YAN, G. 2015. Low parasitemia in submicroscopic infections significantly impacts malaria diagnostic sensitivity in the highlands of Western Kenya. PLoS One, 10, e0121763 %@ 1932-6203. LOOAREESUWAN, S., BUCHACHART, K., WILAIRATANA, P., CHALERMRUT, K., RATTANAPONG, Y., AMRADEE, S., SIRIPIPHAT, S., CHULLAWICHIT, S., THIMASAN, K., ITTIVERAKUL, M., TRIAMPON, A. & WALSH, D. S. 1997. Primaquine-tolerant vivax malaria in Thailand. Ann Trop Med Parasitol, 91, 939-43. LUCHAVEZ, J., BAKER, J., ALCANTARA, S., BELIZARIO, V., JR., CHENG, Q., MCCARTHY, J. S. & BELL, D. 2011. Laboratory demonstration of a prozone-like effect in HRP2-detecting malaria rapid diagnostic tests: implications for clinical management. Malar J, 10, 286. LUCKNER, D., LELL, B., GREVE, B., LEHMAN, L. G., SCHMIDT-OTT, R. J., MATOUSEK, P., HERBICH, K., SCHMID, D., MBA, R. & KREMSNER, P. G. 1998. No influence of socioeconomic factors on severe malarial anaemia, hyperparasitaemia or reinfection. Trans R Soc Trop Med Hyg, 92, 478-81. LUXEMBURGER, C., RICCI, F., NOSTEN, F., RAIMOND, D., BATHET, S. & WHITE, N. J. 1997. The epidemiology of severe malaria in an area of low transmission in Thailand. Trans R Soc Trop Med Hyg, 91, 256-62. LUXEMBURGER, C., THWAI, K. L., WHITE, N. J., WEBSTER, H. K., KYLE, D. E., MAELANKIRRI, L., CHONGSUPHAJAISIDDHI, T. & NOSTEN, F. 1996. The epidemiology of malaria in a Karen population on the western border of Thailand. Trans R Soc Trop Med Hyg, 90, 105-11. MACDONALD, G. 1956. Epidemiological basis of malaria control. Bull World Health Organ, 15, 613-26. MACHADO, P. R., AMPUERO, J., GUIMARAES, L. H., VILLASBOAS, L., ROCHA, A. T., SCHRIEFER, A., SOUSA, R. S., TALHARI, A., PENNA, G. & CARVALHO, E. M. 2010. Miltefosine in the treatment of cutaneous leishmaniasis caused by Leishmania braziliensis in Brazil: a randomized and controlled trial. PLoS Negl Trop Dis, 4, e912. MACHAULT, V., VIGNOLLES, C., BORCHI, F., VOUNATSOU, P., PAGES, F., BRIOLANT, S., LACAUX, J. P. & ROGIER, C. 2011. The use of remotely sensed environmental data in the study of malaria. Geospat Health, 5, 151-68. MACINTYRE, K., KEATING, J., SOSLER, S., KIBE, L., MBOGO, C. M., GITHEKO, A. K. & BEIER, J. C. 2002. Examining the determinants of mosquito-avoidance practices in two Kenyan cities. Malar J, 1, 14. MACKINNON, M. J., MWANGI, T. W., SNOW, R. W., MARSH, K. & WILLIAMS, T. N. 2005. Heritability of malaria in Africa. PLoS Med, 2, e340. MACKINTOSH, C. L., BEESON, J. G. & MARSH, K. 2004. Clinical features and pathogenesis of severe malaria. Trends Parasitol, 20, 597-603. MAGUIRE, J. D., LEDERMAN, E. R., BARCUS, M. J., O'MEARA, W. A., JORDON, R. G., DUONG, S., MUTH, S., SISMADI, P., BANGS, M. J., PRESCOTT, W. R., BAIRD, J. K. & WONGSRICHANALAI, C. 2006. Production and validation of durable, high quality standardized malaria microscopy slides for teaching, testing and quality assurance during an era of declining diagnostic proficiency. Malar J, 5, 92. MAHANDE, A., MOSHA, F., MAHANDE, J. & KWEKA, E. 2007. Feeding and resting behaviour of malaria vector, Anopheles arabiensis with reference to zooprophylaxis. Malar J, 6, 100.
215 MAIER, W. A., BECKER-FELDMAN, H. & SEITZ, H. M. 1987. Pathology of malaria-infected mosquitoes. Parasitol Today, 3, 216-8. MAITLAND, K., WILLIAMS, T. N. & NEWBOLD, C. I. 1997. Plasmodium vevax and P. falciparum: Biological interactions and the possibility of cross-species immunity. Parasitol Today, 13, 227-31. MAJEED, M., HUSSAIN, M. & SARWAR, F. 2016. Prevalence of human malaria; it’s correlation with thrombocytopenia and treatment in patients of district Rahim Yar Khan. Professional Med J, 23, 655-659. MAJID, A., REHMAN, M. U., AHMAD, T., ALI, A., ALI, S., ALI, S., BAIG, D., SALAM, A., AHMED, A. & KHAN, A. M. 2016. Prevalence of Malaria in Human Population of District Mardan, Pakistan. World Journal of Zoology 11, 63-66. MAKANGA, B., YANGARI, P., RAHOLA, N., ROUGERON, V., ELGUERO, E., BOUNDENGA, L., MOUKODOUM, N. D., OKOUGA, A. P., ARNATHAU, C., DURAND, P., WILLAUME, E., AYALA, D., FONTENILLE, D., AYALA, F. J., RENAUD, F., OLLOMO, B., PRUGNOLLE, F. & PAUPY, C. 2016. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc Natl Acad Sci U S A, 113, 5329-34. MALARIASITE. 2017. All About Malaria [Online]. Available: https://www.malariasite.com/ [Accessed 12 December 2017]. MALES, S., GAYE, O. & GARCIA, A. 2008. Long-term asymptomatic carriage of Plasmodium falciparum protects from malaria attacks: a prospective study among Senegalese children. Clin Infect Dis, 46, 516-22. MALHOTRA, I., DENT, A., MUNGAI, P., MUCHIRI, E. & KING, C. L. 2005. Real-time quantitative PCR for determining the burden of Plasmodium falciparum parasites during pregnancy and infancy. J Clin Microbiol, 43, 3630-5. MALI, S., KACHUR, S. P. & ARGUIN, P. M. 2012. Malaria surveillance--United States, 2010. MMWR Surveill Summ, 61, 1-17. MALISA, A. L., PEARCE, R. J., ABDULLA, S., MSHINDA, H., KACHUR, P. S., BLOLAND, P. & ROPER, C. 2010. Drug coverage in treatment of malaria and the consequences for resistance evolution--evidence from the use of sulphadoxine/pyrimethamine. Malar J, 9, 190. MALTHA, J., GILLET, P., CNOPS, L., BOTTIEAU, E., VAN ESBROECK, M., BRUGGEMAN, C. & JACOBS, J. 2011. Evaluation of the rapid diagnostic test SDFK40 (Pf-pLDH/pan-pLDH) for the diagnosis of malaria in a non-endemic setting. Malar J, 10, 7. MANDEL, B. K., WILKINS, E. G. L., DUNBAR, E. M. & WHITE, R. M. 1984. Lecture Notes on the Infective Disease. 4th Edition. MANGOLD, K. A., MANSON, R. U., KOAY, E. S., STEPHENS, L., REGNER, M., THOMSON, R. B., JR., PETERSON, L. R. & KAUL, K. L. 2005. Real-time PCR for detection and identification of Plasmodium spp. J Clin Microbiol, 43, 2435-40. MAPUTO. 2008. Declaration. MARRELLI, M. T. & BROTTO, M. 2016. The effect of malaria and anti-malarial drugs on skeletal and cardiac muscles. Malar J, 15, 524. MARSTON, L. 2010. Introductory Statistics for Health and Nursing Using SPSS, Thousand Oaks, California, Sage Publications, Ltd. MARTENS, P. & HALL, L. 2000. Malaria on the move: human population movement and malaria transmission. Emerg Infect Dis, 6, 103-9. MARTENS, W. J., NIESSEN, L. W., ROTMANS, J., JETTEN, T. H. & MCMICHAEL, A. J. 1995. Potential impact of global climate change on malaria risk. Environ Health Perspect, 103, 458-64. MARTINSEN, E. S., PERKINS, S. L. & SCHALL, J. J. 2008. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): evolution of life-history traits and host switches. Mol Phylogenet Evol, 47, 261-73. MASON, D. P. & MCKENZIE, F. E. 1999. Blood-stage dynamics and clinical implications of mixed Plasmodium vivax-Plasmodium falciparum infections. Am J Trop Med Hyg, 61, 367-74. MAUNY, F., VIEL, J. F., HANDSCHUMACHER, P. & SELLIN, B. 2004. Multilevel modelling and malaria: a new method for an old disease. Int J Epidemiol, 33, 1337-44.
216 MAY, J., FALUSI, A. G., MOCKENHAUPT, F. P., ADEMOWO, O. G., OLUMESE, P. E., BIENZLE, U. & MEYER, C. G. 2000. Impact of subpatent multi-species and multi-clonal plasmodial infections on anaemia in children from Nigeria. Trans R Soc Trop Med Hyg, 94, 399-403. MAYENGUE, P. I., LUTY, A. J., ROGIER, C., BARAGATTI, M., KREMSNER, P. G. & NTOUMI, F. 2009. The multiplicity of Plasmodium falciparum infections is associated with acquired immunity to asexual blood stage antigens. Microbes Infect, 11, 108-14. MAYNARD SMITH, J., SMITH, N. H., O'ROURKE, M. & SPRATT, B. G. 1993. How clonal are bacteria? Proc Natl Acad Sci U S A, 90, 4384-4388. MAYOR, A., MORO, L., AGUILAR, R., BARDAJÍ, A., CISTERÓ, P., SERRA-CASAS, E., SIGAÚQUE, B., ALONSO, P. L., ORDI, J. & MENÉNDEZ, C. 2012. How hidden can malaria be in pregnant women? Diagnosis by microscopy, placental histology, polymerase chain reaction and detection of histidine-rich protein 2 in plasma. Clinical infectious diseases, 54, 1561-1568 %@ 1537-6591. MAYXAY, M., PUKRITRAYAKAMEE, S., CHOTIVANICH, K., IMWONG, M., LOOAREESUWAN, S. & WHITE, N. J. 2001. Identification of cryptic coinfection with Plasmodium falciparum in patients presenting with vivax malaria. Am J Trop Med Hyg, 65, 588-92. MAYXAY, M., PUKRITTAYAKAMEE, S., NEWTON, P. N. & WHITE, N. J. 2004. Mixed-species malaria infections in humans. Trends Parasitol, 20, 233-40. MBOERA, L. E., FANELLO, C. I., MALIMA, R. C., TALBERT, A., FOGLIATI, P., BOBBIO, F. & MOLTENI, F. 2006. Comparison of the Paracheck-Pf test with microscopy, for the confirmation of Plasmodium falciparum malaria in Tanzania. Ann Trop Med Parasitol, 100, 115-22. MBOERA, L. E. & KITUA, A. Y. 2001. Malaria epidemics in Tanzania: An overview. Afr J Health Sci, 8, 17- 23. MCCUTCHAN, T. F., DE LA CRUZ, V. F., LAL, A. A., GUNDERSON, J. H., ELWOOD, H. J. & SOGIN, M. L. 1988. Primary sequences of two small subunit ribosomal RNA genes from Plasmodium falciparum. Mol Biochem Parasitol, 28, 63-8. MCKENZIE, F. E., SIRICHAISINTHOP, J., MILLER, R. S., GASSER, R. A., JR. & WONGSRICHANALAI, C. 2003. Dependence of malaria detection and species diagnosis by microscopy on parasite density. Am J Trop Med Hyg, 69, 372-6. MCMANUS, D. P. & BOWLES, J. 1996. Molecular genetic approaches to parasite identification: their value in diagnostic parasitology and systematics. Int J Parasitol, 26, 687-704. MCMICHAEL, A. J., HAINES, A., SLOOF, R. & KOVATS, S. 1998. Climate change and human health. Climatic Change, 38, 501-506. MCMICHAEL, A. J., WOODRUFF, R. E. & HALES, S. 2006. Climate change and human health: present and future risks. Lancet, 367, 859-69. MCNEMAR, Q. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12, 153-7. MEHMOOD, K. 2005. Malaria in Karachi and other areas in Sindh, Pakistan Armed Forces Med J 4. MENDIS, C., GAMAGE-MENDIS, A. C., DE ZOYSA, A. P., ABHAYAWARDENA, T. A., CARTER, R., HERATH, P. R. & MENDIS, K. N. 1990. Characteristics of malaria transmission in Kataragama, Sri Lanka: a focus for immuno-epidemiological studies. Am J Trop Med Hyg, 42, 298-308. MENDIS, K., RIETVELD, A., WARSAME, M., BOSMAN, A., GREENWOOD, B. & WERNSDORFER, W. H. 2009. From malaria control to eradication: The WHO perspective. Trop Med Int Health, 14, 802-9. MENDIS, K., SINA, B. J., MARCHESINI, P. & CARTER, R. 2001. The neglected burden of Plasmodium vivax malaria. Am J Trop Med Hyg, 64, 97-106. MENDIS, K. N. & CARTER, R. 1995. Clinical disease and pathogenesis in malaria. Parasitol Today, 11, Pti1-16. MENENDEZ, C. 1995. Malaria during pregnancy: a priority area of malaria research and control. Parasitol Today, 11, 178-83. MENS, P. F., BOJTOR, E. C. & SCHALLIG, H. D. 2012. Molecular interactions in the placenta during malaria infection. Eur J Obstet Gynecol Reprod Biol, 152, 126-32.
217 MESSINA, J. P., TAYLOR, S. M., MESHNICK, S. R., LINKE, A. M., TSHEFU, A. K., ATUA, B., MWANDAGALIRWA, K. & EMCH, M. 2011. Population, behavioural and environmental drivers of malaria prevalence in the Democratic Republic of Congo. Malar J, 10, 161. MICHEL, K. & KAFATOS, F. C. 2005. Mosquito immunity against Plasmodium. Insect Biochem Mol Biol, 35, 677-89. MIKHAIL, A. F., LESLIE, T. J., MAYAN, M. I., ZEKRIA, R., MOHAMMAD, N., HASANZAI, M. A., SAFI, N., WHITTY, C. J. & ROWLAND, M. 2011. Field trial of three different Plasmodium vivax-detecting rapid diagnostic tests with and without evaporative cool box storage in Afghanistan. Malar J, 10, 169. MILLER, L. H., BARUCH, D. I., MARSH, K. & DOUMBO, O. K. 2002. The pathogenic basis of malaria. Nature, 415, 673-9. MILLER, L. H., GOOD, M. F. & MILON, G. 1994. Malaria pathogenesis. Science, 264, 1878-83. MILNE, L. M., KYI, M. S., CHIODINI, P. L. & WARHURST, D. C. 1994. Accuracy of routine laboratory diagnosis of malaria in the United Kingdom. J Clin Pathol, 47, 740-2. MINAKAWA, N., MUNGA, S., ATIELI, F., MUSHINZIMANA, E., ZHOU, G., GITHEKO, A. K. & YAN, G. 2005. Spatial distribution of anopheline larval habitats in Western Kenyan highlands: effects of land cover types and topography. Am J Trop Med Hyg, 73, 157-65. MINAKAWA, N., SONYE, G., MOGI, M. & YAN, G. 2004. Habitat characteristics of Anopheles gambiae s.s. larvae in a Kenyan highland. Med Vet Entomol, 18, 301-5. MISHRA, B., SAMANTARAY, J. C., KUMAR, A. & MIRDHA, B. R. 1999. Study of false positivity of two rapid antigen detection tests for diagnosis of Plasmodium falciparum malaria. J Clin Microbiol, 37, 1233. MOBEGI, V. A., DUFFY, C. W., AMAMBUA-NGWA, A., LOUA, K. M., LAMAN, E., NWAKANMA, D. C., MACINNIS, B., ASPELING-JONES, H., MURRAY, L., CLARK, T. G., KWIATKOWSKI, D. P. & CONWAY, D. J. 2014. Genome-wide analysis of selection on the malaria parasite Plasmodium falciparum in West African populations of differing infection endemicity. Mol Biol Evol, 31, 1490-9. MOCKENHAUPT, F. P. 1995. Mefloquine resistance in Plasmodium falciparum. Parasitol Today, 11, 248- 53. MOH 2010. Pakistan national strategic plan for malaria control 2010 - 2015. In: HEALTH, M. O. (ed.). Islamabad. MOHANTY, S., PATEL, D. K., PATI, S. S. & MISHRA, S. K. 2006. Adjuvant therapy in cerebral malaria. Indian J Med Res, 124, 245-60. MOHP 2017. Epidemiology of Malaria Pakistan. Pakistan. MOK, S., ASHLEY, E. A., FERREIRA, P. E., ZHU, L., LIN, Z., YEO, T., CHOTIVANICH, K., IMWONG, M., PUKRITTAYAKAMEE, S., DHORDA, M., NGUON, C., LIM, P., AMARATUNGA, C., SUON, S., HIEN, T. T., HTUT, Y., FAIZ, M. A., ONYAMBOKO, M. A., MAYXAY, M., NEWTON, P. N., TRIPURA, R., WOODROW, C. J., MIOTTO, O., KWIATKOWSKI, D. P., NOSTEN, F., DAY, N. P., PREISER, P. R., WHITE, N. J., DONDORP, A. M., FAIRHURST, R. M. & BOZDECH, Z. 2015. Drug resistance. Population transcriptomics of human malaria parasites reveals the mechanism of artemisinin resistance. Science, 347, 431-5. MOODY, A. 2002. Rapid Diagnostic Tests for Malaria Parasites. Clin Microbiol Rev, 15, 66-78. MOODY, A. H. & CHIODINI, P. L. 2002. Non-microscopic method for malaria diagnosis using OptiMAL IT, a second-generation dipstick for malaria pLDH antigen detection. Br J Biomed Sci, 59, 228-31. MOONEN, B., COHEN, J. M., SNOW, R. W., SLUTSKER, L., DRAKELEY, C., SMITH, D. L., ABEYASINGHE, R. R., RODRIGUEZ, M. H., MAHARAJ, R., TANNER, M. & TARGETT, G. 2010. Operational strategies to achieve and maintain malaria elimination. Lancet, 376, 1592-603. MORELL, V. 1997. How the malaria parasite manipulates its hosts. Science, 278, 223. MORI, Y., NAGAMINE, K., TOMITA, N. & NOTOMI, T. 2001. Detection of loop-mediated isothermal amplification reaction by turbidity derived from magnesium pyrophosphate formation. Biochem Biophys Res Commun, 289, 150-4. MPR 2012. Pakistan Malaria Programme Review.
218 MU, J., JOY, D. A., DUAN, J., HUANG, Y., CARLTON, J., WALKER, J., BARNWELL, J., BEERLI, P., CHARLESTON, M. A., PYBUS, O. G. & SU, X. Z. 2005. Host switch leads to emergence of Plasmodium vivax malaria in humans. Mol Biol Evol, 22, 1686-93. MU, J., MYERS, R. A., JIANG, H., LIU, S., RICKLEFS, S., WAISBERG, M., CHOTIVANICH, K., WILAIRATANA, P., KRUDSOOD, S., WHITE, N. J., UDOMSANGPETCH, R., CUI, L., HO, M., OU, F., LI, H., SONG, J., LI, G., WANG, X., SEILA, S., SOKUNTHEA, S., SOCHEAT, D., STURDEVANT, D. E., PORCELLA, S. F., FAIRHURST, R. M., WELLEMS, T. E., AWADALLA, P. & SU, X. Z. 2010. Plasmodium falciparum genome-wide scans for positive selection, recombination hot spots and resistance to antimalarial drugs. Nat Genet, 42, 268-71. MUELLER, I., GALINSKI, M. R., BAIRD, J. K., CARLTON, J. M., KOCHAR, D. K., ALONSO, P. L. & DEL PORTILLO, H. A. 2009. Key gaps in the knowledge of Plasmodium vivax, a neglected human malaria parasite. Lancet Infect Dis, 9, 555-66. MUHAMMAD, N. & HUSSAIN, A. 2003. Prevalence of malaria in general population of district Buner. JPMI, 17, 75-80. MUKHTAR, M. 2006. Killer number one: the fight against malaria: malaria strategy lags behind the global goals, Humanitarian news and analysis a service of the UN Office for the Coordination of Humanitarian Affairs. Nairobi: IRIN. MUKHTAR, M. 2009. Guidelines for antivector interventions for monsoon season. In: DIRECTORATE OF MALARIA CONTROL, M. O. H. (ed.). Pakistan. MUNIR, M. A., QURESHI, H. & SAFDAR, N. 2014. Malaria Indicator Survey in 38 High Risk Districts of Pakistan- 2013-14. Pakistan Medical Research Council. MURPHY, G. S., BASRI, H., PURNOMO, ANDERSEN, E. M., BANGS, M. J., MOUNT, D. L., GORDEN, J., LAL, A. A., PURWOKUSUMO, A. R., HARJOSUWARNO, S. & ET AL. 1993. Vivax malaria resistant to treatment and prophylaxis with chloroquine. Lancet, 341, 96-100. MURRAY, C. K., BELL, D., GASSER, R. A. & WONGSRICHANALAI, C. 2003. Rapid diagnostic testing for malaria. Trop Med Int Health, 8, 876-83. MURTAZA, G., MEMON, I., MEMON, A., LAL, M. & KALLOR, N. 2009. Malaria morbidity in Sindh and the Plasmodium species distribution. Pak J Med Sci, 25, 646-649. MUSALIKA, A. 2010a. Human genetic polymorphisms and malaria in north east Tanzania. MUSALIKA, A. 2010b. Human genetic polymorphisms and malaria in north east Tanzania. Ph.D, london school of hygiene and tropical medicines MUTABINGWA, T. K. 2005. Artemisinin-based combination therapies (ACTs): best hope for malaria treatment but inaccessible to the needy! Acta Trop, 95, 305-15. MZILAHOWA, T., MCCALL, P. J. & HASTINGS, I. M. 2007. "Sexual" population structure and genetics of the malaria agent P. falciparum. PLoS One, 2, e613. N'GUESSAN, R., DARRIET, F., DOANNIO, J. M., CHANDRE, F. & CARNEVALE, P. 2001. Olyset Net efficacy against pyrethroid-resistant Anopheles gambiae and Culex quinquefasciatus after 3 years' field use in C te d'Ivoire. Med Vet Entomol, 15, 97-104. NAFO TRAORE, F. 2005. Rolling back malaria: opportunities and challenges. Trans R Soc Trop Med Hyg, 99, 403-6. NAGAMINE, K., WATANABE, K., OHTSUKA, K., HASE, T. & NOTOMI, T. 2001. Loop-mediated isothermal amplification reaction using a nondenatured template. Clin Chem, 47, 1742-3. NANDWANI, S., MATHUR, M. & RAWAT, S. 2005. Evaluation of the polymerase chain reaction analysis for diagnosis of falciparum malaria in Delhi, India. Indian J Med Microbiol, 23, 176-8. NASSIR, E., ABDEL-MUHSIN, A. M., SULIAMAN, S., KENYON, F., KHEIR, A., GEHA, H., FERGUSON, H. M., WALLIKER, D. & BABIKER, H. A. 2005. Impact of genetic complexity on longevity and gametocytogenesis of Plasmodium falciparum during the dry and transmission-free season of eastern Sudan. Int J Parasitol, 35, 49-55. NAYAR, J. K., BAKER, R. H., KNIGHT, J. W., SULLIVAN, J. S., MORRIS, C. L., RICHARDSON, B. B., GALLAND, G. G. & COLLINS, W. E. 1997. Studies on a primaquine-tolerant strain of Plasmodium vivax from Brazil in Aotus and Saimiri monkeys. J Parasitol, 83, 739-45.
219 NAZ, S., MEMON A. S., HAQUE M., NADEEM U., JAMAL G., KHAN A., AILAAN A. & SDPI. 2016. Pakistan District education rankings 2016. Islamabad: Alif Ailaan. NDENGA, B., GITHEKO, A., OMUKUNDA, E., MUNYEKENYE, G., ATIELI, H., WAMAI, P., MBOGO, C., MINAKAWA, N., ZHOU, G. & YAN, G. 2006. Population dynamics of malaria vectors in western Kenya highlands. J Med Entomol, 43, 200-6. NDOEN, E., WILD, C., DALE, P., SIPE, N. & DALE, M. 2011. Dusk to dawn activity patterns of anopheline mosquitoes in West Timor and Java, Indonesia. Southeast Asian J Trop Med Public Health, 42, 550-61. NEAFSEY, D. E., GALINSKY, K., JIANG, R. H., YOUNG, L., SYKES, S. M., SAIF, S., GUJJA, S., GOLDBERG, J. M., YOUNG, S., ZENG, Q., CHAPMAN, S. B., DASH, A. P., ANVIKAR, A. R., SUTTON, P. L., BIRREN, B. W., ESCALANTE, A. A., BARNWELL, J. W. & CARLTON, J. M. 2012. The malaria parasite Plasmodium vivax exhibits greater genetic diversity than Plasmodium falciparum. Nat Genet, 44, 1046-50. NEBL, T., DE VEER, M. J. & SCHOFIELD, L. 2005. Stimulation of innate immune responses by malarial glycosylphosphatidylinositol via pattern recognition receptors. Parasitology, 130 Suppl, S45-62. NEI, M. 1973. Analysis of gene diversity in subdivided populations. Proc Natl Acad Sci U S A, 70. NEI, M. 1977. F-statistics and analysis of gene diversity in sub-divided populations. Ann. Hum. Genet. Lond., 41, 225-233. NGASALA, B. 2010. Improved malaria case management in under five in the era of Artemisinin-based combination therapy in Tanzania., Published by Karolinska intitute. NGASALA, B., MUBI, M., WARSAME, M., PETZOLD, M. G., MASSELE, A. Y., GUSTAFSSON, L. L., TOMSON, G., PREMJI, Z. & BJORKMAN, A. 2008. Impact of training in clinical and microscopy diagnosis of childhood malaria on antimalarial drug prescription and health outcome at primary health care level in Tanzania: a randomized controlled trial. Malar J, 7, 199. NIRINGIYE, A. & DOUGLASON, O. G. 2010. Environmental and Socio-economic Determinants of Malaria Prevalence in Uganda. Research Journal of Environmental and Earth Sciences, 2, 194-198. NIZAMANI, M., KALAR, N. & KHUSHK, I. 2006. Burden of malaria in Sind Pakistan: A two years surveillance report. Journal of Liaqat University of Medical and Health Sciences, 5, 76-83. NJAMA-MEYA, D., KAMYA, M. R. & DORSEY, G. 2004. Asymptomatic parasitaemia as a risk factor for symptomatic malaria in a cohort of Ugandan children. Trop Med Int Health, 9, 862-8. NKOGHE, D., AKUE, J. P., GONZALEZ, J. P. & LEROY, E. M. 2011. Prevalence of Plasmodium falciparum infection in asymptomatic rural Gabonese populations. Malar J, 10, 33. NOEDL, H., YINGYUEN, K., LAOBOONCHAI, A., FUKUDA, M., SIRICHAISINTHOP, J. & MILLER, R. S. 2006. Sensitivity and specificity of an antigen detection ELISA for malaria diagnosis. Am J Trop Med Hyg, 75, 1205-8. NOOR, M. & AKBAR, H. 2003. Prevalence of malaria in general populationof distric Buner. J Pak Inst Med Sci, 17 75-80. NOTOMI, T., OKAYAMA, H., MASUBUCHI, H., YONEKAWA, T., WATANABE, K., AMINO, N. & HASE, T. 2000. Loop-mediated isothermal amplification of DNA. Nucleic Acids Res, 28, E63. OBADO, S. O., GLOVER, L. & DEITSCH, K. W. 2016. The nuclear envelope and gene organization in parasitic protozoa: Specializations associated with disease. Mol Biochem Parasitol, 209, 104- 113. OBALDIA, N., 3RD 2015. Determinants of low socio-economic status and risk of Plasmodium vivax malaria infection in Panama (2009-2012): a case-control study. Malar J, 14, 14. OCHOLA, L. I., TETTEH, K. K., STEWART, L. B., RIITHO, V., MARSH, K. & CONWAY, D. J. 2010. Allele frequency-based and polymorphism-versus-divergence indices of balancing selection in a new filtered set of polymorphic genes in Plasmodium falciparum. Mol Biol Evol, 27, 2344-51. OEMIJATI, S. 1992. Risk behavior in malaria transmission in Indonesia. Southeast Asian J Trop Med Public Health, 23 Suppl 1, 47-50. OHRT, C., PURNOMO, SUTAMIHARDJA, M. A., TANG, D. & KAIN, K. C. 2002. Impact of microscopy error on estimates of protective efficacy in malaria-prevention trials. J Infect Dis, 186, 540-6.
220 OKELL, L. C., BOUSEMA, T., GRIFFIN, J. T., OUEDRAOGO, A. L., GHANI, A. C. & DRAKELEY, C. J. 2012a. Factors determining the occurrence of submicroscopic malaria infections and their relevance for control. Nat Commun, 3, 1237. OKELL, L. C., BOUSEMA, T., GRIFFIN, J. T., OUÉDRAOGO, A. L., GHANI, A. C. & DRAKELEY, C. J. 2012b. Factors determining the occurrence of submicroscopic malaria infections and their relevance for control. Nature communications, 3, 1237 %@ 2041-1723. OKELL, L. C., GHANI, A. C., LYONS, E. & DRAKELEY, C. J. 2009. Submicroscopic infection in Plasmodium falciparum-endemic populations: a systematic review and meta-analysis. J Infect Dis, 200, 1509- 17. OMAR, S. A., ADAGU, I. S., GUMP, D. W., NDARU, N. P. & WARHURST, D. C. 2001a. Plasmodium falciparum in Kenya: high prevalence of drug-resistance-associated polymorphisms in hospital admissions with severe malaria in an epidemic area. Ann Trop Med Parasitol, 95, 661-9. OMAR, S. A., ADAGU, I. S. & WARHURST, D. C. 2001b. Can pretreatment screening for dhps and dhfr point mutations in Plasmodium falciparum infections be used to predict sulfadoxine- pyrimethamine treatment failure? Trans R Soc Trop Med Hyg, 95, 315-9. OMER, F. M., DE SOUZA, J. B. & RILEY, E. M. 2003. Differential induction of TGF-beta regulates proinflammatory cytokine production and determines the outcome of lethal and nonlethal Plasmodium yoelii infections. J Immunol, 171, 5430-6. ONER, A. Y., AKIN, H. & KOCAZEYBEK, B. 2004. Detection of Plasmodium vivax and Plasmodium falciparum in blood donors: comparison of new method to the conventional one. Transfus Apher Sci, 30, 3-7. ONG'ECHA, J. M., KELLER, C. C., WERE, T., OUMA, C., OTIENO, R. O., LANDIS-LEWIS, Z., OCHIEL, D., SLINGLUFF, J. L., MOGERE, S., OGONJI, G. A., ORAGO, A. S., VULULE, J. M., KAPLAN, S. S., DAY, R. D. & PERKINS, D. J. 2006. Parasitemia, anemia, and malarial anemia in infants and young children in a rural holoendemic Plasmodium falciparum transmission area. Am J Trop Med Hyg, 74, 376-85. ONGAGNA-YHOMBI, S. Y., CORSTJENS, P., GEVA, E., ABRAMS, W. R., BARBER, C. A., MALAMUD, D. & MHARAKURWA, S. 2013. Improved assay to detect Plasmodium falciparum using an uninterrupted, semi-nested PCR and quantitative lateral flow analysis. Malar J, 12, 74. ORJUELA-SANCHEZ, P., KARUNAWEERA, N. D., DA SILVA-NUNES, M., DA SILVA, N. S., SCOPEL, K. K., GONCALVES, R. M., AMARATUNGA, C., SA, J. M., SOCHEAT, D., FAIRHUST, R. M., GUNAWARDENA, S., THAVAKODIRASAH, T., GALAPATHTHY, G. L., ABEYSINGHE, R., KAWAMOTO, F., WIRTH, D. F. & FERREIRA, M. U. 2010. Single-nucleotide polymorphism, linkage disequilibrium and geographic structure in the malaria parasite Plasmodium vivax: prospects for genome-wide association studies. BMC Genet, 11, 65. OTTEN, M., AREGAWI, M., WERE, W., KAREMA, C., MEDIN, A., BEKELE, W., JIMA, D., GAUSI, K., KOMATSU, R., KORENROMP, E., LOW-BEER, D. & GRABOWSKY, M. 2009. Initial evidence of reduction of malaria cases and deaths in Rwanda and Ethiopia due to rapid scale-up of malaria prevention and treatment. Malar J, 8, 14. OTTO, T. D., RAYNER, J. C., BOHME, U., PAIN, A., SPOTTISWOODE, N., SANDERS, M., QUAIL, M., OLLOMO, B., RENAUD, F., THOMAS, A. W., PRUGNOLLE, F., CONWAY, D. J., NEWBOLD, C. & BERRIMAN, M. 2014. Genome sequencing of chimpanzee malaria parasites reveals possible pathways of adaptation to human hosts. Nat Commun, 5, 4754. OUATTARA, A., DOUMBO, S., SAYE, R., BEAVOGUI, A. H., TRAORE, B., DJIMDE, A., NIANGALY, A., KAYENTAO, K., DIALLO, M., DOUMBO, O. K. & THERA, M. A. 2011. Use of a pLDH-based dipstick in the diagnostic and therapeutic follow-up of malaria patients in Mali. Malar J, 10, 345. OWUSU-AGYEI, S., KORAM, K. A., BAIRD, J. K., UTZ, G. C., BINKA, F. N., NKRUMAH, F. K., FRYAUFF, D. J. & HOFFMAN, S. L. 2001. Incidence of symptomatic and asymptomatic Plasmodium falciparum infection following curative therapy in adult residents of northern Ghana. Am J Trop Med Hyg, 65, 197-203. PAAIJMANS, K. P., WANDAGO, M. O., GITHEKO, A. K. & TAKKEN, W. 2007. Unexpected high losses of Anopheles gambiae larvae due to rainfall. PLoS One, 2, e1146.
221 PAGE, R. D. M. & HOLMES, E. C. 1998a. Molecular evolution, a phylogenetic approach. Blackwell Science, Oxford. PAGE, R. D. M. & HOLMES, E. C. 1998b. Molecular evolution, a phylogenetic approach (Blackwell Science, Oxford). PAJOT, F.-X., LE PON, T. F., MOLEZ, J.-F. & DEGALLIER, N. 1977. Agressivité d’Anopheles (Nyssorhynchus) darlingi Root, 1926 (Diptera, Culicidae) en Guyane française. Cahiers de l’ORSTOM, Série entomologie médicale et parasitology, 15, 15-22. PALMER, C. J., LINDO, J. F., KLASKALA, W. I., QUESADA, J. A., KAMINSKY, R., BAUM, M. K. & AGER, A. L. 1998. Evaluation of the OptiMAL test for rapid diagnosis of Plasmodium vivax and Plasmodium falciparum malaria. J Clin Microbiol, 36, 203-6. PARIKH, R., MATHAI, A., PARIKH, S., CHANDRA SEKHAR, G. & THOMAS, R. 2008. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol, 56, 45-50. PARIS, D. H., IMWONG, M., FAIZ, A. M., HASAN, M., YUNUS, E. B., SILAMUT, K., LEE, S. J., DAY, N. P. & DONDORP, A. M. 2007. Loop-mediated isothermal PCR (LAMP) for the diagnosis of falciparum malaria. Am J Trop Med Hyg, 77, 972-6. PARK, D. J., LUKENS, A. K., NEAFSEY, D. E., SCHAFFNER, S. F., CHANG, H. H., VALIM, C., RIBACKE, U., VAN TYNE, D., GALINSKY, K., GALLIGAN, M., BECKER, J. S., NDIAYE, D., MBOUP, S., WIEGAND, R. C., HARTL, D. L., SABETI, P. C., WIRTH, D. F. & VOLKMAN, S. K. 2012. Sequence-based association and selection scans identify drug resistance loci in the Plasmodium falciparum malaria parasite. Proc Natl Acad Sci U S A, 109, 13052-7. PARK, S. D. E. 2001. Trypanotolerance in West African cattle and the population genetic effects of selection. Ph.D. Thesis. University of Dublin. PARN. 2016. Laboratory diagnosis of malaria. Pakistan antimicrobial resistance network [Online]. Available: http://www.parn.org.pk/ [Accessed 19 January 2016]. PARROCHE, P., LAUW, F. N., GOUTAGNY, N., LATZ, E., MONKS, B. G., VISINTIN, A., HALMEN, K. A., LAMPHIER, M., OLIVIER, M., BARTHOLOMEU, D. C., GAZZINELLI, R. T. & GOLENBOCK, D. T. 2007. Malaria hemozoin is immunologically inert but radically enhances innate responses by presenting malaria DNA to Toll-like receptor 9. Proc Natl Acad Sci U S A, 104, 1919-24. PASVOL, G. 2005. Management of severe malaria: interventions and controversies. Infect Dis Clin North Am, 19, 211-40. PATTANASIN, S., PROUX, S., CHOMPASUK, D., LUWIRADAJ, K., JACQUIER, P., LOOAREESUWAN, S. & NOSTEN, F. 2003. Evaluation of a new Plasmodium lactate dehydrogenase assay (OptiMAL-IT) for the detection of malaria. Trans R Soc Trop Med Hyg, 97, 672-4. PAUL, R. E., DIALLO, M. & BREY, P. T. 2004. Mosquitoes and transmission of malaria parasites - not just vectors. Malar J, 3, 39. PAYNE, D. 1988. Use and limitations of light microscopy for diagnosing malaria at the primary health care level. Bull World Health Organ, 66, 621-6. PERKINS, S. L. & SCHALL, J. J. 2002. A molecular phylogeny of malarial parasites recovered from cytochrome b gene sequences. J Parasitol, 88, 972-8. PERLMANN, P., PERLMANN, H., LOOAREESUWAN, S., KRUDSOOD, S., KANO, S., MATSUMOTO, Y., BRITTENHAM, G., TROYE-BLOMBERG, M. & AIKAWA, M. 2000. Contrasting functions of IgG and IgE antimalarial antibodies in uncomplicated and severe Plasmodium falciparum malaria. Am J Trop Med Hyg, 62, 373-7. PETERS, A. L. & VAN NOORDEN, C. J. 2009. Glucose-6-phosphate dehydrogenase deficiency and malaria: cytochemical detection of heterozygous G6PD deficiency in women. J Histochem Cytochem, 57, 1003-11. PETERSON, I., BORRELL, L. N., EL-SADR, W. & TEKLEHAIMANOT, A. 2009. Individual and household level factors associated with malaria incidence in a highland region of Ethiopia: a multilevel analysis. Am J Trop Med Hyg, 80, 103-11. PHILLIPS, R. S. 2001. Current status of malaria and potential for control. Clin Microbiol Rev, 14, 208-26.
222 PICHYANGKUL, S., SAENGKRAI, P. & WEBSTER, H. K. 1994. Plasmodium falciparum pigment induces monocytes to release high levels of tumor necrosis factor-alpha and interleukin-1 beta. Am J Trop Med Hyg, 51, 430-5. PINIKAHANA, J. 1992. Socio-cultural factors associated with malaria transmission: a review. Indian J Malariol, 29, 121-6. PIPER, R. C., VANDERJAGT, D. L., HOLBROOK, J. J. & MAKLER, M. 1996. Malaria lactate dehydrogenase target for diagnosis and drug development. Annals of Tropical Medicine and Parasitology, 90, 433. PITT, S., PEARCY, B. E., STEVENS, R. H., SHARIPOV, A., SATAROV, K. & BANATVALA, N. 1998. War in Tajikistan and re-emergence of Plasmodium falciparum. Lancet, 352, 1279. POSADA, D. 2008. jModelTest: phylogenetic model averaging. Mol Biol Evol, 25, 1253-6. POSTIGO, M., MENDOZA-LEON, A. & PEREZ, H. A. 1998. Malaria diagnosis by the polymerase chain reaction: a field study in south-eastern Venezuela. Trans R Soc Trop Med Hyg, 92, 509-11. PRAJAPATI, S. K., JOSHI, H., CARLTON, J. M. & RIZVI, M. A. 2013. Neutral polymorphisms in putative housekeeping genes and tandem repeats unravels the population genetics and evolutionary history of Plasmodium vivax in India. PLoS Negl Trop Dis, 7, e2425. PREISER, P. R., WILSON, R. J., MOORE, P. W., MCCREADY, S., HAJIBAGHERI, M. A., BLIGHT, K. J., STRATH, M. & WILLIAMSON, D. H. 1996. Recombination associated with replication of malarial mitochondrial DNA. Embo j, 15, 684-93. PRESTON, M. D., CAMPINO, S., ASSEFA, S. A., ECHEVERRY, D. F., OCHOLLA, H., AMAMBUA-NGWA, A., STEWART, L. B., CONWAY, D. J., BORRMANN, S., MICHON, P., ZONGO, I., OUEDRAOGO, J. B., DJIMDE, A. A., DOUMBO, O. K., NOSTEN, F., PAIN, A., BOUSEMA, T., DRAKELEY, C. J., FAIRHURST, R. M., SUTHERLAND, C. J., ROPER, C. & CLARK, T. G. 2014. A barcode of organellar genome polymorphisms identifies the geographic origin of Plasmodium falciparum strains. Nat Commun, 5, 4052. PRICE, R., NOSTEN, F., SIMPSON, J. A., LUXEMBURGER, C., PHAIPUN, L., TER KUILE, F., VAN VUGT, M., CHONGSUPHAJAISIDDHI, T. & WHITE, N. J. 1999. Risk factors for gametocyte carriage in uncomplicated falciparum malaria. Am J Trop Med Hyg, 60, 1019-23. PRICE, R. N., DOUGLAS, N. M. & ANSTEY, N. M. 2009. New developments in Plasmodium vivax malaria: severe disease and the rise of chloroquine resistance. Curr Opin Infect Dis, 22, 430-5. PRICE, R. N., NOSTEN, F., LUXEMBURGER, C., VAN VUGT, M., PHAIPUN, L., CHONGSUPHAJAISIDDHI, T. & WHITE, N. J. 1997. Artesunate/mefloquine treatment of multi-drug resistant falciparum malaria. Trans R Soc Trop Med Hyg, 91, 574-7. PRICE, R. N., SIMPSON, J. A., NOSTEN, F., LUXEMBURGER, C., HKIRJAROEN, L., TER KUILE, F., CHONGSUPHAJAISIDDHI, T. & WHITE, N. J. 2001. Factors contributing to anemia after uncomplicated falciparum malaria. Am J Trop Med Hyg, 65, 614-22. PRICE, R. N., TJITRA, E., GUERRA, C. A., YEUNG, S., WHITE, N. J. & ANSTEY, N. M. 2007. Vivax malaria: neglected and not benign. Am J Trop Med Hyg, 77, 79-87. PRITCHARD, J. K., STEPHENS, M. & DONNELLY, P. 2000. Inference of population structure using multilocus genotype data. Genetics, 155, 945-959. PRITCHARD, J. K., WEN, X. & FALUSH, D. 2009. Documentation for structure software: Version 2.3. http://pritch.bsd.uchicago.edu/structure.html. PROTOPOPOFF, N., VAN BORTEL, W., SPEYBROECK, N., VAN GEERTRUYDEN, J. P., BAZA, D., D'ALESSANDRO, U. & COOSEMANS, M. 2009. Ranking malaria risk factors to guide malaria control efforts in African highlands. PLoS One, 4, e8022. PRUGNOLLE, F., DURAND, P., NEEL, C., OLLOMO, B., AYALA, F. J., ARNATHAU, C., ETIENNE, L., MPOUDI- NGOLE, E., NKOGHE, D., LEROY, E., DELAPORTE, E., PEETERS, M. & RENAUD, F. 2010. African great apes are natural hosts of multiple related malaria species, including Plasmodium falciparum. Proc Natl Acad Sci U S A, 107, 1458-63. PRUGNOLLE, F., ROUGERON, V., BECQUART, P., BERRY, A., MAKANGA, B., RAHOLA, N., ARNATHAU, C., NGOUBANGOYE, B., MENARD, S., WILLAUME, E., AYALA, F. J., FONTENILLE, D., OLLOMO, B.,
223 DURAND, P., PAUPY, C. & RENAUD, F. 2013. Diversity, host switching and evolution of Plasmodium vivax infecting African great apes. Proc Natl Acad Sci U S A, 110, 8123-8. PUTAPORNTIP, C., BUPPAN, P. & JONGWUTIWES, S. 2011. Improved performance with saliva and urine as alternative DNA sources for malaria diagnosis by mitochondrial DNA-based PCR assays. Clin Microbiol Infect, 17, 1484-91. QIAGEN 2013. Structure of Plasmodium Merozoite. RAB, M. A., FREEMAN, T. W., DURRANI, N., DE POERCK, D. & ROWLAND, M. W. 2001. Resistance of Plasmodium falciparum malaria to chloroquine is widespread in eastern Afghanistan. Ann Trop Med Parasitol, 95, 41-6. RAFI, S., MEMON, M. A., RAO, M. H. & BILLOO, A. G. 1994. A change of Plasmodium species infecting children in Karachi over the last decade. J Pak Med Assoc, 44, 162-4. RAHIM, F. & YOUNAS, M. 2003. Malaria in children: Clinical profile and response to treatment. A district level experience. Journal of Medical Sciences, 11, 21- 25. RAHMAN, S., JALIL, F., KHAN, H., JADOON, M. A., ULLAH, K., REHMAN, M., KHAN, A. M., KHAN, A., HAYAT, A. & IQBAL, Z. 2017. Prevalence of Malaria in district Shangla, Khyber Pakhtunkhwa, Pakistan. JEZS, 5, 678-682. RANTALA, A.-M., TAYLOR, S. M., TROTTMAN, P. A., LUNTAMO, M., MBEWE, B., MALETA, K., KULMALA, T., ASHORN, P. & MESHNICK, S. R. 2010. Comparison of real-time PCR and microscopy for malaria parasite detection in Malawian pregnant women. Malaria journal, 9, 269 %@ 1475- 2875. RAZIQ, F. & KHAN, M. 1995. A survey of malarial parasites in Abbottabad. Pak Med J, Res, 34, 33-34. RAZZAQ, A., KHAN, N. & KHAN, H. 2014. Frequency of slide positivity in clinically suspected malaria cases. Gomal Journal of Medical Sciences, 12. REGMI, K., KUNWAR, A. & ORTEGA, L. 2016. A systematic review of knowledge, attitudes and beliefs about malaria among the South Asian population. Infect Ecol Epidemiol, 6, 30822. REHMAN, A., AHMED, K., USTONOMIYA, Y., NAGATAKE, T., KHAN, F. & MATUSOMOTO, K. 1994. The prevalence of malaria in an endemic area of Bangladesh. J Trop Med Hyg, 22, 13-9. REHMAN, M. & MUTTALIB, A. 1967. Determination of malaria transmission in central part of Karachi city; incrimination of Anopheles stephensi as the vector. Pakistan Journal of Health, 17, 73-84. REICH, D. E., CARGILL, M., BOLK, S., IRELAND, J., SABETI, P. C., RICHTER, D. J., LAVERY, T., KOUYOUMJIAN, R., FARHADIAN, S. F., WARD, R. & LANDER, E. S. 2001. Linkage disequilibrium in the human genome. Nature, 411, 199-204. REID, C. 2000. Implications of Climate Change on Malaria in Karnataka, India. Center for Environmental Studies. Brown University. REISEN, W. K. & BOREHAM, P. F. 1982. Estimates of malaria vectorial capacity for Anopheles culicifacies and Anopheles stephensi in rural Punjab province Pakistan. J Med Entomol, 19, 98-103. REITER, P. 2001. Climate change and mosquito-borne disease. Environ Health Perspect, 109 Suppl 1, 141-61. RENIA, L., HOWLAND, S. W., CLASER, C., CHARLOTTE GRUNER, A., SUWANARUSK, R., HUI TEO, T., RUSSELL, B. & NG, L. F. 2012. Cerebral malaria: mysteries at the blood-brain barrier. Virulence, 3, 193-201. REYBURN, H., MBATIA, R., DRAKELEY, C., BRUCE, J., CARNEIRO, I., OLOMI, R., COX, J., NKYA, W. M., LEMNGE, M., GREENWOOD, B. M. & RILEY, E. M. 2005. Association of transmission intensity and age with clinical manifestations and case fatality of severe Plasmodium falciparum malaria. Jama, 293, 1461-70. REYBURN, H., MBATIA, R., DRAKELEY, C., CARNEIRO, I., MWAKASUNGULA, E., MWERINDE, O., SAGANDA, K., SHAO, J., KITUA, A., OLOMI, R., GREENWOOD, B. M. & WHITTY, C. J. 2004. Overdiagnosis of malaria in patients with severe febrile illness in Tanzania: a prospective study. Bmj, 329, 1212. RHEE, M., SISSOKO, M., PERRY, S., MCFARLAND, W., PARSONNET, J. & DOUMBO, O. 2005. Use of insecticide-treated nets (ITNs) following a malaria education intervention in Piron, Mali: a control trial with systematic allocation of households. Malar J, 4, 35.
224 RICH, S. M., LEENDERTZ, F. H., XU, G., LEBRETON, M., DJOKO, C. F., AMINAKE, M. N., TAKANG, E. E., DIFFO, J. L., PIKE, B. L., ROSENTHAL, B. M., FORMENTY, P., BOESCH, C., AYALA, F. J. & WOLFE, N. D. 2009. The origin of malignant malaria. Proc Natl Acad Sci U S A, 106, 14902-7. RICHARDS, A. K., SMITH, L., MULLANY, L. C., LEE, C. I., WHICHARD, E., BANEK, K., MAHN, M., SHWE OO, E. K. & LEE, T. J. 2007. Prevalence of Plasmodium falciparum in active conflict areas of eastern Burma: a summary of cross-sectional data. Confl Health, 1, 9. RICHIE, T. L. 1988. Interactions between malaria parasites infecting the same vertebrate host. Parasitology, 96 ( Pt 3), 607-39. RICHTER, J., FRANKEN, G., MEHLHORN, H., LABISCH, A. & HAUSSINGER, D. 2010. What is the evidence for the existence of Plasmodium ovale hypnozoites? Parasitol Res, 107, 1285-90. RICKMAN, L. S., LONG, G. W., OBERST, R., CABANBAN, A., SANGALANG, R., SMITH, J. I., CHULAY, J. D. & HOFFMAN, S. L. 1989. Rapid diagnosis of malaria by acridine orange staining of centrifuged parasites. Lancet, 1, 68-71. RIDLEY, R. G. 1997. Plasmodium: drug discovery and development--an industrial perspective. Exp Parasitol, 87, 293-304. RIECKMANN, K. H., DAVIS, D. R. & HUTTON, D. C. 1989. Plasmodium vivax resistance to chloroquine? Lancet, 2, 1183-4. ROBERT, L. S. & JANOVY, J. J. 2009. Gerald D. Schmidt and Larry S.Roberts' Foundation of parasitology, London, Mc Graw Hill. ROBERT, V., MACINTYRE, K., KEATING, J., TRAPE, J. F., DUCHEMIN, J. B., WARREN, M. & BEIER, J. C. 2003. Malaria transmission in urban sub-Saharan Africa. Am J Trop Med Hyg, 68, 169-76. RODULFO, H., DE DONATO, M., MORA, R., GONZALEZ, L. & CONTRERAS, C. E. 2007. Comparison of the diagnosis of malaria by microscopy, immunochromatography and PCR in endemic areas of Venezuela. Braz J Med Biol Res, 40, 535-43. ROGERSON, S. J., HVIID, L., DUFFY, P. E., LEKE, R. F. & TAYLOR, D. W. 2007. Malaria in pregnancy: pathogenesis and immunity. Lancet Infect Dis, 7, 105-17. ROPER, C., ELHASSAN, I. M., HVIID, L., GIHA, H., RICHARDSON, W., BABIKER, H., SATTI, G. M., THEANDER, T. G. & ARNOT, D. E. 1996. Detection of very low level Plasmodium falciparum infections using the nested polymerase chain reaction and a reassessment of the epidemiology of unstable malaria in Sudan. Am J Trop Med Hyg, 54, 325-31. ROPER, M. H., TORRES, R. S., GOICOCHEA, C. G., ANDERSEN, E. M., GUARDA, J. S., CALAMPA, C., HIGHTOWER, A. W. & MAGILL, A. J. 2000. The epidemiology of malaria in an epidemic area of the Peruvian Amazon. Am J Trop Med Hyg, 62, 247-56. ROSENBERG, M. 2005. Evolutionary distance estimation and fidelity of pairwise sequence alignment. BMC Informatics, 6, 106. ROSENBERG, R. & RUNGSIWONGSE, J. 1991. The number of sporozoites produced by individual malaria oocysts. Am J Trop Med Hyg, 45, 574-7. ROSHANRAVAN, B., KARI, E., GILMAN, R. H., CABRERA, L., LEE, E., METCALFE, J., CALDERON, M., LESCANO, A. G., MONTENEGRO, S. H., CALAMPA, C. & VINETZ, J. M. 2003. Endemic malaria in the Peruvian Amazon region of Iquitos. Am J Trop Med Hyg, 69, 45-52. ROUGERON, V., TIEDJE, K. E., CHEN, D. S., RASK, T. S., GAMBOA, D., MAESTRE, A., MUSSET, L., LEGRAND, E., NOYA, O., YALCINDAG, E., RENAUD, F., PRUGNOLLE, F. & DAY, K. P. 2017. Evolutionary structure of Plasmodium falciparum major variant surface antigen genes in South America: Implications for epidemic transmission and surveillance. Ecol Evol, 7, 9376-9390. ROWLAND, M. 2001. Refugee health in the tropics. Trans R Soc Trop Med Hyg, 95. ROWLAND, M., DURRANI, N., HEWITT, S., MOHAMMED, N., BOUMA, M., CARNEIRO, I., ROZENDAAL, J. & SCHAPIRA, A. 1999. Permethrin-treated chaddars and top-sheets: appropriate technology for protection against malaria in Afghanistan and other complex emergencies. Trans R Soc Trop Med Hyg, 93, 465-72. ROWLAND, M., DURRANI, N., HEWITT, S. & SONDORP, E. 1997a. Resistance of falciparum malaria to chloroquine and sulfadoxine-pyrimethamine in Afghan refugee settlements in western Pakistan:
225 surveys by the general health services using a simplified in vivo test. Trop Med Int Health, 2, 1049-56. ROWLAND, M., HEWITT, S., DURRANI, N., BANO, N. & WIRTZ, R. 1997b. Transmission and control of vivax malaria in Afghan refugee settlements in Pakistan. Trans R Soc Trop Med Hyg, 91. ROWLAND, M., RAB, M. A., FREEMAN, T., DURRANI, N. & REHMAN, N. 2002. Afghan refugees and the temporal and spatial distribution of malaria in Pakistan. Soc Sci Med, 55, 2061-72. ROZENDAAL, J. A., VOORHAM, J., VAN HOOF, J. P. & OOSTBURG, B. F. 1989. Efficacy of mosquito nets treated with permethrin in Suriname. Med Vet Entomol, 3, 353-65. RUBIO, J. M., BENITO, A., ROCHE, J., BERZOSA, P. J., GARCIA, M. L., MICO, M., EDU, M. & ALVAR, J. 1999. Semi-nested, multiplex polymerase chain reaction for detection of human malaria parasites and evidence of Plasmodium vivax infection in Equatorial Guinea. Am J Trop Med Hyg, 60, 183-7. SACCONE, C. 1994. The evolution of mitochondrial DNA. Curr Opin Genet Dev, 4, 875-81. SACHIDANANDAM, R., WEISSMAN, D., SCHMIDT, S. C., KAKOL, J. M., STEIN, L. D., MARTH, G., SHERRY, S., MULLIKIN, J. C., MORTIMORE, B. J., WILLEY, D. L., HUNT, S. E., COLE, C. G., COGGILL, P. C., RICE, C. M., NING, Z., ROGERS, J., BENTLEY, D. R., KWOK, P. Y., MARDIS, E. R., YEH, R. T., SCHULTZ, B., COOK, L., DAVENPORT, R., DANTE, M., FULTON, L., HILLIER, L., WATERSTON, R. H., MCPHERSON, J. D., GILMAN, B., SCHAFFNER, S., VAN ETTEN, W. J., REICH, D., HIGGINS, J., DALY, M. J., BLUMENSTIEL, B., BALDWIN, J., STANGE-THOMANN, N., ZODY, M. C., LINTON, L., LANDER, E. S. & ALTSHULER, D. 2001. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature, 409, 928-33. SACHS, J. & MALANEY, P. 2002. The economic and social burden of malaria. Nature, 415, 680-5. SAHAR, S., AKHTAR, T., BILAL, H. & RANA, M. 2010. Prevalence of Plasmodium falciparum, Malarial Parasite in Muzaffargarh District, Punjab. Pakistan: a Two year Study. Pak J Sci, 64, 64-66. SAITOU, N. & NEI, M. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol, 4, 406-425. SCHNEIDER, K. A. & ESCALANTE, A. A. 2013. Fitness components and natural selection: why are there different patterns on the emergence of drug resistance in Plasmodium falciparum and Plasmodium vivax? Malar J, 12, 15. SCHNEIDER, P., BOUSEMA, J. T., GOUAGNA, L. C., OTIENO, S., VAN DE VEGTE-BOLMER, M., OMAR, S. A. & SAUERWEIN, R. W. 2007. Submicroscopic Plasmodium falciparum gametocyte densities frequently result in mosquito infection. Am J Trop Med Hyg, 76, 470-4. SCHOEPFLIN, S., VALSANGIACOMO, F., LIN, E., KINIBORO, B., MUELLER, I. & FELGER, I. 2009. Comparison of Plasmodium falciparum allelic frequency distribution in different endemic settings by high- resolution genotyping. Malar J, 8, 250. SCHOFIELD, C. J. & WHITE, G. B. 1984. House design and domestic vectors of disease. Trans R Soc Trop Med Hyg, 78, 285-92. SCHULMAN, S., OPPENHEIM, J. D. & VANDERBERG, J. P. 1980. Plasmodium berghei and Plasmodium knowlesi: serum binding to sporozoites. Exp Parasitol, 49, 420-9. SCHUMANN, R. R. 2007. Malarial fever: hemozoin is involved but Toll-free. Proc Natl Acad Sci U S A, 104, 1743-4. SEED, C. R., KITCHEN, A. & DAVIS, T. M. 2005. The current status and potential role of laboratory testing to prevent transfusion-transmitted malaria. Transfus Med Rev, 19, 229-40. SERVICE, M. 2002. Characteristics of some major Anopheles vectors of human malaria. . In: IN: WARREL DA., GILLES HM., EDITORS & ARNOLD. (eds.) Essential Malariology 4th edn. . 338 Euston Road, London. SEVERINI, C., MENEGON, M., GRADONI, L. & MAJORI, G. 2002. Use of the Plasmodium vivax merozoite surface protein 1 gene sequence analysis in the investigation of an introduced malaria case in Italy. Acta Trop, 84, 151-7. SHAH, I., ROWLAND, M., MEHMOOD, P., MUJAHID, C., RAZIQUE, F., HEWITT, S. & DURRANI, N. 1997. Chloroquine resistance in Pakistan and the upsurge of falciparum malaria in Pakistani and Afghan refugee populations. Ann Trop Med Parasitol, 91, 591-602.
226 SHAKYA, G., GUPTA, R., PANT, S. D., POUDEL, P., UPADHAYA, B., SAPKOTA, A., KC, K. & OJHA, C. R. 2012. Comparative study of sensitivity of rapid diagnostic (hexagon) test with calculated malarial parasitic density in peripheral blood. J Nepal Health Res Counc, 10, 16-9. SHANKAR, A. H. 2000. Nutritional modulation of malaria morbidity and mortality. J Infect Dis, 182 Suppl 1, S37-53. SHARMA, V. P. 1996. Re-emergence of malaria in India. Indian J Med Res, 103, 26-45. SHEKALAGHE, S., ALIFRANGIS, M., MWANZIVA, C., ENEVOLD, A., MWAKALINGA, S., MKALI, H., KAVISHE, R., MANJURANO, A., SAUERWEIN, R., DRAKELEY, C. & BOUSEMA, T. 2009. Low density parasitaemia, red blood cell polymorphisms and Plasmodium falciparum specific immune responses in a low endemic area in northern Tanzania. BMC Infect Dis, 9, 69. SHEKALAGHE, S. A., BOUSEMA, J. T., KUNEI, K. K., LUSHINO, P., MASOKOTO, A., WOLTERS, L. R., MWAKALINGA, S., MOSHA, F. W., SAUERWEIN, R. W. & DRAKELEY, C. J. 2007. Submicroscopic Plasmodium falciparum gametocyte carriage is common in an area of low and seasonal transmission in Tanzania. Trop Med Int Health, 12, 547-53. SHILLCUTT, S., MOREL, C., GOODMAN, C., COLEMAN, P., BELL, D., WHITTY, C. J. & MILLS, A. 2008. Cost- effectiveness of malaria diagnostic methods in sub-Saharan Africa in an era of combination therapy. Bull World Health Organ, 86, 101-10. SIMPSON, J. A., AARONS, L., COLLINS, W. E., JEFFERY, G. M. & WHITE, N. J. 2002. Population dynamics of untreated Plasmodium falciparum malaria within the adult human host during the expansion phase of the infection. Parasitology, 124, 247-63. SINDEN, R. E. & HARTLEY, R. H. 1985. Identification of the meiotic division of malarial parasites. J Protozool, 32, 742-4. SINGH, B., BOBOGARE, A., COX-SINGH, J., SNOUNOU, G., ABDULLAH, M. S. & RAHMAN, H. A. 1999a. A genus- and species-specific nested polymerase chain reaction malaria detection assay for epidemiologic studies. Am J Trop Med Hyg, 60, 687-92. SINGH, B., KIM SUNG, L., MATUSOP, A., RADHAKRISHNAN, A., SHAMSUL, S. S., COX-SINGH, J., THOMAS, A. & CONWAY, D. J. 2004. A large focus of naturally acquired Plasmodium knowlesi infections in human beings. Lancet, 363, 1017-24. SINGH, N., SHUKLA, M. M. & SHARMA, V. P. 1999b. Epidemiology of malaria in pregnancy in central India. Bull World Health Organ, 77, 567-72. SINGH, N., SHUKLA, M. M., SHUKLA, M. K., MEHRA, R. K., SHARMA, S., BHARTI, P. K., SINGH, M. P., SINGH, A. & GUNASEKAR, A. 2010. Field and laboratory comparative evaluation of rapid malaria diagnostic tests versus traditional and molecular techniques in India. Malar J, 9, 191. SINGH, U. K., KUMAR, R. & SHARMA, V. K. 1994. Increased urinary frequency as a presentation of Plasmodium falciparum malaria. Pediatr Infect Dis J, 13, 1024. SINTASATH, D. M., GHEBREMESKEL, T., LYNCH, M., KLEINAU, E., BRETAS, G., SHILILU, J., BRANTLY, E., GRAVES, P. M. & BEIER, J. C. 2005. Malaria prevalence and associated risk factors in Eritrea. Am J Trop Med Hyg, 72, 682-7. SIPE, N. G. & DALE, P. 2003. Challenges in using geographic information systems (GIS) to understand and control malaria in Indonesia. Malar J, 2, 36. SIRI, J. G., WILSON, M. L., MURRAY, S., ROSEN, D. H., VULULE, J. M., SLUTSKER, L. & LINDBLADE, K. A. 2010. Significance of travel to rural areas as a risk factor for malarial anemia in an urban setting. Am J Trop Med Hyg, 82, 391-7. SIRICHAISINTHOP, J., BUATES, S., WATANABE, R., HAN, E. T., SUKTAWONJAROENPON, W., KRASAESUB, S., TAKEO, S., TSUBOI, T. & SATTABONGKOT, J. 2011. Evaluation of loop-mediated isothermal amplification (LAMP) for malaria diagnosis in a field setting. Am J Trop Med Hyg, 85, 594-6. SMITH, J. M., SMITH, N. H., O'ROURKE, M. & SPRATT, B. G. 1993. How clonal are bacteria? Proc Natl Acad Sci U S A, 90, 4384-8. SNOUNOU, G., VIRIYAKOSOL, S., JARRA, W., THAITHONG, S. & BROWN, K. N. 1993a. Identification of the four human malaria parasite species in field samples by the polymerase chain reaction and detection of a high prevalence of mixed infections. Mol Biochem Parasitol, 58, 283-92.
227 SNOUNOU, G., VIRIYAKOSOL, S., ZHU, X. P., JARRA, W., PINHEIRO, L., DO ROSARIO, V. E., THAITHONG, S. & BROWN, K. N. 1993b. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol Biochem Parasitol, 61, 315-20. SNOW, R. W., CRAIG, M. H., DEICHMANN, U. & LE SUEUR, D. 1999. A preliminary continental risk map for malaria mortality among African children. Parasitol Today, 15, 99-104. SNOW, R. W., GUERRA, C. A., NOOR, A. M., MYINT, H. Y. & HAY, S. I. 2005. The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature, 434, 214-7. SNOW, R. W., OMUMBO, J. A., LOWE, B., MOLYNEUX, C. S., OBIERO, J. O., PALMER, A., WEBER, M. W., PINDER, M., NAHLEN, B., OBONYO, C., NEWBOLD, C., GUPTA, S. & MARSH, K. 1997. Relation between severe malaria morbidity in children and level of Plasmodium falciparum transmission in Africa. Lancet, 349, 1650-4. SNOW, R. W., PESHU, N., FORSTER, D., BOMU, G., MITSANZE, E., NGUMBAO, E., CHISENGWA, R., SCHELLENBERG, J. R., HAYES, R. J., NEWBOLD, C. I. & MARSH, K. 1998. Environmental and entomological risk factors for the development of clinical malaria among children on the Kenyan coast. Trans R Soc Trop Med Hyg, 92, 381-5. SOMI, M. F., BUTLER, J. R., VAHID, F., NJAU, J., KACHUR, S. P. & ABDULLA, S. 2007. Is there evidence for dual causation between malaria and socioeconomic status? Findings from rural Tanzania. Am J Trop Med Hyg, 77, 1020-7. SOOMRO, F., KAKAR, J. & PATHAN, G. 2009. Malarial Parasite; slide positively rate at Shikarpur District Sindh Pakistan. Professional Med J, 16, 377-9. SORNMANI, S. 1992. Current knowledge of risk behavior and risk factors in malaria in Southeast Asia. Southeast Asian J Trop Med Public Health, 23 Suppl 1, 6-8. SORNMANI, S., BUTRAPORN, P., FUNGLADDA, W., OKANURAK, K. & DISSAPONGSA, S. 1983. Migration and disease problems: a study of pattern of migration in an endemic area of malaria in Thailand. Southeast Asian J Trop Med Public Health, 14, 64-8. SOWUNMI, A. & ODUOLA, A. M. 1996. Efficacy of artemether in severe falciparum malaria in African children. Acta Trop, 61, 57-63. STAALSOE, T. & HVIID, L. 1998. The Role of Variant-specific Immunity in Asymptomatic Malaria Infections: Maintaining a Fine Balance. Parasitol Today, 14, 177-8. STAEDKE, S. G., NOTTINGHAM, E. W., COX, J., KAMYA, M. R., ROSENTHAL, P. J. & DORSEY, G. 2003. Short report: proximity to mosquito breeding sites as a risk factor for clinical malaria episodes in an urban cohort of Ugandan children. Am J Trop Med Hyg, 69, 244-6. STEENKESTE, N., INCARDONA, S., CHY, S., DUVAL, L., EKALA, M. T., LIM, P., HEWITT, S., SOCHANTHA, T., SOCHEAT, D., ROGIER, C., MERCEREAU-PUIJALON, O., FANDEUR, T. & ARIEY, F. 2009. Towards high-throughput molecular detection of Plasmodium: new approaches and molecular markers. Malar J, 8, 86. STEFANI, A., HANF, M., NACHER, M., GIROD, R. & CARME, B. 2011. Environmental, entomological, socioeconomic and behavioural risk factors for malaria attacks in Amerindian children of Camopi, French Guiana. Malar J, 10, 246. STENSVOLD, C. R. & NIELSEN, H. V. 2012. Comparison of microscopy and PCR for detection of intestinal parasites in Danish patients supports an incentive for molecular screening platforms. J Clin Microbiol, 50, 540-1. STOW, N. W., TORRENS, J. K. & WALKER, J. 1999. An assessment of the accuracy of clinical diagnosis, local microscopy and a rapid immunochromatographic card test in comparison with expert microscopy in the diagnosis of malaria in rural Kenya. Trans R Soc Trop Med Hyg, 93, 519-20. STRICKLAND, G. T., ZAFAR-LATIF, A., FOX, E., KHALIQ, A. A. & CHOWDHRY, M. A. 1987. Endemic malaria in four villages of the Pakistani province of Punjab. Trans R Soc Trop Med Hyg, 81, 36-41. SUAREZ-MUTIS, M. C., CUERVO, P., LEORATTI, F. M., MORAES-AVILA, S. L., FERREIRA, A. W., FERNANDES, O. & COURA, J. R. 2007. Cross sectional study reveals a high percentage of asymptomatic Plasmodium vivax infection in the Amazon Rio Negro area, Brazil. Rev Inst Med Trop Sao Paulo, 49, 159-64.
228 SUDHINARASET, M., BRIEGLEB, C., AUNG, M., KHIN, H. S. & AUNG, T. 2015. Motivation and challenges for use of malaria rapid diagnostic tests among informal providers in Myanmar: a qualitative study. Malar J, 14, 61. SUH, K. N., KAIN, K. C. & KEYSTONE, J. S. 2004. Malaria. Cmaj, 170, 1693-702. SULEMAN, M. 1988a. Malaria in Afghan refugees in Pakistan. Trans R Soc Trop Med Hyg, 82. SULEMAN, M. 1988b. Malaria in Afghan refugees in Pakistan. Trans R Soc Trop Med Hyg, 82, 44-7. SUMBELE, I., NKUO-AKENJI, T., SAMJE, M., NDZEIZE, T., NGWA, E. & AL., E. 2010. Haematological changes and recovery associated with treated and untreated P. falciparum infection in children in the Mount Cameroon Region. J Clin Med Res 2, 143-151. SUNDARARAJAN, R., KALKONDE, Y., GOKHALE, C., GREENOUGH, P. G. & BANG, A. 2013. Barriers to malaria control among marginalized tribal communities: a qualitative study. PLoS One, 8, e81966. SUNDARARAMAN, S. A., PLENDERLEITH, L. J., LIU, W., LOY, D. E., LEARN, G. H., LI, Y., SHAW, K. S., AYOUBA, A., PEETERS, M., SPEEDE, S., SHAW, G. M., BUSHMAN, F. D., BRISSON, D., RAYNER, J. C., SHARP, P. M. & HAHN, B. H. 2016. Genomes of cryptic chimpanzee Plasmodium species reveal key evolutionary events leading to human malaria. Nat Commun, 7, 11078. SUTCLIFFE, C. G., KOBAYASHI, T., HAMAPUMBU, H., SHIELDS, T., KAMANGA, A., MHARAKURWA, S., THUMA, P. E., GLASS, G. & MOSS, W. J. 2011. Changing individual-level risk factors for malaria with declining transmission in southern Zambia: a cross-sectional study. Malar J, 10, 324. SUTHERLAND, C. J., DRAKELEY, C. J. & SCHELLENBERG, D. 2007. How is childhood development of immunity to Plasmodium falciparum enhanced by certain antimalarial interventions? Malar J, 6, 161. SWAROOP, S. 1949. Forecasting of epidemic malaria in the Punjab, India. Am J Trop Med Hyg, 29, 1-17. TANGPUKDEE, N., DUANGDEE, C., WILAIRATANA, P. & KRUDSOOD, S. 2009. Malaria diagnosis: a brief review. Korean J Parasitol, 47, 93-102. TANNER, M. & VLASSOFF, C. 1998. Treatment-seeking behaviour for malaria: a typology based on endemicity and gender. Soc Sci Med, 46, 523-32. TAREEN, A. M., RAFIQUE, M., WADOOD, A., QASIM, M., RAHMAN, H., SHAH, S. H., KHAN, K. & PIRKANI, G. S. 2012. Malaria burden in human population of Quetta, Pakistan. Eur J Microbiol Immunol (Bp), 2, 201-4. TARIMO, S. D. 2007. Appraisal on the prevalence of malaria and anaemia in pregnancy and factors influencing uptake of intermittent preventive therapy with sulfadoxine-pyrimethamine in Kibaha district, Tanzania. East Afr J Public Health, 4, 80-3. TASAWER, Z., MANAN, F. & BHUTTA, A. 2003. Prevalence of human malaria at Multan-Pakistan. A. J. Med. Sci, 3, 123-126. TEK, F. B., DEMPSTER, A. G. & KALE, I. 2009. Computer vision for microscopy diagnosis of malaria. Malar J, 8, 153. TEMEL, T. 2004. Malaria from the gap: need for cross-sector co-operation in Azerbaijan. Acta Trop, 89, 249-59. TEMPLETON, T. J., ASADA, M., JIRATANH, M., ISHIKAWA, S. A., TIAWSIRISUP, S., SIVAKUMAR, T., NAMANGALA, B., TAKEDA, M., MOHKAEW, K., NGAMJITUEA, S., INOUE, N., SUGIMOTO, C., INAGAKI, Y., SUZUKI, Y., YOKOYAMA, N., KAEWTHAMASORN, M. & KANEKO, O. 2016. Ungulate malaria parasites. Sci Rep, 6, 23230. TEUSCHER, F., GATTON, M. L., CHEN, N., PETERS, J., KYLE, D. E. & CHENG, Q. 2010. Artemisinin-induced dormancy in Plasmodium falciparum: duration, recovery rates, and implications in treatment failure. J Infect Dis, 202, 1362-8. THAKUR, A., ALAM, M. T. & SHARMA, Y. D. 2008. Genetic diversity in the C-terminal 42 kDa region of merozoite surface protein-1 of Plasmodium vivax (PvMSP-1(42)) among Indian isolates. Acta Trop, 108, 58-63. THANG, N. D., ERHART, A., SPEYBROECK, N., HUNG LE, X., THUAN LE, K., HUNG, C. T., KY, P. V., COOSEMANS, M. & D'ALESSANDRO, U. 2008. Malaria in central Vietnam: analysis of risk factors by multivariate analysis and classification tree models. Malar J, 7, 28.
229 THIELE, E. A., SORENSEN, R. E., GAZZINELLI, A. & MINCHELLA, D. J. 2008. Genetic diversity and population structuring of Schistosoma mansoni in a Brazilian village. Int J Parasitol, 38, 389-99. THOMSON, M. C. & CONNOR, S. J. 2001. The development of Malaria Early Warning Systems for Africa. Trends Parasitol, 17, 438-45. THOMSON, S., LOHMANN, R. C., CRAWFORD, L., DUBASH, R. & RICHARDSON, H. 2000. External quality assessment in the examination of blood films for malarial parasites within Ontario, Canada. Arch Pathol Lab Med, 124, 57-60. TIAGO, A., CALÚ, N., CAUPERS, P. & SAMUEL, M. 2011. Normas de Tratamento da Malaria Malaria PNdCd, ed . Maputo : Ministerio da Saude, 42 TIBAYRENC, M. 1999. Toward an integrated genetic epidemiology of parasitic protozoa and other pathogens. Annu Rev Genet, 33, 449-77. TILAYE, T. & DERESSA, W. 2007. Prevalence of urban malaria and assocated factors in Gondar Town, Northwest Ethiopia. Ethiop Med J, 45, 151-8. TILLEY, L., DIXON, M. W. & KIRK, K. 2011. The Plasmodium falciparum-infected red blood cell. Int J Biochem Cell Biol, 43, 839-42. TJITRA, E., ANSTEY, N. M., SUGIARTO, P., WARIKAR, N., KENANGALEM, E., KARYANA, M., LAMPAH, D. A. & PRICE, R. N. 2008. Multidrug-resistant Plasmodium vivax associated with severe and fatal malaria: a prospective study in Papua, Indonesia. PLoS Med, 5, e128. TJITRA, E., SUPRIANTO, S., MCBROOM, J., CURRIE, B. J. & ANSTEY, N. M. 2001. Persistent ICT malaria P.f/P.v panmalarial and HRP2 antigen reactivity after treatment of Plasmodium falciparum malaria is associated with gametocytemia and results in false-positive diagnoses of Plasmodium vivax in convalescence. J Clin Microbiol, 39, 1025-31. TORII, M., NAKAMURA, K., SIEBER, K. P., MILLER, L. H. & AIKAWA, M. 1992. Penetration of the mosquito (Aedes aegypti) midgut wall by the ookinetes of Plasmodium gallinaceum. J Protozool, 39, 449- 54. TORRES-ESTRADA, J. L. & RODRIGUEZ, M. H. 2003. [Physico-chemical signals involved in host localization and in the induction of mosquito bites]. Salud Publica Mex, 45, 497-505. TRAGER, W. & JENSEN, J. B. 2005. Human malaria parasites in continuous culture. 1976. J Parasitol, 91, 484-6. TRAPE, J. F. 1985. Rapid evaluation of malaria parasite density and standardization of thick smear examination for epidemiological investigations. Trans R Soc Trop Med Hyg, 79, 181-4. TRAPE, J. F. 2001. The public health impact of chloroquine resistance in Africa. Am J Trop Med Hyg, 64, 12-7. TRAPE, J. F., LEFEBVRE-ZANTE, E., LEGROS, F., NDIAYE, G., BOUGANALI, H., DRUILHE, P. & SALEM, G. 1992. Vector density gradients and the epidemiology of urban malaria in Dakar, Senegal. Am J Trop Med Hyg, 47, 181-9. TREUTIGER, C. J., HEDLUND, I., HELMBY, H., CARLSON, J., JEPSON, A., TWUMASI, P., KWIATKOWSKI, D., GREENWOOD, B. M. & WAHLGREN, M. 1992. Rosette formation in Plasmodium falciparum isolates and anti-rosette activity of sera from Gambians with cerebral or uncomplicated malaria. Am J Trop Med Hyg, 46, 503-10. TRIPATHI, M. & DAS, A. 2015. Genotyping malariaparasites with DNA barcodes. Tropical Medicine and International Health, 20, 1636. TURRINI, F., SCHWARZER, E. & ARESE, P. 1993. The involvement of hemozoin toxicity in depression of cellular immunity. Parasitol Today, 9, 297-300. TUTEJA, R. 2007. Malaria - an overview. Febs j, 274, 4670-9. TUYISHIMIRE, J. 2013. Spatial modelling of malaria risk factors in Ruhuha sector, Rawanda. UNICEF. 2000. Promoting Rational Use of Drugs and Correct Case Management in Basic Health [Online]. The prescriber. [Accessed]. UTTRA, C. K. M., DEVRAJANI, B. R., SHAIKH, K., SHAIK, K. R. & SHAH, S. Z. A. 2010. Severity of Thrombocytopenia and Prolonged Bleeding Time in Patients with Malaria (A Clinical Study of 162 Malaria Cases). World Applied Science Journal 9, 484-488.
230 VAFA, M., TROYE-BLOMBERG, M., ANCHANG, J., GARCIA, A. & MIGOT-NABIAS, F. 2008. Multiplicity of Plasmodium falciparum infection in asymptomatic children in Senegal: relation to transmission, age and erythrocyte variants. Malar J, 7, 17. VAIDYA, A. B. & ARASU, P. 1987. Tandemly arranged gene clusters of malarial parasites that are highly conserved and transcribed. Mol Biochem Parasitol, 22, 249-57. VAIDYA, A. B., LASHGARI, M. S., POLOGE, L. G. & MORRISEY, J. 1993. Structural features of Plasmodium cytochrome b that may underlie susceptibility to 8-aminoquinolines and hydroxynaphthoquinones. Mol Biochem Parasitol, 58, 33-42. VALECHA, N., ANSARI, M. A., PRABHU, S. & RAZDAN, R. K. 1996. Preliminary evaluation of safety aspects of neem oil in kerosene lamp. Indian J Malariol, 33, 139-43. VALLE, R. M. & TORRES, S. G. 1965. Report of Mexico on the malaria eradication campaign and on the present situation of epidemiological surveillance operations. Salud Publica Mex, 7, 9-31. VAN DEN BROEK, I., HILL, O., GORDILLO, F., ANGARITA, B., HAMADE, P., COUNIHAN, H. & GUTHMANN, J. P. 2006. Evaluation of three rapid tests for diagnosis of P. falciparum and P. vivax malaria in Colombia. Am J Trop Med Hyg, 75, 1209-15. VAN DEN EEDE, P., VAN DER AUWERA, G., DELGADO, C., HUYSE, T., SOTO-CALLE, V. E., GAMBOA, D., GRANDE, T., RODRIGUEZ, H., LLANOS, A., ANNE, J., ERHART, A. & D'ALESSANDRO, U. 2010. Multilocus genotyping reveals high heterogeneity and strong local population structure of the Plasmodium vivax population in the Peruvian Amazon. Malar J, 9, 151. VAN DER HOEK, W., KONRADSEN, F., DIJKSTRA, D. S., AMERASINGHE, P. H. & AMERASINGHE, F. P. 1998. Risk factors for malaria: a microepidemiological study in a village in Sri Lanka. Trans R Soc Trop Med Hyg, 92, 265-9. VAN DOOREN, G. G. & STRIEPEN, B. 2013. The algal past and parasite present of the apicoplast. Annu Rev Microbiol, 67, 271-89. VANDERBERG, J. 1980. Development of infectivity by the P. berghei sporozoite. J Parasitol, 61. VASOO, S. & PRITT, B. S. 2013. Molecular diagnostics and parasitic disease. Clinics in laboratory medicine, 33, 461-503 %@ 0272-2712. VAUGHAN, A. M., ALY, A. S. & KAPPE, S. H. 2008. Malaria parasite pre-erythrocytic stage infection: gliding and hiding. Cell Host Microbe, 4, 209-18. VERNICK, K. D., ODUOL, F., LAZZARO, B. P., GLAZEBROOK, J., XU, J., RIEHLE, M. & LI, J. 2005. Molecular genetics of mosquito resistance to malaria parasites. Curr Top Microbiol Immunol, 295, 383-415. VERON, V., SIMON, S. & CARME, B. 2009. Multiplex real-time PCR detection of P. falciparum, P. vivax and P. malariae in human blood samples. Exp Parasitol, 121, 346-51. VITTOR, A. Y., GILMAN, R. H., TIELSCH, J., GLASS, G., SHIELDS, T., LOZANO, W. S., PINEDO-CANCINO, V. & PATZ, J. A. 2006. The effect of deforestation on the human-biting rate of Anopheles darlingi, the primary vector of Falciparum malaria in the Peruvian Amazon. Am J Trop Med Hyg, 74, 3-11. VITTOR, A. Y., PAN, W., GILMAN, R. H., TIELSCH, J., GLASS, G., SHIELDS, T., SANCHEZ-LOZANO, W., PINEDO, V. V., SALAS-COBOS, E., FLORES, S. & PATZ, J. A. 2009. Linking deforestation to malaria in the Amazon: characterization of the breeding habitat of the principal malaria vector, Anopheles darlingi. Am J Trop Med Hyg, 81, 5-12. VO, T. K., BIGOT, P., GAZIN, P., SINOU, V., DE PINA, J. J., HUYNH, D. C., FUMOUX, F. & PARZY, D. 2007. Evaluation of a real-time PCR assay for malaria diagnosis in patients from Vietnam and in returned travellers. Trans R Soc Trop Med Hyg, 101, 422-8. WAHID, S. 2013. Assessment of exposure, infection and risk for malaria in refugee camps in Khyber Pakhtunkhwa (KP), Pakistan. Ph.D, London School of Hygiene and Tropical Medicine University of London. WAHID, S., STRESMAN, G. H., KAMAL, S. S., SEPULVEDA, N., KLEINSCHMIDT, I., BOUSEMA, T. & DRAKELEY, C. 2016. Heterogeneous malaria transmission in long-term Afghan refugee populations: a cross-sectional study in five refugee camps in northern Pakistan. Malaria Journal, 15, 245.
231 WAITUMBI, J. N., KUYPERS, J., ANYONA, S. B., KOROS, J. N., POLHEMUS, M. E., GERLACH, J., STEELE, M., ENGLUND, J. A., NEUZIL, K. M. & DOMINGO, G. J. 2010. Outpatient upper respiratory tract viral infections in children with malaria symptoms in Western Kenya. Am J Trop Med Hyg, 83, 1010-3. WANG, Y., MA, A., CHEN, S. B., YANG, Y. C., CHEN, J. H. & YIN, M. B. 2014. Genetic diversity and natural selection of three blood-stage 6-Cys proteins in Plasmodium vivax populations from the China- Myanmar endemic border. Infect Genet Evol, 28, 167-74. WARHURST, D. C. 2001. A molecular marker for chloroquine-resistant falciparum malaria. N Engl J Med, 344, 299-302. WARRELL, D. A. G., H. M. 2002. Essential malariology., Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, UK. WATERS, A., HIGGINS, D. & MCCUTCHAN, T. 1993. Evolutionary relatedness of some primate models of Plasmodium. Mol. Biol. Evol., 10, 914-923. WATERS, A. P. & MCCUTCHAN, T. F. 1989. Rapid, sensitive diagnosis of malaria based on ribosomal RNA. Lancet, 1, 1343-6. WEBER, R., SCHLAGENHAUF, P., AMSLER, L. & STEFFEN, R. 2003. Knowledge, attitudes and practices of business travelers regarding malaria risk and prevention. J Travel Med, 10, 219-24. WFP 2009. U.S Global Health Policy. U.S. Global Health Programs. California. WHITE, N. J. 2011. Determinants of relapse periodicity in Plasmodium vivax malaria. Malar J, 10, 297. WHITE, N. J. 2017. Malaria parasite clearance. Malar J, 16, 88. WHO 1999. New Perspectives: Malaria Diagnosis. Report of a Joint WHO/USAID: Informal Consultation held on 25-27 October 1999. World Health Organization. Geneva, Switzerland. WHO. 2009. Malaria rapid diagnostic test performance [Online]. Geneva, Switzerland: World Health Organization. [Accessed]. WHO 2011a. Flooding and communicable diseases fact sheet: risk assessment and preventive measures,” WHO Communicable Diseases Working Group on Emergencies, 2011. WHO 2011b. World malaria report. In: ORGANIZATION, W. H. (ed.). Geneva. WHO 2012. Country profiles (http://www.who.int/malaria/publications/countryprofiles/profile_pak_en.pdf). WHO. 2014a. Malaria rapid diagnostic test performance: results of WHO product testing of malaria RDTs: round 5 (2013) [Online]. Geneva. [Accessed 2017]. WHO 2014b. Policy brief on malaria diagnostics in low-transmission settings. WHO 2017. World Health Organization. World malaria report 2017. Geneva. WHO. 2007. Malaria Elimination: A field manual for low and moderate endemic countries. 98. WILLIAMS, O. & MEEK, S. 2011. Malaria: country profiles [Online]. London: Department of International Development. [Accessed]. WILSON, R. J. & WILLIAMSON, D. H. 1997. Extrachromosomal DNA in the Apicomplexa. Microbiol Mol Biol Rev, 61, 1-16. WINTER, D. J., PACHECO, M. A., VALLEJO, A. F., SCHWARTZ, R. S., AREVALO-HERRERA, M., HERRERA, S., CARTWRIGHT, R. A. & ESCALANTE, A. A. 2015. Whole Genome Sequencing of Field Isolates Reveals Extensive Genetic Diversity in Plasmodium vivax from Colombia. PLoS Negl Trop Dis, 9, e0004252. WIWANITKIT, V. 2009. Headache and malaria: a brief review. Acta Neurol Taiwan, 18, 56-9. WOLFF, C. G., SCHROEDER, D. G. & YOUNG, M. W. 2001. Effect of improved housing on illness in children under 5 years old in northern Malawi: cross sectional study. Bmj, 322, 1209-12. WONGSRICHANALAI, C., BARCUS, M. J., MUTH, S., SUTAMIHARDJA, A. & WERNSDORFER, W. H. 2007. A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT). Am J Trop Med Hyg, 77, 119-27. WOYESSA, A., DERESSA, W., ALI, A. & LINDTJORN, B. 2013. Malaria risk factors in Butajira area, south- central Ethiopia: a multilevel analysis. Malar J, 12, 273. WRIGHT, S. 1978. Evolution and the Genetics of Population, Variability Within and Among Natural Populations. The University of Chicago Press, Chicago.
232 YALE, S. H., ADLAKHA, A., SEBO, T. J. & RYU, J. H. 1993. Bronchiolitis obliterans organizing pneumonia caused by Plasmodium vivax malaria. Chest, 104, 1294-6. YANDA, P. S., WANDIGA, R., KANGALAWE, M., OPONDO, D., OLAGO, A., GITHEKO & DOWNS T 2006. Adaptation to Climate Change/Variability-Induced Highland Malaria and Cholera in the Lake Victoria Region. AIACC Working Paper 43. YANG, Z., MIAO, J., HUANG, Y., LI, X., PUTAPORNTIP, C., JONGWUTIWES, S., GAO, Q., UDOMSANGPETCH, R., SATTABONGKOT, J. & CUI, L. 2006. Genetic structures of geographically distinct Plasmodium vivax populations assessed by PCR/RFLP analysis of the merozoite surface protein 3beta gene. Acta Trop, 100, 205-12. YAR, H., MASOOD, K., MAQBOOL, A. & MALIK, G. 1998. Prevalence of malarial parasite species in Multan district. The Professional 5, 183-187. YASEEN, M. & ALI, N. H. 2015. Frequency and Seasonal Distribution of Plasmodium falciparum and Plasmodium vivax in Children in Tertiary hospital Karachi - Pakistan. .International Journal of Endorsing Health Science Research, 3, 23-25. YASINZAI, M. & KAKARSULEMANKHEL, J. 2003. Incidence of malaria infection in rural areas of District Quetta, Pakistan. On Line J Med Sci, 3, 766-72. YASINZAI, M. & KAKARSULEMANKHEL, J. 2004. A study of prevalence of malaria infection in urban areas of district Quetta, Pakistan. Pak J Zool;, 36, 75-9. YASINZAI, M. & KAKARSULEMANKHEL, J. 2007. Incidence of malaria infection in central areas of Balochistan: Mastung and Khuzdar. Rawal Med J, 32, 176-8. YASINZAI M.I. & KAKARSULEMANKHEL J.K. 2008. Incidence of human malaria infection in desert area of Pakistan: District Kharan. J. Agri. Soc. Sci, 4, 39-41. YASINZAI, M. I. & KAKARSULEMANKHEL, J. K. 2008. Incidence of human malaria infection in northern hilly region of Balochistan, adjoining with NWFP, Pakistan: district Zhob. Pak J Biol Sci, 11, 1620- 4. YASINZAI, M. I., KAKARSULEMANKHEL, J.K. 2008. Incidence of Human Malaria Infection in Desert Area of Pakistan: District Kharan. Journal of Agriculture & Social Sciences, 4, 39-41. ZACARIAS, O. P. & ANDERSSON, M. 2010. Mapping malaria incidence distribution that accounts for environmental factors in Maputo Province--Mozambique. Malar J, 9, 79. ZAKERI, S., KAKAR, Q., GHASEMI, F., RAEISI, A., BUTT, W., SAFI, N., AFSHARPAD, M., MEMON, M. S., GHOLIZADEH, S., SALEHI, M., ATTA, H., ZAMANI, G. & DJADID1, N. D. 2010a. Detection of mixed Plasmodium falciparum & P. vivax infections by nested-PCR in Pakistan, Iran & Afghanistan. Indian J Med Res, 132, 31-35. ZAKERI, S., KAKAR, Q., GHASEMI, F., RAEISI, A., BUTT, W., SAFI, N., AFSHARPAD, M., MEMON, M. S., GHOLIZADEH, S., SALEHI, M., ATTA, H., ZAMANI, G. & DJADID, N. D. 2010b. Detection of mixed Plasmodium falciparum & amp; P. vivax infections by nested-PCR in Pakistan, Iran & amp; Afghanistan. Indian J Med Res, 132, 31-5. ZAKERI, S., KAKAR, Q., GHASEMI, F., RAEISI, A., BUTT, W., SAFI, N., AFSHARPAD, M., MEMON, M. S., GHOLIZADEH, S., SALEHI, M., ATTA, H., ZAMANI, G. & DJADID, N. D. 2010c. Detection of mixed Plasmodium falciparum & P. vivax infections by nested-PCR in Pakistan, Iran & Afghanistan. Indian J Med Res, 132, 31-5. ZAKERI, S., MEHRIZI, A. A., MAMAGHANI, S., NOORIZADEH, S., SNOUNOU, G. & DJADID, N. D. 2006. Population structure analysis of Plasmodium vivax in areas of iran with different malaria endemicity. Am J Trop Med Hyg, 74, 394-400. ZAKERI, S., RAEISI, A., AFSHARPAD, M., KAKAR, Q., GHASEMI, F., ATTA, H., ZAMANI, G., MEMON, M. S., SALEHI, M. & DJADID, N. D. 2010d. Molecular characterization of Plasmodium vivax clinical isolates in Pakistan and Iran using pvmsp-1, pvmsp-3alpha and pvcsp genes as molecular markers. Parasitol Int, 59, 15-21. ZEB, J., KHAN, M. S., ULLAH, N., ULLAH, H., NABI, G. & AZIZ, T. 2015. Epidemiology of Plasmodium species and prevalence of malaria on the basis of Age, Sex, Area, Seasonality and clinical manifestation in the population of District Lower Dir, Khyber Pakhtunkhwa Pakistan. World journal of zoology, 10, 147-152.
233 ZHAO, Y. O., KURSCHEID, S., ZHANG, Y., LIU, L., ZHANG, L., LOELIGER, K. & FIKRIG, E. 2012. Enhanced survival of Plasmodium-infected mosquitoes during starvation. PLoS One, 7, e40556. ZHOU, G., MINAKAWA, N., GITHEKO, A. K. & YAN, G. 2004. Association between climate variability and malaria epidemics in the East African highlands. Proc Natl Acad Sci U S A, 101, 2375-80. ZHOU, G., MUNGA, S., MINAKAWA, N., GITHEKO, A. K. & YAN, G. 2007. Spatial relationship between adult malaria vector abundance and environmental factors in western Kenya highlands. Am J Trop Med Hyg, 77, 29-35. ZHOU, G., SIRICHAISINTHOP, J., SATTABONGKOT, J., JONES, J., BJORNSTAD, O. N., YAN, G. & CUI, L. 2005. Spatio-temporal distribution of Plasmodium falciparum and P. Vivax malaria in Thailand. Am J Trop Med Hyg, 72, 256-62. ZIMMERMAN, P. A. & HOWES, R. E. 2015. Malaria diagnosis for malaria elimination. Curr Opin Infect Dis, 28, 446-54.
234